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

Yongsheng Li1, Yongnan Li2, Ning Deng3

  • 1Department of Preventive Medicine, Medical College, Tarim University, Alar 843300, China.

Diagnostics (Basel, Switzerland)
|April 13, 2023
PubMed
Summary

A new nomogram accurately predicts diabetic peripheral neuropathy (DPN) risk in type 2 diabetes patients. This tool aids early detection and management of DPN, a serious complication.

Keywords:
25(OH)D3diabetic peripheral neuropathynomogramprediction modeltype 2 diabetes mellitus (T2DM)

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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.