The Association of HLA-class I Genes and the Extent of Atherosclerotic Plaques in Patients with Psoriatic Disease
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between HLA susceptibility and disease severity markers and the extent of atherosclerosis in patients with psoriatic disease. METHODS: White patients with psoriatic arthritis (PsA) and psoriasis without PsA (PsC) were recruited. An ultrasound of the carotid arteries was performed and the size of each atherosclerotic plaque was measured. The resulting score, the total plaque area (TPA), represented the extent of atherosclerosis. HLA genotyping was performed using sequence-specific oligonucleotide probes. The association between 10 HLA susceptibility and severity markers of PsC and PsA and the severity of atherosclerosis was assessed by ordinal logistic regression models adjusted for age, sex, and cardiovascular (CV) risk factors. RESULTS: The study involved 411 patients (273 PsA, 138 PsC). Of them, 61.8% had at least 1 atherosclerotic plaque. HLA-B*13:02 and HLA-C*06:02 were associated with more severe atherosclerosis (age- and sex-adjusted OR 2.31, 95% CI 1.23-4.32 and OR 1.68, 95% CI 1.12-2.52, respectively). HLA-B*38:01 was associated with less severe atherosclerosis (OR 0.49, 95% CI 0.28-0.86). These associations remained statistically significant after adjusting for CV risk factors. Higher levels of erythrocyte sedimentation rate (ESR) were associated with more severe atherosclerosis (age- and sex-adjusted OR 1.33, p = 0.02). HLA-B*13:02-positive (p = 0.01) as well as HLA-C*06:02-positive (p = 0.008) patients had higher levels of ESR over time. CONCLUSION: HLA-C*06:02 and B*13:02 alleles are associated with a higher burden of atherosclerosis in patients with psoriatic disease. This association may be mediated by a higher level of systemic inflammation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".