Nonfasting Sample for the Determination of Routine Lipid Profile: Is It an Idea Whose Time Has Come?
Bibliographic record
Abstract
For many years the determination of a routine lipid profile (total, LDL, and HDL cholesterol and triglycerides) has been done routinely in the clinical laboratory using a blood specimen that is collected in the fasting state. The rationale for such a requirement includes 1) the postprandial changes in lipoprotein composition known to occur, particularly the increases in triglycerides (TG)10 concentration which have a direct relation to the meal fat and carbohydrate content, 2) the clinically significant effects of increased TG (>400 mg/dL; 4.5 mmol/L) on the calculation of LDL cholesterol (LDL-C) when using the Friedewald equation, and 3) the use of fasting samples for lipid measurement in many clinical trials and epidemiological studies on which treatment goals are based. However, because most of each person's lifetime is spent in the postprandial state, the wisdom of collecting a fasting sample to determine future risk of cardiovascular disease has been challenged. In addition, recent evidence has demonstrated that nonfasting TG concentrations are a better predictor of future coronary events compared to fasting TG, in both men and women. The Danish Society for Clinical Biochemistry, in 2009, and the UK National Institute of Clinical Excellence (NICE), in 2014, recommended the use of a nonfasting specimen for the determination of routine lipid profile; both entities acknowledge that in certain situations a fasting sample is required. The European Atherosclerosis Society and the European Federation of Clinical Chemistry and Laboratory Medicine will be making a similar recommendation. In contrast, the 2013 guidelines released by the American College of Cardiology/American Heart Association (ACC/AHA) preferred a fasting specimen for lipid testing. Such inconsistencies in published guidelines will complicate the interpretation of the literature and confound metaanalyses. The decision of whether to use a fasting or nonfasting sample, however, will be driven not only by the strong epidemiologic …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".