A Genetics-based Biomarker Risk Algorithm for Predicting Risk of Alzheimer's Disease

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 Bryan-ADRC 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 ADNI study (n=660; 457 individuals categorized as MCI or LOAD).

Results: The positive predictive values (PPV) and negative predictive values (NPV) of the GBRA are in the range of 70-80%. The relatively high odds ratio (approximately 3-5) and significant net reclassification index (NRI) 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 (fMRI) biomarkers. In addition, the GBRA "high" and "low" AD-risk categorizations correlated well with pathological CSF biomarker levels, PET 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 merits further evaluation for its clinical utility in evaluating therapeutic efficacy.

Fig. 1

GBRA AD-risk algorithm. Flowchart and Tables show the process for the generation of the risk assessment for MCI due to AD using the GBRA. Risk of high or low is assigned based on APOE genotype, TOMM40′523 genotype, and current age. Abbreviations: GBRA, genetics-based biomarker risk algorithm; AD, Alzheimer's disease; APOE, apolipoprotein E; TOMM40, translocase of outer mitochondrial membrane 40 homolog; MCI, mild cognitive impairment.

Fig. 2

Age at onset of cognitive impairment as a function of TOMM40′523 genotype in the Bryan-ADRC cohort. The curves represent the fit of a Kaplan-Meier survival analysis model to the data. The red line corresponds to APOE ε4/ε4; the two green lines correspond to APOE ε3/ε4, and the three blue lines correspond to APOE ε3/ε3. Adapted from Crenshaw et al. . Abbreviations: TOMM40, translocase of outer mitochondrial membrane 40 homolog; APOE, apolipoprotein E; VL, very long; S, short; L, long; Bryan-ADRC, Joseph and Kathleen Bryan, Alzheimer's Disease Research Center.

Fig. 3

Comparison of the performance of the full GBRA (age, APOE genotype, and TOMM40′523 genotype) with versions of the risk algorithm based on age alone, age and APOE ε4 carrier status, or APOE genotype and age. Abbreviations: GBRA, genetics-based biomarker risk algorithm; APOE, apolipoprotein E; Bryan-ADRC, Joseph and Kathleen Bryan, Alzheimer's Disease Research Center; ADNI, Alzheimer's Disease Neuroimaging Initiative.

Fig. 4

Comparative receiver operating curves for statistical models based on age, APOE genotype, and TOMM40′523 genotype. (A) Bryan-ADRC cohort and (B) ADNI cohort. Abbreviations: Bryan-ADRC, Joseph and Kathleen Bryan, Alzheimer's Disease Research Center; ADNI, Alzheimer's Disease Neuroimaging Initiative; APOE, apolipoprotein E.

Fig. 5

Age sensitivity of GBRA illustrates the relationship between sensitivity and specificity with age. Baseline corresponds to the algorithm as defined. All age-dependent thresholds were increased or decreased by 2 and 4 years and the resulting sensitivity and specificity were plotted. Abbreviations: GBRA, genetics-based biomarker risk algorithm; Bryan-ADRC, Joseph and Kathleen Bryan, Alzheimer's Disease Research Center; ADNI, Alzheimer's Disease Neuroimaging Initiative.

Fig. 6

Comparative performance of the GBRA, CSF (combination of amyloid β and tau), and fMRI biomarkers. Abbreviations: GBRA, genetics-based biomarker risk algorithm; CSF, cerebrospinal fluid; MRI, magnetic resonance imaging; HC, healthy control; fMRI, functional magnetic resonance imaging.