Blood and Cerebrospinal Fluid Biomarkers of Inflammation in Parkinson’s Disease

Abstract

Given the clear role of inflammation in the pathogenesis of Parkinson’s disease (PD) and its impact on incidence and phenotypical characteristics, this review provides an overview with focus on inflammatory biofluid markers in blood and cerebrospinal fluid (CSF) in PD patient cohorts. In preparation for clinical trials targeting the immune system, we specifically address the following questions: 1) What evidence do we have for pro-inflammatory profiles in blood and in CSF of sporadic and genetic PD patients? 2) Is there a role of anti-inflammatory mediators in blood/CSF? 3) Do inflammatory profiles in blood reflect those in CSF indicative of a cross-talk between periphery and brain? 4) Do blood/CSF inflammatory profiles change over the disease course as assessed in repeatedly taken biosamples? 5) Are blood/CSF inflammatory profiles associated with phenotypical trajectories in PD? 6) Are blood/CSF inflammatory profiles associated with CSF levels of neurodegenerative/PD-specific biomarkers? Knowledge on these questions will inform future strategies for patient stratification and cohort enrichment as well as suitable outcome measures for clinical trials.

Keywords

Parkinson’s disease inflammation interleukin chemokine cytokine blood cerebrospinal fluid

Abbreviations:

AFP

Alpha Fetoprotein

NAP-3 (CXCL1)

Autonomic Nervous System

BDNF

Anti-Neutrophil Activating Protein 3

Complement component 3

Complement component 4

CA-125

Cancer Antigen 125

CEA

Carcinoembryonic Antigen

CRP

C-reactive Protein

CKMB

Creatine Kinase Muscle Brain type

CCL11

Eotaxin

ENA-78 (CXCL5)

AEpithelial-derived neutrophil-activating peptide 78

FactorVII

FABP

Fatty Acid Binding Protein

CX3CL1

Fractalkine

Growth Hormone

IgE

Immunoglobulin E

ICAM-1

Intercellular Adhesion Molecule 1

IFN-γ

Interferon-Gamma

IP-9 (CXCL11)

Interferon-Gamma Induced Protein-9

IP-10 (CXCL10)

Interferon-Gamma Induced Protein-10

IL-1α

Interleukin-1 alpha

IL-1β

Interleukin-1 beta

IL-1RA

Interleukin-1 Receptor Antagonist

IL-2

Interleukin-2

IL-3

Interleukin-3

IL-4

Interleukin-4

IL-5

Interleukin-5

IL-6

Interleukin-6

IL-7

Interleukin-7

IL-8

Interleukin-8

IL-9

Interleukin-9

IL-10

Interleukin-10

IL-12p40

Interleukin-12p40

IL-12p70

Interleukin-12p70

IL-13

Interleukin-13

IL-15

Interleukin-15

IL-16

Interleukin-16

IL-17A

Interleukin-17 A

IL-18

Interleukin-18

IL-21

Interleukin-21

IL-22

Interleukin-22

IL-27

Interleukin-27

Leptin

Lymphotactin

CSF-1

Macrophage Colony CSF-1 Stimulating Factor 1

MDC

Macrophage Derived MDC Chemokine

MIP-1β

Macrophage Inflammatory Protein 1 Alpha

MIP-1α

Macrophage Inflammatory Protein 1 Beta

MIP-3α (CCL20)

Autonomic Nervous System

MIP-3 (CCL23)

Macrophage Inflammatory Protein 3 Alpha

MMP3

Matrix Metallopeptidase 3

MMP9

Matrix Metallopeptidase 9

MMP10

Matrix Metallopeptidase 9

MCP-1(CCL2)

Monocyte Chemoattractant Protein 1

MCP-2 (CCL8)

Monocyte Chemoattractant Protein 2

MCP-4 (CCL13)

Monocyte Chemoattractant Protein 4

MIG (CXCL9)

Monokine Induced by Interferon-Gamma

MEC (CCL28)

Mucosae Associated Epithelial Chemokine

NGAL

Neutrophil Gelatinase-Associated Lipocalin

NLRP3

NLR Family Pyrin Domain Containing 3

PDGF-BB

Platelet-Derived Growth Factor BB

Prolactin

PSA-f

Prostate Specific Antigen (free)

CCL5

RANTES

S100A8/9 (Calprotectin)

S100 Calcium-Binding Protein A8/9

S100B

S100 Calcium-Binding Protein B

SAA

Serum Amyloid A

sIL-2R

Soluble Interleukin-2 Receptor

sTNFR1

Soluble Tumor Necrosis Factor Receptor 1

sTNFR2

Soluble Tumor Necrosis Factor Receptor 2

sVCAM1

Soluble Vascular Cell Adhesion Molecule 1

SCF

Stem Cell Factor

SDF-1

Stromal Cell-Derived Factor-1

TPO

Thyreoperoxidase

TSH

Thyroid Stimulating Hormone

Tissue Factor

TIMP-2

Tissue Inhibitor of Metalloproteinases 2

TGF-α

Transforming Growth Factor Alpha

TGF-β1

Transforming Growth Factor Beta1

TNF-α

Tumor Necrosis Factor beta Factor beta

YKL-40

Chitinase-3-like 1

INTRODUCTION

In recent years, a growing number of epidemiological and genetic studies as well as post-mortem and biofluid marker analyses including unbiased proteomic approaches provide evidence for a relevant influence of inflammation on both incidence and progression in Parkinson’s disease (PD) [1 –8]. In this context, the cerebral and peripheral as well as the innate and adaptive immune system seem involved [9]. The activation of microglia as representative of the cerebral innate immune system was shown postmortem and in vivo in PD patients by positron emission tomography (PET) studies ([¹¹C]PK11195, [¹⁸F]FEPPA, [¹¹C]PBR28) and by increased levels of cytokines in cerebrospinal fluid (CSF) [2, 10]. Microglia is activated by damage-associated molecular patterns (DAMPs), which are generated by damaged cells, misfolded proteins, and protein aggregates. In PD, α-synuclein acts as DAMP resulting in microglia activation with induction of neuroinflammation and release of cytokines/chemokines [11]. Moreover, there is increasing evidence for the involvement of the peripheral innate and adaptive immune system in the pathophysiology of PD. In this context, α-synuclein promotes inflammasome-related cytokine production in the periphery and specific α-synuclein peptides act as antigenic epitopes resulting in helper and cytotoxic T cell responses in peripheral blood mononuclear cells from patients with PD [12, 13].

Postmortem and biofluid (blood, CSF) studies reported that increased inflammatory profiles are associated with clinical subtypes of PD, promoting an accelerated motor and non-motor phenotype [3 , 14–17]. Recent evidence highlights that the involvement of inflammation in PD is maximized in the early disease stages and maintains a chronic profile during the course of the disease [18, 19] (Fig. 1).

Fig. 1

Inflammatory markers in blood and CSF that are associated with clinical trajectories, disease progression, and neurodegenerative/disease-specific protein levels. In line with evidence from post-mortem and cell studies, blood and CSF levels of inflammatory markers indicate a contribution of the innate and adaptive immune system in both CNS and periphery. Of these, the most frequently reported inflammatory markers in blood and CSF that are associated with clinical trajectories, disease progression and neurodegenerative/disease-specific protein levels are IL-1β, IL-6, INF-γ, MCP-1 and TNF-α. Notably, inflammation is maximized in the early disease stages and maintains a chronic profile during the course of the disease. Figure created in BioRender.com.

Despite this clear role for inflammation in the pathogenesis of PD, several open questions remain to be answered in preparation of clinical trials aiming at disease-modification by targeting the immune system: 1) What evidence do we have for pro-inflammatory profiles in blood and in CSF of sporadic and genetic PD patients? 2) Is there a role of anti-inflammatory mediators in blood/CSF? 3) Do inflammatory profiles in blood reflect those in CSF indicative of a cross-talk between periphery and brain? 4) Do blood/CSF inflammatory profiles change over the disease course as assessed in repeatedly taken biosamples? 5) Are blood/CSF inflammatory profiles associated with phenotypical trajectories in PD? 6) Are blood/CSF inflammatory profiles associated with CSF levels of neurodegenerative/PD-specific biomarkers? 7) Which inflammatory markers in blood/CSF are the most promising candidates for clinical trials with regard to patient stratification, cohort enrichment and outcome measures?

This review gives an overview of the current knowledge on these questions with specific focus on blood (plasma, serum) and CSF levels of inflammatory biofluid markers in PD patient cohorts. We did a PubMed search using the search terms “Parkinson, Inflammation, Interleukin, Chemokine, Cytokine, Blood, CSF” and included publications between 01/2011-01/2022. Studies on white blood cells of the myeloid and lymphoid cell lineage as well as on postmortem tissue, imaging, and cell/animal models are not included in the present manuscript as these are reviewed in other articles from this special issue on “The immune system in Parkinson’s disease”.

Please note that the assessed cohorts largely vary in sample size and disease duration. Further, the type of assays used, differs and ranges from singleplex (ELISA) to multiplex immunoassays with different metrics of quality control (lower limit of quantification, number of replicates, variation coefficient, etc.). Additionally, it is well known that females and males differ in their immune system. They show distinct patterns in innate and adaptive immune responses which further change across the lifespan [20]. In general, females show stronger innate and adaptive immune responses than males with increased production of antibodies and anti-inflammatory species such as IL-4 and IL-10. Taking these gender differences into consideration is of importance when analysing immune marker profiles in different cohorts against disease status. However, only few studies stratified their analyses by sex. All these points pose limitations and have to be kept in mind for data interpretation and drawing congruent conclusions.

WHAT EVIDENCE DO WE HAVE FOR PRO-INFLAMMATORY PROFILES IN BLOOD AND IN CSF OF SPORADIC AND GENETIC PD PATIENTS?

In preparation of upcoming clinical trials targeting the immune system, it will be essential to translate basic research findings from cell and animal models into patient cohorts and to define markers that are representative of inflammation, that are suited for patient stratification and cohort enrichment, that are predictive/prognostic for different trajectories and that serve as read-out for target engagement. Moreover, such markers should be longitudinally accessible in a multi-centre design. While specific imaging techniques such as PET might not be readily available all over the world, blood can be repeatedly collected and stored for longitudinal biofluid marker analyses.

Most studies on inflammatory biofluid markers have been conducted cross-sectionally in blood comparing levels of cytokines between patients with sporadic PD and healthy controls.

Inflammatory markers in blood

Over 50 different pro-inflammatory biofluid markers have been assessed in serum or plasma. However, only for 7 markers (CRP, IL-1β, IL-2, IL-6, IL-8, IFN-γ, TNF-α) more than 5 studies are available. The most robust data are published for CRP [21 –34] and IL-1β [25–27 , 35–44]. These two show consistently higher levels in sporadic PD patients compared to healthy controls. Data for IL-2 [25 , 46], IL-6 [21 , 45–50], IL-8 [25 , 52], IFN-γ [25 , 53–55], and TNF-α [25–29 , 56–59] are less clear with some studies reporting higher blood levels but others showing no differences or even lower levels in sporadic PD patients when compared to healthy controls. A detailed overview of all assessed blood markers along with the respective findings and references is given in Table 1.

Table 1

Findings of levels of inflammatory markers in sporadic PD patients in blood (serum, plasma) and CSF compared to healthy controls from 01/2012-12/2021

Blood (PD vs. CON)	CSF (PD vs. CON)
C3	Sun et al. [96] 2019 ↓
C4	Sun et al. [96] 2019 ↓
CCL2 (MCP-1)	Csencsits-Smith et al. [58] 2016 ↑	Hall et al. [63] 2018 ⟶
	Schröder et al. [46] 2018 ⟶	Schröder et al. [46] 2018 ↑
	Usenko et al. [55] 2020 ↑
	Miliukhina et al. [50] 2020 ↓
CCL3 (MIP-1α)	Schröder et al. [46] 2018 ⟶	Schröder et al. [46] 2018 ⟶
	Calvani et al. [52] 2020 ↓
CCL4 (MIP-1β)	Schröder et al. [46] 2018 ⟶
	Calvani et al. [52] 2020 ↑
CCL5 (RANTES)	Mahlknecht et al. [45] 2012 ⟶
	Tang et al. [47] 2014 ↑
	Qin et al. [25] 2016 ↑
	Schröder et al. [46] 2018 ⟶
CCL8 (MCP-2)		Santaella et al. [65] 2020 ↓
CCL11 (Eotaxin)	Schröder et al. [46] 2018 ⟶
CCL13 (MCP-4)	Mahlknecht et al. [45] 2012 ⟶
CCL17 (TARC)	Schröder et al. [46] 2018 ⟶
CCL20 (MIP-3α)	Schröder et al. [46] 2018 ⟶
CCL23 (MIP-3)		Santaella et al. [65] 2020 ↓
CCL28 (MEC)		Santaella et al. [65] 2020 ↑
CRP	Ton et al. [21] 2012 ↓	Hall et al. [63] 2018 ⟶
	Andican et al. [22] 2012 ↑	Moghaddam et al. [87] 2018 ↑
	Lindqvist et al. [23] 2013 ↑
	Sawada et al. [24] 2014 ↑
	Qin et al. [25] 2016 ↑
	Wang et al. [26] 2016 ↑
	Kim et al. [27] 2018 ⟶
	King et al. [28, 29] 2019 ⟶
	Santos-García et al. [30] 2019 ↑
	Qiu et al. [31] 2019 ↑
	Baran et al. [32] 2019 ↑
	Jin et al. [33] 2020 ↑
	Dommershuijsen et al. [34] 2022 ⟶
CSF-1		Santaella et al. [65] 2020 ⟶
CX3CL1 (Fractalkine)	Gupta et al. [78] 2021 ↑	Santaella et al. [65] 2020 ↓
		Hatcher-Martin et al. [89] 2021 ↓
CXCL1 (NAP-3)	Schröder et al. [46] 2018 ⟶	Santaella et al. [65] 2020 ↓
CXCL5 (ENA78)	Schröder et al. [46] 2018 ⟶
CXCL9 (MIG)	Schröder et al. [46] 2018 ⟶
CXCL10 (IP-10)	Schröder et al. [46] 2018 ⟶	Schröder et al. [46] 2018 ⟶
	Csencsits-Smith et al. [58] 2016 ↑	Hu et al. [64] 2019 ⟶
CXCL11 (IP-9)	Schröder et al. [46] 2018 ⟶
CXCL12 (SDF-1)	Bagheri et al. [97] 2018 ↑
FABP	Brockmann et al. [38] 2016 ↑
ICAM-1	Andican et al. [22] 2012 ↑
	Mahlknecht et al. [45] 2012 ⟶
IL-1ß	Koziorowski et al. [35] 2012 ⟶	Milyukhina et al. [37] 2015 ↑
	Hu et al. [36] 2015 ↑	Hu et al. [36] 2015 ↑
	Milyukhina et al. [37] 2015 ↑	Chen et al. [60] 2018 ↑
	Brockmann et al. [38] 2016 ↑	Iwaoka et al. [61] 2020 ↑
	Wang et al. [26] 2016 ↑
	Qin et al. [25] 2016 ↑
	Karpenko et al. [39] 2018 ↑
	Kim et al. [27] 2018 ↑
	Rocha et al. [40] 2018 ⟶
	Alrafiah et al. [41] 2019 ↑
	Lian et al. [42] 2019 ↑
	Chatterjee et al. [43] 2020 ↑
	Fan et al. [44] 2020 ↑
IL-1RA	Karpenko et al. [39] 2018 ↓
IL-2	Mahlknecht et al. [45] 2012 ⟶	Schröder et al. [46] 2018 ↑
	Qin et al. [25] 2016 ↑
	Kim et al. [27] 2018 ↑
	Rocha et al. [40] 2018 ⟶
	Schröder et al. [46] 2018 ⟶
	King et al. [28, 29] 2019 ⟶
sIL-2R	Wang et al. [26] 2016 ↑
IL-4	Qin et al. [25] 2016 ⟶	Schröder et al. [46] 2018 ⟶
	Rocha et al. [40] 2018 ↓
	Schröder et al. [46] 2018 ⟶
	King et al. [28, 29] 2019 ⟶
IL-5	Schröder et al. [46] 2018 ⟶
IL-6	Ton et al. [21] 2012 ↑	Yu et al. [62] 2014: ↑
	Koziorowski et al. [35] 2012 ⟶	Milyukhina et al. [37] 2015 ↑
	Mahlknecht et al. [45] 2012 ⟶	Delgado-Alvarado et al. [48] 2017 ⟶
	Tang et al. [47] 2014 ↑	Chen et al. [60] 2018 ↑
	Milyukhina et al. [37] 2015 ↑	Hall et al. [63] 2018 ⟶
	Wang et al. [26] 2016 ↑	Schröder et al. [46] 2018 ↑
	Qin et al. [25] 2016 ↑	Lian et al. [42] 2019 ↑
	Delgado-Alvarado et al. [48] 2017 ⟶	Iwaoka et al. [61] 2020 ⟶
	Karpenko et al. [39] 2018 ↑
	Kim et al. [27] 2018 ↑
	Rocha et al. [40] 2018 ↓
	Schröder et al. [46] 2018 ⟶
	Alrafiah et al. [41] 2019 ⟶
	King et al. [28, 29] 2019 ↑
	Lian et al. [42] 2019 ↑
	Kwiatek-Majkusiak et al. [49] 2020 ↑
	Miliukhina et al. [50] 2020 ↓
IL-8	Koziorowski et al. [35] 2012 ⟶	Hall et al. [63] 2018 ↑
	Gupta et al. [51] 2016 ↓	Schröder et al. [46] 2018 ⟶
	Qin et al. [25] 2016 ⟶	Hu et al. [64] 2019 ⟶
	Schröder et al. [46] 2018 ⟶	Santaella et al. [65] 2020 ↓
	King et al. [28, 29] 2019 ⟶
	Calvani et al. [52] 2020 ↑
IL-9	Schröder et al. [46] 2018 ⟶	Schröder et al. [46] 2018 ↓
	Calvani et al. [52] 2020 ↓
IL-10	Koziorowski et al. [35] 2012 ⟶	Schröder et al. [46] 2018 ⟶
	Brockmann et al. [38] 2016 ↑	Hu et al. [64] 2019 ⟶
	Qin et al. [25] 2016 ↑
	Karpenko et al. [39] 2018 ⟶
	Kim et al. [27] 2018 ⟶
	Rocha et al. [40] 2018 ↓
	Schröder et al. [46] 2018 ⟶
	King et al. [28, 29] 2019 ⟶
	Rathnayake et al. [54] 2019 ↑
	Martin-Ruiz et al. [57] 2020 ↑
IL-12	Koziorowski et al. [35] 2012 ⟶
IL-12-p40	Brockmann et al. [38] 2016 ↑
IL-13	Schröder et al. [46] 2018 ⟶
	Lin et al. [53] 2019 ↑
IL-16
IL-17A	Rocha et al. [40] 2018 ↓
Schröder et al. [46] 2018 ⟶	Schröder et al. [46] 2018 ⟶	Majbour et al. [68] 2020 ↓
IL-21	Schröder et al. [46] 2018 ⟶	Schröder et al. [46] 2018 ⟶
IL-22	Schröder et al. [46] 2018 ⟶	Schröder et al. [46] 2018 ⟶
IL-27	Kouchaki et al. [59] 2018 ↓
IFN-y	Wang et al. [26] 2016 ⟶	Schröder et al. [46] 2018 ⟶
	Qin et al. [25] 2016 ⟶	Iwaoka et al. [61] 2020 ⟶
	Eidson et al. [56] 2017 ↑
	Rocha et al. [40] 2018 ↓
	Schröder et al. [46] 2018 ⟶
	King et al. [28, 29] 2019 ⟶
	Lin et al. [53] 2019 ↑
	Rathnayake et al. [54] 2019 ↑
	Miliukhina et al. [50] 2020 ↑
	Usenko et al. [55] 2020 ↓
Leptin	Mahlknecht et al. [45] 2012 ⟶
	Rahnemayan et al. [98] 2021 ⟶
NGAL	Eidson et al. [56] 2017 ↑
NLRP3	Chatterjee et al. [43] 2020 ↑
	Fan et al. [44] 2020 ↑
	Roy et al. [99] 2021 ↑
PDGF-BB	Mahlknecht et al. [45] 2012 ↑
	Prolactin	Mahlknecht et al. [45] 2012 ↑
S100A8/9 (Calprotectin)	Dumitrescu et al. [100] 2021 ↑
S100B		Sathe et al. [101] 2012 ↑
SAA (Serum amyloid A)		Hall et al. [63] 2018 ↑
SCF	Brockmann et al. [38] 2016 ↑	Santaella et al. [65] 2020 ⟶
TGF-β1		Chen et al. [60] 2018 ↑
TGF-α		Santaella et al. [65] 2020 ⟶
TIMP-2	Mahlknecht et al. [45] 2012 ⟶
TNF-α	Koziorowski et al. [35] 2012 ↑	Delgado-Alvarado et al. [48] 2017 ↑
	Hu et al. [36] 2015 ↑	Chen et al. [60] 2018 ⟶
	Gupta et al. [51] 2016 ↓	Schröder et al. [46] 2018 ↑
	Wang et al. [26] 2016 ↑	Hu et al. [36, 64] 2015, 2019 ⟶
	Qin et al. [25] 2016 ↑	Iwaoka et al. [61] 2020 ↑
	Csencsits-Smith et al. [58] 2016 ↑
	Eidson et al. [56] 2017 ↑
	Karpenko et al. [39] 2018 ⟶
	Kim et al. [27] 2018 ⟶
	Kouchaki et al. [59] 2018 ↑
	Rocha et al. [40] 2018 ↓
	Schröder et al. [46] 2018 ⟶
	Alrafiah et al. [41] 2019 ⟶
	King et al. [28, 29] 2019 ↑
	Lin et al. [53] 2019 ↑
	Rathnayake et al. [54] 2019 ↑
	Martin-Ruiz et al. [57] 2020 ↑
	Miliukhina et al. [50] 2020 ↓
sTNFR1 and sTNFR2	Rocha et al. [83] 2014 ↑
sVCAM1	Perner et al. [80] 2019 ↑
YKL-40		Hall et al. [63] 2018 ↓

↑: Significantly elevated levels in comparison to controls. ⟶: No significant differences in comparison to controls. ↓: Significantly reduced levels in comparison to controls.

Inflammatory markers in CSF

Studies in CSF are less frequent. Overall, 26 pro-inflammatory markers have been assessed. However, only IL-1β, IL-6, IL-8, and TNF-α have been measured in 4 or more studies. Of these, levels of IL-1β were consistently higher in sporadic PD patients compared to healthy controls [36 , 61] while levels of IL-6 were also mostly higher in sporadic PD patients but with less robustness across studies [37 , 60–63]. Data for IL-8 and TNF-α are less clear with some studies reporting higher CSF levels but others showing no differences or even lower levels in sporadic PD patients when compared to healthy controls [36 , 63–65]. A detailed overview of all assessed CSF markers along with the respective findings and references is given in Table 1.

Genetic-associated PD

LRRK2 (Leucine-rich repeat kinase 2)

There are 3 studies available which explored inflammatory markers in PD patients with LRRK2 mutations as well as in non-manifesting LRRK2 mutation carriers.

Two studies with focus on LRRK2 used serum samples from the Michael J. Fox Foundation LRRK2 Cohort Consortium and assessed 32 and 23 inflammatory markers by multiplex assays. Both studies found similar serum levels of inflammatory markers between PD patients with LRRK2 mutations compared to those with LRRK2-wildtype status [38, 66]. In a subgroup with CSF available, higher levels of VEGF and IL-8 were detected in PD patients with LRRK2 mutations compared to those with LRRK2-wildtype status [66]. A recent study from Israel confirmed the findings of relatively similar inflammatory profiles between PD patients with vs. without LRRK2 mutations as the colleagues also did not find any differences in blood or CSF levels of cytokines [67].

Within the group of PD patients with LRRK2 mutations, higher serum levels of IL-8, MCP-1 and MIP1-β seem associated with a more severe phenotype comprising cognitive impairment, orthostatic hypotension and REM-Sleep-behaviour disorder [14].

While 1 of the 3 studies reports higher serum levels of IL-1β [66], the other 2 studies did not find differences in inflammatory profiles in blood and/or CSF between non-manifesting LRRK2 mutation carriers and healthy controls, even when clinically stratified for prodromal markers [38, 67]. A recent study in Norwegian individuals revealed higher CSF levels of TNF-α in LRRK2 mutation carriers when compared to controls [68]. Given these somewhat variable findings, one has to keep in mind that different prodromal symptoms vary in their predictive value to PD conversion and that the rate of progression from prodromal to manifest PD is age-dependent and varies among patients. Therefore, these cross-sectional results in the prodromal cohorts need to be interpreted with caution and warrant further longitudinal studies, ideally in individuals who convert to PD during study.

GBA (Glucocerebrosidase)

While one small study reports increased plasma levels of IFN-γ, IL-1β, IL-2, and TNF-α in 8 PD patients carrying heterozygous mutations in the GBA gene [50], two larger studies did not find any differences in blood and CSF levels between PD patients as well as healthy individuals with vs. without heterozygous GBA mutations [67, 69].

PRKN/PINK1 (Parkin/PINK1)

It has been shown that dysfunction in Parkin and PINK1 enhances mitochondrial stress with activation of inflammatory processes and neurodegeneration via the STING protein cascade (stimulator of interferon genes) [70]. Consequently, PD patients with bi-allelic and heterozygous mutations in PRKN/PINK1 showed increased serum levels of IL-6 in a gene-dosage manner in 3 cohorts [71].

We conclude that the role of inflammation in PD is reflected in increased inflammatory markers in blood and CSF in sporadic PD patients compared to healthy control individuals. Studies in genetically associated PD (LRRK2, GBA, PRKN/PINK1) are rare and indicate similar profiles as seen in sporadic PD. Data in at-risk and prodromal mutation carriers are sparse and conflicting and need further validation in prospectively followed longitudinal cohorts.

IS THERE A ROLE OF ANTI-INFLAMMATORY MEDIATORS IN BLOOD/CSF?

While many studies focus on pro-inflammatory profiles, it is more and more established that inflammation is a balance between pro- and anti-inflammatory processes.

The best investigated anti-inflammatory marker in the context of PD is IL-10 in blood. However, reports are quite heterogenous. While some studies show higher levels in sporadic PD patients, others find no differences or lower levels in sporadic PD patients compared to healthy controls [25 , 57]. Two studies have been done in CSF and found no differences between sporadic PD patients and healthy controls [46, 64].

Within the group of PD patients, single studies report higher blood levels of IL-10 to be associated with motor and cognitive impairment as well as with higher CSF levels of α-synuclein [55, 57]. Whether such findings represent compensatory upregulation to keep the balance between pro-and anti-inflammatory species remains to be elucidated. A detailed overview of the respective findings in blood and CSF along with references is given in Table 1.

DO INFLAMMATORY PROFILES IN BLOOD REFLECT THOSE IN CSF INDICATIVE OF A CROSS-TALK BETWEEN PERIPHERY AND BRAIN?

When designing clinical trials, one has to decide which biomaterial should to be repeatedly collected for outcome measurements. In this context, blood is more easily accessible than CSF. However, we should know whether blood profiles are representative for profiles in CSF and can serve as proxy for central-nervous system neuroinflammation?

So far, there are 3 comprehensive studies published in sporadic PD that assessed a variety of inflammatory markers by multiplex assay in CSF/serum pairs [16 , 72]. The 2 smaller cohorts comprised 12 and 22 PD patients whereas our own cohort consisted of 453 sporadic PD patients. Importantly, the platform/panel (Mesoscale: V-PLEX: IFN-γ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, TNF-α) that was used in the 2 smaller studies was identical and fully overlapped with the inflammatory markers assessed in our own study (Myriad: AFP, BDNF, CA-125, CEA, CKMB, ENA-78, FactorVII, FABP, GH, IgE, ICAM-1, IL-1α, IL-1β IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12p40, IL-12p70, IL-13, IL-15, IL-16, IL-18, Leptin, Lymphotactin, MDC, MIP-1β, MMP3, MMP9, MCP-1, PSA-f, SCF, TPO, TSH, TF, TNF-α, TNF-β). Results from all 3 studies are quite congruent: (I) Only a fraction of markers is reliably detectable in both CSF and serum. (II) Correlations of cytokines between blood and CSF are sparse. In the 2 smaller studies, none of the markers IL-6, IL-8, IL-10, IFN-γ, TNF-α showed a significant correlation between serum and CSF. The first study with 12 patients additionally assessed CRP which showed a good correlation between serum and CSF. In our larger study of 41 markers, ICAM-1, IL-4, IL-6, IL-12p40, MCP-1, MIP-1β, MMP3, Leptin, and TSH were correlated between CSF and serum in females and in males whereas IL-8, IL-13 and CKMB were correlated only in females.

These findings indicate that inflammatory markers in blood and CSF do not reflect one another and that they might be regulated independently of each other. This poses a challenge for clinical trials. Which biomaterial should to be collected for repeated outcome measurements –the more easily accessible blood or CSF by lumbar puncture?

DO BLOOD/CSF INFLAMMATORY PROFILES CHANGE OVER THE DISEASE COURSE AS ASSESSED IN REPEATEDLY TAKEN BIOSAMPLES?

Inflammatory profiles do not follow a linear pattern as recent evidence highlights that inflammation in PD is maximized in the early disease stages and maintains a chronic profile during the course of the disease. Knowledge of the trajectories of the different biofluid markers in relation to disease progression is important when interpreting therapeutic effects (beneficial/risk) in clinical trials targeting the immune system.

One study used a multiplex assay assessing 37 cytokines in serum of 25 sporadic PD patients every 6 months over 2 years. While levels of IL-4 increased, plasma levels of IL-2, IL-9 and IL-15 decreased over time. Other cytokines did not change significantly over time [58]. Another study reports stable blood levels of CRP over a period of 3 years in 313 PD patients with a mean disease duration of 7.9 years at baseline. Notably, the patients have been stratified by their individual CRP levels at baseline (low, mid, high) [73]. Another study in 47 PD patients showed an increase in serum levels of C3, C4, and IL-6 in 30–50% of patients over a period of 2 years [74].

So far, 1 study reports longitudinal measurements of YKL-40 in repeatedly taken CSF samples of 63 sporadic PD patients. The authors report an increase of CSF levels of YKL-40 over 2 years in the whole PD cohort as well as in subgroups when stratified by disease duration≤or>5 years disease duration [75].

We conclude that large longitudinal studies with repeatedly collected blood and CSF biosamples in de-novo patients over several years are missing. However, such data are of utmost importance and will provide information on how inflammatory profiles evolve over time and in concert with other neurodegenerative/disease-specific biomarkers.

ARE BLOOD/CSF INFLAMMATORY PROFILES ASSOCIATED WITH PHENOTYPICAL TRAJECTORIES IN PD?

In order to enrich cohorts for maximized therapeutic benefit, define clinically reasonable outcome measures and target specific clinical milestones, knowledge on the predictive/prognostic value of inflammatory profiles in relation to clinical trajectories is important.

There are several studies in blood and CSF that investigated inflammatory profiles in relation to phenotypical characteristics of motor and non-motor symptoms, the latter with focus on cognition, depression and sleep. A detailed overview of associations between inflammatory profiles in blood and CSF with phenotypical characteristics is given in Table 2 along with all references.

Table 2

Findings of levels of inflammatory markers in association with clinical characteristics in sporadic PD patients in blood (serum, plasma) and CSF from 01/2012-12/2021

Blood						CSF
	Mortality	Motor function	Cognitive function	Depression	Hallucination	Fatigue Sleep RBD	Mortality	Motor function	Cognitive function	Hallucination	Depression	Fatigue Sleep RBD
C3			Vesely [74, 85] 2018, 2019
C4			Vesely [74] 2018
CCL2 MCP-1								Santaella [88] 2020	Lerche [72] 2022		Lindqvist [23] 2013	Lindqvist [23] 2013
CRP	Sawada [73] 2015	Umemura [81] 2015	Mollenhauer [84] 2019	Sawada [24] 2014				Hall [63] 2018	Lindqvist [23] 2013		Lindqvist [23] 2013	Lindqvist [23] 2013
		Santos-García [30] 2019	Martin-Ruiz [57] 2020					Moghaddam [87] 2018	Hall [63] 2018		Hall [63] 2018
		Li [77] 2021							Moghaddam [87] 2018		Lindqvist [23] 2013
CX3CL1 Fractalkine		Gupta [78] 2021						Hatcher-Martin [89] 2021
CXCL10 IP-10												Lindqvist [23] 2013
FABP			Lerche [72] 2022						Lerche [72] 2022
IL-1ß		Fan [44] 2020										Hu [36] 2015
IL-1RA						Herlofson [102] 2018
IL-2		King [28] 2019
IL-4		King [28] 2019
IL-6	Dufek [86] 2015	Delgado-Alvarado [48] 2017	Vesely [85] 2019	Vesely [74] 2018		Pereira [103] 2016		Lian [42] 2019	Lindqvist [23] 2013
		Green [76] 2019	Martin-Ruiz [57] 2020						Yu [62] 2014
		Lian [42] 2019	Usenko [55] 2020
IL -8									Lerche [72] 2022	Lerche [72] 2022
IL-10				Usenko [55] 2020		Karpenko [104] 2018
				Martin-Ruiz [57] 2020
IL-12p40		Yilmaz [82] 2018		Yilmaz [82] 2018
IL-15		Lerche [72] 2022
IL-17A			Green [76] 2019		Green [76] 2019
IL-18									Lerche [72] 2022
IFN-y		Eidson [56] 2017	Martin-Ruiz [57] 2020
MMP10									Santaella [88] 2020
S100B		Carvalho [79] 2015					Carvalho [79] 2015
SAA Serum amyloid A								Hall [63] 2018	Hall [63] 2018		Hall [63] 2018
SCF									Lerche [72] 2022
TNF-α		Lerche [72] 2022	Usenko [55] 2020
		Martin-Ruiz [57] 2020
		Lerche [72] 2022
sTNFR1 sTNFR2			Rocha [83] 2013
sVCAM1		Perner [80] 2019				Herlofson [102] 2018
YKL-40									Wennström [90] 2015 Hall [75] 2016

Straight = positive associations meaning higher levels of the respective inflammatory marker are associated with worse clinical outcome measure. Italic = Results show an inverse association meaning lower levels of the respective (anti)-inflammatory marker are associated with worse clinical outcome measure.Bold = Studies with longitudinal clinical data. Please note that due to space limitation we only name the first author with year of publication and do not refer to co-authors with “et al.”.

Inflammatory markers in blood: Phenotypical trajectories

It was repeatedly shown cross-sectionally that higher blood levels of IL-6 [42 , 76] and CRP [30, 77] are associated with worse motor function. Other single studies further found that higher blood levels of fractalkine [78], IL-1β [44], IL-15 [72], IFN-γ [56], S100B [79], TNF-α [72], and VCAM1 [80] are related to worse motor function. One longitudinal study could further support the finding that higher CRP levels are associated with more severe motor impairment [81]. Contrary, lower blood levels of the anti-inflammatory makers IL-4 [28] and IL-12p40 [82] were also associated with worse motor function.

Several cross-sectional studies report higher levels of CRP, FABP, IL-6, IL-10, IL-17A, TNF-α, and TNFR to be related with worse cognitive function and dementia [55 , 83]. One longitudinal study supports the finding that higher levels of CRP are associated with cognitive decline over time [84]. Similarly, it was shown longitudinally that higher levels of C3 and C4 are related to reduced memory function [74, 85]. Another longitudinal study created an inflammatory composite score based on Patients’ individual blood levels of CRP, IFN-γ, IL-6, IL-10, and TNF-α. While a higher composite score predicted worse MoCA scores over 3 years, no association was found with phenoconversion to manifest dementia [57].

Two longitudinal studies report an association between higher levels of CRP and IL-6 with overall mortality in PD [73, 86].

Inflammatory markers in CSF: Phenotypical trajectories

Two studies report higher CSF levels of CRP to be associated with worse motor function [63, 87]. Other single studies found that higher CSF levels of IL-6 [42], IL-8 [72], and SAA [63] are related to worse motor function. One longitudinal study reports that higher CSF levels of MCP-1 and MMP-10 are associated with more severe motor impairment [88]. Contrary, lower levels of the anti-inflammatory marker fractalkine and IL-18 were associated with worse motor function [72, 89].

Several cross-sectional studies report higher CSF levels of CRP and IL-6 to be related with worse cognitive function [23 , 87]. Other single studies further found that higher CSF levels of MCP-1, FABP, IL-8, SAA, SCF, and YKL-40 are associated with cognitive impairment [63 , 90]. One longitudinal study supports that higher CSF levels of YKL-40 are associated with cognitive decline over time [75].

Higher CSF levels of CRP and MCP-1 were associated with depression and fatigue [23, 63].

We conclude that higher blood and CSF levels of inflammatory markers are associated with more severe clinical trajectories of motor and non-motor symptoms cross-sectionally as well as longitudinally (Fig. 1).

ARE BLOOD/CSF INFLAMMATORY PROFILES ASSOCIATED WITH CSF LEVELS OF NEURODEGENERATIVE/PD-SPECIFIC BIOMARKERS?

In order to map the complex picture of neurodegeneration with its different pathway-related endpoints (α-synuclein aggregation, concomitant amyloid-beta (Aβ) and tau pathology) and (neuro)inflammation, comprehensive biomarker analyses are needed.

The 3 studies that assessed inflammatory markers by multiplex assay in CSF/serum pairs (described above) also performed additional analyses in relation to CSF levels of neurodegenerative/PD-biomarkers [16 , 72].

Inflammatory markers in blood: CSF levels of neurodegenerative/PD-specific biomarkers

In our own study, we could show that higher blood levels of ICAM-1, IL-18, and MIP-1β are associated with higher CSF levels of t-Tau and p181-Tau. Higher blood levels of FABP, MMP3, TF, and TNF-α are correlated with higher CSF levels of NFL. Higher blood levels of IL-10, IL-12p40, IL-13, IL-16, IL-18, MCP-1, and MIP-1β are associated with higher CSF levels of α-synuclein. Notably, the majority of these associations were only found in female PD patients [72].

Inflammatory markers in CSF: CSF levels of neurodegenerative/PD-specific biomarkers

In the study by Wijeyekoon, higher CSF levels of IL-1β and IL-8 were associated with higher CSF levels of t-Tau whereas no correlation was seen between CSF inflammatory markers with p-Tau, α-synuclein or Aβ₁₄₂ [16]. Our own study revealed that higher CSF levels of FABP, ICAM-1, MMP3, SCF, and TF were associated with higher CSF levels of t-Tau, p181-Tau and α-synuclein in both, males and in females whereas the same association was detected with Aβ₁₄₂ only on males [72].

There is only 1 longitudinal study with repeatedly taken CSF samples over 2 years available. Here, higher baseline CSF levels of YKL-40 were associated with higher baseline CSF levels of t-Tau, p-Tau and α-synuclein but not with Aβ₁₄₂. Similarly, an increase in CSF levels of YKL-40 over 2 years was associated with an increase in CSF levels of t-Tau, p-Tau, and α-synuclein [75].

In conclusion, the studies indicate that associations between inflammatory markers with CSF levels of neurodegenerative/PD-biomarkers are primarily seen with t-Tau, p-Tau, and α-synuclein but not with Aβ₁₄₂.

CONCLUSION

Based on findings of the studies discussed in this review we conclude: (I) The role of inflammation in PD pathogenesis is reflected in increased inflammatory markers in blood and CSF in sporadic PD patients compared to healthy control individuals. (II) Within the group of PD patients, higher blood and CSF levels of inflammatory markers are associated with more severe clinical trajectories of motor and non-motor symptoms cross-sectionally as well as longitudinally. (III) In line with evidence from post-mortem and cell studies, the kind of blood and CSF levels of inflammatory markers indicate a contribution of the innate and adaptive immune system in both CNS and periphery. (IV) Correlations of inflammatory markers in blood and CSF are sparse indicating that they do not reflect one another. This poses a challenge for clinical trials as one has to decide which biomaterial should to be collected for repeated outcome measurements— the more easily accessible blood or CSF by lumbar puncture.

Inflammation is also relevant in the pathogenic mechanisms in other neurodegenerative diseases, among them also Alzheimer’s disease (AD). Interestingly, similar metabolites as reported relevant in PD patients are also elevated in blood and/or CSF in AD patients when compared to healthy controls: CRP, IL-1ß, IL-2, IL-4, IL-6, IL-10, IL-12, IL-18, MCP-1, MCP-3, IL-8, IFN-γ, TGF-β, TNA-α, and YKL-40 [60 , 91–95]. Thus, inflammatory processes seem to be somewhat disease-unspecific and triggered by different disease-related protein species such as α-synuclein, Aβ, and tau. These findings in turn offer the chance to accumulate knowledge on inflammation across different neurodegenerative diseases. We acknowledge the following shortcomings: (I) Longitudinal studies with repeatedly collected blood and CSF biosamples in de-novo patients over several years, ideally accompanied by other disease-related biomarkers are missing. This will provide information on how inflammatory profiles evolve over time and in concert with other neurodegenerative/disease-specific protein levels and on the predictive value for the development of disease-related milestones such as dementia. (II) Studies in (genetically) at-risk and prodromal individuals are just beginning to emerge. So far, no robust conclusions can be drawn as longitudinal data in phenoconverters are missing. (III) The type of assays used (single vs. multiplex, platforms), of cytokines assessed and the cohorts’ characteristics (samples size, disease duration) are quite variable. This limits the informative value of each individual marker and comparability across studies. (IV) There is not the one single inflammatory marker in blood and CSF that is associated with clinical trajectories, disease progression and neurodegenerative/disease-specific protein levels. The most frequently reported markers in this context are IL-1β, IL-6, IL-8, MCP-1, and TNF-α (Fig. 1). However, these are also the most investigated ones so that there might be a bias in interpretation. An elegant way might be a composite score out of the 5–10 top markers and thereby stratifying patients by their intra-individual inflammatory load.

Footnotes

ACKNOWLEDGMENTS

KB received funding from The Michael J. Fox Foundation for Parkinson’s Research (MJFF) within the grant “Influence of Inflammatory profiles on PD Phenotype and Progression” and within the PD-Strat project (FKZ 031L0137B) which was supported by the German Federal Ministry of Education and Research (BMBF) in the frame of ERACoSysMed2.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Nalls

, Pankratz

, Lill

, Do

, Hernandez

, Saad

, DeStefano

, Kara

, Bras

, Sharma

, Schulte

, Keller

, Arepalli

, Letson

, Edsall

, Stefansson

, Liu

, Pliner

, Lee

, ChengR; , International Parkinson’s Disease Genomics Consortium (IPDGC); Parkinson’s Study Group (PSG) Parkinson’s Research: The Organized GENetics Initiative (PROGENI); 23andMe; GenePD; NeuroGenetics Research Consortium (NGRC); Hussman Institute of Human Genomics (HIHG); Ashkenazi Jewish Dataset Investigator; Cohorts for Health and Aging Research in Genetic Epidemiology (CHARGE); North American Brain Expression Consortium (NABEC); United Kingdom Brain Expression Consortium (UKBEC); Greek Parkinson’s Disease Consortium; Alzheimer Genetic Analysis Group Ikram

, Ioannidis

, Hadjigeorgiou

, Bis

, Martinez

, Perlmutter

, Goate

, Marder

, Fiske

, Sutherland

, Xiromerisiou

, Myers

, Clark

, Stefansson

, Hardy

, Heutink

, Chen

, Wood

, Houlden

, Payami

, Brice

, Scott

, Gasser

, Bertram

, Eriksson

, Foroud

, Singleton

(2014) Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease. Nat Genet46, 989–993.

Halliday

, Stevens

(2011) Glia: Initiators and progressors of pathology in Parkinson’s disease. Mov Disord26, 6–17.

Williams-Gray

, Wijeyekoon

, Yarnall

, Lawson

, Breen

, Evans

, Cummins

, Duncan

, Khoo

, Burn

, Barker

, ICICLE-PD study group (2016) Serum immune markers and disease progression in an incident Parkinson’s disease cohort (ICICLE-PD). Mov Disord31, 995–1003.

Witoelar

, Jansen

, Wang

, Desikan

, Gibbs

, Blauwendraat

, Thompson

, Hernandez

, Djurovic

, Schork

, Bettella

, Ellinghaus

, Franke

, Lie

, McEvoy

, Karlsen

, Lesage

, Morris

, Brice

, Wood

, Heutink

, Hardy

, Singleton

, Dale

, Gasser

, Andreassen

, Sharma

, International Parkinson’s Disease Genomics Consortium (IPDGC) North American Brain Expression Consortium (NABEC) and United Kingdom Brain Expression Consortium (UKBEC) Investigators (2017) Genome-wide pleiotropy between Parkinson disease and autoimmune diseases. JAMA Neurol74, 780–792.

Caldi Gomes

, Galhoz

, Jain

, Roser

, Maass

, Carboni

, Barski

, Lenz

, Lohmann

, Klein

, Bahr

, Fischer

, Menden

, Lingor

(2022) Multi-omic landscaping of human midbrains identifies disease-relevant molecular targets and pathways in advanced-stage Parkinson’s disease. Clin Transl Med12, e692.

Nayak

, Salt

, Verma

, Kishore

(2015) Proteomics approach to identify biomarkers in neurodegenerative diseases. Int Rev Neurobiol121, 59–86.

Dixit

, Mehta

, Singh

(2019) Proteomics in human Parkinson’s disease: Present scenario and future directions. Cell Mol Neurobiol39, 901–915.

Magdalinou

, Noyce

, Pinto

, Lindstrom

, Holmen-Larsson

, Holtta

, Blennow

, Morris

, Skillback

, Warner

, Lees

, Pike

, Ward

, Zetterberg

, Gobom

(2017) Identification of candidate cerebrospinal fluid biomarkers in parkinsonism using quantitative proteomics. Parkinsonism Relat Disord37, 65–71.

Harms

, Ferreira

, Romero-Ramos

(2021) Periphery and brain, innate and adaptive immunity in Parkinson’s disease. Acta Neuropathol141, 527–545.

10.

Mogi

, Harada

, Riederer

, Narabayashi

, Fujita

, Nagatsu

(1994) Tumor necrosis factor-alpha (TNF-alpha) increases both in the brain and in the cerebrospinal fluid from parkinsonian patients. Neurosci Lett165, 208–210.

11.

de Araujo

, Cuenca-Bermejo

, Fernandez-Villalba

, Costa

, Silva

VDA

, Herrero

(2021) Role of microgliosis and NLRP3 inflammasome in Parkinson’s disease pathogenesis and therapy. Cell Mol Neurobiol , doi: 10.1007/s10571-020-01027-6.

12.

Sulzer

, Alcalay

, Garretti

, Cote

, Kanter

, Agin-Liebes

, Liong

, McMurtrey

, Hildebrand

, Mao

, Dawson

, Oseroff

, Pham

, Sidney

, Dillon

, Carpenter

, Weiskopf

, Phillips

, Mallal

, Peters

, Frazier

, Lindestam Arlehamn

, Sette

(2017) T cells from patients with Parkinson’s disease recognize alpha-synuclein peptides. Nature546, 656–661.

13.

White

, Wijeyekoon

, Scott

, Gunawardana

, Hayat

, Solim

, McMahon

, Barker

, Williams-Gray

(2018) The peripheral inflammatory response to alpha-synuclein and endotoxin in Parkinson’s disease. Front Neurol9, 946.

14.

Brockmann

, Schulte

, Schneiderhan-Marra

, Apel

, Pont-Sunyer

, Vilas

, Ruiz-Martinez

, Langkamp

, Corvol

, Cormier

, Knorpp

, Joos

, Bernard

, Gasser

, Marras

, Schule

, Aasly

, Foroud

, Marti-Masso

, Brice

, Tolosa

, Berg

, Maetzler

(2017) Inflammatory profile discriminates clinical subtypes in LRRK2-associated Parkinson’s disease. Eur J Neurol24, 427–e426.

15.

Kouli

, Camacho

, Allinson

, Williams-Gray

(2020) Neuroinflammation and protein pathology in Parkinson’s disease dementia. Acta Neuropathol Commun8, 211.

16.

Wijeyekoon

, Moore

, Farrell

, Breen

, Barker

, Williams-Gray

(2020) Cerebrospinal fluid cytokines and neurodegeneration-associated proteins in Parkinson’s disease. Mov Disord35, 1062–1066.

17.

Wijeyekoon

, Kronenberg-Versteeg

, Scott

, Hayat

, Kuan

, Evans

, Breen

, Cummins

, Jones

, Clatworthy

, Floto

, Barker

, Williams-Gray

(2020) Peripheral innate immune and bacterial signals relate to clinical heterogeneity in Parkinson’s disease. Brain Behav Immun87, 473–488.

18.

Lindestam Arlehamn

, Dhanwani

, Pham

, Kuan

, Frazier

, Rezende Dutra

, Phillips

, Mallal

, Roederer

, Marder

, Amara

, Standaert

, Goldman

, Litvan

, Peters

, Sulzer

, Sette

(2020) alpha-Synuclein-specific T cell reactivity is associated with preclinical and early Parkinson’s disease. Nat Commun11, 1875.

19.

Kouli

, Williams-Gray

(2020) Timing is everything: The T-cell response to alpha-synuclein is maximal in early Parkinson’s. Mov Disord35, 1137.

20.

Klein

, Flanagan

(2016) Sex differences in immune responses. Nat Rev Immunol16, 626–638.

21.

Ton

, Jain

, Biggs

, Thacker

, Strotmeyer

, Boudreau

, Newman

, Longstreth

Jr , Checkoway

(2012) Markers of inflammation in prevalent and incident Parkinson’s disease in the Cardiovascular Health Study. Parkinsonism Relat Disord18, 274–278.

22.

Andican

, Konukoglu

, Bozluolcay

, Bayulkem

, Firtiina

, Burcak

(2012) Plasma oxidative and inflammatory markers in patients with idiopathic Parkinson’s disease. Acta Neurol Belg112, 155–159.

23.

Lindqvist

, Hall

, Surova

, Nielsen

, Janelidze

, Brundin

, Hansson

(2013) Cerebrospinal fluid inflammatory markers in Parkinson’s disease–associations with depression, fatigue, and cognitive impairment. Brain Behav Immun33, 183–189.

24.

Sawada

, Oeda

, Umemura

, Tomita

, Hayashi

, Kohsaka

, Yamamoto

, Sudoh

, Sugiyama

(2014) Subclinical elevation of plasma C-reactive protein and illusions/hallucinations in subjects with Parkinson’s disease: Case-control study. PLoS One9, e85886.

25.

Qin

, Zhang

, Cao

, Loh

, Cheng

(2016) Aberrations in peripheral inflammatory cytokine levels in Parkinson disease: A systematic review and meta-analysis. JAMA Neurol73, 1316–1324.

26.

Wang

, Zhang

, Li

, Long

, Wang

, Li

, Xia

, Luo

, Shan

(2016) Relationship between levels of inflammatory cytokines in the peripheral blood and the severity of depression and anxiety in patients with Parkinson’s disease. Eur Rev Med Pharmacol Sci20, 3853–3856.

27.

Kim

, Kim

, Jang

, Kim

, Yoo

, Im

, Choi

, Jeon

(2018) Peripheral blood inflammatory markers in early Parkinson’s disease. J Clin Neurosci58, 30–33.

28.

King

, O’Brien

, Donaghy

, Williams-Gray

, Lawson

, Morris

, Barnett

, Olsen

, Martin-Ruiz

, Burn

, Yarnall

, Taylor

, Duncan

, Khoo

, Thomas

(2019) Inflammation in mild cognitive impairment due to Parkinson’s disease, Lewy body disease, and Alzheimer’s disease. Int J Geriatr Psychiatry34, 1244–1250.

29.

King

, O’Brien

, Donaghy

, Morris

, Barnett

, Olsen

, Martin-Ruiz

, Taylor

, Thomas

(2019) Peripheral inflammationin mild cognitive impairment with possible and probable Lewy bodydisease and Alzheimer’s disease. Int Psychogeriatr31, 551–560.

30.

Santos-Garcia

, de Deus Fonticoba

, Suarez Castro

, Aneiros Diaz

, Paz Gonzalez

, Feal Panceiras

, Garcia Sancho

, Jesus

, Mir

, Aguilar

, Pastor

, Hernandez Vara

, de Fabregues-Boixar

, Puente

, Crespo Cuevas

, Gonzalez-Aramburu

, Infante

, CarrilloPadilla

, Pueyo

, Escalante

, Bernardo

, Solano

, Cots Foraster

, Martinez-Martin

, Group

(2019) High ultrasensitive serumC-reactive protein may be related to freezing of gait in Parkinson’sdisease patients. J Neural Transm (Vienna)126, 1599–1608.

31.

Qiu

, Xiao

, Wu

, Gan

, Huang

, Wang

(2019) C-reactive protein and risk of Parkinson’s disease: A systematic review and meta-analysis. Front Neurol10, 384.

32.

Baran

, Bulut

, Kaya

, Demirpence

, Sevim

, Akil

, Varol

(2019) High-sensitivity C-reactive protein and high mobility group box-1 levels in Parkinson’s disease. Neurol Sci40, 167–173.

33.

Jin

, Gu

, Mao

, Chen

, Liu

(2020) Association of inflammatory factors and aging in Parkinson’s disease. Neurosci Lett736, 135259.

34.

Dommershuijsen

, Ruiter

, Erler

, Rizopoulos

, Ikram

(2022) Peripheral immune cell numbers and C-reactive protein in Parkinson’s disease: Results from a population-based study. J Parkinsons Dis12, 667–678.

35.

Koziorowski

, Tomasiuk

, Szlufik

, Friedman

(2012) Inflammatory cytokines and NT-proCNP in Parkinson’s disease patients. Cytokine60, 762–766.

36.

, Yu

, Zuo

, Cao

, Wang

, Chen

, Du

, Lian

, Wang

, Chan

, Chen

, Wang

, Zhang

(2015) Parkinson disease with REM sleep behavior disorder: Features, alpha-synuclein, and inflammation. Neurology84, 888–894.

37.

Milyukhina

, Karpenko

, Klimenko

(2015) [Clinical parametersand the level of certain cytokines in blood and cerebrospinal fluidof patients with Parkinson’s disease]. Klin Med (Mosk)93, 51–55.

38.

Brockmann

, Apel

, Schulte

, Schneiderhan-Marra

, Pont-Sunyer

, Vilas

, Ruiz-Martinez

, Langkamp

, Corvol

, Cormier

, Knorpp

, Joos

, Gasser

, Schule

, Aasly

, Foroud

, Marti-Masso

, Brice

, Tolosa

, Marras

, Berg

, Maetzler

(2016) Inflammatory profile in LRRK2-associated prodromal and clinical PD. J Neuroinflammation13, 122.

39.

Karpenko

, Vasilishina

, Gromova

, Muruzheva

, Miliukhina

, Bernadotte

(2018) Interleukin-1beta, interleukin-1 receptorantagonist, interleukin-6, interleukin-10, and tumor necrosisfactor-alpha levels in CSF and serum in relation to the clinicaldiversity of Parkinson’s disease. Cell Immunol327, 77–82.

40.

Rocha

, Assis

, Scalzo

, Vieira

ELM

, Barbosa

, de Souza

, Christo

, Reis

, Teixeira

(2018) Reduced activated T lymphocytes (CD4+CD25+) and plasma levels of cytokines in Parkinson’s disease. Mol Neurobiol55, 1488–1497.

41.

Alrafiah

, Al-Ofi

, Obaid

, Alsomali

(2019) Assessment of thelevels of level of biomarkers of bone matrix glycoproteins andinflammatory cytokines from Saudi Parkinson patients. BiomedRes Int2019, 2690205.

42.

Lian

, Guo

, Zuo

, Hu

, Yu

, Jin

, Wang

, Li

, Zhang

(2019) Tremor-dominant in Parkinson disease: The relevance to iron metabolism and inflammation. Front Neurosci13, 255.

43.

Chatterjee

, Roy

, Banerjee

, Choudhury

, Mondal

, Halder

, Basu

, Shubham

, Dey

, Kumar

(2020) Inflammasome and alpha-synuclein in Parkinson’s disease: A cross-sectional study. J Neuroimmunol338, 577089.

44.

Fan

, Pan

, Zhang

, Yang

, Yu

, Zheng

, Ma

, Wang

(2020) Systemic activation of NLRP3 inflammasome and plasma alpha-synuclein levels are correlated with motor severity and progression in Parkinson’s disease. J Neuroinflammation17, 11.

45.

Mahlknecht

, Stemberger

, Sprenger

, Rainer

, Hametner

, Kirchmair

, Grabmer

, Scherfler

, Wenning

, Seppi

, Poewe

, Reindl

(2012) An antibody microarray analysis of serum cytokinesin neurodegenerative Parkinsonian syndromes. ProteomeSci10, 71.

46.

Schroder

, Pawlowski

, Meyer Zu Horste

, Gross

, Wiendl

, Meuth

, Ruck

, Warnecke

(2018) Immune cell activation in thecerebrospinal fluid of patients with Parkinson’s disease. FrontNeurol9, 1081.

47.

Tang

, Chong

, Li

, Liu

, Hou

, Li

(2014) Correlation between serum RANTES levels and the severity of Parkinson’s disease. Oxid Med Cell Longev2014, 208408.

48.

Delgado-Alvarado

, Gago

, Gorostidi

, Jimenez-Urbieta

, Dacosta-Aguayo

, Navalpotro-Gomez

, Ruiz-Martinez

, Bergareche

, Marti-Masso

, Martinez-Lage

, Izagirre

, Rodriguez-Oroz

(2017) Tau/alpha-synuclein ratio and inflammatory proteins in Parkinson’s disease: An exploratory study. Mov Disord32, 1066–1073.

49.

Kwiatek-Majkusiak

, Geremek

, Koziorowski

, Tomasiuk

, Szlufik

, Friedman

(2020) Serum levels of hepcidin and interleukin 6 in Parkinson’s disease. Acta Neurobiol Exp (Wars)80, 297–304.

50.

Miliukhina

, Usenko

, Senkevich

, Nikolaev

, Timofeeva

, Agapova

, Semenov

, Lubimova

, Totolyan

, Pchelina

(2020) Plasma cytokines profile in patients with Parkinson’s diseaseassociated with mutations in GBA gene. Bull Exp Biol Med168, 423–426.

51.

Gupta

, Garg

, Khattri

(2016) Levels of IL-8 and TNF-alpha decrease in Parkinson’s disease. Neurol Res38, 98–102.

52.

Calvani

, Picca

, Landi

, Marini

, Biancolillo

, Coelho-Junior

, Gervasoni

, Persichilli

, Primiano

, Arcidiacono

, Urbani

, Bossola

, Bentivoglio

, Cesari

, Bernabei

, Monaco

MRL

, Marzetti

(2020) A novel multi-marker discovery approach identifies new serum biomarkers for Parkinson’s disease in older people: An EXosomes in PArkiNson Disease (EXPAND) ancillary study. Geroscience42, 1323–1334.

53.

Lin

, Chen

, Chiang

, Liou

, Chang

, Lu

, Chuang

, Tai

, Cheng

, Lin

, Wu

(2019) Altered gut microbiota and inflammatory cytokine responses in patients with Parkinson’s disease. J Neuroinflammation16, 129.

54.

Rathnayake

, Chang

, Udagama

(2019) Selected serum cytokines andnitric oxide as potential multi-marker biosignature panels forParkinson disease of varying durations: A case-control study. BMC Neurol19, 56.

55.

Usenko

, Nikolaev

, Miliukhina

, Bezrukova

, Senkevich

, Gomzyakova

, Beltceva

, Zalutskaya

, Gracheva

, Timofeeva

, Petrova

, Semenov

, Lubimova

, Totolyan

, Pchelina

(2020) Plasma cytokine profile in synucleinophaties with dementia. J Clin Neurosci78, 323–326.

56.

Eidson

, Kannarkat

, Barnum

, Chang

, Chung

, Caspell-Garcia

, Taylor

, Mollenhauer

, Schlossmacher

, Ereshefsky

, Yen

, Kopil

, Frasier

, Marek

, Hertzberg

, Tansey

(2017) Candidate inflammatory biomarkers display unique relationships withalpha-synuclein and correlate with measures of disease severity insubjects with Parkinson’s disease. J Neuroinflammation14, 164.

57.

Martin-Ruiz

, Williams-Gray

, Yarnall

, Boucher

, Lawson

, Wijeyekoon

, Barker

, Kolenda

, Parker

, Burn

, Von Zglinicki

, Saretzki

(2020) Senescence and Inflammatory Markers for Predicting Clinical Progression in Parkinson’s Disease: The ICICLE-PD Study. J Parkinsons Dis10, 193–206.

58.

Csencsits-Smith

, Suescun

, Li

, Luo

, Bick

, Schiess

(2016) Serum lymphocyte-associated cytokine concentrations change more rapidly over time in multiple system atrophy compared to Parkinson disease. Neuroimmunomodulation23, 301–308.

59.

Kouchaki

, Kakhaki

, Tamtaji

, Dadgostar

, Behnam

, Nikoueinejad

, Akbari

(2018) Increased serum levels of TNF-alpha and decreased serum levels of IL-27 in patients with Parkinson disease and their correlation with disease severity. Clin Neurol Neurosurg166, 76–79.

60.

Chen

, Hu

, Cao

, Liu

, Cheng

(2018) Cerebrospinal fluid inflammatory cytokine aberrations in Alzheimer’s disease, Parkinson’s disease and amyotrophic lateral sclerosis: A systematic review and meta-analysis. Front Immunol9, 2122.

61.

Iwaoka

, Otsuka

, Maeda

, Yamahara

, Kato

, Takahashi

, Terayama

(2020) Impaired metabolism of kynurenine and its metabolites in CSF of Parkinson’s disease. Neurosci Lett714, 134576.

62.

, Zuo

, Wang

, Chen

, Hu

, Wang

, Zhang

(2014) Potential biomarkers relating pathological proteins, neuroinflammatory factors and free radicals in PD patients with cognitive impairment: A cross-sectional study. BMC Neurol14, 113.

63.

Hall

, Janelidze

, Surova

, Widner

, Zetterberg

, Hansson

(2018) Cerebrospinal fluid concentrations of inflammatory markers in Parkinson’s disease and atypical parkinsonian disorders. Sci Rep8, 13276.

64.

, Howell

, Ozturk

, Gangishetti

, Kollhoff

, Hatcher-Martin

, Anderson

, Tyor

(2019) CSF cytokines in aging, multiple sclerosis, and dementia. Front Immunol10, 480.

65.

Santaella

, Kuiperij

, van Rumund

, Esselink

RAJ

, van Gool

, Bloem

, Verbeek

(2020) Inflammation biomarker discovery in Parkinson’s disease and atypical parkinsonisms. BMC Neurol20, 26.

66.

Dzamko

, Rowe

, Halliday

(2016) Increased peripheral inflammation in asymptomatic leucine-rich repeat kinase 2 mutation carriers. Mov Disord31, 889–897.

67.

Thaler

, Omer

, Giladi

, Gurevich

, Bar-Shira

, Gana-Weisz

, Goldstein

, Kestenbaum

, Shirvan

, Cedarbaum

, Orr-Urtreger

, Regev

, Shenhar-Tsarfaty

, Mirelman

(2021) Mutations in GBA and LRRK2 are not associated with increased inflammatory markers. J Parkinsons Dis11, 1285–1296.

68.

Majbour

, Aasly

, Hustad

, Thomas

, Vaikath

, Elkum

, van de Berg

WDJ

, Tokuda

, Mollenhauer

, Berendse

, El-Agnaf

OMA

(2020) CSF total and oligomeric alpha-Synuclein along with TNF-alpha as risk biomarkers for Parkinson’s disease: A study in LRRK2 mutation carriers. Transl Neurodegener9, 15.

69.

Galper

, Balwani

, Fahn

, Waters

, Krohn

, Gan-Or

, Dzamko

, Alcalay

(2021) Cytokines and Gaucher biomarkers in glucocerebrosidase carriers with and without Parkinson disease. Mov Disord36, 1451–1455.

70.

Sliter

, Martinez

, Hao

, Chen

, Sun

, Fischer

, Burman

, Li

, Zhang

, Narendra

, Cai

, Borsche

, Klein

, Youle

(2018) Parkin and PINK1 mitigate STING-induced inflammation. Nature561, 258–262.

71.

Borsche

, Konig

, Delcambre

, Petrucci

, Balck

, Bruggemann

, Zimprich

, Wasner

, Pereira

, Avenali

, Deuschle

, Badanjak

, Ghelfi

, Gasser

, Kasten

, Rosenstiel

, Lohmann

, Brockmann

, Valente

, Youle

, Grunewald

, Klein

(2020) Mitochondrial damage-associated inflammation highlights biomarkers in PRKN/PINK1 parkinsonism. Brain143, 3041–3051.

72.

Lerche

, Zimmermann

, Wurster

, Roeben

, Fries

, Deuschle

, Waniek

, Lachmann

, Gasser

, Jakobi

, Joos

, Schneiderhan-Marra

, Brockmann

(2022) CSF and serum levels of inflammatory markers in PD: Sparse correlation, sex differences and association with neurodegenerative biomarkers. Front Neurol13, 834580.

73.

Sawada

, Oeda

, Umemura

, Tomita

, Kohsaka

, Park

, Yamamoto

, Sugiyama

(2015) Baseline C-reactive protein levels and life prognosis in Parkinson disease. PLoS One10, e0134118.

74.

Vesely

, Dufek

, Thon

, Brozman

, Kiralova

, Halaszova

, Koritakova

, Rektor

(2018) Interleukin 6 and complement serum level study in Parkinson’s disease. J Neural Transm (Vienna)125, 875–881.

75.

Hall

, Surova

, Ohrfelt

, Swedish

Bio FS

, Blennow

, Zetterberg

, Hansson

(2016) Longitudinal measurements of cerebrospinal fluid biomarkers in Parkinson’s disease. Mov Disord31, 898–905.

76.

Green

, Khosousi

, Svenningsson

(2019) Plasma IL-6 and IL-17A correlate with severity of motor and non-motor symptoms in Parkinson’s disease. J Parkinsons Dis9, 705–709.

77.

, Zhang

, Gao

, Nie

, Liang

, Zhang

, Wang

(2021) Serum folate, vitamin B12 levels, and systemic immune-inflammation index correlate with motor performance in Parkinson’s disease: A cross-sectional study. Front Neurol12, 665075.

78.

Gupta

, Paliwal

, Babu

(2022) Serum fractalkine and 3-nitrotyrosine levels correlate with disease severity in Parkinson’s disease: A pilot study. Metab Brain Dis37, 209–217.

79.

Carvalho

, Schonwald

, Schumacher-Schuh

, Braga

, Souza

, Oses

, Donis

, Rieder

(2015) Overnight S100B in Parkinson’s Disease: A glimpse into sleep-related neuroinflammation. Neurosci Lett608, 57–63.

80.

Perner

, Perner

, Gaur

, Zimmermann

, Witte

, Heidel

, Grosskreutz

, Prell

(2019) Plasma VCAM1 levels correlate with disease severity in Parkinson’s disease. J Neuroinflammation16, 94.

81.

Umemura

, Oeda

, Yamamoto

, Tomita

, Kohsaka

, Park

, SugiyamaH , Sawada

(2015) Baseline plasma C-reactive protein concentrationsand motor prognosis in Parkinson disease. PLoS One10, e0136722.

82.

Yilmaz

, Strafella

, Bernard

, Schulte

, van den Heuvel

, Schneiderhan-Marra

, Knorpp

, Joos

, Leypoldt

, Geritz

, Hansen

, Heinzel

, Apel

, Gasser

, Lang

, Berg

, Maetzler

, Marras

(2018) Serum inflammatory profile for the discrimination ofclinical subtypes in Parkinson’s disease. Front Neurol9, 1123.

83.

Rocha

, Teixeira

, Scalzo

, Barbosa

, de Sousa

, Morato

, Vieira

, Christo

, Palotas

, Reis

(2014) Plasma levelsof soluble tumor necrosis factor receptors are associated withcognitive performance in Parkinson’s disease. Mov Disord29, 527–531.

84.

Mollenhauer

, Zimmermann

, Sixel-Doring

, Focke

, Wicke

, Ebentheuer

, Schaumburg

, Lang

, Friede

, Trenkwalder

, DeNoPa Study Group (2019) Baseline predictors for progression 4 years after Parkinson’s disease diagnosis in the De Novo Parkinson Cohort (DeNoPa). Mov Disord34, 67–77.

85.

Vesely

, Koritakova

, Bohnen

, Viszlayova

, Kiralova

, Valkovic

, Kurca

, Rektor

(2019) The contribution of cerebrovascular risk factors, metabolic and inflammatory changes to cognitive decline in Parkinson’s disease: Preliminary observations. J Neural Transm (Vienna)126, 1303–1312.

86.

Dufek

, Rektorova

, Thon

, Lokaj

, Rektor

(2015) Interleukin-6 may contribute to mortality in Parkinson’s disease patients: A 4-year prospective study. Parkinsons Dis2015, 898192.

87.

Sanjari Moghaddam

, Valitabar

, Ashraf-Ganjouei

, Mojtahed Zadeh

, Ghazi Sherbaf

, Aarabi

(2018) Cerebrospinal fluid C-reactive protein in Parkinson’s disease: Associations with motor and non-motor symptoms. Neuromolecular Med20, 376–385.

88.

Santaella

, Kuiperij

, van Rumund

, Esselink

RAJ

, van Gool

, Bloem

, Verbeek

(2020) Cerebrospinal factaluid monocyte chemoattrnt protein 1 correlates with progression of Parkinson’s disease. NPJ Parkinsons Dis6, 21.

89.

Hatcher-Martin

, McKay

, Pybus

, Sommerfeld

, Howell

, Goldstein

, Wood

, Hu

, Factor

(2021) Cerebrospinal fluid biomarkers in Parkinson’s disease with freezing of gait: An exploratory analysis. NPJ Parkinsons Dis7, 105.

90.

Wennstrom

, Surova

, Hall

, Nilsson

, Minthon

, Hansson

, Nielsen

(2015) The inflammatory marker YKL-40 is elevated in cerebrospinal fluid from patients with Alzheimer’s but not Parkinson’s disease or dementia with Lewy bodies. PLoS One10, e0135458.

91.

Park

, Han

, Mook-Jung

(2020) Peripheral inflammatory biomarkers in Alzheimer’s disease: A brief review. BMB Rep53, 10–19.

92.

, Zhao

, Xia

, Xu

(2019) Peripheral inflammatory biomarkers in Alzheimer’s disease and mild cognitive impairment: A systematic review and meta-analysis. Psychogeriatrics19, 300–309.

93.

Xie

, Van Hoecke

, Vandenbroucke

(2021) The impact of systemic inflammation on Alzheimer’s disease pathology. Front Immunol12, 796867.

94.

Dhapola

, Hota

, Sarma

, Bhattacharyya

, Medhi

, Reddy

(2021) Recent advances in molecular pathways and therapeutic implications targeting neuroinflammation for Alzheimer’s disease. Inflammopharmacology29, 1669–1681.

95.

de Oliveira

, Kucharska

, Garcez

, Rodrigues

, Quevedo

, Moreno-Gonzalez

, Budni

(2021) Inflammatory cascade in Alzheimer’s disease pathogenesis: A review of experimental findings. Cells10, 2581.

96.

Sun

, Yu

, Zhao

, Song

, Liu

, Jia

, Wang

, Liu

(2019) Peripheral humoral immune response is associated with the non-motor symptoms of Parkinson’s disease. Front Neurosci13, 1057.

97.

Bagheri

, Khorramdelazad

, Hassanshahi

, Moghadam-Ahmadi

, Vakilian

(2018) CXCL12 and CXCR4 in the peripheral blood of patients with Parkinson’s disease. Neuroimmunomodulation25, 201–205.

98.

Rahnemayan

, Mirghafourvand

, Fathalizadeh

, Faramarzi

, Reyhanifard

, Mahmoodpoor

, Sanaie

(2021) Leptin levels in patients with Parkinson’s disease: A systematic review and meta-analysis. Clin Nutr ESPEN41, 104–109.

99.

Roy

, Choudhury

, Banerjee

, Basu

, Kumar

(2021) Soluble LAG-3 and Toll-interacting protein: Novel upstream neuro-inflammatory markers in Parkinson’s disease.Parkinsonism Relat Disord91, 121–123.

100.

Dumitrescu

, Marta

, Danau

, Lefter

, Tulba

, Cozma

, Manole

, Gherghiceanu

, Ceafalan

, Popescu

(2021) Serum and fecalmarkers of intestinal inflammation and intestinal barrierpermeability are elevated in Parkinson’s disease. FrontNeurosci15, 689723.

101.

Sathe

, Maetzler

, Lang

, Mounsey

, Fleckenstein

, Martin

, Schulte

, Mustafa

, Synofzik

, Vukovic

, Itohara

, Berg

, Teismann

(2012) S100B is increased in Parkinson’s disease andablation protects against MPTP-induced toxicity through the RAGE andTNF-alpha pathway. Brain135, 3336–3347.

102.

Herlofson

, Heijnen

, Lange

, Alves

, Tysnes

, Friedman

, Fagundes

(2018) Inflammation and fatigue in early, untreated Parkinson’s disease. Acta Neurol Scand138, 394–399.

103.

Pereira

, Santos

LVD

, Santos

RMS

, Campos

ALF

, Pimenta

, de Oliveira

, Bacheti

, Rocha

, Teixeira

, Christo

, Scalzo

(2016) IL-6 serum levels are elevated in Parkinson’s disease patients with fatigue compared to patients without fatigue. J Neurol Sci370, 153–156.

104.

Karpenko

, Vasilishina

, Gromova

, Muruzheva

, Bernadotte

(2018) Corrigendum to “Interleukin-1beta, interleukin-1 receptor antagonist, interleukin-6, interleukin-10, and tumor necrosis factor-alpha levels in CSF and serum in relation to the clinical diversity of Parkinson’s disease” [Cell. Immunol. 327 (2018) 77-82]. Cell Immunol334, 99.