Browsing by Subject "TrialNet"

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    Beta cell function in type 1 diabetes determined from clinical and fasting biochemical variables
    (Springer Nature, 2019-01) Wentworth, John M.; Bediaga, Naiara G.; Giles, Lynne C.; Ehlers, Mario; Gitelman, Stephen E.; Geyer, Susan; Evans-Molina, Carmella; Harrison, Leonard C.; Medicine, School of Medicine
    AIMS/HYPOTHESIS: Beta cell function in type 1 diabetes is commonly assessed as the average plasma C-peptide concentration over 2 h following a mixed-meal test (CPAVE). Monitoring of disease progression and response to disease-modifying therapy would benefit from a simpler, more convenient and less costly measure. Therefore, we determined whether CPAVE could be reliably estimated from routine clinical variables. METHODS: Clinical and fasting biochemical data from eight randomised therapy trials involving participants with recently diagnosed type 1 diabetes were used to develop and validate linear models to estimate CPAVE and to test their accuracy in estimating loss of beta cell function and response to immune therapy. RESULTS: A model based on disease duration, BMI, insulin dose, HbA1c, fasting plasma C-peptide and fasting plasma glucose most accurately estimated loss of beta cell function (area under the receiver operating characteristic curve [AUROC] 0.89 [95% CI 0.87, 0.92]) and was superior to the commonly used insulin-dose-adjusted HbA1c (IDAA1c) measure (AUROC 0.72 [95% CI 0.68, 0.76]). Model-estimated CPAVE (CPEST) reliably identified treatment effects in randomised trials. CPEST, compared with CPAVE, required only a modest (up to 17%) increase in sample size for equivalent statistical power. CONCLUSIONS/INTERPRETATION: CPEST, approximated from six variables at a single time point, accurately identifies loss of beta cell function in type 1 diabetes and is comparable to CPAVE for identifying treatment effects. CPEST could serve as a convenient and economical measure of beta cell function in the clinic and as a primary outcome measure in trials of disease-modifying therapy in type 1 diabetes.
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    Oral Glucose Tolerance Test Measures of First-phase Insulin Response and Their Predictive Ability for Type 1 Diabetes
    (Oxford University Press, 2022) Baidal, David A.; Warnock, Megan; Xu, Ping; Geyer, Susan; Marks, Jennifer B.; Moran, Antoinette; Sosenko, Jay; Evans-Molina, Carmella; Pediatrics, School of Medicine
    Context: Decreased first-phase insulin response (FPIR) during intravenous glucose tolerance testing (IVGTT) is an early indicator of β-cell dysfunction and predictor of type 1 diabetes (T1D). Objective: Assess whether oral glucose tolerance test (OGTT) measures could serve as FPIR alternatives in their ability to predict T1D in autoantibody positive (Aab+) subjects. Design: OGTT and IVGTT were performed within 30 days of each other. Eleven OGTT variables were evaluated for (1) correlation with FPIR and (2) T1D prediction. Setting: Type 1 Diabetes TrialNet "Oral Insulin for Prevention of Diabetes in Relatives at Risk for T1D" (TN-07) and Diabetes Prevention Trial-Type 1 Diabetes (DPT-1) studies clinical sites. Patients: TN-07 (n = 292; age 9.4 ± 6.1 years) and DPT-1 (n = 194; age 15.1 ± 10.0 years) Aab + relatives of T1D individuals. Main outcome measures: (1) Correlation coefficients of OGTT measures with FPIR and (2) T1D prediction at 2 years using area under receiver operating characteristic (ROCAUC) curves. Results: Index60 showed the strongest correlation in DPT-1 (r = -0.562) but was weaker in TN-07 (r = -0.378). C-peptide index consistently showed good correlation with FPIR across studies (TN-07, r = 0.583; DPT-1, r = 0.544; P < 0.0001). Index60 and C-peptide index had the highest ROCAUCs for T1D prediction (0.778 vs 0.717 in TN-07 and 0.763 vs 0.721 in DPT-1, respectively; P = NS), followed by FPIR (0.707 in TN-07; 0.628 in DPT-1). Conclusions: C-peptide index was the strongest measure to correlate with FPIR in both studies. Index60 and C-peptide index had the highest predictive accuracy for T1D and were comparable. OGTTs could be considered instead of IVGTTs for subject stratification in T1D prevention trials.