Maternal anthropometric variables and clinical factors shape neonatal microbiome

Abstract

Recent studies indicate the existence of a complex microbiome in the meconium of newborns that plays a key role in regulating many host health-related conditions. However, a high variability between studies has been observed so far. In the present study, the meconium microbiome composition and the predicted microbial metabolic pathways were analysed in a consecutive cohort of 96 full-term newborns. The effect of maternal epidemiological variables on meconium diversity was analysed using regression analysis and PERMANOVA. Meconium microbiome composition mainly included Proteobacteria (30.95%), Bacteroidetes (23.17%) and Firmicutes (17.13%), while for predicted metabolic pathways, the most abundant genes belonged to the class “metabolism”. We observed a significant effect of maternal Rh factor on Shannon and Inverse Simpson indexes (p = 0.045 and p = 0.049 respectively) and a significant effect of delivery mode and maternal antibiotic exposure on Jaccard and Bray–Curtis dissimilarities (p = 0.001 and 0.002 respectively), while gestational age was associated with observed richness and Shannon indexes (p = 0.018 and 0.037 respectively), and Jaccard and Bray–Curtis dissimilarities (p = 0.014 and 0.013 respectively). The association involving maternal Rh phenotype suggests a role for host genetics in shaping meconium microbiome prior to the exposition to the most well-known environmental variables, which will influence microbiome maturation in the newborn.

Investigating prenatal and perinatal factors on meconium microbiota: a systematic review and cohort study

“… 80 Thus it is difficult to determine whether the meconium microbiota is shaped by the delivery mode, the use of intrapartum antibiotics, or the combined effect of both. Studies investigating the effect of antenatal 37 , 39 , 40 , 49 , 57 , 58 , 64 , 69 , 81 and intrapartum 13 , 36 , 45 , 72 , 82 antibiotics on the meconium microbiota have shown variable results. In the present cohort study, intrapartum antibiotics seemed to alter the meconium microbiota development in the infants born vaginally, suggesting that maternal exposure to antibiotics during vaginal birth acts rapidly to modify early gut colonization, probably before the actual birth.…”

“…In addition, we collected extensive data on maternal living environments and lifestyles during pregnancy, such as fish and meat consumption, leisure time spent in forests, the presence of older siblings or furry pets in the home, smoking in the home and maternal weight, and established that these prenatal factors had little effect on the microbial composition of meconium. It is therefore understandable that the prenatal factors considered in our systematic review, including maternal diet, 2 , 37 , 84 , 85 residential area, 37 , 44 age, 37 , 44 , 49 , 50 , 53 , 57 , 86 ethnicity, 3 , 49 weight, 3 , 38 , 44 , 49 , 53 , 56 , 57 , 75 , 86 smoking, 3 , 37 , 44 , 87 pregnancy-related health issues, 36 – 40 , 47 , 49 , 54 , 60 , 71 , 75 , 85 , 86 non-pregnancy-related health issues 3 , 37 , 38 , 44 , 45 , 49 , 70 , 76 , 88 , 89 and education, 37 , 44 , 49 together with environmental pollutants, particularly metals and microplastics 58 , 90 and the presence of furry pets 13 , 45 yielded mixed results. Another commonly studied newborn factor, which we did not analyze in our cohort study, was gestational age, 3 , 4 ...…”

“…It is therefore understandable that the prenatal factors considered in our systematic review, including maternal diet, 2 , 37 , 84 , 85 residential area, 37 , 44 age, 37 , 44 , 49 , 50 , 53 , 57 , 86 ethnicity, 3 , 49 weight, 3 , 38 , 44 , 49 , 53 , 56 , 57 , 75 , 86 smoking, 3 , 37 , 44 , 87 pregnancy-related health issues, 36 – 40 , 47 , 49 , 54 , 60 , 71 , 75 , 85 , 86 non-pregnancy-related health issues 3 , 37 , 38 , 44 , 45 , 49 , 70 , 76 , 88 , 89 and education, 37 , 44 , 49 together with environmental pollutants, particularly metals and microplastics 58 , 90 and the presence of furry pets 13 , 45 yielded mixed results. Another commonly studied newborn factor, which we did not analyze in our cohort study, was gestational age, 3 , 4 , 36 – 39 , 44 , 45 , 48 – 50 ,…”

Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data

“…However, a large-scale morphological study on benthic invertebrates showed that ecological status classifications based on abundance and P–A data showed only minor variations [105]. In a microbial context, multiple HTS studies showed similar correlations of both abundance and P–A data with environmental variables [106–108], while some studies showed that correlations differed between data types [109,110]. These results indicate that the impact of data types might depend on the studied environmental variables, but if further research shows that both data types have similar predictive power for environmental assessments, as our results suggest, then P–A data could be used exclusively in future environmental assessment studies.…”

“…However, a large-scale morphological study on benthic invertebrates showed that ecological status classifications based on abundance and P-A data showed only minor variations [105]. In a microbial context, multiple HTS studies showed similar correlations of both abundance and P-A data with environmental variables [106][107][108], while some studies showed that correlations differed between data types [109,110].…”