Genetic risk scores enhance the diagnostic value of plasma biomarkers of brain amyloidosis
Open accessAbstract
Blood-based biomarkers offer strong potential to revolutionize diagnosis, trial enrolment and treatment monitoring in Alzheimer’s disease (AD). However, further advances are needed before these biomarkers can achieve wider deployment beyond selective research studies and specialty memory clinics, including the development of frameworks for optimal interpretation of biomarker profiles. We hypothesized that integrating Alzheimer’s disease genetic risk score (AD-GRS) data would enhance the diagnostic value of plasma AD biomarkers by better capturing extant disease heterogeneity. Analysing 962 individuals from a population-based sample, we observed that an AD-GRS was independently associated with amyloid PET levels (an early marker of AD pathophysiology) over and above APOE ε4 or plasma p-tau181, amyloid-β42/40, glial fibrillary acidic protein or neurofilament light chain. Among individuals with a high or moderately high plasma p-tau181, integrating AD-GRS data significantly improved classification accuracy of amyloid PET positivity, including the finding that the combination of a high AD-GRS and high plasma p-tau181 outperformed p-tau181 alone in classifying amyloid PET positivity (88% versus 68%; P = 0.001). A machine learning approach incorporating plasma biomarkers, demographics and the AD-GRS was highly accurate in predicting amyloid PET levels (90% training set; 89% test set) and Shapley value analyses (an explainer method based in cooperative game theory) indicated that the AD-GRS and plasma biomarkers had differential importance in explaining amyloid deposition across individuals. Polygenic risk for AD dementia appears to account for a unique portion of disease heterogeneity, which could non-invasively enhance the interpretation of blood-based AD biomarker profiles in the population.
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Refinements to clinical pretest probability estimates will probably further improve plasma biomarker diagnostic performance and interpretation.
“Fourth, refinements to the clinical pretest probability estimates (for example, through polygenetic risk scores58 or through basic algorithms incorporating age, APOE genotype and cognitive testing59) will probably further improve plasma biomarker diagnostic performance and interpretation.”
From Diagnosis of Alzheimer's disease using plasma biomarkers adjusted to clinical probability (2024) · cites this paper as [58]
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Diagnosis of Alzheimer's disease using plasma biomarkers adjusted to clinical probability
Therriault, J., Janelidze, S., Benedet, A. L., Ashton, N. J., Arranz Martínez, J., Gonzalez-Escalante, A., Bellaver, B., Alcolea, D., Vrillon, A., Karim, H., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00731-y · Cited by 112 · see citing works
“Fourth, refinements to the clinical pretest probability estimates (for example, through polygenetic risk scores 58 or through basic algorithms incorporating age, APOE genotype and cognitive testing 59 ) will probably further improve plasma biomarker diagnostic performance and interpretation.”
Self-citationScreen, Sample, Stratify: Biomarkers and Machine Learning Compress Dementia Pathways
Battaglia, S., & Tanaka, M. (2026) · Biomedicines · doi:10.3390/biomedicines14010159 · Cited by 2 · see citing works
- Authors
- Ramanan, V. K., Gebre, R. K., Graff-Radford, J., Hofrenning, E., Algeciras-Schimnich, A., Figdore, D. J., Lowe, V. J., Mielke, M. M., Knopman, D. S., Ross, O. A., Jack, C. R., Petersen, R. C., & Vemuri, P.
- Affiliations
- Mayo Clinic (US)
- Department of Neurology, Mayo Clinic , Rochester, MN 55905 , USA
- Department of Laboratory Medicine and Pathology, Mayo Clinic , Rochester, MN 55905 , USA
- Wake Forest University Health Sciences (US)
- Mayo Clinic in Florida (US)
- Corresponding author
- Ramanan, Vijay K
- ORCID
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-06-05
- Volume
- 146
- Issue
- 11
- Pages
- 4508-4519
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- PMC
- PMC10629762
- OpenAlex
- W4379600983
- Cited by
- 24
- References
- 9
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 10
- FWCI
- 4.89
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Brain (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NIH (U01 AG006786, R01 NS097495, R01 AG56366, P50 AG016574, P30 AG062677, R37 AG011378, R01 AG041851, RF1 AG55151, U54 NS100693, R01 AG034676)
- GHR Foundation
- Mayo Clinic
- Alzheimer’s Association
- Mayo Foundation
- Liston Award
- Elsie and Marvin Dekelboum Family Foundation
- Schuler Foundation (C06 RR018898)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- Alzheimer's disease, polygenic risk score, plasma biomarkers, cerebral amyloidosis, amyloid PET, phosphorylated tau 181, APOE ε4, neurofilament light chain, glial fibrillary acidic protein, polygenic risk, disease heterogeneity, blood-based biomarkers, diagnostic accuracy, Shapley value, machine learning prediction
- MeSH terms
- Alzheimer Disease, Amyloidosis, Brain, Humans, Risk Factors, Biomarkers, Amyloid beta-Peptides, tau Proteins, Amyloidogenic Proteins
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