Laske, Christoph ; Müller, Stephan ; Preische, Oliver ; Ruschil, Victoria ; Munk, Matthias H. J. ; Honold, Iris ; Peter, Silke ; Schoppmeier, Ulrich ; Willmann, Matthias (2022)
Signature of Alzheimer’s Disease in Intestinal Microbiome: Results From the AlzBiom Study.
In: Frontiers in Neuroscience, 2022, 16
doi: 10.26083/tuprints-00021278
Artikel, Zweitveröffentlichung, Verlagsversion
 | Es ist eine neuere Version dieses Eintrags verfügbar. |
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Laske, Christoph ; Müller, Stephan ; Preische, Oliver ; Ruschil, Victoria ; Munk, Matthias H. J. ; Honold, Iris ; Peter, Silke ; Schoppmeier, Ulrich ; Willmann, Matthias |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Signature of Alzheimer’s Disease in Intestinal Microbiome: Results From the AlzBiom Study |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | Frontiers Media S.A. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Frontiers in Neuroscience |
| Jahrgang/Volume einer Zeitschrift: | 16 |
| Kollation: | 12 Seiten |
| DOI: | 10.26083/tuprints-00021278 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/21278 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Alzheimer’s disease, intestinal microbiome, taxonomic data, functional data, ensemble learning |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-212780 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin, Gesundheit |
| Fachbereich(e)/-gebiet(e): | 10 Fachbereich Biologie 10 Fachbereich Biologie > Systemische Neurophysiologie |
| Hinterlegungsdatum: | 09 Mai 2022 13:52 |
| Letzte Änderung: | 10 Mai 2022 05:17 |
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| Suche nach Titel in: | TUfind oder in Google |
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