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  • 1
    In: Alzheimer's & Dementia, Wiley, Vol. 17, No. S5 ( 2021-12)
    Abstract: Several 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 clinical biomarkers of AD using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and controls. Method We included 169 patients from the NUDAD project, comprising 33 with AD dementia (66±8 years, 46%F, MMSE 21[19‐24]), 21 with MCI (64±8 years, 43%F, MMSE 27[25‐29] ) and 115 controls (62±8 years, 44%F, MMSE 29[28‐30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Clinical parameters of AD included clinical diagnosis, cerebral spinal fluid (CSF) amyloid and phosphorylated tau (pTau) status, positron emission tomography (PET) amyloid status, and MRI visual scores. Associations between gut microbiota composition and dichotomized clinical parameters of AD were assessed with separate machine learning classification models using XGBoost with nested cross‐validation. The model with the highest area under the curve (AUC) was selected for logistic regression, to assess associations between the 20 best predicting microbes (cumulative sum scaled counts) and the outcome measure from this machine learning model while adjusting for age, sex, and BMI. Result The machine learning prediction for amyloid status (CSF) from microbiota composition had the highest AUC. Top predicting microbes included several short chain fatty acid (SCFA)‐producing species. In the logistic regression models, these microbes were significantly associated with lower odds of amyloid positive status, and included Eubacterium ventriosum group spp. (OR 0.49 (0.30‐0.76) per SD increase in counts, p = 0.002), Marvinbryantia spp. (OR 0.55 (0.34‐0.85), p = 0.009), Coprococcus catus (OR 0.58 (0.36‐0.89), p = 0.017), Roseburia hominis (OR 0.59 (0.38‐0.90), p = 0.018), Odoribacter splanchnicus (OR 0.51 (0.30‐0.82), p = 0.008), Lachnospiraceae spp. (OR 0.58 (0.36‐0.89), p = 0.014), and Ruminococcaceae spp. (OR 0.44 (0.25‐0.71), p = 0.002). Conclusion Gut microbiota composition had the strongest association with amyloid status among the clinical biomarkers examined. We extend on recent studies that observed associations between SCFA levels and AD biomarkers by showing that higher abundances of SCFA‐producing microbes were associated with lower odds of amyloid positive status.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2201940-6
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  • 2
    In: Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, Wiley, Vol. 12, No. 1 ( 2020-01)
    Type of Medium: Online Resource
    ISSN: 2352-8729 , 2352-8729
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2832898-X
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  • 3
    In: Frontiers in Immunology, Frontiers Media SA, Vol. 12 ( 2022-1-31)
    Abstract: Several 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 Methods We 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. Results 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 [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. Conclusions 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.
    Type of Medium: Online Resource
    ISSN: 1664-3224
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2606827-8
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