Abstract
In 2024, stunted child growth affected 150 million children under the age of five years, underscoring its critical impact on global health. Stunting has also been associated with neurodevelopmental delays. This study explores the relationship between stunting, the fecal microbiota, and neurodevelopment in 2–5-year-old children from the Afribiota cross-sectional study in Madagascar. Children were assessed using the Ages and Stages Questionnaire (ASQ-3), covering five developmental domains (communication, personal-social, problem-solving, fine and gross motor). Fecal samples were analyzed via 16S rRNA gene amplicon sequencing. Classical bi- and multivariate analysis was combined with Structural Equation Modelling to evaluate direct and indirect associations between different clinical factors, the microbiota and neurodevelopment. Our study shows that stunting and low socioeconomic status are consistently linked to poorer neurodevelopmental outcomes, while low branched-chain amino acids and hemoglobin levels are associated with stunting. Furthermore, a higher microbial diversity within individuals (α-diversity—specifically the Shannon index-) was directly linked to improved neurodevelopment scores in one of the tested models, while gut microbiota variation between individuals (β-diversity) was not associated with neurodevelopment. These findings support the hypothesis of neurodevelopment being primarily influenced by nutritional and social factors, with a more limited role for microbiota diversity.
Similar content being viewed by others
Data availability
All metataxonomic data are deposited on ENA ([https://www.ebi.ac.uk/ena/browser/view/PRJEB48119](https:/www.ebi.ac.uk/ena/browser/view/PRJEB48119)). Access to the remaining data is regulated through a Scientific Advisory Board, under the supervision of Institut Pasteur. Data requests should be directed to the corresponding author (PV), who will redirect them to the Access Committee.
References
United Nations Children’s Fund (UNICEF), The World Health Organization (WHO) & International Bank for Reconstruction and Development/The World Bank. Levels and trends in child malnutrition: UNICEF/WHO/World Bank Group Joint Child Malnutrition Estimates: Key findings of the 2025 edition., (UNICEF and WHO, New York, 2025).
United Nations Children’s Fund (UNICEF), The World Health Organization (WHO) & International Bank for Reconstruction and Development/The World Bank. Levels and trends in child malnutrition: UNICEF/WHO/World Bank Group Joint Child Malnutrition Estimates: Key findings of the 2023 edition., (UNICEF and WHO, New York, 2023).
Prado, E. L. & Dewey, K. G. Nutrition and brain development in early life. Nutr. Rev. 72, 267–284. https://doi.org/10.1111/nure.12102 (2014).
Leroy, J. L. & Frongillo, E. A. Perspective: What does stunting really mean? A critical review of the evidence. Adv. Nutr. 10, 196–204. https://doi.org/10.1093/advances/nmy101 (2019).
Perkins, J. M. et al. Understanding the association between stunting and child development in low- and middle-income countries: Next steps for research and intervention. Soc. Sci. Med. 193, 101–109. https://doi.org/10.1016/j.socscimed.2017.09.039 (2017).
Nahar, B. et al. Early childhood development and stunting: Findings from the MAL-ED birth cohort study in Bangladesh. Matern Child Nutr. 16, e12864. https://doi.org/10.1111/mcn.12864 (2020).
Miller, A. C., Murray, M. B., Thomson, D. R. & Arbour, M. C. How consistent are associations between stunting and child development? Evidence from a meta-analysis of associations between stunting and multidimensional child development in fifteen low- and middle-income countries. Public Health Nutr. 19, 1339–1347. https://doi.org/10.1017/s136898001500227x (2016).
Sudfeld, C. R. et al. Linear growth and child development in low- and middle-income countries: A meta-analysis. Pediatrics 135, e1266-1275. https://doi.org/10.1542/peds.2014-3111 (2015).
He, P. et al. Prenatal malnutrition and adult cognitive impairment: A natural experiment from the 1959–1961 Chinese famine. Br. J. Nutr. 120, 198–203. https://doi.org/10.1017/s0007114518000958 (2018).
Fink, G. & Rockers, P. C. Childhood growth, schooling, and cognitive development: Further evidence from the Young Lives study. Am. J. Clin. Nutr. 100, 182–188. https://doi.org/10.3945/ajcn.113.080960 (2014).
Adair, L. S. et al. Associations of linear growth and relative weight gain during early life with adult health and human capital in countries of low and middle income: Findings from five birth cohort studies. Lancet (Lond. Engl.) 382, 525–534. https://doi.org/10.1016/s0140-6736(13)60103-8 (2013).
Daniels, M. C. & Adair, L. S. Growth in young Filipino children predicts schooling trajectories through high school. J. Nutr. 134, 1439–1446. https://doi.org/10.1093/jn/134.6.1439 (2004).
Walker, S. P., Chang, S. M., Powell, C. A. & Grantham-McGregor, S. M. Effects of early childhood psychosocial stimulation and nutritional supplementation on cognition and education in growth-stunted Jamaican children: Prospective cohort study. Lancet (Lond. Engl.) 366, 1804–1807. https://doi.org/10.1016/s0140-6736(05)67574-5 (2005).
Fink, G. et al. Schooling and wage income losses due to early-childhood growth faltering in developing countries: National, regional, and global estimates. Am. J. Clin. Nutr. 104, 104–112. https://doi.org/10.3945/ajcn.115.123968 (2016).
Lu, C., Black, M. M. & Richter, L. M. Risk of poor development in young children in low-income and middle-income countries: An estimation and analysis at the global, regional, and country level. Lancet Glob. Health 4, e916–e922. https://doi.org/10.1016/s2214-109x(16)30266-2 (2016).
Bliznashka, L., Perumal, N., Yousafzai, A. & Sudfeld, C. Diet and development among children aged 36–59 months in low-income countries. Arch. Dis. Child 107, 719–725. https://doi.org/10.1136/archdischild-2021-323218 (2022).
McCoy, D. C. et al. Early childhood developmental status in low- and middle-income countries: National, regional, and global prevalence estimates using predictive modeling. PLoS Med. 13, e1002034. https://doi.org/10.1371/journal.pmed.1002034 (2016).
Lozoff, B. et al. Long-lasting neural and behavioral effects of iron deficiency in infancy. Nutr. Rev. 64, S34-43. https://doi.org/10.1301/nr.2006.may.s34-s43 (2006) (discussion S72–91).
Danaei, G. et al. Risk factors for childhood stunting in 137 developing countries: A comparative risk assessment analysis at global, regional, and country levels. PLoS Med. 13, e1002164. https://doi.org/10.1371/journal.pmed.1002164 (2016).
Mertens, A. et al. Causes and consequences of child growth faltering in low-resource settings. Nature 621, 568–576. https://doi.org/10.1038/s41586-023-06501-x (2023).
Moreau, G. B. et al. Childhood growth and neurocognition are associated with distinct sets of metabolites. EBioMedicine 44, 597–606. https://doi.org/10.1016/j.ebiom.2019.05.043 (2019).
Mapping child growth failure across low- and middle-income countries. Nature 577, 231–234. https://doi.org/10.1038/s41586-019-1878-8 (2020).
McCuskee, S. et al. Child malnutrition in Ifanadiana district, Madagascar: Associated factors and timing of growth faltering ahead of a health system strengthening intervention. Glob. Health Action 11, 1452357. https://doi.org/10.1080/16549716.2018.1452357 (2018).
Remonja, C. R. et al. The importance of public health, poverty reduction programs and women’s empowerment in the reduction of child stunting in rural areas of Moramanga and Morondava, Madagascar. PLoS ONE 12, e0186493. https://doi.org/10.1371/journal.pone.0186493 (2017).
Rakotomanana, H., Gates, G. E., Hildebrand, D. & Stoecker, B. J. Determinants of stunting in children under 5 years in Madagascar. Matern Child Nutr. https://doi.org/10.1111/mcn.12409 (2017).
Rakotomanana, H., Gates, G. E., Hildebrand, D. & Stoecker, B. J. Situation and determinants of the infant and young child feeding (IYCF) indicators in Madagascar: Analysis of the 2009 Demographic and Health Survey. BMC Public Health 17, 812. https://doi.org/10.1186/s12889-017-4835-1 (2017).
Vonaesch, P. et al. Factors associated with stunted growth in children under five years in Antananarivo, Madagascar and Bangui, Central African Republic. Matern Child Health J. 25, 1626–1637. https://doi.org/10.1007/s10995-021-03201-8 (2021).
Aiga, H. et al. Risk factors for malnutrition among school-aged children: A cross-sectional study in rural Madagascar. BMC Public Health 19, 773. https://doi.org/10.1186/s12889-019-7013-9 (2019).
Randrianarisoa, M. M. et al. Factors associated with anaemia among preschool- age children in underprivileged neighbourhoods in Antananarivo, Madagascar. BMC Public Health 22, 1320. https://doi.org/10.1186/s12889-022-13716-6 (2022).
Kolling, G., Wu, M. & Guerrant, R. L. Enteric pathogens through life stages. Front. Cell. Infect. Microbiol. 2, 114. https://doi.org/10.3389/fcimb.2012.00114 (2012).
Niehaus, M. D. et al. Early childhood diarrhea is associated with diminished cognitive function 4 to 7 years later in children in a northeast Brazilian shantytown. Am. J. Trop. Med. Hyg. 66, 590–593. https://doi.org/10.4269/ajtmh.2002.66.590 (2002).
Lorntz, B. et al. Early childhood diarrhea predicts impaired school performance. Pediatr. Infect Dis. J. 25, 513–520. https://doi.org/10.1097/01.inf.0000219524.64448.90 (2006).
Vonaesch, P. et al. Identifying the etiology and pathophysiology underlying stunting and environmental enteropathy: Study protocol of the AFRIBIOTA project. BMC Pediatr. 18, 236. https://doi.org/10.1186/s12887-018-1189-5 (2018).
Habib, A. et al. High prevalence of intestinal parasite infestations among stunted and control children aged 2 to 5 years old in two neighborhoods of Antananarivo, Madagascar. PLoS Negl. Trop. Dis. 15, e0009333. https://doi.org/10.1371/journal.pntd.0009333 (2021).
Collard, J. M. et al. High prevalence of small intestine bacteria overgrowth and asymptomatic carriage of enteric pathogens in stunted children in Antananarivo, Madagascar. PLoS Negl. Trop. Dis. 16, e0009849. https://doi.org/10.1371/journal.pntd.0009849 (2022).
Vonaesch, P. et al. Stunted childhood growth is associated with decompartmentalization of the gastrointestinal tract and overgrowth of oropharyngeal taxa. Proc. Natl. Acad. Sci. U.S.A. https://doi.org/10.1073/pnas.1806573115 (2018).
Vonaesch, P. et al. Stunted children display ectopic small intestinal colonization by oral bacteria, which cause lipid malabsorption in experimental models. Proc. Natl. Acad. Sci. U.S.A. 119, e2209589119. https://doi.org/10.1073/pnas.2209589119 (2022).
Diaz Heijtz, R. et al. Normal gut microbiota modulates brain development and behavior. Proc. Natl. Acad. Sci. U. S. A. 108, 3047–3052. https://doi.org/10.1073/pnas.1010529108 (2011).
Pruss, K. M. et al. Effects of intergenerational transmission of small intestinal bacteria cultured from stunted Bangladeshi children with enteropathy. bioRxiv, https://doi.org/10.1101/2024.11.01.621574 (2024).
Leroy, J. L., Ruel, M., Habicht, J. P. & Frongillo, E. A. Linear growth deficit continues to accumulate beyond the first 1000 days in low- and middle-income countries: Global evidence from 51 national surveys. J. Nutr. 144, 1460–1466. https://doi.org/10.3945/jn.114.191981 (2014).
Scharf, R. J. et al. Early childhood growth and cognitive outcomes: Findings from the MAL-ED study. Matern Child Nutr. 14, e12584. https://doi.org/10.1111/mcn.12584 (2018).
Chen, R. Y. et al. Duodenal microbiota in stunted undernourished children with enteropathy. N. Engl. J. Med. 383, 321–333. https://doi.org/10.1056/NEJMoa1916004 (2020).
Portlock, T. et al. Interconnected pathways link faecal microbiota plasma lipids and brain activity to childhood malnutrition related cognition. Nat. Commun. 16, 473. https://doi.org/10.1038/s41467-024-55798-3 (2025).
Vonaesch, P. et al. Putative biomarkers of environmental enteric disease fail to correlate in a cross-sectional study in two study sites in Sub-Saharan Africa. Nutrients https://doi.org/10.3390/nu14163312 (2022).
Alam, M. A. et al. Impact of early-onset persistent stunting on cognitive development at 5 years of age: Results from a multi-country cohort study. PLoS ONE 15, e0227839. https://doi.org/10.1371/journal.pone.0227839 (2020).
Letourneau, N. L., Duffett-Leger, L., Levac, L., Watson, B. & Young-Morris, C. Socioeconomic status and child development: A meta-analysis. J. Emot. Behav. Disord. 21, 211–224. https://doi.org/10.1177/1063426611421007 (2013).
Lawson, G. M., Hook, C. J. & Farah, M. J. A meta-analysis of the relationship between socioeconomic status and executive function performance among children. Dev. Sci https://doi.org/10.1111/desc.12529 (2018).
MAL-ED Network Investigators. Early childhood cognitive development is affected by interactions among illness, diet, enteropathogens and the home environment: findings from the MAL-ED birth cohort study. BMJ Glob. Health 3, e000752. https://doi.org/10.1136/bmjgh-2018-000752 (2018).
Ranjitkar, S. et al. Determinants of cognitive development in the early life of children in Bhaktapur. Nepal. Front Psychol. 10, 2739. https://doi.org/10.3389/fpsyg.2019.02739 (2019).
Vohr, B. R., Wright, L. L., Poole, W. K. & McDonald, S. A. Neurodevelopmental outcomes of extremely low birth weight infants <32 weeks’ gestation between 1993 and 1998. Pediatrics 116, 635–643. https://doi.org/10.1542/peds.2004-2247 (2005).
Wood, N. S. et al. The EPICure study: Associations and antecedents of neurological and developmental disability at 30 months of age following extremely preterm birth. Arch. Dis. Child Fetal Neonatal. Ed 90, F134-140. https://doi.org/10.1136/adc.2004.052407 (2005).
Helderman, J. B. et al. Antenatal antecedents of cognitive impairment at 24 months in extremely low gestational age newborns. Pediatrics 129, 494–502. https://doi.org/10.1542/peds.2011-1796 (2012).
Adams-Chapman, I., Bann, C. M., Vaucher, Y. E. & Stoll, B. J. Association between feeding difficulties and language delay in preterm infants using Bayley Scales of Infant Development-Third Edition. J. Pediatr. 163, 680–685. https://doi.org/10.1016/j.jpeds.2013.03.006 (2013).
Upadhyay, R. P. et al. Cognitive and motor outcomes in children born low birth weight: A systematic review and meta-analysis of studies from South Asia. BMC Pediatr. 19, 35. https://doi.org/10.1186/s12887-019-1408-8 (2019).
Lewis, C. R. et al. Family SES is associated with the gut microbiome in infants and children. Microorganisms https://doi.org/10.3390/microorganisms9081608 (2021).
Tokuno, H. et al. Method for estimating disease risk from microbiome data using structural equation modeling. Front. Microbiol. 14, 1035002. https://doi.org/10.3389/fmicb.2023.1035002 (2023).
Vu, K. et al. From birth to overweight and atopic disease: Multiple and common pathways of the infant gut microbiome. Gastroenterology 160, 128-144.e110. https://doi.org/10.1053/j.gastro.2020.08.053 (2021).
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 2224. https://doi.org/10.3389/fmicb.2017.02224 (2017).
Chibuye, M. et al. Systematic review of associations between gut microbiome composition and stunting in under-five children. NPJ Biofilms Microbiomes 10, 46. https://doi.org/10.1038/s41522-024-00517-5 (2024).
Costea, P. I. et al. Enterotypes in the landscape of gut microbial community composition. Nat. Microbiol. 3, 8–16. https://doi.org/10.1038/s41564-017-0072-8 (2018).
WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards based on length/height, weight and age. Acta Paediatr. Suppl. 450, 76–85. https://doi.org/10.1111/j.1651-2227.2006.tb02378.x (2006).
Squires, J., Bricker, D. & Potter, L. Revision of a parent-completed development screening tool: Ages and stages questionnaires. J. Pediatr. Psychol. 22, 313–328. https://doi.org/10.1093/jpepsy/22.3.313 (1997).
Doria, M. et al. Cultural adaptation and psychometric validation of the Malachi-adapted version of the Ages and Stages 3 Questionnaires for use in the Afribiota project. Manuscript submitted for publication (2024).
Squires, J. & Bricker, D. Ages & Stages Questionnaires (ASQ- 3): A Parent-Completed Child-Monitoring System. 3rd edn, (Paul Brookes Publishing Company, 2009).
Fernald, L., Prado, E., Kariger, P. & Raikes, A. A toolkit for measuring early childhood development in low and middle income countries (World Bank Group, 2017).
Moursi, M. M. et al. Dietary diversity is a good predictor of the micronutrient density of the diet of 6- to 23-month-old children in Madagascar. J. Nutr. 138, 2448–2453. https://doi.org/10.3945/jn.108.093971 (2008).
Habte, T. & Krawinkel, M. Dietary diversity score: A measure of nutritional adequacy or an indicator of healthy diet?. J. Nutr. Health Sci. 3, 303 (2016).
Thurnham, D. I. et al. Adjusting plasma ferritin concentrations to remove the effects of subclinical inflammation in the assessment of iron deficiency: A meta-analysis. Am. J. Clin. Nutr. 92, 546–555. https://doi.org/10.3945/ajcn.2010.29284 (2010).
Sullivan, K. M., Mei, Z., Grummer-Strawn, L. & Parvanta, I. Haemoglobin adjustments to define anaemia. Trop. Med. Int. Health TM & IH 13, 1267–1271. https://doi.org/10.1111/j.1365-3156.2008.02143.x (2008).
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120. https://doi.org/10.1128/aem.01043-13 (2013).
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).
R Core Team. R: A language and environment for statistical computing, <http://www.R-project.org/> (2016).
Zeileis, A. & Hothorn, T. Diagnostic Checking in Regression Relationships. R News 2, 7–10 (2002).
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.4–2. (2017).
Lin, H. & Peddada, S. D. Analysis of compositions of microbiomes with bias correction. Nat. Commun. 11, 3514. https://doi.org/10.1038/s41467-020-17041-7 (2020).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. https://doi.org/10.1186/s13059-014-0550-8 (2014).
Nearing, J. T. et al. Microbiome differential abundance methods produce different results across 38 datasets. Nat. Commun. 13, 342. https://doi.org/10.1038/s41467-022-28034-z (2022).
Yuan, C. & Yang, H. Research on K-value selection method of K-means clustering algorithm. J. 2, 226–235 (2019).
Rosseel, Y. lavaan: An R package for structural equation modeling. J. Stat. Softw. 48, 1–36. https://doi.org/10.18637/jss.v048.i02 (2012).
Acknowledgements
We would like to thank Kurt Long for helpful discussions regarding the SEM model approach. We further would like to thank all field workers, doctors, community health workers and families implicated in Afribiota.
Funding
The Afribiota project was funded by the Total Foundation, Institut Pasteur, the Bill and Melinda Gates Foundation (OPP1204689, INV-004352 and INV-002525), the Fondation Petram and a donation by the Odyssey Re-Insurance company. PV was supported by an Early Postdoctoral Fellowship (P2EZP3_152159), an Advanced Postdoctoral Fellowship (P300PA_177876) as well as a Return Grant (P3P3PA_177877), an Eccellenza Professorial Fellowship (PCEFP3_194545) and a SNSF Starting Grant (TMSGI3_218455) from the Swiss National Science Foundation. This study has been further supported as a part of the NCCR Microbiome, a National Center of Competence and research, funded by the Swiss National Science Foundation (Grant number 180575). JT is a Marie Curie Slodowska Actions Global Fellow.
Author information
Authors and Affiliations
Consortia
Contributions
PS, PV and the Afribiota investigators conceived and implemented the Afribiota cross-sectional study in Madagascar and the Central African Republic. In Madagascar, the study was coordinated by MR and RVR. For this substudy on cognitive development, MVD and PV conceived the study. MVD, VR and TR coordinated the cognitive assessment of children on site. JT and ASF performed statistical analyses. JT wrote the manuscript. JT and PV performed the revisions of the article. The final article was read and approved by all authors.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethical approval
The study protocol of Afribiota was approved by the Institutional Review Board of the Institut Pasteur (2016-06/IRB) and the National Ethical Review Boards of Madagascar (55/MSANP/CE, 19 May 2015). All participants received oral and written information about the study. The legal representatives of the children provided written consent to participate in the study. The present analysis (AfriGutBrain) was approved by the Swiss Cantonal Ethics Commission CER-VD (BASEC-ID 2023-01834).
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Tamarelle, J., Doria, M.V., Rambolamanana, V. et al. Socioeconomic and nutritional determinants outweigh gut microbiota influence on neurodevelopment in young children from Antananarivo, Madagascar. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35174-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-35174-5


