Metabolism open · 2026
Ensemble learning uncovers novel metabolomic biomarkers for early osteoporosis prediction in Tibetan plateau populations.
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Yang Jiawei, Zhou Tao, Zhang Haichen, Zhang Qiong, Chen Lening, Xiao Qianqian, Luo Shusheng, Labasangzhu, Dunyou, Danbalangjie, Hao Weidong, Wei Xuetao
Аннотация
BACKGROUND: Osteoporosis represents a prevalent metabolic bone disorder among middle-aged and elderly populations, with its prevention and early detection holding important implications for clinical practice and public health. While extensive research has characterized osteoporosis in low-altitude populations, plateau regions present unique challenges.
METHODS: This study enrolled 177 adult residents from Lhasa, Tibet. Data collection included demographic characteristics, health profiles, and female reproductive parameters. Osteoporosis is defined by T-score ≤ -2.5 (for males >50 years and postmenopausal women) or Z-score ≤ -2 (for males ≤50 years and premenopausal women). Serum metabolomic profiling identified 3381 metabolites via HPLC-MS/MS. Predictive models were constructed using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) ensemble learning algorithm, with pathway enrichment analysis performed in MetaboAnalyst (P < 0.05).
RESULTS: Our cross-sectional analysis of 177 participants revealed 41 osteoporosis cases (23.2%), predominantly postmenopausal women (36/41). Baseline characteristics showed significant differences in age, gender, difficulty initiating sleep, and menopausal status across groups (all P < 0.05). The 9 metabolites screened by LASSO classification demonstrated exceptional predictive capacity for osteoporosis (AUC: 0.995), while 10 metabolites based on RF better predicted osteopenia progression (R2: 0.848). Z-score-based models outperformed T-score subgroups. Menopausal status and difficulty initiating sleep were identified as high-risk factors, and three metabolites-Dihydro-3-hydroxy-4,4-dimethyl-2(3H)-furanone, gamma-Glutamyltryptophan, and di-homo-gamma-linolenate-emerged as potential early indicators of bone loss. Pathway enrichment highlighted pyrimidine metabolism and related diseases as a potential therapeutic target for bone loss.
CONCLUSIONS: This research proposes a metabolomics-driven model for osteoporosis prediction, establishing an innovative biomarker framework for early clinical intervention in plateau areas.
METHODS: This study enrolled 177 adult residents from Lhasa, Tibet. Data collection included demographic characteristics, health profiles, and female reproductive parameters. Osteoporosis is defined by T-score ≤ -2.5 (for males >50 years and postmenopausal women) or Z-score ≤ -2 (for males ≤50 years and premenopausal women). Serum metabolomic profiling identified 3381 metabolites via HPLC-MS/MS. Predictive models were constructed using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) ensemble learning algorithm, with pathway enrichment analysis performed in MetaboAnalyst (P < 0.05).
RESULTS: Our cross-sectional analysis of 177 participants revealed 41 osteoporosis cases (23.2%), predominantly postmenopausal women (36/41). Baseline characteristics showed significant differences in age, gender, difficulty initiating sleep, and menopausal status across groups (all P < 0.05). The 9 metabolites screened by LASSO classification demonstrated exceptional predictive capacity for osteoporosis (AUC: 0.995), while 10 metabolites based on RF better predicted osteopenia progression (R2: 0.848). Z-score-based models outperformed T-score subgroups. Menopausal status and difficulty initiating sleep were identified as high-risk factors, and three metabolites-Dihydro-3-hydroxy-4,4-dimethyl-2(3H)-furanone, gamma-Glutamyltryptophan, and di-homo-gamma-linolenate-emerged as potential early indicators of bone loss. Pathway enrichment highlighted pyrimidine metabolism and related diseases as a potential therapeutic target for bone loss.
CONCLUSIONS: This research proposes a metabolomics-driven model for osteoporosis prediction, establishing an innovative biomarker framework for early clinical intervention in plateau areas.