Fat versus carbohydrate in insulin resistance, obesity, diabetes and cardiovascular disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review assesses the relative effect of fat versus carbohydrate and the differences between fatty acids and types of carbohydrate on insulin resistance and associated risk factors for diabetes and cardiovascular disease. RECENT FINDINGS: The debate continues over whether high-carbohydrate or high-fat diets have the more deleterious metabolic effects. Large randomized controlled trials have shown that a reduction of fat intake as part of a healthy lifestyle combined with weight reduction and exercise reduce the risk of type 2 diabetes. Carbohydrate as fruit and vegetable together with low-fat dairy products reduce blood pressure. The results of trials of fatty acid type continue to favor the use of monounsaturated fats. However, the advantages over carbohydrate have not always been clear. In terms of carbohydrate, the glycemic index appears to be a better predictor of the metabolic effects of a diet than the sugar content. The fiber content of the carbohydrate food appears to confer benefits in terms of diabetic control. Lower cholesterol and postprandial blood glucose results are associated with viscous fibers. SUMMARY: Diets that are higher in monounsaturated fatty acids, fiber and low glycemic index foods appear to have advantages in insulin resistance, glycemic control and blood lipids in a number of studies. The division of nutrients into total fat (regardless of fatty acids) versus carbohydrate (type and quantity not specified) appears to be less helpful in predicting outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".