Diagnostic markers for Parkinson's disease

Animal Approaches to Studying Risk Factors for Parkinson's Disease: A Narrative Review

Despite recent efforts to search for biomarkers for the pre-symptomatic diagnosis of Parkinson's disease (PD), the presence of risk factors, prodromal signs, and family history still support the classification of individuals at risk for this disease. Human epidemiological studies are useful in this search but fail to provide causality. The study of well-known risk factors for PD in animal models can help elucidate mechanisms related to the disease's etiology and contribute to future prevention or treatment approaches. This narrative review aims to discuss animal studies that investigated four of the main risk factors and/or prodromal signs related to PD: advanced age, male sex, sleep alterations, and depression. Different databases were used to search the studies, which were included based on their relevance to the topic. Although still in a reduced number, such studies are of great relevance in the search for evidence that leads to a possible early diagnosis and improvements in methods of prevention and treatment.

The function of previously unappreciated exerkines secreted by muscle in regulation of neurodegenerative diseases

The initiation and progression of neurodegenerative diseases (NDs), distinguished by compromised nervous system integrity, profoundly disrupt the quality of life of patients, concurrently exerting a considerable strain on both the economy and the social healthcare infrastructure. Exercise has demonstrated its potential as both an effective preventive intervention and a rehabilitation approach among the emerging therapeutics targeting NDs. As the largest secretory organ, skeletal muscle possesses the capacity to secrete myokines, and these myokines can partially improve the prognosis of NDs by mediating the muscle-brain axis. Besides the well-studied exerkines, which are secreted by skeletal muscle during exercise that pivotally exert their beneficial function, the physiological function of novel exerkines, e.g., apelin, kynurenic acid (KYNA), and lactate have been underappreciated previously. Herein, this review discusses the roles of these novel exerkines and their mechanisms in regulating the progression and improvement of NDs, especially the significance of their functions in improving NDs' prognoses through exercise. Furthermore, several myokines with potential implications in ameliorating ND progression are proposed as the future direction for investigation. Elucidation of the function of exerkines secreted by skeletal muscle in the regulation of NDs advances the understanding of its pathogenesis and facilitates the development of therapeutics that intervene in these processes to cure NDs.

Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram

Little is known about electrocardiogram (ECG) markers of Parkinson's disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before disease diagnosis. This case-control study included samples from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). Cases and controls were matched according to specific characteristics (date, age, sex and race). Clinical data were available from May, 2014 onward at LUC and from January, 2015 onward at MLH, while the ECG data were available as early as 1990 in both institutes. PD was denoted by at least two primary diagnostic codes (ICD9 332.0; ICD10 G20) at least 30 days apart. PD incidence date was defined as the earliest of first PD diagnostic code or PD-related medication prescription. ECGs obtained at least 6 months before PD incidence date were modeled to predict a subsequent diagnosis of PD within three time windows: 6 months-1 year, 6 months-3 years, and 6 months-5 years. We applied a novel deep neural network using standard 10-s 12-lead ECGs to predict PD risk at the prodromal phase. This model was compared to multiple feature engineering-based models. Subgroup analyses for sex, race and age were also performed. Our primary prediction model was a one-dimensional convolutional neural network (1D-CNN) that was built using 131 cases and 1058 controls from MLH, and externally validated on 29 cases and 165 controls from LUC. The model was trained on 90% of the MLH data, internally validated on the remaining 10% and externally validated on LUC data. The best performing model resulted in an external validation AUC of 0.67 when predicting future PD at any time between 6 months and 5 years after the ECG. Accuracy increased when restricted to ECGs obtained within 6 months to 3 years before PD diagnosis (AUC 0.69) and was highest when predicting future PD within 6 months to 1 year (AUC 0.74). The 1D-CNN model based on raw ECG data outperformed multiple models built using more standard ECG feature engineering approaches. These results demonstrate that a predictive model developed in one cohort using only raw 10-s ECGs can effectively classify individuals with prodromal PD in an independent cohort, particularly closer to disease diagnosis. Standard ECGs may help identify individuals with prodromal PD for cost-effective population-level early detection and inclusion in disease-modifying therapeutic trials.

Air pollution nanoparticle and alpha-synuclein fibrils synergistically decrease glutamate receptor A1, depending upon nPM batch activity

Background

Epidemiological studies have variably linked air pollution to increased risk of Parkinson's disease (PD). However, there is little experimental evidence for this association. Alpha-synuclein (α-syn) propagation plays central roles in PD and glutamate receptor A1 (GluA1) is involved in memory and olfaction function.

Methods

Each mouse was exposed to one of three different batches of nano-particulate matter (nPM) (300 μg/m³, 5 h/d, 3 d/week), collected at different dates, 2017-2019, in the same urban site. After these experiments, these nPM batches were found to vary in activity. C57BL/6 female mice (3 mo) were injected with pre-formed murine α-synuclein fibrils (PFFs) (0.4 μg), which act as seeds for α-syn aggregation. Two exposure paradigms were used: in Paradigm 1, PFFs were injected into olfactory bulb (OB) prior to 4-week nPM (Batch 5b) exposure and in Paradigm 2, PFFs were injected at 4th week during 10-week nPM exposure (Batches 7 and 9). α-syn pSer129, microglia Iba1, inflammatory cytokines, and Gria1 expression were measured by immunohistochemistry or qPCR assays.

Results

As expected, α-syn pSer129 was detected in ipsilateral OB, anterior olfactory nucleus, amygdala and piriform cortex. One of the three batches of nPM caused a trend for elevated α-syn pSer129 in Paradigm 1, but two other batches showed no effect in Paradigm 2. However, the combination of nPM and PFF significantly decreased Gria1 mRNA in both the ipsi- and contra-lateral OB and frontal cortex for the most active two nPM batches. Neither nPM nor PFFs alone induced responses of microglia Iba1 and expression of Gria1 in the OB and cortex.

Conclusion

Exposures to ambient nPM had weak effect on α-syn propagation in the brain in current experimental paradigms; however, nPM and α-syn synergistically downregulated the expression of Gria1 in both OB and cortex.

Air pollution, surrounding green, road proximity and Parkinson's disease: A prospective cohort study

BACKGROUND

Though growing evidence has linked air pollution to Parkinson's disease (PD), the results remain inconsistent. Less is known about the relevance of road proximity and surrounding green. We aimed to investigate the individual and joint associations of air pollution, road proximity and surrounding green with the incidence of PD in a prospective cohort study.

METHODS

We used data from a prospective cohort of 47,516 participants recruited from July 2015 to January 2018 in Ningbo, China. Long-term exposure to particulate matter with aerodynamic diameter ≤2.5 μm (PM_2.5) and ≤10 μm (PM₁₀) and nitrogen dioxide (NO₂) estimated by land-use regression models, road proximity and surrounding green assessed by Normalized Difference Vegetation Index (NDVI) were calculated based on the residential address for each participant. Cox proportional hazard models were used to analyze the individual and joint effects of air pollution, road proximity, and surrounding green on PD.

RESULTS

In single-exposure models, PM_2.5, PM₁₀, NO₂ and road proximity was associated with increased risk of PD (e.g. Hazard Ratio (HR) = 1.51, 95%CI:1.02, 2.24 per interquartile range (IQR) increase for PM_2.5) while surrounding green was associated with decreased risk of PD (e.g. HR = 0.80, 95%CI:0.65, 0.98 per IQR increase for NDVI in 300 m buffer). In two-exposure models, the associations of PM_2.5 and surrounding green persisted while the associations of NO₂ and road proximity attenuated towards unity.

CONCLUSIONS

We found that PM_2.5 were associated with increased risk of incident PD while surrounding green was associated with decreased risk of PD. Future studies about PD etiology may benefit from including multiple environmental exposures to address potential joint associations.

Serum microRNA expression levels in Turkish patients with Parkinson's disease

Objectives: To determine the serum expression levels of seven candidate microRNAs (miRNA); miR-19a, miR-19b, miR-29a, miR-29c, miR-181, miR-195 and miR-221 in Turkish patients with Parkinson's disease (PD) and explored their potential role in the diagnosis of PD. We further described the relationship between these miRNAs with the clinical findings and treatment of PD.Materials and methods: The study included 51 PD patients and 20 healthy controls. The clinical severity of disease was assessed using the Hoehn Yahr staging scale and the Unified Parkinson's Disease Rating Scale (UPDRS). Venous blood samples were taken after fasting for 12 h, then centrifuged. Obtained serum samples were stored until analysis of miRNA. In the laboratory, expression levels of these miRNAs were analyzed using a real-time PCR instrument. Receiver-operating characteristic analysis and area-under the-curve (AUC) was used to evaluate these miRNA levels as potential diagnostic biomarkers for PD.Results: miR-29c expression levels were increased significantly for PD patients compared to healthy controls. There were no significant differences in levels of other miRNAs between PD patients and controls. The AUC of miR-29c was 0.689. The sensitivity and specificity of this diagnostic test was 54.9% and 80.0%, respectively. miR-195 level was found to have a significant positive correlation only with age. Significant negative correlation was found between miR-29a level and UPDRS total score. miR-19b was found higher in ropinirole drug used group than that of pramipexole group.Conclusion: This study suggests that serum miR-29c expression level might be potential biomarker in the diagnosis of Turkish Parkinson patients.

Clinical Application of Circulating MicroRNAs in Parkinson's Disease: The Challenges and Opportunities as Diagnostic Biomarker

Discovery of evolutionarily conserved, nonprotein-coding, endogenous microRNAs has induced a paradigm shift in the overall understanding of gene regulation. Now, microRNAs are considered and classified as master regulators of gene expression as they regulate a wide range of processes – gene regulation, splicing, translation and posttranscriptional modifications. Besides, dysregulated microRNAs have been related to many diseases, including Parkinson's and related disorders. Several studies proposed that differentially expressed microRNAs as a potential biomarker. So far, there is no accepted clinical diagnostic test for Parkinson's disease based on biochemical analysis of biological fluids. However, circulating microRNAs possess many vital features typical of reliable biomarkers and discriminates Parkinson's patients from healthy control with much higher sensitivity and specificity. Though they show tremendous promise as a putative biomarker, translating these research findings to clinical application is often met with many obstacles. Most of the candidate microRNAs reported as a diagnostic biomarker is not organ-specific, and their overlap is low between studies. Therefore this review aimed to highlight the challenges in the application of microRNA in guiding disease discrimination decisions and its future prospects as a diagnostic biomarker in Parkinson's Disease.

Serum N-Glycosylation in Parkinson's Disease: A Novel Approach for Potential Alterations

In this study, we present the application of a novel capillary electrophoresis (CE) method in combination with label-free quantitation and support vector machine-based feature selection (support vector machine-estimated recursive feature elimination or SVM-RFE) to identify potential glycan alterations in Parkinson’s disease. Specific focus was placed on the use of neutral coated capillaries, by a dynamic capillary coating strategy, to ensure stable and repeatable separations without the need of non-mass spectrometry (MS) friendly additives within the separation electrolyte. The developed online dynamic coating strategy was applied to identify serum N-glycosylation by CE-MS/MS in combination with exoglycosidase sequencing. The annotated structures were quantified in 15 controls and 15 Parkinson’s disease patients by label-free quantitation. Lower sialylation and increased fucosylation were found in Parkinson’s disease patients on tri-antennary glycans with 2 and 3 terminal sialic acids. The set of potential glycan alterations was narrowed by a recursive feature elimination algorithm resulting in the efficient classification of male patients.

Combinatory microRNA serum signatures as classifiers of Parkinson's disease

INTRODUCTION

As current clinical diagnostic protocols for Parkinson's disease (PD) may be prone to inaccuracies there is a need to identify and validate molecular biomarkers, such as circulating microRNAs, which will complement current practices and increase diagnostic accuracy. This study identifies, verifies and validates combinatory serum microRNA signatures as diagnostic classifiers of PD across different patient cohorts.

METHODS

370 PD (drug naïve) and control serum samples from the Norwegian ParkWest study were used for identification and verification of differential microRNA levels in PD which were validated in a blind study using 64 NY Parkinsonism in UMeå (NYPUM) study serum samples and tested for specificity in 48 Dementia Study of Western Norway (DemWest) study Alzheimer's disease (AD) serum samples using miRNA-microarrays, and quantitative (q) RT-PCR. Proteomic approaches identified potential molecular targets for these microRNAs.

RESULTS

Using Affymetrix GeneChip^® miRNA 4.0 arrays and qRT-PCR we comprehensively analyzed serum microRNA levels and found that the microRNA (PARKmiR)-combinations, hsa-miR-335-5p/hsa-miR-3613-3p (95% CI, 0.87-0.94), hsa-miR-335-5p/hsa-miR-6865-3p (95% CI, 0.87-0.93), and miR-335-5p/miR-3613-3p/miR-6865-3p (95% CI, 0.87-0.94) show a high degree of discriminatory accuracy (AUC 0.9-1.0). The PARKmiR signatures were validated in an independent PD cohort (AUC ≤ 0.71) and analysis in AD serum samples showed PARKmiR signature specificity to PD. Proteomic analyses showed that the PARKmiRs regulate key PD-associated proteins, including alpha-synuclein and Leucine Rich Repeat Kinase 2.

CONCLUSIONS

Our study has identified and validated unique miRNA serum signatures that represent PD classifiers, which may complement and increase the accuracy of current diagnostic protocols.

Leucine-rich repeat kinase 2 and Parkinson's disease

Leucine-rich repeat kinase 2 (LRRK2) is a large multidomain protein that is expressed in many tissues and participates in numerous biological pathways. Mutations in LRRK2 are recognized as genetic risk factors for familial Parkinson's disease (PD) and may also represent causal factors in the more common sporadic form of PD. The structure of LRRK2 comprises a combination of GTPase, kinase, and scaffolding domains. This functional diversity, combined with a potentially central role in genetic and idiopathic PD motivates significant effort to further credential LRRK2 as a therapeutic target. Here, we review the current understanding for LRRK2 function in normal physiology and PD, with emphasis on insight gained from proteomic approaches.

Advances in Biomarker Research in Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disease, and the numbers are projected to double in the next two decades with the increase in the aging population. An important focus of current research is to develop interventions to slow the progression of the disease. However, prerequisites to it include the development of reliable biomarkers for early diagnosis which would identify at-risk groups and disease progression. In this review, we present updated evidence of already known clinical biomarkers (such as hyposmia and rapid eye movement (REM) sleep behavior disorder (RBD)) and neuroimaging biomarkers, as well as newer possible markers in the blood, CSF, and other tissues. While several promising candidates and methods to assess these biomarkers are on the horizon, it is becoming increasingly clear that no one candidate will clearly fulfill all the roles as a single biomarker. A multimodal and combinatorial approach to develop a battery of biomarkers will likely be necessary in the future.

Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification

For many complex diseases, an earlier and more reliable diagnosis is considered a key prerequisite for developing more effective therapies to prevent or delay disease progression. Classical statistical learning approaches for specimen classification using omics data, however, often cannot provide diagnostic models with sufficient accuracy and robustness for heterogeneous diseases like cancers or neurodegenerative disorders. In recent years, new approaches for building multivariate biomarker models on omics data have been proposed, which exploit prior biological knowledge from molecular networks and cellular pathways to address these limitations. This survey provides an overview of these recent developments and compares pathway- and network-based specimen classification approaches in terms of their utility for improving model robustness, accuracy and biological interpretability. Different routes to translate omics-based multifactorial biomarker models into clinical diagnostic tests are discussed, and a previous study is presented as example.

Cerebrospinal fluid α-synuclein in the differential diagnosis of parkinsonian syndromes

ABSTRACT: 

Aim: To evaluate the diagnostic value of the level of α-synuclein, DJ-1, and Alzheimer's disease markers in the cerebrospinal fluid (CSF) in patients with parkinsonian syndromes. Materials & methods: We measured α-synuclein, DJ-1 and Alzheimer's disease markers by quantitative immunoassays in 122 CSF samples from patients with Parkinson's disease (PD; n = 38), dementia with Lewy bodies (n = 20), multiple-system atrophy (n = 18), progressive supranuclear palsy (n = 28), corticobasal degeneration (n = 7), unspecified atypical parkinsonian disorders (n = 5) and healthy controls (n = 6). Results: The level of α-synuclein was significantly decreased in CSF from PD patients. For the total tau/α-synuclein ratio, we obtained an area under the curve of 0.80 of the receiver operating characteristics curve for the PD diagnosis. With a cut-off of 1.33, the tau/α-synuclein ratio had 93% diagnostic specificity at 75% sensitivity for PD. Conclusion: CSF α-synuclein measurement may be useful in the differential diagnosis of PD versus other parkinsonian syndromes. CSF α-synuclein is found at low levels in the CSF and requires specifically adjusted assays to be measured.

From micrographia to Parkinson's disease dysgraphia

Micrographia, an abnormal reduction in writing size, is a specific behavioral deficit associated with Parkinson's disease (PD). In recent years, the availability of graphic tablets has made it possible to study micrographia in unprecedented detail. Consequently, a growing number of studies show that PD patients also exhibit impaired handwriting kinematics. Is micrographia still the most characteristic feature of PD-related handwriting deficits? To answer this question, we identified studies that investigated handwriting in PD, either with conventional pencil-and-paper measures or with graphic tablets, and we reported their findings on key spatiotemporal and kinematic variables. We found that kinematic variables (velocity, fluency) differentiate better between control participants and PD patients, and between off- and on-treatment PD patients, than the traditional measure of static writing size. Although reduced writing size is an important feature of PD handwriting, the deficit is not restricted to micrographia stricto sensu. Therefore, we propose the term PD dysgraphia, which encompasses all deficits characteristic of Parkinsonian handwriting. We conclude that the computerized analysis of handwriting movements is a simple and useful tool that can contribute to both diagnosis and follow-up of PD.

Cardiac stress test is normal in pre-motor Parkinson's disease

Cardiac sympathetic denervation is an early nonmotor feature of Parkinson's disease (PD). The aim of the current study was to trace evidence for cardiac dysfunction abnormalities in the premotor phase of PD. We retrospectively reviewed treadmill ergometric tests of a large cohort (n = 16,841) between 2000 and 2012, that attended the Executive Screening Survey (ESS) at Sheba Medical Center. Heart rate and blood pressure profiles as well as exercise capacity were compared between subjects who later developed PD and age- and sex-matched subjects (ratio 1:2) who did not. We identified 28 subjects (24 males) who developed PD at follow-up. The PD group was older than the group of subjects who did not develop PD on first ergometric test (64.82 ± 8.82 vs. 48.91 ± 10.60 years, P < 0.001). The time between the first ergometric test and motor symptoms onset was 4.64 ± 2.86 years. Patients who later developed PD had lower maximal heart rate (P < 0.001) and lower heart rate reserve than healthy controls (P < 0.001); however, compared with age- and sex-matched subjects, subjects who developed PD had similar exercise capacity and heart rate profile during rest, exercise, and recovery, even 1 year before diagnosis. In this study, we did not detect significant signs of sympathetic dysfunction during the premotor phase of PD.

The influence of preanalytical conditions on the DJ-1 concentration in human cerebrospinal fluid

Aim: The purpose of this study was to establish the influence of centrifugation and protease activity on the cerebrospinal fluid (CSF) concentrations of DJ-1 and hemoglobin. Materials & methods: The concentrations of DJ-1 and hemoglobin were determined in 12 (DJ-1) and six (hemoglobin) pairs of CSF samples, with one sample being stored without centrifugation and the other being centrifuged at 2000 × g before storage. The DJ-1 concentration was also determined in centrifuged and uncentrifuged CSF containing protease inhibitors and compared with values determined in centrifuged and uncentrifuged CSF samples without protease inhibitors. Furthermore, specific protein concentrations were determined in CSF from two groups, each comprising 23 patients with Parkinson’s disease. In one group the CSF was centrifuged at 1300−1800 × g, 4°C, 10 min, and in the other at 2000 × g, 4°C, 10 min. Results: Centrifugation at 2000 × g resulted in significantly lower CSF DJ-1 concentrations compared with no centrifugation and centrifugation at a lower g-force. There was a significant difference in the hemoglobin concentration between centrifuged and uncentrifuged CSF. In all centrifuged samples the hemoglobin concentration was <200 ng/ml including blood contaminated samples centrifuged at 2000 × g. When a protease inhibitor cocktail was added to the CSF prior to centrifugation, the DJ-1 concentration was significantly higher. Conclusion: Preanalytical factors such as centrifugation and protease inhibition must be carefully controlled when handling CSF for analysis of DJ-1 and other biomarkers, as DJ-1 was influenced by blood contamination, centrifugation and protease activity.

Recent developments in biomarkers in Parkinson disease

PURPOSE OF REVIEW

Parkinson disease is the second most common neurodegenerative disease after Alzheimer disease, and current demographic trends indicate a life-time risk approaching 4% and predict a doubling of prevalence by 2030. Strategies are being developed to apply recent advances in our understanding of the cause of Parkinson disease to the development of biomarkers that will enable the identification of at-risk individuals, enable early diagnosis and reflect the progression of disease. The latter will be particularly important for the testing of disease-modifying therapies. This review summarizes recent advances in Parkinson disease biomarker development.

RECENT FINDINGS

Recent reports continue to reflect the application of a variety of clinical, imaging or biochemical measurements, alone or in combination, to general Parkinson disease populations. Probably the most promising is the assay of alpha-synuclein in the diagnosis and evolution of Parkinson disease. At present, detection techniques are still being refined, but once accurate and reproducible assays are available, it will be important to define the relationship of these to early diagnosis and progression. Alpha-synuclein concentrations may also be modulated by certain disease-modifying agents in development and so may represent a measure of their efficacy. It has to be accepted that no single measure currently fulfils all the necessary criteria for a biomarker in Parkinson disease, but combinations of measures are more likely to deliver benefit.

SUMMARY

The Parkinson disease biomarker field is approaching a stage when certain combinations of clinical, imaging and biochemical measures may identify a proportion of individuals at risk for developing the disease. However, their general applicability may be limited. Attention is now turning to stratification of Parkinson disease into certain at-risk groups defined by genotype. The application of multimodal screening to these populations may be more rewarding in the short term.

Emerging candidate biomarkers for Parkinson's disease: a review

Parkinson's disease is a chronic neurodegenerative disorder leading to progressive motor impairment affecting more than 1% of the over-65 population. In spite of considerable progress in identifying the genetic and biochemical basis of PD, to date the diagnosis remains clinical and disease-modifying therapies continue to be elusive. A cornerstone in recent PD research is the investigation of biological markers that could help in identifying at-risk population or to track disease progression and response to therapies. Although none of these parameters has been validated for routine clinical practice yet, however some biochemical candidates hold great promise for application in PD patients, especially in the early stages of disease, and it is likely that in the future the diagnosis of PD will require a combination of genetic, imaging and laboratory data. In this review we discuss the most interesting biochemical markers for PD (including the "-omics" techniques), focusing on the methodological challenges in using ex vivo blood/CSF/tissue-based biomarkers and suggesting alternative strategies to overcome the difficulties that still prevent their actual use.

Biomarkers in Parkinson's disease (recent update)

Parkinson's disease (PD) is the second most common neurodegenerative disorder mostly affecting the aging population over sixty. Cardinal symptoms including, tremors, muscle rigidity, drooping posture, drooling, walking difficulty, and autonomic symptoms appear when a significant number of nigrostriatal dopaminergic neurons are already destroyed. Hence we need early, sensitive, specific, and economical peripheral and/or central biomarker(s) for the differential diagnosis, prognosis, and treatment of PD. These can be classified as clinical, biochemical, genetic, proteomic, and neuroimaging biomarkers. Novel discoveries of genetic as well as nongenetic biomarkers may be utilized for the personalized treatment of PD during preclinical (premotor) and clinical (motor) stages. Premotor biomarkers including hyper-echogenicity of substantia nigra, olfactory and autonomic dysfunction, depression, hyposmia, deafness, REM sleep disorder, and impulsive behavior may be noticed during preclinical stage. Neuroimaging biomarkers (PET, SPECT, MRI), and neuropsychological deficits can facilitate differential diagnosis. Single-cell profiling of dopaminergic neurons has identified pyridoxal kinase and lysosomal ATPase as biomarker genes for PD prognosis. Promising biomarkers include: fluid biomarkers, neuromelanin antibodies, pathological forms of α-Syn, DJ-1, amyloid β and tau in the CSF, patterns of gene expression, metabolomics, urate, as well as protein profiling in the blood and CSF samples. Reduced brain regional N-acetyl-aspartate is a biomarker for the in vivo assessment of neuronal loss using magnetic resonance spectroscopy and T2 relaxation time with MRI. To confirm PD diagnosis, the PET biomarkers include [(18)F]-DOPA for estimating dopaminergic neurotransmission, [(18)F]dG for mitochondrial bioenergetics, [(18)F]BMS for mitochondrial complex-1, [(11)C](R)-PK11195 for microglial activation, SPECT imaging with (123)Iflupane and βCIT for dopamine transporter, and urinary salsolinol and 8-hydroxy, 2-deoxyguanosine for neuronal loss. This brief review describes the merits and limitations of recently discovered biomarkers and proposes coenzyme Q10, mitochondrial ubiquinone-NADH oxidoreductase, melatonin, α-synculein index, Charnoly body, and metallothioneins as novel biomarkers to confirm PD diagnosis for early and effective treatment of PD.

Lipid raft disarrangement as a result of neuropathological progresses: a novel strategy for early diagnosis?

Lipid rafts are the preferential site of numerous membrane signaling proteins which are involved in neuronal functioning and survival. These proteins are organized in multiprotein complexes, or signalosomes, in close contact with lipid classes particularly represented in lipid rafts (i.e. cholesterol, sphingolipids and saturated fatty acids), which may contribute to physiological responses leading to neuroprotection. Increasing evidence indicates that alteration of lipid composition in raft structures as a consequence of neuropathologies, such as Alzheimer's disease (AD) and Parkinson's disease (PD), causes a dramatic increase in lipid raft order. These phenomena may correlate with perturbation of signalosome activities, likely contributing to neurodegenerative progression. Interestingly, significant disruption of stable raft microenvironments has been already observed in the first stages of either AD or PD, suggesting that these alterations may represent early events in the neuropathological development. In this regard, the search for biochemical markers, such as specific metabolic products altered in the brain at the first steps of the disease, presently represents an important challenge for early diagnostic strategies. Alterations of these biomarkers may be reflected in either plasma or cerebrospinal fluid, thus representing a potential strategy to predict an accurate diagnosis. We propose that pathologically-linked lipid raft markers may be interesting candidates to be explored at this level, although it has not been studied so far to what extent alteration of different signalosome components may be reflected in peripheral fluids. In this mini-review, we will discuss on relevant aspects of lipid rafts that contribute to the modulation of neuropathological events related to AD and PD. An interesting hypothesis is that anomalies on raft biomarkers measured at peripheral fluids might mirror the lipid raft pathology observed in early stages of AD and PD.

Resting state functional connectivity of the striatum in Parkinson's disease

Classical accounts of the pathophysiology of Parkinson's disease have emphasized degeneration of dopaminergic nigrostriatal neurons with consequent dysfunction of cortico-striatal-thalamic loops. In contrast, post-mortem studies indicate that pathological changes in Parkinson's disease (Lewy neurites and Lewy bodies) first appear primarily in the lower brainstem with subsequent progression to more rostral parts of the neuraxis. The nigrostriatal and histological perspectives are not incompatible, but they do emphasize different anatomical structures. To address the question of which brain structures are functionally most affected by Parkinson's disease, we performed a resting-state functional magnetic resonance imaging study focused on striatal functional connectivity. We contrasted 13 patients with advanced Parkinson's disease versus 19 age-matched control subjects, using methodology incorporating scrupulous attention to minimizing the effects of head motion during scanning. The principal finding in the Parkinson's disease group was markedly lower striatal correlations with thalamus, midbrain, pons and cerebellum. This result reinforces the importance of the brainstem in the pathophysiology of Parkinson's disease. Focally altered functional connectivity also was observed in sensori-motor and visual areas of the cerebral cortex, as well the supramarginal gyrus. Striatal functional connectivity with the brainstem was graded (posterior putamen > anterior putamen > caudate), in both patients with Parkinson's disease and control subjects, in a manner that corresponds to well-documented gradient of striatal dopaminergic function loss in Parkinson's disease. We hypothesize that this gradient provides a clue to the pathogenesis of Parkinson's disease.

Biomarkers in Parkinson's disease: an update

PURPOSE OF REVIEW

This review article is focused upon the most recent biomarker studies of Parkinson's disease. It provides an update on promising areas of biomarker research in a rapidly expanding field, and discusses future directions that might lead to successful development of Parkinson's disease biomarkers.

RECENT FINDINGS

Studies of molecular-genetic and biochemical biomarkers of Parkinson's disease have not only targeted hypothesis-driven measures of specific substrates involved in processes such as protein misprocessing, but also have made use of sophisticated analyses such as transcriptomic, proteomic, and metabolomic approaches. Whereas none of these are yet established as Parkinson's disease biomarkers, brain imaging using the 123I-ioflupane ligand with single-photon emission computed tomography was recently approved in the United States to aid in Parkinson's disease diagnosis, and research on other imaging modalities is ongoing. Neurophysiological tests are also being adapted for biomarker research, and we review recent promising data.

SUMMARY

The search for effective biomarkers for diagnosis and surveillance of Parkinson's disease continues. A battery of biomarkers comprising different modalities might be required to address clinical needs in this complex disorder. Critically, collaborative efforts including centralized tissue repository and clinical research infrastructure that are being organized will advance this field further.

The outdoor air pollution and brain health workshop

Accumulating evidence suggests that outdoor air pollution may have a significant impact on central nervous system (CNS) health and disease. To address this issue, the National Institute of Environmental Health Sciences/National Institute of Health convened a panel of research scientists that was assigned the task of identifying research gaps and priority goals essential for advancing this growing field and addressing an emerging human health concern. Here, we review recent findings that have established the effects of inhaled air pollutants in the brain, explore the potential mechanisms driving these phenomena, and discuss the recommended research priorities/approaches that were identified by the panel.

Disease modification in Parkinson’s disease: are we there yet with currently available therapies?

SUMMARY Management of Parkinson’s disease (PD) is currently based primarily on dopamine-replacement therapy for the alleviation of motor symptoms. Current medical and surgical therapies can provide long-lasting symptomatic benefit, but they do not modify progression of the disease. Research is ongoing to find a therapy that can provide neuroprotection, defined herein as preventing vulnerable neurons from dying. Studies of neuroprotection are limited by a lack of adequate biomarkers of PD progression and by the confounding symptomatic effects of many putative neuroprotective therapies. Studies have shown that levodopa prolongs life, but they have not clearly shown that it modifies disease progression. Trials of dopamine agonists have demonstrated symptomatic effect but no unequivocal neuroprotective benefits. While some studies of monamine oxidase B inhibitors have been promising, they have not conclusively proven disease modification. Exercise provides many benefits to patients with PD, may modify the progression of the disease and should be part of each patient’s treatment plan.

Air pollution & the brain: Subchronic diesel exhaust exposure causes neuroinflammation and elevates early markers of neurodegenerative disease

Background

Increasing evidence links diverse forms of air pollution to neuroinflammation and neuropathology in both human and animal models, but the effects of long-term exposures are poorly understood.

Objective

We explored the central nervous system consequences of subchronic exposure to diesel exhaust (DE) and addressed the minimum levels necessary to elicit neuroinflammation and markers of early neuropathology.

Methods

Male Fischer 344 rats were exposed to DE (992, 311, 100, 35 and 0 μg PM/m³) by inhalation over 6 months.

Results

DE exposure resulted in elevated levels of TNFα at high concentrations in all regions tested, with the exception of the cerebellum. The midbrain region was the most sensitive, where exposures as low as 100 μg PM/m³ significantly increased brain TNFα levels. However, this sensitivity to DE was not conferred to all markers of neuroinflammation, as the midbrain showed no increase in IL-6 expression at any concentration tested, an increase in IL-1β at only high concentrations, and a decrease in MIP-1α expression, supporting that compensatory mechanisms may occur with subchronic exposure. Aβ42 levels were the highest in the frontal lobe of mice exposed to 992 μg PM/m³ and tau [pS199] levels were elevated at the higher DE concentrations (992 and 311 μg PM/m³) in both the temporal lobe and frontal lobe, indicating that proteins linked to preclinical Alzheimer's disease were affected. α Synuclein levels were elevated in the midbrain in response to the 992 μg PM/m³ exposure, supporting that air pollution may be associated with early Parkinson's disease-like pathology.

Conclusions

Together, the data support that the midbrain may be more sensitive to the neuroinflammatory effects of subchronic air pollution exposure. However, the DE-induced elevation of proteins associated with neurodegenerative diseases was limited to only the higher exposures, suggesting that air pollution-induced neuroinflammation may precede preclinical markers of neurodegenerative disease in the midbrain.