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| 001 | 278911 | ||
| 005 | 20250604100731.0 | ||
| 024 | 7 | _ | |a 10.3390/ijms26104735 |2 doi |
| 024 | 7 | _ | |a pmid:40429881 |2 pmid |
| 024 | 7 | _ | |a pmc:PMC12112180 |2 pmc |
| 024 | 7 | _ | |a 1422-0067 |2 ISSN |
| 024 | 7 | _ | |a 1661-6596 |2 ISSN |
| 037 | _ | _ | |a DZNE-2025-00637 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 540 |
| 100 | 1 | _ | |a Bauch, Anne |b 0 |
| 245 | _ | _ | |a Prognostic Value of a Multivariate Gut Microbiome Model for Progression from Normal Cognition to Mild Cognitive Impairment Within 4 Years. |
| 260 | _ | _ | |a Basel |c 2025 |b Molecular Diversity Preservation International |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1748613647_7947 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Little is known about the dysbiosis of the gut microbiome in patients with mild cognitive impairment (MCI) potentially at risk for the development of Alzheimer's disease (AD). So far, only cross-sectional differences and not longitudinal changes and their prognostic significance have been in the scope of research in MCI. Therefore, we investigated the ability of longitudinal taxonomic and functional gut microbiome data from 100 healthy controls (HC) to predict the progression from normal cognition to MCI over a 4-year follow-up period (4yFU). Logistic regression models were built with baseline features that best discriminated between the two groups using an ANOVA-type statistical analysis. The best model for the discrimination of MCI converters was based on functional data using Gene Ontology (GO), which included 14 features. This model achieved an area under the receiver operating characteristic curve (AUROC) of 0.84 at baseline, 0.78 at the 1-year follow-up (1yFU), and 0.75 at 4yFU. This functional model outperformed the taxonomic model, which included 38 genera features, in terms of descriptive performance and showed comparable efficacy to combined analyses integrating functional, taxonomic, and clinical characteristics. Thus, gut microbiome algorithms have the potential to predict MCI conversion in HCs over a 4-year period, offering a promising innovative supplement for early AD identification. |
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| 650 | _ | 7 | |a Alzheimer’s disease |2 Other |
| 650 | _ | 7 | |a gut microbiome |2 Other |
| 650 | _ | 7 | |a longitudinal observational study |2 Other |
| 650 | _ | 7 | |a mild cognitive impairment |2 Other |
| 650 | _ | 7 | |a prediction model |2 Other |
| 650 | _ | 2 | |a Humans |2 MeSH |
| 650 | _ | 2 | |a Cognitive Dysfunction: microbiology |2 MeSH |
| 650 | _ | 2 | |a Cognitive Dysfunction: diagnosis |2 MeSH |
| 650 | _ | 2 | |a Gastrointestinal Microbiome |2 MeSH |
| 650 | _ | 2 | |a Male |2 MeSH |
| 650 | _ | 2 | |a Female |2 MeSH |
| 650 | _ | 2 | |a Prognosis |2 MeSH |
| 650 | _ | 2 | |a Disease Progression |2 MeSH |
| 650 | _ | 2 | |a Aged |2 MeSH |
| 650 | _ | 2 | |a Cognition |2 MeSH |
| 650 | _ | 2 | |a Alzheimer Disease: microbiology |2 MeSH |
| 650 | _ | 2 | |a ROC Curve |2 MeSH |
| 650 | _ | 2 | |a Middle Aged |2 MeSH |
| 700 | 1 | _ | |a Baur, Julia |b 1 |
| 700 | 1 | _ | |a Honold, Iris |b 2 |
| 700 | 1 | _ | |a Willmann, Matthias |b 3 |
| 700 | 1 | _ | |a Weber, Greta Louise |b 4 |
| 700 | 1 | _ | |a Müller, Stephan |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Sodenkamp, Sebastian |0 P:(DE-2719)9003152 |b 6 |u dzne |
| 700 | 1 | _ | |a Peter, Silke |b 7 |
| 700 | 1 | _ | |a Schoppmeier, Ulrich |0 0009-0004-0769-6845 |b 8 |
| 700 | 1 | _ | |a Laske, Christoph |0 P:(DE-2719)2000055 |b 9 |e Last author |u dzne |
| 770 | _ | _ | |a Molecular Research in Human Microbiome 2.0 |
| 773 | _ | _ | |a 10.3390/ijms26104735 |g Vol. 26, no. 10, p. 4735 - |0 PERI:(DE-600)2019364-6 |n 10 |p 4735 |t International journal of molecular sciences |v 26 |y 2025 |x 1422-0067 |
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