Metabolomic Signatures of Dietary Patterns and Incident Radiographic Knee Osteoarthritis: A Case-Cohort Study From the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVE: Dietary factors related to inflammation, obesity, and metabolism may contribute to osteoarthritis (OA) development. This study examines the relationship between metabolomic signatures of dietary patterns and radiographic knee OA incidence. METHODS: This case-cohort study included 603 participants from the Osteoarthritis Initiative, comprising 237 incident radiographic knee OA cases and 366 noncases during a 6-year follow-up. Plasma metabolomes were analyzed using ultra-performance liquid chromatography-tandem mass spectrometry. We averaged metabolite levels at baseline and year 1 as the exposure and identified incident cases with those who had a Kellgren-Lawrence grade ≥ 2 in follow-ups. We selected 46 metabolites significantly associated with major food groups based on previous studies. Principal component analysis identified 4 metabolomic signatures related to specific food groups. Weighted logistic regression was used to examine associations between metabolomic signature scores and knee OA incidence. RESULTS: Among the 603 participants, after adjusting for age, sex, and other covariates, the highest quartiles of 2 metabolomic signatures associated with healthy food groups were linked to a reduced OA risk compared to the lowest quartile (odds ratio [95% CI] 0.69 [0.44-1.08] and 0.61 [0.40-0.92], respectively). However, after further adjustment for BMI, these associations weakened. The proportions of the associations mediated through BMI for these 2 signatures were 43.7% and 81.4%, respectively. The other signatures were null. CONCLUSION: Metabolomic signatures of healthy dietary patterns may be linked to a lower risk of radiographic knee OA, with BMI potentially playing an intermediary role. Further studies are needed to explore causality.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".