Urinary Albumin Excretion Is Increased in Patients with Rheumatoid Arthritis and Associated with Arterial Stiffness
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) is associated with increased risk of cardiovascular disease (CVD). High urinary albumin excretion is a risk factor for CVD in the general population, but its role in atherosclerosis in patients with RA is not well defined. METHODS: We determined the urine albumin to creatinine ratio (UACR) in 136 patients with RA and 79 controls. Individuals with diabetes or a clinical history of CVD were excluded. We measured coronary artery calcium (CAC) with electron beam computer tomography and augmentation index (AIx) using pulse wave analysis. In patients with RA, erythrocyte sedimentation rate and concentrations of vascular cell adhesion protein-1 (VCAM-1), interleukin 10 (IL-10), C-reactive protein, IL-6, tumor necrosis factor-α, and cystatin-C were measured and results correlated with UACR. RESULTS: Patients with RA had higher UACR [median (interquartile range): 7.6 (4.0-15.5) mg/g] than control subjects: 5.6 (3.3-9.0) mg/g; p = 0.02. The presence of CAC was not associated with UACR in RA or control subjects. In patients with RA, UACR was significantly correlated with AIx (rho = 0.24, p = 0.01), higher levels of VCAM-1 (rho = 0.2, p = 0.01), and lower levels of IL-10 (rho = -0.2, p = 0.02). The association between AIx and higher UACR remained significant in multivariate analysis [β coefficient of 1.5 (95% CI 0.1-2.8), p = 0.03 that adjusted for age, sex, and race]. CONCLUSION: Urinary albumin excretion was higher in patients with RA than controls and correlated with increased arterial stiffness, higher VCAM-1, and lower IL-10 concentrations.
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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 | 0.000 | 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 | 0.000 | 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".