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. 2017 Apr;18:109-117.
doi: 10.1016/j.ebiom.2017.02.024. Epub 2017 Mar 8.

Causal Pathways From Enteropathogens to Environmental Enteropathy: Findings From the MAL-ED Birth Cohort Study

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Free PMC article

Causal Pathways From Enteropathogens to Environmental Enteropathy: Findings From the MAL-ED Birth Cohort Study

Margaret N Kosek et al. EBioMedicine. .
Free PMC article

Abstract

Background: Environmental enteropathy (EE), the adverse impact of frequent and numerous enteric infections on the gut resulting in a state of persistent immune activation and altered permeability, has been proposed as a key determinant of growth failure in children in low- and middle-income populations. A theory-driven systems model to critically evaluate pathways through which enteropathogens, gut permeability, and intestinal and systemic inflammation affect child growth was conducted within the framework of the Etiology, Risk Factors and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) birth cohort study that included children from eight countries.

Methods: Non-diarrheal stool samples (N=22,846) from 1253 children from multiple sites were evaluated for a panel of 40 enteropathogens and fecal concentrations of myeloperoxidase, alpha-1-antitrypsin, and neopterin. Among these same children, urinary lactulose:mannitol (L:M) (N=6363) and plasma alpha-1-acid glycoprotein (AGP) (N=2797) were also measured. The temporal sampling design was used to create a directed acyclic graph of proposed mechanistic pathways between enteropathogen detection in non-diarrheal stools, biomarkers of intestinal permeability and inflammation, systemic inflammation and change in length- and weight- for age in children 0-2years of age.

Findings: Children in these populations had frequent enteric infections and high levels of both intestinal and systemic inflammation. Higher burdens of enteropathogens, especially those categorized as being enteroinvasive or causing mucosal disruption, were associated with elevated biomarker concentrations of gut and systemic inflammation and, via these associations, indirectly associated with both reduced linear and ponderal growth. Evidence for the association with reduced linear growth was stronger for systemic inflammation than for gut inflammation; the opposite was true of reduced ponderal growth. Although Giardia was associated with reduced growth, the association was not mediated by any of the biomarkers evaluated.

Interpretation: The large quantity of empirical evidence contributing to this analysis supports the conceptual model of EE. The effects of EE on growth faltering in young children were small, but multiple mechanistic pathways underlying the attribution of growth failure to asymptomatic enteric infections had statistical support in the analysis. The strongest evidence for EE was the association between enteropathogens and linear growth mediated through systemic inflammation.

Funding: Bill & Melinda Gates Foundation.

Keywords: Child growth; Child health; Enteropathogen; Enteropathy; Stunting; Undernutrition.

Figures

Fig. 1
Fig. 1
Conceptual model of the associations between pathogens, markers of gut function and inflammation, systemic inflammation and growth.
Fig. 2
Fig. 2
Timeline for collection of stool, urine, and blood samples and their respective biomarker assays that relate to changes in growth Z-scores.
Fig. 3
Fig. 3
Effect of pathogens on the three fecal biomarkers (N non-diarrheal stools = 27,931) and the L:M test (N urine samples = 4476). The color represents the coefficient from a linear mixed effects model with pathogens found in the same stool as the fecal biomarkers or during the same month as the L:M test. Cells with crosses are not significant (p ≥ 0.05). Pathogens, within their groups (I˗V), are sorted by prevalence (high to low, left to right). In addition to the presence of individual pathogens, age was included using both linear and quadratic terms, stool consistency was included in the biomarker models, and child nested in site was included as a random intercept.
Fig. 4
Fig. 4
The model results for (a) Age 1 (4 ≤ months ≤ 11) and (b) Age 2 (12 ≤ months ≤ 21) using functional pathogen groupings and the specific pathways indicated by the individual fecal biomarkers as well as LMZ and AGP. Arrows show those relationships that had statistical support based on the 95% credibility interval. Red arrows indicate positive associations and blue arrows show negative associations. The pathogen groups reflect 1) viruses; 2) invasive bacteria; 3) non-invasive bacteria; 4) Cryptosporidium; and 5) Giardia.
Fig. 5
Fig. 5
Sensitivity analysis of the DAG model (Fig. 4) to explore the effect of increasing (triangles) or decreasing (squares) the concentration of different biomarkers on mean ΔLAZ and ΔWAZ at the two age periods (Age 1, black; Age 2, gray). Symbols indicate the mean difference (lines, ± 1 standard deviation) in the mean simulated ΔLAZ and ΔWAZ when biomarkers are changed ± 1 standard deviation compared to a simulation using the mean observed biomarker value (i.e., the dotted horizontal line shows a difference of zero).

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