Impact of early disease factors on metabolic syndrome in systemic lupus erythematosus: data from an international inception cohort
Bibliographic record
Abstract
BackgroundThe metabolic syndrome (MetS) may contribute to the increased cardiovascular risk in systemic lupus erythematosus (SLE). We examined the association between MetS and disease activity, disease phenotype and corticosteroid exposure over time in patients with SLE.MethodsRecently diagnosed (<15 months) patients with SLE from 30 centres across 11 countries were enrolled into the Systemic Lupus International Collaborating Clinics (SLICC) Inception Cohort from 2000 onwards. Baseline and annual assessments recorded clinical, laboratory and therapeutic data. A longitudinal analysis of factors associated with MetS in the first 2 years of follow-up was performed using random effects logistic regression.ResultsWe studied 1150 patients with a mean (SD) age of 34.9 (13.6) years and disease duration at enrolment of 24.2 (18.0) weeks. In those with complete data, MetS prevalence was 38.2% at enrolment, 34.8% at year 1 and 35.4% at year 2. In a multivariable random effects model that included data from all visits, prior MetS status, baseline renal disease, SLICC Damage Index >1, higher disease activity, increasing age and Hispanic or Black African race/ethnicity were independently associated with MetS over the first 2 years of follow-up in the cohort.ConclusionsMetS is a persistent phenotype in a significant proportion of patients with SLE. Renal lupus, active inflammatory disease and damage are SLE-related factors that drive MetS development while antimalarial agents appear to be protective from early in the disease course.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".