ARTERIAL STIFFNESS IN DIFFERENT AGE AND CARDIOVASCULAR RISK GROUPS OF PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS
Bibliographic record
Abstract
PV056 / #703 Poster Topic: AS06 - Comorbidities Background/Purpose Systemic Lupus Erythematosus (SLE) is associated with increased cardiovascular morbidity and mortality. Arterial stiffness (ArS) is a marker of vascular aging and atherosclerosis and is a well-recognized predictor of cardiovascular risk in the general population; however, data in SLE is scarce. We compared ArS in SLE vs healthy controls (HC) and assessed potential predictors. Methods ArS was assessed in 194 SLE patients vs 1:1 age/sex/mean arterial pressure (MAP)-matched HC using the carotid-femoral pulse wave velocity (PWV) and augmentation index at 75 beats/min (AIx@75). ArS was examined in different age groups (18-37, 38-57, 58-75 years) and cardiovascular risk groups (low-moderate, high-very high) classified by the Systematic Coronary Risk Evaluation (SCORE). Carotid and femoral ultrasounds were performed to detect atherosclerotic plaque presence. Linear regression models were used to examine potential predictors of ArS, including patient demographic characteristics, Systemic Coronary Risk Evaluation (SCORE), the sum of modifiable cardiovascular risk factors (CVRFs) (among hypertension, dyslipidemia, smoking, exercise, and body weight), the achievement of Lupus Low Disease Activity State (LLDAS) and Definition of Remission in SLE (DORIS) clinical remission, Systemic Lupus International Collaborating Clinics/American College of Rheumatology (SLICC/ACR) damage index, cumulative glucocorticoid exposure, consistent hydroxychloroquine use, cardiovascular disease (CVD)-related medications, and antiphospholipid antibody (aPL) positivity at the time of the assessment. Results SLE patients had increased AIx@75 vs HC (β = 3.353, 95% CI 1.964-6.526, p = 0.019) (Table 1, model A), but not PWV (β = 0.102, 95% CI -0.117, 0.321, p = 0.361). Patients aged 18-37 had higher PWV (β = 0.409, 95% CI 0.095-0.722, p = 0.011) and AIx@75 (β = 10.115, 95% CI 6.111-14.119, p < 0.001) than HC (Table 1, models B and C). Low-moderate CVD risk patients had higher AIx@75 than HC (β = 3.387, 95% CI 0.735-6.039, p = 0.012) (Table 1, model D). PWV and AIx@75 were independently associated with atherosclerotic plaque presence (carotid or femoral) (β = 0.297, 95% CI 0.005-0.589, p = 0.046 and β = 4.867, 95% CI 1.989-7.746, p = 0.001, respectively). In SLE, PWV and AIx@75 were independently associated with age (β = 0.061, 95% CI 0.040-0.082, p < 0.001 and β = 0.426, 95% CI 0.278-0.574, p < 0.001, respectively), MAP (β = 0.051, 95% CI 0.031-0.070, p < 0.001 and β = 0.328, 95% CI 0.189-0.466, p < 0.001, respectively), and the sum of modifiable CVRFs (β = 0.258, 95% CI 0.055-0.461, p = 0.013 and β = 2.035, 95% CI 0.587-3.483, p = 0.006, respectively) (Table 1, models E and G). PWV was additionally associated with SCORE (β = 0.607, 95% CI 0.414-0.800, p < 0.001) (Table 1, model F). Among disease-related factors, AIx@75 was associated with disease duration (β = 0.274, 95% CI 0.081-0,467, p < 0.001) (Table 1, model H), and PWV correlated with past use of corticosteroids in patients aged 58-75 years (β = 1.660, 95% CI 0.185-3.135, p = 0.029). Table 1 Multivariate linear regression models of pulse wave velocity and augmentation index in SLE versus HC (models A, B, C, D), and within SLE (models E, F, G, H) Conclusions Increased ArS in SLE compared to HC is associated with traditional CVRF burden, emphasizing the need for early CVRF evaluation and treatment in SLE, particularly in young low CVD risk patients. ArS screening may help detect high CVD risk in low-moderate risk patients with SLE.
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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 | 0.001 |
| 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) | 0.002 | 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".