Cystic fibrosis-related diabetes onset can be predicted using biomarkers measured at birth.

Cystic fibrosis (CF), caused by pathogenic variants in the CF transmembrane conductance regulator (CFTR), affects multiple organs including the exocrine pancreas, which is a causal contributor to cystic fibrosis-related diabetes (CFRD). Untreated CFRD causes increased CF-related mortality whereas early detection can improve outcomes. Using genetic and easily accessible clinical measures available at birth, we constructed a CFRD prediction model using the Canadian CF Gene Modifier Study (CGS; n = 1,958) and validated it in the French CF Gene Modifier Study (FGMS; n = 1,003). We investigated genetic variants shown to associate with CF disease severity across multiple organs in genome-wide association studies. The strongest predictors included sex, CFTR severity score, and several genetic variants including one annotated to PRSS1, which encodes cationic trypsinogen. The final model defined in the CGS shows excellent agreement when validated on the FGMS, and the risk classifier shows slightly better performance at predicting CFRD risk later in life in both studies. We demonstrated clinical utility by comparing CFRD prevalence rates between the top 10% of individuals with the highest risk and the bottom 10% with the lowest risk. A web-based application was developed to provide practitioners with patient-specific CFRD risk to guide CFRD monitoring and treatment.