Nuclear Magnetic Resonance Lipoprotein Subclasses and the APOE Genotype Influence Carotid Atherosclerosis in Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Patients with systemic lupus erythematosus (SLE) have accelerated atherosclerosis. Since the conventional lipid profile (total plasma cholesterol, triglycerides, low and high density lipoprotein cholesterol) is not consistently altered in SLE, we hypothesized that investigation of lipoprotein subclasses would improve prediction of risk of atherosclerosis in these patients. METHODS: As a quantitative index of atherosclerosis, we measured the carotid intima-media thickness (IMT) in 68 patients with SLE and related the atherosclerosis to a detailed lipoprotein profile generated using nuclear magnetic resonance (NMR). We measured the cholesterol transported by the pool of remnant lipoproteins (RLPc) and evaluated the modulatory effect of the APOE genotype on the lipoprotein subclass profile and atherosclerosis associated with SLE. RESULTS: Circulating lipoprotein remnant particles [RLPc and intermediate density lipoprotein (IDL)] were positively correlated with IMT, and among them, the indicator that explained 20.2% of the variability in carotid atherosclerosis measured in these patients was IDL, as assessed by NMR. Carriers of the APOE2 allele were at increased risk due to a significant accumulation of IDL particles. CONCLUSION: Lipoprotein subclasses are more associated with subclinical atherosclerosis in patients with SLE than the lipid variables that are routinely measured. The IDL fraction, which is significantly modulated by the APOE genotype, is the most strongly, significantly, and positively correlated with IMT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".