ADRD Biomarker Heterogeneity Framework

ADRD Biomarker Heterogeneity Framework

Introduction

Henrik Zetterberg's heterogeneity framework, presented at the AD/PD 2026 conference in Copenhagen, articulates a fundamental reorientation in how the field should interpret biomarker data across Alzheimer Disease and Related Dementias (ADRD). His central thesis is that disease heterogeneity — not a single pathophysiological cascade — is the primary challenge facing biomarker validation, diagnostic classification, and clinical trial design[@zetterberg2026adh].

The framework moves beyond the traditional view of Alzheimer's disease as a linear amyloid → tau → neurodegeneration cascade toward a model in which multiple independent biological processes (amyloid, tau, TDP-43, vascular injury, neuroinflammation, alpha-synuclein, aging-related resilience mechanisms) interact in varying combinations across individuals to produce clinically similar phenotypes. This heterogeneity explains why blood biomarkers that perform well in one cohort may underperform in others, why anti-amyloid therapies have shown heterogeneous response rates, and why single-marker diagnostic thresholds fail to capture the full spectrum of AD pathology.

The Heterogeneity Problem

Why One Size Does Not Fit All

The A/T/N classification framework[@jack2018] was a major advance for standardizing biomarker reporting, but its binary positive/negative framework masks substantial within-group heterogeneity:

ADRD Biomarker Heterogeneity Framework

Introduction

Henrik Zetterberg's heterogeneity framework, presented at the AD/PD 2026 conference in Copenhagen, articulates a fundamental reorientation in how the field should interpret biomarker data across Alzheimer Disease and Related Dementias (ADRD). His central thesis is that disease heterogeneity — not a single pathophysiological cascade — is the primary challenge facing biomarker validation, diagnostic classification, and clinical trial design[@zetterberg2026adh].

The framework moves beyond the traditional view of Alzheimer's disease as a linear amyloid → tau → neurodegeneration cascade toward a model in which multiple independent biological processes (amyloid, tau, TDP-43, vascular injury, neuroinflammation, alpha-synuclein, aging-related resilience mechanisms) interact in varying combinations across individuals to produce clinically similar phenotypes. This heterogeneity explains why blood biomarkers that perform well in one cohort may underperform in others, why anti-amyloid therapies have shown heterogeneous response rates, and why single-marker diagnostic thresholds fail to capture the full spectrum of AD pathology.

The Heterogeneity Problem

Why One Size Does Not Fit All

The A/T/N classification framework[@jack2018] was a major advance for standardizing biomarker reporting, but its binary positive/negative framework masks substantial within-group heterogeneity:

• Amyloid-positive individuals span a continuum from early-stage plaque deposition to advanced plaque load, with different trajectories of biomarker change over time
• Tau-positive individuals show wide variation in regional distribution (Braak stages I–VI), spreading speed, and relationship to clinical symptoms
• Neurodegeneration markers (NfL, MRI atrophy) can result from multiple causes — tau, amyloid, vascular, inflammatory — that produce indistinguishable signal

Sources of Heterogeneity

The three major axes of heterogeneity that Zetterberg's framework identifies are:

Blood Biomarkers Across the Heterogeneity Spectrum

P-Tau Species: Sensitivity vs. Specificity Trade-offs

The phosphorylated tau biomarker family (p-tau181, p-tau217, p-tau231) has emerged as the most specific blood-based readout for AD-type pathology, but their performance varies across heterogeneity axes:

| Biomarker | AD-specificity | Early sensitivity | Cross-disease signal | Key references |
|-----------|---------------|-------------------|---------------------|----------------|
| p-tau217 | Very high | Moderate (appears after Aβ PET+) | Low | [@thijssen2022; @mattsson2019] |
| p-tau231 | High | High (earliest tau marker) | Low | [@hansson2024] |
| p-tau181 | High | Moderate | Low | [@blennow2024] |

P-tau217 shows the strongest correlation with amyloid burden and clinical progression, making it the most widely adopted clinical marker[@hansson2024]. However, its performance is modulated by:

• APOE4 status: APOE4 carriers show elevated baseline p-tau217 levels independent of amyloid status
• Age: Age-adjusted cutoffs significantly improve specificity in older populations
• Ancestry: Current reference ranges derived primarily from European-ancestry cohorts may not generalize across populations[@hajjar2024]

NfL: Non-Specific but Universally Informative

Neurofilament light chain (NfL) reflects the rate of neuronal injury regardless of cause, making it powerful for tracking progression but limited for differential diagnosis. Zetterberg's framework emphasizes NfL as an ensemble-level monitoring tool — useful for clinical trials to track global neurodegeneration, but requiring complementary disease-specific markers to determine the underlying cause of injury.

GFAP: Astrocytic Reactivity as a Disease Stage Indicator

Glial fibrillary acidic protein (GFAP) provides information about astrocytic reactivity that neither p-tau nor NfL captures. Elevated GFAP may indicate an earlier "pre-injury" state in the amyloid → tau → neurodegeneration cascade, useful for identifying individuals in a window where neuroprotective intervention might be most effective.

Multi-Analyte Panels for Heterogeneity Stratification

The framework advocates for multi-analyte panels that simultaneously measure:

• Amyloid: Aβ42/40 ratio, p-tau217
• Tau: p-tau217, p-tau181, p-tau231
• Neurodegeneration: NfL
• Reactive astrogliosis: GFAP
• Synucleinopathy (when applicable): α-synuclein seed amplification assays

Clinical Trial Design Implications

Enrichment Beyond Amyloid Positivity

Traditional AD clinical trial enrichment relied on amyloid PET positivity as the primary inclusion criterion. Zetterberg's framework argues that biomarker-based enrichment must go further to account for heterogeneity:

Outcome Measure Selection

The framework distinguishes between:

• Disease-modifying outcomes: Biomarker levels (p-tau217, NfL) that reflect underlying pathological processes and are less subject to cognitive reserve effects
• Clinical outcomes: Cognitive measures (CDR-SB, ADAS-Cog) that are heavily modulated by heterogeneity in clinical expression

Addressing the Amyloid Therapy Heterogeneity Problem

The heterogeneous clinical responses to anti-amyloid monoclonal antibodies (lecanemab, donanemab, gantenerumab) illustrate why the heterogeneity framework matters:

• Responders vs. non-responders: Only a subset of amyloid-positive individuals show clinical benefit from anti-amyloid therapy. Heterogeneity in co-pathology burden, tau stage, and cognitive reserve likely explains much of this variance
• ARIA as a heterogeneity marker: Amyloid-related imaging abnormalities (ARIA) occur more frequently in APOE4 carriers, suggesting ARIA itself is a manifestation of underlying biological heterogeneity rather than a drug side effect
• Subgroup-specific benefit: Re-analysis of lecanemab and donanemab trial data stratified by biomarker subtypes may reveal that specific subgroups derive substantially greater benefit than the overall population average[@cummings2024]

Relationship to Existing NeuroWiki Pages

This framework builds on and cross-links to the following pages:

• [Blood-Based Biomarkers for Neurodegeneration](/mechanisms/blood-based-biomarkers) — the primary blood biomarker reference page; this page extends it with heterogeneity-specific content
• [Biomarkers of Alzheimer Disease](/mechanisms/biomarkers-alzheimers) — foundational AT(N) framework and biomarker sequence
• [P-Tau217 as Clock for AD Onset Timing](/mechanisms/p-tau217-onset-timing) — p-tau217 as a staging tool, complementing the heterogeneity lens
• [Henrik Zetterberg](/researchers/henrik-zetterberg) — researcher profile with biomarker focus
• [AD/PD 2026 Blood Biomarkers](/biomarkers/adpd-2026-blood-biomarkers) — conference coverage of blood biomarker advances
• [AD/PD 2026 Precision Medicine and Genetic Stratification](/mechanisms/adpd-2026-precision-medicine-genetic-stratification) — overlapping content on genetic stratification
• [Alzheimer's Disease](/diseases/alzheimers-disease) — primary disease page

Key Takeaways

References