MARKERS-NDD Progression Markers (NCT06596746)

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MARKERS-NDD Progression Markers (NCT06596746)

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Overview

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MARKERS-NDD is an observational cohort study focused on identifying and validating progression biomarkers in neurodegenerative diseases, including Progressive Supranuclear Palsy (PSP), Corticobasal Syndrome (CBS), Multiple System Atrophy (MSA), and Parkinson's Disease (PD). This multi-center, international initiative addresses one of the most critical gaps in neurodegenerative disease research: the lack of validated biomarkers that can reliably track disease progression and serve as surrogate endpoints in clinical trials["@markersndd"].

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Overview

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MARKERS-NDD is an observational cohort study focused on identifying and validating progression biomarkers in neurodegenerative diseases, including Progressive Supranuclear Palsy (PSP), Corticobasal Syndrome (CBS), Multiple System Atrophy (MSA), and Parkinson's Disease (PD). This multi-center, international initiative addresses one of the most critical gaps in neurodegenerative disease research: the lack of validated biomarkers that can reliably track disease progression and serve as surrogate endpoints in clinical trials["@markersndd"].

The study represents a paradigm shift in how we approach biomarker discovery for atypical parkinsonism and tauopathies. Rather than focusing solely on diagnostic biomarkers, MARKERS-NDD prioritizes longitudinal progression markers that can quantify the rate of neurodegeneration, predict clinical decline, and potentially serve as endpoints for disease-modifying therapy trials.

Study Details

| Field | Value |
|-------|-------|
| NCT Number | NCT06596746 |
| Status | Recruiting |
| Study Type | Observational, Prospective Cohort |
| Conditions | PSP, CBS, MSA, PD |
| Sponsor | International Consortium of Academic Medical Centers |
| Enrollment | Target: 500+ participants |
| Follow-up Duration | Minimum 2 years, optional extension to 5 years |
| Sites | Multiple international academic centers |

The study employs a standardized, longitudinal design with comprehensive baseline characterization and standardized follow-up assessments at 6-month intervals.

Scientific Rationale

The Need for Progression Biomarkers

The lack of validated progression biomarkers represents a major obstacle to developing disease-modifying therapies for neurodegenerative diseases. Unlike oncology, where tumor size or survival can serve as objective endpoints, neurodegenerative disease progression is measured through clinical rating scales that are inherently subjective, show high variability, and may not capture the full spectrum of disease pathology.

Several factors highlight the urgent need for progression biomarkers:

Clinical Trial Efficiency: Validated biomarkers can reduce trial duration and sample sizes by providing sensitive measures of disease progression[@beyer2011].

Patient Stratification: Biomarkers can identify subgroups with more homogeneous disease course, enabling targeted therapies.

Mechanistic Insights: Changes in biomarker levels can reveal information about underlying disease mechanisms.

Regulatory Acceptance: The FDA and EMA have expressed willingness to consider biomarker-based endpoints for accelerated approval.

Tauopathies and Disease Progression

PSP and CBS are primary 4R-tauopathies characterized by the intracellular accumulation of hyperphosphorylated tau protein in neurons and glia[@hoglinger2017]. The disease course involves:

Regional spread: Tau pathology progresses in a predictable pattern through the brain
Neuronal loss: Progressive death of neurons in affected regions
Network disruption: Degeneration of specific brain networks measured via functional connectivity
Neuroinflammation: Activation of microglia correlating with disease severity

The rate of progression varies substantially between patients and between clinical subtypes. Richardson's syndrome (PSP-RS) typically progresses more rapidly than PSP-parkinsonism (PSP-P)[@williams2005].

Biomarker Modalities Under Investigation

The MARKERS-NDD study evaluates biomarkers across multiple modalities:

Fluid Biomarkers

Cerebrospinal fluid (CSF) and blood biomarkers provide direct insights into CNS pathology:

| Biomarker | Target | Clinical Relevance |
|-----------|--------|---------------------|
| NfL | Neurofilament light chain | Axonal degeneration, progression rate[@krismer2018] |
| p-tau181/217 | Phosphorylated tau | Tau pathology burden |
| t-tau | Total tau | Neuronal injury |
| Alpha-synuclein | Aggregated alpha-synuclein | Synucleinopathy burden |
| GFAP | Glial fibrillary acidic protein | Astrocyte activation |
| YKL-40 | Chitinase-3-like protein | Neuroinflammation |
| UCH-L1 | Ubiquitin C-terminal hydrolase | Neuronal injury |

Neuroimaging Biomarkers

Advanced neuroimaging provides in vivo measures of neurodegeneration:

Volumetric MRI: Regional brain atrophy rates, particularly in brainstem and basal ganglia
Diffusion Tensor Imaging (DTI): White matter microstructural integrity, especially superior cerebellar peduncle
FDG-PET: Regional hypometabolism patterns characteristic of each disorder
Tau PET: In vivo tau pathology visualization (using second-generation tracers like PM-PBB3)
VMAT2 PET: Presynaptic dopaminergic terminal integrity

Digital Biomarkers

Wearable sensor-based assessments provide objective, continuous measures:

Quantitative gait analysis using instrumented walkways
Wearable accelerometer-based motor assessments
Voice and speech analysis for dysarthria quantification
Oculomotor assessment using video-oculography

Objectives

Primary Objectives

Identify progression biomarkers: Discover fluid, imaging, or digital biomarkers that correlate with clinical progression over 2 years

Characterize natural history: Establish comprehensive natural history data for PSP, CBS, MSA, and PD subtypes

Validate biomarker platforms: Validate existing biomarker platforms across different populations and clinical sites

Secondary Objectives

Develop composite endpoints: Create combination biomarker-clinical endpoints for clinical trials

Subtype stratification: Identify biomarkers that distinguish between clinical subtypes and predict progression rates

Cross-disorder comparisons: Compare biomarker trajectories across different neurodegenerative disorders

Technical optimization: Optimize biomarker measurement protocols for clinical trial readiness

Eligibility Criteria

Inclusion Criteria

Age 40-85 years
Clinical diagnosis of one of the following:
PSP (any subtype) according to MDS-PSP criteria[@hoglinger2017]
CBS according to Armstrong criteria[@armstrong2013]
MSA (parkinsonian or cerebellar subtype)
Idiopathic PD (for comparison)
Ability to undergo repeated clinical and imaging assessments
Willingness to participate in long-term follow-up (minimum 2 years)
MRI compatibility (no pacemakers, cochlear implants, or other contraindications)
Capacity to provide informed consent

Exclusion Criteria

Significant comorbid neurological conditions (stroke, traumatic brain injury, normal pressure hydrocephalus)
Active psychiatric conditions that would interfere with assessments
Inability to cooperate with study procedures
Terminal illness with life expectancy < 2 years
Current participation in interventional clinical trials

Study Design

Longitudinal Cohort Design

The study employs a prospective cohort design with the following features:

Comprehensive baseline evaluation: Full clinical, neuropsychological, imaging, and fluid biomarker assessment at enrollment

Standardized follow-up: 6-month visits for 2 years with optional extension to 5 years

Multi-modal biomarker collection: CSF and blood collection, MRI, and digital assessments at each visit

Central quality control: Central reading for all neuroimaging, standardized protocols across sites

Data harmonization: Common data elements across all sites to enable pooled analyses

Sample Size Considerations

Given the rarity of PSP and CBS, the study requires multi-center collaboration to achieve adequate statistical power. The target enrollment of 500+ participants across all conditions ensures:

Sufficient PSP and CBS cases for subtype-specific analyses
Power to detect moderate correlations between biomarkers and progression
Generalizability across different populations and clinical sites

Outcome Measures

Primary Endpoints

Clinical progression rate: Change in disease-specific rating scale (PSPRS, CBDRS) over 12 months

Brain atrophy rate: Annualized change in regional brain volumes on MRI

Fluid biomarker change: Longitudinal change in CSF NfL levels

Secondary Endpoints

Time to disease milestones (wheelchair dependence, severe dysphagia, nursing home placement)
Correlation between fluid and imaging biomarkers
Validation of digital motor assessments against clinical measures
Quality of life trajectory (PDQ-39, PSP-QoL)
Conversion between diagnostic categories (e.g., PD to PDD)

Exploratory Endpoints

Genetic modifiers of progression
Pharmacogenomic predictors of treatment response
Machine learning-based predictive models

Clinical Subtypes and Their Biomarker Profiles

PSP Subtypes

The study captures the spectrum of PSP phenotypes[@respondek2013]:

Richardson's syndrome (PSP-RS): Classic presentation with vertical gaze palsy, postural instability, and symmetric akinesia-rigidity
PSP-parkinsonism (PSP-P): Asymmetric onset, better levodopa response, less prominent gaze palsy
PSP-corticobasal syndrome (PSP-CBS): Features of CBS with PSP pathology
Pure akinesia with gait freezing (PAGF): Predominant gait freezing with minimal other features
Frontal presentation (PSP-F): Predominant frontal cognitive and behavioral symptoms

CBS Subtypes

Corticobasal syndrome presents with heterogeneous features:

Alien limb phenomena: Involuntary limb movements
Apraxia: Motor planning deficits
Cortical sensory loss: Impaired two-point discrimination
Akinesia-rigidity: Often asymmetric

MSA Subtypes

Multiple system atrophy has two main variants[@krismer2018]:

MSA-P (parkinsonian): Dominant parkinsonian features
MSA-C (cerebellar): Dominant cerebellar features (ataxia, nystagmus)

Significance for Clinical Development

Enabling Disease-Modifying Therapy Trials

The MARKERS-NDD study addresses critical barriers to clinical trial success:

Sample size reduction: Sensitive biomarker endpoints can reduce required sample sizes by 30-50%

Trial duration: Biomarker endpoints may enable shorter trials by detecting effects earlier

Stratification: Biomarker-based patient selection may enrich for rapid progressors

Mechanistic proof: Biomarker changes can provide evidence of target engagement

Current Therapeutic Landscape

Several disease-modifying therapies are in development for PSP[@litvan2011]:

| Approach | Examples | Development Stage |
|----------|----------|-------------------|
| Tau aggregation inhibitors | Tolfenamic acid, Lithium | Phase 2 |
| Anti-tau immunotherapies | ABBV-8E12, UCB0107 | Phase 1-2 |
| Anti-tau ASOs | BIIB080, IONIS-MAPTRx | Phase 1-2 |
| Neuroprotective agents | CoQ10, TPN-101 | Phase 2 |

The MARKERS-NDD study will help identify which patients are most likely to benefit from these emerging treatments through biomarker stratification.

Regulatory Implications

Regulatory agencies have expressed increasing flexibility regarding biomarker-based endpoints:

FDA: Guidance on biomarker qualification and use in accelerated approval
EMA: Adaptive pathway approaches for rare diseases
Ishihara Initiative: International collaboration on biomarker validation

The MARKERS-NDD study complements other biomarker initiatives:

PROSPECT-MRI: UK-based PSP MRI biomarker study
4RTNI: US-based tau imaging network
CBS/PSP Genetic Study: Genetic modifiers of disease progression
MSA-SG: MSA study group natural history registry

This study is specifically linked to:

[NADAPT Study](/clinical-trials/nad-replenishment-parkinsonism-nct06162013): NAD replenishment in Parkinsonism
[NCT03872102](/clinical-trials/neuroimaging-parkinsonian-syndromes-nct03872102): Advanced neuroimaging for PSP
[NCT05121012](/clinical-trials/synaptic-loss-msa-nct05121012): Synaptic loss biomarkers in MSA

Future Directions

Extended Follow-up

The optional 5-year extension will enable:

Assessment of very long-term biomarker trajectories
Correlation with end-of-life clinical status
Validation of progression models

Multi-omic Integration

Future analyses will integrate:

Genomics: GWAS and whole-exome sequencing
Proteomics: CSF and blood proteomics
Metabolomics: Metabolic biomarker panels
Transcriptomics: Gene expression profiling

Machine Learning Applications

Advanced computational approaches will:

Develop predictive models combining multiple biomarkers
Identify novel biomarker patterns
Personalize disease progression estimates

External Links

[MARKERS-NDD on ClinicalTrials.gov](https://clinicaltrials.gov/study/NCT06596746)
[CurePSP Foundation](https://curepsp.org/)
[PSP Europe Network](https://www.psp-europe.net/)
[Movement Disorder Society](https://www.movementdisorders.org/)

References

[MARKERS-NDD Study - ClinicalTrials.gov NCT06596746](https://clinicaltrials.gov/study/NCT06596746)[@markersndd]

[Höglinger GU et al., Clinical diagnosis of progressive supranuclear palsy: The MDSPSP criteria (2017)](https://pubmed.ncbi.nlm.nih.gov/28692957/)[@hoglinger2017]

[Armstrong MJ et al., Criteria for the diagnosis of corticobasal degeneration (2013)](https://pubmed.ncbi.nlm.nih.gov/23419831/)[@armstrong2013]

[Krismer F et al., Natural history of multiple system atrophy: A prospective cohort study (2018)](https://pubmed.ncbi.nlm.nih.gov/29513984/)[@krismer2018]

[Beyer MK et al., White matter hyperintensities, cerebrospinal fluid phosphorylated tau, and cognition in Parkinson disease (2011)](https://pubmed.ncbi.nlm.nih.gov/22042276/)[@beyer2011]

[Massoth L et al., Motor and non-motor correlates of axial impairment in atypical parkinsonisms (2021)](https://pubmed.ncbi.nlm.nih.gov/34046623/)[@massoth2021]

[Litvan I et al., Planning and designing clinical trials for progressive supranuclear palsy (2011)](https://pubmed.ncbi.nlm.nih.gov/21328041/)[@litvan2011]

[Respondek G et al., The phenotypic spectrum of progressive supranuclear palsy (2013)](https://pubmed.ncbi.nlm.nih.gov/24072416/)[@respondek2013]

[Boxer AL et al., Clinical syndromes associated with progressive supranuclear palsy (2006)](https://pubmed.ncbi.nlm.nih.gov/16540456/)[@boxer2006]

[Golbe LI et al., Follow-up study of risk factors in progressive supranuclear palsy (2000)](https://pubmed.ncbi.nlm.nih.gov/11025267/)[@golbe2000]

[Nichelli P et al., Neuropsychological testing in atypical parkinsonian syndromes (2014)](https://pubmed.ncbi.nlm.nih.gov/24249399/)[@nichelli2014]

[Williams DR et al., Characteristics of two distinct clinical phenotypes in progressive supranuclear palsy (2005)](https://pubmed.ncbi.nlm.nih.gov/16339797/)[@williams2005]

[Beyer K et al., Biomarkers in neurodegenerative diseases: A systematic review (2019)](https://pubmed.ncbi.nlm.nih.gov/31716079/)[@br2019]

[Zhang J et al., Biomarker-based prediction of progression in Parkinson disease (2015)](https://pubmed.ncbi.nlm.nih.gov/26375586/)[@zhang2015]

[McMillan CT et al., Cognitive decline in the neurodegenerative tauopathies (2011)](https://pubmed.ncbi.nlm.nih.gov/21360039/)[@mcmillan2011]

[Lerche S et al., Falls in progressive supranuclear palsy: A systematic review (2019)](https://pubmed.ncbi.nlm.nih.gov/31378624/)[@lerche2019]

[Shoeibi A et al., Diagnostic accuracy of the Montreal Cognitive Assessment in subtypes of Parkinson disease (2019)](https://pubmed.ncbi.nlm.nih.gov/31432251/)[@shoeibi2019]

[Postuma RB et al., MDS research criteria for prodromal Parkinson disease (2015)](https://pubmed.ncbi.nlm.nih.gov/26587593/)[@postuma2015]

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📊 Evidence Profile

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📗 Cite This Artifact

MARKERS-NDD Progression Markers (NCT06596746)

Overview

Overview

Study Details

Scientific Rationale

The Need for Progression Biomarkers

Tauopathies and Disease Progression

Biomarker Modalities Under Investigation

Fluid Biomarkers

Neuroimaging Biomarkers

Digital Biomarkers

Objectives

Primary Objectives

Secondary Objectives

Eligibility Criteria

Inclusion Criteria

Exclusion Criteria

Study Design

Longitudinal Cohort Design

Sample Size Considerations

Outcome Measures

Primary Endpoints

Secondary Endpoints

Exploratory Endpoints

Clinical Subtypes and Their Biomarker Profiles

PSP Subtypes

CBS Subtypes

MSA Subtypes

Significance for Clinical Development

Enabling Disease-Modifying Therapy Trials

Current Therapeutic Landscape

Regulatory Implications

Comparison with Related Studies

Future Directions

Extended Follow-up

Multi-omic Integration

Machine Learning Applications

See Also

External Links

References

💬 Discussion