Genomic and Serological Rheumatoid Arthritis Biomarkers, <i>MUC5B</i> Promoter Variant, and Interstitial Lung Abnormalities
Bibliographic record
Abstract
Abstract Rationale Rheumatoid arthritis (RA) has been implicated in interstitial lung disease, as the majority of studies have comprised patients with known RA. However, it remains unclear whether an underlying risk for RA in combination with genetic risk for pulmonary fibrosis is associated with radiological markers of early lung injury and fibrosis in broader population samples. Objective We sought to determine whether genetic and serological biomarkers of RA risk in combination with the MUC5B (rs35705950) risk allele (T) are associated with interstitial lung abnormalities (ILAs) on computed tomography scans. Methods Associations of RA-risk HLA-DRB1 alleles (*04:01, *04:08, *04:05, *04:04, and *10:01) and serum RA autoantibodies with ILA in the Multi-Ethnic Study of Atherosclerosis (MESA; n = 4,018) and COPDGene (n = 5,963) cohorts were modeled using logistic regression and adjusted for age, sex, self-reported race and ethnicity, smoking history, body mass index, and principal components of genetic ancestry. Results The prevalence of an RA-risk HLA-DRB1 allele was 16.5% and 21.9% in the MESA and COPDGene cohorts, respectively. ILA was present in 3.9% and 11% of the MESA and COPDGene cohorts, respectively. An RA-risk HLA-DRB1 allele was not significantly associated with ILA in the MESA and COPDGene cohorts. In the MESA cohort, higher serum levels of immunoglobulin (Ig)A rheumatoid factor (RF) and anticyclic citrullinated peptide were associated with odds ratios for ILA of 1.20 (95% confidence interval [CI] = 1.07–1.35) and 1.19 (95% CI = 1.04–1.38), respectively. Among smokers without baseline ILA, per doubling of IgM RF was associated with an odds ratio for ILA 10 years later of 1.25 (95% CI = 1.08–1.44). Associations were not significantly different by MUC5B risk allele status. Conclusions RA-related HLA-DRB1 alleles were not associated with ILA, whereas higher serum levels of IgM RF among smokers without baseline ILA were associated with subsequent ILA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".