Risk prediction of major complications in individuals with diabetes: the Atherosclerosis Risk in Communities Study.

Pubmed ID: 27161077

Pubmed Central ID: PMC4993670

Journal: Diabetes, obesity & metabolism

Publication Date: Sept. 1, 2016

MeSH Terms: Humans, Male, Female, Aged, Cohort Studies, Middle Aged, Coronary Disease, Diabetes Complications, Risk Assessment, Heart Failure, Hospitalization, Prospective Studies, Creatinine, Stroke, Diabetic Angiopathies, Diabetic Nephropathies, Diabetes Mellitus, Glomerular Filtration Rate, Renal Insufficiency, Chronic, Peptide Fragments, Alanine Transaminase, Natriuretic Peptide, Brain, Self Report, C-Reactive Protein, Serum Albumin, Aspartate Aminotransferases, Biomarkers, Troponin T, Cystatin C, beta 2-Microglobulin, Fructosamine, gamma-Glutamyltransferase, Glycation End Products, Advanced, Glycated Hemoglobin, Glycated Serum Albumin

Grants: HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, HHSN268201100012C, R01 DK089174, T32 HL007024, K24 DK106414, HHSN268201100009I, HHSN268201100005G, HHSN268201100008I, HHSN268201100011I, HHSN268201100005I, HHSN268201100007I

Authors: Woodward M, Coresh J, Selvin E, Wagenknecht LE, Matsushita K, Parrinello CM

Cite As: Parrinello CM, Matsushita K, Woodward M, Wagenknecht LE, Coresh J, Selvin E. Risk prediction of major complications in individuals with diabetes: the Atherosclerosis Risk in Communities Study. Diabetes Obes Metab 2016 Sep;18(9):899-906. Epub 2016 Jun 14.

Studies:

Abstract

AIMS: To develop a prediction equation for 10-year risk of a combined endpoint (incident coronary heart disease, stroke, heart failure, chronic kidney disease, lower extremity hospitalizations) in people with diabetes, using demographic and clinical information, and a panel of traditional and non-traditional biomarkers. METHODS: We included in the study 654 participants in the Atherosclerosis Risk in Communities (ARIC) study, a prospective cohort study, with diagnosed diabetes (visit 2; 1990-1992). Models included self-reported variables (Model 1), clinical measurements (Model 2), and glycated haemoglobin (Model 3). Model 4 tested the addition of 12 blood-based biomarkers. We compared models using prediction and discrimination statistics. RESULTS: Successive stages of model development improved risk prediction. The C-statistics (95% confidence intervals) of models 1, 2, and 3 were 0.667 (0.64, 0.70), 0.683 (0.65, 0.71), and 0.694 (0.66, 0.72), respectively (p < 0.05 for differences). The addition of three traditional and non-traditional biomarkers [β-2 microglobulin, creatinine-based estimated glomerular filtration rate (eGFR), and cystatin C-based eGFR] to Model 3 significantly improved discrimination (C-statistic = 0.716; p = 0.003) and accuracy of 10-year risk prediction for major complications in people with diabetes (midpoint percentiles of lowest and highest deciles of predicted risk changed from 18-68% to 12-87%). CONCLUSIONS: These biomarkers, particularly those of kidney filtration, may help distinguish between people at low versus high risk of long-term major complications.