Brain and Systemic Inflammation in De Novo Parkinson's Disease

Talene A Yacoubian; Yu‐Hua Dean Fang; Adam Gerstenecker; Amy Amara; Natividad Stover; Lauren Ruffrage; Christopher Collette; Richard Kennedy; Yue Zhang; Huixian Hong; Hongwei Qin; Jonathan McConathy; Etty N Benveniste; David G Standaert

doi:10.1002/mds.29363

. 2023 Feb 28;38(5):743–754. doi: 10.1002/mds.29363

Brain and Systemic Inflammation in De Novo Parkinson's Disease

Talene A Yacoubian ^1,^✉, Yu‐Hua Dean Fang ², Adam Gerstenecker ¹, Amy Amara ¹, Natividad Stover ¹, Lauren Ruffrage ¹, Christopher Collette ¹, Richard Kennedy ³, Yue Zhang ³, Huixian Hong ⁴, Hongwei Qin ⁴, Jonathan McConathy ², Etty N Benveniste ⁴, David G Standaert ¹

PMCID: PMC11403348 PMID: 36853618

Abstract

Objective

To assess the presence of brain and systemic inflammation in subjects newly diagnosed with Parkinson's disease (PD).

Background

Evidence for a pathophysiologic role of inflammation in PD is growing. However, several key gaps remain as to the role of inflammation in PD, including the extent of immune activation at early stages, potential effects of PD treatments on inflammation and whether pro‐inflammatory signals are associated with clinical features and/or predict more rapid progression.

Methods

We enrolled subjects with de novo PD (n = 58) and age‐matched controls (n = 62). Subjects underwent clinical assessments, including the Movement Disorder Society‐United Parkinson's Disease rating scale (MDS‐UPDRS). Comprehensive cognitive assessment meeting MDS Level II criteria for mild cognitive impairment testing was performed. Blood was obtained for flow cytometry and cytokine/chemokine analyses. Subjects underwent imaging with ¹⁸F‐DPA‐714, a translocator protein 18kd ligand, and lumbar puncture if eligible and consented.

Results

Baseline demographics and medical history were comparable between groups. PD subjects showed significant differences in University of Pennsylvania Smell Identification Test, Schwab and England Activities of Daily Living, Scales for Outcomes in PD autonomic dysfunction, and MDS‐UPDRS scores. Cognitive testing demonstrated significant differences in cognitive composite, executive function, and visuospatial domain scores at baseline. Positron emission tomography imaging showed increased ¹⁸F‐DPA‐714 signal in PD subjects. ¹⁸F‐DPA‐714 signal correlated with several cognitive measures and some chemokines.

Conclusions

¹⁸F‐DPA‐714 imaging demonstrated increased central inflammation in de novo PD subjects compared to controls. Longitudinal follow‐up will be important to determine whether the presence of inflammation predicts cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Keywords: Parkinson's disease, inflammation, TSPO PET, cognition, cytokine

Introduction

Evidence for inflammation in Parkinson's disease (PD) is growing, with innate and adaptive immune systems implicated. Post‐mortem studies reveal activated microglia and T‐cells ¹ , ² , ³ , ⁴ and immunoglobulin deposition ⁵ in brain tissue from PD subjects. Alterations in immune cells are detected in living PD subjects, with most consistent findings pointing to T‐cell and monocyte changes. ⁶ , ⁷ , ⁸ , ⁹ , ¹⁰ , ¹¹ These changes may vary with disease duration or severity, although longitudinal studies to assess for changes in immune cells over time are lacking. Peptides derived from α‐synuclein (αsyn), the key protein that aggregates in PD and the primary component of Lewy bodies, can activate T‐cells from PD patients. ¹² Pro‐inflammatory cytokines and chemokines are elevated in blood and cerebrospinal fluid (CSF) specimens from PD subjects. ¹³ , ¹⁴ , ¹⁵ , ¹⁶ , ¹⁷ , ¹⁸ , ¹⁹ , ²⁰ , ²¹ , ²² , ²³ , ²⁴ , ²⁵ , ²⁶ Brain imaging using ligands against translocator protein 18kd (TSPO) as a marker for neuroinflammation show increased TSPO signal in PD patients. ²⁷ , ²⁸ , ²⁹ , ³⁰ , ³¹ , ³² Animal PD models show inflammatory changes, and manipulation of inflammation can alter neurodegeneration in animal models. ³³ , ³⁴ , ³⁵ Epidemiologic studies suggest that immunomodulation may alter the course of PD: some data suggest that ibuprofen is protective against the development of PD, ³⁶ , ³⁷ , ³⁸ whereas treatment of inflammatory bowel disease with anti‐ tumor necrosis factor (TNF) biologics is associated with reduced PD risk. ³⁹ , ⁴⁰ Despite the wealth of information pointing to inflammatory activation in PD, many gaps remain in our understanding of the role of inflammation in the pathophysiology of PD.

An important question is whether inflammation contributes to PD onset or arises later in the course of the disease in response to neurodegeneration. Genetic studies link polymorphisms in HLA genes to PD risk, ⁴¹ , ⁴² , ⁴³ , ⁴⁴ whereas several genes causal for PD, LRRK2, PRKN, and PINK1, play a role in immune cells, ³⁴ supporting an early role of the immune system. Association of certain infections with PD risk also point to a causal role for inflammation in PD pathogenesis. ⁴⁵ Interestingly, some infections stimulate αsyn levels. ⁴⁶ , ⁴⁷ T‐cell changes are prominent early and may precede disease onset. ⁴⁸ Although these observations suggest a role of inflammation early in PD, most studies of inflammatory markers in human cohorts have examined a mix of subjects with a wide range of disease severity and duration and included patients already on treatment for PD. This is potentially problematic because dopaminergic drugs can alter immune responses. ⁴⁹

Another gap is whether inflammation is associated with clinical features or predicts the subsequent course of disease, including cognitive decline. Most human studies evaluating inflammation in PD have been primarily performed in PD subjects with more advanced disease and lack longitudinal follow‐up with clinical assessment and collection of inflammatory measures. Most studies also do not include detailed cognitive assessments. Additionally, there is a lack of studies evaluating multiple inflammatory measures in the same cohort of patients.

To address gaps in our understanding of the role of neuroinflammation, we enrolled subjects with newly diagnosed, untreated PD (“de novo”) and controls in a longitudinal study designed to comprehensively measure markers of inflammation and track clinical outcomes. Subjects were enrolled within 2 years of PD diagnosis. Clinical evaluation included PD‐related scales and extensive cognitive testing consistent with the Level II diagnostic mild cognitive impairment (MCI) assessment criteria recommended by the Movement Disorders Society (MDS) task force. ⁵⁰ Inflammatory measurements included plasma and CSF cytokine and chemokine measures, TSPO positron emission tomography (PET), and peripheral immune cell phenotyping using flow cytometry. Here, we describe clinical features and measures of inflammation in this cohort at the time of entry into the study.

Methods

Standard Protocol Approvals, Registrations, and Patient Consents

Participants were enrolled between November 2018 and December 2021 at the University of Alabama at Birmingham (UAB). The study was approved by the Institutional Review Board at UAB. Full written informed consent was obtained on each participant.

Participants

Fifty‐eight subjects with newly diagnosed PD and 62 age‐ and sex‐matched controls were enrolled in the Alabama Udall cohort. Inclusion criteria for PD subjects included the diagnosis of PD by United Kingdom Brain Bank criteria ⁵¹ within 2 years of enrollment, age ≥40 at time of diagnosis, and Hoehn and Yahr stage I‐III. Exclusion criteria for PD subjects included atypical parkinsonian syndromes, clinical diagnosis of dementia, PD diagnosis >2 years, previous treatment with PD medications, and the presence of other significant neurological disorders. Inclusion criteria for controls were age ≥40, no current diagnosis of PD or other significant neurological disorder, no history of PD in first‐degree blood relatives, and ≤3 positive responses on the PD Screening Questionnaire. ⁵² Exclusion criteria for all participants included significant autoimmune or inflammatory disorders, active treatment with immunosuppressants, and serious comorbidity that may interfere with study participation.

Clinical Evaluation

Clinical assessment included demographics, medical history, immune disorders and treatment questionnaire, vaccination history, prior and concomitant medication history, family history, behavioral history, PD Screening Questionnaire, vital signs, neurological examination, Epworth Sleepiness Scale, Hamilton Anxiety and Depression scales, MDS‐United Parkinson's Disease rating scale (MDS‐UPDRS), Modified Schwab and England Activities of Daily Living, Montreal Cognitive Assessment (MoCA), PD Quality of Life Questionnaire, Pittsburgh Sleep Quality Index, Scales for Outcomes in Parkinson's Disease‐Autonomic Dysfunction (SCOPA‐AUT), and University of Pennsylvania Smell Identification Test (UPSIT).

Participants underwent comprehensive cognitive assessment meeting Level II diagnostic MCI assessment criteria recommended by the MDS task force. ⁵⁰ This testing included at least two tests across each of five cognitive domains: attention, language, memory, executive function, and visuospatial ability. To better describe patterns of cognitive performance, we included separate domains for verbal memory and visual memory and included a processing speed domain. Tests performed were Wechsler Adult Intelligence Scale‐Fourth Edition (WAIS‐IV) Digit Span and Letter‐Number Sequencing ⁵³ ; Hopkins Verbal Learning Test‐Revised (HVLT‐R) ⁵⁴ ; 10/36 Spatial Recall Test ⁵⁵ ; Judgment of Line Orientation (JLO) ⁵⁶ ; Hooper Visual Organization Test ⁵⁷ ; Boston Naming Test (BNT) ⁵⁸ ; Animal Naming ⁵⁹ ; Delis‐Kaplan Executive Function System (DKEFS) Color/Word Interference ⁶⁰ ; and Trail Making Test (TMT). ⁶¹ Normally distributed z‐scores for each domain were calculated using the best available normative mean and standard deviation (SD). Normative groups were stratified by age and education (when possible). At least two cognitive measures were used for each domain, and an average z‐score for the combined measures represented an aggregated domain score. The cognitive composite score was the average of all domain z‐scores.

Biospecimen Collection

Blood samples were processed for flow cytometry, complete blood count (performed at the UAB Hospital laboratories using a Beckman Coulter DxH 800 Hematology Analyzer), peripheral blood mononuclear cells isolation, plasma, DNA, and TSPO SNP genotyping. Although not required for participation, some subjects underwent CSF collection. Plasma, DNA, and CSF were banked at the UAB Center for Clinical and Translational Science (CCTS) Sample Processing and Analysis Network (SPAN) and at the National Institute of Neurological Disorders and Stroke (NINDS) BioSEND program. Blood and CSF samples were processed within 30 minutes after collection and then stored at −80°C.

Cytokine/Chemokine Analysis

Plasma and CSF cytokine and chemokine levels were measured by the V‐PLEX Proinflammatory Panel 1 and the V‐PLEX Chemokine Panel 1 (Meso Scale Diagnostics, Rockville, MD) using the MSD SECTOR Imager 2400 through the UAB Diabetes Research Center Human Physiology Core.

Flow Cytometry

Human TruStain FcX (BioLegend, San Diego, CA) was used to block 500 μL of fresh blood before incubation with fluorochrome‐conjugated monoclonal antibodies to monocyte, T‐cell, and B‐cell surface markers. Red blood cells (RBC) were lysed using RBC Lysis Buffer (BioLegend). After washing, cells were fixed with 2% paraformaldehyde. Cell viability was measured using the Aqua Live/Dead Kit (ThermoFisher Scientific, Waltham, MA). Antibodies used were from BioLegend: anti‐CD45 Pacific Blue (clone HI30); anti‐CD3 Brilliant Violet 605/Brilliant Violet 650 (clone OKT3); anti‐CD4 PE (clone OKT4); anti‐CD4 eFluor 450 (clone OKT4); anti‐CD8α FITC (clone HIT8a); anti‐CD14 FITC (clone HCD14); anti‐CD16 APC (clone 3G8); anti‐CD25 PE/PerCP‐Cyanine5.5 (clone M‐A251); anti‐CD19 Brilliant Violet 650 (clone HIB19); and anti‐CD127 APC‐Cy7 (clone A019D5). Antibodies at 1:100 were incubated for 20–30 minutes at room temperature. Flow cytometry was performed on a FACSymphony (BD Biosciences, Franklin Lakes, NJ). FlowJo software (Tree Star, Ashland, OR) was used for data analysis, as previously described. ⁶²

PET Imaging

Dynamic PET data were acquired from 0 to 60 minutes after ¹⁸F‐DPA‐714 injection for eligible subjects. As the rs6971 single nucleotide polymorphism (SNP) is associated with TSPO radioligand binding affinity, ⁶³ TSPO SNP genotyping was measured before PET. Those with low TSPO binding affinity were excluded from PET. All eligible subjects were scanned with a GE Signa PET/MR scanner. A sagittal T1‐weighted volumetric magnetic resonance (MR) series was acquired during PET acquisition for brain parcellation. PET images were reconstructed with ordered subset expectation maximization with the zero‐echo‐time (ZTE) magnetic resonance imaging (MRI)‐based attenuation correction. ⁶⁴ All dynamic frames of the PET scan were co‐registered to the last frame to reduce the patient motion during the acquisition. To extract the time‐activity curves for volumes of interest (VOIs), brain parcellation was performed with FreeSurfer (v.7.1.1, Martinos Center, Boston, MA) ⁶⁵ and then applied to the PET data. Partial volume correction was performed with the Geometric Transformation Matrix method in the PETPVC toolbox. ⁶⁶ , ⁶⁷ As FreeSurfer does not provide segmentation of the substantia nigra (SN), we developed an in‐house algorithm for fully automatic SN segmentation (Data S1/Supporting Information).

Ten brain regions were selected as the target VOI: putamen, caudate nucleus, SN, hippocampus, thalamus, brainstem, frontal cortex, temporal cortex, parietal cortex, and occipital cortex. For each region, simplified reference tissue model (SRTM) was used to estimate the binding potential (BP), with the cerebellum cortex serving as the reference region. ⁶⁸ , ⁶⁹ BP was constrained to be non‐negative for SRTM optimization.

Statistical Analysis

Characteristics of the study sample were described using means and SDs for continuous variables and frequencies for categorical variables. Differences between groups were analyzed using independent samples t tests for continuous variables and Pearson χ² tests (or Fisher's exact tests) for categorical variables. For cytokine and chemokine comparisons, samples with levels below the detectable range were considered “0” in the analysis. Similar findings were observed when these values were excluded or when they were replaced with detection limits.

Multiple linear regression analysis was used to examine associations of each regional ¹⁸F‐DPA‐714 BP with disease status, sex, and TSPO genotype. Associations between each regional ¹⁸F‐DPA‐714 BP and clinical scales, cognitive domain scores, flow cytometry, and plasma or CSF cytokines/chemokines were examined in PD subjects only using multiple linear regression analysis, adjusting for TSPO genotype. Results were considered statistically significant at P < 0.05.

Statistical analyses were performed using version 4.2.0 Patched of the R programming environment (https://www.R-project.org).

Data Sharing

Clinical data are uploaded to the NINDS Data Management Resource. Plasma, DNA, and CSF are stored at the NINDS BioSEND repository. Additional data are available to qualified investigators on request.

Results

Referral Data

A total of 231 potential subjects were referred by the end of December 2022 (Supplementary Figure S1). A total of 174 were eligible after screening. Among eligible candidates, 125 consented for the study, and 49 refused to participate. A total of 120 completed the enrollment visits, and five were screen failures at enrollment visit.

Baseline Clinical Data

Among the 120 subjects enrolled, 58 subjects had PD, and 62 were controls. Baseline demographics, medical history, and family history were comparable between groups (Supplementary Tables 1, 2, 3). Controls differed from PD subjects for some vaccinations, with PD having a higher rate of tetanus, diphtheria, and pertussis vaccinations and a lower rate of coronavirus disease 2019 vaccinations at time of enrollment (Supplementary Table S4).

TABLE 1.

Clinical assessment scores in patient cohort at baseline

	PD, n = 58	Control, n = 62	P	N
Age	66.1 (8.55)	64.1 (9.10)	0.180	120
Gender			0.270	120
Female	25 (43.1%)	34 (54.8%)
Male	33 (56.9%)	28 (45.2%)
PDQ‐39 total	17.7 (16.5)	n/a	n/a	57
MoCA total points	25.5 (3.73)	27.5 (1.73)	<0.001	115
UPSIT total score	22.8 (7.84)	32.4 (5.03)	<0.001	119
Schwab and England ADL score	91.4 (10.2)	97.9 (5.17)	<0.001	120
Epworth sleepiness scale total	5.67 (4.21)	5.10 (3.50)	0.419	120
HAM‐A total score	3.28 (3.09)	2.61 (4.96)	0.378	120
HAM‐D total score	2.64 (2.63)	3.16 (2.38)	0.256	120
SCOPA‐AUT score	10.4 (6.63)	5.85 (4.29)	<0.001	120
PSQI total	5.14 (3.70)	5.35 (3.43)	0.740	120
UPDRS total	43.4 (14.8)	8.66 (5.89)	<0.001	119

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Note: Values for control and PD are mean values (SD).

Abbreviations: PD, Parkinson's disease; PDQ‐39, PD Quality of Life Questionnaire 39; MoCA, Montreal Cognitive Assessment; UPSIT, University of Pennsylvania Smell Identification Test; ADL, activities of daily living; SCOOPA‐AUT, Scales for Outcomes in Parkinson's Disease‐Autonomic Dysfunction; PSQI, Pittsburgh Sleep Quality Index; UPDRS, United Parkinson's Disease rating scale; SD, standard deviation.

TABLE 2.

Cognitive data in patient cohort at baseline

Cognitive variable	PD, n = 56	Control, n = 60	P	N
Attention	0.31 (0.89)	0.52 (0.78)	0.176	115
Verbal memory	0.00 (1.07)	0.25 (0.92)	0.171	116
Visual memory	−0.01 (0.90)	0.30 (0.82)	0.054	115
Visuospatial	0.03 (0.93)	0.34 (0.72)	0.046	116
Language	0.34 (0.73)	0.46 (0.66)	0.348	115
Processing speed	0.10 (0.79)	0.26 (0.58)	0.234	115
Executive function	0.05 (0.91)	0.36 (0.72)	0.044	115
Cognitive composite	0.14 (0.60)	0.36 (0.47)	0.033	114

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Note: Values for control and PD are mean z score (SD).

Abbreviations: PD, Parkinson's disease; SD, standard deviation.

TABLE 3.

Baseline cytokine levels in plasma and CSF

	Control	PD	P	N
Plasma	n = 62	n = 56
Eotaxin	192 (74.1)	207 (79.7)	0.302	118
Eotaxin‐3	17.9 (13.7)	18.8 (9.85)	0.678	117
IP‐10	265 (128)	327 (256)	0.109	118
MCP‐1	87.9 (36.0)	146 (474)	0.368	118
MCP‐4	123 (75.9)	127 (57.4)	0.734	118
MDC	1040 (378)	1191 (1032)	0.306	118
MIP‐1α	10.4 (3.55)	55.1 (231)	0.153	118
MIP‐1β	68.8 (23.3)	78.4 (29.8)	0.054	118
TARC	268 (224)	265 (152)	0.912	118
IL‐10	0.28 (0.14)	0.37 (0.42)	0.118	118
IL‐8	7.26 (4.71)	6.47 (2.19)	0.243	118
TNF‐α	1.40 (0.41)	1.49 (0.51)	0.304	118
IFN‐γ	5.62 (3.54)	5.25 (3.25)	0.555	118 ^a
IL‐6	1.08 (0.79)	1.07 (0.88)	0.916	118 ^a
CSF	n = 19	n = 25
IL‐2	0.23 (0.13)	0.87 (2.17)	0.156	44
IL‐8	41.9 (10.3)	40.2 (12.0)	0.619	44
Eotaxin	16.5 (10.1)	19.6 (6.73)	0.254	44 ^a
IP‐10	341 (195)	419 (265)	0.270	44 ^a
MCP‐1	342 (66.7)	324 (107)	0.478	44 ^a
MDC	9.68 (6.55)	13.2 (5.73)	0.073	44 ^a
MIP‐1α	5.43 (2.58)	6.90 (1.81)	0.043	44 ^a
MIP‐1β	9.25 (2.17)	10.7 (4.07)	0.139	44 ^a
TARC	2.48 (1.48)	3.51 (1.42)	0.025	44 ^a
IL‐6	0.93 (0.52)	1.17 (0.71)	0.195	44 ^a

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Note: Values for control and PD are mean values (SD). For both plasma and CSF, those cytokines and chemokines were excluded in which levels were below detection for >30% of subjects. For plasma samples, IL‐2, IL‐4, IL‐13, IL‐1β, and IL‐12p70 were excluded. For CSF samples, IL‐4, IL‐10, IL12p70, IL‐13, IL‐1β, IFN‐γ, TNF‐α, eotaxin‐3, and MCP‐4.

^{^a}

A few subject samples had undetectable values for the measured cytokine or chemokine. These undetectable values were replaced with zeros.

PD subjects showed significant differences in UPSIT, Schwab and England Activities of Daily Living, SCOPA‐AUT, and MDS‐UPDRS total scores (Table 1). PD subjects showed worse performance on the MoCA (Table 1) and showed significant differences in executive function, visuospatial ability, and cognitive composite at baseline compared to controls (Table 2).

Inflammatory Measures in Biospecimens

We measured cytokines and chemokines in the plasma of all subjects and in the CSF of those participants who agreed to undergo lumbar puncture (n = 19 controls and 25 PD subjects) (Table 3). We excluded those cytokines and chemokines in which levels for these molecules were below detection for >30% of subjects. Five molecules were excluded from the plasma analysis; nine were excluded from the CSF analysis (Table 3). Increased plasma macrophage inflammatory protein‐1β (MIP1β; also known as chemokine ligand 4 [CCL4]) was noted among PD subjects compared to controls (P = 0.054) (Table 3). In the CSF, significant increases in macrophage inflammatory protein‐1α (MIP1α/CCL3) (P = 0.043) and thymus‐ and activation‐regulated chemokine (TARC/CCL17) (P = 0.025) and an increased trend in macrophage‐derived chemokine (MDC/CCL22) (P = 0.073) were seen in PD subjects compared to controls (Table 3).

Complete blood counts and flow cytometry were performed on peripheral blood. No significant differences were noted in white blood cell or platelet counts. Small, but statistically significant increases were seen in hemoglobin and hematocrit levels in PD subjects (Supplementary Table S5). PD participants demonstrated increased CD4⁺ T non‐regulatory cell percentages (CD4⁺CD25⁺CD127^+/dim; P = 0.048) with decreased CD4⁺ T regulatory (Tregs) cell percentages (CD4⁺CD25^brightCD127^dim; P = 0.038) compared to controls (Fig. 1A,B). PD subjects showed a higher neutrophil to lymphocyte ratio (NLR) compared to controls (Fig. 1C) (P = 0.004). This increased NLR was related to both an increase in neutrophils and a decrease in lymphocytes in PD subjects (Supplementary Table S5). No changes in CD4⁺ or CD8⁺ T‐cell, CD19⁺ B‐cell, or monocyte percentages were observed at baseline (Supplementary Fig. S2).

FIG 1 — Alterations in T regulatory (Treg) subset, T non‐regulatory subset, and neutrophil to lymphocyte ratio (NLR) in Parkinson's disease (PD). (A) CD45⁺CD3⁺CD4⁺ cells were gated to CD25⁺CD127⁻ Treg and CD25⁺CD127⁺ non‐Treg subsets. (B) Frequencies of Treg and non‐Treg subsets from PD and control subjects were analyzed and are shown as mean ± standard deviation (SD). *P < 0.05 (Student's t test). Red symbols are females, and blue symbols are males. (C) White blood cell count, including neutrophils, lymphocytes, monocytes, eosinophils, and basophils, was done as part of the complete blood count. The NLR was calculated as percentage of neutrophils divided by the percentage of lymphocytes and is shown as mean ± SD. **P < 0.01 (Student's t test). Red symbols are females, and blue symbols are males.

FIG 2 — Parkinson's disease (PD) subjects show higher ¹⁸F‐DPA‐714 binding protein (BP) in the putamen, thalamus, substantia nigra, and cortical brain regions. (A) Comparison of the parametric images of binding potential between the control and PD cohorts. Mean of the BP images for high affinity binders (HAB) subjects is shown here. (B) Raw values for binding potential in control and PD subjects that is not adjusted for sex or translocator protein 18kd (TSPO) genotype. *P < 0.05, **P < 0.01, ***P < 0.001 (Student's t test with Welch's correction). Red symbols are females, and blue symbols are males. Using linear regression to adjust for TSPO genotype and sex, TSPO binding potential is higher in PD subjects in the putamen, thalamus, substantia nigra, and several cortical brain regions.

PET Imaging

Participants underwent TSPO SNP genotyping to determine eligibility for ¹⁸F‐DPA‐714 imaging. Among the 88 participants willing to undergo ¹⁸F‐DPA‐714 imaging, 14.8% were low affinity binders (LAB), 30.7% were mixed affinity binders (MAB), and 54.5% were high affinity binders (HAB). Only MAB and HAB subjects were imaged, with a total of 37 controls and 30 PD subjects who had ¹⁸F‐DPA‐714 imaging successfully completed. We observed a significant increase in unadjusted TSPO BP in PD participants at baseline in several brain regions (Fig. 2). Because TSPO genotype can affect BP, we examined the HAB and MAB binders separately. Differences between control and PD subjects were more prominent among HAB (Supplementary Table S6). Of note, the sample size was smaller and control and PD subjects were not well matched in the MAB group, which may account for the lack of differences in the MAB group.

We performed linear regression analysis including disease status, sex, and genotype in the same statistical model to determine the contributions of each predictor. After adjusting for TSPO genotype and sex, we found that PD subjects still had higher ¹⁸F‐DPA‐714 BP in the putamen, thalamus, SN, temporal cortex, parietal cortex, and occipital cortex compared to controls (Supplementary Table S7; P = 0.013 for putamen, P = 0.022 for thalamus, P = 0.045 for SN, P = 0.027 for temporal cortex, P = 0.012 for parietal cortex, P = 0.00093 for occipital cortex). As expected, TSPO genotype affected ¹⁸F‐DPA‐714 BP independent of diagnosis or sex in several brain regions (Supplementary Table S7) (P = 0.0085 for putamen, P = 0.0073 for brainstem, P = 0.00043 for frontal cortex, P = 0.023 for parietal cortex). We also observed a sex difference between male and female in ¹⁸F‐DPA 714 BP independent of diagnosis and TSPO genotype in the putamen and thalamus (Supplementary Table S7) (P = 0.011 in putamen, P = 0.0043 in thalamus).

Given the differences in ¹⁸F‐DPA‐714 imaging, we performed linear regression analyses to determine whether clinical measures, plasma or CSF cytokines and chemokines, or immune cell changes were associated with ¹⁸F‐DPA‐714 changes among PD subjects (Supplementary Table S8). We present the results for ¹⁸F‐DPA‐714 BP adjusted for TSPO genotype. After adjustment, we observed significant associations between ¹⁸F‐DPA‐714 BP in the thalamus with several cognitive domains, including visuospatial, language, verbal memory, and composite cognitive domain scores (Supplementary Table S8). We noted significant associations between the visuospatial domain and ¹⁸F‐DPA‐714 BP in the caudate, thalamus, and brainstem and between language domain scores and ¹⁸F‐DPA‐714 BP in the putamen and thalamus. As expected with the large number of variables examined in this discovery study, associations were no longer significant after full adjustment for multiple comparisons.

¹⁸F‐DPA‐714 BP also significantly associated with a subset of plasma and CSF cytokines and chemokines among PD participants. We observed significant associations between plasma eotaxin 3/CCL26 and ¹⁸F‐DPA‐714 BP in the putamen, thalamus, and hippocampus (Supplementary Table S8). We also noted significant associations between CSF MDC/CCL22 and ¹⁸F‐DPA‐714 BP in the putamen, thalamus, hippocampus, and brainstem (Supplementary Table S8). These associations were no longer significant after full adjustment for multiple comparisons. No significant associations were observed between PET measures and immune cell phenotyping by flow cytometry, except for NLR with ¹⁸F‐DPA‐714 BP in the SN (Supplementary Table S8).

Discussion

We enrolled subjects with de novo PD and controls in a longitudinal study designed to comprehensively measure markers of inflammation and track clinical outcomes. Baseline ¹⁸F‐DPA‐714 imaging revealed central inflammation in de novo PD patients. PD subjects demonstrated impairments in executive function, visual memory, and cognitive composite domains compared to controls at baseline. ¹⁸F‐DPA‐714 binding associated with certain cognitive domain and composite scores and with a subset of plasma and CSF chemokines.

Our study demonstrates elevations in TSPO binding in early, untreated PD. Previous studies examining TSPO binding in PD populations enrolled smaller numbers of PD subjects from a broad range of PD stage and duration and many who were already on active PD treatment. ²⁷ , ²⁸ , ²⁹ , ³⁰ , ³¹ , ³² Our data clearly demonstrate that increased TSPO binding is present in PD independent of treatment effects. Our multimodal study provides further evidence that TSPO signal as measured by ¹⁸F‐DPA‐714 is a marker of central inflammation, as we found correlations of ¹⁸F‐DPA‐714 signal with chemokine measures, in particular MDC/CCL22. This attractant for monocytes, Th2 T‐cells, and Treg cells is produced by microglia in experimental autoimmune encephalitis models. ⁷⁰ The association between ¹⁸F‐DPA‐714 and MDC/CCL2 suggests that the ¹⁸F‐DPA‐714 signal is partly secondary to microglial activation, although the ¹⁸F‐DPA‐714 signal could mark peripheral immune cell infiltration in response to MDC/CCL2 release. The finding that increased ¹⁸F‐DPA‐714 binding was observed in most brain regions fits with this concept of peripheral immune cell infiltration early in PD. ⁷¹

With our baseline dataset, we performed a discovery‐based correlation analyses of PET with clinical measures to find whether any associations may be present at baseline within the PD group. The most interesting associations were found with cognitive data. ¹⁸F‐DPA‐714 BP was associated with the visuospatial domain score and the cognitive composite score in several brain regions. ¹⁸F‐DPA‐714 BP within the thalamus also associated with several cognitive domains, including verbal memory, visuospatial, and language domains. This observation suggests an important role for inflammation in the thalamus regarding cognitive findings early in PD. Previous MRI studies showed reduced thalamic volume and altered functional connectivity of the thalamus with cortical regions in PD subjects with MCI compared to PD subjects without MCI, and progressive loss of thalamic volume was associated with conversion to MCI in mild PD. ⁷² , ⁷³ Neuronal loss and αsyn aggregates are seen in the intralaminar thalamic nuclei, which play important roles in cognition in PD. ⁷⁴ A meta‐analysis of TSPO PET studies showed increased TSPO binding signal in the thalamus, ³¹ yet to our knowledge, PD subjects have not been evaluated extensively for inflammatory changes within the thalamus.

Other groups also observed an association between immune activation and cognitive changes. Immune markers were elevated in PD subjects at higher risk for dementia, and soluble TNF receptor levels correlated with poorer cognitive scores. ⁷⁵ , ⁷⁶ ¹¹C(R)PK11195 TSPO binding was negatively associated with MMSE scores in a small study of PD with dementia patients. ⁷⁷ Although these data are supportive of pro‐inflammatory signals as predictive of cognitive decline, longitudinal studies measuring inflammatory signals and cognitive outcomes over time are missing. Longitudinal follow‐up of this cohort will be critical in understanding the significance of early inflammatory signals.

We noted an intriguing observation of sex differences in ¹⁸F‐DPA‐714 BP, with females showing reduced BP compared to males, independent of disease. This finding suggests that central inflammation as detected by TSPO PET differs between males and females. We previously noted sex‐related differences in the inflammatory response in PD, including sex differences in monocyte activation in PD. ⁷ Sex‐based differences in the immune response could point to potential reasons for the differences in PD prevalence observed between male and females. In a study examining age and sex effects on ¹¹C‐PBR28 TSPO imaging, healthy females had higher total volume of distribution (Vt) compared to healthy males, but TSPO Vt increased with age in males. ⁷⁸ Males and females were older in our study, which, in addition to structural differences between ¹¹C‐PBR28 and ¹⁸F‐DPA‐714, may account for these differences in the effects of sex on TSPO imaging.

A limitation of TSPO PET is that it is unclear what cell type is responsible for the TSPO binding signal. TSPO is found not only in microglia, but also astrocytes and peripheral immune cells. ⁷⁹ , ⁸⁰ , ⁸¹ , ⁸² Recent studies in rodent models suggest that TSPO signal may be related more to microglial number than microglial activation. ⁸³ Although our observations suggest that increased TSPO signal reflects central inflammation and correlates with cognitive measures, the TSPO signal we observed does not reveal which cell types are responsible. Additionally, second generation TSPO ligands, including ¹⁸F‐DPA‐714, are affected by the SNP rs6971 genotype. ⁶³ We previously showed that ¹⁸F‐DPA‐714 PET can reveal the difference between TSPO binding in HAB and MAB normal controls. ⁸⁴ When we examine our PET data by genotype, much of the difference between control and PD participants is driven by observations in the HAB subjects (Supplementary Table S6). The MAB group was only ~30% of the overall sample and poorly matched between controls and PD such that our MAB subset was underpowered. From a practical point of view, TSPO PET is likely to be most useful in HAB subjects. As HAB subjects are only half the population in our sample, alternative ligands not affected by this TSPO SNP are needed to bring PET imaging of neuroinflammation into the clinical arena.

The gold standard for PET imaging quantification involves arterial blood sampling to measure the arterial input function, yet this is invasive and difficult to perform in a large cohort. We used a reference region‐based approach using the cerebellar gray matter. Although ¹⁸F‐DPA‐714 can bind in cerebellum, ⁸⁵ other studies point to BP estimated using cerebellar gray matter and SRTM as a feasible alternative for AIF‐derived BP. ⁶⁹ , ⁸⁶ Several groups used cerebellar gray matter or cerebellum as the reference region for TSPO‐PET quantification with ¹⁸F‐DPA‐714 and other TSPO tracers. ⁶⁸ , ⁸⁷ , ⁸⁸ , ⁸⁹ , ⁹⁰ , ⁹¹ Cerebellar cortex time‐activity curves in our cohort showed no significant difference between HC and PD subjects (Supplementary Fig. S3), suggesting that the choice of our reference region did not influence our findings.

Previous studies of chemokine and cytokine levels in plasma or CSF pointed to elevations in several pro‐inflammatory molecules, although studies vary in the degree and particular cytokines or chemokines observed. ¹³ , ¹⁴ , ¹⁵ , ¹⁶ , ¹⁷ , ¹⁸ , ¹⁹ , ²⁰ , ²¹ , ²² , ²³ , ²⁴ , ²⁵ , ²⁶ In blood, the most consistent changes are noted in interleukin (IL)‐1β, and some studies have also pointed to changes in IL‐2, interferon (IFN)‐γ, and TNF‐α. ²⁶ Two recent studies have evaluated serum cytokines in early‐stage PD subjects. ¹³ , ²⁵ In the ICICLE study, early stage PD subjects showed increased TNF‐α, IL‐1β, IL‐2, and IL‐10 in sera compared to controls, with subjects showing a “pro‐inflammatory” profile having faster progression on the mini mental status exam over 36 months. ²⁵ In another study in early PD, Ahmad Rastegar et al ¹³ found that MIP‐1α and MCP‐1 predicted motor decline at 2 years in a cohort of PD patients with and without LRRK2 mutations. We found no significant differences in plasma cytokine levels between controls and PD subjects, although we saw a nearly significant increase in MIP‐1β and a trend in MIP‐1α. Potential differences between our findings and those studies performed at earlier stages include the smaller sample size in our group and the inclusion of a substantial number of LRRK2 mutation carriers in the Ahmad Rastegar study.

In the CSF, we observed increases in MIP‐1α and TARC in PD subjects compared to controls. Although elevations in specific inflammatory markers have differed between studies, most consistent changes have been noted in IL‐1β and IL‐6 in the CSF of PD patients. ¹⁴ , ¹⁶ , ¹⁹ , ²¹ , ²⁶ In our cohort, we did see a non‐significant increase in IL‐6, and IL‐1β levels were below detection in a significant subset of subjects. Potential reasons for differences in our cohort and other published cohorts include differences in sample size, differences in disease duration and severity, and use of PD medication in other cohorts. Additionally, diurnal variations in CSF inflammatory molecules are greater in PD subjects compared to controls. ⁹²

In our cohort, we observed an increase in the NLR in PD subjects; this marker of systemic inflammation has been observed in other PD cohorts. ⁹³ We also saw a significant decrease in CD4⁺ Tregs in PD subjects. CD4⁺ Tregs are defined by the surface markers CD25⁺CD127⁻ and have potent anti‐inflammatory and immunosuppressive properties. ⁹⁴ , ⁹⁵ , ⁹⁶ In contrast, CD4⁺ non‐Tregs, defined by surface markers CD25⁺CD127⁺, were significantly increased in PD subjects. CD4⁺ non‐Tregs have pro‐inflammatory properties. ⁹⁵ This decrease in Tregs and increase in non‐Tregs suggests a deficient anti‐inflammatory response, which may promote chronic inflammation in PD. These findings support previous observations of decreased percentages of Tregs in PD participants. ⁶ , ¹⁰ , ¹¹ Of note, with regard to monocyte populations, we previously described a small difference in intermediate monocytes in males with PD, ⁷ but in this larger study we did not replicate this observation.

De novo PD is the earliest phase in which a clinical diagnosis of PD can be made. Rapid eye movement sleep behavior disorder (RBD) is viewed as a premotor stage of PD, as the vast majority of subjects with RBD eventually go on to develop PD. ⁹⁷ , ⁹⁸ Elevated TSPO binding is observed in the SN and occipital cortex in RBD subjects compared to controls, ⁹⁹ , ¹⁰⁰ two regions in which we observed increased TSPO BP. Changes in peripheral immune cell populations and plasma cytokines have been noted in RBD subjects. ¹⁰¹ , ¹⁰² These studies point to the likelihood of immune activation occurring even before the motor stages of PD.

In conclusion, we found elevated ¹⁸F‐DPA‐714 binding in newly diagnosed PD subjects, and this signal correlated with cognitive testing and a subset of plasma and CSF cytokines. This study supports the conclusion that central inflammation is observed early in the disease process in PD, is independent of treatment for PD, and is correlated with cognitive features and peripheral markers of inflammation. These findings suggest that early, inflammatory changes are associated with neurodegeneration in PD. Longitudinal follow‐up of this cohort will be critical to determine the significance of early inflammatory changes and to observe if certain inflammatory changes predict clinical progression. We will continue to collect biospecimens annually to determine if the inflammatory measures change over time in PD. Future studies will need to examine the potential causal relationship between inflammation and neurodegeneration.

Author Roles

(1) Research project: A. Conception, B. Organization, C. Execution; (2) Statistical Analysis: A. Design, B. Execution, C. Review and Critique; (3) Manuscript: A. Writing of the First Draft, B. Review and Critique.

T.A.Y., A.G., E.N.B., J.M., D.G.S.: conceptionalized the research.

T.A.Y., L.R.: organized the research.

T.A.Y., A.W.A., N.S., L.R., C.C., H.H., H.Q., J.M: executed research.

T.A.Y., Y.‐H.D.F., A.G., R.K., Y.Z., H.Q. designed, executed, and reviewed the statistical analysis.

T.A.Y., Y.‐H.D.F., A.G., R.K., Y.Z., E.N.B., D.G.S.: wrote the manuscript.

T.A.Y., Y.‐H.D.F., A.G., N.S., R.K., Y.Z., H.Q., E.N.B., J.M., D.G.S.; edited the final manuscript.

Financial Disclosures in Previous 12 Months

Authors have no financial conflicts of interest concerning the research in this manuscript.

T.A.Y. is a member of the faculty of the University of Alabama at Birmingham and is supported by endowment and University funds. She has a clinical practice and is compensated for these activities through the University of Alabama Health Services Foundation. She has active grants from the American Parkinson Disease Association, Travere Therapeutics, and the National Institutes for Health (NIH) (RF1NS115767‐A1, R01NS112203, P50NS108675, U01NS104326, R13GM109532, and T32GM008361). T.A.Y. serves on the Scientific Advisory Board for Parkinson's Foundation. T.A.Y. has received honorarium for presentations from the Movement Disorders Society and for grant reviews from NIH. She has a US Patent (7,919,262) on the use of 14–3‐3 s in neurodegeneration. Y.H.D.F. is a member of the faculty of the University of Alabama at Birmingham and is supported by University funds. He is an investigator in studies funded by NIH P50NS108675. He has received consulting fees from Popneuron. A.T.G. is a member of the faculty of the University of Alabama at Birmingham and is supported by University funds. A.T.G. is an investigator in studies funded by NIH (K23HD091849, RF1AG059009, and P50NS108675). He has a clinical practice and is compensated for these activities through the University of Alabama Health Services Foundation. Finally, he has received funds in payment for expert testimony and consultation he has provided in capacity, personal injury, and criminal legal cases. A.W.A. was a member of the faculty of the University of Alabama at Birmingham and is supported by University funds. She is currently employed by the University of Colorado and receives grant funding from the NIH (K23NS080912 and R01HD100670) and the McKnight Brain Institute. She has served as a site investigator for studies sponsored by The Michael J. Fox Foundation for Parkinson's Research, NeuroNEXT, Ely Lilly and Company, Biogen Idec and Jazz Pharmaceuticals. N.S. is a member of the faculty of the University of Alabama at Birmingham. She is an investigator in studies funded by F. Hoffmann‐LaRoche, Neuraly, UCB Biopharma SPRL, Cerevel Therapeutics, AbbVie, Praxis Precision Medicines, Dystonia Medical Research Foundation, and NIH grant P50NS108675. She has a clinical practice and is compensated for these activities through the University of Alabama Health Services Foundation. In the last year, she has been a consultant and received honoraria from The Huntington Study Group, and PSG EARSTIM DSMB. L.R. is employed by the University of Alabama at Birmingham. She is supported by studies funded by NIH (P50NS108675), Global Parkinson's Genetics Program (GP2), and The Michael J. Fox Foundation. C.C. is employed by the University of Alabama at Birmingham and is supported by University funds. He is also supported by studies funded by NIH (K23HD091849 and P50NS108675). He also has clinical duties and is compensated for these activities through the University of Alabama Health Services Foundation. R.K. is a member of the faculty of the University of Alabama at Birmingham He receives grant funding from the NIH (R01 NS102257, R01 AG060993, P50 NS108675, RF1 AG059009, R01 AG057684, R01 NS107316, R01 NR017181, and RF1 AG072773), the Veterans Health Administration (IIR 19–413‐2), and the Administration for Community Living (90DPTB0015). He also serves on the Data Safety and Monitoring Board for NIH (R61 AG065619). Y.Z. is an employee of the University of Alabama at Birmingham and is supported by NIH (R01AG057684, R01 NS102257, R01NS107316, P50NS108675, and R01AG060993). H.H. is employed by the University of Alabama at Birmingham and receives salary support from NIH (P50NS108675). H.Q. is a member of the faculty at the UAB and receives salary support through the University. She is an investigator in studies funded by the National Multiple Sclerosis Society, NIH (R01NS57563 and P50NS108675), and a grant from The Michael J. Fox Foundation for Parkinson Research. J.M. is a member of the faculty of the UAB and receives salary support through the University. He has a clinical practice and is compensated for these activities through the University of Alabama Health Services Foundation. He is an investigator in studies funded by the O'Neal Comprehensive Cancer Center, Eli Lilly/Avid, Blue Earth Diagnostics, GE Healthcare, Clovis Oncology, Cytosite Biopharma, and NIH (P50NS108675, R01NS109529, R21CA234764, P20AG068024, P01CA240307, R01CA240589, and U10CA180820). In addition, since January 1, 2021, he has served as a consultant for or received honoraria from Eli Lilly, GE Healthcare, ImaginAb, and Blue Earth Diagnostics. He has served on a scientific advisory board for Novartis/Advanced Accelerator Applications. E.N.B. is a member of the faculty of the UAB and is supported by endowment and University funds. She is an investigator in studies funded by The Michael J. Fox Foundation for Parkinson Research, the Department of Defense (W81CWH‐18‐1‐0036), and NIH (P30CA013148–49, P50NS108675, R01NS109529, R01CA246708, R01AI148711, and R01DK122986–02). In addition, since January 1, 2021, she has served as a consultant for or received honoraria from the SONTAG Brain Tumor Foundation, The Race to Erase MS Foundation, the NIH, the American Association for Medical Colleges, The National Multiple Sclerosis Society, and Emory University. D.G.S. is a member of the faculty of the University of Alabama at Birmingham and is supported by endowment and University funds. DGS is an investigator in studies funded by AbbVie, the American Parkinson Disease Association, The Michael J. Fox Foundation for Parkinson Research, Alabama Department of Commerce, Alabama Innovation Fund, the Department of Defense, and NIH (P50NS108675, R25NS079188, and T32NS095775). DGS has a clinical practice and is compensated for these activities through the University of Alabama Health Services Foundation. In addition, since January 1, 2021 he has served as a consultant for or received honoraria from AbbVie, Sutter Health, Curium Pharma, Appello, Theravance, Sanofi‐Aventis, Alnylam Pharmaceutics, Coave Therapeutics, BlueRock Therapeutics, and F. Hoffman‐La Roche.

Supporting information

Data S1. Supporting Information

MDS-38-743-s002.docx^{(2.2MB, docx)}

Table S8. Regression analysis between regional PET binding potential and clinical, cognitive, cytokine/chemokine, and immune cell phenotyping measures in the PD subjects, adjusting for TSPO genotype. Separate excel file.

MDS-38-743-s001.xlsx^{(32.5KB, xlsx)}

Acknowledgments

This work was supported by the National Institutes of Health (P50NS108675).

Relevant conflict of interest/financial disclosure: Authors have no financial conflicts of interest concerning the research in this manuscript.

Funding agency: The National Institute of Neurological Disorders and Stroke P50NS108675.

Data Availability Statement

Clinical data are uploaded to the NINDS Data Management Resource. Plasma, DNA, and CSF are stored at the NINDS BioSEND repository. Additional data are available to qualified investigators on request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1. Supporting Information

MDS-38-743-s002.docx^{(2.2MB, docx)}

MDS-38-743-s001.xlsx^{(32.5KB, xlsx)}

Data Availability Statement

Clinical data are uploaded to the NINDS Data Management Resource. Plasma, DNA, and CSF are stored at the NINDS BioSEND repository. Additional data are available to qualified investigators on request.

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PERMALINK

Brain and Systemic Inflammation in De Novo Parkinson's Disease

Talene A Yacoubian, MD, PhD

Yu‐Hua Dean Fang, PhD

Adam Gerstenecker, PhD

Amy Amara, MD, PhD

Natividad Stover, MD

Lauren Ruffrage, MS

Christopher Collette, BS

Richard Kennedy, MD, PhD

Yue Zhang, PhD

Huixian Hong, MD, PhD

Hongwei Qin, PhD

Jonathan McConathy, MD, PhD

Etty N Benveniste, PhD

David G Standaert, MD, PhD

Abstract

Objective

Background

Methods

Results

Conclusions

Introduction

Methods

Standard Protocol Approvals, Registrations, and Patient Consents

Participants

Clinical Evaluation

Biospecimen Collection

Cytokine/Chemokine Analysis

Flow Cytometry

PET Imaging

Statistical Analysis

Data Sharing

Results

Referral Data

Baseline Clinical Data

TABLE 1.

TABLE 2.

TABLE 3.

Inflammatory Measures in Biospecimens

FIG 1.

FIG 2.

PET Imaging

Discussion

Author Roles

Financial Disclosures in Previous 12 Months

Supporting information

Acknowledgments

Data Availability Statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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