Metabolic Impact of Intermittent Fasting in Patients With Type 2 Diabetes Mellitus: A Systematic Review and Meta-analysis of Interventional Studies
Bibliographic record
Abstract
CONTEXT: Intermittent fasting (IF) has been proposed as a weight-loss strategy with additional cardiometabolic benefits in individuals with obesity. Despite its growing popularity, the effect of IF in patients with type 2 diabetes (T2DM) remains unclear. OBJECTIVE: We conducted a systematic review and meta-analysis to evaluate the metabolic impact of IF compared to standard diet in patients with T2DM. METHODS: Embase, PubMed, and clinicaltrials.gov between 1950 and August 12, 2020 were searched for randomized, diet-controlled studies evaluating any IF intervention in adults with T2DM. We examined the impact of IF on weight loss and glucose-lowering by calculating pooled estimates of the absolute differences in body weight and glycated hemoglobin A1c (HbA1c) compared to a control group using a random-effects model. RESULTS: Seven studies (n = 338 participants; mean body mass index [BMI] 35.65, mean baseline HbA1c 8.8%) met our inclusion criteria. IF induced a greater decrease in body weight by -1.89 kg (95% CI, -2.91 to -0.86 kg) compared to a regular diet, with no significant between-study heterogeneity (I2 21.0%, P = .28). The additional weight loss induced by IF was greater in studies with a heavier population (BMI > 36) (-3.43 kg [95% CI, -5.72 to -1.15 kg]) and in studies of shorter duration (≤ 4 months) (-3.73 kg [95% CI, -7.11 to -0.36 kg]). IF was not associated with further reduction in HbA1c compared to a standard diet (HbA1c -0.11% [95% CI, -0.38% to 0.17%]). CONCLUSION: Current evidence suggests that IF is associated with greater weight loss in patients with T2DM compared with a standard diet, with a similar impact on glycemic control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".