The prevalence of obesity has tripled since 1975 and constitutes a major clinical and research challenge worldwide (Abarca-Gómez et al., 2017). In 2016, more than 1.2 billion adults were overweight and nearly 650 million adults were obese (World Health Organization [WHO], 2020). Increased body mass index (BMI) is associated with many comorbidities, such as cardiovascular diseases, diabetes, osteoarthritis, and some forms of cancers (Haslam and James, 2005; Wolin et al., 2010). Weight gain results from multiple conditions and complex interactions between the factors that still need better characterization (Blüher, 2019). Disruption of circadian rhythms, which may be driven by societal constraints (e.g., work schedules), has been described as one factor potentially contributing to weight gain (Roenneberg et al., 2012).

To synchronize (entrain) to the rhythmic environment, the central circadian clock in the suprachiasmatic nucleus (SCN) predominantly uses light as a zeitgeber (environmental signal specific for entrainment; Roenneberg and Merrow, 2007; LeGates et al., 2014). The SCN provides an internal rhythmic milieu that is used by the cellular clocks in tissues and organs to entrain to the light-dark cycle of the environment (via the SCN), and also to other clocks of the peripheral circadian system. This system modulates all aspects of physiology (e.g., body temperature, hormone secretion, feeding behavior, alertness, and cognitive function; Mohawk et al., 2012; Astiz et al., 2019), thereby also coordinating metabolism on a daily basis (Marcheva et al., 2013; Reinke and Asher, 2019). Notably, peripheral clocks, for example, in the liver, can also be entrained by food intake (Damiola et al., 2000; Panda and Hogenesch, 2004; Buijs et al., 2013).

Human circadian organization is affected by current lifestyles and behaviors in modern societies, including weak zeitgeber signals (due to less time spent outdoors and artificial light after dusk; Roenneberg and Merrow, 2016). Timing, duration, and intensity of light exposure have strong effects on health and behavioral outcomes (Gonzalez, 2018; Rumanova et al., 2020); exposure to weak (low amplitude) light-dark cycles, for example, is associated with metabolic changes and weight gain (Borniger et al., 2014). Weak or irregular light exposure is often associated with circadian misalignment (e.g., in shift workers or simply early work start times that lead to use alarm clocks), which itself is linked to obesity (Roenneberg et al., 2012; Liu et al., 2018). Disturbances of daily rhythmicity have been related to cardiovascular disease, diabetes, aging, and obesity (Sohail et al., 2015; Sletten et al., 2020). When rodents are under forced desynchrony (modeling circadian disruption), glucose metabolism and insulin sensitivity become impaired (Karatsoreos et al., 2011; de Oliveira et al., 2019). However, the precise mechanisms linking circadian disruption with adverse metabolic consequences are not fully understood.

Few studies have evaluated the relationship between circadian rhythms, light exposure, and body weight in clinical, epidemiological, or field studies. Yet, identifying modifiable behaviors or aspects promoting weight gain is essential to understand the underlying mechanisms and to devise effective interventions. Here, we report the results of studying the association between BMI, light exposure, and activity-rest rhythms in Quilombola communities in southern Brazil. These are characterized as remaining social groups established in the past to escape or resist slavery (and slavery remnants) in Brazil with an established ethnic identity and culture. These communities, already described in a previous study (Pilz et al., 2018), have varied histories of access to electricity and live at different stages of urbanization and, therefore, represent a unique opportunity to study the association between circadian misalignment and metabolic factors under the conditions brought by modern industrialized societies. We hypothesized that later phases of entrainment, irregular light exposure, and higher levels of urbanization are associated with overweight or obesity.

Our study investigated the association of daily behavior and levels of urbanization with BMI. The results show that both activity and light are more fragmented (i.e., higher values of IV) in the overweight or obese group than in the normal-weight participants. Night-time light exposure (light L5) was positively and day-time light exposure (light M10) was negatively associated with having higher than normal BMI. Furthermore, living in more urbanized areas was associated with an increased risk of belonging to the overweight or obese group.

Contrary to previous findings of studies in other populations (Roenneberg et al., 2012; Parsons et al., 2015; Patterson et al., 2018McMahon et al., 2019), SJL and chronotype were not associated with BMI in our sample: here, we used MSF as a marker of chronotype and we did not detect differences in phase of entrainment or SJL between BMI groups either. The lack of a significant difference in this instance may be related to the low levels of SJL in our rural sample, which may not be sufficient to impose a risk. Additionally, SJL reflects circadian misalignment in the context of work structures normally seen in urban or industrialized societies (workdays vs. free days). Other metrics of circadian strain could better show how challenges to the system may be a risk factor to obesity.

In fact, we did see differences in IV and RA between BMI groups. The IV of activity and light was significantly higher in the overweight and obese groups, compared with the normal-weight group, which indicates that higher fragmentation of daily rhythms associates with overweight and obesity. This result supports those of Garaulet et al. (2017), who found highly fragmented activity rhythms associated with obesity and central adiposity in a sample of adolescents and also with an earlier study that suggested higher fragmentation as a predictor of lower weight loss in overweight and obese women undergoing a weight-reduction program (Bandin et al., 2014). Other large epidemiological studies have shown a positive association between obesity, diabetes, metabolic risk, and fragmentation of activity rhythms in urban populations (Van den Berg et al., 2008; Luik et al., 2013; Sohail et al., 2015). Our study adds to the mounting evidence by showing that activity IV also correlates with BMI in a sample comprised of subjects at different urbanization stages. The higher activity and light variability do not seem to be an artifact produced by different actimeter brands (see Supplementary Figure 2). Even adjusting model 2 to actimeter brand, activity was still significantly associated with being overweight or obese.

Few studies have investigated the associations of stability in activity rhythms (measured by IS) with BMI; some of them found significant associations (Luik et al., 2013; Sohail et al., 2015), whereas others did not (Cespedes Feliciano et al., 2017). In our sample, we did not find any significant differences in the IS between groups.

In addition to the higher fragmentation of light exposure and activity rhythms, in our study, lower RA of activity was related to overweight or obesity. RA is calculated as the difference between M10 and L5, normalized by their sum (Van Someren et al., 1999). Lower values indicate a less robust 24-h activity-rest or light-dark pattern, reflecting both lower activity and light during the wake periods and/or higher activity and light during the rest phase. According to the existing literature, this flattening of the rest-activity rhythm indicates that the circadian pacemaker is less entrained to its zeitgebers, correlating with metabolic disruption (Hsieh et al., 2010; Wright et al., 2013). As expected, the overweight group was less active during the day (i.e., had a lower M10), even though the L5 of activity was the same across groups. Although the M10 of activity is lower in the obese group, it was not significantly different when compared to the normal-weight participants. This may be related to the fact that there is a higher proportion of women in the obese group. In a study conducted by Cooper et al. (2000), it was shown that the differences in activity levels between obese and non-obese participants were greater in men than women. Among NPCRA estimates, RA has been widely investigated as a factor inversely correlated with conditions such as psychiatric disorders, cognitive malfunctioning, and metabolic dysfunction (Ng et al., 2015; Cespedes Feliciano et al., 2017; Lyall et al., 2018). Although we observed the inverse association of RA (of both activity and light) with BMI, such relationship did not persist in the multivariate model, possibly due to the low sample size (data not shown). Furthermore, plots and descriptive statistics suggest similar patterns across groups regardless of the actimeter brand except for M10 and RA of activity. M10 and RA of activity seem to be lower in obese compared with the normal group only among those wearing ActTrust and similar in those wearing the Actiwatch-2. ActTrust is more sensitive to movement, but these differences may also reflect the characteristics of both samples collected with each actimeter, since the sample collected using ActTrust was larger and more varied in terms of urbanization (and therefore in variability measures): no electricity and urban groups data were collected using ActTrust. Even if causal relationships underlying these associations are still not clear, there is growing evidence that simple environmental interventions, such as keeping regular light exposure, feeding, and physical activity times, may improve metabolic health outcomes (Dowling et al., 2005; Van Someren and Riemersma-Van Der Lek, 2007).

The NPCRA estimates for light exposure indicate that a weaker signal and irregular patterns may be related to adverse metabolic consequences and are consistent with recent findings in the literature (McFadden et al., 2014; Wyse et al., 2014; Gerhart-Hines and Lazar, 2015). In mammals, light is the main cue for entraining internal circadian rhythms to environmental cycles. The lack of a strong zeitgeber contrast between day and night leads to advancing extreme early types and delaying all other chronotypes (Papatsimpa et al., 2021). The resulting interaction with local time leads to sleeping and eating at the wrong circadian times, constituting potential mediators linking light exposure to BMI, as shown in both humans and rodents (Fonken et al., 2013; Shi et al., 2013; Plano et al., 2017). Several other studies report that daytime bright light therapy improves glycemic control in patients with diabetes (Versteeg et al., 2017), reduces insulin resistance, body weight, and fat mass (Sene-Fiorese et al., 2015), and improves carbohydrate metabolism (Dunai et al., 2007; Danilenko et al., 2013). Our results support the potential of strategies aimed at adjusting light exposure and promoting circadian organization for preventing obesity.

Another important aspect to be discussed regarding the development of obesity is the changes in lifestyle brought by industrialization that may impact health and increase risk for metabolic diseases. The urban environment comprises factors that affect circadian rhythms and health, such as working in night shifts, prolonged exposure to artificial light, stress, pollution, SJL, and poor diets (Wyse et al., 2014; Pot, 2018; Benedito-Silva et al., 2020). One study that compares Brazilian communities with different levels of urbanization demonstrated that urban dwellers had higher BMI and higher levels of insulin, fasting glucose, and insulin resistance (Martins et al., 2020). Here, we found an association between higher levels of urbanization and increased risk of being overweight or obese. One of the factors explaining these results may be the occupations in rural settings. Since their economy is mostly based on agricultural jobs, they have higher levels of activity during the day. Future studies are needed to investigate how levels of physical activity during work could be a factor contributing to the differences in BMI. Another contributing factor could be the difference in light exposure between some of these communities (Pilz et al., 2018). Reduced natural daylight exposure and increased levels of nocturnal light are common features of urbanized societies and may disrupt circadian rhythmicity, contributing to the association between global urbanization and obesity (Rybnikova et al., 2016). Since we observed a significant association of light at night or low light exposure during the day with overweight or obesity, this may be considered as one of the driving forces behind metabolic diseases. Finally, eating habits were greatly changed by urban settings in many aspects, such as timing of eating, frequency of meals, and diet quality. All these factors may be associated with body weight as well (Pot, 2018).

The main strengths of the current work include the use of objective measures to assess daily behavior. Furthermore, we considered some important covariates and potential confounders such as age, sex, and use of alarm clocks, which may influence the calculation of MSF and SJL. Finally, we could compare many communities that differ in access to electricity and urbanization, which represents a unique opportunity to study the impacts of modern lifestyles on human biological rhythms and metabolism. Conversely, there are some limitations to be considered when interpreting our findings: First, the limited generalizability of the results considers the study sample size. Furthermore, its cross-sectional nature precludes inferring causality and direction of the associations. Second, due to our sample size, we may have not had enough statistical power to rule out confounders and detect the effects of some variables on the multivariate models; the wide confidence intervals for effect sizes suggest that further information is needed for greater precision. Third, as a methodological limitation, BMI is a populational measure that does not cover individual characteristics of body composition, including body fat and lean mass percentage, although it is the standard clinical measure of obesity. Future work should consider the use of other estimates of adiposity, such as waist circumference, body fat, and direct measurements of metabolic parameters that were not assessed in this study due to logistic difficulties that prevented collection, storage, and transportation of biological samples. Fourth, when we were unable to measure height and weight or there was any discomfort from participants, BMI measures were self-reported. Thus, we chose to categorize this variable into three groups for it to be more reliable. Fifth, it was not possible to assess food intake, a factor that might also be associated with obesity. Finally, we collected actimetry data in different seasons (80 participants between March and September and 41 participants between September and March). However, we included season of data (March 20 to September 22 or September 22 to March 20) collection as a covariate in the final actigraphy or MCTQ multivariate models and neither was it associated with being overweight or obese, nor did it change the results.

In summary, results from this study show an association between more fragmented rhythms of activity and exposure to light and higher BMI. Also, higher levels of urbanization and lower amplitude of light exposure were associated with increased risk of being overweight or obese. The present findings support the hypothesis that disturbed circadian rhythms and irregular light exposure might be associated with increased risk for obesity, which warrants further investigation, considering the strong potential of preventive measures.

The datasets presented in this article are not readily available because the datasets presented in this article are not readily available because the data in this study cannot at this stage be publicly provided. Participants were not asked for consent to make individual data available. The data contain information that could compromise the privacy of research participants. Requests to access the datasets should be directed to LKP, luisa.pilz@ufrgs.br.

The studies involving human participants were reviewed and approved by Ethics Committee of the Hospital de Clínicas de Porto Alegre. The patients/participants provided their written informed consent to participate in this study.

RL, TR, LKP, and MPH contributed to conception and design of the study. DBC, NBX, LKP, and RL participated in data collection and organized the database. DBC, NBX, and LKP performed the statistical analyses and interpretation of data. DBC wrote the first draft of the manuscript. NBX and LKP wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the manuscript before submission.

The authors alone are responsible for the content and writing of the paper.

TR is the founder and CSO at Chronosulting UG. None of his consulting activities in this context had any relationship with the current study. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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