Biomarkers of Parkinson's Disease: From Basic Research to Clinical Practice

Cerebrospinal Fluid Biomarkers for Diagnosis of Parkinson's disease: A Systematic Review

Parkinson's disease (PD) is a neurodegenerative disorder that presents challenges in early diagnosis, particularly in its prodromal stages. PD is characterized by motor and non-motor symptoms, and it remains challenging to diagnose in its early stages. The use of cerebrospinal fluid (CSF) biomarkers has shown promise as an adjunctive tool for early detection and monitoring of disease progression. The aim of this systematic review was to evaluate the diagnostic potential of CSF biomarkers in PD. We focused on assessing the reliability, sensitivity, specificity, and utility of various CSF biomarkers for the early and accurate diagnosis of PD. A comprehensive search was conducted across multiple databases, including PubMed, Scopus, and Web of Science, to identify relevant studies published from January 2015 to November 2024. Studies were included if they examined CSF biomarkers in human PD patients, and compared to healthy controls or other neurodegenerative diseases. Data on sample size, biomarker types, and diagnostic accuracy were extracted from 34 eligible studies. The methodological quality of the studies was assessed using standard tools, and a qualitative synthesis was performed using PRISMA tools. Analysis was done to assess the diagnostic performance of selected biomarkers. The review identified several promising CSF biomarkers, including α-synuclein, neurofilament light chain (NfL), DJ-1, tau, and exosomal biomarkers. Of these, α-synuclein demonstrated the highest diagnostic accuracy with a sensitivity of 70-85% and specificity of 75-90%. NfL also showed a strong sensitivity (65-85%) for detecting neuronal injury, while DJ-1 exhibited a high specificity for early-stage PD. Multi-biomarker panels, including combinations of α-synuclein, tau, and NfL, demonstrated superior diagnostic accuracy compared to individual biomarkers. The variability in the biomarkers' performance was noted across studies, indicating the need for standardization in biomarker assays and further validation through larger, multicenter studies. CSF biomarkers hold significant promise for improving the diagnosis of PD, particularly when used in combination. However, more research is needed to establish standardized protocols and evaluate their role in clinical practice. Multi-biomarker panels show potential as a diagnostic tool, but further investigation is required to confirm their clinical utility and cost-effectiveness in diverse populations. Future studies should focus on the longitudinal tracking of these biomarkers for monitoring disease progression and therapeutic response.

Joint effect of modifiable risk factors on Parkinson's disease: a large-scale longitudinal study

Introduction

Previous researches have often underestimated the diversity and combined effects of risk factors for Parkinson's disease (PD). This study aimed to identify how multiple modifiable risk factors collectively impact PD.

Methods

The study included 452,492 participants from the UK Biobank, utilizing genetic data and 255 phenotypic variables. A broad exposure association study was conducted across seven domains: socioeconomic status, medical history, psychosocial factors, physical measures, early life, local environment, and lifestyle. Risk scores of each domain for each participant were generated. The joint effects of modifiable and genetic risks assessed using Cox proportional hazards model. Population attributable fraction (PAF) was estimated to quantify contribution ratio of risk factors in different domains to the occurrence of PD.

Results

Multiple risk factors significantly (p < 1.96 × 10-4) associated with PD was observed. The top 5 factors were hand grip strength (hazard ratio (HR) = 0.98, p = 1.59 × 10-24), long-standing illness (HR = 1.38, p = 3.63 × 10-20), self-reported nervousness (HR = 1.56, p = 5.9 × 10-20), ever suffered from mental health concerns (HR = 1.42, p = 5.48 × 10-18) and chest pain (HR = 1.42, p = 1.43 × 10-18). Individuals with unfavorable medical history, psychosocial factors, physical measures, and lifestyle had an increased risk of PD by 33 to 51% compared to those with favorable factors (p < 0.001).

Discussion

Results indicated that addressing modifiable risk factors, especially in physical measures and psychological factors, could potentially prevent up to 33.87% of PD cases. In formulating prevention strategies, it is recommended to prioritize domains such as physical measures, psychosocial factors, lifestyle, and medical history.

Nanobiosensors in neurodegenerative disease diagnosis: A promising pathway for early detection

Neurodegenerative diseases, including Alzheimer's and Parkinson's, are characterized by progressive neuronal loss, leading to cognitive and motor impairments. Early diagnosis remains a challenge due to the slow progression of symptoms and the limitations of current diagnostic methods. Nanobiosensors, leveraging the high sensitivity and specificity of nanotechnology, offer a promising, noninvasive, and cost-effective approach for detecting disease biomarkers at ultra-low concentrations. This review highlights recent advancements in nanobiosensor technology, including the integration of gold nanoparticles, quantum dots, and carbon nanotubes, which have significantly enhanced biomarker detection precision. Furthermore, it examines the advantages of nanobiosensors over traditional diagnostic techniques, such as improved sensitivity, rapid detection, and minimal invasiveness. The potential of these innovative sensors to revolutionize early disease detection and improve patient outcomes is discussed, along with existing challenges in clinical translation, including stability, reproducibility, and regulatory considerations. Addressing these limitations will be crucial for integrating nanobiosensors into routine clinical practice and advancing personalized medicine for neurodegenerative disorders.

Differences in Blood and Cerebrospinal Fluid Between Parkinson's Disease and Related Diseases

It is difficult to distinguish Parkinson's disease (PD) in the early stage from those of various disorders including atypical Parkinson's syndrome (APS), vascular parkinsonism (VP), and even essential tremor (ET), because of the overlap of symptoms. Other, more challenging problems will arise when Parkinson's disease develops into Parkinson's disease dementia (PDD) in the middle and late stages. At this time, the differential diagnosis of PDD and DLB becomes thorny. These complicate the diagnostic process for PD, which traditionally heavily relies on symptomatic assessment and treatment response. Recent advances have identified several biomarkers in the blood and cerebrospinal fluid (CSF), including α-synuclein, lysosomal enzymes, fatty acid-binding proteins, and neurofilament light chain, whose concentration differs in PD and the related diseases. However, not all these molecules can effectively discriminate PD from related disorders. This review advocates for a paradigm shift toward biomarker-based diagnosis to effectively distinguish between PD and similar conditions. These biomarkers may reflect the diversity that exist among different diseases and provide an effective way to accurately understand their mechanisms. This review focused on blood and CSF biomarkers of PD that may have differential diagnostic value and the related molecular measurement methods with high diagnostic performance due to emerging technologies.

Biological Function Analysis of MicroRNAs and Proteins in the Cerebrospinal Fluid of Patients with Parkinson's Disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by alpha-synuclein aggregation into Lewy bodies in the neurons. Cerebrospinal fluid (CSF) is considered the most suited source for investigating PD pathogenesis and identifying biomarkers. While microRNA (miRNA) profiling can aid in the investigation of post-transcriptional regulation in neurodegenerative diseases, information on miRNAs in the CSF of patients with PD remains limited. This review combines miRNA analysis with proteomic profiling to explore the collective impact of CSF miRNAs on the neurodegenerative mechanisms in PD. We constructed separate networks for altered miRNAs and proteomes using a bioinformatics method. Altered miRNAs were poorly linked to biological functions owing to limited information; however, changes in protein expression were strongly associated with biological functions. Subsequently, the networks were integrated for further analysis. In silico prediction from the integrated network revealed relationships between miRNAs and proteins, highlighting increased reactive oxygen species generation, neuronal loss, and neurodegeneration and suppressed ATP synthesis, mitochondrial function, and neurotransmitter release in PD. The approach suggests the potential of miRNAs as biomarkers for critical mechanisms underlying PD. The combined strategy could enhance our understanding of the complex biochemical networks of miRNAs in PD and support the development of diagnostic and therapeutic strategies for precision medicine.

Simple biomarkers to distinguish Parkinson's disease from its mimics in clinical practice: a comprehensive review and future directions

In the last few years, a plethora of biomarkers have been proposed for the differentiation of Parkinson's disease (PD) from its mimics. Most of them consist of complex measures, often based on expensive technology, not easily employed outside research centers. MRI measures have been widely used to differentiate between PD and other parkinsonism. However, these measurements were often performed manually on small brain areas in small patient cohorts with intra- and inter-rater variability. The aim of the current review is to provide a comprehensive and updated overview of the literature on biomarkers commonly used to differentiate PD from its mimics (including parkinsonism and tremor syndromes), focusing on parameters derived by simple qualitative or quantitative measurements that can be used in routine practice. Several electrophysiological, sonographic and MRI biomarkers have shown promising results, including the blink-reflex recovery cycle, tremor analysis, sonographic or MRI assessment of substantia nigra, and several qualitative MRI signs or simple linear measures to be directly performed on MR images. The most significant issue is that most studies have been conducted on small patient cohorts from a single center, with limited reproducibility of the findings. Future studies should be carried out on larger international cohorts of patients to ensure generalizability. Moreover, research on simple biomarkers should seek measurements to differentiate patients with different diseases but similar clinical phenotypes, distinguish subtypes of the same disease, assess disease progression, and correlate biomarkers with pathological data. An even more important goal would be to predict the disease in the preclinical phase.

Vascular Contributions to Healthy Aging and Dementia

Vascular pathologies are among the most common contributors to neurodegenerative changes across the spectrum of normal aging to dementia. Cerebral small vessel disease (SVD) encompasses a wide range of conditions affecting capillaries, small arteries, and arterioles, as well as perivascular spaces and fluid dynamics in the brain, playing a significant role in vascular contributions to cognitive impairment and dementia (VCID). These factors can accelerate the progression of SVD and neuronal degeneration. Since aging is the primary risk factor for Alzheimer's disease (AD) and AD-related dementias (ADRD), this Research Topic aims to gather recent research to better understand vascular contributions to healthy aging and age-related cognitive impairment. Other risk factors include diabetes, lifestyle factors, high cholesterol, vascular inflammation, and immune remodeling, all of which can accelerate cognitive dysfunction progression. This special issue includes a total of 21 articles comprising Reviews, Perspectives, and Original Research articles. The articles cover various technical and biological aspects related to recent progress in aging and dementia research. We aim to promote research exchange across different fields, including imaging, VCID, molecular biology, neuroinflammation, and immunology. Most papers in this special issue focus on understanding the disease mechanisms of AD/ADRD and developing new therapeutic strategies.

Exploring the application and challenges of fNIRS technology in early detection of Parkinson's disease

Background

Parkinson's disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a critical gap in the early and non-invasive detection of PD. Current diagnostic methods are often invasive, expensive, or late in identifying the disease, leading to missed opportunities for early intervention.

Objective

The goal of this study is to explore the efficiency and accuracy of combining fNIRS technology with machine learning algorithms in diagnosing early-stage PD patients and to evaluate the feasibility of this approach in clinical practice.

Methods

Using an ETG-4000 type near-infrared brain function imaging instrument, data was collected from 120 PD patients and 60 healthy controls. This cross-sectional study employed a multi-channel mode to monitor cerebral blood oxygen changes. The collected data were processed using a general linear model and β values were extracted. Subsequently, four types of machine learning models were developed for analysis: Support vector machine (SVM), K-nearest neighbors (K-NN), random forest (RF), and logistic regression (LR). Additionally, SHapley Additive exPlanations (SHAP) technology was applied to enhance model interpretability.

Results

The SVM model demonstrated higher accuracy in differentiating between PD patients and control group (accuracy of 85%, f1 score of 0.85, and an area under the ROC curve of 0.95). SHAP analysis identified the four most contributory channels (CH) as CH01, CH04, CH05, and CH08.

Conclusion

The model based on the SVM algorithm exhibited good diagnostic performance in the early detection of PD patients. Future early diagnosis of PD should focus on the Frontopolar Cortex (FPC) region.

Pathogenic Impact of Fatty Acid-Binding Proteins in Parkinson's Disease-Potential Biomarkers and Therapeutic Targets

Parkinson's disease is a neurodegenerative condition characterized by motor dysfunction resulting from the degeneration of dopamine-producing neurons in the midbrain. This dopamine deficiency gives rise to a spectrum of movement-related symptoms, including tremors, rigidity, and bradykinesia. While the precise etiology of Parkinson's disease remains elusive, genetic mutations, protein aggregation, inflammatory processes, and oxidative stress are believed to contribute to its development. In this context, fatty acid-binding proteins (FABPs) in the central nervous system, FABP3, FABP5, and FABP7, impact α-synuclein aggregation, neurotoxicity, and neuroinflammation. These FABPs accumulate in mitochondria during neurodegeneration, disrupting their membrane potential and homeostasis. In particular, FABP3, abundant in nigrostriatal dopaminergic neurons, is responsible for α-synuclein propagation into neurons and intracellular accumulation, affecting the loss of mesencephalic tyrosine hydroxylase protein, a rate-limiting enzyme of dopamine biosynthesis. This review summarizes the characteristics of FABP family proteins and delves into the pathogenic significance of FABPs in the pathogenesis of Parkinson's disease. Furthermore, it examines potential novel therapeutic targets and early diagnostic biomarkers for Parkinson's disease and related neurodegenerative disorders.