Assessment and Clinical Relevance of Non-Fasting and Postprandial Triglycerides: An Expert Panel Statement
Bibliographic record
Abstract
An Expert Panel group of scientists and clinicians met to consider several aspects related to non-fasting and postprandial triglycerides (TGs) and their role as risk factors for cardiovascular disease (CVD). In this context, we review recent epidemiological studies relevant to elevated non-fasting TGs as a risk factor for CVD and provide a suggested classification of non-fasting TG concentration. Secondly, we sought to describe methodologies to evaluate postprandial TG using a fat tolerance test (FTT) in the clinic. Thirdly, we discuss the role of non-fasting lipids in the treatment of postprandial hyperlipemia. Finally, we provide a series of clinical recommendations relating to non-fasting TGs based on the consensus of the Expert Panel: 1). Elevated non-fasting TGs are a risk factor for CVD. 2). The desirable non-fasting TG concentration is <2 mmol/l (<180 mg/dl). 3). For standardized postprandial testing, a single FTT meal should be given after an 8 h fast and should consist of 75 g of fat, 25 g of carbohydrates and 10 g of protein. 4). A single TG measurement 4 h after a FTT meal provides a good evaluation of the postprandial TG response. 5). Preferably, subjects with non-fasting TG levels of 1-2 mmol/l (89-180 mg/dl) should be tested with a FTT. 6). TG concentration ≤ 2.5 mmol/l (220 mg/dl) at any time after a FTT meal should be considered as a desirable postprandial TG response. 7). A higher and undesirable postprandial TG response could be treated by aggressive lifestyle modification (including nutritional supplementation) and/or TG lowering drugs like statins, fibrates and nicotinic acid.
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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 | 0.124 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.020 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".