Predictive diagnosis of chronic obstructive pulmonary disease using serum metabolic biomarkers and least-squares support vector machine.

Objective: Development of biofluid-based biomarkers is attractive for the diagnosis of chronic obstructive pulmonary disease (COPD) but still lacking. Thus, here we aimed to identify serum metabolic biomarkers for the diagnosis of COPD.

Methods: In this study, we investigated serum metabolic features between COPD patients (n = 54) and normal individuals (n = 74) using a H NMR-based metabolomics approach and developed an integrated method of least-squares support vector machine (LS-SVM) and serum metabolic biomarkers to assist COPD diagnosis.

Results: We observed a hypometabolic state in serum of COPD patients, as indicated by decreases in N-acetyl-glycoprotein (NAG), lipoprotein (LOP, mainly LDL/VLDL), polyunsaturated fatty acid (pUFA), glucose, alanine, leucine, histidine, valine, and lactate. Using an integrated method of multivariable and univariate analyses, NAG and LOP were identified as two important metabolites for distinguishing between COPD patients and controls. Subsequently, we developed a LS-SVM classifier using these two markers and found that LS-SVM classifiers with linear and polynomial kernels performed better than the classifier with RBF kernel. Linear and polynomial LS-SVM classifiers can achieve the total accuracy rates of 80.77% and 84.62% and the AUC values of 0.87 and 0.90 for COPD diagnosis, respectively.

Conclusions: This study suggests that artificial intelligence integrated with serum metabolic biomarkers has a great potential for auxiliary diagnosis of COPD.