Effects of anti-TNF-α treatment on lipid profile in rheumatic diseases: an analytical cohort study
Bibliographic record
Abstract
BACKGROUND: The aim was to assess the influence of long-term treatment with tumor necrosis factor alpha (TNF-α) inhibitors on total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and atherogenic index (AI) in rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) patients. METHODS: A retrospective cohort study was conducted on RA, PsA, and AS patients treated with TNF-α inhibitors for at least 270 days between 2001 and 2011. Levels of TC, TG, LDL, and HDL and the AI were compared with baseline values at 0-6, 6-12, 12-18, and 18-24 months. Patients were further subdivided into three groups according to their HMG CoA reductase inhibitor (statin) treatment status in order to assess their effect on the results. RESULTS: The records of 311 patients (152 RA, 90 PsA, and 69 AS) were reviewed. TC and TG increased following treatment with TNF-α inhibitors, from 180.85 ± 2.12 mg/dl and 116.00 ± 3.55 mg/dl at baseline to 188.12 ± 2.35 mg/dl (p = 0.02) and 132.02 ± 4.63 mg/dl at 0-6 months (p < 0.01), respectively, and to 184.88 ± 2.09 mg/dl (p = 0.02) and 129.36 ± 4.32 mg/dl at 18-24 months (p < 0.01), respectively. AI increased following treatment with TNF-α inhibitors, from -0.032 ± 0.017 at baseline to 0.004 ± 0.019 at 18-24 months (p < 0.01). LDL decreased significantly in patients who were treated with statins before and during the entire study period, from 119.97 ± 2.86 mg/dl at baseline to 104.02 ± 3.57 mg/dl at 18-24 months (p < 0.01), in contrast to an increase in LDL values in patients who did not receive statins during the study. CONCLUSIONS: TNF-α inhibitor treatment was associated with a significant increase in TC and TG levels and the AI. Adding statins to the treatment was associated with a significant decrease in LDL levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".