9 October 2026

7 Alzheimer’s Biomarkers That Inform Trial Design

Biomarker selection is a critical part of Alzheimer’s disease (AD) study design. The choice of which biomarkers to use is based on study parameters and the particular questions the trial is designed to assess, such as population characteristics (early AD vs. moderate to advanced disease) and the drug’s expected mechanism of action.

Misjudge a biomarker’s role, and you’re building diagnostic or safety logic on the wrong foundation. Read on to find out how each one breaks down.

The 7 biomarker types driving Alzheimer’s trial Design

The 2024 Alzheimer’s Association Criteria for Diagnosis and Staging of Alzheimer’s Disease expand the earlier A/T/N framework into a multimodal AT₁T₂NISV profile. Core 1 biomarkers (amyloid PET, validated CSF amyloid-based assays, and sufficiently accurate blood-based assays such as some plasma p-tau217 tests) can establish the biological presence of Alzheimer’s disease. Core 2 biomarkers (including tau PET and selected later-changing tau measures such as MTBR-tau243) are particularly useful for biological staging, prognosis, and assessing the likelihood that AD pathology contributes to symptoms. N and I markers reflect nonspecific injury/degeneration and inflammation, while V and S address common non-AD co-pathologies: vascular brain injury and α-synucleinopathy, respectively.

1. Amyloid (A). Amyloid PET (positron emission tomography), CSF (cerebrospinal fluid) Aβ42/40 ratio, CSF total tau/Aβ42 ratio, and increasingly plasma Aβ42/40 or plasma p-tau-based algorithms when those plasma assays have sufficient validated accuracy. This is the “A” biomarker type, and together with T1 it makes up Core 1, the tier the 2024 criteria treat as sufficient on its own for a biological AD diagnosis. Amyloid specifically is the backbone of diagnostic confirmation: it establishes whether a participant has the underlying amyloid pathology a program is targeting.

2. Tau pathology (T1 and T2). The 2024 criteria split tau into two tiers with different roles. T1 covers early changing fluid markers (p-tau181, p-tau217, and p-tau231) and sits in Core 1 alongside amyloid, meaning an abnormal, adequately validated T1 result can itself support a biological AD diagnosis. T2 covers later-changing, more aggregated tau pathology (tau PET and emerging fluid measures like MTBR-tau243) and is used mainly for staging, prognosis, and building confidence that AD pathology is what’s driving a patient’s symptoms.

3. Neurodegeneration and injury (N). MRI atrophy, FDG-PET hypometabolism, and neurofilament light (NfL) all fall here. Under the 2024 criteria, N is formally classified as non-core, the same category GFAP, sTREM2, and neurogranin (below) fall into: useful for characterizing injury and prognosis, but not specific enough on their own to define Alzheimer’s pathology.

NfL is a good example of why. It’s sensitive and easy to measure in plasma, but on its own it just flags general axonal injury, not a particular disease process, so a negative or unchanged result doesn’t mean much by itself. Layered alongside Core 1/Core 2 amyloid and tau data, though, it adds real supportive evidence of downstream neuronal injury.

4. GFAP (astrocytic activation). Like NfL, GFAP falls into the non-core “I” (inflammation) category under the 2024 criteria, a marker of astrocyte response to injury, not of AD pathology itself. GFAP can rise with early amyloid pathology and has been shown to decline with effective anti-amyloid treatment, making it a useful downstream pharmacodynamic signal even without diagnostic weight on its own.

5. sTREM2 (microglial/immune activation). Also part of the non-core “I” category, sTREM2 reflects the brain’s immune response. Direction of change here is context-dependent: a rise isn’t automatically bad, and a fall isn’t automatically good, which is exactly why non-core markers like this one are read alongside Core 1/Core 2 data rather than interpreted on their own.

6. Neurogranin (synaptic injury). Neurogranin is a non-core CSF marker of synaptic dysfunction, useful for characterizing downstream neuronal effects that amyloid and tau markers don’t directly capture. Like the rest of this group, it’s not specific enough to define AD on its own.

7. Vascular/hemorrhagic burden on MRI. Unlike items 3 through 6, this sits in a separate co-pathology category (V) in the 2024 framework, not the non-core bucket. It’s the safety category, and for anti-amyloid antibody programs it’s not optional. MRI is used to monitor for ARIA (amyloid-related imaging abnormalities), baseline microhemorrhages, and superficial siderosis.

Regulators require ARIA surveillance for beta-amyloid antibody programs, and APOE4 status directly factors into that risk. Protocolized MRI monitoring here is core to any safety and pharmacovigilance plan on an anti-amyloid trial.

When Should Biomarker Collection Start?

What determines the timing is context of use, not a fixed phase schedule.

Biomarker strategy in Alzheimer’s is better treated as a program-level workstream that starts at target validation and is settled, category by category, as each biomarker’s context of use is defined, since Core 1 diagnostic markers, non-core prognostic markers, and co-pathology safety markers each need a different validation path.

The context of use for each biomarker needs to be worked out early enough to affect CMC, assay qualification and validation, specimen collection, site training, endpoint hierarchy, sample-size assumptions, and the statistical analysis plan. Biomarker-based enrichment or eligibility criteria need to be operationally and analytically ready before the trial that relies on them begins, not necessarily before every first-in-human study. And how early each decision needs to be made isn’t uniform: it depends on mechanism, target population, degree of invasiveness, analytical maturity, and whether a given biomarker is exploratory, enrichment-based, pharmacodynamic, safety-related, or registrational.

Teams that delay formalizing the strategy are missing groundwork that should already be done: target engagement data, baseline characterization of how the population progresses, and the practical logistics of sample collection and assay validation. Retrofitting those considerations into an already-designed protocol adds real cost and time that can burn an already lean runway.
FDA guidance reinforces this. The agency encourages early discussion when biomarkers define the intended population, serve as a primary measure of effect, or may require companion-diagnostic development.

There’s also a stratification argument for starting early. APOE4 status and tau positivity both affect how quickly a patient is likely to progress; APOE4 homozygotes, for example, tend to decline faster and have higher susceptibility to ARIA. Knowing that status at baseline informs the decision to either stratify enrollment by risk or analyze subgroups separately.

The Real Cost of Waiting Too Long to Strategize

The clearest risk of deferring biomarker confirmation is enrolling the wrong population. Without a confirmed Core 1 result (amyloid or T1 tau), a program risks including patients who don’t actually have Alzheimer’s pathology at all. Vascular dementia and other conditions can present similarly, especially in earlier disease stages where clinical diagnosis alone is harder to get right. For an amyloid-targeted therapy specifically, confirming that patients actually have amyloid plaques establishes that the target is present to begin with; target engagement itself is demonstrated later, by a treatment-associated change in the biomarker.

If that gap gets discovered after the fact, fixing it means a protocol amendment, which can entail retraining sites, renegotiating or establishing new vendor contracts, and extending a timeline that’s already under pressure. It also means the underlying data may already be weaker. A population that includes patients without true AD pathology dilutes the signal a trial is trying to detect.

Regulatory bodies generally encourage this kind of diagnostic rigor precisely because it makes trial data more trustworthy. However, PET and CSF confirmation isn’t cheap, and smaller biotechs are often balancing scientific rigor against investor timelines.

A practical middle ground that’s gained traction: using a low-cost plasma p-tau217 or Aβ-based prescreen to identify likely amyloid-positive candidates, then reserving PET or CSF confirmation for those who screen positive. This approach keeps diagnostic confirmation in the protocol without requiring every candidate to go through the most expensive test first.

Getting Inclusion/Exclusion Thresholds Right

Amyloid burden cutoffs deserve particular attention, because getting them wrong cuts both ways. Set the threshold too low, and patients with only marginally elevated amyloid can enter the trial, which is a lower starting burden than a typical mild Alzheimer’s or MCI patient, leaving less “room” for a treatment effect to show up against baseline. The risk here has less to do with the drug and more to do with who was allowed to enroll.

Set the threshold too high, on the other hand, and you risk unnecessary screen failures, excluding otherwise viable patients without meaningfully improving the strength of the eventual signal. Some degree of elevated amyloid is common in normal elderly patients and doesn’t necessarily mean they have or will develop Alzheimer’s. Calibrating the cutoff is a genuine balancing act, and getting it right leans as much on patient recruitment strategy as it does on biostatistics modeling.

It’s also worth remembering that cognitive and functional assessments change slowly, especially in mild or early-stage populations. Even over the course of a 12-month study, meaningful cognitive change can be hard to detect. So, effectively, biomarker data carries more of the analytical weight in early-phase readouts than it will later in development.

Where Biomarkers Add the Most Value Across Development

Biomarker utility shifts across the development timeline

Target validation / lead optimization: Build the causal chain from target to pathway modulation to pathological process to clinical effect, and identify which matrices and modalities are even feasible. This is where the biomarker hypothesis map and candidate assay list that everything downstream builds on gets established.

IND-enabling / first-in-human planning: Define the minimum viable biomarker panel and establish sample collection, processing, and analytical-validation plans, so the program has a defensible context-of-use for each biomarker before it ever reaches the clinic.

Proof-of-concept (often Phase Ib): A fast read on whether a drug is doing what it’s designed to do, an early go/no-go signal before investing in a larger population.

Dose-finding studies (Phase IIa or IIb): Biomarkers show which doses are actually hitting the intended target, confirming target engagement alongside safety data.

Later-stage studies: Biomarkers should move in the same direction as cognitive and functional endpoints.

Regulatory history shows why: amyloid PET reduction (the Core 1 biomarker discussed above) has twice been accepted as a stand-alone surrogate endpoint for accelerated approval, first for aducanumab in 2021, then for lecanemab in 2023. But accelerated approval is provisional, not a finish line. Aducanumab never produced convincing clinical-benefit data and was discontinued; lecanemab did, and converted to full traditional approval within the year once its clinical trial showed a real effect on cognition and function.

In practice, biomarker data can open the door to approval, but clinical corroboration is what keeps a drug safe and on the market.

A Note on Trial Design Beyond the Lab (Considering Patients Involved)

Two considerations worth flagging as biomarker and study design conversations happen in parallel:

Digital and wearable biomarkers are still early-stage for Alzheimer’s specifically. They’ve shown more direct relevance in conditions like Parkinson’s, where gait and movement metrics are central to the disease itself. For Alzheimer’s, their role today is more ancillary (sleep patterns, for instance) than central to a biomarker strategy.
As Alzheimer’s trials frequently involve elderly or cognitively impaired patients who need caregiver support, frequent site visits and overnight stays carry a real logistical and emotional burden, one that can itself affect performance and results.

Decentralized, more patient-centered trial models — bringing assessments to the patient rather than the reverse — are increasingly part of how trial teams are addressing that burden.

The Bottom Line

Biomarker strategy in Alzheimer’s programs isn’t a decision you make once, at any one development phase gate. It’s a workstream that starts as early as target validation and is refined, category by category, as each biomarker’s context of use is established. Treat it that way, and you go into your pivotal studies with a confirmed population, a defensible diagnosis, and biomarker data that can corroborate a clinical signal instead of obfuscating it. Wait, and you usually end up paying for that decision later, in timeline, in budget, and sometimes in the credibility of your data.

Getting this right from the start is exactly what an experienced Alzheimer’s clinical trial CRO should be bringing to the table before the first patient is ever dosed. If you’re weighing biomarker strategy for an upcoming Alzheimer’s program, talk to an expert at Avance Clinical. Our dedicated Central Nervous System (CNS) Center of Excellence routinely reviews and redirects early-stage plans to support long-term patient and market access.

About the Authors

Francesco Bibbiani, MD, CPI

Vice President & Therapeutic Area Head, CNS Center of Excellence

Marty Kraut Ph.D., PMP

Director of Operations, CNS Center of Excellence

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