{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T04:08:16Z","timestamp":1791950896159,"version":"4.3.4"},"reference-count":59,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"vor","delay-in-days":104,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Reta Lila Weston Trust"},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/S021612\/1"],"award-info":[{"award-number":["EP\/S021612\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012621","name":"University College London Hospitals Biomedical Research Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012621","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The human microbiome is a complex and dynamic community of microbes, thought to have symbiotic benefit to its host. Influences of the gut microbiome on brain microglia have been identified as a potential mechanism contributing to neurodegenerative diseases, such as Alzheimer\u2019s disease, motor neurone disease and Parkinson\u2019s disease (Boddy SL, Giovannelli I, Sassani M, et al. The gut microbiome: A key player in the complexity of amyotrophic lateral sclerosis (ALS). BMC Med. 2021;19(1):13). We hypothesize that population level differences in the gut microbiome will predict the incidence of Alzheimer\u2019s disease using machine learning methods. Cross-sectional analyses were performed in R, using two large, open-access microbiome datasets (n = 959 and n = 2012). Countries in these datasets were grouped based on Alzheimer\u2019s disease incidence and the gut microbiome profiles compared. In countries with a high incidence of Alzheimer\u2019s disease, there is a significantly lower diversity of the gut microbiome (P &amp;lt; 0.05). A permutational analysis of variance test (P &amp;lt; 0.05) revealed significant differences in the microbiome profile between countries with high versus low incidence of Alzheimer\u2019s disease with several contributing taxa identified: at a species level Escherichia coli, and at a genus level Haemophilus and Akkermansia were found to be reproducibly protective in both datasets. Additionally, using machine learning, we were able to predict the incidence of Alzheimer\u2019s disease within a country based on the microbiome profile (mean area under the curve 0.889 and 0.927). We conclude that differences in the microbiome can predict the varying incidence of Alzheimer\u2019s disease between countries. Our results support a key role of the gut microbiome in neurodegeneration at a population level.<\/jats:p>","DOI":"10.1093\/braincomms\/fcaf059","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T13:58:59Z","timestamp":1742479139000},"source":"Crossref","is-referenced-by-count":6,"title":["Machine learning determines the incidence of Alzheimer\u2019s disease based on population gut microbiome profile"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9478-6192","authenticated-orcid":false,"given":"Amedra","family":"Basgaran","sequence":"first","affiliation":[{"name":"Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London , London WC1N 3BG ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"Lymberopoulos","sequence":"additional","affiliation":[{"name":"Centre for Doctoral Training in AI-enabled Healthcare Systems, Institute of Health Informatics, University College London , London NW1 2DA ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1674-8844","authenticated-orcid":false,"given":"Ella","family":"Burchill","sequence":"additional","affiliation":[{"name":"King's College London, School of Medical Education , London WC2R 2LS ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maryam","family":"Reis-Dehabadi","sequence":"additional","affiliation":[{"name":"Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London , London WC1N 3BG ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikhil","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London , London WC1N 3BG 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