Gender-Specific Association Between Sleep Duration and Body Mass Index in Rural China - Lin Ren; Lin Ren; Li Chang; Yijun Kang; Yaling Zhao; Fangyao Chen; Leilei Pei (Frontiers in Endocrinology, 2022)
BackgroundThis study aimed at investigating the association of sleep duration with body mass index (BMI) by gender among adult residents in rural Hanzhong of Shaanxi province, Northwest China.MethodsA two-level stratified random cluster sampling method was used to select adult residents between the ages of 18 and 80 years. All information including sociodemographic characteristics and lifestyles was collected by face-to-face interview with a structured questionnaire. According to standard methods, trained staff were responsible for anthropometric measurements using calibrated instruments in an empty room. By gender, both ordinary least square regression (OLS) and quantile regression (QR) were used to analyze the relationship between sleep time and BMI controlling for other confounders. The restricted cubic splines with five knots were further used to express the potentially non-linear association between sleep time and BMI.ResultsA total of 3,017 eligible participants were included in the study. After controlling for confounding factors including sociodemographic characteristics and lifestyles, OLS regression did not indicate any significant association of sleep duration with BMI among men and women. Among men, it was clear that there is an inverse U-shaped relationship between sleep time and BMI beyond the 66.0th percentile (BMI ≥24). Among women, quantile regression presented a significant U-shaped relationship between BMI and sleep duration. According to the restricted cubic splines, the women who sleep for approximately 9 h had the lowest BMI, and when sleep duration approached approximately 7 h among men, their BMI would be the highest.ConclusionsThe U-shaped and inverse U-shaped relationships between sleep duration and BMI were clearly observed for women and men, respectively, in our study. The identification of potentially relevant modifiable risk factors may provide better preventive approaches to obesity.