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Estimation of the relationship between body mass index and EQ-5D health utilities in individuals with type 2 diabetes: Evidence from the population-based KORA studies
Authors:Hunger Matthias  Schunk Michaela  Meisinger Christa  Peters Annette  Holle Rolf
Affiliation:Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Health Economics and Health Care Management, 85764 Neuherberg, Germany.
Abstract:ObjectivesObesity is known to be an important risk factor for type 2 diabetes and its related comorbid conditions; however, its specific impact on generic health-related quality of life (HRQL) is less clear. The objective of this study was to estimate the association between body mass index (BMI) and HRQL in individuals with type 2 diabetes.MethodsThe EQ-5D quality of life questionnaire was administered in a follow-up of 10,385 participants aged 33–94 of the population-based German MONICA/KORA surveys. 1033 participants with type 2 diabetes were identified by self-report combined with validated physician diagnoses. Semiparametric additive regression models were used to estimate the effect of BMI on EQ-5D health utilities adjusted for age, sex, education and comorbidities.ResultsBMI was significantly associated with EQ-5D health utilities even after adjustment for macro- and microvascular complications. The functional relationship between BMI and utilities was nonlinear, reflecting optimal health around 26 kg/m2 and significantly decreasing health utilities with increasing levels of overweight and obesity (? 0.09 points between BMI values 26 and 40). Among the diabetic complications, the history of a stroke (? 0.13) and neuropathy (? 0.10) were the strongest predictors of reduced health utility scores.ConclusionsBMI is strongly associated with health utilities in persons with type 2 diabetes. This suggests that lifestyle measures to reduce obesity can markedly improve patients' health-related quality of life and that the negative effect of potential weight gain should be taken into account when determining patient preferences for different type 2 diabetes treatment options.
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