Blood and cerebrospinal fluid differences between Parkinson's disease and related diseases

doi:10.21203/rs.3.rs-4973615/v1

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Blood and cerebrospinal fluid differences between Parkinson's disease and related diseases

https://doi.org/10.21203/rs.3.rs-4973615/v1

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Accurately diagnosing Parkinson’s disease (PD) in its early stages is difficult due to its symptoms overlapping with those of various disorders, including atypical Parkinsonian syndromes, dementia with Lewy bodies (DLB), and even essential tremor. This complicates the diagnostic process for PD, which traditionally heavily relies on symptomatic assessment and treatment response. Recent advances have identified several biomarkers in the blood and cerebrospinal fluid (CSF), including α-synuclein, lysosomal enzymes, fatty acid-binding proteins, and neurofilament light chain, that may potentially be used to diagnosed PD. However, not all can effectively distinguish PD from related disorders or identify its subtypes. This review advocates for a paradigm shift towards biomarker-based diagnosis to effectively distinguish between PD and similar conditions and to categorize PD into its subtypes. These biomarkers may reflect the differences that exist among different diseases and provide an effective way to accurately understand their mechanisms. This review focused on blood and CSF biomarkers of PD that may have differential diagnostic value and the related molecular measurement methods with high diagnostic performance due to emerging technologies.

Parkinson’s disease

Atypical Parkinsonian disorders

Dementia and movement disorders

Differential diagnosis

Biomarkers

Atypical Parkinson’s syndrome (APS) includes progressive supranuclear palsy (PSP), corticobasal syndrome (CBS), multiple system atrophy (MSA), and vascular parkinsonism (VP). During the early stages of APS, the individual conditions are difficult to distinguish from Parkinson's disease (PD) and are often misdiagnosed as PD (Jabbari et al. 2020; Holm et al. 2023). PD dementia (PDD) is a manifestation of PD during its middle and late stages. Distinguishing PDD from dementia with Lewy bodies (DLB) is challenging and may result in indiscriminate clinical pathology studies or clinical trials (Hirschberg et al. 2023). DLB and PDD are collectively referred to as Lewy body disease or synucleinopathy (Harris 2023). Additionally, the motor symptoms of PD should also be differentiated from essential tremor (ET) (Yu et al. 2023; Yoo et al. 2023).

Through an extensive literature search, we found that several blood and cerebrospinal fluid (CSF) biomarkers significantly differ between PD and other related diseases (Fig. 1). These molecules may be defined as differential diagnostic biomarkers of PD (Quadalti et al. 2021). While these biomarkers have significant statistical differences in the study of groups, the traits of discrimination may need to be demonstrated in many ways. The differential diagnostic biomarkers of PD may be used as reference for clinical pathological studies and clinical trials, because differentiating these diseases is the first step for such studies (Dutta et al., 2021).

Figure 1 Flow diagram of the different types of PD’s differential diagnostic biomarkers. DDC: DOPA decarboxylase; GFAP: glial fibrillary acidic protein; NfL: neurofilament light chain protein; 8-OHdG: 8-hydroxy-2'–deoxyguanosine; Hcy: homocysteine; TC: total cholesterol; TG: triglycerides; HDL: high-density lipoprotein cholesterol; Apo A1: apolipoprotein A1.

Through literature review, we first identified diseases that require differentiation from PD as well as the different PD subtypes (see search query). Next, we screened for diagnostic biomarkers using the search statements (“Parkinson’s disease” OR “PD”) AND (“biomarker”) on PubMed and Web of Science. We obtained PD-related biomarkers (see search query) and selected review articles to obtain a comprehensive biomarkers selection. Based on the biomarkers identified, we used the search formulas (“Parkinson's Disease” OR PD) AND ((“Atypical Parkinson's syndrome” OR APS OR “Atypical parkinsonism disorder” OR APD) OR (“progressive supranuclear palsy” OR PSP) OR (“corticobasal syndrome” OR CBS) OR (“multiple system atrophy” OR MSA) OR (“vascular parkinsonism” OR VP) OR (“dementia with Lewy bodies” OR DLB) OR (“essential tremor” OR ET)) AND ((“α-Synuclein” OR “α-Syn”) OR (“DOPA Decarboxylase” OR “DDC”) OR (“Amyloid beta” OR “Aβ”) OR “tau protein” OR “Exosomes” OR (“Neurofilament Light Protein” OR “NfL”) OR “MicroRNAs” OR “FABP3”) to retrieve targeted literature while selecting studies that were appropriate to meet our research objectives. A similar search strategy was used to screen biomarkers for distinguishing PD subtypes. After screening abstracts, 91 articles were identified for further in-depth reading. Midkine (MK) and Kallikrein 10 were two molecules not obtained in the initial search, and no study further elaborated their roles in different PD subtypes. Ultimately, 72 articles were included in this review.

2.1. α-Synuclein (α-syn) and its variants

α-syn is detectable in both CSF and plasma and is the most widely researched biomarker of PD (Tsao et al., 2022; Estaun-Panzano et al. 2023; Tofaris 2022). Phosphorylation of the Ser129 site results in phosphorylated α-syn (PS-129), while pro-aggregating forms of α-syn, such as oligomeric α-syn (o-α-syn), are also found in CSF and blood (Ma et al. 2023; Constantinides et al. 2021; Zubelzu et al. 2022; Chen et al. 2022; Chen et al. 2022). Meanwhile, the pathogenic β-sheet seed is the pathological conformation of α-syn and can be detected in serum (Okuzumi et al. 2023).

Several studies and meta-analyses have confirmed that when compared to the control group, the total α-syn (t-α-syn) levels in the CSF are consistently lower in PD, MSA, PSP, CBS, and VP groups, with no significant differences among them (Zubelzu et al. 2022; Constantinides et al. 2017; Førland et al. 2020; Koníčková et al. 2023; Aerts et al. 2012). Therefore, t-α-syn levels cannot differentiate between PD and APS.

Notably, levels of o-α-syn in PD and other Parkinsonian syndromes reportedly do not differ significantly but are elevated compared to that of the control group (Eusebi et al. 2017).

Utilizing a Bead-based Luminex assay (with a sensitivity of 9 pg/mL), researchers measured the concentration of pS129 in the CSF of patients with PD, MSA, and PSP, revealing differences among them. To differentiate between the different diseases, receiver operating characteristic analysis following the discovery phase indicated that pS-129/t-α-syn was superior to pS-129 alone, with a specificity of ≥ 80%. The sensitivity among the three different Parkinsonian disease groups were as follows: PD vs MSA, 40%; PD vs PSP, 72%; and MSA vs. PSP, 63% (Wang et al. 2012).

Aggregates of α-syn, including propagative α-syn seeds, showed high diagnostic performance in differentiating between PD and MSA (Siderowf et al. 2023; Painous et al. 2024; Parnetti et al. 2019; Goolla et al. 2023; Shahnawaz et al. 2020) Amplified seeds maintain disease-specific properties, allowing for the differentiation of samples from individuals with PD and MSA (Painous et al. 2024) Okuzumi et al. (2023) suggested that the rate of negative results of IP/RT-QuIC in patients with MSA was significantly higher than that in patients with PD. Additionally, their study also examined the distinctive morphological features of seeds in various diseases. The fibril morphology of products derived from IP/RT-QuIC of serum α-syn seeds in patients with synucleinopathies could differentiate PD, DLB, and MSA, allowing for further research in this area. A study found that the intensity of the signal in MSA was greater than that in PD when aggregation was performed in a specific buffered solution, indicating that α-syn seed aggregation from various diseases require different conditions for optimal detection (Martinez-Valbuena et al. 2022). These results suggest that our follow-up study can focus on the structural diversity and disease specificity of α-syn seeds.

2.2 DOPA decarboxylase (DDC)

A primary pathological feature of PD is the degeneration of dopaminergic neurons in the substantia nigra (Stoker and Greenland 2018). DDC is a diagnostic marker of dopaminergic dysfunction and can be detected in CS (Painous et al. 2024).

Several studies have attempted to reveal the differences in DDC between PD and APS (Paslawski et al. 2023; Pereira et al. 2023). CSF levels of DDC may potentially be useful in differentiating among degenerative Parkinsonisms (PD vs. APS) (Paslawski et al. 2023).

2.2 MK

MK is predominantly expressed during midgestation in embryogenesis, but its presence in normal adult brains is minimal. However, MK may recently play a role in various adult brain pathologies (Neumaier et al. 2023).

MK has demonstrated significant diagnostic potential as its levels were notably higher in patients with PD compared to those with APS (Paslawski et al. 2023).

2.3 Kallikrein 10

Kallikreins, which is a subgroup of serine proteases, play various physiological roles. Recent research has emphasized their involvement in carcinogenesis, highlighting several kallikreins as promising candidates for novel biomarkers in cancer and other diseases. This supports the potential utility of kallikreins in clinical diagnostics and therapeutic targeting (Wikipedia, nd).

Kallikrein 10 has exhibited specific changes in APS compared to PD and controls; unfortunately, these changes were not elaborated (Paslawski et al. 2023).

2.4 Classic Alzheimer’s disease (AD) biomarkers

Amyloid-beta-Aβ42, tau protein-τT, and phosphorylated tau protein-τP-181 are classical biomarkers of AD (Sung et al. 2023). Notably, their significance in Parkinson’s syndrome has been re-recognized.

When compared to patients with PD, τT/Aβ42 ratio was increased in patients with MSA (Constantinides et al. 2021; Constantinides et al. 2017). An elevated τT/Aβ42 ratio effectively differentiated MSA from PD, with an optimal cut-off value of 0.344 that yielded a sensitivity of 0.71 and specificity of 0.93 (Constantinides et al. 2017).

2.5 Exosomes

Exosomes from peripheral blood and CSF nerve cells have been used to distinguish PD and MSA (Taha and Bogoniewski 2024; Taha 2023; Yan et al. 2024).

Dutta et al.’s (2021) study confirmed that α-syn concentrations in exosomes were markedly lower in the control group and significantly higher in the MSA group compared to the PD group. They created a ratio using α-syn concentrations of putative oligodendroglial exosomes and putative neuronal exosomes with good sensitivity in distinguishing PD and MSA. By incorporating this ratio along with the α-syn and total exosome concentrations, a multinomial logistic model successfully distinguished PD from MSA, with an area under the curve (AUC) of 0.902, sensitivity of 89.8%, and specificity of 86.0% after application to an independent validation cohort.

Meloni et al. (2023) investigated neural-derived extracellular vesicles (NDEVs) isolated from the blood. Analysis of NDEVs revealed a significant increase in o-α-syn levels in PD compared to APS (CBD and PSP). Additionally, levels of Tau aggregates in NDEVs were significantly elevated in APS compared to PD (p < 0.0001). Receiver operating characteristic analysis showed that the concentration of NDEVs of both oligomeric o-α-syn and Tau aggregates exhibited an “excellent” power of classification that effectively distinguished PD from APS. For o-α-syn, the AUC was 0.817 (95% confidence interval (CI): 0.732–0.885; p < 0.0001), sensitivity of 78.6%, and specificity of 77.5%. For Tau aggregates, the AUC was 0.856 (95% CI: 0.776–0.915; p < 0.0001), sensitivity of 90.0%, and specificity of 75.7%.

Taha et al. (2023) was the first to measure pS129-α-syn levels in neuronal extracellular vesicles (nEVs) and oligodendroglial extracellular vesicles (oEVs). They reported that nEV pS129-α-syn concentrations were highest in healthy controls (HC) followed by PD and MSA, but the differences were not statistically significant. Conversely, oEV concentrations of pS129-α-syn were also highest in HC followed by PD and MSA and was significantly higher in both disease groups. Additionally, their study revealed that the oEV/nEV pS129-α-syn ratio increased in the order of HC < PD < MSA. Furthermore, they also measured total tau, pT181-tau (tau phosphorylated at Thr181) in nEVs and oEVs, and/or serum neurofilament light protein (NfL) levels. Due to the detection sensitivity, pT181-tau was detected in very few samples. Other results were similar to experiments involving plasma or CSF.

2.6 NfL

NfL in CSF (cNfL) and plasma (pNfL) is a marker for neuronal damage that may potentially be used to distinguish between clinically similar conditions, such as frontotemporal dementia from AD and PD from APS (Quadalti et al. 2021).

Among Parkinsonian syndromes, the mean cNfL levels were higher in MSA, PSP, and CBS when compared with PD (Wikipedia, nd; Bridel et al. 2019).

3.1 MicroRNAs

MicroRNAs, which are small non-coding RNAs with 20–22 nucleotides, play a critical role in several mechanisms underlying the pathogenesis of various neurodegenerative diseases, including PD. (Guévremont et al. 2023) More importantly, they can be detected in the serum. (van Wamelen et al. 2020)

miR-30c and miR-148b are specific to individuals with PD, whereas miR-24, miR-223, and miR-324-3p are present in patients with both PD and MSA when compared with healthy individuals (Villar-Menéndez et al. 2014).

4.1 Fatty acid-binding protein 3, heart type (FABP3)

FABP3 is a small cytosolic protein that plays a role in lipid transport (Chiasserini et al. 2017). In the brain, FABP3 plays a regulatory role in the lipid composition of the membrane, suggesting a potential involvement in synapse formation and in the activity of cholinergic and glutamatergic neurons (Parnetti et al. 2019).

Elevated FABP3 levels have been detected in the serum of individuals diagnosed with DLB and PDD (Kawahata et al. 2023). Additionally, FABP3 levels were higher in patients with DLB than in those with PDD. This suggests the potential of FABP3 as a distinctive biomarker for DLB (Kawahata and Fukunaga 2023).

Similar to DLB, FABP3 levels were higher in patients with AD than in those with PD and other neurological disorders (p < 0.001). Notably, a combination of p-tau, FABP3, and α-syn successfully differentiated patients with AD from those with PDD, yielding an AUC of 0.96 (Chiasserini et al. 2017).

4.2 Tau

Despite being a hallmark of AD, Tau proteins are also found in the brains of patients with PD and DLB (Shim et al. 2022).

A study aimed at distinguishing AD, DLB, and PD revealed that t-tau levels were higher in the DLB group than in the control and PD groups, but the differences were not statistically significant. Meanwhile, the t-tau/t-α-syn ratio had a better performance than standalone markers. For AD vs. DLB, the AUC increased from 0.66 for t-tau alone (70% sensitivity and 68% specificity) to 0.74 for the t-tau/t-α-syn ratio (55% sensitivity and 95% specificity) (Førland et al. 2020).

4.3 C-reactive protein (CRP)

CRP concentrations in the CSF are higher in patients with PD and PDD than in patients with PD without dementia and HCs (Lindqvist et al. 2013). However, distinguishing between patients with PD without dementia and HCs based on CRP levels was not feasible.

4.4 Plasma homocysteine (Hcy)

In one study, Hcy levels were measured in patients with (PDD) and without dementia (PDwoD) as well as in HCs. Results showed that individuals with PDD demonstrated higher Hcy levels than PDwoD and HCs (Song et al. 2013).

4.5 Glial fibrillary acidic protein (GFAP)

A study revealed that plasma GFAP levels in patients with PDD were higher than those in HCs, patients with PD with mild cognitive impairment (PD-MCI), and patients with PD with normal cognition (Bartl et al. 2023).

5.1 α-syn in erythrocytes

While pathological α-syn aggregations primarily localize in the central nervous system, peripheral α-syn concentrations, particularly in erythrocytes, are higher than in those in the CSF (Barbour et al. 2008) α-syn in erythrocytes are reportedly excellent biomarkers for diagnosing PD (Yu et al. 2023, Yu et al. 2022).

Total α-syn levels in erythrocytes are higher in patients with ET than in those with PD. Moreover, the proportion of aggregated α-syn levels to t-α-syn levels in erythrocytes is markedly lower in patients with ET than in those with PD and HCs. Receiver operating characteristic curve analysis showed that the ratios of aggregated α-syn to monomeric α-syn concentrations performed well in distinguishing patients with ET from those with PD and HCs, with an AUC of 0.892, sensitivity of 86.67%, and specificity of 97.96% for patients with ET vs. HCs. For ET vs. PD, the AUC was 0.817, with a sensitivity of 80.00% and specificity of 81.25% (Yu et al. 2023).

5.2 NfL

Some studies have compared serum NfL levels in PD and ET. (Hansson et al. 2017) Huang et al. (2022) reported that serum NfL concentrations in patients with PD (16.6 ± 3.5 pg/mL) were significantly higher than that in patients with ET (12.2 ± 2.4 pg/mL) and HCs (11.8 ± 2.4 pg/mL) (both p < 0.01, effect sizes = 1.47 and 1.60, respectively). When the cut-off was set at 13.65 pg/mL, the sensitivity and specificity of distinguishing between PD and ET were 76.7% and 84.1% respectively, with an AUC of 0.854.

Figure 2 Concentrations of NfL in PD were considerably different from those in APS PDD and ET, with NfL concentrations higher in APS and PDD than in PD and the opposite in APS (vascular parkinsonism is not included). This may suggest a different pathogenesis of its disease at the molecular level. NfL: Neurofilament light protein, PD: Parkinson’s diaseae, APS: Atypical parkinson’s syndrome, PSP: progressive supranuclear palsy, CBS: corticobasal syndrome, MSA: multiple system atrophy, PDD: Parkinson's disease dementia, ET: Essential tremor.

6.1 Early-onset PD (EOPD) and late-onset PD (LOPD)

An analysis of plasma microRNA levels in patients with EOPD, patients with LOPD, and HCs revealed a statistically significant difference between patients with PD and HCs. Upregulation of miR-29b-3p and downregulation of miR-297 and miR-4462 in EOPD may be associated with EOPD alone (Arshad et al. 2017).

6.2 Tremor-dominant (TD) PD vs. non-tremor-dominant (NTD) PD

PPD can be classified into TD PD and NTD PD. Individuals with NTD PD have lower levels of serum uric acid (UA) and a significantly lower serum UA/creatinine (Cr) ratio (UA/Cr) than those with TD PD (van Wamelen et al. 2020).

6.3 PD versus PD-MCI

Individuals with PD-MCI have higher mean levels of total cholesterol (TC), triglycerides (TG), and apolipoprotein A1 (apo A1) than subgroups with normal cognition. Therefore, TC, TG, and apo A1 may serve as valuable biomarkers for PD-MCI (Deng et al. 2022). Conversely, serum levels of high-density lipoprotein cholesterol were elevated in patients with PD (Dong et al. 2021; Li et al. 2020).

6.4 PD with hallucinations

Levels of 8-OHdG are reportedly higher in PD with hallucinations, while no such elevation was observed in cases associated with dementia or other clinical features (Hirayama et al. 2018; Faria et al. 2019).

6.5 PD with rapid eye movement sleep behavior disorder (RBD)

Plasma GFAP levels were notably elevated in patients with PD exhibiting RBD compared to those without RBD (Teng et al. 2023).

The importance of novel technologies in identifying biomarkers has been emphasized as these advancements provided increased discriminatory capabilities (Parnetti et al. 2019). Highly accurate methods are changing previously known but non-significant findings.

The measurement techniques, represented by α-syn seed amplification (SAA) that includes real-time quaking-induced conversion (RT-QuIC) and protein misfolding cyclic amplification (PMCA), have shown high specificity (almost 100%) and sensitivity (> 90%) (Shahnawaz et al. 2017; Fairfoul et al. 2016). SAA has been employed to identify misfolded α-syn aggregates in the CSF and peripheral tissues (Ma et al. 2023). RT-QuIC and PMCA can distinguish PD from other NDDs according to variations in α-syn aggregates. The reliability in detection and the adaptability of RT-QuIC across different tissues and biological fluids have allowed this technique to become the benchmark when investigating the aggregation of α-syn in humans (Goolla et al. 2023).

The HANdai Amyloid Burst Inducer technique has been suggested as a viable alternative to the PMCA and RT-QuIC for assessing pro-aggregating proteins in biofluids due to its faster assay speed than PMCA and RT-QuIC (Umemoto et al. 2014). It is currently being studied as a technique for measuring pro-aggregating forms of α-syn.

Combining these technologies, scientists have developed several types of SAA, including immunoprecipitation-based RT-QuIC (IP/RT-QuIC), which enables the detection of pathogenic α-syn seeds in the serum of individuals with synucleinopathy (Okuzumi et al. 2023). Moreover, researchers found that different sources of α-syn seeds have different optimal signal display conditions, making it possible to develop specific SAAs for a single disease such as MSA (Martinez-Valbuena et al. 2022).

Based on the total α-synuclein assay, a modified Luminex assay, namely the Bead-based Luminex assays, was developed. Its sensitivity is approximately 9 pg/mL, providing a highly precise method for measuring pS129 (Wang et al. 2012). Detection systems supported by the Simoa Bead technology can accurately quantify low-concentration proteins and peptides at the level of fM with excellent reproducibility. Kawahata, Sekimori, Oizumi, Takeda, and Fukunaga (Kawahata et al. 2023) used this technique to quantify FABP levels. Currently, Meso Scale Discovery electrochemiluminescence technology has been gradually used to detect molecular markers of neurodegenerative diseases and has shown a higher sensitivity (Zhao et al. 2020)

Novel ELISA assays have been developed by Majbour et al. (2016). Their method expanded the detection limit of α-syn to 50 pg/mL, which is 20-fold lower than that detected in human CSF. Meanwhile, the detection limits of pS129 and recombinant o-α-syn were expanded to 20 pg/mL and 10 pg/mL, respectively. To exceed these limits, technologies, such as novel photochemical, electrochemical, and crystal biosensors should be utilized (Jabbari et al. 2020).

From the perspective of technological evolution, the development of targeted detection methods is an inevitable requirement for the differential diagnosis of PD.

This review focused on the blood and CSF markers in PD as they are easily acquired, non-invasive, and in proximity to the brain in contrast to brain tissue biopsy or urine tests (Parnetti et al. 2019). Thus, facilitating the integration of clinical and scientific research for these biomarkers is essential.

The differences between t-α-syn, o-α-syn, pS-129, aggregates of α-syn, Kallikrein 10, τT/Aβ42 ratio, α-syn concentrations in exosomes, NDEV concentrations of both o-α-syn and Tau aggregates, the oEV/nEV pS129-α-syn ratio, and cNfL levels between PD and APS have been revealed. Future diagnostic studies on PD should focus on the differentially expressed molecules in this disease. miR-30c, miR-148b, miR-24, miR-223, and miR-324-3p exhibit specificity in identifying patient with PD with MSA. FABP3, the t-tau/t-a-syn ratio, total α-syn levels in erythrocytes, and serum NfL concentrations may distinguish PDD from other dementias and movement disorders. Total α-syn levels in erythrocytes and serum NfL levels have distinctly different concentrations in ET compared to PD. CRP, Hcy, GFAP, microRNA, serum UA, TC, TG, A1, and 8-OHdG levels may also be potentially used to support the differentiation between the different PD subtypes (Supplementary Table 1).

Currently, diagnosing PD primarily depends on clinical symptoms, and the relationship between symptoms and prognosis has been partially established (Armstrong and Okun 2020). However, the differences between biomarkers among different neurodegenerative diseases should make us consider the occurrence and development of this disease at a more specific level. Differential diagnostic biomarkers of PD in the blood and CSF represent its unique onset and evolution, which should further research on its etiology and pathogenesis (Kelly et al. 2023).

Clinical pathological studies and clinical trials should accurately classify study participants (Lin et al. 2020). The emergence of biomarkers makes it feasible to accurately identify patients and allows for a more reliable reference for distinguishing PD from other diseases (Tolosa et al. 2021). This is a practical significance of PD’s differential diagnostic biomarkers.

Several biomarkers in PD and related diseases have obvious differences, but only a few have excellent performance by relying on high-precision testing methods. The combination of multiple biomarkers or clinical signs can increase the ability to discriminate between diseases (Quadalti et al. 2021; Dutta et al. 2021; Meloni et al. 2023; Taha et al. 2023; Kawahata et al. 2023). Developing a combined detection method based on multiple biomarkers may not be an urgent need for PD, which is an incurable disease; however, if patients can be accurately classified at disease onset, improving the efficiency of future scientific research and follow-up research in a large population will be beneficial.

Acknowledgments: The manuscript has been carefully reviewed by an experienced editor whose first language is English and who specializes in editing papers written by scientists whose native language is not English.

Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests: Author LH was employed by ICON Plc. The remaining authors have no relevant financial or nonfinancial interests to disclose..

Author Contributions: Study design was performed by YW and writing of the original draft and visualization was performed by JM. Data collection was performed by ZT, YqW, JZ, ZW and LH. Data acquisition and critical revision of the manuscript was performed by SL. Project administration was carried out by YW.

Data Availability Statement: No new data were created.

Ethics Approval: Not applicable.

Consent to Participate: Not applicable.

Consent to Publish：All authors have read and approved the ﬁnal version of the manuscript.

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Blood and cerebrospinal fluid differences between Parkinson's disease and related diseases

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Version 1

Abstract

Figures

Introduction

Search Strategy and Selection Criteria

PD and APS

2.1. α-Synuclein (α-syn) and its variants

2.2 DOPA decarboxylase (DDC)

2.2 MK

2.3 Kallikrein 10

2.4 Classic Alzheimer’s disease (AD) biomarkers

2.5 Exosomes

2.6 NfL

Suffering from both PD and MSA

3.1 MicroRNAs

PDD and Other Dementia and Movement Disorders

4.1 Fatty acid-binding protein 3, heart type (FABP3)

4.2 Tau

4.3 C-reactive protein (CRP)

4.4 Plasma homocysteine (Hcy)

4.5 Glial fibrillary acidic protein (GFAP)

PD and ET

5.1 α-syn in erythrocytes

5.2 NfL

PD and its Subtypes

6.1 Early-onset PD (EOPD) and late-onset PD (LOPD)

6.2 Tremor-dominant (TD) PD vs. non-tremor-dominant (NTD) PD

6.3 PD versus PD-MCI

6.4 PD with hallucinations

6.5 PD with rapid eye movement sleep behavior disorder (RBD)

Emerging Technologies

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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