Topic: Amyloid, tau and blood-test diagnosis
Objective To develop an algorithm that separates patients with Alzheimer disease (AD) from controls. Design Longitudinal case-control study. Setting The Texas Alzheimer's Research Consortium project. Patients We analyzed serum protein-based multiplex biomarker data from 197 patients diagnosed with AD and 203 controls. Main Outcome Measure The total sample was randomized equally into training and test sets and random forest methods were applied to the training set to create a biomarker risk score. Results The biomarker risk score had a sensitivity and specificity of 0.80 and 0.91, respectively, and an area under the curve of 0.91 in detecting AD. When age, sex, education, and APOE status were added to the algorithm, the sensitivity, specificity, and area under the curve were 0.94, 0.84, and 0.95, respectively. Conclusions These initial data suggest that serum protein-based biomarkers can be combined with clinical information to accurately classify AD. A disproportionate number of inflammatory and vascular markers were weighted most heavily in the analyses. Additionally, these markers consistently distinguished cases from controls in significant analysis of microarray, logistic regression, and Wilcoxon analyses, suggesting the existence of an inflammatory-related endophenotype of AD that may provide targeted therapeutic opportunities for this subset of patients.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
O'Bryant, S. E., Xiao, G., Barber, R., Reisch, J., Doody, R., Fairchild, T., et al. (2010). A serum protein-based algorithm for the detection of Alzheimer disease. Archives of neurology. https://doi.org/10.1001/archneurol.2010.215
Vancouver
O'Bryant SE, Xiao G, Barber R, Reisch J, Doody R, Fairchild T, et al. A serum protein-based algorithm for the detection of Alzheimer disease. Archives of neurology. 2010. doi:10.1001/archneurol.2010.215. PMID: 20837851.
BibTeX
@article{obryant2010-20837851,
author = {O'Bryant, S. E. and Xiao, G. and Barber, R. and Reisch, J. and Doody, R. and Fairchild, T. and Adams, P. and Waring, S. and others},
title = {{A serum protein-based algorithm for the detection of Alzheimer disease}},
journal = {Archives of neurology},
year = {2010},
doi = {10.1001/archneurol.2010.215},
pmid = {20837851},
url = {https://doi.org/10.1001/archneurol.2010.215}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
Key findings
- Results The biomarker risk score had a sensitivity and specificity of 0.80 and 0.91, respectively, and an area under the curve of 0.91 in detecting AD.
- Conclusions These initial data suggest that serum protein-based biomarkers can be combined with clinical information to accurately classify AD.
- Additionally, these markers consistently distinguished cases from controls in significant analysis of microarray, logistic regression, and Wilcoxon analyses, suggesting the existence of an inflammatory-related endophenotype of AD that may provide targeted therapeutic opportunities for this subset of patients.
Why it matters
A blood protein-based score, especially when combined with age, sex, education and APOE status, could classify people with Alzheimer's disease versus controls with fairly high accuracy. These are initial data on a diagnostic approach, not a treatment, and the authors note the markers suggest an inflammatory subtype that might offer future therapeutic targets.
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Study details
- Study designcase-control study
- Participants197
- Evidence strengthHigh
How this study scored 59/100
Published in a peer-reviewed journal, and it is a case-control study. Scored zero for effect reported because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Archives of neurology) |
| Journal's citation record | 3 / 4 | Archives of Neurology: journal h-index 353 (OpenAlex, 2026-10-29) |
| Study design | 6 / 12 | A case-control study |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2010 — within 20 year(s) of the newest study here |
| Sample size | 2 / 4 | 197 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 1 / 2 | Randomised (as stated in the record) |
| Times cited | 5 / 7 | Cited 230 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 12.8 citations a year over 18 year(s) since 2010 |
| Impact for its field and year | 4 / 6 | Cited 15.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 15 other studies in this library |
| Still being cited | 1 / 4 | Cited 14 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Sid E. O’Bryant: h-index 63 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Texas Tech University Health Sciences Center: institution h-index 356 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 20837851) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 59 / 100 | — |
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Read the original
- DOI: 10.1001/archneurol.2010.215
- PubMed: 20837851
- Original source →
- Free full text: PubMed Central →
Full text: free to read — PubMed Central lists this copy as free to read: the link above (checked 10 Oct 2026).
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