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Host-microbiome determinants of ready-to-use supplemental food efficacy in acute childhood malnutrition
Zehra Jamil, … , S. Asad Ali, Sean R. Moore
Zehra Jamil, … , S. Asad Ali, Sean R. Moore
Published July 22, 2025
Citation Information: JCI Insight. 2025;10(14):e188993. https://doi.org/10.1172/jci.insight.188993.
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Clinical Research and Public Health Gastroenterology Microbiology

Host-microbiome determinants of ready-to-use supplemental food efficacy in acute childhood malnutrition

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Abstract

Background Ready-to-use supplemental foods (RUSF) are energy-dense meals used to treat moderate and severe acute childhood malnutrition. Weight recovery with RUSF is heterogeneous, therefore we investigated whether environmental enteric dysfunction (EED), systemic inflammation, and gut microbiota predict RUSF response.Methods We followed nutritional status and RUSF outcomes in a rural birth cohort of 416 Pakistani infants. Acha Mum, a chickpea-based RUSF, was administered daily for 8 weeks to children who developed wasting (weight-for-length Z-score <–2).Results Of 187 treated with RUSF, 112 showed no immediate improvement in weight-for-age. Machine learning identified nine biomarkers that collectively predicted RUSF response with 73% accuracy. Gut microbiome composition before and after supplementation predicted response with 93% and 98% accuracy, respectively. Responders showed microbiome restructuring, with increased growth-associated taxa and reduced Gammaproteobacteria relative to nonresponders. A subset of extreme nonresponders—whose microbiome profiles resembled those of responders—displayed markedly abnormal biomarkers of inflammation, suggesting adverse host factors constrain gut microbiota benefits for RUSF efficacy.Conclusion EED, systemic inflammation, and gut microbiota predict acute nutritional responses to Acha Mum, setting the stage for precision use of RUSF and adjunctive therapies in addressing the global burden of childhood malnutrition in low- and middle-income countries.

Authors

Zehra Jamil, Gabriel F. Hanson, Junaid Iqbal, G. Brett Moreau, Najeeha Talat Iqbal, Sheraz Ahmed, Aneeta Hotwani, Furqan Kabir, Fayaz Umrani, Kamran Sadiq, Kumail Ahmed, Indika Mallawaarachchi, Jennie Z. Ma, Fatima Aziz, S. Asad Ali, Sean R. Moore

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Figure 6

Responders and nonresponders harbor distinct fecal microbiota prior to RUSF.

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Responders and nonresponders harbor distinct fecal microbiota prior to R...
(A) α Diversity measurements (number of observed species, Simpson index) of responders and nonresponders at 9 months of age prior to RUSF. (B) Nonmetric multidimensional scaling plot on Bray-Curtis matrices comparing the community composition of the responders and nonresponders prior to the intervention. (C and D) Relative abundance of phylum (C) and Class level (D) taxonomy between controls and controls prior to the nutritional intervention. An OPSL-DA model was constructed to discriminate between responders and nonresponders using the relative abundance of ASVS from their fecal microbiome. The model outperformed all of 1,000 randomly permuted models (P < 0.001). (E) Scatter plot of the X scores on latent variables 1 and 2 (LV1 and LV2), where each point represents one sample. (F) Bar plot shows the variable importance in projection (VIP) scores, artificially oriented in the direction of loadings on LV1 and colored according to their association with responder or nonresponder samples, labeled with genus name and ASV number. VIP scores > 1 indicate a variable with greater than average influence on the projection.

Copyright © 2025 American Society for Clinical Investigation
ISSN 2379-3708

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