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Updated: Jun 1, 2025

Author Spotlight: An Economic and Efficient Method for Quantitative Evaluation of Bone Microarchitecture in a Murine Osteoporosis Model
Published on: September 8, 2023
Combining untargeted and targeted metabolomic profiling reveals principal differences between osteopenia,
Bing Tan1, Yan Cheng1, Junfeng Li1
1The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, 621000, China.
Background:
Osteopenia (ON) and osteoporosis (OP) are highly prevalent among postmenopausal women and poses a challenge for early diagnosis. Therefore, identifying reliable biomarkers for early prediction using metabolomics is critically important.
Methods:
Initially, non-targeted metabolomics was employed to identify differential metabolites in plasma samples from cohort 1, which included healthy controls (HC, n = 23), osteonecrosis (ON, n = 36), and osteoporosis (OP, n = 37). Subsequently, we performed targeted metabolomic validation of 37 amino acids and their derivatives in plasma samples from cohort 2, consisting of healthy controls (HC, n = 10), osteonecrosis (ON, n = 10), and osteoporosis (OP, n = 10).
Results:
The non-targeted metabolomic analysis revealed an increase in differential metabolites with the progression of the disease, showing abnormalities in lipid and organic acid metabolism in ON and OP patients. Several substances were found to correlate positively or negatively with bone mineral density (BMD), for example, N-undecanoylglycine, sphingomyelins, and phosphatidylinositols exhibited positive correlations with BMD, while acetic acid, phenylalanine, taurine, inosine, and pyruvic acid showed negative correlations with BMD. Subsequently, targeted validation of 37 amino acids and their metabolites revealed six amino acids related to ON and OP.
Conclusion:
Significant metabolomic features were identified between HC and patients with ON/OP, with multiple metabolites correlating positively or negatively with BMD. Integrating both targeted and non-targeted metabolomic results suggests that lipid, organic acid, and amino acid metabolism may represent important metabolomic characteristics of patients with OP, offering new insights into the development of metabolomic applications in OP.
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