Abstract 124: Increased Selective Clearance of Cholesteryl-Ester in Patients with Rheumatoid Arthritis is Reversed by Treatment with Tofacitinib
Bibliographic record
Abstract
Inflammation is associated with dysfunctional HDL. Rheumatoid arthritis (RA) is a chronic inflammatory disease with significantly elevated cardiovascular risk in spite of low LDL and HDL cholesterol (C) levels. Using stable isotope methods, we examined underlying metabolic mechanisms for the reduced cholesterol levels in active RA and for the changes with tofacitinib (tofa) treatment. Apolipoprotein and cholesterol kinetics were assessed in 36 RA patients at baseline and following 6 weeks of tofa treatment compared to baseline values in 33 non-RA controls matched for age, gender, and menopausal status. All subjects received constant IV infusions of [3,4] 13 C 2 cholesterol and U- 13 C 6 leucine with collection of fasting blood samples hourly for 22 hours. Whole-body cholesterol efflux and cholesteryl ester (CE) and apolipoprotein production and fractional catabolic rates (FCR) were determined by modeling isotope enrichments. At baseline, total-C, HDL-C, LDL-C, CE and apoA-I concentrations were lower and CE FCR was elevated in RA. No differences were seen in CE production rate, whole body cholesterol efflux or HDL-apoA-I and LDL-apoB production and FCRs. RA patients had elevated serum HDL-SAA, myeloperoxidase and reduced in vitro LCAT activity and mass. Following 6 weeks of tofa therapy, cholesterol and apoAI approached levels of controls and apoB concentrations increased. Tofa treatment reduced CE FCR, increased HDL-apoA-I production rate with no changes in LDL-apoB production rates or LDL-apoB and HDL-apoA-I FCRs. Increased CE FCR in the absence of changes in HDL-apoA-I or LDL-apoB particle clearance suggest increased selective CE uptake, presumably by SR-B1, which was normalized by tofa. Change in CE FCR with treatment was negatively correlated with changes in HDL size and concentration of large HDL particles, but not with LDL size or composition. Change in CE production rate with treatment was positively correlated with change in LCAT mass. This is the first study to evaluate the effect of chronic inflammation on in vivo cholesterol and lipoprotein kinetics in humans. Inflammation increases selective CE uptake explaining the reduced cholesterol levels in RA. Reducing inflammation with tofa restored selective CE uptake to normal rates.
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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) | 0.001 | 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) | 0.003 | 0.001 |
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".