Abstract P351: Remnant Cholesterol and Cardiovascular Disease Outcomes in Chronic Kidney Transplant Recipients
Bibliographic record
Abstract
Remnant cholesterol (RC) is the cholesterol content of circulating triglyceride-rich lipoproteins. Studies employing a simple calculation of RC from routine lipid/lipoprotein measures have demonstrated associations between RC levels and cardiovascular disease (CVD) outcomes in both observational study, and lipid lowering clinical trial cohorts. There are no published data evaluating the potential relationship between remnant cholesterol and CVD in chronic kidney transplant recipients [KTRs], a population with excess risk for fatal and non-fatal CVD. RC was calculated, using non-fasting plasma samples, as total cholesterol - [HDL cholesterol + LDL cholesterol] in n=3002 FAVORIT trial [NCT00064753] participants at randomization (mean 37.6, standard deviation ± 21.3, range 4-230 mg/dl). During a median follow-up of 4.0-years, the cohort experienced n=419 CVD outcomes [myocardial infarction, stroke, resuscitated sudden death, CVD death, and CVD procedural events, pooled]. Multivariable logistic regression modeling revealed that each 10 mg/dl increase of RC conferred a 16.4% increase [95% CI, 3.6-30.8%] in CVD risk adjusted for age, baseline CVD, diabetes, smoking, race, sex, body-mass index, LDL, HDL, natural log triglycerides, estimated glomerular filtration rate, natural log urinary albumin/creatinine, type of kidney graft, graft vintage, and the use of calcineurin inhibitors, steroids, or lipid lowering drugs. Given the residual risk for CVD after recommended LDL levels are achieved, these data suggest that interventions [i.e., such as eicosapentaenoic acid ethyl ester, which can lower RC by ~25-30%; Atherosclerosis 2016; 253: 81-87] targeting elevated RC concentrations in KTRs, merit consideration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".