Effects of 12 Nutritional Interventions on Type 2 diabetes：A Systematic Review with Network Meta-Analysis of Randomized Trials Short Running Title: Effectiveness of 12 Diets in T2DM Management

Effects of 12 Nutritional Interventions on Type 2 diabetes：A Systematic Review with Network Meta-Analysis of Randomized Trials Short Running Title: Effectiveness of 12 Diets in T2DM Management | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Effects of 12 Nutritional Interventions on Type 2 diabetes：A Systematic Review with Network Meta-Analysis of Randomized Trials Short Running Title: Effectiveness of 12 Diets in T2DM Management Yi Liu, Haiyue Li, Qian Zhao, Wenxiang Cui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6499801/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Aug, 2025 Read the published version in Nutrition & Metabolism → Version 1 posted 11 You are reading this latest preprint version 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-hour Postprandial Glucose (2hPG), HbA1c, HOMA-IR, Total Cholesterol (TC), Triglycerides (TG), and BMI, providing evidence to guide clinical nursing. Methods We conducted an NMA of RCTs (Prospero registration: CRD42023429616) examining dietary interventions for T2DM, searching databases from January 1, 2010, to 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 GRADE and CINeMA. Results Our initial search identified 301,997 articles; 18 RCTs involving 1,687 patients met our criteria. Twelve dietary interventions, including MNT, digital models, and LGI + LGL diets, were analyzed. Superior glycemic control was observed in some diets according to SUCRA (P < 0.05), with outcomes ranging from moderate to high quality. Conclusions MNT, LGI diets, and digital models show efficacy in improving key T2DM metrics. LGI + LGL diets potentially reduce TC, TG, and BMI, while low GI diets best improve HOMA-IR. These results support the effectiveness of these interventions, though further large-scale, multi-center RCTs are needed to confirm long-term safety and effects. Trial registration CRD42023429616 Type 2 Diabetes Nutritional Interventions Network Meta-Analysis Randomized Controlled Trials Glycemic Control Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background With aging populations, rising living standards, and changing lifestyles, T2DM incidence is rapidly increasing worldwide [ 1 ], posing a major health threat. T2DM, characterized by insulin resistance and β-cell dysfunction, can lead to cardiovascular disease, retinopathy, neuropathy, and nephropathy. In China, the adult diabetes prevalence is 11.9%, with T2DM as the predominant form [ 2 ]. Globally, about 537 million people had diabetes in 2019, projected to reach 783 million by 2045 [ 3 , 4 ], escalating complications and healthcare costs [ 5 ]. Identifying effective prevention and treatment measures is thus an urgent priority. Many of the risk factors for diabetes are related to poor dietary habits, and modifying diet and lifestyle can effectively prevent and manage T2DM. Mediterranean and low-carbohydrate diets have been proven to help reduce the risk of T2DM [ 6 , 7 ]. Low Glycemic Index (LGI) diets are more effective than High Glycemic Index (HGI) diets in controlling blood glucose and HbA1c levels [ 8 ]. Diets such as LGI and ketogenic diets have been shown to effectively lower HbA1c levels and Fasting Plasma Glucose (FPG) in the short term. Diets rich in whole grains, fruits, and vegetables can improve blood glucose control and are associated with better health indicators such as blood pressure, BMI, and waist circumference [ 9 ]. Network meta-analysis (NMA) extends traditional meta-analysis by comparing multiple interventions simultaneously to help clinicians and patients choose optimal treatment. However, evidence is insufficient to determine which dietary intervention best reduces hyperglycemia and related indicators in T2DM. Therefore, this study will conduct an NMA of RCTs to systematically evaluate the impact of dietary interventions on T2DM outcomes, aiming to provide evidence-based guidelines and assist in clinical decision-making. Ultimately, this research will guide future studies and advance T2DM management [ 10 ]. 2. Methods 2.1 Registration The study was prospectively registered in the PROSPERO International Systematic Review Register (https://www.crd.york.ac.uk/prospero/, accessed on August 31, 2024), with the registration number CRD42023429616. The planning, implementation, and reporting of this study followed the PRISMA guidelines and the NMA reporting requirements [11,12]. 2.2 Search Strategy The search included databases such as CNKI, WanFang, VIP, SINOMED, Web of Science, PubMed, Medline, and the Cochrane Library's Central Register of Controlled Trials (CENTRAL), covering studies from January 1, 2010, to August 31, 2024. Keywords used in the search included: nutritional intervention, nutrition policy, diabetes, T2DM, randomized controlled trials, along with the relevant English terms: Diabetes Mellitus, Type 2, Diabetes Mellitus, type 2 diabetes, randomized controlled trial. Both Chinese and English literature were included (See Supplementary Materials). 2.2.1 Inclusion crieria P: T2DM; no restrictions on gender, nationality, or ethnicity. I: 12 types of dietary interventions: (a) Low Carbohydrate Diet Intervention: Total daily caloric intake controlled at 1980 kcal, with carbohydrates, fats, and proteins accounting for 33%, 45%, and 22%, respectively. (b) Low Fat Diet Intervention: Total daily caloric intake controlled at 1860 kcal, with carbohydrates, fats, and proteins accounting for 48%, 32%, and 20%, respectively. (c) East Asian Alternative Diet Model: Restricts sugar and starch usage, with the caloric ratio of carbohydrates: fats: proteins being 4:3:3, and net carbohydrates accounting for 27% of total caloric intake. (d) Korean Food Exchange Model: Caloric ratio of carbohydrates: fats: proteins is 6:2:2, with sodium intake limited to 600-800 mg per meal. (e) Medical Nutrition Therapy (MNT): Individualized dietary guidance based on factors such as blood glucose, blood lipids, weight, and physical activity, with an emphasis on increasing protein intake. (f) Carbohydrate Counting Method: Carbohydrates, proteins, and fats make up 55%, 20%, and 25% of the total caloric intake, respectively, distributed as 1/5, 2/5, and 2/5 across three meals. (g) Digital Dietary Model: the smaller part includes carbohydrates, snacks, total energy, vegetables, fats, proteins, fruits, etc.; the larger part includes water, beverages, food safety, exercise, weight control, mental health, rational eating, and alcohol consumption. (h) Low Glycemic Index (LGI) Dietary Intervention: A diet with a Glycemic Index (GI) ≤ 45 completely replaces breakfast and dinner. (i) Multifactorial Mediterranean Diet Intervention: Recommends white meat, four or more tablespoons of olive oil per day (1 tablespoon = 13.5 grams), two or more servings of vegetables, three or more servings of fruits, one or fewer servings of red meat or sausage, less animal fat, and less than one cup (100 mL) of carbonated or sugary drinks. (j) Soluble Dietary Fiber Intervention: Add 10g of soluble dietary fiber to breakfast and dinner. (k) LGI+LGL Dietary Intervention: Total daily Glycemic Index (GI) = Σ(food GI × intake amount × available carbohydrate %) ÷ total available carbohydrate amount of all foods; Total daily Glycemic Load (GL) = Σ(food GI × intake amount × available carbohydrate %) ÷ 100. The average GI and GL of 3-day dietary intake are used. (l) PCPA Dietary Intervention: Dietary intervention strategy guided by the PCPA theory (Phases: Advocacy, Building Alliances, Promotion and Mobilization, and Action). C: Conventional diabetes dietary intervention. O: (a) Blood Glucose Control Indicators: Fasting Plasma Glucose (FPG); Postprandial 2-hour Glucose (2hPG); Glycated Hemoglobin (HbA1c); Insulin Resistance Index (HOMA-IR) (b) Cardiovascular Risk Factors Indicators: Total Cholesterol (TC); Triglycerides (TG); Body Mass Index (BMI) S: Randomized Controlled Trials (RCT) 2.2.2. Exclusion Criteria (1) The study design is a cohort study, cross-sectional study, or case-control study; (2) Although the study is a clinical control trial, the grouping lacks randomization, or it is a non-synchronous clinical control study or a self-before-and-after control study; (3) The study subjects include those with type 1 diabetes, other special types of diabetes, gestational diabetes, or high-risk populations for diabetes; (4) The study lacks relevant outcome indicators; (5) The literature is a review, commentary, editorial, case report, extended research from original studies, or non-human trials. 2.3 Data Extraction Two researchers (L and L) followed the search strategy to screen titles and assess relevant studies. Any unclear or ambiguous data in the original texts prompted full-text review. In cases of disagreement, a third researcher (C) conducted in-depth analysis, ensuring objective, accurate final assessments. Microsoft Excel was used to record the first author’s name, country/region, sample size, intervention/control measures, and outcome indicators. 2.4 Risk of Bias Assessment The Cochrane risk of bias tool was used to assess bias across seven domains, categorizing the risk as high, low, or unclear. After completing the assessments, Kappa consistency tests were conducted. Two researchers (L and L) independently evaluated the risk of bias for the final selected studies. Given the inherent difficulties in blinding in diet pattern RCTs, any differences were resolved by discussing with a third team member (C) until consensus was reached. 2.5 Dealing with Missing Data Following the Cochrane Handbook guidelines [13], if post-intervention data with corresponding standard deviations were unavailable, the corresponding standard deviation change scores were used. When standard deviation was not available, estimates were made based on standard errors, p -values, and confidence intervals. 2.6 Statistical Analysis Traditional meta-analysis and network meta-analysis (NMA) were conducted using Stata 17.0 software to compare the effects of 12 dietary interventions on clinical outcomes in T2DM patients (FPG, 2hPG, HbA1c, HOMA-IR, TC, TG, BMI). In NMA results, node size was proportional to the sample size for each intervention, and line thickness reflected the number of available studies. Heterogeneity was assessed using the Cochran Q test, where I² > 50% was considered indicative of heterogeneity, and a random-effects model was used; otherwise, a fixed-effects model was employed. MD, SMD, OR, RR, and their 95% CI were used as effect size indicators. When a closed loop appeared in the network, consistency was tested using the node splitting method. A p -value > 0.05 indicated no significant inconsistency, and a consistency model was used for NMA. The surface under the cumulative ranking area (SUCRA) was used to assess the likelihood of each intervention being the best, with SUCRA values ranging from 0 to 1; higher values indicated better intervention effects. The SUCRA values were used to rank the 12 dietary patterns based on their effectiveness in controlling FPG, 2hPG, HbA1c, HOMA-IR, TC, TG, and BMI. 2.7 Sensitivity Analyses Given that network meta-analysis (NMA) concerns closed loops, we tested for inconsistency between direct and indirect evidence using statistical methods specific to closed loops to detect potential discrepancies. The stability of the combined results using both random-effects and fixed-effects models was assessed. Inconsistencies between these models suggested potential instability in the original findings. Sensitivity analyses were conducted by sequentially excluding individual studies to monitor changes in the combined results, thereby evaluating the influence of specific studies on the overall outcome. Furthermore, we examined the impact of low-quality studies by excluding them from the analysis. 2.8 Credibility of the Evidence Funnel plots were created to analyze publication bias. The GRADE system was used to rate the quality of evidence for traditional meta-analysis results, and the CINeMA online tool was used to assess the quality of evidence for NMA. The combined effects for key outcomes (FPG, 2hPG, HbA1c) and important outcomes (HOMA-IR, TC, TG, BMI) were analyzed. CINeMA provided six ratings: within-study bias, reporting bias, indirectness, imprecision, heterogeneity, and incoherence [14]. 3. Results 3.1. Search Results and Study Selection As of August 31, 2024, a total of 301,997 relevant articles were identified (3,957 in Chinese, 298,040 in English). After excluding duplicate publications and those that did not meet the inclusion criteria (19,930 articles), 3,135 articles were retained for further evaluation. Following a review of titles and abstracts, 18 randomized controlled trials (RCTs) were included [ 15 – 31 ], consisting of 14 Chinese studies and 4 English studies. These studies involved 12 different dietary nutritional interventions. The literature screening flowchart is shown in Fig. 1 ,Table 1 . 3.2. Study Characteristics A total of 18 studies were included, with 4 studies conducted in South Korea, Japan, India, and Spain, and 14 studies conducted in China. The participants were primarily overweight and obese individuals with T2DM. The studies were conducted by teams of clinical healthcare professionals or professional nutritionists. Sixteen of the studies were two-arm trials, while two were three-arm trials. The outcome indicators included: FPG, 2hPG, HbA1c, HOMA-IR, TC, TG, and BMI. The basic characteristics of the included studies are shown in Table 1 . 3.3. Risk of Bias in Included Studies The quality assessment was conducted using Cochrane 5.4.0 guidelines, with an inter-rater consistency Kappa value of 0.897. Eleven studies [ 16 , 17 , 20 – 23 , 26 , 27 , 30 – 31 ] reported the random sequence generation method, while the others only mentioned randomization in the abstract. Seven studies [ 17 , 21 , 26 , 27 , 30 – 31 ] provided information on sample dropout rates and reasons, and the data were mostly complete. The included studies reported both primary and secondary outcome indicators in full, as summarized in Table 2 . 3.4 Traditional Meta-Analysis Results Using Stata, a traditional meta-analysis was conducted for effect indicators with more than two studies directly comparing original research [ 32 ]. Compared to conventional diabetes dietary interventions, all 12 other nutritional interventions showed statistically significant effects ( P < 0.05). Due to high heterogeneity, a random effects model was used for analysis. The sources of heterogeneity were found to be related to measurement tools, intervention duration, and specific intervention methods. The absence of strict randomization, allocation concealment, and blinding may have contributed to the heterogeneity(See Supplementary Materials).. 3.5 Effects of the Interventions The Network Meta-Analysis (NMA) results were reported using the CINeMA evidence grading system [ 33 ]. In the network diagram, each node represents a dietary intervention method, with node size and line thickness reflecting sample size and number of studies, respectively. Direct evidence between two points is connected by a solid line, and indirect comparisons can be made based on the network relationships. Node colors represent the risk of bias in studies: red (high), yellow (medium), and green (low), corresponding to Cochrane quality ratings, ①FPG②2hPG③HbAlc④HOMA-IR⑤TC⑥TG⑦BMI ,The overall inconsistency test showed P > 0.05, and the node-splitting method confirmed that all results had P > 0.05, indicating no significant inconsistency in the loop. The consistency model was used for the analysis. The SUCRA ranking was as follows Table 3 (Fig. 2 – 3 ). 3.6 Pairwise Comparison Results Pairwise comparisons were conducted for seven outcome indicators using league tables, For HbA1c control, Digital Dietary Patterns and Medical Nutrition Therapy (MNT) outperform conventional diabetes diets, Korean food exchange models, and East Asian alternative diets. LGI + LGL diets, carbohydrate counting, and LGI interventions are superior to East Asian alternative diets (P < 0.05). For HOMA-I reduction, LGI interventions surpass MNT and conventional diabetes diets, while soluble fiber interventions outperform conventional diabetes diets (P < 0.05). For BMI reduction, LGI + LGL diets are superior to low-carb diets, MNT, and PCPA interventions, showing greater effectiveness with statistical significance (P < 0.05, See Supplementary Materials). 3.7 SUCRA Results Ranking Cluster Heatmap Figure 9 shows a circular quantity indicating the seven outcome indicators, and the number of sectors represents the twelve dietary interventions. The color-coded sections represent the SUCRA values of the interventions. MNT was the most effective for reducing FPG, while LGI dietary intervention was the preferred choice for reducing 2hPG and HOMA-IR. Digital dietary patterns were the most effective for reducing HbA1c, and LGI + LGL dietary interventions were the most effective for reducing TC, TG, and BMI 3.8 Sensitivity Analyses The network relationships for all seven outcome indicators were closed loops. The global consistency test using the inconsistency model showed a P-value > 0.05, and the local inconsistency test using the node-splitting method also showed a P-value > 0.05, indicating that the results were consistent(34). For traditional meta-analysis, the sensitivity analysis based on heterogeneity degree showed that the results were stable, as there were minimal differences between the random-effect model and the fixed-effect model. Additionally, re-conducting the traditional meta-analysis after excluding low-quality studies yielded no significant change in the overall effect size, suggesting that the results were robust(35). 3.9 Publication Bias The funnel plot for the network meta-analysis showed a symmetrical distribution of data points in the upper part of the funnel, indicating few small-sample studies. This suggests that the results are stable. The scatter points were evenly distributed, and all studies were symmetrically distributed around the vertical line at X = 0, indicating that there is a low likelihood of publication bias in the current research. 3.10 Credibility of the Evidence 3.10.1 GRADE Evidence Rating For traditional meta-analysis, six outcome indicators were assessed: four high-quality outcomes (FPG, 2hPG, HbA1c, BMI) and two moderate-quality outcomes (TC, TG). Among these, three outcomes (FPG, 2hPG, HbA1c) were classified as "critical" and three outcomes (BMI, TC, TG) as "important." The GRADE evidence rating and reasons for upgrading or downgrading for each outcome can be found in Table 4 . 3.10.2 CINeMA Evidence Rating CINeMA assessed the NMA across six domains: within-study bias and between-study bias (no concern, no downgrade), and indirectness, imprecision, heterogeneity, and inconsistency (some concern, downgraded by one level). The evidence quality was rated high for FPG, 2hPG, HbA1c, and BMI, and moderate for HOMA-IR, TC, and TG. Overall, the evidence is of good quality and provides valuable recommendations. 4. Discussion This NMA study included 18 RCTs, involving a total of 1,687 T2DM patients. The quality of the included studies was evaluated using the Cochrane 5.4.0 tool and the Jadad scale, with results indicating a medium to high quality of evidence. The two researchers (L/L) independently assessed the results, yielding a Kappa value of 0.897, which suggests a high level of homogeneity among the included studies. By combining traditional meta-analysis and NMA methods, we ranked 12 dietary interventions and evaluated their effects on various outcomes for T2DM patients, including FPG, 2hPG, HbA1c, HOMA-IR, TC, TG, and BMI. In diabetes management, clearly distinguishing between "critical" and "important" outcome indicators allows healthcare providers to more effectively formulate and adjust treatment plans to minimize the risk of complications. The GRADE evidence rating identified FPG, 2hPG, and HbA1c as "critical" outcomes, and TC, TG, and BMI as "important" outcomes. The CINeMA evidence rating revealed that the quality of evidence for FPG, 2hPG, HbA1c, and BMI was high, while the quality for HOMA-IR, TC, and TG was moderate. Overall, the evidence is of good quality and offers valuable recommendations. The critical outcome indicators, (FPG, 2hPG, and HbA1c), directly reflect the control of diabetes and the potential risks of complications, making them essential for prioritization in treatment planning. On the other hand, the important outcome indicators (TC, TG, and BMI) contribute to a comprehensive assessment of treatment effects. Although they may not directly influence patient survival rates or the occurrence of complications as much as the critical indicators, they provide insight into the patient's overall health status and the comprehensive effects of the treatment. This information, in turn, helps healthcare providers deliver more personalized treatment recommendations for patients. Medical nutrition therapy (MNT) is crucial in preventing and managing diabetes, effectively lowering FPG in T2DM patients, followed by PCPA dietary interventions. MNT reduces β-cell stress through structured dietary habits, optimizing caloric intake to maintain ideal weight. A balanced diet should meet energy needs while controlling total calories, with fat intake at 1g/kg/day, protein at 1–1.2g/kg/day, and carbohydrates derived from remaining caloric needs. Key recommendations include adequate hydration, limiting alcohol, quitting smoking, regular meals, portion control, and keeping salt intake under 6g/day. Healthcare providers should adjust energy intake based on weight to maintain an optimal fat-protein-carb balance. LGI dietary interventions have the highest likelihood (62.1%) of reducing 2hPG and the greatest likelihood (96.9%) of improving HOMA-IR, making LGI an optimal intervention for both. LGI foods have a longer gastrointestinal transit time, lower absorption rates, and slower digestion, which results in a more gradual and lower rise in blood glucose, leading to better control of 2hPG. T2DM patients should be encouraged to follow LGI dietary principles. LGI interventions help lower 2hPG and HOMA-IR through multiple mechanisms. The digital dietary model [ 36 ] ranks first in lowering HbA1c, followed by MNT [ 12 ]. Using a Food Composition Database , this model calculates the BMI and nutrient composition of T2DM patients' diets and builds digital data models using food photography applications. A personalized digital system is then used to manage patients' nutritional patterns. The Twin Precision Nutrition (TPN) model [ 37 ], which utilizes continuous glucose monitoring (CGM) data and food intake information combined with machine learning algorithms, offers personalized daily nutrition guidance to patients, significantly lowering HbA1c levels. This method adjusts the diet based on patients' glucose responses, avoiding glucose fluctuations and effectively controlling blood sugar levels. Platforms like Foodsmart [ 38 ] provide digital nutritional interventions through personalized recipe suggestions, meal planning, and food ordering, which have been shown to significantly impact blood glucose control. These findings suggest that sustained digital support can help T2DM patients maintain healthy dietary habits and blood glucose control in the long term. Clinicians are encouraged to adopt and incorporate such digital nutrition interventions into their practice. The combination of LGI + LGL nutritional intervention demonstrates optimal effects in lowering TC, TG, and BMI, with the effectiveness percentages ranked as follows: 88.3%, 80.6%, and 99.8%, respectively. This highlights that BMI is the most positively impacted parameter. To reduce HOMA-IR, guiding patients in choosing low glycemic index (LGI) foods is the top priority, with an effectiveness ratio of 96.9%. Employing scientific cooking methods and creating rational meal plans can effectively lower HOMA-IR. Meal plans should be tailored based on the patient's height, weight, and physical activity for optimal results. Improving insulin sensitivity, and thus insulin resistance and overall health, can be achieved by increasing dietary protein content and reducing the glycemic index (GI) value [ 39 ]. The integration of MNT, LGI dietary intervention, digital dietary models, and LGI + LGL nutritional interventions forms a comprehensive and effective strategy for diabetes management. These approaches collectively aid in better controlling blood glucose levels, reducing the risk of cardiovascular diseases, and improving the quality of life for diabetic patients. In clinical practice, it is essential to actively promote and apply these dietary interventions to provide more comprehensive and personalized services for diabetes patients. Future clinical settings should establish digital “Internet+” diabetes nutrition intervention platforms that allow patients to access interventions via mobile apps. The platform should incorporate the best evidence provided by this study and offer customized dietary interventions based on MNT principles. Patients can upload metabolic data and food photos for CI calculation and access health education, exercise courses, peer support, nutritional follow-ups, and family connectivity. This approach would overcome the temporal and spatial limitations of traditional in-person dietary consultations and follow-ups. Additionally, a supervision and management system should be implemented to create a seamless “hospital-community-family” management model, addressing the ongoing management challenges of diabetes nutrition intervention [ 40 ]. Integrating online and offline platforms, this model enhances diabetes care efficiency, making comprehensive management a reality and easing the burden on patients through effective nutritional interventions. 5. Strengths and Limitations of the Study This study has several strengths: it comprehensively analyzed 18 RCTs (1687 T2DM patients), applied rigorous quality assessments with tools like Cochrane 5.4.0 and Jadad scales, and employed both traditional meta-analysis and NMA methods. However, it also has limitations. We only included Chinese- and English-language studies published after 2010, which means some relevant RCTs might have been missed. Additionally, blinding can be challenging with nutritional interventions, and issues with allocation concealment and blind design may introduce heterogeneity. Despite these concerns, most of the included studies were moderate-to-high quality, suggesting that our results are solid. Future research should emphasize CONSORT-based RCT designs. 6. Conclusions For patients with T2DM, MNT is the most effective intervention for lowering FPG , while LGI intervention demonstrates the best results in reducing 2hPG . The digital dietary model shows significant effectiveness in lowering HbA1c . Furthermore, the combination of LGI + LGL dietary interventions exhibits potential advantages in reducing TC , TG , and BMI , but further high-quality and long-term studies are required to strengthen its credibility and confirm its long-term benefits. Abbreviations T2DM type 2 diabetes mellitus NMA Network Meta-Analysis FPG Fasting Plasma Glucose 2hPG 2-hour Postprandial Glucose TC Total Cholesterol TG Triglycerides RCTs Randomized Controlled Trials MNT Medical Nutrition Therapy LGI Low Glycemic Index SUCRA surface under the cumulative ranking area HGI High Glycemic Index CENTRAL Cochrane Library's Central Register of Controlled Trials GI Glycemic Index BMI Body Mass Index LGL Low Glycemic Load Declarations Ethics approval and consent to participate Not applicable. This study is a systematic review and network meta-analysis based on previously published data and does not involve any new studies with human participants or animals. Consent for publication Not applicable. Availability of data and materials All data generated or analyzed during this study are included in the published article and its supplementary materials. Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions All authors made a significant contribution to the work reported. Y.L. and H.Y.L. andW.X.C. finished Data collection, Y.L. and H.Y.L. and Q.Z. and W.X.C. finished Data analysis and interpretation, Y.L. and H.Y.L. and W.X.C. finished Manuscript writing,Y.L. and H.Y.L. and W.X.C. finished Manuscript revision. All authors approved the finalversion of the manuscript. 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Tables Table 1 General information about the included studies No. Included Study Country/Region Sample Size (cases) T/C T1 Intervention T2 Intervention Control Intervention Outcome Indicators 1 Yang Bing, 2021 China (Shaanxi) T=47 / C=48 Low-carbohydrate dietary intervention (LCD) – Low-fat dietary intervention (LFD) ①②③⑤⑥ 2 Sang-Man Jin, 2021 South Korea T1=16 / T2=17 / C=20 East Asia alternative diet (EAAD) Korean Food Exchange Mode (KFEM) Routine diabetes dietary intervention (UC) ③ 3 Yuka Omura, 2021 Japan T=64 / C=62 Personalized medical nutrition therapy (MNT) – Routine diabetes dietary intervention (UC) ③ 4 Chen Xianghua, 2020 China (Guangdong) T=40 / C=40 Carbohydrate counting (CHO) – Routine diabetes dietary intervention (UC) ①③⑤⑥ 5 Li Xiaotao, 2020 China (Ningxia) T=63 / C=48 Low-carbohydrate dietary intervention (LCD) – Routine diabetes dietary intervention (UC) ①②③⑦ 6 Xu Yan, 2020 China (Shanghai) T=48 / C=48 Digital nutrition dietary intervention (DN) – Routine diabetes dietary intervention (UC) ①②③ 7 Nivedita Pavithran, 2020 India T=18 / C=18 Low-glycemic index dietary intervention (LGI) – Routine diabetes dietary intervention (UC) ③⑤⑥ 8 Ma Rongwei, 2019 China (Shijiazhuang) T=62 / C=60 Personalized medical nutrition therapy (MNT) – Routine diabetes dietary intervention (UC) ①②③④⑤⑥⑦ 9 Rosario, 2019 Spain T=94 / C=91 Multi-factor + Mediterranean diet (EMID) – Routine diabetes dietary intervention (UC) ③ 10 Wang Ruiping, 2015 China (Kunming) T1=27 / T2=29 / C=29 Low-glycemic index dietary intervention (LGI) Water-soluble dietary fiber intervention (WSDF) Routine diabetes dietary intervention (UC) ①②③④⑤⑥ 11 Wang Xia, 2015 China (Nanjing) T=50 / C=50 Low-glycemic index & low-glycemic load dietary intervention (LGI+LGL) – Routine diabetes dietary intervention (UC) ①②③⑤⑥⑦ 12 Chang Na, 2014 China (Jilin) T=36 / C=39 Personalized medical nutrition therapy (MNT) – Routine diabetes dietary intervention (UC) ②③⑦ 13 Quan Xiaojuan, 2014 China (Shaanxi) T=50 / C=50 Personalized medical nutrition therapy (MNT) – Routine diabetes dietary intervention (UC) ①③⑤⑥ 14 Yao Li, 2013 China (Ningxia) T=48 / C=47 PCPA dietary intervention (PCPA) – Routine diabetes dietary intervention (UC) ⑤⑥ 15 Ma Li, 2012 China (Ningxia) T=48 / C=47 PCPA dietary intervention (PCPA) – Routine diabetes dietary intervention (UC) ①⑦ 16 Chen Min, 2012 China (Shanghai) T=62 / C=79 Low-glycemic index dietary intervention (LGI) – Routine diabetes dietary intervention (UC) ①②③⑤ 17 Han Mingming, 2011 China (Tianjin) T=9 / C=9 Personalized medical nutrition therapy (MNT) – Routine diabetes dietary intervention (UC) ①② 18 Xu Danfeng, 2010 China (Shanghai) T=62 / C=61 Personalized medical nutrition therapy (MNT) – Routine diabetes dietary intervention (UC) ①②③④⑤⑥ Outcome Indicators: ① FPG (fasting plasma glucose) ② 2hPG (2-hour postprandial glucose) ③ HbAlc (glycosylated hemoglobin)④ HOMA-IR (homeostasis model assessment-insulin resistance)⑤ TC (total cholesterol) ⑥ TG (triglycerides)⑦ BMI (body mass index) Abbreviations: UC: Routine diabetes dietary intervention; LCD: Low-carbohydrate dietary intervention LFD: Low-fat dietary intervention; EAAD: East Asia alternative diet; KFEM: Korean Food Exchange Mode; MNT: Personalized medical nutrition therapy; CHO: Carbohydrate counting DN: Digital nutrition dietary intervention; LGI: Low-glycemic index dietary intervention EMID: Multi-factor + Mediterranean diet intervention; WSDF: Water-soluble dietary fiber intervention; LGI+LGL: Low-glycemic index & low-glycemic load dietary intervention; PCPA: PCPA dietary intervention Table 2 Included Study Quality Evaluation (Cochrane Handbook5.3.0) No. Included Study Selection Bias Performance Bias Detection Bias Attrition Bias Reporting Bias Other Bias Jadad Score 1 Yang Bing,2021[8] 3 3 3 3 1 1 1 4 2 Sang-Man Jin2021[9] 1 2 1 1 1 1 1 6 3 Yuka Omura2021[10] 1 1 1 1 1 1 1 6 4 Chen Xianghua, 2020[11] 2 3 3 3 1 1 1 4 5 Li Xiaotao, 2020[12] 3 3 3 3 1 1 1 4 6 Xu Yan, 2020[13] 1 2 1 1 1 1 1 5 7 Nivedita 2020[14] 1 1 1 1 1 1 1 6 8 Ma Rongwei2019[15] 1 3 3 3 1 1 1 4 9 Rosario 2019[16] 1 2 1 1 1 1 1 6 10 Wang Ruiping2015[17] 1 2 2 2 1 1 1 4 11 Wang Xia 2015[18] 1 2 1 1 1 1 1 7 12 Chang Na2014[19] 1 1 1 1 1 1 1 7 13 Quan Xiaojuan2014[20] 1 1 1 1 1 1 1 7 14 Yao Li2013[21] 3 3 3 3 1 1 1 4 15 Ma Li2012[22] 1 3 3 3 1 1 1 4 16 Chen Min 2012[23] 1 1 1 1 1 1 1 7 17 Han Mingming2011[24] 1 1 1 1 1 1 1 7 18 Xu Danfeng2010[25] 1 1 1 1 1 1 1 7 Note: 1 = Low risk; 2 = High risk; 3 = Unclear. Table 3 Effects of the Interventions The SUCRA ranking The inconsistency test (>0.05) node-splitting method Model FPG[17, 20-22, 24, 26, 27, 29, 31, 32, 34] ⑥(77.6%)>⑬(61.3%)>②(59.4%)>⑧(58.4%)>⑦(55.6%) > ③(54.6%) >⑫(53.3%) >⑨(43.8%)>①(19.9%) >⑪(16.1%). P=0.142 P>0.05 The consistency model 2hPG[17, 21, 22, 24, 26-28, 32-34] ⑨(62.1%) > ⑥(61.0%) > ⑪(52.1%) > ③ (50.7%) > ⑫(49.5%) > ⑧ (49.2%) > ②(49%) > ①(26.4%). P=0.648 P>0.05 The consistency model HbA1c[17-29, 32, 34] ⑧(84.6%) > ⑥ (75.4%) > ⑫(72.1%) >⑦ (71.1%) > ⑨(65.3%) > ⑪(52.4%) > ③(41.2%) > ②(39.5%) = ⑩(39.5%) > ①(35.9%) > ⑤(17.1%) > East Asian ④(6%). P=0.983 P>0.05 The consistency model HOMA-IR[24, 26, 34] ⑨(96.9%) > ⑪(64.4%) > ⑥ (32.8%) >①(5.9%). P=0.165 P>0.05 The consistency model TC[17, 20, 23, 24, 26, 27, 29, 30, 32, 34] ⑫(88.3%) > ⑥(70.1%) > ⑬ (46.3%) > ⑦(45.1%) > ⑨(45.0%) > ① (34.0%) > ⑪ (21.3%). P=0.188 (P>0.05) The consistency model TG[17, 20, 23, 24, 26, 27, 29, 30, 32, 34] ⑫(80.6%) > ⑨ (71.0%) > ⑥ (57.8%) >⑪(45.4%) > ⑬(26.5%) > ① (6.4%). P=0.341 (P>0.05) The consistency model BMI[21, 24, 27, 28, 31] ⑫(99.8%) >②(62.6%) > ⑥(42.1%) >⑬(28.0%) > ① (17.5%). P=0.923 (P>0.05) The consistency model ①conventional diabetes diet ②low-carb diet ③low-fat diet④East Asian alternative diet⑤Korean food exchange model⑥MNT⑦carbohydrate counting⑧digital dietary patterns ⑨LGI diet⑩multi-factor Mediterranean diet intervention ⑪water-soluble dietary fiber intervention ⑫LGI+LGL⑬PCPA dietary intervention Table 4 Summary table of GRADE evidence Certainty assessment №；Patients Outcome Outcome Indicator N Study Design Risk of Bias Inconsistency Indirectness Imprecision Other Considerations [Intervention] [Control] Relative (95% CI) Absolute (95% CI) Certainty Importance FPG 10 Randomized trials Not serious Not serious a Not serious Not serious None 494 432 - SMD 0.75 SD lower (0.88 lower to 0.61 lower) ⨁⨁⨁⨁ High Critical 2hPG 8 Randomized trials Not serious Not serious a Not serious Not serious None 329 394 - SMD 0.62 SD lower (0.76 lower to 0.47 lower) ⨁⨁⨁⨁ High Critical HbAlc 12 Randomized trials Not serious Not serious a Not serious Not serious None 649 646 - SMD 0.45 SD lower (0.45 lower to 0.33 lower) ⨁⨁⨁⨁ High Critical TC 8 Randomized trials Not serious Serious a Not serious Not serious None 392 405 - SMD 0.39 SD lower (0.54 lower to 0.25 lower) ⨁⨁⨁◯ Moderate Important TG 7 Randomized trials Not serious Serious a Not serious Not serious None 330 326 - SMD 0.59 SD lower (0.75 lower to 0.43 lower) ⨁⨁⨁◯ Moderate Important BMI 5 Randomized trials Not serious Not serious Not serious Not serious None 259 244 - SMD 0.28 SD lower (0.45 lower to 0.1 lower) ⨁⨁⨁⨁ High Important Note: CI: Confidence interval; MD: Mean difference; OR: Odds ratio; SMD: Standardized mean difference (a) Differences in unit conversions or variations in population/intervention may introduce heterogeneity across studies. Additional Declarations No competing interests reported. Supplementary Files Supplementarydocumentation.docx Cite Share Download PDF Status: Published Journal Publication published 07 Aug, 2025 Read the published version in Nutrition & Metabolism → Version 1 posted Editorial decision: Revision requested 23 May, 2025 Reviews received at journal 22 May, 2025 Reviews received at journal 21 May, 2025 Reviews received at journal 11 May, 2025 Reviewers agreed at journal 06 May, 2025 Reviewers agreed at journal 06 May, 2025 Reviewers agreed at journal 01 May, 2025 Reviewers invited by journal 30 Apr, 2025 Editor assigned by journal 29 Apr, 2025 Submission checks completed at journal 23 Apr, 2025 First submitted to journal 21 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6499801","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":452304591,"identity":"37f219de-a881-46f7-869a-6d3823e4357e","order_by":0,"name":"Yi Liu","email":"","orcid":"","institution":"Yanbian University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Liu","suffix":""},{"id":452304592,"identity":"be3c96ed-9d41-4a43-8116-670e31393d13","order_by":1,"name":"Haiyue Li","email":"","orcid":"","institution":"Yanbian University 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selection.\u003c/p\u003e","description":"","filename":"figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6499801/v1/f4918ed12d2b02635e83cada.jpg"},{"id":82276429,"identity":"d2425bdc-68e2-4d74-893b-86e9f0cdb182","added_by":"auto","created_at":"2025-05-08 14:46:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73802,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork meta-analysis evidence network diagram for FPG.\u003c/p\u003e","description":"","filename":"figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6499801/v1/6f3a35d0c0c9a6fd5382c8a4.jpg"},{"id":82276431,"identity":"58b9efc6-9b19-4711-b8a3-5128e5cda88c","added_by":"auto","created_at":"2025-05-08 14:46:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73124,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork meta-analysis evidence network diagram for 2hPG.\u003c/p\u003e","description":"","filename":"figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6499801/v1/55d303866d9bfdc27d952103.jpg"},{"id":82276435,"identity":"efa7adbb-f938-4dd5-ae0c-6e8a5d73b27f","added_by":"auto","created_at":"2025-05-08 14:46:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133066,"visible":true,"origin":"","legend":"\u003cp\u003eSUCRA result sort clustering heatmap.\u003c/p\u003e","description":"","filename":"figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6499801/v1/e996b051b5af12ef8e1a28de.jpg"},{"id":88815129,"identity":"6d138f3c-9ea6-45e0-a636-028196701b8d","added_by":"auto","created_at":"2025-08-11 16:10:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1819869,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6499801/v1/80fdcfa4-b652-48b9-80e5-bf3408045266.pdf"},{"id":82276433,"identity":"a197aff0-4b47-49ba-a715-848fe072885d","added_by":"auto","created_at":"2025-05-08 14:46:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":234207,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydocumentation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6499801/v1/2dd603bd16fe2038c504598a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of 12 Nutritional Interventions on Type 2 diabetes：A Systematic Review with Network Meta-Analysis of Randomized Trials Short Running Title: Effectiveness of 12 Diets in T2DM Management","fulltext":[{"header":"1. Background","content":"\u003cp\u003eWith aging populations, rising living standards, and changing lifestyles, T2DM incidence is rapidly increasing worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], posing a major health threat. T2DM, characterized by insulin resistance and β-cell dysfunction, can lead to cardiovascular disease, retinopathy, neuropathy, and nephropathy. In China, the adult diabetes prevalence is 11.9%, with T2DM as the predominant form [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Globally, about 537\u0026nbsp;million people had diabetes in 2019, projected to reach 783\u0026nbsp;million by 2045 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], escalating complications and healthcare costs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Identifying effective prevention and treatment measures is thus an urgent priority.\u003c/p\u003e \u003cp\u003eMany of the risk factors for diabetes are related to poor dietary habits, and modifying diet and lifestyle can effectively prevent and manage T2DM. Mediterranean and low-carbohydrate diets have been proven to help reduce the risk of T2DM [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Low Glycemic Index (LGI) diets are more effective than High Glycemic Index (HGI) diets in controlling blood glucose and HbA1c levels [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Diets such as LGI and ketogenic diets have been shown to effectively lower HbA1c levels and Fasting Plasma Glucose (FPG) in the short term. Diets rich in whole grains, fruits, and vegetables can improve blood glucose control and are associated with better health indicators such as blood pressure, BMI, and waist circumference [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNetwork meta-analysis (NMA) extends traditional meta-analysis by comparing multiple interventions simultaneously to help clinicians and patients choose optimal treatment. However, evidence is insufficient to determine which dietary intervention best reduces hyperglycemia and related indicators in T2DM. Therefore, this study will conduct an NMA of RCTs to systematically evaluate the impact of dietary interventions on T2DM outcomes, aiming to provide evidence-based guidelines and assist in clinical decision-making. Ultimately, this research will guide future studies and advance T2DM management [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1\u0026nbsp;Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was prospectively registered in the PROSPERO International Systematic Review Register (https://www.crd.york.ac.uk/prospero/, accessed on August 31, 2024), with the registration number CRD42023429616. The planning, implementation, and reporting of this study followed the PRISMA guidelines and the NMA reporting requirements [11,12].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Search Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe search included databases such as CNKI, WanFang, VIP, SINOMED, Web of Science, PubMed, Medline, and the Cochrane Library\u0026apos;s Central Register of Controlled Trials (CENTRAL), covering studies from January 1, 2010, to August 31, 2024. Keywords used in the search included: nutritional intervention, nutrition policy, diabetes, T2DM, randomized controlled trials, along with the relevant English terms: Diabetes Mellitus, Type 2, Diabetes Mellitus, type 2 diabetes, randomized controlled trial. Both Chinese and English literature were included (See Supplementary Materials).\u003c/p\u003e\n\u003cp\u003e2.2.1 Inclusion crieria\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eP:\u003c/strong\u003e T2DM; no restrictions on gender, nationality, or ethnicity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI:\u003c/strong\u003e 12 types of dietary interventions:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Low Carbohydrate Diet Intervention:\u003c/strong\u003e Total daily caloric intake controlled at 1980 kcal, with carbohydrates, fats, and proteins accounting for 33%, 45%, and 22%, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b) Low Fat Diet Intervention:\u003c/strong\u003e Total daily caloric intake controlled at 1860 kcal, with carbohydrates, fats, and proteins accounting for 48%, 32%, and 20%, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(c) East Asian Alternative Diet Model:\u003c/strong\u003e Restricts sugar and starch usage, with the caloric ratio of carbohydrates: fats: proteins being 4:3:3, and net carbohydrates accounting for 27% of total caloric intake.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(d) Korean Food Exchange Model:\u003c/strong\u003e Caloric ratio of carbohydrates: fats: proteins is 6:2:2, with sodium intake limited to 600-800 mg per meal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(e) Medical Nutrition Therapy (MNT):\u003c/strong\u003e Individualized dietary guidance based on factors such as blood glucose, blood lipids, weight, and physical activity, with an emphasis on increasing protein intake.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(f) Carbohydrate Counting Method:\u003c/strong\u003e Carbohydrates, proteins, and fats make up 55%, 20%, and 25% of the total caloric intake, respectively, distributed as 1/5, 2/5, and 2/5 across three meals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(g) Digital Dietary Model:\u003c/strong\u003e the smaller part includes carbohydrates, snacks, total energy, vegetables, fats, proteins, fruits, etc.; the larger part includes water, beverages, food safety, exercise, weight control, mental health, rational eating, and alcohol consumption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(h) Low Glycemic Index (LGI) Dietary Intervention:\u003c/strong\u003e A diet with a Glycemic Index (GI) \u0026le; 45 completely replaces breakfast and dinner.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(i) Multifactorial Mediterranean Diet Intervention:\u003c/strong\u003e Recommends white meat, four or more tablespoons of olive oil per day (1 tablespoon = 13.5 grams), two or more servings of vegetables, three or more servings of fruits, one or fewer servings of red meat or sausage, less animal fat, and less than one cup (100 mL) of carbonated or sugary drinks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(j) Soluble Dietary Fiber Intervention:\u003c/strong\u003e Add 10g of soluble dietary fiber to breakfast and dinner.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(k) LGI+LGL Dietary Intervention:\u003c/strong\u003e Total daily Glycemic Index (GI) = \u0026Sigma;(food GI \u0026times; intake amount \u0026times; available carbohydrate %) \u0026divide; total available carbohydrate amount of all foods; Total daily Glycemic Load (GL) = \u0026Sigma;(food GI \u0026times; intake amount \u0026times; available carbohydrate %) \u0026divide; 100. The average GI and GL of 3-day dietary intake are used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(l) PCPA Dietary Intervention:\u003c/strong\u003e Dietary intervention strategy guided by the PCPA theory (Phases: Advocacy, Building Alliances, Promotion and Mobilization, and Action).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC:\u003c/strong\u003e Conventional diabetes dietary intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eO:\u003c/strong\u003e(a) \u003cstrong\u003eBlood Glucose Control Indicators:\u0026nbsp;\u003c/strong\u003eFasting Plasma Glucose (FPG); Postprandial 2-hour Glucose (2hPG); Glycated Hemoglobin (HbA1c); Insulin Resistance Index (HOMA-IR)\u003c/p\u003e\n\u003cp\u003e(b) \u003cstrong\u003eCardiovascular Risk Factors Indicators:\u0026nbsp;\u003c/strong\u003eTotal Cholesterol (TC); Triglycerides (TG); Body Mass Index (BMI)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS:\u003c/strong\u003e Randomized Controlled Trials (RCT)\u003c/p\u003e\n\u003cp\u003e2.2.2. Exclusion Criteria\u003c/p\u003e\n\u003cp\u003e(1) The study design is a cohort study, cross-sectional study, or case-control study; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(2) Although the study is a clinical control trial, the grouping lacks randomization, or it is a non-synchronous clinical control study or a self-before-and-after control study; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(3) The study subjects include those with type 1 diabetes, other special types of diabetes, gestational diabetes, or high-risk populations for diabetes; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(4) The study lacks relevant outcome indicators; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(5) The literature is a review, commentary, editorial, case report, extended research from original studies, or non-human trials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Data Extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo researchers (L and L) followed the search strategy to screen titles and assess relevant studies. Any unclear or ambiguous data in the original texts prompted full-text review. In cases of disagreement, a third researcher (C) conducted in-depth analysis, ensuring objective, accurate final assessments. Microsoft Excel was used to record the first author\u0026rsquo;s name, country/region, sample size, intervention/control measures, and outcome indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Risk of Bias Assessment\u003c/strong\u003e\u003cbr\u003eThe Cochrane risk of bias tool was used to assess bias across seven domains, categorizing the risk as high, low, or unclear. After completing the assessments, Kappa consistency tests were conducted. Two researchers (L and L) independently evaluated the risk of bias for the final selected studies. Given the inherent difficulties in blinding in diet pattern RCTs, any differences were resolved by discussing with a third team member (C) until consensus was reached.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Dealing with Missing Data\u003c/strong\u003e\u003cbr\u003eFollowing the Cochrane Handbook guidelines\u0026nbsp;[13], if post-intervention data with corresponding standard deviations were unavailable, the corresponding standard deviation change scores were used. When standard deviation was not available, estimates were made based on standard errors, \u003cem\u003ep\u003c/em\u003e-values, and confidence intervals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Statistical Analysis\u003c/strong\u003e\u003cbr\u003eTraditional meta-analysis and network meta-analysis (NMA) were conducted using Stata 17.0 software to compare the effects of 12 dietary interventions on clinical outcomes in T2DM patients (FPG, 2hPG, HbA1c, HOMA-IR, TC, TG, BMI). In NMA results, node size was proportional to the sample size for each intervention, and line thickness reflected the number of available studies. Heterogeneity was assessed using the Cochran Q test, where\u003cem\u003e\u0026nbsp;I\u0026sup2;\u003c/em\u003e \u0026gt; 50% was considered indicative of heterogeneity, and a random-effects model was used; otherwise, a fixed-effects model was employed. MD, SMD, OR, RR, and their 95% CI were used as effect size indicators. When a closed loop appeared in the network, consistency was tested using the node splitting method. A \u003cem\u003ep\u003c/em\u003e-value \u0026gt; 0.05 indicated no significant inconsistency, and a consistency model was used for NMA. The surface under the cumulative ranking area (SUCRA) was used to assess the likelihood of each intervention being the best, with SUCRA values ranging from 0 to 1; higher values indicated better intervention effects. The SUCRA values were used to rank the 12 dietary patterns based on their effectiveness in controlling FPG, 2hPG, HbA1c, HOMA-IR, TC, TG, and BMI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Sensitivity Analyses\u003c/strong\u003e\u003cbr\u003eGiven that network meta-analysis (NMA) concerns closed loops, we tested for inconsistency between direct and indirect evidence using statistical methods specific to closed loops to detect potential discrepancies. The stability of the combined results using both random-effects and fixed-effects models was assessed. Inconsistencies between these models suggested potential instability in the original findings. Sensitivity analyses were conducted by sequentially excluding individual studies to monitor changes in the combined results, thereby evaluating the influence of specific studies on the overall outcome. Furthermore, we examined the impact of low-quality studies by excluding them from the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Credibility of the Evidence\u003c/strong\u003e\u003cbr\u003eFunnel plots were created to analyze publication bias. The GRADE system was used to rate the quality of evidence for traditional meta-analysis results, and the CINeMA online tool was used to assess the quality of evidence for NMA. The combined effects for key outcomes (FPG, 2hPG, HbA1c) and important outcomes (HOMA-IR, TC, TG, BMI) were analyzed. CINeMA provided six ratings: within-study bias, reporting bias, indirectness, imprecision, heterogeneity, and incoherence [14].\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Search Results and Study Selection\u003c/h2\u003e \u003cp\u003eAs of August 31, 2024, a total of 301,997 relevant articles were identified (3,957 in Chinese, 298,040 in English). After excluding duplicate publications and those that did not meet the inclusion criteria (19,930 articles), 3,135 articles were retained for further evaluation. Following a review of titles and abstracts, 18 randomized controlled trials (RCTs) were included [\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], consisting of 14 Chinese studies and 4 English studies. These studies involved 12 different dietary nutritional interventions. The literature screening flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e,Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Study Characteristics\u003c/h2\u003e \u003cp\u003eA total of 18 studies were included, with 4 studies conducted in South Korea, Japan, India, and Spain, and 14 studies conducted in China. The participants were primarily overweight and obese individuals with T2DM. The studies were conducted by teams of clinical healthcare professionals or professional nutritionists. Sixteen of the studies were two-arm trials, while two were three-arm trials. The outcome indicators included: FPG, 2hPG, HbA1c, HOMA-IR, TC, TG, and BMI. The basic characteristics of the included studies are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Risk of Bias in Included Studies\u003c/h2\u003e \u003cp\u003e The quality assessment was conducted using Cochrane 5.4.0 guidelines, with an inter-rater consistency Kappa value of 0.897. Eleven studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] reported the random sequence generation method, while the others only mentioned randomization in the abstract. Seven studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] provided information on sample dropout rates and reasons, and the data were mostly complete. The included studies reported both primary and secondary outcome indicators in full, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Traditional Meta-Analysis Results\u003c/h2\u003e \u003cp\u003eUsing Stata, a traditional meta-analysis was conducted for effect indicators with more than two studies directly comparing original research [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Compared to conventional diabetes dietary interventions, all 12 other nutritional interventions showed statistically significant effects (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Due to high heterogeneity, a random effects model was used for analysis. The sources of heterogeneity were found to be related to measurement tools, intervention duration, and specific intervention methods. The absence of strict randomization, allocation concealment, and blinding may have contributed to the heterogeneity(See Supplementary Materials)..\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Effects of the Interventions\u003c/h2\u003e \u003cp\u003eThe Network Meta-Analysis (NMA) results were reported using the CINeMA evidence grading system [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In the network diagram, each node represents a dietary intervention method, with node size and line thickness reflecting sample size and number of studies, respectively. Direct evidence between two points is connected by a solid line, and indirect comparisons can be made based on the network relationships. Node colors represent the risk of bias in studies: red (high), yellow (medium), and green (low), corresponding to Cochrane quality ratings,\u003c/p\u003e \u003cp\u003e①FPG②2hPG③HbAlc④HOMA-IR⑤TC⑥TG⑦BMI ,The overall inconsistency test showed P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, and the node-splitting method confirmed that all results had P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, indicating no significant inconsistency in the loop. The consistency model was used for the analysis. The SUCRA ranking was as follows Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Pairwise Comparison Results\u003c/h2\u003e \u003cp\u003ePairwise comparisons were conducted for seven outcome indicators using league tables, For HbA1c control, Digital Dietary Patterns and Medical Nutrition Therapy (MNT) outperform conventional diabetes diets, Korean food exchange models, and East Asian alternative diets. LGI\u0026thinsp;+\u0026thinsp;LGL diets, carbohydrate counting, and LGI interventions are superior to East Asian alternative diets (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For HOMA-I reduction, LGI interventions surpass MNT and conventional diabetes diets, while soluble fiber interventions outperform conventional diabetes diets (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For BMI reduction, LGI\u0026thinsp;+\u0026thinsp;LGL diets are superior to low-carb diets, MNT, and PCPA interventions, showing greater effectiveness with statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, See Supplementary Materials).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 SUCRA Results Ranking Cluster Heatmap\u003c/h2\u003e \u003cp\u003eFigure 9 shows a circular quantity indicating the seven outcome indicators, and the number of sectors represents the twelve dietary interventions. The color-coded sections represent the SUCRA values of the interventions. MNT was the most effective for reducing FPG, while LGI dietary intervention was the preferred choice for reducing 2hPG and HOMA-IR. Digital dietary patterns were the most effective for reducing HbA1c, and LGI\u0026thinsp;+\u0026thinsp;LGL dietary interventions were the most effective for reducing TC, TG, and BMI\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Sensitivity Analyses\u003c/h2\u003e \u003cp\u003eThe network relationships for all seven outcome indicators were closed loops. The global consistency test using the inconsistency model showed a P-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05, and the local inconsistency test using the node-splitting method also showed a P-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05, indicating that the results were consistent(34). For traditional meta-analysis, the sensitivity analysis based on heterogeneity degree showed that the results were stable, as there were minimal differences between the random-effect model and the fixed-effect model. Additionally, re-conducting the traditional meta-analysis after excluding low-quality studies yielded no significant change in the overall effect size, suggesting that the results were robust(35).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Publication Bias\u003c/h2\u003e \u003cp\u003eThe funnel plot for the network meta-analysis showed a symmetrical distribution of data points in the upper part of the funnel, indicating few small-sample studies. This suggests that the results are stable. The scatter points were evenly distributed, and all studies were symmetrically distributed around the vertical line at X\u0026thinsp;=\u0026thinsp;0, indicating that there is a low likelihood of publication bias in the current research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Credibility of the Evidence\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.10.1 GRADE Evidence Rating\u003c/h2\u003e \u003cp\u003eFor traditional meta-analysis, six outcome indicators were assessed: four high-quality outcomes (FPG, 2hPG, HbA1c, BMI) and two moderate-quality outcomes (TC, TG). Among these, three outcomes (FPG, 2hPG, HbA1c) were classified as \"critical\" and three outcomes (BMI, TC, TG) as \"important.\" The GRADE evidence rating and reasons for upgrading or downgrading for each outcome can be found in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.10.2 CINeMA Evidence Rating\u003c/h2\u003e \u003cp\u003eCINeMA assessed the NMA across six domains: within-study bias and between-study bias (no concern, no downgrade), and indirectness, imprecision, heterogeneity, and inconsistency (some concern, downgraded by one level). The evidence quality was rated high for FPG, 2hPG, HbA1c, and BMI, and moderate for HOMA-IR, TC, and TG. Overall, the evidence is of good quality and provides valuable recommendations.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis NMA study included 18 RCTs, involving a total of 1,687 T2DM patients. The quality of the included studies was evaluated using the Cochrane 5.4.0 tool and the Jadad scale, with results indicating a medium to high quality of evidence. The two researchers (L/L) independently assessed the results, yielding a Kappa value of 0.897, which suggests a high level of homogeneity among the included studies. By combining traditional meta-analysis and NMA methods, we ranked 12 dietary interventions and evaluated their effects on various outcomes for T2DM patients, including FPG, 2hPG, HbA1c, HOMA-IR, TC, TG, and BMI.\u003c/p\u003e \u003cp\u003eIn diabetes management, clearly distinguishing between \"critical\" and \"important\" outcome indicators allows healthcare providers to more effectively formulate and adjust treatment plans to minimize the risk of complications. The GRADE evidence rating identified FPG, 2hPG, and HbA1c as \"critical\" outcomes, and TC, TG, and BMI as \"important\" outcomes. The CINeMA evidence rating revealed that the quality of evidence for FPG, 2hPG, HbA1c, and BMI was high, while the quality for HOMA-IR, TC, and TG was moderate. Overall, the evidence is of good quality and offers valuable recommendations. The critical outcome indicators, (FPG, 2hPG, and HbA1c), directly reflect the control of diabetes and the potential risks of complications, making them essential for prioritization in treatment planning. On the other hand, the important outcome indicators (TC, TG, and BMI) contribute to a comprehensive assessment of treatment effects. Although they may not directly influence patient survival rates or the occurrence of complications as much as the critical indicators, they provide insight into the patient's overall health status and the comprehensive effects of the treatment. This information, in turn, helps healthcare providers deliver more personalized treatment recommendations for patients.\u003c/p\u003e \u003cp\u003eMedical nutrition therapy (MNT) is crucial in preventing and managing diabetes, effectively lowering FPG in T2DM patients, followed by PCPA dietary interventions. MNT reduces β-cell stress through structured dietary habits, optimizing caloric intake to maintain ideal weight. A balanced diet should meet energy needs while controlling total calories, with fat intake at 1g/kg/day, protein at 1\u0026ndash;1.2g/kg/day, and carbohydrates derived from remaining caloric needs. Key recommendations include adequate hydration, limiting alcohol, quitting smoking, regular meals, portion control, and keeping salt intake under 6g/day. Healthcare providers should adjust energy intake based on weight to maintain an optimal fat-protein-carb balance.\u003c/p\u003e \u003cp\u003eLGI dietary interventions have the highest likelihood (62.1%) of reducing 2hPG and the greatest likelihood (96.9%) of improving HOMA-IR, making LGI an optimal intervention for both. LGI foods have a longer gastrointestinal transit time, lower absorption rates, and slower digestion, which results in a more gradual and lower rise in blood glucose, leading to better control of 2hPG. T2DM patients should be encouraged to follow LGI dietary principles. LGI interventions help lower 2hPG and HOMA-IR through multiple mechanisms.\u003c/p\u003e \u003cp\u003eThe digital dietary model [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] ranks first in lowering HbA1c, followed by MNT [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Using a \u003cem\u003eFood Composition Database\u003c/em\u003e, this model calculates the BMI and nutrient composition of T2DM patients' diets and builds digital data models using food photography applications. A personalized digital system is then used to manage patients' nutritional patterns. The Twin Precision Nutrition (TPN) model [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], which utilizes continuous glucose monitoring (CGM) data and food intake information combined with machine learning algorithms, offers personalized daily nutrition guidance to patients, significantly lowering HbA1c levels. This method adjusts the diet based on patients' glucose responses, avoiding glucose fluctuations and effectively controlling blood sugar levels. Platforms like Foodsmart [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] provide digital nutritional interventions through personalized recipe suggestions, meal planning, and food ordering, which have been shown to significantly impact blood glucose control. These findings suggest that sustained digital support can help T2DM patients maintain healthy dietary habits and blood glucose control in the long term. Clinicians are encouraged to adopt and incorporate such digital nutrition interventions into their practice.\u003c/p\u003e \u003cp\u003eThe combination of LGI\u0026thinsp;+\u0026thinsp;LGL nutritional intervention demonstrates optimal effects in lowering TC, TG, and BMI, with the effectiveness percentages ranked as follows: 88.3%, 80.6%, and 99.8%, respectively. This highlights that BMI is the most positively impacted parameter. To reduce HOMA-IR, guiding patients in choosing low glycemic index (LGI) foods is the top priority, with an effectiveness ratio of 96.9%. Employing scientific cooking methods and creating rational meal plans can effectively lower HOMA-IR. Meal plans should be tailored based on the patient's height, weight, and physical activity for optimal results. Improving insulin sensitivity, and thus insulin resistance and overall health, can be achieved by increasing dietary protein content and reducing the glycemic index (GI) value [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe integration of MNT, LGI dietary intervention, digital dietary models, and LGI\u0026thinsp;+\u0026thinsp;LGL nutritional interventions forms a comprehensive and effective strategy for diabetes management. These approaches collectively aid in better controlling blood glucose levels, reducing the risk of cardiovascular diseases, and improving the quality of life for diabetic patients. In clinical practice, it is essential to actively promote and apply these dietary interventions to provide more comprehensive and personalized services for diabetes patients.\u003c/p\u003e \u003cp\u003eFuture clinical settings should establish \u003cb\u003edigital \u0026ldquo;Internet+\u0026rdquo; diabetes nutrition intervention platforms\u003c/b\u003e that allow patients to access interventions via mobile apps. The platform should incorporate the best evidence provided by this study and offer customized dietary interventions based on \u003cb\u003eMNT\u003c/b\u003e principles. Patients can upload metabolic data and food photos for CI calculation and access health education, exercise courses, peer support, nutritional follow-ups, and family connectivity. This approach would overcome the temporal and spatial limitations of traditional in-person dietary consultations and follow-ups. Additionally, a supervision and management system should be implemented to create a seamless \u0026ldquo;hospital-community-family\u0026rdquo; management model, addressing the ongoing management challenges of diabetes nutrition intervention [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Integrating online and offline platforms, this model enhances diabetes care efficiency, making comprehensive management a reality and easing the burden on patients through effective nutritional interventions.\u003c/p\u003e"},{"header":"5. Strengths and Limitations of the Study","content":"\u003cp\u003eThis study has several strengths: it comprehensively analyzed 18 RCTs (1687 T2DM patients), applied rigorous quality assessments with tools like Cochrane 5.4.0 and Jadad scales, and employed both traditional meta-analysis and NMA methods. However, it also has limitations. We only included Chinese- and English-language studies published after 2010, which means some relevant RCTs might have been missed. Additionally, blinding can be challenging with nutritional interventions, and issues with allocation concealment and blind design may introduce heterogeneity. Despite these concerns, most of the included studies were moderate-to-high quality, suggesting that our results are solid. Future research should emphasize CONSORT-based RCT designs.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eFor patients with T2DM, \u003cb\u003eMNT\u003c/b\u003e is the most effective intervention for lowering \u003cb\u003eFPG\u003c/b\u003e, while \u003cb\u003eLGI intervention\u003c/b\u003e demonstrates the best results in reducing \u003cb\u003e2hPG\u003c/b\u003e. The \u003cb\u003edigital dietary model\u003c/b\u003e shows significant effectiveness in lowering \u003cb\u003eHbA1c\u003c/b\u003e. Furthermore, the combination of \u003cb\u003eLGI\u0026thinsp;+\u0026thinsp;LGL dietary interventions\u003c/b\u003e exhibits potential advantages in reducing \u003cb\u003eTC\u003c/b\u003e, \u003cb\u003eTG\u003c/b\u003e, and \u003cb\u003eBMI\u003c/b\u003e, but further high-quality and long-term studies are required to strengthen its credibility and confirm its long-term benefits.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2DM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etype 2 diabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNetwork Meta-Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFPG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFasting Plasma Glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e2hPG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e2-hour Postprandial Glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCTs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandomized Controlled Trials\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMNT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedical Nutrition Therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLGI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow Glycemic Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSUCRA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esurface under the cumulative ranking area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHGI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh Glycemic Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCENTRAL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCochrane Library's Central Register of Controlled Trials\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycemic Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLGL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow Glycemic Load\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study is a systematic review and network meta-analysis based on previously published data and does not involve any new studies with human participants or animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in the published article and its supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made a significant contribution to the work reported. Y.L. and H.Y.L. andW.X.C. finished Data collection, Y.L. and H.Y.L. and Q.Z. and W.X.C. finished Data analysis and interpretation, Y.L. and H.Y.L. and W.X.C. finished Manuscript writing,Y.L. and H.Y.L. and W.X.C. finished Manuscript revision. All authors approved the finalversion of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their heartfelt gratitude to the staff and faculty of the School ofNursing at Yanbian University for their continuous support and guidance throughout thisstudy. Special thanks to the clinical teams involved in the data collection process for their invaluable contributions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmerican Diabetes Association Professional Practice Committee. 2.Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2022. 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Modern Preventive Medicine 2013:3775-7+85.\u003c/li\u003e\n\u003cli\u003eMa L, Zhang L, Yao L, Ning YH. The impact of the PCPA dietary intervention model on quality of life and blood sugar levels in elderly diabetic patients in the community. Chinese Journal of Gerontology 2012;32:3175-37. \u003c/li\u003e\n\u003cli\u003eChen M. Study on dietary nutritional intervention for individuals with impaired glucose regulation and type 2 diabetes. Shanghai: Fudan University, 2012.\u003c/li\u003e\n\u003cli\u003eHan MM. Study on energy provision and nutritional intervention in hospitalized patients with type 2 diabetes. Tianjin: Tianjin Medical University, 2011. \u003c/li\u003e\n\u003cli\u003eXu DF. Study on the effects of dietary nutritional intervention on outcomes of impaired glucose regulation and control of type 2 diabetes. Shanghai: Fudan University, 2010.\u003c/li\u003e\n\u003cli\u003eZhang Y, Wang XG, Li CC, Wang HJ Network meta-analysis on the effects of seven types of exercise training on balance function in patients with diabetic peripheral neuropathy. Chinese Journal of Nursing 2022;57:89-97. \u003c/li\u003e\n\u003cli\u003eWang Q, Wang YH, Lai HH, Wang Q,Ding GW,Tian JH,Chen YL,Yang KL,Wu DR,Guo XF,Yang LH,Ge L. Grading the quality of evidence in network meta-analyses: An introduction to the CINeMA online application. Chinese Journal of Evidence-Based Medicine 2020;20:1111-6.\u003c/li\u003e\n\u003cli\u003eZhuo J, Ling J, Jiang Y, Li T,Wang J. Network meta-analysis of hypoglycemia in type 2 diabetes patients caused by SGLT-2 inhibitors. China Pharmacy 2023;34:1509-14. \u003c/li\u003e\n\u003cli\u003eDavid M, Alessandro L, Jennifer T, Douglas GA. Systematic reviews and meta-analyses: The PRISMA statement. Journal of Integrated Traditional and Western Medicine 200;7:889-96. \u003c/li\u003e\n\u003cli\u003eZhang TY. Interpretation of the \u0026quot;Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2017 Edition)\u0026quot; with respect to Traditional Chinese Medicine.In: Proceedings of the Fourth Academic Exchange Conference of the Endocrinology Committee of Jiangxi Province Integrated Traditional and Western Medicine Society andthe Advances in Integrated Traditional and Western Medicine Treatment of Endocrine Diseases Study Class (pp.16-20+15). Endocrinology Department, Jiangxi Province Integrated Traditional and Western Medicine Hospital, 2018.\u003c/li\u003e\n\u003cli\u003eChinese Medical Healthcare International Exchange Promotive Association Nutrition and Metabolism Management Branch, Chinese Nutrition Society Clinical Nutrition Branch, Chinese Medical Association Diabetes Branch. Guidelines for Medical NutritionTherapy of Diabetes in China (2022 Edition). Chinese Journal of Diabetes 2022;14:881-933. \u003c/li\u003e\n\u003cli\u003eGoyenechea E, Holst C, van Baak MA, Saris WH, Jebb S, Kafatos A, et al. Effects of different protein content and glycaemic index ofad libitumdiets on diabetes riskfactors in overweight adults: the DIOGenes multicentre, randomized, dietary intervention trial. Diabetes/Metab Res. 2011;27:705-76. \u003c/li\u003e\n\u003cli\u003eBo Z. Expert Consensus on Telemedicine Management of Diabetes (2020 Edition).Int J Endocrinol. 2021:1-12. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 General information about the included studies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIncluded Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCountry/Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSample Size (cases) T/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT1 Intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT2 Intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eControl Intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOutcome Indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYang Bing, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina (Shaanxi)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=47 / C=48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-carbohydrate dietary intervention (LCD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-fat dietary intervention (LFD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e①②③⑤⑥\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSang-Man Jin, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT1=16 / T2=17 / C=20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEast Asia alternative diet (EAAD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKorean Food Exchange Mode (KFEM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e③\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYuka Omura, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=64 / C=62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePersonalized medical nutrition therapy (MNT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e③\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChen Xianghua, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina (Guangdong)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=40 / C=40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCarbohydrate counting (CHO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e①③⑤⑥\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLi Xiaotao, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina (Ningxia)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=63 / C=48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-carbohydrate dietary intervention (LCD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e①②③⑦\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eXu Yan, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina (Shanghai)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=48 / C=48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDigital nutrition dietary intervention (DN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e①②③\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNivedita Pavithran, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=18 / C=18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-glycemic index dietary intervention (LGI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e③⑤⑥\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMa Rongwei, 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina (Shijiazhuang)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=62 / C=60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePersonalized medical nutrition therapy (MNT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e①②③④⑤⑥⑦\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRosario, 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=94 / C=91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMulti-factor + Mediterranean diet (EMID)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e③\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWang Ruiping, 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina (Kunming)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT1=27 / T2=29 / C=29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-glycemic index dietary intervention (LGI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWater-soluble dietary fiber intervention (WSDF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e①②③④⑤⑥\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWang Xia, 2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina (Nanjing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=50 / C=50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-glycemic index \u0026amp; low-glycemic load dietary intervention (LGI+LGL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e①②③⑤⑥⑦\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChang Na, 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina (Jilin)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=36 / C=39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePersonalized medical nutrition therapy (MNT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e②③⑦\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQuan Xiaojuan, 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina (Shaanxi)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=50 / C=50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePersonalized medical nutrition therapy (MNT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e①③⑤⑥\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYao Li, 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina (Ningxia)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=48 / C=47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePCPA dietary intervention (PCPA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e⑤⑥\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMa Li, 2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina (Ningxia)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=48 / C=47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePCPA dietary intervention (PCPA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e①⑦\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChen Min, 2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina (Shanghai)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=62 / C=79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-glycemic index dietary intervention (LGI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e①②③⑤\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHan Mingming, 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina (Tianjin)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=9 / C=9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePersonalized medical nutrition therapy (MNT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e①②\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eXu Danfeng, 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina (Shanghai)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eT=62 / C=61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePersonalized medical nutrition therapy (MNT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRoutine diabetes dietary intervention (UC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e①②③④⑤⑥\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003eOutcome Indicators:\u003c/p\u003e\n \u003cp\u003e① FPG (fasting plasma glucose) ② 2hPG (2-hour postprandial glucose) ③ HbAlc (glycosylated hemoglobin)④ HOMA-IR (homeostasis model assessment-insulin resistance)⑤ TC (total cholesterol) ⑥ TG (triglycerides)⑦ BMI (body mass index)\u003c/p\u003e\n \u003cp\u003eAbbreviations:\u003c/p\u003e\n \u003cp\u003eUC: Routine diabetes dietary intervention; LCD: Low-carbohydrate dietary intervention\u003c/p\u003e\n \u003cp\u003eLFD: Low-fat dietary intervention; EAAD: East Asia alternative diet; KFEM: Korean Food Exchange Mode; MNT: Personalized medical nutrition therapy; CHO: Carbohydrate counting\u003c/p\u003e\n \u003cp\u003eDN: Digital nutrition dietary intervention; LGI: Low-glycemic index dietary intervention\u003c/p\u003e\n \u003cp\u003eEMID: Multi-factor + Mediterranean diet intervention; WSDF: Water-soluble dietary fiber intervention; LGI+LGL: Low-glycemic index \u0026amp; low-glycemic load dietary intervention; PCPA: PCPA dietary intervention\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 \u0026nbsp;Included Study Quality Evaluation (Cochrane Handbook5.3.0)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eIncluded Study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eSelection Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003ePerformance Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eDetection Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eAttrition Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eReporting Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eOther Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eJadad Score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eYang Bing,2021[8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eSang-Man Jin2021[9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eYuka Omura2021[10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eChen Xianghua, 2020[11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eLi Xiaotao, 2020[12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eXu Yan, 2020[13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eNivedita 2020[14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eMa Rongwei2019[15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eRosario 2019[16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eWang Ruiping2015[17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eWang Xia 2015[18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eChang Na2014[19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eQuan Xiaojuan2014[20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eYao Li2013[21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eMa Li2012[22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eChen Min 2012[23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eHan Mingming2011[24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eXu Danfeng2010[25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 577px;\"\u003e\n \u003cp\u003eNote: 1 = Low risk; 2 = High risk; 3 = Unclear.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Effects of the Interventions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"549\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 232px;\"\u003e\n \u003cp\u003eThe SUCRA ranking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eThe inconsistency test (\u0026gt;0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003enode-splitting method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eFPG[17, 20-22, 24, 26, 27, 29, 31, 32, 34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e⑥(77.6%)\u0026gt;⑬(61.3%)\u0026gt;②(59.4%)\u0026gt;⑧(58.4%)\u0026gt;⑦(55.6%) \u0026gt; ③(54.6%) \u0026gt;⑫(53.3%) \u0026gt;⑨(43.8%)\u0026gt;①(19.9%) \u0026gt;⑪(16.1%).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eP=0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eP\u0026gt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eThe consistency model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2hPG[17, 21, 22, 24, 26-28, 32-34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e⑨(62.1%) \u0026gt; ⑥(61.0%) \u0026gt; ⑪(52.1%) \u0026gt; ③ (50.7%) \u0026gt; ⑫(49.5%) \u0026gt; ⑧ (49.2%) \u0026gt; ②(49%) \u0026gt; ①(26.4%).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eP=0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eP\u0026gt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eThe consistency model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eHbA1c[17-29, 32, 34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e⑧(84.6%) \u0026gt; ⑥ (75.4%) \u0026gt; ⑫(72.1%) \u0026gt;⑦ (71.1%) \u0026gt; ⑨(65.3%) \u0026gt; ⑪(52.4%) \u0026gt; \u0026nbsp; \u0026nbsp; ③(41.2%) \u0026gt; ②(39.5%) = ⑩(39.5%) \u0026gt; \u0026nbsp; \u0026nbsp; ①(35.9%) \u0026gt; \u0026nbsp;⑤(17.1%) \u0026gt; East Asian \u0026nbsp;④(6%).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eP=0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eP\u0026gt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eThe consistency model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eHOMA-IR[24, 26, 34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e⑨(96.9%) \u0026gt; ⑪(64.4%) \u0026gt; ⑥ (32.8%) \u0026gt;①(5.9%).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eP=0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eP\u0026gt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eThe consistency model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTC[17, 20, 23, 24, 26, 27, 29, 30, 32, 34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e⑫(88.3%) \u0026gt; ⑥(70.1%) \u0026gt; ⑬ (46.3%) \u0026gt; ⑦(45.1%) \u0026gt; ⑨(45.0%) \u0026gt; ① (34.0%) \u0026gt; ⑪ (21.3%).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eP=0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e(P\u0026gt;0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eThe consistency model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTG[17, 20, 23, 24, 26, 27, 29, 30, 32, 34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e⑫(80.6%) \u0026gt; ⑨ (71.0%) \u0026gt; ⑥ (57.8%) \u0026gt;⑪(45.4%) \u0026gt; ⑬(26.5%) \u0026gt; ① (6.4%).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eP=0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e(P\u0026gt;0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eThe consistency model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eBMI[21, 24, 27, 28, 31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003e⑫(99.8%) \u0026gt;②(62.6%) \u0026gt; ⑥(42.1%) \u0026gt;⑬(28.0%) \u0026gt; ① (17.5%).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eP=0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e(P\u0026gt;0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eThe consistency model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 549px;\"\u003e\n \u003cp\u003e①conventional diabetes diet ②low-carb diet ③low-fat diet④East Asian alternative diet⑤Korean food exchange model⑥MNT⑦carbohydrate counting⑧digital dietary patterns ⑨LGI diet⑩multi-factor Mediterranean diet intervention ⑪water-soluble dietary fiber intervention ⑫LGI+LGL⑬PCPA dietary intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Summary table of GRADE evidence\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"670\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eCertainty assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e№；Patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eOutcome Indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eStudy Design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eRisk of Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eInconsistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eIndirectness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eImprecision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eOther Considerations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e[Intervention]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e[Control]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eRelative (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eAbsolute (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eCertainty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eImportance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eFPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eRandomized trials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eNot serious a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eSMD 0.75 SD lower (0.88 lower to 0.61 lower)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e⨁⨁⨁⨁ High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eCritical\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e2hPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eRandomized trials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eNot serious a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eSMD 0.62 SD lower (0.76 lower to 0.47 lower)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e⨁⨁⨁⨁ High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eCritical\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eHbAlc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eRandomized trials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eNot serious a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eSMD 0.45 SD lower (0.45 lower to 0.33 lower)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e⨁⨁⨁⨁ High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eCritical\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eRandomized trials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eSerious a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eSMD 0.39 SD lower (0.54 lower to 0.25 lower)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e⨁⨁⨁◯ Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eImportant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eRandomized trials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eSerious a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eSMD 0.59 SD lower (0.75 lower to 0.43 lower)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e⨁⨁⨁◯ Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eImportant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eRandomized trials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eNot serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eSMD 0.28 SD lower (0.45 lower to 0.1 lower)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e⨁⨁⨁⨁ High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eImportant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\" valign=\"top\" style=\"width: 670px;\"\u003e\n \u003cp\u003eNote:\u003cbr\u003e\u0026nbsp;CI: Confidence interval; MD: Mean difference; OR: Odds ratio; SMD: Standardized mean difference\u003cbr\u003e\u0026nbsp;(a) Differences in unit conversions or variations in population/intervention may introduce heterogeneity across studies.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 Diabetes, Nutritional Interventions, Network Meta-Analysis, Randomized Controlled Trials, Glycemic Control","lastPublishedDoi":"10.21203/rs.3.rs-6499801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6499801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNumerous 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-hour Postprandial Glucose (2hPG), HbA1c, HOMA-IR, Total Cholesterol (TC), Triglycerides (TG), and BMI, providing evidence to guide clinical nursing.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted an NMA of RCTs (Prospero registration: CRD42023429616) examining dietary interventions for T2DM, searching databases from January 1, 2010, to 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 GRADE and CINeMA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur initial search identified 301,997 articles; 18 RCTs involving 1,687 patients met our criteria. Twelve dietary interventions, including MNT, digital models, and LGI\u0026thinsp;+\u0026thinsp;LGL diets, were analyzed. Superior glycemic control was observed in some diets according to SUCRA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with outcomes ranging from moderate to high quality.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMNT, LGI diets, and digital models show efficacy in improving key T2DM metrics. LGI\u0026thinsp;+\u0026thinsp;LGL diets potentially reduce TC, TG, and BMI, while low GI diets best improve HOMA-IR. These results support the effectiveness of these interventions, though further large-scale, multi-center RCTs are needed to confirm long-term safety and effects.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eCRD42023429616\u003c/p\u003e","manuscriptTitle":"Effects of 12 Nutritional Interventions on Type 2 diabetes：A Systematic Review with Network Meta-Analysis of Randomized Trials Short Running Title: Effectiveness of 12 Diets in T2DM Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 14:46:15","doi":"10.21203/rs.3.rs-6499801/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-23T04:23:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T03:41:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-21T14:01:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-11T20:37:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4716226083308207977939481347618959993","date":"2025-05-06T10:44:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216829029727924144785193349657667395443","date":"2025-05-06T07:57:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79010096131657102657633536417681019512","date":"2025-05-01T07:30:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-01T01:42:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-29T13:39:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-23T05:41:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nutrition \u0026 Metabolism","date":"2025-04-22T03:09:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a17d797d-323b-4622-82dd-80fb5ab2b01f","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T16:10:33+00:00","versionOfRecord":{"articleIdentity":"rs-6499801","link":"https://doi.org/10.1186/s12986-025-00968-3","journal":{"identity":"nutrition-and-metabolism","isVorOnly":false,"title":"Nutrition \u0026 Metabolism"},"publishedOn":"2025-08-07 15:57:02","publishedOnDateReadable":"August 7th, 2025"},"versionCreatedAt":"2025-05-08 14:46:15","video":"","vorDoi":"10.1186/s12986-025-00968-3","vorDoiUrl":"https://doi.org/10.1186/s12986-025-00968-3","workflowStages":[]},"version":"v1","identity":"rs-6499801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6499801","identity":"rs-6499801","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}