A New Approach for Diagnosing Type 1 Diabetes in Autoantibody-Positive Individuals Based on Prediction and Natural History
Year: 2014
Type: article
Abstract: OBJECTIVE: We assessed whether type 1 diabetes (T1D) can be diagnosed earlier using a new approach based on prediction and natural history in autoantibody-positive individuals. RESEARCH DESIGN AND METHODS: Diabetes Prevention Trial-Type 1 (DPT-1) and TrialNet Natural History Study (TNNHS) participants were studied. A metabolic index, the T1D Diagnostic Index60 (Index60), was developed from 2-h oral glucose tolerance tests (OGTTs) using the log fasting C-peptide, 60-min C-peptide, and 60-min glucose. OGTTs with Index60 ≥2.00 and 2-h glucose <200 mg/dL (Ind60+Only) were compared with Index60 <2.00 and 2-h glucose ≥200 mg/dL (2hglu+Only) OGTTs as criteria for T1D. Individuals were assessed for C-peptide loss from the first Ind60+Only OGTT to diagnosis. RESULTS: Areas under receiver operating characteristic curves were significantly higher for Index60 than for the 2-h glucose (P < 0.001 for both DPT-1 and the TNNHS). As a diagnostic criterion, sensitivity was higher for Ind60+Only than for 2hglu+Only (0.44 vs. 0.15 in DPT-1; 0.26 vs. 0.17 in the TNNHS) OGTTs. Specificity was somewhat higher for 2hglu+Only OGTTs in DPT-1 (0.97 vs. 0.91) but equivalent in the TNNHS (0.98 for both). Positive and negative predictive values were higher for Ind60+Only OGTTs in both studies. Postchallenge C-peptide levels declined significantly at each OGTT time point from the first Ind60+Only OGTT to the time of standard diagnosis (range -22 to -34% in DPT-1 and -14 to -27% in the TNNHS). C-peptide and glucose patterns differed markedly between Ind60+Only and 2hglu+Only OGTTs. CONCLUSIONS: An approach based on prediction and natural history appears to have utility for diagnosing T1D.
Source: Diabetes Care
DOI: 10.2337/dc14-1813
Authors: Jay M. Sosenko, Jay S. Skyler, Linda A. DiMeglio, Craig A. Beam, Jeffrey P. Krischer, Carla J. Greenbaum, David Boulware, Lisa E. Rafkin, Della Matheson, Kevan C. Herold, Jeffrey Mahon, Jerry P. Palmer, the Type 1 Diabetes TrialNet and Diabetes Prevention Trial–Type 1 Study Groups
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Awards: TrialNet: University of Florida Clinical Center and Network, Type 1 Diabetes-A Proposal for Prevention & Intervention, CLINICAL AFFILIATE OF THE TYPE 1 DIABETES TRIALNET, Continuation of UCSF as a Clinical Center for TrialNet, Prediction and Prevention of Type 1 Diabetes (TrialNet), NIDDK Type 1 Diabetes TrialNet Data Coordinating Center, TrialNet at Stanford University, Data Coordinating Center for Type 1 Diabetes TrialNet (UC4)USF: TrialNet UC4-3, NIDDK Type 1 Diabetes TrialNet Data Coordinating Center, Diabetes Research Center, Diabetes Research Center
OpenAlex ID: W2078167380
