Methods to assess impaired post‐prandial metabolism and the impact for early detection of cardiovascular disease risk
Bibliographic record
Abstract
Post-prandial lipaemia has emerged as a key contributor to cardiovascular disease (CVD) risk and progression. Specifically, delayed clearance of chylomicrons (CM) and their remnants increase the delivery of triglyceride and cholesteryl ester to the vessel wall and can accelerate the progression of atherosclerosis, which may be particularly pertinent to individuals with insulin resistance and/or obesity. As the number of studies linking post-prandial metabolism and chronic disease increases, interest has grown in the use of parameters reflecting CM metabolism as a possible indicator of early CVD risk. This, in turn has raised the question of what method might be most appropriate to detect CM and their remnants in plasma accurately. However, the handful of techniques able to measure CM metabolism (triglyceride-rich lipoprotein fractions; remnant-lipoprotein cholesterol; retinyl esters, CM-like emulsion; sodium dodecyl sulphate-polyacrylamide gel electrophoresis; immunoblotting, enzyme-linked immunoabsorbent assays; C(13) breath test; capillary finger prick) differ in their specificity, cost and applicability in research and in the clinical setting. In this review, we explore the scientific and clinical implications of CM methodology to better understand early risk assessment of CVD. We raise ongoing issues of the need to appreciate differential separation of very low-density lipoprotein and CM fractions, as well as to identify the technical basis for imprecision between assays for apolipoprotein B48. We also highlight emerging issues with respect to the practicality of measuring post-prandial metabolism in large clinical studies and offer opinions on the appropriateness of existing techniques in this field.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".