ArticleMetabolism open2026
Ensemble learning uncovers novel metabolomic biomarkers for early osteoporosis prediction in Tibetan plateau populations.
Article in Metabolism open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 ( 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 Conclusions: This research proposes a metabolomics-driven model for osteoporosis prediction, establishing an innovative biomarker framework for early clinical intervention in plateau areas.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.