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Journal of Diabetes Investigation logoLink to Journal of Diabetes Investigation
. 2022 Dec 13;14(2):263–288. doi: 10.1111/jdi.13935

Comparative efficacy of different eating patterns in the management of type 2 diabetes and prediabetes: An arm‐based Bayesian network meta‐analysis

Ben‐tuo Zeng 1, Hui‐qing Pan 2, Feng‐dan Li 3, Zhen‐yu Ye 1, Yang Liu 1,✉, Ji‐wei Du 4,5,✉
PMCID: PMC9889690  PMID: 36514864

ABSTRACT

Aims/Introduction

Diet therapy is a vital approach to manage type 2 diabetes and prediabetes. However, the comparative efficacy of different eating patterns is not clear enough. We aimed to compare the efficacy of various eating patterns for glycemic control, anthropometrics, and serum lipid profiles in the management of type 2 diabetes and prediabetes.

Materials and Methods

We conducted a network meta‐analysis using arm‐based Bayesian methods and random effect models, and drew the conclusions using the partially contextualized framework. We searched twelve databases and yielded 9,534 related references, where 107 studies were eligible, comprising 8,909 participants.

Results

Eleven diets were evaluated for 14 outcomes. Caloric restriction was ranked as the best pattern for weight loss (SUCRA 86.8%) and waist circumference (82.2%), low‐carbohydrate diets for body mass index (81.6%), and high‐density lipoprotein (84.0%), and low‐glycemic‐index diets for total cholesterol (87.5%) and low‐density lipoprotein (86.6%). Other interventions showed some superiorities, but were imprecise due to insufficient participants and needed further investigation. The attrition rates of interventions were similar. Meta‐regression suggested that macronutrients, energy intake, and weight may modify outcomes differently. The evidence was of moderate‐to‐low quality, and 38.2% of the evidence items met the minimal clinically important differences.

Conclusions

The selection and development of dietary strategies for diabetic/prediabetic patients should depend on their holistic conditions, i.e., serum lipid profiles, glucometabolic patterns, weight, and blood pressure. It is recommended to identify the most critical and urgent metabolic indicator to control for one specific patient, and then choose the most appropriate eating pattern accordingly.

Keywords: Diabetes mellitus type 2, Medical nutrition therapy, Prediabetic state

This network meta‐analysis of 107 trials and nearly 9,000 participants provided conclusions on the rankings of popular dietary patterns for glycemic control, weight, blood pressure, and serum lipid profile outcomes. For each outcome, clinicians can use this work to find the most appropriate and effective diet, benefiting from this study. Heterogeneity and sensitivity should be considered, and more high‐quality RCTs for new dietary patterns are needed.

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INTRODUCTION

It was estimated that 10.5% of people aged 20–75 suffered from diabetes mellitus globally, where over 90% were type 2 diabetes (T2DM) 1 . They spend about 966 billion US dollars of health expenditure per year 1 . Since type 2 diabetes has proven to be preventable and controllable 2 , the remission of a prediabetic state (PreD), or impaired glucose tolerance (IGT), was also of concern and was included in the comprehensive prevention of the incidence of type 2 diabetes.

Beyond medications, lifestyle management is the more cost‐effective for type 2 diabetes/prediabetes patients with strong clinical evidence 3 , 4 , 5 , where eating patterns play the leading role. Various patterns of different nutrients and food groups have been investigated and applied to the treatment and management of type 2 diabetes/prediabetes, from the very high‐fat diet in the 18th century 6 to the pattern recommended by American Diabetes Association (ADA) in 2003 7 . From an evidence‐based perspective, hundreds of random controlled trials (RCT), cohorts, and related systematic reviews have quantified the efficacy of popular and widely used eating patterns 8 , 9 , 10 , 11 , 12 , 13 .

However, there are variances in the effectiveness of the diets across different outcomes, e.g., blood glucose, weight, and cardiovascular risk factors. Diabetes Canada guidelines 4 summarized the properties of dietary interventions, pointing out the differences among diets. Consequently, current guidelines strongly recommend an individualized medical nutrition therapy under the supervision of dietitians and multidisciplinary professionals 3 , 4 , 5 . However, how to choose and apply appropriate dietary patterns for professionals remains to be a question, due to the lack of direct evidence comparing the relative efficacy of the interventions. Whether a specific diet is suitable for an individual with specific laboratory profiles and situations is not clear enough, though high‐quality evidence of several patterns has been drawn.

It is not cost‐effective to carry out multi‐arm trials directly comparing several diets. Thus, it is crucial to conduct a network meta‐analysis to synthesize current evidence. Previous network meta‐analyses 14 , 15 have assessed a number of patterns, but the authors only included a limited number of studies and outcomes. Furthermore, short‐term trials were not considered in the analyses, but a short‐term effect may be more common for some patterns 16 . Therefore, this study aimed to evaluate the relative efficacy of different eating patterns on glycemic control, anthropometrics, and serum lipid profiles in the management of patients with type 2 diabetes/prediabetes, and to conclude evidence to promote clinical decision‐making.

MATERIALS AND METHODS

Study design

We conducted an arm‐based Bayesian network meta‐analysis of randomized controlled trials, following the Cochrane Handbook 17 . We reported results according to the Preferred Reporting Items for Systematic reviews and Meta‐Analyses Incorporating Network Meta‐analysis (PRISMA‐NMA) 18 . A protocol was prepared and registered a priori in PROSPERO (CRD42021278268).

Eligibility criteria

We selected peer‐reviewed articles and thesis according to the PICOS principle. Eligibility criteria are displayed in Table 1.

Table 1.

Eligibility criteria

Inclusion Exclusion
Type Criteria Type Criteria
P Adults with type 2 diabetes mellitus or prediabetes I Any prescribed between‐group difference on exercise, antihyperglycemic medications, insulin injection, or other co‐interventions; added a single supplement, or single specified food which did not provide macronutrients; or use meal replacement to provide an appreciable percentage of energy intake; or total energy intake (TEI) <800 kcal/d (3.3 MJ/d); or the adjustment of intervention during the trial
I Contain at least one arm of the interventions as follows: caloric restriction (CR), high‐fiber diet (fiber), dietary approaches to stop hypertension (DASH), high‐protein diet (HPD), high‐fat diet (HFD), low‐carbohydrate diet (LCD), low‐glycemic‐index diet (LGID), Mediterranean diet (Med), Nordic diet (ND), Paleolithic diet (Paleo), Portfolio diet (PfD), and vegetarian/vegan/plant‐based diet (VD). The macronutrients and food group intake can be as prescribed or as actual
C Contained standard diabetes diet, e.g. ADA 2003 diet 7 ; ad libitum; general nutrition counselling; or placebo (no intervention); or contain two or more intervention arms
O Reported at least one outcome as follows, where fasting plasma glucose (FPG) was the primary outcome of this meta‐analysis: glycemic control, including FPG, glycated hemoglobin (HbA1c), fasting insulin (FIns), and insulin resistance (IR); anthropometrics, including weight, body mass index (BMI), waist circumference (WC), waist‐to‐hip ratio (WHR), and body fat rate (BFR), systolic blood pressure (SBP), and diastolic blood pressure (DBP); serum lipid profiles, including triacylglycerol (TG), total cholesterol (TC), low‐density lipoprotein cholesterol (LDL), and high‐density lipoprotein cholesterol (HDL); renal function, including serum creatinine, serum urea, serum uric acid and (estimated) glomerular filtration rate; other dichotomous outcomes, including attrition rate, remission of type 2 diabetes mellitus, incidence of hypoglycemia, incidence of drug or insulin discontinuation, incidence of type 2 diabetes mellitus from Prediabetes Duration less than 4 weeks or 1 month for parallel RCTs or any phase of crossover RCTs; or intermittent intervention
S single‐arm or self‐controlled trials
Other Data availability: trials not completed, or without data analysis and published reports; or articles with inappropriate or insufficient data
S Randomized controlled trials (RCT)
Other Language: English or Chinese
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P, participants; I, interventions; C, comparators; O, outcomes; S, study types.

Search strategy

We conducted searches of databases and trial registers, including PubMed, Web of Science, Embase, CINAHL and Open Dissertation, ProQuest, Scopus, Global Index Medicus, Cochrane Central Register of Controlled Trials, Clinicaltrials.gov, SinoMed, WanFang Med, and CNKI. All publications from the inception to 13 October 2021 were initially retrieved. An updated search was conducted on 17 March 2022 using Scopus and Google Scholar to identify the latest relevant articles. The full search strategy can be found in File S1.

Data selection and extraction

All references identified from the search were imported into EndNote 20 (Clarivate, PA, USA) to move duplicates. After automatic exclusion by filtering title using excluding terms, the reviewers (B.‐T.Z., H.‐Q.P., and F.‐D.L.) assessed the eligibility in the order of title, abstract, and full text. Each reference was decided upon independently by at least two reviewers, and arisen discrepancies were discussed and decided upon by the authors together.

We used MySQL 8.0 (Oracle Corporation, TX, USA) for data extraction and management, and critical information was extracted (see File S2 for fields in MySQL tables). Two authors (B.‐T.Z. and Z.‐Y.Y.) independently extracted the data and checked the consistency.

R 4.1.3 (R Foundation, Vienna, Austria) and Microsoft Excel 2019 (Microsoft Corporation, WA, USA) were used for data conversion and imputation. For continuous outcomes, we calculated the change from baseline and its standard deviation (SD) if not reported by the article. Correlation coefficients for changes from baseline and for crossover RCTs were estimated using reported SDs from included studies (File S3). The median and interquartile range was converted into the mean and SD using methods from Luo 19 and Wan 20 after testing for skewness using methods from Shi et al. 21 . WebPlot Digitizer 22 was applied for extracting data from figures. Ultimately, R package ‘mice’ 23 was used for the imputation of missing values of covariates for meta‐regression.

Risk of bias assessment

The Risk of Bias 2 tool 24 and Risk of Bias 2 for crossover trials 25 were employed to assess the risk of bias (RoB) of parallel and crossover RCTs, respectively. Two reviewers (B.‐T.Z. and H.‐Q.P.) assessed the RoBs independently, with all arisen divergences discussed and consensuses reached.

Data synthesis

Our study synthesized evidence through an arm‐based Bayesian network meta‐analysis in a random effect model. We use R package ‘gemtc’ 1.0‐1 for meta‐analysis, inconsistency test, heterogeneity test, meta‐regression, and sensitivity analysis 26 , 27 . Markov chain Monte Carlo sampling was performed using JAGS 4.3.0 via R package ‘rjags’ 4.12 28 , 29 . Comparison‐adjusted funnel plots, Egger's test, and Begg's test were performed to detect publication bias under a frequentist framework and random effect model using R package ‘netmeta’ 2.1‐0 and ‘metafor’ 3.4‐0 30 , 31 .

Continuous outcomes were presented as the mean difference (MD) or the difference in percentage change from baseline (Percentage MD, PMD, for fasting insulin and insulin resistance) and 95% credible intervals (95% CrI), while relative risk (RR) and 95% CrI were for dichotomous variables.

Quality of the evidence

We rated the quality of evidence of comparisons of experimental diets and control diets based on the GRADE Working Group's network meta‐analysis evidence rating strategies 32 and the GRADE handbook 33 . Conclusions were drawn according to the partially contextualized framework by the GRADE workgroup 34 , where minimal clinically important differences (MCID) and thresholds for moderate and large beneficial/harm effects were identified based on previous studies 13 , 35 , 36 , 37 and consensuses among reviewers.

RESULTS

We identified 9,358 publications and registrations from the initial search, and 176 from the updated search. In total, 111 publications 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 were eligible, where 107 independent studies were identified (Figure 1). All the items excluded via full‐text screening and their reason for exclusion are listed in File S4.

Figure 1.

Figure 1

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PRISMA flowchart of data selection.

Among our prescribed outcomes, data of FPG, HbA1c, FIns, IR, weight, BMI, WC, SBP, DBP, TG, TC, HDL, LDL, and attrition rate were sufficient to form networks and perform a meta‐analysis. However, other outcomes were not analyzed due to scarce data.

Study characteristics

The 107 included studies contained 8,909 participants for data analysis and 8,583 completers. A total of ten experimental diets and 223 arms was reported. The studies reported efficacy of CR, DASH, fiber, HFD, HPD, LCD, LGID, Med, Paleo, and VD, but ND and PfD were not included.

The characteristics of the studies are displayed in Table 2. We included 16 crossover and 91 parallel RCTs. Among them, seven were multi‐arm, and six were multicenter. Four studies reported their outcomes in two or more publications. Only five studies focused on PreD population, considering that there was no significant difference among PreD and T2DM RCTs, we did not distinguish them in the meta‐analysis. Fundings and conflicts of interest of the studies are listed in File S5.

Table 2.

Characteristics of included studies

Study information Intervention Arms and characteristics
Basic information Type Objective RoB Duration (week) Intensity† Arm Size Participants‡ Nutrition intake §
Study ID Origin Sex ratio Age (years) BMI (kg/m2) TEI Pr Fat CHO GI fiber
Al‐Jazzaf 2007 38 Kuwait S P T2DM SC 6 3 control 16 0.563 51.9 ± 12.8 34.6 ± 4.8 2270 17 33 49 73 25
LGID 16 0.438 55.3 ± 8.9 33 ± 3 2376 17 36 47 61 25
LGID 18 0.611 48.2 ± 8 34.4 ± 5.4 1806 18 35 51 62 26
Azadbakht 2011 39 Iran S X T2DM H 8 2 control 31 0.581 2165 15 28 57
DASH 31 0.581 2189 16 29 55
Bahado‐Singh 2015 40 Jamaica S P T2DM SC 24 3 control 24 43 ± 2.3 27.1 ± 0.8 30 32 45 25
LGID 29 42.5 ± 2 26.11 ± 0.78 30 30 45 32
Barnard 2006/9 41 , 42 USA S P T2DM SC 74 2 control 50 0.660 54.6 ± 10.2 35.9 ± 7 1422 21 34 47 18
VD 49 0.551 56.7 ± 9.8 33.9 ± 7.8 1366 15 22 66 27
Brand 1991 43 Australia S X T2DM SC 12 3 control 16 0.375 62 ± 9 25 ± 5 1623 19 31 46 90 26
LGID 16 0.375 62 ± 9 25 ± 5 1654 22 30 44 77 26
Brehm 2009 44 USA S P T2DM SC 52 3 control 52 0.673 56.5 ± 8.91 35.9 ± 3.34 1550 18 28 54
HFD 43 0.605 56.5 ± 8.91 35.9 ± 3.34 1550 16 38 46
Breukelman 2019/21 45 , 46 South Africa S P T2DM H 16 2 control 13 0.692 58.3 ± 5.53 38.2 ± 10.66
LCD 10 0.600 54.2 ± 12.67 38.9 ± 6.06 10
Brinkworth 2004 47 Australia S P T2DM H 64 2 control 19 0.632 62.7 ± 7.85 33.3 ± 5.67 15 30 55 30
HPD 19 0.579 60.9 ± 7.85 33.6 ± 5.23 30 30 40 30
Brunerova 2007 48 Czech S P T2DM H 12 3 control 13 51.2 ± 3.3 34.7 ±  ± 3.6 1800 10 30 60 20
HFD 14 54.7 ± 3.8 33.4 ± 4.5 1800 10 45 45 20
Cao 2011 49 China S P T2DM SC 52 2 control 45 0.378 53.2 ± 6.5 35.9 ± 6.5 1860 20 32 48
LCD 45 0.333 54.2 ± 6.2 35.4 ± 6.2 1980 22 45 33
Ceriello 2014 50 Spain S P T2DM SC 12 2 control 12 0.333 29.2 ± 3.81
Med 12 0.250 29.8 ± 4.85
Chandalia 2000 51 USA S X T2DM H 6 4 control 13 0.077 61 ± 9 32.3 ± 3.9 2308 15 30 55 24
fiber 13 0.077 61 ± 9 32.3 ± 3.9 2308 15 30 55 50
Chen 2020a 52 China (Taiwan) S P T2DM SC 76 3 control 42 0.619 64.1 ± 7.4 26.55 ± 3.69 1469 20 41 41
LCD 43 0.605 63.1 ± 10.5 27.31 ± 4.53 1430 23 46 25
Chen 2020b 53 China S P PreD SC 12 2 control 43 0.628 51.9 ± 11.8 25.2 ± 4
LCD 57 0.596 54.9 ± 11.9 25.5 ± 2.9 25
Choi 2013 54 South Korea S P T2DM SC 12 2 control 38 56 ± 8.6 26.74 ± 2.16 1785 20 20 60
CR 38 55.5 ± 7 27.36 ± 3 1396 20 20 60
Coppell 2010 55 New Zealand S P T2DM SC 24 2 control 48 0.563 58.4 ± 8.8 34.3 ± 5.8
fiber 45 0.622 56.6 ± 8.8 35.1 ± 6.1 15 30 55 40
Coulston 1989 56 USA S X T2DM SC 6 4 control 8 0.375 66 ± 8.49 25.5 ± 2.26 20 20 60
LCD 8 0.375 66 ± 8.49 25.5 ± 2.26 20 40 40
Daly 2006 57 UK M P T2DM SC 12 2 control 39 0.529 59.1 ± 10.57 36.7 ± 9 1434 21 33 45 14
LCD 40 0.510 58.2 ± 11.07 35.4 ± 5 1290 26 40 34 10
Davis 2009 58 USA S P T2DM SC 52 2 control 50 0.260 53 ± 7 37 ± 6 1810 19 31 50 17
LCD 55 0.182 54 ± 6 35 ± 6 1642 23 44 33 15
Ding 2010 59 , 60 China S P T2DM H 24 2 control 37 0.725 58.7 ± 7.74 29.94 ± 3.85 2040 14 37 59
LGID 39 0.625 60.65 ± 6.92 30.24 ± 3.43 2012 14 30 59
Durrer 2021 61 Canada S P T2DM L 12 4 control 90 0.567 59 ± 8 35.1 ± 5.3 1667 22 40 40
LCD 98 0.561 58 ± 11 36 ± 6 984 43 31 27
Elhayany 2010 62 Israel M P T2DM H 52 2 control 55 0.509 56 ± 6.1 31.8 ± 3.3 2221 20 30 50 15
Med 63 0.444 57.4 ± 6.1 31.1 ± 2.8 2221 20 30 50 30
LCD 61 0.492 55.5 ± 6.5 31.4 ± 2.8 2221 20 45 35 30
Esposito 2009 63 Italy S P T2DM L 208 2 control 107 0.514 51.9 ± 10.7 29.5 ± 3.6 1650
Med 108 0.500 52.4 ± 11.2 29.7 ± 3.4 1650
Fabricatore 2011 64 USA S P T2DM SC 40 3 control 39 0.795 52.5 ± 8.12 35.8 ± 4.37 1676 19 33 50 65 16
LGID 40 0.800 52.8 ± 8.85 36.7 ± 5.06 1624 18 40 41 57 18
Fan 2010 66 China S P T2DM SC 12 2 control 60
LGID 60
Fan 2013 65 China S P T2DM SC 12 2 control 25 0.462 69.1 ± 8.2 25.8 ± 2.5
LGID 26 0.462 69.3 ± 7.9 25.7 ± 2.7
Fang 2016 68 China S P T2DM SC 24 2 control 95 0.516 67.3 ± 9.8 15 25 60
LGID 105 0.495 68.2 ± 9.2 15 25 60 55
Fang 2019 67 China S P T2DM SC 24 2 control 283 0.481 56.78 ± 5.34 1835
CR 284 0.451 57.78 ± 5.14 1080
Gannon 2003 70 USA S X T2DM SC 5 4 control 12 0.167 61 31 2266 15 34 53 26
HPD 12 0.167 61 31 2235 30 30 40 29
Gannon 2004 69 USA S X T2DM H 5 4 control 8 0.000 63.3 31 2825 15 30 55 24
LCD 8 0.000 63.3 31 2825 30 50 20 36
Goldstein 2011 71 Israel S P T2DM SC 52 2 control 26 0.538 55 ± 8 33.3 ± 3 1937 19 40 43
LCD 26 0.500 57 ± 9 33.1 ± 3.6 1725 24 58 20
Gram‐Kampmann 2022 72 Denmark S P T2DM SC 24 2 control 20 0.591 55.2 ± 12.66 35.2 ± 6.57 1600 23 28 48 30
LCD 44 0.551 57.3 ± 6.3 32.5 ± 6.3 1642 23 63 13 16
Guldbrand 2012 73 Sweden M P T2DM SC 104 2 control 31 0.581 62.7 ± 11 33.8 ± 5.7 1700 24 31 44
LCD 30 0.533 61.2 ± 9.5 31.6 ± 5 1700 20 47 31
Guo 2014 74 China S P T2DM SC 24 2 control 120 63.2 ± 13.1 26.98 ± 4.15
LGID 123 63.2 ± 13.1 26.42 ± 3.65
Han 2021 75 China S P T2DM SC 24 2 control 61 0.535 53.74 ± 3.48 25.39 ± 3.95 1798 17 28 55
LCD 60 0.333 49.13±13.06 1796 29 58 14
Hashemi 2019 76 Iran S P T2DM SC 12 2 control 40 0.600 1775 18 30 52
DASH 35 0.629 1771 27
He 2017 77 China S P T2DM SC 12 3 control 75 0.413 24.47 ± 3.06
LGID 75 0.467 24.92 ± 3.01 55
Heilbronn 2002 78 Australia S P T2DM SC 8 2 control 21 0.429 57.5 ± 9.6 33.4 ± 4.2 1436 22 17 61 30
LGID 24 0.542 56 ± 9.4 34.2 ± 8.7 1442 22 18 59 29
Hockaday 1978 79 UK S P T2DM SC 52 2 control 39 0.487 50 1500 20 26 54
LCD 54 0.407 53 1500 20 40 40
Hu 2018 80 China S P PreD SC 24 3 control 31 0.452 50.4 ± 3.2 25.9 ±  ± 2.9 15 25 60
LCD 29 0.517 51.9 ± 4.2 25.9 ± 4 20 40 40
Huang 2016 81 China S P T2DM SC 12 2 control 40 0.350 67.43 ± 8.43 25.79 ± 2.86
LGID 40 0.300 67.84 ± 8.71 25.92 ± 2.63
Ikem 2007 82 Nigeria S P T2DM SC 8 2 control 17 0.412 58.2 ± 8.8 24.5 ± 3.4 20 20 60
fiber 35 0.543 57.6 ± 6.3 23.8 ± 3.3 20 20 60 40+
Iqbal 2010 83 USA S P T2DM SC 104 2 CR 74 0.054 60 ± 9.5 36.9 ± 5.3 1574 18 34 47 15
LCD 70 0.157 60 ± 8.9 38.1 ± 5.5 1610 17 34 48 14
Itsiopoulos 2011 84 Australia S X T2DM SC 12 4 control 27 0.407 59 30.7 ± 4.9 1787 18 32 46
Med 27 0.407 59 30.7 ± 4.9 2229 14 39 44
Jenkins 2008 85 Canada S P T2DM L 24 3 control 104 0.394 61 ± 9 31.2 ± 5.8 1690 21 31 48 84
LGID 106 0.387 60 ± 10 30.6 ± 6 1706 21 33 44 70
Jimenez‐Cruz 2003 86 Mexico S X T2DM H 6 2 control 14 0.571 59 ± 9 29.6 ± 5.8 1561 18 20 64 56 25
LGID 14 0.571 59 ± 9 29.6 ± 5.8 1422 21 23 60 44 34
Jönsson 2009 87 Sweden S X T2DM SC 12 2 control 13 0.231 64 ± 6 30 ± 7 1878 20 34 42 55 26
Paleo 13 0.231 64 ± 6 30 ± 7 1581 24 39 32 50 21
Kahleova 2011 88 Czech S P T2DM SC 24 4 control 37 0.514 57.7 ± 4.9 35 ± 4.6 1795 18 37 45 21
VD 37 0.541 54.6 ± 7.8 35.1 ± 6.1 1736 16 37 50 25
Krebs 2012 89 New Zealand M P T2DM L 104 2 control 150 0.656 58 ± 9.2 36.7 ± 6.4 1695 20 30 48 24
HPD 144 0.541 57.7 ± 9.9 36.6 ± 6.7 1714 21 33 46 23
Lasa 2014 90 Spain M P T2DM SC 52 2 control 67 0.522 67.2 ± 6.8 29.8 ± 2.8 2198 17 39 41
Med 74 0.608 67.4 ± 6.3 29.4 ± 2.9 2463 17 42 39
Med 50 0.680 67.1 ± 4.8 30.1 ± 3.1 2479 17 44 37
Lee 2016 91 South Korea S P T2DM SC 12 2 control 47 0.745 58.3 ± 7 23.1 ± 2.4 1560 17 20 64 25
VD 46 0.870 57.5 ± 7.7 23.9 ± 3.4 1496 15 19 72 34
Li 2011 94 China S P T2DM SC 12 2 control 78 51.8 ± 6.2 15 25 60
LGID 78 51.8 ± 6.2 15 25 60
Li 2021 92 China S P T2DM SC 24 2 control 38 0.447 44.62 ± 1.3 35.38 ± 6.27 60
LCD 38 0.474 44.53 ± 1.28 35.41 ± 6.25 30
Li 2022 93 China S P T2DM SC 12 3 control 29 37.1 ± 14.02 29.75 ± 6.07 1500 16 12 73
LCD 24 36.5 ± 13.67 29.04 ± 5.81 1500 16 78 11
Liu 2011 96 China S P T2DM H 12 3 control 56 20 25 55
LGID 40 65
Liu 2016 95 China S P T2DM L 12 3 control 30 0.500 49.7 ± 5.48 21.17 ± 1.37 1800 17 29 54
control 31 0.484 50.2 ± 6.12 21.42 ± 1.34 1800 17 29 54
LCD 30 0.500 49.8 ± 6.02 21.72 ± 1.37 1800 28 30 40
LCD 31 0.516 51.9 ± 5.01 21.21 ± 1.34 1800 28 30 40
Liu 2020 97 China S P T2DM SC 4 3 CR 49 0.429 66.7 ± 8.7 15 25 55
LCD 49 0.469 66.9 ± 8.6 20 75 5
Lousley 1984 98 UK S X T2DM SC 6 2 fiber 11 63.45 ± 7.17 1240 23 16 65 68
LCD 11 63.45 ± 7.17 1240 22 44 37 13
Luger 2013 99 Austria S P T2DM SC 12 2 control 20 0.727 63.7 ± 5.2 33.6 ± 5.3 1235 17 29 50 22
HPD 20 0.364 61 ± 5.7 33 ± 4.2 1273 26 35 38 22
Ma 2008 100 USA S P T2DM SC 52 2 control 21 0.476 51 ± 8.25 35.95 ± 6.75 1779 20 43 38 80 12
LGID 19 0.579 56.31 ± 7.85 35.58 ± 7.46 1674 20 42 38 77 12
Marco‐Benedí 2020 101 Spain S P PreD L 24 3 control 32 0.657 54.6 ± 8.11 32.3 ± 3.7 1600 18 30 52
HPD 35 0.474 56.5 ± 8.59 33.2 ± 3.63 1600 35 30 35
McLaughlin 2007 102 USA S P T2DM SC 16 2 control 15 0.400 56 ± 7 31 ± 2.4 15 45 40
LCD 14 0.429 57 ± 7 31.4 ± 2.4 15 25 60
Mehling 2000 103 Canada S P PreD H 16 2 control 11 0.818 58.8 ± 13.27 29.4 ± 7.3 1714 17 28 53 83 23
LGID 13 0.769 55.2 ± 10.82 29.7 ± 4.33 1695 19 25 55 76 36
LCD 11 0.818 6.88 ± 13.27 30.6 ± 5.64 1894 16 36 47 82 24
Mohammadi 2017 104 Iran S P T2DM SC 10 2 control 15 1.000 49.28 ± 7.75 32.45 ± 2.34 1787 12 40 48
CR 15 1.000 49.63 ± 9.57 34.57 ± 5.62 1595 14 41 45
Mollentze 2019 105 South Africa S P T2DM H 24 3 control 7 1.000 54.53 ± 6.48 40.1 ± 6.46 2477
CR 9 1.000 55.64 ± 7.72 41.3 ± 4.41 2091
Nicholson 1999 106 USA S P T2DM SC 12 4 control 4 0.500 60 1526 18 31 51 20
VD 7 0.429 51 1409 14 11 75 26
Ning 2020 107 China S P T2DM SC 52 2 control 31 0.452 57.63 ± 9.55 34.87 ± 3.25 1500 60
LCD 31 0.419 57.52 ± 9.13 34.82 ± 3.16 1500 20 50 30
Parker 2002 108 Australia S P T2DM SC 8 4 control 28 0.643 62.08 1543 16 26 55 28
HPD 26 0.654 60.32 1587 28 28 42 24
Pavithran 2020a 109 India S P T2DM SC 24 3 control 18 0.333 52 ± 7.7 27.25 ± 2.72
LGID 18 0.500 52 ± 7.7 26.81 ± 5.04 45
Pavithran 2020b 110 India S P T2DM H 24 3 control 40 0.325 51.93 ± 7.43 26.75 ± 3.29 1450 16 21 66
LGID 40 0.375 54.43 ± 7.57 26.4 ± 3.03 1511 16 24 62 45
Pedersen 2014 111 Australia S P T2DM SC 52 3 control 33 0.303 61 35 ± 4.6 1666 21 34 11
HPD 31 0.323 58 36 ± 6.12 2005 26 35 39
Perna 2019 112 Italy S P T2DM SC 13 3 control 9 0.556 67.78 ± 5.87 32.41 ± 2.91 1600 18 23 59
LCD 8 0.750 59.5 ± 9.48 30.3 ± 2.13 1600 22 46 32
Rizkalla 2004 113 France S X T2DM SC 4 2 control 12 0.000 54 ± 6.93 31 ± 3.46 2291 20 37 38 71
LGID 12 0.000 54 ± 6.93 31 ± 3.46 2222 21 37 36 39
Rock 2014 114 USA M P T2DM SC 52 4 control 67 0.473 55.5 ± 9.2 36.2 ± 4.3 20 20 60
HFD 66 0.481 57.3 ± 8.6 36.2 ± 4.7 25 30 45
Ruggenenti 2017 115 Italy S P T2DM L 24 3 control 36 0.289 59.5 ± 7.1 29.6 ± 3.8 1760 18 34 48
CR 34 0.194 60.2 ± 7.2 30 ± 3.9 1571 20 36 44
Ruggenenti 2022 116 Italy S P T2DM L 104 2 control 50 0.180 62.8 ± 8.7 32.1 ± 3.1 1783 17 43 39 21
CR 53 0.245 64.9 ± 7.5 32.3 ± 3.7 1592 18 43 39 20
Saslow 2014/7 117 , 118 USA S P T2DM L 52 2 control 16 0.889 55.1 ± 13.5 36.9 ± 6.93 1681 16 40 36
LCD 14 0.563 64.8 ± 7.7 35.9 ± 6.84 1535 25 62 19
Sato 2017 119 Japan S P T2DM SC 24 2 LCD 30 0.233 60.5 ± 10.5 27.27 ± 3.9 1371 19 34 43
CR 32 0.250 58.4 ± 10 27.11 ± 4.27 1605 16 29 49
Shen 2021 120 China S P T2DM SC 8 2 control 46 0.435 61.78 ± 7.05 23.91 ± 2.12
LGID 46 0.391 62.11 ± 6.71 24.04 ± 2.19 55
Shige 2000 121 Australia S P T2DM SC 12 3 control 12 57.5 ± 11.8 32.6 ± 4.7 1541 17 9 73
HFD 12 58.1 ± 9 33.1 ± 2.8 1596 18 32 50
Skytte 2019 122 Denmark S X T2DM H 6 3 control 28 0.286 64 ± 7.7 30.1 ± 5.2 17 33 50
LCD 28 0.286 64 ± 7.7 30.1 ± 5.2 30 40 30
Stentz 2016 123 USA S P PreD H 24 4 control 12 0.019 41.1 ± 5.89 37.4 ± 5.89 15 30 55
HPD 12 0.250 43.1 ± 4.5 40.5 ± 6.24 30 30 40
Sun 2007 124 China S X T2DM SC 4 4 control 42 0.500 68.6 ± 7.3 25.32 ± 2.7 1471 77
LGID 42 0.500 68.6 ± 7.3 25.32 ± 2.7 1493 55
Sun 2020 125 China S P T2DM SC 12 3 control 30 0.500 57.9 ± 10.4 30 45 25
LCD 30 0.467 57.6 ± 10.3 20 25 55
Tang 2021 126 China S P T2DM SC 12 3 control 45 0.444 40.18 ± 6.32 60
LCD 45 0.467 40.25 ± 6.26 1600 30
Tay 2015 127 Australia S P T2DM L 52 4 control 57 0.491 58 ± 7 35.1 ± 4.1 1700 17 30 53
LCD 58 0.362 58 ± 7 34.2 ± 4.5 1700 28 58 14
Thomsen 2022 128 Denmark S P T2DM L 6 4 control 33 0.545 67 ± 8.8 33.2 ± 5.1 2044 17 33 50 48
LCD 34 0.412 66.4 ± 6.9 33.6 ± 4.6 2058 30 40 30 36
Uusitupa 1993 129 Finland S P T2DM SC 52 2 control 46 0.391 54.16 ± 6.45 33.21 ± 4.78 1713
CR 40 0.475 52.13 ± 6.61 33.88 ± 5.51 1628
Visek 2014 130 Czech S X T2DM H 12 2 control 20 0.400 62.7 ± 5.8 32 ± 4.2 1745 18 41 37 68 18
LGID 20 0.400 62.7 ± 5.8 32 ± 4.2 1676 18 38 38 49 18
Walker 1995 131 Australia S X T2DM SC 12 2 control 24 0.625 58.3 ± 10.29 29.2 ± 3.43 1506 24 23 50 34
HFD 24 0.625 58.3 ± 10.29 29.2 ± 3.43 1554 22 36 40 25
Wang 2009a 132 China S P T2DM SC 12 2 control 53 0.528 50.1 ± 5.2 15 25 60
LGID 56 0.536 49.1 ± 5.6 15 25 60 55
Wang 2009b 135 China S P T2DM SC 24 2 control 20 56.1 ± 1.3 30.23 ± 0.34 1800 15 25 60
LCD 20 57.3 ± 1.2 30.28 ± 0.39 1800 30 50 20
Wang 2015 134 China S P T2DM SC 24 2 control 50 0.360 71.8 ± 10.6 26.05 ± 2.82 20 25 55
LGID 50 0.400 70.5 ± 10.4 25.32 ± 4.01
Wang 2018 133 China S P T2DM L 12 3 control 25 0.480 61.2 ± 11.71 24.62 ± 5.17 1732 18 26 56
LCD 24 0.458 66.79 ± 9.12 24.29 ± 3.36 1808 19 42 39
Watson 2016 136 Australia S P T2DM L 12 4 control 29 0.448 55 ± 8 34.4 ± 4.7 1421 21 22 50 29
HPD 32 0.469 54 ± 8 34.3 ± 5.4 1490 29 30 35 25
Westman 2008 137 USA S P T2DM H 24 2 LGID 29 0.793 50 ± 8.5 37.9 ± 6 1335 20 36 44
LCD 21 0.667 51.2 ± 6.1 37.8 ± 6.7 1550 28 59 13
Wolever 1992 138 Canada S X T2DM SC 6 4 control 6 0.500 63 ± 9.8 32.1 ± 5.88 1388 20 23 57 86 33
LGID 6 0.500 63 ± 9.8 32.1 ± 5.88 1388 20 23 57 58 34
Wolever 2008 139 Canada S P T2DM SC 52 4 control 48 0.500 60.4 ± 7.93 30.1 ± 4.33 1890 20 31 47 63 21
LGID 55 0.661 60.6 ± 7.48 31.6 ± 4.49 1800 21 27 52 55 36
LCD 53 0.463 58.6 ± 8.82 31.1 ± 4.41 2020 19 40 39 59 23
Wu 2020 140 China S P T2DM SC 12 2 control 52 0.442 53.03 ± 6.74 24.28 ± 3.25 1764 17 33 53
LGID 52 0.462 53.16 ± 6.9 24.53 ± 3.12 1679 19 33 51
Xue 2020 141 China S P T2DM SC 24 3 control 40 0.475 60.01 ± 2.54 25.91 ± 1.48
Med 40 0.425 55.23 ± 5.99 25.98 ± 1.72 15 25 60
Yamada 2014 142 Japan S P T2DM SC 24 2 CR 12 0.583 63.2 ± 10.2 27 ± 3 1610 17 32 51
LCD 12 0.417 63.3 ± 13.5 24.5 ± 4.3 1634 25 45 30
Ye 2021 143 China S P T2DM SC 12 3 control 50 67 ± 1.3
LGID 50 68 ± 0.9
Yu 2020 144 China S P T2DM SC 12 3 control 150 0.387 59.98 ± 4.34 21.22 ± 3.34 83
LGID 150 0.407 60.01 ± 4.58 21.25 ± 3.44 69
Zahedi 2021 145 Iran S P T2DM SC 24 2 control 123 0.772 57.8 ± 8.9 31.21 ± 2.49
Med 105 0.771 56.8 ± 9.5 30.14 ± 3.21
Zhao 2018 146 China S P T2DM SC 8 2 control 40 0.275 60 ± 3 15 25 60
LGID 40 0.325 59.1 ± 3.5 55
Zheng 2015 147 China S P T2DM SC 8 2 control 37 0.459 59.8 ± 7.2 23.9 ± 2.7 15 25 60
LGID 37 0.405 60.1 ± 6.7 24.1 ± 2.9 55
Zhou 2011 148 China S P T2DM SC 12 3 control 31 0.581 23.47 ± 3.2
LGID 31 0.710 24.31 ± 3.22
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†Intensity was defined as: 1 = no intervention, 2 = only nutrition consultations or group discussion; 3 = provide detailed menus; 4 = provide prepared/prepackaged foods; 5 = metabolic wards. ‡Age and BMI were presented as mean ± standard deviation. The sex ratio was female percentage. §Macronutrients (protein, fat, and carbohydrate) are presented as the percentage of total energy intake (TEI%). The units of total energy intake and fiber were kcal/d and g/d, respectively. S, single‐center; M, multicenter; P, parallel; X, crossover; T2DM, type 2 diabetes mellitus; PreD, prediabetes; RoB, risk of bias; H, high; SC, some concerns; L, low; CR, caloric restriction; DASH, Dietary Approaches to Stop Hypertension; fiber, high‐fiber diet; HFD, high‐fat diet; HPD, high‐protein diet; LCD, low‐carbohydrate diet; LGID, low‐glycemic‐index diet; Med, Mediterranean diet; Paleo, Paleolithic diet; VD, vegetarian, vegan or plant‐based diet; BMI, body mass index; TEI, total energy intake; Pr, protein; CHO, carbohydrate; GI, glycemic index. Data of nutrition intake were either prescribed or estimated from the mean of reported values from 24 hour self‐reported dietary records. A 60 kg average individual or 2000 kcal average TEI was used for nutrition intake estimation if needed.

Risk of bias assessment

The overall risk of bias of eligible studies was acceptable, but trials of some patterns (fiber and DASH) had a relatively high risk of bias (Table 2). 15.9% of studies were at high risk of bias (Figure 2). Notably, the risk of bias of crossover RCTs was significantly higher than the parallel (P 0.05/2 = 0.006, Mann‐Whitney test), due to the period and carryover effects. Detailed risk of bias ratings of each domain are displayed in File S6.

Figure 2.

Figure 2

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Risk of bias of included studies. aThe ‘period and carryover effects’ domain was only for crossover RCTs (n = 16), and other domains were for all included studies (n = 107).

Main outcomes

The number of nodes and comparisons varied among outcomes (Figure 3 and File S7). File S8 presented all league tables and cumulative ranking curves; File S9 showed forest plots with heterogeneity and inconsistency tests of all outcomes.

Figure 3.

Figure 3

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Efficacy of different eating patterns on glycemic control, anthropometrics, serum lipid profiles, and comparative attrition rate. I, intervention arm; C, control arm; No., number of direct comparisons; Incons., P value of inconsistency test (node‐splitting method); MD, mean difference; PMD, difference in percentage change from baseline; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; FIns, fasting insulin; IR, insulin resistance; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triacylglycerol; TC, total cholesterol; LDL, low‐density lipoprotein cholesterol; HDL, high‐density lipoprotein cholesterol. Thick dashes refer to the null value, and thin dashes refer to the MCID threshold. Unless otherwise specified using “vs”, the effect sizes were experimental patterns vs control. I 2 values were for network heterogeneity, including both direct and indirect comparisons.

Glycemic control

For glycemic control, a high‐fiber diet (fiber) was ranked as the best pattern for reducing FPG (MD −1.3 mmol/L, 95% CrI −2.3 to −0.22, SUCRA 82.7%) (Figure 3a). DASH (−1.2%, −2.2 to −0.23, SUCRA 90.5%) and LGID (−0.71%, −0.93 to −0.49, SUCRA 76.2%) had the highest probability of improving HbA1c compared with control groups (Figure 3b). The effects on reducing FPG and HbA1c were comparable.

FIns and IR were presented as PMD due to the various units reported by studies. Effects on improving insulin‐related conditions were not stable and significant because of the limited sample size. High‐fiber diets achieved a mean of 21% Fins reduction (95% CrI 5.2% to 46%) with a probability of 79.4% to be the best pattern (Figure 3c). IR was reported as the homeostatic model assessment (HOMA)1‐IR and HOMA2‐IR, among which HPD showed the best beneficial effects on improving IR (−22%, −37% to −7.0%, SUCRA 86.3%) (Figure 3d).

Anthropometrics

Calorie restriction was still one of the most effective diet patterns for weight loss (−4.1 kg, −6.1 to −2.0, SUCRA 86.8%) and WC (−4.5 cm, −7.4 to −1.8, SUCRA 82.2%), and the low carbohydrate diet was ranked as the second (−3.0 kg, −4.3 to −1.8, SUCRA 74.3%) for weight loss and the best (−1.2 kg/m2, −1.7 to −0.74, SUCRA 81.6%) for BMI reduction (Figure 3e–g).

As for blood pressure, DASH was found to be the best pattern for lowering SBP (−7.6 mmHg, −15 to −0.29, SUCRA 87.9%) and the second for DBP (−3.7 mmHg, −10 to 2.8, SUCRA 73.7%), while HPD was the most effective for DBP (−3.0 mmHg, −5.9 to −0.068, SUCRA 74.6%) with slight superiority to DASH (Figure 3i‐j).

Lipid profiles

Figure 3k–n illustrate the effects of the different interventions on lipid profiles compared with the control groups. The low‐glycemic‐index diet showed the most remarkable efficacy for lowering TC (−0.46 mmol/L, −0.62 to −0.30, SUCRA 87.5%) and LDL (−0.35 mmol/L, −0.47 to −0.24, SUCRA 86.6%), but were not of beneficial effect on HDL. The Paleo diet was ranked as the best pattern for improving TG (−0.50 mmol/L, −1.1 to 0.13, SUCRA 83.4%), though the outcome was not statistically significant. The low‐carbohydrate diet led to an average increase of 0.12 mmol/L (95% CrI 0.073 to 0.17, SUCRA 84.0%) for HDL compared with control, thus being the best intervention with a small effect size.

Attrition

Since a considerable number of studies did not report standardized flowcharts of follow‐up, we only included trials that reported a loss in at least one arm into the synthesis. An attrition rate was calculated as: the attrition number divided by the product of participant number when allocation and the duration of intervention. The meta‐analysis did not find a significant difference among all patterns (Figure 3h; File S8), suggesting that the participants’ tolerance for each diet be similar.

Heterogeneity and inconsistency test

Generally, the included interventions were of moderate to high heterogeneity (Figure 3, Files S9, and S10), making the results less confident. LCD‐control, CR‐control, LGID‐control, LCD‐CR, and LGID‐LCD pairs were of high heterogeneity in either direct or network comparison, while Med‐control and HPD‐control were with mild heterogeneity in lipid profiles. Significant inconsistency was observed in LCD‐CR for FPG, and CR‐LCD‐control loop for weight and LDL using node‐splitting methods. The evidence of CR, LCD, and LGID showed severe incoherence and inconsistency and should be interpreted prudently.

Meta‐regression

A random effect meta‐regression model with one covariate and exchangeable coefficients was fitted for continuous outcomes. The significance of coefficients was summarized in File S11. Universally, the meta‐regression denoted that weight, BMI, and macronutrient intake significantly modified the efficacy of interventions of most outcomes. On the contrary, coefficients of length, study design, medication, or insulin treatment, duration of disease, and sex ratio were not significant, implying that these factors may not contribute to the effectiveness. Another notable finding that coefficients of sample size and origin (from China or not) showed significance in FPG, weight, and lipid profiles indicated potential publication or selection biases.

Sensitivity analysis

The effect of weight, BMI, and TC showed robustness, but other outcomes were not robust enough (File S12). The exclusion of several articles 51 , 61 , 67 , 97 , 98 , 101 , 126 , 134 , 135 , 140 , 145 significantly changed the SUCRA and the 95% CrI of effect size, mainly in comparisons of CR, LCD, and Med vs. control, contributing to the severe heterogeneity. When testing for different models, i.e., fixed effect or unrelated study effect models, Med, HPD, and VD showed narrower 95% CrIs and became statistically significant for more outcome variables (see File S12). The analysis did not observe the sensitivity of relative effect and between‐study heterogeneity priors, and correlation coefficients.

Publication bias

Potential publication bias of HbA1c, weight and BMI existed (Egger's test P = 0.002; <0.001; and <0.001, respectively). P values for all outcomes and comparison‐adjusted funnel plots are listed in File S13.

Quality of evidence

All MCIDs and thresholds were identified (see File S14, Figure 3, and Table 3). Of all 123 pieces of evidence comparing interventions and control groups, 49 were of moderate quality, and there was no high‐quality evidence (Table 3). At the clinical level, all patterns were not significantly worse than the control diets for each outcome, but most did not show moderate to large beneficial effects. All the quality of evidence should be downgraded when applying to PreD due to the indirectness, because PreD‐related trials were limited.

Table 3.

Summary of findings

Efficacy† Intervention Direct evidence Indirect evidence Network meta‐analysis‡
Mean and 95% CrI Quality Mean and 95% CrI Quality Mean and 95% CrI SUCRA Quality
FPG (MD, mmol/L), MCID = 0.80
Small (0.80 to 1.40) fiber −1.2 (−2.2, −0.057) Me −2.4 (−6.2, 1.4) Me −1.3 (−2.3, −0.22) 0.827 M
LGID −0.93 (−1.2, −0.64) Mb −0.63 (−3.1, 1.8) Me −0.94 (−1.2, −0.65) 0.746 M
DASH −0.92 (−2.5, 0.70) Lae −0.92 (−2.5, 0.70) 0.639 L
LCD −0.72 (−1.0, −0.39) Mbb −1.7 (−2.7, −0.63) −0.82 (−1.1, −0.51) 0.651 Mg*
CR −1.1 (−1.8, −0.51) Mb 0.15 (−0.93, 1.2) VLdee −0.81 (−1.4, −0.25) 0.645 Lg
Trivial (0.00 to 0.80) VD −0.63 (−1.6, 0.29) Me −0.64 (−1.6, 0.29) 0.524 M
Paleo −0.50 (−2.2, 1.2) Lee −0.50 (−2.2, 1.2) 0.460 L
Med −0.45 (−1.1, 0.15) Lbe −0.45 (−1.1, 0.15) 0.405 L
HPD −0.29 (−0.88, 0.30) Lee −0.29 (−0.88, 0.30) 0.308 L
HFD −0.0078 (−0.79, 0.77) VLbee −0.0078 (−0.79, 0.77) 0.171 VL
HbA1c (MD, %), MCID = 0.50
Moderate (0.80 to 1.40) DASH −1.2 (−2.2, −0.23) Ma −1.2 (−2.2, −0.23) 0.905 M
Small (0.50 to 0.90) fiber −0.42 (−1.3, 0.41) Lee −2.5 (−4.5, −0.53) Lbf −0.74 (−1.5, 0.035) 0.712 Lg*
LGID −0.73 (−0.95, −0.52) Lbf 0.35 (−1.1, 1.8) VLeef −0.71 (−0.93, −0.49) 0.762 L
LCD −0.63 (−0.85, −0.41) Lbbf −0.95 (−1.6, −0.29) Mf −0.67 (−0.88, −0.46) 0.721 L
Trivial (0.00 to 0.50) Med −0.46 (−0.90, −0.021) Lef −0.46 (−0.90, −0.021) 0.521 L
Paleo −0.40 (−1.5, 0.71) Lee −0.40 (−1.5, 0.71) 0.471 L
CR −0.51 (−1.1, 0.033) Me −0.11 (−0.74, 0.53) VLbee −0.34 (−0.76, 0.072) 0.412 M
VD −0.32 (−0.89, 0.25) Me −0.32 (−0.89, 0.25) 0.402 M
HPD −0.18 (−0.57, 0.21) Lee −0.18 (−0.57, 0.21) 0.272 L
HFD −0.11 (−0.63, 0.42) Lee −0.11 (−0.63, 0.42) 0.222 L
FIns (PMD), MCID = 0.08
Large (>0.16) fiber −0.22 (−0.52, 0.084) Lae −0.18 (−0.73, 0.37) Lee −0.21 (−0.46, 0.052) 0.794 L
Moderate (0.12 to 0.16) LGID −0.16 (−0.30, −0.025) Mbb 0.041 (−0.32, 0.40) VLdee −0.14 (−0.27, −0.0098) 0.683 M
Small (0.08 to 0.12) LCD −0.10 (−0.21, 0.0060) Lbbe −0.27 (−0.60, 0.055) Lde −0.12 (−0.22, −0.02) 0.628 M†
HPD −0.09 (−0.26, 0.082) Me −0.09 (−0.26, 0.082) 0.530 M
Trivial (0.00 to 0.08) Med −0.075 (−0.26, 0.10) VLbee −0.075 (−0.26, 0.10) 0.476 VL
VD −0.065 (−0.46, 0.33) Lee −0.065 (−0.46, 0.33) 0.467 L
HFD −0.050 (−0.26, 0.16) VLbee −0.050 (−0.26, 0.16) 0.408 VL
Paleo 0.021 (−0.36, 0.41) Lee 0.021 (−0.36, 0.41) 0.306 L
IR (PMD), MCID = 0.05
Large (>0.12) HPD −0.22 (−0.37, −0.07) Me −0.22 (−0.37, −0.07) 0.863 M
LGID −0.16 (−0.28, −0.04) Me −0.16 (−0.28, −0.04) 0.709 M
HFD −0.15 (−0.43, 0.13) Me −0.15 (−0.43, 0.13) 0.631 M
Moderate (0.08 to 0.12) Med −0.098 (−0.19, 0.01) Me −0.098 (−0.19, 0.01) 0.482 M
LCD −0.086 (−0.17, 0.0030) Me −0.086 (−0.17, 0.0030) 0.440 M
Trivial (0.00 to 0.05) Paleo −0.0010 (−0.29, 0.29) Lee −0.0010 (−0.29, 0.29) 0.257 L
Weight (MD, kg), MCID = 3.00
Small (3.00 to 5.00) CR −5.9 (−8.3, −3.4) Lbbf −0.50 (−3.9, 2.9) VLeef −4.1 (−6.1, −2.0) 0.868 Lg*
LCD −2.4 (−3.7, −1.0) Lbbf −7.3 (−11, −3.8) Lbf −3.0 (−4.3, −1.8) 0.743 Lg*
DASH −3.0 (−9.0, 3.1) Lae −3.0 (−9.0, 3.1) 0.654 L
Trivial (0.00 to 3.00) Paleo −3.0 (−10, 4.0) Lee −3.0 (−10, 4.0) 0.637 L
VD −2.1 (−7.1, 3.0) Lee −2.1 (−7.1, 3.0) 0.560 L
LGID −1.1 (−2.8, 0.56) VLbbef 1.1 (−5.6, 7.8) VLeef −1.1 (−2.7, 0.5) 0.435 VL
fiber −0.88 (−5.2, 3.5) VLaee −0.88 (−5.2, 3.5) 0.398 VL
HFD −0.61 (−3.3, 2.1) VLeef −0.61 (−3.3, 2.1) 0.343 VL
Med −0.58 (−3.8, 2.6) Lee −0.58 (−3.8, 2.6) 0.342 L
HPD −0.50 (−3.3, 2.1) VLeef −0.50 (−3.3, 2.1) 0.318 VL
BMI (MD, kg/m2), MCID = 1.05
Small (1.05 to 1.55) LCD −1.1 (−1.6, −0.59) Lbbf −1.8 (−3.3, −0.34) Lbf −1.2 (−1.7, −0.74) 0.816 L
CR −1.3 (−2.2, −0.35) Mbb −0.71 (−2.4, 0.95) Lbe −1.1 (−1.9, −0.34) 0.756 M
Trivial (0.00 to 1.05) Paleo −1.0 (−3.8, 1.8) Me −1.0 (−3.8, 1.8) 0.598 M
LGID −0.74 (−1.3, −0.16) Lbbf −0.022 (−2.5, 2.4) VLeef −0.73 (−1.28, −0.18) 0.543 L
VD −0.69 (−1.9, 0.57) Me −0.69 (−1.9, 0.57) 0.519 M
Med −0.66 (−1.4, 0.14) VLbbef −0.66 (−1.4, 0.14) 0.504 VL
HPD −0.43 (−1.5, 0.59) Lee −0.43 (−1.5, 0.59) 0.391 L
HFD −0.35 (−1.9, 1.1) Lee −0.35 (−1.9, 1.1) 0.375 L
fiber −0.29 (−1.8, 1.2) Lee −0.29 (−1.8, 1.2) 0.353 L
WC (MD, cm), MCID = 4.50
Small (4.50 to 7.00) DASH −4.8 (−11, 1.1) Lae −4.8 (−11, 1.1) 0.776 L
Trivial (0.00 to 4.50) CR −4.5 (−7.4, −1.8) Mbb −4.5 (−7.4, −1.8) 0.822 M
Paleo −4.0 (−12, 3.6) Me −4.0 (−12, 3.6) 0.669 M
LCD −2.8 (−4.7, −1.0) Mbb −4.2 (−11, 2.8) Me −3.0 (−4.7, −1.3) 0.653 M
HFD −2.4 (−6.6, 1.8) Me −2.4 (−6.6, 1.8) 0.539 M
VD −2.3 (−6.4, 1.8) Me −2.3 (−6.4, 1.8) 0.534 M
LGID −2.1 (−4.0, −0.17) Me −1.2 (−8.0, 5.6) VLdee −2.1 (−3.9, −0.27) 0.499 M
fiber −1.1 (−5.3, 3.2) Lee −1.1 (−5.3, 3.2) 0.364 L
Med −0.77 (−4.4, 2.8) Lee −0.77 (−4.4, 2.8) 0.315 L
HPD 0.53 (−2.8, 3.9) VLbee 0.53 (−2.8, 3.9) 0.156 VL
SBP (MD, mmHg), MCID = 6.00
Small (6.00 to 10.00) Paleo −8.9 (−24, 6.4) Me −8.9 (−24, 6.4) 0.807 M
DASH −7.6 (−15, −0.29) Ma −7.6 (−15, −0.29) 0.879 M
Trivial (0.00 to 6.00) HPD −2.7 (−6.3, 0.71) Lbe −2.7 (−6.3, 0.71) 0.634 L
LCD −1.9 (−4.1, 0.38) Lbe −5.5 (−12, 1.3) Lde −2.2 (−4.3, −0.10) 0.594 Mp*
CR −2.3 (−6.8, 2.1) Lbe −0.12 (−8.1, 8.1) Lee −1.8 (−5.7, 2.0) 0.515 L
fiber −1.6 (−10, 7.3) Lee −1.6 (−10, 7.3) 0.477 L
Med −0.82 (−5.4, 3.8) Lee −0.82 (−5.4, 3.8) 0.401 L
LGID −0.92 (−4.0, 2.3) VLbee 3.6 (−6.7, 14) Lee −0.76 (−3.7, 2.2) 0.385 VL
VD −0.11 (−6.2, 6.1) Lee −0.11 (−6.2, 6.1) 0.339 L
HFD 1.2 (−4.1, 6.4) Lee 1.2 (−4.1, 6.4) 0.211 L
DBP (MD, mmHg), MCID = 3.50
Small (3.50 to 7.00) Paleo −4.0 (−13, 5.3) Me −4.0 (−13, 5.3) 0.708 M
DASH −3.7 (−10, 2.8) VLabe −3.7 (−10, 2.8) 0.737 VL
Trivial (0.00 to 3.50) HPD −3.0 (−5.9, −0.068) Mb −3.0 (−5.9, −0.068) 0.746 M
CR −2.6 (−6.4, 1.1) Lbe 0.15 (−6.7, 7.0) Lee −2.0 (−5.2, 1.2) 0.610 L
LCD −2.0 (−3.9, 0.033) Lbe −3.7 (−9.3, 1.9) Lbe −2.0 (−3.8, −0.069) 0.627 Mp*
LGID −0.49 (−3.1, 2.1) Lee 0.18 (−8.2, 8.7) Lee −0.83 (−3.3, 1.7) 0.440 L
fiber −0.73 (−8.2, 6.7) Lee −0.73 (−8.2, 6.7) 0.448 L
Med −0.48 (−4.6, 3.7) VLbbee −0.48 (−4.6, 3.7) 0.400 VL
HFD 0.77 (−3.7, 5.2) Lee 0.77 (−3.7, 5.2) 0.255 L
VD 0.98 (−3.9, 5.9) Lee 0.98 (−3.9, 5.9) 0.243 L
TG (MD, mmol/L), MCID = 0.09
Large (>0.25) Paleo −0.50 (−1.1, 0.13) Me −0.50 (−1.1, 0.13) 0.834 M
LCD −0.26 (−0.38, −0.14) Mb −0.45 (−0.81, −0.094) H −0.29 (−0.40, −0.18) 0.758 M
LGID −0.26 (−0.35, −0.15) Mb −0.26 (−0.35, −0.15) 0.674 M
Moderate (0.15 to 0.25) HPD −0.23 (−0.46, −0.0030) Me −0.23 (−0.46, −0.0030) 0.615 M
Med −0.20 (−0.41, 0.0050) Lbe −0.20 (−0.41, 0.0050) 0.548 L
fiber −0.19 (−0.50, 0.13) Lae −0.19 (−0.50, 0.13) 0.517 L
HFD −0.18 (−0.52, 0.16) Me −0.18 (−0.52, 0.16) 0.499 M
Small (0.09 to 0.15) CR −0.18 (−0.41, 0.048) Me 0.0026(−0.30, 0.31) Lee −0.11 (−0.29, 0.066) 0.361 M
Trivial (0.00 to 0.09) DASH −0.040 (−0.53, 0.45) VLaee −0.040 (−0.53, 0.45) 0.310 VL
VD −0.024 (−0.36, 0.31) VLbee −0.024 (−0.36, 0.31) 0.246 VL
TC (MD, mmol/L), MCID = 0.26
Moderate (0.40 to 0.52) LGID −0.48 (−0.64, −0.31) Mbb −0.18 (−1.0, 0.69) Lee −0.46 (−0.62, −0.30) 0.875 M
Small (0.26 to 0.40) DASH −0.36 (−1.1, 0.42) VLaee −0.36 (−1.1, 0.42) 0.647 VL
fiber −0.29 (−0.79, 0.21) Me −0.47 (−1.4, 0.51) Me −0.33 (−0.76, 0.11) 0.675 M
Trivial (0.00 to 0.26) Paleo −0.20 (−1.3, 0.87) Lee −0.20 (−1.3, 0.87) 0.500 L
Med −0.18 (−0.47, 0.12) Me −0.18 (−0.47, 0.12) 0.483 M
CR −0.23 (−0.53, 0.074) Lbe 0.027 (−0.52, 0.58) Lee −0.17 (−0.43, 0.089) 0.472 L
LCD −0.12 (−0.29, 0.049) Lbe −0.35 (−0.80, 0.11) Lde −0.17 (−0.32, −0.012) 0.470 Mp*
HPD −0.15 (−0.44, 0.14) Me −0.15 (−0.44, 0.14) 0.439 M
HFD −0.12 (−0.52, 0.28) Lee −0.12 (−0.52, 0.28) 0.393 L
VD −0.11 (−0.58, 0.37) Lee −0.11 (−0.58, 0.37) 0.386 L
LDL (MD, mmol/L), MCID = 0.10
Moderate (0.25 to 0.40) DASH −0.37 (−0.89, 0.15) Lae −0.37 (−0.89, 0.15) 0.773 L
LGID −0.37 (−0.48, −0.25) Mb −0.19 (−0.83, 0.45) Lee −0.35 (−0.47, −0.24) 0.866 M
Small (0.10 to 0.25) CR −0.39 (−0.62, −0.15) Mbb 0.096 (−0.26, 0.46) Lee −0.24 (−0.44, −0.039) 0.694 Lg
fiber −0.24 (−0.62, 0.14) Me −0.21 (−1.1, 0.65) Lee −0.24 (−0.35, 0.21) 0.648 M
HPD −0.11 (−0.31, 0.088) Me −0.11 (−0.31, 0.088) 0.443 M
LCD −0.058 (−0.18, 0.062) Me −0.43(−0.75, −0.099) H −0.11 (−0.22, 0.0040) 0.444 Mg*
Paleo −0.10 (−0.92, 0.72) Lee −0.10 (−0.92, 0.72) 0.454 L
Trivial (0.00 to 0.10) HFD −0.067 (−0.35, 0.21) Lee −0.067 (−0.35, 0.21) 0.361 L
VD −0.060 (−0.36, 0.24) Lee −0.060 (−0.36, 0.24) 0.351 L
Med −0.029 (−0.27, 0.21) VLbbee −0.029 (−0.27, 0.21) 0.284 VL
HDL (MD, mmol/L), MCID = 0.10
Small (0.10 to 0.15) LCD 0.11 (0.056, 0.16) Mbb 0.15 (0.027, 0.27) Mb 0.12 (0.073, 0.17) 0.840 M
Trivial (0.00 to 0.10) CR 0.10 (0.0092, 0.19) Mbb 0.044 (−0.095, 0.18) VLbde 0.084 (0.0080, 0.16) 0.657 M
DASH 0.081 (−0.15, 0.31) VLaee 0.081 (−0.15, 0.31) 0.593 VL
LGID 0.083 (0.028, 0.14) Mbb −0.025 (−0.28, 0.23) Lee 0.080 (0.028, 0.13) 0.640 M
Paleo 0.080 (−0.18, 0.34) Lee 0.080 (−0.18, 0.34) 0.584 L
fiber 0.026 (−0.13, 0.19) VLaee 0.22 (−0.041, 0.49) Lde 0.077 (−0.059, 0.22) 0.609 VL
Med 0.062 (−0.039, 0.16) Lbbe 0.062 (−0.039, 0.16) 0.547 L
HFD 0.040 (−0.074, 0.15) Mee 0.040 (−0.074, 0.15) 0.447 M
HPD −0.017 (−0.11, 0.072) Lee −0.017 (−0.11, 0.072) 0.200 L
VD −0.045 (−0.17, 0.079) VLbee −0.045 (−0.17, 0.079) 0.139 VL
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a. limitation (risk of bias); b. inconsistency (unexplained substantial heterogeneity); bb. severe inconsistency (unexplained substantial heterogeneity, downgrade 1 level); c. indirectness (from population, intervention, or outcomes); d. indirectness (intransitivity); e. imprecision; ee. severe imprecision (downgrade 2 levels); f. publication bias; g. incoherence; g*. incoherence (same direction, no downgrading). p*. greater precision. SUCRA, surface under the cumulative ranking curve; H, high quality of evidence; M, moderate; L, low; VL, very low. †‘Small’, ‘moderate’ and ‘large’ referred to beneficial effects. ‡We only identified limitations when more than half of the included studies providing the evidence were at high risk of bias. For inconsistency, an I 2 value of greater than 75% was considered severe inconsistent. Even if meta‐regression was done, we were not confident to explain heterogeneity using covariates, so every comparison with I 2 greater than or near 50% was considered inconsistent. Indirectness from population, intervention, or outcomes was not detected for T2DM because of the aims of this study; however, if applying evidence to PreD populations, indirectness should exist. Intransitivity was determined if the effect size and the SUCRA seemed very unstable in the sensitivity analysis. Unstable SUCRA may denote differences in characteristics among studies that could modify effects in indirect comparison. Imprecision was determined if 95% CrI contained a null value, or the effect size showed statistical instability (change of significance) in the meta‐regression and sensitivity analysis. Severe imprecision referred to those whose CrI was divided by the null value into two parts with a comparable ratio, or the mean was very trivial, close to null. Incoherence was determined if the comparisons showed significant inconsistency (the term in network meta‐analysis). The network quality of evidence was downgraded if incoherence of different directions (i.e., positive and negative) existed. All ratings of the factors were agreed by three authors (B.‐T.Z., F.‐D.L., and J.‐W.D.).

DISCUSSION

This review evaluated the comparative efficacy of ten experimental diets, and the results can provide guidance for diet selection of one specific patient. To manage patients with comorbidities and different levels of glycemic control, we concluded a dietary suggestion table derived from the evidence from the meta‐analysis (Table 4). However, this table should be applied prudently because the evidence was not solid enough.

Table 4.

Dietary suggestions for patients with different profiles

Well‐controlled glycemia Poor‐controlled glycemia Poor insulin sensitivity Hypertriglyceridemia Hypercholesterolemia Low HDL level General obesity Central obesity Hypertension
Well‐controlled glycemia Only poor‐controlled glycemia: LGID; fiber; DASH HPD LCD; Paleo † LGID LCD CR; LCD LCD DASH; HPD
Poor‐controlled glycemia LGID; fiber LGID LGID LCD DASH DASH; LCD DASH
Poor insulin sensitivity LGID LGID LCD LCD LCD † HPD
Hypertriglyceridemia LGID LGID LCD Paleo † HPD
Hypercholesterolemia CR CR DASH DASH
Low HDL level LCD LCD DASH †
General obesity CR Paleo †
Central obesity DASH
Hypertension
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†

The evidence is uncertain.

Quantity of macronutrients

A previous evidence basis has corroborated the efficacy of CR in weight loss, BMI and WC in patients with metabolic diseases or healthy individuals 149 , 150 . However, CR did not lead to a greater improvement of glycemic control, blood pressure, TG, and TC compared with standard diets. Trivial effects on these outcomes may result from weight loss but not the caloric restriction 151 , 152 . The median TEI of the included CR arms was 1594 kcal/d, with a 150–400 kcal negative difference compared with standard diets, significantly slighter than the prescribed (−500 kcal/d). However, the deviance did not lead to the failure of trials. The phenomena were also observed in LCD and LGID.

Carbohydrate restriction acted well in weight, HbA1c, TG, and HDL, where improving HDL was the unique advantage of LCD. Nevertheless, other types of serum lipids, i.e., TC and LDL were not improved. The 75th percentile of carbohydrate intake of the included LCD arms was 40%, indicating that nearly a quarter of included trials did not meet the low‐carbohydrate criteria as prescribed. Nevertheless, the effect size was similar to previous systematic reviews 13 , and the strict following of the instruction as well as a more intensive intervention did not enhance the effects but may even lead to a decrease (File S11).

Increased protein intake without carbohydrate restriction (HPD) effectively improved IR, blood pressure, and TG. Compared with other reviews 153 , an effectiveness of HPD on FPG, HbA1c, and other lipids was not observed, mainly due to the different inclusion criteria: only HPD with protein intake of more than 30% TEI and without carbohydrate restriction was included. This implied the different efficacy of protein and carbohydrate.

As for HFD, no beneficial effect was detected, and fat intake negatively modified the lipid improvement. Despite the numerical impact on specific lipids, it remains to be evaluated whether specific types of fat improved or negatively affected the overall lipoprotein profile 154 . Unfortunately, the included trials did not provide sufficient data to draw a thorough interpretation.

Quality of carbohydrates

The low‐glycemic‐index diet and the high‐fiber diet emphasized more the quality of the carbohydrates. The effects of LGID and high‐fiber diets were similar: both showed more excellent effects on FPG, HbA1c, FIns, TC, and LDL than the other patterns, but did not significantly improve weight‐related outcomes, consistent with other studies 155 , 156 . The dietary GI and fiber of a specific single food were not well‐associated 157 . However, the emphasis on lowering GI may encourage participants to increase fiber intake, because the usually recommended food groups can be both low in GI and high in fiber, e.g., whole grains and nuts.

A recent high‐quality meta‐analysis has also denoted that dietary fiber and low‐GI food were associated with a lower risk of type 2 diabetes mellitus incidence, where fiber may be a stronger protector 158 . Rather than a severe long‐term restriction of carbohydrate intake which leads to higher all‐cause mortality 159 , LGID and increased fiber intake can be better and sustainable approaches for patients with type 2 diabetes mellitus without obesity/overweight, especially in the circumstance that most people lacked fiber intake 160 .

Mediterranean diets

Even if previous cohort studies and RCTs have demonstrated the efficacy of Med in type 2 diabetes mellitus management 161 , our study failed to detect a significant improvement driven by Med. Except for HbA1c, IR, and TG, all other outcomes were of great imprecision and of trivial effect. The effect size was also more trivial than other meta‐analyses 14 , 162 . A small sample size compared with other interventions could be the reason when using random effects models; different calculation of effect size, i.e., MD of change from baseline or of the endpoint may explain the numerical differences.

Moreover, heterogeneity was detected for almost all outcomes of Med‐control comparisons, where the variance and bias of the definition of Med in different trials 163 can be a significant reason. Though several scales have been developed to measure the adherence to Med (e.g., MedDiet Score) 164 , few trials employed it, making this problem difficult to address.

Vegan, vegetarian, or plant‐based diets

The vegetarian/vegan/plant‐based diet did not show any significant beneficial effects in our study. The mean differences of VD were similar to the previous studies 36 , thus not affecting the conclusion but lowering the quality of the evidence. While using fixed effect models, an effectiveness of VD on BMI, WC, and HbA1c was detected, but moderate heterogeneity made it unreasonable to employ fixed effect models.

Notably, the carbohydrate intake in VD trials was relatively high (mean 65.8%TEI). The sensitivity analysis also showed a slight improvement of SUCRA in TG after omitting Lee 2016 91 , which contained about 72%TEI of carbohydrate in VD arms. Researchers should consider a lower carbohydrate intake when conducting a VD, and the effects would promise to be more significant.

Newly developed diets

Evidence of the efficacy of the dietary approaches to stop hypertension (DASH) and Paleo was limited and of low quality due to the sample size, and further investigation is needed. As one of the recommended healthy patterns by Dietary Guidelines for Americans (DGA 2020‐2025) 165 , many studies have addressed DASH's benefit in blood pressure and glycemic control 166 , 167 . However, related RCTs specially for type 2 diabetes mellitus/prediabetic patients were rare. Included studies also outlined the beneficial effects of DASH on blood pressure, TC, LDL, and HbA1c, and DASH was the most effective intervention for HbA1c with a high probability (90.5%). As for Paleolithic diets, Tommy Jönsson and his colleagues also quantified the improvement of leptin and introduced a scale (Paleolithic Diet Fraction) to measure the compliance, based on their trial 87 , 168 , providing a basis for further study.

Limitations

This study had several limitations. First, the heterogeneity and sensitivity lowered the quality of evidence. Second, the sample size of VD, DASH, and Paleo was limited, leading to the imprecision. Third, only five prediabetic trials were included, raising the indirectness of the evidence for the prediabetic population. Moreover, there was not an adequate method to compare the longitudinal dataset of different patterns, though the data of different timepoints have been extracted.

In conclusion, energy, carbohydrate, and dietary glycemic index (GI) restriction, as well as dietary fiber intake, were the most effective approaches with solid and abundant evidence bases. Simultaneously, DASH, Paleolithic diets, and HPD were of satisfactory efficacy in limited outcomes and worth investigation. Mediterranean diets, VD, and HFD did not act well in most outcomes, mainly due to the imprecision. Heterogeneity and sensitivity should be considered when interpreting results.

This work may eliminate some barriers on how to choose the best diet on an individualized basis. Clinicians and dietitians can choose the most important outcome when there is an urgent need to control a patient to match the most appropriate dietary pattern, according to the summary of findings table and the dietary suggestions table of this review.

DISCLOSURE

The authors declare no conflict of interest.

Approval of the research protocol: N/A.

Informed consent: N/A.

Registry and the registration no. of the study/trial: This network meta‐analysis was registered at https://www.crd.york.ac.uk/PROSPERO as CRD42021278268.

Animal studies: N/A.

Supporting information

File S1 | Full search strategy

File S2 | Data extraction template

File S3 | Correlation coefficients for estimation

File S4 | Reason for exclusion

File S5 | Fundings and conflicts of interest of included studies

File S6 | Risk of bias assessment

File S7 | Network plots

File S8 | League tables and cumulative ranking curves

File S9 | Forest plots

File S10 | Heterogeneity and inconsistency test

File S11 | Meta‐regression

File S12 | Sensitivity analysis

File S13 | Publication bias

File S14 | Minimal clinically important difference and thresholds for effects

Click here for additional data file. (81.8MB, pdf)

ACKNOWLEDGMENTS

We acknowledge Professor Lawrence J. Cheskin from George Mason University for his kind reply to our email about the data availability of his registered trial. This research did not receive any funding.

Contributor Information

Yang Liu, Email: liuyang123@xmu.edu.cn.

Ji‐wei Du, Email: dujw@hku-szh.org.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

File S1 | Full search strategy

File S2 | Data extraction template

File S3 | Correlation coefficients for estimation

File S4 | Reason for exclusion

File S5 | Fundings and conflicts of interest of included studies

File S6 | Risk of bias assessment

File S7 | Network plots

File S8 | League tables and cumulative ranking curves

File S9 | Forest plots

File S10 | Heterogeneity and inconsistency test

File S11 | Meta‐regression

File S12 | Sensitivity analysis

File S13 | Publication bias

File S14 | Minimal clinically important difference and thresholds for effects

Click here for additional data file. (81.8MB, pdf)

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