INTERACTION BETWEEN CLOCK GENE AND LIFESTYLE FACTOR IN RELATION TO OBESITY RISK FACTOR

Abstract

Background: Circadian rhythms regulate vital biological processes affecting metabolic pathways. Its dysregulation is associated with obesity, cardiovascular diseases, insulin resistance, and other signs of metabolic syndrome. Circadian rhythms are maintained by a transcriptional auto-regulatory feedback loop including core clock genes. Evidence proposed the implication of the clock genes in obesity. It is well known that circadian rhythm is a regulator of sleep and eating, disruption can lead to alterations in macronutrient choice and overconsumption of high-energy food and short sleep, both are known risk factors for obesity. The interaction between clock genes and lifestyle factors relative to obesity risk factor is not well explored among adults in Qatar. Methods: This cross-sectional study was conducted on 6,000 Qatari adult participants with existing genetic information from the Qatar Biobank (QBB), sequenced through Qatar Genome. A clock gene risk score was constructed to estimate the polygenic risk scores (PRS) using 185 single nucleotide polymorphisms (SNPs) located within 18 clock genes. Dietary intake patterns were constructed using factor analysis based on food frequency intake. The association between exposure variables including clock genes, lifestyle factors, and obesity risk factor was assessed by multivariable logistic regression models. Subgroup analyses were also conducted, and multiplicative interactions were tested. All P-values presented are two-tailed, and P-values <.05 were considered to be statistically significant. All statistical analyses were performed using the STATA 18. Results: There was a positive association between quartiles of PRS and obesity in both before and after adjustment. After adjusting for age, gender, education, smoking, and physical activity levels, the extreme quartile of PRS was still significant for obesity (aOR 1.26; 95%CI 1.08-1.47). Subgroup analysis showed a positive association between PRS quartiles for obesity in women, high education levels, non-smokers, low intake of sweets/fast food, and high consumption of mixed dietary patterns and modern breakfast. Quartiles 2 to 4 of PRS quartiles had a statistically significant association with obesity compared to those with quartile 1 of PRS, suggesting that 75% of the population had SNPs with an increased risk of obesity. Women showed a strong association with obesity compared to men (OR: 1.51, 95% CL: 1.32-1.73), while smokers and ex-smokers had a decreased odds of having obesity by 21% and 12% respectively in comparison to nonsmokers. Participants with a high level of education also showed a decreased risk of obesity (OR: 0.60, 95% CL: 0.54-0.67) compared with a low education level. Conclusion: The available evidence supports the interaction between clock genes and lifestyle factors in regard to obesity. It provides new insight into the gene-environment interaction proposing the risk for obesity among individuals with a high genetic predisposition may vary by sociodemographic and lifestyle factors. This study has a potential impact on the prevention and management of obesity such as arranging physical activities and diet management programs targeting people with a genetic predisposition to obesity like women. Raising awareness about the significance of maintaining a regular circadian rhythm is also recommended.