Circadian clock gene variants and their link with chronotype, chrononutrition, sleeping patterns and obesity in the European prospective investigation into cancer and nutrition (EPIC) study
Background & aims: The circadian clock is involved in the control of daily rhythms and is related to the individual's chronotype, i.e., the morningness-eveneningness preference. Knowledge is limited on the relationship between circadian genes, chronotype, sleeping patterns, chronutrition an...
| Autores: | , , , , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/193104 |
| Acceso en línea: | https://hdl.handle.net/2445/193104 |
| Access Level: | acceso abierto |
| Palabra clave: | Cronobiologia Ritmes circadiaris Antropometria Obesitat Chronobiology Circadian rhythms Anthropometry Obesity |
| Sumario: | Background & aims: The circadian clock is involved in the control of daily rhythms and is related to the individual's chronotype, i.e., the morningness-eveneningness preference. Knowledge is limited on the relationship between circadian genes, chronotype, sleeping patterns, chronutrition and obesity. The aim was to explore these associations within the EPIC-Spain cohort study. Methods: There were 3183 subjects with information on twelve genetic variants of six genes (PER1, PER2, PER3, CRY1, NR1D1, CLOCK). Their association was evaluated with: chronotype and sleeping duration/ quality (assessed by questionnaires), chrononutrition (number of meals and timing of intake assessed by a diet history), and also anthropometric measures of obesity at early and late adulthood (in two points in time), such as weight and waist circumference (assessed by physical measurements). Multivariable logistic and linear regression as well as additive genetic models were applied. Odds ratios (ORs), b coefficients, and p-values corrected for multiple comparisons were estimated. Genetic risk scores (GRS) were built to test gene-outcome associations further. Results: At nominal significance level, the variant rs2735611 (PER1 gene) was associated with a 11.6% decrease in long-term weight gain (per-allele b beta - -0.12), whereas three CLOCK gene variants (rs12649507, rs3749474 and rs4864548), were associated with a similar to 20% decrease in waist circumference gain (per-allele beta similar to -0.19). These and other associations with body measures did not hold after multiple testing correction, except waist-to-hip ratio and rs1801260, rs2070062 and rs4580704 (CLOCK gene). Associations with chrononutrition variables, chronotype and sleep duration/quality failed to reach statistical significance. Conversely, a weighted GRS was associated with the evening/late chronotype and with all other outcomes (p < 0.05). The chronotype-GRS was associated with an increased overweight/ obesity risk (vs normal weight) in both early and late adulthood (OR = 2.2; p = 0.004, and OR = 2.1; p = 0.02, respectively). Conclusion: Genetic variants of some circadian clock genes could explain the link between genetic susceptibility to the individual's chronotype and obesity risk. |
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