Diagnostic Model for Obesity and Type 2 Diabetes Mellitus Based on Feature Selection from Fatty Acids, Amino Acids, and Clinical Characteristics.

Purpose: Given that obesity and type 2 diabetes mellitus (T2DM) share complicated pathophysiological mechanisms, this study aimed to establish a diagnostic model for the two diseases using feature selection from fatty acids, amino acids, and clinical characteristics. Methods: This prospective study included 81 obese patients, 25 T2DM patients and 33 healthy controls. Amino acids and fatty acids in serum were tested using LC-MS/MS method. Anthropometric and laboratory measurements were recorded. All samples were split into a training set and a test set (7/3 ratio). Results: Total 54 variables were significantly different between obesity, T2DM and control groups (p-value < 0.05). In uni-variable logistic regression analysis, 44 variables were significantly associated with disease diagnosis. LASSO, RFE and RF algorithms jointly selected 7 optimal variables (Ala, His, Gln, IL-10, age, FBG, and AHR). The support vector machine (SVM) diagnostic model based on the 7 variables showed robust performance in both the training set (AUC = 0.998) and the validation set (AUC = 0.958). Obesity or T2DM patients had significantly increased Ala (p-value < 0.01) but decreased Gln, His and IL-10 (p-value < 0.01) in serum compared to healthy controls. Gln and His levels were positively correlated with IL-10 level (Cor = 0.46, 0.48, p-value < 0.001). Conclusion: This study developed a 7 feature-based diagnostic model for obesity and T2DM and suggested that Ala, His, Gln, and IL-10 were involved in the common mechanisms and might be potential therapeutic targets.