Individual participant data meta-analysis of metabolomics on sustained knee pain in primary osteoarthritis patients
Bibliographic record
Abstract
OBJECTIVES: Knee pain is the major driver for OA patients to seek healthcare, but after pursuing both conservative and surgical pain interventions, ∼20% of patients continue to report long-term pain following total knee arthroplasty (TKA). This study aimed to identify a metabolomic signature for sustained knee pain after TKA to elucidate possible underlying mechanisms. METHODS: Two independent cohorts from St John's, NL, Canada (n = 430), and Toronto, ON, Canada (n = 495) were included in the study. Sustained knee pain was assessed using the WOMAC pain subscale (five questions) at least 1 year after TKA for primary OA. Those reporting any pain on all five questions were considered to have sustained knee pain. Metabolomic profiling was performed on fasted pre-operative plasma samples using the Biocrates Absolute IDQ p180 kit. Associations between metabolites and pair-wise metabolite ratios with sustained knee pain in each individual cohort were assessed using logistic regression with adjustment for age, sex and BMI. Random-effects meta-analysis using inverse variance as weights was performed on summary statistics from both cohorts. RESULTS: One metabolite, phosphatidylcholine (PC) diacyl (aa) C28:1 (odds ratio = 0.66, P = 0.00026), and three metabolite ratios, PC aa C32:0 to PC aa C28:1, PC aa C28:1 to PC aa C32:0, and tetradecadienylcarnitine (C14:2) to sphingomyelin C20:2 (odds ratios = 1.59, 0.60 and 1.59, respectively; all P < 2 × 10-5), were significantly associated with sustained knee pain. CONCLUSIONS: Though further investigations are needed, our results provide potential predictive biomarkers and drug targets that could serve as a marker for poor response and be modified pre-operatively to improve knee pain and surgical response to TKA.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.040 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".