Crucial nuances in understanding (mis)associations between the neonatal microbiome and Cesarean delivery

Sassin, Alexa M.; Johnson, Grace; Goulding, Alison N.; Aagaard, Kjersti M.

doi:10.1016/j.molmed.2022.07.005

Cited by 14 publications

(9 citation statements)

References 102 publications

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“…There are a growing number of reports suggesting that Caesarean delivery per se is not associated with any appreciable differences in neonatal microbiota compared with vaginal delivery [36,[41][42][43][44][45]. Here, in line with studies reporting that there is an association [35,36,38,39], we confirmed an influence of delivery mode on microbiota composition in the neonatal samples assessed.…”

Section: Discussionsupporting

confidence: 89%

Effects of maternal type 1 diabetes and confounding factors on neonatal microbiomes

Gajecka,

Gutaj,

Jaskiewicz

et al. 2023

Diabetologia

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Aims/hypothesis Body niche-specific microbiota in maternal–neonatal dyads from gravidae with type 1 diabetes have not been quantitatively and functionally examined. Similarly, the impact of pregnancy-specific factors, such as the presence of comorbidities known to occur more frequently among gravidae with type 1 diabetes, including Caesarean delivery, as well as antibiotic prophylaxis, level of glycaemic control during each trimester of pregnancy and insulin administration, has not been adequately considered. The aims of this study were to characterise the maternal and neonatal microbiomes, assess aspects of microbiota transfer from the maternal microbiomes to the neonatal microbiome and explore the impact of type 1 diabetes and confounding factors on the microbiomes. Methods In this observational case–control study, we characterised microbiome community composition and function using 16S rRNA amplicon sequencing in a total of 514 vaginal, rectal and ear-skin swabs and stool samples derived from 92 maternal–neonatal dyads (including 50 gravidae with type 1 diabetes) and in-depth clinical metadata from throughout pregnancy and delivery. Results Type 1 diabetes-specific microbiota were identified among gravidae with type 1 diabetes and their neonates. Neonatal microbiome profiles of ear-skin swabs and stool samples were established, indicating the taxa more prevalent among neonates born to mothers with type 1 diabetes compared with neonates born to control mothers. Without taking into account the type 1 diabetes status of mothers, both delivery mode and intrapartum antibiotic prophylaxis were found to have an influence on neonatal microbiota composition (both p=0.001). In the logistic regression analysis involving all confounding variables, neonatal ear-skin microbiome variation was explained by maternal type 1 diabetes status (p=0.020) and small for gestational age birthweight (p=0.050). Moreover, in women with type 1 diabetes, a relationship was found between HbA1c levels >55 mmol/mol (>7.2%) measured in the first trimester of pregnancy and neonatal ear-skin microbiota composition (p=0.008). In the PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) assessment, pathways concerning carbohydrate biosynthesis were predicted as key elements of the microbial functional profiles dysregulated in type 1 diabetes. Additionally, in SourceTracker analysis, we found that, on average, 81.0% of neonatal microbiota was attributed to maternal sources. An increase in the contribution of maternal rectum microbiota and decrease in the contribution of maternal cervix microbiota were found in ear-skin samples of vaginally delivered neonates of mothers with type 1 diabetes compared with neonates born to control mothers (83.2% vs 59.5% and 0.7% vs 5.2%, respectively). Conclusions/interpretation These findings indicate that, in addition to maternal type 1 diabetes, glycaemic dysregulation before/in the first trimester of pregnancy, mode of delivery and intrapartum antibiotic prophylaxis may contribute to the inoculation and formation of the neonatal microbiomes. Data availability The BioProject (PRJNA961636) and associated SRA metadata are available at http://www.ncbi.nlm.nih.gov/bioproject/961636. Processed data on probiotic supplementation and the PICRUSt analysis are available in the Mendeley Data Repository (https://doi.org/10.17632/g68rwnnrfk.1). Graphical Abstract

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Section: Discussionsupporting

confidence: 89%

Effects of maternal type 1 diabetes and confounding factors on neonatal microbiomes

Gajecka,

Gutaj,

Jaskiewicz

et al. 2023

Diabetologia

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“…However, no pathway could consistently predict mode of delivery at any gestational age which is similar to the results previously reported by Chu et al ( 27 ). Caesarean deliveries, performed for multiple underlying maternal and fetal indications, are associated with varying rates of success at exclusive breastfeeding ( 55 ). In the present study, we planned to perform a sensitivity analysis stratified on feeding mode.…”

Section: Discussionmentioning

confidence: 99%

Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts

Vänni,

Tejesvi,

Paalanne

et al. 2023

mSystems

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Combining and comparing microbiome data from distinct infant cohorts has been challenging because such data are inherently multidimensional and complex. Here, we used an ensemble of machine-learning (ML) models and studied 16S rRNA amplicon sequencing data from 4,099 gut microbiome samples representing 12 prospectively collected infant cohorts. We chose the childbirth delivery mode as a starting point for such analysis because it has previously been associated with alterations in the gut microbiome in infants. In cross-study ensemble models, Bacteroides was the most important feature in all machine-learning models. The predictive capacity by taxonomy varied with age. At the age of 1–2 months, gut microbiome data were able to predict delivery mode with an area under the curve of 0.72 to 0.83. In contrast, ML models trained on taxa were not able to differentiate between the modes of delivery, in any of the cohorts, when the infants were between 3 and 12 months of age. Moreover, no ML model, alternately trained on the functional pathways of the infant gut microbiome, could consistently predict mode of delivery at any infant age. This study shows that infant gut microbiome data sets can be effectively combined with the application of ML analysis across different study populations. IMPORTANCE There are challenges in merging microbiome data from diverse research groups due to the intricate and multifaceted nature of such data. To address this, we utilized a combination of machine-learning (ML) models to analyze 16S sequencing data from a substantial set of gut microbiome samples, sourced from 12 distinct infant cohorts that were gathered prospectively. Our initial focus was on the mode of delivery due to its prior association with changes in infant gut microbiomes. Through ML analysis, we demonstrated the effective merging and comparison of various gut microbiome data sets, facilitating the identification of robust microbiome biomarkers applicable across varied study populations.

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“…In contrast, CS-born neonates miss this crucial exposure as they bypass the birth canal and are instead exposed to environmental microbes prevalent in the hospital setting and on the maternal skin. This microbial incongruence can potentially shape the neonate's GM composition and diversity in distinct ways [3,[63][64][65][66].…”

Section: Mechanisms Involvedmentioning

confidence: 99%

The Impact of Cesarean Section Delivery on Intestinal Microbiota: Mechanisms, Consequences, and Perspectives—A Systematic Review

Inchingolo,

Palumbo

et al. 2024

IJMS

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The relationship between cesarean section (CS) delivery and intestinal microbiota is increasingly studied. CS-born infants display distinct gut microbial compositions due to the absence of maternal birth canal microorganisms. These alterations potentially link to long-term health implications like immune-related disorders and allergies. This correlation underscores the intricate connection between birth mode and the establishment of diverse intestinal microbiota. A systematic literature review was conducted on the PubMed, Scopus, and Web of Science databases by analyzing the articles and examining the intricate interactions between CS delivery and the infant’s intestinal microbiota. The analysis, based on a wide-ranging selection of studies, elucidates the multifaceted dynamics involved in CS-associated shifts in the establishment of fetal microbiota. We also explore the potential ramifications of these microbial changes on neonatal health and development, providing a comprehensive overview for clinicians and researchers. By synthesizing current findings, this review contributes to a deeper understanding of the interplay between delivery mode and early microbial colonization, paving the way for informed clinical decisions and future investigations in the field of perinatal medicine.

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Crucial nuances in understanding (mis)associations between the neonatal microbiome and Cesarean delivery

Cited by 14 publications

References 102 publications

Effects of maternal type 1 diabetes and confounding factors on neonatal microbiomes

Effects of maternal type 1 diabetes and confounding factors on neonatal microbiomes

Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts

The Impact of Cesarean Section Delivery on Intestinal Microbiota: Mechanisms, Consequences, and Perspectives—A Systematic Review

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