{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,12]],"date-time":"2026-11-12T02:21:36Z","timestamp":1794450096046,"version":"4.9.1"},"reference-count":63,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T00:00:00Z","timestamp":1643587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Several studies have reported alterations in gut microbiota composition of Alzheimer\u2019s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>We included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 \u00b1 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 \u00b1 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 \u00b1 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of <jats:italic>[Clostridium] leptum<\/jats:italic> and lower abundance of <jats:italic>[Eubacterium] ventriosum<\/jats:italic> group spp., <jats:italic>Lachnospiraceae<\/jats:italic> spp., <jats:italic>Marvinbryantia<\/jats:italic> spp., <jats:italic>Monoglobus<\/jats:italic> spp., <jats:italic>[Ruminococcus] torques group<\/jats:italic> spp., <jats:italic>Roseburia hominis<\/jats:italic>, and <jats:italic>Christensenellaceae R-7<\/jats:italic> spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of <jats:italic>Lachnospiraceae<\/jats:italic> spp., <jats:italic>Lachnoclostridium<\/jats:italic> spp., <jats:italic>Roseburia hominis<\/jats:italic> and <jats:italic>Bilophila wadsworthia<\/jats:italic> and higher odds of positive p-tau status.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Gut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2021.794519","type":"journal-article","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T07:23:02Z","timestamp":1643613782000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":199,"title":["Gut Microbiota Composition Is Related to AD Pathology"],"prefix":"10.3389","volume":"12","author":[{"given":"Barbara J. H.","family":"Verhaar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heleen M. 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