Weight Loss Associated With Consumption of Apples: A Review
Bibliographic record
Abstract
Apples are known as a major source of polyphenols, dietary fiber, carotenoids, and other nutrients. There are many documents and studies that show fruit polyphenols likely promote anti-obesity effects and exert their beneficial effects via scavenging free radicals, regulating gene expression, and altering signal transduction in target cells and tissues, especially fat tissues.The goal of this review is to presenti the major components of apple and the evidence that indicates its potential to diminution weight gain risk from in vitro, animal, and epidemiological and clinical studies. This review summarizes data about the apple and apple products that been have reported to reduce weight gain by various mechanisms, including antioxidant, antiproliferative, and cell signaling pathways. An extensive search was performed in PubMed, Science Direct, Scopus, and Google Scholar to identify human, animal, and cell culture studies on the association between weight loss and apple consumption, published from inception up to journey 15, 2017. The feeding of apples rats (7-10 mg/kg/d) in different forms in 8 experiments have shown that this caused weight loss during 3 to 28 weeks. In agreement with this, the obtained results from 5 experiments on humans have revealed that consumption of the whole apple or apple juice (240-720 mg/d) in 4-12 weeks by fat people can cause weight loss. Experiments on animals and humans have shown that the consumption of apples in different forms can cause weight loss in overweight ones. However, the main questions are which kind of apple, which part of it, how much, and how long overweight persons should consume them to reduce their body fat and body mass index (BMI). Then, it is necessary to do a meta-analysis to show how these factors affect the body fat percentage and whether this weight-lowering effect is statistically significant or not.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".