Synuclein and dopamine transporter biomarkers among phenoconverters to parkinsonian disorders

This study analyzing Parkinso[n]s Progressions Markers Initiative data reveals that clinical phenoconversion to Parkinsonian disorders does not always align with biological evidence of synucleinopathy or dopaminergic loss, often occurring after the onset of significant functional impairment and varying significantly by prodromal or genetic risk group.

The Gist

The Big Picture: The "False Alarm" and the "Late Arrival"

Imagine Parkinson's disease (PD) isn't a light switch that suddenly flips on, but rather a slow-burning fire. For years, the fire has been smoldering in the background (the "prodromal" stage), damaging the house before you ever see a flame.

For a long time, doctors have defined the moment the disease "starts" as Phenoconversion. This is the moment a patient shows enough symptoms (like a shaky hand or a slow walk) to get a formal diagnosis of Parkinson's.

This study asked two big questions:

1. The "False Alarm" Question: When a doctor says, "You have Parkinson's," is the biology of the brain actually showing Parkinson's? Or is the diagnosis just a guess based on symptoms that might be wrong?
2. The "Late Arrival" Question: By the time the doctor makes the diagnosis, has the fire already burned down half the house? In other words, did the damage happen before the diagnosis was made?

The Tools: The Smoke Detector and the Heat Sensor

To answer these questions, the researchers used two high-tech tools on patients in the PPMI study (a massive global study tracking people at risk for Parkinson's):

1. CSF Alpha-Synuclein Test (The Smoke Detector): This looks at spinal fluid to see if there are clumps of a toxic protein (alpha-synuclein) that causes Parkinson's. If it's positive, the "smoke" is there.
2. DAT Scan (The Heat Sensor): This is an imaging scan that looks at the dopamine-producing cells in the brain. If these cells are dying, the scan lights up red.

The Goal: They wanted to see if the "Smoke Detector" and "Heat Sensor" matched the doctor's diagnosis.

The Participants: The "At-Risk" Groups

The study looked at four groups of people who didn't have Parkinson's yet but were at risk:

• iRBD Group: People who act out their dreams violently during sleep (a very strong warning sign).
• Hyposmia Group: People with a very poor sense of smell.
• Genetic Carriers (NMC): People who carry genes (like LRRK2 or GBA1) that increase their risk.
• Controls: Healthy people with no known risks.

What They Found

1. The "False Alarm" (Biological Mismatch)

When a patient got a clinical diagnosis of Parkinson's, the researchers checked their Smoke Detector and Heat Sensor.

• The Good News: For most people (especially those with smell loss), the biology matched the diagnosis. The smoke and heat were there.
• The Bad News: About 28% to 30% of people diagnosed with Parkinson's did not show the biological signs.
  • Some had a "normal" Heat Sensor (their dopamine cells were fine).
  • Some had a "negative" Smoke Detector (no toxic protein clumps).
  • Analogy: It's like a fire department responding to a call, finding a house that looks like it's on fire, but when they check the sensors, there is no heat and no smoke. They might be looking at a different problem entirely, or the fire is hiding in a way the sensors can't see yet.

Special Note on Genetics: People with the LRRK2 gene were the most likely to have this mismatch. Their "Smoke Detector" often stayed silent even when they had Parkinson's symptoms. This suggests their version of the disease might be different from the "classic" type.

2. The "Late Arrival" (Timing is Off)

The researchers also looked at how much damage had been done before the diagnosis was made. They used a scoring system called NSD-ISS (a ladder of disease stages).

• The Finding: By the time a patient was officially diagnosed, many were already at Stage 4 (meaning they had mild but real functional impairment).
• Analogy: Imagine the disease is a marathon. The "Phenoconversion" (diagnosis) is the moment the runner crosses the finish line. But this study found that for many people, they were already past the finish line and had been walking around the neighborhood for a while before the official timer stopped. The "diagnosis" happened after the real damage had already started.

The Takeaway: Why This Matters

This study is a wake-up call for how we treat Parkinson's research.

1. Diagnosis isn't perfect: Just because a doctor says "Parkinson's" based on symptoms doesn't guarantee the biology is there. We need to use the "Smoke Detector" and "Heat Sensor" to confirm the diagnosis, especially for clinical trials.
2. We are too late: If we wait for the official diagnosis to start treatment, we might be waiting until the "fire" has already done significant damage. We need to find ways to treat people before they meet the strict criteria for a diagnosis.
3. Genetics matter: Different genes (like LRRK2) might behave differently than the standard Parkinson's disease, requiring different tests and treatments.

In short: The study suggests that the "official start" of Parkinson's is often a delayed and sometimes inaccurate marker. To truly stop the disease, we need to look at the biology (the smoke and heat) rather than just waiting for the symptoms to become obvious.

Technical Summary

1. Problem Statement

The transition from a prodromal state (pre-clinical) to a clinically defined neurodegenerative disorder (e.g., Parkinson's Disease [PD], Dementia with Lewy Bodies [DLB]) is traditionally defined by phenoconversion—the emergence of clinical signs meeting diagnostic criteria. However, this binary clinical definition faces several critical limitations:

• Biological Misalignment: Clinical diagnosis may not accurately reflect the underlying biological pathology (synucleinopathy and dopaminergic neurodegeneration), especially in early stages where diagnostic accuracy is lowest.
• Temporal Disconnect: It is unclear if the timing of clinical phenoconversion aligns with the onset of biological changes or meaningful functional impairment.
• Trial Design Challenges: Relying solely on clinical endpoints for disease-modifying trials may be inefficient if the "conversion" event occurs after significant biological damage has already occurred or if the diagnosis is biologically incongruent.

The study aims to investigate the congruency between clinical phenoconversion and biological evidence (using CSF $\alpha$-synuclein seed amplification assays and Dopamine Transporter imaging) across different high-risk prodromal groups.

2. Methodology

• Study Population: A case series analysis of participants from the Parkinson's Progression Markers Initiative (PPMI).
  • Cohorts: Prodromal groups including isolated REM sleep behavior disorder (iRBD), hyposmia, and non-manifesting genetic carriers (NMCs) of GBA1, LRRK2, and SNCA variants. Healthy controls (HC) were also included.
  • Inclusion Criteria: Participants who developed a new clinical diagnosis (PD, DLB, MSA, AD, etc.) assigned by investigators, had at least one follow-up visit, and possessed available biomarker data (CSF $\alpha$-synuclein and DAT imaging) within $\pm$ 2 years of phenoconversion.
  • Sample Size: 121 total phenoconverters identified; 103 had evaluable biomarker data and were included in the final analysis (92 PD, 7 DLB, 2 MSA, 2 AD/other).
• Biomarkers:
  • CSF $\alpha$-synuclein Seed Amplification Assay (CSFaSynSAA): Performed at Amprion to detect Type I (synucleinopathy) and Type II (glial inclusions/MSA) seeds.
  • Dopamine Transporter (DAT) Imaging: SPECT scans to assess nigrostriatal integrity (defined as dysfunction if Specific Binding Ratio < 75% of expected).
• Definitions:
  • Biological Congruency: Defined as the presence of the expected biomarker profile for the clinical diagnosis (e.g., CSFaSynSAA+/DAT+ for PD).
  • NSD-ISS Staging: The Neuronal $\alpha$-Synuclein Disease Integrated Staging System was applied to stage participants based on biomarker status and functional impairment.
• Statistical Analysis: Descriptive statistics were used to compare demographic, clinical, and biological characteristics across groups.

3. Key Contributions

• Conceptual Framework: Introduces the distinction between "biologically congruent" and "biologically incongruent" phenoconverters, challenging the assumption that clinical diagnosis alone is a reliable milestone in the disease continuum.
• Temporal Analysis: Quantifies the delay between the onset of meaningful functional impairment (NSD-ISS Stage $\ge$ 4) and the clinical assignment of phenoconversion.
• Genetic Heterogeneity: Provides specific data on biomarker profiles in genetic carriers (LRRK2, GBA1), highlighting that standard synucleinopathy markers may not capture all pathogenic mechanisms in these subgroups.
• Prodromal DLB Detection: Suggests potential under-recognition of prodromal DLB within iRBD cohorts, as cognitive decline often precedes the formal diagnosis.

4. Key Results

• Conversion Rates:
  • iRBD: Highest annual conversion rate (7.9%).
  • Hyposmia: 4.2%.
  • Genetic Carriers: Very low rates (LRRK2: 1.3%, GBA1: 0.3%, LRRK2+GBA1: 0.9%).
  • Controls: 0.5%.
• Biomarker Congruency:
  • Overall: Only 71.8% (74/103) of clinically diagnosed PD/DLB cases showed the expected "congruent" profile (CSFaSynSAA+/DAT+).
  • Hyposmia: Highly congruent (87% CSFaSynSAA+/DAT+).
  • iRBD: 72% congruent. Notably, 25% showed unexpected profiles (e.g., DAT negative or CSFaSynSAA negative), including cases meeting criteria for SWEDDs (Subjects Without Evidence of Dopaminergic Deficits).
  • LRRK2 Carriers: High heterogeneity. 67% were CSFaSynSAA negative. While 75% were considered "congruent" (either CSFaSynSAA+/DAT+ or CSFaSynSAA-/DAT+), 25% (3/12) were CSFaSynSAA-/DAT- despite a clinical PD diagnosis.
  • GBA1 Carriers: Unexpectedly, 2/3 were CSFaSynSAA-/DAT-, contradicting the view of GBA1 as a pure synucleinopathy.
• Timing and Functional Impairment:
  • Phenoconversion often occurred after the onset of functional impairment.
  • 31.9% of iRBD and 18.4% of hyposmia phenoconverters were already at NSD-ISS Stage $\ge$ 4 (indicating mild but meaningful functional impairment) at the time of clinical diagnosis.
  • Median time to phenoconversion was short for iRBD/hyposmia (13–14 months) but long for genetic carriers (36–85 months).
• Specific Observations:
  • LRRK2: High proportion of normosmia (83%) and female carriers, factors associated with lower CSFaSynSAA positivity.
  • DLB: All 7 DLB cases were from the iRBD group; 6/7 were biologically congruent.
  • MSA: 2 iRBD cases converted to MSA; both were CSFaSynSAA Type I+ (not Type II), suggesting potential misclassification or atypical presentation.

5. Significance and Implications

• Clinical Trials: The findings suggest that clinical phenoconversion is a delayed and potentially unreliable endpoint for disease-modifying trials targeting the prodromal phase. Trials relying solely on clinical diagnosis may miss the window for intervention or include biologically incongruent participants.
• Diagnostic Refinement: The study advocates for biologically anchored definitions of disease progression. Incorporating CSFaSynSAA and DAT imaging is essential to confirm the biological reality of the disease before clinical diagnosis.
• Genetic Stratification: The high rate of biomarker negativity in LRRK2 and GBA1 carriers suggests that unselected genetic cohorts are inefficient for clinical trials. Enrichment strategies based on biomarker positivity are necessary to reduce sample size and follow-up duration.
• Functional Staging: The NSD-ISS staging system appears more sensitive to early disease progression than clinical diagnosis, as functional decline often precedes the formal "phenoconversion" label.
• Future Directions: Longitudinal follow-up is required for "incongruent" converters to determine if they will eventually develop biomarker positivity or if they represent a distinct, non-synucleinopathy pathology.

In conclusion, the paper argues that the field must move beyond binary clinical definitions toward a biologically integrated continuum to accurately capture the onset and progression of synucleinopathies.