Following birth and the initiation of feeding, the human gastrointestinal tract is colonized by a large diversity of bacterial life. An emerging body of literature in adults has begun to establish clear associations between gut microbiome composition and a wide range of health outcomes.^1-6 In contrast, comparatively little is known about the gut microbiome in infants and children, the exposures that shape it, and its lifelong health effects.⁷ Although limited in their size and scope, a number of studies have established associations between intestinal microbiome profiles in infants, delivery mode, and/or breast milk exposure.^8-15 These factors both have long-term health consequences. Cesarean delivery has been associated with an increased risk for obesity, asthma, celiac disease, and type 1 diabetes mellitus,^16-19 whereas breastfeeding has been related to decreased risks for illnesses such as asthma, obesity, infection, metabolic syndrome, and diabetes compared with formula feeding (reviewed in the article by Ip et al²⁰). The underlying mechanisms are not well understood, but there is growing evidence linking exposure to microflora that is present during vaginal delivery with the patterns of the microbiome that become established in infants.²¹ In addition, following delivery, the feeding of human milk primes and matures the infant gastrointestinal system and is believed to promote a unique microbial colonization profile that has yet to be clearly defined in healthy populations.²² The acquisition of specific microbes in succession, as the core microbiome of the gut is created, may be permanently affected by exposure to maternal vaginal microflora and/or to breast milk and could represent a key mechanism underlying differences in immune development that influence lifelong disease risk.

The gut microbiome has an important role in infant health and immune development and may be affected by early-life exposures. Maternal diet may influence the infant gut microbiome through vertical transfer of maternal microbes to infants during vaginal delivery and breastfeeding. We aimed to examine the association of maternal diet during pregnancy with the infant gut microbiome 6 weeks post-delivery in mother-infant dyads enrolled in the New Hampshire Birth Cohort Study. Infant stool samples were collected from 145 infants, and maternal prenatal diet was assessed using a food frequency questionnaire. We used targeted sequencing of the 16S rRNA V4-V5 hypervariable region to characterize infant gut microbiota. To account for differences in baseline and trajectories of infant gut microbial profiles, we stratified analyses by delivery mode. We identified three infant gut microbiome clusters, characterized by increased abundance of Bifidobacterium, Streptococcus and Clostridium, and Bacteroides, respectively, overall and in the vaginally delivered infant stratum. In the analyses stratified to infants born vaginally and adjusted for other potential confounders, maternal fruit intake was associated with infant gut microbial community structure (PERMANOVA, p < 0.05). In multinomial logistic regression analyses, increased fruit intake was associated with an increased odds of belonging to the high Streptococcus/Clostridium group among infants born vaginally (OR (95% CI) = 2.73 (1.36, 5.46)). In infants delivered by Cesarean section, we identified three clusters that differed slightly from vaginally delivered infants, which were characterized by a high abundance of Bifidobacterium, high Clostridium and low Streptococcus and Ruminococcus genera, and high abundance of the family Enterobacteriaceae. Maternal dairy intake was associated with an increased odds of infants belonging to the high Clostridium cluster in infants born by Cesarean section (OR (95% CI) = 2.36 (1.05, 5.30)). Linear models suggested additional associations between maternal diet and infant intestinal microbes in both delivery mode strata. Our data indicate that maternal diet influences the infant gut microbiome and that these effects differ by delivery mode.

Background The impact of degree of prematurity at birth on premature infant gut microbiota has not been extensively studied in comparison to term infants in large cohorts. Methods To determine the effect of gestational age at birth and postnatal exposures on gut bacterial colonization in infants, we analyzed 65 stool samples from 17 premature infants in the neonatal intensive care unit, as well as 13 samples from 13 mostly moderate-to-late premature infants and 189 samples from 176 term infants in the New Hampshire Birth Cohort Study. Gut colonization patterns were determined with 16S rDNA microbiome profiling. Results Gut bacterial alpha-diversity differed between premature and term infants at 6 weeks of age, after adjusting for exposures (p = 0.027). Alpha-diversity varied between extremely premature (<28 weeks gestation) and very premature infants (≥28 but <32 weeks, p = 0.011), as well as between extremely and moderate-to-late premature infants (≥32 and <37 weeks, p = 0.004). Newborn antibiotic use among premature infants was associated with lower Bifidobacterium and Bacteroides abundance (p = 0.015 and p = 0.041). Conclusion Gestational age at birth and early antibiotic exposure have significant effects on the premature infant gut microbiota.

The complex epidemiology of vector-borne diseases creates significant challenges in the design and delivery of prevention and control strategies, especially in light of rapid social and environmental changes. Spatial models for predicting disease risk based on environmental factors such as climate and landscape have been developed for a number of important vector-borne diseases. The resulting risk maps have proven value for highlighting areas for targeting public health programs. However, these methods generally only offer technical information on the spatial distribution of disease risk itself, which may be incomplete for making decisions in a complex situation. In prioritizing surveillance and intervention strategies, decision-makers often also need to consider spatially explicit information on other important dimensions, such as the regional specificity of public acceptance, population vulnerability, resource availability, intervention effectiveness, and land use. There is a need for a unified strategy for supporting public health decision making that integrates available data for assessing spatially explicit disease risk, with other criteria, to implement effective prevention and control strategies. Multi-criteria decision analysis (MCDA) is a decision support tool that allows for the consideration of diverse quantitative and qualitative criteria using both data-driven and qualitative indicators for evaluating alternative strategies with transparency and stakeholder participation. Here we propose a MCDA-based approach to the development of geospatial models and spatially explicit decision support tools for the management of vector-borne diseases. We describe the conceptual framework that MCDA offers as well as technical considerations, approaches to implementation and expected outcomes. We conclude that MCDA is a powerful tool that offers tremendous potential for use in public health decision-making in general and vector-borne disease management in particular.

Zoonoses are a growing international threat interacting at the human-animal-environment interface and call for transdisciplinary and multi-sectoral approaches in order to achieve effective disease management. The recent emergence of Lyme disease in Quebec, Canada is a good example of a complex health issue for which the public health sector must find protective interventions. Traditional preventive and control interventions can have important environmental, social and economic impacts and as a result, decision-making requires a systems approach capable of integrating these multiple aspects of interventions. This paper presents the results from a study of a multi-criteria decision analysis (MCDA) approach for the management of Lyme disease in Quebec, Canada. MCDA methods allow a comparison of interventions or alternatives based on multiple criteria.

Background: The infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life.Results: Stool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 158) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks:p = 0.056; 12 months:p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks:p = 0.376; 12 months:p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R²values demonstrated poor predictive performance across all models assessed (avg: − 5.06% -- 6 weeks; − 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344–6 weeks; 0.265–12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations.Conclusions: Our results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.