Hostname: page-component-89b8bd64d-r6c6k Total loading time: 0 Render date: 2026-05-07T22:20:20.579Z Has data issue: false hasContentIssue false
  • English
  • Français

Sex-specific association between later circadian timing of food intake and adiposity among Chinese young adults living in real-world settings

Published online by Cambridge University Press:  22 November 2024

Yan Huang Open the ORCID record for Yan Huang [Opens in a new window] ,
Yu-xiang Xu ,
Yu-ting Shen ,
Yi Zhou ,
Yu-hui Wan ,
Fang-biao Tao  and
Ying Sun
Yan Huang
Affiliation:
Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, Anhui, China
Yu-xiang Xu
Affiliation:
Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, Anhui, China
Yu-ting Shen
Affiliation:
Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, Anhui, China
Yi Zhou
Affiliation:
Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, Anhui, China
Yu-hui Wan
Affiliation:
Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, Anhui, China MOE Key Laboratory of Population Health Across Life Cycle, Hefei, Anhui, China Anhui Provincial Key Laboratory of Environment and Population Health across the Life Course, Anhui Medical University, Anhui, China
Fang-biao Tao
Affiliation:
Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, Anhui, China MOE Key Laboratory of Population Health Across Life Cycle, Hefei, Anhui, China Anhui Provincial Key Laboratory of Environment and Population Health across the Life Course, Anhui Medical University, Anhui, China Center for Big Data and Population Health of IHM, Anhui Medical University, Anhui, China
Ying Sun*
Affiliation:
Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, Anhui, China MOE Key Laboratory of Population Health Across Life Cycle, Hefei, Anhui, China Anhui Provincial Key Laboratory of Environment and Population Health across the Life Course, Anhui Medical University, Anhui, China Center for Big Data and Population Health of IHM, Anhui Medical University, Anhui, China
*
*Corresponding author: Ying Sun, email yingsun@ahmu.edu.cn
Rights & Permissions [Opens in a new window]

Abstract

Timing of food intake is an emerging aspect of nutrition; however, there is a lack of research accurately assessing food timing in the context of the circadian system. The study aimed to investigate the relation between food timing relative to clock time and endogenous circadian timing with adiposity and further explore sex differences in these associations among 151 young adults aged 18–25 years. Participants wore wrist actigraphy and documented sleep and food schedules in real time for 7 consecutive days. Circadian timing was determined by dim-light melatonin onset (DLMO). The duration between last eating occasion and DLMO (last EO-DLMO) was used to calculate the circadian timing of food intake. Adiposity was assessed using bioelectrical impedance analysis. Of the 151 participants, 133 were included in the statistical analysis finally. The results demonstrated that associations of adiposity with food timing relative to circadian timing rather than clock time among young adults living in real-world settings. Sex-stratified analyses revealed that associations between last EO-DLMO and adiposity were significant in females but not males. For females, each hour increase in last EO-DLMO was associated with higher BMI by 0·51 kg/m2 (P = 0·01), higher percent body fat by 1·05 % (P = 0·007), higher fat mass by 0·99 kg (P = 0·01) and higher visceral fat area by 4·75 cm2 (P = 0·02), whereas non-significant associations were present among males. The findings highlight the importance of considering the timing of food intake relative to endogenous circadian timing instead of only as clock time.

Keywords

Chrono-nutritionCircadian rhythmFood timingAdiposityYoung adults

Information

Type
Research Article
Information
British Journal of Nutrition , Volume 132 , Issue 12 , 28 December 2024 , pp. 1629 - 1636
Check for updates
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of The Nutrition Society

Obesity represents a growing public health challenge(1), and it is particularly detrimental during early young adulthood as it increases the risk of developing chronic diseases later in life(Reference Ellison-Barnes, Johnson and Gudzune2). Obesity is typically defined through global metrics such as BMI, however, is a non-specific marker of body fat and provides no insight in body fat distribution(Reference Ahima and Lazar3). Specifically, Asians generally have a higher body fat than white people of the same age, sex and BMI category(4). Efforts to combat the obesity pandemic and its related metabolic diseases have mainly targeted traditional risk factors, e.g. excess energy intake and insufficient physical activity. However, traditional risk factors cannot fully explain the rapid rise of obesity during the past decades. Over the past 10 years, many causes of obesity and metabolic diseases have been identified, such as the timing of food intake(Reference Beccuti, Monagheddu and Evangelista5–Reference St-Onge, Ard and Baskin7).

Evidence for the importance of food timing for obesity comes from animal models(Reference Arble, Bass and Laposky8–Reference Fonken, Workman and Walton10) as well as observational(Reference Bernardes, Teixeira and Madalena11,Reference Dashti, Gomez-Abellan and Qian12) and experimental(Reference Allison, Hopkins and Ruggieri13,Reference Chellappa, Qian and Vujovic14) studies in humans, although not all studies are consistent(Reference Dote-Montero, Acosta and Sanchez-Delgado15,Reference Fiore, Scapaticci and Neri16) . Previous observational studies linking late eating with higher obesity risk revealed that food timing per se might alter body weight without a significant difference in energy intake and expenditure(Reference Ruiz-Lozano, Vidal and de Hollanda17,Reference Xiao, Garaulet and Scheer18) . However, most studies have utilised clock time to illustrate food timing(Reference St-Onge, Ard and Baskin7), which fails to accurately characterise food timing in the context of the circadian timing system. It is well established that there are individual differences in endogenous circadian timing relative to clock hour(Reference Horne and Ostberg19,Reference Roenneberg, Kuehnle and Juda20) , and that can vary by up to 10 h among college students(Reference McHill, Phillips and Czeisler21). This potential mismatch between clock and circadian timing may account for the contradictory associations between food timing and obesity in human studies.

Experimental evidence suggests the alignment between food timing and circadian rhythms, as with some forms of time-restricted eating or feeding, may prevent weight gain and promote metabolic health(Reference Manoogian, Chow and Taub22). However, food timing relative to endogenous circadian timing has not yet been thoroughly examined in human studies. McHill and colleagues(Reference McHill, Phillips and Czeisler21) examined the association between food timing relative to objective markers of circadian rhythms with adiposity in USA college students. The available evidence emphasises that it may be more physiologically relevant to consider the timing of food intake relative to endogenous circadian timing instead of only as clock time(Reference McHill, Phillips and Czeisler21).

Sex differences in the circadian system have been well characterised. For example, females have a shorter intrinsic circadian period and an earlier circadian phase than males(Reference Cain, Dennison and Zeitzer23,Reference Duffy, Cain and Chang24) . Given previous studies indicated that metabolic response to a meal depends on the timing of food intake relative to the individual’s circadian rhythm(Reference McHill, Phillips and Czeisler21,Reference Asher and Sassone-Corsi25,Reference Sofer, Stark and Madar26) , it is essential to explore the different metabolic responses to later circadian timing of food intake in males and females, which has not been investigated in previous research.

Hence, the objective of the present study was twofold: First, to examine the associations of food timing relative to clock time and endogenous circadian timing with general and visceral adiposity among Chinese college students and second to explore whether these associations differed by sex.

Methods

Participants

Participants were recruited from undergraduate students at local universities in Hefei, Anhui Province (China), using a multimethod approach spanning paper posters, flyers around campus and online advertisement (e.g. social media). The inclusion criteria included the following: (i) having the ability to download phone applications, (ii) not being engaged in a weight loss program, (iii) not taking any medication and (iv) not being engaged in night work within a 12-month period or traveling across time zones within a 3-month period before and during the procedure(Reference Phillips, Vidafar and Burns27). This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects were approved by the Ethics Committee of the Anhui Medical University (No. 20180082). Written informed consent was obtained from all subjects.

Procedure

Pre-Laboratory procedures

Participants wore a wrist actigraphy and documented sleep and food schedules in real -time for 7 consecutive days.

Laboratory procedures

For calculation of circadian timing, participants were admitted to the laboratory for 1 night, arriving at approximately 14.00 h. After admission, participants completed the bioelectrical impedance analysis to assess adiposity. The lights were dimmed (< 10 lux) at 18.00 h. Salivary melatonin was sampled hourly from 19.00 h until 01.00 h. The following morning, participants were asked to void, and then their height and weight were measured in light clothing without shoes before eating or drinking anything. After completion of the laboratory procedures, participants were given breakfast and transported to school.

Measures

Demographics

At inclusion, participants filled in a set of questionnaires on socio-demographics and lifestyle, including age, sex, smoking status (current, former and never), alcohol status (current, former and never), consumption of sugar-sweetened beverages (never, 1–3 times/week, 4–6 times/week and ≥ 1 time/day) and screen time (< 2 h/d, 2–5 h/d and ≥ 5 h/d).

Dim-Light melatonin onset

Melatonin concentrations were determined from serial saliva samples (approximately 2 ml) collected hourly using Salivettes (Starstedt, Nümbrecht), beginning at 19.00 and ending at 01.00 h (seven measurements per subject) under dim-light conditions (< 10 lux), as verified using a portable illuminance meter (TES-1339R; Taishi Corp.). During the sample period, participants were seated in comfortable chairs, watched a dimmed TV (< 10 lux), talked to each other, had access to toilet facilities (also < 10 lux) and a research assistant was present at all times to ensure they did not fall asleep. Snacks and water were offered following each sampling, and participants rinsed their mouths with water 20 min before each sample collection. The saliva sample was centrifuged at 3300 g for 5 min. If there was less than 2 ml of saliva after centrifugation, the sample was centrifuged again at 3500 g for 5 min. Samples were frozen and stored at −80°C before being analysed. Melatonin levels were determined using a commercially available enzyme-linked immunosorbent assay kit from IBL (IBL International GmbH). Dim-light melatonin onset (DLMO) was calculated as the linear interpolated clock time at which evening salivary melatonin concentrations crossed and maintained above a 5 pg/ml threshold(Reference Phillips, Vidafar and Burns27).

Clock time of food intake

Each participant was asked by Tencent Document (https://docs.qq.com/mall/index) to complete a 7-day (5 weekdays and 2 weekend days) ecological momentary assessment of food timing, based on a signal-contingent approach at semifixed intervals (i.e., 07.00–10.00, 10.00–13.00, 13.00–16.00, 16.00–19.00 and 19.00–22.00)(Reference Barchitta, Maugeri and Favara28). To synchronise participants’ food timing information while minimising the burden on participants, students received notifications five times a day via Tencent Document, and each data entry took approximately 1–2 min. Participants reported the start timing of breakfast, lunch, dinner and first and last eating occasion in real-time, with an eating occasion (EO) defined as food or drink containing any amount of calories(Reference Dashti, Gomez-Abellan and Qian12). Eating midpoint was defined as the midpoint between the first and last EO(Reference Zeron-Rugerio, Hernaez and Porras-Loaiza29). Eating jetlag was calculated in hours as follows: Eating midpoint on weekends − Eating midpoint on weekdays, and all analyses were conducted using the absolute value of the estimated eating jetlag(Reference Zeron-Rugerio, Hernaez and Porras-Loaiza29). An experienced research staff instructed study participants on how to accurately complete the food records at the start of the study and reviewed these data with the participants every day to ensure the correctness of self-reported data.

Circadian timing of food intake

The duration between the average clock time of the last EO and DLMO (last EO-DLMO) was used to calculate the circadian timing of food intake. DLMO is a marker of the circadian phase and the beginning of an individual’s biological night(Reference Benloucif, Guico and Reid30). Therefore, the last EO-DLMO was conducted in an attempt to capture the EO that may have occurred during the biological night when energy expenditure was lower(Reference Zitting, Vujovic and Yuan31) and diet-induced thermogenesis tended to be lower as well(Reference Morris, Garcia and Myers32).

Sleep

For 7 consecutive days, participants were instructed to wear an actigraphy (ActiGraph GT3X+) on the non-dominant wrist for 24 h per day (except when bathing or swimming) and to complete an electronic sleep diary. Sleep onset, offset, duration and sleep fragmentation index were manually scored using the Sadeh algorithm in ActiLife software version 6·13·3 (Actigraph) from actigraphy and corroborated with sleep diary times(Reference Sadeh, Sharkey and Carskadon33). The midsleep point refers to the midpoint between actigraphy-derived sleep onset and offset.

BMI

BMI (kg/m2) was estimated using laboratory measurements: height and weight (without shoes and light clothing) were taken using standard protocols, without eating or drinking after the morning void.

Body composition

Body composition, including percent body fat (%), fat mass (FM, kg) and visceral fat area (VFA, cm2), were measured using an InBody S10 body composition analyser (Inbody Co., Seoul, Korea), a bioelectrical impedance analysis device that is portable, non-invasive and non-radiation. The VFA measured by bioelectrical impedance analysis using Inbody ® (Inbody Co., Seoul, Korea) demonstrated a good correlation with VFA observed by the gold standard method – computed tomography(Reference Lee, Park and Ahn34–Reference Nagai, Komiya and Mori36).

Analysis

Categorical variables were compared using the χ 2 test and continuous variables using paired or unpaired t tests. Possible linear and non-linear relationships between last EO (clock time/circadian timing) and adiposity were evaluated with multivariable-adjusted restricted cubic splines. Multiple linear regression models were used to test whether last EO (clock time/circadian timing) and sleep measures predicted adiposity. Further, sex-stratified analyses were performed to explore the sex differences of these associations. Scatter plots and Pearson correlations were used to examine the sex-specific association between later circadian timing of food intake with general and visceral adiposity. Age, sex, alcohol status, sugar-sweetened beverage consumption, screen time and sleep duration were included as covariates in multivariate linear regression models and restricted cubic splines models. Analyses were performed with R Software version 4·2·1 (R Foundation for Statistical Computing) and STATA 16 statistical software (Stata Corporation). Alpha < 0·05 (two-sided) was deemed statistically significant.

Results

General characteristics of the study participants

Of the 151 participants, 133 were included in the statistical analysis finally. Of the eighteen who were not included, five were not included because DLMO was not observed during the sampling period and thirteen did not have a complete 7-day food record (see Flow Chart, Fig. 1). The average age of the study population was 20·7 (s d, 0·8) years, 84 were females.

Fig. 1.

Participant flow chart.

The main characteristics of study participants were presented by sex in Table 1. Compared with the male participants, female participants were more likely to have slightly earlier DLMO (female: 21:53, male: 22:06, P = 0·40) and last EO (clock time) of the day (19:40 v. 19:43, P = 0·83). When considering the circadian timing of last EO, females have the later circadian timing of last EO (i.e. last EO closer to DLMO) than males (–2·2 v. −2·4, P = 0·61). Female participants had higher percent body fat (28·0 v. 18·2, P < 0·001), FM (15·6 v. 12·8, P = 0·012) and VFA (65·9 v. 50·6, P = 0·004) than male participants, while male participants had higher BMI (20·9 v. 22·6, P = 0·003).

Table 1.

Descriptive characteristics of participants by sex among 133 young adults (Numbers and percentages; mean values and standard deviations)

PBF, percent body fat; FM, fat mass; VFA, visceral fat area; DLMO, dim-light melatonin onset; EO, eating occasion; Last EO-DLMO, local time of the last EO minus DLMO.

Data are mean (sd) or n (%) for continuous and categorical variables, respectively. For last EO-DLMO, higher values denote last EO closer to, or after, DLMO.

Bold font is used to indicate statistically significant P value (P < 0·05).

Linear and nonlinear associations between last eating occasion (clock time/circadian timing) with general and visceral adiposity

Multivariable-adjusted restricted cubic splines demonstrated significant positive linear associations between last EO-DLMO with adiposity (BMI: P overall = 0·03, P nonlinear = 0·08; percent body fat: P overall = 0·02, P nonlinear = 0·35; FM: P overall = 0·03, P nonlinear = 0·21; VFA: P overall = 0·02, P nonlinear = 0·14) (Fig. 2 (e)–(h)). In addition, sex-stratified analyses revealed that these linear associations were significant in females but not males (online Supplementary Fig. 1 (a)–(h)).

Fig. 2.

Restricted cubic splines representing the association between last EO (clock time/circadian timing) with general and visceral adiposity among 133 young adults. Heavy central line represents the estimated adjusted beta, with shaded ribbons denoting 95 % confidence interval. For last EO-DLMO, higher values denote last EO closer to, or after, DLMO. Splines were adjusted for age, sex (female, male), alcohol status (current, former and never), sugar-sweetened beverages consumption (never, 1–3 times/week, 4–6 times/week and ≥ 1 time/d), screen time (< 2 h/d, 2–5 h/d and ≥ 5 h/d) and sleep duration (h). DLMO, dim-light melatonin onset; EO, eating occasion; FM, fat mass; Last EO-DLMO, local time of the last EO minus DLMO; PBF, percent body fat; VFA, visceral fat area.

Association between last eating occasion (clock time/circadian timing) with general and visceral adiposity by sex

As shown in Table 2, the results demonstrated no association between the clock time of last EO with general and visceral adiposity, subanalyses after stratification for sex also revealed similar trends. However, when considering the circadian timing of last EO, significant associations were observed, such that individuals with their last EO closer to DLMO had higher BMI, percent body fat, FM and VFA (Table 2). Furthermore, sex-stratified analyses revealed that these associations were significant in females but not males (Fig. 3, Table 2). For females, each hour increase in last EO-DLMO was associated with higher BMI by 0·51 kg/m2 (95 % CI: 0·12, 0·91; P = 0·01), higher percent body fat by 1·05 % (95 % CI: 0·30, 1·80; P = 0·007), higher FM by 0·99 kg (95 % CI: 0·21, 1·77; P = 0·01) and higher VFA by 4·75 cm2 (95 % CI: 0·84, 8·65; P = 0·02), whereas no similar associations were observed among males.

Table 2.

Association between last EO (clock time/circadian time) with general and visceral adiposity by sex among 133 young adults (Unstandardised beta and 95 % confidence intervals)

PBF, percent body fat; FM, fat mass; VFA, visceral fat area; DLMO, dim-light melatonin onset; EO, eating occasion; Last EO-DLMO, local time of the last EO minus DLMO; B = unstandardised beta.

Adjusted for age, sex (female, male), alcohol status (current, former and, never), sugar-sweetened beverages consumption (never, 1–3 times/week, 4–6 times/week and ≥ 1 time/day), screen time (< 2 h/d, 2–5 h/d and ≥ 5 h/d) and sleep duration (h). For last EO-DLMO, higher values denote last EO closer to, or after, DLMO.

Bold font is used to indicate statistically significant P value (P < 0·05).

Fig. 3.

Scatter plots showing correlation between last EO-DLMO with BMI (a), PBF (b), FM (c) and VFA (d) by sex among 133 young adults. Pearson’s correlation coefficients and P value for each analysis are shown in the corresponding graph. For last EO-DLMO, higher values denote last EO closer to, or after, DLMO. DLMO, dim-light melatonin onset; EO, eating occasion; FM, fat mass; Last EODLMO, local time of the last EO minus DLMO; PBF, percent body fat; VFA, visceral fat area.

Association between sleep duration and sleep quality with general and visceral adiposity by sex

The results of the multiple linear regression models examining the associations of sleep duration and sleep quality with general and visceral adiposity are displayed in online Supplementary Tables 1 and 2. There were no significant associations between sleep duration and sleep quality with adiposity and results of stratified analyses by sex were consistent with overall results (both P > 0·05).

Discussion

The findings demonstrated that the timing of food intake relative to endogenous circadian timing rather than clock time was associated with general and visceral adiposity among young adults living in real-world settings. Strong linear associations between later circadian timing of food intake and adiposity were found among young females but not males. The findings highlight the importance of considering the timing of food intake relative to endogenous circadian timing instead of only as clock time. Nutrition strategies aimed to improve metabolic health should consider the circadian timing of food intake, particularly as our society moves toward personalised healthcare strategy.

Research on associations of food timing with adiposity is scant among young adults and has produced mixed results(Reference Fong, Caterson and Madigan37). One possible reason is that the clock time of food intake depends more on cultural and environmental factors such as work and school schedules, which may not accurately reflect the physiological impact of food timing on health. The present study indicated that later circadian timing of food intake (i.e. the timing of food intake relative to DLMO) was linked with higher general and visceral adiposity, while there were no differences when expressed in clock hours. These findings are consistent with a recent study conducted by McHill et al. (Reference McHill, Phillips and Czeisler21) and support the notion that examining the circadian timing of food intake rather than clock time could be more relevant when investigating associations with adiposity in young adults.

One potential mechanism for increased adiposity in response to later circadian timing of food intake is through effects on the thermic effect of food. Thermic effect of food is the energy expenditure in response to food intake(Reference Morris, Garcia and Myers32,Reference Bo, Fadda and Castiglione38,Reference Romon, Edme and Boulenguez39) , which is lower in the biological evening than morning due to the influence of the circadian system(Reference Morris, Garcia and Myers32). Given the association of adiposity with food timing relative to circadian timing rather than clock time, food intake closer to or after DLMO is a decrease in thermic effect of food, contributing to a positive energy balance and weight gain over time.

Another explanatory hypothesis is that the concurrence of food intake with elevated melatonin concentrations increases the probability of impaired glucose tolerance(Reference Garaulet, Lopez-Minguez and Dashti40,Reference Lopez-Minguez, Saxena and Bandin41) . It is known that postprandial glucose concentrations are higher in the biological evening than in the biological morning(Reference Bandin, Scheer and Luque42,Reference Morris, Yang and Garcia43) , and melatonin may further suppress the release of insulin in the biological evening(Reference Rubio-Sastre, Scheer and Gomez-Abellan44). Hence, eating earlier in the daytime to align with circadian rhythms may be preferred for improved blood glucose management, body weight control and related health outcomes, which will limit the concurrence of elevated glucose levels (due to postprandial state) and melatonin concentrations. Furthermore, later circadian timing of food intake may lead to a desynchronisation between the central and peripheral clocks and possibly result in metabolic disturbances(Reference Hernandez-Garcia, Navas-Carrillo and Orenes-Pinero45,Reference Scheer, Hilton and Mantzoros46) . In addition to the function of the circadian system, pathway analyses of the adipose tissue gene expression profiles revealed that late (circadian) eating modified lipid metabolic pathways in a direction consistent with increased adipogenesis and reduced lipolysis, contributing to increased lipid storage(Reference Vujovic, Piron and Qian47).

An interesting observation of the present study is the sex-specific association between later circadian timing of food intake with higher general and visceral adiposity in females but not males. One possible explanation for this observation is that the different metabolic response depends on the timing of food intake relative to an individual’s circadian system. Duffy and colleagues(Reference Duffy, Cain and Chang24) precisely examined the intrinsic circadian period in 157 healthy adults (33·1 % female) who underwent the gold-standard forced desynchrony protocol, and the results demonstrated that females have shorter intrinsic circadian periods than males and a greater proportion of females had intrinsic circadian periods shorter than 24 h. These sex-related circadian timing may explain the differential metabolic effects of food schedules among females v. males. However, whether and how sex modulates the adverse metabolic effects of later circadian timing of food intake is far less investigated. Further studies are needed to confirm these findings and investigate the underlying mechanism.

The absence of associations between sleep duration and sleep quality with adiposity in our results was unexpected, because insufficient sleep and poor sleep quality have been linked to higher weight(Reference Garaulet, Ortega and Ruiz48–Reference Zhao, Sun and He51). However, individuals with insufficient sleep may tend to increase food intake later in the day(Reference Markwald, Melanson and Smith52–Reference Spaeth, Dinges and Goel54) and later timing of food intake (closer to the biological night) is related to weight gain and obesity(Reference McHill, Phillips and Czeisler21).

Limitations of the study should be noted. There were no repeated assessments of circadian timing, thereby limiting interpretations of potential changes in circadian alignment with food timing, though DLMO is an extremely precise measure of circadian timing, and robust stability in DLMO time has been demonstrated in young adults living in real-world settings(Reference McHill, Sano and Hilditch55). A further key limitation of this study is the lack of data on dietary composition, which would have enhanced the analysis. Energy intake, macronutrient composition of the diet and overall diet quality may also interact synergistically with later circadian timing of food intake to influence adiposity(Reference Xiao, Garaulet and Scheer18), so that the observed effects may partly elated to the later circadian timing of food intake. However, the 7-day ecological momentary assessment of food records in this study provides more reliable and accurate data on food timing than questionnaire methods about usual mealtimes(Reference Maugeri and Barchitta56). In addition, we acknowledge that the population of university students may not be representative of the entire population in circadian timing of food intake. Older populations are more likely to have an earlier endogenous circadian phase and therefore may be more vulnerable to the effect of eating at a later clock time(Reference Knutson, Wu and Patel57) because it would be closer to their earlier DLMO. Other populations with known late eating at biological night are night shift workers, who have an increased risk of obesity(Reference Antunes, Levandovski and Dantas58) and metabolic syndrome(Reference De Bacquer, Van Risseghem and Clays59). Examination of the timing of food intake relative to DLMO in diverse populations is needed to extend our findings. Finally, the sample sizes differed between males and females in the current study, with males having a smaller sample size relative to females, which may affect the associations between later circadian timing of food intake and adiposity in males. Although the sex-specific association between later circadian timing of food intake with higher general and visceral adiposity is an important observation, comparable groups of males and females and longitudinal follow-up studies are needed to demonstrate the causality and potential mechanisms underlying this relationship.

Conclusions

In summary, the synthesis of real-world observations and precise laboratory data in the present study identified a new potential risk factor for weight gain and disease: food intake relative to endogenous circadian timing. The finding is particularly important for young adults and even teenagers, whose behaviour tends to be more evening-oriented. Future studies are needed to examine the interactions between circadian timing of food intake and energy intake, macronutrient profile and diet quality to influence weight trajectories, energy balance and metabolism over time. Furthermore, the sex-specific association observed in this study highlights an important, but under-explored, area of research – sex differences in later circadian timing of food intake effects, as well as the need to further investigate the underlying mechanisms.

Acknowledgements

The authors would like to thank all the volunteers for the participation and investigators for their contribution in the study.

This work was supported by the National Natural Science Foundation of China (Y.S., grant numbers 82173537) and Research Funds of Center for Big Data and Population Health of IHM (Y.S., grant numbers JKS2022008).

Y. H.: Conceptualisation, investigation, data curation, formal analysis, writing – review and editing. Y-x. X.: Methodology, data curation and writing – review and editing. Y-t. S.: Investigation and data curation. Y. Z.: Investigation and data curation. Y-h. W.: Formal analysis and supervision. F-b. T.: Conceptualisation and supervision. Y. S.: Conceptualisation, methodology, supervision and writing – review and editing. All authors approved the final submitted version.

The authors declared no conflict of interest.

Supplementary material

For supplementary material/s referred to in this article, please visit https://doi.org/10.1017/S0007114524001636

Footnotes

†

These authors contributed equally to this work.

References

NCD Risk Factor Collaboration (NCD-RisC) (2017) Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet 390, 2627–2642.CrossRefGoogle Scholar
Ellison-Barnes, A, Johnson, S & Gudzune, K (2021) Trends in obesity prevalence among adults aged 18 through 25 years, 1976–2018. JAMA-J Am Med Assoc 326, 2073–2074.CrossRefGoogle ScholarPubMed
Ahima, RS & Lazar, MA (2013) Physiology. The health risk of obesity--better metrics imperative. Science 341, 856–858.CrossRefGoogle ScholarPubMed
WHO Expert Consultation (2004) Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363, 157–163.CrossRefGoogle Scholar
Beccuti, G, Monagheddu, C, Evangelista, A, et al. (2017) Timing of food intake: sounding the alarm about metabolic impairments? A systematic review. Pharmacol Res 125, 132–141.CrossRefGoogle ScholarPubMed
Dashti, HS, Scheer, F, Saxena, R, et al. (2019) Timing of food intake: identifying contributing factors to design effective interventions. Adv Nutr 10, 606–620.CrossRefGoogle ScholarPubMed
St-Onge, MP, Ard, J, Baskin, ML, et al. (2017) Meal timing and frequency: implications for cardiovascular disease prevention: a scientific statement from the American Heart Association. Circulation 135, e96–e121.CrossRefGoogle ScholarPubMed
Arble, DM, Bass, J, Laposky, AD, et al. (2009) Circadian timing of food intake contributes to weight gain. Obesity 17, 2100–2102.CrossRefGoogle ScholarPubMed
Chaix, A, Lin, T, Le, HD, et al. (2019) Time-restricted feeding prevents obesity and metabolic syndrome in mice lacking a circadian clock. Cell Metab 29, 303–319.CrossRefGoogle ScholarPubMed
Fonken, LK, Workman, JL, Walton, JC, et al. (2010) Light at night increases body mass by shifting the time of food intake. P Natl Acad Sci USA 107, 18664–18669.CrossRefGoogle ScholarPubMed
Bernardes, DCN, Teixeira, GP, Madalena, RA, et al. (2023) Late meal intake is associated with abdominal obesity and metabolic disorders related to metabolic syndrome: a chrononutrition approach using data from NHANES 2015–2018. Clin Nutr 42, 1798–1805.CrossRefGoogle Scholar
Dashti, HS, Gomez-Abellan, P, Qian, J, et al. (2021) Late eating is associated with cardiometabolic risk traits, obesogenic behaviors, and impaired weight loss. Am J Clin Nutr 113, 154–161.CrossRefGoogle ScholarPubMed
Allison, KC, Hopkins, CM, Ruggieri, M, et al. (2021) Prolonged, controlled daytime v. delayed eating impacts weight and metabolism. Curr Biol 31, 650–657.CrossRefGoogle Scholar
Chellappa, SL, Qian, J, Vujovic, N, et al. (2021) Daytime eating prevents internal circadian misalignment and glucose intolerance in night work. Sci Adv 7, g9910.CrossRefGoogle ScholarPubMed
Dote-Montero, M, Acosta, FM, Sanchez-Delgado, G, et al. (2023) Association of meal timing with body composition and cardiometabolic risk factors in young adults. Eur J Nutr 62, 2303–2315.CrossRefGoogle ScholarPubMed
Fiore, G, Scapaticci, S, Neri, CR, et al. (2023) Chrononutrition and metabolic health in children and adolescents: a systematic review and meta-analysis. Nutr Rev. 82, 1309–1354.Google Scholar
Ruiz-Lozano, T, Vidal, J, de Hollanda, A, et al. (2016) Timing of food intake is associated with weight loss evolution in severe obese patients after bariatric surgery. Clin Nutr 35, 1308–1314.CrossRefGoogle ScholarPubMed
Xiao, Q, Garaulet, M & Scheer, F (2019) Meal timing and obesity: interactions with macronutrient intake and chronotype. Int J Obes 43, 1701–1711.CrossRefGoogle ScholarPubMed
Horne, JA & Ostberg, O (1977) Individual differences in human circadian rhythms. Biol Psychol 5, 179–190.CrossRefGoogle ScholarPubMed
Roenneberg, T, Kuehnle, T, Juda, M, et al. (2007) Epidemiology of the human circadian clock. Sleep Med Rev 11, 429–438.CrossRefGoogle ScholarPubMed
McHill, AW, Phillips, AJ, Czeisler, CA, et al. (2017) Later circadian timing of food intake is associated with increased body fat. Am J Clin Nutr 106, 1213–1219.CrossRefGoogle ScholarPubMed
Manoogian, E, Chow, LS, Taub, PR, et al. (2022) Time-restricted eating for the prevention and management of metabolic diseases. Endocr Rev 43, 405–436.CrossRefGoogle ScholarPubMed
Cain, SW, Dennison, CF, Zeitzer, JM, et al. (2010) Sex differences in phase angle of entrainment and melatonin amplitude in humans. J Biol Rhythm 25, 288–296.CrossRefGoogle ScholarPubMed
Duffy, JF, Cain, SW, Chang, AM, et al. (2011) Sex difference in the near-24-hour intrinsic period of the human circadian timing system. P Natl Acad Sci USA 108, 15602–15608.CrossRefGoogle ScholarPubMed
Asher, G & Sassone-Corsi, P (2015) Time for food: the intimate interplay between nutrition, metabolism, and the circadian clock. Cell 161, 84–92.CrossRefGoogle ScholarPubMed
Sofer, S, Stark, AH & Madar, Z (2015) Nutrition targeting by food timing: time-related dietary approaches to combat obesity and metabolic syndrome. Adv Nutr 6, 214–223.CrossRefGoogle ScholarPubMed
Phillips, A, Vidafar, P, Burns, AC, et al. (2019) High sensitivity and interindividual variability in the response of the human circadian system to evening light. P Natl Acad Sci USA 116, 12019–12024.CrossRefGoogle ScholarPubMed
Barchitta, M, Maugeri, A, Favara, G, et al. (2022) Development of a web-app for the ecological momentary assessment of dietary habits among college students: the HEALTHY-UNICT Project. Nutrients 14, 330.CrossRefGoogle ScholarPubMed
Zeron-Rugerio, MF, Hernaez, A, Porras-Loaiza, AP, et al. (2019) Eating jet lag: a marker of the variability in meal timing and its association with body mass index. Nutrients 11, 2980.CrossRefGoogle ScholarPubMed
Benloucif, S, Guico, MJ, Reid, KJ, et al. (2005) Stability of melatonin and temperature as circadian phase markers and their relation to sleep times in humans. J Biol Rhythm 20, 178–188.CrossRefGoogle ScholarPubMed
Zitting, KM, Vujovic, N, Yuan, RK, et al. (2018) Human resting energy expenditure varies with circadian phase. Curr Biol 28, 3685–3690.CrossRefGoogle ScholarPubMed
Morris, CJ, Garcia, JI, Myers, S, et al. (2015) The human circadian system has a dominating role in causing the morning/evening difference in diet-induced thermogenesis. Obesity 23, 2053–2058.CrossRefGoogle Scholar
Sadeh, A, Sharkey, KM & Carskadon, MA (1994) Activity-based sleep-wake identification: an empirical test of methodological issues. Sleep 17, 201–207.CrossRefGoogle ScholarPubMed
Lee, DH, Park, KS, Ahn, S, et al. (2015) Comparison of abdominal visceral adipose tissue area measured by computed tomography with that estimated by bioelectrical impedance analysis method in Korean subjects. Nutrients 7, 10513–10524.CrossRefGoogle ScholarPubMed
Nagai, M, Komiya, H, Mori, Y, et al. (2008) Development of a new method for estimating visceral fat area with multi-frequency bioelectrical impedance. Tohoku J Exp Med 214, 105–112.CrossRefGoogle ScholarPubMed
Nagai, M, Komiya, H, Mori, Y, et al. (2010) Estimating visceral fat area by multifrequency bioelectrical impedance. Diabetes Care 33, 1077–1079.CrossRefGoogle ScholarPubMed
Fong, M, Caterson, ID & Madigan, CD (2017) Are large dinners associated with excess weight, and does eating a smaller dinner achieve greater weight loss? A systematic review and meta-analysis. Br J Nutr 118, 616–628.CrossRefGoogle ScholarPubMed
Bo, S, Fadda, M, Castiglione, A, et al. (2015) Is the timing of caloric intake associated with variation in diet-induced thermogenesis and in the metabolic pattern? A randomized cross-over study. Int J Obes 39, 1689–1695.CrossRefGoogle Scholar
Romon, M, Edme, JL, Boulenguez, C, et al. (1993) Circadian variation of diet-induced thermogenesis. Am J Clin Nutr 57, 476–480.CrossRefGoogle ScholarPubMed
Garaulet, M, Lopez-Minguez, J, Dashti, HS, et al. (2022) Interplay of dinner timing and MTNR1B Type 2 diabetes risk variant on glucose tolerance and insulin secretion: a randomized crossover trial. Diabetes Care 45, 512–519.CrossRefGoogle ScholarPubMed
Lopez-Minguez, J, Saxena, R, Bandin, C, et al. (2018) Late dinner impairs glucose tolerance in MTNR1B risk allele carriers: a randomized, cross-over study. Clin Nutr 37, 1133–1140.CrossRefGoogle ScholarPubMed
Bandin, C, Scheer, FA, Luque, AJ, et al. (2015) Meal timing affects glucose tolerance, substrate oxidation and circadian-related variables: a randomized, crossover trial. Int J Obes 39, 828–833.CrossRefGoogle ScholarPubMed
Morris, CJ, Yang, JN, Garcia, JI, et al. (2015) Endogenous circadian system and circadian misalignment impact glucose tolerance via separate mechanisms in humans. P Natl Acad Sci USA 112, E2225–34.CrossRefGoogle ScholarPubMed
Rubio-Sastre, P, Scheer, FA, Gomez-Abellan, P, et al. (2014) Acute melatonin administration in humans impairs glucose tolerance in both the morning and evening. Sleep 37, 1715–1719.CrossRefGoogle ScholarPubMed
Hernandez-Garcia, J, Navas-Carrillo, D & Orenes-Pinero, E (2020) Alterations of circadian rhythms and their impact on obesity, metabolic syndrome and cardiovascular diseases. Crit Rev Food Sci 60, 1038–1047.CrossRefGoogle ScholarPubMed
Scheer, FA, Hilton, MF, Mantzoros, CS, et al. (2009) Adverse metabolic and cardiovascular consequences of circadian misalignment. P Natl Acad Sci USA 106, 4453–4458.CrossRefGoogle ScholarPubMed
Vujovic, N, Piron, MJ, Qian, J, et al. (2022) Late isocaloric eating increases hunger, decreases energy expenditure, and modifies metabolic pathways in adults with overweight and obesity. Cell Metab 34, 1486–1498.CrossRefGoogle ScholarPubMed
Garaulet, M, Ortega, FB, Ruiz, JR, et al. (2011) Short sleep duration is associated with increased obesity markers in European adolescents: effect of physical activity and dietary habits. The HELENA study. Int J Obes 35, 1308–1317.CrossRefGoogle ScholarPubMed
Patel, SR & Hu, FB (2008) Short sleep duration and weight gain: a systematic review. Obesity 16, 643–653.CrossRefGoogle ScholarPubMed
Taheri, S, Lin, L, Austin, D, et al. (2004) Short sleep duration is associated with reduced leptin, elevated ghrelin, and increased body mass index. Plos Med 1, e62.CrossRefGoogle ScholarPubMed
Zhao, B, Sun, S, He, X, et al. (2021) Sleep fragmentation and the risk of obesity: the Sleep Heart Health Study. Obesity 29, 1387–1393.CrossRefGoogle ScholarPubMed
Markwald, RR, Melanson, EL, Smith, MR, et al. (2013) Impact of insufficient sleep on total daily energy expenditure, food intake, and weight gain. P Natl Acad Sci USA 110, 5695–5700.CrossRefGoogle ScholarPubMed
Nedeltcheva, AV, Kilkus, JM, Imperial, J, et al. (2009) Sleep curtailment is accompanied by increased intake of calories from snacks. Am J Clin Nutr 89, 126–133.CrossRefGoogle ScholarPubMed
Spaeth, AM, Dinges, DF & Goel, N (2013) Effects of experimental sleep restriction on weight gain, caloric intake, and meal timing in healthy adults. Sleep 36, 981–990.CrossRefGoogle ScholarPubMed
McHill, AW, Sano, A, Hilditch, CJ, et al. (2021) Robust stability of melatonin circadian phase, sleep metrics, and chronotype across months in young adults living in real-world settings. J Pineal Res 70, e12720.CrossRefGoogle Scholar
Maugeri, A & Barchitta, M (2019) A systematic review of ecological momentary assessment of diet: implications and perspectives for nutritional epidemiology. Nutrients 11, 2696.CrossRefGoogle ScholarPubMed
Knutson, KL, Wu, D, Patel, SR, et al. (2017) Association between sleep timing, obesity, diabetes: the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) Cohort Study. Sleep 40, zsx014.CrossRefGoogle Scholar
Antunes, LC, Levandovski, R, Dantas, G, et al. (2010) Obesity and shift work: chronobiological aspects. Nutr Res Rev 23, 155–168.CrossRefGoogle ScholarPubMed
De Bacquer, D, Van Risseghem, M, Clays, E, et al. (2009) Rotating shift work and the metabolic syndrome: a prospective study. Int J Epidemiol 38, 848–854.CrossRefGoogle ScholarPubMed
Figure 0

Fig. 1. Participant flow chart.

Figure 1

Table 1. Descriptive characteristics of participants by sex among 133 young adults (Numbers and percentages; mean values and standard deviations)

Figure 2

Fig. 2. Restricted cubic splines representing the association between last EO (clock time/circadian timing) with general and visceral adiposity among 133 young adults. Heavy central line represents the estimated adjusted beta, with shaded ribbons denoting 95 % confidence interval. For last EO-DLMO, higher values denote last EO closer to, or after, DLMO. Splines were adjusted for age, sex (female, male), alcohol status (current, former and never), sugar-sweetened beverages consumption (never, 1–3 times/week, 4–6 times/week and ≥ 1 time/d), screen time (< 2 h/d, 2–5 h/d and ≥ 5 h/d) and sleep duration (h). DLMO, dim-light melatonin onset; EO, eating occasion; FM, fat mass; Last EO-DLMO, local time of the last EO minus DLMO; PBF, percent body fat; VFA, visceral fat area.

Figure 3

Table 2. Association between last EO (clock time/circadian time) with general and visceral adiposity by sex among 133 young adults (Unstandardised beta and 95 % confidence intervals)

Figure 4

Fig. 3. Scatter plots showing correlation between last EO-DLMO with BMI (a), PBF (b), FM (c) and VFA (d) by sex among 133 young adults. Pearson’s correlation coefficients and P value for each analysis are shown in the corresponding graph. For last EO-DLMO, higher values denote last EO closer to, or after, DLMO. DLMO, dim-light melatonin onset; EO, eating occasion; FM, fat mass; Last EODLMO, local time of the last EO minus DLMO; PBF, percent body fat; VFA, visceral fat area.

Supplementary material: File

Huang et al. supplementary material

Huang et al. supplementary material
Download Huang et al. supplementary material(File)
File 181.1 KB