Frontiers in Immunology (Jan 2022)

Gut Microbiota Composition Is Related to AD Pathology

  • Barbara J. H. Verhaar,
  • Barbara J. H. Verhaar,
  • Barbara J. H. Verhaar,
  • Heleen M. A. Hendriksen,
  • Francisca A. de Leeuw,
  • Astrid S. Doorduijn,
  • Mardou van Leeuwenstijn,
  • Charlotte E. Teunissen,
  • Frederik Barkhof,
  • Frederik Barkhof,
  • Philip Scheltens,
  • Robert Kraaij,
  • Cornelia M. van Duijn,
  • Cornelia M. van Duijn,
  • Max Nieuwdorp,
  • Majon Muller,
  • Wiesje M. van der Flier,
  • Wiesje M. van der Flier

DOI
https://doi.org/10.3389/fimmu.2021.794519
Journal volume & issue
Vol. 12

Abstract

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IntroductionSeveral studies have reported alterations in gut microbiota composition of Alzheimer’s 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).Materials and MethodsWe included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 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.ResultsThe 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 [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.ConclusionsGut 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.

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