Development of risk assessment models for microvascular complications in type 2 diabetes mellitus patients: a cross-sectional study

This study aimed to develop and validate nomogram risk estimation models for assessing the risk of microvascular complications, specifically diabetic retinopathy (DR) and diabetic kidney disease (DKD), in patients with type 2 diabetes mellitus (T2DM). A retrospective cross-sectional study was conducted involving 6,043 T2DM patients. Clinical and laboratory data were collected. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify significant risk factors from 38 initial variables. These selected variables were then incorporated into multivariate logistic regression analyses to build the risk estimation models. The models were presented as nomograms and were internally validated using bootstrap resampling. Their performance was evaluated by assessing discrimination (using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC)), calibration (using calibration plots), and clinical utility (using Decision Curve Analysis (DCA)). The LASSO regression identified 8 and 15 independent risk factors for DR and DKD, respectively. The DR model included age, DM duration, insulin use, peripheral neuropathy, heart rate, HbA1c, and total protein. The DKD model included carotid atherosclerosis, diabetic foot, DM duration, α-glucosidase inhibitor use, insulin use, hypertension history, systolic blood pressure, lymphocyte count, total cholesterol, triglycerides, albumin, BMI, uric acid, and creatinine. Multivariate logistic analysis confirmed these associations. The nomograms demonstrated acceptable discrimination, with AUCs of 0.703 (training) and 0.732 (validation) for the DR model, and 0.802 (training) and 0.713 (validation) for the DKD model. Calibration curves showed good agreement between estimated and observed probabilities. This study successfully established and validated effective nomogram models for estimating the risk of DR and DKD in T2DM patients. Utilizing readily available clinical parameters, these models demonstrate moderate discriminative ability and clinical utility, potentially aiding in the early identification of high-risk individuals for targeted screening and improved management of microvascular complications in T2DM.