Combining genetic and non-genetic factors to predict the risk of pancreatic cancer in patients with new-onset diabetes mellitus - Yu Zhou; Zhuo Wu; Liangtang Zeng; Rufu Chen (BMC Medicine)
Abstract Background Recent research suggests that new-onset diabetes mellitus (NODM) often results from pancreatic cancer (PC) rather than causing it. Determining if NODM is type 2 diabetes mellitus (T2DM) or PC-related NODM (NODM-PC) aids in the early diagnosis of PC. We developed a NODM-PC risk prediction model to stratify PC risk in patients with NODM. Methods This study utilized data from the UK Biobank, including 238 NODM-PC cases and 14,825 cancer-free T2DM controls. Polygenic risk scores (PRSs) for PC and T2DM were constructed using previously reported single nucleotide polymorphisms (SNPs) separately, while the NODM-PC PRS was developed by combining SNPs from both. Non-genetic factors were selected as candidate predictors based on prior NODM-PC prediction models. We developed three Cox models to estimate the risk of PC diagnosis within 3 years in the NODM population and evaluated them by internal–external cross-validation. Results Elevated NODM-PC PRS and PC PRS scores positively correlated with NODM-PC risk, while T2DM PRS showed an inverse correlation. The NODM-PC PRS achieved the highest AUC at 0.719. Three Cox models were developed: Model 1 included age at T2DM diagnosis, smoking status, HbA1c, PC PRS, and T2DM PRS; Model 2 replaced PC and T2DM PRS with NODM-PC PRS; Model 3 included only non-genetic factors. Model 2 had the highest discrimination (Harrell’s C-index 0.823 (95% CI: 0.806–0.840)), demonstrated the best clinical utility with good calibration, and showed significant classification improvement (continuous net reclassification index: 26.89% and 31.93% for cases, 28.51% and 30.90% for controls, compared to Models 1 and 3). The positive predictive value for the top 1% predicted risk in Model 2 was 13.25%. Conclusions This NODM-PC PRS enhances NODM-PC risk prediction, efficiently identifies individuals at high risk for PC screening, and improves PC screening efficiency at the population level among NODM individuals.