Examining Health Outcomes in Juvenile Idiopathic Arthritis: A Genetic Epidemiology Study
Bibliographic record
Abstract
Objective Juvenile idiopathic arthritis (JIA) is the most common pediatric rheumatic disease; however, little is known about its wider health impacts. This study explores health outcomes associated with JIA genetic liability. Methods We used publicly available genetic data sets to interrogate the genetic correlation between JIA and 832 other health‐related traits using linkage disequilibrium score regression. Two‐sample Mendelian randomization (2SMR) was used to examine four genetic correlates for evidence of causality. Results We found robust evidence (adjusted P [ P adj ] < 0.05) of genetic correlation between JIA and rheumatoid arthritis (genetic correlation [ r g ] = 0.63, P adj = 0.029), hypothyroidism/myxedema ( r g = 0.61, P adj = 0.041), celiac disease (CD) ( r g = 0.58, P adj = 0.032), systemic lupus erythematosus ( r g = 0.40, P adj = 0.032), coronary artery disease (CAD) ( r g = 0.42, P adj = 0.006), number of noncancer illnesses ( r g = 0.42, P adj = 0.016), paternal health ( r g = 0.57, P adj = 0.032), and strenuous sports ( r g = −0.52, P adj = 0.032). 2SMR analyses found robust evidence that genetic liability to JIA was causally associated with the number of noncancer illnesses reported by UK Biobank (UKBB) participants (increase of 0.03 noncancer illnesses per doubling odds of JIA, 95% confidence interval 0.01‐0.05). Conclusion This study illustrates genetic sharing between JIA and a diversity of health outcomes. The causal association between genetic liability to JIA and noncancer illnesses suggests a need for broader health assessments of patients with JIA to reduce their potential comorbid burden. The strength of genetic correlation with hypothyroidism and CD implies that patients with JIA may benefit from CD and thyroid function screening. Strong positive genetic correlation between JIA and CAD supports the need for cardiovascular risk assessment and risk factor modification.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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 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".