Alterations in Circulating Fatty Acid Composition in Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
INTRODUCTION: Circulating fatty acids (FAs) may play a role in the disease pathogenesis of patients with systemic lupus erythematosus (SLE). OBJECTIVES: To compare red blood cell (RBC) and plasma FA composition: (1) between female SLE patients and age-matched healthy female (HF) controls and in SLE with history of cardiovascular disease (CVD) and those with no history (SLE+CVD vs SLE-CVD); and (2) between SLE patients who were or were not receiving prednisone treatment at the time of blood sampling. METHODS: This cross-sectional study consisted of 33 female patients with SLE (11 SLE+CVD, 22 SLE-CVD) and 20 HF controls. Demographics, CVD risk, medication profile, blood biochemistry, and FA composition of RBC and plasma total lipids were determined. RESULTS: Waist circumference and body mass index were higher in SLE patients than in HF controls. These variables along with serum triglycerides, blood glucose, and systolic blood pressure were higher in SLE+CVD than SLE-CVD patients. RBC FA composition showed lower eicosapentaenoic acid (EPA, ω-3 active metabolite) and ω-3 index (EPA+ docosahexaenoic acid) in SLE patients compared with HF controls. The ratio of the RBC inflammatory metabolite, arachidonic acid, to the anti-inflammatory metabolite EPA was also significantly higher in SLE patients than in HF controls. No differences were seen in plasma FA between SLE and HF groups. However, SLE-CVD patients had a more favorable lipid profile than SLE+CVD patients. In SLE patients, the use of prednisone resulted in alteration of both RBC and plasma FA composition. CONCLUSION: SLE patients, regardless of their history of CVD, have altered plasma and RBC FA composition favoring inflammation. The use of prednisone was associated with differences in FA profile.
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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 | 0.001 |
| 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) | 0.002 | 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".