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A Blood-Based Screening Tool for Alzheimer's Disease That Spans Serum and Plasma: Findings from TARC and ADNI
Open accessAbstract
CONTEXT: There is no rapid and cost effective tool that can be implemented as a front-line screening tool for Alzheimer's disease (AD) at the population level. OBJECTIVE: To generate and cross-validate a blood-based screener for AD that yields acceptable accuracy across both serum and plasma. DESIGN, SETTING, PARTICIPANTS: Analysis of serum biomarker proteins were conducted on 197 Alzheimer's disease (AD) participants and 199 control participants from the Texas Alzheimer's Research Consortium (TARC) with further analysis conducted on plasma proteins from 112 AD and 52 control participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The full algorithm was derived from a biomarker risk score, clinical lab (glucose, triglycerides, total cholesterol, homocysteine), and demographic (age, gender, education, APOE*E4 status) data. MAJOR OUTCOME MEASURES: Alzheimer's disease. RESULTS: 11 proteins met our criteria and were utilized for the biomarker risk score. The random forest (RF) biomarker risk score from the TARC serum samples (training set) yielded adequate accuracy in the ADNI plasma sample (training set) (AUC = 0.70, sensitivity (SN) = 0.54 and specificity (SP) = 0.78), which was below that obtained from ADNI cerebral spinal fluid (CSF) analyses (t-tau/Aβ ratio AUC = 0.92). However, the full algorithm yielded excellent accuracy (AUC = 0.88, SN = 0.75, and SP = 0.91). The likelihood ratio of having AD based on a positive test finding (LR+) = 7.03 (SE = 1.17; 95% CI = 4.49-14.47), the likelihood ratio of not having AD based on the algorithm (LR-) = 3.55 (SE = 1.15; 2.22-5.71), and the odds ratio of AD were calculated in the ADNI cohort (OR) = 28.70 (1.55; 95% CI = 11.86-69.47). CONCLUSIONS: It is possible to create a blood-based screening algorithm that works across both serum and plasma that provides a comparable screening accuracy to that obtained from CSF analyses.
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Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins
Ray, S., Britschgi, M., Herbert, C., Takeda-Uchimura, Y., Boxer, A., Blennow, K., Friedman, L. F., Galasko, D. R., Jutel, M., Karydas, A., et al. (2007) · Nature Medicine · doi:10.1038/nm1653 · Cited by 1,048
Plasma Aβ40 and Aβ42 and Alzheimer’s disease: Relation to age, mortality, and risk
Mayeux, R., Honig, L. S., Tang, M.-X., Manly, J., Stern, Y., Schupf, N., & Mehta, P. D. (2003) · Neurology · doi:10.1212/01.wnl.0000091890.32140.8f · Cited by 434
Implementing a screening and diagnosis program for dementia in primary care
Boustani, M., Callahan, C. M., Unverzagt, F. W., Austrom, M. G., Perkins, A. J., Fultz, B. A., Hui, S. L., & Hendrie, H. C. (2005) · Journal of General Internal Medicine · doi:10.1007/s11606-005-0103-7 · Cited by 374
Serum MCP-1 levels are increased in mild cognitive impairment and mild Alzheimer’s disease
Galimberti, D., Fenoglio, C., Lovati, C., Venturelli, E., Guidi, I., Corrà, B., Scalabrini, D., Clerici, F., Mariani, C., Bresolin, N., et al. (2006) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2005.10.007 · Cited by 209
Decreased C-Reactive Protein Levels in Alzheimer Disease
O'Bryant, S. E., Waring, S. C., Hobson, V., Hall, J. R., Moore, C. B., Bottiglieri, T., Massman, P., & Diaz-Arrastia, R. (2010) · Journal of Geriatric Psychiatry and Neurology · doi:10.1177/0891988709351832 · Cited by 103
Self-citationTeunissen, C. E., Veerhuis, R., De Vente, J., Verhey, F. R. J., Vreeling, F., van Boxtel, M. P. J., Glatz, J. F. C., & Pelsers, M. A. L. (2011) · European Journal of Neurology · doi:10.1111/j.1468-1331.2010.03273.x · Cited by 74
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Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Cedarbaum, J., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.11.001 · Cited by 348
Self-citationMultiplex proteomics identifies novel CSF and plasma biomarkers of early Alzheimer’s disease
Whelan, C. D., Mattsson, N., Nagle, M. W., Vijayaraghavan, S., Hyde, C., Janelidze, S., Stomrud, E., Lee, J., Fitz, L., Samad, T. A., et al. (2019) · Acta Neuropathologica Communications · doi:10.1186/s40478-019-0795-2 · Cited by 262
Biomarkers for the Early Detection and Progression of Alzheimer's Disease
Counts, S. E., Ikonomovic, M. D., Mercado, N., Vega, I. E., & Mufson, E. J. (2017) · Neurotherapeutics · doi:10.1007/s13311-016-0481-z · Cited by 224
Metadata
- Authors
- O'Bryant, S. E., Xiao, G., Barber, R., Huebinger, R., Wilhelmsen, K., Edwards, M., Graff-Radford, N., Doody, R., Diaz-Arrastia, R., for the Texas Alzheimer's Research & Care Consortium, & Initiative, F. T. A. D. N.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-12-07
- Volume
- 6
- Issue
- 12
- Pages
- e28092
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2097452416
- Cited by
- 158
- References
- 6
- FWCI
- 9.65
- Type
- journal article
- Language
- en
- Open access
- gold
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- O'Bryant, S. E., Xiao, G., Barber, R., Huebinger, R., Wilhelmsen, K., Edwards, M., Graff-Radford, N., Doody, R., Diaz-Arrastia, R., for the Texas Alzheimer's Research & Care Consortium, & Initiative, F. T. A. D. N. (2011). A Blood-Based Screening Tool for Alzheimer's Disease That Spans Serum and Plasma: Findings from TARC and ADNI. PLoS ONE. https://doi.org/10.1371/journal.pone.0028092
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