Improving Short- and Long-Term Type 2 Diabetes Risk Prediction for Middle-Aged British Adults

Diabetes prediction tools are valuable for prevention and resource planning. Few tools exist to predict long-term diabetes risk for British adults. We aim to develop and validate a diabetes prediction tool for short- and long-term risk.

Prediction model development (Training, n = 4502) and internal validation (Test, n = 1929) used Whitehall II data (WHII); external validation used English Longitudinal Study of Ageing (ELSA) data, with incident diabetes as outcome. Predictors were selected using LASSO-Cox, random survival forests (RSF) and best subset selection. Three candidate Cox models were built from these predictor sets and compared. Performance was assessed with C-indices, calibration and decision curve analysis (DCA). A continuous risk score and quartile-based risk groups were derived; a nomogram was created.

Across WHII Test (235 events) and ELSA (255 events), three models showed broadly similar discrimination, calibration and DCA patterns. LASSO- and RSF-selected models performed comparably and slightly better than best subset. LASSO and RSF both selected ethnicity, body mass index, waist-to-height ratio, height, triglyceride-glucose index, fasting glucose, family history of diabetes and socioeconomic status; LASSO additionally selected smoking, whereas RSF also included age, triglycerides, high-density lipoprotein cholesterol, systolic and diastolic blood pressure. In ELSA, risk-score C-indices (95% CIs) were 0.800 (0.773-0.827) for the LASSO-selected model and 0.801 (0.774-0.827) for the RSF-selected model; risk-group C-indices were 0.737 (0.715-0.759) and 0.727 (0.707-0.748), respectively.

We developed and externally validated a tool to estimate 5-, 10- and 15-year diabetes risk in British middle-aged adults, which may support primary care decision-making.