Sander K R van Zon1, Sijmen A Reijneveld, Peter J van der Most, Morris A Swertz, Ute Bültmann, Harold Snieder. 1. From the Department of Health Sciences (van Zon, Reijneveld, Bültmann), Community and Occupational Medicine, University of Groningen, University Medical Center Groningen; Department of Epidemiology (van der Most, Snieder), Unit of Genetic Epidemiology and Bioinformatics, University of Groningen, University Medical Center Groningen; Department of Genetics (Swertz), University of Groningen, University Medical Center Groningen; and Genomics Coordination Center (Swertz), University of Groningen, University Medical Center Groningen, the Netherlands.
Abstract
OBJECTIVE: A strong genetic predisposition for type 2 diabetes mellitus (T2DM) may aggravate the negative effects of low socioeconomic position (SEP) in the etiology of the disorder. This study aimed to examine cross-sectional and longitudinal associations and interactions of a genetic risk score (GRS) and SEP with T2DM and to investigate whether clinical and behavioral risk factors can explain these associations and interactions. METHODS: We used data from 13,027 genotyped participants from the Lifelines study. The GRS was based on single-nucleotide polymorphisms genome-wide associated with T2DM and was categorized into tertiles. SEP was measured as educational level. T2DM was based on biological markers, recorded medication use, and self-reports. Cross-sectional and longitudinal associations and interactions between the GRS and SEP on T2DM were examined. RESULTS: The combination of a high GRS and low SEP had the strongest association with T2DM in cross-sectional (odds ratio = 3.84, 95% confidence interval = 2.28-6.46) and longitudinal analyses (hazard ratio = 2.71, 1.39-5.27), compared with a low GRS and high SEP. Interaction between a high GRS and a low SEP was observed in cross-sectional (relative excess risk due to interaction = 1.85, 0.65-3.05) but not in longitudinal analyses. Clinical and behavioral risk factors mostly explained the observed associations and interactions. CONCLUSIONS: A high GRS combined with a low SEP provides the highest risk for T2DM. These factors also exacerbated each other's impact cross-sectionally but not longitudinally. Preventive measures should target individual and contextual factors of this high-risk group to reduce the risk of T2DM.
OBJECTIVE: A strong genetic predisposition for type 2 diabetes mellitus (T2DM) may aggravate the negative effects of low socioeconomic position (SEP) in the etiology of the disorder. This study aimed to examine cross-sectional and longitudinal associations and interactions of a genetic risk score (GRS) and SEP with T2DM and to investigate whether clinical and behavioral risk factors can explain these associations and interactions. METHODS: We used data from 13,027 genotyped participants from the Lifelines study. The GRS was based on single-nucleotide polymorphisms genome-wide associated with T2DM and was categorized into tertiles. SEP was measured as educational level. T2DM was based on biological markers, recorded medication use, and self-reports. Cross-sectional and longitudinal associations and interactions between the GRS and SEP on T2DM were examined. RESULTS: The combination of a high GRS and low SEP had the strongest association with T2DM in cross-sectional (odds ratio = 3.84, 95% confidence interval = 2.28-6.46) and longitudinal analyses (hazard ratio = 2.71, 1.39-5.27), compared with a low GRS and high SEP. Interaction between a high GRS and a low SEP was observed in cross-sectional (relative excess risk due to interaction = 1.85, 0.65-3.05) but not in longitudinal analyses. Clinical and behavioral risk factors mostly explained the observed associations and interactions. CONCLUSIONS: A high GRS combined with a low SEP provides the highest risk for T2DM. These factors also exacerbated each other's impact cross-sectionally but not longitudinally. Preventive measures should target individual and contextual factors of this high-risk group to reduce the risk of T2DM.
Authors: América Liliana Miranda-Lora; Jenny Vilchis-Gil; Daniel B Juárez-Comboni; Miguel Cruz; Miguel Klünder-Klünder Journal: Front Endocrinol (Lausanne) Date: 2021-03-12 Impact factor: 5.555
Authors: Chris H L Thio; Sander K R van Zon; Peter J van der Most; Harold Snieder; Ute Bültmann; Ron T Gansevoort Journal: Am J Epidemiol Date: 2021-05-04 Impact factor: 4.897