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SYSTEMATIC REVIEW article

Front. Nutr., 21 January 2026

Sec. Nutrition, Psychology and Brain Health

Volume 12 - 2025 | https://doi.org/10.3389/fnut.2025.1754492

This article is part of the Research TopicDietary Patterns and Neurobehavioral HealthView all 6 articles

Neurobiological insights into the effects of ultra-processed food on lipid metabolism and associated mental health conditions: a scoping review


Emily Poon&#x;Emily Poon1Christine Li&#x;Christine Li1Daniel Schweitzer
Daniel Schweitzer2*Isaac Akefe
Isaac Akefe3*
  • 1Medical School, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
  • 2Centre for Neurosciences, Mater Hospital, South Brisbane, QLD, Australia
  • 3CDU-Menzies School of Medicine, Charles Darwin University, Darwin, NT, Australia

Background: Ultra-processed foods (UPFs) account for approximately 38% of the adult diet, corresponding with a global increase in the prevalence of mental illnesses. Understanding the relationship between UPF consumption and mental health is crucial for public health and clinical practice.

Objectives: To uncover the association between consumption of ultra-processed food (UPF), dysregulated lipid metabolism, and increased risk of mental illnesses, including depression, anxiety, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), eating disorders (ED), and food addiction (FA). In addition, this review explores the potential biological and behavioral mechanisms that may underlie these associations for each disorder.

Methods: Following the PRISMA extension for scoping reviews guideline, a comprehensive search was conducted across PubMed, Web of Science, and EMBASE databases. The retrieved records, screened using Covidence, included English-language studies published between 2020 and 2025 that involved participants without significant comorbidities. Relevant data on associations and proposed mechanisms were extracted and synthesized using a narrative approach.

Results: UPF consumption was associated with dysregulated lipid metabolism and increased risk of Anxiety, Depression, ADHD, Autism, ED, and FA. Dose-dependent increases in risk were identified in all mental illnesses except for autism. Proposed mechanisms for all these increased risks included systemic low-grade inflammation, alterations in neuronal signaling, particularly dopamine and serotonin signaling pathways, and the influence of UPF additives on neurochemical regulation.

Conclusion: There is a strong association between UPF consumption, disrupted lipid metabolism and increased risk of mental disorder in populations without significant comorbidities. Diets rich in minimally processed foods appear protective. The findings support the potential of public health initiatives aimed at reducing UPF consumption to mitigate the mental health burden. Future studies should focus on mechanistic pathways, UPF and minimally processed food consumption patterns to provide evidence for targeted dietary and policy interventions that improve health outcomes.

1 Introduction

The prevalence of ultra-processed foods (UPFs) is rapidly increasing across both developed and developing nations, which may in part be due to their availability, low price and additives which make them highly palatable (1). The NOVA classification system categorizes foods according to their level of processing. Based on this classification scheme, UPFs are defined as foods or beverages that are industrially formulated from food constituents and additives that rarely contain any whole foods (2). Common UPFs include deep-fried foods, packaged snacks, soft drinks, and instant meals (3). These foods are generally nutrient-poor but contain higher amounts of fat, sugar and additives, including preservatives, artificial flavors [such as monosodium glutamate (MSG)] and dyes (2, 4). In Australia, UPFs accounted for, on average, 38.8% of the adult diet (5). Based on findings from recent studies, the consumption of UPF has been increasingly implicated across a myriad of non-communicable diseases, such as cardiovascular disease, neurological disorders, type 2 diabetes, and cancers (68).

Although the prevalence of mental illness has increased over recent years, the reasons for the increased prevalence of mental disorders remain unclear. It has been well-established that most mental disorders are caused by an interplay of a range of factors, including genetic, early developmental factors, environmental factors, and, in some cases, an initiating or precipitating factor (9, 10). The increased prevalence of mental disorders may also be attributable to a range of factors, including increased awareness about mental disorders, reduced levels of stigma, as well as alternative methods of measurement and recording of mental disorders, which have led to an increased level of reporting of mental disorders (11).

The global burden of mental disorders is significant, with more than one billion individuals estimated to be living with a mental disorder as of 2021 (12). The most common mental disorders are anxiety and depressive disorders, which together account for over two-thirds of all mental health conditions (12). The prevalence of mental disorders continues to increase, potentially driven by a range of societal and social factors, such as the COVID-19 pandemic (13).

Mental illness in Australia is associated with significant morbidity. It accounts for approximately 15% of the total disability-adjusted life years (DALYs). It is the second most significant contributor to overall disability, only behind cancer (14). Over the 2022–2023 period, a total of $13.2 billion was collectively spent on mental health-related services by the government and private health services in Australia, with an estimated total cost of $220 billion when accounting for lost productivity (15). At the individual level, several previous studies have shown that mental disorders have the potential to contribute to a range of other problems, including an increased suicide risk as well as reduced quality of life across multiple domains, including impairments in physical and social functioning (16). Hence, it is important to reduce the prevalence of mental illness and understand the different risk factors associated with the development of mental disorders in view of their significant impacts, both at an individual and at a community level (16, 17). Creating a more nuanced understanding of its impact, at a public health and economic level, will be important for developing public policy around cost-effective and evidence-based.

Previous research has established an association between specific dietary patterns and the risk of mental disorders, with multiple studies demonstrating that individual dietary components, including the type and intake levels of saturated fats and sugars, contribute to the development of mental health disorders (18, 19). With the increasing popularity and consumption of UPFs worldwide, the role of food processing is emerging as a central area of investigation and discussion across various studies (1, 2). To date, there have only been a few studies that have established a clear association and linkage between the level of UPF consumption and the development of mental illness, particularly in terms of the development of mood and anxiety disorders, such as major depressive disorder (MDD) (20, 21). In fact, frequent UPF consumption was found to be associated with an increased cross-sectional risk of depression and anxiety-related symptoms, as well as an increased risk of subsequent depression (20). However, previous studies have not comprehensively assessed the causal mechanisms between the consumption of UPFs and mental disorders. Although there is literature based on investigating the cross-sectional association between UPFs and mental disorders, there continues to be a relative lack of high-quality evidence on the prospective effects of UPFs as a risk factor for the development of mental disorders, as well as the pathophysiological mechanisms involved (21, 22).

Recent evidence suggests that dysregulation of lipid metabolism may play a significant role in the development of mental disorders (2325). Furthermore, alterations in lipid metabolism and signaling resulting from UPF consumption may, in part, contribute to the pathophysiology of several important non-communicable diseases, including cardiovascular disease (CVD), type 2 diabetes, and neurodegenerative diseases (6, 25). Mental disorders and non-communicable diseases have been shown to have a strong bidirectional relationship, with several shared pathophysiological pathways mediated through common risk factors, including UPFs (26, 27). There have been several studies which have established a clear association between the constituents of UPFs and the development of several chronic diseases, including mental disorders (6, 24, 28).

Partially hydrogenated vegetable oils contain industrially produced trans-fatty acids (TFAs; commonly found in processed foods such as margarine, baked goods and deep-fried foods), which may adversely affect the blood lipid profile by raising low-density lipoprotein (LDL) cholesterol and lowering high-density lipoprotein (HDL) cholesterol, leading to increased risk of CVD (29). Increased levels of TFAs may also contribute to the pathogenesis of mental disorders as a result of altering the neuronal lipid membrane composition, leading to reduced levels of polyunsaturated fatty acid content, reduced membrane fluidity, and impaired neurotransmission (28, 30, 31). Additionally, saturated fatty acids (SFAs), which are also common in UPFs such as processed meats, fried foods and baked goods, have been linked to chronic low-grade inflammation mediated by macrophage recruitment and release of inflammatory cytokines, notably tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6), which may contribute to neuroinflammation via crossing of the blood-brain-barrier (BBB) (32). SFAs are also thought to directly induce an inflammatory response in the brain by activating toll-like receptor (TLR) receptors in microglia, causing elevated cytokines IL-6, interleukin-1β (IL-1β), and TNF-α, particularly in the hippocampus, which has been shown to correlate with depressive behavior in mice (3234). Furthermore, high UPF intake may decrease the production of short-chain fatty acids (SCFAs) by the gut microbiota, leading to compromised BBB integrity and thus allowing more passage of inflammatory cytokines or potentially harmful toxins (e.g., titanium dioxide food colorant) (24, 35). Thus, the lipid content of UPFs may initiate inflammatory processes that converge on neuroinflammatory pathways.

UPFs have been established as an important trigger of peripheral and central inflammation (24). Inflammatory cytokines adversely affect different components of the central nervous system (CNS) and peripheral nervous system (PNS), which may, in turn, lead to degeneration of neurons and microglial cells, increased BBB permeability, as well as prolonged levels of microglial activation, which impairs neurogenesis (36, 37). These inflammatory changes may be important drivers of mental disorders as well as neurodegenerative conditions (35, 38).

Consumption of refined carbohydrates (or otherwise known as simple carbohydrates), which break down into simple sugars, may contribute to elevated levels of intracellular glucose, thereby leading to an increased production of free radicals that may damage lipids, thereby inducing lipid peroxidation and causing membrane damage (39). This has, in turn, contributed to the development of atherosclerosis, as well as contributing to increased fasting glucose levels, which increases the risk of type 2 diabetes. Furthermore, the inflammatory cytokines (IL-6, TNF-α, IL-1β) linked to UPF consumption may further contribute to elevated levels of insulin resistance as well as to the development and progression of atherosclerotic disease through both endothelial damage and dysfunction, which is mediated through various signaling pathways, including TLR, and Nod-like receptor protein 3 (NLRP3) inflammasome and nuclear factor-kappa B (NF-kB) (4043). Insulin resistance in the brain has been linked to dopaminergic dysfunction manifesting as anxious and depressive behaviors, whilst cerebrovascular disease may potentially contribute to depressive disorders through disrupted neural connectivity and cerebral hypoperfusion (44, 45). In addition, other common additives, such as emulsifiers and stabilizing agents (e.g., carrageenan), are associated with increased levels of glucose intolerance and insulin resistance, which may, in some cases, contribute to the development of metabolic syndrome as well as mental disorders (7).

Other potential causative mechanisms may also include hormonal dysregulation due to exposure to Bisphenol A (BPA) and other contaminants found in food packaging, which, in turn, contribute to alterations to the gut microbiome, as well as other changes along the gut-brain axis (35). However, other factors, such as hyperpalatability, may contribute to hypothalamic-pituitary-adrenal (HPA) axis dysregulation, which may be implicated, in some clinical situations, in the pathological development of addictive eating behaviors via the alteration of hunger and satiety hormones (46). This drives the propensity to consume energy-dense UPFs during “emotional eating,” forming a bidirectional relationship between UPF intake and mental disorder (20, 47). Other purported mechanisms include exposure to contaminants such as advanced glycation end products (AGEs; e.g., acrylamide) formed by high-temperature industrial frying, which has been suggested to contribute to neuroinflammation and neurotoxicity mediated through the generation of free radicals, mitochondrial dysfunction and activation of glial cells such as BV-2 microglial cells, primary astrocytes, and microglia, together inducing neuronal apoptosis (8, 4850). Nevertheless, interestingly, changes in lipid metabolism have not been systematically discussed in the context of the development of mental disorders despite a likely overlap in the pathophysiological mechanisms associated with the development of the major non-communicable diseases.

Hence, this scoping review aims to synthesize existing literature to explore the potential relationships between UPFs, lipid dysregulation, and mental disorders. Specifically, it investigates the underlying molecular pathways through which UPF consumption may disrupt lipid metabolism. Additionally, the review seeks to understand how these metabolic alterations could contribute to a range of clinical manifestations, including depression, mood and anxiety disorders, eating disorders such as bulimia nervosa, UPF addiction, attention deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD).

The types of constituents in the human diet vary across cultures and have evolved. However, within the context of the Western diet, UPFs now account for a growing proportion of total energy intake. At the same time, global prevalence and burden of mental disorders continue to rise exponentially, placing great strain on our health systems. There is currently a need to assess the impact of UPFs across the spectrum of mental disorders. The findings from this study may inform the development of future therapeutic interventions, clinical practices, and policymaking, both at a local level in Australia and at a global public health level.

2 Methodology

2.1 Search strategy

This scoping review was conducted in accordance with the preferred reporting items for systematic reviews and meta-analyses extended for scoping reviews (PRISMA-ScR). The search strategy was developed to capture literature examining the intersection of three core concepts: UPFs, mental health, and lipid metabolism. A comprehensive search was undertaken in PubMed, Web of Science, and EMBASE for studies published between January 2020 and July 2025 to ensure inclusion of the most recent evidence. Search strings were constructed to explore the interactions among all three concepts, associations between UPFs and mental health outcomes, as well as the links between mental health and lipid metabolism. medical subject headings (MeSH) and equivalent controlled vocabulary terms were used where applicable. Key MeSH terms included: “Food, Processed” [Mesh], “Mental Health” [Mesh], “Depressive Disorder” [Mesh], “Depression” [Mesh], “Sleep Deprivation” [Mesh], “Sleep Disorders, Circadian Rhythm” [Mesh], “Binge-Eating Disorder” [Mesh], “Anorexia Nervosa” [Mesh], “Bulimia Nervosa” [Mesh], “Food Addiction” [Mesh], “Autism Spectrum Disorder” [Mesh], “Attention Deficit Disorder with Hyperactivity” [Mesh], “Stress Disorders, Post-Traumatic” [Mesh], “Lipid Metabolism” [Mesh], “Lipid Metabolism Disorders” [Mesh].

The focus on UPFs reflects growing global concern regarding their widespread consumption and potential implications for health and wellbeing. The final database searches were completed on 18 July 2025. A manual search of reference lists and database results was also performed to ensure completeness. All identified records were imported into Covidence, where duplicates were removed, and the remaining articles were screened for eligibility.

2.2 Study inclusion and exclusion criteria

Studies eligible for inclusion were peer-reviewed English journal articles, reviews, or meta-analyses published between 2020 and 2025. For human studies, participants of all ages, genders, and demographics were included. Animal studies were included for mechanistic insights.

Studies were included if they compared the effect of high and low levels of UPF consumption with the risk of developing a psychiatric disorder, or if they investigated the mechanisms related to how UPFs may cause lipid dysregulation in the context of the pathophysiological mechanisms involved in the development of mental health disorders. Disorders included in the literature review were depression and anxiety, addiction-related disorder (food addiction), eating disorders (bulimia nervosa and binge-eating disorder), ADHD and ASD.

Studies were excluded if the study focused on a patient population with existing comorbidities, the intervention was not specifically UPF intake, the outcome was not the risk of mental disorder or lipid dysregulation, the setting was restricted to the COVID-19 pandemic, or the topic was not relevant to the specific psychiatric disorders included.

2.3 Data screening, extraction, and analysis

Title and abstract screening, followed by full-text review, was conducted independently in Covidence by two reviewers (CL, EP) as shown in Figure 1. All included studies were assessed for methodological rigor and risk of bias to ensure reliability. Key study characteristics, including publication year, country, study design, primary findings, and lipid measures, were extracted and summarized in a table using Microsoft Excel (see Supplementary material). Figures were created using BioRender.

Figure 1
Flowchart showing the selection process for a review. Initially, 1,567 studies identified from databases like Scopus and PubMed, with none from other sources. After removing 630 duplicates, 937 studies were screened. Out of these, 711 were excluded, 224 sought for retrieval, with none not retrieved. Eligibility assessment conducted on 224 studies, excluding 101 for reasons like wrong setting, language, or study design. Finally, 123 studies included in the review.

Figure 1. PRISMA flow diagram illustrating the screening of studies included. One hundred twenty-three studies were included in the final data extraction.

2.4 Study quality assessment

A formal critical appraisal was not required for this scoping review. However, the study limitations were noted in the table of data extraction, and discrepancies between studies will be further explored in the Section 4 of the article.

2.5 Data synthesis

Data characteristics, including year, country, study type, key results, mechanisms, lipids discussed, and therapeutic implications, were extracted. Results were categorized by mental illness. Key associations were identified, and then a hypothesized mechanism to explain the associations.

3 Results

3.1 Descriptive analysis of included studies

The analysis of the included studies provides valuable insights into the field's research landscape. Two key aspects of the studies' characteristics are highlighted: the country of origin and the publication timeline.

The distribution of studies across different countries, as shown in Figure 2, reveals a diverse range of research contributions. While some countries have multiple publications, others are represented scarcely and are grouped under the “Other” category. This category includes countries such as Chile, France, Germany, Iran, Italy, Japan, South Korea, Lebanon, Mexico, Norway, Saudi Arabia, Sweden, Switzerland, the Netherlands, and Türkiye. Research from these countries suggests a global interest in the topic. However, the studies' concentration in certain countries may indicate areas of focused research expertise or specific regional interests (Figure 2).

Figure 2
Horizontal bar chart showing the number of studies per country. China leads, followed by Brazil, Others, USA, Australia, UK, Iran, and Spain. The highest number is nearly 30 studies.

Figure 2. Distribution of studies by country of publication. Brazil had the most at 24 studies.

The temporal distribution of publications, shown in Figure 3, reveals a clear upward trend in research activity over time. Most included studies were published between 2022 and 2025, with the highest number of studies published in 2024. This surge likely reflects growing recognition of UPFs and lipid dysregulation as key mechanisms in mental health research, alongside rapid advancements in lipidomics technologies. Increased interdisciplinary collaboration between mental health and lipidomics researchers may have further contributed to the rise in scholarly output. Overall, the recent growth in publications suggests a rapidly expanding interest in this emerging field.

Figure 3
Bar chart showing the number of studies from 2020 to 2025. The numbers increase from 10 in 2020 to 25 in 2023, peaking at 35 in 2024, then decreasing to 30 in 2025.

Figure 3. Distribution of studies by year of publication. The search was conducted in July 2025.

3.2 UPF and depression

3.2.1 Background on known mechanisms of depression

One of the most widely recognized pathophysiological mechanisms involved in the development of mood disorders, such as depression, is the monoamine hypothesis (51). This hypothesis suggests that there are reduced levels of key neurotransmitters, including serotonin, dopamine, and noradrenaline, within the brain that contribute to both the onset and persistence of depressive symptoms (51, 52). This forms the basis of tri-cyclic antidepressants and monoamine oxidase inhibitors in depression therapy, as these drugs increase the levels of the monoamines within the brain, and relieve depression symptoms (51). Alternative hypotheses exist, such as a model involving the HPA axis (53). Here, the HPA axis becomes overactive in response to stressful events, resulting in increased glucocorticoid and cortisol levels (53). Increased glucocorticoids can also result from inflammation in the brain (53).

Another model is the stress-diathesis theory, which suggests that stressful events during life can alter neuroplasticity and transmission, which leads to the symptoms of major depression (54). Gray matter volume changes are different in healthy patients compared to those with MDD (55). Patients who reported higher stressful life events had an increased risk of depression (56).

Depression risk is also a combination of genetic and environmental factors (57, 58). This was supported since first-degree relatives of patients with MDD have a 2.8 times higher risk of MDD (57). Together, these findings suggest that depression is a multifactorial disorder involving complex neurobiological pathways. This section will explore the various mechanisms involved in depression, providing a more comprehensive understanding of its underlying pathophysiology.

3.2.2 Implications of UPF consumption on depression risk

Previous studies have demonstrated a significant association between UPF and depression, which remains after adjusting for a range of confounders (46, 5962) (Table 1). However, in some studies, no significant association was found between UPF and depression (60, 63, 64). The specific food types that were investigated as part of these studies included sugar-sweetened beverages (SSBs) (61, 6569), although there was a stronger association between males and the intake of SSBs than among females (65). A dose-dependent relationship was also found with energy drinks (69). Also, fast foods and fried foods (67, 70), processed meat (71), smoked food (70), and foods containing higher levels of sodium (71) were associated with an elevated lifetime risk of depression. However, further studies are needed to establish whether there is a causal relationship between the intake of UPF and the risk of depression. Some studies have identified a higher lifetime depression risk among females compared to males (72, 73). In contrast, in another study, it was found that there was a higher risk of depression among males who were under 60 years old (74). This may arise due to hormonal or metabolic differences across the sexes. Associations were found globally, including in Korea, Brazil, China, Australia, Spain, and France (21, 7579). Notably, individuals in the highest quartile of UPF intake had a significantly greater risk of depression (35, 62, 78, 80, 81). Additional evidence demonstrates a dose-dependent relationship, in which increasing UPF consumption corresponds to a progressively higher risk of depression (22, 25, 66, 8284). In addition, UPF consumption during pregnancy was associated with an increased odds of depression symptoms in offspring compared to healthy non-clinical controls (85).

Table 1
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Table 1. Summary of studies implicating increased UPF consumption and dysregulated lipid metabolism associated with depression or anxiety.

3.2.3 Mechanisms of UPF and depression

3.2.3.1 UPFs and dysregulated lipid metabolism

Several mechanisms have been proposed to link increased UPF consumption and depression risk (Figure 4; Table 1). A leading hypothesis suggests that UPFs may disrupt normal lipid metabolism, contributing to lipid profile dysregulation that adversely affects brain function and mood regulation (23) (Figure 5). Individuals consuming diets high in UPF content have been shown to exhibit altered lipid profiles, including shifts in key lipid species involved in neuroinflammation, cell membrane integrity, and neurotransmitter signaling, all of which may contribute to depressive symptoms (77). In animal studies, mice fed thermo-induced oxidized oil (TIOO) diets displayed disrupted ceramide and sphingomyelin levels (86). Moreover, the high omega-6 fatty acid content typical of UPFs promotes the formation of proinflammatory eicosanoids, further exacerbating metabolic and inflammatory imbalances (46). High-fructose corn syrup can increase metabolism, hypertriglyceridemia, insulin resistance, blood glucose fluctuations, and oxidative stress (87). Similarly, in another animal study, grain-fed rats exhibited hyperglycemia due to de novo lipogenesis, which led to abnormal lipids and contributed to inflammation and impaired neuronal function (52). The worsened lipid profile was reproduced in Lutz, Arancibia (36). Deranged lipid profiles were present in severe mental illnesses but could be influenced by metabolic comorbidities (23). Altered lipid levels may, in turn, trigger systemic inflammation, leading to endothelial dysfunction, increased visceral adiposity, body weight, and insulin resistance (24).

Figure 4
Diagram showing a brain labeled “Depression” in the center, connected by arrows to four factors: “Lipid Dysregulation,” “Inflammation,” “Altered Neuronal Signaling,” and “Neurotoxicity,” each with corresponding images.

Figure 4. Summary of mechanisms relating to depression induced by UPFs. Mechanisms include lipid dysregulation, inflammation, altered neuronal signaling, and neurotoxicity.

Figure 5
Flowchart illustrating how lipid dysregulation can lead to depression. It includes influences from added sugars, industrial processing, and Omega-6 oils. Added sugars, high in fructose, increase triglycerides. Industrial processing leads to acrylamide and altered ceramide levels, causing gut dysbiosis and reduced lipid membrane fluidity. Omega-6 increases proinflammatory eicosanoids and reduces brain-derived neurotrophic factor (BDNF), impairing serotonin synthesis. Hyperglycemia and refined grains contribute to de novo lipogenesis. All these factors collectively contribute to lipid dysregulation-induced depression.

Figure 5. Mechanisms by which UPFs induce lipid dysregulation. UPF constituents that contribute to lipid dysregulation include high-fructose corn syrup, acrylamide and refined grains. Lipid dysregulation leads to inflammation, impaired serotonin synthesis and gut dysbiosis. TIOO, thermo-induced oxidized oil; BDNF, brain-derived neurotrophic factor; AA, arachidonic acid; DHA, docosahexaenoic acid.

Notably, the consumption of UPF can adversely affect the permeability of important lipid membranes, including the BBB (88). The production of UPF relies on industrial hydrogenation of vegetable oils that form TFAs, which alter the BBB and lead to changes in neuronal communication (24). UPF also result in the reduction of SCFAs that support the BBB (89). The tight junctions of endothelial cells become impaired, facilitating the passage of nanoparticles into the brain and causing damage (24). UPF increases malondialdehyde (MDA) and decreases glutathione (GSH) levels, leading to oxidative stress, lipid peroxidation and reduced lipid membrane fluidity and permeability (52).

3.2.3.2 UPFs and increased inflammation

Refined carbohydrates, sugars, and hydrogenated fat (90) have been identified as contributing to neuroinflammation in the context of excessive UPF consumption (62, 84). UPF consumption over the course of pregnancy and lactation has also been associated with causing neuroinflammation (24). Saturated long-chain fatty acids activate inflammatory signaling of microglia and astrocytes (24). Abnormal microglial function was also identified in mice treated with TIOO (36). Saturated fatty acid-mediated inflammation can alter the neural circuits that control energy balance, leading to excess nutrient consumption and impaired mood regulation (35). Lower levels of brain-derived neurotrophic factor (BDNF) have been identified (43, 46, 89, 9193). Disrupted BDNF synthesis reduces synaptic plasticity (46), as well as glutamate and other excitatory neuron responses, which may contribute to the pathogenesis of depression (22). UPF have also been linked to the development of demyelination of the CNS (94).

The association between the consumption of UFP and depressive symptoms has been linked to changes in systemic inflammation (43, 73, 93). Added sugar, sodium, saturated and TFAs have been found to have a number of pro-inflammatory properties (80) (Figure 6). Elevated white cell count was significantly associated with depression symptoms, mediated by inflammation from UPF (30, 95). SFAs act similarly to bacterial lipopolysaccharides (LPS), activating TLR4 on macrophages and microglia (35). They can also cross the BBB and activate TLRs in the brain (35). This can stimulate inflammatory pathways, leading to the phosphorylation of the IkappaB alpha protein and the production of inflammatory cytokines (35). Elevated markers, such as C-reactive protein (CRP), IL-6, IL-17, and TNF-α, have been identified. Microglial activation can lead to neurotoxicity (36). TIOO mice also exhibited downregulation of neuroprotective factors, including Interleukin 4 (IL-4), Interleukin 10 (IL-10), and M2 microglia (86). Eicosapentaenoic acid (EPA), an omega-3 polyunsaturated fatty acid (PUFA), is anti-inflammatory and has an inverse relationship with inflammatory markers and depression (89). The EPA may reduce inflammatory cytokines, such as TNF-α, IL-6, and IL-1β, by inhibiting the NF-κB pathway (43).

Figure 6
Flowchart illustrating how industrial processing and packaging contribute to depression through inflammation. High-temperature processing creates AGEs, causing oxidative stress and neuroinflammation, leading to increased BBB permeability and HPA axis dysregulation. Industrial processing reduces neuroprotective factors, activating microglia and astrocytes via TLR4 with saturated fatty acids. BPA and phthalates from packaging release pro-inflammatory markers. Emotional eating is also linked to HPA axis dysregulation.

Figure 6. Pathophysiological mechanisms that underpin how UPFs trigger inflammation across different regions of the CNS, thereby contributing to depression. Constituents involved include by-products of industrial processing, contaminants from plastic packaging, and saturated fatty acids. TIOO, thermo-induced oxidized oil; AGE, advanced glycation end-products; ROS, reactive oxygen species; BBB, blood-brain barrier; TLR4, toll-like receptor 4; IL-17, interleukin 17; IL-6, interleukin 6; TNF-α, tumor necrosis factor alpha; CRP, C-reactive protein; BPA, bisphenol A; HPA, hypothalamic-pituitary-adrenal axis.

AGEs (61) are produced in high-temperature cooking such as frying, roasting, and grilling (known as the Maillard reaction) (8, 61). AGEs are proteins or lipids that glycate and bind to the Receptor for Advanced glycation end-products (RAGE) and mediate inflammatory pathways and reactive oxygen species (ROS) (8). AGEs impair the BDNF-TrkB pathway, thereby impairing neuroplasticity, and cause endothelial dysfunction by forming cross-links in the arterial wall and collagen, leading to stiffness and impaired brain microcirculation (8). AGE can adversely impact BBB integrity and immune cell function (36). AGE-RAGE interaction generates free radicals, pro-inflammatory cytokines, and NF-kB activation. Leading to mitochondrial dysfunction, ROS and apoptosis (8, 96) (Figure 6). Increased RAGE downregulates detoxification pathways and activates glial cells (8). Hyperglycaemia can increase AGE generation (52). But UPF consumption is associated with high levels of AGEs, and AGEs have a strong association with MDD and a higher mortality risk of MDD. Shortening (part of the Maillard reaction) increases neuronal nitric oxide synthase (nNOS) activity, which can lead to excessive signaling, reactive nitrogen species, and cell toxicity (97). The inhibition of nNOS reduced depressive behaviors in animal models (97).

3.2.3.3 UPFs and altered neuronal signaling

The consumption of UFP may lead to changes in dopamine and serotonin signaling, which may, in turn, contribute to a heightened risk of mood disorders, including depression (52, 79, 91, 94, 98) (Figure 7). This may occur due to prolonged exposure to inflammatory cytokines (76). Gut dysbiosis and inflammation can alter tryptophan (the precursor to serotonin) metabolism to the kynurenine (KYN) pathway, which is linked to the development of a range of different mental illnesses (24). High palatability may alter dopaminergic pathways (90) or reward pathways (61, 62, 79). Tolerance occurring due to a downregulation of D2 receptors has been identified in human and animal studies, and withdrawal has been apparent in animal studies, e.g., observed tremor, signs of depression, and increased corticotropin-releasing factor (CRF) (99). Other signaling mechanisms may be necessary due to the abnormal dopamine and serotonin signaling. In particular, artificial sweeteners promote purinergic signaling (100), which is associated with depression (66). Mice treated with TIOO have been found to have changes across glutamate signaling pathways (86).

Figure 7
Flowchart showing factors contributing to depression through altered neuronal signaling. Nutrient deficiency and additives like artificial sweeteners affect various signaling pathways. Trans fatty acids increase blood-brain barrier permeability, leading to neuronal damage. These processes cumulatively contribute to mood dysregulation and depression.

Figure 7. Mechanisms by which UPFs cause depression, via altered neuronal signaling. Altered neurotransmission includes downregulation of dopamine, decreased excitatory response and increased purinergic signaling. UPFs also cause endothelial dysfunction and increased blood-brain barrier permeability, which can lead to neuronal damage. FA, fatty acid; Mg, magnesium; Zn, zinc; BDNF, brain-derived neurotropic factor; D2, dopamine receptor D2; BBB, blood-brain barrier; AGEs, advanced glycation end-products.

3.2.3.4 UPF additives induced depression

The additives and flavoring compounds which are used in UPF production have been associated with a higher risk of depression (101). Preservatives, flavorings and colourings may, in some cases, alter the mitochondrial function of neurons (94) (Figure 8). These include polysorbate-80 and carboxymethylcellulose (61). Titanium dioxide can be cytotoxic to glial cells, hippocampal cells and dopaminergic substantia nigra neurons (20, 24). Silver can accumulate in the brain and impair short- and long-term memory (24). Aspartame and saccharin (artificial sweeteners) can inhibit neurotransmitter synthesis and signaling (20, 61, 102). Artificial sweeteners can increase the beta-theta brainwave ratio, which is linked to negative emotions (81). MSG in UPFs can be an excitatory neurotoxin that alters the nervous system function (20, 61, 103). Caffeine can cause nervousness and stimulate emotions in the short term, but can also cause long-term emotional decline (69). Artificial food colorants may trigger histamine release, which can affect the CNS, sleep and behavior (100).

Figure 8
Flowchart showing factors leading to depression induced by neurotoxicity. BPA and phthalates cause DNA damage, increasing nNOS activity and oxidative stress, linked to increased MDA and reduced GSH. Additives like TiO2 and emulsifiers/stabilizers lead to mitochondrial dysfunction. Added sugars and refined carbs contribute to hyperglycemia and insulin resistance. All these factors converge on neurotoxicity-induced depression.

Figure 8. Mechanisms by which UPFs contribute to neurotoxicity-induced depression. Key constituents include plastic components (BPA, phthalates), additives, added sugar and refined carbohydrates. BPA, bisphenol A; DNA, deoxyribonucleic acid; nNOS, neuronal nitric oxide synthase; ROS, reactive oxygen species; MDA, malondialdehyde; GSH, glutathione; TiO2, titanium dioxide.

3.2.3.5 Neurotoxic contaminants from UPF processing and packaging

Industrial processes in the manufacturing of UPF may be associated with depression (80). Industrial by-products like acrylamide, acrolein, polycyclic aromatic hydrocarbons and furan can be neurotoxic and cause neuroinflammation (36, 70, 74, 80) (Figure 8). Acrylamide causes lipid metabolism disturbance, impairs the BBB and causes neuroinflammation through multiple pathways (74). These pathways include the formation of oxidative damage and ROS (36). In zebrafish, chronic acrylamide exposure altered the cerebral lipid metabolism and immune responses (74). These also impaired BBB function and caused neuroinflammation (74). These mechanisms were linked to depression-like behavior (74). Acrylamide also has the potential to affect Peroxisome Proliferator-Activated Receptors (PPAR), and Janus Kinase/Signal Transducer and Activator of Transcription (JAK-STAT) signaling, and hence is a chemical with a strong ability to negatively affect the brain (74). Chemicals in plastic packaging may increase the risk of depression (22). Plastic food packaging and additives increase inflammatory biomarkers CRP, IL-6, and IL-10 (36). Contaminants from processing, such as bisphenols and phthalates from packaging, can cause DNA damage and toxicity to the immune and nervous systems (75, 98). They can also interfere with cell signaling and gut barrier function, which can increase permeability and cause inflammation and metabolic changes (104). Exposure to BPA in childhood can be associated with depression later in life through reduction of volumes in the mesocorticolimbic regions (amygdala, cingulate cortex, ventral putamen) (20, 30).

3.2.3.6 UPFs and dietary deficiencies

Dietary deficiencies arising from consuming UPFs with poor nutritional quality (36, 61, 71, 90, 91, 98, 105, 106) and a lack of omega-3 fatty acids, phospholipids, cholesterol, niacin, folate, fiber, vitamin B6, and B12 (76) has been linked to depression. This is because the healthy nutrients are no longer present to support mental health (76). Antioxidants, fiber, and micronutrients, which are missing in an UPF diet, improve inflammation and reduce oxidative stress, which protects against depression (71, 82, 107). Low magnesium and zinc may impair neuroplasticity (43), and low tryptophan (the precursor of serotonin) availability, which may reduce brain serotonin synthesis (46, 76). This can increase depressive symptoms according to the monoamine hypothesis (51, 52). Increased tryptophan metabolism can lead to a shift toward the KYN pathway (24). These lead to an increase in quinolonic acids (neurotoxic), and a decrease in the kynurenic acids (neuroprotective), overall leading to mental illness such as depression (24). The UPF additives can disrupt the gut microbial environment (76). Gut dysbiosis (90, 93, 98), for instance, through the reduction of SCFAs, may affect serotonin and dopamine synthesis (24). Gut dysbiosis through the absence of important micronutrients and antioxidants can contribute to inflammation and altered transmission (71, 80, 93). The UPF saturated fatty acids increase the ratio of gram-negative bacteria, which increases intestinal permeability and thereby inflammation systemically (35, 52).

3.2.3.7 Other proposed mechanisms

Changes across several key brain regions in response to UPF consumption may lead to reduced volumes of the mesocorticolimbic regions (30, 36), left ventral putamen, amygdala, and the dorsal frontal cortex (95). High sugar and low BDNF have been linked to hippocampal atrophy (92). These brain changes can cause impaired ability to gauge the nutritional content of UPF, causing overeating and metabolic dysfunction (78).

Cardiometabolic dysregulation and metabolic syndromes are known to be important risk factors for a range of different mental illnesses (98, 108). Excessive feeding can cause metabolic dysfunction (109), as well as altered peptide signaling for appetite and satiety, including YY (101), GLP-1, GIP, and CCK (109). UPF interaction with the HPA axis has also been proposed (20, 89, 91, 96). Systemic inflammation may lead to activation of the HPA axis, leading to an increase in depression symptoms (105). SSBs with high fructose can cause corticosterone levels to rise, leading to HPA axis dysregulation (110) (Figure 6). Worsened glycaemic indices (109) of UPF causes blood glucose level (BGL) peaks, leading to compensation by cortisol and adrenaline, which can cause a lower mood (84). UPF have faster energy intake rates, which affect satiety and BGL spikes (87).

Some of the proposed pathophysiological mechanisms that have been proposed to account for the relationship between UPF, as well as the risk of mood disorders such as depression, include genetically based factors, including the MC4R gene, CRY1 gene, and Cav1 gene, which are associated with an elevated risk of developing depression (60). However, epigenetic changes, including high fat and sugar content, result in changes in acetylation patterns (36), may have a synergistic effect (60, 100). Oxidative stress can cause DNA damage and telomere shortening (95).

3.3 UPF and anxiety

3.3.1 Background on known mechanisms of anxiety

As of 2021, an estimated 4.4% of the global population experiences an anxiety disorder, making it the most prevalent mental disorder (12). Current hypotheses regarding the pathophysiology of anxiety disorders emphasize an imbalance between neuronal excitation and inhibition, characterized by reduced Gamma-aminobutyric acid (GABA) inhibition and excessive glutamatergic activity, which contributes to neuronal hyperexcitability and the manifestation of anxious behaviors (111). Additionally, dysregulation of key neurotransmitters, such as elevated noradrenaline and reduced serotonin, particularly within the amygdala, has been implicated in heightened anxiety and impaired stress resilience (112, 113). High intake of UPFs may contribute to the development of anxiety via both pathways.

3.3.2 Association between UPF consumption and anxiety risk

Findings from previous studies, which have specifically investigated the relationship between UPF consumption and the risk of anxiety disorders, have been mixed, but increasingly, there are several recent findings that have shown a possible association between UPF consumption and the risk of anxiety disorders (Table 1). Several studies report that higher UPF intake is linked to elevated cross-sectional odds of anxiety, either as an independent outcome (79, 114) or within broader measures of common mental disorders (CMDs) that encompass anxiety and depressive symptoms (94, 98, 115). The associations appear particularly robust among adolescents (94) and in analyses demonstrating significant dose-response relationships (98), with specific UPF subgroups such as artificially sweetened beverages, processed meats, ready-packaged bread, high-sodium foods, foods high in additives and fried foods emerging as significant dietary contributors (67, 70, 71, 104, 115). However, the evidence is not entirely consistent. While some studies suggest that UPF consumption is linked to an increased risk of depression, they report little to no association with anxiety, highlighting significant heterogeneity in the findings (65, 67, 90), while others report no significant associations with either outcome (63). Overall, although the findings from the recent literature indicate a plausible connection between UPF consumption and anxiety, the evidence remains inconclusive, particularly in view of the limited number of prospective studies evaluating clinically diagnosed anxiety as a distinct outcome.

3.3.3 UPF and dysregulated lipid metabolism in anxiety

Lipid-related mechanisms may play a significant role in the association between UFPs and anxiety (Table 1). One potential pathway involves altered lipid membrane permeability, which several distinct UPF constituents may mediate. TFAs formed during the industrial hydrogenation of vegetable oils are believed to distort phospholipid composition of the brain membrane, increasing membrane rigidity and hence affecting neuronal signaling (24). In animal models, maternal consumption of trans fats during pregnancy and lactation has been linked to increased neuroinflammation and oxidative stress in the offspring's brain, resulting in anxiety-like behaviors and memory impairments (24). Similarly, high intake of refined grains in rats has been shown to elevate MDA levels and reduce GSH, thereby increasing oxidative stress and promoting lipid peroxidation. These changes reduce lipid membrane fluidity, with increased rigidity impairing neurotransmitter binding, similar to the effects of TFAs (52) (Figure 9). Hyperglycaemia further contributes to membrane rigidity through the generation of AGEs, which aggravate oxidative stress and promote free radical formation, causing damage to the lipid bilayer (52).

Figure 9
Flowchart illustrating the process leading to decreased membrane fluidity. Hydrogenated vegetable oils and refined grains contribute to TFA production and reduced glutathione, leading to lipid peroxidation and oxidative stress. High sugar content causes a hyperglycemic state, leading to AGE formation. These processes decrease membrane fluidity, alter neurotransmitter binding, impair neuronal signaling, and result in anxiety-like behaviors.

Figure 9. UPFs cause decreased membrane fluidity, leading to impaired neuronal signaling and anxiety disorders. Key constituents involved include hydrogenated vegetable oils, refined grains and high sugar content. TFAs, trans fatty acids; MDA, malondialdehyde; ROS, reactive oxygen species; AGE, advanced glycation end product; RAGE, receptor for advanced glycation end products.

A second potential pathway involves the interplay between lipid dysregulation and inflammation. Diets high in UPFs may trigger both peripheral and central inflammatory responses, resulting in elevated triglyceride and TNF-α levels in the liver, as well as increased IL-6 concentrations in the prefrontal cortex, as observed in animal models (116, 117) (Figure 10). Peripheral and central inflammation may be linked through the ability of systemic inflammatory mediators to travel through a “leaky” BBB, as UPFs have previously been shown to compromise BBB integrity (117). These inflammatory changes correlated with anxiety-like behaviors in animal models (116, 117). Saturated fatty acids, in particular, were shown to increase cytokine production mediated through the activation of microglia and astrocytes (24). Furthermore, other inflammatory processes, such as metabolic endotoxemia (characterized by elevated plasma LPS) driven by increased gut permeability, may contribute to peripheral and neuroinflammation, manifesting as anxious behaviors (116). The nutrient displacement theory suggests that the displacement of protective anti-inflammatory lipids, such as omega-3 PUFAs (docosahexaenoic acid, EPA), contributes to increased inflammatory signaling via the disinhibition of phospholipase A2, which normally mediates the production of pro-inflammatory mediators (117). Docosahexaenoic acid (DHA) and EPA were shown to reverse elevated TNF-α levels and ameliorate anxious behaviors (117).

Figure 10
Flowchart depicting the process of inflammation leading to anxiety-like behaviors. It begins with increased triglyceride levels and saturated fatty acids, leading to peripheral inflammation and gut permeability. These activate PPAR signaling and TLR, disrupting lipid metabolism and leading to neuroinflammation. This process involves pro-inflammatory cytokine release, changes in omega-6 and omega-3 levels, and ultimately results in anxiety-like behaviors.

Figure 10. Mechanisms underlying UPF's contribution to neuroinflammation. TNF-α, tumor necrosis factor alpha; BBB, blood-brain barrier; PPAR, peroxisome proliferator-activated receptors; PLA2, phospholipase A2; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; LPS, lipopolysaccharide; TLR, toll-like receptor.

The integrity of the BBB may, in some clinical situations, be a lipid-sensitive target of UPFs (Figure 10). Acrylamide, a Maillard reaction contaminant abundant in fried foods, down-regulates endothelial tight-junction proteins, weakening BBB integrity and permitting infiltration of inflammatory mediators, leading to neuroinflammation (74). Beyond altering the permeability of the BBB, acrylamide disrupts multiple lipid metabolic pathways, including cholesterol, arachidonic acid, sphingolipid, and phospholipid metabolism, mainly through the dysregulation of upstream PPAR signaling, which is heavily involved in lipid metabolism (74). These disturbances result in the accumulation of lipid droplets in brain cells, evidencing impaired cholesterol metabolism. Perturbed sphingolipid and phospholipid metabolism led to the upregulation of apoptogenic ceramides and the downregulation of lipids involved in vital cerebral functions, such as neurotransmission, anti-inflammation, and synaptic refinement. Changes in arachidonic acid metabolism appeared to favor an increase in lipid peroxidation, leading to the formation of downstream inflammatory mediators and inducing oxidative stress. This may contribute to anxiety-like behaviors (74).

Lipid dysregulation can also impact neurotransmission. Normally, SCFAs produced by beneficial gut microbiota regulate enzymes critical for the synthesis of serotonin and dopamine. However, UPF-induced gut dysbiosis, such as that observed following sucralose exposure, reduces SCFA availability, thereby impairing the regulation of these key neurotransmitters (24). Peripheral low-grade inflammation exacerbates this effect by diverting tryptophan metabolism (usually a precursor for serotonin) toward the KYN pathway, resulting in reduced peripheral tryptophan availability and lower central serotonin synthesis (24) (Figure 11). Endocrine disruptors, such as bisphenols, exacerbate this dysregulation during fetal development by altering placental tryptophan metabolism, with long-term neurodevelopmental consequences for offspring (24). Supporting this, animal studies show that high-UPF diets reduce serotonin and dopamine concentrations, while increasing Catechol-O-methyltransferase (COMT) expression, which accelerates dopamine degradation and produces anxiety-like behaviors (116). In addition, food additives such as aspartame may further impair neurotransmission through altered expression of genes regulating glutamate/GABA signaling in the amygdala (111). This results in excessive excitatory drive, characterized by downregulation of GABA signaling and upregulation of postsynaptic N-methyl-D-aspartate (NMDA) receptors, which has been shown to manifest as anxiety-like behaviors that persist across generations (111) (Figure 11).

Figure 11
Flowchart illustrating how artificial sweeteners like aspartame and sucralose can lead to neurotransmitter imbalance. It shows pathways involving decreased GABA and NMDA receptors, excitatory signaling, gut dysbiosis, and peripheral inflammation. This results in impaired serotonin and dopamine synthesis, ultimately causing anxiety-like behaviors.

Figure 11. UPFs lead to neurotransmitter imbalance, manifesting as anxiety-like behaviors. Artificial sweeteners and peripheral inflammation play key roles. GABA, gamma-aminobutyric acid; NMDA, N-methyl-D-aspartate; TPH1, tryptophan hydroxylase 1; COMT, catechol-O-methyltransferase; KYN, kynurenine; DA, dopamine; 5-HT, serotonin.

Taken together, these findings highlight how lipid dysregulation may be a unifying mechanism linking high UPF intake to pathophysiological changes underpinning anxiety. Disruption of lipid metabolism not only alters membrane integrity and lipid homeostasis but also promotes inflammation, compromises the BBB, and perturbs neurotransmitter systems (Table 1).

3.4 UPF and eating disorders

3.4.1 Background on known mechanisms of eating disorders

UPF have been implicated in the development and maintenance of eating disorders (ED), including binge-eating disorder (BED) (118). UPF constitute 60%−80% of the dietary makeup of the diet in ED diets, as well as almost 100% of the food that is consumed in BED (118, 119). UPF consists of a small range of ingredients that enable restrictive eating habits (120). This allows the perseverance of unhealthy habits and adherence to uniformly processed foods (120). The presence of a food addiction (FA) as a comorbid problem in the setting of an ED is generally more severe than when it occurs in the absence of a comorbid ED (121). This may in part be due to a so-called “addiction transfer,” which may occur as a potential coping mechanism in mental illness (91). In fact, FA and BED may, in some cases, co-occur (121, 122). BED, bulimia nervosa (BN) and anorexia binge-purge type have also been found to be associated with a condition known as Loss of Control Binge Eating (LCBE), which has a complex etiology (47).

3.4.2 Effect of UPF on the increased risk of eating disorders

In a previous study, it was found that there was a significant correlation between UPFs and the development of ED (91, 123, 124) in a dose-dependent manner (123) (Table 2). That is, UPF were associated with increased severity of eating disorder (121). Other studies have shown that the consumption of UPF was associated with the development of BN and BED (122, 124) but not in the case of anorexia nervosa (AN) (122). Furthermore, the consumption of UPF was found to be a precursor to ED among children (88).

Table 2
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Table 2. Summary of studies implicating increased UPF consumption and dysregulated lipid metabolism associated with autism, ADHD, eating disorders and food addiction.

UPF were highly represented in the diets of patients with ED, and bulimic eating and binge-eating episodes were significantly associated with UPF consumption (119, 122). Binge episodes nearly exclusively contained UPF (47). Further studies have demonstrated that UPF overconsumption is associated with increased eating rates and higher energy intake (125, 126). The consumption of salty ultra-processed foods, including fried snacks, pizza, and fast foods, was associated with higher levels of loss of control eating and overeating (125). Similarly, sweet ultra-processed foods and sugar-sweetened beverages were also linked to loss of control eating and overeating. Sex-specific associations were observed, with salty food intake in girls and sweet food intake in both boys and girls associated with maladaptive compensatory behaviors, including laxative misuse and self-induced vomiting (125, 127).

3.4.3 Mechanisms linking UPF consumption and eating disorders

A range of mechanisms has been proposed to explain the strong association between UPF intake and ED, involving various neurobiological, metabolic, and behavioral pathways (Table 2).

3.4.3.1 Neurobiological pathways

The consumption of UPF may, in some cases, lead to the rewiring of the brain's reward systems, and these synaptic changes may replicate the types of changes that lead to reward and emotional dysfunction in substance use disorders (SUD) (128, 129). LCBE leads to the hyperexcitability of reward systems, increased stress reactivity, and cognitive impairment (47). UPF may also contribute to increased food intake as a result of increased stress activity (47). These changes may lead to a range of changes across dopamine and serotonin neurotransmitter systems across a wide range of brain networks (88, 124, 125, 129). UPF, which consists of carbohydrates and fats, have several important functions which lead to several synergistic actions which alter the brain's reward system (121). The dysregulation across the different neurotransmitter systems may contribute to the formation of habitual and inflexible unhealthy food choices, which have been implicated in the development of BED (24). Among individuals with BED, dysregulation across these neurotransmitter systems may result in a range of changes involving the orbitofrontal cortex and inferior frontal gyrus (124). Changes in the brain's reward pathways may also lead to new UPF cue-induced cravings (88) which can, in some cases, be triggered by a visual trigger (129).

3.4.3.2 Additives and palatability

Additives in UPF, such as added salt, sugar and flavoring, increase the palatability of foods (121, 122, 124, 125, 127, 130). The additives may activate dopamine, which contributes to cravings and binge episodes (131). One of the purported mechanisms responsible for these changes is that the additives in UPF may interact with the striatum and hippocampus, which are known to assess the local calorie content, food taste and flavor (119, 120). The additives may disrupt the brain's ability to perceive the caloric content, taste, and flavor of food, leading to overconsumption (88, 122). Moreover, disruption in the striatal circuits has been found to promote the formation of habits and compulsive eating patterns (24). The additives contained in UPF may also cause neuroinflammation (124).

3.4.3.3 Metabolic and hormonal pathways

UPF has also been linked to changes in insulin sensitivity and satiety, which may also occur in the case of ED (118, 123, 129). Changes in the food matrix may lead to improved absorption and release of dopamine (121). However, hormonal involvement has also been identified as contributing to changes across different neurotransmitter systems (118, 119). Furthermore, increased levels of ghrelin, fasting glucose, and insulin, as well as a reduction in the peptide tyrosine (appetite-suppressing), were found among individuals who had a diet high in UPF (118, 119). In addition, reduced levels of leptin were associated with higher rates of hedonic eating (88). However, Ulug et al. (126) investigated individuals who were on a diet with a high dietary content of UPF. They found no significant changes in the levels of leptin or ghrelin (126).

Some studies have shown that AN may be associated with reduced insulin sensitivity (119). Furthermore, there have been some studies which have shown that BN and BED were associated with increased levels of insulin resistance (119). In fact, artificial sweeteners were associated with higher levels of glucose intolerance (118). Insulin resistance and hyperinsulinemia have been found to promote hunger, contribute to cravings and lead to overeating (118, 129). Some adverse metabolic effects of UPF include changes in lipid composition, resulting in heightened levels of inflammation, endothelial dysfunction, and increased fat, which may, in turn, contribute to increased insulin levels (24).

Other mechanisms that have been implicated as being important to the development of adverse metabolic effects include the maternal consumption of UPF, which may lead to changes in the fetal BBB, among individuals with ED (120). Furthermore, UPF contain reduced levels of fiber content, which may facilitate increased rates of eating as well as an increased consumption of UPF, which in some cases leads to a significant reduction in the levels of satiety (122).

Hence, these recent studies have demonstrated that UPFs have an important and multifaceted role in the onset and maintenance of ED, particularly in terms of the development of BED and BN. Their impact extends beyond calorie load to involve neurobiological alterations, metabolic dysregulation, and behavioral reinforcement of maladaptive eating. Early exposure to UPFs may further predispose individuals to ED, highlighting the importance of dietary interventions in prevention. These findings emphasize that reducing UPF intake could serve as a modifiable risk factor for ED, complementing psychological and medical approaches to prevention and treatment. Moreover, a range of studies provides increasing evidence for integrating the assessment of specific dietary patterns and their frequency into clinical risk assessments, particularly among at-risk populations, to enable earlier and more targeted therapeutic interventions.

3.5 UPF and food addiction

3.5.1 Background on known mechanisms of food addiction

Based on previous studies, it has been found that UPF addiction is present among approximately 14% of adults and 15% of youths globally, although UPF addiction has been found to have a higher prevalence among obese individuals (132). FA and ED, including BED, are typically considered as being separate diseases, but may, in some cases, co-occur (121, 128). Among individuals with ED and concurrent obesity, individuals may have higher rates of severe disease as well as more severe long-term outcomes (121). A range of proposed hypotheses suggests that food addiction and substance addiction may involve similar brain networks (47, 121, 128, 133, 134). Furthermore, UPF may be a form of “addiction transfer” and, in some cases, be a type of coping mechanism in view of its underlying association with post-traumatic stress disorder (PTSD) (91).

3.5.2 Effect of UPF on the increased risk of food addiction

The consumption of UPF was significantly associated with FA (91, 123, 135), and a dose-dependent increase in FA risk (123) (Table 2). The proportion of UPF in the diet was also associated with an increased risk of FA (134). Furthermore, higher levels of UPF were associated with an increased eating rate and energy intake (126). Some of the identified food components associated with an increased risk of FA included salt, fat, sugar, and caffeine (136).

3.5.3 Mechanisms linking UPF with food addiction

Some of the key mechanisms in which UPF may contribute to FA include its effects on the reward system (47, 121, 128, 129) (Table 2). There have been previous neuroimaging-based studies that have shown similar reward patterns and loss of control deficits among individuals with FA and substance use (132, 137). Consumption of UPF resulted in heightened levels of reward activity in the caudate and anterior cingulate regions (137). This may be mediated by the high palatability of UPF (137).

The carbohydrates associated with UPF may be addictive due to their stimulation of dopamine (121, 137). The refined carbohydrates result in a spike in blood glucose levels and a delay in dips in glucose levels. They also cause the release of insulin and may result in a hypoglycemic state, which can lead to a greater activation of the reward centers, leading to an increased urge for overconsumption of UPF (137).

UPF are high in saturated and trans fats, which stimulate a greater reward than foods containing natural fats (137). Furthermore, UPF enhance the palatability and inhibits the effects of high sugar intake as a consequence of reducing the glycaemic index and withdrawal response from UPF (121, 137). The high fat content results in elevated levels of dopamine across the reward circuits of the brain (138). The combination of carbohydrates and fats in UPF leads to a “supra-addictive reward response” (121, 137). Fats can affect and, in some cases, modify the reward pathway by increasing the levels of the inflammatory cytokines IL-6 and TNF-alpha (34). Saturated fats may also activate TLR4s, leading to the stimulation of the inflammatory pathways and cytokine release (34). These cytokines may cross the BBB, thereby inducing neuroinflammation across the reward pathway and altering synaptic plasticity, dendritic spine formation and myelination (34). The high-fat diets may, in turn, influence the integrity of the BBB, leading to elevated levels of serum albumin, which is an observation based on studies involving the analysis of an individual's cerebrospinal fluid (CSF) (34). A high-fat diet may, in turn, lead to the direct stimulation of inflammation or indirectly affect inflammation through the impact of a high-fat diet on the white adipose tissue (34).

The hypothesis that UPF may, in some cases, contribute to FA is supported by other findings that have demonstrated tolerance and withdrawal symptoms associated with the consumption of UPF. UPF addiction has been found to lead to neural responses that are similar to those that are seen in substance addiction, and these responses include a greater anticipatory reward, reduced consummatory reward, and enhanced functional connectivity in reward processing neurons (132). Continuous UPF consumption may also lead to a blunting of emotional states (47). The prolonged consumption of sugars such as fructose (136) and sucrose (47, 138) may also lead to a reduction in the dopamine response as a consequence of the downregulation of D2 receptors, thereby leading to tolerance, which is a finding that has been found across both human and animal studies (99, 135, 137). Furthermore, chronically high lipid levels consumed in the context of high-fat diets may reduce dopamine levels in the nucleus accumbens (138). Altered dopamine and opioid receptor signaling, which are like those seen in substance use and addictive disorders, have been observed in high-fat diet animal studies (34, 132). In animal-based studies, some withdrawal studies have included tremors, cravings, and increased stress that arise as a consequence of increased CRF levels (99). Reduced levels of dopamine in the nucleus accumbens were also reported (99), alongside altered dopamine signaling (88). Thus, UPF may lead to an alteration of dopamine and serotonin signaling similar to what is seen across SUD (129, 131).

Altered levels of satiety may contribute to UPF, leading to FA and overconsumption of UPF (123). In particular, corn syrup, thickeners, and glazing agents intensify flavors and palatability (121, 130, 131, 134). High fructose corn syrup may contribute to changes in gut dysbiosis, glucose tolerance, lipid neogenesis and oxidative stress (133). These changes may lead to a satiety hormone imbalance and signal impairments in the hypothalamus, which are involved in satiety (133). Nevertheless, there has been some dispute about the role of UPF signaling on leptin and ghrelin. Notably, Ulug (126) found that leptin and ghrelin levels were not significantly altered, but Lustig (136) has found that fructose inhibits leptin and stimulates ghrelin, resulting in increased hunger and overconsumption. Additionally, Wiss and LaFata (88) found that UPF was associated with reduced leptin levels, hedonic eating, and decreased satiety. Food cravings have also been found to be stimulated by UPF consumption (88, 126, 131). Reduced glucose and insulin levels from UPF have been linked to overeating in animal studies, although further studies in humans are needed (129).

Altered food matrices may, in some cases, lead to UPF being easier and faster to consume, with greater levels of bioavailability (121). That is, UPF is absorbed more quickly and able to alter levels of dopamine signaling, thereby leading to its addictive actions (121). The rapid consumption and transit of UPFs have been shown to impair dopamine-mediated satiety signaling, delaying the brain's recognition of fullness. This can result in gastric distension and contribute to the overconsumption of UPFs (126).

3.6 UPF and attention deficit hyperactivity disorder

3.6.1 Background on known mechanisms of ADHD

The exact etiology and pathophysiology of ADHD remain incompletely understood. However, current evidence suggests that the disorder arises from a complex interplay of genetic predisposition, altered monoamine neurotransmission, and structural brain abnormalities, which together increase susceptibility. Genome-wide association studies (GWAS) have identified numerous genes associated with ADHD, including those involved in neurodevelopment (e.g., FOXP2), synaptic function (e.g., GRM7, SORCS3), and neuronal signaling pathways, particularly those regulating dopamine and serotonin receptor activity (139141). Collectively, this polygenic component may account for approximately one-third of the heritability of ADHD (142). In addition to genetic vulnerability, dysfunction of dopamine signaling within the prefrontal cortex has been highlighted as a central mechanism underlying weak executive control and attentional deficits (143). Evidence suggests that an imbalance between tonic and phasic dopamine signaling, characterized by reduced baseline activity but exaggerated transient reward responses, leads to abnormally strong reward reinforcement effects and manifests clinically as impulsivity and distractibility (144). Structural brain differences further reinforce this picture. Neuroimaging studies show reduced volume in the frontal lobes, striatum, and interconnecting white matter in individuals with ADHD, which may be attributed to delayed cortical maturation (145, 146). Such alterations disrupt the integrity of fronto-striatal circuits, critical for behavioral regulation, thereby contributing to the hallmark symptoms of hyperactivity, impulsivity, and inattention (145, 146). Further research is needed to explore whether UPFs may interact with these pathophysiological pathways.

3.6.2 Effect of UPF on the increased risk of ADHD

Current evidence based on a limited number of studies indicates that diets high in UPFs are consistently associated with increased ADHD symptoms and risk (Table 2). Early exposure, whether maternal consumption during pregnancy or intake during early childhood, appears particularly influential, correlating with higher scores of hyperactivity, inattention, and overall ADHD symptomatology (147149). Significant food groups include sweetened beverages and sweets (149, 150). In contrast, children with higher overall diet quality, characterized by reduced UPF intake and greater consumption of minimally processed foods, show lower symptom burden and reduced likelihood of an ADHD diagnosis (147). UPF intake and ADHD risk also appear to follow a dose-dependent relationship, with greater frequency of consumption corresponding to higher symptom severity and increased diagnostic risk (150). While some associations are weak, with only a minor increase in risk, the overall trend suggests that repeated exposure to UPFs during fetal and childhood development may contribute to the development or exacerbation of ADHD (147, 149). Further studies are needed to consolidate these findings and explore effects in other populations, such as adults.

3.6.3 Mechanisms of association between UPF intake and ADHD

Increasing evidence suggests that UPF consumption may contribute to ADHD risk and symptom expression through multiple biological pathways, including lipid metabolism, epigenetic programming, exposure to neurotoxins, and dopaminergic dysfunction (Table 2). One well-established factor in ADHD pathophysiology is the role of omega-3 fatty acids, which are crucial for maintaining neuronal membrane integrity and regulating neurotransmission. Individuals with ADHD consistently exhibit lower circulating levels of DHA, EPA, total n-3 PUFAs, and AA (148). Conversely, omega-3 supplementation has been shown to alleviate ADHD symptoms, suggesting a potential neuroprotective role in both the onset and progression of the disorder (148). Diets high in UPFs, typically deficient in omega-3 fatty acids, may further deprive the developing brain of these critical substrates. While evidence highlights omega-3 pathways as central to ADHD, further research is needed to clarify the contributions of other lipid pathways to disease mechanisms.

UPF intake during critical pre- or perinatal periods may also drive risk via epigenetic modifications. For example, high-fat/high-sugar maternal diets have been shown to alter methylation of the insulin-like growth factor gene, which plays an essential role in fetal growth and neurodevelopment, potentially predisposing to ADHD in childhood (147). Epigenome-wide association study (EWAS) in children similarly demonstrates that high-UPF diets are linked to DNA methylation changes in several genes relevant to neurodevelopmental disorders and behavioral regulation (36). Furthermore, contaminants within UPFs may contribute to pathogenesis. BPA, a plastic contaminant known to cross the placental barrier, has been implicated in behavioral disturbances, including hyperactivity (24). Similarly, heavy metal contaminants are also known to contribute to the development of ADHD. High intake of UPFs may cause dysfunction of the metallothionein genes and suppression of the paraoxonase-1 gene (PON1) (151). These neuroprotective genes are important for detoxifying heavy metals (151). UPFs are also characteristically high in refined sugars, which causes an immediate increase in glucose and adrenaline. This combination provides a short-term burst of energy, which can manifest as symptoms including excitement, impulsiveness and poor concentration consistent with ADHD (150). The longer-term effects of sugar can mimic dysfunction in the reward system. Chronic excessive sugar intake can lead to changes in mesolimbic dopamine signaling, via overstimulation of dopamine receptors and subsequent downregulation. This downregulation may result in reduced dopaminergic responses, which may in turn contribute to the development of addictive behaviors and are closely linked to the impaired cortical inhibition observed in ADHD (148).

3.7 UPF and autism spectrum disorder

3.7.1 Background on known mechanisms of ASD

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent deficits in social communication as well as the presence of restricted, repetitive patterns of behavior and interests (152). Notably, ASD is the most common heritable neurodevelopmental disorder, with heritability estimates of approximately 80% (153). Inherited forms of ASD are often attributed to genetic mutations, in contrast to sporadic cases that may frequently arise as a consequence of microdeletions or duplications of chromosomal regions (154). These structural variations may overlap with the types of complex chromosomal changes observed across neurodevelopmental disorders, such as Angelman and Prader-Willi syndromes, as well as single-gene disorders, including neurofibromatosis (NF1 and NF2) and tuberous sclerosis (TSC1 and TSC2) (154). A broad range of genes have been implicated in the pathophysiology of ASD, including gene mutations that have been implicated in chromatin remodeling (e.g., CHD7), synaptic organization and function (e.g., SHANK family, neurexin), intracellular signaling pathways (e.g., G protein-coupled receptors, extracellular signal-regulated kinase (ERK) signaling), as well as several important genes that have been implicated across a range of different neurodevelopmental processes (154).

A number of previous studies that have specifically investigated the types of neurodevelopmental regression that have been seen in ASD have shown that these developmental changes may be associated with mitochondrial dysfunction, associated with impairment of oxidative phosphorylation and in some cases, this may lead to a disruption of the electron transport chain in the brain (155). These changes contribute to reduced energy production across several key brain networks, as well as increased levels of oxidative stress, which may, in turn, lead to significant disruptions across several key neurodevelopmental processes (156). In terms of other pathophysiological processes leading to changes in neural connectivity, several studies have demonstrated that alterations in the excitation-inhibition balance can result in a range of changes across various brain networks. In particular, abnormalities in glutamatergic and GABAergic neurotransmission across key brain regions such as the hippocampus, amygdala, and cerebellum may underlie several characteristic clinical features of ASD, including impairments in learning and memory, atypical visual perception (e.g., hypersensitivity to bright light), repetitive behaviors, and avoidance across social settings (157, 158).

Recent studies have implicated neuroinflammatory changes as being crucial in the development of ASD, with evidence of significant microglial activation and increased production of inflammatory cytokines and chemokines, including Interferon-γ (IFN-γ), IL-1β, IL-6, and TNF-α (159, 160). Furthermore, elevated levels of inflammatory cytokines have also been found in plasma, suggesting that peripheral inflammation may contribute to the development of ASD. These inflammatory markers have been linked to impairments in social interaction and communication, as well as aberrant behaviors seen in ASD (159). Although genetic factors have been implicated as contributing to a substantial proportion of ASD risk, a range of other environmental exposures have also recently been identified as potentially contributing to a heightened risk of developing ASD across the lifespan. Some of the important identified risk factors for ASD include advanced parental age, in utero exposure to drugs, toxins, alcohol, and tobacco smoke, as well as maternal disease and infection during pregnancy (161).

3.7.2 Effect of UPF intake on increased risk of ASD

There continues to be a limited number of research studies that have specifically investigated the potential relationship between the UPF consumption and the subsequent risk of developing ASD, and further studies are needed (Table 2). There have been some studies that have identified a potential association between UPF consumption as well as the subsequent risk of developing ASD. For instance, high levels of UPF consumption have been found to be associated with an increased odds of developing ASD in adolescents, while experimental studies indicate that acrylamide, which is a compound frequently present in UPFs, may, in some cases, induce autism-like behaviors as a result of a number of important pathophysiological mechanisms that result in neuroinflammatory changes as well as oxidative stress (151, 160). These recent experimental findings are biologically plausible, particularly as both oxidative stress and neuroinflammation are increasingly recognized as important contributors to the pathophysiology of ASD (151, 156, 160).

However, not all studies have identified a particular association between the intake of UPF and the subsequent risk of developing ASD. In particular, Vecchione et al. (162) found that there was no significant link between consuming a UPF-rich Western dietary pattern and subsequently receiving an ASD diagnosis, suggesting that confounding factors such as genetic predisposition, overall dietary context, or methodological differences may explain inconsistencies across studies.

Overall, the findings from the current literature are conflicting and inconclusive, although they raise some important questions about whether certain diet-based factors, including diets with a high UPF exposure, may contribute to and influence neurodevelopmental outcomes across both healthy development and in the case of ASD. The possible mechanistic role of acrylamide and other UPF-derived compounds highlights the need for longitudinal human studies, as well as other animal-based studies, to elucidate the pathophysiological mechanisms involved and clarify whether the identified associations are causal. This avenue of translational research also has the potential to further clarify UPF's contribution to ASD risk, which could have significant public health implications, thereby informing dietary recommendations for children, adolescents, and pregnant women during critical neurodevelopmental windows.

3.7.3 Mechanisms of association between UPF intake and ASD

There have been a range of recent studies that have shown that high dietary intake of UPF may contribute to an increased risk of developing ASD, which may be mediated through a range of different pathophysiological mechanisms, including lipid dysregulation, oxidative stress and neuroinflammation (Table 2). In a study that integrated findings from a lipidomic analysis, Hylén et al. (23) demonstrated that elevated inflammatory mediators were associated with reduced levels of endogenous antioxidants and, in particular, ether phospholipids. Based on these findings, it has been suggested that systemic low-grade inflammation may lead to significant oxidative stress through lipid dysregulation, which has been implicated in the pathophysiology of ASD (23). This was supported by findings from Ye et al. (160), in which administering an antioxidant (alpha-lipoic acid) was found to attenuate autism-like behavior in mice.

Furthermore, Ye et al. (160) investigated the effects of acrylamide (common in UPFs such as fried food) and demonstrated that high levels of damage to the native gut microbiome may result in a reduction in the production of SCFAs, while increasing levels of toxic LPS. In fact, previous studies have shown that serum LPS has various effects in the CNS, including the overactivation of microglia and an increased production of proinflammatory cytokines (116). These actions, resulting in neuroinflammation, are thought to lead to impaired synaptic plasticity as well as a range of other cognitive changes across key brain networks, which may contribute to the development of ASD. This may, in turn, manifest with a range of different traits, including learning and memory impairments (160). Further translational research is needed to investigate additional pathophysiological mechanisms and to build on the current findings, particularly in relation to the varied roles that genetic factors, early developmental factors, and environmental factors (e.g., exposure to toxins) may contribute to lipid dysregulation, as well as how these changes contribute to the development of ASD.

4 Discussion

This scoping review examined the association between the consumption of UPF and mental health–related disorders, with a specific focus on neurobiological mechanisms involving lipid metabolism. While previous reviews have linked UPF intake to adverse neuropsychiatric outcomes, these have largely relied on epidemiological associations or broad mechanistic frameworks. In contrast, this review uniquely synthesizes evidence directly interrogating lipid dysregulation as a central pathway linking UPF exposure to mental health outcomes. In comparison to prior narrative syntheses that propose putative mechanisms without systematically evaluating lipid pathways, the present review advances the field by integrating findings from experimental, preclinical, and translational studies to map how UPF consumption alters lipid synthesis, transport, and signaling within the central nervous system. These lipid disturbances are critical for neuronal membrane integrity, synaptic function, and neuroimmune regulation, providing a biologically plausible link between UPF intake and psychiatric vulnerability. Overall, the findings support a growing body of evidence associating high UPF consumption with multiple adverse mental health outcomes alongside consistent disruptions in lipid metabolic pathways. However, the specific causal mechanisms and their relevance across different psychiatric phenotypes remain incompletely defined. Among the proposed mechanisms, dysregulation of lipid metabolism appears to be a leading factor.

In terms of the relationship between the dietary intake of UPF and psychiatric disorders, the most robust and consistent evidence was observed for depression, with several key studies demonstrating a dose-dependent relationship between intake of UPF and subsequent development of depressive symptoms (46, 5962). Higher levels of consumption of UPF during pregnancy were associated with an increased risk of depressive symptoms among offspring. In terms of specific food groups, it was found that the key food groups responsible for these changes included SSBs, fast foods, and fried foods (61, 6570, 85). In the case of anxiety disorders, the evidence for an association between UFP and anxiety disorders was less consistent; only a few studies specifically investigated anxiety as an isolated outcome. Nevertheless, several studies reported an increased risk of developing an anxiety disorder when assessed in the absence of a comorbid mental health disorder, or in combination with a depressive disorder, or in the setting of another comorbid mental health disorder (67, 79, 94, 98, 114, 115).

There was also a strong association between the consumption of UPF and ED. ED, such as BN and BED, as well as FA, were associated with an increased eating rate, increased energy intake, as well as addictive reward responses, and these physiological changes were similar to the types of changes that were seen in the case of SUD (91, 122126). There is emerging evidence to suggest that a high UPF intake may be associated with ADHD, which may adversely affect fetal and early childhood development, with sweetened beverages and sweets identified as key contributors (147150). There have been other cross-sectional studies that have highlighted a potential association between the consumption of UPF and the risk of developing ASD. The association between UPF and the development of ASD may arise as a consequence of dietary contaminants in UPF, such as acrylamide, leading to changes in lipid regulation and metabolism (23, 151, 160).

4.1 Summary of key pathophysiological mechanisms

UPFs may lead to several pathophysiological changes across several important cellular and biochemical pathways, which have been implicated in the development of psychiatric disorders. In some cases, UPFs may adversely affect lipid profiles and compromise membrane integrity, thereby impacting BBB function. Reduced levels of SCFAs may lead to increased levels of BBB permeability, whilst TFAs have been found to increase phospholipid membrane rigidity, leading to altered neuronal signaling (24, 89). Oxidative stress resulting from the consumption of refined grains further reduces the fluidity of the lipid bilayer, thereby impairing neurotransmission (52). AGEs, such as acrylamide, may also contribute to elevated levels of endothelial dysfunction, oxidative stress, and inflammation (8, 97). Additionally, high omega-6 and low omega-3 PUFA intake has been found to promote systemic and neuroinflammation (117). Increased intake of SFAs can activate TLRs in the brain, which mimic bacterial LPS, triggering the activation of microglia and astrocytes, leading to the release of pro-inflammatory cytokines (35). In fact, UPFs may also disrupt various components of the gut-brain axis by increasing intestinal permeability, resulting in elevated circulating LPS, which contributes to heightened levels of peripheral and central inflammation (116). Some of the common pro-inflammatory markers implicated across mental health disorders include IL-1, IL-6, IL-17, IL-1β, and TNF-α (32, 93, 159).

In addition, UPF consumption has been implicated in metabolic disturbances such as hyperglycaemia and insulin resistance, which is a particular issue in the case of BED and BN. Furthermore, UPF commonly have higher glycaemic indexes, resulting in rapid spikes in blood glucose levels (84). These glycaemic fluctuations can influence gut microbiota composition by selectively promoting the growth of glucose-utilizing bacteria while suppressing beneficial fiber-fermenting species. This shift in microbial balance may alter the production of short-chain fatty acids and other metabolites, impacting intestinal barrier function, systemic inflammation, and host metabolic regulation.

These pathological changes drive hunger and cravings, reinforce overeating and contribute to inflammation and endothelial dysfunction (118). At the neurochemical level, UPFs alter serotonin and dopamine signaling, and these changes across these signaling pathways have been implicated in the development of depressive and anxiety disorders (52, 79, 91, 94, 98, 116). Furthermore, a glutamate/GABA imbalance has also been found to be associated with anxiety disorders, which provides further support that anxiety disorders may, in part, be caused by a disruption in the excitatory-inhibitory balance (111).

In summary, UPFs may have the potential to elicit addiction-like responses through overstimulation of dopaminergic reward pathways, which may lead to a downregulation of D2 receptors. These subsequent changes may lead to the development of abnormal tolerance patterns, which may be important in the development of SUD. These changes are evident in BN, BED, FA, and ADHD (88, 99, 124, 125, 129, 148). The synergistic combination of fats and refined carbohydrates and the destruction of the food matrix may, in some cases, accelerate the absorption of nutrients and amplify dopaminergic signaling (121).

4.2 Clinical implications

These recent experimental findings underscore the importance of incorporating diet-based factors, including dietary habits, into clinical practice to reduce the prevalence and adverse effects of UPF consumption. Hence, there is a recommendation for patient education regarding the types of harm posed by UPF consumption, with an emphasis on high-risk food subgroups, including fried foods, sugar-sweetened or artificially sweetened beverages, and processed meats (65, 67, 71, 74, 104, 125, 149, 150, 163). At a pragmatic level, clinicians should encourage practical substitutions, such as replacing sweetened juices with natural alternatives. Moreover, by considering contaminants from packaging, such as BPA, and additives including artificial colourings, like titanium dioxide, it is possible that appropriate measures can reduce the risk posed by these contaminants contained in UPF.

Educational programs should extend beyond the avoidance of harmful foods to include a comprehensive education program that promotes the health benefits of a balanced and healthy dietary intake. Diets which are rich in whole foods, such as the Mediterranean diet, and those with higher omega-3 fatty acid intake have recently been demonstrated as having a protective effect against mental health disorders, although further epidemiological studies are needed (8, 109, 148). Clinicians should continue to emphasize adherence to national dietary guidelines, which typically recommend whole-food-based eating patterns (164, 165). Raising awareness about the detrimental effects of UPFs may facilitate a shift toward preventive approaches in mental healthcare delivery.

Targeted education related to UPF consumption is important for vulnerable populations. Individuals with existing mental disorders should be informed about UPF as a risk factor for mental health conditions. Maternal nutrition during pregnancy is vital due to its influence on neurodevelopmental outcomes. Further, early-life education for children and adolescents is also important in view of the increased vulnerability of children and adolescents to mental health conditions. University students and young adults are another priority group because poor dietary habits are more common among individuals exposed to stressful environments (166).

Based on these findings, we propose that there is a need to integrate nutrition-based interventions with other non-pharmacological therapies as part of evidence-based care across the spectrum of mental health disorders. For mental health conditions that have a relatively strong association with UPF consumption, including FA and ED (namely BN and BED), multidisciplinary-based interventions involving psychiatrists and dietitians, as well as other health professionals, may lead to enhanced patient outcomes. In view of the socioeconomic and lifestyle factors associated with UPF consumption, greater consideration should be given to multidisciplinary-based interventions when managing patients across a broad range of clinical settings, particularly as limited access to whole foods and constraints on cooking ability may be significant barriers to patients implementing appropriate dietary changes.

4.3 Implications for policy making and public health

The current review emphasizes the impact of lifestyle choices on overall health outcomes. Not only has UPF been implicated as adversely affecting cardiovascular and metabolic health, but there is increasing recognition about UPF's impact across several domains of mental health (102). Public health sector policy can help the population make informed choices regarding their diet. These policies should be developed in collaboration with a range of health professionals, such as nutritionists and neuroscientists, to develop multidisciplinary health-based interventions. Such policies may involve a range of incentives to reduce UPF consumption, such as a tax on UPF or marketing restrictions. Policies aimed at implementing reforms in the marketing of UPF could include regulations related to the types of advertising and processing included in packaging. Such policies have been implemented in several countries, including Brazil, Mexico, Chile, and South Africa (167). This measure could include several different actions, such as revisions to the NOVA classification scheme or the use of current health star ratings.

In addition, there are several drivers of UPF consumption, which include a range of demographic-related factors, including obesity (168). In fact, previous studies have found that low socio-economic status and areas with low food security are at higher risk of UPF consumption (5, 169, 170). Thus, policies should ensure the affordability and accessibility of whole and minimally processed foods, such as through community-based food production.

Therefore, education about the potential adverse impacts of UFP and the beneficial effects of minimally processed foods, as well as steps to improve diets, may have several beneficial long-term effects across a diverse range of communities. This could be achieved through school programs and community initiatives, utilizing targeted advertising. These would include how to interpret processed foods rating systems and the dietary guidelines for minimally processed foods.

4.4 Future directions

Future research studies should prioritize longitudinal cohort designs to examine the directionality of associations between UPF consumption and mental health outcomes. Randomized controlled trials that substitute UPFs with minimally processed or whole-food dietary patterns, such as the Mediterranean diet, could be useful in providing stronger therapeutic evidence by assessing the extent to which dietary modification may reduce the severity and/or mitigate the types of psychiatric presentations. Therefore, examining the purported dose-response relationships between UPF consumption and mental health conditions will be important as part of new studies to investigate the potentially “safe” thresholds of UPF consumption that minimize risk.

To reduce the heterogeneity of further findings, future studies should adopt a standardized food classification framework, including the NOVA system, to ensure consistency in defining and quantifying UPF (2). Moreover, further work aimed at incorporating psychiatric diagnoses rather than self-reported questionnaires can strengthen the validity and applicability of mental health outcome measures. Furthermore, the impact of mental health disorders should be assessed individually as opposed to using the non-specific outcome of “common mental disorders.” At the same time, there is a need for new biochemical studies to investigate how UPFs affect key pathophysiological pathways involved in the development of mental disorders, particularly how lipid dysregulation and changes in specific lipid species contribute to heightened levels of neuroinflammation and altered neurotransmission. Integrative translational approaches, which combine a range of investigative methodologies including neuroimaging, metabolic profiling, and inflammatory markers, could lead to new avenues of study and offer new insights into the detrimental effects of UPFs, as well as the efficacy of potential therapeutic interventions. Finally, further studies aimed at developing evidence-based, individualized interventions, including omega-3 fatty acid supplementation, have the potential to inform the development of new targeted interventions that mitigate the detrimental impact of high-UPF diets.

4.5 Limitations

As part of this review, important correlations were identified between the UFP and a range of mental health conditions. However, no longitudinal studies were used. Most studies were cross-sectional, which reduced the ability to make causal inferences as well as generalize the findings to the broader community. More prospective cohort studies could strengthen the evidence. Assessment of the outcomes following the intervention, consumption of UPFs, has yielded inconsistent findings. The cross-sectional studies incorporate a range of self-reporting measures of dietary intake, including food-frequency questionnaires or interviews, which can be inaccurate and prone to recall bias (171). Furthermore, the amounts of food and their categorization were not standardized across the several studies included in the review. The classification of “processed foods” varied, and not all studies utilized the NOVA classification; some studies grouped foods into customized dietary patterns. In addition, outcome measurements were also variable, as some studies measured symptoms of a mental illness, whilst others required a diagnosis. The diagnostic criteria also differed across the different studies.

Based on the findings, there is a need for further work aimed at unraveling the mechanisms through which UFP may contribute to the development of mental health conditions. Further RCT-based studies are not currently feasible; therefore, most studies have been conducted using animals. However, findings from these studies may be challenging to generalize to clinical populations. Conducting longitudinal studies is also challenging, as they could be helpful in assessing temporal relationships. Further work aimed at developing longitudinal-based studies to assess whether there may be a bidirectional relationship between UPF and mental health disorders offers a new avenue for further translational research studies. At the same time, the clinical populations included in the studies typically consisted of a younger cohort, and hence, it may not be easy to generalize the findings to an older clinical cohort. Furthermore, there is currently a lack of clear studies that have been conducted across the neurodevelopmental period, from in utero to the young child, where the brain is susceptible to environmental impacts, and potentially to the impact of UPF. Therefore, further studies are necessary to address this significant gap in the clinical research literature.

5 Conclusion

The global rise in mental health disorders has occurred alongside increasing consumption of ultra-processed foods, highlighting the need to better understand dietary determinants of brain health. This scoping review synthesized evidence published between 2020 and 2025 examining associations between ultra-processed food intake, lipid metabolic disruption, and mental health outcomes. Across studies, higher consumption of ultra-processed foods was consistently associated with an increased risk of depression, anxiety, attention deficit hyperactivity disorder, eating disorders, and food addiction, with largely dose-dependent relationships observed.

These associations are supported by biologically plausible neurobiological mechanisms. Dysregulation of lipid metabolism emerged as a central pathway, with UPF consumption linked to altered fatty acid profiles, neuroinflammatory signaling, and impaired neurotransmitter systems, particularly those involving serotonin and dopamine. Such lipid-mediated disruptions are especially relevant to mood disorders, eating disorders, and food addiction. Additional contributions from food additives, packaging-related chemicals, and gut–brain axis perturbations are also likely, although mechanistic evidence remains limited.

Importantly, these findings have clear translational and therapeutic implications. Dietary interventions that reduce ultra-processed food intake and prioritize minimally processed nutrient-dense foods may represent a feasible, low-risk strategy to support mental health by restoring lipid balance and reducing neuroinflammatory burden. Targeting lipid metabolic pathways through nutritional modification or adjunctive therapies may complement existing pharmacological and psychosocial treatments, particularly for disorders characterized by metabolic and inflammatory dysregulation. Future research should prioritize longitudinal and mechanistic studies to clarify causality, identify sensitive developmental windows, and determine whether dietary modification can be leveraged as a preventive or adjunct therapeutic approach. Collectively, the evidence supports integrating dietary quality into mental health prevention, clinical management, and public health policy, underscoring the importance of limiting ultra-processed food consumption while promoting whole, nutrient-dense dietary patterns.

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 authors.

Author contributions

EP: Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. CL: Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. DS: Conceptualization, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing. IA: Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) 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.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript. Generative AI (ChatGPT) was used solely to assist with the structure, language refinement and clarity; all scientific content, interpretation, and conclusions remain the responsibility of the author(s).

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2025.1754492/full#supplementary-material

References

1. Elizabeth L, Machado P, Zinöcker M, Baker P, Lawrence M. Ultra-processed foods and health outcomes: a narrative review. Nutrients. (2020) 12:1–36. doi: 10.3390/nu12071955

PubMed Abstract | Crossref Full Text | Google Scholar

2. Monteiro CA, Cannon G, Moubarac J-C, Levy RB, Louzada MLC, Jaime PC. The UN decade of nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr. (2018) 21:5–17. doi: 10.1017/S1368980017000234

PubMed Abstract | Crossref Full Text | Google Scholar

3. Petrus RR, do Amaral Sobral PJ, Tadini CC, Gonçalves CB. The NOVA classification system: a critical perspective in food science. Trends Food Sci Technol. (2021) 116:603–8. doi: 10.1016/j.tifs.2021.08.010

Crossref Full Text | Google Scholar

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

PubMed Abstract | Crossref Full Text | Google Scholar

5. Marchese L, Livingstone KM, Woods JL, Wingrove K, Machado P. Ultra-processed food consumption, socio-demographics and diet quality in Australian adults. Public Health Nutr. (2022) 25:94–104. doi: 10.1017/S1368980021003967

PubMed Abstract | Crossref Full Text | Google Scholar

6. Lane MM, Davis JA, Beattie S, Gómez-Donoso C, Loughman A, O'Neil A, et al. Ultraprocessed food and chronic noncommunicable diseases: a systematic review and meta-analysis of 43 observational studies. Obes Rev. (2021) 22:e13146. doi: 10.1111/obr.13146

PubMed Abstract | Crossref Full Text | Google Scholar

7. Levy RB, Rauber F, Chang K, Louzada MLdC, Monteiro CA, Millett C, et al. Ultra-processed food consumption and type 2 diabetes incidence: a prospective cohort study. Clin Nutr. (2021) 40:3608–14. doi: 10.1016/j.clnu.2020.12.018

Crossref Full Text | Google Scholar

8. D'Cunha NM, Sergi D, Lane MM, Naumovski N, Gamage E, Rajendran A, et al. The effects of dietary advanced glycation end-products on neurocognitive and mental disorders. Nutrients. (2022) 14:2421. doi: 10.3390/nu14122421

PubMed Abstract | Crossref Full Text | Google Scholar

9. Harvey SB, Modini M, Joyce S, Milligan-Saville JS, Tan L, Mykletun A, et al. Can work make you mentally ill? A systematic meta-review of work-related risk factors for common mental health problems. Occup Environ Med. (2017) 74:301. doi: 10.1136/oemed-2016-104015

PubMed Abstract | Crossref Full Text | Google Scholar

10. Uher R. Gene–environment interactions in severe mental illness. Front Psychiatry. (2014) 5:48. doi: 10.3389/fpsyt.2014.00048

PubMed Abstract | Crossref Full Text | Google Scholar

11. Szücs A, van der Lubbe SCC, Arias de la Torre J, Valderas JM, Hay SI, Bisignano C, et al. The epidemiology and burden of ten mental disorders in countries of the association of southeast Asian nations (ASEAN), 1990–2021: findings from the global burden of disease study 2021. Lancet Public Health. (2025) 10:e480–91. doi: 10.1016/S2468-2667(25)00098-2

PubMed Abstract | Crossref Full Text | Google Scholar

12. World Health Organisation. World Mental Health Today Latest Data. Geneva: World Health Organisation (2025).

Google Scholar

13. World Health Organization. Mental Disorders. (2022). Available online at: https://www.who.int/news-room/fact-sheets/detail/mental-disorders (Accessed August 17, 2025).

Google Scholar

14. Australian Institue of Health and Welfare. Burden of Disease Mental Health Summary. (2024). Available online at: https://www.aihw.gov.au/mental-health/snapshots/burden-of-disease (Accessed August 17, 2025).

Google Scholar

15. Australian Institue of Health and Welfare. Expenditure on Mental Health Services. (2025). Available online at: https://www.aihw.gov.au/mental-health/topic-areas/expenditure (Accessed August 17, 2025).

Google Scholar

16. Scott D, Happell B. The high prevalence of poor physical health and unhealthy lifestyle behaviours in individuals with severe mental illness. Issues Ment Health Nurs. (2011) 32:589–97. doi: 10.3109/01612840.2011.569846

PubMed Abstract | Crossref Full Text | Google Scholar

17. Australian Institute of Health and Welfare. Expenditure on Mental Health Services. (2025). Available online at: https://www.aihw.gov.au/mental-health/topic-areas/expenditure (Accessed August 14, 2025).

Google Scholar

18. Bremner JD, Moazzami K, Wittbrodt MT, Nye JA, Lima BB, Gillespie CF, et al. Diet, stress and mental health. Nutrients. (2020) 12:2428. doi: 10.3390/nu12082428

PubMed Abstract | Crossref Full Text | Google Scholar

19. Marx W, Lane M, Hockey M, Aslam H, Berk M, Walder K, et al. Diet and depression: exploring the biological mechanisms of action. Mol Psychiatry. (2021) 26:134–50. doi: 10.1038/s41380-020-00925-x

PubMed Abstract | Crossref Full Text | Google Scholar

20. 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) 14:2568. doi: 10.3390/nu14132568

PubMed Abstract | Crossref Full Text | Google Scholar

21. Achour Y, Lucas G, Iceta S, Boucekine M, Rahmati M, Berk M, et al. Dietary patterns and major depression: results from 15,262 participants (International ALIMENTAL Study). Nutrients. (2025) 17:1583. doi: 10.3390/nu17091583

PubMed Abstract | Crossref Full Text | Google Scholar

22. Mazloomi SN, Talebi S, Mehrabani S, Bagheri R, Ghavami A, Zarpoosh M, et al. The association of ultra-processed food consumption with adult mental health disorders: a systematic review and dose-response meta-analysis of 260,385 participants. Nutr Neurosci. (2023) 26:913–31. doi: 10.1080/1028415X.2022.2110188

PubMed Abstract | Crossref Full Text | Google Scholar

23. Hylén U, McGlinchey A, Orešič M, Bejerot S, Humble MB, Särndahl E, et al. Potential transdiagnostic lipid mediators of inflammatory activity in individuals with serious mental illness. Front Psychiatry. (2021) 12:778325. doi: 10.3389/fpsyt.2021.778325

PubMed Abstract | Crossref Full Text | Google Scholar

24. Contreras-Rodriguez O, Solanas M, Escorihuela RM. Dissecting ultra-processed foods and drinks: do they have a potential to impact the brain? Rev Endocr Metab Dis. (2022) 23:697–717. doi: 10.1007/s11154-022-09711-2

PubMed Abstract | Crossref Full Text | Google Scholar

25. Ozojide KOCVUADAAOOOELJG, Aliu V. Ultra-processed food intake, obesity, and mood disorders: an epidemiological study from the national health and nutrition examination survey (NHANES) 2005 to 2018 data. Cureus. (2025) 17:e87975. doi: 10.7759/cureus.87975

PubMed Abstract | Crossref Full Text | Google Scholar

26. Anwar N, Kuppili PP, Balhara YPS. Depression and physical noncommunicable diseases: the need for an integrated approach. WHO South East Asia J Public Health. (2017) 6:12–7. doi: 10.4103/2224-3151.206158

PubMed Abstract | Crossref Full Text | Google Scholar

27. O'Neil A, Jacka FN, Quirk SE, Cocker F, Taylor CB, Oldenburg B, et al. A shared framework for the common mental disorders and non-communicable disease: key considerations for disease prevention and control. BMC Psychiatry. (2015) 15:15. doi: 10.1186/s12888-015-0394-0

PubMed Abstract | Crossref Full Text | Google Scholar

28. Kwon Y. Effect of trans–fatty acids on lipid metabolism: mechanisms for their adverse health effects. Food Rev Int. (2016) 32:323–39. doi: 10.1080/87559129.2015.1075214

Crossref Full Text | Google Scholar

29. Sacks FM, Lichtenstein AH, Wu JHY, Appel LJ, Creager MA, Kris-Etherton PM, et al. Dietary fats and cardiovascular disease: a presidential advisory from the american heart association. Circulation. (2017) 136:e1–23. doi: 10.1161/CIR.0000000000000510

PubMed Abstract | Crossref Full Text | Google Scholar

30. Contreras-Rodriguez O, Reales-Moreno M, Fernández-Barrès S, Cimpean A, Arnoriaga-Rodríguez M, Puig J, et al. Consumption of ultra-processed foods is associated with depression, mesocorticolimbic volume, and inflammation. J Affect Disord. (2023) 335:340–8. doi: 10.1016/j.jad.2023.05.009

PubMed Abstract | Crossref Full Text | Google Scholar

31. Arshad H, Head J, Jacka FN, Lane MM, Kivimaki M, Akbaraly T. Association between ultra-processed foods and recurrence of depressive symptoms: the Whitehall II cohort study. Nutr Neurosci. (2023) 27:42–54. doi: 10.1080/1028415X.2022.2157927

PubMed Abstract | Crossref Full Text | Google Scholar

32. Melo HM, Santos LE, Ferreira ST. Diet-derived fatty acids, brain inflammation, and mental health. Front Neurosci. (2019) 13:265. doi: 10.3389/fnins.2019.00265

PubMed Abstract | Crossref Full Text | Google Scholar

33. Valdearcos M, Robblee MM, Benjamin DI, Nomura DK, Xu AW, Koliwad SK. Microglia dictate the impact of saturated fat consumption on hypothalamic inflammation and neuronal function. Cell Rep. (2014) 9:2124–38. doi: 10.1016/j.celrep.2014.11.018

PubMed Abstract | Crossref Full Text | Google Scholar

34. Huerta-Canseco C, Caba M, Camacho-Morales A. Obesity-mediated lipoinflammation modulates food reward responses. Neuroscience. (2023) 529:37–53. doi: 10.1016/j.neuroscience.2023.08.019

PubMed Abstract | Crossref Full Text | Google Scholar

35. González Olmo BM, Butler MJ, Barrientos RM. Evolution of the human diet and its impact on gut microbiota, immune responses, and brain health. Nutrients. (2021) 13:196. doi: 10.3390/nu13010196

PubMed Abstract | Crossref Full Text | Google Scholar

36. Lutz M, Arancibia M, Moran-Kneer J, Manterola M. Ultraprocessed foods and neuropsychiatric outcomes: putative mechanisms. Nutrients. (2025) 17:1215. doi: 10.3390/nu17071215

PubMed Abstract | Crossref Full Text | Google Scholar

37. Sankowski R, Mader S, Valdés-Ferrer SI. Systemic inflammation and the brain: novel roles of genetic, molecular, and environmental cues as drivers of neurodegeneration. Front Cell Neurosci. (2015) 9:28. doi: 10.3389/fncel.2015.00028

PubMed Abstract | Crossref Full Text | Google Scholar

38. Claudino PA, Bueno NB, Piloneto S, Halaiko D, Azevedo de Sousa LP, Barroso Jara Maia CH, et al. Consumption of ultra-processed foods and risk for Alzheimer's disease: a systematic review. Front Nutr. (2024) 10:1288749. doi: 10.3389/fnut.2023.1288749

PubMed Abstract | Crossref Full Text | Google Scholar

39. Martínez Leo EE, Peñafiel AM, Hernández Escalante VM, Cabrera Araujo ZM. Ultra-processed diet, systemic oxidative stress, and breach of immunologic tolerance. Nutrition. (2021) 91–92:111419. doi: 10.1016/j.nut.2021.111419

PubMed Abstract | Crossref Full Text | Google Scholar

40. Kong P, Cui Z-Y, Huang X-F, Zhang D-D, Guo R-J, Han M. Inflammation and atherosclerosis: signaling pathways and therapeutic intervention. Signal Transduct Target Ther. (2022) 7:131. doi: 10.1038/s41392-022-00955-7

PubMed Abstract | Crossref Full Text | Google Scholar

41. Liu C, Feng X, Li Q, Wang Y, Li Q, Hua M. Adiponectin, TNF-α and inflammatory cytokines and risk of type 2 diabetes: a systematic review and meta-analysis. Cytokine. (2016) 86:100–9. doi: 10.1016/j.cyto.2016.06.028

PubMed Abstract | Crossref Full Text | Google Scholar

42. Tristan Asensi M, Napoletano A, Sofi F, Dinu M. Low-grade inflammation and ultra-processed foods consumption: a review. Nutrients. (2023) 15:1546. doi: 10.3390/nu15061546

PubMed Abstract | Crossref Full Text | Google Scholar

43. Shakya PR, Melaku YA, Page A, Gill TK. Association between dietary patterns and adult depression symptoms based on principal component analysis, reduced-rank regression and partial least-squares. Clin Nutr. (2020) 39:2811–23. doi: 10.1016/j.clnu.2019.12.011

PubMed Abstract | Crossref Full Text | Google Scholar

44. Kleinridders A, Cai W, Cappellucci L, Ghazarian A, Collins WR, Vienberg SG, et al. Insulin resistance in brain alters dopamine turnover and causes behavioral disorders. Proc Nat Acad Sci. (2015) 112:3463–8. doi: 10.1073/pnas.1500877112

PubMed Abstract | Crossref Full Text | Google Scholar

45. Taylor WD, Aizenstein HJ, Alexopoulos GS. The vascular depression hypothesis: mechanisms linking vascular disease with depression. Mol Psychiatry. (2013) 18:963–74. doi: 10.1038/mp.2013.20

PubMed Abstract | Crossref Full Text | Google Scholar

46. Ejtahed HS, Mardi P, Hejrani B, Mahdavi FS, Ghoreshi B, Gohari K, et al. Association between junk food consumption and mental health problems in adults: a systematic review and meta-analysis. BMC Psychiatry. (2024) 24:438. doi: 10.1186/s12888-024-05889-8

PubMed Abstract | Crossref Full Text | Google Scholar

47. Ifland J, Brewerton TD. Binge-type eating disorders and ultra-processed food addiction: phenomenology, pathophysiology and treatment implications. Front Psychiatry. (2025) 16:1584891. doi: 10.3389/fpsyt.2025.1584891

PubMed Abstract | Crossref Full Text | Google Scholar

48. Deng L, Zhao M, Cui Y, Xia Q, Jiang L, Yin H, et al. Acrylamide induces intrinsic apoptosis and inhibits protective autophagy via the ROS mediated mitochondrial dysfunction pathway in U87-MG cells. Drug Chem Toxicol. (2022) 45:2601–12. doi: 10.1080/01480545.2021.1979030

PubMed Abstract | Crossref Full Text | Google Scholar

49. Zhao M, Lewis Wang FS, Hu X, Chen F, Chan HM. Acrylamide-induced neurotoxicity in primary astrocytes and microglia: roles of the Nrf2-ARE and NF-κB pathways. Food Chem Toxicol. (2017) 106:25–35. doi: 10.1016/j.fct.2017.05.007

PubMed Abstract | Crossref Full Text | Google Scholar

50. Liu Z, Song G, Zou C, Liu G, Wu W, Yuan T, et al. Acrylamide induces mitochondrial dysfunction and apoptosis in BV-2 microglial cells. Free Radic Biol Med. (2015) 84:42–53. doi: 10.1016/j.freeradbiomed.2015.03.013

PubMed Abstract | Crossref Full Text | Google Scholar

51. Prins J, Olivier B, Korte SM. Triple reuptake inhibitors for treating subtypes of major depressive disorder: the monoamine hypothesis revisited. Expert Opin Investig Drugs. (2011) 20:1107–30. doi: 10.1517/13543784.2011.594039

PubMed Abstract | Crossref Full Text | Google Scholar

52. Elesawy BH, Alsanie WF, Algahtany MA, Al-Ashkhari JM, Alyarobi AK, Sakr HF. Whole and refined grains change behavior and reduce brain derived neurotrophic factor and neurotrophin-3 in rats. J Food Biochem. (2021) 45:e13867. doi: 10.1111/jfbc.13867

PubMed Abstract | Crossref Full Text | Google Scholar

53. Pariante CM, Lightman SL. The HPA axis in major depression: classical theories and new developments. Trends Neurosci. (2008) 31:464–8. doi: 10.1016/j.tins.2008.06.006

PubMed Abstract | Crossref Full Text | Google Scholar

54. Duman RS. Pathophysiology of depression: the concept of synaptic plasticity1To be presented at ECNP Barcelona, 5-9 October 2002, during the symposium “a new pharmacology of depression: the concept of synaptic plasticity.”. Eur Psychiatry. (2002) 17:306–10. doi: 10.1016/S0924-9338(02)00654-5

Crossref Full Text | Google Scholar

55. Thomas-Odenthal F, Ringwald K, Teutenberg L, Stein F, Alexander N, Bonnekoh LM, et al. Neural foundation of the diathesis-stress model: longitudinal gray matter volume changes in response to stressful life events in major depressive disorder and healthy controls. Mol Psychiatry. (2024) 29:2724–32. doi: 10.1038/s41380-024-02526-4

PubMed Abstract | Crossref Full Text | Google Scholar

56. Colodro-Conde L, Couvy-Duchesne B, Zhu G, Coventry WL, Byrne EM, Gordon S, et al. A direct test of the diathesis-stress model for depression. Mol Psychiatry. (2018) 23:1590–6. doi: 10.1038/mp.2017.130

PubMed Abstract | Crossref Full Text | Google Scholar

57. Shyn SI, Hamilton SP. The genetics of major depression: moving beyond the monoamine hypothesis. Psychiatr Clin North Am. (2010) 33:125–40. doi: 10.1016/j.psc.2009.10.004

PubMed Abstract | Crossref Full Text | Google Scholar

58. Nugent NR, Tyrka AR, Carpenter LL, Price LH. Gene-environment interactions: early life stress and risk for depressive and anxiety disorders. Psychopharmacology. (2011) 214:175–96. doi: 10.1007/s00213-010-2151-x

PubMed Abstract | Crossref Full Text | Google Scholar

59. Dun-Campbell K, Hartwell G, Maani N, Tompson A, van Schalkwyk MCI, Petticrew M. Commercial determinants of mental ill health: an umbrella review. PLoS Global Public Health. (2024) 4:e0003605. doi: 10.1371/journal.pgph.0003605

PubMed Abstract | Crossref Full Text | Google Scholar

60. Hajmir MM, Shiraseb F, Ebrahimi S, Noori S, Ghaffarian-Ensaf R, Mirzaei K. Interaction between ultra-processed food intake and genetic risk score on mental health and sleep quality. Eat Weight Dis. (2022) 27:3609–25. doi: 10.1007/s40519-022-01501-8

PubMed Abstract | Crossref Full Text | Google Scholar

61. Lane MM, Lotfaliany M, Hodge AM, O'Neil A, Travica N, Jacka FN, et al. High ultra-processed food consumption is associated with elevated psychological distress as an indicator of depression in adults from the Melbourne collaborative cohort study. J Affect Disord. (2023) 335:57–66. doi: 10.1016/j.jad.2023.04.124

PubMed Abstract | Crossref Full Text | Google Scholar

62. Leal ACG, Lopes LJ, Rezende-Alves K, Bressan J, Pimenta AM, Hermsdorff HHM. Ultra-processed food consumption is positively associated with the incidence of depression in Brazilian adults (CUME project). J Affect Disord. (2023) 328:58–63. doi: 10.1016/j.jad.2023.01.120

PubMed Abstract | Crossref Full Text | Google Scholar

63. Ribeiro José ME, Costa Ramos IE, de Sousa TM, Canella DS. Food consumption associated with depression, anxiety and stress in students entering a public university. J Nutr Sci. (2025) 14:e3. doi: 10.1017/jns.2024.90

PubMed Abstract | Crossref Full Text | Google Scholar

64. Sangouni AA, Beigrezaei S, Akbarian S, Ghayour-Mobarhan M, Yuzbashian E, Salehi-Abargouei A, et al. Association between dietary behaviors and depression in adolescent girls. BMC Public Health. (2022) 22:1169. doi: 10.1186/s12889-022-13584-0

PubMed Abstract | Crossref Full Text | Google Scholar

65. Liu J, Chen T, Chen M, Ma Y, Ma T, Gao D, et al. Sugar-sweetened beverages and depressive and social anxiety symptoms among children and adolescents aged 7–17 years, stratified by body composition. Front Nutr. (2022) 9:888671. doi: 10.3389/fnut.2022.888671

PubMed Abstract | Crossref Full Text | Google Scholar

66. Samuthpongtorn C, Nguyen LH, Okereke OI, Wang DD, Song M, Chan AT, et al. Consumption of ultraprocessed food and risk of depression. JAMA Netw Open. (2023) 6:E2334770. doi: 10.1001/jamanetworkopen.2023.34770

PubMed Abstract | Crossref Full Text | Google Scholar

67. Sangsefidi ZS, Lorzadeh E, Hosseinzadeh M, Mirzaei M. Dietary habits and psychological disorders in a large sample of Iranian adults: a population-based study. Ann Gen Psychiatry. (2020) 19:8. doi: 10.1186/s12991-020-00263-w

PubMed Abstract | Crossref Full Text | Google Scholar

68. Xie J, Huang Z, Mo Y, Pan Y, Ruan Y, Cao W, et al. Ages-specific beverage consumption and its association with depression and anxiety disorders: a prospective cohort study in 188,355 participants. J Affect Disord. (2025) 371:224–33. doi: 10.1016/j.jad.2024.11.069

PubMed Abstract | Crossref Full Text | Google Scholar

69. Kim H, Park J, Lee S, Lee SA, Park EC. Association between energy drink consumption, depression and suicide ideation in Korean adolescents. Int J Soc Psychiatry. (2020) 66:335–43. doi: 10.1177/0020764020907946

PubMed Abstract | Crossref Full Text | Google Scholar

70. Zhang Q, Li Y, Zhang J, Wu D, Chen Z, Feng X, et al. Association between long-term consumption trajectories of various foods and the risk of anxiety and depression in Chinese children. Crit Public Health. (2025) 35:2503277. doi: 10.1080/09581596.2025.2503277

Crossref Full Text | Google Scholar

71. Lee MF, Orr R, Marx W, Jacka FN, O'Neil A, Lane MM, et al. The association between dietary exposures and anxiety symptoms: a prospective analysis of the Australian longitudinal study on women's health cohort. J Aff Dis. (2025) 389:119651. doi: 10.1016/j.jad.2025.119651

PubMed Abstract | Crossref Full Text | Google Scholar

72. Lee S, Choi M. Ultra-processed food intakes are associated with depression in the general population: the Korea national health and nutrition examination survey. Nutrients. (2023) 15:2169. doi: 10.3390/nu15092169

PubMed Abstract | Crossref Full Text | Google Scholar

73. Mengist B, Lotfaliany M, Pasco JA, Agustini B, Berk M, Forbes M, et al. The risk associated with ultra-processed food intake on depressive symptoms and mental health in older adults: a target trial emulation. BMC Med. (2025) 23:172. doi: 10.1186/s12916-025-04002-4

PubMed Abstract | Crossref Full Text | Google Scholar

74. Wang A, Wan X, Zhuang P, Jia W, Ao Y, Liu X, et al. High fried food consumption impacts anxiety and depression due to lipid metabolism disturbance and neuroinflammation. Proc Natl Acad Sci U S A. (2023) 120:e2221097120. doi: 10.1073/pnas.2221097120

PubMed Abstract | Crossref Full Text | Google Scholar

75. Chen X, Zhang Z, Yang H, Qiu P, Wang H, Wang F, et al. Consumption of ultra-processed foods and health outcomes: a systematic review of epidemiological studies. Nutr J. (2020) 19:86. doi: 10.1186/s12937-020-00604-1

PubMed Abstract | Crossref Full Text | Google Scholar

76. Choi JY, Park SJ, Lee HJ. Healthy and unhealthy dietary patterns of depressive symptoms in middle-aged women. Nutrients. (2024) 16:776. doi: 10.3390/nu16060776

PubMed Abstract | Crossref Full Text | Google Scholar

77. Clayton-Chubb D, Vaughan NV, George ES, Chan AT, Roberts SK, Ryan J, et al. Mediterranean diet and ultra-processed food intake in older Australian adults—associations with frailty and cardiometabolic conditions. Nutrients. (2024) 16:2978. doi: 10.3390/nu16172978

PubMed Abstract | Crossref Full Text | Google Scholar

78. Gómez-Donoso C, Sánchez-Villegas A, Martínez-González MA, Gea A, Mendonça RD, Lahortiga-Ramos F, et al. Ultra-processed food consumption and the incidence of depression in a Mediterranean cohort: the SUN Project. Eur J Nutr. (2020) 59:1093–103. doi: 10.1007/s00394-019-01970-1

PubMed Abstract | Crossref Full Text | Google Scholar

79. Meller FO, Costa CDS, Quadra MR, Miranda VIA, Eugênio FD, Da Silva TJ, et al. Consumption of ultra-processed foods and mental health of pregnant women from the South of Brazil. Br J Nutr. (2024) 132:107–14. doi: 10.1017/S0007114524000783

PubMed Abstract | Crossref Full Text | Google Scholar

80. Godos J, Bonaccio M, Al-Qahtani WH, Marx W, Lane MM, Leggio GM, et al. Ultra-processed food consumption and depressive symptoms in a mediterranean cohort. Nutrients. (2023) 15:504. doi: 10.3390/nu15030504

PubMed Abstract | Crossref Full Text | Google Scholar

81. Zheng L, Sun J, Yu X, Zhang D. Ultra-processed food is positively associated with depressive symptoms among United States adults. Front Nutr. (2020) 7:600449. doi: 10.3389/fnut.2020.600449

PubMed Abstract | Crossref Full Text | Google Scholar

82. de Farias Xavier DE, de Moraes RCS, Viana TAF, Pereira JKG, da Costa PCT, Duarte DB, et al. Food consumption according to the NOVA food classification and its relationship with symptoms of depression, anxiety, and stress in women. Nutrients. (2024) 16:3734. doi: 10.3390/nu16213734

PubMed Abstract | Crossref Full Text | Google Scholar

83. Hecht EM, Rabil A, Martinez Steele E, Abrams GA, Ware D, Landy DC, et al. Cross-sectional examination of ultra-processed food consumption and adverse mental health symptoms. Public Health Nutr. (2022) 25:3225–34. doi: 10.1017/S1368980022001586

PubMed Abstract | Crossref Full Text | Google Scholar

84. Werneck AO, Steele EM, Delpino FM, Lane MM, Marx W, Jacka FN, et al. Adherence to the ultra-processed dietary pattern and risk of depressive outcomes: findings from the NutriNet Brasil cohort study and an updated systematic review and meta-analysis. Clin Nutr. (2024) 43:1190–9. doi: 10.1016/j.clnu.2024.03.028

PubMed Abstract | Crossref Full Text | Google Scholar

85. Da Costa Louzada ML, Dos Santos Costa C, Souza TN, Da Cruz GL, Levy RB, Monteiro CA. Impact of the consumption of ultra-processed foods on children, adolescents and adults' health: scope review. Cadernos de Saude Publica. (2021) 37:e00323020. doi: 10.1590/0102-311x00323020

PubMed Abstract | Crossref Full Text | Google Scholar

86. Lu M, Shi J, Li X, Liu Y. Long-term intake of thermo-induced oxidized oil results in anxiety-like and depression-like behaviors: involvement of microglia and astrocytes. Food Func. (2024) 15:4037–50. doi: 10.1039/D3FO05302D

PubMed Abstract | Crossref Full Text | Google Scholar

87. Wang Z, Lu C, Cui L, Fenfen E, Shang W, Song G, et al. Consumption of ultra-processed foods and multiple health outcomes: an umbrella study of meta-analyses. Food Chem. (2024) 434:137460. doi: 10.1016/j.foodchem.2023.137460

PubMed Abstract | Crossref Full Text | Google Scholar

88. Wiss DA, LaFata EM. Ultra-processed foods and mental health: where do eating disorders fit into the puzzle? Nutrients. (2024) 16:1955. doi: 10.3390/nu16121955

PubMed Abstract | Crossref Full Text | Google Scholar

89. Kunugi H. Depression and lifestyle: focusing on nutrition, exercise, and their possible relevance to molecular mechanisms. Psychiatry Clin Neurosci. (2023) 77:420–33. doi: 10.1111/pcn.13551

PubMed Abstract | Crossref Full Text | Google Scholar

90. Ghernati L, Tamim H, Chokor FAZ, Taktouk M, Assi B, Nasreddine L, et al. Processed and ultra-processed foods are associated with depression and anxiety symptoms in a cross-sectional sample of urban Lebanese adults. Nutr Res. (2025) 133:172–89. doi: 10.1016/j.nutres.2024.11.011

PubMed Abstract | Crossref Full Text | Google Scholar

91. Wiss DA, LaFata EM. Structural equation modeling of adverse childhood experiences, ultra-processed food intake, and symptoms of post-traumatic stress disorder, ultra-processed food addiction, and eating disorder among adults seeking nutrition counseling in Los Angeles, CA. Appetite. (2025) 208:107938. doi: 10.1016/j.appet.2025.107938

PubMed Abstract | Crossref Full Text | Google Scholar

92. Wu H, Gu Y, Meng G, Zhang Q, Liu L, Zhang S, et al. Relationship between dietary pattern and depressive symptoms: an international multicohort study. Int J Behav Nutr Phys Act. (2023) 20:74. doi: 10.1186/s12966-023-01461-x

PubMed Abstract | Crossref Full Text | Google Scholar

93. Yan J, Ren QH, Lin HY, Liu Q, Fu JZ, Sun CQ, et al. Association between dietary patterns and the risk of depressive symptoms in the older adults in rural China. Nutrients. (2022) 14:3538. doi: 10.3390/nu14173538

PubMed Abstract | Crossref Full Text | Google Scholar

94. Gratao LHA, da Silva TPR, Rocha LL, Jardim MZ, de Oliveira T, Cunha CD, et al. Common mental disorders in Brazilian adolescents: association with school characteristics, consumption of ultraprocessed foods and waist-to-height ratio. Cadernos De Saude Publica. (2024) 40:e00068423. doi: 10.1590/0102-311xen068423

Crossref Full Text | Google Scholar

95. DS Freitas R, da Silva J. Impact of ultra-processed foods on human health: a comprehensive review of genomic instability and molecular mechanisms. Nutrition. (2025) 137:112800. doi: 10.1016/j.nut.2025.112800

PubMed Abstract | Crossref Full Text | Google Scholar

96. Yuan S, Zhu T, Gu J, Hua L, Sun J, Deng X, et al. Associations of ultra-processed food intake and its circulating metabolomic signature with mental disorders in middle-aged and older adults. Nutrients. (2025) 17:1582. doi: 10.3390/nu17091582

PubMed Abstract | Crossref Full Text | Google Scholar

97. Wang P, Kong FZ, Hong XH, Zhang L, Zhao WH, Yang JC, et al. Neuronal nitric oxide synthase regulates depression-like behaviors in shortening-induced obese mice. Nutrients. (2022) 14:4302. doi: 10.3390/nu14204302

PubMed Abstract | Crossref Full Text | Google Scholar

98. Hosseininasab D, Shiraseb F, Bahrampour N, da Silva A, Hajinasab MM, Bressan J, et al. Ultra-processed food consumption and quality of life: a cross-sectional study in Iranian women. Front Public health. (2024) 12:1351510. doi: 10.3389/fpubh.2024.1351510

PubMed Abstract | Crossref Full Text | Google Scholar

99. Parnarouskis L, Gearhardt AN. Preliminary evidence that tolerance and withdrawal occur in response to ultra-processed foods. Curr Addict Rep. (2022) 9:282–9. doi: 10.1007/s40429-022-00425-8

Crossref Full Text | Google Scholar

100. Warner JO. Artificial food additives: hazardous to long-term health? Arch Dis Child. (2024) 109:882–5. doi: 10.1136/archdischild-2023-326565

PubMed Abstract | Crossref Full Text | Google Scholar

101. Juul F, Bere E. Ultra-processed foods – a scoping review for Nordic nutrition recommendations 2023. Food Nutr Res. (2024) 68:10616. doi: 10.29219/fnr.v68.10616

PubMed Abstract | Crossref Full Text | Google Scholar

102. 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) 43:1386–94. doi: 10.1016/j.clnu.2024.04.016

PubMed Abstract | Crossref Full Text | Google Scholar

103. Prescott SL, D'Adamo CR, Holton KF, Ortiz S, Overby N, Logan AC. Beyond plants: the ultra-processing of global diets is harming the health of people, places, and planet. Int J Environ Res Public Health. (2023) 20:6461. doi: 10.3390/ijerph20156461

PubMed Abstract | Crossref Full Text | Google Scholar

104. Canhada SL, Vigo Á, Giatti L, Fonseca MDJ, Lopes LJ, Cardoso LDO, et al. Associations of ultra-processed food intake with the incidence of cardiometabolic and mental health outcomes go beyond specific subgroups—the Brazilian longitudinal study of adult health. Nutrients. (2024) 16:4291. doi: 10.3390/nu16244291

PubMed Abstract | Crossref Full Text | Google Scholar

105. Ferreira NV, Gomes Gonçalves N, Khandpur N, Steele EM, Levy RB, Monteiro C, et al. Higher ultraprocessed food consumption is associated with depression persistence and higher risk of depression incidence in the Brazilian longitudinal study of adult health. J Acad Nutr Diet. (2025) 125:630–40. doi: 10.1016/j.jand.2024.10.012

PubMed Abstract | Crossref Full Text | Google Scholar

106. Mesas AE, González AD, de Andrade SM, Martínez-Vizcaíno V, López-Gil JF, Jiménez-López E. Increased consumption of ultra-processed food is associated with poor mental health in a nationally representative sample of adolescent students in Brazil. Nutrients. (2022) 14:5207. doi: 10.3390/nu14245207

PubMed Abstract | Crossref Full Text | Google Scholar

107. Medina-Reyes EI, Rodríguez-Ibarra C, Déciga-Alcaraz A, Díaz-Urbina D, Chirino YI, Pedraza-Chaverri J. Food additives containing nanoparticles induce gastrotoxicity, hepatotoxicity and alterations in animal behavior: the unknown role of oxidative stress. Food Chem Toxicol. (2020) 146:111814. doi: 10.1016/j.fct.2020.111814

PubMed Abstract | Crossref Full Text | Google Scholar

108. Tian YR, Deng CY, Xie HC, Long QJ, Yao Y, Deng Y, et al. Ultra-processed food intake and risk of depression: a systematic review. Nutr Hosp. (2023) 40:160–76. doi: 10.20960/nh.03723

PubMed Abstract | Crossref Full Text | Google Scholar

109. Henney AE, Gillespie CS, Alam U, Hydes TJ, Boyland E, Cuthbertson DJ. Ultra-processed food and non-communicable diseases in the United Kingdom: a narrative review and thematic synthesis of literature. Obes Rev. (2024) 25:e13682. doi: 10.1111/obr.13682

PubMed Abstract | Crossref Full Text | Google Scholar

110. Chen Y, Yang H, Sheng B, Zhou L, Li D, Zhang M, et al. Consumption of sugary beverages, genetic predisposition and the risk of depression: a prospective cohort study. Gen Psychiatry. (2024) 37:e101446. doi: 10.1136/gpsych-2023-101446

PubMed Abstract | Crossref Full Text | Google Scholar

111. Jones SK, McCarthy DM, Vied C, Stanwood GD, Schatschneider C, Bhide PG. Transgenerational transmission of aspartame-induced anxiety and changes in glutamate-GABA signaling and gene expression in the amygdala. Proc Natl Acad Sci U S A. (2022) 119:e2213120119. doi: 10.1073/pnas.2213120119

PubMed Abstract | Crossref Full Text | Google Scholar

112. Goddard AW, Ball SG, Martinez J, Robinson MJ, Yang CR, Russell JM, et al. Current perspectives of the roles of the central norepinephrine system in anxiety and depression. Depress Anxiety. (2010) 27:339–50. doi: 10.1002/da.20642

PubMed Abstract | Crossref Full Text | Google Scholar

113. Stein DJ, Stahl S. Serotonin and anxiety: current models. Int Clin Psychopharmacol. (2000) 15:S1–6. doi: 10.1097/00004850-200008002-00002

PubMed Abstract | Crossref Full Text | Google Scholar

114. Oliveira IFRD, Pereira NG, Monteiro LF, Rezende LMTD, Lira CABD, Monfort-Pañego M, et al. Factors influencing the quality of life and mental health of Brazilian federal education network employees: an epidemiological cross-sectional study. Heliyon. (2025) 11:e42029. doi: 10.1016/j.heliyon.2025.e42029

PubMed Abstract | Crossref Full Text | Google Scholar

115. Bahrami G, Mohammadifard N, Haghighatdoost F, Emamjomeh A, Najafi F, Farshidi H, et al. The association between soft drinks consumption and risk of mental disorders among Iranian adults: the LIPOKAP study. J Affect Disord. (2024) 363:8–14. doi: 10.1016/j.jad.2024.07.033

PubMed Abstract | Crossref Full Text | Google Scholar

116. Jantsch J, Rodrigues FDS, Fraga GF, Eller S, Silveira AK, Moreira JCF, et al. Calorie restriction mitigates metabolic, behavioral and neurochemical effects of cafeteria diet in aged male rats. J Nutr Biochem. (2023) 119:109371. doi: 10.1016/j.jnutbio.2023.109371

PubMed Abstract | Crossref Full Text | Google Scholar

117. Neto J, Jantsch J, de Oliveira S, Braga MF, Castro L, Diniz BF, et al. DHA/EPA supplementation decreases anxiety-like behaviour, but it does not ameliorate metabolic profile in obese male rats. Br J Nutr. (2022) 128:964–74. doi: 10.1017/S0007114521003998

PubMed Abstract | Crossref Full Text | Google Scholar

118. Ayton A, Ibrahim A. The Western diet: a blind spot of eating disorder research? - A narrative review and recommendations for treatment and research. Nutr Rev. (2020) 78:579–96. doi: 10.1093/nutrit/nuz089

Crossref Full Text | Google Scholar

119. Ayton A, Ibrahim A, Dugan J, Galvin E, Wright OW. Ultra-processed foods and binge eating: a retrospective observational study. Nutrition. (2021) 84:111023. doi: 10.1016/j.nut.2020.111023

PubMed Abstract | Crossref Full Text | Google Scholar

120. Mottis G, Kandasamey P, Peleg-Raibstein D. The consequences of ultra-processed foods on brain development during prenatal, adolescent and adult stages. Front Public Health. (2025) 13:1590083. doi: 10.3389/fpubh.2025.1590083

PubMed Abstract | Crossref Full Text | Google Scholar

121. Gearhardt AN, Bueno NB, Difeliceantonio AG, Roberto CA, Jiménez-Murcia S, Fernandez-Aranda F. Social, clinical, and policy implications of ultra-processed food addiction. BMJ. (2023) 383:e075354. doi: 10.1136/bmj-2023-075354

PubMed Abstract | Crossref Full Text | Google Scholar

122. Figueiredo N, Kose J, Srour B, Julia C, Kesse-Guyot E, Péneau S, et al. Ultra-processed food intake and eating disorders: cross-sectional associations among French adults. J Behav Addict. (2022) 11:588–99. doi: 10.1556/2006.2022.00009

PubMed Abstract | Crossref Full Text | Google Scholar

123. Pereira T, Mocellin MC, Curioni C. Association between ultraprocessed foods consumption, eating disorders, food addiction and body image: a systematic review. BMJ Open. (2024) 14:e091223. doi: 10.1136/bmjopen-2024-091223

PubMed Abstract | Crossref Full Text | Google Scholar

124. Via E, Contreras-Rodríguez O. Binge-eating precursors in children and adolescents: neurodevelopment, and the potential contribution of ultra-processed foods. Nutrients. (2023) 15. doi: 10.3390/nu15132994

PubMed Abstract | Crossref Full Text | Google Scholar

125. Luo Y, Morales JC, Dunton GF, Mason TB. Assessment of EMA binge-eating symptoms in adolescents: factor analysis and associations with social context and food types. Appetite. (2025) 214:108212. doi: 10.1016/j.appet.2025.108212

PubMed Abstract | Crossref Full Text | Google Scholar

126. Ulug E, Acikgoz Pinar A, Yildiz BO. Impact of ultra-processed foods on hedonic and homeostatic appetite regulation: a systematic review. Appetite. (2025) 213:108139. doi: 10.1016/j.appet.2025.108139

PubMed Abstract | Crossref Full Text | Google Scholar

127. de Souza ALG, de Almeida AA, Noll P, Noll M. Unhealthy life habits associated with self-induced vomiting and laxative misuse in Brazilian adolescents. Sci Rep. (2021) 11:2482. doi: 10.1038/s41598-021-81942-w

PubMed Abstract | Crossref Full Text | Google Scholar

128. Lafata EM, Gearhardt AN. Ultra-processed food addiction: an epidemic? Psychother Psychosom. (2022) 91:363–72. doi: 10.1159/000527322

PubMed Abstract | Crossref Full Text | Google Scholar

129. Schulte EM, Chao AM, Allison KC. Advances in the neurobiology of food addiction. Curr Behav Neurosci Rep. (2021) 8:103–12. doi: 10.1007/s40473-021-00234-9

Crossref Full Text | Google Scholar

130. Calcaterra V, Cena H, Rossi V, Santero S, Bianchi A, Zuccotti G. Ultra-processed food, reward system and childhood obesity. Children. (2023) 10:804. doi: 10.3390/children10050804

PubMed Abstract | Crossref Full Text | Google Scholar

131. Rezazadegan M, Amani R. Ultra processed food addiction among people: a mini-review of the evidence. J Nutr Food Secur. (2025) 10:174–7. doi: 10.18502/jnfs.v10i1.17769

Crossref Full Text | Google Scholar

132. LaFata EM, Allison KC, Audrain-McGovern J, Forman EM. Ultra-processed food addiction: a research update. Curr Obes Rep. (2024) 13:214–23. doi: 10.1007/s13679-024-00569-w

PubMed Abstract | Crossref Full Text | Google Scholar

133. Laurent J, Martin AR, Tompkins CL. Persistent and unsuccessful attempts to cut down on ultra-processed foods and the associated challenges for dietary adherence. Curr Addict Rep. (2022) 9:275–81. doi: 10.1007/s40429-022-00418-7

Crossref Full Text | Google Scholar

134. Whatnall M, Clarke E, Collins CE, Pursey K, Burrows T. Ultra-processed food intakes associated with “food addiction” in young adults. Appetite. (2022) 178:106260. doi: 10.1016/j.appet.2022.106260

PubMed Abstract | Crossref Full Text | Google Scholar

135. Silva Júnior AED, Gearhardt AN, Bueno NB. Association between food addiction with ultra-processed food consumption and eating patterns in a Brazilian sample. Appetite. (2023) 186:106572. doi: 10.1016/j.appet.2023.106572

PubMed Abstract | Crossref Full Text | Google Scholar

136. Lustig RH. Ultraprocessed food: addictive, toxic, and ready for regulation. Nutrients. (2020) 12:3401. doi: 10.3390/nu12113401

PubMed Abstract | Crossref Full Text | Google Scholar

137. Gearhardt AN, Schulte EM. Is food addictive? A review of the science. Annu Rev Nutr. (2021) 41:387–410. doi: 10.1146/annurev-nutr-110420-111710

PubMed Abstract | Crossref Full Text | Google Scholar

138. Jurema Santos GC de Sousa Fernandes MS Carniel PG da Silva Garcêz A Góis Leandro C Canuto R. Dietary intake in children and adolescents with food addiction: a systematic review. Addict Behav Rep. (2024) 19:100531. doi: 10.1016/j.abrep.2024.100531

Crossref Full Text | Google Scholar

139. Dark C, Homman-Ludiye J, Bryson-Richardson RJ. The role of ADHD associated genes in neurodevelopment. Dev Biol. (2018) 438:69–83. doi: 10.1016/j.ydbio.2018.03.023

PubMed Abstract | Crossref Full Text | Google Scholar

140. Drechsler R, Brem S, Brandeis D, Grünblatt E, Berger G, Walitza S, et al. Current concepts and treatments in children and adolescents. Neuropediatrics. (2020) 51:315–35. doi: 10.1055/s-0040-1701658

Crossref Full Text | Google Scholar

141. van der Laan CM, Ip HF, Schipper M, Hottenga J-J, St Pourcain B, Zayats T, et al. Genome-wide association meta-analysis of childhood ADHD symptoms and diagnosis identifies new loci and potential effector genes. Nat Genet. (2025) 57:2427–35. doi: 10.1038/s41588-025-02295-y

PubMed Abstract | Crossref Full Text | Google Scholar

142. Faraone SV, Larsson H. Genetics of attention deficit hyperactivity disorder. Mol Psychiatry. (2019) 24:562–75. doi: 10.1038/s41380-018-0070-0

PubMed Abstract | Crossref Full Text | Google Scholar

143. MacDonald HJ, Kleppe R, Szigetvari PD, Haavik J. The dopamine hypothesis for ADHD: an evaluation of evidence accumulated from human studies and animal models. Front Psychiatry. (2024) 15:1492126. doi: 10.3389/fpsyt.2024.1492126

PubMed Abstract | Crossref Full Text | Google Scholar

144. Véronneau-Veilleux F, Robaey P, Ursino M, Nekka F. A mechanistic model of ADHD as resulting from dopamine phasic/tonic imbalance during reinforcement learning. Front Comput Neurosci. (2022) 16:849323. doi: 10.3389/fncom.2022.849323

PubMed Abstract | Crossref Full Text | Google Scholar

145. Cupertino RB, Soheili-Nezhad S, Grevet EH, Bandeira CE, Picon FA, Tavares MEA, et al. Reduced fronto-striatal volume in attention-deficit/hyperactivity disorder in two cohorts across the lifespan. Neuroimage Clin. (2020) 28:102403. doi: 10.1016/j.nicl.2020.102403

PubMed Abstract | Crossref Full Text | Google Scholar

146. Hai T, Swansburg R, Kahl CK, Frank H, Stone K, Lemay J-F, et al. Right superior frontal gyrus cortical thickness in pediatric ADHD. J Atten Disord. (2022) 26:1895–906. doi: 10.1177/10870547221110918

PubMed Abstract | Crossref Full Text | Google Scholar

147. Borge TC, Biele G, Papadopoulou E, Andersen LF, Jacka F, Eggesbø M, et al. The associations between maternal and child diet quality and child ADHD – findings from a large Norwegian pregnancy cohort study. BMC Psychiatry. (2021) 21:139. doi: 10.1186/s12888-021-03130-4

PubMed Abstract | Crossref Full Text | Google Scholar

148. Ferreira RC, Marin AH, Vitolo MR, Campagnolo PDB. Early ultra-processed foods consumption and hyperactivity/inattention in adolescence. Revista de Saude Publica. (2024) 58:46. doi: 10.11606/s1518-8787.2024058005636

PubMed Abstract | Crossref Full Text | Google Scholar

149. Kvalvik LG, Klungsøyr K, Igland J, Caspersen IH, Brantsæter AL, Solberg BS, et al. Association of sweetened carbonated beverage consumption during pregnancy and ADHD symptoms in the offspring: a study from the Norwegian mother, father and child cohort study (MoBa). Eur J Nutr. (2022) 61:2153–66. doi: 10.1007/s00394-022-02798-y

PubMed Abstract | Crossref Full Text | Google Scholar

150. Yan W, Lin S, Wu D, Shi Y, Dou L, Li X. Processed food–sweets patterns and related behaviors with attention deficit hyperactivity disorder among children: a case–control study. Nutrients. (2023) 15:1254. doi: 10.3390/nu15051254

PubMed Abstract | Crossref Full Text | Google Scholar

151. Osman B, Sunderland M, Devine EK, Thornton L, Jacka F, Teesson M. Prevalence of noncommunicable diseases and developmental conditions in 5014 Australian adolescents, and their correlations with diet, other lifestyle behaviours and mental health. Aust N Z J Public Health. (2025) 49:100225. doi: 10.1016/j.anzjph.2025.100225

PubMed Abstract | Crossref Full Text | Google Scholar

152. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR). Washington, DC: American Psychiatric Association Publishing (2022). doi: 10.1176/appi.books.9780890425787

Crossref Full Text | Google Scholar

153. Kainer D, Templeton AR, Prates ET, Jacboson D, Allan ERO, Climer S, et al. Structural variants identified using non-Mendelian inheritance patterns advance the mechanistic understanding of autism spectrum disorder. Hum Genet Genomics Adv. (2023) 4:100150. doi: 10.1016/j.xhgg.2022.100150

PubMed Abstract | Crossref Full Text | Google Scholar

154. Genovese A, Butler MG. The autism spectrum: behavioral, psychiatric and genetic associations. Genes. (2023) 14:677. doi: 10.3390/genes14030677

PubMed Abstract | Crossref Full Text | Google Scholar

155. Frye RE. Mitochondrial dysfunction in autism spectrum disorder: unique abnormalities and targeted treatments. Semin Pediatr Neurol. (2020) 35:100829. doi: 10.1016/j.spen.2020.100829

PubMed Abstract | Crossref Full Text | Google Scholar

156. Khaliulin I, Hamoudi W, Amal H. The multifaceted role of mitochondria in autism spectrum disorder. Mol Psychiatry. (2025) 30:629–50. doi: 10.1038/s41380-024-02725-z

PubMed Abstract | Crossref Full Text | Google Scholar

157. Lee E, Lee J, Kim E. Excitation/inhibition imbalance in animal models of autism spectrum disorders. Biol Psychiatry. (2017) 81:838–47. doi: 10.1016/j.biopsych.2016.05.011

PubMed Abstract | Crossref Full Text | Google Scholar

158. Uzunova G, Pallanti S, Hollander E. Excitatory/inhibitory imbalance in autism spectrum disorders: implications for interventions and therapeutics. World J Biol Psychiatry. (2016) 17:174–86. doi: 10.3109/15622975.2015.1085597

PubMed Abstract | Crossref Full Text | Google Scholar

159. Onore C, Careaga M, Ashwood P. The role of immune dysfunction in the pathophysiology of autism. Brain Behav Immun. (2012) 26:383–92. doi: 10.1016/j.bbi.2011.08.007

PubMed Abstract | Crossref Full Text | Google Scholar

160. Ye J, Fan H, Shi R, Song G, Wu X, Wang D, et al. Dietary lipoic acid alleviates autism-like behavior induced by acrylamide in adolescent mice: the potential involvement of the gut-brain axis. Food Funct. (2024) 15:3395–410. doi: 10.1039/D3FO05078E

PubMed Abstract | Crossref Full Text | Google Scholar

161. Masini E, Loi E, Vega-Benedetti AF, Carta M, Doneddu G, Fadda R, et al. An overview of the main genetic, epigenetic and environmental factors involved in autism spectrum disorder focusing on synaptic activity. Int J Mol Sci. (2020) 21:8290. doi: 10.3390/ijms21218290

PubMed Abstract | Crossref Full Text | Google Scholar

162. Vecchione R, Wang S, Rando J, Chavarro JE, Croen LA, Fallin MD, et al. Maternal dietary patterns during pregnancy and child autism-related traits: results from two US cohorts. Nutrients. (2022) 14:2729. doi: 10.3390/nu14132729

PubMed Abstract | Crossref Full Text | Google Scholar

163. Lane MM, Travica N, Gamage E, Marshall S, Trakman GL, Young C, et al. Sugar-sweetened beverages and adverse human health outcomes: an umbrella review of meta-analyses of observational studies. Annu Rev Nutr. (2024) 44:383–404. doi: 10.1146/annurev-nutr-062322-020650

PubMed Abstract | Crossref Full Text | Google Scholar

164. National Health and Medical Research Council. Australian Dietary Guidelines. Canberra: National Health and Medical Research Council (2013).

Google Scholar

165. Ministry of Health of Brazil. Dietary Guidelines for the Brazilian Population. Ministry of Health of Brazil (2015).

Google Scholar

166. Yang BW, Zou P, Chen Q, Sun L, Ling X, Yang H, et al. Lifestyle-related risk factors correlated with mental health problems: a longitudinal observational study among 686 male college students in Chongqing, China. Front Public Health. (2022) 10:1040410. doi: 10.3389/fpubh.2022.1040410

PubMed Abstract | Crossref Full Text | Google Scholar

167. 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) 9:462–70. doi: 10.1016/S2213-8587(21)00078-4

PubMed Abstract | Crossref Full Text | Google Scholar

168. Machado PP, Steele EM, Levy RB, da Costa Louzada ML, Rangan A, Woods J, et al. Ultra-processed food consumption and obesity in the Australian adult population. Nutr Diabetes. (2020) 10:39. doi: 10.1038/s41387-020-00141-0

PubMed Abstract | Crossref Full Text | Google Scholar

169. 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 Ac. (2022) 19:148. doi: 10.1186/s12966-022-01389-8

PubMed Abstract | Crossref Full Text | Google Scholar

170. Leung CW, Fulay AP, Parnarouskis L, Martinez-Steele E, Gearhardt AN, Wolfson JA. Food insecurity and ultra-processed food consumption: the modifying role of participation in the supplemental nutrition assistance program (SNAP). Am J Clin Nutr. (2022) 116:197–205. doi: 10.1093/ajcn/nqac049

PubMed Abstract | Crossref Full Text | Google Scholar

171. Shim JS, Oh K, Kim HC. Dietary assessment methods in epidemiologic studies. Epidemiol Health. (2014) 36:e2014009. doi: 10.4178/epih/e2014009

PubMed Abstract | Crossref Full Text | Google Scholar

172. Bujtor M, Turner AI, Torres SJ, Esteban-Gonzalo L, Pariante CM, Borsini A. Associations of dietary intake on biological markers of inflammation in children and adolescents: a systematic review. Nutrients. (2021) 13:1–29. doi: 10.3390/nu13020356

PubMed Abstract | Crossref Full Text | Google Scholar

173. Almeida ALS, Sousa TM, Caldeira TCM, Claro RM. Association between adherence to the food guide golden rule and health characteristics among adult Brazilian women: a cross-sectional study with VIGITEL data, 2018-2021. Epidemiol Serv Saude. (2025) 34:e20240232. doi: 10.1590/s2237-96222025v34e20240232.b

PubMed Abstract | Crossref Full Text | Google Scholar

174. de Sousa TM, Caldeira TCM, Ramos IEC, Canella DS, Claro RM. Association between depression and ultra-processed food consumption: a population-based study (Vigitel, 2023). Public Health. (2024) 234:187–90. doi: 10.1016/j.puhe.2024.06.015

PubMed Abstract | Crossref Full Text | Google Scholar

175. Maltos-Gómez F, Brito-López A, Uriarte-Ortiz JB, Sánchez DPG, Muñoz-Comonfort A, Sampieri-Cabrera R. Association between diet, physical activity, smoking, and ultra-processed food and cardiovascular health, depression, and sleep quality. Cureus J Med Sci. (2024) 16:e66561. doi: 10.7759/cureus.66561

PubMed Abstract | Crossref Full Text | Google Scholar

176. He P, Tang J, Yang T, Liu Y, Zhang Z, Yang Q, et al. Association between ultra-processed food-related knowledge and intake behavior and anxiety among Chinese college students. Nutr Health. (2025) 2601060251339558. doi: 10.1177/02601060251339558

PubMed Abstract | Crossref Full Text | Google Scholar

177. Milà-Guasch M, Ramírez S, Llana SR, Fos-Domènech J, Dropmann LM, Pozo M, et al. Maternal emulsifier consumption programs offspring metabolic and neuropsychological health in mice. PLoS Biol. (2023) 21:e3002171. doi: 10.1371/journal.pbio.3002171

PubMed Abstract | Crossref Full Text | Google Scholar

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

PubMed Abstract | Crossref Full Text | Google Scholar

179. Sun M, He Q, Li G, Zhao H, Wang Y, Ma Z, et al. Association of ultra-processed food consumption with incident depression and anxiety: a population-based cohort study. Food Funct. (2023) 14:7631–41. doi: 10.1039/D3FO01120H

PubMed Abstract | Crossref Full Text | Google Scholar

180. Xue X, Wang Z, Qi Y, Chen N, Zhao K, Zhao M, et al. Multimorbidity patterns and influencing factors in older Chinese adults: a national population-based cross-sectional survey. J Glob Health. (2025) 15:04051. doi: 10.7189/jogh.15.04051

PubMed Abstract | Crossref Full Text | Google Scholar

181. Yu X, Pu H, Voss M. Overview of anti-inflammatory diets and their promising effects on non-communicable diseases. Br J Nutr. (2024) 132:898–918. doi: 10.1017/S0007114524001405

PubMed Abstract | Crossref Full Text | Google Scholar

182. Zhang H, Li M, Mo L, Luo J, Shen Q, Quan W. Association between western dietary patterns, typical food groups, and behavioral health disorders: an updated systematic review and meta-analysis of observational studies. Nutrients. (2024) 16:125. doi: 10.3390/nu16010125

PubMed Abstract | Crossref Full Text | Google Scholar

183. Peper E, Shuford J. Reflections on the increase in autism, ADHD, anxiety, and depression: part 2 – exposure to neurotoxins and ultraprocessed foods. NeuroRegulation. (2024) 11:219–28. doi: 10.15540/nr.11.2.219

Crossref Full Text | Google Scholar

184. Zupo R, Castellana F, Boero G, Matera E, Colacicco G, Piscitelli P, et al. Processed foods and diet quality in pregnancy may affect child neurodevelopment disorders: a narrative review. Nutr Neurosci. (2024) 27:361–81. doi: 10.1080/1028415X.2023.2197709

PubMed Abstract | Crossref Full Text | Google Scholar

185. Silva SA, do Carmo AS, Carvalho KMB. Lifestyle patterns associated with common mental disorders in Brazilian adolescents: results of the study of cardiovascular risks in adolescents (ERICA). PLoS ONE. (2021) 16:e0261261. doi: 10.1371/journal.pone.0261261

PubMed Abstract | Crossref Full Text | Google Scholar

186. Simplício APM, Viola PCAF, Lavôr LCC, Sousa PVL, de Carvalho CA, Rodrigues LARL, et al. Unhealthy dietary pattern associated with common mental disorders in adults and older adults: a population-based study. Curr Nutr Food Sci. (2024) 20:1155–64. doi: 10.2174/1573401319666230503155748

Crossref Full Text | Google Scholar

187. van Zonneveld SM, van den Oever EJ, Haarman BCM, Grandjean EL, Nuninga JO, van de Rest O, et al. An anti-inflammatory diet and its potential benefit for individuals with mental disorders and neurodegenerative diseases—a narrative review. Nutrients. (2024) 16:2646. doi: 10.3390/nu16162646

PubMed Abstract | Crossref Full Text | Google Scholar

188. Werneck AO, Costa CS, Horta B, Wehrmeister FC, Gonçalves H, Menezes AMB, et al. Prospective association between ultra-processed food consumption and incidence of elevated symptoms of common mental disorders. J Affect Disord. (2022) 312:78–85. doi: 10.1016/j.jad.2022.06.007

PubMed Abstract | Crossref Full Text | Google Scholar

189. Morales-Suarez-Varela M, Rocha-Velasco OA. Impact of ultra-processed food consumption during pregnancy on maternal and child health outcomes: a comprehensive narrative review of the past five years. Clin Nutr ESPEN. (2025) 65:288–304. doi: 10.1016/j.clnesp.2024.12.006

PubMed Abstract | Crossref Full Text | Google Scholar

190. Oddy WH, Allen KL, Trapp GSA, Ambrosini GL, Black LJ, Huang R-C, et al. Dietary patterns, body mass index and inflammation: Pathways to depression and mental health problems in adolescents. Brain Behav Immun. (2018) 69:428–39.

PubMed Abstract | Google Scholar

191. Hu D, Cheng L, Jiang W. Sugar-sweetened beverages consumption and the risk of depression: A meta-analysis of observational studies. J Affect Disord. (2019) 245:348–55.

PubMed Abstract | Google Scholar

Keywords: anxiety, autism, depression, eating disorders, food addiction, lipid metabolism, mental health, neuroinflammation

Citation: Poon E, Li C, Schweitzer D and Akefe I (2026) Neurobiological insights into the effects of ultra-processed food on lipid metabolism and associated mental health conditions: a scoping review. Front. Nutr. 12:1754492. doi: 10.3389/fnut.2025.1754492

Received: 26 November 2025; Revised: 20 December 2025;
Accepted: 24 December 2025; Published: 21 January 2026.

Edited by:

Javier Diaz-Castro, University of Granada, Spain

Reviewed by:

Mahsa Rezazadegan, Isfahan University of Medical Sciences, Iran
Soroush Taherkhani, Iran University of Medical Sciences, Iran

Copyright © 2026 Poon, Li, Schweitzer and Akefe. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Daniel Schweitzer, ZGFuaWVsLnNjaHdlaXR6ZXIyQG1hdGVyLm9yZy5hdQ==; Isaac Akefe, aXNhYWMuYWtlZmVAY2R1LmVkdS5hdQ==

These authors have contributed equally to this work

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