Association between serum total cholesterol level and renal outcome in systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVE: To determine whether an elevated serum total cholesterol level in a first-available sample obtained at a systemic lupus erythematosus (SLE) clinic is associated with worse renal outcome in patients with SLE. METHODS: Survival analysis methods were used on prospectively gathered data on 1,060 patients with SLE who were registered in the University of Toronto Lupus Databank. The effect of total cholesterol and 15 additional variables on the outcomes of renal deterioration, end-stage renal disease (ESRD), and death was assessed using Cox proportional hazards methods. RESULTS: In 474 (45%) of the 1,060 patients, the total cholesterol level exceeded 5.2 mmoles/liter. In the entire study group, the median total cholesterol level was 5.1 mmoles/liter (range 1.6-17.1). During a mean followup period of 8.8 years, 93 patients (9%) experienced renal deterioration, 42 patients (4%) had ESRD, and 161 deaths occurred, 48 (30%) of which were associated with renal dysfunction (renal death), and 113 (70%) of which were not associated with renal dysfunction (nonrenal death). Kaplan-Meier survival estimates for each outcome were statistically significantly different between patients with normal versus those with elevated total cholesterol levels (cutoff 5.2 mmoles/ liter), with a worse outcome observed among those with an elevated total cholesterol concentration. In multivariate analyses, total cholesterol level (hazard ratio [HR] 1.17, 95 confidence interval [95% CI] 1.01-1.36), serum creatinine level (HR 1.06, 95% CI 1.04-1.07), proteinuria (HR 2.44, 95% CI 1.25-4.76), the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (HR 1.44, 95% CI 1.16-1.80), and corticosteroid dose (HR 1.01, 95% CI 1.00-1.02) were associated with renal deterioration. Significant predictors of ESRD were baseline proteinuria (HR 6.24, 95% CI 1.96-19.88) and serum creatinine level (HR 1.15, 95% CI 1.08-1.22). The total cholesterol level was correlated with death (HR 1.20, 95% CI 1.11-1.29), retaining statistical significance for renal death (HR 1.33, 95% CI 1.20-1.47) but not for nonrenal death (HR 1.12, 95% CI 0.99-1.25). CONCLUSION: Those results indicate that an elevated serum total cholesterol level in a first-available sample obtained at an SLE clinic is associated with adverse renal outcomes and mortality.
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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 | 0.003 |
| 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".