Proposing a “Lipemic Index” As a Nutritional and Research Tool
Bibliographic record
Abstract
Recent studies have demonstrated the value of non-fasting serum triglycerides (TG) as risk markers for cardiovascular and cerebrovascular disease. This underscores the importance of knowing the postprandial lipid/lipoprotein responses to different foods. A systematic approach is needed to make use of postprandial lipid data as a practical nutritional tool, similar to the well known glycemic index (GI), which is a measure of the effect of carbohydrates on blood glucose levels. Using GI as a model, we propose that a similar and parallel nutritional tool called Lipemic Index (LI) be developed to facilitate the planning of a healthy diet. LI could also serve as a tool in human nutrition research. LI would refer to the postprandial increase of serum TG after a test meal with a specific food relative to a reference meal. The reference meal could take the form of a fat load that has a fixed amount (e.g. 50-70 g) of a mixture of saturated, polyunsaturated and monounsaturated fats in known proportions. It is possible that a test meal may have a greater degree of postprandial lipemia (PPL) than the reference meal and, unlike GI, the LI may exceed 100%. We recommend total plasma TG as the blood parameter to follow after consumption of the fat load. The TG incremental area under the curve (iAUC) will be calculated from the curve drawn from hourly measurements of plasma TG up to 6 hours using the trapezoid rule. The LI of the test meal (%) will equal the iAUC of the test meal divided by the iAUC of the reference meal x 100. Consideration will be given to the impact of background diet, other nutrients in the test meal and gender differences on LI testing. The establishment of LI into practice will be complicated and challenging. However, it is important for work to begin on establishing a practical and quantifiable index of PPL, in order to benefit clinical management of patients as well as research.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".