Acute whole apple consumption did not influence postprandial lipaemia: a randomised crossover trial
Bibliographic record
Abstract
Whole apples are a source of pectin and polyphenols, both of which show potential to modulate postprandial lipaemia (PPL). The present study aimed to explore the effects of whole apple consumption on PPL, as a risk factor for CVD, in generally healthy but overweight and obese adults. A randomised, crossover acute meal trial was conducted with seventeen women and nine men (mean BMI of 34·1 (sem 0·2) kg/m2). Blood samples were collected for 6 h after participants consumed an oral fat tolerance test meal that provided 1 g fat/kg body weight and 1500 mg acetaminophen per meal for estimating gastric emptying, with and without three whole raw Gala apples (approximately 200 g). Plasma TAG (with peak postprandial concentration as the primary outcome), apoB48, chylomicron-rich fraction particle size and fatty acid composition, glucose, insulin and acetaminophen were analysed. Differences between with and without apples were identified by ANCOVA. Apple consumption did not alter postprandial TAG response, chylomicron properties, glucose or acetaminophen (P > 0·05), but did lead to a higher apoB48 peak concentration and exaggerated insulin between 20 and 180 min (P < 0·05). Overall, as a complex food matrix, apples did not modulate postprandial TAG when consumed with a high-fat meal in overweight and obese adults, but did stimulate insulin secretion, potentially contributing to an increased TAG-rich lipoprotein production.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".