Risk Assessment for Hip and Knee Osteoarthritis Using Polygenic Risk Scores
Bibliographic record
Abstract
OBJECTIVE: Polygenic risk scores (PRS) allow risk stratification using common single-nucleotide polymorphisms (SNPs), and clinical applications are currently explored for several diseases. This study was undertaken to assess the risk of hip and knee osteoarthritis (OA) using PRS. METHODS: We analyzed 12,732 individuals from a population-based cohort from the Rotterdam Study (n = 11,496), a clinical cohort (Cohort Hip and Cohort Knee [CHECK] study; n = 908), and a high-risk cohort of overweight women (Prevention of Knee OA in Overweight Females [PROOF] study; n = 328), for the association of the PRS with prevalence/incidence of radiographic OA, of clinical OA, and of total hip replacement (THR) or total knee replacement (TKR). The hip PRS and knee PRS contained 44 and 24 independent SNPs, respectively, and were derived from a recent genome-wide association study meta-analysis. Standardized PRS (with Z transformation) were used in all analyses. RESULTS: We found a stronger association of the PRS for clinically defined OA compared to radiographic OA phenotypes, and we observed the highest PRS risk stratification for TKR/THR. The odds ratio (OR) per SD was 1.3 for incident THR (95% confidence interval [95% CI] 1.1-1.5) and 1.6 (95% CI 1.3-1.9) for incident TKR in the Rotterdam Study. The knee PRS was associated with incident clinical knee OA in the CHECK study (OR 1.3 [95% CI 1.1-1.5]), but not for the PROOF study (OR 1.2 [95% CI 0.8-1.7]). The OR for OA increased gradually across the PRS distribution, up to 2.1 (95% CI 1.4-3.2) for individuals with the 10% highest PRS compared to the middle 50% of the PRS distribution. CONCLUSION: Our findings validated the association of PRS across OA definitions. Since OA is becoming frequent and primary prevention is not commonly applicable, PRS-based risk assessment could play a role in OA prevention. However, the utility of PRS is dependent on the setting. Further studies are needed to test the integration of genetic risk assessment in diverse health care settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".