Novel time-saving OGTT sparing HbA1c-HOMA2 based algorithm for the diagnosis of cystic fibrosis-related diabetes.

Aims: The diagnosis of cystic fibrosis-related diabetes (CFRD) faces several challenges. We propose a novel screening algorithm to alleviate the burden of cystic fibrosis (CF).

Methods: Through a retrospective cross-sectional single-centre study, HbA1c and HOMA2 indices were assessed in multiple models as alternative diagnostic tools from OGTT data. We sought to establish specific thresholds for CFRD screening with oral glucose tolerance test (OGTT) as gold standard. We evaluated various straightforward or sequential approaches, in terms of diagnostic accuracy while also quantify the potential reduction in OGTTs through these different methods.

Results: HOMA indices were recovered in 72 patients. We devised a composite index that combines HbA1c and HOMA-B: Diabetes Predicting Index in cystic fibrosis (DIPIc) = (HbA1c(%) × 3.455) - (HOMA-B(%) ×  0.020) - 19.294. This index yields the highest screening accuracy according to receiver-operating characteristics curves. Using a stepwise algorithm that incorporates DIPIc decreases the requirement for annual OGTTs. A CFRD exclusion cutoff less than -1.7445 (sensitivity 98 %), in conjunction with a CFRD diagnostic threshold greater than 0.4543 (specificity 98 %) allows for 71 % OGTT sparing.

Conclusion: The composite index DIPIc is a suitable, less invasive screening method for CFRD, which enables to avoid many OGTTs.