Composition of the maternal gastrointestinal microbiome as a predictor of neonatal birth weight.

BACKGROUND: The biological mechanism by which the maternal gastrointestinal microbiota contributes to fetal growth and neonatal birth weight is currently unknown. The purpose of this study was to explore how the composition of the maternal microbiome in varying pre-gravid body mass index (BMI) groups are associated with neonatal birth weight adjusted for gestational age. METHODS: Retrospective, cross-sectional metagenomic analysis of bio-banked fecal swab biospecimens (n = 102) self-collected by participants in the late second trimester of pregnancy. RESULTS: Through high-dimensional regression analysis using principal components (PC) of the microbiome, we found that the best performing multivariate model explained 22.9% of the variation in neonatal weight adjusted for gestational age. Pre-gravid BMI (p = 0.05), PC3 (p = 0.03), and the interaction of the maternal microbiome with maternal blood glucose on the glucose challenge test (p = 0.01) were significant predictors of neonatal birth weight after adjusting for potential confounders including maternal antibiotic use during gestation and total gestational weight gain. CONCLUSIONS: Our results indicate a significant association between the maternal gastrointestinal microbiome in the late second trimester and neonatal birth weight adjusted for gestational age. Moderated by blood glucose at the time of the universal glucose screening, the gastrointestinal microbiome may have a role in the regulation of fetal growth. IMPACT: Maternal blood glucose in the late second trimester significantly moderates the relationship between the maternal gastrointestinal microbiome and neonatal size adjusted for gestational age. Our findings provide preliminary evidence for fetal programming of neonatal birth weight through the maternal gastrointestinal microbiome during pregnancy.

A Comparison of Hypothesis-Driven and Data-Driven Research: A Case Study in Multimodal Data Science in Gut-Brain Axis Research.

Data science, bioinformatics, and machine learning are the advent and progression of the fourth paradigm of exploratory science. The need for human-supported algorithms to capture patterns in big data is at the center of personalized healthcare and directly related to translational research. This paper argues that hypothesis-driven and data-driven research work together to inform the research process. At the core of these approaches are theoretical underpinnings that drive progress in the field. Here, we present several exemplars of research on the gut-brain axis that outline the innate values and challenges of these approaches. As nurses are trained to integrate multiple body systems to inform holistic human health promotion and disease prevention, nurses and nurse scientists serve an important role as mediators between this advancing technology and the patients. At the center of person-knowing, nurses need to be aware of the data revolution and use their unique skills to supplement the data science cycle from data to knowledge to insight.

A framework to guide research and practice response to emerging infectious diseases: Genomic-to-global considerations.

Newly emerging infectious diseases (EIDs), like the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, are becoming increasingly common. Due to geographic, political, social, behavioral, and genomic differences, some populations are more vulnerable to infectious disease spread than others. The purpose of this article is to present a framework for research and practice response to emergent infectious diseases that addresses multiple transdisciplinary actions to limit exposure or mitigate adverse outcomes for individuals and communities. Recent experience with new strains of emergent infectious diseases reinforces the importance of intervening at multiple levels, from genomics to political messaging to create multipronged, transdisciplinary interventions to contain the threat. In particular, incorporation of genomics into public health nursing practice of infectious diseases management can enhance existing regional-, community-, and individual-level health promotion and protection efforts, thus impacting long-term health outcomes.

Moving beyond Table 1: A critical review of the literature addressing social determinants of health in chronic condition symptom cluster research.

Variability in the symptom experience in patients diagnosed with chronic conditions may be related to social determinants of health (SDoH). The purpose of this critical review was to (1) summarize the existing literature on SDoH and symptom clusters (i.e., multiple, co-occurring symptoms) in patients diagnosed with common chronic conditions, (2) evaluate current variables and measures used to represent SDoH, (3) identify gaps in the evidence base, and (4) provide recommendations for the incorporation of SDoH into future symptom cluster research. We identified 118 articles including information on SDoH in chronic condition symptom cluster research. Articles primarily focused on cancer populations. Few articles had the explicit purpose of investigating relationships between SDoH and symptom clusters, and the inclusion of SDoH was often limited to variables used to describe samples. Future studies should be designed to "move beyond Table 1" in their utilization of SDoH as variables and examine relationships between SDoH and symptom clusters. Attention should be paid to the appropriateness of measures being used to collect information on SDoH, and analysis methods that estimate causal connections between variables should be considered. Research regarding the relationship of SDoH with symptom clusters in patients with chronic conditions has the potential to reveal mechanisms of symptom disparities and guide changes to alleviate these disparities.

Older Adult Engagement With Symptom Reporting in a COVID-19 Citizen Science Application.

The current study examined the frequency and predictors of older adults' engagement with symptom reporting in COVIDWATCHER, a mobile health (mHealth) citizen science application. Citizen science is a type of participatory research that leverages information provided by community members. There were 1,028 COVIDWATCHER participants who engaged with symptom reporting between April 2020 and January 2021. Approximately 13.5% (n = 139) were adults aged ≥65 years. We used a Wilcoxon test to compare the mean frequency of engagement with symptom reporting by older adults (i.e., aged ≥65 years) to younger adults (i.e., aged ≤64 years) and multivariable linear regression to explore the predictors of engagement with symptom reporting. There was a significant difference in engagement with symptom reporting between adults aged ≥65 years compared to those aged ≤64 years (p < 0.001). In our final model, age (ß = 26.0; 95% confidence interval [14.8, 34.2]) was a significant predictor for engagement with symptom reporting. These results help further our understanding of older adult engagement with mHealth-enabled citizen science for symptom reporting. [Journal of Gerontological Nursing, 49(4), 6-11.].

Informatics and data science perspective on Future of Nursing 2020-2030: Charting a pathway to health equity.

The Future of Nursing 2020 to 2030 report explicitly addresses the need for integration of nursing expertise in designing, generating, analyzing, and applying data to support initiatives focused on social determinants of health (SDOH) and health equity. The metrics necessary to enable and evaluate progress on all recommendations require harnessing existing data sources and developing new ones, as well as transforming and integrating data into information systems to facilitate communication, information sharing, and decision making among the key stakeholders. We examine the recommendations of the 2021 report through an interdisciplinary lens that integrates nursing, biomedical informatics, and data science by addressing three critical questions: (a) what data are needed?, (b) what infrastructure and processes are needed to transform data into information?, and (c) what information systems are needed to "level up" nurse-led interventions from the micro-level to the meso- and macro-levels to address social determinants of health and advance health equity?

Gut microbiota composition is associated with newborn functional brain connectivity and behavioral temperament.

The gut microbiome appears to play an important role in human health and disease. However, only little is known about how variability in the gut microbiome contributes to individual differences during early and sensitive stages of brain and behavioral development. The current study examined the link between gut microbiome, brain, and behavior in newborn infants (N = 63; M [age] = 25 days). Infant gut microbiome diversity was measured from stool samples using metagenomic sequencing, infant functional brain network connectivity was assessed using a resting state functional near infrared spectroscopy (rs-fNIRS) procedure, and infant behavioral temperament was assessed using parental report. Our results show that gut microbiota composition is linked to individual variability in brain network connectivity, which in turn mediated individual differences in behavioral temperament, specifically negative emotionality, among infants. Furthermore, virulence factors, possibly indexing pathogenic activity, were associated with differences in brain network connectivity linked to negative emotionality. These findings provide novel insights into the early developmental origins of the gut microbiome-brain axis and its association with variability in important behavioral traits. This suggests that the gut microbiome is an important biological factor to consider when studying human development and health.

A primer on investigating the role of the microbiome in brain and cognitive development.

Incorporating information regarding the gut microbiota into psychobiological research promises to shed new light on how individual differences in brain and cognitive development emerge. However, the investigation of the gut-brain axis in development is still in its infancy and poses several challenges, including data analysis. Considering that the gut microbiome is an eco-system containing millions of bacteria, one needs to utilize a breadth of methodologies and data analytic techniques. The present review serves two purposes. First, this review will inform developmental psychobiology researchers about the emerging study of the gut-brain axis in development and second, this review will propose methodologies and data analytic strategies for integrating microbiome data in developmental research.

Review of enhanced recovery programs in benign gynecologic surgery.

INTRODUCTION AND HYPOTHESIS: Enhanced recovery programs (ERPs) are evidence-based protocols designed to improve functional rehabilitation after surgery. ERPs have gained widespread acceptance in many surgical disciplines, and their use leads to significant improvements in patient outcomes while reducing hospital length of stay (LOS). There remains a paucity of data on the use of ERPs in benign gynecologic surgery. The purpose of this review was to evaluate current literature on the use of ERP concepts in benign gynecologic surgery. METHODS: A systematic search of PubMed, CINAHL, Web of Science, and the Cochrane databases was conducted, cross-referencing search terms related to gynecologic surgery and ERP concepts. The search was limited to publications available in English. Studies published prior to 2000, and those involving gynecologic oncology, nonadult patients, and outpatient surgery were excluded. RESULTS: Nine studies were included in the analysis. Due to heterogeneity of the included studies, no statistical pooling was possible and comparison between studies was limited to their respective themes. Primary study outcomes included LOS, postoperative nausea and vomiting (PONV), pain management, patient satisfaction, and hospital costs. Five studies investigated ERPs, two evaluated measures to improve PONV, and four focused on unique aspects of pain management. Across the studies, ERPs that focused on the patients' basic symptoms and recovery were found to have equal, if not better, outcomes than standard practice. CONCLUSIONS: This integrative review supports the implementation of ERPs in benign gynecologic surgery. The results showed that the use of ERPs decreased LOS, improved pain scores, and reduced hospital costs, without increasing perioperative complications. We suggest additional randomized controlled trials of ERP concepts in benign gynecologic surgery to support their more widespread use and application.

How Narrative Focus and a Statistical Map Shape Health Policy Support Among State Legislators.

This study attempts to advance theorizing about health policy advocacy with combinations of narrative focus and a statistical map in an attempt to increase state legislators' support for policies to address the issue of obesity by reducing food deserts. Specifically, we examine state legislators' responses to variations in narrative focus (individual vs. community) about causes and solutions for food deserts in U.S. communities, and a statistical map (presence vs. absence) depicting the prevalence of food deserts across the United States. Using a Web-based randomized experiment (N=496), we show that narrative focus and the statistical map interact to produce different patterns of cognitive response and support for policies to reduce the prevalence of food deserts. The presence of a statistical map showing the prevalence of food deserts in the United States appeared to matter only when combined with an individual narrative, offsetting the fact that the individual narrative in isolation produced fewer thoughts consistent with the story's persuasive goal and more counterarguments in opposition to environmental causes and solutions for obesity than other message conditions. The image did not have an impact when combined with a story describing a community at large. Cognitive responses fully mediated message effects on intended persuasive outcomes. We conclude by discussing the study's contributions to communication theory and practice.

The role of prenatal maternal sex steroid hormones in weight and adiposity at birth and growth trajectories during infancy.

Objective: Intrauterine factors can impact fetal and child growth and may underlie the developmental origins of childhood obesity. Sex steroid hormone exposure during pregnancy is a plausible target because of the impact on placental vascularization, nutrient transportation, bone growth, adipogenesis, and epigenetic modifications. In this study we assessed maternal sex steroid hormones in each trimester in relation to birthweight, neonatal adiposity, and infant growth trajectories, and evaluate sensitive windows of development. Methods: Participants from a prospective pregnancy cohort who delivered at term were included in the analysis (n=252). Estrone, estradiol, and estriol, as well as total and free testosterone throughout gestation were assessed using high-performance liquid chromatography and tandem mass spectrometry. Path analyses were used to assess the direct associations of sex steroid hormones in each trimester with birth outcomes and infant growth trajectories (birth to 12 months) adjusting for covariates and considering moderation by sex. Results: The associations between prenatal sex steroid hormones and fetal/infant growth varied by sex and hormone assessment timing. First trimester estrone were associated with higher birthweight z-scores (ß=0.37, 95%CI: 0.02, 0.73) and truncal skinfold thickness (TST) at birth (ß=0.94, 95%CI: 0.34, 1.54) in female infants. Third trimester total testosterone was associated with higher TST at birth (ß=0.61, 95%CI: 0.02, 1.21) in male infants. First trimester estrone/estradiol and first and third trimesters testosterone were associated with lower probabilities of high stable weight trajectory compared to low stable weight trajectory (Estrone: ß=-3.87, 95%CI: -6.59, -1.16; First trimester testosterone: ß=-3.53, 95%CI: -6.63, -0.43; Third trimester testosterone: ß=-3.67, 95%CI: -6.66, -0.69) during infancy in male infants. Conclusions: We observed associations between prenatal sex steroid hormone exposure and birthweight, neonatal adiposity and infant growth that were sex and gestational timing dependent. Our findings suggest further investigation on additional mechanisms linking prenatal sex steroid exposure and fetal/postnatal growth is needed.

Screening for Partner Postpartum Depression: A Systematic Review.

BACKGROUND: Postpartum depression is a significant mental health condition affecting an estimated 7% to 20% of women, with higher rates among individuals with increased risk factors. Most research on postpartum depression has focused on mothers, with less recognition of the mental health changes experienced by their partners. Research suggests almost 20% of partners may experience postpartum depression, yet our understanding is limited. An enhanced understanding of postpartum depression in a birthing person's partner is critical, given the mental and physical health sequelae associated with depression. PURPOSE: The purpose of this review was to systematically examine the current tools available to assess partner postpartum depression. METHODS: We conducted a systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible studies were identified using selected key terms in PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, and Ovid MEDLINE. Studies were included if they assessed partner depressive symptoms and identified the specified use of a tool or screening measure. RESULTS: Seventeen studies met inclusion criteria. Seven different measures were used to assess postpartum depression. The Edinburgh Postnatal Depression Scale (EPDS) was used in 16 out of the 17 studies for depression assessment. CLINICAL IMPLICATIONS: Routine screening of partners for postpartum depression should be recommended as part of standard care. Nurses are critical liaisons for assessing risk and connecting relevant and timely resources to birthing people and their partners. Identifying the available screening tools may help to avoid adverse clinical outcomes associated with increased symptom severity and burden.

Clinical research staff perceptions on a natural language processing-driven tool for eligibility prescreening: An iterative usability assessment.

BACKGROUND: Participant recruitment is a barrier to successful clinical research. One strategy to improve recruitment is to conduct eligibility prescreening, a resource-intensive process where clinical research staff manually reviews electronic health records data to identify potentially eligible patients. Criteria2Query (C2Q) was developed to address this problem by capitalizing on natural language processing to generate queries to identify eligible participants from clinical databases semi-autonomously. OBJECTIVE: We examined the clinical research staff's perceived usability of C2Q for clinical research eligibility prescreening. METHODS: Twenty clinical research staff evaluated the usability of C2Q using a cognitive walkthrough with a think-aloud protocol and a Post-Study System Usability Questionnaire. On-screen activity and audio were recorded and transcribed. After every-five evaluators completed an evaluation, usability problems were rated by informatics experts and prioritized for system refinement. There were four iterations of system refinement based on the evaluation feedback. Guided by the Organizational Framework for Intuitive Human-computer Interaction, we performed a directed deductive content analysis of the verbatim transcriptions. RESULTS: Evaluators aged from 24 to 46 years old (33.8; SD: 7.32) demonstrated high computer literacy (6.36; SD:0.17); female (75 %), White (35 %), and clinical research coordinators (45 %). C2Q demonstrated high usability during the final cycle (2.26 out of 7 [lower scores are better], SD: 0.74). The number of unique usability issues decreased after each refinement. Fourteen subthemes emerged from three themes: seeking user goals, performing well-learned tasks, and determining what to do next. CONCLUSIONS: The cognitive walkthrough with a think-aloud protocol informed iterative system refinement and demonstrated the usability of C2Q by clinical research staff. Key recommendations for system development and implementation include improving system intuitiveness and overall user experience through comprehensive consideration of user needs and requirements for task completion.

Metagenomic characterization of the maternal prenatal gastrointestinal microbiome by pregravid BMI.

OBJECTIVE: The incidence of women entering into pregnancy with BMI indicating overweight or obesity is rising with concurrent increases in adverse complications such as gestational diabetes. Although several studies have examined the compositional changes to the microbiome across BMI classifications, there has been no investigation regarding changes in microbial function during pregnancy. METHODS: A total of 105 gastrointestinal microbiome biospecimens were used in this analysis. Biospecimens were sequenced by using the Illumina NovaSeq 6000 shotgun metagenomics platform. RESULTS: Findings indicate an enrichment in microbiota from the phylum Firmicutes across all pregravid BMI groups with a decrease in α diversity in groups with BMI indicating obesity or overweight compared with a group with BMI indicating normal weight (p = 0.02). More specifically, women with BMI indicating obesity or overweight had enrichment in Bifidobacterium bifidum and B. adolescentis. Women with BMI > 25 kg/m2 had a higher abundance of microbiota that support biotin synthesis and regulate epithelial cells in the lower gastrointestinal tract. These epithelial cells are responsible for host adaptability to dietary lipid variation and caloric absorption. CONCLUSIONS: Our analysis suggests that there are differences in microbial composition and function between BMI groups. Future research should consider how these changes contribute to specific clinical outcomes during pregnancy.

Predictors of Unrelieved Symptoms in All of Us Research Program Participants With Chronic Conditions.

CONTEXT: Over half of American adults are diagnosed with a chronic condition, with an increasing prevalence being diagnosed with multiple chronic conditions. These adults are at higher risk for having unrelieved, co-occurring symptoms, known as symptom clusters. OBJECTIVES: To identify symptom phenotypes of patients diagnosed with four common chronic conditions, specifically, cancer, chronic obstructive pulmonary disease, heart failure, and/or type 2 diabetes mellitus, and to understand factors that predict membership in symptomatic phenotypes. METHODS: We conducted a retrospective, cross-sectional analysis using participant responses (N=14,127) to All of Us Research Program, a National Institutes of Health biomedical database, survey questions. We performed hierarchical clustering to generate symptom phenotypes of fatigue, emotional distress, and pain and used multinomial regression to determine if demographic, healthcare access and utilization, and health-related variables predict symptom phenotype. RESULTS: Four phenotypes, one asymptomatic or mildly symptomatic and three highly symptomatic (characterized by severe symptoms, severe pain, and severe emotional distress), were identified. The percentage of participants belonging to the severe symptoms phenotype increased with the number of chronic conditions. Most notably, foregoing or delaying medical care and rating mental health as poor or fair increased the odds of belonging to a highly symptomatic phenotype. CONCLUSION: We found meaningful relationships between demographic, healthcare access and utilization, and health-related factors and symptom phenotypes. With the increasing trends of American adults with one or more chronic conditions and a demand to individualize care in the precision health era, it is critical to understand the factors that lead to unrelieved symptoms.

Women's Experiences of Symptoms of Suspected or Confirmed COVID-19 Illness During the Pandemic.

OBJECTIVE: To explore experiences of symptoms of suspected or confirmed COVID-19 illness among women using the CovidWatcher mobile citizen science app. DESIGN: Convergent parallel mixed-methods design. PARTICIPANTS: Twenty-eight self-identified women consented for follow-up after using CovidWatcher. Participants' ages ranged from 18 to 83 years old. METHODS: We collected data via semistructured, virtual interviews and surveys: the COVID-19 Exposure and Family Impact Survey and Patient-Reported Outcomes Measurement Information System measures. We used directed content analysis to develop codes, categories, themes, and subthemes from the qualitative data and summarized survey data with descriptive statistics. RESULTS: We derived five themes related to symptom experiences: (a) Physical Symptoms, (b) Mental Health Symptoms, (c) Symptom Intensity, (d) Symptom Burden, and (e) Symptom Trajectories. Subthemes reflected more nuanced experiences of suspected or confirmed COVID-19 disease. For those without COVID-19, anxiety and mental health symptoms were still present. Of those who attested to one of the PROMIS-measured symptoms, all but one had at least mild severity in one of their reported symptoms. CONCLUSION: This study demonstrates the cross-cutting impact of the COVID-19 pandemic on individuals who identify as women. Future research and clinical practice guidelines should focus on alleviating physical and mental health symptoms related to the ongoing pandemic, regardless of COVID-19 diagnosis. Furthermore, clinicians should consider how patients can use symptom reconciliation apps and tracking systems.

Association between microbial composition, diversity, and function of the maternal gastrointestinal microbiome with impaired glucose tolerance on the glucose challenge test.

Over the last two decades, the incidence of gestational diabetes (GDM) has almost doubled resulting in almost 9% of pregnant women diagnosed with GDM. Occurring more frequently than GDM is impaired glucose tolerance (IGT), also known as pre-diabetes, but it has been understudied during pregnancy resulting in a lack of clinical recommendations of maternal and fetal surveillance. The purpose of this retrospective, cross-sectional study was to examine the association between microbial diversity and function of the maternal microbiome with IGT while adjusting for confounding variables. We hypothesized that reduced maternal microbial diversity and increased gene abundance for insulin resistance function will be associated with IGT as defined by a value greater than 140 mg/dL on the glucose challenge test. In the examination of microbial composition between women with IGT and those with normal glucose tolerance (NGT), we found five taxa which were significantly different. Taxa higher in participants with impaired glucose tolerance were Ruminococcacea (p = 0.01), Schaalia turicensis (p<0.05), Oscillibacter (p = 0.03), Oscillospiraceae (p = 0.02), and Methanobrevibacter smithii (p = 0.04). When we further compare participants who have IGT by their pre-gravid BMI, five taxa are significantly different between the BMI groups, Enterobacteriaceae, Dialister micraerophilus, Campylobacter ureolyticus, Proteobacteria, Streptococcus Unclassified (species). All four metrics including the Shannon (p<0.00), Simpson (p<0.00), Inverse Simpson (p = 0.04), and Chao1 (p = 0.04), showed a significant difference in alpha diversity with increased values in the impaired glucose tolerance group. Our study highlights the important gastrointestinal microbiome changes in women with IGT during pregnancy. Understanding the role of the microbiome in regulating glucose tolerance during pregnancy helps clinicians and researchers to understand the importance of IGT as a marker for adverse maternal and neonatal outcomes.

Nursing science as a federally-recognized STEM degree: A call to action for the United States with global implications.

Nursing science contributes to advancements in patient care, public health, and innovation within numerous scientific domains. Despite commonality with United States Department of Education definitions of a science, technology, engineering, and mathematics (STEM) educational programs, nursing continues to be excluded from Department of Homeland Security STEM classification. This exclusion prevents societal recognition of nursing as a science and limits attraction of clinicians and nurse scientists born outside of the United States due to omission from various federal visa provisions the Department of Homeland Security classification provides. We evaluated existing Department of Homeland Security STEM-classified educational programs and identified methodological and content congruency among STEM-classified programs and nursing. We provide clear evidence that nursing contributes impactful STEM research; and argue that inclusion is critical for advancement of the profession and the potential to mitigate the faculty shortage. Beyond evaluation of nursing as a STEM field, we offer a policy-focused solution for development and diversification of the nursing workforce.

A systematic review on natural language processing systems for eligibility prescreening in clinical research.

OBJECTIVE: We conducted a systematic review to assess the effect of natural language processing (NLP) systems in improving the accuracy and efficiency of eligibility prescreening during the clinical research recruitment process. MATERIALS AND METHODS: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards of quality for reporting systematic reviews, a protocol for study eligibility was developed a priori and registered in the PROSPERO database. Using predetermined inclusion criteria, studies published from database inception through February 2021 were identified from 5 databases. The Joanna Briggs Institute Critical Appraisal Checklist for Quasi-experimental Studies was adapted to determine the study quality and the risk of bias of the included articles. RESULTS: Eleven studies representing 8 unique NLP systems met the inclusion criteria. These studies demonstrated moderate study quality and exhibited heterogeneity in the study design, setting, and intervention type. All 11 studies evaluated the NLP system's performance for identifying eligible participants; 7 studies evaluated the system's impact on time efficiency; 4 studies evaluated the system's impact on workload; and 2 studies evaluated the system's impact on recruitment. DISCUSSION: NLP systems in clinical research eligibility prescreening are an understudied but promising field that requires further research to assess its impact on real-world adoption. Future studies should be centered on continuing to develop and evaluate relevant NLP systems to improve enrollment into clinical studies. CONCLUSION: Understanding the role of NLP systems in improving eligibility prescreening is critical to the advancement of clinical research recruitment.

Metabolic and Microbial Changes Associated With Diet and Obesity During Pregnancy: What Can We Learn From Animal Studies?

The intestinal microbiota changes throughout pregnancy and influences maternal metabolic adaptations to support fetal growth. Obesity induces alterations to the microbiota that include decreased microbial diversity and shifts in microbial composition, though specific species changes are inconsistent between published studies. In animal models, probiotics and exercise moderate maternal weight gain and partially correct the maternal microbiota. Supplemental Escherichia coli, however, exacerbate maternal obesity during the perinatal period, lending weight to the theory that inflammation-induced gut epithelial barrier leak influences metabolic dysregulation. Although birth weight is not always altered when offspring are exposed to an obesogenic diet during gestation, insulin resistance and lipid metabolism are impacted through adulthood in association with this exposure and can lead to increased body weight in adulthood. Postnatal offspring growth is accelerated in response to maternal overnutrition during lactation. Offspring microbiota, metabolism, and behavior are altered in response to early exposure to high fat and high sucrose diets. Consequences to this exposure include impaired glucose and insulin homeostasis, fatty liver, and neurobehavioral deficits that can be ameliorated by improving the microbial environment. In this mini review, we provide an overview of the use of translational animal models to understand the mechanisms associated with changes to the gastrointestinal microbiota due to maternal obesity and the microbial impact on the metabolic changes of pregnancy.