Why biomarkers matter

From classic cerebrospinal fluid (CSF) assays to the new generation of blood-based tests, biomarkers are providing unprecedented access to the molecular footprint of Alzheimer’s. They can identify disease before symptoms arise, monitor progression, and stratify patients for targeted trials². They’re also more scalable and better suited for repeat testing than imaging. For researchers navigating experimental reproducibility and assay choice, biomarkers offer both a biological window into disease and a practical toolkit for improving translational workflows.

Classic Alzheimer’s disease biomarkers

CSF biomarkers have long been the gold standard in Alzheimer’s diagnostics, including:

• Amyloid-β (Aβ42): Levels drop in CSF when Aβ accumulates in brain plaques. The Aβ42/40 ratio improves specificity and is one of the earliest signals of amyloid pathology³.
• Total tau (t-tau): Reflects neuronal injury, but isn’t specific to AD.
• Phosphorylated tau (p-tau): Typically measured at pTau181 or pTau231, levels rise as tau tangles form, and correlate more directly with AD⁴.

Together, these markers laid the groundwork for the A/T/N framework, which classifies Alzheimer’s based on amyloid (A), tau (T), and neurodegeneration (N)⁵.

Broadening the lens

While Aβ and tau are crucial, they don’t tell the whole story. Newer Alzheimer’s disease biomarkers are helping researchers capture additional dimensions, such as:

• Neurofilament light (NfL): Reflects axonal damage and tracks neurodegeneration across diseases. It’s not specific to AD but is useful for monitoring disease severity⁶.
• Glial fibrillary acidic protein (GFAP): Indicates astrocyte activation. Elevated in early AD and may offer prognostic insight^7,8.
• YKL-40: A neuroinflammatory marker produced by astrocytes and microglia. Levels rise before symptom onset and may help differentiate inflammatory-driven pathology⁹.
• Plasma pTau217: A blood-based tau marker that performs comparably to CSF pTau and can distinguish AD from other dementias¹⁰.

By expanding the biomarker landscape, researchers can build more nuanced models of disease progression and underlying biology.

Shifting from CSF to blood

Traditionally, AD biomarker detection required lumbar punctures or PET scans, both costly and invasive methods poorly suited to large-scale screening. Now, advanced technologies like Single Molecule Array (Simoa) and mass spectrometry (MS) are making it possible to detect pTau217, GFAP, and NfL in blood^11,12.

This shift is a turning point, making AD biomarker detection:

• Less invasive: Easier to collect and repeat.
• Scalable: Suitable for large trials or screening programs.
• Accessible: Potentially usable in primary care, not just memory clinics.

Tests like PrecivityAD® are already FDA-approved for clinical use¹³. While not flawless, blood biomarkers are advancing rapidly and could soon support earlier, broader access to Alzheimer’s diagnoses.

Platforms and assays

Depending on whether they're working with CSF, plasma, or both, you may encounter a few different platforms in AD research, including:

• ELISA: Reliable and widely used for CSF biomarkers, but has limited sensitivity with plasma¹⁴.
• Simoa: Ultra-sensitive digital immunoassay, enabling detection of p-tau and NfL in blood in emerging clinical assays^15,16.
• Mass spectrometry (MS): Highly specific, used in Aβ42/40 testing and biomarker validation¹⁷.

No single platform is perfect, but collectively, they enable the biomarker surge we see today.

Building smarter panels

Since no single marker captures Alzheimer’s complexity, multi-analyte panels are becoming the norm in research and clinical trial design, combining:

• Aβ42/40 (amyloid)
• pTau217 or pTau231 (tau pathology)
• GFAP and YKL-40 (glial response)
• NfL (neurodegeneration)

Some panels also integrate APOE genotype, digital biomarkers (eg, cognitive app performance), or gait data for added context. Machine learning models are increasingly used to optimize these Alzheimer’s disease biomarker combinations for prediction, staging, or therapeutic response¹⁸.

What’s still in the way?

Despite all the momentum, there are still hurdles to overcome before AD biomarkers are widely adopted, including:

• Standardization: Different labs and platforms sometimes yield different results.
• Cutoffs: Defining what’s “positive” for each marker is still in flux.
• Specificity: NfL and GFAP rise in multiple conditions, not just AD.

Until these obstacles are overcome, biomarkers will be interpreted cautiously, especially outside the research setting. Consistent reagents and validated protocols remain key to improving reproducibility and moving toward everyday clinical use.

Biomarkers and the future of AD research

Biomarkers have evolved from niche CSF tools to a rapidly expanding, multi-platform ecosystem with promise for early diagnosis, personalized treatment, and better clinical trials. For researchers, they offer new ways to design studies, track outcomes, and uncover the biology of neurodegeneration. Accessible, composite biomarker panels powered by blood tests, machine learning, and real-time monitoring are still a way off. Still, every new AD biomarker discovery is a step in the right direction.

References

1. Jack Jr., C. R.  et al.  NIA-AA Research Framework: Toward a Biological Definition of Alzheimer’s Disease.  Alzheimers Dement.  14, 535–562 (2018). https://doi.org/10.1016/j.jalz.2018.02.018.
2. Schindler, S. E.  et al.  Acceptable Performance of Blood Biomarker Tests of Amyloid Pathology — Recommendations from the Global CEO Initiative on Alzheimer’s Disease.  Nat. Rev. Neurol.  20, 426–439 (2024). https://doi.org/10.1038/s41582-024-00977-5.
3. Nakamura, A.  et al.  High Performance Plasma Amyloid-β Biomarkers for Alzheimer’s Disease.  Nature  554, 249–254 (2018). https://doi.org/10.1038/nature25456.
4. Blennow, K. & Zetterberg, H. Biomarkers for Alzheimer’s Disease: Current Status and Prospects for the Future.  J. Intern. Med.  284, 643–663 (2018). https://doi.org/10.1111/joim.12816.
5. Jack, C. R.  et al.  A/T/N: An Unbiased Descriptive Classification Scheme for Alzheimer Disease Biomarkers.  Neurology  87, 539–547 (2016). https://doi.org/10.1212/WNL.0000000000002923.
6. Khalil, M.  et al.  Neurofilaments as Biomarkers in Neurological Disorders.  Nat. Rev. Neurol.  14, 577–589 (2018). https://doi.org/10.1038/s41582-018-0058-z.
7. Cicognola, C.  et al.  Plasma Glial Fibrillary Acidic Protein Detects Alzheimer Pathology and Predicts Future Conversion to Alzheimer Dementia in Patients with Mild Cognitive Impairment.  Alzheimers Res. Ther.  13, 68 (2021). https://doi.org/10.1186/s13195-021-00804-9.
8. Benedet, A. L.  et al.  Differences Between Plasma and Cerebrospinal Fluid Glial Fibrillary Acidic Protein Levels Across the Alzheimer Disease Continuum.  JAMA Neurol.  78, 1471–1483 (2021). https://doi.org/10.1001/jamaneurol.2021.3671.
9. Craig-Schapiro, R.  et al.  YKL-40: A Novel Prognostic Fluid Biomarker for Preclinical Alzheimer’s Disease.  Biol. Psychiatry  68, 903–912 (2010). https://doi.org/10.1016/j.biopsych.2010.08.025.
10. Palmqvist, S.  et al.  Discriminative Accuracy of Plasma Phospho-Tau217 for Alzheimer Disease vs Other Neurodegenerative Disorders.  JAMA  324, 772–781 (2020). https://doi.org/10.1001/jama.2020.12134.
11. Bayoumy, S.  et al.  Clinical and Analytical Comparison of Six Simoa Assays for Plasma P-Tau Isoforms P-Tau181, P-Tau217, and P-Tau231.  Alzheimers Res. Ther.  13, 198 (2021). https://doi.org/10.1186/s13195-021-00939-9.
12. Blood Biomarkers Promise to Revolutionize Alzheimer’s Diagnosis.
13. PrecivityAD®.  PrecivityAD®.  https://precivityad.com (accessed 17 Jul 2025).
14. Pais, M. V., Forlenza, O. V. & Diniz, B. S. Plasma Biomarkers of Alzheimer’s Disease: A Review of Available Assays, Recent Developments, and Implications for Clinical Practice.  J. Alzheimers Dis. Rep.  7, 355–380 (2023). https://doi.org/10.3233/adr-230029.
15. Sahrai, H.  et al.  SIMOA-Based Analysis of Plasma NFL Levels in MCI and AD Patients: A Systematic Review and Meta-Analysis.  BMC Neurol.  23, 331 (2023). https://doi.org/10.1186/s12883-023-03377-2.
16. Li, D. & Mielke, M. M. An Update on Blood-Based Markers of Alzheimer’s Disease Using the SiMoA Platform.  Neurol. Ther.  8, 73–82 (2019). https://doi.org/10.1007/s40120-019-00164-5.
17. Klafki, H.-W.  et al.  Diagnostic Performance of Automated Plasma Amyloid-β Assays Combined with Pre-Analytical Immunoprecipitation.  Alzheimers Res. Ther.  14, 127 (2022). https://doi.org/10.1186/s13195-022-01071-y.
18. Patel, N.  et al.  Emerging Blood Biomarkers in Alzheimer’s Disease: A Proteomic Perspective.  Clin. Chim. Acta  576, 120397 (2025). https://doi.org/10.1016/j.cca.2025.120397.