Type 1 diabetes prediction in autoantibody-positive individuals: performance, time and money matter.

ABSTRACT:

Aims/hypothesis

Efficient prediction of clinical type 1 diabetes is important for risk stratification and monitoring of autoantibody-positive individuals. In this study, we compared type 1 diabetes predictive models for predictive performance, cost and participant time needed for testing.

Methods

We developed 1943 predictive models using a Cox model based on a type 1 diabetes genetic risk score (GRS2), autoantibody count and types, BMI, age, self-reported gender and OGTT-derived glucose and C-peptide measures. We trained and validated the models using halves of a dataset comprising autoantibody-positive first-degree relatives of individuals with type 1 diabetes (n=3967, 49% female, 14.9 ± 12.1 years of age) from the TrialNet Pathway to Prevention study. The median duration o

SUBMITTER: Ferrat LA 

PROVIDER: S-EPMC12245984 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature