The gut microbiome and early-life growth in a population with high prevalence of stunting

Abstract

Stunting affects one-in-five children globally and is associated with greater infectious morbidity, mortality and neurodevelopmental deficits. Recent evidence suggests that the early-life gut microbiome affects child growth through immune, metabolic and endocrine pathways. Using whole metagenomic sequencing, we map the assembly of the gut microbiome in 335 children from rural Zimbabwe from 1–18 months of age who were enrolled in the Sanitation, Hygiene, Infant Nutrition Efficacy Trial (SHINE; NCT01824940), a randomized trial of improved water, sanitation and hygiene (WASH) and infant and young child feeding (IYCF). Here, we show that the early-life gut microbiome undergoes programmed assembly that is unresponsive to the randomized interventions intended to improve linear growth. However, maternal HIV infection is associated with over-diversification and over-maturity of the early-life gut microbiome in their uninfected children, in addition to reduced abundance of Bifidobacterium species. Using machine learning models (XGBoost), we show that taxonomic microbiome features are poorly predictive of child growth, however functional metagenomic features, particularly B-vitamin and nucleotide biosynthesis pathways, moderately predict both attained linear and ponderal growth and growth velocity. New approaches targeting the gut microbiome in early childhood may complement efforts to combat child undernutrition.

Bifidobacterium longummodifies a nutritional intervention for stunting in Zimbabwean infants

“…formicigenerans. These species were also important predictors of infant age in a microbiome-age model previously developed from this same cohort 64 . Age-discriminatory taxa in the same genera were also identified in previously reported microbiome-age models 14 .…”

“…In our PCoA model, infant microbiome species maturation predominantly reflected decreased abundance of B. longum and increased abundance of P. copri , F. prausnitzii , D. longicatena , and D. formicigenerans . These species were also important predictors of infant age in a microbiome-age model previously developed from this same cohort 64 . Age-discriminatory taxa in the same genera were also identified in previously reported microbiome-age models 14 .…”

“…Models also included IYCF, infant sex, mother-infant FUT2+/FUT3-discordance, infant age at specimen collection, an indicator of whether infants met the minimum dietary diversity score at specimen collection, and LAZ at specimen collection. We did not include WASH arm because, in prior analyses, the SHINE WASH intervention did not affect stunting or LAZ at 18mo 51 nor infant gut microbiome composition 64 . P-values were adjusted for multiple hypothesis testing to preserve the false discovery rate 113 .…”

A microbiota-directed complementary food intervention in 12-18-month-old Bangladeshi children improves linear growth

“…One recent example of the importance of this kind of approach employed a metagenomic analysis of fecal samples from 335 infants/young children living in rural Zimbabwe where there is a high prevalence of stunting. This study revealed that genomic features of the fecal microbial community, in particular, the representation carbohydrate degradation pathways (rather than taxonomic representation), were predictive of linear growth of children during the period of complementary feeding 52 . This finding is consistent with our own studies of growth responses to MDCF-2 in children with MAM; namely that improvements in ponderal growth are driven by specific strains (MAGs) of P. copri that express the appropriate repertoire of carbohydrate active enzymes (CAZymes) required to utilize MDCF-2 glycans 28 .…”

The use of machine learning in paediatric nutrition

“…Robertson et al . [4 ▪ ] also examined stunting, focusing on a group of children in Zimbabwe who had been enrolled in a randomized trial of improved sanitation and feeding practices. They also used XGBoost and found weak associations between growth and taxonomic classifications of the microbiome, but significant associations between growth and the functional metagenome.…”