Disease State Fingerprint in Frontotemporal Degeneration with Reference to Alzheimer's Disease and Mild Cognitive Impairment

  • Miguel Ángel Muñoz-Ruiz
    Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
  • Päivi Hartikainen
    Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
  • Anette Hall
    Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
  • Jussi Mattila
    VTT Technical Research Centre of Finland, Tampere, Finland
  • Juha Koikkalainen
    VTT Technical Research Centre of Finland, Tampere, Finland
  • Sanna-Kaisa Herukka
    Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
  • Valtteri Julkunen
    Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
  • Ritva Vanninen
    Department of Clinical Radiology, Kuopio University Hospital, Kuopio, Finland
  • Yawu Liu
    Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
  • Jyrki Lötjönen
    VTT Technical Research Centre of Finland, Tampere, Finland
  • Hilkka Soininen
    Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland

書誌事項

公開日
2013-05-21
権利情報
  • https://journals.sagepub.com/page/policies/text-and-data-mining-license
DOI
  • 10.3233/jad-122260
公開者
SAGE Publications

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説明

<jats:sec specific-use="heading-level-1"> <jats:title>Background:</jats:title> <jats:p>Disease State Index and Disease State Fingerprint represent a novel tool which collates data information from different sources, helping the clinician in the diagnosis and follow-up of dementia diseases. It has been demonstrated that it is applicable in the diagnosis of Alzheimer’s disease (AD).</jats:p> </jats:sec> <jats:sec specific-use="heading-level-1"> <jats:title>Objective:</jats:title> <jats:p>We applied this novel tool to classify frontotemporal dementia (FTD) cases in comparison with controls, AD, and mild cognitive impairment (MCI) subjects.</jats:p> </jats:sec> <jats:sec specific-use="heading-level-1"> <jats:title>Methods:</jats:title> <jats:p>Thirty seven patients with FTD, 35 patients with AD, 26 control subjects, and 64 subjects with MCI were included in the study. The Disease State Index encompassed data from cognitive performance assessed by Mini-Mental State Examination, cerebrospinal fluid biomarkers, MRI volumetric and morphometric parameters as well as APOE genotype.</jats:p> </jats:sec> <jats:sec specific-use="heading-level-1"> <jats:title>Results:</jats:title> <jats:p>We applied the Disease State Index for comparisons at the group level. The data showed that FTD patients could be differentiated with a high accuracy, sensitivity, and specificity from controls (0.84, 0.84, 0.83) and from MCI (0.79, 0.78, 0.80). However, the correct accuracy was lower in the FTD versus AD comparison (0.69, 0.70, 0.71). In addition, we demonstrated the use of Disease State Fingerprint by comparing one particular FTD case with control, AD, and MCI population data.</jats:p> </jats:sec> <jats:sec specific-use="heading-level-1"> <jats:title>Conclusion:</jats:title> <jats:p>The results suggest that the Disease State Fingerprint and the underlying Disease State Index are particularly useful in differentiating between normal status and disease in patients with dementia, but it may also help to distinguish between the two dementia diseases, FTD and AD.</jats:p> </jats:sec>

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