Influence of Interesterification of a Stearic Acid‐Rich Spreadable Fat on Acute Metabolic Risk Factors
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it