ReviewIdentification of blood-based biomarkers for diagnosis and prognosis of Parkinson’s disease: A systematic review of proteomics studies
Graphical Abstract
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.
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