Cerebrospinal fluid NFL in the differential diagnosis of parkinsonian disorders: A meta-analysis

Extracellular Vesicles for the Diagnosis of Parkinson's Disease: Systematic Review and Meta-Analysis

Parkinson's disease (PD) biomarkers are needed by both clinicians and researchers (for diagnosis, identifying study populations, and monitoring therapeutic response). Imaging, genetic, and biochemical biomarkers have been widely studied. In recent years, extracellular vesicles (EVs) have become a promising material for biomarker development. Proteins and molecular material from any organ, including the central nervous system, can be packed into EVs and transported to the periphery into easily obtainable biological specimens like blood, urine, and saliva. We performed a systematic review and meta-analysis of articles (published before November 15, 2022) reporting biomarker assessment in EVs in PD patients and healthy controls (HCs). Biomarkers were analyzed using random effects meta-analysis and the calculated standardized mean difference (Std.MD). Several proteins and ribonucleic acids have been identified in EVs in PD patients, but only α-synuclein (aSyn) and leucine-rich repeat kinase 2 (LRRK2) were reported in sufficient studies (n = 24 and 6, respectively) to perform a meta-analysis. EV aSyn was significantly increased in neuronal L1 cell adhesion molecule (L1CAM)-positive blood EVs in PD patients compared to HCs (Std.MD = 1.84, 95% confidence interval = 0.76-2.93, P = 0.0009). Further analysis of the biological sample and EV isolation method indicated that L1CAM-IP (immunoprecipitation) directly from plasma was the best isolation method for assessing aSyn in PD patients. Upcoming neuroprotective clinical trials immediately need peripheral biomarkers for identifying individuals at risk of developing PD. Overall, the improved sensitivity of assays means they can identify biomarkers in blood that reflect changes in the brain. CNS-derived EVs in blood will likely play a major role in biomarker development in the coming years. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The 2022 Lady Estelle Wolfson lectureship on neurofilaments

Neurofilament proteins (Nf) have been validated and established as a reliable body fluid biomarker for neurodegenerative pathology. This review covers seven Nf isoforms, Nf light (NfL), two splicing variants of Nf medium (NfM), two splicing variants of Nf heavy (NfH),α -internexin (INA) and peripherin (PRPH). The genetic and epigenetic aspects of Nf are discussed as relevant for neurodegenerative diseases and oncology. The comprehensive list of mutations for all Nf isoforms covers Amyotrophic Lateral Sclerosis, Charcot-Marie Tooth disease, Spinal muscular atrophy, Parkinson Disease and Lewy Body Dementia. Next, emphasis is given to the expanding field of post-translational modifications (PTM) of the Nf amino acid residues. Protein structural aspects are reviewed alongside PTMs causing neurodegenerative pathology and human autoimmunity. Molecular visualisations of NF PTMs, assembly and stoichiometry make use of Alphafold2 modelling. The implications for Nf function on the cellular level and axonal transport are discussed. Neurofilament aggregate formation and proteolytic breakdown are reviewed as relevant for biomarker tests and disease. Likewise, Nf stoichiometry is reviewed with regard to in vitro experiments and as a compensatory mechanism in neurodegeneration. The review of Nf across a spectrum of 87 diseases from all parts of medicine is followed by a critical appraisal of 33 meta-analyses on Nf body fluid levels. The review concludes with considerations for clinical trial design and an outlook for future research.

Diffusion Magnetic Resonance Imaging Microstructural Abnormalities in Multiple System Atrophy: A Comprehensive Review

Multiple system atrophy (MSA) is a neurodegenerative disease characterized by autonomic failure, ataxia, and/or parkinsonism. Its prominent pathological alterations can be investigated using diffusion magnetic resonance imaging (dMRI), a technique that exploits the characteristics of water random motion inside brain tissue. The aim of this report was to review currently available literature on the application of dMRI in MSA and to describe microstructural abnormalities, diagnostic applications, and pathophysiological correlates. Sixty-four published studies involving microstructural investigation using dMRI in MSA were included. Widespread microstructural abnormalities of white matter were described, especially in the middle cerebellar peduncle, corticospinal tract, and hemispheric fibers. Gray matter degeneration was identified as well, with diffuse involvement of subcortical structures, especially in the putamina. Diagnostic applications of dMRI were mostly explored for the differential diagnosis between MSA parkinsonism and Parkinson's disease. Recently, machine learning algorithms for image processing and disease classification have demonstrated high diagnostic accuracy, showing potential for translation into clinical practice. To a lesser extent, clinical correlates of microstructural abnormalities have also been investigated, and abnormalities related to motor, ocular, and cognitive impairments were described. dMRI in MSA has contributed to in vivo identification of known pathological abnormalities. Translation into clinical practice of the latest advancements for the differential diagnosis between MSA and other forms of parkinsonism seems feasible. Current limitations involve the possibility of correctly diagnosing MSA in the very early stages, when the clinical diagnosis is most uncertain. Furthermore, pathophysiological correlates of microstructural abnormalities remain understudied. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Neurofilaments in neurologic disorders and beyond

Many neurologic diseases can initially present as a diagnostic challenge and even when a diagnosis is made, monitoring of disease activity, progression and response to therapy may be limited with existing clinical and paraclinical assessments. As such, the identification of disease specific biomarkers provides a promising avenue by which diseases can be effectively diagnosed, monitored and used as a prognostic indicator for long-term outcomes. Neurofilaments are an integral component of the neuronal cytoskeleton, where assessment of neurofilaments in the blood, cerebrospinal fluid (CSF) and diseased tissue has been shown to have value in providing diagnostic clarity, monitoring disease activity, tracking progression and treatment efficacy, as well as lending prognostic insight into long-term outcomes. As such, this review attempts to provide a glimpse into the structure and function of neurofilaments, their role in various neurologic and non-neurologic disorders, including uncommon conditions with recent knowledge of neurofilament-related pathology, as well as their applicability in future clinical practice.

CSF and Circulating NfL as Biomarkers for the Discrimination of Parkinson Disease From Atypical Parkinsonian Syndromes: Meta-analysis

PURPOSE OF REVIEW

To evaluate whether CSF and circulating neurofilament light chain (NfL), a marker of axonal damage, could discriminate Parkinson disease (PD) from atypical parkinsonian syndromes (APSs).

RECENT FINDINGS

MEDLINE and Scopus were systematically searched, and 15 studies were included (1,035 patients with PD and 930 patients with APS). CSF NfL levels were 1.26 SDs higher in the APS group compared to the PD group (g = 1.26 [95% confidence interval 0.99-1.53]), and circulating NfL levels were 1.53 SDs higher in the APS group compared to the PD group (g = 1.53 [95% confidence interval 1.15-1.91]); 4 studies, 307 patients with PD, 197 patients with APS. Pooled areas under the curve were 0.941 (0.916-0.965) and 0.874 (0.802-0.946) for CSF and circulating NfL, corresponding to average sensitivities of 86% (79%-90%) and 91% (86%-95%), and specificity of 88% (82%-92%) and 76% (62%-85%), respectively.

SUMMARY

These results strongly support the high diagnostic accuracy of both CSF and circulating NfL in differentiating PD from APS, highlighting their usefulness as promising biomarkers.

Cerebrospinal Fluid Biomarkers in Multiple System Atrophy Relative to Parkinson's Disease: A Meta-Analysis

Objective

To investigate the differences of candidate cerebrospinal fluid (CSF) biomarkers associated with multiple system atrophy (MSA) and Parkinson's disease (PD).

Method

Here, a systematic review and meta-analysis were conducted on studies related to CSF biomarkers associated with MSA and PD obtained from PubMed, Web of Science, Embase, and Cochrane databases. Data were pooled where appropriate and used to calculate standardized mean differences (SMDs) with 95% confidence intervals (CI). Heterogeneity was assessed using the I² statistic while Egger's test was used to test for existing publication bias.

Results

MSA patients had higher CSF t-tau (SMD = 0.41, 95% CI: 0.10 to 0.72) and YKL-40 (SMD = 0.63, 95% CI 0.12 to1.15) as well as DJ-1 (SMD = 1.05, 95% CI 0.67 to 1.42) levels than PD patients, while CSF p-tau (SMD = −0.17, 95% CI, -0.31 to -0.02) and Aβ-42 (SMD = −0.33, 95% CI, -0.55 to -0.12) levels in MSA patients were lower than those in PD patients. There were no differences in CSF's GFAP and Flt3 ligand levels in both MSA and PD patients.

Conclusion

The study revealed the differences in CSF biomarker levels between MSA and PD cohorts that can be further explored to clinically distinguish MSA from PD.

Plasma Neurofilament Light Chain as a Predictive Biomarker for Post-stroke Cognitive Impairment: A Prospective Cohort Study

BACKGROUND

Plasma neurofilaments light chain (pNfL) is a marker of axonal injury. The purpose of this study was to examine the role of pNfL as a predictive biomarker for post-stroke cognitive impairment (PSCI).

METHODS

A prospective single-center observational cohort study was conducted at the General Hospital of Western Theater Command between July 1, 2017 and December 31, 2019. Consecutive patients ≥18 years with first-ever acute ischemic stroke (AIS) of anterior circulation within 24 h of symptom onset were included. PSCI was defined by the Montreal Cognitive Assessment (MOCA) (MOCA < 26) at 90 days after stroke onset.

RESULTS

A total of 1,694 patients [male, 893 (52.70%); median age, 64 (16) years] were enrolled in the cohort analysis, and 1,029 (60.70%) were diagnosed with PSCI. Patients with PSCI had significantly higher pNfL [median (IQR), 55.96 (36.13) vs. 35.73 (17.57) pg/ml; P < 0.001] than Non-PSCI. pNfL was valuable for the prediction of PSCI (OR 1.044, 95% CI 1.038-1.049, P < 0.001) after a logistic regression analysis, even after adjusting for conventional risk factors including age, sex, education level, NIHSS, TOAST classification, and infarction volume (OR 1.041, 95% CI 1.034-1.047, P < 0.001). The optimal cutoff value of the pNfL concentration was 46.12 pg/ml, which yielded a sensitivity of 71.0% and a specificity of 81.5%, with the area under the curve (AUC) at 0.785 (95% CI 0.762-0.808, P < 0.001).

CONCLUSION

This prospective cohort study showed that the pNfL concentration within 48 h of onset was an independent risk factor for PSCI 90 days after an anterior circulation stroke, even after being adjusted for potential influencing factors regarded as clinically relevant.

CLINICAL TRIAL REGISTRATION

www.chictr.org.cn, identifier ChiCTR1800020330.

Blood neurofilament light chain in Parkinson disease and atypical parkinsonisms: A protocol for systematic review and meta-analysis

BACKGROUND

Neurofilament light chain (NfL), an index of neuroaxonal injury, is a promising diagnostic and prognostic fluid biomarker with high translational value in many neurodegenerative disorders. Blood NfL measurement has been an exciting and active field of research in idiopathic Parkinson disease (PD) and atypical parkinsonisms. However, blood NfL levels in these parkinsonisms from existing literature were inconsistent. No comprehensive meta-analysis has ever been conducted.

METHODS

Three major biomedical electronic databases PubMed, Embase, and Web of Science were comprehensively searched from inception to July 10, 2020. This protocol will be prepared based on the guidelines recommended by the statement of Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Original observational studies that measured blood (serum/plasma) NfL concentrations in patients with parkinsonisms (multiple system atrophy [MSA], progressive supranuclear palsy [PSP], corticobasal syndrome [CBS], and dementia with Lewy bodies [DLB]), and healthy controls (HCs) will be included. Quality assessment of the included studies will be performed using the Newcastle Ottawa Scale (NOS). Meta-analyses will be conducted using the STATA software version 13.0. The standardized mean differences as the measure of effect size and 95% confidence intervals were calculated for each comparison of blood NfL levels. Heterogeneity analysis, sensitivity analysis, publication bias, subgroup analysis, and meta-regression analysis will be carried out to test the robustness of the results.

RESULTS

The meta-analysis will obtain the effect sizes of blood NfL levels in the following comparisons: PD versus HC, MSA versus HC, PSP versus HC, CBS versus HC, DLB versus HC, MSA versus PD, PSP versus PD, CBS versus PD, and DLB versus PD.

CONCLUSIONS

The present meta-analysis will provide the quantitative evidence of NfL levels in idiopathic PD and atypical parkinsonisms, hoping to facilitate differential diagnoses in clinical practice.

REGISTRATION NUMBER

INPLASY202070091.

Cerebrospinal Fluid Biomarkers in Parkinson's Disease: A Critical Overview of the Literature and Meta-Analyses

Parkinson’s disease (PD) is a common neurodegenerative disorder; however, well-established biochemical markers have not yet been identified. This review article covers several candidate cerebrospinal fluid (CSF) biomarkers for PD based on the recent literature and meta-analysis data. The decrease of α-synuclein in PD is supported by meta-analyses with modest reproducibility, and a decrease of amyloid β42 is seen as a prognostic marker for cognitive decline. Tau, phosphorylated tau (p-tau), and neurofilament light chains have been used to discriminate PD from other neurodegenerative disorders. This article also describes more hopeful biochemical markers, such as neurotransmitters, oxidative stress markers, and other candidate biomarkers.

What Have We Learned from Cerebrospinal Fluid Studies about Biomarkers for Detecting LRRK2 Parkinson's Disease Patients and Healthy Subjects with Parkinson's-Associated LRRK2 Mutations?

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene are the most common known cause of autosomal dominant Parkinson’s disease (PD) and sporadic PD (sPD). The clinical presentation of LRRK2 PD is similar to sPD, and except for genetic testing, no biochemical or imaging markers can differentiate LRRK2 PD from sPD. Discovery of such biomarkers could indicate neuropathological mechanisms that are unique to or increased in LRRK2 PD. This review discusses findings in 17 LRRK2 - related CSF studies found on PubMed. Most of these studies compared analyte concentrations between four diagnostic groups: LRRK2 PD patients, sPD patients, asymptomatic control subjects carrying PD-associated LRRK2 mutations (LRRK2 CTL), and healthy control subjects lacking LRRK2 mutations (CTL). Analytes examined in these studies included Aβ1-42, tau, α-synuclein, oxidative stress markers, autophagy-related proteins, pteridines, neurotransmitter metabolites, exosomal LRRK2 protein, RNA species, inflammatory cytokines, mitochondrial DNA (mtDNA), and intermediary metabolites. FINDINGS: Pteridines, α-synuclein, mtDNA, 5-hydroxyindolacetic acid, β-D-glucose, lamp2, interleukin-8, and vascular endothelial growth factor were suggested to differentiate LRRK2 PD from sPD patients; 8-hydroxy-2’-deoxyguanosine (8-OHdG), 8-isoprostane (8-ISO), 2-hydroxybutyrate, mtDNA, lamp2, and neopterin may differentiate between LRRK2 CTL and LRRK2 PD subjects; and soluble oligomeric α-synuclein, 8-OHdG, and 8-ISO might differentiate LRRK2 CTL from CTL subjects. CONCLUSIONS: The low numbers of investigations of each analyte, small sample sizes, and methodological differences limit conclusions that can be drawn from these studies. Further investigations are indicated to determine the validity of the analytes identified in these studies as possible biomarkers for LRRK2 PD patients and/or LRRK2 CTL subjects.

The path to biomarker-based diagnostic criteria for the spectrum of neurodegenerative diseases

Introduction: The postmortem examination still represents the reference standard for detecting the pathological nature of chronic neurodegenerative diseases (NDD). This approach displays intrinsic conceptual limitations since NDD represent a dynamic spectrum of partially overlapping phenotypes, shared pathomechanistic alterations that often give rise to mixed pathologies.Areas covered: We scrutinized the international clinical diagnostic criteria of NDD and the literature to provide a roadmap toward a biomarker-based classification of the NDD spectrum. A few pathophysiological biomarkers have been established for NDD. These are time-consuming, invasive, and not suitable for preclinical detection. Candidate screening biomarkers are gaining momentum. Blood neurofilament light-chain represents a robust first-line tool to detect neurodegeneration tout court and serum progranulin helps detect genetic frontotemporal dementia. Ultrasensitive assays and retinal scans may identify Aβ pathology early, in blood and the eye, respectively. Ultrasound also represents a minimally invasive option to investigate the substantia nigra. Protein misfolding amplification assays may accurately detect α-synuclein in biofluids.Expert opinion: Data-driven strategies using quantitative rather than categorical variables may be more reliable for quantification of contributions from pathophysiological mechanisms and their spatial-temporal evolution. A systems biology approach is suitable to untangle the dynamics triggering loss of proteostasis, driving neurodegeneration and clinical evolution.

Diagnostic utility of fluid biomarkers in multiple system atrophy: a systematic review and meta-analysis

BACKGROUND

Multiple system atrophy (MSA) is an adult onset, fatal neurodegenerative disease. However, no reliable biomarker is currently available to guide clinical diagnosis and help to determine the prognosis. Thus, a comprehensive meta-analysis is warranted to determine effective biomarkers for MSA and provide useful guidance for clinical diagnosis.

METHODS

A comprehensive literature search was made of the PubMed, Embase, Cochrane and Web of Science databases for relevant clinical trial articles for 1984-2019. Two review authors examined the full-text records, respectively, and determined which studies met the inclusion criteria. We estimated the mean difference, standard deviation and 95% confidence intervals.

RESULTS

A total of 28 studies and 11 biomarkers were included in our analysis. Several biomarkers were found to be useful to distinguish MSA patients from healthy controls, including the reduction of phosphorylated tau, α-synuclein (α-syn), 42-amino-acid form of Aβ and total tau (t-tau), the elevation of neurofilament light-chain protein (NFL) in cerebrospinal fluid, the elevation of uric acid and reduction of homocysteine and coenzyme Q10 in plasma. Importantly, α-syn, NFL and t-tau could be used to distinguish MSA from Parkinson's disease (PD), indicating that these three biomarkers could be useful biomarkers in MSA diagnosis.

CONCLUSION

The findings of our meta-analysis demonstrated diagnostic biomarkers for MSA. Moreover, three biomarkers could be used in differential diagnosis of MSA and PD. The results could be helpful for the early diagnosis of MSA and the accuracy of MSA diagnosis.

Discriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learning

BACKGROUND

Progressive supranuclear palsy (PSP), a neurodegenerative conditions may be difficult to discriminate clinically from idiopathic Parkinson's disease (PD). It is critical that we are able to do this accurately and as early as possible in order that future disease modifying therapies for PSP may be deployed at a stage when they are likely to have maximal benefit. Analysis of gait and related tasks is one possible means of discrimination.

RESEARCH QUESTION

Here we investigate a wearable sensor array coupled with machine learning approaches as a means of disease classification.

METHODS

21 participants with PSP, 20 with PD, and 39 healthy control (HC) subjects performed a two minute walk, static sway test, and timed up-and-go task, while wearing an array of six inertial measurement units. The data were analysed to determine what features discriminated PSP from PD and PSP from HC. Two machine learning algorithms were applied, Logistic Regression (LR) and Random Forest (RF).

RESULTS

17 features were identified in the combined dataset that contained independent information. The RF classifier outperformed the LR classifier, and allowed discrimination of PSP from PD with 86 % sensitivity and 90 % specificity, and PSP from HC with 90 % sensitivity and 97 % specificity. Using data from the single lumbar sensor only resulted in only a modest reduction in classification accuracy, which could be restored using 3 sensors (lumbar, right arm and foot). However for maximum specificity the full six sensor array was needed.

SIGNIFICANCE

A wearable sensor array coupled with machine learning methods can accurately discriminate PSP from PD. Choice of array complexity depends on context; for diagnostic purposes a high specificity is needed suggesting the more complete array is advantageous, while for subsequent disease tracking a simpler system may suffice.

New markers in Parkinson's disease

Parkinson's disease (PD) is a chronic, debilitating neurodegenerative disorder characterized clinically by a variety of progressive motor and nonmotor symptoms. Currently, there is a dearth of diagnostic tools available to predict, diagnose or mitigate disease risk or progression, leading to a challenging dilemma within the healthcare management system. The search for a reliable biomarker for PD that reflects underlying pathology is a high priority in PD research. Currently, there is no reliable single biomarker predictive of risk for motor and cognitive decline, and there have been few longitudinal studies of temporal progression. A combination of multiple biomarkers might facilitate earlier diagnosis and more accurate prognosis in PD. In this review, we focus on the recent developments of serial biomarkers for PD from a variety of clinical, biochemical, genetic and neuroimaging perspectives.

Longitudinal analyses of cerebrospinal fluid α-Synuclein in prodromal and early Parkinson's disease

BACKGROUND

Aggregation of α-synuclein is central to the pathophysiology of PD. Biomarkers related to α-synuclein may be informative for PD diagnosis/progression.

OBJECTIVES

To analyze α-synuclein in CSF in drug-naïve PD, healthy controls, and prodromal PD in the Parkinson's Progression Markers Initiative.

METHODS

Over up to 36-month follow-up, CSF total α-synuclein and its association with MDS-UPDRS motor scores, cognitive assessments, and dopamine transporter imaging were assessed.

RESULTS

The inception cohort included PD (n = 376; age [mean {standard deviation} years]: 61.7 [9.62]), healthy controls (n = 173; age, 60.9 [11.3]), hyposmics (n = 16; age, 68.3 [6.15]), and idiopathic rapid eye movement sleep behavior disorder (n = 32; age, 69.3 [4.83]). Baseline CSF α-synuclein was lower in manifest and prodromal PD versus healthy controls. Longitudinal α-synuclein decreased significantly in PD at 24 and 36 months, did not change in prodromal PD over 12 months, and trended toward an increase in healthy controls. The decrease in PD was not shown when CSF samples with high hemoglobin concentration were removed from the analysis. CSF α-synuclein changes did not correlate with longitudinal MDS-UPDRS motor scores or dopamine transporter scan.

CONCLUSIONS

CSF α-synuclein decreases early in the disease, preceding motor PD. CSF α-synuclein does not correlate with progression and therefore does not reflect ongoing dopaminergic neurodegeneration. Decreased CSF α-synuclein may be an indirect index of changes in the balance between α-synuclein secretion, solubility, or aggregation in the brain, reflecting its overall turnover. Additional biomarkers more directly related to α-synuclein pathophysiology and disease progression and other markers to be identified by, for example, proteomics and metabolomics are needed. © 2019 International Parkinson and Movement Disorder Society.

Corticobasal degeneration and corticobasal syndrome: A review

Corticobasal degeneration (CBD) is a rare neurodegenerative disorder. The most common presentation of CBD is the corticobasal syndrome (CBS), which is a constellation of cortical and extrapyramidal symptoms and signs. Clinical-pathological studies have illustrated that CBD can present with diverse clinical phenotypes, including a non-fluent, agrammatic primary progressive aphasia syndrome, a behavioral, dysexecutive and visuospatial syndrome, as well as a progressive supranuclear palsy-like syndrome. Conversely, multiple pathologies, such as CBD, Alzheimer's disease and progressive supranuclear palsy may underlie a patient with CBS. This clinical-pathological overlap emphasizes the need for biomarkers that will assist in the accurate diagnosis of patients with CBS. This review presents an overview of the pathological, genetic, clinical and therapeutic characteristics of CBD, with an emphasis on the imaging (structural and functional) and biochemical (cerebrospinal fluid) biomarkers of CBD.