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. 2025 Aug 26;25:886. doi: 10.1186/s12884-025-07999-x

The mechanism of probiotics in pregnancy outcomes in overweight or obese pregnant women based on meta-analysis, network pharmacology and molecular docking

Ting Zhong 1, Jihan Sun 2, Miao Miao 3, Guiju Sun 1,✉
PMCID: PMC12379535  PMID: 40859213

Abstract

Background

The prevalence of obesity among women of reproductive age is increasing worldwide. Obesity significantly increases the risk of adverse pregnancy outcomes. The effectiveness of probiotics in improving the pregnancy outcomes of overweight or obese pregnant women is still controversial.

Methods

PubMed, Embase, Scopus, Cochrane, and Web of Science were searched for relevant articles up to May 30, 2025. Revman 5.4 was used for the meta-analysis. In network pharmacology, the gutMGene database was used to obtain the bioactive components of probiotics, and the SwissTargetPrediction platform was used to predict the targets of the active components. The related targets of diseases were obtained through OMIM and GeneCards databases and the bioactive compound–target network was constructed. AutoDockTools software was used for molecular docking verification.

Results

Eight randomized controlled trials (RCTs) involving 1563 participants were included in the meta-analysis.The results showed no significant difference between probiotic and control in improving adverse pregnancy outcomes.However, subgroup analyses indicated that the combination of Bifidobacterium and Lactobacillus significantly reduced the risk of small-for-gestational-age (SGA) (RR = 0.54, 95% CI [0.30,0.96], P = 0.04). Network pharmacology identified 46 bioactive metabolites and 166 targets. The key bioactive metabolites of probiotics were arctigenin, aglycone, 10-Oxo-11-octadecenoic acid, doconexent, 10-keto-12Z-octadecenoic acid, kaempferol, quercetin, ponciretin, caffeic acid, and equol. The core targets were STAT3, ESR1, HSP90AA1, CCND1, AKT1, EGFR, BCL2, SRC, MTOR, and TP53. Molecular docking validated high-affinity interactions.

Conclusion

These findings suggest that probiotics can improve some adverse pregnancy outcomes in overweight or obese pregnant women through multiple components, targets and pathways, and provide a basis for further research. In the future, more in vitro and in vivo studies are needed to verify the efficacy of probiotics.

The study has been registered in the international prospective register of systematic reviews (PROSPERO) under the number CRD42024576090.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-025-07999-x.

Keywords: Probiotics, Obesity, Pregnancy outcomes, Network pharmacology, Molecular docking

Introduction

Obesity is a growing epidemic worldwide and has become an increasingly common health burden. According to the latest statistics from the World Health Organization (WHO), the prevalence of obesity has tripled since 1975 [1]. The global obesity rate among women of reproductive age is increasing, with the rate expected to reach 20% by 2025 [2]. Of the more than $400 billion in excess direct health care costs, overweight or obese women account for approximately 75% [3]. Obesity has become a major public health issue affecting the health of pregnant women and their children. Obesity significantly increases the risk of adverse pregnancy outcomes, such as preeclampsia and eclampsia, gestational diabetes mellitus (GDM), and the cesarean section rate [4]. Liu et al. studied the correlation between obesity and pregnancy complications and reported that a 10%−15% reduction in prepregnancy body mass index (BMI) was associated with a significant reduction in the risk of gestational hypertension, cesarean section and macrosomia [5]. A retrospective study revealed that the probability of cesarean delivery increased with BMI at delivery [6]. Each increasing BMI class predicted a 1.21-fold greater odds of cesarean delivery, even after adjustment for maternal age, year of birth, gestational diabetes mellitus, and hypertensive disorders of pregnancy. Compared with normal-weight women, obese women have higher rates of miscarriage and preterm birth [7]. Obesity not only affects pregnant women but also affects the health of their offspring. Rooney et al. followed 795 women and 802 children during pregnancy and 10 to 15 years after delivery and reported that maternal obesity is a very strong predictor of childhood, adolescence, and early adult obesity in their offspring [8]. Given the short- and long-term health problems caused by obesity, finding effective therapies to control maternal obesity is critical to ensuring maternal and child health.

According to the researchers’ findings, the gut microbiota is closely related to obesity [9–11]. Song et al. [12] identified significant differences in gut microbiota composition between overweight and normol weight pregnant women during the second trimester. In addition, Gomez et al. [13] showed that overweight pregnant women had higher abundance and homogeneity of intestinal flora than obese pregnant women.The gut microbiota influences the host’s energy intake and metabolic processes, not only by helping the host to digest and absorb nutrients from food, but also by potentially affecting fat synthesis and storage [14–16]. These findings suggest that the microbiome may be a potential target for restoring intestinal flora homeostasis in obese pregnant women and improving pregnancy outcomes.

Probiotics are living microorganisms that confer health benefits to the host when administered in adequate amounts, including Bifidobacterium, Lactobacillus, Lactococcus and yeast, which can improve the intestinal microenvironment and prevent systemic diseases and inflammation [17–19]. Probiotics can directly act on the intestinal mucosal barrier, regulate the body’s metabolism, and may restore the imbalance of the intestinal flora caused by obesity, thereby improving the health of obese pregnant women and their offspring [20–22]. The results of a 10-year follow-up study of probiotic intervention in overweight or obese pregnant women showed that perinatal probiotic intervention can inhibit excessive weight gain in the first years of life [23].Several studies have demonstrated that gut colonization begins prenatally [24, 25]and that the establishment of the gut microbiota in early life may reduce the risk of chronic diseases in adulthood, including obesity and type 2 diabetes [26–28]. Thus, intervention in the gut microbiota during pregnancy is an excellent opportunity to improve offspring health. At present, there is no consensus on the effect of probiotic intervention on overweight or obese pregnant women. High-quality meta-analyses generate the highest level of evidence and address the problem of inconsistent results from a single study.

Network pharmacology is a comprehensive discipline that combines systems biology and network informatics and can elucidate the mechanism of action in complex biological systems from the perspective of multiple components, multiple targets and pathways [29, 30]. Molecular docking is a computer simulation technique that can simulate the interaction between molecules and proteins at the atomic level and predict the affinity and binding mode between therapeutic molecules and target molecules [31]. In this study, we conducted a meta-analysis to evaluate the efficacy of probiotics in improving pregnancy outcomes in obese pregnant women. In addition, we constructed a bioactive compound–target network to reveal the potential therapeutic value of the interaction. A network pharmacology method was used to explore the potential pharmacological mechanism of probiotics in improving adverse pregnancy outcomes, and molecular docking was further performed to determine the binding efficiency of probiotic metabolic compounds and putative targets.

Methods

Meta-analysis

This meta-analysis was conducted following Cochrane’s Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines. The protocol of this study has been registered in the international prospective register of systematic reviews (PROSPERO) under the number CRD42024576090.

Search strategy

We conducted a literature search of PubMed, Embase, Scopus, Cochrane, and Web of Science for relevant articles up to May 30, 2025. Moreover, we conducted grey literature searches by utilizing professional databases such as OpenGrey and ClinicalTrials.gov.The search strings included (“probiotics” OR “Lactobacillus” OR “Bifidobacterium” OR “saccharomyces” OR “VSL#3” OR “synbiotics”) AND (“overweight” OR “obesity” OR “adiposity” OR “body mass” OR “BMI” OR “body fatness”) AND (“pregnancy” OR “gestation” OR “Delivery, Obstetric”) AND (“randomized controlled trial” OR “controlled clinical trial”). The full search strategy can be found in Appendix 1.

Inclusion and exclusion criteria

The included studies met the following criteria: (1) pregnant women with a BMI ≥ 25 kg/m2; (2) study design was a randomized controlled trial (RCT); (3) intervention was probiotic; (4) participant age > 18 years; and (5) at least one outcome of interest was reported, categorized into maternal outcomes and infant outcomes, include birth weight, GDM, preeclampsia, gestational weight gain, gestational hypertension, cesarean delivery, SGA, large-for-gestational-age (LGA), preterm delivery, and admission to the neonatal intensive care unit (NICU).

The exclusion criteria were as follows: (1) experimental trials involving animals or nonhumans; (2) abstracts, case reports, expert opinions, reviews, letters, or editorials; and (3) non-randomized controlled trials (non-RCTs).

Data extraction and quality assessment

The following data were extracted from the selected articles by two independent reviewers: author first name, publication year, study design, sample size, baseline characteristics of the participants, composition, dose and duration of intervention.The risk of bias was independently assessed by both reviewers via Cochrane Risk of Bias(RoB) 2.0 tool.The risk assessment indicators evaluates six domains: randomization process, deviation from intended interventions, missing outcome data, measurement of the outcome, selection of the reported result, and overall risk of bias. Each study was assessed as low risk, high risk, or some concern. We used the GRADE method to assess the quality of evidence outcomes [32]. The evaluation incorporated factors including risk of bias, publication bias, imprecision of results, heterogeneity, and indirectness of evidence. Ultimately, the quality of evidence was categorized as “high,” “moderate,” “low,” or “very low”.Consistency of the study was assessed by kappa statistics. Any disagreements between reviewers were resolved through discussion or consultation with a senior author.

Statistical analysis

RevMan 5.4 was used for the meta-analysis. Binary variables are presented as relative risk ratios (RRs) and 95% confidence intervals (CIs). The standardized mean difference (SMD) was used as the effect size if the results were continuous variables. Sensitivity analyses were performed by sequentially removing individual studies to examine their impact on overall outcomes. Clinical heterogeneity was assessed by comparing participant inclusion criteria (e.g. pre-pregnancy BMI, gestational age) and outcome definitions. Methodological heterogeneity was assessed by RoB2.0. Additionally, heterogeneity was determined through Q test combined with I² value. Fixed-effects model was used when I² ≤ 50% and clinical/methodological heterogeneity was low, and random-effects model was used for I² >50% or significant clinical/methodological diversity. P < 0.05 was considered statistically significant. Subgroup analysis was performed based on probiotic genera, duration and form of administration.

Network pharmacology and molecular docking

Acquisition of bioactive compounds and targets of probiotics

The bioactive compounds generated by probiotics were identified via gutMGene [33] (http://bioannotation.cn/gutmgene/) (accessed on June 1, 2025). We adopted PubChem [34] (https://pubchem.ncbi.nlm.nih.gov/) (accessed on June 2, 2025) to obtain a simplified molecular input line entry system (SMILES) format of bioactive metabolites and SwissTargetPrediction (STP) [35] (http://www.swisstargetprediction.ch/) (accessed on June 3, 2025) to search for targets linked to the metabolites. A probability > 0 was used as the screening criterion to predict the targets of the metabolites.

Retrieval of disease targets

We used GeneCards [36] (https://www.genecards.org/) and OMIM [37] (https://www.omim.org/) (accessed on June 4, 2025) to retrieve disease targets. The final targets were obtained by combining the search results and removing duplicates.

Identification of intersection targets

With the help of VENNY 2.1.0, we obtained the intersection of the predicted targets of the bioactive compounds and known targets of the disease.

Network topology analysis

The intersection targets and their corresponding bioactive compounds were imported into Cytoscape 3.7.2 to visualize the bioactive compound–target network diagram [38]. The network was analyzed via the CytoHubba plugin to identify key bioactive metabolites.

Protein interaction network construction

The protein–protein interaction (PPI) network of the intersection targets was constructed via STRING [39] (https://string-db.org/) (accessed on June 5, 2025). The organism and minimum interaction score were set as “Homo sapiens” and “highest confidence > 0.7”. Cytoscape 3.7.2 software was used to visualize the PPI network, and the CytoNCA plugin was used to analyze the topology of the network and identify the most important proteins in the network.

Enrichment analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of intersection targets were performed with R 4.4.1. The main biological processes and signaling pathways with significant differences were screened out under the condition of P < 0.05, and the results were visualized.

Molecular docking

The proteins with Betweenness, Closeness, Degree, Eigenvector, LAC, and Network values greater than the median in the PPI network were calculated. After reviewing the relevant literature, the five core proteins most closely related to the disease were selected for molecular docking with the active metabolites of the top four degree values in the bioactive compound–target network. The 2D structures of the key active metabolites were obtained from PubChem, and the energy minimization process was performed with the help of Chem3D. The protein structure was obtained from PDB (http://www.rcsb.org) (accessed on June 7, 2025). Pymol and AutoDock Tools 1.5.7 [40] software were used to remove water molecules, remove ligands, and add hydrogen atoms to small molecules and target proteins. Finally, the binding energy was calculated via molecular docking via AutoDock Vina [41], and the docking results were visualized via PyMOL.

Results

Meta-analysis

Study selection and study characteristics

Figure 1 shows the flowchart from the search to the meta-analysis.A total of 1,674 records were found through an electronic database search, while 39 records were from the register. After using the literature management software to remove duplicates, 1131 records were left for the initial screening of titles and abstracts. A total of 989 records were excluded based on the inclusion/exclusion criteria independently assessed by two investigators, leaving 142 records requiring further attempts to obtain full text. The full text of some documents is not available due to database access restrictions such as paywalls, lack of institutional subscription to specific journals, and grey literature without public full text.We were able to obtain 11 articles through interlibrary loan and direct contact with authors, and in the end 121 records were not available in full text. Twenty-one records were included in the full-text eligibility assessment, and after further review, eight trials met the inclusion criteria for this review.There was good inter-investigator agreement, with Kappa values of 0.89 and 0.82 for the title/abstract and full-text screening stages, respectively, suggesting that the screening criteria were clear and reproducible.

Fig. 1.

Fig. 1

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Flow chart from the search to the meta-analysis

Table 1 summarized the characteristics of the 8 included articles. One article used a single probiotic, four articles used two kinds of probiotics, and three articles used three kinds of probiotics. Seven articles were supplemented with probiotics in capsule form, while one article was supplemented with probiotics in the form of yogurt. The intervention was initiated in the first or second trimester of pregnancy, five articles until the end of pregnancy, two articles until 6 months postpartum, and one article for 4 weeks. The intervention probiotics used vary, but most studies have used two probiotics, Lactobacillus and Bifidobacterium, with different subtypes of strains. All participants were randomly assigned to receive either probiotics daily or placebo/traditional yogurt.

Table 1.

Characteristics of the included studies

Author, Year Study Design Comparators Sample size Prepregnancy BMI (kg/m2) Gestational weeks Regimen of Intervention Dose of Intervention Form of intervention Administration duration
Pellonperä, O. 2020 [42] Db-RCT probiotics + placebo 109 29.9 ± 4.7 13.7 ± 2.1 Lactobacillus rhamnosus HN001 and Bifidobacterium animalis ssp. lactis 420 1010 CFU capsule From the randomisation to the end of pregnancy
placebo + placebo 110 29.7 ± 4.2 13.9 ± 2.0 microcrystalline cellulose -
Lindsay, K. L.2014 [43] Db-RCT probiotic 63 - 13.8 ± 2.3 Lactobacillus salivarius UCC118 109 CFU capsule From 24 weeks’ gestation to 28 weeks’ gestation
placebo 75 - 13.9 ± 2.4 - -
Callaway, L. K.2019 [44] Db-RCT probiotic 204 31.9 ± 7.5 - Lactobacillus rhamnosus(LGG) and Bifidobacterium animalis subspecies lactis(BB-12) > 109 CFU capsule From < 20 weeks’ gestation until birth
placebo 207 31.6 ± 7.2 - microcrystalline cellulose and dextrose anhydrate capsules -
Halkjar, S. I.2019 [45] Db-RCT probiotic 25 31.7 ± 1.8 15.5 ± 1.5 Streptococcus thermophilus DSM 24,731, bifidobacteria (Bifidobacterium breve DSM 24,732, Bifidobacterium longum DSM 24,736, Bifidobacterium infantis DSM 24,737) and lactobacilli (Lactobacillus acidophilus DSM 24,735, Lactobacillus plantarum DSM 24,730, Lactobacillus paracasei DSM 24,733, Lactobacillus delbrueckii subsp. bulgaricus DSM 24,734) 450 billion CFU capsule From14 − 20 weeks’ gestation until birth
placebo 25 32.1 ± 1.3 15.1 ± 1.4 microcrystalline cellulose, magnesium stearate, and silicon dioxide -
Saros, L.2023 [46] Db-RCT probiotics + placebo 81 28.4 (26.5; 31.0) 13.8 ± 2.1 Lacticaseibacillus rhamnosus HN001 and Bifidobacterium animalis ssp. lactis 420 1010 CFU capsule From enrollment(13.9 ± 2.1 gestational weeks) until 6 months postpartum
placebo + placebo 85 9.2 (26.5; 31.8) 13.9 ± 2.0 microcrystalline cellulose -
Pellonperä, O.2019 [47] Db-RCT probiotics + placebo 109 29.9 ± 4.7 13.7 ± 2.1 Lactobacillus rhamnosus HN001 and Bifidobacterium animalis ssp. lactis 420 1010 CFU capsule From < 18 gestational weeks to 6 months postpartum
placebo + placebo 110 29.7 ± 4.2 13.9 ± 2.0 microcrystalline cellulose -
Asgharian, H.2019 [48] Db-RCT Probiotic yoghurt 65 29.2 ± 3.3 - Lactobacillus acidophilus La5 and Bifidobacterium lactis Bb12 + Streptococcus thermophilus and Lactobacillus delbrueckii subsp. Bulgaricus 5 × 1010+109 CFU yoghurt From 24 weeks of gestation until delivery
Conventional yoghurt 65 30.3 ± 4.1 - Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus 109 CFU
Okesene-Gafa, K.2019 [49] 2 × 2 factorial, RCT probiotic 115 38.9 ± 6.5 15.2 ± 1.8 Lactobacillus rhamnosus GG and Bifidobacterium lactis BB12 minimum 6.5 × 109 CFU capsule From 12–17 weeks’ gestation until delivery
placebo 115 38.2 ± 5.7 15.1 ± 1.8 microcrystalline cellulose and dextrose anhydrate -
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Quality and risk of bias within studies

The risk of bias assessment is shown in Fig. 2. A total of 4 studies were categorised as “some concerns” areas, while 4 studies showed “low risk”. All 8 randomized controlled trials were double-blinded.The quality of GRADE evidence for preeclampsia, cesarean delivery, and gestational hypertension was low, and moderate for the remaining outcomes.

Fig. 2.

Fig. 2

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The risk of bias assessment

Maternal outcomes

Figure 3 shows that the probiotic group had a similar effect to the control group in terms of the risk for gestational diabetes mellitus (RR = 1.07; 95% CI [0.86, 1.32]; I2 = 13%; 6 articles), preeclampsia (RR = 1.74; 95% CI [0.99, 3.08]; I2 = 0%; 5 articles), gestational hypertension (RR = 1.34; 95% CI [0.94, 1.92]; I2 = 0%; 5 articles), and cesarean delivery (RR = 0.97; 95% CI [0.83, 1.13]; I2 = 0%; 7 articles); similarly, no significant difference was found in gestational weight gain (SMD = 0.03; 95% CI [− 0.14, 0.21]; P = 0.72; I²=8%).

Fig. 3.

Fig. 3

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Forest plot for the overall meta-analysis of maternal outcomes: (A) gestational diabetes mellitus; (B) preeclampsia; (C) gestational hypertension; (D) cesarean delivery; (E) gestational weight gain

Infant outcomes

Figure 4 shows no difference in the risk of preterm birth (RR = 1.03; 95% CI [0.66, 1.61]; I2 = 22%; 6 articles), admission to the NICU (RR = 0.98; 95% CI [0.62, 1.54]; I2 = 0%; 4 articles), SGA (RR = 0.58; 95% CI [0.33, 1.01]; I2 = 0%; 5 articles) or LGA (RR = 1.00; 95% CI [0.78, 1.29]; I2 = 0%; 7 articles) between the probiotic and control groups. The pooled results of birth weight from the included studies suggested that probiotics have effects similar to those of the control (SMD = 0.04; 95% CI [− 0.07, 0.15]; P = 0.45), with no significant heterogeneity among the studies (I2 = 2%, heterogeneity P = 0.41).

Fig. 4.

Fig. 4

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Forest plot for the overall meta-analysis of infant outcomes: (A) preterm birth; (B) admission to the NICU; (C) SGA; (D) LGA; (E) birth weight

Subgroup analysis of probiotic genera

We conducted subgroup analysis of the included studies according to probiotic genera, duration and form of administration. There was no significant difference in the results of most subgroup analyses. Notably, the risk of SGA was lower in the two-probiotic combination group (Bifidobacterium and Lactobacillus genera) than in the control group, with an RR of 0.54 (95% CI 0.30 to 0.96, P = 0.04) (Fig. 5). The results of the remaining subgroup analyses are presented in Appendix 2.

Fig. 5.

Fig. 5

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Forest plots of subgroup analysis of probiotic genera

Network pharmacology and molecular docking

The meta-analysis revealed that the combination of Bifidobacterium and Lactobacillus could reduce the risk of SGA, so we further explored the network pharmacology and molecular docking mechanism of Bifidobacterium and Lactobacillus in improving the pregnancy outcome of SGA.

Prediction of active metabolites and targets of probiotics

After searching the gutMGene database and excluding the components without targets in the STP database, 37 active components and 1511 targets were obtained for Bifidobacterium, and 15 active components and 638 targets for Lactobacillus were obtained. After pooling and removing duplicates, 46 active ingredients and 679 targets of probiotics were obtained.

Prediction of SGA-related targets

The GeneCards and OMIM databases were searched, and ultimately, 1129 SGA-related targets were obtained. With the help of VENNY 2.1.0, 166 probiotic targets intersecting with SGA were obtained and plotted in a Venn diagram (Fig. 6).

Fig. 6.

Fig. 6

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Venn diagram of probiotic and SGA targets

Bioactive compound–target network

Cytoscape 3.7.2 software was used to construct the bioactive compound–target network of probiotics and SGA, which included 718 nodes and 2,133 edges. The degree value of each node (bioactive component) is calculated based on its number of direct connections in the network. The metric identifies highly interconnected components.Using the CytoNCA plugin to analyze the topology of the network and identify the most important proteins in the network, components were ranked in descending order of degree values.Utimately, we selected the key bioactive ingredients with the top 10 degree values. Arctigenin, aglycone, 10-oxo-11-octadecenoic acid, doconexent, 10-keto-12Z-octadecenoic acid, kaempferol, quercetin, ponciretin, caffeic acid, and equol may play important roles in the prevention of SGA by probiotics (Fig. 7).

Fig. 7.

Fig. 7

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Network diagram of bioactive compounds and their targets. (B: The bioactive compounds of Bifidobacterium L: The bioactive compounds of Lactobacillus P: Shared bioactive compounds of Bifidobacterium and Lactobacillus)

PPI network

The PPI network was constructed via the STRING database and Cytoscape 3.7.2, which included 161 nodes and 1286 edges. Core targets were obtained after three rounds of iterative topological screening.Parameters for each topological network were computed via the CytoNCA plugin, including closeness, degree, eigenvector, LAC, and network.Targets with parameter values greater than the median of the current network were retained in each round, and the screening was repeated after reconstructing the sub-network. The final 10 targets consistently satisfy the threshold conditions in all rounds.The size and color of each node were proportional to the degree value, and the top 10 targets were STAT3, ESR1, HSP90AA1, CCND1, AKT1, EGFR, BCL2, SRC, MTOR, and TP53 (Fig. 8).

Fig. 8.

Fig. 8

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Construction of the PPI network and screening of core targets

GO and KEGG enrichment analysis

GO enrichment analysis revealed 2936 entries (P < 0.05), of which 2640 were related to biological processes (BP), including positive regulation of phosphorylation, positive regulation of protein phosphorylation, response to xenobiotic stimulus and response to peptide hormone. Ninety-three entries were related to the cellular component (CC) category, involving membrane rafts, membrane microdomains, focal adhesion and cell − substrate junctions. A total of 203 entries were related to the molecular function (MF) category, involving protein serine/threonine kinase activity, protein serine kinase activity, protein tyrosine kinase activity, and phosphatase binding. The top 30 disease-related signaling pathways were selected for visualization (Fig. 9). KEGG enrichment analysis revealed 172 signaling pathways (P < 0.05), which were associated mainly with the PI3K − Akt signaling pathway, EGFR tyrosine kinase inhibitor resistance, prostate cancer, and endocrine resistance. The top 30 signaling pathways were visualized (Fig. 10).

Fig. 9.

Fig. 9

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GO function analysis

Fig. 10.

Fig. 10

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KEGG function analysis

Molecular docking

ESR1 (PDB code: 5W9D), HSP90AA1 (PDB code: 8SBT), EGFR (PDB code: 8PO4), MTOR (PDB code: 8PPZ), and TP53 (PDB code: 2BIM) were selected as protein receptors for molecular docking with arctigenin, aglycone,10-Oxo-11-octadecenoic acid, and doconexent.The above target proteins were core targets screened by network pharmacology and the five most relevant to the disease were selected by reviewing the literature. The compounds were obtained by screening based on the four active ingredients with the highest degree values.The results revealed that the binding energies of the key active ingredients and the core targets were all less than − 4.5 kcal/mol (1 cal = 4.2 J), and the binding energies of most of the results were less than − 7.0 kcal/mol (Table 2). In the Doconexent-HSP90AA1 interaction, the binding energy is − 8.9 kcal/mol through hydrophobic interactions and hydrogen bonding. Aglycones are linked to EGFR through salt bridges, hydrogen bonds, and hydrophobic interactions, with a combined energy of −8.7 kcal/mol. The integrated energy of arctigenin-MTOR was − 8.3 kcal/mol, and the molecules were connected by π-stacking, hydrogen bonding and hydrophobic interactions, whereas aglycone-ESR1 was bound by hydrogen bonding and hydrophobic interactions, and the integrated energy was − 9.4 kcal/mol. Aglycone and TP53 are linked by hydrophobic interactions and hydrogen bonding, with a binding energy of −8.3 kcal/mol. These findings indicate that the key active metabolites have good binding ability between their core targets and that most of them have strong binding ability [50]. These results suggested that the active ingredients in Bifidobacterium and Lactobacillus could spontaneously bind to the core target proteins and play a preventive role in SGA. Pymol software was used to visualize some of the results (Fig. 11).

Table 2.

Molecular Docking energy scoring results (kcal/mol)

Receptors Ligands HSP90AA1 EGFR MTOR ESR1 TP53
Arctigenin -7.4 -8.5 -8.3 -8.2 -7.0
Aglycone -8.2 -8.7 -8.0 -9.4 -8.3
Doconexent -8.5 -7.5 -8.2 -6.3 -5.1
10-Oxo-11-octadecenoic acid -6.9 -6.3 -6.3 -5.4 -4.5
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Fig. 11.

Fig. 11

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Molecular docking model diagram

Discussion

Meta-analysis findings and interpretation

In the meta-analysis, we identified eight trials involving 1563 pregnant women and showed that probiotics did not improve adverse pregnancy outcomes in overweight or obese pregnant women.The results of the subgroup analysis revealed that the combination of Bifidobacterium and Lactobacillus reduced the risk of SGA in overweight or obese pregnant women. This inconsistency may stem from two key factors.Firstly, different probiotic strains have different physiological functions, metabolites, and immunomodulatory properties, and the meta-analysis included several different strain combinations, including single strains, strains of other genera such as Streptococcus, or mixtures of Bifidobacterium and Lactobacillus in different proportions. They may have little or no effect on the metabolic and inflammatory status of obese pregnant women, and some combinations may even have a negative effect, thus offsetting the potential benefits of the Bifidobacterium and Lactobacillus combination. Secondly, there may have been significant differences between the groups of obese pregnant women in the eight included trials.These differences included baseline BMI, comorbidities (e.g., insulin resistance, pre-diabetic status during pregnancy), dietary composition, and baseline gut flora status. All of these factors may significantly influence the body’s response to probiotic interventions.

Network pharmacology and molecular docking findings and mechanism interpretation

The mechanisms by which Bifidobacterium and Lactobacillus reduce the risk of SGA vary depending on the different bacterial genera.Bifidobacterium specialize in breaking down oligogalactans into short-chain fatty acids, thereby increasing nutrient bioavailability and inhibiting maternal and fetal inflammatory responses [51]. Lactobacillus maintain mucosal barriers and attenuate upstream infections [52].Based on these mechanisms targeting specific bacterial genera, we further applied network pharmacology and molecular docking techniques to find the molecular pathways and targets behind this protective effect.We identified 46 bioactive metabolites and 166 intersection targets of probiotic metabolites with SGA via network pharmacology analysis. We found that key genes (STAT3, ESR1, HSP90AA1, CCND1, AKT1, EGFR, BCL2, SRC, MTOR, and TP53) regulated PI3K − Akt, EGFR tyrosine kinase inhibitor resistance and other numerous signaling pathways. The molecular docking results indicated that arctigenin, aglycone,10-Oxo-11-octadecenoic acid, and doconexent exhibited good affinities for ESR1, HSP90AA1, EGFR, MTOR and TP53.In the PPI network, we identified the core genes as ESR1, HSP90AA1, EGFR, MTOR, and TP53. ESR1 is a nuclear transcription factor that is expressed mainly in the female reproductive tract. ESR1 is essential for embryo transport and implantation, as well as the development and function of the placenta [53, 54]. HSP90AA1, heat shock protein 90α, is a protein that plays an important role in organisms. The expression of HSP90AA1 is associated with certain biological pathways during fetal heart development, which may be essential for the normal development of the fetal heart [55]. EGFR plays a key role in placental development and fetal growth, and changes in EGFR expression can affect fetal development, thereby increasing the risk of adverse pregnancy outcomes [56]. MTOR is a protein kinase that plays important roles in cell growth, proliferation, and metabolism, and clinical studies have shown that MTOR can regulate fetal birth weight and the metabolic health trajectory of offspring [57, 58]. TP53 is involved in the female-specific molecular process of “X chromosome inactivation”, which is required to ensure proper neural tube development. If TP53 is dysfunctional, it may lead to impaired normal development of the neural tube in female embryos, thereby increasing the risk of developing neural tube defects such as spina bifida [58].The GO functional enrichment results revealed that probiotics improved SGA mainly through biological processes such as positive regulation of phosphorylation, positive regulation of protein phosphorylation, response to xenobiotic stimuli and response to peptide hormones. KEGG analysis revealed that probiotics improved the outcome of SGA mainly through the PI3K − Akt signaling pathway. The PI3K/AKT signaling pathway regulates cell biological processes, including cell proliferation, survival, apoptosis and metabolism, by activating PI3K and leading to AKT phosphorylation. A study on preeclamptic mice supplemented with AmEVs revealed that AmEVs promoted fetal growth and improved placental pathology by increasing the expression of EGFR and activating the PI3K‒Akt signaling pathway, thereby promoting placental function [59].

Innovation and limitations

To the best of our knowledge, this is the first study to comprehensively collate available data and integrate meta-analyses, network pharmacology and molecular docking to support the efficacy of probiotics in improving pregnancy outcomes in obese pregnant women. Currently, most studies are limited to a single approach such as individual meta-analysis. In this study, we systematically analysed the pathway of probiotics in reducing SGA risk in obese pregnant women through the three-dimensional system of clinical evidence-target prediction-molecular mechanism validation, filling the gap of research. While the existing meta-analysis were mostly focused on general pregnant women, the present study is the first to limit the target group to overweight or obese pregnant women, revealing the specific benefits of probiotics in this high-risk group, and providing a key basis for precise clinical intervention.However, there are two limitations to this study. Firstly, the limited number of included studies may have affected the effectiveness of probiotics on outcome indicators. Of the eight included studies, only five assessed the effect of probiotic supplementation on SGA, four assessed the effect on GWG as well as admission to NICU, and most of these were assessed as secondary outcome indicators, so the evidence for outcome is somewhat indirect. To address this, we assessed the quality of the evidence using the GRADE method, which informs the interpretation of the results. We comprehensively assessed the clinical heterogeneity, methodological heterogeneity, and statistical heterogeneity and found that the heterogeneity of the included studies was low. Secondly, regardless of probiotic strains, counts, and duration of intervention included in the study may introduce potential bias into the study results. Callaway et al. demonstrated the beneficial effects of probiotic supplementation (Lactobacillus rhamnosus and Bifidobacterium animalis Lactis) at a dose of > 1 × 109 colony-forming units(CFU) per day on SGA in overweight pregnant women. However, Halkjær et al. [45] showed that probiotic supplementation (Streptococcus thermophilus, Bifidobacterium bifidum, and Lactobacillus animalis) with 450 billion CFU per day had no effect on the risk of SGA in overweight pregnant women.In addition, most of the study participants included in our meta-analysis enrolled in mid-pregnancy.A longer administration period may be required to find the efficacy of probiotics in overweight or obese pregnant women. Different modes of administration are also an important factor influencing the efficacy of probiotics. A recent meta-analysis of probiotic yoghurts targeting the pregnant women showed a significant reduction in the risk of GDM and fasting blood glucose levels [60]. This finding contrasts with the results of our meta-analysis of the administration of various probiotic (capsules, yogurt) in overweight or obese pregnant women, which did not observe an overall effect on adverse outcomes, including gestational diabetes.In the subgroup analyses, we analysed strain type, duration and form of administration as a potential factor, effectively excluding these confounding factor from interfering with the results.

Research implications

The results of this comprehensive analysis have important clinical and public health implications for the management of obese pregnant women. Probiotics, as a safe and easy-to-use dietary supplement [61–64], may improve placental function and promote fetal growth by modulating key signalling pathways (e.g., the PI3K-Akt pathway) as well as by acting on a number of core targets identified in this study that are relevant to fetal development (e.g., ESR1, HSP90AA1, and EGFR). This finding is expected to be translated into clinical practice in the future, for example, through the development of probiotic formulas specifically targeting obese pregnant women, the development of personalised probiotic supplementation regimens, or the combination of probiotics with other interventions to reduce the risk of SGA and other adverse pregnancy outcomes. Meanwhile, the multi-component, multi-target and multi-pathway regulatory properties of probiotics revealed in this study also inform the development of comprehensive therapeutic strategies. This may help to break through the limitations of single-target drug therapy and provide a more comprehensive solution for improving maternal and infant health in high-risk pregnancy populations. Our findings provide a baseline for subsequent probiotic intervention studies in obese pregnant women, but further in vitro and in vivo studies are still needed to deeply verify the therapeutic mechanism of probiotics and promote their clinical application.

Conclusion

The meta-analysis results showed that probiotics did not reduce the risk of adverse pregnancy outcomes. However, in the subgroup analysis, the combined use of Bifidobacterium and Lactobacillus could reduce the risk of SGA. The results of network pharmacology showed that probiotics may combat obesity-related adverse pregnancy outcomes by modulating pathways such as the PI3K-Akt signaling pathway, mainly through interactions with core targets. Molecular docking further confirmed the potential binding affinity between these key metabolites and their targets, suggesting that modulation of these specific targets and pathways may be a key mechanism by which probiotics alleviate adverse pregnancy outcomes associated with obesity.Nevertheless, further in vitro and in vivo studies are needed to verify the mechanism, as this study was based only on data analysis.

6.Declarations.

Supplementary Information

12884_2025_7999_MOESM1_ESM.docx (33.3KB, docx)

Supplementary Material 1: Appendix 1. Search strategy and GEAGE evidence profile.

12884_2025_7999_MOESM2_ESM.xlsx (53KB, xlsx)

Supplementary Material 2: Appendix 2. Subgroup analysis results.

12884_2025_7999_MOESM3_ESM.docx (266.4KB, docx)

Supplementary Material 3: Appendix 3. The results of network pharmacology.

12884_2025_7999_MOESM4_ESM.docx (827KB, docx)

Supplementary Material 4: Appendix 4. Checklist.

Acknowledgements

Not applicable.

Abbreviations

RCT

randomized controlled trial

SGA

small-for-gestational-age

LGA

large for gestational age

NICU

neonatal intensive care unit

WHO

World Health Organization

GDM

gestational diabetes mellitus

BMI

body mass index

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

CC

cellular component

MF

molecular function

CFU

colony-forming units

Authors’ contributions

Conceptualization, T.Z. and J.S.; methodology, T.Z.; software, T.Z.; validation, J.S and M.M.; investigation, T.Z.; resources, J.S.; data curation, M.M.; writing—original draft preparation, T.Z.; writing—review and editing, J.S.; visualization, M.M.; supervision, G.S.; funding acquisition, G.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the National Natural Science Foundation of China [No.82173509].

Data availability

Data is provided within the manuscript or supplementary information files.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

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

Supplementary Materials

12884_2025_7999_MOESM1_ESM.docx (33.3KB, docx)

Supplementary Material 1: Appendix 1. Search strategy and GEAGE evidence profile.

12884_2025_7999_MOESM2_ESM.xlsx (53KB, xlsx)

Supplementary Material 2: Appendix 2. Subgroup analysis results.

12884_2025_7999_MOESM3_ESM.docx (266.4KB, docx)

Supplementary Material 3: Appendix 3. The results of network pharmacology.

12884_2025_7999_MOESM4_ESM.docx (827KB, docx)

Supplementary Material 4: Appendix 4. Checklist.

Data Availability Statement

Data is provided within the manuscript or supplementary information files.

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