Construction and validation of a risk prediction model for vitamin D deficiency in patients with type 2 diabetes mellitus

ObjectivesPatients with type 2 diabetes mellitus (T2DM) have a high prevalence of vitamin D deficiency, but convenient and efficient screening tools are lacking in clinical practice. This study aimed to construct and validate a predictive model for vitamin D deficiency risk in T2DM patients based on routine clinical indicators.MethodsClinical data were retrospectively collected from 618 T2DM patients hospitalized in the Department of Endocrinology of a tertiary general hospital between January 2024 and December 2024. Patients were randomly divided into a training cohort (n = 432) and a validation cohort (n = 186) at a ratio of 7:3. LASSO regression was used to screen predictors, and a logistic regression model was constructed to generate a nomogram. The discrimination, calibration, and clinical utility of the model were evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), respectively.ResultsThe prevalence of vitamin D deficiency in T2DM patients was 71.8%. LASSO regression combined with multivariate logistic regression showed that female (OR = 3.53, 95%CI: 1.99–6.25, P < 0.001), elevated triglycerides (TG) (OR = 1.56, 95%CI: 1.15–2.12, P = 0.004), elevated glycated hemoglobin (HbA1c) (OR = 1.17, 95%CI: 1.03–1.32, P = 0.048), and urinary albumin-to-creatinine ratio (UACR) ≥ 300 mg/g (OR = 9.68, 95%CI: 2.58–29.24, P < 0.001) were independent risk factors for vitamin D deficiency, whereas age ≥ 65 years (OR = 0.34, 95%CI: 0.19–0.59, P < 0.001) was a potential protective factor. The nomogram model based on these five variables achieved an AUC of 0.7468 (95%CI: 0.6994–0.7942) in the training cohort and 0.7557 (95%CI: 0.6753–0.8362) in the validation cohort. Calibration curves revealed favorable consistency between predicted and actual probabilities (Hosmer–Lemeshow test: training cohort P = 0.436, validation cohort P = 0.672). DCA indicated net benefit within the clinically defined threshold range.ConclusionWe developed a nomogram using Gender, Age, TG, HbA1c, and UACR for predicting vitamin D deficiency risk in T2DM inpatients. Internal validation indicated promising performance. While the model may assist in identifying high-risk individuals in hospital settings, its clinical utility and generalizability remain to be confirmed through external validation.