Associations of HLA Polymorphisms with Chronic Kidney Disease in Japanese Rheumatoid Arthritis Patients
Bibliographic record
Abstract
Objectives: The prevalence of chronic kidney disease (CKD) was reported to be higher in rheumatoid arthritis (RA) patients than in normal healthy individuals. Human leukocyte antigen (HLA) was associated with RA or CKD. Few studies on the association of HLA with CKD in RA have been reported. Here, we investigated the association of HLA polymorphisms with CKD in Japanese RA patients. Methods: HLA-DRB1 genotyping was conducted in 351 Japanese RA patients with CKD (estimated glomerular filtration rate [eGFR] lower than 60 [mL/min/1.73 m2]) and 959 without CKD (eGFR equal to or higher than 60 [mL/min/1.73 m2]). Associations of allele carrier frequencies of DRB1 with CKD were examined in the RA patients. Results: There was an association of DRB1*13:02 with CKD in RA, but this did not achieve statistical significance (p = 0.0265, odds ratio [OR] 1.70, pc = 0.7412, 95% confidence interval [CI] 1.09–2.64). The DR6 serological group was associated with CKD in RA (p = 0.0008, OR 1.65, 95% CI 1.24–2.20). A gene-dosage effect of DR6 was not detected. Logistic regression analysis showed that the association of DR6 with CKD in RA was independent of clinical characteristics. Conclusions: The present study first revealed the independent predisposing association of DR6 with CKD in Japanese RA patients, although DR6 is known to be protective against RA. Our data suggest direct or indirect roles of HLA for the development of CKD in RA, but the mechanisms are not clear.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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 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".