Cold climate and obesity in China: a cross-sectional ecological analysis.

Background: Most research on obesity has focused on dietary, behavioral, and socioeconomic determinants. Environmental exposures such as climate, however, may also shape geographic variation in obesity, yet evidence from China remains scarce. Understanding whether cold exposure contributes to obesity distribution is important for broadening the framework of obesity prevention. Methods: We conducted an ecological analysis of 26 mainland Chinese provinces. Annual cold days (<4 °C) were averaged from 2000 to 2021. Overweight and obesity prevalence in 2019, derived from a national surveillance dataset of 15.8 million adults, were linked to climatic exposures. Associations were examined using ordinary least squares (OLS) regression and geographically weighted regression (GWR) to account for spatial heterogeneity. Sex differences were tested with pooled log-log models including interaction terms. Results: OLS residuals exhibited spatial autocorrelation, which was resolved by GWR. GWR revealed pronounced geographic variation: associations between cold days and overweight/obesity were strongest in transitional eastern provinces (Shanghai, Jiangsu, Zhejiang, Anhui; coefficients 0.40-0.48), whereas northern and western provinces showed relatively weaker or nonsignificant associations. In pooled models, the cold-overweight association was stronger in women than men ( Conclusion: Chronic cold exposure contributes to the spatial distribution of obesity in China, with the strongest effects in transitional eastern provinces. Climate should be recognized alongside socioeconomic and behavioral determinants in obesity epidemiology and considered in the design of regionally tailored prevention strategies.