Document Type

Journal Article

Publication Title

Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring

Publisher

Wiley

School

Centre of Excellence for Alzheimer's Disease Research and Care / School of Medical and Health Sciences

Funders

Funding information available in the Acknowledgements section of the PDF

Comments

Keret, O., Staffaroni, A. M., Ringman, J. M., Cobigo, Y., Goh, S. Y. M., Wolf, A., ... & Dominantly Inherited Alzheimer Network. (2021). Pattern and degree of individual brain atrophy predicts dementia onset in dominantly inherited Alzheimer's disease. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 13(1), e12197. https://doi.org/10.1002/dad2.12197

Abstract

Introduction: Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD-MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD-MC and could help predict risk for dementia during trial enrollment. Methods: We created a dementia risk score by entering standardized gray-matter volumes from 231 DIAD-MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD-MC followed longitudinally. Results: Our risk score separated asymptomatic versus demented DIAD-MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%-98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion: Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD-MC participants for prevention trials.

DOI

10.1002/dad2.12197

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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