Faster reaction times of CSF alpha-synuclein seed amplification assay predict the diffuse malignant subtype of Parkinson's disease at 10-year follow-up

Abstract

Background

Data-driven approaches identified Mild Motor Predominant (MMP), Intermediate (IM), and Diffuse Malignant (DM) as Parkinson's Disease (PD) subtypes with different motor and non-motor impairment at diagnosis. It remains unclear whether these subtypes remain stable over time or whether they represent distinct biological substrates. The alpha-synuclein seed amplification assay in CSF (CSF-αSyn-SAA) might provide further insights.

Objective

to evaluate the association between baseline CSF-αSyn-SAA parameters and 10-year clinical evolution of PD subtypes.

Methods

323 sporadic PD patients from PPMI dataset were classified as MMP, IM, or DM at baseline and 10-year follow-up based on motor, cognitive, sleep and dysautonomia features. CSF-αSyn-SAA parameters were collected at baseline using 150-h protocol. CSF Aβ1-42, tTau and pTau181, CSF and serum NfL were also considered at baseline.

Results

Reaction times (T50, TTT) and area under the curve (AUC) respectively were shorter and larger in DM compared to IM/MMP. The difference in baseline amplification parameters was more evident when comparing subtypes based on 10-year clinical features (T50, η2 = 0.036; TTT, η2 = 0.031; AUC, η2 = 0.033; all p-values < 0.05) than when comparing subtypes based on baseline clinical features (T50, η2 = 0.012; TTT, η2 = 0.012; AUC, η2 = 0.013; all p < 0.05). Shorter T50 and TTT at baseline, or larger AUC, were associated with greater risk of DM versus MMP at 10-year follow-up (T50, OR = 4.1, p = 0.004; TTT, OR = 5.5, p < 0.001; AUC, OR = 3.5, p = 0.010). Aβ, Tau and NfL were similar between groups.

Conclusions

Baseline CSF-αSyn-SAA parameters predicted long-term PD progression. Faster reactions were associated with a more severe 10-year PD phenotype considering motor and non-motor features.

Plain language title

Parameters of Alpha-Synuclein Seed Amplification Assay on Cerebrospinal Fluid predict the clinical subtype of Parkinson's Disease 10 years later

Plain language summary

Background

The diagnosis of Parkison's Disease is drastically changing by the development of Alpha-Synuclein Seed Amplification Assay. The assay enables, for the first time, the detection of pathological forms of alpha-synuclein in cerebrospinal fluid in living patients. Alpha-Synuclein Seed Amplification is very accurate in discerning individuals with Parkinson's Disease versus healthy subjects, but it remains unknown whether it can also inform about prognosis.

Objective

We assessed the ability of the assay to predict the 10-year clinical progression of Parkinson's Disease.

Methods

Public data from an international cohort were used. At time of diagnosis, we classified 323 individuals with Parkinson's Disease into three clinical subtypes: Mild Motor Predominant, Intermediate, and Diffuse Malignant. These subtypes were characterized by a progressively increasing burden of motor and non-motor symptoms. Subjects with available follow-up data were re-classified using the same subtypes after 10 years of disease. Time-dependent signal changes of Alpha-Synuclein Seed Amplification Assay in Cerebrospinal Fluid were measured at baseline and used to predict the 10-year phenotype.

Results

Firstly, we found that subtypes were not stable categories. Around a half of participants changed subtype over time, mostly shifting towards a more aggressive one. Notably, our results showed that faster reactions on Alpha-Synuclein Seed Amplification Assay at baseline were associated with a 10-year phenotype more aggressive in terms of motor symptoms, dysautonomia, sleep and cognitive impairment, i.e the Diffuse Malignant subtype.

Conclusion

Characteristics of the assay underlying the final positivity or negativity outcomes performed at a milder and early stage of PD may identify a subgroup of subjects that are more likely to undergo a more rapid clinical deterioration. Further studies, however, are needed to confirm and expand this result.

Keywords

Parkinson's disease seed amplification assay clinical phenotype prognosis

Introduction

Phenotypic heterogeneity is a well-recognized trait of Parkinson's Disease (PD).^1,2 The identification of PD subtypes is important for prognostic implication and understanding pathophysiological basis that could lead to more rationale treatment. Subtypes have been initially based on the motor phenotypes.^3,4 More recently data driven approaches and cluster analysis allowed to identify subtypes without a priori hypothesis.^5–8 Recent works by Fereshtehnejad et al. was able to identify three main PD phenotypes, namely a Mild Motor Predominant (MMP), an Intermediate (IM), and a Diffuse Malignant (DM), leveraging unsupervised cluster analysis of different motor and non-motor symptoms (REM sleep behavior disorder (RBD), dysautonomia, early cognitive impairment) in large clinical cohorts.^7,8 Subtypes have been noted to change over time within individual subjects, but most studies have been limited to relatively short periods of no longer than 3–4 years.^9,10 Whether these subtypes represent a snapshot of between-subject variability or biological differences that persist over time is unclear. The lack of imaging or biochemical biomarkers has prevented establishing the connections between clinical presentation and underlying biology.

Seed amplification assays (SAA), originally developed for prions disease, has been welcomed as the first reliable in vivo biomarker for PD diagnosis.^11–13 Beyond the binary outcome (positive/negative), the parameters of the amplification kinetics of alpha-synuclein SAA on CSF (CSF-αSyn-SAA) may provide information about the α-syn conformation and properties of aggregates.^12,14,15 A few studies demonstrated that amplification parameters correlate with certain clinical features (e.g., dysautonomia and cognitive impairment).^15–18 Recently, the speed of seeding was shown to predict the phenoconversion of idiopathic RBD (iRBD) to PD.¹⁹ However, it remains unknown whether CSF-αSyn-SAA parameters at baseline may serve as a biomarker that can predict long-term clinical progression of PD.

Here, we classified 323 PD subjects from Parkinson's Progression Markers Initiative (PPMI) repository into MMP, IM and DM at baseline and after 10 years of follow-up. The clinical trajectory of these subtypes over time was assessed. CSF-αSyn-SAA amplification parameters at the time of diagnosis were compared in MMP versus IM/DM and used to predict the long-term phenotype. Markers of axonal degeneration (i.e Neurofilament Light Chain) and Alzheimer's disease (AD) co-pathology (i.e., Amyloid Beta Peptide, Total and Serine 181 Phosphorylated Tau) were also considered for a comprehensive analysis.

Methods

Participants

Clinical and biochemical data were obtained from the PPMI cohort (data-cut: 20240729). PPMI is an international multi-center study collecting clinical, genetical, radiological, and biochemical markers from subjects with PD longitudinally followed-up. As per PPMI design, diagnosis of PD is based on abnormal dopamine transporter (DAT)-SPECT and two of either resting tremor, bradykinesia, rigidity, or asymmetric resting tremor, or asymmetric bradykinesia. Only subjects with a sporadic form of disease and positivity on the CSF-αSyn-SAA (n = 323) were considered for this study. A description of the CSF-αSyn-SAA is provided below. PD participants carrying pathogenic variants in any of the following genes - GBA, LRRK2, PINK1, VPS35 and PRKN - were excluded.

Clinical assessment

Demographic information and clinical features were collected at baseline (n = 323) and 10 years follow-up when available (n = 146). Baseline assessment was performed within 2 years from diagnosis in a drug-naïve condition. The following parameters were considered for the participants: sex, age, disease duration, Movement Disorder Society-Unified PD Rating scale (MDS-UPDRS) part III in off state, part II and IV, Hoehn and Yahr scale (H&Y) in off state, postural instability–gait difficulty (PIGD) score, levodopa equivalent daily dose (LEDD), RBD Screening Questionnaire (RBDSQ), Scales for Outcomes in PD-Autonomic dysfunction (SCOPA-AUT), Montreal cognitive assessment (MoCA), Benton judgment of line orientation (BJLO), Hopkins Verbal Learning Test (HVLT), Letter number sequencing (LNS), Semantic fluency test (SF), Symbol digit test (SDT).

Clinical subtyping

Each subject was assigned to either Mild Motor Predominant (MMP), Intermediate (IM) or Diffuse Malignant (DM) PD subtype based on composite scores including motor and non-motor components (RBD, dysautonomia, early cognitive impairment).⁷ The motor component was calculated with MDS-UPDRS part II, III and PIGD scores. The non-motor components included three domains: domain 1 (RBDSQ), domain 2 (SCOPA-AUT), domain 3 (BJLO, HVLT, LNS, SF, SDT). MMP was defined as motor and all three non-motor scores below the 75th percentile calculated on the baseline cohort (n = 323); DM as either motor score plus ≥ 1/3 non-motor score > 75th percentile, or all three non-motor scores > 75th percentile; subjects not included in the previous categories were labelled as IM. The subtype membership was calculated at baseline for all participants (n = 323) by means of clinical scores obtained in proximity of diagnosis, at a drug-naïve stage. A smaller subgroup (n = 146) had long-term follow-up information which were used to re-define the clinical subtype after 10 years of disease. In the latter case, new percentiles, based on the 10-year follow-up evaluation, were used to adjust the classification for the clinical worsening that each participant inevitably experienced during the years. Baseline and 10-year follow-up subtypes for a single subject could coincide or not (subtype shifting) depending on the specific disease course.

Biochemical assessment: alpha-synuclein seed amplification assay

Technical aspects of the CSF-αSyn-SAA were discussed in a previous paper.^12,13 Briefly, the 200-μL reaction mixture included 0·3 mg/mL recombinant α-synuclein (Amprion, San Diego, CA, USA), 0·5 mol/L NaCl (Lonza, Basel, Switzerland), 100 mmol/L PIPES-NaOH (pH 6·50; MilliporeSigma, Burlington, MA, USA), and 20% volume per volume CSF. Recombinant α-synuclein was expressed with C-terminal His-tag in Escherichia coli BL21(DE3) and purified using immobilised metal affinity chromatography. All samples were analysed with a single batch of the substrate. One 3/32 inch Si3N4 bead (Tsubaki Nakashima, Osaka, Japan) was added per well using a house-made bead dispenser. Beads were blocked with 1% bovine serum albumin 100 mmol/L PIPES-NaOH (pH 6·50) for 1 h and washed twice with 100 mmol/L PIPES-NaOH (pH 6·50). Samples were run in three technical replicates within 96-well plates, using three FLUOstar Omega readers set to 37°C. Each plate was shaken for 1 min every 29 min, and fluorescence was measured after each cycle for 150 h.¹³ The reaction provided a curve with the following parameters: Fmax (highest fluorescence from each well; RFU), T50 (time to reach 50% of the Fmax; hours), TTT (time to reach a 5000 RFU threshold; hours), Slope (RFU/hours) and AUC (area under the curve; RFU*hours) (Figure 1). A categorical outcome (positive/negative/inconclusive) was based on the fluorescence of three replicates. Only PD subjects with a positivity on CSF-αSyn-SAA were included in the study (n = 323). A negative (n = 23 subjects) or inconclusive (n = 4 subjects) response was excluded. The mean of three replicates was calculated for each amplification parameter. The CSF-αSyn-SAA was performed exclusively at baseline.

Figure 1.

Schematic representation of the CSF alpha-synuclein seed amplification assay (CSF-αSyn-SAA) reaction curve with amplification parameters. The seed amplification assay was performed as described in the text. The extent of aggregation was monitored for 150 h by the increase in fluorescence. The reaction provided a curve with the following parameters: Fmax (highest fluorescence from each well; RFU), T50 (time to reach 50% of the Fmax; hours), TTT (time to reach a 5000 RFU threshold; hours), Slope (RFU/hours) and AUC (area under the curve; RFU*hours). Abbreviations: RFU, relative fluorescence unit. Created in BioRender. Pisani, A. (2025) https://BioRender.com/0s5i5om.

Biochemical assessment: AD biomarkers and neurofilament light chain

CSF AD biomarkers including Amyloid Beta Peptide 1-42 (Aβ1-42), Total Tau (tTau), Phosphorylated Tau at Serine 181 (pTau181), were considered for the study in addition to the ratios of tTau/Aβ1-42 and pTau181/Aβ1-42. CSF and Serum NfL levels were also analyzed. Elecsys (Roche) and Simoa-Quanterix (Biogen) assays were used for dosages on CSF and Serum respectively. All the above measurements referred to the baseline assessment and were available for the following subsample of subjects: CSF AD biomarkers for 311, CSF NfL for 169, and Serum NfL for 284 out of 323 participants.

Statistical analysis

Distribution of variables was evaluated with Shapiro-Wilk test. Categorical variables were compared by Chi-Square test followed by Post-Hoc Test with Bonferroni-Adjustment. Comparison between groups was assessed by Kruskal-Wallis's Test; Dunn-Bonferroni Post-Hoc Analysis for multiple comparisons was applied; eta squared (η2) was used to measure the effect size. For parametric variables one-way ANOVA and Bonferroni Post-Hoc Analysis were used. Multinomial Logistic Regression (MLR) was used to predict the 10-year PD clinical subtype given baseline CSF-αSyn-SAA parameters. Only CSF-αSyn-SAA parameters whose mean/distribution significantly differed between the 10-year PD clinical subtypes were included in the model. Multicollinearity was defined by Variation Inflation Factor (VIF) greater than 5.0. Predictors were expressed as quantiles above and below the median. Age, sex and disease duration at baseline were used as covariates. Significance was set at p < 0.05. Statistical analysis was performed by using IBM-SPSS Version-28.

Standard protocol approvals, registrations, and patient consents

Information about ethical approval and patient consents can be retrieved on the PPMI website (www.ppmi-info.org).

Data availability

Data are available from authors upon reasonable request.

Results

Clinical signature of mild motor predominant, intermediate and diffuse malignant PD subtypes

Following the assignment rules described above, MMP subtype was characterized by milder motor and non-motor disability compared to the IM/DM both at baseline and 10-year follow-up (Table 1). At baseline, MMP scored lower on MDS-UPDRS-part III in OFF state, HY in OFF state, SCOPA-AUT and RBDSQ (p < 0.001 for all listed variables), with no sex, age and disease duration differences between groups (Table 1). Similar results were found at 10-year follow-up between MMP vs IM/DM (MDS-UPDRS-part III in OFF state, p = 0.002; HY in OFF state, p < 0.001; SCOPA-AUT, p < 0.001; RBDSQ, p < 0.001) (Table 1). The mean (±SD) LEDD at 10-year follow-up across subtypes was 915.1 (±673.7) without significant differences between subtypes.

Table 1.

Comparison of clinical-demographic features between Mild Motor Predominant, intermediate and diffuse malignant subtypes of Parkinson's disease at baseline (n = 323) and 10-year follow-up (n = 146).

	Mild Motor Predominant	Intermediate	Diffuse Malignant	p value	Post HocAnalysis
Baseline
n	182	101	40
Sex (f, %)	66, 36.2%	37, 36.6%	12, 30%	p = 0.730	-
Age (y)	61.1 (9.3)	62.5 (9.2)	63.6 (9.9)	p = 0.180	-
Duration (y)	0.6 (0.6)	0.5 (0.5)	0.7 (0.6)	p = 0.075	-
LEDD (mg/day)	0	0	0
MDS-UPDRS-part III in OFF state	18.4 (7.5)	22.1 (8.7)	29.8 (9.1)	p < 0.001	MMP versus IMMMP versus DMIM versus DM*
HY in OFF state	1.5 (0.5)	1.7 (0.5)	1.8 (0.4)	p < 0.001	MMP versus IMMMP versus DM*
MDS-UPDRS-part IV	0	0	0
MOCA	27.4 (2.1)	26.8 (2.5)	26.6 (2.6)	p = 0.066	-
SCOPA-AUT	6.5 (3.4)	11.5 (6.7)	17.1 (6.3)	p < 0.001	All Comparisons**
RBDSQ	2.8 (1.5)	5.2 (2.8)	7.1 (2.9)	p < 0.001	MMP versus IMMMP versus IMIM versus DM*
10-Year Follow-Up
n	44	64	38
Sex (f, %)	19, 43.2%	21, 32.8%	7, 18.4%	p = 0.056	-
Age (y)	68.2 (8.7)	69.0 (8.6)	71.5 (8.3)	p = 0.434	-
LEDD (mg/day)	841.6 (473.5)	906.7 (592.8)	1014.4 (950.4)	p = 0.799	-
MDS-UPDRS-part III in OFF state	32.3 (12.6)	38.7 (16.7)	49.7 (17.8)	p = 0.002	MMP versus DM*
HY in OFF state	2.1 (0.4)	2.1 (0.3)	2.2 (0.5)	p < 0.001	MMP versus DMIM versus DM
MDS-UPDRS-part IV	3.8 (3.7)	4.1 (4.2)	5.2 (3.6)	p = 0.111	-
MOCA	27.7 (2.5)	27.0 (2.8)	25.4 (5.2)	p = 0.368	-
SCOPA-AUT	10.4 (4.4)	13.8 (5.7)	20.7 (7.5)	p < 0.001	MMP versus IM**
RBDSQ	3.2 (1.9)	5.6 (3.4)	7.5 (3.5)	p < 0.001	MMP versus IMMMP versus DM

Values were given in mean (±standard deviation). Kruskal-Wallis/Chi-Square p value: statistical significance is marked in bold. Post-Hoc p value: *p < 0.005; **p < 0.001. Abbreviations: MMP, Mild Motor Predominant; IM, Intermediate; DM, Diffuse Malignant; n, number; y, years; m, male; f, female; LEDD, levodopa equivalent daily dose; MDS-UPDRS-part III, Movement Disorder Society-Unified PD Rating scale part III; MDS-UPDRS-part IV, Movement Disorder Society-Unified PD Rating scale part IV; HY, Hoehn and Yahr scale; MoCA, Montreal cognitive assessment; SCOPA-AUT, Scales for Outcomes in PD-Autonomic dysfunction; RBDSQ, RBD Screening Questionnaire.

Stability of clinical subtypes over time

The 146 subjects with available data at BL and at 10-year follow-up were classified by the three clinical subtypes at both time points. Around half of the subjects changed subtype from baseline to follow-up (83 out of 146, 56.8%). Clinical subtype remained stable for the remainders (63 out of 146, 43.2%) (Table 2). Among subjects in the MMP subtype at baseline, 37.6% (n = 35 out of 93) remained in the same group at 10-year follow-up. The rest of the subject (n = 58 out of 93) moved to a less benign clinical phenotype (48.4% were classified as IM and 14.0% as DM at 10-year follow-up). In the IM at baseline, 43.9% of subjects remained within the same subtype, 19.5% were classified as MMP, and 36.6% as DM at 10-year follow-up. The DM membership at baseline was reconfirmed at 10-year follow-up for most participants (83.3%). Only two subjects changed subtype from DM to IM/MMP at follow-up.

Table 2.

Shift of Parkinson's disease subtypes from baseline to 10-year follow-up assessment.

		Mild Motor PredominantAt 10-Year Follow-Up	IntermediateAt 10-Year Follow-Up	Diffuse MalignantAt 10-Year Follow-Up	Total
Mild Motor PredominantAt Baseline	Count	35	45	13	93
Mild Motor PredominantAt Baseline	Percentage within Baseline Subtype	37.6%	48.4%	14.0%	100%
IntermediateAt Baseline	Count	8	18	15	41
IntermediateAt Baseline	Percentage withinBaseline Subtype	19.5%	43.9%	36.6%	100%
Diffuse MalignantAt Baseline	Count	1	1	10	12
Diffuse MalignantAt Baseline	Percentage withinBaseline Subtype	8.3%	8.3%	83.3%	100%

Amplification parameters of CSF-αSyn-SAA and clinical subtypes at baseline

Comparison between CSF-αSyn-SAA parameters and clinical subtypes at baseline showed longer T50/TTT and smaller AUC in MMP compared to IM/DM (values reported as mean ± SD; T50: MMP 72.5 ± 11.7 vs IM 69.2 ± 9.5 vs DM 68.5 ± 8.3 h, χ²(2, 323) = 6.098, p = 0.047; TTT: MMP 67.1 ± 12.5 vs IM 63.6 ± 9.1 vs DM 62.4 ± 7.7 h, χ²(2, 323) = 6.115, p = 0.047; AUC: MMP 25792471.6 ± 4186018.3 vs IM 26977933.6 ± 3217124.8 vs DM 27327623.5 ± 2792542.6 RFU*hours, χ²(2, 323) = 6.189, p = 0.045) (Table 3, Figure 2). Despite an overall significance, the post-hoc analysis did not show any significant differences by the pairwise comparison.

Figure 2.

Box and whiskers plots illustrating the differences in baseline CSF-αSyn-SAA parameters across PD subtypes based on baseline (A) and 10-year follow-up (B) clinical features. Significance of ANOVA/Kruskal-Wallis's Tests was reported in the bottom right corner of each graph. Only significant post-hoc comparisons were reported. Abbreviations: CSF-αSyn-SAA, Alpha-synuclein seed amplification assay on CSF; MMP, Mild Motor Predominant; IM, Intermediate; DM, Diffuse Malignant; T50, time to reach 50% of the Fmax; TTT, time to reach a 5000 RFU threshold; AUC, area under the curve.

Table 3.

Comparison of CSF-αSyn-SAA amplification parameters, CSF AD biomarkers, CSF and Serum NfL between mild motor predominant, intermediate and diffuse malignant subtypes of Parkinson's disease at baseline.

	Mild Motor Predominantat Baseline	Intermediateat Baseline	Diffuse Malignantat Baseline	p value	Post HocAnalysis
CSF-αSyn-SAAAmplification Parameters at Baseline
n	182	101	40
Fmax (RFU)	84256.8(23929.2)	82374.0(24720.8)	85422.4(29984.8)	p = 0.757	–
T50 (hours)	72.5 (11.7)	69.2 (9.5)	68.5 (8.3)	p = 0.047	No significant comparison
TTT (hours)	67.1 (12.5)	63.6 (9.1)	62.4 (7.7)	p = 0.047	No significant comparison
AUC (RFU*hours)	25792471.6(4186018.3)	26977933.6(3217124.8)	27327623.5(2792542.6)	p = 0.045	No significant comparison
Slope (RFU/hours)	33.6 (10.7)	33.1 (9.6)	32.0 (9.5)	p = 0.922	–
CSF AD Biomarkers at Baseline
n	174	99	38
Aβ1-42 (pg/mL)	872.0 (328.8)	934.0 (342.1)	839.9 (368.9)	p = 0.182	–
tTau (pg/mL)	162.6 (56.2)	170.0 (59.0)	176.1 (66.6)	p = 0.352	–
pTau181 (pg/mL)	13.9 (5.1)	14.4 (5.3)	15.4 (6.5)	p = 0.394	–
tTau/Aβ1-42	0.18 (0.04)	0.18 (0.05)	0.24 (0.12)	p = 0.327	–
pTau181/Aβ1-42	0.015 (0.003)	0.016 (0.004)	0.020 (0.010)	p = 0.171	–
CSF NfL at Baseline
n	103	44	22
CSF NfL(pg/mL)	92.2 (44.7)	94.8 (41.9)	104.0 (45.6)	p = 0.412	–
Serum NfL at Baseline
n	159	91	34
Serum NfL (pg/mL)	12.2 (5.4)	12.9 (6.5)	14.3 (6.7)	p = 0.205	–

Values were given in mean (±standard deviation). Statistical significance is marked in bold. Abbreviations: MMP, Mild Motor Predominant; IM, Intermediate; DM, Diffuse Malignant; n, number. Fmax, highest fluorescence from each well; T50, time to reach 50% of the Fmax; TTT, time to reach a 5000 RFU threshold; AUC, area under the curve; AD biomarkers, Alzheimer's Disease biomarkers; NfL, Neurofilament Light Chain; Aβ1-42, Amyloid Beta Peptide 1-42; tTau, Total Tau; pTau181, Phosphorylated Tau at Serine 181.

Amplification parameters of CSF-αSyn-SAA at baseline as predictors of 10-year follow-up clinical subtypes

When CSF-αSyn-SAA parameters obtained at baseline were compared between the 10-year clinical subtypes, the longer T50/TTT and smaller AUC were noted in MMP compared to IM/DM (values reported as mean ± SD; T50: MMP 73.9 ± 11.2 vs IM 72.3 ± 12.6 vs DM 68.1 ± 7.3 h, χ²(2, 146) = 7.150, p = 0.028; TTT: MMP 68.1 ± 11.1 vs IM 66.7 ± 13.7 vs DM 62.0 ± 7.4 h, χ²(2, 146) = 6.512, p = 0.039; AUC: MMP 25511406.0 ± 3716051.8 vs IM 25834362.9 ± 4503639.5 vs DM 27453849.4 ± 2458992.2 RFU*hours, χ²(2, 146) = 6.744, p = 0.034) (Table 4, Figure 2). At 10-year follow-up, the differences between groups were larger (10-Year Follow-Up: T50-η2 = 0.036; TTT-η2 = 0.031, AUC-η2 = 0.033, all p values < 0.05; Baseline: T50-η2 = 0.012, TTT-η2 = 0.012, AUC-η2 = 0.013, all p < 0.05) and the post-hoc analysis showed a significant longer T50 and TTT, and smaller AUC at the 10-year-follow-up in MMP subtype versus DM (p = 0.023; p = 0.033; p = 0.029). A MLR was run to predict the 10-year subtypes based on the baseline T50, TTT and AUC (Table 5, Figure 3). Amplification parameters were expressed as quantiles above and below the median. Given the presence of multicollinearity between T50, TTT and AUC, each predictor was entered separately (VIF: T50 7.55, TTT 18.41, AUC 28.04). Age, sex and disease duration at baseline were used as covariates. MLR showed that subjects with T50 and TTT below the median at baseline, or AUC above the median at baseline, were associated with a greater risk of being classified as DM or IM versus MMP subtype at 10-year follow-up (T50-DMvsMMP: OR = 4.1, 95%CI = 1.6–10.8, p = 0.004; T50-IMvsMMP, OR = 3.1, 95%CI = 1.3–7.2, p = 0.009; TTT-DMvsMMP, OR = 5.5, 95%CI = 2.1–14.8, p < 0.001; TTT-IMvsMMP, OR = 3.0, 95%CI = 1.3–6.9, p = 0.009; AUC-DMvsMMP, OR = 3.5, 95%CI = 1.3–9.1, p = 0.010) (Table 5, Figure 3).

Figure 3.

Forest plots of multinomial logistic regression comparing the odds of being classified as DM (A), or IM (B), versus MMP at 10-year follow-up, based on CSF-αSyn-SAA amplification parameters at baseline. The plots illustrate odds ratios (ORs) and 95% confidence interval (CI) for each CSF-αSyn-SAA amplification parameter, dichotomously expressed as values below and above the median. Only CSF-αSyn-SAA amplification parameters whose mean/distribution significantly differed between 10-year PD subtypes were considered. Age, sex and disease duration at baseline were used as covariates. Median of T50: 69.7 h; Median of TTT: 63.6 h; Median of AUC: 26909171.8 RFU*hours. Statistical significance is marked in bold. Abbreviations: PD, Parkinson's Disease; CSF-αSyn-SAA, Alpha-synuclein seed amplification assay on CSF; MMP, Mild Motor Predominant; IM, Intermediate; DM, Diffuse Malignant; T50, time to reach 50% of the Fmax; TTT, time to reach a 5000 RFU threshold; AUC, area under the curve; OR, odds ratio, 95% CI, 95% confidence interval.

Table 4.

Association between baseline CSF-αSyn-SAA amplification parameters, CSF AD biomarkers, CSF and Serum NfL and Parkinson's disease clinical subtype at 10-year follow-up.

	Mild Motor Predominantat 10-Year Follow-Up	Intermediateat 10-Year Follow-Up	Diffuse Malignantat 10-Year Follow-Up	p value	Post HocAnalysis
CSF-αSyn-SAAAmplification Parameters at Baseline
n	44	64	38	–	–
Fmax (RFU)	85251.4(24397.9)	84752.4(23727.1)	92425.8(27467.4)	p = 0.285	–
T50 (hours)	73.9 (11.2)	72.3 (12.6)	68.1 (7.3)	p = 0.028	MMP versus DMp = 0.023
TTT (hours)	68.1 (11.1)	66.7 (13.7)	62.0 (7.4)	p = 0.039	MMP versus DMp = 0.033
AUC (RFU*hours)	25511406.0(3716051.8)	25834362.9(4503639.5)	27453849.4(2458992.2)	p = 0.034	MMP versus DMp = 0.029
Slope (RFU/hours)	35.5 (10.0)	33.6 (11.7)	33.8 (9.3)	p = 0.440
CSF AD Biomarkers at Baseline
n	44	64	38	–	–
Aβ1-42 (pg/mL)	995.5 (331.1)	885.6 (321.2)	923.4 (304.3)	p = 0.190	–
tTau (pg/mL)	171.5 (53.6)	153.9 (46.7)	155.7 (46.8)	p = 0.230	–
pTau181 (pg/mL)	14.4 (5.0)	13.0 (4.3)	13.2 (4.1)	p = 0.374	–
tTau/Aβ1-42	0.17 (0.03)	0.18 (0.04)	0.17 (0.04)	p = 0.197	–
pTau181/Aβ1-42	0.014 (0.003)	0.015 (0.003)	0.014 (0.003)	p = 0.226	–
CSF NfL at Baseline
n	25	39	25	–	–
CSF NfL (pg/mL)	93.7 (47.5)	81.2 (36.1)	84.2 (30.8)	p = 0.541	–
Serum NfL at Baseline
n	41	60	36	–	–
Serum NfL (pg/mL)	12.4 (5.2)	10.6 (4.3)	10.8 (4.2)	p = 0.194	–

Table 5.

Multinomial logistic regression examining the relationship between CSF-αSyn-SAA amplification parameters at baseline and Parkinson's disease subtypes at 10-year follow-up. CSF-αSyn-SAA amplification parameters were expressed dichotomously as values below and above the median of each parameter.

		OR	95% CI Lower Bound	95% CI Upper Bound	Sig.	Model Fitting Information
		OR	95% CI Lower Bound	95% CI Upper Bound	Sig.	χ² (df)	Sig.
T50 below the median	DM compared to MMP	4.1	1.6	10.8	p = 0.004	19.105 (8)	p = 0.014
T50 below the median	IM compared to MMP	3.1	1.3	7.2	p = 0.009
T50 above the median	DM compared to MMP	0.2	0.1	0.6	p = 0.004
T50 above the median	IM compared to MMP	0.3	0.1	0.7	p = 0.009
TTT below the median	DM compared to MMP	5.5	2.1	14.8	p < 0.001	22.228 (8)	p = 0.005
TTT below the median	IM compared to MMP	3.0	1.3	6.9	p = 0.009
TTT above the median	DM compared to MMP	0.2	0.1	0.4	p < 0.001
TTT above the median	IM compared to MMP	0.3	0.1	0.7	p = 0.009
AUC below the median	DM compared to MMP	0.3	0.1	0.7	p = 0.010	15.903 (8)	p = 0.044
AUC below the median	IM compared to MMP	0.5	0.2	1.0	p = 0.069
AUC above the median	DM compared to MMP	3.5	1.3	9.1	p = 0.010
AUC above the median	IM compared to MMP	2.1	0.9	4.8	p = 0.069

Only CSF-αSyn-SAA amplification parameters whose mean/distribution significantly differed between 10-year PD subtypes were considered for Multinomial Logistical Regression. Age, sex and disease duration at baseline were used as covariates. CSF-αSyn-SAA amplification parameters were expressed dichotomously as values below and above the median of each parameter. Median of T50: 69.7 h; Median of TTT: 63.6 h; Median of AUC: 26909171.8 RFU*hours. Statistical significance is marked in bold. Abbreviations: PD, Parkinson's Disease; CSF-αSyn-SAA, Alpha-synuclein seed amplification assay on CSF; MMP, Mild Motor Predominant; IM, Intermediate; DM, Diffuse Malignant; T50, time to reach 50% of the Fmax; TTT, time to reach a 5000 RFU threshold; AUC, area under the curve; OR, odds ratio, CI, confidence interval; χ² (df), Chi-Square (degrees of freedom).

An additional series of Binomial Logistic Regressions (BLR) was conducted to explore whether baseline T50, TTT, and AUC predicted 10-year follow-up impairment in each of the four clinical domains (motor, cognitive, RBD, and dysautonomia) used to define the clinical subtypes. Impairment at 10-year follow-up for each domain was dichotomized by comparing subjects in the worst-performing quartile (first quartile) versus those in the remaining quartiles (second, third and fourth quartiles). In total, twelve BLR models were run: one for each combination of clinical domain (motor impairment, cognitive impairment, RBD, dysautonomia) and CSF-αSyn-SAA amplification parameter (T50, TTT, AUC). In each model, the 10-year follow-up clinical domain was entered as the outcome and the baseline CSF-αSyn-SAA amplification parameter as the predictor. Age, sex, disease duration at baseline were included as covariates, in addition with the remaining clinical domains. The analysis showed that TTT values below the median at baseline significantly predicted a greater motor impairment at 10-year follow-up (OR = 3.9, 95%CI = 1.3–11.9, p = 0.014). No significant associations were found when baseline T50 or AUC were used as predictors, nor when 10-year follow-up cognitive impairment, RBD, or dysautonomia were used as the outcome (Supplementary Tables 1–4).

Eventually, the same three baseline amplification parameters (T50, TTT, AUC) were compared between subjects who changed subtype over time versus those who did not (Supplementary Table 5–7). Subjects with MMP subtype at baseline who shifted towards IM/DM at follow-up were associated with a higher percentage of TTT values below the median compared to subjects that remained in the MMP subtype (MMP to IM/DM vs stable MMP subtype, 51.7% vs 26.6%, p = 0.029; relative risk of shifting from MMP to IM/DM if TTT below the median: OR = 2.7, 95%CI = 1.1–6.6, p = 0.031, model fitting information - χ²(1) = 4.887, p < 0.027). A similar trend with the T50 quantile below the median was observed in subjects who shifted from IM to DM at follow-up (shifted to DM vs shifted to MMP vs stable on IM, 73.3% vs 25.0% vs 55.6%, p = 0.084).

AD biomarkers, NfL and clinical subtypes

Baseline levels of CSF AD biomarkers (including ratios) as well as CSF and Serum NfL did not differ between groups. No statistical difference was noted with either baseline or 10-year follow-up clinical subtypes (Table 3 and 4).

Discussion

Clinical subtypes of PD can be helpful for patient counseling and prediction of disease progression, but it is uncertain whether they mark distinct clinic-pathological paths. The recent development of CSF-αSyn-SAA as an accurate biomarker of synucleinopathy could provide biological basis for the subtype classifications. In this study, we assessed the 10-year clinical trajectory of MMP, IM and DM subtypes of PD. CSF-αSyn-SAA parameters at baseline were used to characterize each subtype and predict the long-term clinical progression. Markers of axonal degeneration and AD co-pathology were also investigated for a comprehensive analysis.

A half of participants changed subtype during follow-up, mostly shifting towards a more aggressive phenotype. Shorter reaction times (T50 and TTT) and a larger AUC of the CSF-αSyn-SAA amplification was observed in DM subtype compared to IM/MMP subtype. The difference in amplification parameters at baseline was more evident when comparing subtypes established on the 10-year clinical features than when comparing subtypes based on the baseline clinical features. Therefore, CSF-αSyn-SAA parameters at baseline were predictors of the long-term clinical outcome of PD subjects at 10 years. CSF AD biomarkers, and CSF and serum NfL did not significantly differ between subtypes.

The need to identify PD subtypes aligns with the main goal of precision medicine.²⁰ In the absence of reliable fluid or imaging biomarkers, clinical classifications have been the only feasible strategy so far to this goal. Stratifications based exclusively on PD symptoms however seem to lack consistency over time, which challenges their interpretation.^9,10,21,22 We observed a large shift across clinical subtypes after 10-year follow-up as well. Although a bidirectional change was noted, the majority of subjects transitioned to a more malignant phenotype. This supports and extends the similar analyses run over shorter follow-up periods (1 to 5 years).^9,10 Even when considering only the motor symptoms, a tendency to shift from more benign to more severe phenotype over time was shown previously.^4,21–23 These observations highlight the need for a better biological basis for classifying clinical subtypes and disease stages, which appear to shift and overlap partially with progression of the disease.

CSF-αSyn-SAAs includes cyclical process of fragmentation and elongation where αSyn seeds in CSF are amplified incorporating recombinant αSyn into new aggregates, which are detected by fluorescent dyes.¹² The speed of the SAA reaction reflects the seed concentration when the assay is performed in vitro using recombinant seeds in artificial buffers.^12,14,15 However, human biofluids can introduce factors that affect the amplification process and serial dilution studies show only weak correlation with amplification parameters and clinical and other biomarkers.²⁴ Whether CSF-αSyn-SAA can be used as a quantitative assay is still under investigation. Two independent groups have recently proposed the use of a scoring system based on the number of positive signals across a dilution series as a quantitative measure of α-synuclein seeds.^25,26 Other authors have introduced a variant of the assay, the so-called seeding amplification immunoassay (SAIA), which not only amplifies disease-associated α-synuclein seeds but also quantifies them using an anti-α-synuclein antibody.²⁷ In our analysis, the DM subtype exhibited the shortest T50 and TTT, suggesting a possible higher concentration of seeds. A different speed in the SAA reaction was already observed in PD subjects with versus without cognitive impairment and dysautonomia.^15,18,28 Interestingly, the difference in times of reaction was more pronounced across the subtypes based on 10-year follow-up than across the subtypes based on the baseline clinical phenotypes. The MLR confirmed that TTT and T50 below the median at baseline, or AUC above the median, increased the risk of being classified as DM or IM after 10 years by more than 3–5 folds. Moreover, faster SAA reactions were more frequent in subjects who shifted towards a more malignant subtype at follow-up (frequency of TTT values below the median: subjects who shifted subtypes versus subjects with stable subtype, 51.7% vs 26.6%, p = 0.029). Our findings indicate that SAA parameters at the time of diagnosis might have a prognostication value to predict the long-term clinical outcome of PD after 10-years. Notably, in an exploratory analysis considering separately the clinical domains used to define subtypes (motor impairment, cognitive impairment, RBD and dysautonomia), TTT values below the median predicted greater motor impairment at the 10-year follow-up. In contrast, T50 and AUC values—which had previously shown significant predictive power for the 10-year clinical subtype—did not predict long-term outcomes within any specific motor or non-motor domain. These findings suggest that amplification parameters may predict long-term global deterioration more effectively than the worsening of specific clinical features. The prognostic value of CSF-αSyn-SAA has been investigated only in a few studies to date. A first study reported faster SAA reactions in iRBD subjects who showed the highest rate of phenoconversion to PD.¹⁹ Other studies identified similar biochemical alterations in PD subjects who experienced an earlier development of PD-related dementia.¹⁷ More recently, Orrù et al. compared data from three international cohorts, including PPMI. In their time-to-event analysis, the authors found that TTT predicted cognitive decline (MoCA ≤ 21) as part of an unfavourable clinical outcome during a median follow-up of 4.5 years.²⁹ Significant variability and overlap of amplification parameters were noted across groups in all the above studies including ours, which prevents the use of SAA as a quantitative test at an individual level for now.

We found no differences in CSF beta-amyloid, total and phosphorylated tau levels between groups. Beta-amyloid and tau represent the main biological biomarkers to aid AD diagnosis. However, these markers are now also used in PD to define the degree of the AD co-pathology and predict cognitive decline.³⁰ In line with sporadic reports, we were expecting to find reduced CSF Aβ1-42 levels in the DM group, which scored the lowest on the neuropsychological testing as per definition.⁷ However, subjects enrolled in this study were overall well-preserved in terms of cognitive functions, despite some inter-group differences. The average MOCA score never fell below the threshold for frank dementia. Moreover, the only neuropathological study performed so far did not detect differences in AD co-pathology between MMP, IM and DM subtypes, outlining the need for further investigations.³¹

CSF and Serum NfL were also similar in all subtypes. NfL is a marker of large fiber degeneration whose application in PD is limited since their levels are similar to controls, much lower than in MSA, where the rapid neuronal death typically leads to more significant changes in the biomarker concentration.³² The literature reports differences in NfL levels between PD subtypes with distinct prognosis.^33–37 Differences between subtypes, however, were consistently very small across all the studies. A single work included MMP, IM and DM subtypes, but the authors used a simplified calculator, limiting a possible comparison between cohorts.³⁸

Our study has several strengths. First, it is based on a large, well-characterized, international cohort. This enabled the use of a large sample size and long-term follow-up information. Clinical subtypes, MMP, IM and DM, originate from a cluster analysis, which led to unbiased inclusion of a large number of motor and non-symptoms of PD. Although the role of CSF-αSyn-SAA is most robust for diagnosis of synucleinopathies, the current study provides insight into the use of the SAA parameters as a quantitative and predictive test. Among the limitations of the study, data were available only in a limited number of subjects at 10-year follow-up. The amplification parameters are variable and the correlations with clinical subtypes are not robust. The assay data is available only from the baseline, preventing the assessment of their longitudinal change.

In conclusion, our results showed that CSF-αSyn-SAA parameters predict the long-term clinical course of PD better than markers of AD co-pathology or axonal degeneration. These data suggest the use of the assay as a semiquantitative test, but further development of assays that can provide more robust quantitation will be necessary to validate these analyses.

Supplemental Material

sj-docx-1-pkn-10.1177_1877718X261417468 - Supplemental material for Faster reaction times of CSF alpha-synuclein seed amplification assay predict the diffuse malignant subtype of Parkinson's disease at 10-year follow-up

Supplemental material, sj-docx-1-pkn-10.1177_1877718X261417468 for Faster reaction times of CSF alpha-synuclein seed amplification assay predict the diffuse malignant subtype of Parkinson's disease at 10-year follow-up by Piergiorgio Grillo, Giulietta Maria Riboldi, Antonio Pisani, Un Jung Kang and Seyed-Mohammad Fereshtehnejad in Journal of Parkinson's Disease

Footnotes

Acknowledgment

Dr Kang is supported by NIH (R01 NS131658, U01 NS113851, U01 NS122419, RF1 NS126406, R01 NS133742).

Dr Riboldi is supported by grants from Michael J Fox Foundation, Parkinson's Foundation, Department of Defense (PD210038), NIH (R01 NS116006; R01 NS133742), and received a previous research grant from Prevail Therapeutics.

Dr Grillo is supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) – A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022). Dr Grillo is also supported by the Marlene and Paolo Fresco Institute Post-Doctoral Clinical Fellowship.

Dr Fereshtehnejad is supported by a grant from Parkinson Canada.

Progression Markers Initiative (PPMI) database (www.ppmi-info.org/access-data-specimens/download-data), RRID:SCR_006431. For up-to-date information on the study, visit .

PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson's Research and funding partners, including 4D Pharma, Abbvie, AcureX, Allergan, Amathus Therapeutics, Aligning Science Across Parkinson's, AskBio, Avid Radiopharmaceuticals, BIAL, BioArctic, Biogen, Biohaven, BioLegend, BlueRock Therapeutics, Bristol-Myers Squibb, Calico Labs, Capsida Biotherapeutics, Celgene, Cerevel Therapeutics, Coave Therapeutics, DaCapo Brainscience, Denali, Edmond J. Safra Foundation, Eli Lilly, Gain Therapeutics, GE HealthCare, Genentech, GSK, Golub Capital, Handl Therapeutics, Insitro, Jazz Pharmaceuticals, Johnson & Johnson Innovative Medicine, Lundbeck, Merck, Meso Scale Discovery, Mission Therapeutics, Neurocrine Biosciences, Neuron23, Neuropore, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi, Servier, Sun Pharma Advanced Research Company, Takeda, Teva, UCB, Vanqua Bio, Verily, Voyager Therapeutics, the Weston Family Foundation and Yumanity Therapeutics.

ORCID iDs

Piergiorgio Grillo

Giulietta Maria Riboldi

Antonio Pisani

Un Jung Kang

Seyed-Mohammad Fereshtehnejad

Ethical considerations

Information about ethical approval and patient consents can be retrieved on the PPMI website ().

Consent to participate

Information about ethical approval and patient consents can be retrieved on the PPMI website ().

Consent for publication

Not applicable

Author contribution

PG: conception, design, analysis, execution, writing

AP: design, editing of final version of the manuscript

GMR: conception, design, analysis, execution, editing of final version of the manuscript

UJK: conception, design, analysis, execution, editing of final version of the manuscript

SMF: conception, design, analysis, execution, editing of final version of the manuscript

Funding

The study utilized public data from the Parkinson's Progression Marker Initiative (PPMI) database. Dr Grillo was supported by the Marlene and Paolo Fresco Institute Post-Doctoral Clinical Fellowship.

Funding statement for the preceding 12 months

Dr Un Kang receives consulting compensation as a SAB member of Amprion, Inc.

Dr Giulietta Riboldi: nothing to disclose.

Dr Piergiorgio Grillo: nothing to disclose.

Dr Antonio Pisani: nothing to disclose.

Dr Seyed-Mohammad Fereshtehnejad: nothing to disclose.

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr Kang is on the Scientific Advisory Board of Amprion, Inc.

Data availability

Data are available from authors upon reasonable request.

Supplemental material

Supplemental material for this article is available online.

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