Table 1_Development and validation of a predictive model for sarcopenia in patients with type 2 diabetes based on the synergistic mechanism of insulin resistance and inflammation.docx

Eight machine learning algorithms were subsequently developed and evaluated.</p>Results<p>The prevalence of sarcopenia among the patients with T2DM was 29.20%. Eight predictors were selected: age, sex, body mass index, serum albumin, alanine aminotransferase, hemoglobin, metabolic score for insulin resistance, and monocyte-to-high-density lipoprotein cholesterol ratio. LightGBM demonstrated the best overall predictive performance in the validation set, yielding an area under the receiver operating characteristic curve (AUC) of 0.928, an accuracy of 0.882, a sensitivity of 0.897, a specificity of 0.876, a precision of 0.745, and an F1 score of 0.814.

Calibration and decision curve analysis indicated that the model yielded clinical net benefit. A web-based calculator developed using LightGBM enabled three-tier risk stratification: low (<15%), moderate (15%–40%), and high (>40%).</p>Conclusion<p>A machine learning model for predicting sarcopenia risk in patients with T2DM was developed and internally validated, resulting in the implementation of an online risk calculator.

This mechanism-based model demonstrated promising internal predictive performance and may provide a practical and cost-effective screening approach for diabetes management.</p>