Associations of Body Mass Index With Pain and the Mediating Role of Inflammatory Biomarkers in People With Hand Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To examine the association of body mass index (BMI) with pain in people with hand osteoarthritis (OA), and explore whether this association, if causal, is mediated by systemic inflammatory biomarkers. METHODS: In 281 Nor-Hand study participants, we estimated associations between BMI and hand pain, as measured by the Australian/Canadian Osteoarthritis Hand Index (AUSCAN; range 0-20) and Numerical Rating Scale (NRS; range 0-10); foot pain, as measured by NRS (range 0-10); knee/hip pain, as measured by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC; range 0-20); painful total body joint count; and pain sensitization. We fit natural-effects models to estimate natural direct and natural indirect effects of BMI on pain through inflammatory biomarkers. RESULTS: Each 5-unit increase in BMI was associated with more severe hand pain (on average increased AUSCAN by 0.64 [95% confidence interval (95% CI) 0.23, 1.08]), foot pain (on average increased NRS by 0.65 [95% CI 0.36, 0.92]), knee/hip pain (on average increased WOMAC by 1.31 [95% CI 0.87, 1.73]), generalized pain, and pain sensitization. Mediation analyses suggested that the effects of BMI on hand pain and painful total body joint count were partially mediated by leptin and high-sensitivity C-reactive protein (hsCRP), respectively. Effect sizes for mediation by leptin were larger for the hands than for the lower extremities, and were statistically significant for the hands only. CONCLUSION: In people with hand OA, higher BMI is associated with greater pain severity in the hands, feet, and knees/hips. Systemic effects of obesity, measured by leptin, may play a larger mediating role for pain in the hands than in the lower extremities. Low-grade inflammation, measured by hsCRP, may contribute to generalized pain in overweight/obese individuals.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".