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.