Journal Article

A genetics-based biomarker risk algorithm for predicting risk of Alzheimer’s disease

January 13, 2016

A genetics-based biomarker risk algorithm for predicting risk of Alzheimer’s disease. Alzheimer’s & Dementia, January 13, 2016. Michael W. Lutz, Scott S. Sundseth, Daniel K. Burns, Ann M. Saunders, Kathleen M. Hayden, James R. Burke, Kathleen A. Welsh-Bohmer, Allen D. Roses.

Author Affiliations

a Joseph and Kathleen Bryan Alzheimer’s Disease Research Center, Duke University Medical Center, Durham, NC, USA
b Department of Neurology, Duke University Medical Center, Durham, NC, USA
c Cabernet Pharmaceuticals, Durham, NC, USA
d Zinfandel Pharmaceuticals, Durham, NC, USA
e Department of Social Sciences and Health Policy, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
f Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA

Abstract

Background
A straightforward, reproducible blood-based test that predicts age-dependent risk of Alzheimer’s disease (AD) could be used as an enrichment tool for clinical development of therapies. This study evaluated the prognostic performance of a genetics-based biomarker risk algorithm (GBRA) established on a combination of apolipoprotein E (APOE)/translocase of outer mitochondrial membrane 40 homolog (TOMM40) genotypes and age, then compare it to cerebrospinal fluid (CSF) biomarkers, neuroimaging, and neurocognitive tests using data from two independent AD cohorts.

Methods
The GBRA was developed using data from the prospective Joseph and Kathleen Bryan, Alzheimer’s Disease Research Center study (n = 407; 86 conversion events [mild cognitive impairment {MCI} or late-onset Alzheimer’s disease {LOAD}]). The performance of the algorithm was tested using data from the Alzheimer’s Disease Neuroimaging Initiative study (n = 660; 457 individuals categorized as MCI or LOAD).

Results
The positive predictive values and negative predictive values of the GBRA are in the range of 70%–80%. The relatively high odds ratio (approximately 3–5) and significant net reclassification index scores comparing the GBRA to a version based on APOE and age alone support the value of the GBRA in risk prediction for MCI due to LOAD. Performance of the GBRA compares favorably with CSF and imaging (functional magnetic resonance imaging) biomarkers. In addition, the GBRA “high” and “low” AD-risk categorizations correlated well with pathologic CSF biomarker levels, positron emission tomography amyloid burden, and neurocognitive scores.

Conclusions
Unlike dynamic markers (i.e., imaging, protein, or lipid markers) that may be influenced by factors unrelated to disease, genomic DNA is easily collected, stable, and the technical methods for measurement are robust, inexpensive, and widely available. The performance characteristics of the GBRA support its use as a pharmacogenetic enrichment tool for LOAD delay-of-onset clinical trials and merit further evaluation for its clinical utility in evaluating therapeutic efficacy.

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