The Potential Role of Yogurt in Weight Management and Prevention of Type 2 Diabetes
Bibliographic record
Abstract
Yogurt is a semisolid fermented milk product that originated centuries ago and is viewed as an essential food and important source of nutrients in the diet of humans. Over the last 30 years, overweight and obesity have become characteristic of Western and developing countries, which has led to deleterious health outcomes, including cardiovascular disease, type 2 diabetes, hypertension, and other chronic conditions. Recent epidemiological and clinical evidence suggests that yogurt is involved in the control of body weight and energy homeostasis and may play a role in reducing the risk for type 2 diabetes partly via the replacement of less healthy foods in the diet, its food matrix, the effect of specific nutrients such as calcium and protein on appetite control and glycemia, and alteration in gut microbiota. This review will discuss the specific properties that make yogurt a unique food among the dairy products, epidemiological and clinical evidence supporting yogurt's role in body weight, energy balance, and type 2 diabetes, including its potential mechanisms of action and gaps that need to be explored. Key teaching points • Several epidemiological and clinical studies have suggested a beneficial effect of yogurt consumption in the control of body weight and energy homeostasis, although this remains controversial. • Yogurt possesses unique properties, including its nutritional composition; lactic acid bacteria, which may affect gut microbiota; and food matrix, which may have a potential role in appetite and glycemic control. • Potential mechanisms of action of yogurt include an increase in body fat loss, decrease in food intake and increase in satiety, decrease in glycemic and insulin response, altered gut hormone response, replacement of less healthy foods, and altered gut microbiota. • The relative energy and nutrient content and contribution of a standard portion of yogurt to the overall diet suggest that the percentage daily intake of these nutrients largely contributes to nutrient requirements and provides a strong contribution to the regulation of energy metabolism.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".