TY - JOUR
T1 - Risk prediction of major complications in individuals with diabetes
T2 - the Atherosclerosis Risk in Communities Study
AU - Parrinello, C. M.
AU - Matsushita, K.
AU - Woodward, M.
AU - Wagenknecht, L. E.
AU - Coresh, J.
AU - Selvin, E.
N1 - Publisher Copyright:
© 2016 John Wiley & Sons Ltd
PY - 2016/9/1
Y1 - 2016/9/1
N2 - 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.
AB - 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.
KW - cardiovascular disease
KW - diabetes complications
KW - population study
KW - type 2 diabetes
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U2 - 10.1111/dom.12686
DO - 10.1111/dom.12686
M3 - Article
AN - SCOPUS:84979031794
SN - 1462-8902
VL - 18
SP - 899
EP - 906
JO - Diabetes, Obesity and Metabolism
JF - Diabetes, Obesity and Metabolism
IS - 9
ER -