MétaCan
Menu
Back to cohort
Record W4410049582 · doi:10.1038/s44324-025-00060-7

Metagenomic analysis revealing links between age, gut microbiota and bone loss in Chinese adults

2025· article· en· W4410049582 on OpenAlexaff
Yufeng Li, Zhun Shi, Xiuying Zhang, Huahui Ren, Hongyi Ji, Fangming Yang, Zuodi Fu, Xiangshuang Kong, Xiaoguang Cheng, Junhua Li, Kui Wu, Yiyi Zhong, Huanzi Zhong, Linong Ji

Bibliographic record

Venuenpj Metabolic Health and Disease · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsMD Precision (Canada)
FundersPeking University People's HospitalPeking University
KeywordsMetagenomicsGut floraBiologyComputational biologyGeneticsImmunologyGene

Abstract

fetched live from OpenAlex

Accumulating evidence has linked gut microbiota to bone health. However, investigations into the impacts of aging, gut microbiota, and their interactions in the development of osteoporosis remain inconclusive. We employed quantitative computed tomography to measure lumbar bone mass density (BMD) and analyzed shotgun metagenomic data in 684 Chinese adults. Our analyses revealed significant positive associations between BMD and abundances of multiple Lachnospiraceae species, including Lachnospira eligens, Blautia wexlerae, and Roseburia hominis, as well as pathways involved in L-arginine biosynthesis and butyrate production-independent of age, diet habits, and lifestyles. Moreover, we demonstrated that individuals with enterotype Bacteroides exhibited a more pronounced age-related decline in BMD compared to those with enterotype Prevotella, a pattern we validated in an independent cohort. Our findings offer valuable insights into BMD-related gut microbial features and interactions between aging, gut microbiota, and bone loss, opening potential avenues for microbiota-based prevention and treatment strategies for osteoporosis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.299
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2025
Admission routes1
Has abstractyes

Explore more

Same venuenpj Metabolic Health and DiseaseSame topicGut microbiota and healthFrench-language works237,207