Abstract
BackgroundBody 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.MethodsThis 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.ResultsParticipants’ mean BMI was 24.19 ± 3.51 kg/m2 (range 14.33–52.82 kg/m2). 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.ConclusionThis 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.
Highlights
Body mass index (BMI) is an accepted measurement that is widely used to quantify overweight and obesity at the population level
Given that the effect of risk factors may vary by BMI distribution, we aimed to examine the effects of socioeconomic status, lifestyle factors, and other factors on different BMI status by Quantile regressions (QRs)
Male participants consumed more red meat (χ2 = 14.184, P < 0.001) and alcohol (χ2 = 34.101, P < 0.001) than females, but they showed no difference in consumption of vegetables, fruits, salt, and oil (Table 1)
Summary
Body mass index (BMI) is an accepted measurement that is widely used to quantify overweight and obesity at the population level. Many studies have focused on obesity in China, there are noticeable demographic and socioeconomic disparities given the wide variation in China [12] Behaviors such as tobacco smoking, alcohol intake, and physical activity have been shown to be associated with individual weight gain or loss [13,14,15]. Most previous studies that examined the effect of health behaviors on changes in BMI used either multiple linear or logistic regression analyses, which do not capture distribution variations in different BMI quantiles [16,17,18]. The former methods may mask some important relations in various quantiles of BMI distribution. This enabled us to observe the effect of risk factors on BMI ranging from low to high quantiles along the entire BMI distribution
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
