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@ARTICLE{Krger:272963,
      author       = {Krüger, Dennis M and Pena Centeno, Tonatiuh and Liu,
                      Shiwei and Park, Tamina and Kaurani, Lalit and Pradhan,
                      Ranjit and Huang, Yen-Ning and Risacher, Shannon L and
                      Burkhardt, Susanne and Schütz, Anna-Lena and Wan, Yang and
                      Shaw, Leslie M and Brodsky, Alexander S and DeStefano, Anita
                      L and Lin, Honghuang and Schroeder, Robert and Krunic, Andre
                      and Hempel, Nina and Sananbenesi, Farahnaz and Blusztajn,
                      Jan Krzysztof and Saykin, Andrew J and Delalle, Ivana and
                      Nho, Kwangsik and Fischer, Andre},
      collaboration = {Initiative, Alzheimer's Disease Neuroimaging},
      title        = {{T}he plasma mi{RNA}ome in {ADNI}: {S}ignatures to aid the
                      detection of at-risk individuals.},
      journal      = {Alzheimer's and dementia},
      volume       = {20},
      number       = {11},
      issn         = {1552-5260},
      address      = {Hoboken, NJ},
      publisher    = {Wiley},
      reportid     = {DZNE-2024-01342},
      pages        = {7479 - 7494},
      year         = {2024},
      abstract     = {MicroRNAs are short non-coding RNAs that control
                      proteostasis at the systems level and are emerging as
                      potential prognostic and diagnostic biomarkers for
                      Alzheimer's disease (AD).We performed small RNA sequencing
                      on plasma samples from 847 Alzheimer's Disease Neuroimaging
                      Initiative (ADNI) participants.We identified microRNA
                      signatures that correlate with AD diagnoses and help predict
                      the conversion from mild cognitive impairment (MCI) to
                      AD.Our data demonstrate that plasma microRNA signatures can
                      be used to not only diagnose MCI, but also, critically,
                      predict the conversion from MCI to AD. Moreover, combined
                      with neuropsychological testing, plasma microRNAome
                      evaluation helps predict MCI to AD conversion. These
                      findings are of considerable public interest because they
                      provide a path toward reducing indiscriminate utilization of
                      costly and invasive testing by defining the at-risk segment
                      of the aging population.We provide the first analysis of the
                      plasma microRNAome for the ADNI study. The levels of several
                      microRNAs can be used as biomarkers for the prediction of
                      conversion from MCI to AD. Adding the evaluation of plasma
                      microRNA levels to neuropsychological testing in a clinical
                      setting increases the accuracy of MCI to AD conversion
                      prediction.},
      keywords     = {Humans / Alzheimer Disease: blood / Alzheimer Disease:
                      genetics / Alzheimer Disease: diagnosis / MicroRNAs: blood /
                      MicroRNAs: genetics / Cognitive Dysfunction: blood /
                      Cognitive Dysfunction: genetics / Cognitive Dysfunction:
                      diagnosis / Aged / Female / Male / Biomarkers: blood /
                      Neuropsychological Tests: statistics $\&$ numerical data /
                      Disease Progression / Aged, 80 and over / Neuroimaging /
                      Alzheimer's disease (Other) / blood biomarker (Other) /
                      cognitive decline (Other) / microRNA (Other) / mild
                      cognitive impairment (Other) / plasma (Other) / small
                      non‐coding RNA (Other) / MicroRNAs (NLM Chemicals) /
                      Biomarkers (NLM Chemicals)},
      cin          = {AG Fischer / Bioinformatics Unit (Göttingen) / AG
                      Sananbenesi},
      ddc          = {610},
      cid          = {I:(DE-2719)1410002 / I:(DE-2719)1440016 /
                      I:(DE-2719)1410004},
      pnm          = {352 - Disease Mechanisms (POF4-352) / 899 - ohne Topic
                      (POF4-899)},
      pid          = {G:(DE-HGF)POF4-352 / G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39291752},
      pmc          = {pmc:PMC11567822},
      doi          = {10.1002/alz.14157},
      url          = {https://pub.dzne.de/record/272963},
}