Genetic Subtypes of Prediabetes, Healthy Lifestyle, and Risk of Type 2 Diabetes

Author:

Li Yang1,Chen Guo-Chong2ORCID,Moon Jee-Young1,Arthur Rhonda1,Sotres-Alvarez Daniela3,Daviglus Martha L.4,Pirzada Amber4,Mattei Josiemer5ORCID,Perreira Krista M.6,Rotter Jerome I.7,Taylor Kent D.7,Chen Yii-Der Ida7,Wassertheil-Smoller Sylvia1,Wang Tao1,Rohan Thomas E.1,Kaufman Joel D.8,Kaplan Robert1,Qi Qibin1ORCID

Affiliation:

1. 1Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY

2. 2Department of Nutrition and Food Hygiene, School of Public Health, Soochow University, Suzhou, China

3. 3Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC

4. 4Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL

5. 5Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA

6. 6Department of Social Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC

7. 7The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA

8. 8Environmental and Occupational Health Sciences, Medicine, and Epidemiology, University of Washington, Seattle, WA

Abstract

Prediabetes is a heterogenous metabolic state with various risks for development of type 2 diabetes (T2D). In this study, we used genetic data on 7,227 US Hispanic/Latino participants without diabetes from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) and 400,149 non-Hispanic White participants without diabetes from the UK Biobank (UKBB) to calculate five partitioned polygenetic risk scores (pPRSs) representing various pathways related to T2D. Consensus clustering was performed in participants with prediabetes in HCHS/SOL (n = 3,677) and UKBB (n = 16,284) separately based on these pPRSs. Six clusters of individuals with prediabetes with distinctive patterns of pPRSs and corresponding metabolic traits were identified in the HCHS/SOL, five of which were confirmed in the UKBB. Although baseline glycemic traits were similar across clusters, individuals in cluster 5 and cluster 6 showed an elevated risk of T2D during follow-up compared with cluster 1 (risk ratios [RRs] 1.29 [95% CI 1.08, 1.53] and 1.34 [1.13, 1.60], respectively). Inverse associations between a healthy lifestyle score and risk of T2D were observed across different clusters, with a suggestively stronger association observed in cluster 5 compared with cluster 1. Among individuals with a healthy lifestyle, those in cluster 5 had a similar risk of T2D compared with those in cluster 1 (RR 1.03 [0.91, 1.18]). This study identified genetic subtypes of prediabetes that differed in risk of progression to T2D and in benefits from a healthy lifestyle. Article Highlights

Funder

National Institute of Environmental Health Sciences

National Institute of Diabetes and Digestive and Kidney Diseases

Publisher

American Diabetes Association

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