Red Cell Distribution Width and Absolute Lymphocyte Count Associate With Biomarkers of Inflammation and Subsequent Mortality in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Morbidity and mortality in rheumatoid arthritis (RA) is partly mitigated by maintaining immune and hematologic homeostasis. Identification of those at risk is challenging. Red cell distribution width (RDW) and absolute lymphocyte count (ALC) associate with cardiovascular disease (CVD) and mortality in the general population, and with disease activity in RA. How these variables relate to inflammation and mortality in RA was investigated. METHODS: In a retrospective single Veterans Affairs (VA) Rheumatology Clinic cohort of 327 patients with RA treated with methotrexate (MTX)+/- a tumor necrosis factor (TNF) inhibitor (TNFi), we evaluated RDW and ALC before and during therapy and in relation to subsequent mortality. Findings were validated in a national VA cohort (n = 13,914). In a subset of patients and controls, we evaluated inflammatory markers. RESULTS: < 0.001). The highest mortality was observed in those with both high RDW and low ALC. This remained after adjusting for age and comorbidities and was validated in the national RA cohort. In the immunology cohort, soluble and cellular inflammatory markers were higher in patients with RA than in controls. ALC correlated with age, plasma TNF receptor II, natural killer HLA-DR mean fluorescence intensity, and CD4CM/CD8CM HLA-DR/CD38%, whereas RDW associated with age and ALC. MTX initiation was followed by an increase in RDW and a decrease in ALC. TNFi therapy added to MTX resulted in an increase in ALC. CONCLUSION: RDW and ALC before disease-modifying antirheumatic drug therapy are associated with biomarkers of monocyte/macrophage inflammation and subsequent mortality. The mechanistic linkage between TNF signaling and lymphopenia found here warrants further investigation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".