Prognostic nutritional index and diabetic peripheral neuropathy in type 2 diabetes: a machine learning approach.

Abstract

Background: The prognostic nutritional index (PNI), an indicator of nutritional status, has been linked to various diabetic complications. However, its relationship with diabetic peripheral neuropathy (DPN) remains unclear. This study aimed to explore the association between PNI and DPN using machine learning (ML) approaches.

Methods: A total of 625 patients with type 2 diabetes (T2D) were enrolled, with 282 diagnosed with DPN. PNI was calculated based on serum albumin and lymphocyte count. Random forest (RF) and eXtreme Gradient Boosting (XGBoost) models were developed to predict DPN using clinical and biochemical data. SHapley Additive exPlanations (SHAP) were applied to determine feature importance. Multivariate logistic regression was used to evaluate the relationship between PNI quartile and DPN risks.

Results: Both RF and XGBoost models exhibited strong performance. The RF model achieved a recall of 78.4%, specificity of 87.8%, and accuracy of 84.0%, while the XGBoost model showed a recall of 77.4%, specificity of 92.1%, and accuracy of 84.8%. SHAP analysis identified lower PNI as a key factor for DPN. Multivariate logistic regression revealed that patients in the lowest PNI quartile had a significantly higher DPN risk compared to those in the highest quartile (OR: 3.271, 95% CI: 1.782-6.006, P < 0.001). Additionally, lower PNI levels were associated with impaired peripheral nerve function, including reduced motor and sensory nerve conduction velocity and action potential amplitudes.

Conclusions: Lower PNI levels were associated with increased DPN risk and poorer nerve function, highlighting the importance of nutritional status in DPN management. Further longitudinal studies are needed to confirm these findings.