Disease progression joint model predicts time to type 1 diabetes onset: Optimizing future type 1 diabetes prevention studies

Abstract

Clinical trials seeking type 1 diabetes prevention are challenging in terms of identifying patient populations likely to progress to type 1 diabetes within limited (i.e., short-term) trial durations. Hence, we sought to improve such efforts by developing a quantitative disease progression model for type 1 diabetes. Individual-level data obtained from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies were used to develop a joint model that links the longitudinal glycemic measure to the timing of type 1 diabetes diagnosis. Baseline covariates were assessed using a stepwise covariate modeling approach. Our study focused on individuals at risk of developing type 1 diabetes with the presence of two or more diabetes-related autoantibodies (AAbs). The developed model successfully quantified how patient features measured at baseline, including HbA1c and the presence of different AAbs, alter the timing of type 1 diabetes diagnosis with reasonable accuracy and precision (<30% RSE). In addition, selected covariates were statistically significant (p < 0.0001 Wald test). The Weibull model best captured the timing to type 1 diabetes diagnosis. The 2-h oral glucose tolerance values assessed at each visit were included as a time-varying biomarker, which was best quantified using the sigmoid maximum effect function. This model provides a framework to quantitatively predict and simulate the time to type 1 diabetes diagnosis in individuals at risk of developing the disease and thus, aligns with the needs of pharmaceutical companies and scientists seeking to advance therapies aimed at interdicting the disease process.

FIGURE 1

Data curation and disease progression model development workflow. AAb, autoantibody; GLU120, 120 min of oral glucose tolerance test; GLU0, fasting glucose measurement (0 min of oral glucose tolerance test); HbA1c, glycated hemoglobin; NLME, nonlinear mixed effects; T1D, type 1 diabetes; TTE, time to event.

FIGURE 2

Distribution of covariates in the training and test datasets at derived baseline. FDR, first degree relative with type 1 diabetes; GADA AAb, glutamic acid decarboxylase autoantibody; GLU0, fasting glucose measurement (0 min of oral glucose tolerance test); HbA1c, glycated hemoglobin; IA2A AAb, islet antigen‐2 autoantibody; PEP120, C peptide level (120 min of oral glucose tolerance test).

FIGURE 3

Effect of the time‐varying biomarker GLU120 on the Weibull scale parameter Te′. The data shown in this figure was obtained from 100 simulations for one subject. The red line represents the smooth line obtained using the generalized additive model smoothing method. GLU120, 120 min of oral glucose tolerance test; Te′, scale parameter of Weibull function.

FIGURE 4

Visual predictive check model diagnostic plots of the final T1D disease progression joint model using training dataset (top panel) and test dataset (bottom panel), for the longitudinal glucose measure (left panel) and time to T1D diagnosis (right panel). The visual predictive check plots (a) and (c) show the median (black solid line) and the 5th and 95th percentiles (lower and upper black dashed lines, respectively) of the observed data. The red shaded areas indicate the 95% CIs of the model prediction of the median, and the blue shaded areas show 95% CIs of the model prediction for the 5th and 95th percentiles. The solid lines – red for the median and blue for the 5th and 95th percentiles – represent the model prediction. The visual predictive check plots (b) and (d) show the Kaplan Meier curve (black solid line) of the observed data. The gray shaded area indicates the 90% CIs of the observed Kaplan Meier curve. The black dashed line represents the median of the model prediction. The blue shaded areas indicate the 90% CIs of the model prediction of the median. CI, confidence interval; GLU120, 120 min of oral glucose tolerance test; T1D, type 1 diabetes.

FIGURE 5

Predicated influence of selected covariates to the time to T1D diagnosis. GADA, glutamic acid decarboxylase autoantibody; HbA1c, glycated hemoglobin; IA2A, islet antigen‐2 autoantibody.