A Review of Modulation of Gut Microbiota to Mitigate Gestational Diabetes: Implications for Maternal and Child Health

Abstract

The increasing prevalence of gestational diabetes mellitus (GDM) is a pressing global public health concern. Despite extensive identification of GDM risk factors and the implementation of screening programs, there has been a notable lack of significant reduction in maternal, fetal, and neonatal complications. It is vital to recognize that the health trajectory of future generations begins before birth, during intrauterine life and even before conception. The development of ‘diabetogenic’ and ‘obesogenic’ changes in the DNA of offspring can be triggered not only by adverse intrauterine conditions, but also by changes in germ cell DNA at earlier stages. Accumulating evidence suggests that the increasing prevalence and persistent metabolic effects of GDM may be mediated by epigenetic modifications.

The composition of the human gut microbiota is emerging as a key determinant of human metabolic processes. Consequently, dysbiosis of the gut microbiota via epigenetic mechanisms may contribute to metabolic complications in both the mother and the developing fetus. Given that diet plays a critical role in maintaining a healthy microbiota profile and that certain epigenetic alterations are reversible, it is pertinent to emphasize that not only the pregnant woman but also both partners attempting conception could influence the future health of the child.

The aim of this article is to review the role of gut microbiota modulation in preventing and managing gestational diabetes mellitus and the health benefits for mother and child.

Introduction

The increasing prevalence of gestational diabetes mellitus (GDM) is an urgent public health problem worldwide, affecting approximately 1–28% of pregnant women, making it one of the most common complications of pregnancy [1]. According to the American Diabetes Association (ADA), GDM is defined as diabetes diagnosed in the second or third trimester, provided that overt diabetes has been excluded before or at the latest in early pregnancy [2]. Although glucose homeostasis is usually restored shortly after birth, GDM is associated with a variety of short- and long-term maternal and neonatal complications. From a public health perspective, the increased risk of long-term complications such as maternal type 2 diabetes mellitus (T2DM) and/or cardiovascular disease, increased adiposity or even obesity, impaired glucose metabolism, hypertension, hyperlipidaemia, non-alcoholic fatty liver disease in the offspring, and precocious puberty, and even more rapid epigenetic aging are of great importance [3,4].

It is believed that in the early prenatal period, developmental plasticity of external and internal factors may induce changes in gene expression by epigenetic mechanisms. Hence, an unfavorable intrauterine environment in GDM patients may induce epigenetic modifications and subsequently be responsible for the increasing prevalence and long-term metabolic consequences of GDM. Alterations in gene expression without nucleotide sequence variations are known as epigenetic modifications. Dynamic, predominantly reversible, changes in chromatin structure and gene expression are the result of responses to external and internal environmental factors. Major epigenetic processes include DNA methylation, histone modifications, regulation of non-coding RNA, and chromatin remodelling [5,6].

A growing body of evidence suggests that the gut microbiota functions almost like an additional organ, actively participating in shaping and maintaining physiological functions of the human body. It metabolizes dietary components and produces a number of epigenetically active metabolites. The exact mechanism of the relationship between the composition of the intestinal microbiome and the development of diabetes is not precisely known. The gut microbiota, which derive energy from indigestible polysaccharides, provide an additional source of calories to the host [7,8]. The lipopolysaccharides (LPS) produced by the gut microbiota affect the development of systemic chronic inflammation, thereby predisposing to the onset of obesity and diabetes. The microbiota can also epigenetically regulate host genes associated with energy storage and consumption [9,10].

According to Slupecka-Ziemilska et al [4], the first 1000 days of life, encompassing the pre-conception and neonatal periods, are thought to have a lasting effect on an individual’s susceptibility to chronic disease in the future. Parental nutrition, particularly maternal, has been shown to influence the early establishment of the fetal and neonatal microbiome, creating specific epigenetic signatures that can persist and potentially predispose to the development of metabolic complications later in life [5].

Elucidating the relationship between maternal diet, gut microbiome, and epigenetics may provide valuable insights into the mechanisms involved in the intergenerational transmission of diabetes. Knowing that not all epigenetic changes are stable over time, it may be possible to reduce the burden of gestational diabetes and its consequences through appropriate dietary modification of the microbiota. Therefore, the aim of this article is to review the role of gut microbiota modulation in preventing and managing gestational diabetes mellitus and the health benefits for mother and child.

Insights Into the Pathophysiology of GDM

Physiological alterations in pregnancy are determined by the supply of energy and nutritional substrates vital to the proper development of the fetus. As a result of pancreatic β-cell adaptation-hypertrophy and proliferation, together with altered insulin sensitivity of tissues, insulin production and secretion increase [11,12]. The first and second trimester feature a shift in metabolic balance towards anabolic processes. Initially, insulin secretion rises in response to a glucose load, while tissue sensitivity to insulin remains unchanged or only slightly increases. Despite physiological changes promoting hyperglycemia, healthy pregnant women maintain significantly lower glycemic levels than non-pregnant ones due to compensatory hyperinsulinemia and fetal glucose disposal. Insulin sensitivity tends to increase starting at the 14^th week onwards, but there is a gradual build-up in insulin resistance, which culminates towards the end of pregnancy [13,14]. In the third trimester of pregnancy, catabolic processes become dominant and lipolysis rises. Maternal fat deposits are being used up and insulin resistance increases due to an upsurge in hormones antagonistic to insulin. Growing insulin resistance leads to a reduction in maternal glucose consumption, maternal adipose tissue is preferentially used up, and the blood glucose, a pivotal, easily accessible energy substrate for the organs of the growing fetus, becomes more abundant [15,16].

The phenomenon of insulin resistance, typical for the second half of pregnancy, is associated with a decrease in insulin receptor sensitivity, which is mediated by hormones that act antagonistically to insulin, such as placental lactogen and growth hormone, estrogens, progesterone, prolactin, adiponectin, and leptin [17–19]. Increased concentrations of pro-diabetogenic hormones and “physiological” pro-inflammatory changes at a cellular level contribute to an overall increase in insulin resistance.

Although the pathophysiology of GDM is not completely understood, it is thought that abnormal placental hormone expression together with maternal metabolic dysfunction, as well as inadequate insulin synthesis and function, play pivotal roles in certain cases of GDM, and subclinical, genetically determined insulin secretion abnormalities existing before pregnancy can also be recognized. Elevated levels of pro-inflammatory markers such as, TNF-alpha, IL-6, and CRP, and impaired insulin signalling pathways are also observed in the course of GDM [20–23].

Gut Microbiota and Metabolic Health

The microbiome is a collection of all microorganisms – bacteria, viruses, fungi, and/or archaea, as well as their genomes, together with the accompanying environmental conditions. Bacteria are the most abundant component, followed by fungi and viruses, and the scarcest in both quantitative and qualitative terms are the archaea.

The range of microorganisms residing in a particular environment is defined as the microbiota, which is found in many places: the mouth, skin, upper respiratory tract, digestive system, and reproductive system [24,25].

Among them, the gut microbiota is considered to be the most numerous and active. When analyzing the microbial groups in the different sections of the human gastrointestinal tract, it should be noted that the large intestine and oral cavity sections have the richest microbiome, while the pharynx and oesophagus are the most deficient in this respect. Considering bacteria alone, the oral cavity and small intestine are the most inhabited, while those in the throat and esophagus are the least abundant. It is estimated that the gastrointestinal tract of a healthy person contains approximately 100 billion microorganisms, and the highest density and diversity of these organisms is in the lower gastrointestinal tract. Among them, more than 1000 species of bacteria have been identified in the gut, at least 80% of which can only be identified via genetic methods. The composition of the gut microbiota changes throughout the lifespan, actively responding to both external and internal factors to maintain homeostasis [26–28].

The small intestine plays a crucial role in immune system function, primarily through the gut-associated lymphoid tissue (GALT), which provides protection against pathogens. Additionally, it is integral to the processes of nutrient digestion and absorption, with its microbial composition predominantly consisting of bacteria from the phyla Firmicutes, Proteobacteria, Verrucomicrobia, Bacterioidota, and Actinobacteria, as well as families such as Enterobacteriaceae and Lactobacillaceae. In contrast, the large intestine exhibits lower oxygen levels and reduced concentrations of antimicrobial compounds, fostering a greater microbial diversity. Anerobic bacteria dominate this environment, utilizing fiber fermentation as their primary carbon source. This section of the gastrointestinal tract is responsible for the breakdown and fermentation of carbohydrates, the synthesis of vitamins, and the absorption of residual undigested nutrients. The predominant microbial taxa include the phyla Bacteroidota, Firmicutes, Actinobacteria, Proteobacteria, and Verrucomicrobia, along with families such as Enterobacteriaceae, Bacteroidaceae, Prevotellaceae, Rikenellaceae, Lachnospiraceae, and Ruminococcaceae [29].

The 2 dominant bacterial phyla in the gastrointestinal tract are Firmicutes and Bacteroidetes (80–90%). Based on 16S rRNA results, Firmicutes is a major genus in the intestinal gut of humans and Lachnospiraceae and Ruminococcaceae are the most numerous families of this genus, comprising 50% and 30%, respectively, of the overall intestinal microbiota, while Proteobacteria, Actinobacteria, Fusobacteria, and Verrucomicrobia are less abundant. The Firmicutes are mainly gram-positive bacteria, predominantly of the genera Bacillus, Clostridium, Enterococcus, Lactobacillus, and Ruminicoccus, while the Bacteroidetes are generally gram-negative bacteria, mostly belonging to the genera Bacteroides, Alistipes, Parabacteroides, and Prevotella [30].

An altered Firmicutes/Bacteroidetes (F/B) ratio is a sign of dysbiosis. Recently, there have been substantial revisions in bacterial taxonomy, particularly at the phylum level. Bacillota is now replacing Firmicutes, and Bacteroidota is replacing Bacteroidetes. Therefore, to ensure consistency and accuracy according to the updated taxonomy of bacteria, the full term ‘Bacillota to Bacteroidota ratio’ should be used. An increased F/B ratio is usually observed in obesity and decreased in inflammatory bowel disease (IBD). An elevated F/B ratio has been suggested as a possible biomarker of obesity and other metabolic syndromes [31–34].

However, dysbiosis can also be caused by disorders in other phyla (eg, Proteobacteria), which do not always result in changes of the F/B ratio. A growing body of evidence suggests that the gut microbiota acts as an additional organ, actively participating in shaping and maintaining the physiological functions of the human body. It metabolizes dietary components and produces a number of epigenetically active metabolites affecting enzymes such histone deacetylases and DNA methyltransferases and regulates non-coding RNAs [35]. Microbiota metabolites include short-chain fatty acids (SCFAs), biotin, folate, and trimethylamine N-oxide.

Biotin, serving as a coenzyme for carboxylase enzymes, is essential for gluconeogenesis, fatty acid synthesis, and oxidation. It can improve diabetic conditions by lowering blood sugar and regulating lipid levels. Additionally, it enhances the expression of proliferating cell nuclear antigen genes, boosting B lymphocyte conversion efficiency in the blood. Other metabolites include trimethylamine-N-oxide (TMAO) and inosine-5-monophosphate (IMP), involved in white adipose tissue formation and lipid metabolism, respectively [36].

Trimethylamine N-oxide is made from choline and L-carnitine, and many studies have shown it inhibits cholesterol metabolism, induces platelet aggregation and thrombosis, and promotes atherosclerosis. TMAO is known for its pro-inflammatory properties; when it is produced in excess, it can exacerbate diseases such as atherosclerosis and type 2 diabetes [37].

Natural forms of folate can be produced by many microorganisms commonly found in the gastrointestinal microbiota, such as the genera Bifidobacterium-phylum Actinomycetota and Lactobacillus-phylum Firmicutes and yeasts [38]. Folate is vital for biosynthesis of the nucleic acids, DNA methylation reactions, and the regeneration of homocysteine from methionine and other crucial metabolic pathways in the human body.

Short-chain fatty acids (SCFAs) – propionate, acetate, and butyrate, and in small amounts, valeric and caproic – are the metabolic products of bacteria attached to the lumen of the human gut. The basic substrate for gut bacteria to produce SCFAs are polysaccharides (starchy, starch-like, and non-starchy) that have not previously been digested by enzymes of the digestive system. In the large intestine (the proximal part of the colon and cecum), due to the availability of carbohydrates and water, the dietary polysaccharides oligosaccharides and disaccharides are hydrolysed into monosaccharides, which provide a substrate for further hydrolysis by the bacterial enzyme system [39,40]. Fermentation of carbohydrates in the proximal part of the colon produces short-chain fatty acids (SCFAs), H₂, and CO₂, while fermentation of amino acids or proteins generates branched SCFAs (BSCFAs), H₂, CO₂, CH₄, phenols, and amines. The molar ratios of acetate, propionate, and butyrate produced in the colon are 60: 25: 15 respectively, but these ratios may be modulated and vary in different segments of the gut depending on a number of factors, including diet, age, and medical condition [41].

SCFAs provide a nutritional source for colonic epithelial cells and influence the immune response and integrity of the epithelium. SCFAs, which supply the colonic epithelial cells, influence the immune response and also the integrity of the epithelium [36,42].

Butyrate is mainly produced via butyryl-CoA: acetate CoA-transferase routes and the phosphate butyryl transferase/butyrate kinase routes by bacteria of the Clostridium, Eubacterium, and Fusobacterium genera. Particularly efficient producers of butyric acid are Clostridium leptum, Roseburia spp., Faecalibacterium prausnitzii, and Coprococcus spp. Acetate is produced by most enteric bacteria, such as Lactobacillus spp., Bifidobacterium spp., Akkermansia muciniphila, Bacteroides spp., Prevotella spp., Ruminococcus spp, and Streptococcus spp. via the Wood-Ljungdahl and acetyl-CoA pathways. Propionate is produced by Phascolarctobacterium succinatutens, Bacteroides spp., Dialister spp., Megasphaera elsdenii, Veillonella spp., Coprococcus catus, Roseburia inulinivorans, Ruminococcus obeum, and Salmonella spp. via succinate, acrylate, and propanediol pathways [43,44]. The metabolic product of one group of bacteria can be a substrate for the production of another metabolite by another group, a process called ‘cross-feeding,’ which most commonly runs from acetate to butyrate, and is almost non-existent for propionate and acetate.

The previously produced butyrate and propionate can be degraded to acetate by Acetobacterium, Acetogenium, Eubacterium, and Clostridium spp. [45]. The process can be reversed if the number of butyrate-producing bacteria, such as Faecalibacterium prausnitzii or Roseburia spp., increases [46]. The acetic acid produced by Bacteroidetes can then be used to produce butyric acid and propionic acid by representatives of Firmicutes. Megasphaera elsdenii, which resides in the human colon, can convert lactate to butyrate [47]. About 70–90% of butyrate is metabolized by colonocytes, for which it is the primary energy source, while about 90% of propionate is metabolized in the liver; the rest passes into the peripheral blood [48].

SCFA concentrations change depending on alterations in diet. Their total production increases with a diet rich in products with a high soluble fiber content, while it decreases in a diet limited in fiber or one that is high-fat [49]. In low-fiber dietary conditions, butyrate-producing bacteria disappear in favor of acetic acid- and propionic acid-producing Bacteroides [50].

The degradation of SCFSAs formed by bacterial fermentation in the human body takes place in colon epithelial cells, where the main substrate is butyrate – the primary energy source for colonocytes in the liver cells, which metabolize most (50–70%) of the acetate as well as the remaining butyrate and propionate after gluconeogenesis, and in muscles, generating energy by oxidizing the remaining acetate [51].

SCFAs are a key source of energy for intestinal cells, the integrity of the epithelial intestinal barrier depends on them, and they play a defensive role by regulating the expression of related genes. SCFAs provide the building blocks for sugar and lipid synthesis by impacting energy homeostasis and metabolism. SCFAs have also been shown to inhibit tumor cell proliferation and promote apoptosis [52]. SCFAs, acting as communicators between the microbiome and the immune system, help to maintain balance in the anti- and pro-inflammatory responses. A particular role of short-chain fatty acids is to enhance proliferation and increase the functional capacity of T-regulatory cells (Treg) by inhibiting the enzyme histone deacetylase. Butyric acid has the greatest inhibitory potential in this area [53].

Butyric acid has the strongest immunomodulatory effect, particularly by strong promotion of Treg differentiation [54]. In human monocytes, butyrate exerts anti-inflammatory effects by inhibiting IL-12 NO, IL-1b, and TNF-α production and increasing IL-10 production [55] while reducing NF-κB activity (with the NF-κB relationship butyrate>propionate>acetate) [56,57]. Macrophages, in the presence of butyrate, reduce the secretion of LPS-induced pro-inflammatory mediators such as IL-6 and IL-12. This mechanism depends on histone deacetylase inhibition, independent of Toll-like receptors (TLRs) and G protein-coupled receptors (GPRs) [51,58]. Butyrate also, most significantly among SCFSAs, promotes the integrity of the intestinal epithelium, inhibition of lipolysis, and stimulation of glucose uptake, and induction of triglyceride synthesis by butyrate suggests its level affects carbohydrate and fat metabolism [59].

Acetic acid accounts for over 50–60% of SCFA content and is crucial in maintaining a balance between the microbiota and the host immune system. In addition, acetate accumulates in the hypothalamus, inducing activation of acetyl-coenzyme A carboxylase and altering the expression profile of regulatory neuropeptides, leading to appetite suppression [60].

Propionic acid can be bound by GPR43 and GPR41 receptors, similarly to acetic acid and exert immunomodulatory effects. A unique property of propionate is its effect on blood pressure regulators, causing an increase or decrease in blood pressure depending on which receptors it acts. It is also thought that it reduces cholesterol concentrations and fat storage [61].

SCFAs, by binding to GPR41 and GPR43 protein-coupled receptors, increase intestinal expression of peptide YY and glucagon-like peptide-1 (GLP-1), resulting in decreased intestinal transit time and increased insulin sensitivity. The peptide YY is an anorexigenic neuropeptide that stimulates the satiety center to produce proopiomelanocortin (POMC) and the α-melanotropic hormone (α-MSH), leading to reduced appetite [61–63].

Acetate, acting via the GLP-1 gut hormone, improves insulin resistance, while propionate and butyrate promote intestinal gluconeogenesis [58,59,62].

SCFAs, by upregulating the transcription of tight junction proteins, enhance the production of glucagon-like peptide-2 (GLP-2) and decrease inflammation in colonic epithelial cells, thereby contributing to a decline in gastrointestinal permeability. The integrity of the intestinal barrier is essential for reducing the passage of lipopolysaccharides (LPS), which are also a structural component of the cell walls of gram-negative bacteria, into the bloodstream. LPS can induce a pro-inflammatory immune response, thus contributing to development of insulin resistance [64].

SCFAs also influence the action of the hormones leptin and ghrelin, called the hormones of satiety and hunger, respectively. They act together but in opposing ways and regulate each other. They are thought to be involved in glucose and lipid metabolism, eating behaviors, and energy balance. They participate in the hormonal regulation of food intake with opposing effects on appetite [63,65,66].

SCFAs, especially acetate and butyrate, also inhibit histone deacetylases and therefore can modify host metabolism through epigenetic pathways [67]. Butyrate, acting as an inhibitor of histone deacetylase, suppresses nuclear factor-B (NF-B) activation, thereby upregulating the expression of peroxisome proliferator-activated receptor gamma (PPAR gamma), further leading to a decrease in TNF production [68,69].

Effects of Physiological and GDM-Complicated Pregnancy on Gut Microbiota

Intestinal microbiota during pregnancy is associated with pre-pregnancy BMI and weight gain. The gut microbiota in the first trimester of pregnancy appears to be similar to that of non-pregnant women. As gestational age increases, the microbiota changes in quantity and number [70].

A decrease in alpha diversity and an increase in beta diversity is observed. Alpha diversity is assessed based on observed richness, defined as the number of taxa, or evenness, which reflects the relative abundances of taxa within an average sample of a given habitat type, while beta diversity is quantified as the variability in community composition, determined by the identity of taxa present, among samples within the same habitat [71]. At the phylum level, there is an increase in Actinobacteria and Proteobacteria. Firmicutes also gradually start to prevail, as does the microbiota in obese individuals. The number of the Faecalibacterium genus and other short-chain fatty acid producers decreases [72], causing a reduction in the output of the anti-inflammatory butyric acid [73]. Both in normal pregnancies and those complicated by GDM, the abundance of Blautia and Collinsella increases. Compared to healthy individuals, the abundance of Blautia (the phylum) was found to be significantly decreased in patients with type 2 diabetes, children with diabetes, and overweight/obese patients with non-alcoholic fatty liver [72,74]. Blautia, a genus of commensal obligate anaerobic bacteria, exerts an inflammatory effect via up-regulation of intestinal regulatory T lymphocytes and the production of SCFAs, which is positively correlated with circulating insulin. The Collinsella genus belongs to the Coriobacteriaceae family and the phylum Actinobacteria. As with other members of the Coriobacteriaceae family, they can influence metabolism by modifying intestinal cholesterol absorption, decreasing hepatic glycogenesis, and increasing triglyceride synthesis and insulin production. Collinsella, by reducing the expression of tight junction proteins in the epithelial cells and inducing the expression of IL-17, stimulates gut permeability. Its abundance has been linked to T2DM, rheumatoid arthritis, and cholesterol metabolism [72,75–77,80].

These alterations facilitate changes in metabolic, immunological, and hormonal processes that are beneficial for the proper development of the fetus. The number of microorganisms engaged in energy production and storage, such as Proteobacteria, Firmicutes, and Akkermansia, considerably rises in the third trimester, causing greater maternal obesity and insulin resistance as it promotes energy accumulation and ensures an increase in fetal metabolism [72].

It has been suggested that the physiological adaptation of the microbiome present during pregnancy is disrupted in women with GDM, due to increased inflammation, insulin resistance, and, often, higher pre-pregnancy BMI and excessive pregnancy weight gain in this population [9]. Interestingly, differences were found regarding the intestinal microbiota not only in the third trimester, but even in the first trimester among women who later develop carbohydrate intolerance [78]. Moreover, these changes persist up to 8 months after delivery [79]. In GDM, the Firmicute/Bacteroides ratio further rises compared to healthy pregnant women. The microbiota of GDM women has similarities with the microbiota reported in patients with T2DM and obesity [77,79,81].

There is growing evidence that significant dysbiosis of the gut microbiota in pregnant women can contribute to development of GDM. Higher abundance of bacteria such as Collinsella, Blautia, Prevotella, Faecalibacterium prausnitzii, and Ruminococcus appears to be associated with an increased risk of GDM. On the other hand, depletion was noted in the presence of other bacterial taxa such Faecalibacterium, Bifidobacterium spp., Eubacterium spp., Marvinbryantia, Acetivibrio, Anaerosporobacter, and Bacteroides spp. in women with GDM compared to normoglycemic ones [82]. Table 1 shows the results of a study in which differences were compared in the microbiota of healthy and gestational diabetes patients.

Table 1.

Recent studies with comparisons between dominant taxonomies in group of women without and with GDM. In this table, recent studies are present in which the composition of gut microbiota is compared between normoglycemic women (control group) and women with gestational diabetes mellitus (GDM). The studies employed 16S rRNA sequencing or whole metagenome shotgun sequencing methods to identify and compare dominant taxonomic groups. The taxonomies are listed separately for each group (normoglycemic and GDM), with differences in microbial community structure and the potential relevance of these differences in GDM pathophysiology highlighted.

Study	Number of woman surveyed GDM/control (total)	Gestational age (weeks)	Methods	Normoglycemic women	Women with GDM
Ye et al [84]	36/16 (52)	24–28	16s rRNA sequencing	Faecalibacterium spp.	Blautia, Eubacterium hallii group
Cortez et al [85]	26/42 (68)	28–36	16s rRNA sequencing	Bacteroides spp.	Lachnospiraceae, Phascolarctobacterium, Christensenellaceae
Crusell et al [80]	50/157 (207)	27–33	16S rRNA gene amplicon sequencing of the V1–V2 region	Anaerosporobacter, Marvinbryantia, Acetivibrio	Actinobacteria, Collinsella, Coriobacteriaceae, Rothia, Micrococcaceae, Desulfovibrio, Leuconostocaceae, Granulicatella, Mogibacterium
Su et al [86]	21/32 (53)	24–28	16s rRNA sequencing	Clostridiales, Clostridia, Proteobacteria, Actinobacteria	Bacteroidetes, Bacteroides spp., Weisella spp., Fusicatenibacter spp., Parabacteroides, Roseburia, Flavonifractor
Wei et al [87]	15/18 (33)	24–28	16s rRNA sequencing	Bacteroidetes, Bacteroidia, Bacteroidales	Ruminococcus bromii, Clostridium colinum, Streptococcus infantis, Clostridiales, Clostridia, Firmicutes
Hu et al [88]	201/201 (402)	6–15 & 24–28	16s rRNA sequencing	Rothia, Actinomyces, Bifidobacterium, Adlercreutzia, Coriobacteriaceae, Lachnospiraceae spp.	Enterobacteriaceae, Ruminococcaceae spp., Veillonellaceae
Chen et al [89]	30/28 (58)	28	16s rRNA microarrary	Oscillospira, Bifidobacterium, Ruminococcaceae, Clostridiales, Gemmiger	Bacteroidetes spp., Dialister spp., Campylobacter spp., Enterococceae spp.
Ma et al [90]	70/70 (140)	10–14 & 42 days after delivery	16s rRNA sequencing	Parasutterella, Parabacteroides, Megasphaera, Dialister, Ruminococcaceae Ruminococcaceae Ruminococcaceae Eubacterium xylanophilum group, Eubacterium eligens group	Eisenbergiella, Tyzzerella, Lachnospiraceae NK4A136
Kuang et al [91]	43/81 (124)	21–29	Whole metagenome shotgun sequencing	Ruminiclostridium, Roseburia, Eggerthella, Fusobacterium, Haemophilus, Mitsukella, Aggregatibacter	Klebsiella varicella, Parabacteroides distasonis, Catenibacterium mitsuokai, Coprococcus comes, Citrobacter spp.

GDM – Gestational Diabetes Mellitus, 16S rRNA – 16S ribosomal RNA, rRNA – ribosomal RNA.

Fetal Programming and Transgenerational Transmission of Metabolic Disorders

The main epigenetic reprogramming processes are DNA methylation, histone modifications, non-coding RNA regulation, and chromatin remodelling. Among them, DNA methylation is the most widely studied and best identified. It mainly occurs within CpG islands and is catalyzed by DNA methyltransferases (DNMTs). Almost two-thirds of the CpG islands of the human genome are located in the promoter sites of basal metabolism genes. Generally speaking, promoter hypermethylation leads to transcriptional silencing, and methylation of the repressor of a given gene enhances the expression of this particular gene. Methylation is a reversible process. Studies have assessed global methylation status, but, unfortunately, there are currently no sensitive and specific methods for evaluating site-specific methylation status [91].

Histone modification (acetylation, methylation, phosphorylation, and ubiquitination) is another relevant mechanism of epigenetic processes. Histone acetylation and deacetylation are crucial for gene control. Histone acetylation is regulated by 2 classes of enzymes: histone acetyltransferases (HATs) and histone deacetylases (HDACs). HAT enzymes catalyze the transfer of an acetyl group from acetyl-CoA to the conserved lysine amino acids on the histone proteins, while HDAC eliminates acetyl groups, allowing for tight-wrapping of histones on DNA. Acetylation renders chromatin DNA strands available for transcription factors as well as other proteins controlling gene expression. HDACs have an important role in suppressing gene expression by the reversal of core histone hyperacetylation [92].

Gene expression is also controlled by histone modification mediated by a non-coding RNA (miRNA) [93]. MicroRNAs are single-stranded RNA molecules that bind to target sites located in the 3′-untranslated region (3′UTR) at the target mRNA to promote gene silencing. MicroRNAs are engaged in the post-transcriptional control of gene expression, influencing both translation and mRNA stability. In principle, the binding of miRNAs to mRNAs leads to inhibition or destabilization of the translation process [94].

The 2 main stages of the epigenetic reprogramming, when globally erasure and re-establishment occur, take place in primary germ cells (PGCs) and from fertilization to the pre-implantation phase. In the first one, during gametogenesis, the somatic epigenetic information must be erased to enable germ cell cellular totipotency and broad developmental potential (sex- and germ cell-specific epigenetic marks crucial for sexual reproduction are being recreated in the remethylation process). During gametogenesis, de novo methylation occurs. Imprinting of the given genes formed in the parental germ cells is passed onto the offspring and memorized. In the gamete phase, fusion comes to basic epigenetic modification in somatic cells. The parental genome especially experiences many changes, but imprinting established in germ cells is escaped [95]. Epigenetic inheritance via the female germline relies on sustaining epigenetics in daughter cells and passing on parental epigenetics to offspring.

Widespread erasure of a major part of the parental epigenetic marks, including DNA methylation and histone modifications and the subsequent re-establishment of the zygotic epigenome together with the relaxation of chromatin structure (accessibility of key DNA sequences for transcriptional regulators essential in gene activation) following fertilization, can efficiently convert gametes into totipotent embryos and contribute to preventing the inadvertent inheritance of spontaneously acquired epigenetic marks by the next generation, which could be harmful to the offspring’s survival. Interestingly, some epigenetic marks (parental histone modifications) are inherited by early embryos (genomic imprinting) and some of them can be functional [96].

The formation of a new methylation profile enables the totipotential cells of the embryo to transform into specific cell lines and is therefore essential for further embryonic development. It is also crucial for the proper development of following generations.

Intrauterine epigenome alterations aim to adjust the fetus to the postnatal environment and can have both beneficial and detrimental effects regarding the risk of future metabolic dysfunction.

Early embryogenesis is the most pivotal time for reestablishing genome-wide epigenetic profiles. The type of alterations determines whether they will have favorable or unfavorable effects on the health of the forming organism. However, the whole period of intrauterine development is particularly vulnerable to the disruptive effects of internal and external factors. In the maternal lineage, exposure throughout pregnancy indicates direct exposure both to the mother and the developing fetus (intergenerational), as well as to primary germinal cells of the growing fetus (transgenerational inheritance) [95,97].

Deleterious factors acting prenatally can disturb the normal pattern of fetal development, leading to a greater tendency to disease in extrauterine life.

In pregnancy complicated by GDM, fetal hyperglycaemia and hyperinsulinemia appear to be significant drivers promoting the further onset of a wide range of disorders such as insulin resistance, diabetes, obesity, metabolic syndrome, cardiovascular disorders, and even cancer. In this process, abnormal pancreatic development, placental disorders, a deficit in polyunsaturated fatty acids, or changes in the microbiota seem to be of great significance. Despite ample evidence of the in-utero programming of carbohydrate metabolism disorders in GDM pregnancy, the exact mechanisms underlying the influence of intrauterine hyperglycaemia on the development of diabetes mellitus or obesity in the future is still unknown. The growing body of evidence shows that during early developmental plasticity, external and internal factors can induce changes in gene expression by epigenetic mechanisms. In this way, the unfavorable intrauterine environment in GDM patients can trigger epigenetic modifications and, in consequence, cause the rising incidence and long-term metabolic consequences of GDM.

The most significant factors shaping the human microbiome, both at individual and population levels, are genetics, ethnicity, age, environmental exposure to potentially pathogenic bacteria, pharmacotherapy, chronic diseases, diet, access to medical care and pharmacotherapy, physical exercise, sleep duration, and level of stress exposure.

The type of diet, and especially the intake of dietary fiber, has been recognized as a major determinant of the composition of the intestinal microbiome. Indigestible carbohydrates, known as glycans (resistant starch, inulin, lignin, pectin, cellulose, and fructo-oligosaccharides), mainly come from plant sources, but also from animal, fungal, and algal ones. Microbial fermentation of non-digestible carbohydrate fractions (fiber and resistant starch) produces SCFAs, which are responsible for maintaining the integrity of the intestinal epithelium and stimulating colonic epithelial cells, influencing regulation of the immune response in the gut [98].

Knowing that altered microbiota in pregnant women can increase the risk of GDM development, it is important to determine if we can change the mother’s microbiota and if this can be done safely for the mother and fetus. It is also worth considering whether restoration of proper composition and diversity of gut microbiota reduces the risk of GDM and the frequency of adverse pregnancy outcomes.

Microbiota can be modulated by dietary interventions, probiotics, prebiotics, and symbiotics. Changes in diet, such as increasing the consumption of fermented foods and fiber-rich products, can affect microbiota [99]. A prebiotic is a “substrate that is selectively utilized by host microorganisms conferring a health benefit” [100] and a symbiotic is “a mixture comprising live microorganisms and substrate(s) selectively utilized by host microorganisms that confers a health benefit on the host” [101] (Figure 1).

Figure 1.

Key points discussed in the article, highlighting the main mechanisms and potential strategies for gut microbiota modulation in the prevention of GDM metabolic complications. The gut microbiota plays a fundamental role in metabolic regulation and may influence both the development of gestational diabetes mellitus (GDM) and the long-term health of offspring. 1. Appropriate nutritional and probiotic interventions may help prevent the metabolic consequences of GDM in future generations by influencing epigenetic mechanisms of inheritance. 2, 3. Alterations in the composition of the intestinal microbiota can modulate epigenetic mechanisms that regulate insulin resistance, for example, by affecting DNA methylation of genes involved in glucose and lipid metabolism. Intestinal dysbiosis leads to an increased production of pro-inflammatory cytokines, such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), which exacerbate insulin resistance. Short-chain fatty acids (SCFAs), produced through the microbial fermentation of dietary fiber, can influence gene expression by modulating histone modifications and microRNA (miRNA) regulation. These changes may improve insulin signalling and reduce inflammation. Additionally, microbial metabolites can regulate hormone levels such as leptin and ghrelin, which are involved in appetite and energy homeostasis. 4, 5. Gut microbiota may also impact the development of gestational diabetes mellitus and metabolic programming of offspring via epigenetic gene regulation. Modulation of microbiota through dietary strategies, including probiotics and prebiotics, offers a promising approach to reducing the risk of metabolic complications associated with GDM. Figure created using Canva (version current as of 2025; Canva Pty Ltd, Australia).

Gut Microbiota Modulation During Pregnancy

Probiotic Interventions

Probiotics, defined as “live microorganisms that, when administered in adequate amounts, confer a health benefit on the host” [102], have shown promise in modulating gut microbiota to support metabolic processes such as glucose regulation. Their potential role in preventing and managing gestational diabetes mellitus (GDM) has generated significant research interest, as GDM poses risks for both maternal and fetal health outcomes.

Probiotics influence host metabolism and glycemic control through several proposed mechanisms, although the precise pathways remain under investigation. A key metabolic action of probiotics involves the reduction of intestinal glucose absorption, which can lower blood glucose and insulin levels [103]. Additionally, probiotics can enhance gut barrier integrity by reducing intestinal permeability and upregulating adhesion proteins in the intestinal epithelium, leading to decreased systemic inflammation and improved insulin sensitivity [104].

Moreover, probiotics facilitate the production of short-chain fatty acids (SCFAs) such as propionate and butyrate. These SCFAs play a crucial role in enhancing secretion of glucagon-like peptide-1 (GLP-1), which stimulates insulin release and thereby contributes to improved glycemic control [105,106]. This SCFA-mediated pathway underscores the value of probiotics in supporting glucose homeostasis, although further research is needed to clarify the mechanisms involved.

The potential of probiotics to prevent GDM has been explored in several clinical trials. Wickens et al conducted a randomized, placebo-controlled trial demonstrating a reduction in GDM incidence among women supplemented with Lactobacillus rhamnosus HN001. Notably, women aged ≥35 or those with a previous history of GDM showed a significant reduction in GDM prevalence, suggesting that specific probiotic strains may be particularly beneficial for high-risk populations. The authors hypothesized that the observed effect was due to modulation of gut microbiota, which improved insulin sensitivity and reduced inflammation without adverse maternal or fetal outcomes [107].

Similarly, Dolatkhah et al (2015) and Karamali et al (2016) found that probiotic supplementation in GDM patients led to significant reductions in fasting glucose levels. These trials support the therapeutic potential of multi-strain probiotics (eg, Lactobacillus acidophilus, Bifidobacterium, and Streptococcus thermophilus) in lowering glycemic indices in pregnant women with GDM, although the findings are strain-specific and may vary with dosage and treatment duration [108,109].

While beneficial effects were reported in several studies, conflicting results also exist. Callaway et al (2019) found that supplementation with Lactobacillus rhamnosus and Bifidobacterium animalis subspecies lactis BB-12 did not lower GDM risk in overweight and obese women. Surprisingly, fasting glucose levels were higher in the probiotic group compared to controls, indicating that patient-specific factors such as BMI may modulate probiotic efficacy [110].

Further supporting this, Lindsay et al (2014) did not observe any significant differences in fasting glucose, metabolic profile, or pregnancy outcomes among obese women taking Lactobacillus salivarius UCC118 compared to the placebo [111]. Similarly, Halkjaer et al (2020) found no reduction in GDM risk with a multi-strain probiotic (Vivomixx^®) administered from the early second trimester until delivery. These studies show that while probiotics offer potential metabolic benefits, outcomes can vary depending on the strain, patient population, and treatment timing [112].

Given the individualized nature of the gut microbiome, a “one-size-fits-all” approach to probiotic supplementation may be inadequate. Personalized probiotics, tailored to a patient’s unique gut microbial composition, could optimize therapeutic benefits, particularly for metabolic disorders such as GDM. However, such approaches require advanced microbiome profiling and an understanding of the interactions between specific probiotic strains and host metabolic pathways. Although probiotics are widely considered safe, some studies found potential risks associated with their use. For instance, Didari et al (2014) noted that certain probiotics, including species of Bifidobacterium and Lactobacillus, may be associated with rare adverse outcomes such as sepsis and gut ischemia in immunocompromised patients [113]. Additionally, Suez et al (2018) suggested that probiotics can delay restoration of the native gut microbiota after antibiotic therapy, warranting caution in specific populations [115]. However, studies in pregnant women, such as in that by Wickens et al (2017), found no significant adverse effects on maternal or fetal outcomes, indicating a good safety profile when appropriately administered [107]. With these caveats in mind, it seems reasonable to pay more attention to appropriate dietary modifications involving the use of probiotic foods rather than the routine use of similar probiotics in all patients (Table 2).

Table 2.

Recent studies on the influence of probiotics in the group of women with and without GDM. In this table, a summary is given on recent studies in which the effect has been examined of various probiotic strains on women with gestational diabetes mellitus (GDM) and their influence on GDM outcomes, including the risk of GDM development, glycemic control, and insulin resistance. The table includes details on the type of probiotics used, the timing of administration and the observed effects in pregnant women with and without GDM.

Author	Probiotic used	Participating women	Administration time	Effect
Wickens et al [108]	Lactobacillus rhamnosus HN001	Pregnant women with normal BMI	From 14–16 weeks of pregnancy until 6 months post birth if still breast feeding	Positive-reduction in risk of GDM
Dolatkhah et al [109]	Lactobacillus acidophilus, Bifidobacterium, Streptococcus thermophilus, Lactobacillus delbrueckii bulgaricus	Pregnant women with newly diagnosed GDM between 24 and 28 weeks of gestation	Eight weeks	Positive-decrease in fasting glycaemia
Karamali et al [110]	L. Acidophilus, L. casei, B. Bifidum	Primiparous mothers diagnosed with GDM between 24 and 28 weeks of gestation	Six weeks	Positive-lower fasting glycemia and serum insulin levels
Kijmanawat et al [106]	Lactobacillus acidophilus, Bifidobacterium bifidum	Newly diagnosed women with GDM between 24–28 weeks of gestation	Four weeks	Positive-less increase in insulin resistance and lower fasting glucose and insulin levels
Luoto et al [116]	Lactobacillus rhamnosus GG, Bifidobacterium lactis Bb12, diet	Pregnant women with normal BMI	From first trimester of pregnancy to end of exclusive breastfeeding	Positive-reduction in risk of GDM
Callaway et al [111]	Lactobacillus rhamnosus, Bifidobacterium animalis subspecies lactis BB-12	Overweight and obese pregnant women	From second trimester until birth	Does not prevent GDM
Lindsay et al [112]	Lactobacillus salivarius UCC118	Pregnant women with early pregnancy body mass index from 30.0 to 39.9	From 24 to 28 weeks of pregnancy	Lack of effect on fasting glycaemia
Asemi et al [104]	Lactobacillus acidophilus LA5 and BB-12	Primigravida in third trimester	Nine weeks	No influence on fasting blood glucose
Pellonpera et al [117]	Lactobacillus rhamnosus HN001, Bifidobacterium animalis ssp. lactis 420, fish oil	Overweight and obese pregnant women	Before 18 gestational weeks, throughout pregnancy and until 6 months postpartum	Does not reduce risk of GDM or improve glucose metabolism
Halkjaer et al [113]	Vivomixx® (Streptococcus thermophilus DSM 24731, Bifidobacterium breve DSM 24732, Bifidobacterium longum DSM 24736, Bifidobacterium infantis DSM 24737, Lactobacillus acidophilus DSM 24735, Lactobacillus plantarum DSM 24730, Lactobacillus paracasei DSM 24733, Lactobacillus delbrueckii subsp. bulgaricus DSM 24734)	Obese pregnant women	From early second trimester until delivery	No significant difference in GDM and HbA1c levels
Shahriari et al [118]	Lactobacillus acidophilus LA1, Bifidobacterium longum sp54 cs, Bifidobacterium bifidum sp9 cs	Pregnant women with risk factor for GDM	From 14 weeks to 24 weeks of pregnancy	Does not reduce risk of GDM

GDM – gestational diabetes mellitus; BMI – body mass index; HbA1c – glycated hemoglobin; L. – Lactobacillus; B. – Bifidobacterium; spp. – species (plural); ssp. – subspecies.

Diet Composition and Its Role in Modulating Gut Microbiota – Prebiotics

The composition and quantity of dietary intake are key factors in the modulation of gut microbiota, which, in turn, profoundly impact human health. A growing body of research shows the role of diet in shaping the microbiome’s structure and function, influencing host metabolism, immunity, and even psychological well-being. The interaction between dietary nutrients and the microbiome is complex, with potential benefits or risks contingent upon nutrient types, ratios, and interactions within the gut environment. Diet-driven shifts in microbial populations not only alter bacterial abundance but also affect bacterial growth dynamics and metabolic output, thus having far-reaching implications for human health. Regarding the type of food consumed, a distinction is made between 3 enterotypes of the large intestine: a diet rich in animal fats and protein promotes Bacteroides (Enterotype 1), abundant in fiber-Prevotella (Enterotype 2), and a diet high in starch-Ruminococcus (Enterotype 3) [29].

Role of Carbohydrates and Glycans in Gut Microbiota Modulation

Indigestible carbohydrates, also known as glycans, are an important dietary component in microbiome modulation. Glycans are diverse carbohydrates present in a variety of foods, including vegetables, fruits, cereals, legumes, and some dairy products. These compounds are a heterogeneous group with structural complexity ranging from simple sugars to complex polysaccharides. The structural diversity of glycans – encompassing both monosaccharides and complex glycoconjugates – dictates their metabolic fate within the gut and their susceptibility to degradation by host and microbial enzymes. Unlike digestible carbohydrates, many glycans cannot be broken down by human enzymes alone and instead rely on the gut microbiota for enzymatic hydrolysis and fermentation. This symbiotic interaction results in production of bioavailable monosaccharides and short-chain fatty acids (SCFAs), which are critical energy sources for both bacterial and host cells [118].

Butyrate, a key SCFA, enhances mucin production in the gut, thus reinforcing the integrity of epithelial tight junctions and reducing intestinal permeability. Enhanced intestinal barrier function helps prevent leakage of potentially harmful bacteria and endotoxins from the intestinal lumen into the circulatory system, a process linked to systemic inflammation and metabolic diseases [119].

Low dietary fiber intake is associated with a gut microbiota favoring lactate fermentation. Bacteria capable of using host-secreted mucus glycoproteins and other ‘non-fiber’ energy sources for growth, affect the integrity of the colonic intestinal barrier, pathogen overgrowth, and proliferation and insulin resistance [120].

However, specific beneficial bacteria, such as those from the Eubacterium genus, can metabolize lactate to SCFAs, counteracting the potential adverse effects associated with lactate accumulation. Moreover, lactate produced by lactic acid bacteria, such as Bifidobacterium, can serve as an alternative energy source in SCFA synthesis pathways, highlighting the metabolic flexibility and adaptive capacity of gut microbes in response to dietary intake. Thus, the modulation of dietary fibers and their conversion to SCFAs not only has protective effects on the gut barrier but also plays a central role in metabolic health and prevention of chronic diseases associated with inflammation [121].

Glycome–Microbiome Interaction

The glycome, encompassing all glycans and glycoconjugates within an organism, plays a vital role in human nutrition and gut ecology. The glycome’s interaction with the gut microbiota is a dynamic process, in which most dietary glycans (excluding starch and glycogen) depend on microbial enzymes for their breakdown and utilization. Recent advances in glycomics have provided critical insights into how different dietary patterns affect glycan profiles within the gut, revealing complex metabolic interactions with the microbiome that have significant health implications [122].

Studies on glycomics have plasticity; shown how specific dietary glycans function as prebiotics, selectively promoting the growth of beneficial bacteria while inhibiting pathogenic species. These prebiotic effects support gut health, aid immune function, and contribute to homeostasis of the host’s internal environment. For instance, particular strains within the Clostridium, Roseburia, and Faecalibacterium genera, as well as Bacteroides, utilize specific glycans to produce SCFAs that support the host’s physiological functions. This microbiota-driven metabolic flexibility allows the host to adapt to various diets while maintaining a stable gut ecosystem [123].

The potential Role of Dietary Interventions Targeting Microbiota in Healthy and GDM Mothers

The alteration in microbial composition during late pregnancy is hypothesized to contribute to the metabolic adaptations necessary for fetal development but may also exacerbate insulin resistance contributing to the onset of GDM. [124]. The potential role of dietary interventions targeting microbial stability and diversity in influencing these metabolic shifts is an area of growing interest, as such interventions could support metabolic resilience during pregnancy. Specific dietary components, including carbohydrates, proteins, fats, and fibers, significantly impact gut microbiota composition and function, influencing blood glucose levels and overall metabolic health in GDM. The role of carbohydrates in GDM management remains a subject of debate, with varied recommendations on their amount and type. A minimum intake of 175 grams per day, constituting 35–50% of total calories, is advised by the American Diabetes Association (ADA) to avoid ketonemia while supporting glycemic control. Carbohydrates with a lower glycemic index (GI) are preferred due to their association with lower postprandial glucose levels. In addition, fiber intake has been shown to promote beneficial gut microbiota profiles that enhance postprandial glucose control, improving insulin sensitivity and reducing inflammation [125]. Protein intake necessary for fetal growth is recommended at 71 grams per day [126]. Fat intake, particularly from unsaturated sources, has been associated with improved inflammatory and glycemic outcomes, while intake of saturated fats should be limited. Lipid composition has been noted to influence gut microbial communities, and diets high in unsaturated fats support beneficial bacterial populations linked to improved insulin sensitivity [127].

The gut microbiota comprises various bacterial species, each with unique metabolic capabilities and roles in maintaining host health. Key taxa that contribute to beneficial SCFA production include Clostridium clusters (eg, Roseburia, Coprococcus, Ruminococcus, Dorea, Lachnospira, Faecalibacterium), and Bacteroides, and others such as Phascolarctobacterium and Holdemania. Gomez-Arango et al (2018) studied the role of dietary fiber intake in regulating microbial composition. Low fiber intake was strongly associated with the Collinsella genus, as well as with the Prevotella, Methanobrevibacter, Dialister, and Biophila genera. In contrast, high fiber intake was associated with higher abundance of the genus Akkermansia, followed by the genera Holdemania, Phascolarctobacterium, Odoribacter, Megasphaera, Coprococcus, and Butyricimonas. Fiber intake was corrected for total energy intake in all women to ensure that differences in bacterial networks were not due to differences in total energy intake. Even after correction, fiber deficiency was still linked to Collinsella and Bilophila. Bifidobacterium, Streptococcus, Lactococcus, Blautia, Turicibacter, Adlercreutzia, Eubacterium, and Sutterella were also more common in the low-fiber group. In contrast, higher fiber consumption was still associated with the genera Holdemania, Phascolarctobacterium, and Coprococcus, as well as the genera Bacteroides, Dorea, Faecalibacterium, Roseburia, Acidaminococcus, Lachnospira, Ruminococcus, and Desulfovibrio. The study also found that higher Collinsella levels during early pregnancy in overweight and obese women was positively correlated with increased circulating insulin levels, regardless of maternal BMI, but was negatively correlated with fiber intake. This suggests that, particularly in overweight and obese individuals, high levels of Collinsella, a genus associated with inflammatory conditions, may play a role in the development of insulin resistance during pregnancy [128].

Röytiö et al assessed correlations between gut microbiota richness and GlycA, and between a specific microbiota genera and serum lipoproteins. Fiber intake was positively correlated with the presence of bacteria of Firmicutes and Bacteroidetes types. The lowest relative abundance of the Bacteroidaceae family (belonging to the Bacteroidetes type) was found in women consuming high fiber and moderate fat. The Lachnospira genus (of the Lachnospiraceae family) showed a negative association with various lipoprotein particles and serum triglycerides, and the Blautia genus (also of the Lachnospiraceae family) had a positive association with various lipoprotein particles. Low fiber intake was strongly associated with abundance of the Bacteroidaceae family, especially in the Bacteroides genus. The authors concluded that the absolute amount of fiber and fat consumed was linked to richness of the gut microbiota, and also to maternal inflammatory status, measured with GlycA (glycoprotein acetylation) [129].

Ferrocino et al enrolled 41 patients with GDM between the 24^th and 28^th gestational week into a prospective observational exploratory study and assessed microbiota alterations during pregnancy in relation to nutrients. At the end of the study (38^th gestational week), α-diversity was significantly increased, together with an increase of Firmicutes and reduction of Bacteroidetes and Actinobacteria. They noted significant depletion in the abundance of Bacteroides, Collinsella, and Rikenellaceae and a significant increase in Blautia, Butyricicoccus, Clostridium, Coprococcus, Dorea, Faecalibacterium, L-Ruminococcus, and Lachnospiraceae at the end of the study. All enrolled patients consumed a low-fiber and high-fat diet. Only 1/3 of them substantially changed their dietary habits after having received nutritional recommendations. Interestingly, α- and β-diversity values were not significantly different between adherent and non-adherent woman, and a common microbiota signature was observed: a significant increase in Blautia, Coprococcus, Dorea, and Lachnospiraceae and decrease in Rikenellaceae abundance. The researchers found that patients who followed dietary recommendations showed a significant decrease in Bacteroides and a better metabolic and inflammatory pattern at the end of the study, while non-adherents presented a higher abundance of Faecalibacterium and L-Ruminococcus [130].

The study conducted by Vavreckova et al (2022) was focused on the first trimester to find a microbiota pattern that can predict the development of GDM. The authors found that the gut microbiota of normoglycemic women was associated with increased abundance of the Prevotellaceae family, order Fusobacteriales order and Sutterella genus. Pregnant women who developed impaired insulin resistance later in pregnancy showed a higher abundance of the Enterococcus or Erysipelotrichaceae UCG-003 genera. In the cohort of pregnant women with impaired FPG/oGTT in the third trimester, a negative correlation was found between Holdemanella and Blautia. Positive correlations between valerate and the Akkermansia genus were found in women with impaired FPG in the first and third trimesters. Holdemanella showed negative a correlation with Blautia and Candida enrichment. They noted that Coprococcus and Akkermansia were positively correlated with acetate and valerate levels, respectively, in women with GDM. The researchers concluded that as early as the first trimester of pregnancy, significant differences in the composition of the gut microbiota were observed between women with GDM and healthy women, highlighting that in their study cohort, most pregnant women were overweight and obese. They believe that the specific microbial pattern already observed in early pregnancy and its correlation with plasma lipid or SCFA levels may help to identify women at increased risk of developing GDM [78].

A study by Wu et al (2022) identified specific changes in gut microbiota composition among women with GDM during the second trimesters of pregnancy and assessed the effect of a 2-week dietary intervention on these changes, finding a marked shift in the microbiota composition at the phylum and genus levels in GDM samples compared to healthy ones, and were able to identify the microbial pattern of GDM patients after the intervention. The authors showed significantly higher alpha diversity of the gut microbiota in women with GDM compared to those without GDM during the second trimester of pregnancy. Women with GDM had a significantly higher abundance of Acidobacteria compared to healthy individuals. Interestingly, the abundance of Acidobacteria was positively correlated with fasting blood glucose levels. In particular, 2 types of Acidobacteria – Acidothermus and Granulicella – were significantly more common in women with GDM. Partial least squares discriminant analysis (PLS-DA) identified 49 key bacterial genera distinguishing women with and without GDM. Among these, those with GDM showed greater abundance of the Citrobacter, Burkholderia, Eubacterium, Holdemania, and Tyzzerella genera. In contrast, the Ruminococcaceae, Akkermansia, and Coprococcus genera were more common in women without GDM. In women with GDM who followed the diet instructions, the Firmicutes/Bacteroidetes ratio did not change from baseline, but an increase in this ratio was observed in women without GDM who did not follow the diet. An increase in the Firmicutes/Bacteroidetes ratio is usually associated with obesity and other metabolic disorders. After the dietary intervention, decreases in the abundance of Acidothermus, Granulicella, Bryobacter, and Candidatus Solibacter were observed in most women with GDM [131].

Consuming a diet rich in fiber and moderate in fat, especially saturated fats, appears to be beneficial for maintaining a balanced gut microflora and thus reducing the risk of metabolic disorders (Table 3).

Table 3.

Studies on influence of diet regarding bacterial taxonomies in the group of women with and without GDM. In this table, a summary is given of studies in which the impact has been investigated of dietary interventions on the gut microbiota composition of pregnant women with and without gestational diabetes mellitus (GDM). It includes the type of diet, the participants’ demographics, methods used for microbiota analysis and the outcomes related to bacterial taxonomies, insulin resistance and metabolic markers. In these studies, various nutritional components are highlighted, such as fiber intake, fat content and carbohydrate levels, as well as their effects on specific bacterial taxa.

Author	Participating women	Nutritional component	Method	Outcome
Gomez-Arango et al (2018) [129]	57 overweight and 73 obese pregnant women at 16 weeks gestation or less	Participants were ranked based on fiber intake and only the top and bottom 10% of dietary fiber intake (30.8 (29.4–35.9) g/day) (n=13) and (10.3 (9.3–10.5) g/day) (n=13), respectively) were included in the analysis. To ensure that the differences in bacterial networks were not due to differences in overall energy intake, fiber consumption was corrected for total energy intake in all women. The women were ranked again and only the top (0.23 (0.21–0.25) g/kJ) and bottom (0.32 (0.29–0.43) g/kJ) 10% were included in the network analysis	Gut microbiota profiles were assessed via 16S rRNA	Collinsella abundance showed positive correlations with circulating insulin (P=0.0006), independent of maternal BMI, and with C-peptide levels and HOMA-IR but negative with dietary fiber intake (p=0.025). Direct associations between Roseburia and fiber intake were found (β=0.09; 95% CI 0.02 0.16; P=0.01). Collinsella and Blautia were directly and inversely, respectively, associated with insulin and HOMA-IR values; Blautia was inversely correlated with HbA1c
Röytiö et al (2017) [130]	100 overweight and obese pregnant women at less than 18 weeks gestation	Patients were divided into 3 groups. Group 1 was the low-fiber/moderate-fat group (n 57) and consisted of women whose fiber intake was below the reference value (<25 g/d) and whose total fat intake was within the reference intakes (25–40 E%). Group 2 was the high-fiber/moderate-fat group (n18), and these women consumed the recommended level of fiber (≥25 g/d), and the total fat intake was within the reference intake level. Group 3 was the low-fiber/high-fat group (n 13), and their fat intake exceeded the referenced level of total fat intake – that is ≥40% of total energy	Fecal microbiota composition was analyzed using 16S rRNA gene sequencing	The high-fiber/moderate-fat group had a lower relative abundance of Bacteroidaceae (25.6 (SD 10.6)%) in comparison to the low-fiber/high-fat group (42·4 (SD 14.6)%, P=0·01) and the low-fiber/moderate-fat group (33.2 (SD 11.9)%, P=0.02). Similar differences were detected in the Bacteroides genus, for which differences were observed between the high-fiber/moderate-fat and low-fiber/moderate-fat (25.6 (SD 10.6) v. 33.2 (SD 11.9)%, P=0.02) and between high-fiber/moderate-fat and low-fiber/high-fat groups (25.6 (SD 10.6) v. 42.4 (14.6)%, P=0.01)
Ferrocino I et al (2018) [131]	41 GDM patients (assessment at 24–28 weeks and at 38 weeks of gestational age)	All the patients with GDM routinely received dietary counselling and nutritional recommendations in line with guidelines (carbohydrates 45% total energy, rapidly absorbed sugars <10% total energy, proteins 18–20% total energy, fats 35% total energy, at least 20–25 g/day fiber intake, no alcohol). Out of all participants, 34.1% (14/41) declared to be adherent to the given dietary recommendations	The fecal microbiota was assessed via 16S amplicon-based sequencing	Reduced intake of sugars and increased consumption of fiber, oligosaccharides and polyunsaturated fatty acids (PUFA) caused an impressive decrease in Bacteroides in adherents, and higher abundance of Faecalibacterium and L-Ruminococcus together with minor Operational Taxonomic Units (OTUs) in non-adherents. Direct associations between Roseburia and fiber intake were noted (β=0.09; 95% CI 0.02 0.16; P=0.01), and between L-Ruminococcus and oligosaccharides (β=0.02; 95% CI 0.01 0.03; P=0.006), In multiple regression models, Faecalibacterium was significantly associated with fasting glucose; Collinsella (directly) and Blautia (inversely) with insulin, and with Homeostasis-Model Assessment Insulin-Resistance, while Sutterella correlated with C-reactive protein levels
Vavreckova M. et al (2022) [79]	104 pregnant women including obese individuals (first trimester body mass index median was 26.73); Samples were taken during the first and the third trimesters of pregnancy	The total group of pregnant women was divided into 4 subgroups: healthy, pregnant women with impaired FPG in the first trimester, pregnant women with impaired FPG in the third trimester, and pregnant women with impaired oGTT in the third trimester. The authors correlated microbiota changes with basic biochemical parameters and SCFA levels in plasma. They did not assess correlations with diet components	Stool samples were assessed for bacterial 16S rRNA gene sequencing via the Illumina MiSeq Approach	Gut microbiota of normoglycemic women was associated with increased abundance of the Prevotellaceae family, Fusobacteriales order and Sutterella genus. Women who developed impaired insulin resistance later in pregnancy had a higher abundance of the Enterococcus or Erysipelotrichaceae UCG-003 genera A positive correlation between Roseburia and Bacteroides was found in healthy women but not in the GDM group. In the groups of pregnant women with impaired FPG/o GTT in the third trimester, negative correlations were observed between Holdemanella with Blautia and with yeast Candida, respectively. A positive correlation between Collinsella and Bifidobacterium was noted in pregnant women with impaired FPG in the third trimester. In normoglycemic women, a very strong negative correlation was found between the Subdoligranulum genus and serum levels of LDL, non-HDL, and cholesterol. In women with later onset of GDM, a positive correlation between the Escherichia/Shigella group abd LDL, non-HDL, and triglycerides and negative correlation between the Prevotella genus and total cholesterol were observed. Positive correlations were indicated between valerate and the Akkermansia genus in pregnant women with impaired FPG in the first and third trimesters, respectively. In the GDM2 group, the Phascolarctobacterium genus positively correlated with 2-hydroxybutyrate levels
Wu N et al 2022 [132]	27 GDM patients and 30 healthy controls in the third trimester, The BMI value of the GDM group was significantly higher than the value of the healthy group (24.2±4.4, versus was 21.4±2.8) Stool samples were taken 2 times: at the enrolment and at the end of the study after the two-week duration	GDM participants received 2 weeks of dietary management and nutritional recommendations 35–45% carbohydrates (80% complex carbohydrates with a low glycemic index and 20% simple carbohydrates), 18–20% protein (50% animal and 50% vegetable) and 35% fat (16% mono-unsaturated, 10% polyunsaturated and 9% saturated with moderately low saturated fat levels and fiber intake of at least 20–25 g/day	DNA was extracted from stool samples using the QIAamp DNA Stool Mini kit protocol (Qiagen, Germany)	After the dietary intervention, decreases in the abundance of Acidothermus, Granulicella, Bryobacter, and Candidatus Solibacter were observed in most women with GDM. In women with GDM who followed the diet, the Firmicutes/Bacteroidetes (F/B) ratio did not change from baseline. An increase in F/B ratio was noted in women without GDM who did not follow the diet. After 2 weeks of diet therapy, the relative abundance of Bacteroidetes in GDM samples showed an increased trend, and the abundance of Firmicutes slightly decreased, while the ratio of Firmicutes/Bacteroidetes did not increase. A decreased abundance in pathogenic taxa (Acidaminococcaceae, Enterobacteriaceae and Bacteroidaceae) and increased Bifidobacteriaceae and butyric acid-producing bacteria (Prevotellaceae and Lachnospiraceae) were seen compared to the GDM microbial samples at enrolment

GDM – gestational diabetes mellitus; BMI – body mass index; FPG – fasting plasma glucose; oGTT – oral glucose tolerance test; LDL – low-density lipoprotein cholesterol; HDL – high-density lipoprotein cholesterol; PUFA – polyunsaturated fatty acids; HOMA-IR – homeostatic model assessment for insulin resistance; SCFA – short-chain fatty acids; OTU – operational taxonomic unit; F/B – Firmicutes to Bacteroidetes ratio.

Future Directions

Future research should be aimed at elucidating the specific mechanisms through which gut microbiota influence glucose metabolism and inflammatory pathways during pregnancy. In particular, identifying microbial signatures predictive of GDM risk could inform early diagnostic strategies. The rapid development of molecular techniques, including RNA sequencing of the gut microbiome, may be helpful in this area. Longitudinal studies are needed to assess the long-term effects of microbiota-targeted interventions – such as probiotics, prebiotics, and dietary modifications – on both maternal and offspring’s metabolic health. Additionally, integrating microbiome profiling with clinical parameters and genetic predispositions could support the development of personalized nutritional approaches. Multi-center trials with diverse populations are needed to validate findings and ensure broad applicability. Translating this knowledge to clinical practice may lead to more effective, tailored strategies for the prevention and management of GDM.

The development of molecular techniques such as RNA sequencing of the intestinal microbiome may allow us to find answers to many questions about the epigenetic mechanisms of genome regulation in pregnant patients.

Conclusions

In individuals with GDM, the composition and function of the gut microbiota significantly diverge from those in their non-GDM counterparts. Distinct microbial community profiles have been revealed in studies, with GDM patients showing an increased presence of pathways related to membrane transport, lipopolysaccharide biosynthesis, and phosphotransferase systems, all of which play roles in inflammatory and metabolic processes. Conversely, the microbiomes of non-GDM controls are enriched with pathways related to amino acid metabolism, suggesting potential metabolic adaptations unique to GDM. These findings show the role of the gut microbiota in influencing metabolic and immune functions during pregnancy and suggest that dietary strategies modulating these microbiota functions could be beneficial in managing GDM. If the results of the conducted studies indicate significant differences regarding the microbiota in healthy and GDM subjects, then modulating the gut microbiota through at least a short-term dietary intervention, especially regarding SCFA-producing bacteria, may be a promising strategy for treatment and prevention of metabolic disorders in GDM mothers and affected fetuses. Raising awareness of the impact of an adequate diet, not only by pregnant women but also by parents-to-be during the pre-conception period, appears to be a safe and cost-effective way to reduce the incidence of metabolic diseases and their transmission to subsequent generations.