Association of Polymorphisms Modulating Low-density Lipoprotein Cholesterol with Susceptibility, Severity, and Progression of Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Dyslipidemia, a risk factor for cardiovascular diseases, is more prevalent in patients with rheumatoid arthritis (RA) than in the general population. We investigated whether single-nucleotide polymorphisms (SNP) modulating low-density lipoprotein (LDL) cholesterol affect susceptibility, severity, and progression of RA. METHODS: We enrolled 302 patients with RA and 1636 healthy controls, and investigated the SNP modulating LDL cholesterol. Clinical characteristics of RA, serum adipocytokine concentrations, and radiographic severity were analyzed according to genotype score based on the number of unfavorable alleles. The influence of genotype score on radiographic progression was also investigated using multivariable logistic models. RESULTS: We identified 3 SNP (rs688, rs693, and rs4420638) modulating LDL cholesterol in Koreans, which correlated well with LDL cholesterol levels in both patients with RA and controls. Among them, 2 SNP, rs688 and rs4420638, were more prevalent in patients with RA than in controls. In patients with RA carrying more unfavorable alleles (genotype score ≥ 3), disease activity measures, serum adipocytokine levels, and radiographic severity were all increased. The genotype score was an independent risk factor for radiographic progression of RA over 2 years, and its effect was greater than the influence of conventional risk factors. CONCLUSION: SNP modulating LDL cholesterol influence the risk, activity, and severity of RA. These results provide the first evidence that genetic mechanisms linked to dyslipidemia may directly contribute to the susceptibility and prognosis of RA, a representative of chronic inflammatory diseases, explaining the high incidence of dyslipidemia in RA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".