Abstract Description

The first 1000 days of life are fundamentally important for childhood development. Undernutrition and stunting are global public health concerns and are considered irreversible after two years of age. Environmental enteric dysfunction (EED) which is a subclinical gut disorder associated with poor environmental living conditions, has been implicated as a contributor to stunting. However, diagnostic methods for EED remain unreliable, invasive and largely inaccessible to children living in informal settlements are vulnerable. Here, we investigate how living conditions impact the gut microbiome, linear growth and the presence of biomarkers associated with EED in children living in informal settlements. DNA was extracted from stool samples, sequenced using the 16S rRNA gene marker and the resultant data was used to analyse the diversity and composition of the gut microbiome. A machine learning approach will be used to develop a predictive model to predict the risk of stunting and EED using socioeconomic and environmental conditions, relative abundances from 16S rRNA gene sequencing and shotgun metagenomic sequencing as inputs. We expect that children from informal settlements will have reduced diversity, distinct gut microbiome signatures and altered functional pathways compared to those from formal settlements. This study will provide new insights into how differences in housing environments influence the gut microbiome. By linking social and environmental conditions to the gut microbiome, the findings will highlight how living conditions contribute to health outcomes.