ArticleInternational journal of obesity (2005)2026
Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to understand how obesity risk varies according to multiple lifestyle behavior recommendations.
Article in International journal of obesity (2005), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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4 authors.
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Abstract
backgroundThe combined and interactive effects of multiple lifestyle behaviours on obesity risk are not well understood. We used Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to examine how adherence to public health recommendations for five lifestyle behaviours affects BMI and obesity risk.
methodsThe sample included 139,540 men and 125,455 women from the UK Biobank. We categorized fruit and vegetable intake, physical activity, sleep duration and alcohol intake as binary variables (meeting vs. not meeting guidelines), and smoking status into three categories (previous, current, never). These categories were combined to form 48 unique strata, representing all possible combinations of the five behaviours. Linear and binary logistic MAIHDA models were used, with individuals nested within strata, and BMI and obesity status (obesity vs. normal weight) as outcomes. Three models were employed: Model 1 (null), Model 2 (with fixed effects for lifestyle behaviours), and Model 3 (with confounders and fixed effects). Variance Partition Coefficient (VPC), Proportional Change in Variance (PCV), and predicted BMI and obesity risk were estimated.
resultsFor both sexes, strata with the lowest obesity risk were associated with meeting most recommendations, while strata with the highest risk were linked to meeting few. Logistic Model 1 VPCs revealed 7% of variance in obesity risk among males and 5% among females was explained by between-strata differences. In Model 3, VPCs attenuated to 0.5% among males and 0.1% among females, suggesting differences in obesity risk were largely additive effects. PCVs from Model 3 also indicated primarily additive rather than interactive effects. Results were similar for BMI in the linear models.
conclusionsUsing a novel statistical approach, this study shows that additive effects of multiple lifestyle behaviours predominantly explain differences in BMI and obesity risk. Meeting more public health lifestyle recommendations is important in mitigating obesity risk.
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