Resistin Levels in Lupus and Associations with Disease-specific Measures, Insulin Resistance, and Coronary Calcification
Bibliographic record
Abstract
OBJECTIVE: To evaluate levels of resistin in female subjects with systemic lupus erythematosus (SLE) compared to age and race-matched controls and to determine the relationship between resistin and systemic inflammation, disease measures, and coronary artery calcification (CAC). METHODS: Resistin levels were measured on stored samples from 159 women with SLE and 70 controls as an extension of a previous cross-sectional study. Spearman correlations and multivariable regressions were used to examine whether resistin levels were associated with SLE, disease-specific and inflammatory markers, insulin resistance, and CAC. RESULTS: In a multivariable linear regression model, a diagnosis of SLE was significantly associated with higher resistin levels independent of age, race, renal function, body mass index (BMI), high-sensitivity CRP (hsCRP), hypertension, diabetes, and steroid use. In SLE, resistin levels correlated positively with Systemic Lupus International Collaborating Clinics Damage Index, glomerular filtration rate (GFR), hsCRP, erythrocyte sedimentation rate, homocysteine, and disease duration (all p < 0.03). Resistin level did not correlate with markers of insulin resistance or body adiposity, including homeostatic model assessment or BMI. Resistin levels were significantly elevated in SLE cases with CAC compared to cases without CAC (16.58 vs 13.10 ng/ml, respectively; p = 0.04). In multivariate logistic regression, the association was not present after adjustment for age, race, and GFR. CONCLUSION: SLE was independently associated with higher resistin levels. Among subjects with SLE, higher resistin level correlated positively with renal dysfunction, inflammatory markers, and disease damage but not with insulin resistance or BMI. SLE cases with CAC had higher resistin levels than cases without CAC; however, this relationship was dependent on other established risk factors.
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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 | 0.000 |
| 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".