Training and External Validation of a Predict Nomogram for Type 2 Diabetic Peripheral Neuropathy

Area of Science:

• Endocrinology and Metabolic Diseases
• Neurology
• Biostatistics and Predictive Modeling

Background:

• Diabetic peripheral neuropathy (DPN) is a significant complication of type 2 diabetes mellitus (T2DM), associated with high rates of disability and mortality.
• Early identification and intervention are crucial for managing DPN and preventing its progression.
• Existing methods for DPN prediction may lack accuracy or accessibility for widespread clinical use.

Purpose of the Study:

• To develop and externally validate a predictive nomogram for the early identification of DPN risk in patients with T2DM.
• To provide clinicians with a reliable tool for assessing DPN risk and facilitating timely management strategies.
• To identify key clinical and biochemical factors associated with DPN development in T2DM.

Main Methods:

• Retrospective study of 3012 T2DM patients from Xinjiang Medical University (training cohort) and 901 T2DM patients from Suzhou BenQ Hospital (external validation cohort).
• Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression to identify independent predictors of DPN.
• Nomogram performance was assessed using Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA).

Main Results:

• A nomogram was established incorporating Age, 25-hydroxyvitamin D3 [25(OH)D3], Duration of T2DM, high-density lipoprotein (HDL), hemoglobin A1c (HbA1c), and fasting blood glucose (FBG).
• The nomogram achieved high predictive accuracy with Areas Under the Curve (AUC) of 0.8256 in the training cohort and 0.8608 in the validation cohort.
• The model demonstrated excellent calibration and clinical utility as confirmed by DCA.

Conclusions:

• The developed and validated nomogram is an accurate and effective tool for predicting DPN risk in the T2DM population.
• This predictive model supports early DPN detection, enabling prompt clinical intervention and improved patient outcomes.
• The nomogram offers a valuable resource for clinicians in the personalized risk assessment and management of DPN.