Hydroxychloroquine and Glycemia in Women with Rheumatoid Arthritis and Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To determine the relationship between current hydroxychloroquine (HCQ) use and 2 indicators of glycemic control, fasting glucose and insulin sensitivity, in nondiabetic women with systemic lupus erythematosus (SLE) or rheumatoid arthritis (RA). METHODS: Nondiabetic women with SLE (n = 149) or RA (n = 177) recruited between 2000 and 2005 for a cross-sectional evaluation of cardiovascular risk factors were characterized by HCQ usage status. Unadjusted and multivariately adjusted mean fasting glucose, median insulin, and insulin resistance [assessed by the homeostasis model assessment (HOMA-IR) calculation] were compared among HCQ users and nonusers for disease-specific groups. RESULTS: More women with SLE were taking HCQ than those with RA (48% vs 18%; p < 0.0001; mean dose ~ 400 mg vs ~ 200 mg). For women with SLE or RA, after adjustment for age, waist circumference, disease duration, prednisone dosage, C-reactive protein, menopausal status, nonsteroidal antiinflammatory drugs, and disease-specific indicators, serum glucose was lower in HCQ users than in nonusers (SLE: 85.9 vs 89.3 mg/dl, p = 0.04; RA: 82.5 vs 86.6 mg/dl, p = 0.05). In women with SLE, HCQ use also was associated with lower (log)HOMA-IR (0.97 vs 1.12, p = 0.09); in those with RA, no differences in (log)HOMA-IR were seen. HCQ usage was not associated with fasting insulin levels in either patient group. CONCLUSION: HCQ use was associated with lower fasting glucose in women with SLE or RA and also lower (log)HOMA-IR in the SLE group. The use of HCQ may be beneficial for reducing cardiovascular risk by improving glycemic control in these patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".