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"].
...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 sitesSecondary 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 readinessEligibility 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 analysesSample 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 levelsSecondary 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 engagementCurrent 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
See Also
- [Progressive Supranuclear Palsy](/diseases/progressive-supranuclear-palsy)
- [Corticobasal Syndrome](/diseases/corticobasal-syndrome)
- [Multiple System Atrophy](/diseases/multiple-system-atrophy)
- [Tau Protein](/proteins/tau)
- [Neurofilament Light Chain](/proteins/nfl)
- [Biomarkers in Neurodegeneration](/mechanisms/neuroimaging-biomarkers)
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]