Abstract 680: Lipoproteins Large to Small Particle Size Ratio is Significant Predictor in CAD and Stroke Outcomes
Bibliographic record
Abstract
Coronary heart disease (CHD) is responsible for approximately 735,000 heart attacks (HA) events and 375,000 deaths in the USA each year. Stroke, a leading cause of mortality accounts for approximately 800,000 cases each year. Predisposing factors include high blood pressure, diabetes and high levels of low density lipoprotein cholesterol (LDL-C). Coronary artery disease (CAD) development involves cholesterol deposits, plaque formation, and poor perfusion. Established is the association between diabetes, LDL-C serum concentration, and CAD risk. Present therapy focuses on increasing high density lipoprotein cholesterol (HDL-C) serum concentration levels. In clinical observations we analyzed LipoProfile data of subjects treated for diabetes, CAD, HBP and other chronic diseases. A broad age range was represented and a specific medication was employed for therapy. Dietary and exercise changes were promoted. NMR lipoprofile at specific durations determined LDL, HDL, and VLDL particle size and density. Favorable health outcomes were noticeable in all treated individuals when a specific size of HDL-LP, LDL-LP, and VLDL-SP post-treatment. Health benefits were noticeable even in the presence of comorbidities. This observation appears consistent with results from prior studies exploring the value of HDL particle in predicting disease outcomes. Our observations revealed correlations between defined particle size of lipoproteins including HDL-LP, HDL-SP, and LDL-LP as determined through NMR LipoProfile, post-treatment with a selected antihyperlipidemic agent. These lipoproteins were valuable predictors of improved health outcomes. Health benefits were probably also derived from any dietary and exercise changes implemented. We feel that this evidence is among the earliest to associate specific size ratios of lipoproteins with improved health outcomes, and this apparently is also supported by some additional novel findings that will be introduced. We feel this report will garner broader appreciation for interventions focused on influencing particle size of lipoproteins, rather than on lowering LDL-C levels, which is the presently established standard now employed for management and therapy.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".