Disentangling osteoarthritis-specific genetic effects from obesity to identify novel therapeutic targets
Bibliographic record
Abstract
Abstract Osteoarthritis (OA) significantly impairs mobility and quality of life for hundreds of millions of individuals. Given the limited non-surgical treatment options for OA, genetics may help identify new strategies for treatment. However, many genetic associations with OA arise from its genetic correlation with obesity (measured by body mass index [BMI]), which makes it difficult to find OA-specific genetic associations. This study used a genome-wide association study (GWAS)-by-subtraction approach to separate genetic effects specific to OA from those shared with BMI, using GWAS of 12 OA traits from the Genetics of Osteoarthritis Consortium. Subsequent proteome-wide Mendelian randomization and colocalization analyses across four large proteomics cohorts identified 27 candidate causal proteins influencing OA via pathways not fully mediated by BMI. Among these, extracellular matrix and bone remodeling mediators such as COL6A2, SMAD3, SPP1, and TNFSF11 (RANKL) were highlighted as promising therapeutic targets. Colocalization with expression quantitative trait loci in osteoclasts and other relevant tissues provided additional biological support. Further, actionability assessments identified several proteins already targeted by existing therapies, such as the approved TNFSF11 (RANKL) inhibitor, denosumab, suggesting repurposing opportunities to modulate subchondral bone turnover in OA. This integrative proteogenomic framework clarifies biological mechanisms of OA beyond BMI-related pathways and offers potential targets for intervention.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".