Abstract
Background:
Alpha-synuclein seed amplification assay on CSF (CSF-αSyn-SAA) has shown high accuracy for Parkinson’s Disease (PD) diagnosis. The analysis of CSF-αSyn-SAA parameters may provide useful insight to dissect the heterogeneity of synucleinopathies.
Objective:
To assess differences in CSF-αSyn-SAA amplification parameters in participants with PD stratified by REM sleep behavior disorder (RBD), dysautonomia, GBA and LRRK2 variants.
Methods:
Clinical and CSF-αSyn-SAA data from the PPMI dataset were utilized. CSF-αSyn-SAA parameters included Fmax (maximum fluorescence), T50 (time to reach 50% of Fmax), TTT (time to threshold), Slope, and AUC (area under the curve). Sporadic PD (n=371) was stratified according to RBD and dysautonomia (DysA) symptoms. Genetic PD included carriers of pathogenic variants of GBA (GBA-PD, n=52) and LRRK2 (LRRK2-PD, n=124) gene.
Results:
CSF-αSyn-SAA was positive in in 77% of LRRK2-PD, 92.3% of GBA-PD and 93.8% of Sporadic PD. The LRRK2-PD cohort showed longer T50 and TTT, and smaller AUC than GBA-PD (p=0.029, p=0.029, p=0.016, respectively) and sporadic PD (p=0.034, p=0.033, p=0.014, respectively). In the sporadic cohort, CSF-αSyn-SAA parameters were similar between PD with (n=157) and without (n=190) RBD, whereas participants with DysA (n=193) presented shorter T50 (p=0.026) and larger AUC (p=0.029) than those without (n=150).
Conclusion:
CSF-αSyn-SAA parameters vary across genetic and non-genetic PD subtypes at the group level. These differences are mostly driven by the presence of LRRK2 variants and DysA. Significant overlaps in the amplification parameters values exist between groups limiting their use at the individual level. Further studies are necessary to understand the mechanisms of CSF-αSyn-SAA parameters differences.
Introduction
The alpha-synuclein seed amplification assay (SAA) is revolutionizing the diagnosis of Parkinson’s Disease (PD), historically lacking fluid biomarkers in support of underlying pathology (1,2). SAA detects the self-replicating activity of misfolded alpha-synuclein with impressive sensitivity and specificity when the analysis is performed on cerebrospinal fluid (CSF) samples (CSF-αSyn-SAA) (1,3–6). The test allows an effective in vivo diagnosis of PD by its high correlation with the pathological hallmark of this disease. In fact, fluorescence values have been associated to the types of synuclein pathology in the brain such as those associated with PD and dementia with Lewy bodies (DLB) vs. multiple system atrophy (MSA) (7, 8), revealing the possibility that amplification curves may harbor information about the structure of the synuclein seeds to dissect the heterogeneity of these synucleinopathies.
The clinical spectrum of synucleinopathies ranges from relative mild conditions to very aggressive and debilitating forms (7). The timeline of symptoms onset and progression is heterogenous and various subtypes have shown to predict severity and progression (7–12). In our recent study, we showed for example that PD with dysautonomia is more prone to progress severely than PD without it (10). Similarly, among the genetic forms of PD, subjects with pathogenic variants in the GBA gene usually present a greater burden of non-motor symptoms and a more rapid course compared to subjects with pathogenic variants in the LRRK2 gene (9,13,14). What drives this phenotypic variability is not known, but some authors also try to link body vs. brain-first pathology as a driver of differential progression (15,16). However, the underlying molecular and biological substrates to the clinical heterogeneity of PD has not been elucidated so far. One could hypothesize that different protein strains with various neuropathological trajectory may underly different PD subtypes.
In this perspective, we analyzed the amplification parameters of CSF-αSyn-SAA in a large cohort of participants from the Parkinson’s Progression Marker Initiative (PPMI) repository and compared clinical and genetic subtypes according to some of the most relevant clinical (such RBD and dysautonomia) and genetic traits (GBA and LRRK2 variants), which have been shown to prognosticate progression(8–10,13,17).
Methods
Study design and participants
The study was based on PPMI cohort whose data were downloaded in September 2023 (datacut 06/12/2023). PPMI is an international, multi-center, longitudinal observational study that characterizes participants with PD under clinical, genetic, imaging, and biochemical aspects. Complete descriptions of data collected by the PPMI study can be found at www.ppmi-info.org. Diagnosis of PD required the presence of both abnormal dopamine transporter (DAT)-SPECT and two of either resting tremor, bradykinesia, rigidity, or asymmetric resting tremor, or asymmetric bradykinesia. For participants with genetic PD neuroimaging positivity was not mandatory. The original cohort assignment based on the individual site determination was used. The sporadic cohort included only drug-naïve participants with newly diagnosed (within 2 years) PD, here referred as de novo PD. As for the genetic cohort, only participants carrying GBA (N409S, n=47; N409S / c.762–2A>G, n=1; and N409S/N409S, n=3) or LRRK2 (G2019S, n=120; G2019S/G2019S, n=3; unspecified variant, n=1) pathogenic variants were selected, whereas other forms of genetic PD were ruled out for paucity of cases and related biochemical data. According to the PPMI design, genetic PD had longer disease duration and were pharmacologically treated compared to sporadic.
Clinical assessment
For all participants the following demographic and clinical data were considered: sex, age, disease duration, RBD Screening Questionnaire (RBDSQ), Scales for Outcomes in PD-Autonomic dysfunction (SCOPA-AUT), Movement Disorder Society-Unified PD Rating scale (MDS-UPDRS) part-III in OFF state, levodopa equivalent daily dose (LEDD), Hoehn and Yahr scale (H&Y), Montreal cognitive assessment (MoCA). Score and demographic information at baseline (BL) visit were considered. Participants were further stratified according to the presence of probable RBD (pRBD) and dysautonomia (DysA). The first was clinically determined by a positive response to RBD Screening Questionnaire (RBDSQ)-Question 6 (≥ 1), which is highly sensitive and strongly associated with PSG-proven diagnosis (12). The latter was instead defined by a total score on SCOPA-AUT equal or greater than 8. The threshold was chosen based on the 75° percentile of the total score in the healthy control group consistently with our previous work (10).
Biochemical assessments
Only participants with paired CSF-αSyn-SAA data were included in the study. The absence of CSF-αSyn-SAA data was considered a major exclusion criterion. The CSF-αSyn-SAA assay was previously described (18). The outcome of the assay provided a categorical response (positive/negative) based on the fluorescence of three replicates. Seven participants with inconclusive response were excluded (sporadic PD, n=4; LRRK2-PD, n=3). Multiple parameters were provided for each test, including: Fmax (highest raw fluorescence from each well; RFU), T50 (time to reach 50% of the Fmax; hours), TTT (time to reach a 5,000 RFU threshold; hours), Slope (RFU/hours) and AUC (area under the curve; RFU*hours). Given that CSF-αSyn-SAA was run in triplicate, the mean of three values for each parameter was used as per previous works (2,19). The statistical analysis with the median of triplicates was also performed and shown as supplementary materials (Supplementary Tables 2–5). The association of CSF αSyn-SAA parameters with PD subtypes and clinical features was investigated only in participants with positive test results. For each participant we consider the mean of the three triplicates of the performed assay.
Statistical analysis
Distribution of variables was evaluated with the Shapiro-Wilk test. Non-normally distributed continuous variables were IDF.NORMAL(fractional rank, mean, standard deviation)-transformed when needed. Categorical variables were compared by chi-square test followed by post hoc test (z-test for independent proportions) with Bonferroni adjustment as needed. Comparison between two groups was assessed by non-parametric (Mann-Whitney U) or parametric (Student’s T) tests as appropriate. Differences between more than two groups were assessed by Kruskal-Wallis’s test; Dunn-Bonferroni post hoc analysis for multiple comparisons was applied when necessary. For normally distributed variables ANOVA and Bonferroni post hoc test were chosen. One-way ANCOVA was performed to compare numerical variables between groups and adjust for potential confounding factors; Bonferroni post-hoc analysis was run for pairwise comparison when one-way ANCOVA included more than two groups. Spearman’s coefficient was used to explore correlation between two quantitative variables. Significance was set at p<0.05. Statistical analysis was performed by using IBM-SPSS Version 28. Data are available from authors upon reasonable request.
Data Sharing
The data that support the findings of this study are openly available in PPMI repository at www.ppmi-info.org.
Results
The initial cohort included 547 participants with sporadic PD (n=371), GBA-PD (n=52) or LRRK2-PD (n=124). The proportions of positive and negative outcomes on CSF-αSyn-SAA test between groups were consistent with the previous report (Supplementary Table 1) (19). In the LRRK2-PD group, a total of 83 (66.9%) out of 124 participants tested positive, showing the highest frequency of negative tests (33.1% vs. 7.7% in GBA-PD, and 6.2% in sporadic PD).
CSF-αSyn-SAA parameters and clinical subtypes
Participants with sporadic PD and positive CSF-αSyn-SAA were initially stratified according to the presence of pRBD and/or DysA, which have been shown to prognosticate the progression of PD symptoms(8,10). pRBD+ PD (n=157) had higher RBDSQ (5.89±2.63 vs 2.49±1.34, p<0.001) and SCOPA-AUT total scores (11.41±6.65 vs 7.58±5.00, p<0.001) compared to pRBD− PD (n=190) but no significant difference was noted in any of the CSF-αSyn-SAA amplification parameters (Table 1). Participants with DysA+ PD (n=193, mean age=63.15) were older than DysA− PD (n=150, mean age=60.21, p=0.004) and had higher MDS-UPDRS part-III score (22.00±8.78 vs 20.30±9.02, p=0.048) (Table 2, Figure 1). DysA+ PD scored higher on RBDSQ (4.63±2.74 vs 3.26±2.23, p<0.001) and SCOPA-AUT (13.12±5.40 vs 4.36±2.01, p<0.001) scales compared to DysA− PD. When considering CSF-αSyn-SAA amplification parameters, DysA+ PD showed shorter T50 and TTT (70.32±10.07 vs 72.88±11.91, p=0.018; 64.71±10.22 vs 67.35±12.50, p=0.034), and larger AUC (26615142.40±3497116.23 vs 25711771.20±4192209.15, p=0.027). After adjusting for age and MDS-UPDRS part-III the significance persisted for T50 (F(1,338)=5.011, p=0.026) and AUC (F(1,338)=4.827, p=0.029), but not for TTT (F(1,338)=3.137, p=0.077).
Table 1. Sporadic PD with (pRBD+) vs without (pRBD−) probable RBD: clinical-demographic data and parameters of CSF-αSyn-SAA.
Only participants with positive SAA were included. Values are given in mean (±standard deviation). Statistical significance is marked in bold. Abbreviations: n, number; y, years; m, male; f, female; CSF-αSyn-SAA, alpha-synuclein seed amplification assays performed on cerebrospinal fluid samples; LEDD, levodopa equivalent daily dose; MDS-UPDRS-part III in OFF state, Movement Disorder Society-Unified PD Rating scale part III; HY, Hoehn and Yahr scale; MoCA, Montreal cognitive assessment; SCOPA-AUT, Scales for Outcomes in PD-Autonomic dysfunction; RBDSQ, RBD Screening Questionnaire; Fmax, highest raw fluorescence from each well; T50, time to reach 50% of the Fmax; TTT, time to reach a 5,000 RFU threshold; AUC, area under the curve.
| pRBD+ | pRBD− | p value | |
|---|---|---|---|
| Demographic Parameters | |||
| n | 157 | 190 | - |
| Sex (m/f) | 106/51 | 119/71 | p=0.343 |
| Age (y) | 62.60 (9.04) | 61.44 (9.36) | p=0.234 |
| Duration (y) | 0.62 (0.62) | 0.57 (0.56) | p=0.872 |
| Clinical Parameters | |||
| LEDD (mg/day) | 0 | 0 | - |
| MDS-UPDRS part III (OFF state) | 21.52 (8.57) | 21.15 (9.27) | p=0.609 |
| HY | 1.62 (0.49) | 1.56 (0.50) | p=0.304 |
| MoCA | 27.08 (2.49) | 27.17 (2.18) | p=0.970 |
| SCOPA-AUT | 11.41 (6.65) | 7.58 (5.00) | p<0.001 |
| RBDSQ | 5.89 (2.63) | 2.49 (1.34) | p<0.001 |
| Parameters of CSF-αSyn-SAA | |||
| Fmax (RFU) | 84056.80 (28371.96) | 84081.91 (23218.67) | p=0.939 |
| T50 (hours) | 70.17 (10.01) | 72.40 (11.57) | p=0.098 |
| TTT (hours) | 64.88 (10.28) | 66.54 (12.06) | p=0.330 |
| Slope (RFU/hours) | 33.08 (8.98) | 33.54 (11.33) | p=0.737 |
| AUC (RFU*hours) | 26644643.20 (3452879.51) | 25903922.90 (4081291.48) | p=0.132 |
Table 2. Sporadic PD with (DysA+) vs without (DysA−) dysautonomia: clinical-demographic data and parameters of CSF-αSyn-SAA. Only participants with positive SAA were included.
Values are given in mean (±standard deviation). Statistical significance is marked in bold. Abbreviations: n, number; y, years; m, male; f, female; CSF-αSyn-SAA, alpha-synuclein seed amplification assays performed on cerebrospinal fluid samples; LEDD, levodopa equivalent daily dose; MDS-UPDRS-part III in OFF state, Movement Disorder Society-Unified PD Rating scale part III; HY, Hoehn and Yahr scale; MoCA, Montreal cognitive assessment; SCOPA-AUT, Scales for Outcomes in PD-Autonomic dysfunction; RBDSQ, RBD Screening Questionnaire; Fmax, highest raw fluorescence from each well; T50, time to reach 50% of the Fmax; TTT, time to reach a 5,000 RFU threshold; AUC, area under the curve.
| DysA+ | DysA− | p value | |
|---|---|---|---|
| Demographic Parameters | |||
| n | 193 | 150 | - |
| Sex (m/f) | 119/74 | 105/45 | p=0.107 |
| Age (y) | 63.15 (8.74) | 60.21 (9.73) | p=0.004 |
| Duration (y) | 0.62 (0.59) | 0.55 (0.58) | p=0.085 |
| Clinical Parameters | |||
| LEDD (mg/day) | 0 | 0 | - |
| MDS-UPDRS-part III (OFF state) | 22.00 (8.78) | 20.30 (9.02) | p=0.048 |
| HY | 1.60 (0.49) | 1.57 (0.50) | p=0.522 |
| MoCA | 27.00 (2.28) | 27.36 (2.33) | p=0.064 |
| SCOPA-AUT | 13.12 (5.40) | 4.36 (2.01) | p<0.001 |
| RBDSQ | 4.63 (2.74) | 3.26 (2.23) | p<0.001 |
| Parameters of CSF-αSyn-SAA | |||
| Fmax (RFU) | 83379.11 (27051.70) | 85194.51 (23595.69) | p=0.380† |
| T50 (hours) | 70.32 (10.07) | 72.88 (11.91) | p=0.026 † |
| TTT (hours) | 64.71 (10.22) | 67.35 (12.50) | p=0.077† |
| Slope (RFU/hours) | 32.62 (10.30) | 34.37 (10.20) | p=0.057† |
| AUC (RFU*hours) | 26615142.40 (3497116.23) | 25711771.20 (4192209.15) | p=0.029 † |
Adjusted for Age and MDS-UPDRS-part III in OFF state
Figure 1. Violin plot illustrating parameters of CSF-αSyn-SAA in Sporadic PD with (DysA+) vs without (DysA−) dysautonomia [A], and GBA-PD vs LRRK2-PD vs Sporadic PD [B].
Fmax, highest raw fluorescence from each well; T50, time to reach 50% of the Fmax; TTT, time to reach a 5,000 RFU threshold; AUC, area under the curve; as for section A (DysA+ vs DysA−): *p-value<0.05, adjusted for age and MDS-UPDRS part-III score in OFF state; as for section B (GBA-PD vs LRRK2-PD vs Sporadic PD): *p-value<0.05, adjusted for MDS-UPDRS-part III, pRBD (positive answer to RBDSQ-question 6) and DysA (SCOPA-AUT total score equal or greater than 8).
Participants were then stratified according to combined pRBD and DysA status in four groups: PD with one of either pRBD (pRBD+DysA−), DysA (pRBD−DysA+), both conditions (pRBD+DysA+) or none of them (pRBD−DysA−) (Table 3). There was an overall difference in terms of age (DysA−/pRBD−, 60.32±9.46 vs DysA−/pRBD+, 59.94±10.47 vs DysA+/pRBD−, 62.79±9.14 vs DysA+/pRBD+, 63.57±8.35, p=0.029) between groups, but no significance was noted on the pairwise comparisons. In a model including age as a covariate, T50 and TTT were significantly different between groups (T50 (hours): DysA−/pRBD−, 73.92±11.74 vs DysA−/pRBD+, 70.37±12.07 vs DysA+/pRBD−,70.53±11.17 vs DysA+/pRBD+, 70.21±9.23, F(3, 336)=3.070, p=0.028; TTT (hours): DysA−/pRBD−, 68.52±12.81 vs DysA−/pRBD+, 64.54±11.38 vs DysA+/pRBD−, 64.16±10.60 vs DysA+/pRBD+, 65.18±9.97, F(3, 336)=2.631, p=0.050), but significance was lost after post hoc analysis.
Table 3. Comparison of clinical-demographic data and parameters of CSF-αSyn-SAA between groups based on the presence of probable RBD (pRBD) and/or dysautonomia (DysA). Only participants with positive SAA were included.
Values are given in mean (±standard deviation). Statistical significance is marked in bold. Abbreviations: n, number; y, years; m, male; f, female; CSF-αSyn-SAA, alpha-synuclein seed amplification assays performed on cerebrospinal fluid samples; LEDD, levodopa equivalent daily dose; MDS-UPDRS-part III in OFF state, Movement Disorder Society-Unified PD Rating scale part III; HY, Hoehn and Yahr scale; MoCA, Montreal cognitive assessment; SCOPA-AUT, Scales for Outcomes in PD-Autonomic dysfunction; RBDSQ, RBD Screening Questionnaire; Fmax, highest raw fluorescence from each well; T50, time to reach 50% of the Fmax; TTT, time to reach a 5,000 RFU threshold; AUC, area under the curve.
| DysA−/pRBD− | DysA−/pRBD+ | DysA+/pRBD− | DysA+/pRBD+ | p value | |
|---|---|---|---|---|---|
| Demographic Parameters | |||||
| n | 106 | 44 | 83 | 109 | |
| Sex (m/f) | 73/33 | 31/12 | 46/37 | 72/37 | p=0.154 |
| Age (y) | 60.32 (9.46) | 59.94 (10.47) | 62.79 (9.14) | 63.57 (8.35) | p=0.029 |
| Duration (y) | 0.55 (0.56) | 0.54 (0.64) | 0.59 (0.57) | 0.64 (0.61) | p=0.198 |
| Clinical Parameters | |||||
| LEDD (mg/day) | 0 | 0 | 0 | 0 | - |
| MDS-UPDRS-part III (OFF state) | 20.82 (9.60) | 19.05 (7.38) | 21.70 (8.83) | 22.26 (8.81) | p=0.191 |
| HY | 1.53 (0.50) | 1.66 (0.48) | 1.61 (0.49) | 1.58 (0.49) | p=0.442 |
| MOCA | 27.28 (2.36) | 27.55 (2.29) | 27.06 (1.95) | 26.93 (2.51) | p=0.262 |
| SCOPA-AUT | 4.13 (2.03) | 4.91 (1.85) | 11.98 (4.13)a,b | 14.03 (6.07)a,b | p<0.001 |
| RBDSQ | 2.36 (1.30) | 5.39 (2.53)a,c | 2.68 (1.35) | 6.06 (2.61)a,c | p<0.001 |
| Parameters of CSF-αSyn-SAA | |||||
| Fmax (RFU) | 85061.34 (23441.92) | 85515.24 (24232.55) | 83157.30 (22975.43) | 83188.80 (29764.22) | p=0.835† |
| T50 (hours) | 73.92 (11.74) | 70.37 (12.07) | 70.53 (11.17) | 70.21 (9.23) | p=0.028 † ‡ |
| TTT (hours) | 68.52 (12.81) | 64.54 (11.38) | 64.16 (10.60) | 65.18 (9.97) | p=0.050 † ‡ |
| Slope (RFU/hours) | 34.64 (10.74) | 33.71 (8.82) | 32.40 (11.82) | 32.77 (9.05) | p=0.274† |
| AUC (RFU*hours) | 25314851.9 (4265416.12) | 26667985.7 (3891973.50) | 26622779.1 (3745621.12) | 26596576.6 (3321435.64) | p=0.060† |
Adjusted for age;
Pairwise comparison test did not show any significant difference;
Compared with DysA−/pRBD−, p<0.001;
Compared with DysA−/pRBD+, p<0.001;
Compared with DysA+/pRBD−, p<0.001
CSF-αSyn-SAA parameters and genetic subtypes
We then compared the CSF-αSyn-SAA amplification parameters among different genetic forms of PD, specifically GBA- and LRRK2-PD, versus sporadic PD (sPD). Only participants with a positive CSF-αSyn-SAA were considered for amplification parameter comparison. All clinical demographical variables, aside from age and sex, were significantly different between groups (Table 4, Figure 1). Post-hoc analysis revealed that LEDD and disease duration were significantly lower in the sporadic group compared to the genetic PD groups (LEDD Sporadic PD vs GBA-PD, 0±0 vs 554.24±323.25, p<0.001; LEDD Sporadic PD vs LRRK2-PD 0±0 vs 586.48±451.19, p<0.001; Disease Duration Sporadic PD vs GBA-PD: 0.59±0.59 vs 3.48±2.42, p<0.001; Disease Duration Sporadic PD vs LRRK2-PD 0.59±0.59 vs 3.10 ±2.07, p<0.001). Moreover, GBA-PD presented higher MDS-UPDRS part-III scores and higher frequency of pRBD compared sporadic PD and LRRK2-PD (MDS-UPDRS part-III GBA-PD vs Sporadic PD, 29.44±10.73 vs 21.31±8.93, p<0.001; MDS-UPDRS part-III GBA-PD vs LRRK2-PD 29.44±10.73 vs 23.70±11.74, p=0.019; pRBD GBA-PD vs Sporadic PD, 69.6% vs 45.2%, p<0.05; pRBD GBA-PD vs LRRK2-PD 69.6% vs 36.1%, p<0.05). GBA-PD showed significantly worse performance on MoCA testing and more severe dysautonomia vs sPD (MoCA 26.13±2.69 vs 27.12±2.30, p=0.015; DysA 75.0% vs 56.3%, p<0.05). MDS-UPDRS part III score correlated with LEDD (r(446)=0.201, p<0.001), disease duration (r(446)=0.183, p<0.001), MoCA (r(446)=−0.178, p<0.001) and age (r(447)=0.124, p=0.008). CSF-αSyn-SAA amplification parameters showed a significant difference in T50, TTT and AUC between groups also when the analysis was adjusted for MDS-UPDRS part III, pRBD and DysA (F(2, 434)=4.215, p=0.015; F(2, 434)=4.241, p=0.015; F(2, 434)=5.068, p=0.007). MDS-UPDRS part III score was chosen as covariate to mitigate the clinical heterogeneity of the cohort without reducing the power of the statistical test since it correlated with clinical-demographic variables (age, disease duration, LEDD, MoCA). DysA and pRBD were added as covariates on the basis of the results obtained with the clinical subtypes. Post Hoc test showed that LRRK2-PD had the longest T50 and TTT, and the smallest AUC (p=0.034, p=0.033, p=0.014 compared to Sporadic PD; p=0.029, p=0.029, p=0.016, compared to GBA-PD). However, significant variability (SD) and overlaps existed between groups for all the SAA amplification parameters.
Table 4. Comparison of clinical-demographic data and parameters of CSF-αSyn-SAA between GBA-PD, LRRK2-PD and Sporadic PD. Only participants with positive SAA were included.
Values are given in mean (±standard deviation). Statistical significance is marked in bold. Abbreviations: n, number; y, years; m, male; f, female; CSF-αSyn-SAA, alpha-synuclein seed amplification assays performed on cerebrospinal fluid samples; LEDD, levodopa equivalent daily dose; MDS-UPDRS-part III in OFF state, Movement Disorder Society-Unified PD Rating scale part III; HY, Hoehn and Yahr scale; MoCA, Montreal cognitive assessment; SCOPA-AUT, Scales for Outcomes in PD-Autonomic dysfunction; RBDSQ, RBD Screening Questionnaire; Fmax, highest raw fluorescence from each well; T50, time to reach 50% of the Fmax; TTT, time to reach a 5,000 RFU threshold; AUC, area under the curve.
| GBA-PD | LRRK2-PD | Sporadic PD | p value | |
|---|---|---|---|---|
| Demographic Parameters | ||||
| n | 48 | 83 | 348 | |
| Sex (m/f) | 27/21 | 52/31 | 226/122 | p=0.491 |
| Age (y) | 62.35 (9.45) | 60.87 (8.57) | 61.92 (9.24) | p=0.476 |
| Duration (y) | 3.48 (2.42) | 3.10 (2.07) | 0.59 (0.59)a,b | p<0.001 |
| Clinical Parameters | ||||
| LEDD (mg/day) | 554.24 (323.25) | 586.48 (451.19) | 0 (0)a,b | p<0.001 |
| MDS-UPDRS-part III ( OFF state) | 29.44 (10.73) | 23.70 (11.74)d | 21.31 (8.93)b | p<0.001 |
| HY | 1.83 (1.67) | 1.79 (0.58) | 1.59 (0.49)c,d | p=0.001 |
| MoCA | 26.13 (2.69) | 26.73 (2.68) | 27.12 (2.30)d | p=0.016 |
| DysA (yes, %) | 35, 75.0% | 56, 68.3% | 193, 56.3%d | p=0.012 |
| pRBD (yes, %) | 32, 69.6% | 30, 36.1%d | 157, 45.2%d | p=0.001 |
| Parameters of CSF-αSyn-SAA | ||||
| Fmax (RFU) | 89040.37 (22823.72) | 83351.18 (24252.50) | 84181.07 (25686.05) | p=0.485† |
| T50 (hours) | 70.06 (11.72) | 75.33 (13.93)d,e | 71.37 (10.92) | p=0.015 † |
| TTT (hours) | 64.26 (11.61) | 70.26 (14.40)d,e | 65.77 (11.29) | p=0.015 † |
| Slope (RFU/hours) | 35.37 (9.37) | 36.46 (10.65) | 33.34 (10.30) | p=0.114† |
| AUC (RFU*hours) | 26548470.3 (3941237.63) | 24396060.8d,e (4840198.96) | 26247268.1 (3819920.33) | p=0.007 † |
Adjusted for MDS-UPDRS-part III in OFF state, pRBD (positive answer to RBDSQ-question 6) and DysA (SCOPA-AUT total score equal or greater than 8).
Compared with LRRK2-PD, p<0.001;
Compared with GBA-PD, p<0.001;
Compared with LRRK2-PD, p<0.05;
Compared with GBA-PD, p<0.05;
Compared with Sporadic PD, p<0.05.
Discussion
This study aimed to assess the differences in CSF-αSyn-SAA amplification parameters across clinical and genetic PD subtypes. Participants with both sporadic and genetic PD were included; the first underwent stratification according to the presence of RBD and/or DysA, the latter were divided into LRRK2- and GBA-PD. DysA emerged as the main driver of the differences in the sporadic cohort with shorter T50 and larger AUC. LRRK2-PD, already known to have the highest occurrence of negative SAA results showed the longest T50/TTT and the smallest AUC (19).
αSyn-SAA amplifies circulating αSyn seeds by a cyclical process by which the aggregates are fragmented and elongated with the recombinant αSyn (18). The detection of amplificated seed by thioflavin T generates fluorescent signal that can be monitored. The current assay results are binary, providing either positive or negative results based on amplification parameters. Numerous studies have shown high accuracy for diagnosis with a sensitivity ranging between 76% to 97% (1,3,20). Most strikingly, CSF-αSyn-SAA shows 99% sensitivity and specificity when only cases with post-mortem confirmation of the diagnosis by Lewy Body (LB) pathology is included from various studies (21,22). SAA was negative in 33% of LRRK2-PD (vs 7.7% of GBA-PD and 6.2% of Sporadic PD), consistent with the absence of LB in a significant proportion of LRRK2-PD (19,23).
Interestingly, our study showed that the data from the LRRK2-PD cohort with positive CSF-αSyn-SAA exhibit a different profile of amplification parameters compared to GBA-PD and Sporadic PD. The amplification curve of SAAs can be described by parameters including Fmax, T50, TTT, Slope, and AUC. In our cohort, LRRK2-PD was characterized by the longest T50 and TTT, and the smallest AUC independently from presence of DysA, RBD, and MDS-UPDRS part-III (the latter correlating with LEDD, disease duration, MoCA and age) (Table 4, Figure 1). Although dopaminergic therapy and disease duration do not appear to influence CSF-αSyn-SAA parameters, the genetic PD groups had a longer disease duration and higher LEDD compared to sPD due to study design, where participants with genetic forms of PD can be enrolled at any stage of disease (2,24). Moreover, as expected by literature GBA-PD presented more severe motor, cognitive, sleep and autonomic impairment (17,25–27). Therefore, results were adjusted for clinical and demographic features (Table 4, Figure 1). In a recent study, Brockman et al found that GBA-PD associated with severe pathogenic variants (n=27) had shorter TTT (in their paper referred as lag phase, LAG) and higher AUC compared to PD carrying different mutation status, including LRRK2-PD (n=7) (24). By considering an independent assay and larger population of LRRK2-PD (n=83), our results reinforce the association between the differences in the amplification parameters and the genetic status
The differences in CSF-αSyn-SAA parameters could reflect different underlying pathology. There is strong evidence for example that subjects with MSA have very different strains of alpha synuclein that demonstrate different amplification parameters from PD/DLB (28–30). Some previous studies have also shown that longer time of reaction (T50/TTT) corresponds to lower burden of LB pathology(22,31). Although quantitative analysis of LB burden in LRRK2-PD vs. sporadic PD is not available, some have hypothesized that LRRK2-PD is a brain-first/amygdala-predominant disease characterized by a centrifugal/descending LB deposition that proceeds at a slower pace in contraposition to the more aggressive body-first PD where the neuropathological trajectory follows the inverse pathway from the gut toward the brain (15,26,32,33). However, the correlations of amplification parameters with clinical features in PD are still very discordant between studies (2,31). In addition, we observed a significant overlap between the range of CSF-αSyn-SAA amplification parameters across groups.
If the stratification of genetic PD was intrinsically guided by the pathogenic variant, identifying valid subtypes for sporadic de novo PD was less obvious and open to multiple possibilities. The decision of adopting RBD and DysA as grouping markers was based on growing evidence linking these non-motor conditions, often appearing in the prodromal phase, to a peculiar clinic-pathological trajectory (10,26). In our cohort only PD with DysA showed different CSF-αSyn-SAA amplification parameters compared to PD without DysA in terms of shorter T50 and TTT (for the latter only a strong tendency was noted after adjusting for age and MDS-UPDRS part-III), and larger AUC. No correlation between RBD status and CSF-αSyn-SAA amplification parameters were noticed. In our previous work we assessed the contribution of DysA and RBD as independent predictor of PD clinical course (10). The study showed that although these two symptoms associated closely, DysA predicted severe progression of motor and non-motor symptoms better than RBD across the 3-year follow-up period(10). Here, we found consistent results that showed a more significant contribution of DysA over RBD to the differences of the amplification parameters. DysA group might have a greater spreading of αSyn seeds as hypothesized by the body-first model characterized by earlier and more extensive involvement of peripheral/autonomic and central nervous system (16,26). However, this observation remains pure speculative since the dualistic model of disease still needs further confirmation and a reliable way to distinguish brain from body first-PD using clinical information still lacks. Originally, prodromal RBD was proposed as marker of the body first subtype(26). In our case, even if the sporadic cohort only included De Novo PD, there was no information about the exact moment of RBD onset which theoretically could have started for some of the participants after the appearance of the motor signs (disease duration ~ 6 months) thus not configuring the condition of “prodromal RBD”.
The study has a few strengths. First, the analysis is based on data from a very large sample of participants coming from a well-established international cohort. The extensive clinical characterization allowed to define the subgroups and assess the biochemical differences accordingly. Our work explores potential quantitative application of CSF-αSyn-SAA which is firmly established only as a binary diagnostic marker and in need of quantitative insights (2). The main limitation of the study is the too small sample size for sub-analysis based on specific pathogenic variants within the single genetic subgroup (i.e, GBA-PD, LRRK2-PD). The sporadic group was clinically different from the genetics due to inclusion criteria of the study, as explained above, although a statistical correction helped us to overcome the issue. Finally, longitudinal data of CSF-αSyn-SAA were not available. Finally, it is important to consider that our data showed significant variability and overlaps for the different SAA amplification parameters between groups. Therefore, the described SAA amplification parameters cannot be used to characterize genetic status at an individual level at this time.
In conclusion, our analysis showed an association of CSF-αSyn-SAA amplification parameters with clinical and genetic PD subtypes. Further studies are necessary to understand the mechanism and significance of amplification parameter differences identified in our results.
Supplementary Material
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 a grant from Fresco Post-Doctoral Clinical Fellowship.
Funding Sources for study:
the study utilized public data from the Parkinson’s Progression Marker Initiative (PPMI) database. Initial analysis of samples was funded by Amprion and by a research grant from MJFF to Amprion (MJFF-16712).
Footnotes
Financial Disclosures of all authors (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. Luis Concha-Marambio is funded internally by Amprion and by the MJFF (grants MJFF-025017, MJFF-024735, MJFF-021233, MJFF-024261).
Financial disclosure/conflict of Interest related to research covered in this article:
Dr. Kang is on the Scientific Advisory Board of Amprion, Inc. Dr. Concha is an employee of Amprion, Inc.
Dr. Luis Concha-Marambio is an employee of Amprion and declares employee stock option ownership and invention of patents related to SAA assigned to Amprion (US11970520B2, US11959927B2, US20190353669A1, US20230084155A1, and US20210223268A1).
References
- 1.Kang UJ, Boehme AK, Fairfoul G, Shahnawaz M, Ma TC, Hutten SJ, et al. Comparative study of cerebrospinal fluid α-synuclein seeding aggregation assays for diagnosis of Parkinson’s disease. Mov Disord [Internet]. 2019. Apr 1 [cited 2024 Jun 17];34(4):536–44. Available from: https://pubmed.ncbi.nlm.nih.gov/30840785/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Russo MJ, Orru CD, Concha-Marambio L, Giaisi S, Groveman BR, Farris CM, et al. High diagnostic performance of independent alpha-synuclein seed amplification assays for detection of early Parkinson’s disease. Acta Neuropathol Commun [Internet]. 2021. Dec 1 [cited 2024 Jun 17];9(1):1–13. Available from: https://actaneurocomms.biomedcentral.com/articles/10.1186/s40478-021-01282-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Bargar C, Wang W, Gunzler SA, LeFevre A, Wang Z, Lerner AJ, et al. Streamlined alpha-synuclein RT-QuIC assay for various biospecimens in Parkinson’s disease and dementia with Lewy bodies. Acta Neuropathol Commun [Internet]. 2021. Dec 1 [cited 2024 Jun 17];9(1):1–13. Available from: https://actaneurocomms.biomedcentral.com/articles/10.1186/s40478-021-01175-w [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Fairfoul G, McGuire LI, Pal S, Ironside JW, Neumann J, Christie S, et al. Alpha-synuclein RT-QuIC in the CSF of patients with alpha-synucleinopathies. Ann Clin Transl Neurol [Internet]. 2016. Oct 1 [cited 2024 Jun 28];3(10):812–8. Available from: https://pubmed.ncbi.nlm.nih.gov/27752516/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Saborio GP, Permanne B, Soto C. Sensitive detection of pathological prion protein by cyclic amplification of protein misfolding. Nature 2001 411:6839 [Internet]. 2001. Jun 14 [cited 2024 Jun 28];411(6839):810–3. Available from: https://www.nature.com/articles/35081095 [DOI] [PubMed] [Google Scholar]
- 6.Atarashi R, Wilham JM, Christensen L, Hughson AG, Moore RA, Johnson LM, et al. Simplified ultrasensitive prion detection by recombinant PrP conversion with shaking. Nat Methods [Internet]. 2008. Mar [cited 2024 Jun 28];5(3):211–2. Available from: https://pubmed.ncbi.nlm.nih.gov/18309304/ [DOI] [PubMed] [Google Scholar]
- 7.Lázaro DF, Lee VMY. Navigating through the complexities of synucleinopathies: Insights into pathogenesis, heterogeneity, and future perspectives. Neuron [Internet]. 2024. Jun [cited 2024 Jun 17]; Available from: https://pubmed.ncbi.nlm.nih.gov/38861985/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Fereshtehnejad SM, Zeighami Y, Dagher A, Postuma RB. Clinical criteria for subtyping Parkinson’s disease: biomarkers and longitudinal progression. Brain [Internet]. 2017. Jul 1 [cited 2024 Jun 17];140(7):1959–76. Available from: https://pubmed.ncbi.nlm.nih.gov/28549077/ [DOI] [PubMed] [Google Scholar]
- 9.Riboldi GM, Di Fonzo AB. GBA, Gaucher Disease, and Parkinson’s Disease: From Genetic to Clinic to New Therapeutic Approaches. Cells [Internet]. 2019. Apr 1 [cited 2024 Jun 17];8(4). Available from: https://pubmed.ncbi.nlm.nih.gov/31010158/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Riboldi GM, Russo MJ, Pan L, Watkins K, Kang UJ. Dysautonomia and REM sleep behavior disorder contributions to progression of Parkinson’s disease phenotypes. NPJ Parkinsons Dis [Internet]. 2022. Dec 1 [cited 2024 Jun 17];8(1). Available from: https://pubmed.ncbi.nlm.nih.gov/36042235/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Rocha EM, De Miranda B, Sanders LH. Alpha-synuclein: Pathology, mitochondrial dysfunction and neuroinflammation in Parkinson’s disease. Neurobiol Dis [Internet]. 2018. Jan 1 [cited 2022 Nov 21];109(Pt B):249–57. Available from: https://pubmed.ncbi.nlm.nih.gov/28400134/ [DOI] [PubMed] [Google Scholar]
- 12.Postuma RB, Arnulf I, Hogl B, Iranzo A, Miyamoto T, Dauvilliers Y, et al. A single-question screen for rapid eye movement sleep behavior disorder: a multicenter validation study. Mov Disord [Internet]. 2012. Jun [cited 2024 Jun 17];27(7):913–6. Available from: https://pubmed.ncbi.nlm.nih.gov/22729987/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Rocha EM, Keeney MT, Di Maio R, De Miranda BR, Greenamyre JT. LRRK2 and idiopathic Parkinson’s disease. Trends Neurosci [Internet]. 2022. Mar 1 [cited 2024 Jun 17];45(3):224–36. Available from: https://pubmed.ncbi.nlm.nih.gov/34991886/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Greenberg J, Astudillo K, Frucht SJ, Flinker A, Riboldi GM. Clinical prediction of GBA carrier status in Parkinson’s disease. Clin Park Relat Disord [Internet]. 2024. Jan 1 [cited 2024 Jul 29];10. Available from: https://pubmed.ncbi.nlm.nih.gov/38645305/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Just MK, Gram H, Theologidis V, Jensen PH, Nilsson KPR, Lindgren M, et al. Alpha-Synuclein Strain Variability in Body-First and Brain-First Synucleinopathies. Front Aging Neurosci. 2022. May 26;14:907293. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Borghammer P, Horsager J, Andersen K, Van Den Berge N, Raunio A, Murayama S, et al. Neuropathological evidence of body-first vs. brain-first Lewy body disease. Neurobiol Dis. 2021. Dec 1;161:105557. [DOI] [PubMed] [Google Scholar]
- 17.Jia F, Fellner A, Kumar KR. Monogenic Parkinson’s Disease: Genotype, Phenotype, Pathophysiology, and Genetic Testing. Genes (Basel) [Internet]. 2022. Mar 1 [cited 2024 Jun 17];13(3). Available from: https://pubmed.ncbi.nlm.nih.gov/35328025/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Concha-Marambio L, Pritzkow S, Shahnawaz M, Farris CM, Soto C. Seed amplification assay for the detection of pathologic alpha-synuclein aggregates in cerebrospinal fluid. Nat Protoc [Internet]. 2023. Apr 1 [cited 2024 Jun 17];18(4):1179–96. Available from: https://pubmed.ncbi.nlm.nih.gov/36653527/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Siderowf A, Concha-Marambio L, Lafontant DE, Farris CM, Ma Y, Urenia PA, et al. Assessment of heterogeneity among participants in the Parkinson’s Progression Markers Initiative cohort using α-synuclein seed amplification: a cross-sectional study. Lancet Neurol [Internet]. 2023. May 1 [cited 2024 Jun 17];22(5):407–17. Available from: https://pubmed.ncbi.nlm.nih.gov/37059509/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Rossi M, Candelise N, Baiardi S, Capellari S, Giannini G, Orrù CD, et al. Ultrasensitive RT-QuIC assay with high sensitivity and specificity for Lewy body-associated synucleinopathies. Acta Neuropathol [Internet]. 2020. Jul 1 [cited 2024 Jun 17];140(1):49–62. Available from: https://pubmed.ncbi.nlm.nih.gov/32342188/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Mammana A, Baiardi S, Quadalti C, Rossi M, Donadio V, Capellari S, et al. RT-QuIC Detection of Pathological α-Synuclein in Skin Punches of Patients with Lewy Body Disease. Mov Disord [Internet]. 2021. Sep 1 [cited 2024 Jul 15];36(9):2173–7. Available from: https://pubmed.ncbi.nlm.nih.gov/34002890/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Bentivenga GM, Mammana A, Baiardi S, Rossi M, Ticca A, Magliocchetti F, et al. Performance of a seed amplification assay for misfolded alpha-synuclein in cerebrospinal fluid and brain tissue in relation to Lewy body disease stage and pathology burden. Acta Neuropathol [Internet]. 2024. Jun 1 [cited 2024 Jul 15];147(1):1–13. Available from: https://cris.unibo.it/handle/11585/961160 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Kalia L V, Lang AE, Hazrati LN, Fujioka S, Wszolek ZK, Dickson DW, et al. Clinical Correlations With Lewy Body Pathology in LRRK2-Related Parkinson Disease. JAMA Neurol [Internet]. 2015. Jan 1 [cited 2024 Jun 17];72(1):100. Available from: /pmc/articles/PMC4399368/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Brockmann K, Quadalti C, Lerche S, Rossi M, Wurster I, Baiardi S, et al. Association between CSF alpha-synuclein seeding activity and genetic status in Parkinson’s disease and dementia with Lewy bodies. Acta Neuropathol Commun [Internet]. 2021. Dec 1 [cited 2024 Jun 17];9(1):1–11. Available from: https://actaneurocomms.biomedcentral.com/articles/10.1186/s40478-021-01276-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Yahalom G, Greenbaum L, Israeli-Korn S, Fay-Karmon T, Livneh V, Ruskey JA, et al. Carriers of both GBA and LRRK2 mutations, compared to carriers of either, in Parkinson’s disease: Risk estimates and genotype-phenotype correlations. Parkinsonism Relat Disord [Internet]. 2019. May 1 [cited 2024 Jun 17];62:179–84. Available from: https://pubmed.ncbi.nlm.nih.gov/30573413/ [DOI] [PubMed] [Google Scholar]
- 26.Horsager J, Knudsen K, Sommerauer M. Clinical and imaging evidence of brain-first and body-first Parkinson’s disease. Neurobiol Dis. 2022. Mar 1;164:105626. [DOI] [PubMed] [Google Scholar]
- 27.Schneider SA, Alcalay RN. Neuropathology of genetic synucleinopathies with parkinsonism: Review of the literature. Movement Disorders [Internet]. 2017. Nov 1 [cited 2024 Jun 17];32(11):1504–23. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/mds.27193 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Singer W, Schmeichel AM, Shahnawaz M, Schmelzer JD, Boeve BF, Sletten DM, et al. Alpha-Synuclein Oligomers and Neurofilament Light Chain in Spinal Fluid Differentiate Multiple System Atrophy from Lewy Body Synucleinopathies. Ann Neurol [Internet]. 2020. Sep 1 [cited 2024 Jun 17];88(3):503–12. Available from: https://pubmed.ncbi.nlm.nih.gov/32557811/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Shahnawaz M, Mukherjee A, Pritzkow S, Mendez N, Rabadia P, Liu X, et al. Discriminating α-synuclein strains in Parkinson’s disease and multiple system atrophy. Nature [Internet]. 2020. Feb 13 [cited 2024 Jun 17];578(7794):273–7. Available from: https://pubmed.ncbi.nlm.nih.gov/32025029/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.De Luca CMG, Elia AE, Portaleone SM, Cazzaniga FA, Rossi M, Bistaffa E, et al. Efficient RT-QuIC seeding activity for α-synuclein in olfactory mucosa samples of patients with Parkinson’s disease and multiple system atrophy. Transl Neurodegener [Internet]. 2019. Aug 8 [cited 2024 Jun 17];8(1). Available from: https://pubmed.ncbi.nlm.nih.gov/31406572/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Bellomo G, De Luca CMG, Paoletti FP, Gaetani L, Moda F, Parnetti L. α-Synuclein Seed Amplification Assays for Diagnosing Synucleinopathies: The Way Forward. Neurology [Internet]. 2022. Aug 2 [cited 2024 Jun 17];99(5):195–205. Available from: https://pubmed.ncbi.nlm.nih.gov/35914941/ [DOI] [PubMed] [Google Scholar]
- 32.Borghammer P, Horsager J, Andersen K, Van Den Berge N, Raunio A, Murayama S, et al. Neuropathological evidence of body-first vs. brain-first Lewy body disease. Neurobiol Dis [Internet]. 2021. Dec 1 [cited 2024 Jun 17];161. Available from: https://pubmed.ncbi.nlm.nih.gov/34763110/ [DOI] [PubMed] [Google Scholar]
- 33.Borghammer P, Horsager J, Andersen K, Van Den Berge N, Raunio A, Murayama S, et al. Neuropathological evidence of body-first vs. brain-first Lewy body disease. Neurobiol Dis [Internet]. 2021. Dec 1 [cited 2022 Apr 22];161. Available from: https://pubmed.ncbi.nlm.nih.gov/34763110/ [DOI] [PubMed] [Google Scholar]
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