Determinants of linear growth faltering among children with moderate-to-severe diarrhea in the Global Enteric Multicenter Study

Brander, Rebecca L; Pavlinac, Patricia B; Walson, Judd L.; John‐Stewart, Grace; Weaver, Marcia R.; Faruque, A. S. G.; Zaidi, Anita K. M.; Sur, Dipika; Sow, Samba O.; Hossain, M. Jahangir; Alonso, Pedro L.; Breiman, Robert F.; Nasrin, Dilruba; Nataro, James P.; Levine, Myron M.; Kotloff, Karen L.

doi:10.1186/s12916-019-1441-3

Cited by 34 publications

(37 citation statements)

References 53 publications

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“…Diarrhea is a leading cause of mortality accounting for more than 1.6 million deaths worldwide [1]. It causes nearly 5,25,000 deaths among children under 5 years of age and leads to malnutrition, stunted growth and anemia [2][3][4][5][6][7]. It is particularly prevalent in the low and middle-income countries owing to poor hygiene and sanitation.…”

Section: Introductionmentioning

confidence: 99%

Metagenomic analysis of gut microbiome and resistome of diarrheal fecal samples from Kolkata, India, reveals the core and variable microbiota including signatures of microbial dark matter

Mukhopadhyay

2020

Gut Pathog

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Background Metagenomic analysis of the gut microbiome and resistome is instrumental for understanding the dynamics of diarrheal pathogenesis and antimicrobial resistance transmission (AMR). Metagenomic sequencing of 20 diarrheal fecal samples from Kolkata was conducted to understand the core and variable gut microbiota. Five of these samples were used for resistome analysis. The pilot study was conducted to determine a microbiota signature and the source of antimicrobial resistance genes (ARGs) in the diarrheal gut. Results 16S rRNA amplicon sequencing was performed using Illumina MiSeq platform and analysed using the MGnify pipeline. The Genome Taxonomy Database (GTDB-Tk) was used for bacterial taxonomic identification. Diarrheal etiology was determined by culture method. Phylum Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria were consistently present in 20 samples. Firmicutes was the most abundant phylum in 11 samples. The Bacteroidetes/Firmicutes ratio was less than 1 in 18 samples. 584 genera were observed. 18 of these were present in all the 20 samples. Proteobacteria was the dominant phylum in 6 samples associated with Vibrio cholerae infection. Conservation of operational taxonomic units (OTUs) among all the samples indicated the existence of a core microbiome. Asymptomatic carriage of pathogens like Vibrio cholerae and Helicobacter pylori was found. Signature of Candidate phyla or “microbial dark matter” occurred. Significant correlation of relative abundance of bacterial families of commensals and pathogens were found. Whole-genome sequencing (WGS) on Illumina MiSeq system and assembly of raw reads using metaSPAdes v3.9.1 was performed to study the resistome of 5 samples. ABRicate was used to assign ARG function. 491 resistance determinants were identified. In 80% of the samples tetracycline resistance was the most abundant resistance determinant. High abundance of ARGs against β-lactams, aminoglycosides, quinolones and macrolides was found. Eschericia sp. was the major contributor of ARGs. Conclusions This is the first comparative study of the gut microbiome associated with different diarrheal pathogens. It presents the first catalogue of different bacterial taxa representing the core and variable microbiome in acute diarrheal patients. The study helped to define a trend in the gut microbiota signature associated with diarrhea and revealed which ARGs are abundantly present and the metagenome-assembled genomes (MAGs) contributing to AMR.

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Section: Introductionmentioning

confidence: 99%

Metagenomic analysis of gut microbiome and resistome of diarrheal fecal samples from Kolkata, India, reveals the core and variable microbiota including signatures of microbial dark matter

Mukhopadhyay

2020

Gut Pathog

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“…The limited number of studies that aim to identify children most likely to growth falter after acute diarrhea have resulted in CPRs with varying discriminative and generalizability. Our full CPRs were better at identifying growth faltering than Brander et al (28) (AUC=0.67, 95% CI: 0.64, 0.69), which was not externally validated, and worse than Hanieh(35) et al (AUC: 0.85, 95% CI 0.80, 0.90), which only used data from a single country. The top predictors of growth faltering identified by random forests in our analysis were consistent with existing knowledge of the drivers of growth faltering – child demographics, child symptoms, and indicators of household wealth.…”

Section: Discussionmentioning

confidence: 57%

“…Children were excluded if follow-up observations occurred <49 or >91 days after enrollment, or if HAZ measurements were implausible (28), defined as: a) HAZ>6 or HAZ<-6; b) change in HAZ>3; c) >1.5cm loss of height from enrollment to follow-up; d) growth of >8cm or >4cm at 49-60 day follow-up for children ≤6 months and >6 months old, respectively; e) growth >10cm or >6cm at 61-91 day follow-up for children ≤6 months and >6 months old, respectively.…”

Section: Methodsmentioning

confidence: 99%

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Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering (stunting) presenting for diarrheal care

Ahmed

Brintz

Pavlinac

et al. 2022

Preprint

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Background: Nearly 150 million children under-5 years of age were stunted in 2020. We aimed to develop a clinical prediction rule (CPR) to identify children likely to experience additional stunting following acute diarrhea, to enable targeted approaches to prevent this irreversible outcome. Methodology: We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) study to build predictive models of linear growth faltering (decrease of ≥0.5 or ≥1.0 in height-for-age z-score [HAZ] at 60 day follow-up) in children ≤59 months presenting with moderate-to-severe diarrhea (MSD), and community controls, in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using 5-fold cross-validation. We used the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study to A) re-derive, and B) externally validate our GEMS-derived CPR. Results: Of 7642 children in GEMS, 1745 (22.8%) experienced severe growth faltering (≥0.5 decrease in HAZ). In MAL-ED, we analyzed 5683 diarrhea episodes from 1322 children, of which 961(16.9%) episodes experienced severe growth faltering. Top predictors of growth faltering in GEMS were: age, HAZ at enrollment, respiratory rate, temperature, and number of people living in the household. The maximum AUC was 0.74 (95% CI: 0.74, 0.75) with 20 predictors, while 2 predictors yielded an AUC of 0.70 (95% CI: 0.70, 0.71). Results were similar in the MAL-ED re-derivation. A 2-variable CPR derived from children 0-23 months in GEMS had an AUC=0.63 (95% CI 0.62, 0.65), and AUC=0.64 (95% CI: 0.60, 0.68) when externally validated in MAL-ED. Conclusions: Our findings indicate that use of prediction rules could help identify children at risk of poor outcomes after an episode of diarrheal illness.

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“…The prevention of diarrhea is critical for optimal growth and development among children. Specifically, it has been shown that the control of diarrhea during infancy and toddlerhood is critical to prevent stunted and faltered linear growth later in life (Moore et al 2001;Checkley et al 2003;Brander et al 2019). Infants and toddlers living in poor or remote communities are most at risk of diarrhea due to a lack of healthcare services and effective interventions.…”

Section: Introductionmentioning

confidence: 99%

Examination of the Cameroon DHS data to investigate how water access and sanitation services are related to diarrhea and nutrition among infants and toddlers in rural households

Dharod

Nounkeu

Paynter

et al. 2021

Journal of Water and Health

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Undernutrition among children is a significant issue in rural areas in Cameroon, with diarrhea being one of the major contributing risk factors. To gain a better understanding of the risk factors of diarrhea, the main objective of this epidemiological study was to examine associations between water access and sanitation service with diarrhea, diet diversity, and anemia among infants and toddlers in rural households in Cameroon. The study involved household- and individual-level data of 2,129 rural-dwelling infants and toddlers from the Demographic and Health Survey database. About one-third of infants and toddlers were experiencing diarrhea, with higher odds among those who lived in households with limited water service (p = 0.028). The odds of having diarrhea were 50% higher among infants and toddlers when households did not have sanitation facilities (p = 0.007). The lack of improved water and sanitation was also associated with a low intake of various food groups among older infants and toddlers. The prevalence of anemia was high, but no significant differences were seen by water and sanitation services. The achievement of SDG #6 will help address undernutrition and achieve other SDGs, including improving health and wellbeing.

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Determinants of linear growth faltering among children with moderate-to-severe diarrhea in the Global Enteric Multicenter Study

Cited by 34 publications

References 53 publications

Metagenomic analysis of gut microbiome and resistome of diarrheal fecal samples from Kolkata, India, reveals the core and variable microbiota including signatures of microbial dark matter

Metagenomic analysis of gut microbiome and resistome of diarrheal fecal samples from Kolkata, India, reveals the core and variable microbiota including signatures of microbial dark matter

Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering (stunting) presenting for diarrheal care

Examination of the Cameroon DHS data to investigate how water access and sanitation services are related to diarrhea and nutrition among infants and toddlers in rural households

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