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Record W4412653395 · doi:10.1002/oby.24350

Timing Matters: Early Eating Mitigates Genetic Susceptibility for Obesity

2025· article· en· W4412653395 on OpenAlexaff
Divya Joshi, Marie Pigeyre

Bibliographic record

VenueObesity · 2025
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsHamilton Health SciencesPopulation Health Research InstituteMcMaster UniversityImpact
Fundersnot available
KeywordsObesityMedicineGenetic predispositionPsychologyInternal medicine

Abstract

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Obesity is a multifactorial and highly heritable condition, influenced by the interplay between genetic predisposition and modifiable lifestyle behaviors. While the contribution of diet composition and physical activity to energy balance is well established, growing evidence highlights the role of circadian rhythms, particularly meal timing, in regulating metabolic health [1]. Disruptions in the synchrony between endogenous biological rhythms and external behavioral cues, such as the timing of food intake, have been associated with increased risk of obesity, insulin resistance, and related cardiometabolic disorders. However, the mechanisms and the extent to which timing of meal intake interacts with genetic susceptibility to influence obesity-related outcomes remain not fully clear. In this issue of Obesity, a study by Rocío De la Peña-Armada et al. [2], conducted in the Obesity, Nutrigenetics, Timing, and Mediterranean (ONTIME) cohort, addresses this gap by examining the independent and interactive effects of meal timing and polygenic risk for body mass index (BMI) on weight-related outcomes. The authors included 1195 adults with overweight or obesity who were participating in a standardized, multimodal weight loss intervention in Spain. The intervention comprised dietary counseling, physical activity, and behavioral therapy but notably did not advise on meal timing, thereby allowing for natural variation in chrononutritional behavior. Meal timing was assessed via self-reported usual times for breakfast and dinner, from which the midpoint of food intake was calculated and used as a marker of chrononutritional behavior [2]. Participants were classified as early or late eaters based on the tertiles of this midpoint. Genetic predisposition to obesity was quantified using a genome-wide polygenic score for BMI (PGS-BMI), generated through the polygenic scores of continuous shrinkage (PGS-CS) method, which modeled the effects of ~900,000 single-nucleotide polymorphisms (SNPs) using a Bayesian framework [3]. This approach offers improved prediction of complex traits over traditional polygenic risk scoring methods by accounting for the continuous shrinkage of SNP effects and linkage disequilibrium. The study reports robust and clinically relevant findings [2]. Later meal timing was independently associated with higher baseline BMI, slower weight loss during intervention, and poorer long-term weight maintenance. Specifically, each 1-h delay in meal intake midpoint was associated with nearly a 1-kg/m2 increase in BMI, a slower weight loss rate of 0.05 kg/week, and a 3% increase in weight regained after an average of 12 years. These associations persisted even after adjusting for potential confounders, such as total energy intake, macronutrient distribution, sleep duration, physical activity, and educational level. Notably, the authors observed a significant interaction between meal timing and polygenic risk. Individuals in the highest PGS-BMI tertile who were late eaters had substantially higher BMI compared with their early-eating counterparts (mean difference of 3 kg/m2). In contrast, among early eaters, BMI did not differ significantly across PGS-BMI tertiles, suggesting that early meal timing may mitigate the phenotypic expression of genetic obesity risk. No significant interactions were observed for weight loss success or long-term maintenance, which may point to distinct physiological mechanisms underlying weight loss versus weight regulation. These findings have important implications for both research and clinical practice. First, they reinforce the growing recognition of meal timing as a modifiable behavioral target in obesity prevention and management, particularly for individuals with high genetic risk. Second, they support the integration of genetic information to tailor lifestyle interventions and optimize their effectiveness, which is one of the goals of precision nutrition [4]. The gene–environment interaction observed between circadian behavior and genetic susceptibility likely involves multiple pathways [5], including circadian misalignment of peripheral metabolic clocks (e.g., in the liver and adipose tissue), altered energy expenditure and substrate utilization during late eating, and hormonal dysregulation involving leptin, insulin, and cortisol. These effects appear to occur independently of total caloric intake and macronutrient composition, emphasizing that “when we eat” is an essential dimension of dietary behavior, in addition to “what we eat.” However, cultural and contextual factors must be considered when translating these findings. Mediterranean populations, such as the one studied, often follow later but more structured meal patterns than those in Western countries, which may influence the expression of circadian risk factors and the applicability of interventions. As such, regional differences and mealtime social norms should inform public health messaging and future interventional designs. Overall, this study [2] adds to the evidence that early meal timing may attenuate the impact of genetic obesity susceptibility. These results advocate for the inclusion of chronobiological principles in personalized obesity prevention and treatment strategies and highlight the need for future research, including randomized controlled trials and mechanistic studies, to establish causality and refine intervention strategies tailored to individual genetic profiles. The authors declare no conflicts of interest. Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.288
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2025
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