New oral fat tolerance tests feature tailoring of the polyunsaturated/saturated fatty acid ratio to elicit a specific postprandial response
Bibliographic record
Abstract
The impact of dietary fat on postprandial metabolic biomarkers for obesity-related chronic diseases, such as type-2 diabetes and cardiovascular disease, has received significant recent attention. However, there is no standard method to evaluate the postprandial response to dietary fat alone. Our goals were to develop a novel oral fat tolerance test (OFTT) consisting solely of emulsified lipids tailored for specific fatty acid compositions and to evaluate the functionality of specific ratios of polyunsaturated/saturated fatty acid (P/S) loading on postprandial triacylglyceride (TAG) concentrations. Two OFTTs of emulsified lipids were prepared with specific P/S ratios of 0.2 and 2.0. Physical characteristics of the fat blends, including TAG composition, melting point, and emulsion droplet size were quantified. Healthy, older (age>45 y) men (n=8) underwent an 8 h postprandial study wherein they received the OFTT treatment (either the P/S ratio of 0.2 or 2.0), with a total lipid load of 1 g/kg subject body mass. All subjects received both treatments separated by at least 1 week. Both the P/S 0.2 and 2.0 OFTT significantly elevated (p<0.05) blood TAG and free fatty acid concentrations for 8 h without increasing blood glucose or serum insulin concentrations. The predominant fatty acids contained in the P/S 0.2 (palmitic acid, 16:0) and 2.0 (linoleic acid, 18:2(n-6)) OFTT blends were significantly elevated in the blood (p<0.05) during their respective postprandial periods. We concluded that blood TAGs are elevated in a specific pattern through the administration of novel OFTTs with specific P/S blends without eliciting an insulin or glucose response.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".