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@ARTICLE{Bauch:278911,
      author       = {Bauch, Anne and Baur, Julia and Honold, Iris and Willmann,
                      Matthias and Weber, Greta Louise and Müller, Stephan and
                      Sodenkamp, Sebastian and Peter, Silke and Schoppmeier,
                      Ulrich and Laske, Christoph},
      title        = {{P}rognostic {V}alue of a {M}ultivariate {G}ut {M}icrobiome
                      {M}odel for {P}rogression from {N}ormal {C}ognition to
                      {M}ild {C}ognitive {I}mpairment {W}ithin 4 {Y}ears.},
      journal      = {International journal of molecular sciences},
      volume       = {26},
      number       = {10},
      issn         = {1422-0067},
      address      = {Basel},
      publisher    = {Molecular Diversity Preservation International},
      reportid     = {DZNE-2025-00637},
      pages        = {4735},
      year         = {2025},
      abstract     = {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.},
      keywords     = {Humans / Cognitive Dysfunction: microbiology / Cognitive
                      Dysfunction: diagnosis / Gastrointestinal Microbiome / Male
                      / Female / Prognosis / Disease Progression / Aged /
                      Cognition / Alzheimer Disease: microbiology / ROC Curve /
                      Middle Aged / Alzheimer’s disease (Other) / gut microbiome
                      (Other) / longitudinal observational study (Other) / mild
                      cognitive impairment (Other) / prediction model (Other)},
      cin          = {AG Gasser / ICRU},
      ddc          = {540},
      cid          = {I:(DE-2719)1210000 / I:(DE-2719)1240005},
      pnm          = {353 - Clinical and Health Care Research (POF4-353) / 899 -
                      ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-353 / G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40429881},
      pmc          = {pmc:PMC12112180},
      doi          = {10.3390/ijms26104735},
      url          = {https://pub.dzne.de/record/278911},
}