The effect of short term higher versus lower fat intake on plasma triglycerides, VLDL‐TG fatty acid composition and hepatic fatty acid synthesis
Bibliographic record
Abstract
Background: Low fat high carbohydrate (LF) diets increase plasma triglyceride (TG) levels, but the role of hepatic de novo fatty acid (DNFA) synthesis is uncertain. We previously found a relationship between TG level and DNFA synthesis. Objectives: To use isotopic methods to examine the effect of dietary fat on plasma TG FAs. We hypothesized that eucaloric higher fat (HF) intake would result in lower TG levels and VLDL‐TG saturated FA composition and DNFA synthesis changes. Methods: Six subjects were fed 2 diets differing in fat energy (LF<25%, HF>35%) for 3d (crossover, 1‐mo washout). Blood samples were drawn before and 24h after deuterium‐labeled water consumption. Results: Plasma and VLDL TG were lower following HF intake. Composition of VLDL‐TG FAs showed a higher amount of saturated FAs after LF intake. No significant change was found in total DNFA synthesis between diets, but LF resulted in more palmitic and stearic acid synthesis. A relationship was found between FA synthesis and plasma TG only for LF. Conclusion: When compared to LF, HF lowered plasma TG and resulted in important differences in VLDL‐TG FA composition that may have been influenced by DNFA synthesis. Funded by the CDA. Nutrition & Metabolism Biochemistry of Vitamins and Minerals (400‐ASN) Energy and Nutrient Metabolism (401‐ASN) Human and Clinical Nutrition (402‐ASN) Metabolic and Disease Processes (403‐ASN) Late Breaking Category ‐ Nutrition & Metabolism 400‐ASN Biochemistry of Vitamins and Minerals 401‐ASN Energy and Nutrient Metabolism 402‐ASN Human and Clinical Nutrition 403‐ASN Metabolic and Disease Processes
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".