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@ARTICLE{Millar:266753,
      author       = {Millar, Peter R and Gordon, Brian A and Wisch, Julie K and
                      Schultz, Stephanie A and Benzinger, Tammie Ls and Cruchaga,
                      Carlos and Hassenstab, Jason J and Ibanez, Laura and Karch,
                      Celeste and Llibre-Guerra, Jorge J and Morris, John C and
                      Perrin, Richard J and Supnet-Bell, Charlene and Xiong,
                      Chengjie and Allegri, Ricardo F and Berman, Sarah B and
                      Chhatwal, Jasmeer P and Chrem Mendez, Patricio A and Day,
                      Gregory S and Hofmann, Anna and Ikeuchi, Takeshi and Jucker,
                      Mathias and Lee, Jae-Hong and Levin, Johannes and Lopera,
                      Francisco and Niimi, Yoshiki and Sánchez-González, Victor
                      J and Schofield, Peter R and Sosa-Ortiz, Ana Luisa and
                      Vöglein, Jonathan and Bateman, Randall J and Ances, Beau M
                      and McDade, Eric M},
      collaboration = {Network, Dominantly Inherited Alzheimer},
      title        = {{A}dvanced structural brain aging in preclinical autosomal
                      dominant {A}lzheimer disease.},
      journal      = {Molecular neurodegeneration},
      volume       = {18},
      number       = {1},
      issn         = {1750-1326},
      address      = {London},
      publisher    = {Biomed Central},
      reportid     = {DZNE-2024-00016},
      pages        = {98},
      year         = {2023},
      abstract     = {'Brain-predicted age' estimates biological age from
                      complex, nonlinear features in neuroimaging scans. The brain
                      age gap (BAG) between predicted and chronological age is
                      elevated in sporadic Alzheimer disease (AD), but is
                      underexplored in autosomal dominant AD (ADAD), in which AD
                      progression is highly predictable with minimal confounding
                      age-related co-pathology.We modeled BAG in 257
                      deeply-phenotyped ADAD mutation-carriers and 179
                      non-carriers from the Dominantly Inherited Alzheimer Network
                      using minimally-processed structural MRI scans. We then
                      tested whether BAG differed as a function of mutation and
                      cognitive status, or estimated years until symptom onset,
                      and whether it was associated with established markers of
                      amyloid (PiB PET, CSF amyloid-β-42/40), phosphorylated tau
                      (CSF and plasma pTau-181), neurodegeneration (CSF and plasma
                      neurofilament-light-chain [NfL]), and cognition (global
                      neuropsychological composite and CDR-sum of boxes). We
                      compared BAG to other MRI measures, and examined
                      heterogeneity in BAG as a function of ADAD mutation
                      variants, APOE ε4 carrier status, sex, and
                      education.Advanced brain aging was observed in
                      mutation-carriers approximately 7 years before expected
                      symptom onset, in line with other established structural
                      indicators of atrophy. BAG was moderately associated with
                      amyloid PET and strongly associated with pTau-181, NfL, and
                      cognition in mutation-carriers. Mutation variants, sex, and
                      years of education contributed to variability in BAG.We
                      extend prior work using BAG from sporadic AD to ADAD, noting
                      consistent results. BAG associates well with markers of
                      pTau, neurodegeneration, and cognition, but to a lesser
                      extent, amyloid, in ADAD. BAG may capture similar signal to
                      established MRI measures. However, BAG offers unique
                      benefits in simplicity of data processing and
                      interpretation. Thus, results in this unique ADAD cohort
                      with few age-related confounds suggest that brain aging
                      attributable to AD neuropathology can be accurately
                      quantified from minimally-processed MRI.},
      keywords     = {Humans / Alzheimer Disease / Amyloid beta-Peptides:
                      metabolism / Brain: metabolism / Amyloid / Aging /
                      Biomarkers / Positron-Emission Tomography / tau Proteins:
                      genetics / tau Proteins: metabolism / Alzheimer disease
                      (Other) / Brain aging (Other) / Machine learning (Other) /
                      Structural MRI (Other) / Amyloid beta-Peptides (NLM
                      Chemicals) / Amyloid (NLM Chemicals) / Biomarkers (NLM
                      Chemicals) / tau Proteins (NLM Chemicals)},
      cin          = {AG Jucker / Clinical Research (Munich)},
      ddc          = {570},
      cid          = {I:(DE-2719)1210001 / I:(DE-2719)1111015},
      pnm          = {352 - Disease Mechanisms (POF4-352) / 353 - Clinical and
                      Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-352 / G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38111006},
      pmc          = {pmc:PMC10729487},
      doi          = {10.1186/s13024-023-00688-3},
      url          = {https://pub.dzne.de/record/266753},
}