CLOCK 3111 T/C SNP Interacts with evening preference, appetite hormones, late eating and sleep reduction for obesity and food intake in obese Iranian adults

Abstract

Abstract Background Previous studies have shown that the Circadian locomotor output cycles protein kaput (CLOCK) gene (rs1801260) variant may be associated with obesity risk. Moreover, lifestyle and biochemical parameters have been shown to elicit favorable effects on the obesity risk potentially. Therefore, this study seeks to investigate the effect of lifestyle, biochemical parameters, and CLOCK interaction on food intake and risk of obesity. Methods This cross-sectional study comprised 403 overweight and/or obese subjects aged 20–50 from Iran. The CLOCK rs1801260 data was measured by the PCR-RFLP method. Dietary intake, food timing, sleep duration, appetite, and chronotype were assessed by using validated questionnaires. Ghrelin and Glucagon-like peptide-1 (GLP-1) were measured by radioimmunoassay in plasma samples. Participants were also divided into three groups based on rs1801260 genotype. Univariate linear regression models were used to assess the interaction between CLOCK and study parameters on body weight, and logistic regression models were used for interaction terms between CLOCK and study parameters on food intakes. Results After controlling confounding factors, our findings showed significant interactions between the C-allele carrier group with chronotype (Pinteraction = 0.048), appetite (Pinteraction = 0.035), lunch time (Pinteraction = 0.016), dinner time (Pinteraction = 0.047), GLP-1 (Pinteraction = 0.035), and ghrelin (Pinteraction = 0.022) on obesity. Also, there was a significant interaction between evening type, high appetite, short sleep and late lunch with C-allele on food intake. Conclusion The results of the present study indicate that differences in sleep, appetite hormones, eating behaviors and chronotype influence the risk of obesity differently by CLOCK genotype. These results highlight that diet, gene variants, lifestyle factors, and their interaction should be considered in obesity risk assessment.