Osteopenia Metabolomic Biomarkers for Early Warning of Osteoporosis

Overview

Journal Metabolites

Publisher MDPI

Date 2025 Jan 24

PMID 39852408

Authors

Jie Wang

Dandan Yan

Suna Wang

Aihua Zhao

Xuhong Hou

Xiaojiao Zheng

Jingyi Guo

Li Shen

Yuqian Bao

Wei Jia

Xiangtian Yu

Cheng Hu

Zhenlin Zhang

Affiliations

Soon will be listed here.

Abstract

: This study aimed to capture the early metabolic changes before osteoporosis occurs and identify metabolomic biomarkers at the osteopenia stage for the early prevention of osteoporosis. : Metabolomic data were generated from normal, osteopenia, and osteoporosis groups with 320 participants recruited from the Nicheng community in Shanghai. We conducted individual edge network analysis (iENA) combined with a random forest to detect metabolomic biomarkers for the early warning of osteoporosis. Weighted Gene Co-Expression Network Analysis (WGCNA) and mediation analysis were used to explore the clinical impacts of metabolomic biomarkers. : Visual separations of the metabolic profiles were observed between three bone mineral density (BMD) groups in both genders. According to the iENA approach, several metabolites had significant abundance and association changes in osteopenia participants, confirming that osteopenia is a critical stage in the development of osteoporosis. Metabolites were further selected to identify osteopenia (nine metabolites in females; eight metabolites in males), and their ability to discriminate osteopenia was improved significantly compared to traditional bone turnover markers (BTMs) (female AUC = 0.717, 95% CI 0.547-0.882, versus BTMs: = 0.036; male AUC = 0.801, 95% CI 0.636-0.966, versus BTMs: = 0.007). The roles of the identified key metabolites were involved in the association between total fat-free mass (TFFM) and osteopenia in females. : Osteopenia was identified as a tipping point during the development of osteoporosis with metabolomic characteristics. A few metabolites were identified as candidate early-warning biomarkers by machine learning analysis, which could indicate bone loss and provide new prevention guidance for osteoporosis.

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