Gut Microbiomics of Sustained Knee Pain in Patients With Knee Osteoarthritis
Bibliographic record
Abstract
Objective To examine whether gut microbes were associated with postsurgery-sustained knee pain in patients with knee osteoarthritis (OA) by a gut microbiomics approach. Methods Patients receiving total knee replacement (TKR) because of primary knee OA were recruited. Sustained knee pain status at ≥ 1 year after TKR was defined by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Fasting plasma and fecal samples were collected. Metabolomic profiling was performed on fasting plasma. 16S rRNA sequencing was performed on fecal samples to determine microbial composition. Results Twenty patients with TKR because of primary knee OA were included in the study, with 10 experiencing sustained postsurgery pain and 10 without such pain. Age, sex, and BMI (kg/m2) were matched. Linear discriminant analysis of microbiome data identified 13 bacterial taxa that were highly abundant in the pain group and 5 that were highly abundant in the nonpain group (P< 0.05 for all). Plasma metabolomic profiling measured 622 metabolites. The correlation analysis indicated the 18 taxa were significantly correlated with 231 metabolites (P< 0.05 for all). Sparse partial least squares discriminant analysis showed that 30/231 metabolites explained 29% of total variance and can be used to clearly separate patients with sustained knee pain from patients in the nonpain group. Pathway enrichment analysis showed that these significant metabolites were enriched in the arachidonic acid metabolic pathway, bile acid biosynthesis, and linoleic acid metabolism. Conclusion Gut microbes may play a significant role in sustained knee pain in patients with knee OA after TKR, potentially through their activation of inflammatory pathways, lipid metabolism, and central sensitization.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".