Wang Jie, Yan Dandan, Wang Suna, Zhao Aihua, Hou Xuhong, Zheng Xiaojiao, Guo Jingyi, Shen Li, Bao Yuqian, Jia Wei, Yu Xiangtian, Hu Cheng, Zhang Zhenlin
Department of Osteoporosis, Metabolic Bone Disease and Genetic Research Unit, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China.
Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai 200233, China.
Metabolites. 2025 Jan 20;15(1):66. doi: 10.3390/metabo15010066.
: 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.
本研究旨在捕捉骨质疏松症发生前的早期代谢变化，并识别骨质减少阶段的代谢组学生物标志物，以早期预防骨质疏松症。代谢组学数据来自正常、骨质减少和骨质疏松组，共招募了320名来自上海泥城社区的参与者。我们进行了个体边缘网络分析（iENA）并结合随机森林来检测骨质疏松症早期预警的代谢组学生物标志物。使用加权基因共表达网络分析（WGCNA）和中介分析来探索代谢组学生物标志物的临床影响。在男女的三个骨密度（BMD）组之间观察到代谢谱的视觉分离。根据iENA方法，几种代谢物在骨质减少参与者中具有显著的丰度和关联变化，证实骨质减少是骨质疏松症发展的关键阶段。进一步选择代谢物以识别骨质减少（女性9种代谢物；男性8种代谢物），与传统骨转换标志物（BTMs）相比，它们区分骨质减少的能力显著提高（女性AUC = 0.717，95% CI 0.547 - 0.882，与BTMs相比： = 0.036；男性AUC = 0.801，95% CI 0.636 - 0.966，与BTMs相比： = 0.007）。所确定的关键代谢物的作用涉及女性总去脂体重（TFFM）与骨质减少之间的关联。骨质减少被确定为具有代谢组学特征的骨质疏松症发展过程中的一个转折点。通过机器学习分析确定了一些代谢物作为候选早期预警生物标志物，它们可以指示骨质流失，并为骨质疏松症提供新的预防指导。
