Effects of Dietary Macronutrients on Plasma Lipid Levels and the Consequence for Cardiovascular Disease
Bibliographic record
Abstract
Despite gaining focus, cardiovascular disease (CVD) remains the leading cause of death worldwide. Health promotion agencies have traditionally recommended diets that are low in fat in order to reduce CVD risk however, much debate remains about which dietary approaches are the most efficient for effective disease prevention. Common markers of CVD include elevated plasma triglycerides (TG) and low-density lipoprotein (LDL) cholesterol levels, as well as reduced high-density lipoprotein (HDL) cholesterol levels. While weight loss alone can significantly reduce markers of CVD, manipulating dietary macronutrient content contributes to the beneficial effects of weight loss and furthers the improvement of lipid profiles even without the alteration of total caloric intake. Considering the recent attention to diets that are low in carbohydrates rather than fat, it remains to be elucidated the beneficial effects of each diet type when establishing new recommendations for CVD prevention. This review aims to examine the effects of different macronutrient compositions on lipid markers, thus providing insight into the potential roles of various diet types in the targeted prevention against CVD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".