title: Risk prediction of major complications in individuals with diabetes: the Atherosclerosis Risk in Communities Study.
abstract: 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.
  
  
  
  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.
  
  
  
  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%).
  
  
  
  These biomarkers, particularly those of kidney filtration, may help distinguish between people at low versus high risk of long-term major complications.
authors.affiliation: Department of Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
authors.forename: C M
authors.initials: CM
authors.lastname: Parrinello
authors.affiliation: Department of Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
authors.forename: K
authors.initials: K
authors.lastname: Matsushita
authors.affiliation: Department of Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.|Nuffield Department of Population Health, George Institute for Global Health, University of Oxford, Oxford, UK.|George Institute for Global Health, University of Sydney, Sydney, Australia.
authors.forename: M
authors.initials: M
authors.lastname: Woodward
authors.affiliation: Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA.
authors.forename: L E
authors.initials: LE
authors.lastname: Wagenknecht
authors.affiliation: Department of Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.|Department of Medicine, Division of General Internal Medicine, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: J
authors.initials: J
authors.lastname: Coresh
authors.affiliation: Department of Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.|Department of Medicine, Division of General Internal Medicine, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: E
authors.initials: E
authors.lastname: Selvin
mesh_terms: D000368:Aged; D000410:Alanine Transaminase / Q000097:blood; D001219:Aspartate Aminotransferases / Q000097:blood; D015415:Biomarkers / Q000097:blood; D002097:C-Reactive Protein / Q000378:metabolism; D015331:Cohort Studies; D003327:Coronary Disease / Q000453:epidemiology*; D003404:Creatinine / Q000097:blood; D055316:Cystatin C / Q000097:blood; D048909:Diabetes Complications / Q000453:epidemiology*; D003920:Diabetes Mellitus / Q000453:epidemiology* / Q000378:metabolism; D003925:Diabetic Angiopathies / Q000453:epidemiology; D003928:Diabetic Nephropathies / Q000453:epidemiology / Q000378:metabolism; D005260:Female; D019270:Fructosamine / Q000097:blood; D005919:Glomerular Filtration Rate; D006442:Glycated Hemoglobin / Q000378:metabolism; D017127:Glycation End Products, Advanced; D006333:Heart Failure / Q000453:epidemiology*; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D020097:Natriuretic Peptide, Brain / Q000097:blood; D010446:Peptide Fragments / Q000097:blood; D011446:Prospective Studies; D051436:Renal Insufficiency, Chronic / Q000453:epidemiology* / Q000378:metabolism; D018570:Risk Assessment; D057566:Self Report; D012709:Serum Albumin / Q000378:metabolism; D020521:Stroke / Q000453:epidemiology*; D020107:Troponin T / Q000097:blood; D001613:beta 2-Microglobulin / Q000097:blood; D005723:gamma-Glutamyltransferase / Q000097:blood; D000093423:Glycated Serum Albumin
chemical_list: D000410:Alanine Transaminase; D001219:Aspartate Aminotransferases; D015415:Biomarkers; D002097:C-Reactive Protein; D003404:Creatinine; D055316:Cystatin C; D019270:Fructosamine; D006442:Glycated Hemoglobin; D017127:Glycation End Products, Advanced; D020097:Natriuretic Peptide, Brain; D010446:Peptide Fragments; D012709:Serum Albumin; D020107:Troponin T; D001613:beta 2-Microglobulin; D005723:gamma-Glutamyltransferase; D000093423:Glycated Serum Albumin; C517652:hemoglobin A1c protein, human; C109794:pro-brain natriuretic peptide (1-76)
keywords: cardiovascular disease; diabetes complications; population study; type 2 diabetes