Worldwide Increasing Use of Nonfasting Rather Than Fasting Lipid Profiles
Bibliographic record
Abstract
BACKGROUND: Historically, lipids and lipoproteins were measured in the fasting state for cardiovascular risk prediction; however, since 2009 use of nonfasting lipid profiles has increased substantially worldwide. For patients, nonfasting lipid profiles are convenient and avoid any risk of hypoglycemia. For laboratories, blood sampling in the morning and extra visits for patients who have not fasted are avoided. For patients, clinicians, hospitals, and society, nonfasting sampling allows same-day visits with first blood sampling followed by a short wait for test results before clinical consultation. Therefore, nonfasting compared to fasting lipid profiles will save money and time and may improve patient compliance with cardiovascular prevention programs. CONTENT: We report on the progression of endorsement and implementation of nonfasting lipid profiles for cardiovascular risk prediction worldwide and summarize the recommendations from major medical societies and health authorities in different countries. We also describe practical advantages and disadvantages for using nonfasting lipid profiles. Further, we include a description of why fasting has been the standard historically, the barriers against implementation of nonfasting lipid profiles, and finally we suggest the optimal content of a nonfasting lipid profile. SUMMARY: Lipid, lipoprotein, and apolipoprotein concentrations vary minimally in response to normal food intake and nonfasting lipid profiles are equal or superior to fasting profiles for cardiovascular risk prediction. Major guidelines and consensus statements in Europe, the United States, Canada, Brazil, Japan, India, and Australia now endorse use of nonfasting lipid profiles in some or all patients; however, there are still gaps in endorsement and implementation of nonfasting lipid profiles worldwide.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".