Proteomic Predictors of Incident Diabetes: Results From the Atherosclerosis Risk in Communities (ARIC) Study

Mary R. Rooney, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Jingsha Chen, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Justin B. Echouffo-Tcheugui, Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Keenan A. Walker, Laboratory of Behavioral Neuroscience, Intramural Research Program, National Institute on Aging, Baltimore, MD.
Pascal Schlosser, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Aditya Surapaneni, Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY.
Olive Tang, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Jinyu Chen, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Christie M. Ballantyne, Department of Medicine, Baylor College of Medicine, Houston, TX.
Eric Boerwinkle, Department of Epidemiology, Human Genetics and Environmental Science, University of Texas Health Science Center, Houston, TX.
Chiadi E. Ndumele, Department of Cardiology, Johns Hopkins University, Baltimore, MD.
Ryan T. Demmer, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN.
James S. Pankow, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN.
Pamela L. Lutsey, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN.
Lynne E. Wagenknecht, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC.
Yujian Liang, Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
Xueling Sim, Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
Rob van Dam, Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington DC.
E Shyong Tai, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Morgan E. Grams, Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY.
Elizabeth Selvin, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Josef Coresh, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.

Document Type

Journal Article

Publication Date

1-27-2023

Journal

Diabetes care

DOI

10.2337/dc22-1830

Abstract

OBJECTIVE: The plasma proteome preceding diabetes can improve our understanding of diabetes pathogenesis. RESEARCH DESIGN AND METHODS: In 8,923 Atherosclerosis Risk in Communities (ARIC) Study participants (aged 47-70 years, 57% women, 19% Black), we conducted discovery and internal validation for associations of 4,955 plasma proteins with incident diabetes. We externally validated results in the Singapore Multi-Ethnic Cohort (MEC) nested case-control (624 case subjects, 1,214 control subjects). We used Cox regression to discover and validate protein associations and risk-prediction models (elastic net regression with cardiometabolic risk factors and proteins) for incident diabetes. We conducted a pathway analysis and examined causality using genetic instruments. RESULTS: There were 2,147 new diabetes cases over a median of 19 years. In the discovery sample (n = 6,010), 140 proteins were associated with incident diabetes after adjustment for 11 risk factors (P < 10-5). Internal validation (n = 2,913) showed 64 of the 140 proteins remained significant (P < 0.05/140). Of the 63 available proteins, 47 (75%) were validated in MEC. Novel associations with diabetes were found for 22 the 47 proteins. Prediction models (27 proteins selected by elastic net) developed in discovery had a C statistic of 0.731 in internal validation, with ΔC statistic of 0.011 (P = 0.04) beyond 13 risk factors, including fasting glucose and HbA1c. Inflammation and lipid metabolism pathways were overrepresented among the diabetes-associated proteins. Genetic instrument analyses suggested plasma SHBG, ATP1B2, and GSTA1 play causal roles in diabetes risk. CONCLUSIONS: We identified 47 plasma proteins predictive of incident diabetes, established causal effects for 3 proteins, and identified diabetes-associated inflammation and lipid pathways with potential implications for diagnosis and therapy.

Department

Exercise and Nutrition Sciences

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