Altered gut microbiota and host pathways in obesity-related knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: To investigate gut microbial alteration and their functional consequences in obesity (OB)-related knee osteoarthritis (OA) by integrating microbiome with metabolomic, proteomic, and dietary data. METHODS: Fecal and fasting plasma samples were collected from 91 knee OA patients and 12 OA-free controls, classified into four subgroups based on OB and OA status: 66 OB+OA+, 25 OB-OA+, 5 OB+OA-, and 7 OB-OA-. 16S rRNA gene sequencing was performed to profile gut microbiota. MaAsLin2 modelling was applied, and dietary intake was incorporated into the models. Plasma metabolomics (n=630 metabolites) and proteomics (n=5,416 proteins) were integrated with microbial signatures to assess functional associations. RESULTS: OB+OA+ patients exhibited significantly lower a- and β-diversity than OB-OA+ (p<0.05). Seventeen microbial taxa were identified to be significantly associated with OB+OA+ (all p<7.65×10-5 after correcting tests for 654 ASVs), and 16 of them remained significant after adjustment for age, sex, antibiotic use, and dietary intake. PICRUSt2-based predictive analysis on these taxa suggested that bile acid biosynthesis was upregulated in OB+OA+ group. These taxa were correlated with 376 metabolites (p<0.05) with enrichment in fatty acid biosynthesis, linoleic/arachidonic acid metabolism, and propanoate metabolism pathways. They were also associated with 146 proteins (p<0.001) with enrichment in PI3K-Akt signalling, ECM-receptor interaction, and lipid/atherosclerosis pathways. CONCLUSIONS: OB+OA+ patients exhibited significant gut microbial dysbiosis associated with systemic metabolic and proteomic alterations relevant to OA pathophysiology. The microbiome-metabolome-proteome axis may provide mechanistic insights into worsened OA outcomes in OB individuals and could inform microbiome-targeted interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".