Influence of Interesterification of a Stearic Acid‐Rich Spreadable Fat on Acute Metabolic Risk Factors
Bibliographic record
Abstract
Chemical and enzymatic interesterification are used to create spreadable fats. However, a comparison between the two processes in terms of their acute metabolic effects has not yet been investigated. A randomised crossover study in obese (plasma TAG > 1.69 mmol/L, and BMI > 30 (BMI = kg/m(2)) or waist circumference > 102 cm, n = 11, age = 59.3 +/- 1.8 years) and non-obese (plasma triacylglycerol (TAG) < 1.69 mmol/L, and BMI < 30 or waist circumference < 102 cm, n = 10, age = 55.8 +/- 2.2 years) men was undertaken to compare the effects of chemical versus enzymatic interesterification on postprandial risk factors for type 2 diabetes (T2D) and cardiovascular disease (CVD). TAG, cholesterol, glucose, insulin and free fatty acid concentrations were measured for 6 h following consumption of 1 g fat/kg body mass of non-interesterified (NIE), chemically interesterified (CIE), enzymatically interesterified (EIE) stearic acid-rich fat spread or no fat, each with 50 g available carbohydrate from white bread. Interesterification did not affect postprandial glucose, insulin, free fatty acids or cholesterol (P > 0.05). Following ingestion of NIE, increases in serum oleic acid were observed, whereas both oleic and stearic acids were increased with CIE and EIE (P < 0.05). While postprandial TAG concentrations in non-obese subjects were not affected by fat treatment (P > 0.05), obese subjects had an 85% increase in TAGs with CIE versus NIE (P < 0.05). The differences in TAG response between non-obese and obese subjects suggest that interesterification may affect healthy individuals differently compared to those already at risk for T2D and/or 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".