Bidirectional causal relationship between obesity and osteoarthritis: Insights from a two-sample Mendelian randomization study
Bibliographic record
Abstract
Objective: Osteoarthritis (OA) is a prevalent chronic disease associated with disability worldwide, and obesity is a key modifiable risk factor for OA. The study's aim was to investigate the causal relationship between obesity and OA. Method: This study employed a two-sample Mendelian randomization (MR) approach to investigate the bidirectional causal relationship between obesity, using body mass index (BMI) as its proxy, and OA of the knee, hip, and hand. Genetic instruments were derived from large-scale GWAS meta-analyses, including ∼681,000 individuals for BMI and ∼827,000 individuals (177,000 OA cases) for OA. Inverse variance weighted with multiplicative random effects analysis was performed as primary analysis, and in addition sensitivity analyses relying on different assumptions were performed. Results: The MR analysis revealed that genetically predicted BMI had a causal effect on increased risk of knee (OR 1.91, 95 % CI 1.80-2.03), hip (OR 1.52, 95 % CI 1.41-1.64) and hand OA (OR 1.21, 95 % CI 1.04-1.23). Sensitivity analyses confirmed the robustness of these associations. However, there was no evidence for a causal effect from knee, hip or hand OA on BMI. Conclusion: This study provides strong evidence supporting a causal effect of obesity (measured by BMI) on OA, with a more pronounced effect in weight-bearing knee & hip joints compared to non-weight-bearing hand joint. There was no causal evidence for the reverse direction. Future research could look more in depth into differences in the genetic variants that may represent different biological underlying mechanisms.
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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 | 0.060 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".