Lipoprotein Subclasses Determined by Nuclear Magnetic Resonance Spectroscopy and Coronary Atherosclerosis in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Patients with rheumatoid arthritis (RA) are at increased risk of atherosclerosis, but routine lipid measurements differ little from those of people without RA. We examined the hypothesis that lipid subclasses determined by nuclear magnetic resonance spectroscopy (NMR) differed in patients with RA compared to controls and are associated with disease activity and the presence of coronary-artery atherosclerosis. METHODS: We measured lipoprotein subclasses by NMR in 139 patients with RA and 75 control subjects. Lipoproteins were classified as large low-density lipoprotein (LDL; diameter range 21.2-27.0 nm), small LDL (18.0-21.2 nm), large high-density lipoprotein (HDL; 8.2-13.0 nm), small HDL (7.3-8.2 nm), and total very low-density lipoprotein (VLDL; >or= 27 nm). All subjects underwent an interview and examination; disease activity was quantified by the 28-joint Disease Activity Score (DAS28) and coronary artery calcification (CAC) was measured with electron-beam computed tomography. RESULTS: Concentrations of small HDL particles were lower in patients with RA (18.2 +/- 5.4 nmol/l) than controls (20.0 +/- 4.4 nmol/l; p = 0.003). In patients with RA, small HDL concentrations were inversely associated with DAS28 (rho = -0.18, p = 0.04) and C-reactive protein (rho = -0.25, p = 0.004). Concentrations of small HDL were lower in patients with coronary calcification (17.4 +/- 4.8 nmol/l) than in those without (19.0 +/- 5.8 nmol/l; p = 0.03). This relationship remained significant after adjustment for the Framingham risk score and DAS28 (p = 0.025). Concentrations of small LDL particles were lower in patients with RA (1390 +/- 722 nmol/l) than in controls (1518 +/- 654 nmol/l; p = 0.05), but did not correlate with DAS28 or CAC. CONCLUSION: Low concentrations of small HDL particles may contribute to increased coronary atherosclerosis in patients with RA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 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".