Associations between gut microbiota composition and AD biomarkers. (31st December 2021)

• Record Type: Journal Article
• Authors: Verhaar, Barbara J.H.; Hendriksen, Heleen M.A.; Leeuw, Francisca A.; Doorduijn, Astrid S.; Leeuwenstijn, Mardou; Teunissen, Charlotte E.; Berckel, Bart N.M.; Barkhof, Frederik; Scheltens, Philip; Kraaij, Robert; Duijn, Cornelia M.; Nieuwdorp, Max; Muller, Majon; Flier, Wiesje M.
• Is Part Of: Alzheimer's & dementia. Volume 17(2021)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-31
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.057781
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: 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.