Postprandial Hypertriglyceridaemia Revisited in the Era of Non-Fasting Lipid Profile Testing: A 2019 Expert Panel Statement, Main Text
Bibliographic record
Abstract
Residual vascular risk exists despite the aggressive lowering of Low-Density Lipoprotein Cholesterol (LDL-C). A contributor to this residual risk may be elevated fasting, or non-fasting, levels of Triglyceride (TG)-rich lipoproteins. Therefore, there is a need to establish whethe a standardised Oral Fat Tolerance Test (OFTT) can improve atherosclerotic Cardiovascular (CV) Disease (ASCVD) risk prediction in addition to a fasting or non-fasting lipid profile. An expert panel considered the role of postprandial hypertriglyceridaemia (as represented by an OFTT) in predicting ASCVD. The panel updated its 2011 statement by considering new studies and various patient categories. The recommendations are based on expert opinion since no strict endpoint trials have been performed. Individuals with fasting TG concentration <1 mmol/L (89 mg/dL) commonly do not have an abnormal response to an OFTT. In contrast, those with fasting TG concentration ≥2 mmol/L (175 mg/dL) or nonfasting ≥2.3 mmol/L (200 mg/dL) will usually have an abnormal response. We recommend considering postprandial hypertriglyceridaemia testing when fasting TG concentrations and non-fasting TG concentrations are 1-2 mmol/L (89-175 mg/dL) and 1.3-2.3 mmol/L (115-200 mg/dL), respectively as an additional investigation for metabolic risk prediction along with other risk factors (obesity, current tobacco abuse, metabolic syndrome, hypertension, and diabetes mellitus). The panel proposes that an abnormal TG response to an OFTT (consisting of 75 g fat, 25 g carbohydrate and 10 g proteins) is >2.5 mmol/L (220 mg/dL). Postprandial hypertriglyceridaemia is an emerging factor that may contribute to residual CV risk. This possibility requires further research. A standardised OFTT will allow comparisons between investigational studies. We acknowledge that the OFTT will be mainly used for research to further clarify the role of TG in relation to CV risk. For routine practice, there is a considerable support for the use of a single non-fasting sample.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.021 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".