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Recent advances in biomarkers for Parkinson’s disease focusing on biochemicals, omics and neuroimaging

  • Rutong Ren , Yi Sun , Xin Zhao and Xiaoping Pu EMAIL logo
Published/Copyright: January 12, 2015
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 53 Issue 10

Abstract

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, involving progressive loss of the nigro-striatal dopaminergic neurons. Cardinal symptoms including tremors, muscle rigidity, drooping posture, drooping, walking difficulty, and autonomic symptoms appear when a significant number of nigrostriatal dopaminergic neurons have already been destroyed. Hence, reliable biomarkers are needed for early and accurate diagnosis to measure disease progression and response to therapy. We review the current status of protein and small molecule biomarkers involved in oxidative stress, protein aggregation and inflammation etc. which are present in cerebrospinal fluid, human blood, urine or saliva. In recent years, advances in genomics, proteomics, metabolomics, and functional brain imaging techniques have led to new insights into the pathoetiology of PD. Further studies in the novel discovery of PD biomarkers will provide avenues to treat PD patients more effectively with few or no side effects.

Keywords: biomarker; neuroimaging; omics; Parkinson’s disease; pathogenesis

Introduction

Parkinson’s disease (PD) is the second most common neurodegenerative disease, estimated to occur in approximately 1% of individuals >60 years of age, with 4.1–4.6 million affected worldwide. This number is predicted to more than double by 2030 as populations age [1]. Clinically, PD is characterized by bradykinesia, postural irregularity, resting tremor, and rigidity.

Experienced neurologists can typically diagnose PD with 90% accuracy on the basis of widely accepted diagnostic criteria for PD [2], which relies largely on clinical history and physical examination in which a subjective component cannot be eliminated. However, the misdiagnosis rate of PD can range from 10% to 50% by movement disorder specialists [3]. In particular, essential tremor and atypical Parkinsonian syndromes, such as progressive supranuclear palsy (PSP) and multiple system atrophy (MSA), may initially mimic PD. When patients fulfill the clinical criteria of PD, they may have lost up to 70% of dopaminergic neurons in some parts of the substantia nigra [4], which suggests a timely and accurate diagnosis is urgently needed. Apart from their diagnostic utility, biomarkers for PD are also needed for monitoring disease progression and efficacy of interventions.

The term ‘biomarker’ was defined in 2001 by the Biomarkers Definitions Working Group [National Institutes of Health (NIH)] as ‘a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention’. An ideal PD biomarker should meet the following qualifications: high sensitivity and specificity validated by neuropathological examination, satisfactory test-retest reproducibility, inexpensive, non-invasive, and offer the ability to monitor disease progression without being biased by age. To date, there is no suitable biomarker for PD that fulfills all of these criteria. In this review, we will focus on two areas of research: biochemical markers, and new techniques ranging from neuroimaging to omics, both of which have clear potential for applications in diagnosing PD and monitoring its progression.

Promising biomarkers associated with pathogenesis

Today, the strongest evidence for the causation of PD suggests a multifactorial intervention of genetic and environmental factors, with possible mechanisms ranging from mitochondrial dysfunction, oxidative stress, protein aggregation, impaired protein degradation, dysregulated autophagy, and inflammation [5]. Given the diversity of possible disease mechanisms, biomarkers related to pathogenesis offer the most promising approaches for early diagnosis of PD (Table 1). We will discuss biomarkers linked to different pathogenic mechanisms in the following sections.

Table 1

Biomarker candidates in Parkinsonian disorders in PD patients compared to controls that reflect PD pathogenesis.

MechanismsBiomarker candidatesCSF/brainPotential utilitiesBlood/plasmaPotential utilitiesSaliva/urinePotential utilities
Mitochondrial dysfunction and oxidative stressDJ-1↑[6]

↓[7]		90% sensitivity and 70% specificity for patients with PD vs. controls [7]No statistical difference [7]–Salivary DJ-1↑[8, 9]Correlated with PD severity [8]
OxDJ-1↑(in multiple brain regions brain, [10])–––––
UA––↓[11–13]Correlated with PD severity [11, 12] and cognitive impairment [13]––
8-OHdG↑[14]Correlated with PD severity [14]8-OHdG/2-dG↑[15]; Leucocyte 8-OHdG↑[16]–Urinary 8-OHdG↑[17]Correlated with hallucinations [17]
AOPPNo statistical difference [18]–↑[18]Correlated with PD severity [18]––
Abnormal protein accumulation and aggregationα-SynucleinOligomeric α-Synuclein↑[19]92% sensitivity and 58% specificityfor patients with PD vs. controls [7]α-Synuclein↓[20]/No statistical difference [21]–Salivary α-synuclein↓[9]Correlated with PD severity [9]
α-Synuclein↓[7, 22]71% sensitivity and 53% specificity patients with PD vs. controls, DLB, and MSA [22]Phosphorylated α-synuclein↑[21, 23]
No correlation between the α-synuclein level and PD severity [7, 19, 22, 24]Antibody towards monomeric α-synuclein↑[25]
TauTotal tau↓[26]Distinguish PD from MSA [26]––––
Phosphorylated-tau↓[26]
Aβ(1-42)↓[26]–––––
Flt3 ligandNo statistical difference [26]Distinguish PD from MSA [26]––––
FractalkineNo statistical difference [26]–––––
Tissue transglutaminase↑[27]–––––
Osteopontin↑[28]–↑[28]–––
Impaired protein degradationHsc 70 protein––↓[29]–––
β-Glucocerebrosidase↓[30]–––––
UCH-L1↓[31]–––––
InflammationHs-CRP––↑[32]–––
sTNFR1 and sTNFR2––↑[33]Predicting cognitive impairment [33]––
EGF––↓[34]Predicting cognitive impairment [34]––

Mitochondrial dysfunction and oxidative stress

Evidence suggests that defects of the respiratory chain (complex I), increased accumulation of mitochondrial DNA mutations, abnormal mitochondrial calcium homoeostasis, defective autophagic removal of mitochondria (mitophagy), and increased oxidative stress are all involved in the pathogenesis of PD [5].

DJ-1

DJ-1 is a causative gene of a familial form of PD, namely PARK7, and acts as a sensor of oxidative stress by regulating the expression of antioxidative defense genes to protect the cells from oxidative stress. DJ-1 gene knock-out human neuroblastoma SH-SY5Y cells were susceptible to neurotoxicity induced by 1-methyl-4-phenylpyridiniumion (MPP+), 6-hydroxydopamine (6-OHDA) and rotenone, whereas over expression of DJ-1 reduced oxidative stress and protected neurons against dopamine toxicity [35]. Previous studies have found both higher and lower cerebrospinal fluid (CSF) DJ-1 levels in PD compared with non-PD controls (Table 1). The CSF DJ-1 levels (measured with quantitative immunoblotting) in the early stages of PD (Hoehn and Yahr stage I-II) were significantly upregulated compared to those in the advanced stages of PD (Hoehn and Yahr stage III-IV) and non-PD controls (p<0.001) [6]. However, decreased levels of DJ-1 (measured with quantitative sensitive Luminex assays) were found in a large cohort of CSF samples of PD patients compared with healthy individuals and Alzheimer’s disease (AD) patients. The sensitivity and specificity for patients with PD versus controls were 90% and 70% for DJ-1, and there was no correlation between the DJ-1 level and the severity of PD [7]. The discrepancy between the results of the two studies might be related to methodological variations or contamination of CSF by blood. A limitation of the latter study is that Hong and colleagues did not evaluate CSF DJ-1 in other synucleinopathies or related disorders (MSA or PSP), i.e., diseases that clinically overlap with PD [7]. In blood, although there was a moderate decrease in DJ-1 levels in patients with PD or AD compared with healthy individuals, no statistical difference was observed in this cohort between any groups, even when the extent of hemolysis and platelet contamination were controlled for. Nonetheless, in an initial discovery study of 119 subjects, seven DJ-1 isoforms were reliably detected, and blood levels of those with 4-hydroxy-2-nonenal modifications were found to be altered in late-stage PD [36]. In saliva, DJ-1 could be an indicator of PD progression. Kang and colleagues found a correlation between salivary concentration of DJ-1 and putamen nucleus uptake of labeled dopamine transporters 99mTc-TRODAT-1 in the PD group. Although salivary DJ-1 levels were not affected by Unified Parkinson’s Disease Rating Scale (UPDRS) scores, gender, age, and pharmacotherapy, DJ-1 levels in Hoehn and Yahr stage III-IV of PD were higher than those in Hoehn and Yahr stage I-III as well as those in healthy controls [8].

Both the crystal structure of DJ-1 and a substantial number of experiments have indicated that the cysteine residue at position 106 (Cys-106) is preferentially oxidized in cells exposed to oxidative stress and is now accepted to be the key residue involved in the antioxidative action of DJ-1 [10]. Using specific monoclonal antibodies against Cys106 oxidized DJ-1 (oxDJ-1), oxDJ-1 immunoreactivity was prominently observed in neuromelanin containing neurons and neuron processes of the substantia nigra, as well as in astrocytes in the striatum, in neurons and glia in the red nucleus, and in the inferior olivary nucleus, all of which are related to regulation of movement [37]. These observations suggest that in addition to the quantity of DJ-1, its qualitative change, oxidation, is an interesting candidate for a PD biomarker [10, 37].

Others

Some antioxidants or oxidation products have been reported to have obvious potential in diagnosing PD and monitoring its progression. Recent studies have provided evidence that uric acid (UA), a natural antioxidant, may play a role in the development and progression of PD. Patients with low UA levels in serum may be more prone to developing PD, and an inverse relationship between UA and severity of PD was robust for men but weak for women [11]. Another study in the Chinese population reported similar results, which suggests that the serum UA level could be a useful biomarker of PD diagnosis and disease progression [12]. Maetzler and co-workers have reported a strong inverse correlation between the serum UA and the cognitive impairment, and found that low serum UA levels predicted poor cognitive scores [13]. In addition, techniques for detection of UA are growing more efficient. Using the novel short graphene oxide nanoribbons (GONRs), which provide improved electrochemical signals at the same concentrations of analytes and test conditions, the minimum detection limits of UA could reach 98 nM [38].

8-Hydroxydeoxyguanosine (8-OHdG) is produced when reactive oxygen radicals react with guanine residues in DNA and is a reliable marker of oxidative stress markers. Increased levels of oxidative stress have been reported in CSF, plasma and urine of patients with various neurodegenerative disorders. A study has found that the concentration of 8-OHdG in CSF of PD patients was greater than that in CSF of controls (p<0.0001) and was positively correlated with the duration of disease [r(s)=0.87, p<0.001] [14]. Bolner and colleagues studied the ratio between 8-OHdG and 2-dG (which is related to the efficacy of the DNA repairing mechanisms) in plasma as a marker of oxidative stress in PD. Their results indicated that in plasma samples, only the 8-OHdG/2-dG ratio but not the 8-OHdG level was significantly higher in PD compared to healthy controls, suggesting that the ratio of 8-OHdG/2-dG might be a reliable diagnostic tool [15]. Another study in blood demonstrated the leucocyte 8-OHdG levels were continuously increased with advanced PD Hoehn and Yahr stages [16]. Hallucinations was reportedly occur in 50% of patients with PD [39] and have a persistent and progressive nature, and Hirayamaa and colleagues observed that the significant correlation between urinary 8-OHdG levels and hallucinations suggests that hallucinations are likely to have unique but unidentified mechanisms that lead to excessive production of 8-OHdG [17].

Protein halogenation is a type of oxidative stress induced by phagocytic overstimulation, and its role in PD has caught people’s attention. These protein products are not cytotoxic, but they are known to form inflammatory mediators after conjugation with serum albumin. Jose’-Manuel and colleagues detected that advanced oxidized protein products (AOPP), markers of protein halogenation, are reliably enhanced in serum of patients with PD relative to control subjects, and to a lesser extent in the CSF. Levels of AOPP are progressively reduced over time, and the duration of PD is larger in Hoehn and Yahr stage II-III patients with low serum levels [18].

Abnormal protein accumulation and aggregation

α-Synuclein

One of the pathological hallmarks of PD is the presence of Lewy bodies in surviving neurons. Lewy bodies consist of insoluble aggregated proteins with α-synuclein being the major component, which has now become a focus of biomarker research.

Two recognized studies have suggested a decreased total α-synuclein level in CSF of aged individuals with PD [7, 22]. Hong and co-workers have reported the sensitivity and specificity for patients with PD versus controls were 92% and 58% for α-synuclein [7], while Mollenhauer and colleagues found that total CSF α-synuclein at a concentration of 1.6 pg/μL or less provides 71% sensitivity and 53% specificity for diagnosing PD [22]. The reasons for the differences in the sensitivity and specificity may be that Mollenhauer and colleagues recruited a cohort that was larger and diagnostically more diverse, including dementia with Lewy bodies (DLB) and MSA, which are atypical Parkinsonian disorders [22]. No correlation between the α-synuclein level and disease severity was found in these two studies, which was later confirmed by another study [24]. The oligomeric form of α-synuclein was increased in CSF of patients with Lewy body disease (PD and DLB combined) compared to non-Lewy body disease subjects (controls and tauopathies). Furthermore, a higher oligomer/total-α-synuclein ratio in CSF of PD provided a greater sensitivity of 89.3% and specificity of 90.6% [19]. In a recent finding, Foulds and colleagues pointed out that approximately 90% of α-synuclein deposited in Lewy bodies is phosphorylated at Ser-129, whereas only 4% of total α-synuclein in normal brain is phosphorylated. Although their preliminary results still require confirmation, they do suggest that the phosphorylated form of α-synuclein is more promising as a diagnostic marker than the non-phosphorylated protein [40].

A number of studies have been conducted on α-synuclein levels in plasma in spite of the risk for contamination with erythrocytes or platelets. Compared to healthy controls, Gorostidi and colleagues found a significant decrease in plasma total α-synuclein levels in idiopathic PD patients and the reduction was less significant in patients who were LRRK2 mutation carriers [20]. However, Foulds and colleagues had a substantially different conclusion: there were no differences in the plasma total α-synuclein levels between PD patients and controls, which may be due to large individual-to-individual variation [21]. For log-transformed data, plasma total α-synuclein levels increased with time for up to 20 years after the appearance of initial symptoms (p=0.012) [23]. At the same time, they found that the mean level of phosphorylated α-synuclein, but not total α-synuclein, was higher in the plasma of PD patients compared to the healthy controls (p=0.012). Their results suggested that the plasma level of phosphorylated α-synuclein may serve as a valuable diagnostic tool, whereas the level of total α-synuclein could act as a surrogate marker for the progression of PD [21, 23]. Yanamandra and colleagues found significantly higher antibody levels against monomeric α-synuclein in the sera of PD patients compared to controls (p<0.0001), though this response decreased with PD progression. This indicates a protective role of autoimmunity in maintaining homeostasis and clearing protein species whose imbalance may lead to amyloid assembly [25].

Ivana and colleagues observed that α-synuclein levels tended to decrease while DJ-1 levels tended to increase in the saliva of PD patients, and that α-synuclein might correlate with severity of motor symptoms in PD [9]. Incidentally, α-synuclein is found in peripheral tissues, such as the gastrointestinal tract and salivary gland. A large scale study demonstrated accumulation of α-synuclein within mucosal and submucosal nerve fibers as well as ganglia, which was more extensively detected with an antibody against phosphorylated α-synuclein than with an antibody against non-phosphorylated α-synuclein [41]. A small study on salivary gland biopsies in living patients with PD demonstrated 12 of the needle core biopsies had microscopically evident submandibular gland tissue to assess and 9/12 had Lewy type α-synucleinopathy (LTS), but only 1/15 minor salivary gland biopsies tested positive for LTS. This result suggested that this tissue biopsy method may be important for tissue confirmation of PD in patients being considered for invasive procedures and in research studies of other PD biomarkers [42].

In summary, α-synuclein is normally found in the peripheral tissues, all blood cells, and bodily fluids (CSF, saliva and plasma) and exists in its various conformations (phosphorylated, glycosylated, nitrated, ubiquitinated or truncated forms). However, neither plasma nor CSF α-synuclein is presently a reliable marker of PD, as abnormal α-synuclein accumulation is commonly used as a marker of a group of diseases called synucleinopathies [43, 44]. It should more readily distinguish PD from tauopathies, such as PSP or AD, but would not distinguish PD so easily from DLB or MSA. In order to develop a diagnostic test or a biomarker for PD, more detailed studies are needed on different modified forms of α-synuclein from different peripheral tissues for their potential as a surrogate marker of CSF or plasma α-synuclein [43, 44].

Tau and others

Tau and amyloid β (Aβ) are important pathological proteins implicated in cognitive impairment and have been investigated in many studies in the past [45]. Shi and coworkers measured total tau, phosphorylated tau, Aβ peptide 1-42 [Aβ (1-42)], Flt3 ligand, and fractalkine levels in CSF in a large cohort of PD patients at different stages, as well as in healthy and diseased controls, and found that the ratio of phosphorylated-tau/tau can be used to distinguish PD from MSA (a disease that overlaps with PD clinically). Moreover, with the CSF Flt3 ligand, PD could be clearly differentiated from MSA with an excellent sensitivity (99%) and specificity (95%) [26]. Furthermore, the authors identified CSF fractalkine/Aβ (1-42) as positively correlated with PD severity in cross-sectional samples, as well as with PD progression in longitudinal samples [26].

There are still several candidate biomarkers related with protein aggregation. For example, tissue transglutaminase, which may contribute to α-synuclein aggregation by promoting crosslinking, increased almost 10-fold in PD patients in comparison to controls (p=0.001), although the overlap between controls and certain PD patients indicated low sensitivity of this potential diagnostic marker [27]. CSF and serum levels of osteopontin, a CNS-derived protein that has been associated with various neuroprotective actions, were elevated in PD patients compared to controls and its higher serum levels were associated with more severe motor symptoms [28]. The CSF axonal damage biomarker, neurofilament heavy chain (NFH-SMI35), differentiated PD from PSP with a sensitivity of 76.5% and a specificity of 94.4%, and was most prominent in the more rapidly progressing syndromes PSP and MSA rather than in PD or corticobasal degeneration (CBD) [46].

Impaired protein degradation

Two main protein degradation systems, the autophagy-lysosomal pathway and ubiquitin-proteasome pathway, are involved in the degradation of misfolded, mutant, denatured or otherwise damaged proteins such as α-synuclein, tau and Aβ. The impairment of the two pathways is recognized to play a pathogenetic role in PD. Lysosomal-associated membrane protein (lamp) 2A and heat shock cognate (hsc) 70 protein are the two key regulators of chaperone-mediated autophagy; their expression was assessed in peripheral blood mononuclear cells (PBMC) of patients with sporadic PD and compared to healthy subjects in a recent study. A significant reduction of hsc70 levels was observed in PBMC of PD patients, and no difference in lamp2A expression was evident between patients and controls [29]. β-Glucocerebrosidase is a lysosomal hydrolase encoded by GBA1, whose mutations represent a recognized risk factor for PD. In a new study, it was found that β-glucocerebrosidase activity was reduced in CSF of PD patients (p<0.05) [30]. A combination of β-glucocerebrosidase activity, oligomer/total α-synuclein ratio, and age gave the best performance in discriminating PD from neurological controls [sensitivity 82%; specificity 71%, area under the receiver operating characteristic curve (ROC)=0.87], which demonstrates the possibility of detecting lysosomal dysfunction in CSF and further supports the need to combine different biomarkers for improving the diagnostic accuracy of PD [30]. The ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) is a pivotal component of the ubiquitin proteasome system. It was reported that the UCH-L1 levels in CSF were significantly decreased in PD compared with controls and atypical Parkinsonian disorders [31]. The combined determination of α-synuclein and UCH-L1 levels could not only be used to separate patients with synucleinopathies from those with tauopathies [p<0.015; area under curve (AUC)=0.63], but also discriminate between PD and APD (p<0.0003; AUC=0.69) [31].

Inflammation

Neuroinflammation, comprising microglial activation and astrogliosis in the substantia nigra of PD patients, has repeatedly been shown to be an important contributor to the pathogenesis of PD. Increased levels of proinflammatory cytokines and alterations in growth factor levels in CSF or plasma of PD patients are molecular indicators of inflammation. This is evidenced by increased CSF levels of several interleukins (ILs), including IL-1-β, IL-6 and IL-8, and decreased levels of the components of the complement system in PD patients [5]. Furthermore, it was found that high-sensitivity C-reactive protein (hs-CRP) levels in the early PD group were higher than those in healthy controls [32]. Most people with PD eventually develop cognitive impairment. Among PD patients, the increased level of soluble tumor necrosis factor receptor (sTNFR) 1 and sTNFR2 in plasma, which are known to antagonize the biological effect of TNF-α, were negatively correlated with cognitive test scores [33]. Chen-Plotkin and colleagues found 11 proteins that exhibited plasma levels correlating with baseline cognitive performance in the discovery cohort. Among them, epidermal growth factor (EGF) was the best candidate for predicting cognitive impairment of PD patients. The low levels of EGF not only correlated with poor cognitive test scores at the baseline, but also predicted an eight-fold greater risk of cognitive decline to dementia-range Mattis Dementia Rating Scale-2 scores in an independent replication cohort of 113 PD patients with intact baseline cognition. Therefore, inflammation might be associated with cognitive impairment of PD patients [34]. It can thus be concluded that inflammation might be associated with cognitive impairment of PD patients.

New techniques for discovering novel biomarkers

In the last decade, a variety of new techniques, ranging from neuroimaging and genomics to proteomics and metabolomics, have been employed to identify novel markers that could facilitate diagnosis and monitoring of PD progression (Table 2). In the following sections, we will discuss a few markers revealed by each platform, along with potential caveats and shortcomings.

Table 2

Biomarker candidates discovered in omics and neuroimaging studies.

New techniquesCSF/BrainBlood/Plasma
Gene expression profilingSNCA CpG island demethylation [47]3897 methylated CpG islands [48]
Cytochrome P450 2E1 demethylation [49]
2908 methylated CpG islands [48]
ProteomicsFerritin-L, seipin, γ-glutamyl hydrolase and nebulette [50]

Ubiquitin, β2-microglobulin, and two secretogranin 1 [chromogranin B] fragments [52]		Sero-transferrin and clusterin↑, complement component 4B, ApoA-I, α2-antiplasmin and coagulation factor V↓ [51]
SAP↑ [53]
The peak at m/z 6250 [54]10 autoantibody↑[55]
Metabolomics3-hydroxykynurenine↑[56]Hypoxanthine [57]
Oxidized glutathione↓[56]Aluminium, copper, iron, manganese and zinc [58]
N8-acetyl spermidine↑[59]
NeuroimagingDAT tracer tracer2β-carbomethoxy-3β-(4-[123I] iodophenyl) tropane [60]–
VMAT2 tracer [18F]AV-133
α-Synuclein tracer [61]
2-[2-(2-dimethylaminothiazol-5-yl) ethenyl]-6-[2-(fluoro)ethoxy] benzoxazole [62]

Gene expression profiling

Epidemiological studies demonstrated that only 5%–10% of PD patients have a family history of PD, and that most cases of PD are sporadic. Through the use of genome-wide association studies (GWASs), genetic screening has successfully identified a common high-risk gene locus glucocerebrosidase, and many common low-risk loci (e.g., SNCA, MAPT, LRRK2). The mutation of SNCA could lead to changes in the level and conformation of α-synuclein. The last few years have seen growing evidence of a possible contribution of SNCA gene epigenetic modifications to PD. By using cultured cells in vitro, Matsumoto and colleagues identified a region of the SNCA CpG island whose demethylation leads to increased SNCA expression [47]. Similarly, decreased methylation of the cytochrome P450 2E1 (CYP2E1) gene and increased expression of CYP2E1 mRNA were found in the brains of PD patients [49]. Masliah and colleagues investigated genome-wide DNA methylation in brain and blood samples from PD patients and detected 2908 differentially methylated CpG islands in the brain and 3897 in blood, representing about 0.6% and 0.8% of the total array probes interrogated, respectively [48]. Therefore, it is reasonable to propose that DNA methylation could be a novel biomarker for PD. Genetic variations in the LRRK2 gene were linked to familial PD and the G2019S mutation of LRRK2 has been associated with both familial and idiopathic PD [63]. The G2019S mutation in the kinase domain is a dominant mutation that has been shown to increase LRRK2 kinase activity in vitro, and therefore selective LRRK2 inhibitors have become a potential target of anti-PD drug development [64]. It was reported that a potent and selective lead compound can penetrate the blood brain barrier and inhibit wild-type or mutant LRRK2 activity, raising the possibility that imaging of LRRK2 activity in the brain with highly selective LRRK2 radiotracers for PET or SPECT might be used as a new diagnostic tool [65]. Using gene expression profiling combined with bioinformatics analysis, Diao and colleagues identified a total of 1004 genes associated with PD initiation and found HLF, E2F1 and STAT4 have altered expression levels in PD patients [66]. However, genomic approaches are only able to identify the susceptible population, but they cannot provide information about whether the disease has started or how advanced it is. Therefore, they should be used in combination with other biomarkers for PD diagnosis.

Proteomics

Protein isolation combined with identification and bioinformatics analysis supports selecting, identifying and quantifying potential PD biomarkers. Recent advances in proteomics, including both upstream and downstream protocols, have fuelled a transition from mere protein identification to functional analysis. Some studies have reported proteomics research in brain tissues, CSF, and blood.

Using a high-throughput shotgun proteomics strategy, Licker and colleagues studied postmortem nigral tissues dissected from pathologically confirmed PD cases and identified 204 proteins exhibiting significant changes in expression levels between PD patients and controls. These proteins were found to be involved in novel or known pathogenic processes including mitochondrial dysfunction, oxidative stress, or cytoskeleton impairment. They further characterized four candidates including ferritin-L, seipin, γ-glutamyl hydrolase and nebulette, which might be relevant to PD pathogenesis [50].

By screening the CSF proteome using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), Constantinescu and co-workers identified four proteins [ubiquitin, β2-microglobulin, and two secretogranin 1 (chromogranin B) fragments] that differentiated healthy controls and PD patients from patients with atypical Parkinsonian disorders [52]. By analyzing CSF proteomic patterns, the peak at m/z 6250 was found to be highly expressed in healthy individuals, less expressed in PD patients and least expressed in MSA patients. Although it could provide a satisfactory AUC value in the ROC analysis (0.956) in discrimination of the early-stage patients with PD or MSA, there were not enough values of this type in the discrimination of other pairs among PD, MSA, and control [54]. However, the biggest shortcoming of proteomics analysis of CSF is that contamination by blood, with its high protein content, can dramatically alter the CSF proteomic pattern. To counter this shortcoming, standardizing methods in sample collection, storage, preparation, analysis, and data mining were recently developed to ensure data reproducibility [67].

Using two-dimensional liquid chromatography-tandem mass spectrometry (2DLC-MS) coupled with isobaric tags for relative and absolute quantification (iTRAQ) labeling, Zhang and colleagues identified eight proteins that were upregulated in PD including sero-transferrin and clusterin, and 18 downregulated proteins including complement component 4B, ApoA-I, α-2-antiplasmin and coagulation factor V. These proteins may be involved in oxidative stress, mitochondrial dysfunction, abnormal protein aggregation and inflammation. Importantly, significantly decreased expression levels of ApoA-I were detected in the early stages of PD [51]. By utilizing 2D electrophoresis (2DE) and MS, IgGκL and human serum amyloid P component (SAP) were found to be differentially expressed between healthy individuals and PD patients. The SAP level was increased approximately five-fold in PD samples, and the ELISA procedure revealed a significant (p<0.001) increase in SAP concentration (65.9±18.7 μg/mL) in the plasma of PD patients (healthy individuals, 35.0±12.5 μg/mL), with a sensitivity of 94.1% and a specificity of 87.5% [53]. With the help of a bioinformatics platform, Alberio and colleagues analyzed more than 51,000 scientific papers dealing with PD and tracked back 35 PD-related proteins as being present in at least two published 2DE maps of human plasma. Then, nine different proteins (haptoglobin, transthyretin, apolipoprotein A-1, serum amyloid P component, apolipoprotein E, complement factor H, fibrinogen γ, thrombin, complement C3) split into 32 spots were identified as a potential diagnostic pattern in plasma [68]. To identify autoantibodies in human sera, Han and colleagues used Invitrogen’s ProtoArray v5.0 Human Protein Microarrays (Cat. No.-PAH0525020, Invitrogen, Carlsbad, CA, USA), each containing 9486 unique human protein antigens (www.invitrogen.com/protoarray), and the diagnostic value of each of these autoantibodies was evaluated, resulting in the selection of 10 autoantibody biomarkers that can effectively differentiate PD sera from control sera with a sensitivity of 93.1% and a specificity of 100% [55].

Metabolomics

Metabolomics is a platform for detecting and analyzing a series of molecules with low molecular weights. By using ultra performance liquid chromatography (UPLC)/tandem MS and gas chromatography (GC)/MS, LeWitt and colleagues detected and provided chemical identifications for 243 of the several hundred small-molecular weight constituents of CSF. In PD, the mean 3-hydroxykynurenine concentration was increased by one third, and mean oxidized glutathione was decreased by 40% in the PD specimens [56], which is consistent with earlier reports. Interestingly, N-acetylhistidine, one of the four N-acetylated amino acids, was reduced by almost half compared to the control value [56]. This observation was consistent with a recent finding that N-terminal acetylation of amino acids is a major factor that governs aggregation for oligomers of α-synuclein. Johansen and co-workers found that both familial and idiopathic PD patients showed significantly reduced UA levels and a significant decrease in the levels of hypoxanthine and in the ratios of major metabolites of the purine pathway in plasma compared to controls. Furthermore, these findings showed that LRRK2 patients with the G2019S mutation have unique metabolomic profiles that distinguish them from patients with idiopathic PD. More importantly, asymptomatic LRRK2 carriers can be separated from gene negative family members, which raise the possibility that metabolomic profiles could be useful in predicting which LRRK2 carriers will eventually develop PD [57]. By using inductively coupled plasma-atomic emission spectrophotometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) to determine the concentration variations of elements between PD and normal samples, an element linkage map was established. The partial least squares discriminant analysis (PLS-DA) showed aluminium, copper, iron, manganese and zinc are the elements that distinguish PD patients from controls. Furthermore, aluminium is a key element involved in triggering phosphorus, which subsequently leads to an imbalance of homeostasis in PD serum [58]. In a pilot study, N8-acetyl spermidine was found to be significantly elevated in the rapid progressors compared to both control subjects and slow progressors. The exploratory data indicate that a fast motor progression disease phenotype can be distinguished early in disease using high resolution mass spectrometry-based metabolic profiling, and that altered polyamine metabolism may be a predictive marker of rapidly progressing PD [59].

Neuroimaging

Imaging techniques that evaluate potential structural, ultrastructural, biochemical, or perfusion pattern changes in PD are being tested. The main pathological changes of PD occur in the substantia nigra, with degeneration of dopaminergic neurons. Functional brain imaging using tracers that penetrate the blood brain barrier are able to identify diseased regions in the brain with either single photon emission computed tomography (SPECT) or positron emission tomography (PET). To date, the most widely accepted biomarkers for nigrostriatal neurodegeneration are those employing neuroimaging methodologies. 6-[18F]-fluoro-L-dopa (18F-dopa) was first used to measure and assess presynaptic dopaminergic neuronal integrity. 18F-dopa, as an analog of L-dopa, was taken up by axonal terminals of dopaminergic neurons, which reflect the density of the axonal terminal plexus, aromatic amino acid decarboxylase (AADC) activity and further underestimate the severity of the nigrostriatal degeneration. Using serial 18F-dopa PET, a longitudinal study by Pavese and colleagues found that patients with symptomatic PD showed an average 0.5% annual reduction in putamen 18F-dopa uptake over 5 years, while caudate 18F-dopa uptake declined by a mean annual rate of 2% [69]. Radiotracer imaging of the vesicular monoamine transporter type 2 (VMAT2) and dopamine transporter (DAT) provides information on presynaptic dopaminergic function. In 2011, the US Food and Drug Administration approved the use of the DAT ligand [123I] ioflupane, with SPECT for evaluating Parkinsonian syndromes and for distinguishing PD from essential tremors. An animal study in vivo demonstrated that the striatal uptake of 2β-carbomethoxy-3β-(4-[123I]iodophenyl) tropane differed significantly between different 6-OHDA dose groups. The results were highly correlated with both striatal DAT- and TH-immunoreactive fiber densities and to TH-immunoreactive cell numbers in the rat substantia nigra, but no clear progression of the lesions was observed [60]. VMAT2 is imaged using 11C- or 18F-dihydrotetrabenazine (DTBZ) PET and is the newest approach for the assessment of nigrostriatal projections. Nevertheless, the application of 11C-DTBZ is limited because its short half-life requires a cyclotron on-site. Using [18F]AV-133, a novel 18F-labeled tetrabenazine derivative that selectively binds to VMAT2 with high affinity, the imaging showed evident progressive loss of striatal uptake of [18F]AV-133 which had a linear correlation with the clinical rating scores and the bradykinesia subscores [61]. While α-synuclein imaging could be a useful diagnostic marker and a means to monitor therapies aimed at reducing α-synuclein levels, developing an α-synuclein PET tracer poses a greater challenge than was faced for the DAT or VMAT2 tracers. To identify molecules that bind selectively and strongly to α-synuclein, researchers in the Michael J. Fox Foundation for Parkinson’s Research opted to employ a small-molecule, medicinal chemistry approach beginning with a screen of 100,000 compounds that were selected based on computational chemistry utilizing information derived from the α-synuclein structure [70]. Kikuchi and colleagues have reported that 2-[2-(2-dimethylaminothiazol-5-yl) ethenyl]-6-[2-(fluoro)ethoxy] benzoxazole could bind to α-synuclein-containing glial cytoplasmic inclusions in the post-mortem brain, and that PET data demonstrated elevated signals in the glial cytoplasmic inclusion-rich brain regions including subcortical white matter, putamen, globus pallidus, primary motor cortices and anterior and posterior cingulate cortex when compared to the normal control. Their results also demonstrated that PD and DLB showed distribution volume patterns that were very different from those of MSA [62].

Conclusions and future directions

A number of different biochemical biomarkers have been summarized in this review (Table 1), and different omics and neuroimaging methodologies are likely to be needed in the future. However, sensitive, specific and thoroughly validated diagnostic CSF markers for PD have not yet been identified. Due to heterogeneity in disease pathology and pathological overlap with other neurodegenerative disorders, it is likely that an optimal biomarker will combine multiple methodological approaches, i.e., a panel of markers will be needed for PD diagnosis/progression. For example, in their study of AD biomarkers, Abdul and colleagues have recently used a combination of 10 plasma proteins to predict conversion to dementia with an accuracy of 87%, sensitivity of 85%, and specificity of 88% [71]. Already, initiatives in PD are currently testing collaborative approaches for a combination of markers, possibly incorporating, in addition to imaging, clinical batteries, molecular genetics, and other disease state-based markers, as well as omics-based measures. It is also important to realize that PD, or any other major neurodegenerative disorder for that matter, is not a single disease; there are many subtypes of diseases. The biomarkers used for diagnosis of PD or subtypes of diseases will likely be different, and in future studies the choice of biomarker will depend upon its purpose. Many develop cognitive impairment or dementia, and biomarkers (such as EGF, Aβ-42) that could identify at-risk patients would be useful for prognostication.

At present, no treatment has been proven to influence the progressive course of the disease by protecting neurons, and the lack of validated biomarkers remains a major barrier to success. Research on PD biomarkers will not only improve the diagnosis of PD, but also provide new targets for anti-Parkinsonian drug discovery. Specifically, some biomarkers subject to oxidation, misfolding and aggregation are the focus of drug development, such as DJ-1 and α-synuclein. Research progress on anti-Parkinsonian drugs targeting DJ-1 or oxDJ-1 and many compounds have been isolated by virtual screening [72]. A recent review demonstrated that compounds that promote α-synuclein mono-ubiquitination should be used in concert with compounds that boost the proteolytic pathways, which may therefore ease the accumulation of α-synuclein in PD [73]. Also, in-depth studies on anti-Parkinsonian medicines have proved very valuable for PD biomarkers. For example, using iTRAQ-based quantitative proteomics, extracts of Acanthopanax senticosus harm were found to correct the abnormal expressions of 16 proteins out of 84 potential biomarkers that are associated with the formation of Lewy bodies [74].

It is envisaged that further advances in molecular neurobiology, omics analyses, functional neuroimaging will provide better opportunities for discovery of early, non-invasive, sensitive, specific, economical diagnostic biomarkers for the safe and effective treatment of PD. Further studies in the novel discovery of PD biomarkers will provide avenues to treat PD patients more effectively with few or no side effects.

Corresponding author: Xiaoping Pu, State Key Laboratory of Natural and Biomimetic Drugs, Peking University, and Department of Molecular and Cellular Pharmacology, School of Pharmaceutical Sciences, Peking University, Beijing 100191, P.R. China, Phone: +86 10 82802431, E-mail:

Acknowledgments

This work was supported by Science and Technology Major Projects: Significant New-Drugs Creation (No. 2012ZX09103201-042) and National Special Equipment Development Program (No. 2013YQ030651).

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

Financial support: None declared.

Employment or leadership: None declared.

Honorarium: None declared.

Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

References

1. Dorsey ER, Constantinescu R, Thompson JP, Biglan KM, Holloway RG, Kieburtz K, et al. Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 2007;68:384–6.10.1212/01.wnl.0000247740.47667.03Search in Google Scholar PubMed

2. Hughes AJ, Daniel SE, Ben-Shlomo Y, Lees AJ. The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain 2002;125:861–70.10.1093/brain/awf080Search in Google Scholar PubMed

3. Meara J, Bhowmick BK, Hobson P. Accuracy of diagnosis in patients with presumed Parkinson’s disease. Age Ageing 1999;28:99–102.10.1093/ageing/28.2.99Search in Google Scholar PubMed

4. Fearnley JM, Lees AJ. Ageing and Parkinson’s disease: substantia nigra regional selectivity. Brain 1991;114:2283–301.10.1093/brain/114.5.2283Search in Google Scholar PubMed

5. van Dijk KD, Teunissen CE, Drukarch B, Jimenez CR, Groenewegen HJ, Berendse HW, et al. Diagnostic cerebrospinal fluid biomarkers for Parkinson’s disease: a pathogenetically based approach. Neurobiol Dis 2010;39:229–41.10.1016/j.nbd.2010.04.020Search in Google Scholar PubMed PubMed Central

6. Waragai M, Wei J, Fujita M, Nakai M, Ho GJ, Masliah E, et al. Increased level of DJ-1 in the cerebrospinal fluids of sporadic Parkinson’s disease. Biochem Biophys Res Commun 2006;345:967–72.10.1016/j.bbrc.2006.05.011Search in Google Scholar PubMed

7. Hong Z, Shi M, Chung KA, Quinn JF, Peskind ER, Galasko D, et al. DJ-1 and alpha-synuclein in human cerebrospinal fluid as biomarkers of Parkinson’s disease. Brain 2010;133:713–26.10.1093/brain/awq008Search in Google Scholar PubMed PubMed Central

8. Kang WY, Yang Q, Jiang XF, Chen W, Zhang LY, Wang XY, et al. Salivary DJ-1 could be an indicator of Parkinson’s disease progression. Front Aging Neurosci 2014;6:102.10.3389/fnagi.2014.00102Search in Google Scholar PubMed PubMed Central

9. Devic I, Hwang H, Edgar JS, Izutsu K, Presland R, Pan C, et al. Salivary alpha-synuclein and DJ-1: potential biomarkers for Parkinson’s disease. Brain 2011;134:e178.10.1093/brain/awr015Search in Google Scholar PubMed PubMed Central

10. Saito Y. Oxidized DJ-1 as a possible biomarker of Parkinson’s disease. J Clin Biochem Nutr 2014;54:138–44.10.3164/jcbn.13-108Search in Google Scholar PubMed PubMed Central

11. Pan M, Gao H, Long L, Xu Y, Liu M, Zou J, et al. Serum uric acid in patients with Parkinson’s disease and vascular parkinsonism: a cross-sectional study. Neuroimmunomodulation 2013;20:19–28.10.1159/000342483Search in Google Scholar PubMed

12. Zhang HN, Guo JF, He D, Lei LF, Wang YQ, Wang CY, et al. Lower serum UA levels in Parkinson’s disease patients in the Chinese population. Neurosci Lett 2012;514:152–5.10.1016/j.neulet.2012.02.077Search in Google Scholar PubMed

13. Maetzler W, Stapf AK, Schulte C, Hauser AK, Lerche S, Wurster I, et al. Serum and cerebrospinal fluid uric acid levels in lewy body disorders: associations with disease occurrence and amyloid-beta pathway. J Alzheimers Dis 2011;27:119–26.10.3233/JAD-2011-110587Search in Google Scholar PubMed

14. Isobe C, Abe T, Terayama Y. Levels of reduced and oxidized coenzyme Q-10 and 8-hydroxy-2’-deoxyguanosine in the cerebrospinal fluid of patients with living Parkinson’s disease demonstrate that mitochondrial oxidative damage and/or oxidative DNA damage contributes to the neurodegenerative process. Neurosci Lett 2010;469:159–63.10.1016/j.neulet.2009.11.065Search in Google Scholar PubMed

15. Bolner A, Pilleri M, De Riva V, Nordera GP. Plasma and urinary HPLC-ED determination of the ratio of 8-OHdG/2-dG in Parkinson’s disease. Clin Lab 2011;57:859–66.Search in Google Scholar

16. Chen CM, Liu JL, Wu YR, Chen YC, Cheng HS, Cheng ML, et al. Increased oxidative damage in peripheral blood correlates with severity of Parkinson’s disease. Neurobiol Dis 2009;33:429–35.10.1016/j.nbd.2008.11.011Search in Google Scholar PubMed

17. Hirayama M, Nakamura T, Watanabe H, Uchida K, Hama T, Hara T, et al. Urinary 8-hydroxydeoxyguanosine correlate with hallucinations rather than motor symptoms in Parkinson’s disease. Parkinsonism Relat Disord 2011;17:46–9.10.1016/j.parkreldis.2010.11.004Search in Google Scholar PubMed

18. Garcia-Moreno JM, Martin de Pablos A, Garcia-Sanchez MI, Mendez-Lucena C, Damas-Hermoso F, Rus M, et al. May serum levels of advanced oxidized protein products serve as a prognostic marker of disease duration in patients with idiopathic Parkinson’s disease? Antioxid Redox Signal 2013;18:1296–302.10.1089/ars.2012.5026Search in Google Scholar PubMed

19. Tokuda T, Qureshi MM, Ardah MT, Varghese S, Shehab SA, Kasai T, et al. Detection of elevated levels of alpha-synuclein oligomers in CSF from patients with Parkinson disease. Neurology 2010;75:1766–72.10.1212/WNL.0b013e3181fd613bSearch in Google Scholar PubMed

20. Gorostidi A, Bergareche A, Ruiz-Martinez J, Marti-Masso JF, Cruz M, Varghese S, et al. Alphalpha-synuclein levels in blood plasma from LRRK2 mutation carriers. PLoS One 2012;7:e52312.10.1371/journal.pone.0052312Search in Google Scholar PubMed PubMed Central

21. Foulds PG, Mitchell JD, Parker A, Turner R, Green G, Diggle P, et al. Phosphorylated alpha-synuclein can be detected in blood plasma and is potentially a useful biomarker for Parkinson’s disease. FASEB J 2011;25:4127–37.10.1096/fj.10-179192Search in Google Scholar PubMed

22. Mollenhauer B, Locascio JJ, Schulz-Schaeffer W, Sixel-Doring F, Trenkwalder C, Schlossmacher MG. alpha-Synuclein and tau concentrations in cerebrospinal fluid of patients presenting with parkinsonism: a cohort study. Lancet Neurol 2011;10:230–40.10.1016/S1474-4422(11)70014-XSearch in Google Scholar

23. Foulds PG, Diggle P, Mitchell JD, Parker A, Hasegawa M, Masuda-Suzukake M, et al. A longitudinal study on alpha-synuclein in blood plasma as a biomarker for Parkinson’s disease. Sci Rep 2013;3:2540.10.1038/srep02540Search in Google Scholar PubMed PubMed Central

24. van Dijk KD, Bidinosti M, Weiss A, Raijmakers P, Berendse HW, van de Berg WD. Reduced alpha-synuclein levels in cerebrospinal fluid in Parkinson’s disease are unrelated to clinical and imaging measures of disease severity. Eur J Neurol 2014;21:388–94.10.1111/ene.12176Search in Google Scholar PubMed

25. Yanamandra K, Gruden MA, Casaite V, Meskys R, Forsgren L, Morozova-Roche LA. Alpha-synuclein reactive antibodies as diagnostic biomarkers in blood sera of Parkinson’s disease patients. PLoS One 2011;6:e18513.10.1371/journal.pone.0018513Search in Google Scholar PubMed PubMed Central

26. Shi M, Bradner J, Hancock AM, Chung KA, Quinn JF, Peskind ER, et al. Cerebrospinal fluid biomarkers for Parkinson disease diagnosis and progression. Ann Neurol 2011;69:570–80.10.1002/ana.22311Search in Google Scholar PubMed PubMed Central

27. Vermes I, Steur EN, Jirikowski GF, Haanen C. Elevated concentration of cerebrospinal fluid tissue transglutaminase in Parkinson’s disease indicating apoptosis. Mov Disord 2004;19:1252–4.10.1002/mds.20197Search in Google Scholar PubMed

28. Maetzler W, Berg D, Schalamberidze N, Melms A, Schott K, Mueller JC, et al. Osteopontin is elevated in Parkinson’s disease and its absence leads to reduced neurodegeneration in the MPTP model. Neurobiol Dis 2007;25:473–82.10.1016/j.nbd.2006.10.020Search in Google Scholar PubMed

29. Sala G, Stefanoni G, Arosio A, Riva C, Melchionda L, Saracchi E, et al. Reduced expression of the chaperone-mediated autophagy carrier hsc70 protein in lymphomonocytes of patients with Parkinson’s disease. Brain Res 2014;1546:46–52.10.1016/j.brainres.2013.12.017Search in Google Scholar PubMed

30. Parnetti L, Chiasserini D, Persichetti E, Eusebi P, Varghese S, Qureshi MM, et al. Cerebrospinal fluid lysosomal enzymes and alpha-synuclein in Parkinson’s disease. Mov Disord 2014;29:1019–27.10.1002/mds.25772Search in Google Scholar PubMed PubMed Central

31. Mondello S, Constantinescu R, Zetterberg H, Andreasson U, Holmberg B, Jeromin A. CSF alpha-synuclein and UCH-L1 levels in Parkinson’s disease and atypical parkinsonian disorders. Parkinsonism Relat Disord 2014;20:382–7.10.1016/j.parkreldis.2014.01.011Search in Google Scholar PubMed

32. Song IU, Chung SW, Kim JS, Lee KS. Association between high-sensitivity C-reactive protein and risk of early idiopathic Parkinson’s disease. Neurol Sci 2011;32:31–4.10.1007/s10072-010-0335-0Search in Google Scholar

33. Rocha NP, Teixeira AL, Scalzo PL, Barbosa IG, de Sousa MS, Morato IB, et al. Plasma levels of soluble tumor necrosis factor receptors are associated with cognitive performance in Parkinson’s disease. Mov Disord 2014;29:527–31.10.1002/mds.25752Search in Google Scholar

34. Chen-Plotkin AS, Hu WT, Siderowf A, Weintraub D, Goldmann Gross R, Hurtig HI, et al. Plasma epidermal growth factor levels predict cognitive decline in Parkinson disease. Ann Neurol 2011;69:655–63.10.1002/ana.22271Search in Google Scholar

35. Lev N, Barhum Y, Pilosof NS, Ickowicz D, Cohen HY, Melamed E, et al. DJ-1 protects against dopamine toxicity: implications for Parkinson’s disease and aging. J Gerontol A Biol Sci Med Sci 2013;68:215–25.10.1093/gerona/gls147Search in Google Scholar

36. Lin X, Cook TJ, Zabetian CP, Leverenz JB, Peskind ER, Hu SC, et al. DJ-1 isoforms in whole blood as potential biomarkers of Parkinson disease. Sci Rep 2012;2:954.10.1038/srep00954Search in Google Scholar

37. Saito Y, Miyasaka T, Hatsuta H, Takahashi-Niki K, Hayashi K, Mita Y, et al. Immunostaining of oxidized DJ-1 in human and mouse brains. J Neuropathol Exp Neurol 2014;73:714–28.10.1097/NEN.0000000000000087Search in Google Scholar

38. Sun CL, Su CH, Wu JJ. Synthesis of short graphene oxide nanoribbons for improved biomarker detection of Parkinson’s disease. Biosens Bioelectron [Epub ahead of print 2014 Aug 23]. pii: S0956-5663(14)00634-4. doi: 10.1016./j.bios.2014.08.046.Search in Google Scholar

39. Williams DR, Lees AJ. Visual hallucinations in the diagnosis of idiopathic Parkinson’s disease: a retrospective autopsy study. Lancet Neurol 2005;4:605–10.10.1016/S1474-4422(05)70146-0Search in Google Scholar

40. Foulds P, Mann DM, Allsop D. Phosphorylated alpha-synuclein as a potential biomarker for Parkinson’s disease and related disorders. Expert Rev Mol Diagn 2012;12:115–7.10.1586/erm.12.5Search in Google Scholar PubMed

41. Hilton D, Stephens M, Kirk L, Edwards P, Potter R, Zajicek J, et al. Accumulation of alpha-synuclein in the bowel of patients in the pre-clinical phase of Parkinson’s disease. Acta Neuropathol 2014;127:235–41.10.1007/s00401-013-1214-6Search in Google Scholar PubMed

42. Adler CH, Dugger BN, Hinni ML, Lott DG, Driver-Dunckley E, Hidalgo J, et al. Submandibular gland needle biopsy for the diagnosis of Parkinson disease. Neurology 2014;82:858–64.10.1212/WNL.0000000000000204Search in Google Scholar PubMed PubMed Central

43. Malek N, Swallow D, Grosset KA, Anichtchik O, Spillantini M, Grosset DG. Alpha-synuclein in peripheral tissues and body fluids as a biomarker for Parkinson’s disease – a systematic review. Acta Neurol Scand 2014;130:59–72.10.1111/ane.12247Search in Google Scholar PubMed

44. Gao L, Tang H, Nie K, Wang L, Zhao J, Gan R, et al. Cerebrospinal fluid alpha-synuclein as a biomarker for Parkinson’s disease diagnosis: a systematic review and meta-analysis. Int J Neurosci 2014;9:1–26.Search in Google Scholar

45. Constantinescu R, Mondello S. Cerebrospinal fluid biomarker candidates for parkinsonian disorders. Front Neurol 2012;3:187.Search in Google Scholar

46. Brettschneider J, Petzold A, Sussmuth SD, Landwehrmeyer GB, Ludolph AC, Kassubek J, et al. Neurofilament heavy-chain NfH(SMI35) in cerebrospinal fluid supports the differential diagnosis of Parkinsonian syndromes. Mov Disord 2006;21:2224–7.10.1002/mds.21124Search in Google Scholar PubMed

47. Matsumoto L, Takuma H, Tamaoka A, Kurisaki H, Date H, Tsuji S, et al. CpG demethylation enhances alpha-synuclein expression and affects the pathogenesis of Parkinson’s disease. PLoS One 2010;5:e15522.10.1371/journal.pone.0015522Search in Google Scholar PubMed PubMed Central

48. Masliah E, Dumaop W, Galasko D, Desplats P. Distinctive patterns of DNA methylation associated with Parkinson disease: identification of concordant epigenetic changes in brain and peripheral blood leukocytes. Epigenetics 2013;8:1030–8.10.4161/epi.25865Search in Google Scholar PubMed PubMed Central

49. Kaut O, Schmitt I, Wullner U. Genome-scale methylation analysis of Parkinson’s disease patients’ brains reveals DNA hypomethylation and increased mRNA expression of cytochrome P450 2E1. Neurogenetics 2012;13:87–91.10.1007/s10048-011-0308-3Search in Google Scholar PubMed

50. Licker V, Turck N, Kövari E, Burkhardt K, Côte M, Surini-Demiri M, et al. Proteomic analysis of human substantia nigra identifies novel candidates involved in Parkinson’s disease pathogenesis. Proteomics 2014;14:784–94.10.1002/pmic.201300342Search in Google Scholar PubMed

51. Zhang X, Yin X, Yu H, Liu X, Yang F, Yao J, et al. Quantitative proteomic analysis of serum proteins in patients with Parkinson’s disease using an isobaric tag for relative and absolute quantification labeling, two-dimensional liquid chromatography, and tandem mass spectrometry. Analyst 2012;137:490–5.10.1039/C1AN15551BSearch in Google Scholar

52. Constantinescu R, Andreasson U, Li S, Podust VN, Mattsson N, Anckarsater R, et al. Proteomic profiling of cerebrospinal fluid in parkinsonian disorders. Parkinsonism Relat Disord 2010;16:545–9.10.1016/j.parkreldis.2010.06.011Search in Google Scholar PubMed

53. Chen HM, Lin CY, Wang V. Amyloid P component as a plasma marker for Parkinson’s disease identified by a proteomic approach. Clin Biochem 2011;44:377–85.10.1016/j.clinbiochem.2011.01.002Search in Google Scholar PubMed

54. Ishigami N, Tokuda T, Ikegawa M, Komori M, Kasai T, Kondo T, et al. Cerebrospinal fluid proteomic patterns discriminate Parkinson’s disease and multiple system atrophy. Mov Disord 2012;27:851–7.10.1002/mds.24994Search in Google Scholar PubMed

55. Han M, Nagele E, DeMarshall C, Acharya N, Nagele R. Diagnosis of Parkinson’s disease based on disease-specific autoantibody profiles in human sera. PLoS One 2012;7:e32383.10.1371/journal.pone.0032383Search in Google Scholar PubMed PubMed Central

56. Lewitt PA, Li J, Lu M, Beach TG, Adler CH, Guo L. 3-hydroxykynurenine and other Parkinson’s disease biomarkers discovered by metabolomic analysis. Mov Disord 2013;28:1653–60.10.1002/mds.25555Search in Google Scholar PubMed

57. Johansen KK, Wang L, Aasly JO, White LR, Matson WR, Henchcliffe C, et al. Metabolomic profiling in LRRK2-related Parkinson’s disease. PLoS One 2009;4:e7551.10.1371/journal.pone.0007551Search in Google Scholar PubMed PubMed Central

58. Ahmed SS, Santosh W. Metallomic profiling and linkage map analysis of early Parkinson’s disease: a new insight to aluminum marker for the possible diagnosis. PLoS One 2010;5:e11252.10.1371/journal.pone.0011252Search in Google Scholar PubMed PubMed Central

59. Roede JR, Uppal K, Park Y, Lee K, Tran V, Walker D, et al. Serum metabolomics of slow vs. rapid motor progression Parkinson’s disease: a pilot study. PLoS One 2013;8:e77629.10.1371/journal.pone.0077629Search in Google Scholar PubMed PubMed Central

60. Back S, Raki M, Tuominen RK, Raasmaja A, Bergstrom K, Mannisto PT. High correlation between in vivo [123I]beta-CIT SPECT/CT imaging and post-mortem immunohistochemical findings in the evaluation of lesions induced by 6-OHDA in rats. EJNMMI Res 2013;3:46.10.1186/2191-219X-3-46Search in Google Scholar PubMed PubMed Central

61. Liu Y, Yue F, Tang R, Tao G, Pan X, Zhu L, et al. Progressive loss of striatal dopamine terminals in MPTP-induced acute parkinsonism in cynomolgus monkeys using vesicular monoamine transporter type 2 PET imaging ([F]AV-133). Neurosci Bull 2013;30:409–16.10.1007/s12264-013-1374-3Search in Google Scholar PubMed PubMed Central

62. Kikuchi A, Takeda A, Okamura N, Tashiro M, Hasegawa T, Furumoto S, et al. In vivo visualization of alpha-synuclein deposition by carbon-11-labelled 2-[2-(2-dimethylaminothiazol-5-yl)ethenyl]-6-[2-(fluoro)ethoxy]benzoxazole positron emission tomography in multiple system atrophy. Brain 2010;133: 1772–8.10.1093/brain/awq091Search in Google Scholar PubMed

63. Hardy J. Genetic analysis of pathways to Parkinson disease. Neuron 2010;68:201–6.10.1016/j.neuron.2010.10.014Search in Google Scholar PubMed PubMed Central

64. Estrada AA, Chan BK, Baker-Glenn C, Beresford A, Burdick DJ, Chambers M, et al. Discovery of highly potent, selective, and brain-penetrant aminopyrazole leucine-rich repeat kinase 2 (LRRK2) small molecule inhibitors. J Med Chem 2014;57: 921–36.10.1021/jm401654jSearch in Google Scholar PubMed

65. Feng Y, Chambers JW, Iqbal S, Koenig M, Park H, Cherry L, et al. A small molecule bidentate-binding dual inhibitor probe of the LRRK2 and JNK kinases. ACS Chem Biol 2013;8:1747–54.10.1021/cb3006165Search in Google Scholar PubMed PubMed Central

66. Diao H, Li X, Hu S, Liu Y. Gene expression profiling combined with bioinformatics analysis identify biomarkers for Parkinson disease. PLoS One 2012;7:e52319.10.1371/journal.pone.0052319Search in Google Scholar PubMed PubMed Central

67. Waybright TJ. Preparation of human cerebrospinal fluid for proteomics biomarker analysis. Methods Mol Biol 2013;1002:61–70.10.1007/978-1-62703-360-2_5Search in Google Scholar PubMed

68. Alberio T, Bucci EM, Natale M, Bonino D, Di Giovanni M, Bottacchi E, et al. Parkinson’s disease plasma biomarkers: an automated literature analysis followed by experimental validation. J Proteomics 2013;90:107–14.10.1016/j.jprot.2013.01.025Search in Google Scholar PubMed

69. Pavese N, Khan NL, Scherfler C, Cohen L, Brooks DJ, Wood NW, et al. Nigrostriatal dysfunction in homozygous and heterozygous parkin gene carriers: an 18F-dopa PET progression study. Mov Disord 2009;24:2260–6.10.1002/mds.22817Search in Google Scholar PubMed

70. Eberling JL, Dave KD, Frasier MA. alpha-synuclein imaging: a critical need for Parkinson’s disease research. J Parkinsons Dis 2013;3:565–7.10.3233/JPD-130247Search in Google Scholar PubMed

71. Hye A, Riddoch-Contreras J, Baird AL, Ashton NJ, Bazenet C, Leung R, et al. Plasma proteins predict conversion to dementia from prodromal disease. Alzheimers Dement 2014;10:799–807.e2.10.1016/j.jalz.2014.05.1749Search in Google Scholar PubMed PubMed Central

72. Kitamura Y, Watanabe S, Taguchi M, Takagi K, Kawata T, Takahashi-Niki K, et al. Neuroprotective effect of a new DJ-1-binding compound against neurodegeneration in Parkinson’s disease and stroke model rats. Mol Neurodegener 2011;6:48.10.1186/1750-1326-6-48Search in Google Scholar PubMed PubMed Central

73. Rott R, Szargel R, Shani V, Bisharat S, Engelender S. Alpha-synuclein ubiquitination and novel therapeutic targets for Parkinson’s disease. CNS Neurol Disord Drug Targets 2014;13:630–7.10.2174/18715273113126660195Search in Google Scholar PubMed

74. Li XZ, Zhang SN, Wang KX, Liu SM, Lu F. iTRAQ-based quantitative proteomics study on the neuroprotective effects of extract of Acanthopanax senticosus harm on SH-SY5Y cells overexpressing A53T mutant alpha-synuclein. Neurochem Int 2014;72C:37–47.10.1016/j.neuint.2014.04.012Search in Google Scholar PubMed

Received: 2014-7-30
Accepted: 2014-11-20
Published Online: 2015-1-12
Published in Print: 2015-9-1

©2015 by De Gruyter

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  2. Editorial
  3. New endocrine biomarkers and cardiovascular disease: is it time for routinely screening?
  4. Reviews
  5. Quantitative detection of amyloid-β peptides by mass spectrometry: state of the art and clinical applications
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  8. Serum calcitonin negative medullary thyroid carcinoma: a systematic review of the literature
  9. Genetics and Molecular Diagnostics
  10. ABCB1 (MDR-1) pharmacogenetics of tacrolimus in renal transplanted patients: a Next Generation Sequencing approach
  11. Novel association of FCGR2A polymorphism with age-related macular degeneration (AMD) and development of a novel CFH real-time genotyping method
  12. General Clinical Chemistry and Laboratory Medicine
  13. Assessing quality on the Sigma scale from proficiency testing and external quality assessment surveys
  14. Combining antibody tests and taking into account antibody levels improves serologic diagnosis of celiac disease
  15. Application of a point of care creatinine device for trend monitoring in kidney transplant patients: fit for purpose?
  16. LC-MS/MS method for hepcidin-25 measurement in human and mouse serum: clinical and research implications in iron disorders
  17. The relationship of fibroblast growth factors 21 and 23 and α-Klotho with platelet activity measured by platelet volume indices
  18. Neurofilament medium polypeptide (NFM) protein concentration is increased in CSF and serum samples from patients with brain injury
  19. Methods to identify saline-contaminated electrolyte profiles
  20. An international study of how laboratories handle and evaluate patient samples after detecting an unexpected APTT prolongation
  21. The influence of excipients commonly used in freeze drying on whole blood coagulation dynamics assessed by rotational thromboelastometry
  22. Reference Values and Biological Variations
  23. Biological variation of plasma osmolality obtained with capillary versus venous blood
  24. Mining of hospital laboratory information systems: a model study defining age- and gender-specific reference intervals and trajectories for plasma creatinine in a pediatric population
  25. Cancer Diagnostics
  26. Fascin is a circulating tumor marker for head and neck cancer as determined by a proteomic analysis of interstitial fluid from the tumor microenvironment
  27. Diabetes
  28. First trimester concentrations of the TTR-RBP4-retinol complex components as early markers of insulin-treated gestational diabetes mellitus
  29. Corrigendum
  30. Corrigendum to: Performance criteria and quality indicators for the pre-analytical phase
  31. Letters to the Editors
  32. Pediatric reference intervals for calculated free testosterone, bioavailable testosterone and free androgen index in the CALIPER cohort
  33. Two novel genomic rearrangements identified in suicide subjects using a-CGH array
  34. Association between physical fitness and mean platelet volume in professional soccer players
  35. Laboratory biomarkers and frailty: presentation of the FRAILOMIC initiative
  36. Spuriously high platelet counts by various automated hematology analyzers in a patient with disseminated intravascular coagulation
  37. To avoid fasting time, more risk than benefits
  38. Daily communication decreases the number of pre-analytical errors in primary care
  39. On-line flagging monitoring – a new quality management tool for the analytical phase
  40. Diagnosis of α1-antitrypsin deficiency using capillary zone electrophoresis
  41. FTL gene mutation and persistent hyperferritinemia without iron deficiency anemia after phlebotomy
  42. Analytical and clinical evaluation of a new immunoassay for therapeutic drug monitoring of etanercept
https://doi.org/10.1515/cclm-2014-0783
Keywords for this article
biomarker; neuroimaging; omics; Parkinson’s disease; pathogenesis

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. New endocrine biomarkers and cardiovascular disease: is it time for routinely screening?
  4. Reviews
  5. Quantitative detection of amyloid-β peptides by mass spectrometry: state of the art and clinical applications
  6. Recent advances in biomarkers for Parkinson’s disease focusing on biochemicals, omics and neuroimaging
  7. Mini Review
  8. Serum calcitonin negative medullary thyroid carcinoma: a systematic review of the literature
  9. Genetics and Molecular Diagnostics
  10. ABCB1 (MDR-1) pharmacogenetics of tacrolimus in renal transplanted patients: a Next Generation Sequencing approach
  11. Novel association of FCGR2A polymorphism with age-related macular degeneration (AMD) and development of a novel CFH real-time genotyping method
  12. General Clinical Chemistry and Laboratory Medicine
  13. Assessing quality on the Sigma scale from proficiency testing and external quality assessment surveys
  14. Combining antibody tests and taking into account antibody levels improves serologic diagnosis of celiac disease
  15. Application of a point of care creatinine device for trend monitoring in kidney transplant patients: fit for purpose?
  16. LC-MS/MS method for hepcidin-25 measurement in human and mouse serum: clinical and research implications in iron disorders
  17. The relationship of fibroblast growth factors 21 and 23 and α-Klotho with platelet activity measured by platelet volume indices
  18. Neurofilament medium polypeptide (NFM) protein concentration is increased in CSF and serum samples from patients with brain injury
  19. Methods to identify saline-contaminated electrolyte profiles
  20. An international study of how laboratories handle and evaluate patient samples after detecting an unexpected APTT prolongation
  21. The influence of excipients commonly used in freeze drying on whole blood coagulation dynamics assessed by rotational thromboelastometry
  22. Reference Values and Biological Variations
  23. Biological variation of plasma osmolality obtained with capillary versus venous blood
  24. Mining of hospital laboratory information systems: a model study defining age- and gender-specific reference intervals and trajectories for plasma creatinine in a pediatric population
  25. Cancer Diagnostics
  26. Fascin is a circulating tumor marker for head and neck cancer as determined by a proteomic analysis of interstitial fluid from the tumor microenvironment
  27. Diabetes
  28. First trimester concentrations of the TTR-RBP4-retinol complex components as early markers of insulin-treated gestational diabetes mellitus
  29. Corrigendum
  30. Corrigendum to: Performance criteria and quality indicators for the pre-analytical phase
  31. Letters to the Editors
  32. Pediatric reference intervals for calculated free testosterone, bioavailable testosterone and free androgen index in the CALIPER cohort
  33. Two novel genomic rearrangements identified in suicide subjects using a-CGH array
  34. Association between physical fitness and mean platelet volume in professional soccer players
  35. Laboratory biomarkers and frailty: presentation of the FRAILOMIC initiative
  36. Spuriously high platelet counts by various automated hematology analyzers in a patient with disseminated intravascular coagulation
  37. To avoid fasting time, more risk than benefits
  38. Daily communication decreases the number of pre-analytical errors in primary care
  39. On-line flagging monitoring – a new quality management tool for the analytical phase
  40. Diagnosis of α1-antitrypsin deficiency using capillary zone electrophoresis
  41. FTL gene mutation and persistent hyperferritinemia without iron deficiency anemia after phlebotomy
  42. Analytical and clinical evaluation of a new immunoassay for therapeutic drug monitoring of etanercept
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