Comparison of weight loss effects among overweight/obese adults: A network meta-analysis of mediterranean, low carbohydrate, and low-fat diets

Background & aims: Eating patterns significantly impact the weight loss process. This study aimed to investigate the influence of primary eating patterns on weight loss measures in overweight and obese adults using network meta-analysis. Methods: We systematically searched PubMed, Scopus, Web of Science (WOS), and Google Scholar until May 2, 2023. Our network meta-analysis followed the PRISMA extension guidelines for Comparing Mediterranean, low carbohydrate and low fat diet effects on weight loss among overweight/obese adults. We conducted a Frequentist random-effects network meta-analysis. Summary effects were presented as mean differences (MD) along with corresponding standard deviations (SD). P-scores were used for treatment ranking within the network. Results: Initial literature searches yielded 1574 citations. Ultimately, 1004 participants from 7 RCTs (or 9 trials) met inclusion criteria. All diets resulted in weight loss. Comparatively, the low-carbohydrate diet exhibited a significant decrease in weight loss compared to the Mediterranean diet (MD = -2.70 kg, 95% CI: -4.65, -0.75). Indirect evidence revealed that both the low-carbohydrate diet (MD = -6.31 kg, 95% CI: -11.23, -1.39) and the low-fat diet (MD = -5.61 kg, 95% CI: -10.61, -0.61) significantly reduced weight among overweight/obese adults compared to the standard hypolipemic diet. Rankings indicated the low-carbohydrate diet as the most effective dietary intervention for enhancing weight loss (Pscore = 0.8994) and reducing body fat (P-score = 0.7060). Conclusions: Overall, a low-carb diet appears to be among the most effective approaches for weight loss and body fat reduction. However, it's essential to consider that its efficacy may vary based on factors such as age, gender, genetics, and lifestyle habits. (c) 2024 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.