Signature of Alzheimer's Disease in Intestinal Microbiome: Results From the AlzBiom Study

Christoph Laske; Stephan Müller; Oliver Preische; Victoria Ruschil; Matthias H J Munk; Iris Honold; Silke Peter; Ulrich Schoppmeier; Matthias Willmann

doi:10.3389/fnins.2022.792996

Signature of Alzheimer's Disease in Intestinal Microbiome: Results From the AlzBiom Study

Front Neurosci. 2022 Apr 19:16:792996. doi: 10.3389/fnins.2022.792996. eCollection 2022.

Authors

Christoph Laske^{1

2

3}, Stephan Müller², Oliver Preische^{2

3}, Victoria Ruschil^{2

4}, Matthias H J Munk^{2

5}, Iris Honold², Silke Peter⁶, Ulrich Schoppmeier⁶, Matthias Willmann⁷

Affiliations

¹ Section for Dementia Research, Department of Psychiatry and Psychotherapy, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
² Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.
³ German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.
⁴ Department of Neurology, Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
⁵ Department of Biology, Technische Universität Darmstadt, Darmstadt, Germany.
⁶ Institute of Medical Microbiology and Hygiene, University of Tübingen, Tübingen, Germany.
⁷ Eurofins Medical Lab Gelsenkirchen, Gelsenkirchen, Germany.

Abstract

Background: Changes in intestinal microbiome composition have been described in animal models of Alzheimer's disease (AD) and AD patients. Here we investigated how well taxonomic and functional intestinal microbiome data and their combination with clinical data can be used to discriminate between amyloid-positive AD patients and cognitively healthy elderly controls.

Methods: In the present study we investigated intestinal microbiome in 75 amyloid-positive AD patients and 100 cognitively healthy controls participating in the AlzBiom study. We randomly split the data into a training and a validation set. Intestinal microbiome was measured using shotgun metagenomics. Receiver operating characteristic (ROC) curve analysis was performed to examine the discriminatory ability of intestinal microbiome among diagnostic groups.

Results: The best model for discrimination of amyloid-positive AD patients from healthy controls with taxonomic data was obtained analyzing 18 genera features, and yielded an area under the receiver operating characteristic curve (AUROC) of 0.76 in the training set and 0.61 in the validation set. The best models with functional data were obtained analyzing 17 GO (Gene Ontology) features with an AUROC of 0.81 in the training set and 0.75 in the validation set and 26 KO [Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog] features with an AUROC of 0.83 and 0.77, respectively. Using ensemble learning for these three models including a clinical model with the 4 parameters age, gender, BMI and ApoE yielded an AUROC of 0.92 in the training set and 0.80 in the validation set.

Discussion: In conclusion, we identified a specific Alzheimer signature in intestinal microbiome that can be used to discriminate amyloid-positive AD patients from healthy controls. The diagnostic accuracy increases from taxonomic to functional data and is even better when combining taxonomic, functional and clinical models. Intestinal microbiome represents an innovative diagnostic supplement and a promising area for developing novel interventions against AD.

Keywords: Alzheimer’s disease; ensemble learning; functional data; intestinal microbiome; taxonomic data.