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Enregistrement W4412653395 · doi:10.1002/oby.24350

Timing Matters: Early Eating Mitigates Genetic Susceptibility for Obesity

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

Notice bibliographique

RevueObesity · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueDiet and metabolism studies
Établissements canadiensHamilton Health SciencesPopulation Health Research InstituteMcMaster UniversityImpact
Organismes subventionnairesnon disponible
Mots-clésObesityMedicineGenetic predispositionPsychologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,595

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,288
Écart entre enseignants0,268 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2025
Routes d'admission1
Résumé présentoui

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