Effect of Intermittent Fasting Strategies on Cardiometabolic Risk Factors: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Intermittent fasting (IF) is a popular trending diet, yet there is limited evidence-based support considering its clinical impact on cardiometabolic outcomes. In an effort to inform the European Association for the Study of Diabetes (EASD) clinical practice guidelines for nutrition therapy, we conducted a network meta-analysis of randomized controlled trials (RCTs) comparing IF strategies and continuous energy restriction (CER) on cardiometabolic outcomes using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. MEDLINE, EMBASE, and Cochrane databases were searched through Nov 2020. We included RCTs assessing the effect of IF strategies (alternate-day fasting (ADF), whole-day periodic fasting (WDF), time-restricted feeding (TRF)), CER, and ad libitum diet. Outcomes included body weight, fasting glucose and LDL-cholesterol. Two independent researchers extracted data and assessed risk of bias. A network meta-analysis was performed and data were expressed as mean differences (MD) with 95% confidence intervals (CI). The certainty of the evidence was assessed using GRADE. We identified 19 RCTs (n = 590) including adults of varying health backgrounds. ADF and CER both showed a benefit for body weight reduction compared to ad libitum diet (18 trials, n = 520; MD −3.95 kg [95% CI −6.09, −1.81] and MD −2.85 kg [95% CI −4.99, −0.71], respectively). For fasting glucose (17 trials, n = 590), TRF showed a benefit compared to ad libitum diet (MD −0.39 mmol/L [95% CI −0.59, −0.20]), to CER (MD −0.25 mmol/L [95% CI, −0.45 to −0.06]) and to WDF (MD −0.20 mmol/L [95% CI, −0.45, −0.05]). Furthermore, ADF showed a benefit in reducing LDL-cholesterol (17 trials, n = 590) compared to ad libitum diet (MD −0.21 mmol/L [95% CI −0.40, −0.1]), and to CER (MD −0.15 mmol/L [95% CI −0.31, −0.01]). The certainty of the evidence ranged from high to moderate due to variable downgrades for imprecision. Current evidence provides a good indication that IF strategies have similar benefits to CER for weight loss but may have additional benefits for fasting glucose and LDL-cholesterol. Long-term high quality RCTs are needed to clarify the effect of different IF strategies on cardiometabolic outcomes. Diabetes and Nutrition Study Group of the EASD, Canadian Institutes of Health Research (CIHR), Diabetes Canada.
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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 | 0.029 | 0.061 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".