Association Between Polygenic Risk Score and the Progression from Mild Cognitive Impairment to Alzheimer's Disease.

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2021-10-13

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Abstract

Mild cognitive impairment (MCI) is a heterogeneous condition and MCI patients are at increased risk of progression to dementia due to Alzheimer's disease (AD). In this study, we aim to evaluate the associations between polygenic risk scores (PRSs) and 1) time to AD progression from MCI, 2) changes in longitudinal cognitive impairment, and 3) biomarkers from cerebrospinal fluid and imaging. We constructed PRS by using 40 independent non-APOE SNPs from well-replicated AD GWASs and tested its association with the progression time from MCI to AD by using 767 MCI patients from the ADNI study and 1373 patients from the NACC study. PRSs calculated with other methods were also computed. We found that the PRS constructed with SNPs that reached genome-wide significance predicted the progression from MCI to AD (beta = 0.182, se = 0.061, p = 0.003) after adjusting for the demographic and clinical variables. This association was replicated in the NACC dataset (beta = 0.094, se = 0.037, p = 0.009). Further analyses revealed that PRS was associated with the increased ADAS-Cog11/ADAS-Cog13/ADASQ4 scores, tau/ptau levels, and cortical amyloid burdens (PIB and AV45), but decreased hippocampus and entorhinal cortex volumes (p <  0.05). Mediation analysis showed that the effect of PRS on the increased risk of AD may be mediated by Aβ 42 (beta = 0.056, SE = 0.026, p = 0.036). Our findings suggest that PRS can be useful for the prediction of time to AD and other clinical changes after the diagnosis of MCI.

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10.3233/jad-210700

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Liu, Hongliang, Michael Lutz, Sheng Luo and undefined Alzheimer’s Disease Neuroimaging Initiative (2021). Association Between Polygenic Risk Score and the Progression from Mild Cognitive Impairment to Alzheimer's Disease. Journal of Alzheimer's disease : JAD. pp. 1–13. 10.3233/jad-210700 Retrieved from https://hdl.handle.net/10161/23954.

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Scholars@Duke

Lutz

Michael William Lutz

Professor in Neurology

Developing and using computational biology methods to understand the genetic basis of disease with a focus on Alzheimer’s Disease.   Recent work has focused on identification and validation of clinically-relevant biomarkers for Alzheimer’s disease and Alzheimer’s disease with Lewy bodies.

Luo

Sheng Luo

Professor of Biostatistics & Bioinformatics

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