Gender Differences in Plasma Lipid Response to Dietary Fat
Bibliographic record
Abstract
The relationship between type of dietary fat, cardiovascular disease risk, and lipid/lipoprotein profiles has been studied since the early 1900s. For the most part, observational data from international comparisons, migration studies, and prospective studies have identified a positive relationship between saturated fatty acid (SFA) intake and coronary heart disease (CHD) risk, although in the latter case these observations were attenuated and in some cases became non-significant after adjusting for other dietary factors. Data from large-scale primary and secondary intervention studies support a positive relationship between CHD and SFA. The majority of data available were derived from male subjects, and if female subjects were included, few studies assessed the effect of gender on responsiveness. Recent evidence has emerged suggesting that females respond differently to diet with respect to CHD progression. This review discusses controlled clinical intervention studies that included data for both genders and their responses to dietary fat perturbations and lipoprotein profiles. The scope was limited for the most part to reports that included identifiers in the title or abstract that indicated data for female and male subjects were reported separately, although a statistical comparison between the genders may not have been reported. Overall, whether the studies assessed the effect of the ratio of SFA to monounsaturated fatty acids (MUFA), SFA to polyunsaturated fatty acids (PUFA), MUFA to PUFA, individual SFA, or SFA to trans fatty acids, female and male subjects responded similarly, and when differences were identified there was no consistent pattern.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".