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A Risk Score for Type 1 Diabetes Derived from Autoantibody Positive Participants in The Diabetes Prevention Trial- Type 1

ABSTRACT

Objective: The accurate prediction of type 1 diabetes (T1D) is essential for appropriately identifying prevention trial participants. Moreover, improved prediction accuracy might ultimately result in an earlier diagnosis. Thus, we have developed a risk score for the prediction of T1D.

Research Design and Methods: Diabetes Prevention Trial-Type 1 (DPT-1) participants, islet-cell autoantibody (ICA) positive relatives of T1D patients (n=670), were randomly divided into development and validation samples. Risk score values were calculated for the validation sample from development sample model coefficients obtained through forward stepwise proportional hazards regression.

Results: A risk score based on a model including log-BMI, age, log-fasting C-peptide, and post-challenge glucose and C-peptide sums from 2-hr oral glucose tolerance tests (OGTTs) was derived from the development sample. The baseline risk score strongly predicted T1D in the validation sample (chi-square=82.3, p<0.001). Its strength of prediction was almost the same as (chi-square=83.3) a risk score additionally dependent on a decreased first-phase insulin response variable from intravenous glucose tolerance tests (IVGTTs). Neither type nor number of biochemical autoantibodies contributed significantly to the risk score model. A final T1D Risk Score was then derived from all participants with the same variables as those in the development sample model. The change in the T1D Risk Score from baseline to one year was in itself also highly predictive of T1D (p<0.001).

Conclusions: A risk score based on age, BMI and OGTT indices, without dependence on IVGTTs or additional autoantibodies, appears to accurately predict T1D in ICA-positive relatives.

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