Osteoporotic fractures are a major cause of disability and mortality in type 2 diabetes mellitus (T2DM). To support early detection, a study published in Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy developed a nomogram integrating multidimensional clinical factors.

The single-center retrospective analysis included 868 patients with T2DM, divided into training and validation sets (8:2 ratio). Logistic regression identified six independent predictors: female gender (OR 2.681, P=0.04), age (OR 1.068 per year, P=0.003), lower BMI (OR 0.912, P=0.01), reduced blood lactic acid (OR 0.747, P=0.011), lower lumbar T-score (OR 0.644, P=0.001), and lower femoral neck T-score (OR 0.412, P<0.001).

The resulting nomogram showed strong calibration with the Hosmer-Lemeshow test (P=0.406), high discrimination with an AUC of 0.831, and consistent clinical benefit across thresholds on decision curve analysis. External validation confirmed good generalizability.

This prediction tool enables early identification of high-risk patients, supporting timely interventions to reduce fracture risk and improve outcomes in T2DM.
By enabling individualized fracture risk prediction, the model offers clinicians a valuable tool for early detection and targeted intervention, helping reduce complications and improve outcomes in patients with type 2 diabetes.