Affiliations: [a]
Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA
| [b]
Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
| [c] Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, USA
| [d]
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
Correspondence:
[*]
Correspondence to: Sheng Luo, Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 77030, USA. Tel.: +1 919 668 8038; Fax: +1 919 668 8038; E-mail: sheng.luo@duke.edu.
Note: [1] Data used in 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 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: Background: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). Objective: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. Methods: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. Results: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-PET and AV45-PET), 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 Aβ42 (beta = 0.056, SE = 0.026, p = 0.036). Conclusion:Our findings suggest that PRS can be useful for the prediction of time to AD and other clinical changes after the diagnosis of MCI.
