Association of Increased Serum Lipopolysaccharide, But Not Microbial Dysbiosis, With Obesity-Related Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To test the hypothesis that an altered gut microbiota (dysbiosis) plays a role in obesity-associated osteoarthritis (OA). METHODS: Stool and blood samples were collected from 92 participants with a body mass index (BMI) ≥30 kg/m2 , recruited from the Johnston County Osteoarthritis Project. OA patients (n=50) had hand and knee OA (Kellgren/Lawrence [K/L] grade≥2 or arthroplasty). Controls (n=42) had no hand OA and a K/L grade of 0-1 for the knees. Compositional analysis of stool samples was carried out by 16S ribosomal RNA amplicon sequencing. Alpha- and beta-diversity and differences in taxa relative abundances were determined. Blood samples were used for multiplex cytokine analysis and measures of lipopolysaccharide (LPS) and LPS binding protein. Germ-free mice were gavaged with patient- or control-pooled fecal samples and fed a 40% fat, high-sucrose diet for 40weeks. Knee OA was evaluated histologically. RESULTS: On average, OA patients were slightly older than the controls, consisted of more women, and had a higher mean BMI, higher mean Western Ontario and McMaster Universities Osteoarthritis Index pain score, and higher mean K/L grade. There were no significant differences in alpha or beta-diversity or genus level composition between patients and controls. Patients had higher plasma levels of osteopontin (P=0.01) and serum LPS (P<0.0001) compared to controls. Mice transplanted with patient or control microbiota exhibited a significant difference in alpha diversity (P=0.02) and beta diversity, but no differences in OA severity were observed. CONCLUSION: The lack of differences in the gut microbiota, but increased serum LPS levels, suggest the possibility that increased intestinal permeability allowing for greater absorption of LPS, rather than a dysbiotic microbiota, may contribute to the development of OA associated with obesity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".