The Effect of Microbiome-Modulating Agents (MMAs) on Type 1 Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Gut microbiome-modulating agents (MMAs), including probiotics, prebiotics, postbiotics, and synbiotics, are shown to ameliorate type 1 diabetes (T1D) by restoring the microbiome from dysbiosis. The objective of this systematic review and meta-analysis was to assess the impact of MMAs on hemoglobin A1c (HbA1c) and biomarkers associated with (T1D). A comprehensive search was conducted in PubMed, Web of Science, Embase, Cochrane Library, National Knowledge Infrastructure, WeiPu, and WanFang Data up to 30 November 2023. Ten randomized controlled trials (n = 630) were included, with study quality evaluated using the Cochrane risk-of-bias tool. Random-effect models with standardized mean differences (SMDs) were utilized. MMA supplementation was associated with improvements in HbA1c (SMD = −0.52, 95% CI [−0.83, −0.20]), daily insulin usage (SMD = −0.41, 95% confidence interval (CI) [−0.76, −0.07]), and fasting C-peptide (SMD = 0.99, 95% CI [0.17, 1.81]) but had no effects on FBG, CRP, TNF-α, IL-10, LDL, HDL, and the Shannon index. Subgroup analysis of HbA1c indicated that a long-term intervention (>3 months) might exert a more substantial effect. These findings suggest an association between MMAs and glycemic control in T1D. Further large-scale clinical trials are necessary to confirm these findings with investigations on inflammation and gut microbiota composition while adjusting confounding factors such as diet, physical activity, and the dose and form of MMA intervention.


Introduction
Type 1 diabetes (T1D) refers to an autoimmune disease leading to the self-destruction of insulin-producing pancreatic ß cells and insulin deficiency, which leads to impaired glucose metabolism [1].T1D places heavy burdens on public health due to the rapid increase in prevalence rate and its complex condition for glucose management, especially in resource-limited countries [2].Insulin therapy is the most accepted treatment for T1D, which requires subcutaneous insulin injection several times per day [3].It causes several challenges, including high expenses, weight gain, risk of hypoglycemia, and low adherence [4].Therefore, novel and economic therapy with high adherence and accessibility is needed to slow down the progression of T1D [5].
The gut microbiome has been shown to impact the occurrence and pathogenesis of T1D in recent years [6].Case-control studies indicate that compared with healthy control subjects, T1D is associated with a significantly lower microbiota diversity, a higher relative abundance of Bacteroides, Ruminococcus, Blautia, and Streptococcus genera, and a lower relative abundance of Bifidobacterium, Roseburia, and Faecalibacterium [7].An imbalanced Bacteroidetes-to-Firmicutes ratio leads to dysbiosis, which changes intestinal mucosa and alters gut permeability, resulting in a leaky gut [8,9].In T1D subjects, the disharmonized intestinal microenvironment causes an increased level of proinflammatory cytokines and lipopolysaccharides (LPSs), and they enter into the bloodstream with greater accessibility since the tight junctions between colonocytes are damaged, resulting in an increased level of inflammatory substances in the bloodstream [10].As a result, the inflammation status causes islet autoimmunity, leading to decreased fasting C-peptide (FCP) and elevated glycemic levels.FCP reflects endogenous insulin production and provides insights into residual beta-cell activity, which is commonly used to assess the effectiveness of interventions aimed at preserving or enhancing insulin secretion [11].Glycated hemoglobin (HbA1c), a widely used biomarker for assessing long-term glucose control in individuals with diabetes, reflects the average blood glucose levels over the past 2-3 months, providing information about the effectiveness of diabetes management strategies [12].
Tackling dysbiosis is suggested to be a novel strategy for treating T1D, and using MMAs is considered to be a feasible way of restoring the gut microbiota [13].MMAs are substances that regulate the gut microbiota, including probiotics, prebiotics, synbiotics, and postbiotics.Supplementation with probiotics in T1D adults has shown improved glycemic control and increased synthesis of Glucagon-like peptide-1 [14].Prebiotics play a role in an increase in the number of lactic acid-producing bacteria and have immuno-modulatory properties [15].Postbiotics from various microbiomes inhibit the growth of pathogenic bacteria [16].Synbiotics exert a synergistic effect on the restoration of the gut microbiota [17].Therefore, MMAs might play a role in maintaining gut microbiota homeostasis, stabilizing blood glucose levels, and reducing the level of proinflammatory cytokines while increasing anti-inflammatory cytokines, resulting in a slower T1D progression [18].
While there are abundant reviews exploring the relationship between MMAs and glycemic control, the majority of the literature predominantly focuses on type 2 diabetes (T2D).Conversely, the literature specifically addressing T1D is notably limited, with only two existing reviews identified.One review, encompassing five randomized controlled trials (RCTs) up to 8 October 2022, examined the impact of probiotics and synbiotics on glycemic control, focusing on outcomes such as fasting blood glucose (FBG), HbA1c, fasting C-peptide (FCP), and daily insulin usage (DIU) [19].However, it did not delve into the outcomes related to T1D pathogenesis, such as inflammatory cytokines and gut microbiota composition.Another recent review aimed to explore the effects of probiotic and synbiotic interventions on both T1D and T2D [20].Despite its inclusion of a large overall sample size, individuals with T1D represented only 2.8% (n = 84), and the review excluded patients with diabetes under 18, a demographic where T1D is prevalent.Moreover, this review was unable to differentiate the outcomes between T1D and T2D, which is crucial due to their distinct pathophysiologies, treatment modalities, and potential responses to interventions.Consequently, there remains a gap in the literature regarding quantitative review studies on T1D.To address this gap, the current meta-analysis updates the evidence up to 30 November 2023, incorporating ten studies covering children, adolescents, and adults and employing a more comprehensive set of outcome measures.

Data Sources and Literature Search
This meta-analysis was recorded in the International Prospective Register for Systematic Reviews (PROSPERO) under registration number CRD42023395896.The review adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [21], which can be found in Supplementary S1, and a comprehensive literature search was undertaken by two independent researchers across seven databases, namely PubMed, Web of Science, Embase, Cochrane Library, National Knowledge Infrastructure (CNKI), WeiPu (VIP), and WanFang Data (WanFang), until 30 November 2023.Published reviews and their references were also manually searched to identify any additional studies meeting the inclusion criteria.A combination of MeSH terms and free text were utilized, encompassing terms such as 'type 1 diabetes', 'probiotics', 'synbiotics', and 'randomized controlled trials'.Boolean operators were employed for sensitivity ('OR') and precision ('AND'), customized to the syntax of each individual database.As an example, the search methodology applied in PubMed was structured as ('Diabetes Mellitus, Type

Inclusion and Exclusion Criteria
A study was included if the following criteria were met: (1) RCT; (2) the literature was published before 30 November 2023; (3) the subjects must be diagnosed specifically with T1D; notably, no specific criteria were set for participants' age or disease duration, aiming to encompass a broad spectrum of eligible studies; (4) interventions were limited to probiotics, synbiotics, prebiotics, and postbiotics with no requirement on duration; and (5) the primary outcome was HbA1c, and the secondary outcomes were FBG, FCP, DIU, C-reactive protein (CRP), interleukin-10 (IL-10), tumor necrosis factor-α (TNF-α), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and Shannon index.
The exclusion criteria were as follows: (1) the subjects had other types of disease; (2) the probiotics were taken within three months before the trial; and (3) duplicate studies.

Selection and Data Extraction Process
Rayyan is a screening tool used for systematic reviews and meta-analyses, facilitating the efficient selection and management of relevant studies [22], and was employed in this review.During the initial round of title and abstract screening, both reviewers independently assessed all 831 records.Subsequently, in the second phase of full-text screening, a panel of two reviewers collectively evaluated the articles.Any disparities or disagreements that emerged during this process were addressed through collaborative discussion between the two reviewers, persisting until unanimous agreements were reached.
Two authors conducted data extraction independently, encompassing key aspects including (1) first author, publication year, and study country; (2) study design and intervention duration; (3) comprehensive details regarding the intervention and placebo, including specific probiotic strain, dosage, and daily intake time; (4) baseline characteristics like age, disease duration, and body mass index (BMI); and (5) metabolic outcomes, which were measured both before and after interventions.The extracted data underwent a verification process by both authors.In instances where data were not explicitly presented in the publications, the data analyst sought information in Supplementary Materials.If the necessary details remained elusive, the corresponding authors were contacted via email to solicit the missing data.A systematic follow-up protocol was implemented: After the initial contact, a one-week interval was allowed for a response.If no reply was received, a second contact attempt was made.In the event of continued non-response after the second attempt, the study was excluded from the analysis.

Quality Assessment
The quality assessment of each RCT was independently conducted by two reviewers utilizing the Cochrane risk-of-bias tool (ROB2) [23].Additionally, the ROBVIS tool [24] was employed for visualization purposes.Adhering to the ROB2 guidelines, the following biases were systematically assessed: (1) bias arising from the randomization process; (2) bias due to deviations from intended interventions; (3) bias due to missing outcome data; (4) bias in the measurement of the outcome; and (5) bias in the selection of the reported result.The tool automatically synthesized the overall risk of bias, represented as low risk in green, some concerns in yellow, and high risk in red.Any disparities in the assessment were meticulously resolved through collaborative discussions between the two reviewers, persisting until consensus was achieved.The overall certainty of evidence across the studies was graded according to the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) working group guidelines.The quality of evidence was classified into four categories, namely high, moderate, low, and very low, according to the corresponding evaluation criteria.

Data Synthesis and Statistical Analyses
For the synthesis and quantitative analysis of data, Review Manager (Revman) 5.3 software was employed in this study.Continuous data were presented as the mean difference with standard deviation (m ± SD).In cases where data were initially expressed as median with interquartile range (IQR) or range, the skewness was assessed using the website (www.math.hkbu.edu.hk,accessed on 1 April 2024) [25,26].If the data were not significantly skewed, transformation into mean with SD was undertaken.The standardized mean difference (SMD) with 95% confidence intervals (CIs) was calculated using randomeffect models.In random-effect models, the treatment effect estimates observed in studies may vary due to genuine disparities in treatment effects across each study, along with sampling variability.This diversity in treatment effects could be attributed to discrepancies in study populations (e.g., patient age), interventions administered (e.g., drug dosage), duration of follow-up, and other variables.Thus, a random-effect model was utilized by facilitating the extension of findings beyond the included studies by assuming that these studies represented random samples from a broader population.Statistical significance was established at p < 0.05.Heterogeneity was evaluated through I 2 , and I 2 values of 25%, 50%, and 75% were suggested to be indicators of low, moderate, and high heterogeneity, respectively [27].Sensitivity analyses were performed for results displaying high heterogeneity to assess whether the combined outcomes and heterogeneity altered, aiming to evaluate the robustness of the findings.Subgroup analyses were further performed based on the different MMAs used, age, disease duration, and intervention duration differences.In cases where more than 10 studies were included, potential publication bias was investigated utilizing funnel plots [28].

Literature Search Results
This review initially identified 831 records, of which 132 were excluded due to duplication.During the process of screening titles and abstracts, 680 studies were eliminated, primarily on the basis of irrelevant diseases, including type 2 diabetes (T2D), gestational diabetes mellitus (GDM), and latent autoimmune diabetes in adults (LADA); non-human studies including in vivo and in vitro studies; non-interventional studies, such as crosssectional, cohort, and case-control studies; and reviews and protocols.In total, 19 full articles were reviewed for eligibility, and eventually, 10 clinical trials were included with 630 patients' records in this meta-analysis (Figure 1).The exclusion reasons for the other nine articles are indicated in Supplementary S2.

Basic Characteristics of the Included Studies
Table 1 Summarizes essential data from the 10 included RCTs.One trial included children with three age groups [29].Thus, each age group was considered as an individual report, and 12 subgroups were obtained eventually.A total of 630 participants (315 in the intervention group and 315 in the control group) underwent re-analysis.All clinical trials included both genders, maintaining a balanced male-to-female ratio (1.02).
Beyond post-intervention assessments, three studies also measured outcomes 3 months and 6 months after intervention completion [15,34,37].None of the trials reported any significant adverse events in the MMA intervention group.

Basic Characteristics of the Included Studies
Table 1 Summarizes essential data from the 10 included RCTs.One trial included children with three age groups [29].Thus, each age group was considered as an individual report, and 12 subgroups were obtained eventually.A total of 630 participants (315 in the intervention group and 315 in the control group) underwent re-analysis.All clinical trials included both genders, maintaining a balanced male-to-female ratio (1.02).

Effects of MMA Intervention on HbA1c
The effect of MMAs on HbA1c was reported by 10 studies (n = 600) [29, 30,[32][33][34]36,37], as depicted in Figure 3.The overall effect (SMD = −0.52,95% CI [−0.83, −0.20], p < 0.01) indicated a significant improvement in HbA1c with MMA intervention but with moderate heterogeneity (I 2 = 70%, p < 0.01, Tau 2 = 0.18).The sensitivity analysis revealed that the omission of any single study did not significantly alter the result.Subgroup analyses were conducted based on four parameters, namely the duration of intervention, the MMAs used, age, and the disease duration of the T1D patients, which are discussed in Section 3.4.5.Placebo capsule: same substances but without the bacteria 1 capsule, TID † Note: normally distributed quantitative variables are presented as the mean ± SD. ‡ Non-normally distributed quantitative variables are presented as the median (interquartile range, IQR).Functional abbreviations: CFU, colony forming unit; QD, once a day; TID, three times a day; CGM, continuous glucose monitoring; mo, months; AI, after intervention, meaning the study included a follow-up for a period of time when intervention completed.Study outcome abbreviations: HbA1c, glycated hemoglobin; AUC C-peptide , area under the curve of the C-peptide level during 2 h responses to a mixed meal; FCP, fasting C-peptide; DIU, daily insulin usage; TNF-α, tumor necrosis factor-α; FBG, fasting blood glucose; SCFA, short-chain fatty acid; CRP, C-reactive protein; HDL, high-density lipoprotein; LDL, low-density lipoprotein; NA, Data not applicable; BID, twice a day.

Effects of MMA Intervention on HbA1c
The effect of MMAs on HbA1c was reported by 10 studies (n = 600) [29, 30,[32][33][34]36,37], as depicted in Figure 3.The overall effect (SMD = −0.52,95% CI [−0.83, −0.20], p < 0.01) indicated a significant improvement in HbA1c with MMA intervention but with moderate heterogeneity (I 2 = 70%, p < 0.01, Tau 2 = 0.18).The sensitivity analysis revealed that the omission of any single study did not significantly alter the result.Subgroup analyses were conducted based on four parameters, namely the duration of intervention, the MMAs used, age, and the disease duration of the T1D patients, which are discussed in Section 3.4.5.

Publication Bias
Publication bias was assessed for HbA1c since it was the only biomarker that exceeded 10 studies and subgroups.The plot shows a concentrated distribution of all studies, suggesting an absence of publication bias (Supplementary S3).

Grading of Evidence
An evaluation of the quality of evidence using the GRADE approach is presented in Table 2.The quality of evidence was moderate for HbA1c due to inconsistency (I 2 = 70%), low quality for FCP, and very low quality for DIU, owing to limitations on imprecision (n = 336 and n = 250 for sample size, respectively), as well as limitations on inconsistency for FCP (I 2 = 90%) and risk of bias in DIU and publication bias (two studies had an overall uncertain risk, and one was with high risk of bias).

⊕ Very low
Each circle represents a level of evidence quality: ⊕ indicates very low quality, ⊕⊕ indicates low quality, ⊕⊕⊕ indicates moderate quality.a There was significant heterogeneity for HbA1c (I 2 = 70%), and FCP (I 2 = 90%).b Two studies for DIU were evaluated with uncertain risk of bias and one study with a high risk of bias.c The sample size for FCP and DIU were 336 and 250, respectively, which was less than 400.

Discussion
This systematic review and meta-analysis focused on probiotics, prebiotics, synbiotics, and postbiotics as adjuvant therapy in T1D management.With the inclusion of 10 clinical trials, comprising a sample size of 630 T1D patients, significant improvements were observed in HbA1c, FCP, and DIU, while no effects were found in FBG, CRP, TNF-α, IL-10, HDL, LDL, and the Shannon index.Subgroup analyses based on HbA1c revealed the effects of the intervention period, types of MMAs, age, and the disease duration of the patients.A considerable effect on HbA1c was found in the subgroup receiving multistrain probiotics or synbiotics, a supplementation period for more than 3 months, and in patients under 18 years old with long-term T1D.The grading of the quality of evidence indicated moderate quality of evidence in HbA1c and low/very low quality of evidence in FCP and DIU.
Comprising roughly 1000 species and weighing approximately 1.5 kg, the gut microbiota is integral to human health, and alterations in its composition, known as dysbiosis, have been implicated in the pathogenesis of T1D [38].The gut microbiome composition was different in healthy versus TID in both human and animal models [39].Animal studies indicated higher alpha-diversity in the gut microbiota of non-obese diabetic (NOD) mice compared with mice that later progressed to T1D [40], and in mice with reduced T1D progression, a higher Bacteroidetes-to-Firmicutes ratio was observed [41].Similar to animal models, case-control studies indicated a significantly lower Bacteroidetes-to-Firmicutes ratio in the T1D group [7,8].The "Teddy study (The Environmental Determinants of Diabetes in the Young)" showed a lower abundance of Streptococcus thermophilus and Lactococcus lactis in children at the onset of T1D with respect to healthy subjects [42].In addition, children with T1D were observed with decreased numbers of bacteria that were essential to maintain gut integrity such as lactic acid-producing bacteria, butyrate-producing bacteria, and mucin-degrading bacteria.Aberrant gut microbiota composition might play a pivotal role in the development of T1D mainly by modulating the formation of SCFA [43], compromising the gut barrier by loosening the tight junction between cells, allowing pathogenic substances such as TNF-α to enter the bloodstream, and triggering autoimmune responses underlying T1D [44].
The modulation of the gut microbiota is a strategy aiming at reversing dysbiosis by using different types of MMAs [45].Single-strain probiotics, multistrain probiotics, synbiotics, prebiotics, and postbiotics were included in this review, with multistrain probiotics appearing to exert a greater efficacy, aligning with the literature [46].Treating NOD mice with probiotic strains belonging to families Bifidobacteriaceae and Lactobacillaceae and the Streptococcus thermophilus genus has been shown to ameliorate T1D [47].The mechanism of action might be through the downregulation of the proinflammatory TLR signaling pathway, which decreases the level of proinflammatory cytokines, including IL-6, IL-1β, and TNF-α while increasing that of anti-inflammatory cytokines, such as transforming growth factor-β (TGF-β) and IL-10 [48].However, the quantitative analysis results on the Shannon index, CRP, TNF-α, IL-10, and IFN-γ revealed negative values, which might be attributed to the inability to stratify different MMAs, given the limited number of outcomes covering the same indicators, each with only two trials examined.They employed different MMAs (varied strains of multistrain probiotics, synbiotics, inulin, and sodium butyrate) targeting distinct mechanisms for gut modulation and T1D amelioration.In this review, one study showed an enriched composition of beneficial gut microbiota, including Bifidobacterium animalis, Lactobacillus salivarius, and Akkermansia muciniphila, and an improved level of TGF-β1 and TNF-α after supplementing with Lactobacillus salivarius and Bifidobacterium animalis [34].This was aligned with an intervention study using Jinshuangqi (a triple live probiotic tablet sold in China consisting of Bifidobacterium longum, Lactobacterium bulagricumi, and Streptococcus thermophilus), indicating a decreased level of IFN-γ, Bifidobacterium, and Lactobacillus and a restored Th1/Th2 cell balance in children with T1D [49].The use of synbiotics (a combination of probiotics and prebiotics) resulted in a significant increase in the levels of SCFAs, ketones, carbon disulfides, and methyl acetates, which was observed to have a greater efficacy on blood glycemic control and inflammation than probiotic usage alone [50,51].One study in this review indicated a decreased CRP level and an increase in total antioxidant capacity [34].
Sodium butyrate is the most common type of postbiotic, indicating promising glycemic control in streptozotocin (STZ)-induced T1D mice by improving the islet morphology and downregulating the NF-κB-mediated inflammatory signal pathway [50].In an antibioticdriven T1D mice model, butyrate ameliorated disease in the female offspring of NOD mice, and in their formal study, butyrate directly shaped pancreatic immune tolerance and dampened T1D progression [51].Nevertheless, human studies did not support any of these findings [28,29], nor an increase in fecal butyrate.Unlike inulin, a type of prebiotic derived from chicory increased SCFA and interleukin-22 potentially by preventing and/or treating T1D in NOD mice and mitigating symptoms among individuals with T2D through the inhibition of JNK and P38 MAPK pathways [52].Clinical evidence demonstrated an improvement in gut integrity and higher relative abundance of Streptococcus, Roseburia inulinivorans, Terrisporobacter, and Faecalitalea with inulin supplementation in children with T1D [13].This suggests that the oral intake of postbiotic metabolites from gut microbiota might not act directly and efficiently in promoting the intestinal environment like other supplementations.
Intervention duration and the characteristics of the T1D patients might also play important roles, as revealed in this meta-analysis.MMA intervention for over 3 months demonstrated a significant decrease in HbA1c levels.The between-group heterogeneity for different intervention periods significantly decreased (I 2 = 0% for ≥3 months, I 2 = 8% for >3 months), and the test for subgroup difference reached significance (I 2 = 95.6%),indicating that variations in the intervention period might serve as a probable source of heterogeneity.Different types of MMAs might also be a source of heterogeneity, as indicated by the test in subgroup differences (I 2 = 82.3%).MMA intervention might be more effective in lowering HbA1c in children and adolescents and those with long-term T1D.This could be attributed to the greater adaptability of children's intestinal flora and the altered glucose metabolism in long-term T1D cases, indicating the increased efficacy of MMA intervention over time [53].However, the subgroup difference tests revealed that age (I 2 = 31.7%)and disease duration (and I 2 = 0) might not be the source of heterogeneity.
Other potential sources of heterogeneity included dietary factors, physical activity, and the dose and form of MMAs.Only one RCT recorded the dietary factor at baseline and post-intervention, though it reported an unchanged effect after adjusting this confounding factor.The diet also plays a role as studies have shown that HbA1c is lower in patients following a diet with balanced-glycemic-index food [54].Similarly, a moderate level of PA resulted in better glycemic control in T1D patients [55], but no trials included in this review reported any information on this factor.Lastly, the dose and form of MMAs used may contribute to heterogeneous results, but they are incomparable between different types of MMA, since the unit for probiotics is CFU, while it is gram in postbiotics and prebiotics.While the moderate heterogeneity aligned with the literature [20], the combined effect varied, which might be attributed to the differentiation between T1D and T2D.This review further demonstrated an improvement in DIU and FCP, contradicting the negative results from another review [19].Furthermore, three studies conducted post-interventional biochemical examinations, revealing no significant difference between the MMA and control groups [15,34,37].This transient effect aligned with another systematic review, indicating the absence of consistent effects on gut microbiota composition alterations after four to eight weeks of probiotic intervention, suggesting that individuals may require a longer duration of treatment to have therapeutic effects [56].This is the first systematic review and meta-analysis exclusively focusing on the effect of MMAs and T1D and attempting to investigate inflammation and gut microbiota indicators in addition to glycemic control.However, it is not without limitations.Despite including more than twice the number of studies and sample size compared to previously published research [17,18], the number of ten RCTs was not sufficient for the extrapolation of the results.Meanwhile, even though most of the included studies presented a low or unclear risk of bias, the overall quality and reliability might be compromised due to unclear reporting and missing data.Unclear reporting in studies may lead to difficulties in assessing the true risk of bias, and missing data may result in attrition bias, which may skew the results and reduce the precision of the estimated effects.This review endeavored to quantitatively analyze the gut microbiota composition and inflammatory cytokines post-MMA interventions.However, due to variations in outcome measures, different properties, and targeted outcomes of MMAs, the scope of quantitative analysis was limited, raising the possibility that the insignificant effects on biomarkers other than HbA1c, FCP, and DIU may have been by chance.In addition, statistical heterogeneity was observed in the analyses, confounding factors such as ethnicity differences, dietary factors PA, dose, and form of the intervention were not analyzed in this review.

Conclusions
In conclusion, this systematic review and meta-analysis suggested that MMA supplementation is associated with improved HbA1c, DIU, and FCP, with moderate quality of evidence in HbA1c and low/very low quality of evidence in FCP and DIU.Multistrain probiotics and synbiotics might exhibit a more significant effect under long-term intervention (<3 months.)Despite the moderate-to-high heterogeneity found in HbA1c and FCP, the evidence supports the potential of MMAs as an adjuvant therapy for glycemic control.The study findings did not substantiate a favorable association between MMA intervention and FBG, CRP, TNF-α, IL-10, LDL, HDL, and the Shannon index, but this might be by chance due to the insufficient number of included studies.Further large-scale clinical trials are necessary to confirm these findings with investigations on inflammation and gut microbiota composition while adjusting confounding factors such as diet, physical activity, and the dose and form of MMA intervention.

Figure 1 .
Figure 1.Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram.

Table 1 .
Characteristics of 10 RCTs that investigated the effect of MMAs on T1D.

Table 2 .
GRADE profile of MMAs for glycemic indices.