Elsevier

Ageing Research Reviews

Volume 73, January 2022, 101514
Ageing Research Reviews

Review
Identification of blood-based biomarkers for diagnosis and prognosis of Parkinson’s disease: A systematic review of proteomics studies

https://doi.org/10.1016/j.arr.2021.101514Get rights and content

Highlights

  • Proteomics as a potential tool for the discovery of blood-based biomarker.

  • 23 candidate biomarker with 12 consistent regulations across at least two cohorts.

  • ApoA -I demonstrate significant downregulation in PD.

  • ApoA-I association with statin, inflammation and oxidative stress in PD.

  • ApoA-I as a potential candidate biomarker for PD.

Abstract

Parkinson’s Disease (PD), a neurodegenerative disorder, is characterised by the loss of motor function and dopamine neurons. Therapeutic avenues remain a challenge due to lack of accuracy in early diagnosis, monitoring of disease progression and limited therapeutic options. Proteomic platforms have been utilised to discover biomarkers for numerous diseases, a tool that may benefit the diagnosis and monitoring of disease progression in PD patients. Therefore, this systematic review focuses on analysing blood-based candidate biomarkers (CB) identified via proteomics platforms for PD. This study systematically reviewed articles across six databases (EMBASE, Cochrane, Ovid Medline, Scopus, Science Direct and PubMed) published between 2010 and 2020. Of the 504 articles identified, 12 controlled-PD studies were selected for further analysis. A total of 115 candidate biomarkers (CB) were identified across selected 12-controlled studies, of which 23 CB were found to be replicable in more than two cohorts. Using the PANTHER Go-Slim classification system and STRING network, the gene function and protein interactions between biomarkers were analysed. Our analysis highlights Apolipoprotein A-I (ApoA-I), which is essential in lipid metabolism, oxidative stress, and neuroprotection demonstrates high replicability across five cohorts with consistent downregulation across four cohorts. Since ApoA-I was highly replicable across blood fractions, proteomic platforms and continents, its relationship with cholesterol, statin and oxidative stress as PD biomarker, its role in the pathogenesis of PD is discussed in this paper. The present study identified ApoA-I as a potential biomarker via proteomics analysis of PD for the early diagnosis and prediction of disease progression.

Introduction

The prevalence of neurodegenerative disorders is all-time high worldwide and is the leading cause of disability in the ageing population (Dorsey et al., 2018). Parkinson’s Disease (PD) is the second common neurodegenerative disease with a wide range of phenotypes and limited treatments (Kowal et al., 2013). There is an increasing burden on the disease with an estimated prediction of the disease doubling by 2030 in the world (Mizusawa, 2015, Muangpaisan et al., 2009, Tan, 2013). PD is categorised by the progressive loss of dopaminergic neurons in substantia nigra (SN) pars compacts over time that affects the motor functions (Emamzadeh, 2017). Bradykinesia, rigidity, resting tremors, postural instability, dysarthria, and dystonia are the primary motor symptoms (DeMaagd and Philip, 2015, Postuma et al., 2015, Váradi, 2020). Whereas depression, dementia, rapid eye movement (REM), sleep disorder, olfactory dysfunction are examples of non-motor symptoms that co-occur in PD (Neikrug et al., 2014). These signs and symptoms of PD often appear after 50–80% of dopaminergic neurons are lost (DeMaagd and Philip, 2015, El‐Agnaf et al., 2006, Emamzadeh, 2017).

PD accounts for 2–3% of the world population over 65 years old (Ntetsika et al., 2021, Pringsheim et al., 2014). In 2016, a total of 6.1 million cases of PD was reported globally, which is an increase of 144% since 1990, which was 2.5 million (Dorsey et al., 2018). Such a trend is expected to rise in the ageing population and increase age-standardized prevalence rates (Dorsey et al., 2018). To date, the diagnosis of PD is based on clinical observation, but the accuracy of early diagnosis is limited due to the wide range of symptoms, most of which are similar to other neurodegenerative disorders (Chiu et al., 2016, Hughes et al., 2001; Lin et al., 2017). PD symptoms resemble other neurodegenerative disorders, resulting in a lack of accuracy (75 – 90%) in clinical diagnosis(Hughes et al., 2001; Lin et al., 2017). Hence, the disease is treated symptomatically due to a paucity of disease-modifying treatment that can stop nor delay the disease progression (Ntetsika et al., 2021). Therefore, there is a need to identify biomarkers that can predict the disease more accurately, which would benefit the development of therapy while allowing a more precise disease monitoring that ultimately improves PD patients' prognosis.

Biomarkers benefit in the early diagnosis and prediction of disease progression, which is a tool currently needed in PD evaluation and validation. In recent years, proteomics has garnered attention, particularly in discovering disease biomarkers and elucidating disease pathogenesis (Chan et al., 2016, Vaiopoulou et al., 2012). Proteomics study utilizes extensive scale techniques focusing on detection, identification and characterization of protein or peptide expressions and their changes (Chan et al., 2016, Vaiopoulou et al., 2012). In PD, proteomic analysis has enabled a better understanding of the molecular mechanisms underlying PD and develops biomarkers for early detection (Lu et al., 2014, Mila et al., 2009, Rite et al., 2007, Xie et al., 2011). It can also distinguish between different protein isoforms and detect the post-translational modifications that may possess functional importance (Lu et al., 2014, Mann and Pandey, 2000). Additionally, proteomics-based approaches provide unbiased, high-throughput, and quantitative results of a wide range of proteins with high reproducibility, making it an ideal tool to discover potential biomarkers in PD (Hedl et al., 2019). However, despite widespread recognition that such predictive tools are critical to the field, there are currently no clinical or research-based tests to predict PD disease progression (Lewczuk et al., 2018, Posavi et al., 2019). Therefore, this systematic review aims to identify putative blood-based biomarkers using proteomics platform to potentially improve the accuracy in early diagnosis and prediction of the rate of PD progression.

Section snippets

Methods

The preferred reporting items for systematic reviews and meta-analyses (PRISMA) checklist was used to conduct this systematic review (Moher et al., 2010). In addition, the International Prospective Registry of Systematic Reviews (PROSPERO) was used to prospectively register a protocol.

Literature search and selection results

Among the six databases, only five with the exception of Cochrane, yielded articles on the key search terms (Parkinson's Disease AND Proteins AND Proteomes AND Proteomics AND Biomarkers AND Humans). Although the initial search with any of the keywords "Parkinson's disease, proteins, biomarkers and humans" displayed 32 studies using the Cochrane database, the search using all the keywords resulted in none (n = 0). A total of 504 articles were identified through the other five databases (Fig. 1).

Discussion

The current systematic review demonstrated differentially expressed blood-based biomarkers. The current reliable tool for PD diagnosis relies on neuroimaging, is not only costly but lacks specificity to PD without any mechanistic insight in PD patients (Bajaj et al., 2013, Pagano et al., 2016, Politis, 2014). Fluid biomarkers permit the measurement of multiple biomarkers, providing insight into molecular pathways involved in PD (Gaetani et al., 2020). Current systematic review investigated

Conclusion

The proteomics approach is becoming a promising platform for discovering biomarkers due to the accuracy, specificity, and sensitivity in diagnosis and monitoring of the disease progression, which till date remains a challenge in PD. The present systematic review discovered over 23 CB replicable across two to five cohorts, proteomic platforms and even continents. Of those, ApoA-I, Haptoglobin, Clusterin, Transthyretin, ITIH4 were highly replicable with consistent regulation across at least three

Funding

This study was supported by an internal research grant awarded by the Jeffrey Cheah School of Medicine and Health Sciences. School, Monash University Malaysia.

CRediT authorship contribution statement

AKR and SB conceptualised and designed the study as well as supervised the study; SSC did the database search using the key-words identified by AKR, SB and SSC. SSC and SB independently reviewed the full-text articles for suitability to be included in this study and conflicts were reviewed by AKR; SSC wrote the manuscript; KBM and MNAK supported SSC in data analysis and manuscript editing; All authors were involved in critically reviewing and revising the the manuscript.

Declaration of Competing Interest

The authors do not have any competing interest to declare on this manuscript.

Acknowledgments

The authors would like to thank the Head of School, Jeffrey Cheah School of Medicine and Health Sciences. School, Monash University Malaysia for providing financial support for this study.

References (129)

  • D.M. Figueroa et al.

    Chapter 13 - Apolipoproteins as context-dependent regulators of lung inflammation

  • L. Gaetani et al.

    CSF and Blood Biomarkers in Neuroinflammatory and Neurodegenerative Diseases: Implications for Treatment

    Trends Pharm. Sci.

    (2020)
  • A.S. Geller et al.

    Genetic and secondary causes of severe HDL deficiency and cardiovascular disease

    J. Lipid Res

    (2018)
  • D.C. Goff et al.

    2013 ACC/AHA guideline on the assessment of cardiovascular risk: A report of the American college of cardiology/American heart association task force on practice guidelines

    J. Am. Coll. Cardiol.

    (2014)
  • Y. Hu et al.

    Distinct patterns of apolipoprotein C-I, C-II, and C-III isoforms are associated with markers of Alzheimer’s disease

    J. Lipid Res.

    (2021)
  • Ö. Karayel et al.

    Accurate MS-based Rab10 Phosphorylation Stoichiometry Determination as Readout for LRRK2 Activity in Parkinson’s Disease

    Mol. Cell Proteom.

    (2020)
  • K. Kawata et al.

    Chapter 22 - Blood and cerebrospinal fluid biomarkers

  • J. Kim et al.

    The role of apolipoprotein E in Alzheimer’s disease

    Neuron

    (2009)
  • Y. Kitamura et al.

    Proteomic Profiling of Exosomal Proteins for Blood-based Biomarkers in Parkinson’s Disease

    Neuroscience

    (2018)
  • M. Koch et al.

    Apolipoproteins and their subspecies in human cerebrospinal fluid and plasma

    Alzheimer’S. Dement. (Amst., Neth. )

    (2017)
  • B.P. Lucke-Wold et al.

    Sleep disruption and the sequelae associated with traumatic brain injury

    Neurosci. Biobehav. Rev.

    (2015)
  • M. Malec-Litwinowicz et al.

    The relation between plasma α-synuclein level and clinical symptoms or signs of Parkinson’s disease

    Neurol. Neurochir. Pol.

    (2018)
  • D. Moher et al.

    Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

    Int J. Surg.

    (2010)
  • A.B. Neikrug et al.

    Parkinson’s disease and REM sleep behavior disorder result in increased non-motor symptoms

    Sleep. Med.

    (2014)
  • E. Rassart et al.

    Apolipoprotein D

    Biochim. Et. Biophys. Acta (BBA) - Protein Struct. Mol. Enzymol.

    (2000)
  • I. Alecu et al.

    Dysregulated lipid metabolism and its role in α-synucleinopathy in Parkinson’s disease

    Front. Neurosci.

    (2019)
  • M.S. Arredouani et al.

    Haptoglobin dampens endotoxin‐induced inflammatory effects both in vitro and in vivo

    Immunology

    (2005)
  • N. Bajaj et al.

    Clinical utility of dopamine transporter single photon emission CT (DaT-SPECT) with (123I) ioflupane in diagnosis of parkinsonian syndromes

    J. Neurol., Neurosurg., Psychiatry

    (2013)
  • A.M. Bennet et al.

    Association of apolipoprotein E genotypes with lipid levels and coronary risk

    Jama

    (2007)
  • A. Brahimaj et al.

    Serum Levels of Apolipoproteins and Incident Type 2 Diabetes: A Prospective Cohort Study

    Diabetes Care

    (2017)
  • K. Bykov et al.

    Confounding of the association between statins and Parkinson disease: systematic review and meta‐analysis

    Pharmacoepidemiol Drug Saf.

    (2017)
  • S.T. Camargos et al.

    Familial Parkinsonism and early onset Parkinson’s disease in a Brazilian movement disorders clinic: phenotypic characterization and frequency of SNCA, PRKN, PINK1, and LRRK2 mutations

    Mov. Disord.: Off. J. Mov. Disord. Soc.

    (2009)
  • P.P. Chan et al.

    Current application of proteomics in biomarker discovery for inflammatory bowel disease

    World J. Gastrointest. Pathophysiol.

    (2016)
  • C.C. Chiu et al.

    Increased Rab35 expression is a potential biomarker and implicated in the pathogenesis of Parkinson’s disease

    Oncotarget

    (2016)
  • DeMaagd, G., & Philip, A. (2015). Parkinson's Disease and Its Management: Part 1: Disease Entity, Risk Factors,...
  • J. Diniz Pereira et al.

    Alzheimer’s disease and type 2 diabetes mellitus: A systematic review of proteomic studies

    J. Neurochem

    (2021)
  • S. Do Carmo et al.

    Neuroprotective effect of apolipoprotein D against human coronavirus OC43-induced encephalitis in mice

    J. Neurosci.

    (2008)
  • M.H. Dominiczak et al.

    Apolipoproteins: metabolic role and clinical biochemistry applications

    Ann. Clin. Biochem

    (2011)
  • E.R. Dorsey et al.

    Global, regional, and national burden of Parkinson’s disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

    Lancet Neurol.

    (2018)
  • O.M.A. El‐Agnaf et al.

    Detection of oligomeric forms of α‐synuclein protein in human plasma as a potential biomarker for Parkinson’s disease

    FASEB J.

    (2006)
  • D.A. Elliott et al.

    Apolipoproteins in the brain: implications for neurological and psychiatric disorders

    Clin. Lipido

    (2010)
  • F.N. Emamzadeh

    Role of Apolipoproteins and α-Synuclein in Parkinson’s Disease

    J. Mol. Neurosci.

    (2017)
  • F. Fang et al.

    Lipids, Apolipoproteins, and the Risk of Parkinson Disease: A Prospective Cohort Study and a Mendelian Randomization Analysis

    Circ. Res.

    (2019)
  • C.C. de Farias et al.

    Parkinson’s Disease is Accompanied by Intertwined Alterations in Iron Metabolism and Activated Immune-inflammatory and Oxidative Stress Pathways

    CNS Neurol. Disord. Drug Targets

    (2017)
  • P.A.C. Freitas et al.

    Glycated albumin: a potential biomarker in diabetes

    Arch. Endocrinol. Metab.

    (2017)
  • M.D. Ganfornina et al.

    Apolipoprotein D is involved in the mechanisms regulating protection from oxidative stress

    Aging Cell

    (2008)
  • X. Gao et al.

    Prospective study of statin use and risk of Parkinson disease

    Arch. Neurol.

    (2012)
  • N. García-Mateo et al.

    Schwann cell-derived Apolipoprotein D controls the dynamics of post-injury myelin recognition and degradation

    Front Cell Neurosci.

    (2014)
  • K. Georgila et al.

    Apolipoprotein A-I (ApoA-I), Immunity, Inflammation and Cancer

    Cancers

    (2019)
  • N. Grima et al.

    Sleep Disturbances in Traumatic Brain Injury: A Meta-Analysis

    J. Clin. Sleep. Med

    (2016)
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