The impact of food-based dietary strategies on achieving type 2 diabetes remission: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Conventional wisdom once asserted that diabetes was irreversible. However, contemporary research indicates that dietary changes may contribute to achieving diabetes remission in persons with type 2 diabetes (T2D). We aimed to determine the effectiveness of food-based dietary approaches for T2D remission. METHODS: We systematically searched Medline, EMBASE, and Web of Science, along with exploring grey literature, to identify longitudinal studies. Data extraction and quality assessment adhered to predetermined criteria, and the results of the included studies were analyzed using a narrative synthesis and graphical display. RESULTS: We included 52 original studies-40 % were rated as low-risk of bias. Overall, studies showed the low-carbohydrate Mediterranean diet (LCMD), compared to a low-fat diet, was more effective for achieving T2D remission in newly diagnosed patients who also had a weight loss of up to 6 kg. Compared to both the traditional Mediterranean diet and the American Diabetic Association diet, the LCMD was also more effective at diabetes remission for persons with T2D with any duration of diabetes; however, more substantial weight loss of 8 kg was required. Other diets that appeared effective for T2D remission included low-calorie diets and diets high in plant protein sources. Less weight loss was needed to achieve remission on plant-based diets than a low-calorie diet and low-carbohydrate diet. CONCLUSIONS: Diets high in plant protein sources may support T2D remission, particularly among newly diagnosed patients. For patients with a duration of over 2 years, the combination of plant-based diets with greater weight loss should be considered to induce remission.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".