Header

UZH-Logo

Maintenance Infos

Significantly different roles of economic affluence in sex-specific obesity prevalence rates: understanding more modifications within female body weight management


You, Wenpeng; Henneberg, Maciej (2022). Significantly different roles of economic affluence in sex-specific obesity prevalence rates: understanding more modifications within female body weight management. Scientific Reports, 12:15757.

Abstract

Socioeconomic status has been associated with obesity prevalence increase in both males and females worldwide. We examined the magnitude of the difference between the two relationships and explored the independence of both relationships. Country specific data on gross domestic product (GDP) per capita, sex-specific obesity prevalence rates, urbanisation, total calories availability and level of obesity, genetic background accumulation (measured by the Biological State Index, Ibs) were obtained for 191 countries. Curvilinear regressions, bivariate and partial correlations, linear mixed models and multivariate linear regression analyses were used to examine the relationship between GDP and obesity prevalence rates in males and females respectively. Fisher's r-to-z transformation, F-test and R2 increment in multivariate regression were used to compare results for males and females. GDP significantly correlated with sex-specific obesity prevalence rates, but significantly more strongly with male obesity prevalence in bivariate correlation analyses. These relationships remained independent of calories availability, Ibs and urbanization in partial correlation model. Stepwise multiple regression identified that GDP was a significant predictor of obesity prevalence in both sexes. Multivariate stepwise regression showed that, when adding GDP as an obesity prevalence predictor, the absolute increment of R2 in male fit model (0.046) was almost four (4) times greater than the absolute increment in female model fit (0.012). The Stepwise analyses also revealed that 68.0% of male but only 37.4% of female obesity prevalence rates were explained by the total contributing effects of GDP, Ibs, urbanization and calories availability. In both Pearson's r and nonparametric analyses, GDP contributes significantly more to male obesity than to female obesity in both developed and developing countries. GDP also determined the significant regional variation in male, but not female obesity prevalence. GDP may contribute to obesity prevalence significantly more in males than in females regardless of the confounding effects of Ibs, urbanization and calories. This may suggest that aetiologies for female obesity are much more complex than for males and more confounders should be included in the future studies when data are available.

Abstract

Socioeconomic status has been associated with obesity prevalence increase in both males and females worldwide. We examined the magnitude of the difference between the two relationships and explored the independence of both relationships. Country specific data on gross domestic product (GDP) per capita, sex-specific obesity prevalence rates, urbanisation, total calories availability and level of obesity, genetic background accumulation (measured by the Biological State Index, Ibs) were obtained for 191 countries. Curvilinear regressions, bivariate and partial correlations, linear mixed models and multivariate linear regression analyses were used to examine the relationship between GDP and obesity prevalence rates in males and females respectively. Fisher's r-to-z transformation, F-test and R2 increment in multivariate regression were used to compare results for males and females. GDP significantly correlated with sex-specific obesity prevalence rates, but significantly more strongly with male obesity prevalence in bivariate correlation analyses. These relationships remained independent of calories availability, Ibs and urbanization in partial correlation model. Stepwise multiple regression identified that GDP was a significant predictor of obesity prevalence in both sexes. Multivariate stepwise regression showed that, when adding GDP as an obesity prevalence predictor, the absolute increment of R2 in male fit model (0.046) was almost four (4) times greater than the absolute increment in female model fit (0.012). The Stepwise analyses also revealed that 68.0% of male but only 37.4% of female obesity prevalence rates were explained by the total contributing effects of GDP, Ibs, urbanization and calories availability. In both Pearson's r and nonparametric analyses, GDP contributes significantly more to male obesity than to female obesity in both developed and developing countries. GDP also determined the significant regional variation in male, but not female obesity prevalence. GDP may contribute to obesity prevalence significantly more in males than in females regardless of the confounding effects of Ibs, urbanization and calories. This may suggest that aetiologies for female obesity are much more complex than for males and more confounders should be included in the future studies when data are available.

Statistics

Citations

Dimensions.ai Metrics
2 citations in Web of Science®
2 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

8 downloads since deposited on 09 Jan 2023
2 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Evolutionary Medicine
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Multidisciplinary
Uncontrolled Keywords:Multidisciplinary
Language:English
Date:21 September 2022
Deposited On:09 Jan 2023 09:34
Last Modified:28 Jun 2024 01:37
Publisher:Nature Publishing Group
ISSN:2045-2322
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1038/s41598-022-19633-3
PubMed ID:36130963
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)