Abstract
The prenatal period is a critical developmental juncture with enduring effects on offspring health trajectories. An individual’s gut microbiome is associated with health and developmental outcomes across the lifespan. Prenatal stress can disrupt an infant’s microbiome, thereby increasing susceptibility to adverse outcomes. This cross-species systematic review investigates whether maternal prenatal stress affects the offspring’s gut microbiome. The study analyzes 19 empirical, peer-reviewed research articles, including humans, rodents, and non-human primates, that included prenatal stress as a primary independent variable and offspring gut microbiome characteristics as an outcome variable. Prenatal stress appeared to correlate with differences in beta diversity and specific microbial taxa, but not alpha diversity. Prenatal stress is positively correlated with Proteobacteria, Bacteroidaceae, Lachnospiraceae, Prevotellaceae, Bacteroides, and Serratia. Negative correlations were observed for Actinobacteria, Enterobacteriaceae, Streptococcaceae, Bifidobacteria, Eggerthella, Parabacteroides, and Streptococcus. Evidence for the direction of association between prenatal stress and Lactobacillus was mixed. The synthesis of findings was limited by differences in study design, operationalization and timing of prenatal stress, timing of infant microbiome sampling, and microbiome analysis methods.
Keywords: animal models, disease susceptibility, gut microbiome, infant, maternal–fetal relations, prenatal exposure delayed effects, stress
1 |. Introduction
The gut microbiome is robustly associated with health and developmental outcomes across the lifespan (Adane et al. 2021; Flanigan et al. 2018; Manzari et al. 2019). Various microbes and bacterial communities are associated with disorders, such as autism, attention deficit and hyperactivity disorder, anxiety, depression, Parkinson disease, and irritable bowel syndrome (Checa-Ros et al. 2021; Dinan and Cryan 2017), as well as immune system and metabolic processes (Dabke, Hendrick, and Devkota 2019; Lambring et al. 2019). Despite these established associations, the various developmental mechanisms contributing to the development of the gut microbiome across the lifespan are not fully elucidated. An emerging area of inquiry focuses on the potential impact of the prenatal environment on the offspring gut microbiome. The goal of this systematic review was to synthesize the literature investigating the association between maternal prenatal stress and the offspring’s gut microbiome across species. With this work, we aim to better understand the complexities of fetal programming and inform strategies for optimizing health across the lifespan.
1.1 |. What Is the Gut Microbiome and Why Does It Matter for Fetal Programming
The gut microbiome is a dynamic system of trillions of microorganisms, including viruses, fungi, and archaea living in the gastrointestinal tract. The gut microbiome communicates with the brain via interconnected neuroendocrine, immune, and neural pathways, and this communication system begins at or before birth (Bäckhed et al. 2015; Ferretti et al. 2018; Penders et al. 2006; Stewart et al. 2018; Walker et al. 2017). The gut microbiome plays a key role in many physiological functions important for human health, including aiding in digestion (Donohoe et al. 2011; Martens et al. 2011; Tremaroli and Bäckhed 2012), synthesizing vitamins and essential nutrients (Donohoe et al. 2011; Morrison and Preston 2016; Murugesan et al. 2018), regulating the immune system (Maslowski et al. 2009), and neural development (Kelsey et al. 2021; Mancini et al. 2023; Oliphant et al. 2021), among others.
Gut bacteria have been most widely studied in the context of human health and development. Gut bacteria are involved in digesting complex carbohydrates, proteins, and fibers, leading to the production of short-chain fatty acids that support gut barrier integrity, regulate immune responses, and provide energy, contributing to overall human health (McBurney et al. 2019). Compared to bacteria, the abundance of viruses is proportionally lower; however, viruses also play a significant role in the microbiome functioning. For example, bacteriophages, a type of virus found in the gut, help regulate bacterial populations and are associated with individual health benefits (Coker 2022). Gut fungi, despite being far less abundant than bacteria, significantly impact human health by serving as a reservoir for pathogenic microbes and potentially contributing to inflammatory disease and metabolic disorders (Huffnagle and Noverr 2013).
In studying the gut microbiome, researchers focus on various measures of bacterial composition and function, including alpha diversity, beta diversity, and taxonomic abundance. Table 1 provides a definition of alpha and beta diversity and explains what various indices measure for each. Taxa refers to the groups into which organisms are classified, with research focusing on changes in the presence or abundance of certain taxa to understand relationships among individual and environmental conditions and the microbiome’s structure and function. More recently, technology has advanced to allow for the examination of other microbes, including fungi and viruses, as well as the presence of functional pathways (i.e., the metabolic activities and interactions that are driven by specific microbes). In this systematic review, we will focus on the bacterial gut microbiome.
TABLE 1 |.
Microbiome diversity metrics, definitions, and interpretation.
| Construct | Definition | Interpretation and distinction |
|---|---|---|
|
| ||
| Alpha diversity | A within-subjects measure assessing the variety of microorganisms within an individual’s gut microbiota | Higher values indicate greater within-individual diversity |
| Shannon index | A measure of the number of bacterial groups in a community (richness) and the distribution of those groups (evenness) | Higher Shannon diversity values indicate a greater number of species, are present and those species are relatively evenly distributed |
| Chaol index | A measure of the number of bacterial groups in a community (i.e., an individual’s gut microbiome) | Higher Chao1 diversity scores reflect a larger number of bacterial groups, wherein many species are only observed once or twice |
| Observed species | A measure of the number of distinct species present in the sample | Higher values reflect a greater number of species observed in a sample |
| Faith’s phylogenetic diversity | A measure of the evolutionary divergence of a bacterial community calculated by summing the lengths of each branch of the community’s phylogenetic tree | Higher values mean that species in the community are more distantly related to one another based on their evolutionary history |
| Simpson index | A measure of community richness and species dominance that gives more weight to highly abundant species | Higher values indicate a community with many species, but that tends to be dominated by one or more species |
| Pielou’s evenness | A metric of how equally individuals in a bacterial community are distributed across species | Higher values indicate more even distribution, so species are relatively equally abundant |
| Beta diversity | A between-subjects measure that assesses similarity and dissimilarity of microbial communities between individuals? (i.e., comparison of the microbiota between two individuals) | Higher values indicate that two communities (i.e., the microbiota of two individuals) are less similar to one another |
| Bray-Curtis distance | A measure of dissimilarity between individual microbial communities that accounts for the presence and abundance of species | Higher values indicate that the two microbial communities are more distinct from one another in terms of either the presence of certain species, the abundance of certain species, or both |
| W metric | ||
| Manhattan distance | A metric of dissimilarity between two communities calculated by summing the absolute differences in abundances of taxa | Higher values indicate greater dissimilarity between communities in terms of absolute abundances of specific taxa |
| Weighted UniFrac | A metric of similarity/dissimilarity between microbial communities that accounts for the number of shared branches on the phylogenetic tree between two communities and incorporates the abundances of taxa in the calculation | Higher weighted UniFrac distances indicate greater dissimilarity between two microbial communities in terms of abundance and shared phylogeny |
| Unweighted UniFrac | A metric of similarity/dissimilarity between microbial communities that takes into account the number of shared branches in the phylogenetic tree between communities, emphasizing the presence/absence of taxa. It provides equal weight to all taxa, allowing rarer taxa to exert a more significant influence on the calculation | Higher values indicate greater dissimilarity based on the presence/absence of taxa and shared phylogeny |
The composition of an individual’s gut microbiome is influenced by a variety of personal and environmental factors, such as geographic location, cohabitation, and antibiotic use, which can have profound and long-lasting impacts (Gacesa et al. 2022). Additionally, diet is a significant determinant of the composition of the gut microbiome (Gacesa et al. 2022). Diets rich in fiber promote greater alpha and beta diversity, as well as the growth of beneficial bacteria like Bifidobacterium and Lactobacillus (Singh et al. 2017). In contrast, diets high in refined sugars and saturated fats have been shown to decrease bacterial diversity and encourage the proliferation of less beneficial bacteria (Singh et al. 2017). The composition of the gut microbiome and exercise are also interconnected, with moderate exercise generally promoting positive changes in gut microbiome composition, including increased alpha and beta diversity and increased abundance of beneficial bacteria (e.g., Clostridiales, Roseburia, Lachnospiraceae, and Erysipelotrichaceae) (Clauss et al. 2021).
The infant gut microbiome undergoes a relatively consistent pattern of development over the first year of life (Bäckhed et al. 2015; Ferretti et al. 2018; Penders et al. 2006; Stewart et al. 2018). At birth, infants are exposed to a diverse community of maternal and environmental microbes, some of which survive and colonize the infant’s gut, depending on whether they are adapted for life within the gut (Ferretti et al. 2018; Penders et al. 2006). After initial colonization, aerobic and facultative bacteria, such as those from the families Lactobacillaceae and Enterobacteriaceae, tend to dominate the infant gut in the first days after birth, followed by replacement with the anaerobic bacteria from the families Bifidobacteriaceae and Clostridiaceae in the first weeks of life (Penders et al. 2006; Sanidad and Zeng 2020). Gradually, facultative anaerobes are replaced by obligate anaerobes, including those from the families Bacteroidaceae, Porphyromonadaceae, Lachnospiraceae, and Ruminococcaceae (Moore and Townsend 2019; Sanidad and Zeng 2020).
Despite these typical trends in development, exposures at and around birth can disrupt the microbial composition of the infant gut. For example, the mode of delivery (e.g., vaginal or cesarean section (CS)) influences clear and sustained differences in the infant gut microbial composition (Korpela and de Vos 2018; Moore and Townsend 2019; Penders et al. 2006). Delivery via CS has been positively associated with the abundance of bacterial groups bacilli and enterobacteria and negatively associated with the genera Bifidobacterium and Bacteroides (Korpela and de Vos 2018; Penders et al. 2006). Additionally, the selected feeding method largely influences the development of the gut microbiome in infancy. For example, breastfeeding, compared to formula feeding, is associated with higher levels of microorganisms from genera Bifidobacterium and Lactobacillus (Azad et al. 2013; Bäckhed et al. 2015; Lee et al. 2015; Stewart et al. 2018), which have been recognized for their positive effects on human health (Heeney, Gareau, and Marco 2018; O’Callaghan and Van Sinderen 2016; Reid and Burton 2002). Conversely, exclusive formula feeding has been associated with increased relative abundance of inflammatory microbes like Roseburia, Clostridium difficile, and Anaerostipes, and more closely resemble the gut microbiome of adults (Bäckhed et al. 2015; Penders et al. 2006; Stewart et al. 2018). Other notable contributors include maternal and infant genetic factors, infant health, pre- and postnatal antibiotic exposure, household family members, household pets, and the timing and type of complementary food. Given this critical window for the gut microbiome, and the importance of the gut microbiome for human health and development, understanding which factors shape the gut microbiome in early developmental periods is of critical importance.
1.2 |. The Gut Microbiome and Infant Health and Development
The composition of the infant gut microbiome has been correlated with various health and developmental outcomes. For example, infants with greater abundance of Bacteroidetes have been shown to perform better in cognitive and language assessments (Tamana et al. 2021). Similarly, in preterm infants, a lower abundance of Bacteroidetes and Lachnospiraceae is associated with decreased head circumference growth, the earliest validated marker for neurodevelopment (Oliphant et al. 2021). Alterations in the infant gut microbiome are also associated with infant temperament (Fox et al. 2022), externalizing disorders (Cassidy-Bushrow et al. 2023; Laue et al. 2022; Laue et al. 2020), and internalizing disorders (Laue et al. 2022; Loughman et al. 2020), metabolic disturbances (Koleva, Bridgman, and Kozyrskyj 2015), Type 1 diabetes mellitus (Kostic et al. 2015), digestive disorders (Hofman et al. 2022), and allergic conditions (e.g., food allergy, asthma, and atopic dermatitis) (Peroni et al. 2020; Salameh et al. 2020; Ta et al. 2020). These findings suggest that the gut microbiome plays a large role in shaping health and development across the lifespan.
1.3 |. Prenatal Stress and Infant Health and Development
The prenatal period, spanning from conception to birth, is a critical juncture in the continuum of human development. During pregnancy, the mother’s body undergoes significant physical and hormonal changes, including increased production of estrogen, progesterone, and cortisol (Jung et al. 2011). Simultaneously, the mother’s brain undergoes significant changes, including reduction in gray matter volume, greater neuroplasticity, and heightened emotional responsiveness (Pawluski et al. 2016; Rehbein et al. 2022). In tandem with these maternal changes, fetal development occurs across three trimesters, each marked by significant growth and maturation (Munoz 2024). In the first trimester (weeks 1–12), the embryo forms, and major organs like the brain, heart, and digestive system begin to develop, with the neural tube closing and the heart starting to beat. During the second trimester (weeks 13–26), the fetus experiences rapid growth, with more detailed development of organs and sensory systems. The nervous system continues to mature. By the third trimester (weeks 27–40), the fetus gains weight, and organs, like the lungs and brain, undergo critical final development. The digestive system begins practicing functions like swallowing, and the fetus prepares for birth, with the brain, lungs, and immune system continuing to mature up until delivery (Munoz 2024).
The Developmental Origins for Health and Disease (DOHaD) theory posits that experiences and exposures during the prenatal period can exert enduring effects on health trajectories (Barker 2004). Within this context, prenatal stress has gained significant attention in research (Kingston et al. 2012; Sly, Blake, and Islam 2021). Infants born to mothers who experience higher levels of prenatal stress are more likely to experience adverse health outcomes, including adverse birth outcomes (e.g., stillbirth, preterm birth, and low birth weight) (Adane et al. 2021; Zhu et al. 2010), altered immune responses (e.g., allergies, asthma, and eczema) (Flanigan et al. 2018), higher risk for neurodevelopmental disorders (e.g., autism spectrum disorder and attention deficit and hyperactivity disorder) (Manzari et al. 2019), and chronic health conditions (e.g., obesity) (Burgueño et al. 2020). Understanding these impacts highlights a need to explore the underlying mechanisms linking prenatal stress to infant health and development.
Pregnancy itself imposes stress necessitating adaptations across social, physical, and emotional functioning (Rackers et al. 2018). To achieve survival of fetus and mother, pregnancy requires adaptations in physiological stress response systems, including the hypothalamic–pituitary–adrenal (HPA) axis, the autonomic nervous system (ANS), and the immune system (Brunton et al. 2008; Kuo et al. 2000; Mor and Cardenas 2010; Redpath et al. 2019). As pregnancy progresses, sympathetic activity and levels of cortisol increase, like during periods of distress (Glynn, Davis, and Sandman 2013; Kudo, Shinohara, and Kodama 2014; Stojanov et al. 2021). The immune system also adapts through increased immunological tolerance and a shift from cell-mediated to antibody-dominated responses, enhancing protection for the fetus, while increasing maternal susceptibility to infections and inflammation (Mor and Cardenas 2010). Stressors occurring in pregnancy can either buffer or further strain these systems, having impacts on the pregnant person and the developing fetus (Howland 2023). For individuals with a history of chronic stress, these systems have become hypo- or hyper-reactive, and physiological responses may be strained further with pregnancy (Crowley et al. 2016; Howland 2023). Therefore, the effects of stress can be cumulative, as the compromised immune system struggles to regulate bacterial entry from the gastrointestinal tract, further straining the immune system. This increased stress then exacerbates the strain on the HPA axis and the ANS. These systems are critical to fetal development, particularly in the development of the offspring’s own HPA axis and ANS. These effects are thought to last into the offspring’s adulthood (Howland 2023).
Health behaviors are a critical factor linking prenatal stress to infant health and development. During pregnancy, a woman’s nutritional needs increase significantly to support fetal growth, including higher demands for protein, iron, calcium, and folic acid. Physiologic stress can increase preference for calorie dense foods or suppress one’s appetite, thereby impairing the individual’s ability to meet nutritional demands (Bernabé et al. 2019). Alternatively, regular physical activity can alleviate prenatal stress and improve maternal and fetal health by downregulating the nervous system, enhancing mood, and reducing the risk of gestational complications (Luft et al. 2022; Marques et al. 2015). In addition to these physiological and behavioral impacts of prenatal stress and their association with infant health outcomes, evidence also suggests that prenatal stress impacts the infant gut microbiome (Aatsinki et al. 2020; Galley et al. 2023; Hu et al. 2019; Naudé et al. 2020; Querdasi et al. 2023; Rojas et al. 2023; Sun et al. 2021; Wei et al. 2022; Zijlmans et al. 2015). This might be a mechanism by which prenatal stress impacts offspring development.
1.4 |. Prenatal Stress and the Infant Gut Microbiome
There are several mechanisms by which maternal prenatal maternal stress may influence the infant gut microbiome. The maternal gut and vaginal microbiomes undergo a series of normal changes throughout pregnancy to promote fetal development and the metabolic changes needed to support pregnancy and are the main source of early colonizers of the gut microbiome for vaginally delivered infants (Jašarević and Bale 2019). However, maternal stress during pregnancy may influence the composition of the maternal gut and vaginal microbiomes (Hantsoo et al. 2019; Jašarević et al. 2015, 2017), thereby influencing the type of bacteria that colonize the newborn gut (Jašarević et al. 2018). Likewise, stress-induced changes in the maternal gut microbiome during pregnancy may also influence the metabolites and nutrients passed to the developing fetus, influencing brain and immune system development (Macpherson, De Agüero, and Ganal-Vonarburg 2017; Moog et al. 2018). As such, maternal stress during pregnancy may influence the offspring gut microbiome via changes to the uterine environment and by altering vertical transmission pathways via the maternal gut and vaginal microbiomes (Jašarević and Bale 2019).
The microbiome has the potential to reflect the culmination of forces both impacted by and buffering prenatal stress (Warner et al. 2023). For example, greater self-efficacy in navigating stress during pregnancy and postpartum has been associated with greater diversity in the maternal microbiome (Long et al. 2023), with dietary fiber and social support also impacting and reflecting the maternal gut microbiome composition (Peñalver Bernabé et al. 2023). Although stress has long been associated with inflammatory and immune responses (Coussons-Read et al. 2005, 2007), newer inquiries hypothesize that the gut microbiome may act as the intermediary (Kimmel et al. 2023).
1.5 |. The Present Study
Prenatal stress is an exposure gaining increasing attention for its impact on the infant gut microbiome and subsequent health outcomes (Kimmel et al. 2023; Warner et al. 2023); however, differences in study designs have posed challenges for synthesizing findings. The earliest work in this area originated with animals (e.g., rhesus monkeys and rodents) (Bailey et al. 2004; Golubeva et al. 2015), which allows for experimental manipulation of a stressor, an approach not possible in human studies that rely on observational design. However, findings from animal studies have the capacity to translate to humans (Courtine et al. 2007; Italia et al. 2020) and provide insight into causal mechanisms, whereas human studies allow for a broader consideration of context and increased external validity. Combined findings from human and animal studies provide a more robust understanding of the phenomenon than either design in isolation.
Differences in the conceptualization operationalization of stress have posed challenges in comparing findings across studies. Many of the related studies have relied on the measurement of perceived stress (Galley et al. 2023; Hu et al. 2019); however, the relation between perceived stress and physiological stress is nuanced, with an individual’s appraisal and coping mechanisms playing a role in how stress impacts the body (Lehrer et al. 2020; Lu, Wei, and Li 2021; Marin-Farrona et al. 2020). Alternately, some studies have reported prenatal stress as measured by exposure to stressors (i.e., events or circumstances that may trigger the stress response) (Hu et al. 2019; Naudé et al. 2020), which can occur at nested levels, including the individual, family, community, and societal levels (Reupert 2017). The stressors are also important to include in this investigation of prenatal stress because stressors induce stress responses in the mother (Lu, Wei, and Li 2021), potentially impacting the developing fetus. Still other studies have included self-reported psychosocial stress, such as anxiety or depression (Querdasi et al. 2023; Rackers et al. 2018; Rojas et al. 2023), that often occurs due to high or chronic levels of stress (Juszczyk et al. 2021; Westfall et al. 2021). Alternatively, some studies have relied on the measurement of the stress response system, such as HPA axis activation via hair or salivary cortisol concentration (Aatsinki et al. 2020; Zijlmans et al. 2015). However, the uterine environment is likely impacted by physiological responses to both (a) stressor exposures and (b) stress responses. Synthesizing these findings across mammalian species has the potential to inform the strongest conclusions for the impact of prenatal stress on the infant gut microbiome. Given how quickly the field has evolved, a comprehensive synthesis of the existing literature for the association between clearly operationalized maternal prenatal stress and the offspring’s gut microbiome is needed.
This systematic review aims to address this gap by analyzing and synthesizing findings across mammalian species. The primary objective of this study was to synthesize findings from previous research to elucidate patterns in the connection between the offspring gut microbiome and prenatal stress that could have an impact on the fetal environment. The purpose of this review was to provide insights into fetal programming and whether evidence exists for a link between prenatal stress and the composition of the offspring gut microbiome.
2 |. Methods
2.1 |. Search Strategy
This scoping review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) Checklist and was performed with guidance from a senior academic librarian. Articles published before June 2022 were obtained from PubMed, Embase, and Scopus using the following search string: (a) “prenatal exposure delayed effects,” “pregnancy trimesters,” “pregnancy,” “pregnant women,” “mother(s),” “prenatal,” “antenatal,” “pregnancy(ies),” “pregnant,” “trimester(s),” “maternal,” or “mother(s),” AND (b) “stress, physiological,” “stress, psychological,” “stress(es/ed/ing),” “stressor(s),” “stressful,” “distress(es/ed/ing),” “adversity,” or “adverse,” AND (c) “infant(s)”, “infancy,” “newborn(s),” “neonate(s),” “neonatal,” or “offspring,” AND (d) “gastrointestinal microbiome,” “gastrointestinal tract,” “gut,” “gastrointestinal,” “ gastric,” “GI tract(s),” “digestive tract(s),” “intestine(s),” “intestinal,” “stomach,” “enteric,” AND “microbiota,” “microbiome(s),” “microbiota(s),” “microflora,” “flora,” “microbe(s),” “microbial,” or “bacteria.” The review was not registered. The full search strategy for the systematic review is included in Supporting Information File A.
2.2 |. Study Selection
This systematic review includes original, peer-reviewed, empirical studies investigating the effects of prenatal stress on offspring gut microbiome across human and animal studies. The exclusion criteria included studies that were not peer-reviewed (e.g., dissertation work, gray literature), theoretical papers, studies published in a language other than English, and case studies. In humans, prenatal stress was defined as stressors, perceptions of stress, and resulting behavioral and physiological phenotypes of stressors that occurred during human pregnancy. Examples of such include perceived stress, psychopathology (e.g., anxiety or depressive symptoms), intimate partner violence (IPV), physiological markers of stress (e.g., cortisol), economic hardship, and experiences of discrimination. This review did not include physical stressors such as unhealthy diets, exposure to environmental toxins, drugs, or medications. These stressors can be toxic to the fetus but do not necessarily induce a stress response in the mother, which was how we operationalized stress in this study. In animals, stress was defined as experimental manipulation to induce a stress response. The outcome was a measure of the offspring gut microbiome, including alpha diversity, beta diversity, and/or species richness, relative abundance, or ratios of specific bacterial taxa. This review did not examine the impact of the effect of prenatal stress on the maternal vaginal microbiome, nor the relationship of the vaginal microbiome with the infant gut microbiome because we aimed to elucidate whether evidence exists for a relationship between prenatal stress and the infant gut microbiome, regardless of alterations to the vaginal microbiome.
Duplicate removal, screening of abstract and title, and full-text reviews were conducted using Covidence, a web-based collaboration software platform that streamlines the production of systematic and other literature reviews (Covidence Systematic Review Software 2023). Two separate reviewers, using the above-mentioned criteria, screened each title and abstract. This search yielded 2853 studies reporting on the effect of prenatal stress on the offspring gut microbiome. A total of 1833 duplicates were removed, leaving 1020 studies to be screened. A total of 20 studies were included for full-text review, and 4 were excluded for the following reasons: not having gut microbiome as an outcome, language other than English, and not including prenatal stress. In total, 16 studies met inclusion criteria (Figure 1 for PRISMA flow diagram). Three studies published in 2023 and meeting the inclusion criteria were added later in the process (Galley et al. 2023; Querdasi et al. 2023; Rojas et al. 2023).
FIGURE 1 |.
Preferred Reporting for Systematic reviews and Meta-Analyses (PRISMA) flow diagram.
2.3 |. Data Collection, Synthesis, and Analysis
The team developed a standardized, data extraction tool in Microsoft Excel to collect the following information: (a) prenatal stress exposure (e.g., perceived stress or exposure to IPV in human studies, experimental manipulation in animal studies) (b) alpha diversity, (c) beta diversity, and (d) taxonomic findings. Taxonomic results were separated by levels of taxonomy. To explore possible causes of heterogeneity among study results, the team also included the following in the data extraction tool: timing of stress exposure (e.g., third trimester of pregnancy), timing of offspring gut microbiome sample collection, method used to sequence gut microbiome samples (e.g., 16S rRNA sequencing), and descriptive data (e.g., sample size, study design, and country). Two team members reviewed and extracted data for each study. To ensure all relevant data were captured, the two team members brought any questions about extraction to the team for further discussion until consensus was reached. Extracted data were summarized in two tables, one for studies with rodents, and one for studies with humans or non-human primates (see Tables 2 and 3). To aid with interpretation, the team created a table outlining diversity metrics, definitions, and interpretation (see Table 1). Two figures were developed to visually display results of individual studies and synthesis (see Figures 2 and 3). Findings were aggregated and reported in a table to indicate confidence in the body of evidence for infant gut microbiome outcomes (see Table 4).
TABLE 2 |.
Characteristics and key findings from rodent studies.
| Reference | Animal | Prenatal stressor | Timing of stressor | Offspring age for gut microbiome sample | Sequencing/genetic analysis | Alpha diversity | Beta diversity | Offspring taxonomic differences associated with PNS | |
|---|---|---|---|---|---|---|---|---|---|
|
| |||||||||
| 1 | Jašarević et al. (2015) | Mice | One stressor per day of the following: - fox odor (60 min) - restraint (15 min) - constant light (36 h) - novel noise overnight - cage changes (three times throughout the light cycle) - saturated bedding overnight - novel object exposure overnight |
PND 1–7 | 2 days | 16S rRNA, qPCR | Not reported | Not reported |
Genus PNS positively associated with Bacteroides (18.3% vs. 4.4%) and Clostridium (17.4% vs. 9.3%) (p < 0.05) (males) PNS negatively associated with Lactobacillus (males and females) (F1,36 = 5.29, p = 0.027) |
| 2 | Brawner et al. (2020) | Mice | Restraint and bright light (2 h, twice daily) | PND 7–20 | 14 days | 16S rRNA (V4), qPCR | PNS positively associated with Shannon index for females (p = 0.0443) but not for males (p = 0.1118) | PNS associated with differences in microbial communities between experimental groups for females (p = 0.0016), but not for males (p = 0.1301) (unweighted PCA) |
Family PNS positively associated with Lachnospiraceae (p = 0.0093) and an unidentified Clostridiales family (p = 0.0044) (females) PNS not associated with any significant differences in family level bacterial taxa for males: Lachnospiraceae (p = 0.2810) and for Clostridiales (p = 0.1466) (males) |
| 3 | Sun et al. (2021) | Mice | Two of the following daily (randomly selected): - restraint (60 min) - water saturated bedding overnight - lights on during the dark phase - white noise generated by a speaker (60 min) - water avoidance (60 min) |
PND 10-birth | 3 weeks (P1) 8 weeks (P2) | 16S rRNA sequencing (V3 and V4), qPCR | PNS not associated with Chao1 at P1 or P2 (p = 0.77) | PNS associated with differences in microbial communities between experimental groups (R = 0.267, p = 0.001) (weighted UniFrac ANISOM) |
Class PNS positively associated with Clostridia (LDA > 4.5) and Deltaproteobacteria at P2 (LDA > 3.5) Order PNS positively associated with Desulfovibrionales at P1 (LDA > 3) and Clostridiales (LDA > 4.5), Verrucomicrobiales (LDA > 4), Desulfovibrionales (LDA > 3.5) at P2 PNS negatively associated with Bifidobacterialesa at P1 and Bacillalesa at P2 Phylum PNS not associated with differences in specific phylum at P1 or P2 Family PNS positively associated with Desulfovibrionaceae at P1 (LDA > 3.5) and P2a and Lachnospiraceae (LDA > 4.5), Prevotellaceae (LDA > 4), and Verrucomicrobiaceae (LDA > 4) at P2 PNS negatively associated with Bifidobacteriaceae at P1 (LDA < −2.5) and Planococcaceae (LDA < −3.5) at P2 Genus PNS negatively associated with Streptococcusa and Enterococcusa at P1, Desulfovibrio at P1 and P2, and Prevotella (LDA > 4), Anaerotruncus (LDA > 3.5), Butyricicoccus (LDA > 3.5) at P2 PNS negatively associated with Bifidobacteriuma, Blautiaa, and Robinsoniellaa at P1 and Olsenella (LDA < −3.5), Rikenella (LDA < −3.5), Sporosarcina (LDA < −3.5) at P2 |
| 4 | Jašarević et al. (2017) | Mice | One stressor per day of the following: - fox odor (60 min) - restraint (15 min) - constant light (36 h) - novel noise overnight - cage changes (three times throughout the light cycle) - saturated bedding overnight - novel object exposure overnight |
PND 1–7 | 2 days (P1) 6 days (P2) 28 days (P3) |
16S rRNA sequencing (V4), qPCR | PNS not associated with any significant differences in Shannon index, phylogenetic diversity, dominance, or McIntosh Evennessa | PNS associated with differences in microbial communities between experimental groups for females at P1 (p < 0.05) and for males at P3 (p < 0.001) (PCoA of unweighted UniFrac) |
Order PNS not associated with differences in Rickettsiales (males and females) Family PNS not associated with differences in Rikenellaceae, Enterococcaceae, Bradyrhizobiacceae, or Desulfovibrionaceae (males and females) Genus PNS negatively associated with Lactobacillus (p < 0.05) at P1 (males and females) PNS positively associated with Desulfovibrio (p < 0.01) and Clostridiales (p < 0.05) at P3 (males) Prenatally stress positively associated with Dehalobacterium (p < 0.05) and Lachnospiraceae (p < 0.05) at P3 (males) PNS negatively associated with Flexispira (p < 0.001) at P3 (males) PNS negatively associated with Helicobacter (p < 0.05) at P3 (males and females) PNS not associated with differences in Haemophilus, Sphingobium, Odoribacter, Bacteroides, Prevotella, Rikenella, Paenibacillus, Candidatus Arthromitus, Oscillospira, Ruminococcus, Sutterella, or Aggregatibacter (males and females) |
| 5 | Gur et al. (2017) | Mice | Restraint (2 h daily) | PND 10–16 | 60–70 days Note: only included female offspring | 16S rRNA sequencing (V1-V3), qPCR | Not reported | PNS associated with differences in microbial communities between experimental groups (adonis statistics, PCoA plot of unweighted UniFrac distances, p < 0.001) |
Phylum PNS positively associated with Proteobacteriaa PNS negatively associated with Bacteroidetes (t (30) = 3.83, p < 0.001) and Firmicutes (t (30) = 3.59, p < 0.001) Family PNS positively associated with Bifidobacteriaceae (t (3) = 3.00, p = 0.005). PNS negatively associated with Rikenellaceae (t (30) = 3.37, p = 0.002) and S24–7 7 (t (30) = 5.87, p < 0.0001) |
| 6 | Golubeva et al. (2015) | Rats | Restraint and bright light (45 min, three times daily) | PND 14–20 | 4 months | 16S rRNA gene 454 pyrosequencing | PNS not associated with any significant differences in observed species (535(475;644) in control and 501(435;588) in prenatally stressed rats) or Chao1 (930(819;1226) in control and 877(741;1134) in prenatally stress group) | Not reported |
Phylum PNS not associated with any significant differences in Actinobacteria, Verrucomicrobia, Deferribacteres, Cyanobacteria, Proteobacteria, Tenericutes, Bacteroidetes, Firmicutes, and unassigned organism Family PNS negatively associated with Streptococcaceae (p = 0.04) Genus PNS positively associated with Oscillibacter (p = 0.029), Anaerotruncus (p = 0.029), and Peptococcus (p = 0.014) |
| 7 | Jašarević et al. (2018) | Mice | One stressor per day of the following: - fox odor (60 min) - restraint (15 min) - constant light (36 h) - novel noise overnight - cage changes (three times throughout the light cycle) - saturated bedding overnight - novel object exposure overnight |
PND 1–7 | 2 days and as adults Note: only included male offspring | - 16S rRNA sequencing (V4), qPCR - Whole-metagenomic shotgun sequencing and analysis |
PNS not associated with any significant differences in Shannon indexa | PNS combined with chronic stress in adulthood was associated with differences in microbial communities between experimental groups (MDS + PCA of the Bray-Curtis)a |
Order PNS negatively associated with Pasteurellales, Lactobacillales, and Enterobacteriales Phylum PNS negatively associated with Proteobacteria and Firmicutes Family PNS positively associated with Peptococcaceae, Pseudomonadaceae, Peptococcaceae, Comamonadaceae, and Microbacteriaceae PNS negatively associated with Enterobacteriaceae, Streptococcaceae, and Pasteurellaceae Genus PNS negatively associated with Pasteurella, Gallibacterium, Mannheimia, Escherichia, Haemophilus, and Streptococcus PNS positively associated with Pseudomonas and Acidovorax Species PNS negatively associated with Streptococcus suis PNS combined with chronic stress in adulthood positively associated with Streptomyces lentus (males only) PNS negatively associated with Lactobacillus reuteri, Escherichia coli, Lactobacillus murinus ASF361, Pasteurella pneumotropica, Streptococcus acidominimus, Streptococcus thoraltensis DSM 12221, Streptococcus thoraltensis, Actinobacillus pleuropneumoniae, Streptococcus lutetiensis 033, Streptococcus agalactiae FSL S3–229, Streptococcus hyovaginalis DSM 12219, Streptococcus agalactiae, Streptococcus hyovaginalis, Pasteurella multocida, and Streptococcus sp. FF10a |
| 8 | Zhang et al. (2021) | Mice | Restraint (2 h per day) | PND 0.5-birth | 6–8 weeks old | 16S rDNA sequencing (V3 and V4) qPCR | PNS negatively associated with Chao1 (p < 0.01), Shannon index (p < 0.01), and Abundance-based coverage estimator (p < 0.01) | PNS associated with differences in microbial communities between experimental groups (nonmetric multidimensional scaling, PCA and PCoA, ANISOM R = 0.206, p = 0.001) |
Class PNS negatively associated with Verrucomicrobia (LDA < −3) Phylum PNS negatively associated with Verrucomicrobia (LDA < −3) Family PNS positively associated with Prevotellaceae Bacteroides (LDA > 3.5), Bacteroidaceae (LDA > 3.5) PNS negatively associated with Muribaculaceae (LDA < −4) Genus PNS positively associated with Bacteroides (LDA > 3.5), Alloprevotella (LDA > 3), Butyricicoccusa, and Lactobacillusa, Ruminiclostridium (LDA > 3) Species PNS associated with differences in abundance of 9 out of 81 species |
| 9 | Zheng et al. (2020) | Rats | Cold stress (4C artificial intelligence climate chamber 24/7) | PND 14-birth | 21 days old | 16S rRNA gene sequencing (V3-V4) and qPCR | Not reported | PNS associated with differences in microbial communities between experimental groups with sex-specific differences (non-metric multidimensional scaling, weighted and unweighted PCoA, and multi response permutation procedure) (female: A = 0.051, p = 0.001) |
Class PNS positively associated with Bacilli (p < 0.05, LDA = 4) (females) Order PNS positively associated with Lactobacillales (p < 0.05, LDA = 4) (females) Phylum Not reported Family PNS positively associated with Lactobacillaceae and Bacteroidaceae (p < 0.05, LDA = 4) (females) PNS negatively associated with Lachnospiraceae and Prevotellaceae (p < 0.05, LDA = 4) (males) Genus PNS positively associated with Lactobacillus and Bacteroides (p < 0.05, LDA = 4) (females) Species PNS positively associated with Lactobacillus_gasseri (p < 0.05, LDA = 4) (females) |
| 10 | Gur et al. (2019) | Mice | Restraint (2 h daily) | PND 10–16 | 60–70 days Note: only included male offspring | 16S rRNA gene sequencing (V1-V3), qPCR | PNS not associated with any significant differences in whole tree, Chao1, and observed OTUs (p > 0.05) | PNS associated with differences in microbial communities between experimental groups (adonis statistic and weighted UniFrac) (p < 0.001) |
Genus PNS negatively associated with Bacteroides (p < 0.001) and Parabacteroides (p < 0.001) |
Abbreviations: LDA, Linear discriminant analysis scores; PCR, polymerase chain reaction; PND, prenatal day; PNS, prenatal stress.
indicates no statistical measure values or levels of significance were reported.
TABLE 3 |.
Summary of design and findings from primate studies examining the relationship between prenatal stress and offspring microbiome (n = 9).
| Reference species | Sample size location | Prenatal stress measured | Timing of stress | Offspring age for gut microbiome sample | Sequencing/Genetic analysis | Alpha and beta diversity | Offspring taxonomic differences associated with PNS |
|---|---|---|---|---|---|---|---|
|
| |||||||
| Querdasi et al. (2023) Human | N = 450 Singapore | Anxiety (STAI) | Second to third trimester (26–28 weeks) | 2 years | 16S rRNA (V4), qPCR |
Alpha STAI positively associated with: Pielou evenness (β = 0.16, p = 0.034) STAI not associated with: Observed features (β = −0.05, p = 0.58), Shannon index (β = 0.10, p = 0.24), or Faith’s PD (β = −0.15, p = 0.10) Beta STAI not associated with difference in microbial composition: weighted UniFrac (F = 0.26, p = 0.97), unweighted UniFrac (F = 0.91, p = 0.54) Jaccard (F = 0.77, p = 0.87), and Bray-Curtis (F = 0.68, p = 0.82) |
Species STAI positively associated with two unidentified species from the genus Streptococcus (β = 0.26, q = 0.08 and β = 0.11, q = 0.24) STAI negatively associated with one unidentified species from the genus Ruminococcus (β = −0.08, q = 0.21) |
| Rojas et al. (2023) Human | N = 131 Canada | Depression (EPDS depression subscale) Anxiety (SCL-90-R anxiety subscale) Salivary cortisol (AUCg) | First, second, and third-trimesters T1: 1–13 weeks T2: 14–26 weeks T3: 27–40 weeks | 4 years (M = 4.4, SD = 0.5, range = 3.1–4.9) | 16S rRNA sequencing (V3 and V4), qPCR |
Alpha EPDS (T2) positively associated with: Shannon (r = 0.19, p = 0.039) EPDS (T3) positively associated with: Shannon index (r = 0.19, p = 0.034), Observed OTUs (r = 0.28, p = 0.011, and Chao1 (r = 0.18, p = 0.045) SCL-90-R (T2) positively associated with: observed OTUs (r = 0.21, p = 0.048), Faith’s phylogenetic diversity (r = 0.28, p = 0.008), Shannon index (r = 0.213, p = 0.042), and Chao1 (r = 0.22, p = 0.039) Cortisol (T1) positively associated with: Faith’s PD (r = 0.32, p = 0.036) Cortisol (T2) negatively associated with: Shannon index (r = −0.27, p = 0.008) |
Genus EPDS (T2 and T3) positively associated with: Ruminococcaceae NK4A214 (rs = 0.30, q = 0.064; rs = 0.32, q = 0.010, respectively) EPDS (T3) negatively associated with: Parasutterella (rs = −0.32, q = 0.010) and Eggerthella (rs = −0.28, q = 0.031) Salivary cortisol (T2) negatively associated with: Ruminococcaceae NK4A214 (rs = −0.30, q = 0.068), Coprococcus2 (rs = −0.30, q = 0.068), and Ruminococcaceae UCG005 (rs = −0.28, q = 0.073) Salivary cortisol (T2) positively associated with: Butyricicoccus (rs = 0.29, q = 0.068), Blautia (rs = 0.29, q = 0.068), and Lachnospiraceae unclassified (rs = 0.28, q = 0.068) Species Salivary cortisol (T2) positively associated with: Eubacterium eligens (rs = 0.27, q = 0.073) |
| Galley et al. (2023) Humans | N = 25 at 5–7 months postpartum; N = 20 at 11–13 months postpartum United States | Anxiety (OASIS) depression (PHQ-9) perceived Stress (PSS) | First, second, and third trimester T0/T1: 8–16 weeks T2: 20–26 weeks T3: 30–36 weeks | 5–7 months (P1) and 11–13 months (P2) | Full-length 16S sequencing |
Alpha PHQ9 (T0/T1) negatively associated with: Faith’s PD at P2 (r = −0.47, p = 0.018) |
Species OASIS at T0/T1 negatively associated with P1: Bifidobacterium dentium (FC = 30.00, p < 0.0001), Ruminococcus gnavus (FC = 6.92, p 0.053), and Lactobacillus rhamnosus (FC = 21.62, p < 0.0001) PHQ-9 at T0/T1 negatively associated with P1: Eggerthella lenta (FC = 23.19, p < 0.0001), Bifidobacterium dentium (FC = 22.13, p < 0.0001), Streptococcus salivarius (FC = 7.93, p 0.026), and Lactobacillus rhamnosus (FC = 20.05, p < 0.0001) PSS at T0/T1 negatively associated with P1: Bifidobacterium dentium (FC = 22.18, p < 0.0001) PSS at T2 negatively associated with P1: Bifidobacterium dentium (FC = 22.53, p < 0.0001) |
| Wei et al. (2022) | N = 410 China | Dichotomous psychopathology score: Allocation to symptoms or no-symptoms group if above a threshold score for either CES-D or SAS) Depression (CES-D) Anxiety (SAS) | Third trimester (32–36 weeks) | <24 h | Total genomic DNA extraction, 16S rRNA (V3-V4), qPCR |
Alpha Dichotomous psychopathology symptoms score positively associated with: Chao1 index (p < 0.001), Observed species (p < 0.001), Shannon index (p = 0.004), and Simpson index (p = 0.008) (Wilcoxon rank sum test for each) SAS positively associated with: Chao1 index (p = 0.008) and Observed species (p = 0.023) CES-D positively associated with: Chao1 index (p = 0.001) and Observed species (p = 0.003) Chao1 index (β = 6.005, p = 0.002), Observed species (β = 3.730, p = 0.011) Beta Dichotomous psychopathology symptoms score associated with differences in microbial composition (Bray-Curtis (adonis p value = 0.047), Weighted UniFrac, and Unweighted UniFrac distance matrices (adonis p value = 0.024) |
Phylum Pathophysiology symptoms (CES-D and/or SAS) positively associated with Proteobacteria (p = 0.026) Pathophysiology symptoms (CES-D and/or SAS) negatively associated with Actinobacteria (p = 0.012) Family Pathophysiology symptoms (CES-D and/or SAS) positively associated with Burkholderiaceae (p < 0.001) Genus Pathophysiology symptoms (CES-D and/or SAS) positively associated with Lactobacillus (p < 0.001), Ralstonia (p < 0.001), and Burkholderia (p < 0.001) |
| Naudé et al. (2020) Human | N = 101 South Africa | Depression (BDI-II) Prenatal Distress (SRQ) Post-Traumatic Stress Disorder (MPSS) Intimate Partner Violence (IPVQ) | Second to third trimester (Mean 27.4 weeks +/-4.2 weeks) | 4–12 weeks (P1) and 20–28 weeks (P2) | 16S rRNA gene (V4), qPCR |
Alpha Not measured Beta BDI-II, SRQ, and MPSS not associated with differences in microbial compositiona |
Class Proportion of Gammaproteobacteria decreased for all infants from birth to P1 and P2, with findings more pronounced among those with low SRQ (rate ratio = 1.19, p = 0.007) Family SRQ negatively associated with Veillonellaceae at P2 (rate ratio = 0.52, p = 0.01) |
| Aatsinki et al. (2020) Human | IV = 446 Finland | Chronic stress score: above a threshold for 2+ time points for EPDS, SCL-90, EPC, PRAQ-R2, and HCC Depression/Anxiety (EPDS) Anxiety (SCL-90, anxiety subscale) Daily hassles (EPC) Pregnancy-related worries and anxieties (PRAQ-R2) Hair cortisol concentration (HCC) from 5 cm hair segment closest to scalp |
Second and third trimesters T1: 14 weeks (mean 15, SD = 1.2, range = 13–21) T2: 24 weeks (mean = 25, SD = 1.3, range = 23–30) T3: 34 weeks (mean = 35, SD = 1.1, range = 33–40) Self-report measures at T1, T2, and T3; HCC at T2 | Mean (SD) 64.3 days (13.4) | 16S rRNA (V4) sequencing and next-generation sequencing |
Alpha Chronic stress scores for EPC, SCL, EPDS, PRAQ, and HCC not associated with Shannon index and richness (FDR ≥ 0.27) Beta Chronic stress score not associated with differences in microbial composition (Bray-Curtis and Permutational Analysis of Variation) (FDR ≥ 0.97) |
Phylum EPC, SCL, EPDS, and PRAQ positively associated with genera from Proteobacteria phylum Genus Chronic EPC positively associated with Actinomyces, Butyricimonas, Prevotella, Dialister, Finegoldia, Erwinia, Haemophilus, and Serratia (FDR < 0.01, absolute log2 fold change > 1) Chronic EPC negatively associated with Actinobaculum, Parabacteroides, Anaerotruncus, Epulopiscium, Eubacterium, Megamonas, Phascolarctobacterium, and Akkermansia (FDR < 0.01, absolute log2 fold change > 1) Chronic SCL positively associated with Actinomyces, Rothia, Dialister, Finegoldia, Staphylococcus, Veillonella, Campylobacter, Citrobacter, and Serratia (FDR < 0.01, absolute log2 fold change > 1) Chronic SCL negatively associated with Epulopiscium, Eubacterium, and Akkermansia (FDR < 0.01, absolute log2 fold change > 1) Chronic EPDS positively associated with Prevotella, Butyricimonas, Coprococcus, Dialister, Veillonella, Citrobacter, and Serratia (FDR < 0.01, absolute log2 fold change > 1) Chronic EPDS negatively associated with Propionibacterium, Slackia, Paraprevotella, Anaerotruncus, Eubacterium, Pseudoramibacter, Staphylococcus, and Desulfovibrio (FDR < 0.01, absolute log2 fold change > 1) Chronic PRAQ positively associated with Actinomyces, Dialister, Dorea, Finegoldia, Veillonella, Campylobacter, Haemophilus, and Serratia (FDR < 0.01, absolute log2 fold change > 1) Chronic PRAQ negatively associated with Paraprevotella, Epulopiscium, Megasphaera, and Akkermansia (FDR < 0.01, absolute log2 fold change > 1) Chronic HCC negatively associated with Slackia, Actinobaculum, Paraprevotella, Butyricimonas, Ruminococcus, Phascolarctobacterium, Anaerotruncus, Enterococcus, Lactobacillus, and Citrobacter (FDR < 0.01, absolute log2 fold change > 1) |
| Hu et al. (2019) Human | N = 75 United States | Depression/Anxiety (EPDS) Anxiety (STAI) Pregnancy-related anxiety (PRAQ) Perceived Stress (PSS-14) Stressful life events (PERI) | Second trimester (14–27 weeks) | Within 48 h | 16S rRNA sequencing (V3-V4) |
Alpha EPDS, STAI, PRAQ, PSS-14, and PERI not associated with Shannon indexa Beta PRAQ score associated with differences in microbial composition at the genus level (Bray-Curtis and Permutational Analysis of Variation) (p < 0.001) |
Phylum PRAQ negatively associated with Proteobacteria (rs = −0.40, p = 0.002) Family PRAQ negatively associated with abundance of Enterobacteriaceae (rs = −0.43, p < 0.001) Genus PRAQ negatively associated with unidentified genus from the Enterobacteriaceae family (rs = −0.54, p < 0.001) |
| Zijlmans et al. (2015) Human | N = 56 The Netherlands | Prenatal cumulative stress index: scores for self-report measures were split at the median, with 0 = low reported stress and 1 = high reported stress. Those who scored above the median for 4 or 5 variables were classified as “high reported PNS.” Those above the median for 2 or fewer were scored as “low reported PNS.” The reported PNS and cortisol measure were combined, creating for four groups: high reported stress + high cortisol, high reported stress + low cortisol, low reported stress + high cortisol, and low reported stress + low cortisol. General anxiety symptoms (STAI) Pregnancy-related anxiety (PRAQ, two subscales) Daily hassles (EPC) Pregnancy-related daily hassles (PES) Cortisol measure: Salivary cortisol concentrations (AUCg) |
Third-trimester (M = 35.29 weeks, SD = 1.22) | 7 days: M = 6.7 days, SD = 0.7) (P1) 14 days: M = 12.5 days, SD = 4.0) (P2) 28 days: M = 24.8 days, SD = 8.9) (P3) 80 days: M = 83.8 days, SD = 19.4) (P4) 115 days: M = 112.3 days, SD = 15.4) (P5) | 16S rRNA sequencing (VI and V6), qPCR |
Alpha Prenatal cumulative stress index (high vs. low stress) positively associated with Simpson indexa Beta Prenatal cumulative stress index associated with differences in microbial composition at P2 (p < 0.05), P3 (p < 0.05), and P4 (p < 0.01) (PCoA using Manhattan distances) |
Genus Prenatal cumulative stress index (STAI, PRAQ, EPC, and PES) positively associated with a proteobacterial group containing Escherichia, Serratia, Haemophilus, Proteus, and Enterobacter at P1-P5a Prenatal cumulative stress index (STAI, PRAQ, EPC, and PES) negatively associated with an Actinobacteria group containing Actinomycetaceae, Bifidobacterium, Collinsella, and Eggerthella at P1, P2, P4, and P5a Prenatal cumulative stress index (STAI, PRAQ, EPC, and PES) negatively associated a lactic acid bacteria group containing Bifidobacterium, Collinsella, Eggerthella, Atopobium, Lactobacillus, Lactococcus, and Aerococcus at P1, P2, P3, and P5a Prenatal cumulative stress index (STAI, PRAQ, EPC, and PES) positively associated with Akkermansia at P1, P2, and P3a Prenatal cumulative stress index (STAI, PRAQ, EPC, and PES) negatively associated with Akkermansia at P4 and P5a High reported stress and high cortisol concentrations positively associated with a proteobacterial group containing Escherichia, Serratia, Haemophilus, Proteus, and Enterobacter at P2-P5a Bacilli remained high throughout the study period, whereas it declined considerably in the low stress group over timea |
| Bailey, Lubach, and Coe (2004) Rhesus Monkey | N = 24 United States | Acoustical startle and darkness during light cycle (three beeps lasting 1-s randomly broadcast during 10 min, five times per week for 6 weeks) | 50–92 days gestation (early stress period) 105–147 days (late stress period) *Average term pregnancy is 169 days in the rhesus monkey | 2 days and 2, 8, 16, and 24 weeks | Cell culture |
Alpha Not measured Beta Not measured |
Genus Early and late stress negatively associated with Lactobacillus across the first 24 weeks (F (2, 21) = 4.78, p < 0.05) Late stress negatively associated with Bifidobacteria across the first 24 weeks (p < 0.05) Species 43% of early stress and 13% of late stress infants shed Shigella flexneri at least once during the 24-week period, compared to 0 of the control infants |
Abbreviations: FDR, False discovery rate; PCR, polymerase chain reaction; PND, prenatal day; PNS, prenatal stress.
indicates no statistical measure values or levels of significance were reported.
FIGURE 2 |.
Timing and type of prenatal stress exposure and offspring gut microbiota findings by rodent study.
FIGURE 3 |.
Timing and type of prenatal stress exposure and timing of gut microbiome sampling with associated taxonomic findings in primate studies.
TABLE 4 |.
Synthesized findings for the direction of association between prenatal stress and specific microbiota.
| Microbiota Taxonomic rank | Direction of association (number of studies) | Alternative findings |
|---|---|---|
|
| ||
| PHYLA | ||
| Proteobacteria | Positive (5) | Negative (2) |
| Phylum | Human studies (3)1,3,4a Rodent studies (1)5 |
Human studies (1)2 Rodent studies (1)6 No association (1) Rodent studies (1)7 |
| Actinobacteria | Negative (2) | No association (1) |
| Phylum | Human studies (2)1,4b | Rodent studeies (1)7 |
| FAMILY | ||
| Lachnospiraceae | Positive (3) | Negative (1) |
| Family | Rodent studies (3)8–10 | Rodent studies (1)11 |
| Bacteroidaceae | Positive (2) | — |
| Family | Rodent studies (2)11,12 | |
| Streptococcaceae | Negative (2) | |
| Family | Rodent studies (2)6,7 | |
| Enterobacteriaceae | Negative (2) | — |
| Family | Human studies (1)2 Rodent studies (1)6 |
|
| Prevotellaceae | Positive (2) | Negative (1) |
| Family | Rodent studies (2)8,12 | Rodent study11 |
| GENERA | ||
| Lactobacillus | Negative (5) | Positive (3) |
| Genera | Rodent9 Human/Monkey10,3,4,13c |
Rodent11,12 Human2 |
| Streptococcus | Negative (4) | — |
| Genera | Human studies (1)15 Rodent studies (3)7,8,13 |
|
| Bifidobacteria | Negative (3) | — |
| Genera | Human/Monkey studies (2)10,4c Rodent studies (1)8 |
|
| Eggerthella | Negative (3) | — |
| Genera | Human studies (3)15,16,4c | |
| Bacteroides | Positive (3) | No association (1) |
| Genera | Rodent studies (3)11–13 | Rodent studies (1)9 |
| Akkermansia | Negative (later postpartum) (2) | — |
| Genera | Human studies (1)3,4 Positive (early postpartum) (1) Human studies (2)3 |
|
| Parabacteroides | Negative (2) | — |
| Genera | Human studies (1)3 Rodent studies (1)17 |
|
| Serratia | Positive (2) | — |
| Genera | Human studies (2)3,4c | |
| Anaerotruncus | Negative (2) | Positive (1) |
| Genera | Human studies (2)3,8 | Rodent studies (1)7 |
| Butyricicoccus | Positive (2) | Negative (1) |
| Genera | Human studies (1)16 Rodent studies (1)12 |
Rodent studies (1)8 |
| Desulfovibrio | Negative (2) | Positive (1) |
| Genera | Human studies (1)3 Rodent studies (1)8 |
Rodent studies (1)9 |
| Haemophilus | Positive (2) | Negative (1) |
| Genera | Human studies (2)3,4c | Rodent studies (1)6 No Association (1) Rodent studies (1)9 |
Note: Microbiota listed in order of most prevalent findings within the taxonomic rank of phyla, family, and genera.
Denotes Proteobacteria group containing Escherichia, Serratia, Haemophilus, Proteus, and Enterobacter.
Denotes Actinobacteria group containing Actinomycetaceae, Bifidobacterium, Collinsella, and Eggerthella.
Denotes genera is one component of the larger measured bacterial group.
3 |. Results
3.1 |. Rodent Studies
3.1.1 |. Study Design
Data extracted from the rodent studies using the template were synthesized and presented in Table 2. The screening returned 10 rodent studies. Two of the studies included rats (Golubeva et al. 2015; Zheng et al. 2020), and eight included mice (Brawner et al. 2020; Gur et al. 2017, 2019; Jašarević et al. 2015, 2017, 2018; Sun et al. 2021; Zhang et al. 2021). Three of the studies were reported by the same lead author (Jašarević et al. 2015, 2017, 2018); however, for each study, the team used a different sample of rodents with multiple technical and biological replications of the experiments. Six studies were conducted in the United States (Brawner et al. 2020; Gur et al. 2017, 2019; Jašarević et al. 2015, 2017, 2018), three in China (Sun et al. 2021; Zhang et al. 2021; Zheng et al. 2020), and one in Ireland (Golubeva et al. 2015).
All of the rodent studies used a randomized controlled design with varying types of stress administered. Stressors included cold stress (Zheng et al. 2020), restraint (Brawner et al. 2020; Golubeva et al. 2015; Gur et al. 2017, 2019; Zhang et al. 2021), bright light (Brawner et al. 2020; Golubeva et al. 2015; Jašarević et al. 2015, 2017, 2018; Sun et al. 2021), and fox odor exposure, restraint, constant light, novel noise, cage changes, saturated bedding, novel object exposure, water avoidance, and light exposure during the dark phase (Jašarević et al. 2015, 2017, 2018; Sun et al. 2021). Studies reported wide variation in the time of offspring gut microbiome sampling, ranging from 2 days after delivery to adulthood.
The timing of prenatal stress varied across studies. In mice and rats, the first trimester equivalent is approximately PND 0–10, during which time major organ systems and structures, such as the neural tube, begin to form (West 1987). The second trimester equivalent is PND 11–20, during which organ development continues and the fetus grows rapidly. The third-trimester equivalent, during which rapid brain development occurs, is in the first few weeks after birth (West 1987). Three studies used first trimester equivalent stress, from PND 1 to 7 only (Jašarević et al. 2015, 2017, 2018). Two studies used stress only in the first and second trimester equivalents (Gur et al. 2017, 2019). Five studies used stress only in the second trimester equivalent (Golubeva et al. 2015; Gur et al. 2017, 2019; Sun et al. 2021; Zheng et al. 2020). And two studies used stress in both the first and second trimester equivalents (Brawner et al. 2020).
3.1.2 |. Alpha Diversity
Of the 10 rodent studies included in the review, 7 measured alpha diversity (Brawner et al. 2020; Golubeva et al. 2015; Gur et al. 2019; Jašarević et al. 2017, 2018; Sun et al. 2021; Zhang et al. 2021) with various indices. See Table 1 for definitions and interpretation of specific diversity metrics. A majority of the rodent studies that measured alpha diversity (5/7 studies) found no difference in community richness, evenness, or phylogenetic diversity between prenatally stressed and control groups (Golubeva et al. 2015; Gur et al. 2019; Jašarević et al. 2017, 2018; Sun et al. 2021). Two studies reported differences in community richness and evenness between the experimental groups, although findings were in opposite directions (Brawner et al. 2020; Zhang et al. 2021). Increased alpha diversity was observed in the prenatally stressed offspring in a study using restraint stress throughout pregnancy (Zhang et al. 2021), whereas decreased alpha diversity was observed in the study that used restraint stress combined with bright light stress in the first and second trimesters (PND 7–20) (Brawner et al. 2020). The timing and type of prenatal stress and timing of gut microbiome sampling had no clear relationship with differences in alpha diversity.
3.1.3 |. Beta Diversity
Seven rodent studies reported beta diversity outcomes (Brawner et al. 2020; Gur et al. 2017, 2019; Jašarević et al. 2017, 2018; Sun et al. 2021; Zhang et al. 2021), with all noting significant differences in clustering of bacterial communities between the experimental groups. One of these studies only found differences in microbial communities between experimental groups when prenatally stressed offspring were exposed to chronic stress as adults (Jašarević et al. 2018). Beta diversity had no apparent relationships with the timing or type of prenatal stress, nor with the timing of gut microbiome sampling.
3.1.4 |. Taxonomic Findings
Figure 2 illustrates the timing and type of prenatal stress exposure for each rodent study, as well as taxonomic findings that were repeated across studies. The limited number of studies with similar design makes it difficult to determine if observed taxonomic differences were related to the timing of prenatal stress exposure or gut microbiome sample collection.
The type of prenatal stress exposure may have influenced differences in Lachnospiraceae, Lactobacillus, and Streptococcus between experimental groups. The three studies that observed an increase in Lachnospiraceae in the prenatally stressed offspring each used two or more stressors (Brawner et al. 2020; Jašarević et al. 2017; Sun et al. 2021), whereas the study that noted a decrease in Lachnospiraceae used cold stress only (Zheng et al. 2020). The three studies that observed a negative association of prenatal stress offspring intestinal Streptococcus all included restraint and bright light stress (Golubeva et al. 2015; Jašarević et al. 2015; Sun et al. 2021). The studies that noted a decrease in Lactobacillus in the prenatally stressed group both multiple stressors (Jašarević et al. 2015, 2017). Of the studies that noted increased Lactobacillus in the prenatally stressed offspring, one used cold stress alone (Zheng et al. 2020), and the other used restraint stress alone (Zhang et al. 2021).
The timing of prenatal stress may have been related to differences in Lactobacillus between experimental groups. The two studies that observed decreased Lactobacillus in the prenatally stressed groups, both used first trimester equivalent prenatal stress only (PND 1–7) (Jašarević et al. 2015, 2017). The two that observed increased Lactobacillus both also included later prenatal stress in the second trimester equivalent period (Zhang et al. 2021; Zheng et al. 2020). This suggests that early prenatal stress may be associated with decreased Lactobacillus in offspring and later prenatal stress may be associated with increased abundance of Lactobacillus.
3.2 |. Primate Studies
3.2.1 |. Study Design
Table 3 reports the data extracted for each primate study. The screening returned eight human studies (Aatsinki et al. 2020; Galley et al. 2023; Hu et al. 2019; Naudé et al. 2020; Querdasi et al. 2023; Rojas et al. 2023; Wei et al. 2022; Zijlmans et al. 2015) and one rhesus monkey study (Bailey et al. 2004). All of the primate studies used a prospective cohort design. Sample sizes ranged from 24 to 450 participants. A majority (n = 5) included a sample size of greater than 100 (Aatsinki et al. 2020; Naudé et al. 2020; Querdasi et al. 2023; Rojas et al. 2023; Wei et al. 2022). Studies represented geographic locations including the United States (n = 3), South Africa, the Netherlands, China, Canada, Singapore, and Finland.
All of the human studies included self-reported measures of various components of prenatal stress. Stress categories include (a) psychosocial stress (e.g., anxiety symptoms, depressive symptoms, post-traumatic stress disorder (PTSD) symptoms, prenatal distress, and perceived stress), (b) stressors (problems encountered in relationships, health, legal matters, finances, work, friendships, and home/family, including IPV), and (c) physiologic stress (e.g., hair and salivary cortisol).
Within the stress category of psychosocial stress, each of the eight human studies measured anxiety and/or distress, using validated instruments, including the Self-Rating Anxiety Scale (SAS) (Wei et al. 2022), the Self-Report Questionnaire (SRQ) (Naudé et al. 2020), the State-Trait Anxiety Inventory (Hu et al. 2019; Querdasi et al. 2023; Zijlmans et al. 2015), the Pregnancy-Related Anxiety Questionnaire (PRAQ) (Aatsinki et al. 2020; Naudé et al. 2020; Zijlmans et al. 2015), the Symptom Checklist 90 (SCL) Anxiety Subscale (Aatsinki et al. 2020; Rojas et al. 2023), the Overall Anxiety Severity and Impairment Scale (OASIS) (Galley et al. 2023), and the Edinburgh Postnatal Depression Scale (EPDS), which includes three items to measure anxiety symptoms, in addition to depressive symptoms (Hu et al. 2019). Six measured depressive symptoms (Aatsinki et al. 2020; Galley et al. 2023; Hu et al. 2019; Naudé et al. 2020; Rojas et al. 2023; Wei et al. 2022) with validated instruments, including the Center for Epidemiologic Studies Depression Scale (CES-D) (Wei et al. 2022), the Beck Depression Inventory-II (BDI) (Naudé et al. 2020), the EPDS (Hu et al. 2019; Rojas et al. 2023), and the Patient Health Questionnaire-9 (PHQ-9) (Galley et al. 2023). Two studies measured perceived stress with the Perceived Stress Scale-14 (PSS) (Galley et al. 2023; Hu et al. 2019). One study measured PTSD symptoms using the modified Post-Traumatic Stress Disorder Symptoms Scale (MPSS) (Naudé et al. 2020).
Four studies measured stressors with a variety of instruments. Two studies used the Everyday Problem Checklist (EPC) (Aatsinki et al. 2020; Zijlmans et al. 2015), one used the Pregnancy Experience Scale (PES) (Zijlmans et al. 2015), and one study used the Psychiatric Epidemiology Research Interview (PERI) Life Events subscale (Hu et al. 2019). One study measured IPV with the IPV Questionnaire (IPVQ) (Naudé et al. 2020).
In addition to self-report measures, three studies also included physiological stress measures. Two measured salivary cortisol concentrations (Rojas et al. 2023; Zijlmans et al. 2015), and the third measured cortisol concentrations in hair samples of 5 cm, accounting for stress over the prior 5-month time period (Aatsinki et al. 2020). All three quantified cortisol as area under the curve with respect to ground (Aatsinki et al. 2020; Rojas et al. 2023; Zijlmans et al. 2015).
The timing of stress measurement varied across studies. Two studies measured stress once around the start of the third trimester (Naudé et al. 2020; Querdasi et al. 2023), whereas two measured stress at one time point near the end of pregnancy (Wei et al. 2022; Zijlmans et al. 2015). One study reported measuring stress in the second trimester but did not report the estimated gestational age of measurement (Hu et al. 2019). Three studies measured stress at three time points across pregnancy (Aatsinki et al. 2020; Galley et al. 2023; Rojas et al. 2023). The specific timing of measurement varied across studies. One study included two points of measurement in the second trimester and one in the third trimester (Aatsinki et al. 2020). The other two studies measured prenatal stress at one time point in each of the three trimesters (Galley et al. 2023; Rojas et al. 2023). The rhesus monkeys experienced stress in early gestation (50–92 days) and again in late gestation (105–147 days) (Bailey et al. 2004). The average term pregnancy is 169 days in the rhesus monkey.
The timing of gut microbiome sample collection varied widely, ranging from less than 24 h after birth to 4 years old. Four studies collected microbiome samples at two or more time points within the first year after birth (Bailey et al. 2004; Galley et al. 2023; Naudé et al. 2020; Zijlmans et al. 2015). Figure 3 depicts the timing and type of prenatal stress, the timing of gut microbiome sampling for each primate study, and the taxonomic findings that were repeated across studies.
3.2.2 |. Alpha Diversity
Of the nine primate studies, eight reported alpha diversity findings (Aatsinki et al. 2020; Galley et al. 2023; Hu et al. 2019; Querdasi et al. 2023; Rojas et al. 2023; Wei et al. 2022; Zijlmans et al. 2015). The studies had notable variation in recorded metrics of alpha diversity (e.g., Chao1 index (n = 2), observed species (n = 3), Shannon index (n = 5), Simpson index (n = 2), Pielou evenness (n = 1), and Faith’s phylogenetic diversity (n = 3).
Findings for the impact of stress on alpha diversity, when measured as richness or evenness, were mixed. Using the Shannon diversity index, three studies observed no associations between prenatal self-reported stress and alpha diversity of the offspring gut microbiome (Aatsinki et al. 2020; Hu et al. 2019; Querdasi et al. 2023). Alternately, two studies observed a positive relationship between self-reported stress and alpha diversity (Rojas et al. 2023; Wei et al. 2022). One study found an inverse relationship between maternal salivary cortisol concentration in the second trimester and alpha diversity of the offspring gut microbiome (Rojas et al. 2023). Using the Chao1 Index, two studies noted a positive relationship between self-reported stress and the alpha diversity of the offspring gut microbiome (Rojas et al. 2023; Wei et al. 2022). Two studies also observed a positive association between self-reported stress and offspring gut alpha diversity using observed units (Rojas et al. 2023; Wei et al. 2022), whereas one study found no relationship (Querdasi et al. 2023). Both studies that used the Simpson index observed a positive relationship between prenatal stress and offspring alpha diversity (Wei et al. 2022; Zijlmans et al. 2015).
When measured as phylogenetic diversity, findings were also mixed. One study observed a positive relationship (Rojas et al. 2023), one observed no statistically significant relationship (Querdasi et al. 2023), and one observed an inverse relationship between prenatal stress and offspring gut microbiome (Galley et al. 2023).
The timing of prenatal stress appears to play a potential role in the alpha diversity of the offspring microbiome. Of those studies that noted increased alpha diversity in the offspring, three of the four noted this relationship with stress measured in late second trimester (e.g., 26–28 weeks estimated gestational age) or the third trimester (Querdasi et al. 2023; Wei et al. 2022; Zijlmans et al. 2015). Comparing findings across studies found no clear patterns for the impact of the type of stress or timing of gut microbiome sampling on differences in alpha diversity.
3.2.3 |. Beta Diversity
Six of the nine primate studies measured beta diversity (Aatsinki et al. 2020; Hu et al. 2019; Naudé et al. 2020; Querdasi et al. 2023; Wei et al. 2022; Zijlmans et al. 2015). A number of analysis techniques were used to examine beta diversity of the offspring gut microbiome: Bray–Curtis using permutational analysis of variation (n = 3), weighted or unweighted UniFrac distance matrices (n = 2), principal coordinates analysis using Manhattan distances (n = 1), and Jaccard (n = 1). On the basis of the reported findings, the relationship between prenatal stress and beta diversity is unclear. Three of the six noted a relationship between prenatal stress and beta diversity of the infant gut microbiota using Bray–Curtis, weighted and unweighted UniFrac, and principal coordinate analysis using Manhattan distance (Hu et al. 2019; Wei et al. 2022; Zijlmans et al. 2015). Three studies found no relationship between prenatal stress and beta diversity using the “w” metric, weighted and unweighted UniFrac, Jaccard, Bray–Curtis, and permutational analysis of variation (Aatsinki et al. 2020; Naudé et al. 2020; Querdasi et al. 2023).
The timing of microbiome sample collection may be related to beta diversity findings. Three studies with noted differences in beta diversity collected the microbiome sample in the first hours to weeks of life (Hu et al. 2019; Wei et al. 2022; Zijlmans et al. 2015). Three studies found no differences in beta diversity between the groups at varying times of infant gut microbiome sampling (e.g., 4 weeks to 2 years old) (Aatsinki et al. 2020; Naudé et al. 2020; Querdasi et al. 2023). Comparing findings across studies found no clear patterns for the impact of the timing or type of stress on differences in beta diversity.
3.2.4 |. Gut Microbiome Taxonomy Findings
Studies most commonly reported findings at the phylum, family, and/or genus level of taxonomy. One study clustered microbiota into groups containing specific genera and analyzed the association between prenatal stress and these bacterial groups (Zijlmans et al. 2015). One study reported findings at the level of class (Naudé et al. 2020). Four reported species-specific findings, with no observable trends in the associations between prenatal stress and gut microbiota species noted across studies (Bailey et al. 2004; Galley et al. 2023; Querdasi et al. 2023; Rojas et al. 2023).
The abundance of offspring gut Proteobacteria appears to be positively associated with prenatal stress. One human study identified a positive correlation between prenatal stress and gut Proteobacteria (Wei et al. 2022). One study found an increased abundance of bacteria from a proteobacterial group containing Escherichia, Serratia, Haemophilus, Proteus, and Enterobacter (Zijlmans et al. 2015). One additional study observed a positive association between chronic prenatal stress (e.g., EPC, SCL, EPDS, and PRAQ) and the abundance of bacterial genera (e.g., Campylobacter, Citrobacter, Desulfovibrio, Erwinia, Haemophilus, and Serratia) from the Proteobacteria phylum (Aatsinki et al. 2020). Conversely, one study noted a negative association between prenatal stress and the abundance of Proteobacteria (Hu et al. 2019).
Findings suggest a potential relationship between prenatal stress and relative abundance of Akkermansia that varied based on the time of microbiome sampling. One study observed a positive association between prenatal stress and relative abundance of Akkermansia at 2 months old (Aatsinki et al. 2020). A second study also observed this positive association when the gut microbiome was sampled at 2 months of age or older (Zijlmans et al. 2015); however, in that same study, with earlier microbiome sampling (i.e., 7, 14, and 28 days), prenatal stress was inversely related to Akkermansia (Zijlmans et al. 2015).
The direction of association between prenatal stress and Lactobacillus varied across studies. Two studies observed a negative association between prenatal stress and the relative abundance of offspring gut Lactobacillus (Aatsinki et al. 2020; Bailey et al. 2004). One study observed a positive association between prenatal stress and offspring gut Lactobacillus collected with the infant’s first stool (Wei et al. 2022).
Findings support a potential relationship between the timing and type of prenatal stress and decreased Lactobacillus. To elaborate, both studies that observed a negative association measured stress in early and late pregnancy and included multiple types of stress (Aatsinki et al. 2020; Bailey et al. 2004).
The timing of offspring microbiome sampling may also have contributed to differences in the abundance of offspring Lactobacillus and Akkermansia between those with higher and lower prenatal stress. Both studies that noted decreased abundance of Lactobacillus collected stool samples when the offspring were around 2 months old (Aatsinki et al. 2020; Bailey et al. 2004) and at 2 days, 2 weeks, 16 weeks, and 24 weeks old (Bailey et al. 2004). The study that observed increased Lactobacillus collected the first infant stool, which occurred in the first 24 h after birth (Wei et al. 2022). Prenatal stress was associated with a greater abundance of Akkermansia from 2.5 to 4 months of age (Aatsinki et al. 2020; Zijlmans et al. 2015) and decreased abundance from 7 to 28 days (Zijlmans et al. 2015). Differences in the measurement of stress pose challenges to interpreting the effect of timing or type of prenatal stress on other offspring gut taxonomic outcomes.
4 |. Discussion
This study is one of the first systematic reviews of the influence of prenatal maternal stress on the offspring gut microbiome. We reviewed 19 studies, 10 from rodents and 9 from primates, including 8 human studies and 1 rhesus monkey study. Methodological differences (e.g., study design, timing and operationalization of prenatal stress, timing of microbiome sampling, and analytical approaches) presented challenges in comparing and interpreting results across studies. However, the inclusion of both animal and human studies allows us to capture the strengths of multiple research approaches and study designs. Highly controlled randomized control trials (RCTs) with animals provide a high degree of internal consistency and allow for causal inference, whereas observational human studies provide more external validity and maximize the potential for translational impact. Although our review examined associations between prenatal stress and offspring gut microbiome, it is also important to consider the health implications of the gut microbiome. The included studies identified associations of prenatal stress with various offspring health and developmental outcomes, including brain development, mood and socioemotional functioning, motor skills, social behaviors, and gastrointestinal disorders in infancy, early childhood, and adulthood (Brawner et al. 2020; Gur et al. 2017, 2019; Jašarević et al. 2015, 2017, 2018; Querdasi et al. 2023; Sun et al. 2021; Wei et al. 2022; Zhang et al. 2021).
4.1 |. Prenatal Stress and Alpha Diversity
Across both human and animal studies, we were not able to identify a consistent pattern of association between prenatal stress and alpha diversity. Of the 14 studies that measured alpha diversity, 7 observed no associations with prenatal stress (Aatsinki et al. 2020; Golubeva et al. 2015; Gur et al. 2019; Hu et al. 2019; Jašarević et al. 2017, 2018; Sun et al. 2021). Five studies noted a positive association between prenatal stress and alpha diversity (Brawner et al. 2020; Querdasi et al. 2023; Rojas et al. 2023; Wei et al. 2022; Zijlmans et al. 2015), whereas two found inverse associations (Galley et al. 2023; Zhang et al. 2021). One study had mixed findings depending on the type of stress measurement for which observed anxiety and depressive symptoms were positively associated with alpha diversity, but salivary cortisol concentration was negatively associated (Rojas et al. 2023).
There are a number of possible reasons why we found no clear patterns of association between prenatal stress and alpha diversity. In humans, alpha diversity increases rapidly across the first year of life and continues to increase through ages 3–4 making it difficult to compare findings from different developmental time points across this period (Stewart et al. 2018). In the human studies, the age of microbiome collection ranged from newborns and infants, for whom the gut is still undergoing considerable rapid change, to 4-year-olds, for whom the gut is thought to have stabilized to more adult-like patterns (Stewart et al. 2018).
Our varied alpha diversity findings may have also been related to varied timing, type, and duration of prenatal stress. None of the rodent studies were able to measure third-trimester stress, in which the third-trimester equivalent development occurs after birth (West 1987). Alternately, only two of the human studies included stress measurement in the first trimester (Rojas et al. 2023; Galley et al. 2023), both of which found an association with offsprings’ Faith’s phylogenetic diversity. Therefore, there is a need for more human studies to measure prenatal stress across early pregnancy to gage impacts on the developing fetus and subsequent impacts on the gut microbiome.
Metrics of alpha diversity were varied across studies, potentially impacting our findings. Most of the primate studies (n = 7) relied on the Shannon index to measure associations between prenatal stress and alpha diversity, with five finding no associations with this metric (Aatsinki et al. 2020; Hu et al. 2019; Jašarević et al. 2017, 2018; Querdasi et al. 2023). Alternatively, two studies observed a positive relationship between prenatal stress and the Simpson diversity index (Wei et al. 2022; Zijlmans et al. 2015), a metric not used in the other studies. Observed findings for other measures of alpha diversity were mixed (e.g., Chao1, Faith’s PD, observed species) or limited to only one study (dominance, McIntosh Evenness Abundance-based Coverage Estimator, Pielou evenness, observed OTUs). Thus, we were not able to draw any conclusions about the association between prenatal stress and these other measures of offspring gut microbiome alpha diversity, indicating a significant need for more human studies to include more robust measurements of alpha diversity.
4.2 |. Prenatal Stress and Beta Diversity
Prenatal stress appears to be related to differences in offspring beta diversity, or variance between individuals. Of the 13 studies that assessed beta diversity, 10 studies (7 rodents and 3 humans) found that variation in prenatal stress was associated with between-subjects variability in composition and diversity of the offspring gut microbiome. In humans, three of the four studies that identified significant associations with beta diversity observed these findings in gut microbiome samples collected within the first hours and weeks of life, ranging from within 24 h to 80 days after birth. The human findings suggest that prenatal stress exposure may be associated with differences in the composition of the gut microbiome in early development. In support of this idea, one study found that the proportion of Gammaproteobacteria decreased over time, especially for infants born to mothers with low prenatal psychological distress (SRQ) (Naudé et al. 2020). Together, the studies suggest that prenatal stress may differentiate the gut microbiomes of individuals early in development, but the persistence of these differences across development may be dependent on postnatal experiences.
4.3 |. Prenatal Stress and Taxonomic Findings
Table 4 depicts the repeated taxonomic findings for the rodent and primate studies and synthesized findings for the direction of the relationship between prenatal stress and the specific taxonomic findings. For phyla level taxonomic findings, prenatal stress appears to be positively associated with the abundance of Proteobacteria and negatively associated with Actinobacteria.
These are important findings, considering implications for child health and development. Actinobacteria are thought to play a key role in cancer prevention through the production of metabolites to inhibit the growth of tumor cells and the production and short-change fatty acids that enhance the immune response (Pongen et al. 2023). Additionally, increased Proteobacteria is a common finding in many diseases, including cardiovascular, respiratory, and gastrointestinal conditions (Amar et al. 2013; Larsen et al. 2015; Rizzatti et al. 2017). Furthermore, preclinical work has shown expansion of Proteobacteria to be associated with chronic psychosocial stress (Langgartner et al. 2017). Therefore, the presence of increased Proteobacteria in these prenatally stressed offspring may reflect fetal programming of impaired stress response systems. Both Actinobacteria and Proteobacteria may serve as markers of microbiota instability, predisposing prenatally stressed fetuses to subsequent disease risk across the lifespan.
For family level findings, Bacteroidaceae, Lachnospiraceae, and Prevotellaceae appear to be increased in prenatally stressed offspring, whereas Enterobacteriaceae and Streptococcaceae appeared to be inversely related to prenatal stress. Findings suggest a positive relationship between prenatal stress and the genera, Bacteroides and Serratia, and an inverse relationship between prenatal stress and Bifidobacteria, Eggerthella, Parabacteroides, and Streptococcus.
Findings for the association between prenatal stress and Lactobacillus were mixed across species. Five reported a negative association and three reported a positive association between prenatal stress and offspring Lactobacillus (Jašarević et al. 2017; Bailey et al. 2004; Galley et al. 2023; Aatsinki et al. 2020; Zijlmans et al. 2015). Findings were also mixed for the relationship between prenatal stress and the following taxa: Prevotellaceae, Anaerotruncus, Butyricicoccus, Desulfovibrio, and Haemophilus. Some of the other genera affected, including Bacteroides and Bifidobacterium, have well-documented functions in the infant gut, including as some of the earliest colonizers, in promoting immune system development, producing short-chain fatty acids, and aiding in digestion (Milani et al. 2017). Others do not have as robust documentation in the child development literature but have been generally associated with diseases in later life, such as Desulfovibrio (Singh et al. 2023).
Notably, the directional relationships between prenatal stress and the abundance of certain bacteria believed to be beneficial to the human gut do not align with expectations. Specifically, Bacteroides and Butyricicoccus tended to be more abundant in the gut microbiota of offspring exposed to prenatal stress. Bacteroides is one genus that is thought to have positive commensal role when residing in the gut, including protection against pathogenic bacteria, maintenance of gut barrier integrity, metabolic benefit, and influence on mood and behavior (Deng et al. 2018; Rhee et al. 2021; Wang et al. 2021; Yuan et al. 2021). Bacteroides was also previously thought to be inversely associated with stress; however, newer evidence suggests that Bacteroides may have some positive associations, with a key differentiating factor being the presence of dietary fiber (Peñalver Bernabé et al. 2023). Additionally, in some circumstances, Bacteroides is known to have negative health impacts, such as enhancing pathogenicity of virulent organisms and contributing to the development of intra-abdominal abscesses (Wexler and Goodman 2017). These unexpected results highlight the dynamic nature of the gut microbiome in early development, supporting the idea that microbes can exert positive or negative effects on development, depending on context. Therefore, there is a need for more research as to the functions of these bacteria using longitudinal research, with multiple measurements of the gut microbiome, to better understand the function and co-existence of these bacteria in the early life gut microbiome.
4.3.1 |. Differential Findings
A number of possible reasons could explain our inconsistent findings for the relationship between prenatal stress and specific taxa. First, biological differences between species included in these studies may have contributed to varying abundance of taxa. In support of this idea, four of the five primate studies with significant Lactobacillus findings observed a negative association between prenatal stress and offspring gut Lactobacillus (Bailey et al. 2004; Aatsinki et al. 2020; Zijlmans et al. 2015; Galley et al. 2023), whereas two of three rodent studies observed positive associations (Zheng et al. 2020; Zhang et al. 2021). This suggests that in humans, prenatal stress may be associated with a depletion of Lactobacillus in the offspring. Thus, infants exposed to prenatal stress may be at increased risk for adverse infectious and chronic conditions, as well as mood and behavioral disorders that are more prevalent when Lactobacillus is depleted (Heeney, Gareau, and Marco 2018).
Second, variations in the type of prenatal stress may have contributed to differences in findings. The studies that noted a decrease in Lactobacillus in the prenatally stressed group both included multiple stressors (Jašarević et al. 2015, 2017). Of the studies that noted increased Lactobacillus in the prenatally stressed offspring, one used cold stress alone (Zheng et al. 2020), and the other used restraint stress alone (Zhang et al. 2021). This lends support to the DOHaD theory that increasing adverse experiences during the prenatal period can exert enduring effects on health trajectories (Barker 2004). Additionally, in three of the rodent studies, multiple stressors (e.g., bright light, noise, restraint, fox urine, and saturated bedding) were positively associated with the abundance of Lachnospiraceae (Brawner et al. 2020; Jašarević et al. 2017; Sun et al. 2021). Alternately, in one rodent study, cold stress was negatively associated with Lachnospiraceae (Zheng et al. 2020). In humans, cold stress is associated with a decrease in anxiety symptoms, whereas other forms of stress are associated with increased anxiety and depressive symptoms. This suggests that cold stress might have different effects on the mother and offspring, compared to other types of stress.
Additionally, timing of prenatal stress might have contributed to difficulty comparing findings across studies. Only two human studies and one monkey study measured first trimester stress. However, first trimester stress may have more profound impacts on the infant gut microbiome, compared to the third trimester. During the first trimester, the fetal nervous system and digestive system are being formed and these systems are particularly sensitive to teratogens (Munoz 2024). Alternately, sympathetic activity and levels of cortisol naturally increase across pregnancy, with third-trimester levels comparable to periods of distress (Glynn, Davis, and Sandman 2013; Kudo, Shinohara, and Kodama 2014; Stojanov et al. 2021). As such, stress encountered later in pregnancy may not impact the infant microbiome as profoundly as if encountered earlier in pregnancy. This could explain the differential Lactobacillus findings, in which early prenatal stress was associated with decreased Lactobacillus (Bailey et al. 2004; Jašarević et al. 2015, 2017), whereas later prenatal stress was associated with increased Lactobacillus (Wei et al. 2022; Zheng et al. 2020).
Finally, variation in the timing of gut microbiome sampling may have also impacted our ability to compare findings across studies. We found that for rodents, earlier gut microbiome sampling was associated with a decreased abundance of Akkermansia in the prenatally stressed groups. For Lactobacillus, a positive association was noted with an infant’s first stool (Wei et al. 2022), compared to negative associations with stool samples collected at later time points (Aatsinki et al. 2020; Bailey et al. 2004). Additionally, weaning may be an important consideration, as breastmilk consumption has been shown to be associated with a distinct microbiome profile (Moore and Townsend 2019; Sanidad and Zeng 2020). To that end, Sun et al. (2021) noted differences in beta diversity between the experimental groups after the mice had weaned, but not prior to weaning.
4.3.2 |. Summary of Findings Across Studies
Despite heterogeneity in study designs, operationalization of stress, microbiome sampling times, and analytical approaches, we still found consistent effects of prenatal stress on the infant gut microbiome, including stability of beta diversity results, positive associations with Proteobacteria, Lachnospiraceae, Bacteroidaceae, and Prevotellaceae, and negative associations with Actinobacteria and Enterobacteriaceae, with additional impacts on specific genera (see Table 4). Although the studies varied greatly in design, the common factors among all studies were (a) prenatal stress and (b) offspring gut microbiome alterations. This suggests that the type of stress may not be as important as the severity and/or the mother’s appraisal of the stress.
4.4 |. Mechanisms Linking Prenatal Stress to Offspring Gut Microbiome
This systematic review examined associations of prenatal stress with the offspring gut microbiome; however, several of the included studies also examined mechanistic pathways. Findings from the rodent and non-human primate studies help inform the understanding of human prenatal stress effects. Two rodent studies noted a direct effect of the prenatal vaginal gut microbiome on offspring gut microbiome colonization at birth (Jašarević et al. 2017, 2018). This suggests that prenatal stress likely impacts the offspring gut microbiome through direct transfer of vaginal microbes at birth.
Another hypothesized mechanism linking prenatal stress to offspring gut microbiome and health outcomes involves fetal programming leading to heightened reactivity of the fetus’ HPA axis and ANS (Howland 2023). This hypothesis is supported by several rodent studies that observed prenatally stressed offspring demonstrated a greater release of corticosterone when exposed to various types of stressors (Golubeva et al. 2015; Gur et al. 2019; Jašarević et al. 2018; Zhang et al. 2021). These findings suggest that prenatal stress may contribute to alterations in the offspring gut microbiome through fetal programming of an impaired stress response system that results in differential microbial colonization after birth.
Another hypothesis is that prenatal stress alters the uterine environment via transfer of metabolites that are encountered by the developing fetus. In-line with this theory, one rodent study found that prenatal stress contributed to increased levels of cytokines and neurotrophins in the placenta and offspring brains, with structural brain differences persisting into adulthood (Gur et al. 2017). Similarly, researchers theorize that prenatal stress disrupts fetal gut and brain development that contributes to differential offspring microbial colonization via the gut–brain-axis. In support of this theory, several studies observed structural and biological brain differences in prenatally stressed offspring (Gur et al. 2017, 2019; Jašarević et al. 2015; Zhang et al. 2021). Four rodent studies also identified links between prenatal stress and alterations to the fetal gut physiology (e.g., colonic tissue damage, deficient distal colon innervation), with long-term impacts on offspring microbial intestinal colonization (Brawner et al. 2020; Golubeva et al. 2015; Jašarević et al. 2015, 2018).
On the basis of the existing rodent studies, it is clear that prenatal stress is associated with offspring gut microbiome differences and that the mechanisms linking these factors are multifactorial. However, more research is needed to better understand how prenatal stress contributes to the gut microbiome via (a) impacts on the gut via brain alterations that communicate with the gut and/or (b) through anatomical and physiological changes that make the gut more hospitable to certain microbes. Additionally, the interaction among self-efficacy, dietary fiber intake, physical exercise, and the prenatal and offspring gut microbiome, as well as the immune-specific effects of pregnancy on the prenatal maternal gut microbiome, require further investigation.
4.5 |. Strengths and Limitations
This systematic review adds to the literature by examining the relationship between maternal prenatal stress and the offspring gut microbiome across species. Strengths of this approach include a cross-species design, which allows us to evaluate the causal relationships identified in rodent studies in conjunction with correlational but more directly translatable results from human studies (Courtine et al. 2007; Italia et al. 2020). Although methodologies varied, we observed consistent findings across studies. This cross-species design, therefore, increases the translational implications of our findings. The findings from rodent and non-human primate studies provide critical insights into the mechanisms of prenatal stress that are highly relevant to human development. Rodent models have been instrumental in elucidating the role of the HPA axis in fetal programming, showing how prenatal stress can dysregulate cortisol levels, leading to altered neurodevelopmental and behavioral outcomes (Kapoor et al. 2006). These mechanisms are conserved across species, including humans, suggesting that similar pathways may be involved in human prenatal stress and infant microbiome responses (Kapoor et al. 2006). Non-human primate studies, given their closer anatomical and gestational similarities to humans, as compared to rodents, have further enhanced our understanding of this complex phenomenon by demonstrating how maternal stress can lead to changes in offspring gut microbiome (Bailey et al. 2004), as well as brain structure, immune function, and stress reactivity—findings that are directly translatable to human health contexts (Berghänel et al. 2016; Pryce et al. 2011). The cross-species findings highlight key biological pathways that inform how prenatal stress increases susceptibility to neurodevelopmental and metabolic disorders in humans, which can guide future interventions. For instance, understanding these mechanisms opens the door to developing prenatal interventions targeting stress reduction and HPA axis regulation, which could mitigate the long-term health impacts of prenatal stress in vulnerable populations. Thus, cross-species studies not only deepen our understanding of the biological effects of prenatal stress but also inform strategies for human health promotion and disease prevention.
Another strength involves the diversity of studies included in the review. These studies draw from populations in different countries, at prenatal and offspring developmental age ranges, and with varying operationalizations of prenatal stress, which allows us to make some broad inferences about the general effects of prenatal stress on offspring gut microbiome. However, there are a number of limitations. First, our review was limited by a small number of studies available. We only identified 19 studies that met our inclusion criteria, with wide variation in design, making it difficult to synthesize findings across studies.
In addition to the limited number of studies, differences in the measurement of stress in humans posed challenges to interpreting relationships between prenatal stress and the offspring gut microbiome across studies. In the human studies, the published work primarily used instruments to measure various components of psychosocial stress, with minimal overlap between studies. Our systematic review did not include anxiety or depressive symptoms as keywords; however, a majority of studies reporting stress included these symptoms as stress measures. Additionally, few of the human studies measured stressors, which were the source of measurement in the animal studies, whereas only three included physiological measures of stress. We also noted a gap in the measurement of prenatal stress during the first trimester for the human studies and third-trimester equivalent for the rodent studies. Finally, we noticed a significant gap in the measurement of family- and community-level stressors such as social disadvantage, social risk, and area deprivation, which are known to contribute to chronic stress for individuals and may be the key to transmitting health inequities across generations. These gaps limit our ability to detect patterns in results or make strong inferences about the effects of type or timing of prenatal stress on offspring gut microbiome development.
There are also a number of methodological inconsistencies between studies that limit how well we can compare results. A few of the studies presented in this review measured the gut microbiome at multiple points throughout development; however, most only considered one time point. Given that the gut microbiome undergoes rapid and dramatic development throughout the first few years of life (Stewart et al. 2018), a lack of attention to the temporal dynamics of gut microbiome development may mask associations between prenatal stress and offspring gut microbiome development. Furthermore, beneficial characteristics of the gut microbiome at one time point may not be protective of health at a different point in time. For example, greater alpha diversity in adults is health protective, whereas in infancy, it is associated with adverse health outcomes (Chen et al. 2019; Schoch et al. 2022; Zhong et al. 2020). Therefore, changes in diversity and taxonomy over time may be a more important indicator of health than characteristics at one or a few points in time.
Another methodological limitation of the studies reviewed here is the different sequencing technologies used to identify the microbes present in the gut. Most studies reviewed used 16S RNA sequencing, which is a method that identifies the types and relative abundances of bacteria in the gut by sequencing a specific section of bacterial ribosomal RNA gene (Durazzi et al. 2021; Lane et al. 1985; Quince et al. 2017). This technology is widely used in gut microbiome studies; however, it is unable to identify other important components of the gut microbiome, such as viruses and fungi, which are also known to play important roles in immune and inflammatory processes (Jaswal et al. 2023; Pérez 2021). In recent years, 16S RNA sequencing has been complemented or replaced in many studies by more advanced sequencing technologies. One such method, whole genome shotgun sequencing, used in a few of the rodent studies we reviewed, has advantages such as detection of other microorganisms present in the gastrointestinal tract (viruses, fungi) (Quince et al. 2017). Shotgun sequencing also allows for examination of the functional potential of gut microbes, yielding insight into the mechanisms by which the gut microbiome may impact health and development. The differences in these technologies make them challenging to combine across studies for meta-analyses, although new technology is facilitating this task (Morton 2023). Future longitudinal work is needed using multiple gut microbiome measurements across development and shotgun metagenomic sequencing to include the assessment of viruses and fungi. Further, this work should occur in conjunction with robust measurement of the social environment and thoughtful consideration of the type and timing of stressor measures to better understand how prenatal stress influences offspring gut microbiome.
There are many factors besides prenatal stress that are known to influence the human gut microbiome in early life, including method of delivery, nutrition, breastfeeding, postnatal experiences, exposure to medications, illness, and others (Stewart et al. 2018). Importantly, not all the studies reviewed here controlled for the vaginal microbiome, which limits our ability to parse between stress-related uterine exposure to metabolites or from vaginal alterations that are transferred at birth. Therefore, the field needs more dyadic studies, starting earlier in pregnancy, including investigation of both prenatal maternal gut and vaginal microbiome, as well as infant gut microbiome, and controlling for early life experiences that are known to impact the gut microbiome.
4.6 |. Next Steps for Research
This cross-species review supports future hypothesis-driven human studies. In particularly, future studies should focus on delineating mechanisms, test for cross-species relevance in humans, and consider creative quasi-experimental models to better characterize causality.
Future research should also focus on more precisely delineating the mechanistic pathways and effects identified in the rodent and non-human primate studies. For example, research in humans should include longitudinal studies that examine the maternal vaginal microbiome and its role in the differences observed in the infant gut microbiome due to prenatal stress, as demonstrated in rodent studies (Jašarević et al. 2015, 2017, 2018). Additionally, future research may consider imaging of the neonatal brain or gastrointestinal tissue to explore whether prenatal stress is leading to fetal brain impacts or colonic tissue damage, as with the rodent studies (Brawner et al. 2020; Sun et al. 2021). Informed by the rodent studies (Gur et al. 2019; Jašarević et al. 2018), future studies may also benefit from examining biomarkers of heightened reactivity of the fetus’ HPA axis and ANS (e.g., corticosterone, adrenocorticotropic hormone, hair cortisol concentration, heart rate variability, norepinephrine, and epinephrine levels).
Future studies should focus on evaluating the taxa that we found to be consistently altered across studies, as well as assessing beta diversity, to gain a deeper understanding of the impact of prenatal stress on the infant gut microbiome. Longitudinal studies starting in pregnancy and utilizing cutting-edge microbiome technology like strain-tracking could help identify a prenatal stress to infant colonization pathway. Additionally, probiotic supplementation has been associated with improved mental health symptoms and decreased inflammatory markers in patients with psychiatric disorders (Huffnagle and Noverr 2013). It would be useful to explore whether prenatal probiotic supplementation of Lactobacillus and Bifidobacterium strains, as modeled in previous prenatal interventions, is successful in (a) reducing prenatal stress and (b) reducing microbiome alterations in prenatally stress infants. Future studies should also include considerations of behaviors associated with stress and the microbiome, such as diet and physical activity (Clauss et al. 2021; Gacesa et al. 2022; Singh et al. 2017), and their impacts on prenatal and infant microbiome composition.
Because our findings suggest that it is not the specific type of stress, but rather the severity or appraisal of the stress, it may be beneficial for studies to move toward a measure of cumulative risk to gather a more holistic picture of physiological impacts on the mother and subsequent impacts fetal microbiome development. Evidence suggests that cumulative prenatal stress is associated with greater biological dysregulation in mothers (Katrinli et al. 2023; Suglia et al. 2010). However, more research is needed to examine how cumulative prenatal stress impacts the infant’s gut microbiome, and the long-term impacts on health and development.
Furthermore, although ethical considerations prevent our ability to design randomized controlled trials with intentional prenatal stressors, we can design studies targeting prenatal stress reduction, to begin to explore causality in humans. For example, one maternal prenatal mindfulness intervention was associated with positive alterations of the infant’s meconium (Zhang et al. 2022). Additional intervention studies should explore the microbiome impacts of prenatal stress reduction techniques, such as yoga and exercise, which have been associated with stress reduction benefits in the perinatal period (Corrigan et al. 2022; Hicks and Yeo 2023). Furthermore, targeted approaches for those with heightened stressors, such as socioeconomic disadvantage or absence of a romantic partner, may include direct cash transfers or social support measures to reduce prenatal stress in vulnerable populations (Bedaso et al. 2021; González and Trommlerová 2022). Finally, because the first trimester appears to be a particularly sensitive period for stress impacts on the infant gut microbiome, human research must include women from the beginning of pregnancy. This could potentially be achieved by enrolling women who are planning to conceive or at their first trimester prenatal appointment. Alternatively, we could reach women earlier in pregnancy through a population-based study examining mental health symptoms in young women, with a subset of participants who conceive during the study being enrolled in a follow-up study. Finally, given our findings, policy changes to reduce maternal stress, such as paid parental leave (for both parents), access to mental health services and comprehensive prenatal care, workplace flexibility, affordable childcare, and financial support, may improve the health of families.
5 |. Conclusion
This systematic review provides a synthesis of the evidence supporting a link between prenatal stress and the composition of the infant gut microbiome, employing a cross-species design that bridges findings from rodent studies with findings from diverse human populations. Overall, findings suggest that prenatal stress does not impact infant gut alpha diversity but rather could have significant impacts on beta diversity and the abundance of specific taxa. Despite heterogeneity in research methodologies, consistent effects were observed for the impact of prenatal stress on Proteobacteria (+), Actinobacteria (−), Enterobacteriaceae (−), Lachnospiraceae (+), Bacteroidaceae (+), Streptococcaceae (−), and Prevotellaceae (+), with additional impacts on specific genera. Notably, the genera Bacteroides (+), Bifidobacterium (−), and Lactobacillus (−) were impacted by prenatal stress and have been recognized for their impact on infant health and development. Synthesis of findings was limited by differences in study design, operationalization and timing of prenatal stress, timing of infant microbiome sampling, and microbiome analysis methods.
This cross-species review highlights the need for future hypothesis-driven human studies to investigate the mechanisms by which prenatal stress affects the infant gut microbiome, with a focus on cross-species relevance and creative quasi-experimental models to explore causality. Future research should prioritize longitudinal studies from the beginning of pregnancy, examining the maternal vaginal microbiome, biomarkers of stress reactivity, and potential interventions like probiotics and stress-reduction techniques, while also accounting for social and environmental factors. Additionally, new sequencing technologies, like strain tracking, should be used to more directly infer how health effects are passed from mother to offspring. In summary, this systematic review advances the science surrounding DOHaD, providing evidence for a link between prenatal stress and the infant microbiome, possibly explaining one mechanism contributing to the relationship between prenatal stress and infant health outcomes.
Supplementary Material
Funding:
This work was supported by the National Institute of Health (NIH) and National Institute of Nursing Research (NINR) under Award Number T32NR007091–27.
Footnotes
Conflicts of Interest
The authors declare no conflicts of interest.
Disclosure
The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent official views of the NIH or the NINR.
Ethics Statement
This study design involved a systematic review of published research. As such, the authors did not seek ethical or IRB approval.
Consent
The authors have nothing to report.
Permission to Reproduce Material from Other Sources
The authors have nothing to report.
Supporting Information
Additional supporting information can be found online in the Supporting Information section.
Data Availability Statement
The data that support the findings of this study are available in PubMed.
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Data Availability Statement
The data that support the findings of this study are available in PubMed.