The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Meta-analysis of effects of exclusive breastfeeding on infant gut microbiota across populations
Open accessAbstract
Previous studies on the differences in gut microbiota between exclusively breastfed (EBF) and non-EBF infants have provided highly variable results. Here we perform a meta-analysis of seven microbiome studies (1825 stool samples from 684 infants) to compare the gut microbiota of non-EBF and EBF infants across populations. In the first 6 months of life, gut bacterial diversity, microbiota age, relative abundances of Bacteroidetes and Firmicutes, and predicted microbial pathways related to carbohydrate metabolism are consistently higher in non-EBF than in EBF infants, whereas relative abundances of pathways related to lipid metabolism, vitamin metabolism, and detoxification are lower. Variation in predicted microbial pathways associated with non-EBF infants is larger among infants born by Caesarian section than among those vaginally delivered. Longer duration of exclusive breastfeeding is associated with reduced diarrhea-related gut microbiota dysbiosis. Furthermore, differences in gut microbiota between EBF and non-EBF infants persist after 6 months of age. Our findings elucidate some mechanisms of short and long-term benefits of exclusive breastfeeding across different populations.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887
Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
DeSantis, T. Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E. L., Keller, K., Huber, T., Dalevi, D., Hu, P., & Andersen, G. L. (2006) · Applied and Environmental Microbiology · doi:10.1128/aem.03006-05 · Cited by 10,891
Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
Langille, M. G. I., Zaneveld, J., Caporaso, J. G., McDonald, D., Knights, D., Reyes, J. A., Clemente, J. C., Burkepile, D. E., Vega Thurber, R. L., Knight, R., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2676 · Cited by 9,116
Antibiotics, birth mode, and diet shape microbiome maturation during early life
Bokulich, N. A., Chung, J., Battaglia, T., Henderson, N., Jay, M., Li, H., D. Lieber, A., Wu, F., Perez-Perez, G. I., Chen, Y., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aad7121 · Cited by 1,469
Persistent gut microbiota immaturity in malnourished Bangladeshi children
Subramanian, S., Huq, S., Yatsunenko, T., Haque, R., Mahfuz, M., Alam, M. A., Benezra, A., DeStefano, J., Meier, M. F., Muegge, B. D., et al. (2014) · Nature · doi:10.1038/nature13421 · Cited by 1,270
Chua, M. C., Ben‐Amor, K., Lay, C., Goh, A. E. N., Chiang, W. C., Rao, R., Chew, C., Chaithongwongwatthana, S., Khemapech, N., Knol, J., et al. (2017) · Journal of Pediatric Gastroenterology and Nutrition · doi:10.1097/mpg.0000000000001623 · Cited by 117
High-Resolution Analyses of Overlap in the Microbiota Between Mothers and Their Children
Schanche, M., Avershina, E., Dotterud, C., Øien, T., Storrø, O., Johnsen, R., & Rudi, K. (2015) · Current Microbiology · doi:10.1007/s00284-015-0843-5 · Cited by 35
Cited by
Effect of Diet on the Gut Microbiota: Rethinking Intervention Duration
Leeming, E. R., Johnson, A. J., Spector, T. D., & Le Roy, C. I. (2019) · Nutrients · doi:10.3390/nu11122862 · Cited by 817
You Are What You Eat—The Relationship between Diet, Microbiota, and Metabolic Disorders—A Review
Moszak, M., Szulińska, M., & Bogdański, P. (2020) · Nutrients · doi:10.3390/nu12041096 · Cited by 397
Galazzo, G., van Best, N., Bervoets, L., Dapaah, I. O., Savelkoul, P. H., Hornef, M. W., Hutton, E. K., Morrison, K., Holloway, A. C., McDonald, H., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.01.024 · Cited by 308
Kim, H., Sitarik, A. R., Woodcroft, K., Johnson, C. C., & Zoratti, E. (2019) · Current Allergy and Asthma Reports · doi:10.1007/s11882-019-0851-9 · Cited by 181
Cowan, C. S. M., Dinan, T. G., & Cryan, J. F. (2020) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13156 · Cited by 176
Beyond the gut: decoding the gut–immune–brain axis in health and disease
Park, J. C., Chang, L., Kwon, H.-K., & Im, S.-H. (2025) · Cellular & Molecular Immunology · doi:10.1038/s41423-025-01333-3 · Cited by 149
The human microbiome in clinical translation: from bench to bedside
Bautista, J., Echeverría, C. E., Maldonado-Noboa, I., Ojeda-Mosquera, S., Hidalgo-Tinoco, C., & López-Cortés, A. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1632435 · Cited by 55
The road not taken: host genetics in shaping intergenerational microbiomes
Zeng, S., Wang, S., Ross, R. P., & Stanton, C. (2022) · Trends in Genetics · doi:10.1016/j.tig.2022.05.016 · Cited by 9
Metadata
- Authors
- Ho, N. T., Li, F., Lee-Sarwar, K. A., Tun, H. M., Brown, B. P., Pannaraj, P. S., Bender, J. M., Azad, M. B., Thompson, A. L., Weiss, S. T., Azcarate-Peril, M. A., Litonjua, A. A., Kozyrskyj, A. L., Jaspan, H. B., Aldrovandi, G. M., & Kuhn, L.
- Venue
- Nature Communications
- Published
- 2018-10-09
- Volume
- 9
- Issue
- 1
- Pages
- 4169-4169
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2895153150
- Cited by
- 466
- References
- 7
- FWCI
- 3.37
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
