Feature selection approaches identify potential plasma metabolites in postmenopausal osteoporosis patients.

INTRODUCTION

Postmenopausal women with osteoporosis (PMOP) are prone to fragility fractures. Osteoporosis is associated with alterations in the levels of specific circulating metabolites.

OBJECTIVES

To analyze the metabolic profile of individuals with PMOP and identify novel metabolites associated with bone mineral density (BMD).

METHODS

We performed an unsupervised metabolomics analysis of plasma samples from participants with PMOP and of normal controls (NC) with normal bone mass. BMD values for the lumber spine and the proximal femur were determined using dual-energy X-ray absorptiometry. Principal component analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA) were performed for metabolomic profile analyses. Metabolites with P < 0.05 in the t-test, VIP > 1 in the PLS-DA model, and SNR > 0.3 between the PMOP and NC groups were defined as differential abundant metabolites (DAMs). The SHapley additive explanations (SHAP) method was utilized to determine the importance of permutation of each DAM in the predictive model between the two groups. ROC analysis and correlation analysis of metabolite relative abundance and BMD/T-scores were conducted. KEGG pathway analysis was used for functional annotation of the candidate metabolites.

RESULTS

Overall, 527 annotated molecular markers were extracted in the positive and negative total ion chromatogram (TIC) of each sample. The PMOP and NC groups could be differentiated using the PLS-DA model. Sixty-eight DAMs were identified, with most relative abundances decreasing in the PMOP samples. SHAP was used to identify 9 DAM metabolites as factors distinguishing PMOP from NC. The logistic regression model including Triethanolamine, Linoleic acid, and PC(18:1(9Z)/18:1(9Z)) metabolites demonstrated excellent discrimination performance (sensitivity = 97.0, specificity = 96.6, AUC = 0.993). The correlation analysis revealed that the abundances of Triethanolamine, PC(18:1(9Z)/18:1(9Z)), 16-Hydroxypalmitic acid, and Palmitic acid were significantly positively correlated with the BMD/T score (Pearson correlation coefficients > 0.5, P < 0.05). Most candidate metabolites were involved in lipid metabolism based on KEGG functional annotations.

CONCLUSION

The plasma metabolomic signature of PMOP patients differed from that of healthy controls. Marker metabolites may help provide information for the diagnosis, therapy, and prevention of PMOP. We highlight the application of feature selection approaches in the analysis of high-dimensional biological data.