Development and Validation of a Predictive Model for Diabetic Peripheral Neuropathy Risk in Type 2 Diabetes

The study developed and validated a predictive nomogram for assessing the risk of diabetic peridiabetic pathology (DPN) in diabetes, integrating multiple key metabolic factors such as limb pain and diabetic nephropathy, including clinical variables such as blood pressure, white blood cell count, and lipid profile. The LELGE map provides flexible, non-invasive tools that allow for more personalised interventions and improved care for early identification of at-risk populations. The analysis showed that there was a significant association between the course of diabetes and the incidence of DPN, and the risk assessment of the disease to prevent disease progression was very necessary. In addition, the study highlighted the critical role of lipid disorders in the pathogenesis and progression of DPN (especially in early diabetes), and the validation of the model suggests that its clinical utility has been affirmed with greater specificity in different medical settings. While the results of this study are encouraging, they also acknowledge their limitations, including the lack of drug data and the demand for a larger and diverse sample population, and the overall findings highlight the presentation and pave the way for further refinement and expansion of applicability studies in the future, while enhancing the potential for diabetes management, reducing healthcare costs and improving patient outcomes.