https://www.selleckchem.com/Bcl-2.html ion measures for enterprise workers to reduce the impact of large-scale public health events like the COVID-19 on their quality of life. Body mass index (BMI) is an accepted measurement that is widely used to quantify overweight and obesity at the population level. Previous studies have described the distribution variation of BMI through applying common statistical approaches, such as multiple linear or logistic regression analyses. This study proposed that associations between BMI and socioeconomic characteristics, diet, and lifestyle factors varied across the conditional BMI distribution. This study was based on a sample of 10,023 Chinese adults who participated in the monitoring of chronic diseases and associated risk factors in Shaanxi Province, Northwest China, in 2013. Cross-quantile factors were observed in the relationships between major risk factors and BMI through quantile regression (QR) and ordinary least squares (OLS) regression. Participants' mean BMI was 24.19 ± 3.51 kg/m (range 14.33-52.82 kg/m ). The QR results showed that living in urban areas was associated with BMI in the low and central quantiles (10th-60th). Participants with 6-9 years of education were 0.23-0.38 BMI units higher in the first half of the BMI quantiles compared with those with ≤6 years of education. There was a positive association between consumption of red meat and BMI; however, the association diminished from the 10th to the 50th quantile. Intake of oil and alcohol were positively associated with all BMI quantiles. Cigarette smoking per day was negatively associated with BMI, which showed a U-shaped distribution. The above results were also observed in the OLS. This study implies that in addition to socioeconomic characteristics, limiting oil and alcohol intake may decrease BMI score. Consuming more red meat could be a strategy to increase BMI. This study implies that in addition to socioeconomic characteristics, limiting oil and alcohol