Obesity and climate change: co-crises with common solutions

Abstract

The global obesity crisis involves an unprecedented and rapid change to the human phenotype. Conferring vast levels of avoidable morbidity and mortality at enormous cost, it has proved refractory to previous policy-led action. This article reviews recent developments in our understanding of obesity and its links to the climate co-crisis, aiming to inform evidence-based, societal-level actions to address both. Recent therapeutic developments now offer transformative interventions for millions of people living with obesity. However, treating all affected adults and children with major bariatric surgery or lifelong anti-obesity medication is unsustainable given the risks and costs. The obesity crisis has been driven primarily by the transformation of our food environment toward diets dominated by ultra-processed foods (UPFs) that exert multiple addictive and obesogenic mechanisms. Emerging evidence shows that not all UPFs have the same impact: processed meat and low-fiber, energy-dense UPFs are linked with poorer outcomes compared with less energy-dense, high-fiber, plant-rich UPFs, indicating that more nuanced classifications would be helpful. This food system also contributes significantly to climate change and other environmental harms, primarily through ruminant meat consumption. Both climate change and obesity are driven by unsustainable, but profitable, consumption. Solutions exist but have not been adequately implemented owing to a lack of political will. They require food system reforms that replace energy-dense UPFs with unprocessed foods and reduce animal-sourced foods. Accumulating evidence supports prioritizing actions to remove market distortions via increasing cost transparency, taxing unhealthy foods (redirecting the proceeds to public health), combating marketing, effective food labeling, facilitating healthy food choices, promoting healthy living environments, and public and professional education. New economic models, market demand shifts, and technological innovation should all be harnessed to overcome economic and political barriers, and food system reform should be integral to future actions to achieve the Sustainable Development Goals. This transformation to improve both human and planetary health will require interdisciplinary scientific advocacy and coalition-building across society. During the COVID-19 pandemic, societies recognized how rapid, concerted, science-led action can effectively address a global threat; a similar societal shift is required to motivate the political action needed to address the obesity crisis.

Key points

• Half the global population is projected to be living with excess weight and/or obesity within 10 years, placing an unsustainable burden on healthcare systems.
• The food environments driving obesity via increased consumption of energy-dense and ultra-processed foods (UPFs) are also driving climate change.
• Although bariatric surgery and incretinomimetic drugs offer important new therapeutic options, they cannot substitute for societal-level, systems-wide reform of obesogenic ecosystems.
• Reforms necessary to tackle the obesity and climate co-crises include the following: cost transparency to address market distortions, taxes on energy-dense UPFs, facilitation of healthy food choices (including subsidies to producers and consumers), food labeling, living environments that enable healthy diets and activity, and education of the public and professionals.
• Transitioning to non-obesogenic and sustainable food systems that are healthy for the planet would likely be very economically advantageous to societies.

Introduction

Over 2.6 billion people—38% of the world’s population—are now living with excess weight and/or obesity. This fraction is projected to reach 50% by 2035 based on current rates of change (1). The global increase in obesity since the 1980s has been the most rapid and dramatic change in the human phenotype throughout our entire evolution.

At the simplest biological level, obesity is the accumulation of excess adipose tissue due to a prolonged surplus of energy intake over that expended. When the extent of adiposity overwhelms the capacity to buffer the energy excess, obesity becomes a complex multifactorial noncommunicable disease (NCD) and a “gateway” condition for many conditions that collectively induce the majority of the global disease burden (2–4). Obesity was first recognized as a disease by the World Health Organization in 1948. While some anthropologists contend that increased body weight is an evolutionary adaptation to changing lifestyle (5), many national and international organizations have reaffirmed its classification as a disease, and a recent international commission defined clinical obesity as a chronic, systemic disease state directly caused by excess adiposity (6). Excess weight, conventionally defined as a body mass index (BMI) >25 kg/m², is estimated to cause almost 500,000 deaths/year in the United States alone (7) and at least 5 million adult deaths/year globally (8, 9). It is now recognized that BMI is an imprecise measure of adiposity, affected by ethnic variations in muscle mass, and that it does not estimate visceral obesity—a major determinant of obesity-associated health outcomes. BMI is now recommended only for use as a screening tool or for epidemiology; excess adiposity should be confirmed by at least one anthropometric measure, such as waist circumference, waist-to-hip ratio, or waist-to-height ratio (6).

The global obesity epidemic is now increasing in children (1, 10) with serious lifelong consequences (11) that can be passed on epigenetically (12). Excess weight and obesity confer enormous direct and indirect economic costs, estimated at 2.19% of gross domestic product (GDP) globally in 2019 and rising (13).

While reduced energy expenditure through sedentary lifestyle patterns and automation may be a potential contributor, it is not the main driver of the obesity pandemic (14–17). Similarly, the rise in obesity does not appear to be due to increased energy intake per se, as evidence suggests that Americans have not been consuming more calories since 2000, especially relative to their increased body size (18). The most compelling evidence indicates that the obesity epidemic is being driven by changes in our food environment. Owing to modern food processing, our diet is increasingly diverging from that consumed during our evolution. Specifically, there has been an increase in consumption of energy-dense “Western”-style diets that are characterized by being high in refined grains, red and processed meats, confectionery, ultra-processed foods (UPFs), and sugar-sweetened beverages (SSBs). These dietary changes have been accompanied by a reduction in home-prepared, minimally processed foods. According to a recent meta-analysis, UPFs represent up to 80% of daily energy intake in the United States and Canada (19), and their increased consumption has been reported across the world (20, 21).

In low- and middle-income countries (LMICs), the rise in obesity has occurred despite the continued prevalence of malnutrition, with a resulting growth in particular conditions termed the double burden of malnutrition (DBM) and the triple burden of malnutrition (TBM). DBM refers to the coexistence of undernutrition along with overweight or obesity within an individual (22), while TBM refers to the coexistence of undernutrition along with overweight or obesity and also micronutrient deficiency or anemia within an individual (23). These conditions particularly cluster in richer households in poorer LMICs and in poorer households in richer LMICs (24) and have been exacerbated by the rapid expansion of UPFs in the global food system, resulting in the displacement of traditional healthy diets with foods of poor nutritional value (25). Over 70% of countries, mainly LMICs such as Indonesia and Guatemala, currently experience DBM (26). While progress has been made in reducing malnutrition-related disability and mortality, these gains have been more than offset by increasing rates of obesity (27).

The trends in both malnutrition and obesity are largely due to the rapid transition of dietary habits towards energy-dense UPFs (26). In developing countries, consumption of UPFs is first adopted in richer households but soon spreads to poorer households in the same pattern as the spread of obesity (28). Coexisting malnutrition with obesity is associated with even greater risk of multiple NCDs, particularly for mothers (29), and young children can suffer stunted growth and poor cognition and educational performance (30). Such exposures during fetal and neonatal development and throughout childhood have potential lifelong consequences for the risk of obesity and other NCDs. This was originally proposed by David Barker in 1995 (31) and is now well-established (32–34).

There have been many prominent policy proposals to combat obesity in recent years (35–39). However, actions have generally been slow and inadequate, such that no nation has reversed or even halted the upward obesity trajectory. This is in stark contrast to the global COVID-19 pandemic, which caused considerable mortality and disruption worldwide but demonstrated how populations can, in general, accept dramatic upheavals to their way of life, at least for temporary periods. The COVID-19 pandemic underscored the negative impact of obesity on health outcomes, as it is a risk factor for severe COVID-19, hospitalization, and death (40). It also illustrated how rapid, concerted, science-led action can effectively address and mitigate a global public health threat. While COVID-19 presented an immediate survival threat for which societies accepted radical policies, this has not been the case for obesity, as the threat has evolved slowly over decades and has been relatively unacknowledged and/or a low priority for national health and economic strategies, despite the clear consequences it has on health and the economy. The sluggish response may also be due to social prejudices holding that obesity is self-inflicted (41). In contrast to COVID-19, obesity will require much more sustained systemic changes.

Over the same period, there has been global acceptance that climatic changes resulting from human activities are driving extreme weather events, causing considerable damage and suffering across the planet (42). Global heating now kills one person every minute around the world, accounting for around 546,000 deaths per year over the period 2012–2021, which is up 63% from the 1990s (43). Scientists warn that there is a high risk of a climate catastrophe that will threaten the health and survival of human civilizations within decades (44–46). The obesity and climate crises both relate to energy balance and, at least in part, to problems of over-consumption of food and goods from which powerful commercial entities profit—resulting in strong resistance to their mitigation from vested interests. They both represent systems problems arising from common problems in the natural and food environment that are deeply social and cultural. Addressing both crises will require political courage and urgent action at multiple levels of society, and both require all sectors to act globally.

There are two key options available to address obesity. The first option is pharmacological or surgical intervention. Until recently, doctors could only advise patients to lose weight via diet and lifestyle interventions, with limited effectiveness. The recent development of effective weight-loss medications, together with bariatric surgery for more severe cases, provides transformative treatment options for people affected by obesity. The application of glucagon-like peptide-1 receptor agonists (GLP-1RAs) for treating obesity was named Science magazine’s breakthrough of the year in 2023 (47). However, there are deep concerns regarding the global scalability, affordability, acceptability, and long-term safety of applying medication or surgery to large populations, particularly from childhood, especially in light of the potential need for life-long medication. There is also a danger that, with effective treatments being available, policymakers will avoid addressing the food environment changes driving the obesity crisis. This is analogous to COVID-19 policymaking in some countries, where reliance was placed on rapid vaccine development rather than effective public health measures.

The second option is a food system transition toward healthier, less-processed, whole food diets to reduce the obesogenic drive, and toward more plant-based foods that would also address the multiple environmental harms from current food systems, including climate change. National dietary guidelines are becoming increasingly plant-based over time, with recent revisions to Canadian (48) German (49), Spanish (50), and Danish (51) guidelines all recommending reduced or no intake of animal-sourced products associated with high greenhouse gas (GHG) emissions. However, often there are important disconnects between national dietary guidelines and relevant government policies and subsidies (52). A food system transformation that is healthy for people and the planet would necessitate a paradigm shift in the food environment driven by appropriate policy approaches.

The growing public acceptance of the climate crisis is resulting in a societal shift in attitude and behaviors, with some people around the world shifting to more sustainable lifestyles (53), including food choices (54), consistent with a food system transformation that would help tackle obesity. Economies are also shifting, with increased investments in cleaner technologies transforming major industries such as automobile manufacturing and energy production. This presents a critical moment to harness these societal transitions and enable sustainable living to protect the environment and avoid climate catastrophe while reducing obesity and its co-morbidities. If the global momentum for more sustainable living could be channeled towards shifting consumer demand to foods that are both non-obesogenic and that can be produced without environmental harm, similar investments could be applied to the necessary transformation of our food systems (55). It is important that food consumption is framed within a global environmental paradigm, as is already the case for fuel consumption, recycling, reusing, and upcycling.

There is also mounting recognition that preventing a climate catastrophe and protecting the environment will be less harmful and much cheaper than trying to adapt to the consequences (56). Likewise, preventing obesity by changing our food environment offers similar economic and well-being benefits over pharmacological or surgical interventions. This would avoid a future where people are condemned to surgery or lifelong medication in order to stay healthy in an environmentally unsustainable, obesogenic world. In the words attributed to Desmond Tutu, “There comes a point where we need to stop just pulling people out of the river. We need to go upstream and find out why they’re falling in.”

Here, we review the recent developments in our understanding of obesity and its treatment. Given the very broad scope of this article, we have not engaged in a formal systematic review but provide a narrative literature review. We also consider what can be learned from the co-crisis of climate change. We discuss the need for integrated multisector obesity prevention strategies that promote both a healthy lifestyle and a healthy planet. A transformative social movement and fundamental changes to our food systems will be required, combined with a realignment of education and healthcare to address these major societal challenges. This will require a variety of soft and hard policy measures and their evaluation and monitoring to ensure effective implementation.

Epidemiology: current and projected scale of the obesity pandemic

The World Health Organization (WHO) describes the global obesity epidemic as one of today’s most blatantly visible—yet most neglected—public health problems (57). A clear-sighted appreciation of the current and rising scale and impact of the crisis is vital to inform action.

Obesity prevalence is rising dramatically

In 2020, 2.6 billion people aged over 5 years—roughly 38% of the world’s population—were living with excess weight or obesity (1). Obesity alone accounted for 988 million people, or 14% of the population (1). Adult obesity (individuals aged ≥20 years) was found in 18% of women and 14% of men; in children (5–19 years), the rates were 10% in boys and 8% in girls. Obesity rates continue to rise globally: in 147 out of 200 countries, the lifetime risk of having a high BMI (>25kg/m²) is now more than 50%, and in 62 countries, it exceeds 80%. The global lifetime risk of class II obesity (a BMI >35kg/m²: the common threshold for clinical intervention) is more than 10% (58). In the United States, 80% of adults in some states are already overweight or obese. If current trends continue, projections indicate that, across the United States, one in three adolescents and two in three adults will be living with obesity by 2050 (59). There are wide geographical differences in the proportion of men and women with obesity, ranging from as low as 4% and 8%, respectively, in the South-East Asian region to as high as 32% and 37%, respectively, for the Americas (North, Central, and South America) (1) (Figure 1).

Figure 1

Figure 1. Current and projected rates of overweight and obesity prevalence in adults and children globally and by World Health Organization region. Data from the World Obesity Federation (1).

Obesity prevalence has nearly tripled over the past 50 years. While prevalence is highest in high-income countries, a large share of the increase (around 55%) has occurred in rural areas in LMICs across South-East Asia, Latin America, Central Asia, and North Africa (26). Significantly closing the gap in prevalence between urban and less-well-resourced rural areas creates “the ticking time bomb of obesity” (26). Projections suggest that by 2030, 50% of adults will be living with high BMI and 17% of men and 22% of women will be living with obesity (58) and by 2035 over 4 billion people worldwide will be living with excess weight or obesity—53% more than in 2020 (1) (Figure 1). The global obesity rate is expected to rise from 14% to 24% over this period. While a gradual rise in prevalence is forecasted for all age groups, the upward trajectory is expected to be greatest in LMICs. In some Pacific Island countries (Tonga, Samoa, Federated States of Micronesia, and Kiribati), more than two-thirds of the adult population are projected to be living with obesity by 2035.

The greatest increases are predicted in children and adolescents. Between 1975 and 2016, the prevalence of obesity increased from 0.7% to 5.6% in girls and from 0.9% to 7.8% in boys (60). This year, 2025, marks a turning point for children and adolescents (aged 5–19 years) when, for the first time, the global prevalence of obesity is now greater than that of underweight individuals (9.4% versus 9.2%) (61). Between 2020 and 2035, obesity prevalence is projected to rise from 10% to 20% in boys and from 8% to 18% in girls (1). The highest predicted annual increases are for India, Bangladesh, Myanmar, and Tanzania at 9.1%, 8.6%, 8.4%, and 8.4% per year, respectively (1). Obesity-related diseases are also being diagnosed at younger ages. Obesity in childhood is particularly damaging and can result in impaired mobility, poorer school performance, early onset puberty, stunting, skeletal anomalies, sleep apnea, stigma-induced anxiety, and mood disorders. Obesity in childhood tracks into adulthood (11) and has the potential to impact quality of life, future economic potential, and adult healthcare costs.

Obesity increases mortality and NCD rates

Obesity is a key risk factor for many of the major NCDs. Improvements in public health over recent decades have reduced the burden of disease due to communicable, maternal, and neonatal diseases, but, simultaneously, there has also been a considerable rise in the global burden due to NCDs, particularly obesity, diabetes, cardiovascular diseases, and cancer (62).

These NCDs have been termed a syndemic as they frequently overlap and co-exist within populations, share common mechanisms, and are driven by shared social, environmental, and economic factors (63). From a public health perspective, viewing these co-existing conditions as a syndemic may help convince policymakers that systemic actions involving bundles of policies in diverse but interlinked portfolios (including agriculture, industry, transport, economics, education, and healthcare) are required to tackle them (64).

Despite improvements in their treatment, the global burden due to this syndemic is projected to continue to increase due to population growth and aging (65). The food environment plays a central role in driving this syndemic (66), and obesity can be viewed as a gateway condition for many of the other NCDs (2–4). In 2019, more than 5 million deaths globally were attributed to obesity-related ill-health, with more than half occurring amongst persons aged under 70 years (13). A high BMI is among the top three causes of death in all geopolitical regions except Sub-Saharan Africa (26). The Organisation for Economic Co-operation and Development (OECD) estimates that excess weight and obesity reduce life expectancy in member countries by an average of 2.7 years (67).

Obesity drives cardiovascular risk factors, including dyslipidemia, type 2 diabetes (T2D), hypertension, and sleep disorders, and the development of cardiovascular disease independently of other cardiovascular risk factors (68). One in five adults with obesity also has T2D, with the risk increasing with higher BMI (69). Obesity has also been linked with numerous common cancers, including breast, colorectal, esophageal, kidney, gallbladder, uterine, pancreatic, and liver cancer (70–72). Excess BMI leads to a 17% increased risk of cancer-specific mortality (70) and is the third-highest risk factor for cancer after smoking and alcohol use (9).

The effect of obesity in driving cancer incidence among children and young adults is of particular concern. A recent analysis of a population-based cancer registry in China, covering around 12–14 million individuals from 2007–2021, found that almost half of incident cancers were obesity related. Obesity-related cancers increased in incidence by 3.6%/year overall, but the rate of increase was highest in younger generations, e.g., 15.28% in those aged 25–29 years versus 1.55% in those aged 60–64 years. The alarming rise documented in the obesity-related cancer incidence rate ratio in successive generations since the 1980s points to an emerging major public health concern (73).

Obesity also increases the risk of osteoarthritis, non-alcoholic fatty liver disease, cirrhosis, gall bladder disease, gout, some pulmonary diseases, and autoimmune diseases (e.g., type 1 diabetes).

Economic impact of obesity

Obesity also presents a huge economic burden. In 2020, the economic costs of obesity ranged from 1.2% of GDP in the African region to 3.2% in the Americas (13). Indirect costs arising from premature mortality and lost productivity accounted for more than two-thirds of this cost, with the remainder being direct medical costs. The OECD estimated that obesity effectively reduces the workforce by 54 million persons/year across 52 countries (67). Given current trends, the global economic costs of obesity (including healthcare and economic productivity) are projected to rise from just below US$2 trillion in 2020 to over US$3 trillion by 2030, over US$4 trillion by 2035, and to US$18 trillion by 2060—reaching 3.29% of total GDP (1, 13).

Obesity pathogenesis and drivers

The lack of progress on addressing obesity may be partly due to the common misconception that it results simply from an energy imbalance caused by increased consumption of energy-dense foods accompanied by reduced energy expenditure associated with sedentary lifestyles. This has perpetuated the belief that the solution is for individuals to eat less and exercise more, which stigmatizes individuals who cannot sustain such habits. In reality, the evidence suggests that much more complicated mechanisms are at play.

Obesity develops due to complex interactions between multiple factors. These include genetic predisposition, environmental exposures (primarily nutrition), epigenetics and developmental programming, and endocrine, metabolic, immune, microbiome, social/cultural, political, economic, and psychological/behavioral factors. The causes of the global obesity epidemic are not fully clear, but its rapid onset and worldwide spread across many cultures and all age groups exclude many factors, such as changes in genetic predisposition.

Obesity models

There are several theories for the obesity epidemic, including the energy storage model (74), the developmental origins of obesity model (75), the energy balance model (76), and the carbohydrate-insulin model (77). The latter two paradigms can be simplistically summarized as “push” and “pull” models, respectively. The push model postulates that obesity is driven by increased consumption of readily available, inexpensive, energy-dense foods. In contrast, the pull model postulates that consumption of food with a high glycemic load stimulates insulin, which alters fuel partitioning, inhibiting mitochondrial respiration and fatty acid utilization (78, 79) and promoting fat deposition into adipose tissue, stimulating hunger and further consumption. These different models have generated considerable academic debate, especially regarding whether all calories have the same metabolic or adipogenic potential and the circular question of whether people develop obesity due to excessive caloric intake or whether their obesity drives excessive caloric intake. Excessive caloric intake resulting in severe, early-onset obesity can be driven by mutations in appetite regulation genes, including melanocorticopin 4 receptor, leptin, and the leptin receptor; however, such cases are relatively uncommon, and the greatest proportion of obesity is likely driven by shifts in the modern food environment affecting individuals according to their genetic predisposition. Despite these debates, the models have significant overlap, and it is possible that both the push and pull models are correct and operate to varying extents in different contexts and/or in different individuals (80). There is, however, common agreement that (i) the epidemic is caused essentially by a change in the food environment, (ii) obesity is an endocrine disorder, and (iii) it is not a personal health issue but a public health crisis.

Energy intake and activity

The obesity epidemic has been most evident in the United States, where the obesity prevalence has tripled since 1980 (81). Yet, this does not appear to be due to increased energy intake: evidence from self-reported recall data and food availability estimates indicates that consumption and availability of dietary energy increased rapidly until around 2000 and subsequently declined by a small but significant amount (18). Theories blaming obesity on macronutrients have been proposed, initially implicating fats and then sugars (82). However, this is also not consistent with evidence suggestive of slight dietary improvements in the United States since around 2000 (83–86). Similarly, although energy expenditure appears to have decreased early in the epidemic, evidence from the United States and Europe suggests that, since 1990, exercise reductions associated with occupation, travel, and being within the home have been offset by an increase in leisure-time exercise (14, 16, 17). More energy is expended to move as individuals become larger; therefore, reduced physical activity does not necessarily equate to reduced absolute energy expenditure. Longitudinal data also suggest that increased adiposity predicts less physical activity but reduced physical activity does not predict increased adiposity (15).

Higher rates of cardiovascular disease and obesity have been associated with the global increase in urbanization, with the fraction of the global population living in urban settings having risen from 36.6% in 1970 to 44.8% in 1994, and being projected to reach 61.1% by 2025 (87, 88). The lower prevalence of obesity in rural areas was proposed to be due to food consumption being restricted by lower incomes (89) and because energy expenditure was higher due to agricultural work and domestic activities such as water and firewood collection (90). However, this does not now apply to all regions: in middle-income countries, agriculture is increasingly mechanized, cars are used for transport, and homes are supplied with water and domestic fuels (91). In addition, the theory that increased urbanization has driven obesity is also not supported by more recent data, as the majority of the global increase in obesity has been in rural areas: 55% of the global increase in mean BMI from 1985 to 2017 occurred in rural areas, and, for some LMICs, rural areas accounted for 80% of the rise (92).

Reduced physical activity does not appear to play a major role in driving the obesity epidemic, which appears to be primarily due to the amount and quality of food intake (93). However, a sedentary lifestyle is a risk factor for weight gain. It is also clear that physical activity has many benefits for cardiometabolic health independent of BMI changes (94). Reducing weight by combining physical activity with diet induces additional improvements in metabolic health, as compared with a similar loss of weight by diet alone (95). As such, a more nuanced analysis of energy balance with greater appreciation of energy partitioning and diet quality is required.

Ultra-processed foods: the key driver of obesity?

As a response to food security concerns in the mid-20th century, investments in the “Green Revolution” drove a dramatic increase in crop yields, with much of the increased grain then used to feed cows, pigs, and chickens, which, together with the use of antibiotics, enabled the industrialization of livestock production. Contemporaneously, Western lifestyles moved away from home-prepared meals made with minimally processed whole foods to ready-to-eat meals, snacks, and convenience foods (96–98). Consumer demand for ready-to-eat foods drove a change in food production that enabled the growth of large national and international food conglomerates, which, in response to demand and to enhance profits, led to the development of UPFs (99). The rise of UPF consumption is now broadly seen as the key factor driving the obesity epidemic (Figure 2).

Figure 2

Figure 2. Food system transition driving ultra-processed food (UPF) consumption—the key driver of obesity.

What are UPFs?

While food processing, such as cooking, fermenting, pickling, curing, and smoking, has been essential throughout history, UPFs are industrially produced, containing ingredients that would not be found in most normal homes. Today, the classification of food as UPFs is based on the NOVA system, which classifies foods into four broad groups: unprocessed or minimally processed foods, processed culinary ingredients, processed foods, and UPFs. In NOVA, UPFs are conceptually defined as formulations of food substances and additives containing little if any whole food, designed to replace the other NOVA food groups and their culinary preparation while maximizing industry profits. Operationally, they are defined as formulations containing one or more markers of food ultra-processing, either sensory-related classes of food additives or food substances of no culinary use (97, 99). Reservations have been raised that classifying foods based on processing is too broad, lacks clarity over the specific food components of concern, and is not consistent with nutritional recommendations, which are generally based on specifying nutrients and food groups (100). In many high-income countries, UPFs are already dominant in the food system and contribute considerably to daily nutrient and energy intake, whereby restricting or removing UPFs requires replacement with appropriate unprocessed or minimally processed foods such that nutrient deficiencies do not occur. Further, the health effects are not the same across UPF subgroups. In a European study, high consumption of animal-based UPFs and SSBs increased risks of cardiometabolic disease and cancer multimorbidity, while breads, cereals, and plant-based UPFs did not (101). Similarly, in an analysis of three large prospective cohorts in the United States, SSBs and processed meats largely explained the overall UPF association with cardiovascular disease outcomes, and for stroke, when SSBs and processed meats were excluded, the association with UPF became inverse (102). Another prospective study in the United States found an association between UPF consumption and mortality, but ultra-processed vegetables and legumes were associated with a reduced risk of mortality (103). However, this apparent heterogeneity of health effects across UPF subgroups is contentious and may reflect biases and flawed comparisons with “the rest of the diet” rather than with non-ultra-processed counterparts (104). However, despite possible limitations of the NOVA system, nearly all of the evidence from hundreds of studies linking food processing levels with obesity risk has been obtained using this system (105).

The industrialization of food production involves the deconstruction of whole foods, obtained cheaply due to the farming revolution, into their component parts. These are then subjected to mechanical, thermal, fermentative, enzymatic, and decontamination treatments. Finally, they are reformulated to add “value” and enhance profitability. This may involve modification, fortification with vitamins and minerals, enhancement of taste with artificial flavorings and sweeteners, enhancement of appearance with artificial colorants, foaming or anti-foaming agents, clarifying agents, and bleaching agents, enhancement of texture and “palatability” with more artificial additives (such as acidity regulators, emulsifiers, gelling agents, glazing agents, and thickeners), and, finally, enhancement of shelf life with artificial stabilizers, antioxidants, and preservatives (106, 107).

UPF consumption: implications for diet quality and obesity

As a result of these changes in farming and lifestyle, together with aggressive marketing, UPF sales have grown rapidly across all countries (20, 21). UPFs now form a major part—in some countries, the main part—of Western diets. The proportion of the diet comprised of UPFs is directly correlated with national wealth (28). An Australian study indicated that 56% of grocery energy was from UPFs, and these were cheaper and purchased by lower lower socioeconomic status groups (108). In the United States and Canada, around half of daily energy intake is now obtained primarily from cookies, pastries and sweet bread, packaged bread, fast foods, SSBs, and processed sweets (19). According to a recent meta-analysis, UPFs comprise almost 80% of all calories consumed in these two countries, and their consumption was inversely related to the intake of unprocessed foods (19). This is evidence that UPFs have displaced whole foods and formed the main part of Western diets. A higher UPF intake is associated with lower intake of fruits, vegetables, legumes, and seafood. Consequently, increased amounts of refined grains, starchy vegetables, and chemical additives and reduced amounts of non-starchy vegetables and whole grains, fibers, probiotics, prebiotics, flavonoids, and phytonutrients are now consumed.

Accumulating evidence indicates that the consumption of a UPF-dominant diet is detrimental to health. Meta-analyses, systematic and umbrella reviews, including many cohort studies across the globe covering millions of individuals, showed that high UPF diets increased the risk of all-cause mortality and were associated with 32 specific adverse health outcomes (109–113). According to prespecified evidence classification criteria, convincing evidence (Class 1) supported direct associations with cardiovascular disease, common mental health disorders, overweight and obesity, and T2D (110). A recent systematic review found 104 prospective observational studies assessing the effects of UPFs in the diet on chronic disease risk, of which 92 found that greater consumption of UPFs increased the risk of one or more chronic diseases, with 78 of these reporting significant linear trends (28). In meta-analyses of outcomes, which were included in at least four of the studies, significant associations were found with 12 outcomes, including excess weight or obesity, abdominal obesity, and T2D (28). A study assessing the contribution of dietary UPFs to premature deaths across eight countries with very different levels of consumption estimated that in Colombia, UPFs account for just 4% of all-cause premature mortality, whereas in the United States and the United Kingdom, the estimate was 14% (112).

In contrast to changes in energy intake or energy expenditure, the increase in UPF consumption has paralleled the rise in obesity in adults (114–116) and children (117). UPF consumption predicted increased general and abdominal obesity in all prospective epidemiological studies systematically reviewed (118). A systematic review of observational studies found a dose-response relationship, with every 10% increase in daily UPF consumption being associated with a 6% increase in obesity (119). A prospective cohort study across nine European countries found, after multivariate adjustment, that every additional standard deviation of UPF consumption was associated with a weight gain of 0.12 kg [95% confidence interval (CI): 0.09–0.15] over 5 years (120). In the United Kingdom, a longitudinal study of over 9,000 children followed from age 7 to 24 years found accelerated BMI growth in those in the highest quintile for UPF consumption, with similar increases for other adiposity measures (121). A study of children and adolescents in 185 countries found that SSB intake increased globally by an average of 23% between 1990 and 2018 and paralleled the increase in prevalence of obesity in these populations (122). Evidence indicates that UPFs and SSBs account for at least a third of total energy intake for adolescents in Argentina, Belgium, Chile, and Mexico and at least half in Australia, Canada, the United States, and the United Kingdom (61). Consumption of UPFs in early childhood may have lasting effects as taste preferences are set early childhood, and early exposure may determine lifelong preferences for sweet, salty, and artificially flavored foods (123).

The most convincing evidence for a role in obesity comes from Latin America, where UPF classification has been widely accepted and the transition to UPF-dominated diets has been rapid and was strongly correlated to equally rapid increases in national obesity prevalence (124). In 2013, the cross-national annual retail sales per capita of UPFs, after controlling for confounders (national income, urbanization, and deregulation), correlated strongly with the national prevalence of obesity across 14 nations, and for 12 of these Latin American countries, the temporal changes from 2000 to 2009 in annual retail sales per capita of UPFs correlated strongly with the change in national mean BMI (125).

How UPFs drive obesity

The relationship between food and obesity is complex. Obesity is not determined by energy intake alone. Rather, different foods can promote obesity by inducing a variety of metabolic changes (Figure 3). In recent decades, the food industry has invested heavily in developing convenient, highly palatable foods using chemicals whose health effects are poorly understood. More than 10,000 chemical additives are estimated to be present in foods in the United States (126). Of 766 introduced there since 2000, only 10 were formally approved by the Food and Drug Administration; the other 756 were self-approved by industrial companies (127). This has become a very active field of research, but only after the realization that a metabolic health crisis had been created. Many unknowns remain regarding the long-term health effects of the numerous stabilizers, emulsifiers, preservatives, colorants, and other chemicals now present in food and the concomitant reductions in fiber, probiotics, flavonoids, and phytonutrients. It is increasingly clear that there are also many complex interactions between all of these agents and factors; more research is required to clarify their effects, particularly potential epigenetic effects on appetite, satiety, and metabolic pathways. With so many chemicals now present in foods that have numerous potential interactions, the traditional strategies for testing and regulation that examine one chemical at a time are impractical and prohibitively expensive. This has led to suggestions for a new approach to determine toxic chemical exposures utilizing many recently developed technologies. There are now several initiatives such as the “Human Exposome Project,” analogous to the “Human Genome Project,” which incorporates the Implementation Moonshot Project for Alternative Chemical Testing (IMPACT), which aims to revolutionize traditional toxicology (128, 129), using multiomics technologies, biomonitoring, big data analytics, microphysiological systems, and artificial intelligence to identify chemical exposures within the complex environment that impact human health.

Figure 3

Figure 3. Ultra-processed foods (UPFs) may promote obesity by various mechanisms. Key characteristics of UPFs, such as high energy density, soft texture, hyper-palatability (fat, carbohydrate, and sugar ratios), non-nutritive sweeteners, and low cost in conjunction with reductions in dietary fiber and changes in complex carbohydrate intake, can interact in complex ways with multiple interconnected physiological systems and processes to drive obesity. The figure illustrates the relationships between the key mechanisms outlined in the text but is not intended to be exhaustive.

Energy density and palatability

The highly palatable nature of many UPFs, along with their low cost and convenience, alters the way and the amount we eat. Accumulating evidence indicates that energy-dense UPFs promote adiposity by altering energy partitioning within the body. The food industry has engineered the precise formulations of fats, carbohydrates, sugars, and salt to maximize palatability such that neuroendocrine signaling to reward centers is reinforced (130, 131). Indeed, neurological signals induced by highly palatable UPFs are very similar to those activated by addictive substances (132). Furthermore, these potentiated reward signals may be perturbed in people with obesity, such that they do not perceive the food addiction (133).

UPFs tend to have lower nutrient density, higher energy density, and lower cost compared with unprocessed foods (134). In contrast, healthier foods are generally more expensive (135). This aligns with the recognized associations between lower socioeconomic status, overt poverty, lower education, and obesity prevalence (136). Another factor may be eating rate: faster eating is associated with higher total energy intake (137). As UPFs are generally softer and more energy dense than conventional foods, they promote rapid energy consumption, further compounding total energy consumption (138). In a recent randomized clinical trial, a time-restricted eating regimen was as effective as calorie restriction at reducing energy intake and inducing weight loss (139, 140). The most compelling evidence of the obesogenic effect of UPFs comes from randomized crossover trials. A month-long, inpatient, randomized, crossover feeding trial assessed ad libitum energy intake of adults presented with a diet of UPFs compared with an unprocessed diet matched for palatability, calories, sugars, carbohydrates, fats, sodium, and fiber (141). On the UPF diet, participants consumed around 500 kCal/day more and gained more weight than when on the unprocessed diet. On further analyses, eating rate was associated with energy intake for both diets, but the additional energy intake on the UPF diet was related to energy density and palatability (142). A similar, 1-week, randomized, crossover feeding trial in Japan tested UPF and non-UPF diets matched for total energy, macronutrient levels, and energy density (143). On the UPF diet, participants consumed more daily calories and gained more weight; further, the eating rate was faster with lower chewing frequency and a significantly lower number of chews per calorie consumed, indicating mechanisms for the increased caloric intake observed. Outside such controlled environments, an 8-week randomized crossover trial in free-living participants compared a UPF-diet with a diet composed of minimally processed foods (MPF); both diets followed national healthy dietary guidelines (144). Whilst both diets caused weight loss, the degree of weight, BMI, and fat mass loss on the MPF diet was greater than that on the UPF diet, together with significantly lower energy intake. Favorable improvements in craving control and hedonic appetite were observed on the MPF diet despite greater weight loss and lower flavor and taste ratings. In the prespecified sensitivity analysis, the results were consistent.

Modern food processing has resulted in an increasing divergence between what we now consume and what we have traditionally eaten. One emerging strand of research is the discordant activation of central neuronal pathways affecting reward, satiety, and appetite that do not match the nutritional content of the UPFs being consumed. It is relatively rare for natural, unprocessed foods to be rich in both fats and carbohydrates, whereas many UPFs are deliberately high in both to enhance palatability. High dietary intake of simple carbohydrates (sugars) alters the gut microbiome, which, in turn, alters the gut immune system, resulting in greater lipid absorption and systemic inflammation (145). The gut-brain neuronal signaling differs when consuming fats (146) or carbohydrates (147). Consuming foods rich in both may reinforce reward center activation (131). The promotion of hedonic eating, eating when not hungry, and overriding homeostatic food intake controls has been designed by food producers.

The multiple ingredients of UPFs are generally either industrially synthesized or purified from raw foods, resulting in an acellular composition lacking the structural matrix of normal raw foods. Independent of the food nutrient composition, this lack of matrix has many effects on human health similar to a lack of fiber, including the extent of chewing required (thereby impacting momentum of and volume of consumption), satiety control, digestive tract transit speed, gastrointestinal hormonal secretions, nutrient bioaccessibility/bioavailability, microbiota diversity, and metabolic utilization (148, 149). The dominance of UPFs in the diet is associated with a reduction of diversity in the diet and an altered pattern of nutrient intake with higher consumption of free sugars, sodium, and total-, saturated- and trans-fats and lower consumption of many micronutrients, including vitamins A, E, C, B9, B12, zinc, calcium, iron, magnesium, potassium, and phosphorus (150, 151). The full effects of these changes in diet diversity and the mix of macro- and micronutrients on weight gain are far from fully understood (152).

Sweetness

Generally, the sweetness of natural foods is directly linked to their sugar content. In contrast, many UPFs contain artificial (e.g., saccharin, sucralose, or aspartame) or natural non-nutritive sweeteners (e.g., steviol glycosides, monk fruit, or xylitol). These sweeteners cause a mismatch between the sweetness and the sugar consumed. Although these additives do not contain calories, they can impact metabolic responses. For example, a sweetness/sugar consumption mismatch impairs diet-induced thermogenesis, which normally reduces energy partitioning, to promote storage in adipose tissue (153). Some non-nutritive sweeteners enhance glucose absorption by increasing intestinal mucosa expression of sodium glucose cotransporter-1 and glucose transporter 2 (154). The use of non-nutritive sweeteners does not necessarily reduce the intake of sugars since activation of neural reward centers requires glucose utilization rather than sweet taste (155). In addition, sucralose consumption along with sucrose blunts the brain response to sucrose, reducing insulin sensitivity and impairing glucose tolerance (156). Non-nutritive sweeteners also impact glucose tolerance by adversely altering the intestinal microbiome (157).

One of the most important sweeteners is high-fructose corn syrup (HFCS), introduced in the 1960s due to its low cost (in part due to subsidy structures) and plentiful supply. The sudden large increase in dietary fructose stimulated considerable interest in its potential role in the obesity epidemic. Despite the concern, the pattern of HFCS consumption has not been compatible with the global increase in obesity, either temporally or geographically (158). Although HFCS is not implicated in the global obesity epidemic, there is compelling evidence that high consumption adversely affects metabolic health (159, 160). For instance, the incidence of T2D is 20% higher in countries with high HFCS consumption compared with those with low consumption (158).

Altered complex carbohydrates

The nature of complex carbohydrates (starches) in our diet has also changed, in parallel with the increase in UPFs, with shifts in consumption of the main starches, amylose, and amylopectin. Amylose, the main starch in legumes such as beans, lentils, and chickpeas, consists of simple glucose chains that are only digested from their ends. This results in slower processing in the upper intestine and allows more amylose to pass into the large intestine, where much of it is utilized by the microbiome via bacterial fermentation, limiting the absorption of glucose into the host. In contrast, amylopectin, the main starch in wheat, rice, pasta, and potatoes, has a complex, highly branched structure, allowing it to be digested by several enzymes and more rapidly absorbed in the upper intestine, presenting the liver and pancreas with a greater glucose load and hence a greater insulin response. Amylose consumption has decreased in the transition to UPFs, whereas the consumption of amylopectin has increased. Cereal crops in which the starch branching enzymes are inhibited to increase amylose content are currently being engineered (161).

Reduced fiber

The reduction in dietary fiber due to the removal of plant structures from UPFs also impacts how the gut processes food and partitions consumed energy. Dietary fibers form a gel on the lining of the duodenum, reducing nutrient absorption by up to 25–30%, lowering the glucose load, and increasing the flux of nutrients to the microbiome in the large intestine (162). This also affects the response of gastrointestinal hormones to food entering the gut, with reduced glucose-dependent insulinotropic polypeptide (GIP) and ghrelin and increased cholecystokinin (CCK) and peptide YY (PYY) impacting insulin secretion and central regulation of satiety and appetite (163). This gel lining the duodenum is lacking with low fiber consumption, facilitating greater small intestine absorption and reducing nutrient supply to the large intestine. This then enhances insulin secretion and lipid deposition but also starves the gut microbiome of nutrients, switching it to digesting the colonic mucus barrier, resulting in “leaky gut,” inflammation, and insulin resistance (164).

Microbiome and obesity

Changes in the gut microbiome are another potential driver of the obesity pandemic, receiving significant attention recently. The gut microbiome comprises roughly the same number of cells as there are human cells in the whole body, but due to bacterial diversity, its total genome is estimated to have 150-fold more genes than the human genome—and these are capable of producing considerably more enzymes for digesting our food (165). The gut microbiome consists of 95% anaerobic bacteria that can consume between 7 and 22% of ingested energy via fermentation (166, 167).

Individuals vary considerably in their metabolic response to the ingestion of identical meals, with variants in the human genome contributing little to these differences (168). In contrast, our microbiome strongly determines our metabolic response to food, and microbiome composition is highly associated with the quality of dietary intake, with microbiome diversity increased by healthy plant-based foods (169). Microbiota composition also mediates the effect of some ingested nutrients on visceral fat mass, a major risk factor for cardiometabolic disease (170). In addition, a healthy microbiome facilitates the formation of secondary bile acids, which have important metabolic actions (171). Microbiome bacterial diversity is reduced in individuals with obesity, although whether this dysbiosis contributes to, or results from, obesity remains unclear. Nevertheless, dysbiosis can have many effects on the host metabolism (166, 167).

A further related concern comes from the prophylactic use of low-dose antibiotics in agriculture, not just to prevent infections but historically as growth promoters. In 2015, it was estimated that approximately 80% of antibiotics sold in the United States were used in livestock agriculture (172). In some parts of the world, antibiotics have been added to food or water for livestock for over 70 years, with residual antibiotics being present in the food chain. This led to the hypothesis that ingestion of antibiotic-contaminated foods could cause gut microbiome dysbiosis, thereby contributing to obesity (173). Antibiotic use may cause livestock microbiome dysbiosis; possible zoonotic transfer of consequent gut microbes to humans could also contribute to obesity (174). However, the use of antibiotics in livestock is not uniform and has varied considerably between countries and over time (175), and patterns of use do not seem to relate to the global rise in human obesity. There is, however, some evidence that antibiotic use in human infants younger than 6 months, or exposure to more than three courses of antibiotics in childhood, and the use of broad-spectrum antibiotics may increase the risk of subsequent obesity (176).

Evidence from mouse models linking the microbiome causatively to obesity engendered interest in microbiota transfer as a potential treatment. However, studies in humans have failed to replicate the effects seen in mice (177).

Genetic variants and obesity

The recent rise in obesity levels across most societies cannot be attributed to changes in the human genetic code. Genetic differences between individuals (and ethnic groups) may favor weight gain and poorer outcomes in terms of metabolic disease (e.g., cardiovascular disease and T2D). The idea that famines throughout evolution favored genetic variants promoting adiposity, a “thrifty genotype” (178), has been challenged by an alternative view that, once we no longer needed to escape predators or forage for food, there was no evolutionary pressure for variants favoring leanness, a “drifty genotype” (179). Studies of families and twins indicate that heritability explains 40–70% of the individual variation in BMI (180) as well as more precise measures of adiposity, including abdominal fat (181).

There are rare, highly penetrant genetic variants associated with excessive weight gain that control feeding circuits. Initially implicated genes were identified from “genetic experiments”: the variants are usually recessive in humans, and their effects are therefore the result of consanguineous marriage. For example, mutations in genes encoding for the satiety hormone leptin and its receptor result in dramatic obesity associated with impaired satiety and feeding behaviors (182). Leptin interacts with satiety circuits in feeding centers in the ventromedial and lateral hypothalamus (183), whose importance is demonstrated by the severely obese phenotypes of human subjects when target genes are disrupted (184). These findings point strongly to feeding behavior—specifically, constant craving for food—as the driver for weight gain, rather than any shift towards enhanced deposition of fat or a “slowing” of metabolism.

Subsequently, genome-wide BMI association studies over the past 16 years have described more than 1,100 common variants associated with obesity, which together account for only around 6% of BMI variation (183, 185–188). For the vast majority of these variants, it is not known which loci may have a causal effect on BMI or what mechanism may have been involved. The fat mass and obesity-associated (FTO) gene was identified in the first of these screens (185), but later studies showed its effect was more likely caused by the neighboring iroquois homeobox 3 (IRX3) gene (189, 190). Other implicated loci also encode genes controlling circuits involved in feeding and satiety (191–193).

Population and twin studies have indicated the presence of gene–environment interactions, and epigenetic studies are now helping to clarify the mechanisms involved. A study of 1,850 twins aged 4 years found that BMI heritability was 86% for those living in lower socioeconomic environments but only 39% for those in higher socioeconomic environments (194). The heritability of adiposity is markedly attenuated by a healthy diet (195–197) or by increased physical activity (198–200). Studies also demonstrate that excess energy exposure in utero and infancy can lead to early weight gain and obesity via epigenetic alterations to gene expression (201–203). These findings indicate that genetic variance and epigenetic mechanisms can affect the susceptibility of an individual to gain weight in response to an unhealthy environment or lifestyle.

The role of epigenetics in obesity is one of the most rapidly emerging research fields with many studies of DNA modifications (mainly methylation thus far), histone modifications, non-coding RNAs, and, more recently, chromatin structure, as described in many recent reviews (204–208). As it is now clear that many epigenetic marks are modifiable, there is much hope that such studies will eventually yield epigenetic markers of exposures, such as foods and the microbiome, and methylation risk scores that will predict an individual’s risk of developing obesity. They may even give insights into intergenerational epigenetic inheritance of obesity. The impact of early-life exposures on subsequent risk of obesity and other NCDs, as posited in the Developmental Origins of Health and Disease (DOHaD) model, is now recognized to mainly operate via epigenetics (207–209).

As many genetic variants each account for a very small amount of susceptibility to gain weight, we are a long way from being able to apply these for any individualized, precision medicine approaches to predicting risk or prognosis.

Psychological and behavioral drivers

It is vital to understand the processes of hunger, wanting, craving, satiation, and post-meal satiety, which stimulate or inhibit eating behavior. People living with obesity, particularly those who binge eat or have a higher BMI status, exhibit weakened within-meal satiation (210). Rather than the rate of food intake naturally decelerating during a meal, as ingestion generates inhibitory signals, eating continues at the same pace. Similarly, post-meal satiety is weaker and hunger rebounds sooner after consumption. These differences, which reflect the underlying biology of obesity, make it far harder for people with obesity to limit their food intake. With uncontrolled hunger and disinhibition of eating behavior, reward-driven eating dominates, placing greater demands on the cognitive and behavioral resources of those with obesity (211). Indeed, the inability to control their hunger is the key barrier to weight loss for adolescents living with obesity (212).

Weight loss through dieting results in persistent changes in the hormones regulating appetite and aggravates underlying hunger (213). Consequently, people living with obesity face a double challenge. Consistent food restriction or dieting increases hunger and food cue responsiveness, undermining inhibitory control and other executive functions. This, in turn, undermines the ability to cope and maintain dietary preferences, particularly in an environment with constant exposure to food cues through pervasive food advertising. This, in turn, negatively affects mood, reinforcing food-related coping strategies. Weakened appetite control is further undermined by physiological and neurochemical responses to reductions in body mass—the so-called “adipostat” (214).

Compounding these neurohumoral responses driving food ingestion are psychological factors that impact mental health and secondarily drive food intake. Whether in clinical samples (e.g., patients waiting to undergo bariatric surgery) or in population studies, obesity is associated with levels of psychological disorders that exceed population norms, including depression, anxiety, substance abuse, and eating disorders. Meta-analyses indicate that greater consumption of UPFs is associated with depression both cross-sectionally and prospectively (215). The relationship between obesity and depression appears bidirectional, with depression increasing the likelihood of developing obesity and vice versa (216). This relationship is apparent even in children as young as 7 years (217). A high proportion of adolescents living with obesity report poor mental health and low self-esteem (212) Struggling with weight at an earlier age is not only associated with higher adult weight and poorer weight management outcomes but also with increased feelings of helplessness (218).

Personal circumstances and life events have well-established impacts on both mental health and weight gain. Adverse life events or past trauma are associated with obesity (219). Stress is associated with visceral obesity (220) and mood impairment, undermining dietary restraint and thus leading to increased vulnerability to temptations (the urge to break the diet) and lapses (when the diet is broken). In turn, these reinforce feelings of failure and guilt, undermining general self-esteem and the success of weight management (221).

Social ecosystems and living environments

The built environment shapes the living space individuals must negotiate, their food access, and their liberty to be physically mobile. Environments affect the prevalence of obesity by influencing both energy intake, via access to healthy foods (222, 223), and energy expenditure, via access to an environment conducive to physical activity (224–227). Specifically, sociodemographic risk factors for obesity include safety, food security, food access, access to safe public transportation, car commute times, and safe walking pathways (228–230). The past century has witnessed significant shifts in the built environment through rapid urbanization, mass transport, increased mechanization, and labor-saving technologies, which have affected all domains of life (231). Coupled with the growth of service industries, these factors have profoundly changed the food environment and reduced physical activity (26).

Food environment: a key driver of obesity prevalence

Studies of dietary habits of communities stratified by socioeconomic status have shown that poorer neighborhoods have greater access to corner and convenience stores (232) and fast-food restaurants, with residents consuming more fried foods, oil, and less-healthy snacks, and not meeting recommended daily intakes of fruit and vegetables (233). Such areas with high density of fast-food and “junk food” relative to healthy food access are often called “food swamps” (234). Food swamps strongly relate to high obesity prevalence (234) and obesity co-morbidities (235). For example, among 3038 United States counties, those with the highest food swamp scores had a 77% (95% CI: 1.43–2.19) increased age-adjusted odds of high obesity-related cancer mortality versus areas with low scores (235). Even after adjusting for age, race, and poverty rates, counties with a high food swamp score had almost 30% increased odds of high obesity-related cancer mortality (1.29; 95% CI: 1.03–1.63). “Food deserts,” where residents have limited access to nutritious foods, also exist within cities, rural areas, and remote areas (222) and are related to increased risk of obesity (234) and associated health outcomes (235).

The existence of food swamps and deserts could also be considered as a commercial strategy whereby certain lower socioeconomic status areas are profiled and provisioned with low-quality, energy-dense and nutrition-poor foods (with high rates of UPFs) for consumers with lower food literacy and little choice (236, 237).

Physical activity

There is a strong association between neighborhood characteristics and obesity. For example, a recent systematic review showed that green space, parks, and recreation facilities are beneficial for children’s weight (238). The availability of schools, parks, and playgrounds plays a key role in promoting physical activity in rural communities in the United States (239). Likewise, studies of adults in Spain and throughout Latin America found that higher exposure to green space was associated with reduced rates of obesity (240–242).

A review of 13 studies concluded that walkability generally promoted more active lifestyles, although not all of the studies supported this conclusion (243). However, changes in the built environment have reduced the walkability of neighborhoods: for example, settings may not be considered safe or may lack footpaths or amenities within walking distance. An analysis of data from 93,280 individuals with obesity from 10 countries found that higher intersection density was associated with higher levels of BMI and obesity, although, unlike other studies, it did not establish a link between urban sprawl and higher BMI or obesity (242).

Education

There are complex, often bidirectional and culturally determined relationships between education and obesity. In high-income countries, there is a strong link between childhood obesity and a lower level of maternal education (244). However, in China and other LMICs, the opposite is apparent, with childhood obesity relating to a higher level of maternal education (245). This may be confounded by other cultural factors, such as excess weight being regarded as a sign of financial success.

Children with obesity show different educational outcomes compared to their peers, indicating early divergence in trajectories. Prospective Swedish data show that childhood obesity predicts fewer years of education achieved by early adulthood (246). This may be explained by lower parental education influencing expectations and trajectories but also by the known discrimination against children who live with obesity. For example, teacher-dependent weight bias is associated with the award of lower academic marks in children who have obesity, particularly girls (247, 248). There is further evidence of subjective downgrading of assessments of children who have obesity, regardless of their objective performance (249). The stigma of obesity plus lower educational achievement imposes a double disadvantage on girls. Employment discrimination, a key determinant of financial success, is evident against adult women living with obesity (250). Income gradients are also evident in women who live with excess weight or obesity and are, in turn, linked to less frequent access to health screening and interventions (251).

The importance of education is discussed later with respect to its foundational importance to the success of public health measures to improve diet and lifestyle.

Obesity management: options and limits

Appropriate management of excess weight and obesity is patient-centered, such that the clinician considers the patient’s needs and wishes (252–254). A recent series of Cochrane Collaboration systematic reviews of interventions for childhood obesity concluded that dietary interventions alone result in little to no difference. For children aged 5–11 years, school-based physical activity interventions, alone or combined with dietary interventions, may show modest beneficial effects over the short and medium term but not at long-term follow-up. In adolescents aged 12–18 years, there was low certainty in evidence that physical activity interventions may have a small beneficial effect in the medium and long term (255–257). Recent medical and surgical interventions have transformed weight loss for adults, and there are no relevant and contemporary Cochrane reviews for adult obesity.

Dietary management

Key to obesity management is a comprehensive behavioral program that includes both diet and physical activity that helps the patient either lose or maintain a healthy weight (see Supplementary Table 1). Dietary modifications are estimated to account for 80% of weight loss following behavioral programs and physical activity for the remaining 20% (258).

There is strong evidence for many different types of diets that vary in micronutrient content, nutrient or energy density, or other weight loss strategies (e.g., meal timing, intermittent fasting, or use of meal replacements) (see Supplementary Table 2). Although many of these diets were introduced more than 10 years ago, they remain valid and in agreement with the current treatment paradigm. There has been much recent emphasis on dietary patterns, defined as quantities, proportions, varieties, or the combination of foods, drinks, or dietary nutrients and frequent habitual consumption of these foodstuffs (see Supplementary Table 3 for examples) (259). Attention has also been given to meal patterns, relating to when individuals eat rather than what they eat, for example, intermittent fasting, time-restricted eating, or other forms of timing or regularity of eating plans. There have been some indications that intermittent fasting can result in cardiovascular benefits (including blood pressure and improved dyslipidemia) and weight loss (260). A comprehensive review found improved overall health outcomes for intermittent fasting (261). A review of intermittent fasting concluded that it may be helpful for the treatment of obesity, producing weight losses of 0.8–13.0% (262). Benefits of other types of fasting regimens have also been reported (263). However, studies so far have been small and short term. Whether intermittent fasting can be sustained in the long term given an individual’s work life, family life, and level of motivation is an open question.

Long-term adherence to a diet is key for success and can be enhanced if the hunger that accompanies weight loss is controlled, the diet is individualized, and food intake is self-monitored (264). Weight loss is accompanied by adaptations in the gut, including altered release of appetite-regulating hormones such as ghrelin, CCK, and PYY, resulting in an increased drive to eat that may offset the loss of weight. The induction of ketosis appears to blunt these changes (265, 266). This has increased interest in very low-energy diets (VLEDs), of the order of 800 calories daily, to promote rapid weight loss with the benefit of ketosis (267). Clinical trials have indicated that VLEDs can enable sustainable weight reduction with greater adherence than gradual weight loss (267, 268). Many patients find that faster weight loss enhances motivation and improves compliance. Weight reductions of 10–15% and remission from T2D have been reported for VLEDs, offering an intervention with a response intermediate between the more extreme intervention of bariatric surgery and conventional diets (269). Although the changes in gastrointestinal hormones following a VLED are not as great as those induced by bariatric surgery, the changes in ratings of postprandial hunger and fullness were comparable (270). A potential problem is that VLEDs do not provide sufficient nutrients and nutrient supplements are required. Ketones appear on day 3 and can suppress hunger better than most medications. Hunger hormone levels change very soon after weight loss starts (271), and ketones blunt some of these changes (272, 273). In addition, ketones enter the same pathway that glucose uses to suppress hunger. As patients approach their target weight, or earlier in patients who cannot avoid carbohydrates, hunger-suppressing medications may become necessary over the long term because the hormone changes leading to increased hunger remain. Hunger can be managed by a range of medications, individualized for the patient, including semaglutide, tirzepatide, bupropion/naltrexone, and phentermine. As multiple hormones suppress hunger after a meal, it may be necessary to use low doses of multiple medicines.

Medical mitigation

Given the difficulties in achieving and maintaining weight loss via diet and physical activity, the global rise in obesity incidence, and the overwhelming medication market potential, the search for effective and safe medical interventions has exploded. Owing to past failures of evidence-based medical treatments, an enormous industry selling alternative weight-loss products—worth US$33 billion/year in the United States alone—has grown, even though such products generally result in only a 3% loss in weight (274). However, there have been significant recent breakthroughs in medical treatments. Here we outline these and their implications, though potential future agents fall outside the scope of this review.

Effectiveness and safety of incretinomimetics

Currently, incretinomimetics appear to be the most efficacious anti-obesity agents: these include the GLP-1RA agonists, semaglutide and liraglutide, and the combined GLP-1RA and GIP agonist, tirzepatide. A multitude of other agents are in development for T2D, including the GLP-1/GIP/glucagon (“triple-G”) agonist retatrutide, and non-incretinomimetic agents. Recent results indicate a 24% loss of weight after 48 weeks of treatment with retatrutide (275). It has also recently been suggested that GLP-1RAs may provide a novel means of directing other drugs (e.g., glutamate receptor agonists) to relevant neurons involved in satiety, achieving even greater effects on body weights than the incretin itself (276).

These medications have a multitude of effects that manifest as altered eating and drinking behaviors that reduce energy intake, improve satiety with earlier cessation of food intake during a meal, and reduce food cravings (277, 278). Efficacy in weight loss and weight loss durability (while on trial) have been demonstrated in large, randomized clinical trials (summarized in Supplementary Table 4).

However, there are important safety and side effect considerations. Indeed, 25 anti-obesity medications have been withdrawn since 1975 (279). Safety remains a fundamental concern, both short and long term, since obesity will not be “cured” by a 3-month or 3-year treatment period. The incretinomimetic clinical trials for obesity have been relatively short-term (Supplementary Table 4). Currently, medication-specific adverse effects from incretinomimetics are predominantly gastroenterological and often dose-related, particularly in the initial dose-escalation phase, including nausea, vomiting, diarrhea, and constipation. Gastro-esophageal reflux disease (GERD) symptoms can worsen. Reports of pancreatitis indicate cautious use of these agents in susceptible patients, including those with underlying pancreatic disease or excess ethanol consumption and possibly prior colonic or neuroendocrine malignancies. The rapid weight loss that can occur during therapy requires ongoing clinical management, including careful dosage adjustment for existing medications and, in the elderly, a concurrent exercise program to avoid the risk of sarcopenia (280). Given the relative novelty of GLP-1RAs, medium-term evidence on safety in people with normoglycemic obesity is only just being reported. For example, the incidence of death from cardiovascular causes, non-fatal myocardial infarction, or stroke was reduced by 20% at a mean of almost 40 months of follow-up with semaglutide (47, 281).

With the current agents, once stopped, the weight lost is regained within a year, implying a requirement for lifelong intervention. The real-world long-term tolerability of current agents is low: only 27% of patients remained on the prescribed drug after 1 year in one large study (282). As obesity increasingly affects younger people, and hence pharmacotherapy is extended to adolescents and potentially children, incretinomimetics and other effective agents require long-term safety surveillance, including their effects during growth and pubertal development, before and after pregnancy, and on cardiovascular, gastroenterological, mental, and cognitive health. A final concern is the impact of the “medicalization” of such a large proportion of the world’s population on the constrained resources of health systems.

Effective pharmacotherapy may undermine the importance of ongoing lifestyle change, with attendant health impacts. Alternatively, it may create space for lifestyle change for people who have not been able to make sustained lifestyle and food behavioral changes in a hostile food environment.

Costs and access parity

The need for continued treatment, poor tolerability, and high cost indicate that these newer medications are currently not cost-effective, especially when compared to bariatric surgery (283, 284). At a population level, 93 million American adults currently meet the criteria for prescription of GLP-1RA: at current prices, the annual cost would amount to $US600 billion, which is equivalent to all other prescription drugs combined (285). The population-level costs of these interventions are set to balloon with obesity projected to affect more than 50% of many populations (1, 286) and the age of onset decreasing into early childhood.

The United States Congressional Budget Office estimates that authorizing the use of anti-obesity medications to all Medicare beneficiaries with obesity, and some classified as overweight, would increase net federal spending by about US$35 billion between 2026 and 2034. It projects that total direct federal costs of covering these agents would increase from US$1.6 billion in 2026 to US$7.1 billion in 2034, far exceeding total savings from beneficiaries’ improved health (287).

In Denmark, reimbursement of proprietary semaglutide alone for the 900,000 citizens with BMI ≥30 would reportedly cost the state the equivalent of US$3.5–4 billion/year based on 2023 prices (288).

The idea that wider use and competition will reduce costs is not encouraging, given trends for prices for other medications such as insulin. Initial indications suggest that pharmaceutical companies have already planned strategies for price protection with some GLP-1RAs and their delivery systems covered by 20 patents each, extending protection to 2040 or beyond (289).

Examination of a US database revealed that only 2.4% of people with obesity in 2010–2019 accessed pharmacotherapy (290). Obvious factors may include socio-demographics; however, this was prior to the reporting of the efficacy of newer GLP-1RAs. Frameworks for the fair allocation of such drugs that are limited in supply are currently being debated (291). Evidence on variations in access to these therapies within and between nations is needed, particularly in nations where socio-demographic factors affect healthcare access. Medical treatments could be made more widely available and affordable by using GLP-1RAs for initial weight loss and then switching to older, less effective drugs and/or lifestyle modifications for maintenance (261 Such options need to be evaluated.

Re-engineering the human body: bariatric surgery

Despite recent advances in pharmacotherapy, surgery remains the most effective treatment for morbid obesity, given the strength of evidence for durability in long-term weight loss and resolution of the consequent disease sequelae. Surgical techniques have evolved for over six decades following the observation that patients undergoing subtotal gastrectomy for cancer subsequently experienced sustained weight loss (292). The original aims of bariatric surgery were to reduce gastric capacity or to create a diversion bypassing sections of the gastric tract, thereby restricting food intake and/or absorption. However, the actual mechanisms of weight loss are more complex and may also involve changes in neural signals affecting central appetite control, the release of gut hormones, changes in gut microbiota, and bile acids (293).

Bariatric surgery has become more prevalent with increasing obesity rates and is now almost a routine surgical procedure in many countries. In 2018, around 400,000 patients worldwide underwent bariatric surgery (294), with an estimated 250,000 procedures in the United States alone (295). Recent guidelines for bariatric surgery are for individuals with a BMI ≥35 kg/m² and those with a BMI of 30.0–34.9 kg/m² when obesity-related comorbidities are present (296). The lower limit was recommended for patients with T2D for whom adequate glycemic control could not be achieved by lifestyle or optimal medications (297, 298).

The quality of surgical expertise and post-operative care has improved following the implementation of standardized protocols, with the incidence of complications and mortality reduced to 1.4% and 0.04%, respectively, for minimally invasive procedures in the United States (299, 300). However, bariatric surgery remains underutilized relative to need, with around 0.5% of patients who meet the indications undergoing surgery in the United States (299). This is despite the improvements in safety and despite surgery remaining the most cost-effective intervention over time—its initial high cost being far outweighed by the accrued long-term savings in health costs due to greater weight loss and T2D remission (301–304). The recent development of effective incretinomimetics and other drugs for obesity may reduce requests for and uptake of bariatric surgery in the future.

Procedures and effectiveness

Around 98% of bariatric surgical procedures are now laparoscopic, with many undertaken as day cases (299, 305). Various procedures have evolved, differing in their complexity, particularly in the number of new intestinal connections, or anastomoses, required (Figure 4). The most straightforward is vertical sleeve gastrectomy (VSG), which involves no anastomosis but just a vertical dissection to drastically reduce the size of the stomach. VSG has become the most popular bariatric procedure in the last 10 years owing to its excellent efficacy and safety profile (306, 307). In contrast, Roux-en-Y gastric bypass (RYGB) involves the creation of a small gastric pouch and two anastomoses so that ingested food bypasses most of the stomach and the first portion of the digestive tract. Simpler procedures have gained popularity, including the “mini-gastric bypass” or one-anastomosis gastric bypass (OAGB) (308) and the single anastomosis duodeno-ileal bypass (SADI-S). These achieve results comparable to or better than RYGB but with shorter operating times and lower complication rates (309, 310). Robotic bariatric surgery has recently become popular with results comparable to laparoscopic surgery despite longer operative time and higher costs (311, 312).

Figure 4

Figure 4. Bariatric surgery procedures include (A) vertical sleeve gastrectomy, which involves no anastomosis but just a vertical dissection to reduce the size of the stomach, (B) Roux-en-Y gastric bypass (RYGB), which involves the creation of a small gastric pouch and two anastomoses so that ingested food bypasses most of the stomach and the first portion of the digestive tract, (C) “mini-gastric bypass” or one-anastomosis gastric bypass (OAGB), and (D) single anastomosis duodeno-ileal bypass (SADI-S).

Compared with optimal medical therapy plus lifestyle management, bariatric surgery resulted in almost double the weight loss after 12 years, and a significantly higher T2D remission rate, according to combined data from four randomized trials (298). Remission from T2D progressively declined following surgery from 51% at 1 year to 13% at 12 years; this might be explained by progressive lapses in adherence to post-operative lifestyle regimens or to continued β-cell functional decline despite the sustained weight loss (313). Given the slow development of T2D with progressive β-cell failure, earlier surgical intervention may achieve better long-term remission. Trials comparing outcomes following bariatric surgery versus the new GLP-1RAs are currently underway (314).

Bariatric surgery also results in sustained improvements in most adverse sequelae associated with morbid obesity and extends life expectancy (315, 316). Cardiovascular health is significantly improved along with long-term improvements in nonalcoholic steatohepatitis (NASH) and liver disease (317–320). The risks of several obesity-related cancers are also significantly reduced (321–323). Bariatric surgery can help patients meet BMI eligibility criteria for access to renal and heart transplantation (324, 325) or even obviate the need for heart transplantation (326, 327). Bariatric surgery also results in improvements in osteoarthritis, reducing the need for pain management and orthopedic surgery (328, 329). Bariatric surgery can reduce depressive symptoms, improve memory, attention, and executive function, and induce long-term changes in brain structure (330–332). Nevertheless, 52.5% of adolescents in one study still showed mild cognitive impairment 10 years after bariatric surgery (333). Longitudinal studies will be required to establish whether cognitive deficits are more immutable when obesity occurs at an early age.

Risks and limits of surgery

Bariatric surgery remains a major re-engineering of the human body, and there are potential adverse events. After 12 years, patients who underwent surgery had higher rates of anemia and fractures (possibly due to greater weight loss or potential nutritional deficiencies), and more adverse gastrointestinal events, than those treated with medical plus lifestyle management (298). The most common procedure, VSG, is associated with a high rate of GERD (334), and, most concerning, 8% of patients develop the premalignant condition Barrett’s esophagus (335). The profound weight loss and anatomical changes also have significant effects on the uptake and pharmacokinetics of orally administered drugs (336, 337). This necessitates careful consideration of alternatives or adjustment of dosages to ensure efficacy and avoid toxicities.

Bariatric surgery has long-term consequences and requires life-long multidisciplinary follow-up, monitoring, and aftercare to achieve optimal outcomes and minimize the risk of complications. Even more essential is careful patient selection, preoperative evaluation, and preoperative work-up (338). This should include education and counseling to manage expectations and ensure adherence to strict postoperative nutrition and eating behaviors. Weight loss prior to surgery may reduce the risk of surgical complications (338, 339), and initial findings suggest that GLP-1RAs may be appropriate (340).

The challenge of weight loss maintenance

Many weight loss strategies do not appear to have long-term durability (or “fail”) due to a number of adaptive responses that have evolved over millennia to defend the body’s energy stores. These include a rise in the level of ghrelin (341) and a fall in the many hormones that suppress hunger, including leptin, CCK, GLP-1, amylin, and pancreatic polypeptide, promoting food seeking and hunger. These hormonal changes are upregulated with even minor weight loss (even as little as 5 kg) (272) and persist long term (342) in the context of reduced energy expenditure (343). The resilience and effectiveness of these adaptive responses to weight reduction require additional interventions to prevent weight regain and remain a strong argument for the primacy of the prevention of obesity.

The challenge of holistic care in medical mitigation

Well-developed strategies for the management of other chronic diseases should be adopted for people with obesity. For example, there is much to learn from care models for ischemic heart disease, T2D, and even cancer risk reduction in highly susceptible groups. Since there are many similar comorbidities and complications of obesity, prevention and treatment strategies applied in each of these conditions could be replicated for the care of people living with obesity. These approaches include optimizing blood pressure, lipid, and glucose levels and supportive management of sleep apnea or hypoventilation syndrome and GERD. Specific physical regimens are recommended for people impaired by degenerative joint disease.

Cancer risk mitigation is a specific health need for people living with obesity, and yet this group experiences barriers to screening for bowel, breast, and cervical cancer (344). Women with obesity less frequently pursue breast and cervical cancer screening (345) and have larger breast cancers when detected by screening (346). Women with oligoamenorrhea require interventions that reduce endometrial cancer risk.

In conclusion, medical mitigation of obesity and its consequences requires intense lifestyle support, pharmacotherapy, and consideration of bariatric procedures. The co-morbidities of obesity require medical intervention, perhaps for life. This paradigm has an immense impact on personal and national health resources, particularly in resource-poor settings. Given the potential long-term consequences, the inflated costs of interventions (347), and limitations on medical resources, the future must be in obesity prevention (348).

The obesity and climate co-crises

Rapid global economic growth throughout the 20th century is at risk of being undone in the 21st century due to human-induced climate change, environmental damage, and biodiversity loss (349). Similarly, the dramatic global health gains seen in the second half of the 20th century are at risk of reversing due to the syndemic of obesity, undernutrition, and climate change (36). The obesity and climate crises are inter-linked: climate change aggravates obesity, and obesity aggravates factors contributing to climate change—with both crises resulting from forms of overconsumption of food and goods (Figure 5). In this section, we review these inter-relationships as a basis for reflection on future action to tackle both crises.

Figure 5

Figure 5. Inter-relationships between the obesity and climate co-crises.

Climate change aggravates obesity risks

Increasing temperature may make a very small contribution to obesity through reduced adaptive thermogenesis. Adaptive thermogenesis is a core function to maintain body temperature and can amount to up to 10% of total energy expenditure; as environmental temperatures rise, less energy is expended in adaptive thermogenesis, and more consumed calories are partitioned to adipose storage (350, 351). Individuals living with obesity are less able to tolerate increased temperatures owing to a diminished ability to dissipate heat (352), resulting in a greater risk of heatstroke and heat-related poor health (353). In the 2003 heatwave in Europe, obesity doubled the risk of death in the elderly (352). The recent Lancet Countdown on Health and Climate estimated that, on average globally, the number of heatwave days that people over the age of 65 years were exposed to in 2024 had increased by 304% compared to the average exposure over the period 1986–2005 (43).

Obesity accelerates climate change via food systems

Obesity prevalence rates in different world regions correlate with GHG emissions (36). Obesity contributes to increased GHG emissions from transport systems (through the movement of increased weight) and, most importantly, via food systems (354). Maintaining obesity requires sustained energy over-consumption, which increases energy demands and hence the associated emissions. Compared with a normal BMI, the over-consumption required to sustain obesity generates 20% higher GHG emissions, mainly from the additional food production (354). An analysis of the effects of changes in the weight of individuals between 1975 and 2014 on food requirements across 186 countries estimated that increased weight (adjusted for changes in age distribution) added a further 13% to the food energy requirements over this period, equivalent to the needs of 286 million adults (355).

At the global level, the increased consumption of UPFs that underlie the obesity crisis, the processed meats in those UPFs, and the large-scale monoculture of crops required for producing UPFs accelerate the contribution of food production to climate change and environmental damage, including via land clearance, degradation, and biodiversity loss. While the emphasis has been on transforming energy systems to phase out the burning of fossil fuels, the evidence clearly shows that our current food systems are also a major contributor to climate change. Food production accounts for 25–33% of global GHG emissions, depending on how widely you draw the system boundary, for example, whether decomposition emissions and what forms of land use change are included (356, 357). It has been estimated that current food systems will add nearly 1°C to global warming by the end of this century (358). Even if all fossil fuel emissions were halted immediately, the current food system on its own could be enough to breach 1.5°C and potentially 2°C climate targets (359).

Agriculture occupies around 40% of ice-free land across the planet, 70–80% of which is used for animal protein production (360, 361), and is responsible for 70% of freshwater use (362). Food production is the largest cause of land clearance and hence loss of ecosystems and biodiversity (363). Clearance of forests, savannas, and grasslands and draining of wetlands releases carbon dioxide (CO₂) and also removes important sites of carbon sequestration. This has created a vicious cycle with changes in land use and climate change then threatening mass species extinctions and the collapse of the natural world; over 30 years, the global population of flying insects has decreased by 70%, and over 15 years, farmland bird populations have reduced by 30% (364). This compromises food production, as flying insects are responsible for the fertilization of many crops.

Climate change affects food systems in various other ways. Increasing atmospheric CO₂ reduces the yield and nutrient content of most crops, such as maize, rice, and soy, although wheat yields increase (365, 366). However, estimations of the impact of climate change must also account for the effects of increases in extreme weather events and losses to pests. The chance of multi-breadbasket failures due to extreme weather events increases rapidly at 1.5 and 2°C (367). Global crop losses to insect pests due to climate change increase by 10–25% per degree of warming (368). Likely, the impacts of climate change on food systems have been systemically underestimated, similar to the impacts on sea level rise, ice-sheet loss, and other large-scale transitions (369).

The food systems contributing to obesity also have local socioeconomic impacts. The activities of transnational food corporations (TNCs) in poorer nations result in loss of intergenerational subsistence farms, often subsumed into mega-plantations utilizing cheap local labor—exacerbating local poverty and land degradation, particularly where governments do not have robust controls or monitoring (370, 371).

Animal-based foods confer the most environmental impact

Our foods and their production methods vary considerably in terms of their impact both on our health and on the climate. The increasing global consumption of UPFs that adversely affect human health has also accelerated the environmental damage attributed to food production. UPFs are generally manufactured using ingredients extracted from a small number of high-yield mono-crops, such as soy, maize, wheat, and oil-seed crops, and—more importantly—animal-based ingredients that are obtained from animals fed on the same crops, all of which use large amounts of land, water, fertilizers, and herbicides and accelerate climate change (372–374).

Animal-based foods account for a large proportion of the environmental impact of food production (364). Grazing livestock alone contributes 9.3% of total global GHG emissions (375). If current dietary trends continue, GHG emissions from food production would increase by 80% by 2050 (376). In particular, the production of ruminant meat, primarily beef and dairy, has increased dramatically. This has a particularly high environmental impact, primarily due to the greater use of land, freshwater, and fertilizers in beef production: beef requires 28-fold more land and 11-fold more freshwater than poultry production (377). On average, the production of 100 g of protein from beef generates 50 kg CO₂, compared with 20 kg from lamb, 5.7 kg from chicken, 0.84 kg from beans, and 0.44 kg from peas (361) (Figure 6). Moreover, livestock production is responsible for 37% of anthropogenic emissions of methane (52, 364), which has an 85-fold greater warming potential than CO₂ over a 20-year timeframe. Methane has particular environmental significance as it is oxidized in the troposphere and cleared from the atmosphere within 12 years (378). Therefore, although reducing CO₂ emissions is essential to prevent further global warming, decreasing the huge number of ruminants could reduce near-term temperatures, as the methane that they produce would rapidly disappear. Hence, reducing meat consumption is a critical practical measure to rapidly reduce global warming. For the livestock sector to comply with Paris Agreement 1.5°C climate targets, climate experts estimated that a 61% reduction in global emissions due to livestock would be needed by 2036 (379).

Figure 6

Figure 6. Environmental costs of different foods. Figure shows (A) carbon dioxide and (B) land use to produce 100 g of protein (361).

As with fossil fuel emissions, the contributions of food production to climate change differ greatly between world regions. According to estimates, if everyone in the world adopted the lifestyle and diet of the average middle-class American, then the planet could only support a quarter of its current population (354).

Reforming obesogenic ecosystems

Following current trends, more than 50% of all humans will be living with excess weight or obesity by 2035, costing the global economy around US$4 trillion/year and conferring a potentially unsustainable burden on health systems. It is a societal crisis that requires societal solutions. There is a growing consensus that the primary driver has been a dramatic change in the food environment (36). The food environment is determined by individual factors, such as living conditions, socioeconomic status, food literacy, and personal preferences, but also by societal/environmental factors, such as food security and the availability and affordability of healthy food choices. United Nations (UN) agencies highlight that current global food systems are failing to provide affordable and healthy foods, while simultaneously driving substantial harm to the environment (380). In short, current food systems are failing to eradicate malnutrition and contribute to the global pandemic of obesity, cardiometabolic diseases, and cancer. Concurrently, the food system has been a major contributor to climate change, causing widespread environmental, land, and soil degradation, loss of biodiversity and ecosystems, considerable GHG emissions, and workforce exploitation (380). Both food and environmental impacts threaten our healthcare systems (36).

Changes to the food system required to help achieve the targets necessary to prevent climate catastrophes could also address the nutritional requirements to mitigate the obesity pandemic and many other chronic diseases. However, there are strong political and market incentives to avoid or delay systems change. This leads to a “papering over the cracks” of the problem, for example, the use of medical or surgical interventions to treat obesity or strategies such as geoengineering for climate change, both of which can have long-term consequences that are not easily reversed (381). Indeed, the GLP-1RAs have considerably improved the treatment options for the millions currently living with obesity, and their effectiveness should help sway the argument from obesity being due to a failure of willpower to it being recognized as a disease associated with a hormonal imbalance. However, a downside could be that politicians may argue that effective treatments remove the need for societal, policy-level action (sometimes characterized as “nanny-state” interventions): such arguments should be negated by the economic and health benefits of prevention. This is similar to the concerns that CO₂ removal strategies to combat climate change may make policymakers and the public imagine that there is no need to reduce the burning of fossil fuels because the emissions can be offset, which has been termed the “moral hazard” (382).

Our current food environment is characterized by the ready availability of cheap and unhealthy foods that are addictive. There is overwhelming evidence that a diet dominated by energy-dense UPFs is detrimental to human health and a critical factor in the obesity pandemic. Some have argued that, as the precise mechanism linking UPF consumption to obesity has not been verified, it is too soon to recommend changes (383). However, there are many potential mechanisms, and likely many of these are working simultaneously. There is also no evidence suggesting it would be safe or healthy for humans to rapidly switch to consuming the complex, artificial mixtures and formulations typical in UPFs. With such strong evidence of harm, gold-standard evidence from long-term, randomized clinical trials is unlikely to be obtained and would probably be unethical owing to the anticipation of adverse outcomes.

There are major, complex environmental factors contributing to the challenge of food selection, including food access, food insecurity, and food literacy, particularly for people residing in lower socioeconomic status areas. To change this ecosystem, healthy food options need to be made more available and more affordable and unhealthy foods made less attractive by ensuring that their harms to health and the environment are accurately reflected in their costs. The Food Insecurity in people living with Obesity Initiative (FIO) articulates a multipronged approach to tackling this issue in the United Kingdom (384). Different strategies and approaches will likely be necessary in nations with other approaches to public health, where responsibility for health is devolved to the individual regardless of their health literacy or access to resources.

In this section, we review recent evidence that i) supports a transition to food systems that are both healthier and more environmentally sustainable, ii) explains why previous obesity strategies and policies have failed, and iii) indicates which options are key to reforming obesogenic ecosystems (Figure 7).

Figure 7

Figure 7. Reforming food systems and environments to address obesity and climate co-crises. Multicomponent interventions are needed to “move the dial” on obesity rates via actions to reduce unhealthy, unsustainable food consumption and increase healthy, sustainable food consumption. Multisectoral engagement spans health, agriculture, transport, planning, recreation and parks, industry, education, and the media. The figure illustrates the key interventions and drivers or “catalyzing” factors.

Transitioning to a healthy and sustainable food system

The transformation of agriculture was largely successful in enabling the production of sufficient food for expanding populations at an affordable cost. The drive for large-scale farms with a monoculture of crops and industrialization of animal farming and food production enhanced profits but caused environmental damage, contributed to climate change, and has chronic adverse effects on human health (385). It is now recognized that a new transition is required to develop sustainable food systems.

A healthy and sustainable food system that also counters the obesity epidemic will need a gradual shift away from the high proportion of energy-dense, low-fiber UPFs in diets. A recent systematic review concluded that for this to be accomplished, policies are needed to address the UPF products themselves, but also food supply chains, food environments, UPF corporations, and fast-food and supermarket corporations (386). In 2019, the EAT-Lancet Commission on healthy diets from sustainable food systems (364) used the planetary boundaries framework (387) to develop safe operating boundaries for the global food system. It concluded that a transition to a healthy and sustainable food system will require a >50% reduction in the consumption of unhealthy foods, particularly red meat, UPFs, and sugars, replaced by a >100% increase in the consumption of healthy foods, such as fruits, vegetables, nuts, and legumes (364). However, even a small increase in consumption of red meat or dairy foods would make it difficult or impossible to maintain the planet in a safe operating space. The Commission estimated that the adoption of a largely plant-based diet by 2050 could reduce food-related GHG emissions by 80%, that more efficient food production could reduce emissions a further 10%, and halving food waste could reduce another 5%.

The Commission recommended diets aimed at improving both human and planetary health. These include a range of food groups but with between zero to moderate animal product intake at a much lower level than in high-income countries today (364). The Commission used a Mediterranean diet as an example of a healthy, sustainable diet that traditionally was largely plant-based and low in red meat but high in legumes and fat, predominantly olive oil. The energy-reduced Mediterranean diet can mitigate the potential negative effects of age-dependent changes in body composition with a reduction in total and visceral fat and a maintenance of lean body mass (388).

The Commission has recently published an update in which they emphasize that the evidence has become even more compelling for an urgent need for a radical transformation of global food systems to ensure the health of humans and to ensure that the planet remains within safe boundaries for sustaining life (389). They report that food systems are the largest single cause of the transgression of five of the six planetary boundaries that have already been breached, these being biodiversity, freshwater flows, biogeochemical flows (nitrogen and phosphorus cycles), and novel entities (pesticides and antimicrobial use) (389). Food systems have also had a notable impact on the climate and ocean acidification boundaries, with agricultural and food systems contributing 30% of total global GHG emissions. That diets proposed by the Lancet Commission as sustainable for a healthy planet can also reduce the risk of obesity was confirmed by a recent umbrella review of food groups associated with excess weight and obesity (390). The risk of being overweight or obese was lowest in those with a high intake of whole grains, legumes, nuts, and fruits and highest in those with a high intake of red meat and SSBs (390).

Recent studies have analyzed the environmental impacts of different diets and attempted to define an ideal diet that, in contrast to diets previously designed solely for weight loss, would be healthy for both humans and the planet. An analysis of dietary data from a large United Kingdom population estimated that the GHG emissions associated with a vegan diet were 74.9% lower than for high meat diets, and the environmental impacts of high meat consumption were at least 30% higher compared to those whose diet included low levels of meat (391). Similarly, a comparison of the environmental impacts of a Mediterranean diet with the current Spanish diet and a cafeteria-style American diet, using food balance sheets from the UN Food and Agriculture Organization (FAO), indicated that meat product consumption was the main contributor to large differences in GHG emissions. Emissions could be 72% lower if the Spanish population adopted a Mediterranean diet (392). Recent comparisons of common American diets also showed ruminant meat consumption to be the main factor driving environmental impact (393, 394).

Recent large prospective cohort studies have quantified the human and planetary co-benefits of sustainable diets. For example, in Europe, it was estimated that up to 10–39% of incident cancers and 19–63% of deaths could be prevented over 20 years by different levels of adherence to the EAT-Lancet diet. Higher adherence could potentially reduce food-associated GHG emissions and land use by up to 50% and 62%, respectively, compared with lower adherence (395). In the United States, adherence to this diet was associated with lower risk of total mortality, and mortality due to cardiovascular disease, cancer, and other NCDs, together with lower environmental impacts (396).

A recent meta-analysis concluded that an ideal, sustainable diet should be very similar to the diet that our ancestors evolved to consume and should be based on whole foods, specifically a high-fiber, largely plant-based diet consisting mainly of whole grains, legumes, tubers, roots, bulbs, nuts, seeds, vegetables, and fruits, with reduced quantities of meat and dairy (52).

There are further climate and health benefits from dietary change, given the large-scale land use of animal agriculture. Dietary shifts offer the option of returning large areas of land to nature, resulting in carbon sequestration, biodiversity protection, recreational areas, better floodwater retention, and more. If people in high-income nations were to adopt an EAT-Lancet Commission diet, then dietary GHG emissions could be reduced by around 60% (364, 397). This potential could double if the land currently occupied by animal agriculture were returned to nature, drawing down carbon into the land (397, 398). This so-called “double dividend” would also result in additional nature areas closer to urban centers that could help address access to nature, along with mental and physical challenges. It may also result in increased resilience to climate change (e.g., via better floodwater retention versus pasture) and to shocks to the food supply (by releasing land that can be used as a buffer against interruptions) (399). A recent modeling study indicated that a transition to the EAT-Lancet diet would make the pathway to the climate target of 1.5°C much more feasible due to the reduction of GHG emissions, especially methane from ruminants, requiring less CO₂ removal and reducing pressure on GHG prices, energy prices, and food expenditures (400).

Despite a wealth of data, there is a lack of wider appreciation of the problems with the current food system and the changes and targets necessary to achieve a sustainable food system that would ensure both human and planetary health. Achieving the required changes will be complex owing to the need for alignment between many stakeholders, including medical and public health professionals, nutritionists, dietitians, educators, policymakers, agriculture, retailers, food industries, and the general public. For example, the recent Danish Plant-Based Action Plan covers a suite of measures along the supply chain to encourage increased intake of plant-based foods (401).

The required transformation of our food system presents particular challenges for LMICs, where food insecurity, malnutrition, and poor diets are common. The rapid rise in UPF consumption in LMICs adds to these existing challenges (20). In LMICs, any food transformation needs to also address sanitation, adequate clean water, and, where appropriate, fortification and micronutrient supplement strategies. Food already represents a much greater proportion of income in such countries, and affordability is critical (402). With economic improvements resulting in higher incomes, food can become more affordable, but this may not translate into healthier diets as it is generally accompanied by rapid expansion in the availability of UPFs and SSBs and increased exposure to advertising of unhealthy foods (20, 403, 404). Many LMICs currently import a substantial portion of their vegetables, fruits, nuts, and legumes, making them especially vulnerable to international price variations (389). Targeted policy interventions may be required in such countries to build infrastructure, such as storage and transport, and to ensure the affordability of a healthy diet. Furthermore, a large proportion of the population may be employed in the agriculture sector in many LMICs, which can also constitute up to 40% of national GDP, in contrast to less than 5% for high-income countries (405). These factors present additional challenges for transforming food systems within LMICs.

Why have previous obesity strategies and policies failed?

Many national and international institutions have produced reports and recommendations to address the obesity pandemic. In response, many governments and international bodies have endorsed or enacted policies in attempts to reduce obesity rates, but until recently, none have had any meaningful effect. At the highest international level, for example, the WHO agreed a Global Action Plan for the Prevention and Control of Non-Communicable Diseases 2013–2020, with a target to prevent any further increase in the rates of obesity by 2025 (406). Yet, the World Obesity Federation (WOF) concluded that not one country was on target to meet this goal, and most countries had a ≤5% chance of doing so (407). WHO has subsequently updated its objective with the Global Noncommunicable Diseases Compact 2020–2030 and, in 2022, published the Acceleration Plan to Stop Obesity (408).

Evaluating obesity strategies to date

The failure to reduce obesity rates indicates that most national public health policies have been largely ineffective. A review of regulatory approaches to control the obesity pandemic adopted across the United States and European Union (EU) between 2004 and 2013 concluded that these had been limited in reach and scope with no widespread consistent policies adopted (409). Policies were frequently restricted to subpopulations, such as schools, rather than the general population, and governments generally preferred providing consumer information over direct taxes or market restrictions. The policies that have been adopted were generally too brief for effects on obesity or health to become apparent (409).

A more global review of policies concluded that governments have generally lacked sufficient commitment to take effective action; this was at least partly due to insufficient influence from consumers and civil society coupled with the food industry’s ability to moderate policies to protect commercial interests (410). In the United Kingdom, analysis of 14 strategies and 689 specific policies between 1992 and 2020 identified a number of issues (411). Only one strategy was based on an independent evaluation of previous government strategies, and only 24% of the proposed policies included any details for monitoring or evaluation. Only 19% cited any evidence to support the policy, and only 9% included any costings or allocated budget. Many of the proposed policies were similar or identical to ones previously proposed, while several were proposed multiple times but with no reference to their prior proposal and failure to be implemented. The majority of policies were not set out in a manner that would lead to implementation, and they relied heavily on behavioral changes by individuals rather than changes to the food environment (411). A major contributor to such ineffective policies in countries such as the United Kingdom and United States has been conflicts of interest (412, 413). To counter such issues, countries such as Brazil and Mexico have adopted evidence-based policy recommendations developed by experts without commercial conflicts of interest (387, 414).

Commercial obstacles to obesity strategies and polices

A recent analysis of 1,500 climate change policies implemented in 41 countries between 1998 and 2022 found that only 63 were successful (415). A common feature between climate and obesity policy is that, in both cases, powerful commercial entities invest large amounts in lobbying policymakers who are persuaded to choose policies that have no effect and often focus on individual actions rather than those that are more likely to work at a systemic level but at the expense of the dominant commercial actors (416). This is despite the accumulation of evidence indicating that obesity is due to system-level factors and not due to individual fallibility, including strong evidence that people who migrate, over time, adopt the obesity characteristics of their new location (417, 418).

Obesity-reduction strategies relying on industry self-regulation and public-private engagement have proved ineffective, owing to conflicts of interest (419–421). The UPF, SSB, and fast-food companies, and animal agriculture industries, hold disproportionate influence over food environments, particularly for children, and leverage vast financial resources and immense political influences to resist policies aimed at creating healthier and more equitable food environments (61). Indeed, a fundamental transformation of the food environment will involve changes resisted by these very powerful food and agricultural sectors. These sectors together account for some of the largest employers globally: in the United States, one in 10 jobs is in the food production sector (422). Economies of scale have enabled TNCs to generate huge profits by selling processed foods at low prices. Over the period 2009 to 2023, the global sales of UPFs grew from US$1.5 trillion to US$1.9 trillion, with eight TNCs dominating the market and accounting for 42% of the sector’s total assets in 2021 (423). These eight TNCs all have headquarters based in North America and Western Europe, with nearly half located near the decision-making centers of Washington, DC and Brussels (423). The over-consumption of food driving obesity was estimated in 2015 to be worth US$60 billion per year to the food industry in the United States alone, equivalent to around US$80 billion today (424). Between 1962 and 2021, more than half of the shareholder payouts in the food sector were distributed just by the TNCs manufacturing UPFs (425). These profits have attracted investors such that the TNCs are now themselves dominated by a small number of hedge-fund managers and private equity firms who generally oppose public health proposals from other shareholders in favor of maximizing financial returns (425–427). The success of TNCs or managed funds in creating wealth attracts support from governments wanting to promote national economic growth.

A recent international synthesis of the evidence for global action on UPFs concluded that the main obstacle to effective policies was the internationally coordinated political activities of the TNCs that included direct lobbying, infiltrating government agencies, and legal actions to restrict regulation, promote TNC-friendly governance, and manufacture scientific doubt (423). Corporate financial influence has had a negative effect on the adoption of WHO-recommended policies to restrict the consumption of unhealthy foods across the globe (428). Similar to climate controls, the ability of national governments to legislate to control their obesity epidemics can be limited by “free trade” deals that empower TNCs, who have used World Trade Organization (WTO) mechanisms to restrain the ability of governments to regulate their own food systems (429–431). The TNCs have also employed intensive lobbying of UN and WTO bodies to oppose food regulation policies (432). Similar to the strategies employed by the tobacco industry, these powerful bodies have also skewed the scientific debate by sponsoring research, scientific conferences, and organizations to counter the impact of evidence that implicates their food products in driving obesity, and the strategies suggested to restrict these (433–436). With sales of UPFs and ultra-processed beverages becoming saturated in high-income countries, the TNCs have focused on the potential of middle-income countries, where the sales of UPFs are projected to match those of high-income countries this year and those of ultra-processed beverages are already greater (434). The success of the food industry strategies can be seen in China, where the industry-funded International Life Sciences Institute is embedded within the Chinese Centre for Disease Control and Prevention and successfully reframed Chinese obesity policy to emphasize interventions based on physical activity rather than diet, which is at odds with the consensus scientific evidence (437).

In contrast to national-level policies, commercial pressures have been less effective at curtailing municipal authorities. The Milan Urban Food Policy Pact was launched in 2014 with recommendations for food policies, including 620 specific actions covering governance, food production, supply, distribution and waste, social and economic equity, and sustainable diets and nutrition. With 55% of the global population now living in urban centers, projected to rise to 68% by 2050 (438), and with 290 cities (with a combined population of 490 million people) already signed up to the Pact, this has the potential to significantly transform food systems (439).

In 2019, the Lancet Commission on “The Global Syndemic of Obesity, Undernutrition, and Climate Change” called for governance levers to be strengthened at local, national, and international levels; for more policy actions from governments; to reduce commercial entity influence and lobbying on such policies or to make them more transparent; to strengthen accountability for policy actions; and for the creation of sustainable and health-promoting business models that prioritize societal and environmental benefits and not just profits (36).

Removing market distortions: cost transparency

As described, large TNCs have been successful in marketing UPFs, making them ubiquitous in the food environment and generating huge profits (440). The mass production of UPFs adds considerable profit for the food industry and provides cheap food for consumers. Generally, food producers add more salt, low-quality fats, and sugars to UPFs to make them highly palatable and then heavily promote them to increase sales and profits despite their known adverse effects on health (441). Unhealthy diets are much cheaper than healthy diets, and this price differential most impacts lower socioeconomic groups (442, 443). However, the low price of processed foods does not reflect their true cost to society and our environment, often termed negative externalities (380).

These distortions need to be addressed systemically and across the policymaking domain in order for people to make informed, healthy choices while living sustainably. A common libertarian refrain claims that a “nanny state” would intrude into private lives and restrict individual choice (444, 445). However, the current system already makes explicit and implicit decisions on the food choices available to people. Incentives that actively “tilt” the playing field to incentivize further externalization of costs are common. For example, an estimated 82% of EU agricultural subsidies are embedded in animal products that are responsible for 84% of the GHG emissions from EU food production but only supply 35% of the calories (446).

The first step in enabling free transparent choices and making food systems sustainable is to inform consumers of the true costs of food (447) (Figure 8). For example, Americans spend a total of US$1.1 trillion a year on food, yet when the impacts of the food system on rising healthcare costs, climate change, and biodiversity loss were factored in, costs were three-fold higher—at least US$3.2 trillion a year (448).

Figure 8

Figure 8. Comparing consumer and true costs of foods, taking into account negative externalities. Estimated annual costs in the United States. Data from the Rockefeller Foundation (448).

Taxing unhealthy foods to redress their true societal cost

Internalizing all negative externalities into the cost of food would likely make many foods unaffordable in most countries. However, an understanding of all of the true costs would enable policymakers to use levers for reducing external costs while informing citizens of the real cost of their choices. Many nations have introduced taxes on unhealthy foods, the most common being taxes on SSBs adopted to varying degrees by 103 countries covering 51% of the global population (449). Meta-analysis of 62 studies indicated that such taxes resulted in average reductions of 15% in sales and 18% in intake of SSBs (450).

Latin American nations (451, 452) and Pacific islands (453) have consistently been at the forefront in applying policies to control obesity. This is likely a result of their recent rapid trends in obesity prevalence that are more clearly linked to food system changes. The influence of the food industry to impede political actions in these nations may also be less entrenched than elsewhere, as TNCs are generally not based in these countries. For example, in 2014, Mexico imposed a tax of 1 peso/L on SSBs, representing a small 5.3% increase in the purchase price, and an 8% ad valorem tax on non-essential energy-dense foods. After 3 years, SSB purchases decreased by 7.6% and taxed foods by 6%, with larger reductions in lower socioeconomic status groups (454, 455). Within 2 years, there was a 1.3% decrease in the prevalence of excess weight and obesity in adolescent girls (456). Modeling studies have indicated that if Mexico increased these taxes then obesity rates could be reduced with huge savings for the national economy (457), with a 30% tax projected to reduce obesity by 9.1% after 10 years, which translates to 3.8 million fewer cases, with a saving of US$17.9 billion in costs to the economy (458). In the United States, California saw reductions in the rates of excess weight and obesity of 5.5% in children aged 2–5 years and 4.2% in those aged 6–11 years within 4–6 years after city-level SSB taxes were implemented (459). In 2016, the Indian state of Kerala levied an indirect tax of 14.5% on fast foods sold at branded restaurants (rather than targeting specific nutrients), with the revenue generated directed to public health initiatives. The tax only remained for 11 months before being dropped when a new federal tax system was implemented. However, it proved effective with reductions in fast food purchases of 3.9–5.6% over the duration of the tax and in the following period (460).

Combating marketing and implementing effective food labeling

The high profits made from UPFs enable considerable advertising budgets, price promotions, and other marketing techniques that drive further demand. In 2024, just three of the TNCs manufacturing UPFs spent a combined US$13.2 billion on marketing, which was nearly four times that of WHO’s total operating budget (423). Advertisements for UPFs are ubiquitous in many societies, with particularly high exposure among lower socioeconomic status groups (461). Children are most vulnerable as they are highly impressionable, motivated by immediate gratification, and have underdeveloped nutritional knowledge. Marketing for adolescents is also of particular concern since food choices made at this critical stage of development set the foundation for a healthy life (123, 462). This problem has been intensified by digital marketing, which has enabled children and adolescents to be targeted with precision with personalized UPF advertisements and gaming platforms that blur the boundaries between entertainment and advertisement (463). The WHO has recently strengthened its guidelines for mandatory policies to protect children from marketing for unhealthy foods, although some consider that the recommendations should go further (464). Evidence indicates causality between food marketing and childhood obesity (465), while a systematic review of policies to restrict marketing to children concluded that these can be effective (466).

Nutritional- or harm-based labels on food products, like those on cigarettes, have also been proven to work and have been adopted in over 20 countries. Graphic labels depicting health effects have the most impact on consumer choice; lists of nutrient contents have the least impact (467, 468). A systematic review of the effect of front-of-pack nutrition labeling on food industry practices found that labels with numerical information had no impact, but labels with intuitive information led to product reformulation, generally reducing sodium, sugar, or calorie content (469). More consistent effects on product reformulation were observed if the food labeling was mandatory compared with voluntary policies that have been found to have little impact (470). The effects of labels specifically warning that foods are UPFs are not yet known and online experiments have yielded differing results: a study of 600 adults in the United States indicated that it would discourage them from purchasing such products (471) whereas a similar online study of 1004 adults in Brazil found that such labeling would not affect purchasing intentions (472).

In the United States, analyses of purchasing datasets from some states show that listing calories on menus in restaurants and supermarket prepared foods can result in a 5% reduction in calories per purchase (473, 474). WHO recommends that for a healthy diet, less than 30% of total energy intake should be from fat, with saturated fat under 10% of total energy, and free sugars under 10% of total energy. For salt, the recommendation is less than 5 grams per day. Various countries have introduced labeling of foods to help people adhere to these guidelines by flagging foods that are high in fats, sugars, and salt (HFSS). In 2016, Chile was the first nation to introduce mandatory, national front-of-pack warning labels on unhealthy foods and beverages, restrictions on marketing such products to children, and a ban on their sales in schools. Unhealthy foods had to have a distinctive black octagon on the front of the pack displaying the words “high in” sugar, sodium, saturated fat, and/or calories. In addition, harmful nutrients are treated as added ingredients rather than intrinsic components of foods, and their content is regulated (475). After Phase 1, there were decreases in household purchases of total calories (4%), sugars (10%), saturated fat (4%), and warning-labeled foods (24%) (476). Even larger changes are expected after Phase 2 and Phase 3. In Phase 3, the thresholds for HFSS were lowered to 10 g sugar, 4 g saturated fat, 400 mg sodium, and 275 kcal energy per 100 g for solids or per 100ml for liquids. This was accompanied by a complete ban on television advertisements of warning-labeled foods between 6 am and 10 pm and a ban on the sale, promotion, or marketing of such foods within schools. An initial analysis indicated that this resulted in improved diets with increased intake of healthy nutrients in children within schools (477). Warning labels on foods can also have indirect effects by creating incentives for food manufacturers and retailers to reformulate their products (478). Recent evidence indicates that this has been the case after full implementation of Phase 3 in Chile, with many producers reformulating packaged foods with reduced contents of fats, sugars, and salt (479). Similar warning labels have now been adopted across much of Latin America (Figure 9). Such policies are more effective when they are coordinated with other mutually reinforcing policies, such as those in Chile (475) and in Colombia, where food labeling is reinforced with taxes on UPFs and SSBs (480).

Figure 9

Figure 9. World Health Organization Region of the Americas (AMRO) countries that had adopted a front-of-pack nutrition labeling (FORNL) system as of August 2022. ©Elsevier, 2023. Re-printed with permission from (468).

With the rapid growth of obesity in children, the extension of warning labels to UPFs and HFSS foods that are targeted at infants and young children and stricter regulation of their marketing are particularly needed (466, 481–483).

Facilitating healthy food choices

Strategies to make healthy foods more affordable and readily available include subsidies, either at the level of producer or consumer, particularly for fruit and vegetables; primary care-based nutritional interventions, such as vouchers or prescriptions for healthy meals; and provision of healthy meals in schools, hospitals, and other institutions. Strategies for improving the availability of healthy foods should increase the availability and affordability of unprocessed or minimally processed foods, including ready-to-consume and ready-to-heat minimally processed foods (386). There have been insufficient studies of the impact of pricing strategies such as price reductions, subsidies, or financial incentives for healthy food, and these have mainly been of short duration. However, they generally indicate that availability, purchases, and consumption of fruit and vegetables can be increased with potential health benefits (484–486). A recent meta-analysis of 14 studies indicated that a 20% price reduction could increase fruit and vegetable purchases by 16.6% (486). Subsidies for healthy foods can be funded by levying taxes on unhealthy foods such as UPFs (487, 488).

Financial incentives can be established through links between supermarkets, gymnasiums, health centers, and health insurance or taxation schemes, depending on the national context. Targeted food subsidies can have indirect effects on the food environment. For example, in the United States, subsidies introduced in the Special Supplemental Nutrition Program for Women, Infants, and Children resulted in increased availability and variety of healthy foods, especially in low-income neighborhoods, as grocery stores responded to demand (489). Subsidies targeting families with young children can also help to develop a healthy preference learning environment at home. The United States “Food is Medicine Movement” aims to overcome the interlinked issues of lack of available, affordable healthy food, and the growing healthcare costs. This involves people with obesity and cardiometabolic diseases being treated with medically tailored meals, prescription programs, and subsidies for healthy foods such as fruit and vegetables (490, 491). The Food is Medicine Movement has also created community teaching kitchens to provide education on healthy dietary behaviors to communities and especially undergraduate students in medicine, nursing, nutrition, and dietetics, as well as doctors and other healthcare professionals (492). These can be established by healthcare professionals in collaboration with schools, universities, and medical colleges, together with local agriculture, including farms, community gardens, and urban agriculture groups. They can be delivered by various combinations of dietitians, physicians, other healthcare workers, and chefs in different formats, including virtual (videos or podcasts) or in-person classes in community facilities, schools, colleges, and businesses. A similar program of community-based teaching kitchens developed by a celebrity chef, Jamie Oliver, has been implemented in the United Kingdom and Australia (493). These can build cross-disciplinary communities, increasing awareness and engagement with the critical role of nutrition and the food system in maintaining healthy lifestyles and a healthy planet (492). These initiatives have often been transient; long-term funding and structures need to be established for them to have greater impact.

In addition to improving the availability of healthy foods, there have been a few studies of behavioral interventions aimed at promoting more environmentally sustainable diets. A systematic review indicated that the use of multicomponent interventions through education, persuasion, and environmental restructuring was most effective, but that more high-quality studies were needed (494).

Living environments enabling healthy diets and activity

Given that living environments help shape health both via dietary patterns and physical activity levels, a systems approach is needed. This requires the engagement of all sectors of society and government—including transport, planning, recreation and parks, industry, education, and the media—rather than health alone.

Changes in the built environment that would enhance physical activity could also reduce climate impacts. The WHO Global Action Plan on Physical Activity 2018–2030 recommends a multifaceted approach combining both upstream policy actions that support the integration of physical activity into multiple settings with downstream, individually focused approaches that empower people to become active (495). A survey of physical activity surveillance, policy, and research in 164 countries found large disparities between countries with only modest progress between 2015 and 2020 (496). Urban and transport planning need to be strengthened and closely integrated to develop compact, highly connected neighborhoods with access to safe and expedient public transport options, thereby reducing car dependency (495). Commitment to a more holistic approach to urban planning is essential (497).

Education: public and professional

Public health strategies may provide education to improve diets and lifestyles, but individuals’ access is fundamentally dependent on their basic education level, language, and literacy. Many consumers do not possess sufficient knowledge to assess nutrient content from food labels (498). More nutrition education is needed to help consumers make healthier choices and to increase awareness of the role that nutrition plays in preventing chronic diseases. Education needs to be implemented throughout schools and colleges in a culturally appropriate manner (499). National food guidelines need to be updated to not just consider individual nutrients but also view food systems and environments holistically in order to address the interplay between all components and characteristics of food. This should include formulation and processing, with recommendations to favor unprocessed natural whole foods and avoid UPFs—as have been adopted to varying degrees in Brazil, Canada, Ecuador, Denmark, Germany, Peru, and Uruguay (20).

Better education regarding nutrition and obesity is also particularly important for healthcare professionals. Health professionals need to be trained and equipped in the prevention, management, and treatment of obesity and in addressing weight bias, which remains a systemic barrier to access and effective implementation of care. Obesity bias is widespread among healthcare professionals and is associated with poor patient outcomes (500, 501). Many medical students start with unconscious obesity bias (502, 503). Moreover, evidence indicates that physicians are not adequately prepared educationally to tackle the obesity crisis. A systematic review concluded that there is a paucity of education on obesity in medical schools worldwide (504). For example, a survey of United States medical schools found an average of 10 hours of obesity education, and fewer than 40% indicated that their medical program covered any obesity-related topic well; only 10% were confident that their students were “very prepared” to manage patients with obesity (505). A United Kingdom-based survey revealed that 70% of medical students could only recall 2 hours devoted to nutrition throughout their 7 years of training; only 26% of doctors were confident in their nutrition knowledge, and 74% gave nutritional advice less than once a month in their clinical practice (506, 507). A similar Canadian survey found that over 87% of medical students thought more time should have been dedicated to nutrition education (508). A survey of United States medical interns found that only 29% considered that they had sufficient education in nutrition (509), and although 94% of internal medicine residents agreed that nutritional counselling was essential, only 14% suggested that they were adequately trained to deliver this (510). In 2022, the United States House of Representatives passed a resolution calling on medical schools to ensure more meaningful nutrition education in their medical curricula, but changes still need to be implemented (511).

A new way forward: catalyzing policy action to tackle obesity

For more than a decade, WHO and repeated Lancet features and Commissions on obesity have called for a switch in emphasis from individual responsibility and the view that the solution was for people to “eat less and move more” to acknowledgement that personal choices for diet and physical activity are greatly constrained by our obesogenic environments and these can only be addressed by more governmental action (36, 440, 512). This was recently reiterated by the WOF (513). To overcome the global lack of progress on policies attempting to limit the obesity pandemic, the WOF and other obesity organizations have developed a framework with five objectives based around the acronym “ROOTS”: Recognition of obesity; Obesity monitoring; Obesity prevention; Treatments of obesity; and a Systems-based approach (407). This ROOTS approach is proposed to be applied to the management of obesity and to the environmental, social, and commercial roots of obesity.

In this final section, we outline approaches to catalyze actions on these fronts and to overcome key barriers to change.

Promoting system-level thinking

The challenge is to shift the focus of policymakers from the misconception that obesity can be addressed by nudges to correct the “failures” of individuals to government actions that enable societal or environmental interventions that directly address the systemic drivers of obesity. Governments should recognize that obesity is a disease and invest in its prevention, management, and treatment. Such recognition could reduce the potential harm of weight stigma, which has deleterious effects contributing to additional weight gain (514). Governments must introduce policies that are informed by evidence and address the primary drivers of obesity from a societal perspective. With obesity onset increasingly occurring in childhood, with serious long-term consequences through to adulthood, interventions must address the entire life-course. For these actions to be effective, they need to include a clear plan for implementation, clear targets and timeframes, accepting appropriate time-lags between changes in dietary behavior and obesity prevalence, and an established process for ongoing independent monitoring and evaluation.

Experiences in Latin America and the Pacific Islands are beginning to demonstrate effective multipronged approaches with the potential to transform our food system. These include taxes, tariffs, subsidies, warning labels on unhealthy foods, education, and making healthy foods affordable and available through primary care-based nutritional interventions, vouchers or prescriptions for healthy meals, provision of healthy meals in schools, hospitals, and other institutions, and via community measures such as the facilitation of local markets, local whole food production, and communal kitchens. Such measures may be particularly valuable in LMICs where financial resources are limited and where provisions such as school meal coverage are currently very low and would cost a significant proportion of GDP (515). An example is the National School Feeding Program that has been adopted in Brazil and currently feeds over 40 million children: this requires that food procured for schools to be at least 90% unprocessed or minimally processed with a maximum of 10% processed or UPF and 30% to be obtained from local family farmers (516, 517). The use of local family farms increased the proportion of fruit, vegetables, and legumes in school meals (518), generated local employment, and stimulated national GDP (519). Another innovative approach has been taken in Vietnam with their Home-Grown School Feeding Program, which promotes the use of school gardens to produce fresh foods for the children (520).

Overcoming commercial pressures opposing change

As described, for consumers, the attractions of UPFs have been their affordability, convenience, and palatability, but the main drivers of UPF proliferation have been commercial. Removing the current distortions and constraints and enabling genuine informed choices will require multi-faceted, interdisciplinary solutions involving policymakers, industrial leaders, institutions, educators, and the public coming together in collaborative policy shifts to change behaviors and transform our food system. Increasing transparency and awareness of the true costs of food is key to helping transform food systems without compromising wealth creation. It should not be a choice, or a contest, between healthy profits or healthy people. As with the transition to green energy, rather than being framed as a threat to economic growth, the emphasis should be on new opportunities. A transition of the food system could provide many opportunities for new developments and employment for the food industry, provided appropriate investment is made available. Schemes such as the Just Energy Partition Partnerships initiated at the 26th Conference of the Parties to the UN Framework Convention on Climate Change (COP26), involving a consortium of countries supporting fair and inclusive economic development (521), could be adopted not just for energy but also for food production. For example, sugar consumption is a major contributor to human obesity, but its production is important for the economies of several countries. A reduction in human consumption could, however, provide several opportunities, such as switching land to grow other food crops or rewilding for environmental gains. In addition, sugar crops could be retained but the sugar utilized for other purposes, such as the production of microbial protein, biofuels, and bioplastics that could mitigate the economic consequences (522). It has been estimated that switching sugar from direct human consumption to microbial protein production could meet the protein needs of 521 million adults with considerable environmental gains (522). Indeed, microbial protein production is just one of many opportunities for developing alternative foods with a much lower environmental footprint, such as synthetic meats, algae, insect-derived proteins, hybrid foods combining any of the previous with plant-based materials, genetically modified crops, and 3D-printed foods (523–526). One recent example is rice-based meat developed by combining technologies involving hybrid rice grains, nanocoating, and animal cell culture (527). Most of these new experimental foods are being developed in response to environmental and sustainability concerns; however, there is no evidence, as yet, that they would impact human obesity. Their impact on obesity should be a major consideration in their development and evaluated before widespread adoption. The challenge for science and the food industry is to provide innovative technologies to enable a further transition of the food industry that is both economically sound and, most importantly, benefits both human and planetary health. These new developments will also challenge the bluntness of the NOVA classification of UPFs, as most of the experimental foods would currently be classified as UPF, and this may require a new nomenclature for advanced processed foods that are healthy for both the environment and humans.

The most important barriers to change are the power imbalances between the TNCs and the health sector, conflicts of interest, and industry interference (528, 529). Governments have a responsibility to promote both the health and wealth of their populations, and when corporations profit from products, such as tobacco and foods that harm health, this creates conflicts. Governments could assert sovereignty over their food systems and the rights of their citizens to healthy and challenge foreign investments by TNCs in foods that are detrimental to health, national economies, and sustainable development (423). A bold move in this direction has been made in Mexico with their new General Law on Adequate and Sustainable Nutrition (530). The challenge is to convince communities and governments to confront powerful TNCs in such ways with policies that transform the food environment to promote better health and less environmental harm, but without compromising economic development. This calls for an innovative economic system that promotes not just financial success but also human, social, and land health (531). This will clearly require engagement with debates and policy arenas with other sectors and disciplines beyond health and science. For instance, if superannuation policyholders were engaged, they could demand that funds be invested only in ethical industries. TNCs have long recognized this: for example, when taxes on SSBs were imposed in some markets, TNCs invested in new markets, lobbying policymakers, and promoting the role of physical activity in combating obesity rather than regulating foods and beverages (437).

Changes in market demand considerations could also help drive change. While food producers and retailers may be reluctant to forego the enormous profit from foodstuffs driving obesity, they would respond to demand and economic pressures in the same manner as industries are responding to societal shifts caused by climate breakdown. Global economies rely on competition, and if the market demands healthier foods and eschews UPFs, food producers would be financially imprudent to continue to pursue current trends despite declining sales. Surveys of young adults show increased interest in sustainable methods of food production and healthy eating practices (532, 533), although the targeted marketing of unhealthy foods to adolescents requires action since food choices made at this critical stage of development are recognized to set the foundation for a healthy life (462).

Central to overcoming commercial obstacles to food system transformation will be the emphasis on the economic imperatives: that actions will be less damaging to wealth creation than inaction and taxes and tariffs can provide resources for the required investments and services (13). In 2001, Thailand established the Thai Health Promotion Foundation to promote public health using resources financed by an additional 2% tax levy on alcohol and tobacco products (534). Such a system could be extended to use taxes and tariffs on unhealthy foods, UPFs, and SSBs to promote healthy local whole foods and the development of healthcare infrastructure. In turn, a healthier population reduces healthcare costs, is more productive, and pays more taxes. However, the classification process of UPFs will be important to avoid discriminating against plant-based products that would have both health and environmental benefits. WHO has a number of guidelines in development scheduled for 2027: a guideline for the optimal intake of animal-sourced foods, which will incorporate considerations of food processing and sustainability, an operational definition of UPFs to support national policy making, and a guideline on the consumption of UPFs (535). A more nuanced definition of UPFs is needed to distinguish unhealthy UPFs from those that are made with less-processed ingredients, such as whole grains, fruits, vegetables, and legumes, or which generally have neutral or positive health associations (536).

Globally, it has been estimated that reducing projected excess weight and obesity by 5% annually from current trends or keeping it at 2019 levels will translate into average global annual reductions of US$429 billion or US$2.2 trillion in costs, respectively, between 2020 and 2060 (13). According to the Food System Economics Commission, “The economic value of the human suffering and planetary harm … [resulting from food systems] … is well above US$10 trillion/year, more than food systems contribute to global GDP. In short, our food systems are destroying more value than they create” (55).

Obesity and the Sustainable Development Goals

A recent analysis of the relationships between global obesity and human development concluded that the increasing prevalence of obesity was linked to economic development, with a shift from agrarian to more industrialized societies, with a concomitant shift to more convenience and processed foods (537). In 2015, the UN General Assembly approved Sustainable Development Goals (SDGs) to address the health and development challenges faced by humanity, to be achieved by 2030 (538). These SDGs were widely endorsed by governments around the world. Despite obesity being a major global health issue with many interrelationships to the SDGs, and which clearly poses challenges to human development, it was surprisingly omitted from the original SDGs, and sustainable agriculture was only included as part of the goal to end hunger (407). With the realization that the SDGs cannot be met by 2030, there have been recent proposals to allow more time and extend the goals to 2050 and include the incorporation of an objective to achieve food systems that are aligned with the climate goals and have net-zero emissions (539). This acknowledges that targets to lessen the harms of climate change cannot be met without reforming current food systems but still omits any mention of obesity despite it compromising human health and development. In 2021, the UN held its first Food System Summit from which a collaboration emerged, the Food System Countdown to 2030 Initiative (FSCI), to provide a framework for the development of new food systems when the current SDGs expire (540). The FSCI framework objective is that a new food system transition should ensure that “all people have access to healthy diets, produced in sustainable, resilient ways that restore nature and deliver just and equitable livelihoods” (540). The FSCI proposes that a sustainable new food system should consider human diets, nutrition and health, the environment, natural resources, livelihoods, poverty, equity, governance, and resilience (541).

Role of interdisciplinary advocacy

The medical and public health communities need to provide evidence both for the drivers of, and solutions to, the obesity crisis, to educate the public and, most importantly, to engage and educate policymakers. The drivers of political action are complex and require the engagement of multiple actors that can muster public opinion and ultimately political will to enact change (528). Trust in nutrition science among the public and policymakers needs to be improved (542). Co-framing the parallel and overlapping threats and common solutions involved in reducing obesity, improving health, reforming unsustainable food systems, protecting the environment, and avoiding climate breakdown should help to build the networks and coalitions across society required to overcome the complexities and barriers to convince governments to make radical changes (538). Examples of effective coalitions driving political change are the Chilean Labelling and Marketing Law, which was established after the Ministry of Health worked with experts and advocacy groups to sustain political momentum (452), and the Ghanian Government, which assembled a similar health coalition to deliver coordinated policies for front-of-pack labeling, marketing restrictions, food taxes, and public food procurement (543). Health professionals and policy advocates need to build networks and alliances to present a united approach to policymakers and frame proposed policies so that they align with the ideology and objectives of their politicians and provide them with clear evidence, particularly for the cost-effectiveness of proposals. A set of mutually reinforcing policies is required. The recent Lancet EAT commission concluded that evidence indicated individual policies and interventions were insufficient to bring about the changes needed to the food system and that bundles of multiple coherent interventions needed to be implemented simultaneously (389). Economically, prevention should be the prime objective with linked policies introduced to address all components of the obesogenic environment in a coordinated manner, as initiated in Chile (544). People living with obesity should be involved throughout—in policy setting, service design, guidelines, and professional training.

Conclusion

Obesity is a complex disease—a societal problem requiring societal solutions. Recent developments in treatment have provided important new options for the millions currently living with obesity to reduce morbidity and mortality. However, the availability of effective surgical and medical treatments could be used to justify circumventing strategies for prevention. This would perpetuate the cycle where market forces ensure considerable profits from both producing unhealthy foods that promote obesity and then producing drugs to alleviate the consequent harm.

There is now good evidence for effective prevention strategies by improving the food system, such as taxes, front-of-pack warning labels on unhealthy foods, and increased availability of healthy foods. In addition to the health benefits, these measures can provide economic benefits that can then be used to subsidize the provision of healthy foods (13). The stigma surrounding obesity has been a major obstacle and has been exploited by stakeholders who have framed obesity as a lifestyle issue and a matter of personal responsibility, to which strategies should be addressed. This has contributed to national strategies with emphasis on personal responsibility and the reliance on small single policies that have been insufficient in terms of having an impact on population weight. The recent international consensus recognition that obesity is a disease shifts the responsibility to that of society and policymakers. Reframing obesity as a disease should also help to reduce the influence of commercial entities and critically enhance the involvement of people living with obesity in the policy-making process.

At the societal level, ecosystem changes are paramount to address the global challenge of obesity in a sustainable manner. Although many factors have been postulated to contribute to the global obesity epidemic, it is widely agreed that the primary driver has been the transformation of our food environment and, in particular, the adoption of UPF-dominated diets. Specific policies that explicitly target UPFs are needed to counter their impact on obesity and other NCDs (386). These will be assisted by the new UPFs guidelines in development by WHO (535), particularly a new operational definition of UPFs. In countries where UPFs are already dominant, policies to reduce UPF consumption will be needed, but in many LMICs, UPF prevention strategies will be required, and these will need to be integrated with policies addressing undernutrition and the multiple nutritional challenges facing their populations (545).

The transformed food system has also been a major contributor to climate change, which, like obesity, is driven by unsustainable, but profitable, consumption. Both climate and obesity issues require a change to the entire food environment, which will have to overcome strong political and market obstacles empowered by the commercial success of TNCs. At some stage, the costs incurred by wildfires, floods, droughts, crop failures, and other consequences of climate change will be so great that policymakers will be compelled to take more effective actions. The challenge for climate scientists is to convince policymakers to act before too many of the planet’s safe operating boundaries have been crossed.

Similarly, the costs of the obesity pandemic will eventually be so great that the case for prevention will be compelling. And again, the challenge for the health community is how to convince policymakers to act before more harm is incurred. The economic argument is clear, the cost to transform the food system to address both co-crises is affordable. The global cost for implementing a core bundle of five interventions proven effective at combating obesity (subsidies and taxes, marketing restrictions, food labeling, school policies, and reformulations) was estimated by the UN International Children’s Emergency Fund (UNICEF) to be US$24.6 billion over a 10-year period based on 2024 values, compared with the global economic burden of obesity of US$4 trillion by 2035 (61). For many high- and middle-income countries, this can largely be achieved by redirecting existing government subsidies: the public funding for agricultural subsidies for the 54 OECD countries in 2023 was US$400 billion, whereas the cost to totally transform the global food system is estimated as US$215 billion (55). This would not be feasible for many LMICs with little agricultural subsidies to reallocate but could be achieved with more equitable international funding; currently, only 4.5% (around US$12 billion) of all international development funds are targeted at agriculture, forestry, and fishing (55).

The need to transform the food system to avoid a climate catastrophe may help to drive coalitions across society to deliver momentum and foster the political will for the necessary transformations that will simultaneously tackle the obesity pandemic. For both the climate and obesity co-crises, there are considerable economic benefits in preventing and addressing the known causes rather than mitigating them. In the post-COVID-19 era, societies may be more accepting of such broad structural changes.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsci.2025.1613595/full#supplementary-material

Supplementary Table 1 | Comprehensive lifestyle intervention for obesity treatment and management.

Supplementary Table 2 | Diets found to be effective for weight loss.

Supplementary Table 3 | Dietary patterns designated as healthy or potentially unhealthy.

Supplementary Table 4 | Incretinomimetic trials in people with normoglycaemic obesity: the evidence base to February 2024.

Statements

Author contributions

PB: Conceptualization, Writing – original draft, Writing – review & editing.

CMC: Conceptualization, Writing – original draft, Writing – review & editing.

JCGH: Conceptualization, Writing – original draft, Writing – review & editing.

MM: Conceptualization, Writing – original draft, Writing – review & editing.

JP: Conceptualization, Writing – original draft, Writing – review & editing.

GAR: Conceptualization, Writing – original draft, Writing – review & editing.

KS: Conceptualization, Writing – original draft, Writing – review & editing.

JMPH: Conceptualization, Visualization, Writing – original draft, Writing – review & editing.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Funding

The authors declared that financial support was received for this work and/or its publication. GAR was supported by a Wellcome Trust Investigator Award (WT212625/Z/18/Z), Medical Research Council (MRC) Programme grant (MR/R022259/1), Diabetes UK (BDA 16/0005485) and National Institute of Diabetes and Digestive and Kidney Diseases (NIH-NIDDK; R01DK135268, 1R01DK139630-01A1) project grants, a Canadian Institutes of Health Research (CIHR)-Juvenile Diabetes Research Foundation (JDRF) Team grant (CIHR-IRSC TDP-186358 and JDRF 4-SRA-2023-1182-S-N), University of Montreal Hospital Research Centre (CRCHUM) start-up funds, and an Innovation Canada John R. Evans Leader Award (CFI 42649).

PB is supported by a British Academy Global Professorship award and a REAPRA Senior Fellowship.

The funders were not involved in the development of this article or the decision to submit it for publication.

Conflict of interest

GAR has received grant funding from, and is a consultant for, Sun Pharmaceuticals Inc. This company was not involved in any way in the preparation of the present article.

JCGH has consultancies with Dupont IFF and Allurion. All payments go to the University of Leeds to support research. These companies were not involved in any way in the preparation of the present article.

The remaining authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer PV declared a shared affiliation with the author GAR to the handling editor at the time of review.

The author JMPH declared that they were an editorial board member of Frontiers at the time of submission. This had no impact on the peer review process and the final decision.

Generative AI statement

The authors declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. World Obesity Federation. World obesity atlas 2023. London: WOF (2023). Available at: https://data.worldobesity.org/publications/WOF-Obesity-Atlas-V5.pdf.

Google Scholar

2. Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, and Straif K. Body fatness and cancer—Viewpoint of the IARC Working Group. N Engl J Med (2016) 375(8):794–8. doi: 10.1056/NEJMsr1606602

PubMed Abstract | Crossref Full Text | Google Scholar

3. Kyrgiou M, Kalliala I, Markozannes G, Gunter MJ, Paraskevaidis E, Gabra H, et al. Adiposity and cancer at major anatomical sites: umbrella review of the literature. BMJ (2017) 356:j477. doi: 10.1136/bmj.j477

PubMed Abstract | Crossref Full Text | Google Scholar

4. Corbin KD, Driscoll KA, Pratley RE, Smith SR, Maahs DM, Mayer-Davis EJ, et al. Obesity in type 1 diabetes: pathophysiology, clinical impact, and mechanisms. Endocr Rev (2018) 39(5):629–663. doi: 10.1210/er.2017-00191

PubMed Abstract | Crossref Full Text | Google Scholar

5. Gale E. The species that changed itself. New York, NY: Penguin Books (2020).

Google Scholar

6. Rubino F, Cummings DE, Eckel RH, Cohen RV, Wilding JPH, Brown WA, et al. Definition and diagnostic criteria of clinical obesity. Lancet Diabetes Endocrinol (2025) 13(3):221–62. doi: 10.1016/S2213-8587(24)00316-4

PubMed Abstract | Crossref Full Text | Google Scholar

7. Ward ZJ, Willett WC, Hu FB, Pacheco LS, Long MW, and Gortmaker SL. Excess mortality associated with elevated body weight in the USA by state and demographic subgroup: a modelling study. EClinicalmedicine (2022) 48:101429. doi: 10.1016/j.eclinm.2022.101429

PubMed Abstract | Crossref Full Text | Google Scholar

8. World Health Organization. Global health risks: mortality and burden of disease attributable to selected major risks. WHO (2009). Available at: https://iris.who.int/bitstream/handle/10665/44203/9789241563871_eng.pdf

Google Scholar

9. GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet (2020) 396(10258):1223–49. doi: 10.1016/S0140-6736(20)30752-2

PubMed Abstract | Crossref Full Text | Google Scholar

10. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. Lancet (2024) 403(10431):1027–50. doi: 10.1016/S0140-6736(23)02750-2

PubMed Abstract | Crossref Full Text | Google Scholar

11. Simmonds M, Llewellyn A, Owen CG, and Woolacott N. Predicting adult obesity from childhood obesity: a systematic review and meta-analysis. Obes Rev (2016) 17(2):95–107. doi: 10.1111/obr.12334

PubMed Abstract | Crossref Full Text | Google Scholar

12. Panera N, Mandato C, Crudele A, Bertrando S, Vajro P, and Alisi A. Genetics, epigenetics and transgenerational transmission of obesity in children. Front Endocrinol (Lausanne) (2022) 13:1006008. doi: 10.3389/fendo.2022.1006008

PubMed Abstract | Crossref Full Text | Google Scholar

13. Okunogbe A, Nugent R, Spencer G, Powis J, Ralston J, and Wilding J. Economic impacts of overweight and obesity: current and future estimates for 161 countries. BMJ Glob Health (2022) 7(9):e009773. doi: 10.1136/bmjgh-2022-009773

PubMed Abstract | Crossref Full Text | Google Scholar

14. Carlson SA, Densmore D, Fulton JE, Yore MM, and Kohl HW 3rd. Differences in physical activity prevalence and trends from 3 U.S. surveillance systems: NHIS, NHANES, and BRFSS. J Phys Act Health (2009) 6(suppl 1):S18–27. doi: 10.1123/jpah.6.s1.s18

PubMed Abstract | Crossref Full Text | Google Scholar

15. Hjorth MF, Chaput JP, Ritz C, Dalskov SM, Andersen R, Astrup A, et al. Fatness predicts decreased physical activity and increased sedentary time, but not vice versa: support from a longitudinal study in 8- to 11-year-old children. Int J Obes (Lond) (2014) 38(7):959–65. doi: 10.1038/ijo.2013.229

PubMed Abstract | Crossref Full Text | Google Scholar

16. Du Y, Liu B, Sun Y, Snetselaar LG, Wallace RB, and Bao W. Trends in adherence to the physical activity guidelines for Americans for aerobic activity and time spent on sedentary behavior among US adults, 2007 to 2016. JAMA Netw Open (2019) 2(7):e197597. doi: 10.1001/jamanetworkopen.2019.7597

PubMed Abstract | Crossref Full Text | Google Scholar

17. Speakman JR, de Jong JMA, Sinha S, Westerterp KR, Yamada Y, Sagayama H, et al. Total daily energy expenditure has declined over the past three decades due to declining basal expenditure, not reduced activity expenditure. Nat Metab (2023) 5(4):579–88. doi: 10.1038/s42255-023-00782-2

PubMed Abstract | Crossref Full Text | Google Scholar

18. Mozaffarian D. Perspective: obesity—an unexplained epidemic. Am J Clin Nutr (2022) 115(6):1445–50. doi: 10.1093/ajcn/nqac075

PubMed Abstract | Crossref Full Text | Google Scholar

19. Martini D, Godos J, Bonaccio M, Vitaglione P, and Grosso G. Ultra-processed foods and nutritional dietary profile: a meta-analysis of nationally representative samples. Nutrients (2021) 13(10):3390. doi: 10.3390/nu13103390

PubMed Abstract | Crossref Full Text | Google Scholar

20. Baker P, MaChado P, Santos T, Sievert K, Backholer K, Hadjikakou M, et al. Ultra-processed foods and the nutrition transition: global, regional and national trends, food systems transformations and political economy drivers. Obes Rev (2020) 21(12):e13126. doi: 10.1111/obr.13126

PubMed Abstract | Crossref Full Text | Google Scholar

21. Dicken SJ, Qamar S, and Batterham RL. Who consumes ultra-processed food? A systematic review of sociodemographic determinants of ultra-processed food consumption from nationally representative samples. Nutr Res Rev (2023) 37(2):416–56. doi: 10.1017/S0954422423000240

PubMed Abstract | Crossref Full Text | Google Scholar

22. Wells JC, Sawaya AL, Wibaek R, Mwangome M, Poullas MS, Yajnik CS, et al. The double burden of malnutrition: aetiological pathways and consequences for health. Lancet (2020) 395(10217):75–88. doi: 10.1016/S0140-6736(19)32472-9

PubMed Abstract | Crossref Full Text | Google Scholar

23. Prentice AM. The triple burden of malnutrition in the era of globalization. Nestle Nutr Inst Workshop Ser (2023) 97:51–61. doi: 10.1159/000529005

PubMed Abstract | Crossref Full Text | Google Scholar

24. Seferidi P, Hone T, Duran AC, Bernabe-Ortiz A, and Millett C. Global inequalities in the double burden of malnutrition and associations with globalisation: a multilevel analysis of Demographic and Health Surveys from 55 low-income and middle-income countries, 1992–2018. Lancet Glob Health (2022) 10(4):e482–90. doi: 10.1016/S2214-109X(21)00594-5

PubMed Abstract | Crossref Full Text | Google Scholar

25. Colozza D. Determinants of food retail outlet choice in an urban food environment: a qualitative study in Indonesia. BMC Public Health (2025) 25(1):3737. doi: 10.1186/s12889-025-23574-7

PubMed Abstract | Crossref Full Text | Google Scholar

26. Shekar M and Popkin B, editors. Obesity: health and economic consequences of an impending global challenge. Washington, DC: World Bank (2020). doi: 10.1596/978-1-4648-1491-4

Crossref Full Text | Google Scholar

27. Chong B, Jayabaskaran J, Kong G, Chan YH, Chin YH, Goh R, et al. Trends and predictions of malnutrition and obesity in 204 countries and territories: an analysis of the Global Burden of Disease Study 2019. EClinicalMedicine (2023) 57:101850. doi: 10.1016/j.eclinm.2023.101850

PubMed Abstract | Crossref Full Text | Google Scholar

28. Monteiro CA, Louzada MLC, Steele-Martinez E, Cannon G, Andrade GC, Baker P, et al. Ultra-processed foods and human health: the main thesis and the evidence. Lancet (2025) 406(10520):2667–84. doi: 10.1016/S0140-6736(25)01565-X

PubMed Abstract | Crossref Full Text | Google Scholar

29. Ramasubramani P, Krishnamoorthy Y, and Rajaa S. Prevalence and socio-demographic factors associated with double and triple burden of malnutrition among mother-child pairs in India: findings from a nationally representative survey (NFHS-5). Heliyon (2024) 10(18):e37794. doi: 10.1016/j.heliyon.2024.e37794

PubMed Abstract | Crossref Full Text | Google Scholar

30. Ji N, Kumar A, Joe W, Kuriyan R, Sethi V, Finkelstein JL, et al. Prevalence and correlates of double and triple burden of malnutrition among children and adolescents in India: the comprehensive national nutrition survey. J Nutr (2024) 154(10):2932–47. doi: 10.1016/j.tjnut.2024.08.021

PubMed Abstract | Crossref Full Text | Google Scholar

31. Barker DJ. Fetal origins of coronary heart disease. BMJ (1995) 311(6998):171–4. doi: 10.1136/bmj.311.6998.171

PubMed Abstract | Crossref Full Text | Google Scholar

32. Dev A, Patra R, Kale SA, James Marianadin LP, Mohanty SS, and Benny J. From womb to tomb: a life-course perspective on the origins of non-communicable diseases. Cureus (2025) 17(9):e93168. doi: 10.7759/cureus.93168

PubMed Abstract | Crossref Full Text | Google Scholar

33. Jopling E and Nelson CA. Early life adversity and risk for non-communicable health outcomes: challenges and opportunities for a maturing field. BMC Med (2025) 23(1):534. doi: 10.1186/s12916-025-04388-1

PubMed Abstract | Crossref Full Text | Google Scholar

34. Umano GR, Bellone S, Buganza R, Calcaterra V, Corica D, De Sanctis L, et al. Early roots of childhood obesity: risk factors, mechanisms, and prevention strategies. Int J Mol Sci (2025) 26(15):7388. doi: 10.3390/ijms26157388

PubMed Abstract | Crossref Full Text | Google Scholar

35. Various authors. Obesity 2015 [series]. Lancet (2025). Available at: https://www.thelancet.com/series/obesity-2015

Google Scholar

36. Swinburn BA, Kraak VI, Allender S, Atkins VJ, Baker PI, Bogard JR, et al. The global syndemic of obesity, undernutrition, and climate change: the Lancet Commission report. Lancet (2019) 393(10173):791–846. doi: 10.1016/S0140-6736(18)32822-8

PubMed Abstract | Crossref Full Text | Google Scholar

37. World Health Organization. WHO acceleration plan to stop obesity. Geneva: WHO (2023). Available at: https://iris.who.int/bitstream/handle/10665/370281/9789240075634-eng.pdf

Google Scholar

38. Speakman JR, Sørensen TIA, Hall KD, and Allison DB. Unanswered questions about the causes of obesity. Science (2023) 381(6661):944–6. doi: 10.1126/science.adg2718

PubMed Abstract | Crossref Full Text | Google Scholar

39. Branca F, Ursu P, and Aguayo V. A plan for accelerated action on obesity. Lancet Glob Health (2023) 11(8):e1170–1. doi: 10.1016/S2214-109X(23)00257-7

PubMed Abstract | Crossref Full Text | Google Scholar

40. Holly JMP, Biernacka K, Maskell N, and Perks CM. Obesity, diabetes and COVID-19: an infectious disease spreading from the east collides with the consequences of an unhealthy western lifestyle. Front Endocrinol (Lausanne) (2020) 11:582870. doi: 10.3389/fendo.2020.582870

PubMed Abstract | Crossref Full Text | Google Scholar

41. Puhl RM and Heuer CA. Obesity stigma: important considerations for public health. Am J Public Health (2010) 100(6):1019–28. doi: 10.2105/AJPH.2009.159491

PubMed Abstract | Crossref Full Text | Google Scholar

42. Masson-Delmotte V, Zhai P, Pirani A, Connors SL, Péan C, Berger S, et al., editors. Climate change: the physical science basis. Contribution of Working Group I to the sixth assessment report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press (2021). doi: 10.1017/9781009157896

Crossref Full Text | Google Scholar

43. Romanello M, Walawender M, Hsu S-C, Moskeland A, Palmeiro-Silva Y, Scamman D, et al. The 2025 report of the Lancet countdown on health and climate change. Lancet (2025). doi: 10.1016/S0140-6736(25)01919-1

PubMed Abstract | Crossref Full Text | Google Scholar

44. Costello A, Romanello M, Hartinger S, Gordon-Strachan G, Huq S, Gong P, et al. Climate change threatens our health and survival within decades. Lancet (2023) 401(10371):85–7. doi: 10.1016/S0140-6736(22)02353-4

PubMed Abstract | Crossref Full Text | Google Scholar

45. Kemp L, Xu C, Depledge J, Ebi KL, Gibbins G, Kohler TA, et al. Climate endgame: exploring catastrophic climate change scenarios. Proc Natl Acad Sci USA (2022) 119(34):e2108146119. doi: 10.1073/pnas.2108146119

PubMed Abstract | Crossref Full Text | Google Scholar

46. Ripple WJ, Wolf C, Gregg JW, Rockström J, Mann ME, Oreskes N, et al. The 2024 state of the climate report: perilous times on planet Earth. BioScience (2024) 74(12):812–24. doi: 10.1093/biosci/biae087

Crossref Full Text | Google Scholar

47. Couzin-Frankel J. Obesity meets its match. Science (2023) 382(6676):1226–7. doi: 10.1126/science.adn4691

PubMed Abstract | Crossref Full Text | Google Scholar

48. Government of Canada. Canada’s food guide [online]. Available at: https://food-guide.canada.ca/en/

Google Scholar

49. German Nutrition Society. Food-based dietary guidelines for Germany. (2024) [online]. Available at: https://www.dge.de/english/fbdg/.

Google Scholar

50. Food and Agriculture Organization of the United Nations. Food-based dietary guidelines – Spain [online] (2022). Available at: http://www.fao.org/nutrition/educacion-nutricional/food-dietary-guidelines/regions/spain/es/

Google Scholar

51. Ministry of Food, Agriculture and Fisheries of Denmark. The Danish official dietary guidelines [online] (2021). Available at: https://en.foedevarestyrelsen.dk/food/nutrition-and-health/the-official-dietary-guidelines

Google Scholar

52. Goldfarb G and Sela Y. The ideal diet for humans to sustainably feed the growing population – review, meta-analyses, and policies for change. F1000Res (2023) 10:1135. doi: 10.12688/f1000research.73470.2

PubMed Abstract | Crossref Full Text | Google Scholar

53. Kenny TA, Woodside JV, Perry IJ, and Harrington JM. Consumer attitudes and behaviors toward more sustainable diets: a scoping review. Nutr Rev (2023) 81(12):1665–79. doi: 10.1093/nutrit/nuad033

PubMed Abstract | Crossref Full Text | Google Scholar

54. Wijayatunga NN, Chang Y, Brown AW, Webster AD, Sollid K, Ahn JJ, et al. Perceptions and preferences for environmentally sustainable food and associated factors: a cross-sectional analysis of a nationally representative survey of United States consumers. Am J Clin Nutr (2024) 120(4):804–13. doi: 10.1016/j.ajcnut.2024.07.026

PubMed Abstract | Crossref Full Text | Google Scholar

55. Ruggeri Laderchi C, Lotze-Campen H, DeClerck F, Bodirsky BL, Collignon Q, Crawford MS, et al. The economics of the food system transformation. Food System Economics Commission (2024). Available at: https://foodsystemeconomics.org/wp-content/uploads/FSEC-GlobalPolicyReport-February2024.pdf

Google Scholar

56. Kotz M, Levermann A, and Wenz L. The economic commitment of climate change. Nature (2024) 628(8008):551–7. doi: 10.1038/s41586-024-07219-0

PubMed Abstract | Crossref Full Text | Google Scholar

57. World Health Organization. Controlling the global obesity epidemic [online]. Available at: https://www.who.int/activities/controlling-the-global-obesity-epidemic

Google Scholar

58. World Obesity Federation. World obesity atlas 2025. London: WOF. Available at: https://data.worldobesity.org/publications/?cat=23

Google Scholar

59. GBD 2021 US Burden of Disease and Forecasting Collaborators. Burden of disease scenarios by state in the USA, 2022–50: a forecasting analysis for the Global Burden of Disease Study 2021. Lancet (2024) 404(10469):2341–70. doi: 10.1016/S0140-6736(24)02246-3

PubMed Abstract | Crossref Full Text | Google Scholar

60. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet (2017) 390(10113):2627–42. doi: 10.1016/S0140-6736(17)32129-3

PubMed Abstract | Crossref Full Text | Google Scholar

61. United Nations Children’s Fund. Feeding profit: how food environments are failing children - child nutrition report 2025 New York, NY: UNICEF (2025). Available at: https://data.unicef.org/resources/feeding-profit-2025-child-nutrition-report/

Google Scholar

62. GBD 2023 Disease and Injury and Risk Factor Collaborators. Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023. Lancet (2025) 406(10513):1873–922. doi: 10.1016/S0140-6736(25)01637-X

PubMed Abstract | Crossref Full Text | Google Scholar

63. Chew NWS, Mehta A, Goh R, Koh J, Chen Y, Chong B, et al. The global cardiovascular-liver-metabolic syndemic: epidemiology, trends and challenges. Nat Rev Cardiol (2025). doi: 10.1038/s41569-025-01220-4

PubMed Abstract | Crossref Full Text | Google Scholar

64. Frühbeck G, Busetto L, and Carbone F. The obesity syndemic in the European Community: towards a systems thinking approach for preventive policies. Eur Heart J (2024) 45(25):2181–2. doi: 10.1093/eurheartj/ehae066

PubMed Abstract | Crossref Full Text | Google Scholar

65. Chong B, Jayabaskaran J, Jauhari SM, Chia J, le Roux CW, Mehta A, et al. The global syndemic of modifiable cardiovascular risk factors projected from 2025 to 2050. J Am Coll Cardiol (2025) 86(3):165–77. doi: 10.1016/j.jacc.2025.04.061

PubMed Abstract | Crossref Full Text | Google Scholar

66. Garrido G, Severo FC, Bonfim SMV, Dias LF, Domingos ALG, Jones AD, et al. The role of food consumption in the global syndemic: a scoping review and conceptual model. Int J Environ Res Public Health (2025) 22(6):897. doi: 10.3390/ijerph22060897

PubMed Abstract | Crossref Full Text | Google Scholar

67. Organization for Economic Co-operation and Development. The heavy burden of obesity: the economics of prevention. A quick guide for policy makers. Paris: OECD Publishing (2019). Available at: https://www.oecd-ilibrary.org/social-issues-migration-health/the-heavy-burden-of-obesity_3c6ec454-en

Google Scholar

68. Powell-Wiley TM, Poirier P, Burke LE, Després J-P, Gordon-Larsen P, Lavie CJ, et al. Obesity and cardiovascular disease: a scientific statement from the American Heart Association. Circulation (2021) 143(21):e984–1010. doi: 10.1161/CIR.0000000000000973

PubMed Abstract | Crossref Full Text | Google Scholar

69. Aras M, Tchang BG, and Pape J. Obesity and diabetes. Nurs Clin North Am (2021) 56(4):527–41. doi: 10.1016/j.cnur.2021.07.008

PubMed Abstract | Crossref Full Text | Google Scholar

70. Pati S, Irfan W, Jameel A, Ahmed S, and Shahid RK. Obesity and cancer: a current overview of epidemiology, pathogenesis, outcomes, and management. Cancers (Basel) (2023) 15(2):485. doi: 10.3390/cancers15020485

PubMed Abstract | Crossref Full Text | Google Scholar

71. Loomans-Kropp HA and Umar A. Analysis of body mass index in early and middle adulthood and estimated risk of gastrointestinal cancer. JAMA Netw Open (2023) 6(5):e2310002. doi: 10.1001/jamanetworkopen.2023.10002

PubMed Abstract | Crossref Full Text | Google Scholar

72. Shi M and Cao Y. Obesity and gastrointestinal cancer: a life course perspective. JAMA Netw Open (2023) 6(5):e239921.doi: 10.1001/jamanetworkopen.2023.9921

PubMed Abstract | Crossref Full Text | Google Scholar

73. Liu C, Yuan YC, Guo MN, Xin Z, Chen GJ, Ding N, et al. Rising incidence of obesity-related cancers among younger adults in China: a population-based analysis (2007–2021). Medicine (2024) 5(11):1402–12.e2. doi: 10.1016/j.medj.2024.07.012

PubMed Abstract | Crossref Full Text | Google Scholar

74. Bray GA. Weight homeostasis. Annu Rev Med (1991) 42:205–16. doi: 10.1146/annurev.me.42.020191.001225

PubMed Abstract | Crossref Full Text | Google Scholar

75. Oken E and Gillman MW. Fetal origins of obesity. Obes Res (2003) 11(4):496–506. doi: 10.1038/oby.2003.69

PubMed Abstract | Crossref Full Text | Google Scholar

76. Hall KD, Farooqi IS, Friedman JM, Klein S, Loos RJF, Mangelsdorf DJ, et al. The energy balance model of obesity: beyond calories in, calories out. Am J Clin Nutr (2022) 115(5):1243–54. doi: 10.1093/ajcn/nqac031

PubMed Abstract | Crossref Full Text | Google Scholar

77. Ludwig DS, Aronne LJ, Astrup A, de Cabo R, Cantley LC, Friedman MI, et al. The carbohydrate-insulin model: a physiological perspective on the obesity pandemic. Am J Clin Nutr (2021) 114(6):1873–85. doi: 10.1093/ajcn/nqab270

PubMed Abstract | Crossref Full Text | Google Scholar

78. Istfan N, Hasson B, Apovian C, Meshulam T, Yu L, Anderson W, et al. Acute carbohydrate overfeeding: a redox model of insulin action and its impact on metabolic dysfunction in humans. Am J Physiol Endocrinol Metab (2021) 321(5):E636–51. doi: 10.1152/ajpendo.00094.2021

PubMed Abstract | Crossref Full Text | Google Scholar

79. Bikman BT, Shimy KJ, Apovian CM, Yu S, Saito ER, Walton CM, et al. A high-carbohydrate diet lowers the rate of adipose tissue mitochondrial respiration. Eur J Clin Nutr (2022) 76(9):1339–42. doi: 10.1038/s41430-022-01097-3

PubMed Abstract | Crossref Full Text | Google Scholar

80. Ludwig DS and Sørensen TIA. An integrated model of obesity pathogenesis that revisits causal direction. Nat Rev Endocrinol (2022) 18(5):261–2. doi: 10.1038/s41574-022-00635-0

PubMed Abstract | Crossref Full Text | Google Scholar

81. Temple NJ. The origins of the obesity epidemic in the USA–lessons for today. Nutrients (2022) 14(20):4253. doi: 10.3390/nu14204253

PubMed Abstract | Crossref Full Text | Google Scholar

82. Drewnowski A. The real contribution of added sugars and fats to obesity. Epidemiol Rev (2007) 29:160–71. doi: 10.1093/epirev/mxm011

PubMed Abstract | Crossref Full Text | Google Scholar

83. Popkin BM and Hawkes C. Sweetening of the global diet, particularly beverages: patterns, trends, and policy responses. Lancet Diabetes Endocrinol (2016) 4(2):174–86. doi: 10.1016/S2213-8587(15)00419-2

PubMed Abstract | Crossref Full Text | Google Scholar

84. Rehm CD, Peñalvo JL, Afshin A, and Mozaffarian D. Dietary intake among US adults, 1999–2012. JAMA (2016) 315(23):2542–53. doi: 10.1001/jama.2016.7491

PubMed Abstract | Crossref Full Text | Google Scholar

85. Shan Z, Rehm CD, Rogers G, Ruan M, Wang DD, Hu FB, et al. Trends in dietary carbohydrate, protein, and fat intake and diet quality among US adults, 1999–2016. JAMA (2019) 322(12):1178–87. doi: 10.1001/jama.2019.13771

PubMed Abstract | Crossref Full Text | Google Scholar

86. Frank SM, Jaacks LM, Adair LS, Avery CL, Meyer K, Rose D, et al. Adherence to the Planetary Health Diet Index and correlation with nutrients of public health concern: an analysis of NHANES 2003–2018. Am J Clin Nutr (2024) 119(2):384–92. doi: 10.1016/j.ajcnut.2023.10.018

PubMed Abstract | Crossref Full Text | Google Scholar

87. Yusuf S, Reddy S, Ôunpuu S, and Anand S. Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization. Circulation (2001) 104(22):2746–53. doi: 10.1161/hc4601.099487

PubMed Abstract | Crossref Full Text | Google Scholar

88. Wagner KH and Brath H. A global view on the development of non communicable diseases. Prev Med (2012) 54(suppl):S38–41. doi: 10.1016/j.ypmed.2011.11.012

PubMed Abstract | Crossref Full Text | Google Scholar

89. Subramanian S and Deaton A. The demand for food and calories. J Polit Econ (1996) 104:133–62. doi: 10.1086/262020

Crossref Full Text | Google Scholar

90. Assah FK, Ekelund U, Brage S, Mbanya JC, and Wareham NJ. Urbanization, physical activity, and metabolic health in sub-Saharan Africa. Diabetes Care (2011) 34(2):491–6. doi: 10.2337/dc10-0990

PubMed Abstract | Crossref Full Text | Google Scholar

91. Ng SW and Popkin BM. Time use and physical activity: a shift away from movement across the globe. Obes Rev (2012) 13(8):659–80. doi: 10.1111/j.1467-789X.2011.00982.x

PubMed Abstract | Crossref Full Text | Google Scholar

92. NCD Risk Factor Collaboration (NCD-RisC). Rising rural body-mass index is the main driver of the global obesity epidemic in adults. Nature (2019) 569(7755):260–4. doi: 10.1038/s41586-019-1171-x

PubMed Abstract | Crossref Full Text | Google Scholar

93. Bleich S, Cutler D, Murray C, and Adams A. Why is the developed world obese? Annu Rev Public Health (2008) 29:273–95. doi: 10.1146/annurev.publhealth.29.020907.090954

PubMed Abstract | Crossref Full Text | Google Scholar

94. Valenzuela PL, Santos-Lozano A, Saco-Ledo G, Castillo-García A, and Lucia A. Obesity, cardiovascular risk, and lifestyle: cross-sectional and prospective analyses in a nationwide Spanish cohort. Eur J Prev Cardiol (2023) 30(14):1493–501. doi: 10.1093/eurjpc/zwad204

PubMed Abstract | Crossref Full Text | Google Scholar

95. Beals JW, Kayser BD, Smith GI, Schweitzer GG, Kirbach K, Kearney ML, et al. Dietary weight loss-induced improvements in metabolic function are enhanced by exercise in people with obesity and prediabetes. Nat Metab (2023) 5(7):1221–35. doi: 10.1038/s42255-023-00829-4

PubMed Abstract | Crossref Full Text | Google Scholar

96. Popkin BM, Adair LS, and Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev (2012) 70(1):3–21. doi: 10.1111/j.1753-4887.2011.00456.x85

PubMed Abstract | Crossref Full Text | Google Scholar

97. Monteiro CA, Cannon G, Moubarac JC, Levy RB, Louzada MLC, and Jaime PC. The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr (2018) 21(1):5–17. doi: 10.1017/S136898001700023486

PubMed Abstract | Crossref Full Text | Google Scholar

98. Zobel EH, Hansen TW, Rossing P, and von Scholten BJ. Global changes in food supply and the obesity epidemic. Curr Obes Rep (2016) 5(4):449–55. doi: 10.1007/s13679-016-0233-8

PubMed Abstract | Crossref Full Text | Google Scholar

99. Monteiro CA, Cannon G, Levy RB, Moubarac J-C, Louzada ML, Rauber F, et al. Ultra-processed foods: what they are and how to identify them. Public Health Nutr (2019) 22(5):936–41. doi: 10.1017/S1368980018003762

PubMed Abstract | Crossref Full Text | Google Scholar

100. Trumbo PR, Bleiweiss-Sande R, Campbell JK, Decker E, Drewnowski A, Erdman JW, et al. Toward a science-based classification of processed foods to support meaningful research and effective health policies. Front Nutr (2024) 11:1389601. doi: 10.3389/fnut.2024.1389601

PubMed Abstract | Crossref Full Text | Google Scholar

101. Cordova R, Viallon V, Fontvieille E, Peruchet-Noray L, Jansana A, Wagner KH, et al. Consumption of ultra-processed foods and risk of multimorbidity of cancer and cardiometabolic diseases: a multinational cohort study. Lancet Reg Health Eur (2023) 35:100771. doi: 10.1016/j.lanepe.2023.100771

PubMed Abstract | Crossref Full Text | Google Scholar

102. Mendoza K, Smith-Warner SA, Rossato SL, Khandpur N, Manson JE, Qi L, et al. Ultra-processed foods and cardiovascular disease: analysis of three large US prospective cohorts and a systematic review and meta-analysis of prospective cohort studies. Lancet Reg Health Am (2024) 37:100859. doi: 10.1016/j.lana.2024.100859

PubMed Abstract | Crossref Full Text | Google Scholar

103. Wang L, Steele EM, Du M, Luo H, Zhang X, Mozaffarian D, et al. Association between ultra-processed food consumption and mortality among US adults: prospective cohort study of the national health and nutrition examination survey, 2003–2018. J Acad Nutr Diet (2025) 125(7):875–87.e7. doi: 10.1016/j.jand.2024.11.014

PubMed Abstract | Crossref Full Text | Google Scholar

104. Monteiro CA and Rezende LFM. Are all ultra-processed foods bad for health? Lancet Reg Health Eur (2024) 46:101106. doi: 10.1016/j.lanepe.2024.101106

PubMed Abstract | Crossref Full Text | Google Scholar

105. Monteiro CA, Martínez-Steele E, and Cannon G. Reasons to avoid ultra-processed foods. BMJ (2024) 384:q439. doi: 10.1136/bmj.q439

PubMed Abstract | Crossref Full Text | Google Scholar

106. Schatzker M. The Dorito effect: the surprising new truth about food and flavor. New York, NY: Simon & Schuster (2015).

Google Scholar

107. Van Tulleken C. Ultra-processed people: why do we all eat stuff that isn’t food… and why can’t we stop? London, UK: Cornerstone Press - Penguin UK (2023).

Google Scholar

108. Coyle DH, Huang L, Shahid M, Gaines A, Di Tanna GL, Louie JCY, et al. Socio-economic difference in purchases of ultra-processed foods in Australia: an analysis of a nationally representative household grocery purchasing panel. Int J Behav Nutr Phys Act (2022) 19(1):148. doi: 10.1186/s12966-022-01389-8

PubMed Abstract | Crossref Full Text | Google Scholar

109. Taneri PE, Wehrli F, Roa-Díaz ZM, Itodo OA, Salvador D, Raeisi-Dehkordi H, et al. Association between ultra-processed food intake and all-cause mortality: a systematic review and meta-analysis. Am J Epidemiol (2022) 191(7):1323–35. doi: 10.1093/aje/kwac039

PubMed Abstract | Crossref Full Text | Google Scholar

110. Lane MM, Gamage E, Du S, Ashtree DN, McGuinness AJ, Gauci S, et al. Ultra-processed food exposure and adverse health outcomes: umbrella review of epidemiological meta-analyses. BMJ (2024) 384:e077310. doi: 10.1136/bmj-2023-077310

PubMed Abstract | Crossref Full Text | Google Scholar

111. Barbaresko J, Bröder J, Conrad J, Szczerba E, Lang A, and Schlesinger S. Ultra-processed food consumption and human health: an umbrella review of systematic reviews with meta-analyses. Crit Rev Food Sci Nutr (2025) 65(11):1999–2007. doi: 10.1080/10408398.2024.2317877

PubMed Abstract | Crossref Full Text | Google Scholar

112. Nilson EAF, Delpino FM, Batis C, MaChado PP, Moubarac JC, Cediel G, et al. Premature mortality attributable to ultraprocessed food consumption in 8 countries. Am J Prev Med (2025) 68(6):1091–99. doi: 10.1016/j.amepre.2025.02.018

PubMed Abstract | Crossref Full Text | Google Scholar

113. Dai S, Wellens J, Yang N, Li D, Wang J, Wang L, et al. Ultra-processed foods and human health: an umbrella review and updated meta-analyses of observational evidence. Clin Nutr (2024) 43(6):1386–94. doi: 10.1016/j.clnu.2024.04.016

PubMed Abstract | Crossref Full Text | Google Scholar

114. Srour B, Kordahi MC, Bonazzi E, Deschasaux-Tanguy M, Touvier M, and Chassaing B. Ultra-processed foods and human health: from epidemiological evidence to mechanistic insights. Lancet Gastroenterol Hepatol (2022) 7(12):1128–40. doi: 10.1016/S2468-1253(22)00169-8

PubMed Abstract | Crossref Full Text | Google Scholar

115. Dicken SJ and Batterham RL. The role of diet quality in mediating the association between ultra-processed food intake, obesity and health-related outcomes: a review of prospective cohort studies. Nutrients (2021) 14(1):23. doi: 10.3390/nu14010023

PubMed Abstract | Crossref Full Text | Google Scholar

116. Dicken SJ and Batterham RL. Ultra-processed food and obesity: what is the evidence? Curr Nutr Rep (2024) 13(1):23–38. doi: 10.1007/s13668-024-00517-z

PubMed Abstract | Crossref Full Text | Google Scholar

117. Costa CS, Del-Ponte B, Assunção MCF, and Santos IS. Consumption of ultra-processed foods and body fat during childhood and adolescence: a systematic review. Public Health Nutr (2018) 21(1):148–59. doi: 10.1017/S1368980017001331

PubMed Abstract | Crossref Full Text | Google Scholar

118. Mambrini SP, Menichetti F, Ravella S, Pellizzari M, De Amicis R, Foppiani A, et al. Ultra-processed food consumption and incidence of obesity and cardiometabolic risk factors in adults: a systematic review of prospective studies. Nutrients (2023) 15(11):2583. doi: 10.3390/nu15112583

PubMed Abstract | Crossref Full Text | Google Scholar

119. Moradi S, Entezari MH, Mohammadi H, Jayedi A, Lazaridi AV, Kermani MAH, et al. Ultra-processed food consumption and adult obesity risk: a systematic review and dose-response meta-analysis. Crit Rev Food Sci Nutr (2023) 63(2):249–60. doi: 10.1080/10408398.2021.1946005

PubMed Abstract | Crossref Full Text | Google Scholar

120. Cordova R, Kliemann N, Huybrechts I, Rauber F, Vamos EP, Levy RB, et al. Consumption of ultra-processed foods associated with weight gain and obesity in adults: a multi-national cohort study. Clin Nutr (2021) 40(9):5079–88. doi: 10.1016/j.clnu.2021.08.009

PubMed Abstract | Crossref Full Text | Google Scholar

121. Chang K, Khandpur N, Neri D, Touvier M, Huybrechts I, Millett C, et al. Association between childhood consumption of ultraprocessed food and adiposity trajectories in the Avon longitudinal study of parents and children birth cohort. JAMA Pediatr (2021) 175(9):e211573. doi: 10.1001/jamapediatrics.2021.1573

PubMed Abstract | Crossref Full Text | Google Scholar

122. Lara-Castor L, Micha R, Cudhea F, Miller V, Shi P, Zhang J, et al. Intake of sugar sweetened beverages among children and adolescents in 185 countries between 1990 and 2018: population based study. BMJ (2024) 386:e079234. doi: 10.1136/bmj-2024-079234

PubMed Abstract | Crossref Full Text | Google Scholar

123. De Cosmi V, Scaglioni S, and Agostoni C. Early taste experiences and later food choices. Nutrients (2017) 9(2):107. doi: 10.3390/nu9020107

PubMed Abstract | Crossref Full Text | Google Scholar

124. Matos RA, Adams M, and Sabaté J. Review: the consumption of ultra-processed foods and non-communicable diseases in Latin America. Front Nutr (2021) 8:622714. doi: 10.3389/fnut.2021.622714

PubMed Abstract | Crossref Full Text | Google Scholar

125. Pan American Health Organization. Ultra-processed food and drink products in Latin America: trends, impact on obesity, policy implications. Washington, DC: PAHO (2015). Available at: https://www.paho.org/en/documents/ultra-processed-food-and-drink-products-latin-america-trends-impact-obesity-policy

Google Scholar

126. Neltner TG, Kulkarni NR, Alger HM, Maffini MV, Bongard ED, Fortin ND, et al. Navigating the U.S. food additive regulatory program. Comprehensive Reviews in Food Science and Food Safety (2011) 10:342–68. doi: 10.1111/j.1541-4337.2011.00166.x

Crossref Full Text | Google Scholar

127. Backhaus O and Benesh M. EWG analysis: almost all new food chemicals greenlighted by industry, not the FDA [online] (2022). Available at: https://www.ewg.org/news-insights/news/2022/04/ewg-analysis-almost-all-new-food-chemicals-greenlighted-industry-not-fda

Google Scholar

128. Hartung T. How AI can deliver the Human Exposome Project. Nat Med (2025) 31(6):1738. doi: 10.1038/s41591-025-03749-w

PubMed Abstract | Crossref Full Text | Google Scholar

129. Sillé FCM, Busquet F, Fitzpatrick S, Herrmann K, Leenhouts-Martin L, Luechtefeld T, et al. The implementation moonshot project for alternative chemical testing (IMPACT) toward a human exposome project. ALTEX (2024) 41(3):344–62. doi: 10.14573/altex.2407081

PubMed Abstract | Crossref Full Text | Google Scholar

130. DiFeliceantonio AG, Coppin G, Rigoux L, Edwin Thanarajah SE, Dagher A, Tittgemeyer M, et al. Supra-additive effects of combining fat and carbohydrate on food reward. Cell Metab (2018) 28(1):33–44.e3. doi: 10.1016/j.cmet.2018.05.018

PubMed Abstract | Crossref Full Text | Google Scholar

131. Small DM and DiFeliceantonio AG. Processed foods and food reward. Science (2019) 363(6425):346–7. doi: 10.1126/science.aav0556

PubMed Abstract | Crossref Full Text | Google Scholar

132. Gearhardt AN and DiFeliceantonio AG. Highly processed foods can be considered addictive substances based on established scientific criteria. Addiction (2023) 118(4):589–98. doi: 10.1111/add.16065

PubMed Abstract | Crossref Full Text | Google Scholar

133. Perszyk EE, Hutelin Z, Trinh J, Kanyamibwa A, Fromm S, Davis XS, et al. Fat and carbohydrate interact to potentiate food reward in healthy weight but not in overweight or obesity. Nutrients (2021) 13(4):1203. doi: 10.3390/nu13041203

PubMed Abstract | Crossref Full Text | Google Scholar

134. Gupta S, Hawk T, Aggarwal A, and Drewnowski A. Characterizing ultra-processed foods by energy density, nutrient density, and cost. Front Nutr (2019) 6:70. doi: 10.3389/fnut.2019.00070

PubMed Abstract | Crossref Full Text | Google Scholar

135. Rao M, Afshin A, Singh G, and Mozaffarian D. Do healthier foods and diet patterns cost more than less healthy options? A systematic review and meta-analysis. BMJ Open (2013) 3(12):e004277. doi: 10.1136/bmjopen-2013-004277

PubMed Abstract | Crossref Full Text | Google Scholar

136. Drewnowski A and Specter SE. Poverty and obesity: the role of energy density and energy costs. Am J Clin Nutr (2004) 79(1):6–16. doi: 10.1093/ajcn/79.1.6

PubMed Abstract | Crossref Full Text | Google Scholar

137. Robinson E, Almiron-Roig E, Rutters F, de Graaf C, Forde CG, Tudur Smith C, et al. A systematic review and meta-analysis examining the effect of eating rate on energy intake and hunger. Am J Clin Nutr (2014) 100(1):123–51. doi: 10.3945/ajcn.113.081745

PubMed Abstract | Crossref Full Text | Google Scholar

138. Forde CG, Mars M, and de Graaf K. Ultra-processing or oral processing? A role for energy density and eating rate in moderating energy intake from processed foods. Curr Dev Nutr (2020) 4(3):nzaa019. doi: 10.1093/cdn/nzaa019

PubMed Abstract | Crossref Full Text | Google Scholar

139. Lin S, Cienfuegos S, Ezpeleta M, Gabel K, Pavlou V, Mulas A, et al. Time-restricted eating without calorie counting for weight loss in a racially diverse population: a randomized controlled trial. Ann Intern Med (2023) 176(7):885–95. doi: 10.7326/M23-0052

PubMed Abstract | Crossref Full Text | Google Scholar

140. Pavlou V, Cienfuegos S, Lin S, Ezpeleta M, Ready K, Corapi S, et al. Effect of time-restricted eating on weight loss in adults with type 2 diabetes: a randomized clinical trial. JAMA Netw Open (2023) 6(10):e2339337. doi: 10.1001/jamanetworkopen.2023.39337

PubMed Abstract | Crossref Full Text | Google Scholar

141. Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, et al. Ultra-processed diets cause excess calorie intake and weight gain: an inpatient randomized controlled trial of ad libitum food intake. Cell Metab (2019) 30(1):67–77.e3. doi: 10.1016/j.cmet.2019.05.008

PubMed Abstract | Crossref Full Text | Google Scholar

142. Fazzino TL, Courville AB, Guo J, and Hall KD. Ad libitum meal energy intake is positively influenced by energy density, eating rate and hyper-palatable food across four dietary patterns. Nat Food (2023) 4(2):144–7. doi: 10.1038/s43016-022-00688-4

PubMed Abstract | Crossref Full Text | Google Scholar

143. Hamano S, Sawada M, Aihara M, Sakurai Y, Sekine R, Usami S, et al. Ultra-processed foods cause weight gain and increased energy intake associated with reduced chewing frequency: a randomized, open-label, crossover study. Diabetes Obes Metab (2024) 26(11):5431–43. doi: 10.1111/dom.15922

PubMed Abstract | Crossref Full Text | Google Scholar

144. Dicken SJ, Jassil FC, Brown A, Kalis M, Stanley C, Ranson C, et al. Ultraprocessed or minimally processed diets following healthy dietary guidelines on weight and cardiometabolic health: a randomized, crossover trial. Nat Med (2025) 31(10):3297–308. doi: 10.1038/s41591-025-03842-0

PubMed Abstract | Crossref Full Text | Google Scholar

145. Kawano Y, Edwards M, Huang Y, Bilate AM, Araujo LP, Tanoue T, et al. Microbiota imbalance induced by dietary sugar disrupts immune-mediated protection from metabolic syndrome. Cell (2022) 185(19):3501–19.e20. doi: 10.1016/j.cell.2022.08.005

PubMed Abstract | Crossref Full Text | Google Scholar

146. Tellez LA, Medina S, Han W, Ferreira JG, Licona-Limón P, Ren X, et al. A gut lipid messenger links excess dietary fat to dopamine deficiency. Science (2013) 341(6147):800–2. doi: 10.1126/science.1239275

PubMed Abstract | Crossref Full Text | Google Scholar

147. Tellez LA, Han W, Zhang X, Ferreira TL, Perez IO, Shammah-Lagnado SJ, et al. Separate circuitries encode the hedonic and nutritional values of sugar. Nat Neurosci (2016) 19(3):465–70. doi: 10.1038/nn.4224

PubMed Abstract | Crossref Full Text | Google Scholar

148. Fardet A. Ultra-processing should be understood as a holistic issue, from food matrix, to dietary patterns, food scoring, and food systems. J Food Sci (2024) 89(7):4563–73. doi: 10.1111/1750-3841.17139

PubMed Abstract | Crossref Full Text | Google Scholar

149. Fardet A and Rock E. Chronic diseases are first associated with the degradation and artificialization of food matrices rather than with food composition: calorie quality matters more than calorie quantity. Eur J Nutr (2022) 61(5):2239–53. doi: 10.1007/s00394-021-02786-8

PubMed Abstract | Crossref Full Text | Google Scholar

150. MaChado P, Cediel G, Woods J, Baker P, Dickie S, Gomes FS, et al. Evaluating intake levels of nutrients linked to non-communicable diseases in Australia using the novel combination of food processing and nutrient profiling metrics of the PAHO Nutrient Profile Model. Eur J Nutr (2022) 61(4):1801–12. doi: 10.1007/s00394-021-02740-8

PubMed Abstract | Crossref Full Text | Google Scholar

151. Houshialsadat Z, Cediel G, Sattamini I, Scrinis G, and MaChado P. Ultra-processed foods, dietary diversity and micronutrient intakes in the Australian population. Eur J Nutr (2024) 63(1):135–44. doi: 10.1007/s00394-023-03245-2

PubMed Abstract | Crossref Full Text | Google Scholar

152. Scrinis G and Monteiro C. From ultra-processed foods to ultra-processed dietary patterns. Nat Food (2022) 3(9):671–3. doi: 10.1038/s43016-022-00599-4

PubMed Abstract | Crossref Full Text | Google Scholar

153. Veldhuizen MG, Babbs RK, Patel B, Fobbs W, Kroemer NB, Garcia E, et al. Integration of sweet taste and metabolism determines carbohydrate reward. Curr Biol (2017) 27(16):2476–85.e6. doi: 10.1016/j.cub.2017.07.018

PubMed Abstract | Crossref Full Text | Google Scholar

154. Pepino MY. Metabolic effects of non-nutritive sweeteners. Physiol Behav (2015) 152(Pt B):450–5. doi: 10.1016/j.physbeh.2015.06.024

PubMed Abstract | Crossref Full Text | Google Scholar

155. Tellez LA, Ren X, Han W, Medina S, Ferreira JG, Yeckel CW, et al. Glucose utilization rates regulate intake levels of artificial sweeteners. J Physiol (2013) 591(22):5727–44. doi: 10.1113/jphysiol.2013.263103

PubMed Abstract | Crossref Full Text | Google Scholar

156. Dalenberg JR, Patel BP, Denis R, Veldhuizen MG, Nakamura Y, Vinke PC, et al. Short-term consumption of sucralose with, but not without, carbohydrate impairs neural and metabolic sensitivity to sugar in humans. Cell Metab (2020) 31(3):493–502.e7. doi: 10.1016/j.cmet.2020.01.014

PubMed Abstract | Crossref Full Text | Google Scholar

157. Suez J, Cohen Y, Valdés-Mas R, Mor U, Dori-Bachash M, Federici S, et al. Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance. Cell (2022) 185(18):3307–28.e19. doi: 10.1016/j.cell.2022.07.016

PubMed Abstract | Crossref Full Text | Google Scholar

158. Goran MI, Ulijaszek SJ, and Ventura EE. High fructose corn syrup and diabetes prevalence: a global perspective. Glob Public Health (2013) 8(1):55–64. doi: 10.1080/17441692.2012.736257

PubMed Abstract | Crossref Full Text | Google Scholar

159. Stanhope KL, Schwarz JM, Keim NL, Griffen SC, Bremer AA, Graham JL, et al. Consuming fructose-sweetened, not glucose-sweetened, beverages increases visceral adiposity and lipids and decreases insulin sensitivity in overweight/obese humans. J Clin Invest (2009) 119(5):1322–34. doi: 10.1172/JCI37385

PubMed Abstract | Crossref Full Text | Google Scholar

160. Sindhunata DP, Meijnikman AS, Gerdes VEA, and Nieuwdorp M. Dietary fructose as a metabolic risk factor. Am J Physiol Cell Physiol (2022) 323(3):C847–56. doi: 10.1152/ajpcell.00439.2021

PubMed Abstract | Crossref Full Text | Google Scholar

161. Wang J, Hu P, Chen Z, Liu Q, and Wei C. Progress in high-amylose cereal crops through inactivation of starch branching enzymes. Front Plant Sci (2017) 8:469. doi: 10.3389/fpls.2017.00469

PubMed Abstract | Crossref Full Text | Google Scholar

162. Barber TM, Kabisch S, Pfeiffer AFH, and Weickert MO. The health benefits of dietary fibre. Nutrients (2020) 12(10):3209. doi: 10.3390/nu12103209

PubMed Abstract | Crossref Full Text | Google Scholar

163. Kabisch S, Weickert MO, and Pfeiffer AFH. The role of cereal soluble fiber in the beneficial modulation of glycometabolic gastrointestinal hormones. Crit Rev Food Sci Nutr (2022) 64(13):4331–47. doi: 10.1080/10408398.2022.2141190

PubMed Abstract | Crossref Full Text | Google Scholar

164. Desai MS, Seekatz AM, Koropatkin NM, Kamada N, Hickey CA, Wolter M, et al. A dietary fiber-deprived gut microbiota degrades the colonic mucus barrier and enhances pathogen susceptibility. Cell (2016) 167(5):1339–53.e21. doi: 10.1016/j.cell.2016.10.043

PubMed Abstract | Crossref Full Text | Google Scholar

165. Rowland I, Gibson G, Heinken A, Scott K, Swann J, Thiele I, et al. Gut microbiota functions: metabolism of nutrients and other food components. Eur J Nutr (2018) 57(1):1–24. doi: 10.1007/s00394-017-1445-8

PubMed Abstract | Crossref Full Text | Google Scholar

166. Armstrong LE, Casa DJ, and Belval LN. Metabolism, bioenergetics and thermal physiology: influences of the human intestinal microbiota. Nutr Res Rev (2019) 32(2):205–17. doi: 10.1017/S0954422419000076

PubMed Abstract | Crossref Full Text | Google Scholar

167. Puljiz Z, Kumric M, Vrdoljak J, Martinovic D, Ticinovic Kurir T, Krnic MO, et al. Obesity, gut microbiota, and metabolome: from pathophysiology to nutritional interventions. Nutrients (2023) 15(10):2236. doi: 10.3390/nu15102236

PubMed Abstract | Crossref Full Text | Google Scholar

168. Berry SE, Valdes AM, Drew DA, Asnicar F, Mazidi M, Wolf J, et al. Human postprandial responses to food and potential for precision nutrition. Nat Med (2020) 26(6):964–73. doi: 10.1038/s41591-020-0934-0

PubMed Abstract | Crossref Full Text | Google Scholar

169. Asnicar F, Berry SE, Valdes AM, Nguyen LH, Piccinno G, Drew DA, et al. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat Med (2021) 27(2):321–32. doi: 10.1038/s41591-020-01183-8

PubMed Abstract | Crossref Full Text | Google Scholar

170. Le Roy CI, Bowyer RCE, Castillo-Fernandez JE, Pallister T, Menni C, Steves CJ, et al. Dissecting the role of the gut microbiota and diet on visceral fat mass accumulation. Sci Rep (2019) 9(1):9758. doi: 10.1038/s41598-019-46193-w

PubMed Abstract | Crossref Full Text | Google Scholar

171. Wang K, Liao M, Zhou N, Bao L, Ma K, Zheng Z, et al. Parabacteroides distasonis alleviates obesity and metabolic dysfunctions via production of succinate and secondary bile acids. Cell Rep (2019) 26(1):222–35.e5. doi: 10.1016/j.celrep.2018.12.028

PubMed Abstract | Crossref Full Text | Google Scholar

172. Martin MJ, Thottathil SE, and Newman TB. Antibiotics overuse in animal agriculture: a call to action for health care providers. Am J Public Health (2015) 105(12):2409–10. doi: 10.2105/AJPH.2015.302870

PubMed Abstract | Crossref Full Text | Google Scholar

173. Riley LW, Raphael E, and Faerstein E. Obesity in the United States – dysbiosis from exposure to low-dose antibiotics? Front Public Health (2013) 1:69. doi: 10.3389/fpubh.2013.00069

PubMed Abstract | Crossref Full Text | Google Scholar

174. Smith HJ. An ethical investigation into the microbiome: the intersection of agriculture, genetics, and the obesity epidemic. Gut Microbes (2020) 12(1):1760712. doi: 10.1080/19490976.2020.1760712

PubMed Abstract | Crossref Full Text | Google Scholar

175. Mulchandani R, Wang Y, Gilbert M, and Van Boeckel TP. Global trends in antimicrobial use in food-producing animals: 2020 to 2030. PloS Glob Public Health (2023) 3(2):e0001305. doi: 10.1371/journal.pgph.0001305

PubMed Abstract | Crossref Full Text | Google Scholar

176. Vallianou N, Dalamaga M, Stratigou T, Karampela I, and Tsigalou C. Do antibiotics cause obesity through long-term alterations in the gut microbiome? A review of current evidence. Curr Obes Rep (2021) 10(3):244–62. doi: 10.1007/s13679-021-00438-w

PubMed Abstract | Crossref Full Text | Google Scholar

177. Dalby MJ. Questioning the foundations of the gut microbiota and obesity. Philos Trans R Soc Lond B Biol Sci (2023) 378(1888):20220221. doi: 10.1098/rstb.2022.0221

PubMed Abstract | Crossref Full Text | Google Scholar

178. Neel JV. Diabetes mellitus: a “thrifty” genotype rendered detrimental by “progress”? Am J Hum Genet (1962) 14(4):353–62

Google Scholar

179. Speakman JR. The evolution of body fatness: trading off disease and predation risk. J Exp Biol (2018) 221(suppl 1):jeb167254. doi: 10.1242/jeb.167254

PubMed Abstract | Crossref Full Text | Google Scholar

180. Elks CE, Den Hoed M, Zhao JH, Sharp SJ, Wareham NJ, Loos RJF, et al. Variability in the heritability of body mass index: a systematic review and meta-regression. Front Endocrinol (2012) 3:29. doi: 10.3389/fendo.2012.00029

PubMed Abstract | Crossref Full Text | Google Scholar

181. Samaras K, Spector TD, Nguyen TV, Baan K, Campbell LV, and Kelly PJ. Independent genetic factors determine the amount and distribution of fat in women after the menopause. J Clin Endocrinol Metab (1997) 82(3):781–5. doi: 10.1210/jcem.82.3.3803

PubMed Abstract | Crossref Full Text | Google Scholar

182. Farooqi IS, Jebb SA, Langmack G, Lawrence E, Cheetham CH, Prentice AM, et al. Effects of recombinant leptin therapy in a child with congenital leptin deficiency. N Engl J Med (1999) 341(12):879–84. doi: 10.1056/NEJM199909163411204

PubMed Abstract | Crossref Full Text | Google Scholar

183. Loos RJF and Yeo GSH. The genetics of obesity: from discovery to biology. Nat Rev Genet (2022) 23(2):120–33. doi: 10.1038/s41576-021-00414-z

PubMed Abstract | Crossref Full Text | Google Scholar

184. Yeo GS, Farooqi IS, Aminian S, Halsall DJ, Stanhope RG, and O’Rahilly S. A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nat Genet (1998) 20(2):111–2. doi: 10.1038/2404

PubMed Abstract | Crossref Full Text | Google Scholar

185. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science (2007) 316(5826):889–94. doi: 10.1126/science.1141634

PubMed Abstract | Crossref Full Text | Google Scholar

186. Scuteri A, Sanna S, Chen WM, Uda M, Albai G, Strait J, et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PloS Genet (2007) 3(7):e115. doi: 10.1371/journal.pgen.0030115

PubMed Abstract | Crossref Full Text | Google Scholar

187. Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res (2019) 47(D1):D1005–12. doi: 10.1093/nar/gky1120

PubMed Abstract | Crossref Full Text | Google Scholar

188. Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet (2018) 27(20):3641–9. doi: 10.1093/hmg/ddy271

PubMed Abstract | Crossref Full Text | Google Scholar

189. Ragvin A, Moro E, Fredman D, Navratilova P, Drivenes Ø, Engström PG, et al. Long-range gene regulation links genomic type 2 diabetes and obesity risk regions to HHEX, SOX4, and IRX3. Proc Natl Acad Sci USA (2010) 107(2):775–80. doi: 10.1073/pnas.0911591107

PubMed Abstract | Crossref Full Text | Google Scholar

190. Smemo S, Tena JJ, Kim KH, Gamazon ER, Sakabe NJ, Gómez-Marín C, et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature (2014) 507(7492):371–5. doi: 10.1038/nature13138

PubMed Abstract | Crossref Full Text | Google Scholar

191. Heydet D, Chen LX, Larter CZ, Inglis C, Silverman MA, Farrell GC, et al. A truncating mutation of Alms1 reduces the number of hypothalamic neuronal cilia in obese mice. Dev Neurobiol (2013) 73(1):1–13. doi: 10.1002/dneu.22031

PubMed Abstract | Crossref Full Text | Google Scholar

192. Grarup N, Moltke I, Andersen MK, Dalby M, Vitting-Seerup K, Kern T, et al. Loss-of-function variants in ADCY3 increase risk of obesity and type 2 diabetes. Nat Genet (2018) 50(2):172–4. doi: 10.1038/s41588-017-0022-7

PubMed Abstract | Crossref Full Text | Google Scholar

193. Saeed S, Bonnefond A, Tamanini F, Mirza MU, Manzoor J, Janjua QM, et al. Loss-of-function mutations in ADCY3 cause monogenic severe obesity. Nat Genet (2018) 50(2):175–9. doi: 10.1038/s41588-017-0023-6

PubMed Abstract | Crossref Full Text | Google Scholar

194. Schrempft S, van Jaarsveld CHM, Fisher A, Herle M, Smith AD, Fildes A, et al. Variation in the heritability of child body mass index by obesogenic home environment. JAMA Pediatr (2018) 172(12):1153–60. doi: 10.1001/jamapediatrics.2018.1508

PubMed Abstract | Crossref Full Text | Google Scholar

195. Samaras K, Kelly PJ, Chiano MN, Arden N, Spector TD, and Campbell LV. Genes versus environment. The relationship between dietary fat and total and central abdominal fat. Diabetes Care (1998) 21(12):2069–76. doi: 10.2337/diacare.21.12.2069

PubMed Abstract | Crossref Full Text | Google Scholar

196. Qi Q, Chu AY, Kang JH, Huang J, Rose LM, Jensen MK, et al. Fried food consumption, genetic risk, and body mass index: gene-diet interaction analysis in three US cohort studies. BMJ (2014) 348:g1610. doi: 10.1136/bmj.g1610

PubMed Abstract | Crossref Full Text | Google Scholar

197. Wang T, Heianza Y, Sun D, Zheng Y, Huang T, Ma W, et al. Improving fruit and vegetable intake attenuates the genetic association with long-term weight gain. Am J Clin Nutr (2019) 110(3):759–68. doi: 10.1093/ajcn/nqz136

PubMed Abstract | Crossref Full Text | Google Scholar

198. Samaras K, Kelly PJ, Chiano MN, Spector TD, and Campbell LV. Genetic and environmental influences on total-body and central abdominal fat: the effect of physical activity in female twins. Ann Intern Med (1999) 130(11):873–82. doi: 10.7326/0003-4819-130-11-199906010-00002

PubMed Abstract | Crossref Full Text | Google Scholar

199. Wang B, Gao W, Lv J, Yu C, Wang S, Pang Z, et al. Physical activity attenuates genetic effects on BMI: results from a study of Chinese adult twins. Obes (Silver Spring) (2016) 24(3):750–6. doi: 10.1002/oby.21402

PubMed Abstract | Crossref Full Text | Google Scholar

200. Kilpelainen TO, Qi L, Brage S, Sharp SJ, Sonestedt E, Demerath E, et al. Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of 218,166 adults and 19,268 children. PloS Med (2011) 8(11):e1001116. doi: 10.1371/journal.pmed.1001116

PubMed Abstract | Crossref Full Text | Google Scholar

201. Huypens P, Sass S, Wu M, Dyckhoff D, Tschöp M, Theis F, et al. Epigenetic germline inheritance of diet-induced obesity and insulin resistance. Nat Genet (2016) 48(5):497–9. doi: 10.1038/ng.3527

PubMed Abstract | Crossref Full Text | Google Scholar

202. Plagemann A, Harder T, Brunn M, Harder A, Roepke K, Wittrock-Staar M, et al. Hypothalamic proopiomelanocortin promoter methylation becomes altered by early overfeeding: an epigenetic model of obesity and the metabolic syndrome. J Physiol (2009) 587(20):4963–76. doi: 10.1113/jphysiol.2009.176156

PubMed Abstract | Crossref Full Text | Google Scholar

203. Ravelli GP, Stein ZA, and Susser MW. Obesity in young men after famine exposure in utero and early infancy. N Engl J Med (1976) 295(7):349–53. doi: 10.1056/NEJM197608122950701

PubMed Abstract | Crossref Full Text | Google Scholar

204. van Dijk SJ, Molloy PL, Varinli H, Morrison JL, Muhlhausler BS, and Members of EpiSCOPE. Epigenetics and human obesity. Int J Obes (Lond) (2015) 39(1):85–97. doi: 10.1038/ijo.2014.34

PubMed Abstract | Crossref Full Text | Google Scholar

205. Ling C and Rönn T. Epigenetics in human obesity and type 2 diabetes. Cell Metab (2019) 29(5):1028–44. doi: 10.1016/j.cmet.2019.03.009

PubMed Abstract | Crossref Full Text | Google Scholar

206. Alfano R, Robinson O, Handakas E, Nawrot TS, Vineis P, and Plusquin M. Perspectives and challenges of epigenetic determinants of childhood obesity: a systematic review. Obes Rev (2022) 23(suppl 1):e13389. doi: 10.1111/obr.13389

PubMed Abstract | Crossref Full Text | Google Scholar

207. Keller M, Svensson SIA, Rohde-Zimmermann K, Kovacs P, and Böttcher Y. Genetics and epigenetics in obesity: what do we know so far? Curr Obes Rep (2023) 12(4):482–501. doi: 10.1007/s13679-023-00526-z

PubMed Abstract | Crossref Full Text | Google Scholar

208. Keller M, Vogel M, Garten A, Svensson SIA, Rossi E, Kovacs P, et al. Epigenetics of childhood obesity. Horm Res Paediatr (2025). doi: 10.1159/000543467

PubMed Abstract | Crossref Full Text | Google Scholar

209. Bianco-Miotto T, Craig JM, Gasser YP, van Dijk SJ, and Ozanne SE. Epigenetics and DOHaD: from basics to birth and beyond. J Dev Orig Health Dis (2017) 8(5):513–9. doi: 10.1017/S2040174417000733

PubMed Abstract | Crossref Full Text | Google Scholar

210. Hellström PM. Satiety signals and obesity. Curr Opin Gastroenterol (2013) 29(2):222–7. doi: 10.1097/MOG.0b013e32835d9ff8

PubMed Abstract | Crossref Full Text | Google Scholar

211. Roberts CA, Christiansen P, and Halford JCG. Pharmaceutical approaches to weight management: behavioural mechanisms of action. Curr Opin Physiol (2019) 12:26–32. doi: 10.1016/j.cophys.2019.04.017

Crossref Full Text | Google Scholar

212. Halford JCG, Bereket A, Bin-Abbas B, Chen W, Fernández-Aranda F, Garibay Nieto N, et al. Misalignment among adolescents living with obesity, caregivers, and healthcare professionals: ACTION Teens global survey study. Pediatr Obes (2022) 17(11):e12957. doi: 10.1111/ijpo.12957

PubMed Abstract | Crossref Full Text | Google Scholar

213. Sumithran P, Prendergast LA, Delbridge E, Purcell K, Shulkes A, Kriketos A, et al. Long-term persistence of hormonal adaptations to weight loss. N Engl J Med (2011) 365(17):1597–604. doi: 10.1056/NEJMoa1105816

PubMed Abstract | Crossref Full Text | Google Scholar

214. Busetto L, Bettini S, Makaronidis J, Roberts CA, Halford JCG, and Batterham RL. Mechanisms of weight regain. Eur J Intern Med (2021) 93:3–7. doi: 10.1016/j.ejim.2021.01.002

PubMed Abstract | Crossref Full Text | Google Scholar

215. Lane MM, Gamage E, Travica N, Dissanayaka T, Ashtree DN, Gauci S, et al. Ultra-processed food consumption and mental health: a systematic review and meta-analysis of observational studies. Nutrients (2022) 14(13):2568. doi: 10.3390/nu14132568

PubMed Abstract | Crossref Full Text | Google Scholar

216. Luppino FS, de Wit LM, Bouvy PF, Stijnen T, Cuijpers P, Penninx BWJH, et al. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry (2010) 67(3):220–9. doi: 10.1001/archgenpsychiatry.2010.2

PubMed Abstract | Crossref Full Text | Google Scholar

217. Patalay P and Hardman CA. Comorbidity, Codevelopment, and temporal associations between body mass index and internalizing symptoms from early childhood to adolescence. JAMA Psychiatry (2019) 76(7):721–9. doi: 10.1001/jamapsychiatry.2019.0169

PubMed Abstract | Crossref Full Text | Google Scholar

218. Caterson ID, Alfadda AA, Auerbach P, Coutinho W, Cuevas A, Dicker D, et al. Gaps to bridge: misalignment between perception, reality and actions in obesity. Diabetes Obes Metab (2019) 21(8):1914–24. doi: 10.1111/dom.13752

PubMed Abstract | Crossref Full Text | Google Scholar

219. Palmisano GL, Innamorati M, and Vanderlinden J. Life adverse experiences in relation with obesity and binge eating disorder: a systematic review. J Behav Addict (2016) 5(1):11–31. doi: 10.1556/2006.5.2016.018

PubMed Abstract | Crossref Full Text | Google Scholar

220. Tenk J, Mátrai P, Hegyi P, Rostás I, Garami A, Szabó I, et al. Perceived stress correlates with visceral obesity and lipid parameters of the metabolic syndrome: a systematic review and meta-analysis. Psychoneuroendocrinology (2018) 95:63–73. doi: 10.1016/j.psyneuen.2018.05.014

PubMed Abstract | Crossref Full Text | Google Scholar

221. Randle M, Ahern AL, Boyland E, Christiansen P, Halford JCG, Stevenson-Smith J, et al. A systematic review of ecological momentary assessment studies of appetite and affect in the experience of temptations and lapses during weight loss dieting. Obes Rev (2023) 24(9):e13596. doi: 10.1111/obr.13596

PubMed Abstract | Crossref Full Text | Google Scholar

222. White M. Food access and obesity. Obes Rev (2007) 8(suppl 1):99–107. doi: 10.1111/j.1467-789X.2007.00327.x

PubMed Abstract | Crossref Full Text | Google Scholar

223. Ghosh-Dastidar B, Cohen D, Hunter G, Zenk SN, Huang C, Beckman R, et al. Distance to store, food prices, and obesity in urban food deserts. Am J Prev Med (2014) 47(5):587–95. doi: 10.1016/j.amepre.2014.07.005

PubMed Abstract | Crossref Full Text | Google Scholar

224. Frank LD, Andresen MA, and Schmid TL. Obesity relationships with community design, physical activity, and time spent in cars. Am J Prev Med (2004) 27(2):87–96. doi: 10.1016/j.amepre.2004.04.011

PubMed Abstract | Crossref Full Text | Google Scholar

225. Frank LD, Saelens BE, Powell KE, and Chapman JE. Stepping towards causation: do built environments or neighborhood and travel preferences explain physical activity, driving, and obesity? Soc Sci Med (2007) 65(9):1898–914. doi: 10.1016/j.socscimed.2007.05.053

PubMed Abstract | Crossref Full Text | Google Scholar

226. Creatore MI, Glazier RH, Moineddin R, Fazli GS, Johns A, Gozdyra P, et al. Association of neighborhood walkability with change in overweight, obesity, and diabetes. JAMA (2016) 315(20):2211–20. doi: 10.1001/jama.2016.5898

PubMed Abstract | Crossref Full Text | Google Scholar

227. Frank LD, Adhikari B, White KR, Dummer T, Sandhu J, Demlow E, et al. Chronic disease and where you live: built and natural environment relationships with physical activity, obesity, and diabetes. Environ Int (2022) 158:106959. doi: 10.1016/j.envint.2021.106959

PubMed Abstract | Crossref Full Text | Google Scholar

228. Brown BB, Werner CM, Smith KR, Tribby CP, and Miller HJ. Physical activity mediates the relationship between perceived crime safety and obesity. Prev Med (2014) 66:140–4. doi: 10.1016/j.ypmed.2014.06.021

PubMed Abstract | Crossref Full Text | Google Scholar

229. Malambo P, De Villiers A, Lambert EV, Puoane T, and Kengne AP. Associations of perceived neighbourhood safety from traffic and crime with overweight/obesity among South African adults of low-socioeconomic status. PloS One (2018) 13(10):e0206408. doi: 10.1371/journal.pone.0206408

PubMed Abstract | Crossref Full Text | Google Scholar

230. Burdette HL, Wadden TA, and Whitaker RC. Neighborhood safety, collective efficacy, and obesity in women with young children. Obes (Silver Spring) (2006) 14(3):518–25. doi: 10.1038/oby.2006.67

PubMed Abstract | Crossref Full Text | Google Scholar

231. Hills AP, Farpour-Lambert NJ, and Byrne NM. Precision medicine and healthy living: the importance of the built environment. Prog Cardiovasc Dis (2019) 62(1):34–8. doi: 10.1016/j.pcad.2018.12.013

PubMed Abstract | Crossref Full Text | Google Scholar

232. Johnson KA, Showell NN, Flessa S, Janssen M, Reid N, Cheskin LJ, et al. Do neighborhoods matter? A systematic review of modifiable risk factors for obesity among low socio-economic status black and Hispanic children. Child Obes (2019) 15(2):71–86. doi: 10.1089/chi.2018.0044

PubMed Abstract | Crossref Full Text | Google Scholar

233. Liou D and Karasik JA. Living environment considerations on obesity prevention behaviors and self-efficacy among Chinese Americans. Int J Environ Res Public Health (2021) 18(17):9322. doi: 10.3390/ijerph18179322

PubMed Abstract | Crossref Full Text | Google Scholar

234. Cooksey-Stowers K, Schwartz MB, and Brownell KD. Food swamps predict obesity rates better than food deserts in the United States. Int J Environ Res Public Health (2017) 14(11):1366. doi: 10.3390/ijerph14111366

PubMed Abstract | Crossref Full Text | Google Scholar

235. Bevel MS, Tsai MH, Parham A, Andrzejak SE, Jones S, and Moore JX. Association of food deserts and food swamps with obesity-related cancer mortality in the US. JAMA Oncol (2023) 9(7):909–16. doi: 10.1001/jamaoncol.2023.0634

PubMed Abstract | Crossref Full Text | Google Scholar

236. Wrigley N. Food deserts in British cities: policy context and research priorities. Urban Stud (2002) 39(11):2029–40. doi: 10.1080/0042098022000011344

Crossref Full Text | Google Scholar

237. Guy CM, Clarke G, and Eyre H. Food retail change and the growth of food deserts: a case study of Cardiff. Int J Retail Distrib Manag (2004) 32(2):72–88. doi: 10.1108/09590550410521752

Crossref Full Text | Google Scholar

238. Daniels KM, SChinasi LH, Auchincloss AH, Forrest CB, and Diez Roux AV. The built and social neighborhood environment and child obesity: a systematic review of longitudinal studies. Prev Med (2021) 153:106790.doi: 10.1016/j.ypmed.2021.106790

PubMed Abstract | Crossref Full Text | Google Scholar

239. McCormack LA, Meendering JR, Burdette L, Prosch N, Moore L, and Stluka S. Quantifying the food and physical activity environments in rural, high obesity communities. Int J Environ Res Public Health (2021) 18(24):13344. doi: 10.3390/ijerph182413344

PubMed Abstract | Crossref Full Text | Google Scholar

240. O’Callaghan-Gordo C, Espinosa A, Valentin A, Tonne C, Pérez-Gómez B, Castaño-Vinyals G, et al. Green spaces, excess weight and obesity in Spain. Int J Hyg Environ Health (2020) 223(1):45–55. doi: 10.1016/j.ijheh.2019.10.007

PubMed Abstract | Crossref Full Text | Google Scholar

241. Blas-Miranda NB, Lozada-Tequeanes AL, Miranda-Zuñiga JA, and Jimenez MP. Green space exposure and obesity in the Mexican adult population. Int J Environ Res Public Health (2022) 19(22):15072. doi: 10.3390/ijerph192215072

PubMed Abstract | Crossref Full Text | Google Scholar

242. Anza-Ramirez C, Lazo M, Zafra-Tanaka JH, Avila-Palencia I, Bilal U, Hernández-Vásquez A, et al. The urban built environment and adult BMI, obesity, and diabetes in Latin American cities. Nat Commun (2022) 13(1):7977. doi: 10.1038/s41467-022-35648-w

PubMed Abstract | Crossref Full Text | Google Scholar

243. Yang S, Chen X, Wang L, Wu T, Fei T, Xiao Q, et al. Walkability indices and childhood obesity: a review of epidemiologic evidence. Obes Rev (2021) 22(suppl 1):e13096. doi: 10.1111/obr.13096

PubMed Abstract | Crossref Full Text | Google Scholar

244. Ruiz M, Goldblatt P, Morrison J, Porta D, Forastiere F, Hryhorczuk D, et al. Impact of low maternal education on early childhood overweight and obesity in Europe. Paediatr Perinat Epidemiol (2016) 30(3):274–84. doi: 10.1111/ppe.12285

PubMed Abstract | Crossref Full Text | Google Scholar

245. Li C, Zhang M, Tarken AY, Cao Y, Li Q, and Wang H. Secular trends and sociodemographic determinants of thinness, overweight and obesity among Chinese children and adolescents aged 7–18 years from 2010 to 2018. Front Public Health (2023) 11:1128552. doi: 10.3389/fpubh.2023.1128552

PubMed Abstract | Crossref Full Text | Google Scholar

246. Hagman E, Danielsson P, Brandt L, Svensson V, Ekbom A, and Marcus C. Childhood obesity, obesity treatment outcome, and achieved education: a prospective cohort study. J Adolesc Health (2017) 61(4):508–13. doi: 10.1016/j.jadohealth.2017.04.009

PubMed Abstract | Crossref Full Text | Google Scholar

247. Yu B. Kindergarten obesity and academic achievement: the mediating role of weight bias. Front Psychol (2021) 12:640474. doi: 10.3389/fpsyg.2021.640474

PubMed Abstract | Crossref Full Text | Google Scholar

248. Watson A, D’Souza NJ, Timperio A, Cliff DP, Okely AD, and Hesketh KD. Longitudinal associations between weight status and academic achievement in primary school children. Pediatr Obes (2023) 18(1):e12975. doi: 10.1111/ijpo.12975

PubMed Abstract | Crossref Full Text | Google Scholar

249. Moon RC. The associations between childhood obesity, academic performance, and perception of teachers: from kindergarten to fifth grade. Child Obes (2020) 16(6):403–11. doi: 10.1089/chi.2019.0330

PubMed Abstract | Crossref Full Text | Google Scholar

250. Campos-Vazquez RM and Gonzalez E. Obesity and hiring discrimination. Econ Hum Biol (2020) 37:100850. doi: 10.1016/j.ehb.2020.100850

PubMed Abstract | Crossref Full Text | Google Scholar

251. Sassenou J, Ringa V, Zins M, Ozguler A, Paquet S, Panjo H, et al. Women with obesity in cervical cancer screening. The double penalty: underscreening and income inequalities. Obes Res Clin Pract (2021) 15(3):212–5. doi: 10.1016/j.orcp.2021.03.003

PubMed Abstract | Crossref Full Text | Google Scholar

252. Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the obesity society. J Am Coll Cardiol (2014) 63(25 Pt B):2985–3023. doi: 10.1016/j.jacc.2013.11.004

PubMed Abstract | Crossref Full Text | Google Scholar

253. Jensen MD and Ryan DH. New obesity guidelines: promise and potential. JAMA (2014) 311(1):23–4. doi: 10.1001/jama.2013.282546

PubMed Abstract | Crossref Full Text | Google Scholar

254. Bray GA, Frühbeck G, Ryan DH, and Wilding JPH. Management of obesity. Lancet (2016) 387(10031):1947–56. doi: 10.1016/S0140-6736(16)00271-3

PubMed Abstract | Crossref Full Text | Google Scholar

255. Spiga F, Davies AL, Tomlinson E, Moore TH, Dawson S, Breheny K, et al. Interventions to prevent obesity in children aged 5 to 11 years old. Cochrane Database Syst Rev (2024) 5(5):CD015328. doi: 10.1002/14651858.CD015328.pub2

PubMed Abstract | Crossref Full Text | Google Scholar

256. Spiga F, Tomlinson E, Davies AL, Moore TH, Dawson S, Breheny K, et al. Interventions to prevent obesity in children aged 12 to 18 years old. Cochrane Database Syst Rev (2024) 5(5):CD015330. doi: 10.1002/14651858.CD015330.pub2

PubMed Abstract | Crossref Full Text | Google Scholar

257. Phillips SM, Spiga F, Moore TH, Dawson S, Stockton H, Rizk R, et al. Interventions to prevent obesity in children aged 2 to 4 years old. Cochrane Database Syst Rev (2025) 6(6):CD015326. doi: 10.1002/14651858.CD015326.pub2

PubMed Abstract | Crossref Full Text | Google Scholar

258. Curioni CC and Lourenço PM. Long-term weight loss after diet and exercise: a systematic review. Int J Obes (Lond) (2005) 29(10):1168–74. doi: 10.1038/sj.ijo.0803015

PubMed Abstract | Crossref Full Text | Google Scholar

259. Champagne CM. Dietary patterns as a basis for diet selection. In: Bray GA and Bouchard C, editors. Handbook of obesity - Volume 2 (5th edition). Boca Raton, FL: CRC Press (2023). 215–24. doi: 10.1201/9781003432807-25

Crossref Full Text | Google Scholar

260. Dong TA, Sandesara PB, Dhindsa DS, Mehta A, Arneson LC, Dollar AL, et al. Intermittent fasting: a heart healthy dietary pattern? Am J Med (2020) 133(8):901–7. doi: 10.1016/j.amjmed.2020.03.030

PubMed Abstract | Crossref Full Text | Google Scholar

261. Patterson RE and Sears DD. Metabolic effects of intermittent fasting. Annu Rev Nutr (2017) 37:371–93. doi: 10.1146/annurev-nutr-071816-064634

PubMed Abstract | Crossref Full Text | Google Scholar

262. Welton S, Minty R, O’Driscoll T, Willms H, Poirier D, Madden S, et al. Intermittent fasting and weight loss: systematic review. Can Fam Phys (2020) 66(2):117–25

PubMed Abstract | Google Scholar

263. Lessan N and Ali T. Energy metabolism and intermittent fasting: the Ramadan perspective. Nutrients (2019) 11(5):1192. doi: 10.3390/nu11051192

PubMed Abstract | Crossref Full Text | Google Scholar

264. Gibson AA and Sainsbury A. Strategies to improve adherence to dietary weight loss interventions in research and real-world settings. Behav Sci (Basel) (2017) 7(3):44. doi: 10.3390/bs7030044

PubMed Abstract | Crossref Full Text | Google Scholar

265. Gibson AA, Seimon RV, Lee CM, Ayre J, Franklin J, Markovic TP, et al. Do ketogenic diets really suppress appetite? A systematic review and meta-analysis. Obes Rev (2015) 16(1):64–76. doi: 10.1111/obr.12230

PubMed Abstract | Crossref Full Text | Google Scholar

266. Martins C, Nymo S, Coutinho SR, Rehfeld JF, Hunter GR, and Gower BA. Association between fat-free mass loss, changes in appetite, and weight regain in individuals with obesity. J Nutr (2023) 153(5):1330–7. doi: 10.1016/j.tjnut.2023.03.026

PubMed Abstract | Crossref Full Text | Google Scholar

267. Purcell K, Sumithran P, Prendergast LA, Bouniu CJ, Delbridge E, and Proietto J. The effect of rate of weight loss on long-term weight management: a randomised controlled trial. Lancet Diabetes Endocrinol (2014) 2(12):954–62. doi: 10.1016/S2213-8587(14)70200-1

PubMed Abstract | Crossref Full Text | Google Scholar

268. Wadden TA, Neiberg RH, Wing RR, Clark JM, Delahanty LM, Hill JO, et al. Four-year weight losses in the Look AHEAD study: factors associated with long-term success. Obes (Silver Spring) (2011) 19(10):1987–98. doi: 10.1038/oby.2011.230

PubMed Abstract | Crossref Full Text | Google Scholar

269. Brown A and Leeds AR. Very low-energy and low-energy formula diets: effects on weight loss, obesity co-morbidities and type 2 diabetes remission – an update on the evidence for their use in clinical practice. Nutr Bull (2019) 44(1):7–24. doi: 10.1111/nbu.12372

Crossref Full Text | Google Scholar

270. Aukan MI, Skårvold S, Brandsaeter IØ, Rehfeld JF, Holst JJ, Nymo S, et al. Gastrointestinal hormones and appetite ratings after weight loss induced by diet or bariatric surgery. Obes (Silver Spring) (2023) 31(2):399–411. doi: 10.1002/oby.23655

PubMed Abstract | Crossref Full Text | Google Scholar

271. Edwards KAL, Prendergast LA, Kalfas S, Sumithran P, and Proietto J. Impact of starting BMI and degree of weight loss on changes in appetite-regulating hormones during diet-induced weight loss. Obesity (2022) 30(4):911–9. doi: 10.1002/oby.23404

PubMed Abstract | Crossref Full Text | Google Scholar

272. Sumithran P, Prendergast LA, Delbridge E, Purcell K, Shulkes A, Kriketos A, et al. Ketosis and appetite-mediating nutrients and hormones after weight loss. Eur J Clin Nutr (2013) 67(7):759–64. doi: 10.1038/ejcn.2013.90

PubMed Abstract | Crossref Full Text | Google Scholar

273. Chearskul S, Delbridge E, Shulkes A, Proietto J, and Kriketos A. Effect of weight loss and ketosis on postprandial cholecystokinin and free fatty acid concentrations. Am J Clin Nutr (2008) 87(5):1238–46. doi: 10.1093/ajcn/87.5.1238

PubMed Abstract | Crossref Full Text | Google Scholar

274. Schirmer B. Metabolic bariatric surgery-A vastly underused treatment. JAMA Surg (2024) 159(5):477–8. doi: 10.1001/jamasurg.2023.7458

PubMed Abstract | Crossref Full Text | Google Scholar

275. Jastreboff AM, Kaplan LM, Frías JP, Wu Q, Du Y, Gurbuz S, et al. Triple-hormone-receptor agonist retatrutide for obesity - a phase 2 trial. N Engl J Med (2023) 389(6):514–26. doi: 10.1056/NEJMoa2301972

PubMed Abstract | Crossref Full Text | Google Scholar

276. Petersen J, Ludwig MQ, Juozaityte V, Ranea-Robles P, Svendsen C, Hwang E, et al. GLP-1-directed NMDA receptor antagonism for obesity treatment. Nature (2024) 629(8014):1133–41. doi: 10.1038/s41586-024-07419-8

PubMed Abstract | Crossref Full Text | Google Scholar

277. Blundell J, Finlayson G, Axelsen M, Flint A, Gibbons C, Kvist T, et al. Effects of once-weekly semaglutide on appetite, energy intake, control of eating, food preference and body weight in subjects with obesity. Diabetes Obes Metab (2017) 19(9):1242–51. doi: 10.1111/dom.12932

PubMed Abstract | Crossref Full Text | Google Scholar

278. Friedrichsen M, Breitschaft A, Tadayon S, Wizert A, and Skovgaard D. The effect of semaglutide 2.4 mg once weekly on energy intake, appetite, control of eating, and gastric emptying in adults with obesity. Diabetes Obes Metab (2021) 23(3):754–62. doi: 10.1111/dom.14280

PubMed Abstract | Crossref Full Text | Google Scholar

279. Onakpoya IJ, Heneghan CJ, and Aronson JK. Post-marketing withdrawal of anti-obesity medicinal products because of adverse drug reactions: a systematic review. BMC Med (2016) 14(1):191. doi: 10.1186/s12916-016-0735-y

PubMed Abstract | Crossref Full Text | Google Scholar

280. Johnson TA, Incze MA, and Silverstein WK. Heightened vigilance needed when patients are prescribed GLP-1 and GIP agonists. JAMA Intern Med (2024) 184(10):1158–9. doi: 10.1001/jamainternmed.2024.3732

PubMed Abstract | Crossref Full Text | Google Scholar

281. Lincoff AM, Brown-Frandsen K, Colhoun HM, Deanfield J, Emerson SS, Esbjerg S, et al. Semaglutide and cardiovascular outcomes in obesity without diabetes. N Engl J Med (2023) 389(24):2221–32. doi: 10.1056/NEJMoa2307563

PubMed Abstract | Crossref Full Text | Google Scholar

282. Gleason PP, Urick BY, Marshall LZ, Friedlander N, Qiu Y, and Leslie RS. Real-world persistence and adherence to glucagon-like peptide-1 receptor agonists among obese commercially insured adults without diabetes. J Manag Care Spec Pharm (2024) 30(8):860–7. doi: 10.18553/jmcp.2024.23332

PubMed Abstract | Crossref Full Text | Google Scholar

283. Saumoy M, Gandhi D, Buller S, Patel S, Schneider Y, Cote G, et al. Cost-effectiveness of endoscopic, surgical and pharmacological obesity therapies: a microsimulation and threshold analyses. Gut (2023) 72(12):2250–9. doi: 10.1136/gutjnl-2023-330437

PubMed Abstract | Crossref Full Text | Google Scholar

284. Haseeb M, Chhatwal J, Xiao J, Jirapinyo P, and Thompson CC. Semaglutide vs endoscopic sleeve gastroplasty for weight loss. JAMA Netw Open (2024) 7(4):e246221. doi: 10.1001/jamanetworkopen.2024.6221

PubMed Abstract | Crossref Full Text | Google Scholar

285. Mozaffarian D. GLP-1 agonists for obesity-A new recipe for success? JAMA (2024) 331(12):1007–8. doi: 10.1001/jama.2024.2252

PubMed Abstract | Crossref Full Text | Google Scholar

286. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the global burden of disease study 2013. Lancet (2014) 384(9945):766–81. doi: 10.1016/S0140-6736(14)60460-8

PubMed Abstract | Crossref Full Text | Google Scholar

287. Congress Budget Office. How would authorizing Medicare to cover anti-obesity medications affect the federal budget? [online] (2024). Available at: https://www.cbo.gov/publication/60816

Google Scholar

288. Reuters. Wegovy reimbursement would cost Denmark up to $4 bln each year -ministry [online]. (2023). Available at: https://www.reuters.com/business/healthcare-pharmaceuticals/wegovy-reimbursement-would-cost-denmark-up-4-bln-each-year-ministry-2023-08-28/

Google Scholar

289. Alhiary R, Kesselheim AS, Gabriele S, Beall RF, Tu SS, and Feldman WB. Patents and regulatory exclusivities on GLP-1 receptor agonists. JAMA (2023) 330(7):650–7. doi: 10.1001/jama.2023.13872

PubMed Abstract | Crossref Full Text | Google Scholar

290. Elangovan A, Shah R, and Smith ZL. Pharmacotherapy for obesity-trends using a population level national database. Obes Surg (2021) 31(3):1105–12. doi: 10.1007/s11695-020-04987-2

PubMed Abstract | Crossref Full Text | Google Scholar

291. Emanuel EJ, Dellgren JL, McCoy MS, and Persad G. Fair allocation of GLP-1 and dual GLP-1-GIP receptor agonists. N Engl J Med (2024) 390(20):1839–42. doi: 10.1056/NEJMp2400978

PubMed Abstract | Crossref Full Text | Google Scholar

292. Mason EE and Ito C. Gastric bypass in obesity. Surg Clin North Am (1967) 47(6):1345–51. doi: 10.1016/S0039-6109(16)38384-0

PubMed Abstract | Crossref Full Text | Google Scholar

293. Akalestou E, Miras AD, Rutter GA, and le Roux CW. Mechanisms of weight loss after obesity surgery. Endocr Rev (2022) 43(1):19–34. doi: 10.1210/endrev/bnab022

PubMed Abstract | Crossref Full Text | Google Scholar

294. Welbourn R, Hollyman M, Kinsman R, Dixon J, Liem R, Ottosson J, et al. Bariatric surgery worldwide: baseline demographic description and one-year outcomes from the fourth IFSO global registry report 2018. Obes Surg (2019) 29(3):782–95. doi: 10.1007/s11695-018-3593-1

PubMed Abstract | Crossref Full Text | Google Scholar

295. American Society for Metabolic and Bariatric Surgery. Estimate of bariatric surgery numbers, 2011–2023 [online]. Available at: https://asmbs.org/resources/estimate-of-bariatric-surgery-numbers/

Google Scholar

296. Eisenberg D, Shikora SA, Aarts E, Aminian A, Angrisani L, Cohen RV, et al. 2022 American society of metabolic and bariatric surgery (ASMBS) and international federation for the surgery of obesity and metabolic disorders (IFSO) indications for metabolic and bariatric surgery. Obes Surg (2023) 33(1):3–14. doi: 10.1016/j.soard.2022.08.013

PubMed Abstract | Crossref Full Text | Google Scholar

297. Rubino F, Nathan DM, Eckel RH, Schauer PR, Alberti KGMM, Zimmet PZ, et al. Metabolic surgery in the treatment algorithm for type 2 diabetes: a joint statement by International Diabetes Organizations. Diabetes Care (2016) 39(6):861–77. doi: 10.2337/dc16-0236

PubMed Abstract | Crossref Full Text | Google Scholar

298. Courcoulas AP, Patti ME, Hu B, Arterburn DE, Simonson DC, Gourash WF, et al. Long-term outcomes of medical management vs bariatric surgery in type 2 diabetes. JAMA (2024) 331(8):654–64. doi: 10.1001/jama.2024.0318

PubMed Abstract | Crossref Full Text | Google Scholar

299. Campos GM, Khoraki J, Browning MG, Pessoa BM, Mazzini GS, and Wolfe L. Changes in utilization of bariatric surgery in the United States from 1993 to 2016. Ann Surg (2020) 271(2):201–9. doi: 10.1097/SLA.0000000000003554

PubMed Abstract | Crossref Full Text | Google Scholar

300. Arterburn DE, Telem DA, Kushner RF, and Courcoulas AP. Benefits and risks of bariatric surgery in adults: a review. JAMA (2020) 324(9):879–87. doi: 10.1001/jama.2020.12567

PubMed Abstract | Crossref Full Text | Google Scholar

301. Hoerger TJ. Economics and policy in bariatric surgery. Curr Diabetes Rep (2019) 19(6):29. doi: 10.1007/s11892-019-1148-z

PubMed Abstract | Crossref Full Text | Google Scholar

302. Xia Q, Campbell JA, Ahmad H, Si L, de Graaff B, and Palmer AJ. Bariatric surgery is a cost-saving treatment for obesity—a comprehensive meta-analysis and updated systematic review of health economic evaluations of bariatric surgery. Obes Rev (2020) 21(1):e12932. doi: 10.1111/obr.12932

PubMed Abstract | Crossref Full Text | Google Scholar

303. Noparatayaporn P, Thavorncharoensap M, Chaikledkaew U, Bagepally BS, and Thakkinstian A. Incremental net monetary benefit of bariatric surgery: systematic review and meta-analysis of cost-effectiveness evidences. Obes Surg (2021) 31(7):3279–90. doi: 10.1007/s11695-021-05415-9

PubMed Abstract | Crossref Full Text | Google Scholar

304. Lauren BN, Lim F, Krikhely A, Taveras EM, Woo Baidal JA, Bellows BK, et al. Estimated cost-effectiveness of medical therapy, sleeve gastrectomy, and gastric bypass in patients with severe obesity and type 2 diabetes. JAMA Netw Open (2022) 5(2):e2148317. doi: 10.1001/jamanetworkopen.2021.48317

PubMed Abstract | Crossref Full Text | Google Scholar

305. Alalwan AA, Friedman J, Park H, Segal R, Brumback BA, and Hartzema AG. US national trends in bariatric surgery: a decade of study. Surgery (2021) 170(1):13–7. doi: 10.1016/j.surg.2021.02.002

PubMed Abstract | Crossref Full Text | Google Scholar

306. Winckelmann LA, Gribsholt SB, Madsen LR, Richelsen B, Svensson E, Jørgensen NB, et al. Roux-en-Y gastric bypass versus sleeve gastrectomy: nationwide data from the Danish quality registry for treatment of severe obesity. Surg Obes Relat Dis (2022) 18(4):511–9. doi: 10.1016/j.soard.2021.12.015

PubMed Abstract | Crossref Full Text | Google Scholar

307. Clapp B, Ponce J, DeMaria E, Ghanem O, Hutter M, Kothari S, et al. American Society for Metabolic and Bariatric Surgery 2020 estimate of metabolic and bariatric procedures performed in the United States. Surg Obes Relat Dis (2022) 18(9):1134–40. doi: 10.1016/j.soard.2022.06.284

PubMed Abstract | Crossref Full Text | Google Scholar

308. Aleman R, Lo Menzo E, Szomstein S, and Rosenthal RJ. Efficiency and risks of one-anastomosis gastric bypass. Ann Transl Med (2020) 8(suppl 1):S7. doi: 10.21037/atm.2020.02.03

PubMed Abstract | Crossref Full Text | Google Scholar

309. Surve A, Cottam D, Richards C, Medlin W, and Belnap L. A matched cohort comparison of long-term outcomes of Roux-en-Y gastric bypass (RYGB) versus single-anastomosis duodeno-ileostomy with sleeve gastrectomy (SADI-S). Obes Surg (2021) 31(4):1438–48. doi: 10.1007/s11695-020-05131-w

PubMed Abstract | Crossref Full Text | Google Scholar

310. Ghiassi S, Nimeri A, Aleassa EM, Grover BT, Eisenberg D, Carter J, et al. American Society for Metabolic and Bariatric Surgery position statement on one-anastomosis gastric bypass. Surg Obes Relat Dis (2023) 20(4):319–35. doi: 10.1016/j.soard.2023.11.003

PubMed Abstract | Crossref Full Text | Google Scholar

311. Bauerle WB, Mody P, Estep A, Stoltzfus J, and El Chaar M. Current trends in the utilization of a robotic approach in the field of bariatric surgery. Obes Surg (2023) 33(2):482–91. doi: 10.1007/s11695-022-06378-1

PubMed Abstract | Crossref Full Text | Google Scholar

312. Ho K, Hsu CH, Maegawa F, Ashouri Y, Ho H, Ajmal S, et al. Operative time and 30-day outcomes in bariatric surgery: comparison between robotic and laparoscopic approach: 4-year MBSAQIP database analysis. J Am Coll Surg (2022) 235(1):138–44. doi: 10.1097/XCS.0000000000000246

PubMed Abstract | Crossref Full Text | Google Scholar

313. Wadden TA, Kushner RF, and Chao AM. Bariatric surgery produces long-term benefits in patients with type 2 diabetes: evidence supporting its expanded use and coverage. JAMA (2024) 331(8):643–5. doi: 10.1001/jama.2023.28141

PubMed Abstract | Crossref Full Text | Google Scholar

314. Dicker D, Sagy YW, Ramot N, Battat E, Greenland P, Arbel R, et al. Bariatric metabolic surgery vs glucagon-like Peptide-1 receptor agonists and mortality. JAMA Netw Open (2024) 7(6):e2415392. doi: 10.1001/jamanetworkopen.2024.15392

PubMed Abstract | Crossref Full Text | Google Scholar

315. Carlsson LMS, Sjöholm K, Jacobson P, Andersson-Assarsson JC, Svensson PA, Taube M, et al. Life expectancy after bariatric surgery in the Swedish Obese Subjects Study. N Engl J Med (2020) 383(16):1535–43. doi: 10.1056/NEJMoa2002449

PubMed Abstract | Crossref Full Text | Google Scholar

316. Syn NL, Cummings DE, Wang LZ, Lin DJ, Zhao JJ, Loh M, et al. Association of metabolic-bariatric surgery with long-term survival in adults with and without diabetes: a one-stage meta-analysis of matched cohort and prospective controlled studies with 174–772 participants. Lancet (2021) 397(10287):1830–41. doi: 10.1016/S0140-6736(21)00591-2

PubMed Abstract | Crossref Full Text | Google Scholar

317. Doumouras AG, Wong JA, Paterson JM, Lee Y, Sivapathasundaram B, Tarride JE, et al. Bariatric surgery and cardiovascular outcomes in patients with obesity and cardiovascular disease: a population-based retrospective cohort study. Circulation (2021) 143(15):1468–80. doi: 10.1161/CIRCULATIONAHA.120.052386

PubMed Abstract | Crossref Full Text | Google Scholar

318. Courcoulas AP, King WC, Belle SH, Berk P, Flum DR, Garcia L, et al. Seven-year weight trajectories and health outcomes in the longitudinal assessment of bariatric surgery (LABS) study. JAMA Surg (2018) 153(5):427–34. doi: 10.1001/jamasurg.2017.5025

PubMed Abstract | Crossref Full Text | Google Scholar

319. Aminian A, Al-Kurd A, Wilson R, Bena J, Fayazzadeh H, Singh T, et al. Association of bariatric surgery with major adverse liver and cardiovascular outcomes in patients with biopsy-proven nonalcoholic steatohepatitis. JAMA (2021) 326(20):2031–42. doi: 10.1001/jama.2021.19569

PubMed Abstract | Crossref Full Text | Google Scholar

320. Lassailly G, Caiazzo R, Ntandja-Wandji LC, Gnemmi V, Baud G, Verkindt H, et al. Bariatric surgery provides long-term resolution of nonalcoholic steatohepatitis and regression of fibrosis. Gastroenterology (2020) 159(4):1290–301.e5. doi: 10.1053/j.gastro.2020.06.006

PubMed Abstract | Crossref Full Text | Google Scholar

321. Aminian A, Wilson R, Al-Kurd A, Tu C, Milinovich A, Kroh M, et al. Association of bariatric surgery with cancer risk and mortality in adults with obesity. JAMA (2022) 327(24):2423–33. doi: 10.1001/jama.2022.9009

PubMed Abstract | Crossref Full Text | Google Scholar

322. Khalid SI, Maasarani S, Wiegmann J, Wiegmann AL, Becerra AZ, Omotosho P, et al. Association of bariatric surgery and risk of cancer in patients with morbid obesity. Ann Surg (2022) 275(1):1–6. doi: 10.1097/SLA.0000000000005035

PubMed Abstract | Crossref Full Text | Google Scholar

323. Lim PW, Stucky CH, Wasif N, Etzioni DA, Harold KL, Madura JA 2nd, et al. Bariatric surgery and longitudinal cancer risk: a review. JAMA Surg (2024) 159(3):331–8. doi: 10.1001/jamasurg.2023.5809

PubMed Abstract | Crossref Full Text | Google Scholar

324. Soliman BG, Tariq N, Law YY, Yi S, Nwana N, Bosetti R, et al. Effectiveness of bariatric surgery in increasing kidney transplant eligibility in patients with kidney failure requiring dialysis. Obes Surg (2021) 31(8):3436–43. doi: 10.1007/s11695-021-05435-5

PubMed Abstract | Crossref Full Text | Google Scholar

325. Tsai C, Dolan P, Moss N, Sandoval AF, Roldan J, and Herron DM. Sleeve gastrectomy facilitates weight loss and permits cardiac transplantation in patients with severe obesity and a left ventricular assist device (LVAD). Surg Endosc (2023) 37(11):8655–62. doi: 10.1007/s00464-023-10264-x

PubMed Abstract | Crossref Full Text | Google Scholar

326. Lim CP, Fisher OM, Falkenback D, Boyd D, Hayward CS, Keogh A, et al. Bariatric surgery provides a “bridge to transplant” for morbidly obese patients with advanced heart failure and may obviate the need for transplantation. Obes Surg (2016) 26(3):486–93. doi: 10.1007/s11695-015-1789-1

PubMed Abstract | Crossref Full Text | Google Scholar

327. Samaras K, Connolly SM, Lord RV, Macdonald P, and Hayward CS. Take heart: bariatric surgery in obese patients with severe heart failure. Two case reports. Heart Lung Circ (2012) 21(12):847–9. doi: 10.1016/j.hlc.2012.05.783

PubMed Abstract | Crossref Full Text | Google Scholar

328. Fonseca Mora MC, Milla Matute CA, Ferri F, Lo Menzo E, Szmostein S, and Rosenthal RJ. Reduction of invasive interventions in severely obese with osteoarthritis after bariatric surgery. Surg Endosc (2020) 34(8):3606–13. doi: 10.1007/s00464-019-07138-6

PubMed Abstract | Crossref Full Text | Google Scholar

329. Murr MM, Streiff WJ, and Ndindjock R. A literature review and summary recommendations of the impact of bariatric surgery on orthopedic outcomes. Obes Surg (2021) 31(1):394–400. doi: 10.1007/s11695-020-05132-9

PubMed Abstract | Crossref Full Text | Google Scholar

330. Custers E, Vreeken D, Kleemann R, Kessels RPC, Duering M, Brouwer J, et al. Long-term brain structure and cognition following bariatric surgery. JAMA Netw Open (2024) 7(2):e2355380. doi: 10.1001/jamanetworkopen.2023.55380

PubMed Abstract | Crossref Full Text | Google Scholar

331. Pino JMV, Silva VF, Campos RMS, Mônico-Neto M, de Araujo KA, Seva DC, et al. Impact of bariatric surgery on circulating metabolites and cognitive performance. Obes Surg (2024) 34(4):1102–12. doi: 10.1007/s11695-024-07096-6

PubMed Abstract | Crossref Full Text | Google Scholar

332. Tao B, Tian P, Hao Z, Qi Z, Zhang J, Liu J, et al. Bariatric surgery improves cognition function in the patients with obesity: a meta-analysis. Obes Surg (2024) 34(3):1004–17. doi: 10.1007/s11695-024-07086-8

PubMed Abstract | Crossref Full Text | Google Scholar

333. Burke E, Jenkins T, Boles RE, Mitchell JE, Inge T, Gunstad J, et al. Cognitive function 10 years after adolescent bariatric surgery. Surg Obes Relat Dis (2024) 20(7):614–20. doi: 10.1016/j.soard.2024.01.008

PubMed Abstract | Crossref Full Text | Google Scholar

334. Salminen P, Grönroos S, Helmiö M, Hurme S, Juuti A, Juusela R, et al. Effect of laparoscopic sleeve gastrectomy vs roux-en-y gastric bypass on weight loss, comorbidities, and reflux at 10 years in adult patients with obesity: the SLEEVEPASS randomized clinical trial. JAMA Surg (2022) 157(8):656–66. doi: 10.1001/jamasurg.2022.2229

PubMed Abstract | Crossref Full Text | Google Scholar

335. Yeung KTD, Penney N, Ashrafian L, Darzi A, and Ashrafian H. Does sleeve gastrectomy expose the distal esophagus to severe reflux? A systematic review and meta-analysis. Ann Surg (2020) 271(2):257–65. doi: 10.1097/SLA.0000000000003275

PubMed Abstract | Crossref Full Text | Google Scholar

336. Konstantinidou SK, Argyrakopoulou G, Dalamaga M, and Kokkinos A. The effects of bariatric surgery on pharmacokinetics of drugs: a review of current evidence. Curr Nutr Rep (2023) 12(4):695–708. doi: 10.1007/s13668-023-00498-5

PubMed Abstract | Crossref Full Text | Google Scholar

337. Dvořáčková E, Pilková A, Matoulek M, Slanař O, and Hartinger JM. Bioavailability of orally administered drugs after bariatric surgery. Curr Obes Rep (2024) 13(1):141–53. doi: 10.1007/s13679-023-00548-7

PubMed Abstract | Crossref Full Text | Google Scholar

338. Carter J, Chang J, Birriel TJ, Moustarah F, Sogg S, Goodpaster K, et al. ASMBS position statement on preoperative patient optimization before metabolic and bariatric surgery. Surg Obes Relat Dis (2021) 17(12):1956–76. doi: 10.1016/j.soard.2021.08.024

PubMed Abstract | Crossref Full Text | Google Scholar

339. Kermansaravi M, Lainas P, Shahmiri SS, Yang W, Jazi AD, Vilallonga R, et al. The first survey addressing patients with BMI over 50: a survey of 789 bariatric surgeons. Surg Endosc (2022) 36(8):6170–80. doi: 10.1007/s00464-021-08979-w

PubMed Abstract | Crossref Full Text | Google Scholar

340. Ilanga M, Heard JC, McClintic J, Lewis D, Martin G, Horn C, et al. Use of GLP-1 agonists in high risk patients prior to bariatric surgery: a cohort study. Surg Endosc (2023) 37(12):9509–13. doi: 10.1007/s00464-023-10387-1

PubMed Abstract | Crossref Full Text | Google Scholar

341. Cummings DE, Weigle DS, Frayo RS, Breen PA, Ma MK, Dellinger EP, et al. Plasma ghrelin levels after diet-induced weight loss or gastric bypass surgery. N Engl J Med (2002) 346(21):1623–30. doi: 10.1056/NEJMoa012908

PubMed Abstract | Crossref Full Text | Google Scholar

342. Edwards KL, Prendergast LA, Kalfas S, Sumithran P, and Proietto J. Impact of starting BMI and degree of weight loss on changes in appetite-regulating hormones during diet-induced weight loss. Obesity (Silver Spring) (2022) 30(4):911–9. doi: 10.1002/oby.23404

PubMed Abstract | Crossref Full Text | Google Scholar

343. Leibel RL, Rosenbaum M, and Hirsch J. Changes in energy expenditure resulting from altered body weight. N Engl J Med (1995) 332(10):621–8. doi: 10.1056/NEJM199503093321001

PubMed Abstract | Crossref Full Text | Google Scholar

344. Graham Y, Hayes C, Cox J, Mahawar K, Fox A, and Yemm H. A systematic review of obesity as a barrier to accessing cancer screening services. Obes Sci Pract (2022) 8(6):715–27. doi: 10.1002/osp4.606

PubMed Abstract | Crossref Full Text | Google Scholar

345. Cohen SS, Palmieri RT, Nyante SJ, Koralek DO, Kim S, Bradshaw P, et al. Obesity and screening for breast, cervical, and colorectal cancer in women: a review. Cancer (2008) 112(9):1892–904. doi: 10.1002/cncr.23408

PubMed Abstract | Crossref Full Text | Google Scholar

346. Hunt KA and Sickles EA. Effect of obesity on screening mammography: outcomes analysis of 88,346 consecutive examinations. AJR Am J Roentgenol (2000) 174(5):1251–5. doi: 10.2214/ajr.174.5.1741251

PubMed Abstract | Crossref Full Text | Google Scholar

347. Barber MJ, Gotham D, Bygrave H, and Cepuch C. Estimated sustainable cost-based prices for diabetes medicines. JAMA Netw Open (2024) 7(3):e243474. doi: 10.1001/jamanetworkopen.2024.3474

PubMed Abstract | Crossref Full Text | Google Scholar

348. Iacobucci G. UK clinics told to stop prescribing antidiabetes drugs for weight loss, after shortages. BMJ (2023) 382:1693. doi: 10.1136/bmj.p1693

PubMed Abstract | Crossref Full Text | Google Scholar

349. Woodard DL, Davis SJ, and Randerson JT. Economic carbon cycle feedbacks may offset additional warming from natural feedbacks. Proc Natl Acad Sci USA (2019) 116(3):759–64. doi: 10.1073/pnas.1805187115

PubMed Abstract | Crossref Full Text | Google Scholar

350. Celi FS, Le TN, and Ni B. Physiology and relevance of human adaptive thermogenesis response. Trends Endocrinol Metab (2015) 26(5):238–47. doi: 10.1016/j.tem.2015.03.003

PubMed Abstract | Crossref Full Text | Google Scholar

351. van den Berg SAA, van Marken Lichtenbelt W, Willems van Dijk K, and Schrauwen P. Skeletal muscle mitochondrial uncoupling, adaptive thermogenesis and energy expenditure. Curr Opin Clin Nutr Metab Care (2011) 14(3):243–9. doi: 10.1097/MCO.0b013e3283455d7a

PubMed Abstract | Crossref Full Text | Google Scholar

352. Vandentorren S, Bretin P, Zeghnoun A, Mandereau-Bruno L, Croisier A, Cochet C, et al. August 2003 heat wave in France: risk factors for death of elderly people living at home. Eur J Public Health (2006) 16(6):583–91. doi: 10.1093/eurpub/ckl063

PubMed Abstract | Crossref Full Text | Google Scholar

353. Speakman JR. Obesity and thermoregulation. Handb Clin Neurol (2018) 156:431–43. doi: 10.1016/B978-0-444-63912-7.00026-6

PubMed Abstract | Crossref Full Text | Google Scholar

354. Magkos F, Tetens I, Bügel SG, Felby C, Schacht SR, Hill JO, et al. The environmental foodprint of obesity. Obes (Silver Spring) (2020) 28(1):73–9. doi: 10.1002/oby.22657

PubMed Abstract | Crossref Full Text | Google Scholar

355. Vásquez F, Vita G, and Müller DB. Food security for an aging and heavier population. Sustainability (2018) 10(10):3683. doi: 10.3390/su10103683

Crossref Full Text | Google Scholar

356. Vermeulen SJ, Campbell BM, and Ingram JSI. Climate change and food systems. Annu Rev Environ Resour (2012) 37(1):195–222. doi: 10.1146/annurev-environ-020411-130608

Crossref Full Text | Google Scholar

357. Crippa M, Solazzo E, Guizzardi D, Monforti-Ferrario F, Tubiello FN, and Leip A. Food systems are responsible for a third of global anthropogenic GHG emissions. Nat Food (2021) 2(3):198–209. doi: 10.1038/s43016-021-00225-9

PubMed Abstract | Crossref Full Text | Google Scholar

358. Ivanovich CC, Sun T, Gordon DR, and Ocko IB. Future warming from global food consumption. Nat Clim Change (2023) 13(3):297–302. doi: 10.1038/s41558-023-01605-8

Crossref Full Text | Google Scholar

359. Clark MA, Domingo NGG, Colgan K, Thakrar SK, Tilman D, Lynch J, et al. Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets. Science (2020) 370(6517):705–8. doi: 10.1126/science.aba7357

PubMed Abstract | Crossref Full Text | Google Scholar

360. Foley JA, Defries R, Asner GP, Barford C, Bonan G, Carpenter SR, et al. Global consequences of land use. Science (2005) 309(5734):570–74. doi: 10.1126/science.1111772

PubMed Abstract | Crossref Full Text | Google Scholar

361. Poore J and Nemecek T. Reducing food’s environmental impacts through producers and consumers. Science (2018) 360(6392):987–92. doi: 10.1126/science.aaq0216

PubMed Abstract | Crossref Full Text | Google Scholar

362. Steffen W, Richardson K, Rockström J, Cornell SE, Fetzer I, Bennett EM, et al. Sustainability. Planetary boundaries: guiding human development on a changing planet. Science (2015) 347(6223):1259855. doi: 10.1126/science.1259855

PubMed Abstract | Crossref Full Text | Google Scholar

363. Tilman D, Clark M, Williams DR, Kimmel K, Polasky S, and Packer C. Future threats to biodiversity and pathways to their prevention. Nature (2017) 546(7656):73–81. doi: 10.1038/nature22900

PubMed Abstract | Crossref Full Text | Google Scholar

364. Willett W, Rockström J, Loken B, Springmann M, Lang T, Vermeulen S, et al. Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet (2019) 393(10170):447–92. doi: 10.1016/S0140-6736(18)31788-4

PubMed Abstract | Crossref Full Text | Google Scholar

365. Myers SS, Zanobetti A, Kloog I, Huybers P, Leakey ADB, Bloom AJ, et al. Increasing CO₂ threatens human nutrition. Nature (2014) 510(7503):139–42. doi: 10.1038/nature13179

PubMed Abstract | Crossref Full Text | Google Scholar

366. Jägermeyr J, Müller C, Ruane AC, Elliott J, Balkovic J, Castillo O, et al. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat Food (2021) 2(11):873–85. doi: 10.1038/s43016-021-00400-y

PubMed Abstract | Crossref Full Text | Google Scholar

367. Gaupp F, Hall J, Mitchell D, and Dadson S. Increasing risks of multiple breadbasket failure under 1.5 and 2 °C global warming. Agric Syst (2019) 175:34–45. doi: 10.1016/j.agsy.2019.05.010

Crossref Full Text | Google Scholar

368. Deutsch CA, Tewksbury JJ, Tigchelaar M, Battisti DS, Merrill SC, Huey RB, et al. Increase in crop losses to insect pests in a warming climate. Science (2018) 361(6405):916–9. doi: 10.1126/science.aat346

PubMed Abstract | Crossref Full Text | Google Scholar

369. Zommers Z, Marbaix P, Fischlin A, Ibrahim ZZ, Grant S, Magnan AK, et al. Burning embers: towards more transparent and robust climate-change risk assessments. Nat Rev Earth Environ (2020) 1(10):516–29. doi: 10.1038/s43017-020-0088-0

Crossref Full Text | Google Scholar

370. Monteiro CA and Cannon G. The impact of transnational “big food” companies on the south: a view from Brazil. PloS Med (2012) 9(7):e1001252. doi: 10.1371/journal.pmed.1001252

PubMed Abstract | Crossref Full Text | Google Scholar

371. Nyarko E and Bartelmeß T. Drivers of consumer food choices of multinational corporations’ products over local foods in Ghana: a maximum difference scaling study. Global Health (2024) 20(1):22. doi: 10.1186/s12992-024-01027-x

PubMed Abstract | Crossref Full Text | Google Scholar

372. Anastasiou K, Baker P, Hadjikakou M, Hendrie GA, and Lawrence M. A conceptual framework for understanding the environmental impacts of ultra-processed foods and implications for sustainable food systems. J Clean Prod (2022) 368:133155. doi: 10.1016/j.jclepro.2022.133155

Crossref Full Text | Google Scholar

373. Seferidi P, Scrinis G, Huybrechts I, Woods J, Vineis P, and Millett C. The neglected environmental impacts of ultra-processed foods. Lancet Planet Health (2020) 4(10):e437–8. doi: 10.1016/S2542-5196(20)30177-7

PubMed Abstract | Crossref Full Text | Google Scholar

374. Leite FHM, Khandpur N, Andrade GC, Anastasiou K, Baker P, Lawrence M, et al. Ultra-processed foods should be central to global food systems dialogue and action on biodiversity. BMJ Glob Health (2022) 7(3):e008269. doi: 10.1136/bmjgh-2021-008269

PubMed Abstract | Crossref Full Text | Google Scholar

375. Koch CA, Sharda P, Patel J, Gubbi S, Bansal R, and Bartel MJ. Climate change and obesity. Horm Metab Res (2021) 53(9):575–87. doi: 10.1055/a-1533-2861

PubMed Abstract | Crossref Full Text | Google Scholar

376. Tilman D and Clark M. Global diets link environmental sustainability and human health. Nature (2014) 515(7528):518–22. doi: 10.1038/nature13959

PubMed Abstract | Crossref Full Text | Google Scholar

377. Eshel G, Shepon A, Makov T, and Milo R. Land, irrigation water, greenhouse gas, and reactive nitrogen burdens of meat, eggs, and dairy production in the United States. Proc Natl Acad Sci USA (2014) 111(33):11996–2001. doi: 10.1073/pnas.1402183111

PubMed Abstract | Crossref Full Text | Google Scholar

378. Shindell D, Sadavarte P, Aben I, Bredariol TD, Dreyfus G, Höglund-Isaksson L, et al. The methane imperative. Front Sci (2024) 2:1349770. doi: 10.3389/fsci.2024.1349770

Crossref Full Text | Google Scholar

379. Harwatt H, Hayek MN, Behrens P, and Ripple WJ. Options for a Paris-compliant livestock sector. Timeframes, targets and trajectories for livestock sector emissions from a survey of climate scientists. Harvard Law School (2024). Available at: https://animal.law.harvard.edu/wp-content/uploads/Paris-compliant-livestock-report.pdf

Google Scholar

380. Food and Agriculture Organization of the United Nations, International Fund for Agricultural Development, United Nations Children’s Emergency Fund, World Food Program, and World Health Organization. The state of food security and nutrition in the world 2022. Rome: FAO (2022). doi: 10.4060/cc0639en

Crossref Full Text | Google Scholar

381. Wan JS, Chen CCJ, Tilmes S, Luongo MT, Richter JH, and Ricke K. Diminished efficacy of regional marine cloud brightening in a warmer world. Nat Clim Change (2024) 14(8):808–14. doi: 10.1038/s41558-024-02046-7

Crossref Full Text | Google Scholar

382. Fuss S, Canadell JG, Peters GP, Tavoni M, Andrew RM, Ciais P, et al. Betting on negative emissions. Nat Clim Change (2014) 4(10):850–3. doi: 10.1038/nclimate2392

Crossref Full Text | Google Scholar

383. Valicente VM, Peng CH, Pacheco KN, Lin L, Kielb EI, Dawoodani E, et al. Ultraprocessed Foods and obesity risk: a critical review of reported mechanisms. Adv Nutr (2023) 14(4):718–38. doi: 10.1016/j.advnut.2023.04.006

PubMed Abstract | Crossref Full Text | Google Scholar

384. Lonnie M, Hunter E, Stone RA, Dineva M, Aggreh M, Greatwood H, et al. Food insecurity in people living with obesity: improving sustainable and healthier food choices in the retail food environment-the FIO Food project. Nutr Bull (2023) 48(3):390–9. doi: 10.1111/nbu.12626

PubMed Abstract | Crossref Full Text | Google Scholar

385. Ambikapathi R, Schneider KR, Davis B, Herrero M, Winters P, and Fanzo JC. Global food systems transitions have enabled affordable diets but had less favourable outcomes for nutrition, environmental health, inclusion and equity. Nat Food (2022) 3(9):764–79. doi: 10.1038/s43016-022-00588-7

PubMed Abstract | Crossref Full Text | Google Scholar

386. Scrinis G, Popkin BM, Corvalan C, Duran AC, Nestle M, Lawrence M, et al. Policies to halt and reverse the rise in ultra-processed food production, marketing, and consumption. Lancet (2025) 406(10520):2685–702. doi: 10.1016/S0140-6736(25)01566-1

PubMed Abstract | Crossref Full Text | Google Scholar

387. Rockström J, Steffen W, Noone K, Persson A, Chapin FS 3rd, Lambin EF, et al. A safe operating space for humanity. Nature (2009) 461(7263):472–5. doi: 10.1038/461472a

PubMed Abstract | Crossref Full Text | Google Scholar

388. Konieczna J, Ruiz-Canela M, Galmes-Panades AM, Abete I, Babio N, Fiol M, et al. An energy-reduced Mediterranean diet, physical activity, and body composition: an interim subgroup analysis of the PREDIMED-plus randomized clinical trial. JAMA Netw Open (2023) 6(10):e2337994. doi: 10.1001/jamanetworkopen.2023.37994

PubMed Abstract | Crossref Full Text | Google Scholar

389. Rockström J, Thilsted SH, Willett WC, Gordon LJ, Herrero M, Hicks CC, et al. The EAT-Lancet Commission on healthy, sustainable, and just food systems. Lancet (2025) 406(10512):1625–700. doi: 10.1016/S0140-6736(25)01201-2

PubMed Abstract | Crossref Full Text | Google Scholar

390. Kristoffersen E, Hjort SL, Thomassen LM, Arjmand EJ, Perillo M, Balakrishna R, et al. Umbrella review of systematic reviews and meta-analyses on the consumption of different food groups and the risk of overweight and obesity. Nutrients (2025) 17(4):662. doi: 10.3390/nu17040662

PubMed Abstract | Crossref Full Text | Google Scholar

391. Scarborough P, Clark M, Cobiac L, Papier K, Knuppel A, Lynch J, et al. Vegans, vegetarians, fish-eaters and meat-eaters in the UK show discrepant environmental impacts. Nat Food (2023) 4(7):565–74. doi: 10.1038/s43016-023-00795-w

PubMed Abstract | Crossref Full Text | Google Scholar

392. Sáez-Almendros S, Obrador B, Bach-Faig A, and Serra-Majem L. Environmental footprints of Mediterranean versus Western dietary patterns: beyond the health benefits of the Mediterranean diet. Environ Health (2013) 12:118. doi: 10.1186/1476-069X-12-118

PubMed Abstract | Crossref Full Text | Google Scholar

393. Dixon KA, Michelsen MK, and Carpenter CL. Modern diets and the health of our planet: an investigation into the environmental impacts of food choices. Nutrients (2023) 15(3):692. doi: 10.3390/nu15030692

PubMed Abstract | Crossref Full Text | Google Scholar

394. O’Malley K, Willits-Smith A, and Rose D. Popular diets as selected by adults in the United States show wide variation in carbon footprints and diet quality. Am J Clin Nutr (2023) 117(4):701–8. doi: 10.1016/j.ajcnut.2023.01.009

PubMed Abstract | Crossref Full Text | Google Scholar

395. Laine JE, Huybrechts I, Gunter MJ, Ferrari P, Weiderpass E, Tsilidis K, et al. Co-benefits from sustainable dietary shifts for population and environmental health: an assessment from a large European cohort study. Lancet Planet Health (2021) 5(11):e786–96. doi: 10.1016/S2542-5196(21)00250-3

PubMed Abstract | Crossref Full Text | Google Scholar

396. Bui LP, Pham TT, Wang F, Chai B, Sun Q, Hu FB, et al. Planetary Health Diet Index and risk of total and cause-specific mortality in three prospective cohorts. Am J Clin Nutr (2024) 120(1):80–91. doi: 10.1016/j.ajcnut.2024.03.019

PubMed Abstract | Crossref Full Text | Google Scholar

397. Sun Z, Scherer L, Tukker A, Spawn-Lee SA, Bruckner M, Gibbs HK, et al. Dietary change in high-income nations alone can lead to substantial double climate dividend. Nat Food (2022) 3(1):29–37. doi: 10.1038/s43016-021-00431-5

PubMed Abstract | Crossref Full Text | Google Scholar

398. Hayek MN, Harwatt H, Ripple WJ, and Mueller ND. The carbon opportunity cost of animal-sourced food production on land. Nat Sustain (2021) 4(1):21–4. doi: 10.1038/s41893-020-00603-4

Crossref Full Text | Google Scholar

399. Sun Z, Scherer L, Zhang Q, and Behrens P. Adoption of plant-based diets across Europe can improve food resilience against the Russia–Ukraine conflict. Nat Food (2022) 3(11):905–10. doi: 10.1038/s43016-022-00634-4

PubMed Abstract | Crossref Full Text | Google Scholar

400. Humpenöder F, Popp A, Merfort L, Luderer G, Weindl I, Bodirsky BL, et al. Food matters: dietary shifts increase the feasibility of 1.5 °C pathways in line with the Paris Agreement. Sci Adv (2024) 10(13):eadj3832. doi: 10.1126/sciadv.adj3832

PubMed Abstract | Crossref Full Text | Google Scholar

401. Ministry of Food, Agriculture and Fisheries of Denmark. Danish action plan for plant-based foods. Ministry of Food, Agriculture and Fisheries of Denmark (2023). Available at: https://en.fvm.dk/Media/638484294982868221/Danish-Action-Plan-for-Plant-based-Foods.pdf

Google Scholar

402. Mayén A-L, Marques-Vidal P, Paccaud F, Bovet P, and Stringhini S. Socioeconomic determinants of dietary patterns in low- and middle-income countries: a systematic review. Am J Clin Nutr (2014) 100(6):1520–31. doi: 10.3945/ajcn.114.089029

PubMed Abstract | Crossref Full Text | Google Scholar

403. Popkin BM and Ng SW. The nutrition transition to a stage of high obesity and noncommunicable disease prevalence dominated by ultra-processed foods is not inevitable. Obes Rev (2022) 23(1):e13366. doi: 10.1111/obr.13366

PubMed Abstract | Crossref Full Text | Google Scholar

404. Carducci B, Chen Y, Luo H, Webb P, and Fanzo J. Nutrition transition patterns in the context of global food systems transformation. Res Square [preprint] (2024). doi: 10.21203/rs.3.rs-4657489/v1

Crossref Full Text | Google Scholar

405. Vittis Y, Obersteiner M, Godfray HCJ, and Springmann M. Labour requirements for healthy and sustainable diets at global, regional, and national levels: a modelling study. Lancet Planet Health (2025) 9(10):101342. doi: 10.1016/j.lanplh.2025.101342

PubMed Abstract | Crossref Full Text | Google Scholar

406. World Health Organization. Global action plan for the prevention and control on NCDs 2013–2020. Geneva: WHO (2015). Available at: https://www.who.int/news/item/10-05-2015-global-action-plan-for-the-prevention-and-control-of-ncds-2013-2020

Google Scholar

407. Ralston J, Cooper K, and Powis J. Obesity, SDGs and ROOTS: a framework for impact. Curr Obes Rep (2021) 10(1):54–60. doi: 10.1007/s13679-020-00420-y

PubMed Abstract | Crossref Full Text | Google Scholar

408. World Health Organization. Controlling the global obesity epidemic [online]. Available at: https://www.who.int/activities/controlling-the-global-obesity-epidemic

Google Scholar

409. Sisnowski J, Handsley E, and Street JM. Regulatory approaches to obesity prevention: a systematic overview of current laws addressing diet-related risk factors in the European Union and the United States. Health Policy (2015) 119(6):720–31. doi: 10.1016/j.healthpol.2015.04.013

PubMed Abstract | Crossref Full Text | Google Scholar

410. Lyn R, Heath E, and Dubhashi J. Global implementation of obesity prevention policies: a review of progress, politics, and the path forward. Curr Obes Rep (2019) 8(4):504–16. doi: 10.1007/s13679-019-00358-w

PubMed Abstract | Crossref Full Text | Google Scholar

411. Theis DRZ and White M. Is obesity policy in England fit for purpose? Analysis of government strategies and policies, 1992–2020. Milbank Q (2021) 99(1):126–70. doi: 10.1111/1468-0009.12498

PubMed Abstract | Crossref Full Text | Google Scholar

412. Millstone E and Lang T. An approach to conflicts of interest in UK food regulatory institutions. Nat Food (2023) 4(1):17–21. doi: 10.1038/s43016-022-00666-w

PubMed Abstract | Crossref Full Text | Google Scholar

413. Mialon M, Serodio PM, Crosbie E, Teicholz N, Naik A, and Carriedo A. Conflicts of interest for members of the US 2020 dietary guidelines advisory committee. Public Health Nutr (2022) 27(1):e69. doi: 10.1017/S1368980022000672

PubMed Abstract | Crossref Full Text | Google Scholar

414. Monteiro CA, Cannon G, Moubarac J-C, Martins APB, Martins CA, Garzillo J, et al. Dietary guidelines to nourish humanity and the planet in the twenty-first century. A blueprint from Brazil. Public Health Nutr (2015) 18(13):2311–22. doi: 10.1017/S1368980015002165

PubMed Abstract | Crossref Full Text | Google Scholar

415. Stechemesser A, Koch N, Mark E, Dilger E, Klösel P, Menicacci L, et al. Climate policies that achieved major emission reductions: Global evidence from two decades. Science (2024) 385(6711):884–92. doi: 10.1126/science.adl6547

PubMed Abstract | Crossref Full Text | Google Scholar

416. Chater N and Loewenstein G. The i-frame and the s-frame: how focusing on individual-level solutions has led behavioral public policy astray. Behav Brain Sci (2022) 46:e147. doi: 10.1017/S0140525X22002023

PubMed Abstract | Crossref Full Text | Google Scholar

417. Awudi DA, Walker AN, Weeto MM, Priddy CB, Akan OD, Baduweh CA, et al. Unhealthy diets increase the likelihood of being overweight or obese among African migrant students in China, but not among African non-migrant students: a cross-sectional study. Front Nutr (2024) 11:1291360. doi: 10.3389/fnut.2024.1291360

PubMed Abstract | Crossref Full Text | Google Scholar

418. Bates LM, Acevedo-Garcia D, Alegría M, and Krieger N. Immigration and generational trends in body mass index and obesity in the United States: results of the National Latino and Asian American Survey, 2002–2003. Am J Public Health (2008) 98(1):70–7. doi: 10.2105/AJPH.2006.102814

PubMed Abstract | Crossref Full Text | Google Scholar

419. Buse K, Tanaka S, and Hawkes S. Healthy people and healthy profits? Elaborating a conceptual framework for governing the commercial determinants of non-communicable diseases and identifying options for reducing risk exposure. Global Health (2017) 13(1):34. doi: 10.1186/s12992-017-0255-3

PubMed Abstract | Crossref Full Text | Google Scholar

420. Seferidi P, Millett C, and Laverty AA. Industry self-regulation fails to deliver healthier diets, again. BMJ (2021) 372:m4762. doi: 10.1136/bmj.m4762

PubMed Abstract | Crossref Full Text | Google Scholar

421. Yates J, Gillespie S, Savona N, Deeney M, and Kadiyala S. Trust and responsibility in food systems transformation. Engaging with Big Food: marriage or mirage? BMJ Glob Health (2021) 6(11):e007350. doi: 10.1136/bmjgh-2021-007350

PubMed Abstract | Crossref Full Text | Google Scholar

422. Mozaffarian D, Andrés JR, Cousin E, Frist WH, and Glickman DR. The White House Conference on Hunger, Nutrition and Health is an opportunity for transformational change. Nat Food (2022 August) 3(8):561–3. doi: 10.1038/s43016-022-00568-x

PubMed Abstract | Crossref Full Text | Google Scholar

423. Baker P, Slater S, White M, Wood B, Contreras A, Corvalán C, et al. Towards unified global action on ultraprocessed foods: understanding commercial determinants, countering corporate power, and mobilising a public health response. Lancet (2025) 406(10520):2703–26. doi: 10.1016/S0140-6736(25)01567-3

PubMed Abstract | Crossref Full Text | Google Scholar

424. Lobstein T, Jackson-Leach R, Moodie ML, Hall KD, Gortmaker SL, Swinburn BA, et al. Child and adolescent obesity: part of a bigger picture. Lancet (2015) 385(9986):2510–20. doi: 10.1016/S0140-6736(14)61746-3

PubMed Abstract | Crossref Full Text | Google Scholar

425. Wood B, Robinson E, Baker P, Paraje G, Mialon M, van Tulleken C, et al. What is the purpose of ultra-processed food? An exploratory analysis of the financialisation of ultra-processed food corporations and implications for public health. Global Health (2023) 19(1):85. doi: 10.1186/s12992-023-00990-1

PubMed Abstract | Crossref Full Text | Google Scholar

426. Clapp J. The rise of financial investment and common ownership in global agrifood firms. Rev Int Polit Econ (2019) 26(4):604–29. doi: 10.1080/09692290.2019.1597755

Crossref Full Text | Google Scholar

427. Slater S, Lawrence M, Wood B, Serodio P, and Baker P. Corporate interest groups and their implications for global food governance: mapping and analysing the global corporate influence network of the transnational ultra-processed food industry. Global Health (2024) 20(1):16. doi: 10.1186/s12992-024-01020-4

PubMed Abstract | Crossref Full Text | Google Scholar

428. Allen LN, Wigley S, and Holmer H. Assessing the association between Corporate Financial Influence and implementation of policies to tackle commercial determinants of non-communicable diseases: a cross-sectional analysis of 172 countries. Soc Sci Med (2022) 297:114825. doi: 10.1016/j.socscimed.2022.114825

PubMed Abstract | Crossref Full Text | Google Scholar

429. Baker P, Kay A, and Walls H. Trade and investment liberalization and Asia’s noncommunicable disease epidemic: a synthesis of data and existing literature. Global Health (2014) 10(66):66. doi: 10.1186/s12992-014-0066-8

PubMed Abstract | Crossref Full Text | Google Scholar

430. Barlow P, Labonte R, McKee M, and Stuckler D. Trade challenges at the World Trade Organization to national noncommunicable disease prevention policies: a thematic document analysis of trade and health policy space. PloS Med (2018) 15(6):e1002590. doi: 10.1371/journal.pmed.1002590

PubMed Abstract | Crossref Full Text | Google Scholar

431. Garton K, Thow AM, and Swinburn B. International trade and investment agreements as barriers to food environment regulation for public health nutrition: a realist review. Int J Health Policy Manag (2021) 10(12):745–65. doi: 10.34172/ijhpm.2020.189

PubMed Abstract | Crossref Full Text | Google Scholar

432. Thow AM, Jones A, Hawkes C, Ali I, and Labonté R. Nutrition labelling is a trade policy issue: lessons from an analysis of specific trade concerns at the World Trade Organization. Health Promot Int (2018) 33(4):561–71. doi: 10.1093/heapro/daw109

PubMed Abstract | Crossref Full Text | Google Scholar

433. Hawkes C and Buse K. Public health sector and food industry interaction: it’s time to clarify the term ‘partnership’ and be honest about underlying interests. Eur J Public Health (2011) 21(4):400–1. doi: 10.1093/eurpub/ckr077

PubMed Abstract | Crossref Full Text | Google Scholar

434. Moodie R, Bennett E, Kwong EJL, Santos TM, Pratiwi L, Williams J, et al. Ultra-processed profits: the political economy of countering the global spread of ultra-processed foods – a synthesis review on the market and political practices of transnational food corporations and strategic public health responses. Int J Health Policy Manag (2021) 10(12):968–82. doi: 10.34172/ijhpm.2021.45

PubMed Abstract | Crossref Full Text | Google Scholar

435. Steele S, Sarcevic L, Ruskin G, and Stuckler D. Confronting potential food industry ‘front groups’: case study of the international food information Council’s nutrition communications using the UCSF food industry documents archive. Global Health (2022) 18(1):16. doi: 10.1186/s12992-022-00806-8

PubMed Abstract | Crossref Full Text | Google Scholar

436. Atli Gunnarsson J, Ruskin G, Stuckler D, and Steele S. Big food and drink sponsorship of conferences and speakers: a case study of one multinational company’s influence over knowledge dissemination and professional engagement. Public Health Nutr (2023) 26(5):1094–111. doi: 10.1017/S1368980022002506

PubMed Abstract | Crossref Full Text | Google Scholar

437. Greenhalgh S. Making China safe for Coke: how Coca-Cola shaped obesity science and policy in China. BMJ (2019) 364:k5050. doi: 10.1136/bmj.k5050

Crossref Full Text | Google Scholar

438. United Nations Department of Economic and Social Affairs. World urbanization prospects: the 2018 revision. United Nations (2019). Available at: https://population.un.org/wup/assets/WUP2018-Report.pdf

Google Scholar

439. Food and Agriculture Organization of the United Nations. The Milan urban food policy pact: monitoring framework. Rome: FAO (2019). Available at: https://openknowledge.fao.org/server/api/core/bitstreams/d394b597-51e0-44b2-a3dc-dac8c1a51e91/content

Google Scholar

440. Roberto CA, Swinburn B, Hawkes C, Huang TTK, Costa SA, Ashe M, et al. Patchy progress on obesity prevention: emerging examples, entrenched barriers, and new thinking. Lancet (2015) 385(9985):2400–9. doi: 10.1016/S0140-6736(14)61744-X

PubMed Abstract | Crossref Full Text | Google Scholar

441. Stuckler D, McKee M, Ebrahim S, and Basu S. Manufacturing epidemics: the role of global producers in increased consumption of unhealthy commodities including processed foods, alcohol, and tobacco. PloS Med (2012) 9(6):e1001235. doi: 10.1371/journal.pmed.1001235

PubMed Abstract | Crossref Full Text | Google Scholar

442. Pedroni C, Castetbon K, Desbouys L, Rouche M, and Vandevijvere S. The cost of diets according to nutritional quality and sociodemographic characteristics: a population-based assessment in Belgium. J Acad Nutr Diet (2021) 121(11):2187–2200.e4. doi: 10.1016/j.jand.2021.05.024

PubMed Abstract | Crossref Full Text | Google Scholar

443. Hoenink JC, Garrott K, Jones NRV, Conklin AI, Monsivais P, and Adams J. Changes in UK price disparities between healthy and less healthy foods over 10 years: an updated analysis with insights in the context of inflationary increases in the cost-of-living from 2021. Appetite (2024) 197:107290. doi: 10.1016/j.appet.2024.107290

PubMed Abstract | Crossref Full Text | Google Scholar

444. Magnusson RS. Case studies in nanny state name-calling: what can we learn?Public Health (2015) 129(8):1074–82. doi: 10.1016/j.puhe.2015.04.023

PubMed Abstract | Crossref Full Text | Google Scholar

445. Kersh R. Of nannies and nudges: the current state of U.S. obesity policymaking. Public Health (2015) 129(8):1083–91. doi: 10.1016/j.puhe.2015.05.018

PubMed Abstract | Crossref Full Text | Google Scholar

446. Kortleve AJ, Mogollón JM, Harwatt H, and Behrens P. Over 80% of the European Union’s Common Agricultural Policy supports emissions-intensive animal products. Nat Food (2024) 5(4):288–92. doi: 10.1038/s43016-024-00949-4

PubMed Abstract | Crossref Full Text | Google Scholar

447. von Braun J and Hendriks SL. Full-cost accounting and redefining the cost of food: implications for agricultural economics research. Agric Econ (2023) 54(4):451–4. doi: 10.1111/agec.12774

Crossref Full Text | Google Scholar

448. The Rockefeller Foundation. True cost of food: measuring what matters to transform the U.S. Food system. The Rockefeller Foundation (2021). Available at: https://www.rockefellerfoundation.org/report/true-cost-of-food-measuring-what-matters-to-transform-the-u-s-food-system/

Google Scholar

449. Hattersley L and Mandeville KL. Global coverage and design of sugar-sweetened beverage taxes. JAMA Netw Open (2023) 6(3):e231412. doi: 10.1001/jamanetworkopen.2023.1412

PubMed Abstract | Crossref Full Text | Google Scholar

450. Andreyeva T, Marple K, Marinello S, Moore TE, and Powell LM. Outcomes following taxation of sugar-sweetened beverages: a systematic review and meta-analysis. JAMA Netw Open (2022) 5(6):e2215276. doi: 10.1001/jamanetworkopen.2022.15276

PubMed Abstract | Crossref Full Text | Google Scholar

451. Popkin BM and Reardon T. Obesity and the food system transformation in Latin America. Obes Rev (2018) 19(8):1028–64. doi: 10.1111/obr.12694

PubMed Abstract | Crossref Full Text | Google Scholar

452. Pérez-Escamilla R, Lutter CK, Rabadan-Diehl C, Rubinstein A, Calvillo A, Corvalán C, et al. Prevention of childhood obesity and food policies in Latin America: from research to practice. Obes Rev (2017) 18(suppl 2):28–38. doi: 10.1111/obr.12574

PubMed Abstract | Crossref Full Text | Google Scholar

453. Dodd R, Reeve E, Sparks E, George A, Vivili P, Win Tin ST, et al. The politics of food in the Pacific: coherence and tension in regional policies on nutrition, the food environment and non-communicable diseases. Public Health Nutr (2020) 23(1):168–80. doi: 10.1017/S1368980019002118

PubMed Abstract | Crossref Full Text | Google Scholar

454. Hernández-F M, Batis C, Rivera JA, and Colchero MA. Reduction in purchases of energy-dense nutrient-poor foods in Mexico associated with the introduction of a tax in 2014. Prev Med (2019) 118:16–22. doi: 10.1016/j.ypmed.2018.09.019

PubMed Abstract | Crossref Full Text | Google Scholar

455. Batis C, Castellanos-Gutiérrez A, Sánchez-Pimienta TG, Reyes-García A, Colchero MA, Basto-Abreu A, et al. Comparison of dietary intake before vs after taxes on sugar-sweetened beverages and nonessential energy-dense foods in Mexico, 2012 to 2018. JAMA Netw Open (2023) 6(7):e2325191. doi: 10.1001/jamanetworkopen.2023.25191

PubMed Abstract | Crossref Full Text | Google Scholar

456. Gračner T, Marquez-Padilla F, and Hernandez-Cortes D. Changes in weight-related outcomes among adolescents following consumer price increases of taxed sugar-sweetened beverages. JAMA Pediatr (2022) 176(2):150–8. doi: 10.1001/jamapediatrics.2021.5044

PubMed Abstract | Crossref Full Text | Google Scholar

457. Salgado Hernández JC, Basto-Abreu A, Junquera-Badilla I, Moreno-Aguilar LA, Barrientos-Gutiérrez T, and Colchero MA. Building upon the sugar beverage tax in Mexico: a modelling study of tax alternatives to increase benefits. BMJ Glob Health (2023) 8(suppl 8):e012227. doi: 10.1136/bmjgh-2023-012227

PubMed Abstract | Crossref Full Text | Google Scholar

458. Basto-Abreu A, Torres-Alvarez R, Barrientos-Gutierrez T, Pereda P, and Duran AC. Estimated reduction in obesity prevalence and costs of a 20% and 30% ad valorem excise tax to sugar-sweetened beverages in Brazil: a modeling study. PloS Med (2024) 21(7):e1004399. doi: 10.1371/journal.pmed.1004399

PubMed Abstract | Crossref Full Text | Google Scholar

459. Young DR, Hedderson MM, Sidell MA, Lee C, Cohen DA, Liu EF, et al. City-level sugar-sweetened beverage taxes and youth body mass index percentile. JAMA Netw Open (2024) 7(7):e2424822. doi: 10.1001/jamanetworkopen.2024.24822

PubMed Abstract | Crossref Full Text | Google Scholar

460. Agarwal S, Ghosh P, and Zhan C. Association between a state-level fat tax and fast food purchases. JAMA Netw Open (2023) 6(10):e2337983. doi: 10.1001/jamanetworkopen.2023.37983

PubMed Abstract | Crossref Full Text | Google Scholar

461. Backholer K, Gupta A, Zorbas C, Bennett R, Huse O, Chung A, et al. Differential exposure to, and potential impact of, unhealthy advertising to children by socio-economic and ethnic groups: a systematic review of the evidence. Obes Rev (2021) 22(3):e13144. doi: 10.1111/obr.13144

PubMed Abstract | Crossref Full Text | Google Scholar

462. Neufeld LM, Andrade EB, Ballonoff Suleiman A, Barker M, Beal T, Blum LS, et al. Food choice in transition: adolescent autonomy, agency, and the food environment. Lancet (2022) 399(10320):185–97. doi: 10.1016/S0140-6736(21)01687-1

PubMed Abstract | Crossref Full Text | Google Scholar

463. Fretes G, Veliz P, Narvaez AM, Williams D, Sibille R, Arts M, et al. Digital marketing of unhealthy foods and non-alcoholic beverages to children and adolescents: a narrative review. Curr Dev Nutr (2025) 9(2):104545. doi: 10.1016/j.cdnut.2025.104545

PubMed Abstract | Crossref Full Text | Google Scholar

464. Dillman Carpentier FR, Stoltze FM, and Popkin BM. Comprehensive mandatory policies are needed to fully protect all children from unhealthy food marketing. PloS Med (2023) 20(9):e1004291. doi: 10.1371/journal.pmed.1004291

PubMed Abstract | Crossref Full Text | Google Scholar

465. Norman JA, Kelly B, Boyland EJ, and McMahon AT. The impact of marketing and advertising on food behaviours: evaluating the evidence for a causal relationship. Curr Nutr Rep (2016) 5(3):139–49. doi: 10.1007/s13668-016-0166-6

Crossref Full Text | Google Scholar

466. Boyland E, McGale L, Maden M, Hounsome J, Boland A, and Jones A. Systematic review of the effect of policies to restrict the marketing of foods and non-alcoholic beverages to which children are exposed. Obes Rev (2022) 23(8):e13447. doi: 10.1111/obr.13447

PubMed Abstract | Crossref Full Text | Google Scholar

467. An R, Liu J, Liu R, Barker AR, Figueroa RB, and McBride TD. Impact of sugar-sweetened beverage warning labels on consumer behaviors: a systematic review and meta-analysis. Am J Prev Med (2021) 60(1):115–26. doi: 10.1016/j.amepre.2020.07.003

PubMed Abstract | Crossref Full Text | Google Scholar

468. Crosbie E, Gomes FS, Olvera J, Rincón-Gallardo Patiño S, Hoeper S, and Carriedo A. A policy study on front-of-pack nutrition labeling in the Americas: emerging developments and outcomes. Lancet Reg Health Am (2022) 18:100400. doi: 10.1016/j.lana.2022.100400

PubMed Abstract | Crossref Full Text | Google Scholar

469. Ganderats-Fuentes M and Morgan S. Front-of-package nutrition labeling and its impact on food industry practices: a systematic review of the evidence. Nutrients (2023) 15(11):2630. doi: 10.3390/nu15112630

PubMed Abstract | Crossref Full Text | Google Scholar

470. Bablani L, Ni Mhurchu C, Neal B, Skeels CL, Staub KE, and Blakely T. The impact of voluntary front-of-pack nutrition labelling on packaged food reformulation: a difference-in-differences analysis of the Australasian Health Star Rating scheme. PloS Med (2020) 17(11):e1003427. doi: 10.1371/journal.pmed.1003427

PubMed Abstract | Crossref Full Text | Google Scholar

471. D’Angelo Campos A, Ng SW, Duran AC, Khandpur N, Taillie LS, Christon FO, et al. “Warning: ultra-processed”: an online experiment examining the impact of ultra-processed warning labels on consumers’ product perceptions and behavioral intentions. Int J Behav Nutr Phys Act (2024) 21(1):115. doi: 10.1186/s12966-024-01664-w

PubMed Abstract | Crossref Full Text | Google Scholar

472. D’Angelo Campos A, Ng SW, McNeel K, and Hall MG. How promising are “ultraprocessed” front-of-package labels? A formative study with US adults. Nutrients (2024) 16(7):1072. doi: 10.3390/nu16071072

PubMed Abstract | Crossref Full Text | Google Scholar

473. Petimar J, Zhang F, Rimm EB, Simon D, Cleveland LP, Gortmaker SL, et al. Changes in the calorie and nutrient content of purchased fast food meals after calorie menu labeling: a natural experiment. PloS Med (2021) 18(7):e1003714. doi: 10.1371/journal.pmed.1003714

PubMed Abstract | Crossref Full Text | Google Scholar

474. Petimar J, Grummon AH, Zhang F, Gortmaker SL, Moran AJ, Polacsek M, et al. Assessment of calories purchased after calorie labeling of prepared foods in a large supermarket chain. JAMA Intern Med (2022) 182(9):965–73. doi: 10.1001/jamainternmed.2022.3065

PubMed Abstract | Crossref Full Text | Google Scholar

475. Corvalán C, Reyes M, Garmendia ML, and Uauy R. Structural responses to the obesity and non-communicable diseases epidemic: the Chilean Law of Food Labeling and Advertising. Obes Rev (2013) 14(suppl 2):79–87. doi: 10.1111/obr.12099

PubMed Abstract | Crossref Full Text | Google Scholar

476. Taillie LS, Bercholz M, Popkin B, Reyes M, Colchero MA, and Corvalán C. Changes in food purchases after the Chilean policies on food labelling, marketing, and sales in schools: a before and after study. Lancet Planet Health (2021) 5(8):e526–33. doi: 10.1016/S2542-5196(21)00172-8

PubMed Abstract | Crossref Full Text | Google Scholar

477. Fretes G, Corvalán C, Reyes M, Taillie LS, Economos CD, Wilson NLW, et al. Changes in children’s and adolescents’ dietary intake after the implementation of Chile’s law of food labeling, advertising and sales in schools: a longitudinal study. Int J Behav Nutr Phys Act (2023) 20(1):40. doi: 10.1186/s12966-023-01445-x

PubMed Abstract | Crossref Full Text | Google Scholar

478. Hawkes C, Smith TG, Jewell J, Wardle J, Hammond RA, Friel S, et al. Smart food policies for obesity prevention. Lancet (2015) 385(9985):2410–21. doi: 10.1016/S0140-6736(14)61745-1

PubMed Abstract | Crossref Full Text | Google Scholar

479. Rebolledo N, Ferrer-Rosende P, Reyes M, Smith Taillie L, and Corvalán C. Changes in the critical nutrient content of packaged foods and beverages after the full implementation of the Chilean Food Labelling and Advertising Law: a repeated cross-sectional study. BMC Med (2025) 23(1):46. doi: 10.1186/s12916-025-03878-6

PubMed Abstract | Crossref Full Text | Google Scholar

480. Daniels JP. Colombia introduces junk food tax. Lancet (2023) 402(10417):2062. doi: 10.1016/S0140-6736(23)02628-4

PubMed Abstract | Crossref Full Text | Google Scholar

481. Baker P, Smith JP, Garde A, Grummer-Strawn LM, Wood B, Sen G, et al. The political economy of infant and young child feeding: confronting corporate power, overcoming structural barriers, and accelerating progress. Lancet (2023) 401(10375):503–24. doi: 10.1016/S0140-6736(22)01933-X

PubMed Abstract | Crossref Full Text | Google Scholar

482. Dunford EK and Popkin BM. Ultra-processed food for infants and toddlers; dynamics of supply and demand. Bull World Health Organ (2023) 101(5):358–60. doi: 10.2471/BLT.22.289448

PubMed Abstract | Crossref Full Text | Google Scholar

483. Boyland E, Davies N, Wilton M, Jones A, Maden M, Curtis F, et al. Impact of food, beverage, and alcohol brand marketing on consumptive behaviors and health in children and adults: a systematic review and meta-analysis. Obes Rev (2025) 26(9):e13932. doi: 10.1111/obr.13932

PubMed Abstract | Crossref Full Text | Google Scholar

484. Gittelsohn J, Trude ACB, and Kim H. Pricing strategies to encourage availability, purchase, and consumption of healthy foods and beverages: a systematic review. Prev Chronic Dis (2017) 14:E107.doi: 10.5888/pcd14.170213

PubMed Abstract | Crossref Full Text | Google Scholar

485. Comini L, Lopes SO, Rocha DMUP, Silva MMDC, and Hermsdorff HHM. The effects of subsidies for healthy foods on food purchasing behaviors, consumption patterns, and obesity/overweight: a systematic review. Nutr Rev (2025) 83(7):e1722–39. doi: 10.1093/nutrit/nuae153

PubMed Abstract | Crossref Full Text | Google Scholar

486. Huangfu P, Pearson F, Abu-Hijleh FM, Wahlich C, Willis K, Awad SF, et al. Impact of price reductions, subsidies, or financial incentives on healthy food purchases and consumption: a systematic review and meta-analysis. Lancet Planet Health (2024) 8(3):e197–212. doi: 10.1016/S2542-5196(24)00004-4

PubMed Abstract | Crossref Full Text | Google Scholar

487. Wright A, Smith KE, and Hellowell M. Policy lessons from health taxes: a systematic review of empirical studies. BMC Public Health (2017) 17(1):583. doi: 10.1186/s12889-017-4497-z

PubMed Abstract | Crossref Full Text | Google Scholar

488. Andreyeva T, Marple K, Moore TE, and Powell LM. Evaluation of economic and health outcomes associated with food taxes and subsidies: a systematic review and meta-analysis. JAMA Netw Open (2022) 5(6):e2214371. doi: 10.1001/jamanetworkopen.2022.14371

PubMed Abstract | Crossref Full Text | Google Scholar

489. Andreyeva T, Luedicke J, Middleton AE, Long MW, and Schwartz MB. Positive influence of the revised Special Supplemental Nutrition Program for Women, Infants, and Children food packages on access to healthy foods. J Acad Nutr Diet (2012) 112(6):850–58. doi: 10.1016/j.jand.2012.02.019

PubMed Abstract | Crossref Full Text | Google Scholar

490. Mozaffarian D, Blanck HM, Garfield KM, Wassung A, and Petersen R. A food is medicine approach to achieve nutrition security and improve health. Nat Med (2022) 28(11):2238–40. doi: 10.1038/s41591-022-02027-3

PubMed Abstract | Crossref Full Text | Google Scholar

491. Bleich SN, Dupuis R, and Seligman HK. Food is medicine movement-key actions inside and outside the government. JAMA Health Forum (2023) 4(8):e233149. doi: 10.1001/jamahealthforum.2023.3149

PubMed Abstract | Crossref Full Text | Google Scholar

492. Cole A, Pethan J, and Evans J. The role of agricultural systems in teaching kitchens: an integrative review and thoughts for the future. Nutrients (2023) 15(18):4045. doi: 10.3390/nu15184045

PubMed Abstract | Crossref Full Text | Google Scholar

493. Clarke B, Kwon J, Swinburn B, and Sacks G. Policy processes leading to the adoption of ‘Jamie’s Ministry of Food’ programme in Victoria, Australia. Health Promot Int (2022) 37(1):daab079. doi: 10.1093/heapro/daab079

PubMed Abstract | Crossref Full Text | Google Scholar

494. Wadi NM, Cheikh K, Keung YW, and Green R. Investigating intervention components and their effectiveness in promoting environmentally sustainable diets: a systematic review. Lancet Planet Health (2024) 8(6):e410–22. doi: 10.1016/S2542-5196(24)00064-0

PubMed Abstract | Crossref Full Text | Google Scholar

495. World Health Organization. Global action plan on physical activity 2018–2030: more active people for a healthier world. Geneva: WHO (2018). Available at: https://www.who.int/publications/i/item/9789241514187

Google Scholar

496. Ramirez Varela AR, Hallal PC, Grueso JM, Pedisic Z, Salvo D, Nguyen A, et al. Status and trends of physical activity surveillance, policy, and research in 164 countries: findings from the Global Observatory for Physical Activity—GoPA! 2015 and 2020 surveys. J Phys Act Health (2022) 20(2):112–28. doi: 10.1123/jpah.2022-0464

PubMed Abstract | Crossref Full Text | Google Scholar

497. Giles-Corti B, Moudon AV, Lowe M, Cerin E, Boeing G, Frumkin H, et al. What next? Expanding our view of city planning and global health, and implementing and monitoring evidence-informed policy. Lancet Glob Health (2022) 10(6):e919–26. doi: 10.1016/S2214-109X(22)00066-3

PubMed Abstract | Crossref Full Text | Google Scholar

498. Persoskie A, Hennessy E, and Nelson WL. US consumers’ understanding of nutrition labels in 2013: the importance of health literacy. Prev Chronic Dis (2017) 14:E86. doi: 10.5888/pcd14.170066

PubMed Abstract | Crossref Full Text | Google Scholar

499. Hayes D, Contento IR, and Weekly C. Position of the academy of nutrition and dietetics, society for nutrition education and behavior, and school nutrition association: comprehensive nutrition programs and services in schools. J Nutr Educ Behav (2018) 50(5):433–9.e1. doi: 10.1016/j.jneb.2018.03.001

PubMed Abstract | Crossref Full Text | Google Scholar

500. Teachman BA and Brownell KD. Implicit anti-fat bias among health professionals: is anyone immune? Int J Obes Relat Metab Disord (2001) 25(10):1525–31. doi: 10.1038/sj.ijo.0801745

PubMed Abstract | Crossref Full Text | Google Scholar

501. Phelan SM, Burgess DJ, Yeazel MW, Hellerstedt WL, Griffin JM, and van Ryn M. Impact of weight bias and stigma on quality of care and outcomes for patients with obesity. Obes Rev (2015) 16(4):319–26. doi: 10.1111/obr.12266

PubMed Abstract | Crossref Full Text | Google Scholar

502. Geller G and Watkins PA. Addressing medical students’ negative bias toward patients with obesity through ethics education. AMA J Ethics (2018) 20(10):E948–59. doi: 10.1001/amajethics.2018.948

PubMed Abstract | Crossref Full Text | Google Scholar

503. Essel KD, Fotang J, Deyton L, and Cotter EW. Discovering the roots: a qualitative analysis of medical students exploring their unconscious obesity bias. Teach Learn Med (2023) 35(2):143–56. doi: 10.1080/10401334.2022.2041421

PubMed Abstract | Crossref Full Text | Google Scholar

504. Mastrocola MR, Roque SS, Benning LV, and Stanford FC. Obesity education in medical schools, residencies, and fellowships throughout the world: a systematic review. Int J Obes (Lond) (2020) 44(2):269–79. doi: 10.1038/s41366-019-0453-6

PubMed Abstract | Crossref Full Text | Google Scholar

505. Butsch WS, Robison K, Sharma R, Knecht J, and Smolarz BG. Medicine residents are unprepared to effectively treat patients with obesity: results from a U.S. internal medicine residency survey. J Med Educ Curric Dev (2020) 7:2382120520973206. doi: 10.1177/2382120520973206

PubMed Abstract | Crossref Full Text | Google Scholar

506. Macaninch E, Buckner L, Amin P, Broadley I, Crocombe D, Herath D, et al. Time for nutrition in medical education. BMJ Nutr Prev Health (2020) 3(1):40–8. doi: 10.1136/bmjnph-2019-000049

PubMed Abstract | Crossref Full Text | Google Scholar

507. Jones G, Macaninch E, Mellor D, Spiro A, Martyn K, Butler T, et al. Putting nutrition education on the table: development of a curriculum to meet future doctors’ needs. BMJ Nutr Prev Health (2022) 5(2):208–16. doi: 10.1136/bmjnph-2022-000510

PubMed Abstract | Crossref Full Text | Google Scholar

508. Gramlich LM, Olstad DL, Nasser R, Goonewardene L, Raman M, Innis S, et al. Medical students’ perceptions of nutrition education in Canadian universities. Appl Physiol Nutr Metab (2010) 35(3):336–43. doi: 10.1139/H10-016

PubMed Abstract | Crossref Full Text | Google Scholar

509. Frantz DJ, McClave SA, Hurt RT, Miller K, and Martindale RG. Cross-sectional study of U.S. interns’ perceptions of clinical nutrition education. J Parenter Enteral Nutr (2016) 40(4):529–35. doi: 10.1177/0148607115571016

PubMed Abstract | Crossref Full Text | Google Scholar

510. Vetter ML, Herring SJ, Sood M, Shah NR, Kal, et al. What do resident physicians know about nutrition? An evaluation of attitudes, self-perceived proficiency and knowledge. J Am Coll Nutr (2008) 27(2):287–98. doi: 10.1080/07315724.2008.10719702

PubMed Abstract | Crossref Full Text | Google Scholar

511. Agusala B, Broad Leib E, and Albin J. The time is ripe: the case for nutrition in graduate medical education in the United States. J Med Educ Curric Dev (2024) 11:23821205241228651. doi: 10.1177/23821205241228651

PubMed Abstract | Crossref Full Text | Google Scholar

512. Gortmaker SL, Swinburn BA, Levy D, Carter R, Mabry PL, Finegood DT, et al. Changing the future of obesity: science, policy, and action. Lancet (2011) 378(9793):838–47. doi: 10.1016/S0140-6736(11)60815-5

PubMed Abstract | Crossref Full Text | Google Scholar

513. Buse K, Ralston J, and Barquera S. Let’s talk differently about obesity. BMJ (2024) 384:q537. doi: 10.1136/bmj.q537

PubMed Abstract | Crossref Full Text | Google Scholar

514. Schumacher LM, Ard J, and Sarwer DB. Promise and unrealized potential: 10 years of the American Medical Association classifying obesity as a disease. Front Public Health (2023) 11:1205880. doi: 10.3389/fpubh.2023.1205880

PubMed Abstract | Crossref Full Text | Google Scholar

515. Springmann M, Hansoge M, Schulz L, Pastorino S, and Bundy D. The health, environmental, and cost implications of providing healthy and sustainable school meals for every child by 2030: a global modelling study. Lancet Planet Health (2025):101278. doi: 10.1016/j.lanplh.2025.06.002

PubMed Abstract | Crossref Full Text | Google Scholar

516. Hawkes C, Brazil BG, Castro IRR, and Jaime PC. How to engage across sectors: lessons from agriculture and nutrition in the Brazilian School Feeding Program. Rev Saude Publica (2016) 50:47. doi: 10.1590/S1518-8787.2016050006506

PubMed Abstract | Crossref Full Text | Google Scholar

517. Coutinho DR, Foss MC, Levy M, and De Paula PCB. Direito E inovação em compras PÚBLICAS: O caso do programa nacional de alimentação escolar. Rev Estud Inst (2022) 8(2):203–28. doi: 10.21783/rei.v8i2.726

Crossref Full Text | Google Scholar

518. Soares P, Martinelli SS, Melgarejo L, Cavalli SB, and Davó-Blanes MC. Using local family farm products for school feeding programmes: effect on school menus. Br Food J (2017) 119(6):1289–300. doi: 10.1108/BFJ-08-2016-0377

Crossref Full Text | Google Scholar

519. Oliveira TD, Pereda P, de Pellegrini E, and Duran AC. Socioeconomic effects of the direct procurement from family farming in the Brazilian school feeding program. SSRN [preprint] (2025). Available at: https://ssrn.com/abstract=4940245 or http://dx.doi.org/10.2139/ssrn.4940245

Google Scholar

520. Di Prima S, Nguyen Dinh D, Reurings DD, Wright EP, Essink D, and Broerse JEW. Home-grown school feeding: implementation lessons from a pilot in a poor ethnic minority community in Vietnam. Food Nutr Bull (2022) 43(3):271–302. doi: 10.1177/03795721221088962

PubMed Abstract | Crossref Full Text | Google Scholar

521. Kramer K. Just energy transition partnerships: an opportunity to leapfrog from coal to clean energy. International Institute for Sustainable Development (2022). Available at: https://www.iisd.org/articles/insight/just-energy-transition-partnerships

Google Scholar

522. Shepon A, Sun Z, Makov T, and Behrens P. The environmental and social opportunities of reducing sugar intake. Proc Natl Acad Sci USA (2024) 121(48):e2314482121. doi: 10.1073/pnas.2314482121

PubMed Abstract | Crossref Full Text | Google Scholar

523. Matassa S, Boon N, Pikaar I, and Verstraete W. Microbial protein: future sustainable food supply route with low environmental footprint. Microb Biotechnol (2016) 9(5):568–75. doi: 10.1111/1751-7915.12369

PubMed Abstract | Crossref Full Text | Google Scholar

524. Kołodziejczak K, Onopiuk A, Szpicer A, and Poltorak A. Meat analogues in the perspective of recent scientific research: a review. Foods (2021) 11(1):105. doi: 10.3390/foods11010105

PubMed Abstract | Crossref Full Text | Google Scholar

525. He J, Tang M, Zhong F, Deng J, Li W, Zhang L, et al. Current trends and possibilities of typical microbial protein production approaches: a review. Crit Rev Biotechnol (2024) 44(8):1515–32. doi: 10.1080/07388551.2024.2332927

PubMed Abstract | Crossref Full Text | Google Scholar

526. Kaplan DL and McClements DJ. Hybrid alternative protein-based foods: designing a healthier and more sustainable food supply. Front Sci (2025) 3:1599300. doi: 10.3389/fsci.2025.1599300

Crossref Full Text | Google Scholar

527. Park S, Lee M, Jung S, Lee H, Choi B, Choi M, et al. Rice grains integrated with animal cells: a shortcut to a sustainable food system. Matter (2024) 7(3):1292–313. doi: 10.1016/j.matt.2024.01.015

Crossref Full Text | Google Scholar

528. Baker P, Hawkes C, Wingrove K, Demaio AR, Parkhurst J, Thow AM, et al. What drives political commitment for nutrition? A review and framework synthesis to inform the United Nations Decade of Action on Nutrition. BMJ Glob Health (2018) 3(1):e000485. doi: 10.1136/bmjgh-2017-000485

PubMed Abstract | Crossref Full Text | Google Scholar

529. Gilmore AB, Fabbri A, Baum F, Bertscher A, Bondy K, Chang HJ, et al. Defining and conceptualising the commercial determinants of health. Lancet (2023) 401(10383):1194–213. doi: 10.1016/S0140-6736(23)00013-2

PubMed Abstract | Crossref Full Text | Google Scholar

530. Pineda E, Hernández-F M, Ortega-Avila AG, Jones A, and Rivera JA. Mexico’s bold new law on adequate and sustainable nutrition. Lancet (2025) 405(10481):764–67. doi: 10.1016/S0140-6736(24)01493-4

PubMed Abstract | Crossref Full Text | Google Scholar

531. Ralston J and Nugent R. Toward a broader response to cardiometabolic disease. Nat Med (2019) 25(11):1644–6. doi: 10.1038/s41591-019-0642-9

PubMed Abstract | Crossref Full Text | Google Scholar

532. Powell PK, Durham J, and Lawler S. Food choices of young adults in the United States of America: a scoping review. Adv Nutr (2019) 10(3):479–88. doi: 10.1093/advances/nmy116

PubMed Abstract | Crossref Full Text | Google Scholar

533. Larson N, Laska MN, and Neumark-Sztainer D. Do young adults value sustainable diet practices? Continuity in values from adolescence to adulthood and linkages to dietary behaviour. Public Health Nutr (2019) 22(14):2598–608. doi: 10.1017/S136898001900096X

PubMed Abstract | Crossref Full Text | Google Scholar

534. Pongutta S, Suphanchaimat R, Patcharanarumol W, and Tangcharoensathien V. Lessons from the Thai health promotion Foundation. Bull World Health Organ (2019) 97(3):213–20. doi: 10.2471/BLT.18.220277

PubMed Abstract | Crossref Full Text | Google Scholar

535. De-Regil LM, Montez J, Mahy L, da Silva Gomes F, Engelhardt K, Engesveen K, et al. Global action on ultra-processed foods: a health, equity, and sustainability imperative. Lancet (2025): 406(10520):2608–10. doi: 10.1016/S0140-6736(25)02326-8

PubMed Abstract | Crossref Full Text | Google Scholar

536. Mozaffarian D. Regulatory policy to address ultraprocessed foods. N Engl J Med (2025) 392(24):2393–6. doi: 10.1056/NEJMp2503241

PubMed Abstract | Crossref Full Text | Google Scholar

537. Munir M, Zakaria ZA, Nisar H, Ahmed Z, Korma SA, and Esatbeyoglu T. Global human obesity and global social index: relationship and clustering. Front Nutr (2023) 10:1150403. doi: 10.3389/fnut.2023.1150403

PubMed Abstract | Crossref Full Text | Google Scholar

538. United Nations. The sustainability development goals report 2019. New York, NY: UN (2019). Available at: https://unstats.un.org/sdgs/report/2019

Google Scholar

539. Fuso Nerini F, Mazzucato M, Rockström J, van Asselt H, Hall JW, Matos S, et al. Extending the Sustainable Development Goals to 2050 - a road map. Nature (2024) 630(8017):555–8. doi: 10.1038/d41586-024-01754-6

PubMed Abstract | Crossref Full Text | Google Scholar

540. Fanzo J, Haddad L, Schneider KR, Béné C, Covic NM, Guarin A, et al. Viewpoint: rigorous monitoring is necessary to guide food system transformation in the countdown to the 2030 global goals. Food Policy (2021) 104:102163. doi: 10.1016/j.foodpol.2021.102163

Crossref Full Text | Google Scholar

541. Schneider KR, Fanzo J, Haddad L, Herrero M, Moncayo JR, Herforth A, et al. The state of food systems worldwide in the countdown to 2030. Nat Food (2023) 4(12):1090–110. doi: 10.1038/s43016-023-00885-9

PubMed Abstract | Crossref Full Text | Google Scholar

542. Garza C, Stover PJ, Ohlhorst SD, Field MS, Steinbrook R, Rowe S, et al. Best practices in nutrition science to earn and keep the public’s trust. Am J Clin Nutr (2019) 109(1):225–43. doi: 10.1093/ajcn/nqy337

PubMed Abstract | Crossref Full Text | Google Scholar

543. Laar A, Amoah JM, Massawudu LM, Pereko KKA, Yeboah-Nkrumah A, Amevinya GS, et al. Making food-related health taxes palatable in sub-Saharan Africa: lessons from Ghana. BMJ Glob Health (2023) 8(suppl 8):e012154. doi: 10.1136/bmjgh-2023-012154

PubMed Abstract | Crossref Full Text | Google Scholar

544. Popkin BM, Barquera S, Corvalan C, Hofman KJ, Monteiro C, Ng SW, et al. Towards unified and impactful policies to reduce ultra-processed food consumption and promote healthier eating. Lancet Diabetes Endocrinol (2021) 9(7):462–70. doi: 10.1016/S2213-8587(21)00078-4

PubMed Abstract | Crossref Full Text | Google Scholar

545. Hawkes C, Ruel MT, Salm L, Sinclair B, and Branca F. Double-duty actions: seizing programme and policy opportunities to address malnutrition in all its forms. Lancet (2020) 395(10218):142–55. doi: 10.1016/S0140-6736(19)32506-1

PubMed Abstract | Crossref Full Text | Google Scholar