Can 2 apples a day decrease cholesterol and modulate the gut microbiome in mildly hypercholesterolaemic subjects?
Bibliographic record
Abstract
Apples are a rich source of polyphenols and fiber. An important proportion of these bioactive components escape digestion in the upper intestinal tract and reach the colon where they can be transformed by the gut microbiota. A randomized, controlled, crossover, intervention was performed (AVAG-AGER study) to test the hypothesis that 2 apples a day can beneficially modulate the gut microbiome and cardiovascular health in mild hypercholesterolaemic subjects. \nForty volunteers consumed 2 apples (Renetta Canada variety) or 100 ml of a sugar matched control drink, daily for 8 weeks separated by 4-week washout period. Blood, urine and faecal samples were collected before and after each treatment. \nWe combined targeted and untargeted analytical strategies for metabolomic fingerprinting of body fluids.LC-HRMS Orbitrap was used for untargeted assays to identify the putative biomarkers of intake and for the validation of apple consumption markers. Additionally, targeted assay with use of UHPLC-MS/MS was employed for bile acids and carnitines quantitative profiling with isotopic dilution method. Changes in faecal populations were identified using fluorescence in situ hybridization (FISH) and 16S rRNA gene profiling \nResults show a significant diet interaction for total cholesterol (P=0.04) and a trend for vascular cell adhesion molecule-1 (VCAM-1) (P=0.076), providing experimental evidence of the positive role of a regular consumption of fresh apple in the diet of mild hypercholesterolaemic subjects. Metabolomics analysis allowed to further investigate the compliance and the putative mechanisms of actions, with the identification of a number of dose dependent biomarkers – including various microbial classes of apple polyphenols, as well as significant changes of bile acids in plasma of treated volunteers. FISH and 16S rRNA analysis indicates a small change in selected bacterial groups for the same group of subjects. \nConsuming 2 apples a day may in conclusion beneficially affect cardiovascular health and modulate both microbial composition and metabolic output.
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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) | 0.001 | 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) | 0.002 | 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".