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Discovery of potential biomarkers for osteoporosis using LC-MS/MS metabolomic methods

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Abstract

Summary

Our study focused on the associations of metabolites with BMD and osteoporosis, finding that several metabolites are associated with BMD, and metabolites combined with bone turnover markers tend to be more sensitive in distinguishing osteoporosis in both males and postmenopausal females, which might be meaningful for the early diagnosis of osteoporosis.

Introduction

Our study aimed to evaluate the association of metabolites with bone, trying to find new metabolic markers that are distinguishing for low bone mineral density (BMD).

Methods

Our study recruited 320 participants, including 138 males and 182 postmenopausal females from the Shanghai area. Bone turnover markers (BTMs), including osteocalcin, PINP and β-CTX, and other biochemical traits were tested. BMD values of the lumber spine (L1–4), femoral neck and total hip were determined using dual-energy X-ray absorptiometry and the serum metabolome profiles including 221 metabolites from five groups (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids and hexose) were assessed by mass spectrometry.

Results

No visual separation in the metabolic profiles between different BMD groups was observed in principal component analysis (PCA) or partial least squares discriminant analysis (PLS-DA) models. We compared metabolites in three groups with different BMD levels in males and postmenopausal females separately and further filtering these metabolites via random forest-based feature selection, a commonly applied machine learning algorithm which could select the features with the greatest impact on osteoporosis, then metabolites with the highest importance (≥ 5%) (5 in males and 9 in postmenopausal females) were selected to construct better models for osteoporosis classification. After adding these selected metabolites to the model, the area under the curve (AUC) of receiver operating characteristic (ROC) curves increased significantly (BTMs: AUC 0.729, 95% CI 0.647–0.802, p < 0.0001, model 1: AUC = 0.828, 95% CI 0.754–0.888, p < 0.0001; model 1 versus model of BTMs: p = 0.0158) compared to the AUC of the BTM-only model in males. Similar results were also observed in postmenopausal females (BTMs: AUC = 0.638, 95% CI 0.562–0.708, p = 0.0025; model 2: AUC = 0.741, 95% CI 0.669–0.803, p < 0.0001; model 1 versus model of BTMs: p = 0.0182).

Conclusion

Metabolites combined with traditional BTMs tend to better markers for distinguishing osteoporosis in both males and postmenopausal females than BTMs alone.

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Acknowledgements

We thank the participants of the research study. We are grateful for the assistance of the nursing and medical staff at the Shanghai Clinical Centre for Diabetes.

Funding

This work was supported by National Key Research and Development Project of China [2016YFC0903303]; National Natural Science Foundation of China grants [81570713, 91649112]; Outstanding Academic Leaders of Shanghai Health System [2017BR008]; the National Program for Support of Top-notch Young Professionals; Yangtze River Scholar.

Author information

Author notes

  1. J. Wang and D. Yan contributed equally to this work.

Authors and Affiliations

  1. Department of Osteoporosis, Metabolic Bone Disease and Genetic Research Unit, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, 200233, China

    J. Wang & Z.-L. Zhang

  2. Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, 200233, China

    D. Yan, Y. Bao, C. Hu & W. Jia

  3. Center for Translational Medicine, and Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, 200233, China

    A. Zhao, X. Zheng & W. Jia

  4. Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, 200233, China

    X. Hou, P. Chen, C. Hu & W. Jia

  5. Institute for Metabolic Disease, Fengxian Central Hospital Affiliated to Southern Medical University, Shanghai, 201499, China

    C. Hu

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  1. J. Wang
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Corresponding authors

Correspondence to C. Hu or Z.-L. Zhang.

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Ethics statement

This study was approved by the institutional review board of The Shanghai Jiao Tong University Affiliated Sixth People’s Hospital with the approval number 2015-KY-002(T). It was conducted in accordance with the principles of the Second Revision of the Declaration of Helsinki, and written informed consent was signed by every participant.

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Wang, J., Yan, D., Zhao, A. et al. Discovery of potential biomarkers for osteoporosis using LC-MS/MS metabolomic methods. Osteoporos Int 30, 1491–1499 (2019). https://doi.org/10.1007/s00198-019-04892-0

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