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The effect of probiotics on gestational diabetes mellitus: an umbrella meta-analysis

BMC Endocrine Disorders volume 24, Article number: 253 (2024) Cite this article

Abstract

Background

Prior studies indicated the positive effects of probiotics on glycemic regulation in patients with gestational diabetes mellitus (GDM). Nonetheless, the results remain inconclusive. To address this, we conducted an umbrella meta-analysis to evaluate the impact of probiotics on glycemic indicators in GDM.

Methods

A comprehensive search was conducted on the PubMed and Scopus databases to identify all relevant meta-analyses of randomized clinical trials published until July 2024. The outcomes included serum hemoglobin A1C (HbA1c), fasting blood insulin (FBI), fasting blood sugar (FBS), homeostatic model assessment for insulin resistance (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), homeostatic model assessment of beta cell function (HOMA-B), C-peptide, and oral glucose tolerance test (OGTT). Standardized mean difference (SMD) was used to test the effects.

Results

In total, 27 studies, comprising 33,378 participants, were included in the analysis. Probiotics resulted in a significant decrease in FBS (SMD: -0.39, 95% CI: -0.56 to -0.23), especially when administered for ≤ 7 weeks. Significant reductions were also observed in FBI (SMD: -1.99, 95% CI: -2.41 to -1.58), HOMA-IR (SMD: -0.61, 95% CI: -0.72 to -0.50), and HOMA-B (SMD: -24.58, 95% CI: -30.59 to -18.56). Moreover, supplementation with probiotics significantly improved QUICKI (SMD: 0.007, 95% CI: 0.004 to 0.01). There was significant evidence of heterogeneity and publication bias. No significant effects were observed on 1-h OGTT, 2-h OGTT, HbA1c, and C-peptide. No dose-specific effect was observed.

Conclusions

Supplementation with probiotics could improve glycemic control in women with GDM. The effects of probiotics on HOMA-IR, HOMA-B, and fasting insulin were clinically important, while, their effect on FBS was not clinically important.

Peer Review reports

Background

Gestational diabetes mellitus (GDM), distinguished by glucose intolerance, is among the prevalent pregnancy complications typically emerging in the second or third trimester of pregnancy, affecting approximately 5–20% of pregnant mothers [1]. The pathogenesis of GDM is multifaceted, encompassing genetic and environmental factors, such as obesity, maternal age, multiple pregnancies, and a history of diabetes [2]. This condition is linked to unfavorable consequences for both mothers and newborns, including preeclampsia, miscarriage, and an elevated risk of type 2 diabetes (T2DM) in mothers after childbirth [3]. Additionally, infants may face respiratory problems, birth defects, and excessive birth weight [4]. To minimize the likelihood of these health consequences, the optimal management for GDM is suggested to be adherence to a healthy diet, physical activity and pharmacological interventions like insulin, as the first line, as well as metformin and sulfonylureas [5, 6]. Sulfonylureas and metformin are discouraged since they can cross the placenta [6]. Although medications have some advantages, they can lead to remarkable side effects, including birth-related complications, neonatal hypoglycemia, and large-for-gestational-age infants [5]. Pregnant mothers may also encounter various challenges after the consumption of antidiabetic drugs, including hypoglycemia, dizziness, abdominal discomfort, and diarrhea [7]. Given the limitations of lifestyle changes and pharmaceutical treatments in managing GDM effectively, it is essential to explore alternative approaches to improve insulin resistance and hyperglycemia.

Dysregulation of gut microbiota has been associated with insulin resistance and metabolic disorders in pregnancy [4, 8]. Women with GDM exhibit decreased alpha diversity compared to non-GDM individuals during mid- and late gestation [9]. In pregnancy, there is an increase in Actinobacteria and Proteobacteria phyla and a decline in beneficial strains like Faecalibacterium prausnitzii and Roseburia intestinalis [4]. Furthermore, in GDM, the Firmicutes/Bacteroidetes ratio elevates towards late pregnancy [9]. These alterations in gut microbiota composition align with the accumulation of fat mass, elevated blood glucose levels, and insulin resistance [10]. Accordingly, the manipulation of the gut flora through the use of probiotics is emerging as an encouraging therapeutic approach for managing GDM. Despite the growing body of evidence, the results of randomized clinical trials (RCTs) [11, 12] on the efficacy of probiotics in managing GDM have been inconsistent, with remarkable differences in treatment duration, probiotic strains used, dose of treatment, and participant characteristics. The meta-analyses of RCTs have also revealed contradictory findings. While some meta-analyses have suggested an improvement in FBS [3, 13], other studies failed to identify any effect on FBS [4, 14,15,16,17,18,19]. This heterogeneity has led to uncertainty regarding the overall effectiveness of probiotics as a therapeutic approach for GDM. An umbrella meta-analysis, which synthesizes findings from multiple meta-analyses, can provide a comprehensive overview of the current evidence, clarify the potential benefits of probiotics, and identify gaps in the literature that require further investigation. This umbrella meta-analysis was conducted to evaluate the effect of probiotics on glycemic parameters in pregnant women with GDM by analyzing existing literature.

Methods

This umbrella meta-analysis was conducted according to the guidelines outlined in the PRISMA statement [20].

Search strategy

Two researchers carried out a literature search on PubMed and Scopus databases to obtain all relevant studies published in English up to July 2024. The search was limited to English-language publications. The search strategy included both text terms and medical subject headings (MeSH). The search strategy included the following terms: (“probiotic” OR “prebiotic” OR “synbiotic” OR “probiotics” OR “prebiotics” OR “synbiotics”) AND (“gestational diabetes” OR “GDM”) AND (“meta-analysis” OR “meta analysis”). A supplementary hand search of references within pertinent studies was also conducted to include missing studies.

Inclusion criteria

Two authors assessed the eligibility of publications separately, and any discrepancies were resolved through a group discussion. The calculated kappa for the inter-rater reliability between the two authors was 0.81 for the data screening and selection process. The inclusion criteria were as follows: (1) Participants: pregnant women with GDM, (2) Intervention: supplementation with probiotics alone or in combination with prebiotics (synbiotics), (3) Comparator: placebo, (4) Outcomes: the outcomes were HbA1c, FBI, FBS, HOMA-IR, QUICKI, HOMA-B, 1-h OGTT, 2-h OGTT, and C-peptide, and (5) Study type: meta-analyses of RCTs. Exclusion criteria included review articles, letters, editorials, protocols, and studies with irrelevant interventions or outcomes.

Data extraction and quality assessment

Two investigators independently conducted data extraction, and any differences were resolved through discussion. The following data were extracted from the studies: the first author’s name, country, sample size, risk of bias (RoB) assessment, year of publication, number of studies, dose and duration of supplementation, and effect sizes. When necessary, the corresponding authors were contacted to obtain any necessary information that was not reported in the studies. The methodological quality of the included studies was measured using A Measurement Tool to Assess Systematic Reviews-2 (AMSTAR-2) criteria [21]. This tool provides a structured approach to assess the critical domains of systematic reviews by considering factors like the appropriateness of the research question, the comprehensiveness of the search strategy, the study selection process, data extraction methods, and the assessment of bias in the included studies.

Data synthesis

The Stata software (version 17) was used to analyze the data. The results were pooled using the standardized mean difference (SMD) and a 95% confidence interval (CI) as the effect size. Q-statistic test and the I2 test were applied to investigate heterogeneity, where a value of I2 ≥ 50% or p < 0.10 indicated significant heterogeneity [22, 23]. Given the expected heterogeneity among the studies, the data were pooled with the use of a random effects model. In addition, we performed subgroup analysis to investigate the sources of heterogeneity, such as the dose of probiotics, duration of supplementation, type of intervention, study quality, and sample size. Sensitivity analysis was also carried out to measure the impact of each study on the pooled results by systematically excluding one study at a time. To investigate publication bias, the funnel plot and the Egger’s test were employed [24]. In cases where the p-values from the Egger’s tests were less than 0.05, the trim-and-fill analyses [25] were additionally carried out to address potential publication biases. Meta-regression analysis was done to evaluate the influence of publication year, sample size, dosage and duration of supplementation, and the proportion of high-quality RCTs in each meta-analysis on the pooled estimates.

Results

Study characteristics

In total, 119 studies were identified by the search strategy. Finally, 27 meta-analyses [3,4,5,6, 8, 9, 14,15,16,17,18,19, 26,27,28,29,30,31,32,33,34,35,36,37,38,39,40], with a total sample size of 33,378 participants, were included. The flow diagram of study selection is reported in Fig. 1. In all studies, the intervention was multistrain probiotics. The sample size ranged from 225 to 9,443 subjects. The dose of probiotics varied from 0.5 × 10^9 to 823 × 10^9 colony-forming units (CFU). The duration of supplementation was between 6 and 14 weeks. Data was reported for FBS in 25 studies [3,4,5,6, 8, 9, 14,15,16,17,18,19, 26,27,28,29,30,31, 33,34,35,36,37, 39, 40], FBI in 20 studies [3, 5, 6, 9, 14,15,16, 19, 26,27,28,29,30,31,32,33,34, 38,39,40], HOMA-IR in 21 studies [3,4,5,6, 9, 14, 16, 18, 19, 26,27,28,29,30,31, 33, 34, 37,38,39,40], HOMA-B in 5 studies [5, 6, 14, 30, 33], QUICKI in 12 studies [5, 9, 14, 26, 27, 30, 31, 33, 34, 38,39,40], 1-h OGTT in 2 studies [15, 30], 2-h OGTT in 3 studies [15, 30, 35], HbA1c in 2 studies [33, 34], and C-peptide in 2 studies [14, 33]. The characteristics of the included studies are presented in Table 1.

Table 1 Characteristics of studies included in umbrella meta-analysis
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Fig. 1
figure 1

Flow diagram of the study

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Quality assessment

According to the AMSTAR-2 criteria, 16 studies were rated as moderate quality and 11 studies were rated as high quality (Table S1).

Results of the umbrella meta-analysis

The meta-analysis found that probiotics significantly reduced FBS (SMD: -0.39, 95% CI: -0.56 to -0.23). A significant heterogeneity was observed (I2 = 76.1%, P = 0.0001) (Fig. 2). In the subgroup analysis, the favorable impact of probiotics on FBS was found across different subgroups. The beneficial effect of probiotics was solely evident when the supplementation period was < 7 weeks (Table 2). A significant reduction in FBI was also found (SMD: -1.99, 95% CI: -2.41 to -1.58), with considerable heterogeneity (I2 = 82.9%, P = 0.0001). However, no significant effects were observed on 1-h OGTT, 2-h OGTT, HbA1c, and C-peptide (Fig. 2; Table 2). Moreover, supplementation with probiotics significantly improved QUICKI (SMD: 0.007, 95% CI: 0.004 to 0.01) and reduced HOMA-IR (SMD: -0.61, 95% CI: -0.72 to -0.50) and HOMA-B (SMD: -24.58, 95% CI: -30.59 to -18.56) (Fig. 3).

Table 2 Overall and subgroup analyses for the effect of probiotics on glycemic indices in women with gestational diabetes
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Fig. 2
figure 2

Pooled analysis for the effect of probiotics on (A) FBS (fasting blood sugar, (B) FBI (fasting blood insulin), (C) 1-hour OGTT (oral glucose tolerance test), (D) 2-hour OGTT, (E) HbA1c (glycated hemoglobin), and (F) C-peptide

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Fig. 3
figure 3

Pooled analysis for the effect of probiotics on (A) HOMA-IR (homeostatic model assessment for insulin resistance, (B) QUICKI (quantitative insulin sensitivity check index), and (C) HOMA-B (homeostatic model assessment of beta cell function)

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Sensitivity and meta-regression analysis

In the sensitivity analysis, no study significantly impacted the pooled effect sizes, indicating the reliability of the results (Fig. S1 to Fig. S5). Additionally, in the meta-regression analysis, the pooled effect sizes were not affected by publication year, the proportion of high-quality studies in each meta-analysis, sample size, and the dose and duration of treatment (Table S2).

Publication bias

Although a significant publication bias was identified for the majority of outcomes (Fig. 4), the trim-and-fill analysis did not alter the pooled estimates. This indicates the minimal impact of publication bias on the results.

Fig. 4
figure 4

Funnel plots for publication bias for outcomes

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Grade assessment

Based on the GRADE criteria, the quality of evidence was moderate for HOMA-B and low for the other outcomes (Table S3).

Discussion

This analysis indicated that probiotics improved glycemic indices in GDM patients. The results revealed that probiotics reduce serum FBS, FBI, HOMA-B, and HOMA-IR index, but increase QUICKI. However, no significant effects were observed on 1-h OGTT, 2-h OGTT, HbA1c, and C-peptide. The positive effect of probiotics on FBS was evident when probiotics were given for a short term (≤ 7 weeks). Other indices of glycemic control were not affected by the treatment duration and intervention dosage. The effects of probiotics on HOMA-IR, HOMA-B, and fasting insulin were clinically important, while, their effect on FBS was not clinically important.

GDM can result in various adverse outcomes if not adequately managed, emphasizing the necessity for safe and efficient therapies. Studies have observed shifts in gut microbiota composition in pregnant women, showing a reduction in favorable bacteria regulating metabolism and an elevation in bacteria with detrimental metabolic impacts. These alterations can disrupt host energy metabolism [4, 8]. Introducing exogenous probiotics to reshape gut microbiota represents a novel approach to GDM management. In this study, the concurrent reduction in FBS and FBI levels, alongside improvements in HOMA-IR and QUICKI, suggests an enhancement in insulin sensitivity rather than insulin secretion. This is in agreement with previous findings in GDM [41, 42], T2DM [43], and metabolic syndrome [44]. However, our analysis did not reveal a significant impact of probiotics on 1-hour OGTT, 2-hour OGTT, HbA1c, and C-peptide. These results should be interpreted with caution due to the heterogeneity and limited number of studies analyzed for the outcomes. The findings from the present meta-analysis have several clinical utilities. Probiotics may provide a safe and effective non-pharmacological adjunct for managing GDM, potentially reducing the need for insulin or other medications that may have side effects. Effective management of GDM through probiotics may also lower the possibility of future metabolic diseases in both mothers and infants, promoting better long-term health outcomes.

Other studies have also shown that probiotics could improve various metabolic parameters. An umbrella meta-analysis by Zarezadeh et al. [45] revealed that probiotics have beneficial effects on FBS, HbA1c, HOMA-IR, and insulin levels. A period of less than 8 weeks of probiotic supplementation at moderate dosages (10^8 or 10^9 CFU) was an effective approach for improving glycemic parameters. Another umbrella review suggested that probiotics could be used as a complementary therapy for controlling high blood pressure [46]. Additionally, an umbrella meta-analysis indicated that synbiotic supplementation can slightly improve lipid profiles and anthropometric indices and might be a therapeutic option for obesity and its related disorders [47]. Probiotics have also been shown to reduce inflammatory biomarkers [48] and biomarkers of oxidative stress [49].

The Minimal Clinically Important Difference (MCID) is a critical concept in clinical research that quantifies the smallest change in a patient-reported outcome that is perceived as beneficial by the patient and would necessitate a change in their management. Initially defined by Jaeschke et al. in 1989 [50], the MCID serves to bridge the gap between statistical significance and clinical relevance, emphasizing the importance of patient perspectives in evaluating treatment efficacy. It reflects the threshold at which changes in health status are meaningful enough to impact clinical decisions, thus guiding healthcare providers in assessing the effectiveness of interventions. The MCID for FBS, HOMA-IR, HOMA-B, and fasting insulin is reported to be 1.6 mmol/L, 0.05 units, 5 units, and 1.5 IU/mL, respectively [51]. In our study, the pooled effect size for FBS, HOMA-IR, HOMA-B, and fasting insulin was − 0.35, -0.61, -24.58, and − 1.99, respectively. Therefore, the effects of probiotics on HOMA-IR, HOMA-B, and fasting insulin were clinically important, while, their effect on FBS was not clinically important. The positive impact of probiotics intake on glycemic indices in GDM is mediated through various mechanisms, comprising modulation of gut microbiota, improvement of insulin sensitivity, reduction of inflammation, increased production of short-chain fatty acids (SCFAs), and reduction of oxidative stress [52, 53]. Probiotics, such as Lactobacillus casei, Bifidobacterium bifidum, and Lactobacillus acidophilus can alter the gut microbial composition and promote the growth of advantageous bacteria. This shift in the gut flora could result in improved glucose metabolism [54]. Probiotics can decrease inflammation by affecting the immune system and decreasing the production of inflammatory cytokines, such as tumor necrosis factor-a (TNF-a) and interleukin-6 (IL-6) [19]. Chronic inflammation is a well-identified contributor to insulin resistance, and its reduction can improve glycemic control [55]. Probiotics can stimulate the production of SCFAs, such as acetate, propionate, and butyrate, by fermenting dietary fiber [56]. SCFAs can improve insulin sensitivity, suppress gluconeogenesis, and reduce glucose levels [31]. Probiotics enhance glucagon-like peptide-1 (GLP-1) secretion, subsequently reducing glucose levels via the following mechanisms: (a) enhancing insulin release and slowing gastric emptying [57], (b) modification of gene expression of proteins associated with glucose metabolism, including PPAR-gamma, glucose transporter type 4, ghrelin, leptin, and glucose-6-phosphatase [58], and (c) decreasing toll-like receptor activity, thereby increasing insulin sensitivity in muscle. This can result in lower FBS and FBI levels, as well as improved HOMA-IR and QUICKI [59]. Probiotics also reduce oxidative stress by elevating the antioxidant enzyme activities, resulting in a reduction in the production of reactive oxygen species [60]. Oxidative stress is known to be related to insulin resistance and impaired glucose tolerance [61].

To our knowledge, this umbrella meta-analysis represents the first investigation assessing the impact of probiotics on glycemic parameters in GDM. The strength of our umbrella meta-analysis is inclusion of a high number of studies and a thorough evaluation of diverse metabolic factors associated with glycemic regulation. The results were obtained from meta-analyses with moderate to high quality, increasing the reliability of the findings. Moreover, the sources of heterogeneity were examined using subgroup and meta-regression analyses by considering various factors, especially dose and duration of intervention. Several limitations should be acknowledged in this study. First, significant heterogeneity was found among the included studies, reducing the generalizability of the results. Random effects models were employed to minimize the influence of heterogeneity on the combined estimates. In the subgroup analysis, differences in sample sizes, supplementation dose, duration of treatment, and study quality were recognized as the origins of the heterogeneity. Second, a significant publication bias was detected. The search strategy was limited to publications in the English language, which may have resulted in the omission of some smaller studies. Nevertheless, using the trim-and-fill analysis, we revealed that the influence of publication bias on the pooled estimates is insignificant. Third, studies have highlighted that different probiotic strains may have diverse metabolic impacts [62]. While all studies in this analysis administered multistrain probiotics, information regarding the impact of different strains on outcomes was scarce. Nevertheless, research indicates that utilizing a combination of various probiotic strains offers greater efficacy compared to single-strain probiotics, as the synergistic interaction among multiple strains may enhance their overall effects [33]. Additionally, the timing of intervention might influence the results [63], a factor that was not investigated in the included studies. Consequently, subgroup analysis considering intervention timing and probiotic strains could not be conducted. These factors likely contributed to increased heterogeneity among the studies, emphasizing the need for their consideration in the future studies. Another limitation of this study is that no dose-specific effects were observed in the analysis, thus, the optimal dosage of probiotics for improving glycemic parameters in GDM remain unclear. Moreover, while some glycemic indicators showed significant improvements, other important measures such as HbA1c, C-peptide, and OGTT did not demonstrate significant changes. This inconsistency in outcomes may limit the overall conclusions about the effectiveness of probiotics.

Conclusion

In conclusion, this analysis indicated that probiotics could offer beneficial effects on the indices of glucose metabolism in patients with GDM. Yet, to generalize these findings effectively, more clinical trials with larger sample sizes are essential, given the heterogeneity observed across current studies. Moreover, further research is necessary to explore the impact of various probiotic strains and their optimal timing of intervention on individuals with GDM.

Data availability

All data generated or analyzed during this study are included in this published article. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

GDM:

Diabetes mellitus

HbA1c:

hemoglobin A1C

FBI:

fasting blood insulin

FBS:

fasting blood sugar

HOMA-IR:

homeostatic model assessment for insulin resistance

QUIKI:

quantitative insulin sensitivity check index

HOMA-B:

homeostatic model assessment of beta cell function

OGTT:

C-peptide, oral glucose tolerance test

SMD:

standardized mean difference

T2DM:

type 2 diabetes

RCTs:

randomized clinical trials

MeSH:

medical subject headings

RoB:

risk of bias

AMSTAR-2:

A Measurement Tool to Assess systematic Reviews-2

CI:

confidence interval

CFU:

colony-forming units

JBI:

Joanna Briggs Institute scale

SCFAs:

short-chain fatty acids

TNF-a:

tumor necrosis factor-a

IL-6:

interleukin-6

GLP-1:

glucagon-like peptide-1

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  1. Department of Gynecology and Obstetrics, Shanxi Provincial Children’s Hospital (Shanxi Provincial Maternal and Child Health Hospital), No.13 Xinmin North Street, Taiyuan, Shanxi, 030001, China

    Guixia Sun, Hongli Hou & Shanshan Yang

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  1. Guixia Sun
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GS performed data collection, data analysis, article preparation, supervision, and re-writing of the manuscript. HH and SY performed the analysis, writing the draft of the manuscript, methodology, investigation, and collation of the literature. GS and SY conceived of the study idea and article review. All authors read and approved the final manuscript.

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Sun, G., Hou, H. & Yang, S. The effect of probiotics on gestational diabetes mellitus: an umbrella meta-analysis. BMC Endocr Disord 24, 253 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12902-024-01751-w

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BMC Endocrine Disorders

ISSN: 1472-6823

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