Effects of 12 nutritional interventions on type 2 diabetes: a systematic review with network meta-analysis of randomized trials.

IF 4.1 2区 医学 Q2 NUTRITION & DIETETICS
Yi Liu, Haiyue Li, Qian Zhao, Wenxiang Cui
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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.

Trial registration: CRD42023429616.

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12种营养干预对2型糖尿病的影响:随机试验网络荟萃分析的系统综述
背景:大量试验证实饮食干预有益于2型糖尿病(T2DM)的管理,但最佳模式尚不清楚。我们通过网络meta分析(NMA)评估了12种干预措施对空腹血糖(FPG)、餐后2小时血糖(2hPG)、HbA1c、胰岛素抵抗稳态模型评估(HOMA-IR)、总胆固醇(TC)、甘油三酯(TG)和BMI的影响,为指导临床护理提供依据。方法:我们进行了随机对照试验(rct)的NMA (PROSPERO注册号:CRD42023429616),检索了2010年1月1日至2024年8月31日期间发表的8个数据库的研究。两位审稿人独立筛选研究,提取数据,并使用Cochrane偏倚风险工具评估偏倚。使用Stata 17.0对关键和重要结果进行分析,并通过分级建议评估、发展和评估(GRADE)和网络元分析(CINeMA)评分对证据质量进行评估。结果:纳入18项随机对照试验,共1687例患者。在12项被评估的饮食干预措施中,MNT在降低FPG方面排名最高(supra = 77.6%;smd = -0.75;95% CI: -0.88 ~ -0.61)。数字饮食模型对降低HbA1c最有效(supra = 84.6%;smd = -1.06;95% CI: -2.11 ~ -0.01),而LGI饮食在2hPG中均优于LGI饮食(SUCRA = 62.1%;smd = -0.62;95% CI: -0.76 ~ -0.47)和HOMA-IR (SUCRA = 96.9%;smd = -10.13;95% CI: -15.96 ~ -4.30)。LGI + LGL干预在降低TC (SUCRA = 88.3%)、TG (SUCRA = 80.6%)和BMI (SUCRA = 99.8%)方面最有效,两组比较差异有统计学意义(P)。结论:这些发现强调了MNT、LGI、数字饮食模型和LGI + LGL干预在改善T2DM患者血糖控制和代谢结局方面的潜力。然而,需要进一步的大规模、多中心随机对照试验来验证其长期疗效和安全性。试验注册:CRD42023429616。
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来源期刊
Nutrition & Metabolism
Nutrition & Metabolism 医学-营养学
CiteScore
8.40
自引率
0.00%
发文量
78
审稿时长
4-8 weeks
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.
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