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Lutz MW, Sundseth SS, Burns DK, et al. A Genetics-based Biomarker Risk Algorithm for Predicting Risk of Alzheimer's Disease. Alzheimers Dement (N Y). 2016;2(1):30-44doi: 10.1016/j.trci.2015.12.002.
Lutz, M. W., Sundseth, S. S., Burns, D. K., Saunders, A. M., Hayden, K. M., Burke, J. R., Welsh-Bohmer, K. A., & Roses, A. D. (2016). A Genetics-based Biomarker Risk Algorithm for Predicting Risk of Alzheimer's Disease. Alzheimer's & dementia (New York, N. Y.), 2(1), 30-44. https://doi.org/10.1016/j.trci.2015.12.002
Lutz, Michael W, et al. "A Genetics-based Biomarker Risk Algorithm for Predicting Risk of Alzheimer's Disease." Alzheimer's & dementia (New York, N. Y.) vol. 2,1 (2016): 30-44. doi: https://doi.org/10.1016/j.trci.2015.12.002
Lutz MW, Sundseth SS, Burns DK, Saunders AM, Hayden KM, Burke JR, Welsh-Bohmer KA, Roses AD. A Genetics-based Biomarker Risk Algorithm for Predicting Risk of Alzheimer's Disease. Alzheimers Dement (N Y). 2016 Jan 01;2(1):30-44. doi: 10.1016/j.trci.2015.12.002. PMID: 27047990; PMCID: PMC4817110.
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Alzheimers Dement (N Y). 2016 Jan 01;2(1):30-44. doi: 10.1016/j.trci.2015.12.002.

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

Alzheimer's & dementia (New York, N. Y.)

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

Affiliations

  1. Joseph and Kathleen Bryan Alzheimer's Disease Research Center, Duke University Medical Center, Durham, North Carolina; Department of Neurology, Duke University Medical Center, Durham, North Carolina.
  2. Cabernet Pharmaceuticals, Durham, North Carolina.
  3. Zinfandel Pharmaceuticals, Durham, North Carolina.
  4. Department of Social Sciences and Health Policy, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina; Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina.
  5. Joseph and Kathleen Bryan Alzheimer's Disease Research Center, Duke University Medical Center, Durham, North Carolina; Department of Neurology, Duke University Medical Center, Durham, North Carolina; Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina.
  6. Joseph and Kathleen Bryan Alzheimer's Disease Research Center, Duke University Medical Center, Durham, North Carolina; Department of Neurology, Duke University Medical Center, Durham, North Carolina; Cabernet Pharmaceuticals, Durham, North Carolina; Zinfandel Pharmaceuticals, Durham, North Carolina.

PMID: 27047990 PMCID: PMC4817110 DOI: 10.1016/j.trci.2015.12.002

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.

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