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Aging (Albany NY). 2019 Aug 29;11(16):6217-6236. doi: 10.18632/aging.102184. Epub 2019 Aug 29.

Risk estimation before progression to mild cognitive impairment and Alzheimer's disease: an AD resemblance atrophy index.

Author information

1
BrainNow Research Institute, Shenzhen, Guangdong Province, China.
2
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, China.
3
Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, China.
4
Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, China.
5
Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Shatin, China.
6
Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, China.
7
Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Abstract

To realize an individual-level risk evaluation of progression of early Alzheimer's disease (AD), we applied an AD resemblance atrophy index (AD-RAI) to differentiate the subjects at risk of progression from normal subjects (NC) to mild cognitive impairment (MCI) and from MCI to AD. We included 183 subjects with a two-year follow-up: 50 NC stable (NCs), 23 NC-to-MCI converters (NCc), 50 MCI stable (MCIs), 35 MCI-to-AD converters (MCIc), 25 AD stable (ADs). ANCOVA analyses were used to identify baseline brain atrophy in converters compared with non-converters. To explore the relative merits of AD-RAI over individual regional volumetric measures in prediction of disease progression, we searched for the optimal cutoff for each measure in logistic regressions and plotted the longitudinal trajectories of these brain volumetric measures in converters and non-converters. Baseline AD-RAI performed the best in differentiating NCc from NCs (odds ratio 26.35, AUC 0.740) and MCIc from MCIs (odds ratio 8.91, AUC 0.771). The AD-RAI presented greater increase in the second year for NCc vs. NCs but not for MCIc vs. MCIs. Baseline AD-RAIs were also associated with CSF-based and PET-based AD biomarkers. These results showed the potential of AD-RAI in early risk estimation before progression to MCI/AD at an individual-level.

KEYWORDS:

Alzheimer’s disease; atrophy index; automated brain volumetry; biomarker; conversion

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