Affiliations:
Department of Nuclear Medicine, Kosin University Gospel Hospital, University of Kosin College of Medicine, Busan, Republic of Korea
Correspondence:
[*]
Correspondence to: Heeyoung Kim, MD, PhD, Department of Nuclear Medicine, Kosin University Gospel Hospital, University of Kosin College of Medicine, 262, Gamcheon-ro, Seo-gu, Busan 49267, Republic of Korea. Tel.: +82 51 990 6662; E-mail: nmkimh@gmail.com.
Note: [1] These authors contributed equally to this work.
Note: [2] The data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data, but did not participate in the analysis or writing of this report. A complete listing of the ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Abstract: Background:To diagnose mild cognitive impairment (MCI) patients at risk of progression to dementia is clinically important but challenging. Objective:We classified MCI patients based on cognitive trajectories and compared biomarkers among groups. Methods:This study analyzed amnestic MCI patients with at least three Clinical Dementia Rating (CDR) scores available over a minimum of 36 months from the Alzheimer’s Disease Neuroimaging Initiative database. Patients were classified based on their progression using trajectory modeling with the CDR-sum of box scores. We compared clinical and neuroimaging biomarkers across groups. Results:Of 569 eligible MCI patients (age 72.7±7.4 years, women n = 223), three trajectory groups were identified: stable (58.2%), slow decliners (24.6%), and fast decliners (17.2%). In the fifth year after diagnosis, the CDR-sum of box scores increased by 1.2, 5.4, and 11.8 points for the stable, slow, and fast decliners, respectively. Biomarkers associated with cognitive decline were amyloid-β 42, total tau, and phosphorylated tau protein in cerebrospinal fluid, hippocampal volume, cortical metabolism, and amount of cortical and subcortical amyloid deposits. Cortical metabolism and the amount of amyloid deposits were associated with the rate of cognitive decline. Conclusion:Data-driven trajectory analysis provides new insights into the various cognitive trajectories of MCI. Baseline brain metabolism, and the amount of cortical and subcortical amyloid burden can provide additional information on the rate of cognitive decline.
