Abstract
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.
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Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
The authors are greatful to Novogene Bioinformatics Technology Co., Ltd. for providing helps in metabolome data analysis and experimental technical support.
Funding
This study was supported by grants from National Natural Science Foundation of China (81702067); the Shaanxi Provincial Key Research and Development Program (2020GXLH-Y-027, 2021SF-030); the Fundamental Research Funds for the Central Universities (G2020KY0516).
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Jihan Wang and Dageng Huang designed the project, and co-wrote the manuscript. Jihan Wang recruited the participants, collected the clinical data. Yangyang Wang analyzed the metabolomics data, and did statistical analysis. Yuhong Zeng revised the manuscript. All authors read and approved the final manuscript.
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The current study was approved by Institutional Ethical Review Board, Northwestern Polytechnical University (Approval number: 202002023). Each participant signed informed consent.
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11306_2022_1937_MOESM1_ESM.tif
Fig. S1 ROC curve of combination test of M150T272, M279T81, and M769T49 in distinguishing between PMOP and NC. The ROC analysis was performed by MedCalc 19.0.4. Supplementary file1 (TIF 115 kb)
11306_2022_1937_MOESM2_ESM.tif
Fig S2. Correlation analysis between the relative abundance of DAMs and BMD/T-score in positive TIC mode. There were 37 DAMs in PMOP group compared to NC group in the positive TIC mode. Detailed information about ID and name of the 37 DAMs is shown in Table S1 (positive TIC). Supplementary file2 (TIF 2521 kb)
11306_2022_1937_MOESM3_ESM.tif
Fig S3. Correlation analysis between the relative abundance of DAMs and BMD/T-score in negative TIC mode. There were 31 DAMs in PMOP group compared to NC group in the negative TIC mode. Detailed information about ID and name of the negative DAMs is shown in Table S2 (negative TIC). Supplementary file3 (TIF 2210 kb)
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Wang, J., Wang, Y., Zeng, Y. et al. Feature selection approaches identify potential plasma metabolites in postmenopausal osteoporosis patients. Metabolomics 18, 86 (2022). https://doi.org/10.1007/s11306-022-01937-0
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DOI: https://doi.org/10.1007/s11306-022-01937-0