{"title":"Effects of 12 nutritional interventions on type 2 diabetes: a systematic review with network meta-analysis of randomized trials.","authors":"Yi Liu, Haiyue Li, Qian Zhao, Wenxiang Cui","doi":"10.1186/s12986-025-00968-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Numerous trials confirm dietary interventions benefit type 2 diabetes mellitus (T2DM) management, but the optimal model is unclear. We evaluated 12 interventions through a Network Meta-Analysis (NMA) on their effects on Fasting Plasma Glucose (FPG), 2-h Postprandial Glucose (2hPG), HbA1c, Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), Total Cholesterol (TC), Triglycerides (TG), and BMI, providing evidence to guide clinical nursing.</p><p><strong>Methods: </strong>We conducted an NMA of randomized controlled trials (RCTs) (PROSPERO registration: CRD42023429616), searching eight databases for studies published between January 1, 2010, and August 31, 2024. Two reviewers independently screened studies, extracted data, and assessed bias using the Cochrane Risk of Bias tool. Key and important outcomes were analyzed using Stata 17.0, with evidence quality assessed via the Grading of Recommendations Assessment, Development and Evaluation (GRADE) and Confidence in Network Meta-Analysis (CINeMA) scores.</p><p><strong>Results: </strong>Eighteen RCTs comprising 1,687 patients were included. Among 12 evaluated dietary interventions, MNT ranked highest in reducing FPG (SUCRA = 77.6%; SMD = -0.75; 95% CI: -0.88 to -0.61). Digital dietary models were most effective for reducing HbA1c (SUCRA = 84.6%; SMD = -1.06; 95% CI: -2.11 to -0.01), while LGI diets were superior for both 2hPG (SUCRA = 62.1%; SMD = -0.62; 95% CI: -0.76 to -0.47) and HOMA-IR (SUCRA = 96.9%; SMD = -10.13; 95% CI: -15.96 to -4.30). The LGI + LGL intervention was most effective in reducing TC (SUCRA = 88.3%), TG (SUCRA = 80.6%), and BMI (SUCRA = 99.8%), with statistically significant differences observed in pairwise comparisons (P < 0.05). The quality of evidence was rated as high for FPG, 2hPG, HbA1c, and BMI, and moderate for HOMA-IR, TC, and TG.</p><p><strong>Conclusions: </strong>These findings highlight the potential of MNT, LGI, digital dietary models, and LGI + LGL interventions to improve glycemic control and metabolic outcomes in patients with T2DM. However, further large-scale, multicenter RCTs are warranted to validate their long-term efficacy and safety.</p><p><strong>Trial registration: </strong>CRD42023429616.</p>","PeriodicalId":19196,"journal":{"name":"Nutrition & Metabolism","volume":"22 1","pages":"94"},"PeriodicalIF":4.1000,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12329975/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nutrition & Metabolism","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12986-025-00968-3","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NUTRITION & DIETETICS","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Numerous trials confirm dietary interventions benefit type 2 diabetes mellitus (T2DM) management, but the optimal model is unclear. We evaluated 12 interventions through a Network Meta-Analysis (NMA) on their effects on Fasting Plasma Glucose (FPG), 2-h Postprandial Glucose (2hPG), HbA1c, Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), Total Cholesterol (TC), Triglycerides (TG), and BMI, providing evidence to guide clinical nursing.
Methods: We conducted an NMA of randomized controlled trials (RCTs) (PROSPERO registration: CRD42023429616), searching eight databases for studies published between January 1, 2010, and August 31, 2024. Two reviewers independently screened studies, extracted data, and assessed bias using the Cochrane Risk of Bias tool. Key and important outcomes were analyzed using Stata 17.0, with evidence quality assessed via the Grading of Recommendations Assessment, Development and Evaluation (GRADE) and Confidence in Network Meta-Analysis (CINeMA) scores.
Results: Eighteen RCTs comprising 1,687 patients were included. Among 12 evaluated dietary interventions, MNT ranked highest in reducing FPG (SUCRA = 77.6%; SMD = -0.75; 95% CI: -0.88 to -0.61). Digital dietary models were most effective for reducing HbA1c (SUCRA = 84.6%; SMD = -1.06; 95% CI: -2.11 to -0.01), while LGI diets were superior for both 2hPG (SUCRA = 62.1%; SMD = -0.62; 95% CI: -0.76 to -0.47) and HOMA-IR (SUCRA = 96.9%; SMD = -10.13; 95% CI: -15.96 to -4.30). The LGI + LGL intervention was most effective in reducing TC (SUCRA = 88.3%), TG (SUCRA = 80.6%), and BMI (SUCRA = 99.8%), with statistically significant differences observed in pairwise comparisons (P < 0.05). The quality of evidence was rated as high for FPG, 2hPG, HbA1c, and BMI, and moderate for HOMA-IR, TC, and TG.
Conclusions: These findings highlight the potential of MNT, LGI, digital dietary models, and LGI + LGL interventions to improve glycemic control and metabolic outcomes in patients with T2DM. However, further large-scale, multicenter RCTs are warranted to validate their long-term efficacy and safety.
期刊介绍:
Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects.
The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases.
Key areas we wish to encourage submissions from include:
-how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes;
-the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components;
-how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved;
-how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.