Unbiased approaches to biomarker discovery in neurodegenerative diseases

Multilayer Exponential Family Factor models for integrative analysis and learning disease progression

Current diagnosis of neurological disorders often relies on late-stage clinical symptoms, which poses barriers to developing effective interventions at the premanifest stage. Recent research suggests that biomarkers and subtle changes in clinical markers may occur in a time-ordered fashion and can be used as indicators of early disease. In this article, we tackle the challenges to leverage multidomain markers to learn early disease progression of neurological disorders. We propose to integrate heterogeneous types of measures from multiple domains (e.g., discrete clinical symptoms, ordinal cognitive markers, continuous neuroimaging, and blood biomarkers) using a hierarchical Multilayer Exponential Family Factor (MEFF) model, where the observations follow exponential family distributions with lower-dimensional latent factors. The latent factors are decomposed into shared factors across multiple domains and domain-specific factors, where the shared factors provide robust information to perform extensive phenotyping and partition patients into clinically meaningful and biologically homogeneous subgroups. Domain-specific factors capture remaining unique variations for each domain. The MEFF model also captures nonlinear trajectory of disease progression and orders critical events of neurodegeneration measured by each marker. To overcome computational challenges, we fit our model by approximate inference techniques for large-scale data. We apply the developed method to Parkinson's Progression Markers Initiative data to integrate biological, clinical, and cognitive markers arising from heterogeneous distributions. The model learns lower-dimensional representations of Parkinson's disease (PD) and the temporal ordering of the neurodegeneration of PD.

New Insights into the Gut Microbiota in Neurodegenerative Diseases from the Perspective of Redox Homeostasis

An imbalance between oxidants and antioxidants in the body can lead to oxidative stress, which is one of the major causes of neurodegenerative diseases. The gut microbiota contains trillions of beneficial bacteria that play an important role in maintaining redox homeostasis. In the last decade, the microbiota-gut-brain axis has emerged as a new field that has revolutionized the study of the pathology, diagnosis, and treatment of neurodegenerative diseases. Indeed, a growing number of studies have found that communication between the brain and the gut microbiota can be accomplished through the endocrine, immune, and nervous systems. Importantly, dysregulation of the gut microbiota has been strongly associated with the development of oxidative stress-mediated neurodegenerative diseases. Therefore, a deeper understanding of the relationship between the gut microbiota and redox homeostasis will help explain the pathogenesis of neurodegenerative diseases from a new perspective and provide a theoretical basis for proposing new therapeutic strategies for neurodegenerative diseases. In this review, we will describe the role of oxidative stress and the gut microbiota in neurodegenerative diseases and the underlying mechanisms by which the gut microbiota affects redox homeostasis in the brain, leading to neurodegenerative diseases. In addition, we will discuss the potential applications of maintaining redox homeostasis by modulating the gut microbiota to treat neurodegenerative diseases, which could open the door for new therapeutic approaches to combat neurodegenerative diseases.

Protective Effects of Polysaccharides in Neurodegenerative Diseases

Neurodegenerative diseases (NDs) are characterized by progressive degeneration and necrosis of neurons, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease and others. There are no existing therapies that correct the progression of these diseases, and current therapies provide merely symptomatic relief. The use of polysaccharides has received significant attention due to extensive biological activities and application prospects. Previous studies suggest that the polysaccharides as a candidate participate in neuronal protection and protect against NDs. In this review, we demonstrate that various polysaccharides mediate NDs, and share several common mechanisms characterized by autophagy, apoptosis, neuroinflammation, oxidative stress, mitochondrial dysfunction in PD and AD. Furthermore, this review reveals potential role of polysaccharides in vitro and in vivo models of NDs, and highlights the contributions of polysaccharides and prospects of their mechanism studies for the treatment of NDs. Finally, we suggest some remaining questions for the field and areas for new development.

The circadian system in Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy

Circadian organization of physiology and behavior is an important biologic process that allows organisms to anticipate and prepare for predictable changes in the environment. Circadian disruptions are associated with a wide range of health issues. In patients with neurodegenerative diseases, alterations of circadian rhythms are among the most common and debilitating symptoms. Although a growing awareness of these symptoms has occurred during the last decade, their underlying neuropathophysiologic circuitry remains poorly understood and, consequently, no effective therapeutic strategies are available to alleviate these health issues. Recent studies have examined the neuropathologic status of the different neural components of the circuitry governing the generation of circadian rhythms in neurodegenerative diseases. In this review, we will dissect the potential contribution of dysfunctions in the different nodes of this circuitry to circadian alterations in patients with parkinsonism-linked neurodegenerative diseases (namely, Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy). A deeper understanding of these mechanisms will provide not only a better understanding of disease neuropathophysiology but also holds promise for the development of more effective and mechanisms-based therapies.

Microstructural differences in white matter tracts across middle to late adulthood: a diffusion MRI study on 7167 UK Biobank participants

White matter fiber tracts demonstrate heterogeneous vulnerabilities to aging effects. Here, we estimated age-related differences in tract properties using UK Biobank diffusion magnetic resonance imaging data of 7167 47- to 76-year-old neurologically healthy people (3368 men and 3799 women). Tract properties in terms of generalized fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity were sampled on 76 fiber tracts; for each tract, age-related differences were estimated by fitting these indices against age in a linear model. This cross-sectional study demonstrated 4 age-difference patterns. The dominant pattern was lower generalized fractional anisotropy and higher axial diffusivity, radial diffusivity, and mean diffusivity with age, constituting 45 of 76 tracts, mostly involving the association, projection, and commissure fibers connecting the prefrontal lobe. The other 3 patterns constituted only 14 tracts, with atypical age differences in diffusion indices, and mainly involved parietal, occipital, and temporal cortices. By analyzing the large volume of diffusion magnetic resonance imaging data available from the UK Biobank, the study has provided a detailed description of heterogeneous age-related differences in tract properties over the whole brain which generally supports the myelodegeneration hypothesis.

Classification algorithms applied to blood-based transcriptome meta-analysis to predict idiopathic Parkinson's disease

Diagnosis of Parkinson's disease (PD) remains a challenge in clinical practice, mostly due to lack of peripheral blood markers. Transcriptomic analysis of blood samples has emerged as a potential means to identify biomarkers and gene signatures of PD. In this context, classification algorithms can assist in detecting data patterns such as phenotypes and transcriptional signatures with potential diagnostic application. In this study, we performed gene expression meta-analysis of blood transcriptome from PD and control patients in order to identify a gene-set capable of predicting PD using classification algorithms. We examined microarray data from public repositories and, after systematic review, 4 independent cohorts (GSE6613, GSE57475, GSE72267 and GSE99039) comprising 711 samples (388 idiopathic PD and 323 healthy individuals) were selected. Initially, analysis of differentially expressed genes resulted in minimal overlap among datasets. To circumvent this, we carried out meta-analysis of 17,712 genes across datasets, and calculated weighted mean Hedges' g effect sizes. From the top-100- positive and negative gene effect sizes, algorithms of collinearity recognition and recursive feature elimination were used to generate a 59-gene signature of idiopathic PD. This signature was evaluated by 9 classification algorithms and 4 sample size-adjusted training groups to create 36 models. Of these, 33 showed accuracy higher than the non-information rate, and 2 models built on Support Vector Machine Regression bestowed best accuracy to predict PD and healthy control samples. In summary, the gene meta-analysis followed by machine learning methodology employed herein identified a gene-set capable of accurately predicting idiopathic PD in blood samples.

Proteomics as a reliable approach for discovery of blood-based Alzheimer's disease biomarkers: A systematic review and meta-analysis

In order to gauge the impact of proteomics in discovery of Alzheimer's disease (AD) blood-based biomarkers, this study had systematically reviewed articles published between 1984-2019. Articles that fulfilled the inclusion criteria were assessed for risk of bias. A meta-analysis was performed for replicable candidate biomarkers (CB). Of the 1651 articles that were identified, 17 case-control and two cohort studies, as well as three combined case-control and longitudinal designs were shortlisted. A total of 207 AD and mild cognitive impairment (MCI) CB were discovered, with 48 reported in >2 studies. This review highlights six CB, namely alpha-2-macroglobulin (α2M)_ps, pancreatic polypeptide (PP)_ps, apolipoprotein A-1 (ApoA-1)_ps, afamin_p, insulin growth factor binding protein-2 (IGFBP-2)_ps and fibrinogen-γ-chain_p, all of which exhibited consistent pattern of regulation in >three independent cohorts. They are involved in AD pathogenesis via amyloid-beta (Aβ), neurofibrillary tangles, diabetes and cardiovascular diseases (CVD). Meta-analysis indicated that ApoA-1_ps was significantly downregulated in AD (SMD = -1.52, 95% CI: -1.89, -1.16, p < 0.00001), with low inter-study heterogeneity (I² = 0%, p = 0.59). α2M_ps was significantly upregulated in AD (SMD = 0.83, 95% CI: 0.05, 1.62, p = 0.04), with moderate inter-study heterogeneity (I² = 41%, p = 0.19). Both CB are involved in Aβ formation. These findings provide important insights into blood-based AD biomarkers discovery via proteomics.

Altered Plasma Acylcarnitines and Amino Acids Profile in Spinocerebellar Ataxia Type 7

Spinocerebellar ataxia type 7 (SCA7), a neurodegenerative disease characterized by cerebellar ataxia and retinal degeneration, is caused by an abnormal CAG repeat expansion in the ATXN7 gene coding region. The onset and severity of SCA7 are highly variable between patients, thus identification of sensitive biomarkers that accurately diagnose the disease and monitoring its progression are needed. With the aim of identified SCA7-specific metabolites with clinical relevance, we report for the first time, to the best of our knowledge, a metabolomics profiling of circulating acylcarnitines and amino acids in SCA7 patients. We identified 21 metabolites with altered levels in SCA7 patients and determined two different sets of metabolites with diagnostic power. The first signature of metabolites (Valine, Leucine, and Tyrosine) has the ability to discriminate between SCA7 patients and healthy controls, while the second one (Methionine, 3-hydroxytetradecanoyl-carnitine, and 3-hydroxyoctadecanoyl-carnitine) possess the capability to differentiate between early-onset and adult-onset patients, as shown by the multivariate model and ROC analyses. Furthermore, enrichment analyses of metabolic pathways suggest alterations in mitochondrial function, energy metabolism, and fatty acid beta-oxidation in SCA7 patients. In summary, circulating SCA7-specific metabolites identified in this study could serve as effective predictors of SCA7 progression in the clinics, as they are sampled in accessible biofluid and assessed by a relatively simple biochemical assay.

Characterization of Parkinson's disease using blood-based biomarkers: A multicohort proteomic analysis

BACKGROUND

Parkinson's disease (PD) is a progressive neurodegenerative disease affecting about 5 million people worldwide with no disease-modifying therapies. We sought blood-based biomarkers in order to provide molecular characterization of individuals with PD for diagnostic confirmation and prediction of progression.

METHODS AND FINDINGS

In 141 plasma samples (96 PD, 45 neurologically normal control [NC] individuals; 45.4% female, mean age 70.0 years) from a longitudinally followed Discovery Cohort based at the University of Pennsylvania (UPenn), we measured levels of 1,129 proteins using an aptamer-based platform. We modeled protein plasma concentration (log10 of relative fluorescence units [RFUs]) as the effect of treatment group (PD versus NC), age at plasma collection, sex, and the levodopa equivalent daily dose (LEDD), deriving first-pass candidate protein biomarkers based on p-value for PD versus NC. These candidate proteins were then ranked by Stability Selection. We confirmed findings from our Discovery Cohort in a Replication Cohort of 317 individuals (215 PD, 102 NC; 47.9% female, mean age 66.7 years) from the multisite, longitudinally followed National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP) Cohort. Analytical approach in the Replication Cohort mirrored the approach in the Discovery Cohort: each protein plasma concentration (log10 of RFU) was modeled as the effect of group (PD versus NC), age at plasma collection, sex, clinical site, and batch. Of the top 10 proteins from the Discovery Cohort ranked by Stability Selection, four associations were replicated in the Replication Cohort. These blood-based biomarkers were bone sialoprotein (BSP, Discovery false discovery rate [FDR]-corrected p = 2.82 × 10-2, Replication FDR-corrected p = 1.03 × 10-4), osteomodulin (OMD, Discovery FDR-corrected p = 2.14 × 10-2, Replication FDR-corrected p = 9.14 × 10-5), aminoacylase-1 (ACY1, Discovery FDR-corrected p = 1.86 × 10-3, Replication FDR-corrected p = 2.18 × 10-2), and growth hormone receptor (GHR, Discovery FDR-corrected p = 3.49 × 10-4, Replication FDR-corrected p = 2.97 × 10-3). Measures of these proteins were not significantly affected by differences in sample handling, and they did not change comparing plasma samples from 10 PD participants sampled both on versus off dopaminergic medication. Plasma measures of OMD, ACY1, and GHR differed in PD versus NC but did not differ between individuals with amyotrophic lateral sclerosis (ALS, n = 59) versus NC. In the Discovery Cohort, individuals with baseline levels of GHR and ACY1 in the lowest tertile were more likely to progress to mild cognitive impairment (MCI) or dementia in Cox proportional hazards analyses adjusting for age, sex, and disease duration (hazard ratio [HR] 2.27 [95% CI 1.04-5.0, p = 0.04] for GHR, and HR 3.0 [95% CI 1.24-7.0, p = 0.014] for ACY1). GHR's association with cognitive decline was confirmed in the Replication Cohort (HR 3.6 [95% CI 1.20-11.1, p = 0.02]). The main limitations of this study were its reliance on the aptamer-based platform for protein measurement and limited follow-up time available for some cohorts.

CONCLUSIONS

In this study, we found that the blood-based biomarkers BSP, OMD, ACY1, and GHR robustly associated with PD across multiple clinical sites. Our findings suggest that biomarkers based on a peripheral blood sample may be developed for both disease characterization and prediction of future disease progression in PD.

Alzheimer's disease and other neurodegenerative dementias in comorbidity: A clinical and neuropathological overview

Neuropathological diagnostic criteria of neurodegenerative disorders are based on the presence of specific inclusions in a specific area of brain tissue that correlate with clinical manifestations. Concomitant neurodegenerative disorders correspond to a combination of two (or more) different fully developed diseases in the same patient. Concomitant neurodegenerative pathology represents the presence of definite neurodegeneration and deposits of pathological proteins specific for another disease, which is not, however, fully developed. Very frequent overlaps include Alzheimer's disease and alpha-synuclein inclusions. Nevertheless, careful neuropathological investigations reveal an increasing frequency of different co-pathologies in examined brains. In Alzheimer's disease, protein TDP-43 may co-aggregate, but it is not clear whether this is atypical isolated Alzheimer's disease or overlap of Alzheimer's disease with early frontotemporal lobar degeneration. Comorbidities of Alzheimer's disease and tauopathies are relatively rare. A combination of vascular pathology with primary neurodegeneration (mostly Alzheimer's disease or dementia with Lewy bodies) is historically called mixed dementia. Overlap of different neuropathologically confirmed neurodegenerations could lead to atypical and unusual clinical presentations and may be responsible for faster disease progression. Several CSF biomarkers have been evaluated for their utility in diagnostic processes in different neurodegenerative dementias; however, evidence regarding their role in neurodegenerative overlaps is still limited.

Metabolic Profiling Reveals Biochemical Pathways and Potential Biomarkers of Spinocerebellar Ataxia 3

Spinocerebellar ataxia 3, also known as Machado-Joseph disease (SCA3/MJD), is a rare autosomal-dominant neurodegenerative disease caused by an abnormal expansion of CAG repeats in the ATXN3 gene. In the present study, we performed a global metabolomic analysis to identify pathogenic biochemical pathways and novel biomarkers implicated in SCA3 patients. Metabolic profiling of serum samples from 13 preclinical SCA3 patients, 13 symptomatic SCA3 patients, and 15 healthy controls were mapped using ultra-high-performance liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry techniques. The symptomatic SCA3 patients showed a metabolic profile significantly distinct from those of the preclinical SCA3 patients and healthy controls. The principal differential metabolites were involved in the amino acid (AA) metabolism and fatty acid metabolism pathways. In addition, four candidate serum biomarkers, FFA 16:1 (palmitoleic acid), FFA 18:3 (linolenic acid), L-Proline and L-Tryptophan, were selected to discriminate between symptomatic SCA3 patients and healthy controls by receiver operator curve analysis with an area under the curve of 0.979. Our study demonstrates that symptomatic SCA3 patients present distinct metabolic profiles with perturbed AA metabolism and fatty acid metabolism, and FFA 16:1, FFA 18:3, L-Proline and L-Tryptophan are identified as potential disease biomarkers.

Analysis of repetitive element expression in the blood and skin of patients with Parkinson's disease identifies differential expression of satellite elements

Repetitive elements (RE) constitute the majority of the human genome and have a range of functions both structural and regulatory on genomic function and gene expression. RE overexpression has been observed in several neurodegenerative diseases, consistent with the observation of aberrant expression of RE posing a mutagenic threat. Despite reports that associate RE expression with PD no study has comprehensively analysed the role of these elements in the disease. This study presents the first genome-wide analysis of RE expression in PD to date. Analysis of RNA-sequencing data of 12 PD patients and 12 healthy controls identified tissue-specific expression differences and more significantly, differential expression of four satellite elements; two simple satellite III (repName = CATTC_n and _GAATG_n) a high-copy satellite II (HSATII) and a centromeric satellite (ALR_Alpha) in the blood of PD patients. In support of the growing body of recent evidence associating REs with neurodegenerative disease, this study highlights the potential importance of characterization of RE expression in such diseases.

Chronotherapies for Parkinson's disease

Parkinson's disease (PD) is the second-most common progressive neurodegenerative disorder. Although the clinical diagnosis of PD is still based on its cardinal motor dysfunctions, several non-motor symptoms (NMS) have been established as integral part of the disease. Unlike motor disorders, development of therapies against NMS are still challenging and remain a critical unmet clinical need. During the last decade, several studies have characterised the molecular, physiological and behavioural alterations of the circadian system in PD patients. As a consequence, and given the ubiquitous nature of circadian rhythms in the entire organism, the biological clock has emerged as a potential therapeutic target to ease suffering from both motor and NMS in PD patients. Here we discuss the emerging field of using bright light, physical exercise and melatonin as chronotherapeutic tools to alleviate motor disorders, sleep/wake alterations, anxiety and depression in PD patients. We also highlight the potential of these readily available therapies to improve the general quality of life and wellbeing of PD patients. Finally, we provide specific data- and mechanisms-driven recommendations that might help improve the therapeutic benefit of light and physical exercise in PD patients.

Aging-Related Changes in Cognition and Cortical Integrity are Associated With Serum Expression of Candidate MicroRNAs for Alzheimer Disease

Evidence has shown that microRNAs (miRNAs) are involved in molecular pathways responsible for aging and prevalent aging-related chronic diseases. However, the lack of research linking circulating levels of miRNAs to changes in the aging brain hampers clinical translation. Here, we have investigated if serum expression of brain-enriched miRNAs that have been proposed as potential biomarkers in Alzheimer’s disease (AD) (miR-9, miR-29b, miR-34a, miR-125b, and miR-146a) are also associated with cognitive functioning and changes of the cerebral cortex in normal elderly subjects. Results revealed that candidate miRNAs were linked to changes in cortical thickness (miR-9, miR-29b, miR-34a, and miR-125b), cortical glucose metabolism (miR-29b, miR-125b, and miR-146a), and cognitive performance (miR-9, miR-34a, and miR-125b). While both miR-29b and miR-125b were related to aging-related structural and metabolic cortical changes, only expression levels of miR-125b were associated with patterns of glucose consumption shown by cortical regions that correlated with executive function. Together, these findings suggest that serum expression of AD-related miRNAs are biologically meaningful in aging and may play a role as biomarkers of cerebral vulnerability in late life.

Diagnostic biomarkers for Parkinson's disease: focus on α-synuclein in cerebrospinal fluid

Diagnostic biomarkers are measures that detect or confirm the presence of a disease or identify individuals with a subtype of the disease. For Parkinson's disease, unlike other neurodegenerative diseases such as Alzheimer's disease and Creutzfeldt-Jakob disease, diagnostic biomarkers remain elusive as none are yet available or approved for clinical use. A biomarker to diagnose early or prodromal Parkinson's disease with high accuracy would significantly enhance clinical practice as well as advance clinical therapeutic trials. Multiple lines of evidence support a role of α-synuclein in the pathophysiology of Parkinson's disease and hence major ongoing efforts to identify biomarkers for Parkinson's disease are aimed at measuring α-synuclein in peripheral tissues and biofluids, including cerebrospinal fluid. This work is still in the early stages of biomarker development and has been accompanied by both losses and victories. Here, α-synuclein in cerebrospinal fluid as a diagnostic marker for Parkinson's disease is reviewed, including measures of total α-synuclein, oligomeric and phosphorylated α-synuclein, and seeding activity of α-synuclein.

Questions concerning the role of amyloid-β in the definition, aetiology and diagnosis of Alzheimer's disease

The dominant hypothesis of Alzheimer’s disease (AD) aetiology, the neuropathological guidelines for diagnosing AD and the majority of high-profile therapeutic efforts, in both research and in clinical practice, have been built around one possible causal factor, amyloid-β (Aβ). However, the causal link between Aβ and AD remains unproven. Here, in the context of a detailed assessment of historical and contemporary studies, we raise critical questions regarding the role of Aβ in the definition, diagnosis and aetiology of AD. We illustrate that a holistic view of the available data does not support an unequivocal conclusion that Aβ has a central or unique role in AD. Instead, the data suggest alternative views of AD aetiology are potentially valid, at this time. We propose that an unbiased way forward for the field, beyond the current Aβ-centric approach, without excluding a role for Aβ, is required to come to an accurate understanding of AD dementia and, ultimately, an effective treatment.

Finding useful biomarkers for Parkinson's disease

The recent advent of an "ecosystem" of shared biofluid sample biorepositories and data sets will focus biomarker efforts in Parkinson's disease, boosting the therapeutic development pipeline and enabling translation with real-world impact.

Whole-Transcriptome Sequencing: a Powerful Tool for Vascular Tissue Engineering and Endothelial Mechanobiology

Among applicable high-throughput techniques in cardiovascular biology, whole-transcriptome sequencing is of particular use. By utilizing RNA that is isolated from virtually all cells and tissues, the entire transcriptome can be evaluated. In comparison with other high-throughput approaches, RNA sequencing is characterized by a relatively low-cost and large data output, which permits a comprehensive analysis of spatiotemporal variation in the gene expression profile. Both shear stress and cyclic strain exert hemodynamic force upon the arterial endothelium and are considered to be crucial determinants of endothelial physiology. Laminar blood flow results in a high shear stress that promotes atheroresistant endothelial phenotype, while a turbulent, oscillatory flow yields a pathologically low shear stress that disturbs endothelial homeostasis, making respective arterial segments prone to atherosclerosis. Severe atherosclerosis significantly impairs blood supply to the organs and frequently requires bypass surgery or an arterial replacement surgery that requires tissue-engineered vascular grafts. To provide insight into patterns of gene expression in endothelial cells in native or bioartificial arteries under different biomechanical conditions, this article discusses applications of whole-transcriptome sequencing in endothelial mechanobiology and vascular tissue engineering.

Updating Our Definitions of Parkinson's Disease for a Molecular Age

Clinical definitions of Parkinson's disease (PD) are over 200 years old, while neuropathological definitions- which are still the basis of how we define the disease now- are over 100 years old. We argue that for both clinical care and therapeutic development, these definitions need updating for the molecular age in which we live. We highlight specific instances in which genetic or biochemical biomarkers are increasingly used for clinical trial enrollment in the neurodegenerative diseases, suggesting that molecular definition(s) of PD are already emerging. We review candidate biomarkers for PD-related pathologies and highlight the need for further validation.

Blood-Based Biomarker Screening with Agnostic Biological Definitions for an Accurate Diagnosis Within the Dimensional Spectrum of Neurodegenerative Diseases

The discovery, development, and validation of novel candidate biomarkers in Alzheimer's disease (AD) and other neurodegenerative diseases (NDs) are increasingly gaining momentum. As a result, evolving diagnostic research criteria of NDs are beginning to integrate biofluid and neuroimaging indicators of pathophysiological mechanisms. More than 10% of people aged over 65 suffer from NDs. There is an urgent need for a refined two-stage diagnostic model to first initiate an early, sensitive, and noninvasive process in primary care settings. Individuals that meet detection criteria will then be channeled to more specific, costly (positron-emission tomography), and invasive (cerebrospinal fluid) assessment methods for confirmatory biological characterization and diagnosis.A reliable and sensitive blood test for AD and other NDs is not yet established; however, it would provide the golden screening gate for an efficient primary care management. A limitation to the development of a large-scale blood-screening biomarker-based test is the traditional application of clinically descriptive criteria for the categorization of single late-stage ND constructs. These are genetically and biologically heterogeneous, reflected in multiple pathophysiological mechanisms and subsequent pathologies throughout a dimensional continuum. Evidence suggests that a shared, "open-source" integrated multilevel categorization of NDs that clusters individuals based on descriptive clinical phenotypes and pathophysiological biomarker signatures will provide the next incremental step toward an improved diagnostic process of NDs. This intermediate objective toward unbiased biomarker-guided early detection of individuals at risk for NDs is currently carried out by the international pilot Alzheimer Precision Medicine Initiative Cohort Program (APMI-CP).

Circulating brain-enriched microRNAs as novel biomarkers for detection and differentiation of neurodegenerative diseases

Background

Minimally invasive specific biomarkers of neurodegenerative diseases (NDs) would facilitate patient selection and disease progression monitoring. We describe the assessment of circulating brain-enriched microRNAs as potential biomarkers for Alzheimer’s disease (AD), frontotemporal dementia (FTD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS).

Methods

In this case-control study, the plasma samples were collected from 250 research participants with a clinical diagnosis of AD, FTD, PD, and ALS, as well as from age- and sex-matched control subjects (n = 50 for each group), recruited from 2003 to 2015 at the University of Pennsylvania Health System, including the Alzheimer’s Disease Center, the Parkinson’s Disease and Movement Disorders Center, the Frontotemporal Degeneration Center, and the Amyotrophic Lateral Sclerosis Clinic. Each group was randomly divided into training and confirmation sets of equal size. To evaluate the potential of circulating microRNAs enriched in specific brain regions affected by NDs and present in synapses as biomarkers of NDs, the levels of 37 brain-enriched and inflammation-associated microRNAs in the plasma of all participants were measured using individual qRT-PCR. A “microRNA pair” approach was used for data normalization.

Results

MicroRNA pairs and their combinations (classifiers) capable of differentiating NDs from control and from each other were defined using independently and jointly analyzed training and confirmation datasets. AD, PD, FTD, and ALS are differentiated from control with accuracy of 0.89, 0.90, 0.88, and 0.83 (AUCs, 0.96, 0.96, 0.94, and 0.93), respectively; NDs are differentiated from each other with accuracy ranging from 0.77 (AUC, 0.87) for AD vs. FTD to 0.93 (AUC, 0.98) for AD vs. ALS. The data further indicate sex dependence of some microRNA markers. The average increase in accuracy in distinguishing ND from control for all and male/female groups is 0.06; the largest increase is for ALS, from 0.83 for all participants to 0.92/0.98 for male/female participants.

Conclusions

The work presented here suggests the possibility of developing microRNA-based diagnostics for detection and differentiation of NDs. Larger multicenter clinical studies are needed to further evaluate circulating brain-enriched microRNAs as biomarkers for NDs and to investigate their association with other ND biomarkers in clinical trial settings.

Electronic supplementary material

The online version of this article (doi:10.1186/s13195-017-0316-0) contains supplementary material, which is available to authorized users.

Parkinson's Disease Biomarkers: Where Are We and Where Do We Go Next?

Background

Objective measures of Parkinson's disease (PD) are needed for purposes of diagnosis and prognostication, as well as identification of those at risk of PD. In this qualitative review, we provide an overview of the current state of the field of PD biomarker development, delineate challenges, and discuss how the field is evolving.

Methods

A search of PubMed was conducted for articles pertaining to objective biomarkers for PD. Articles were selected based on relevance and methodology; where available, meta-analyses, systematic reviews, and comprehensive qualitative review articles were preferentially referenced.

Results

There are several potential sources of objective PD biomarkers including biofluids, peripheral tissue, imaging, genetics, and technology based objective motor testing. Approaches to biomarker identification include the candidate biomarker approach and unbiased discovery methods, each of which has advantages and disadvantages. Several emerging techniques hold promise in each of these areas. Advances in technology and bioinformatics, and the increasing availability of biobanks, are expected to facilitate future PD biomarker development.

Conclusions

The field of objective biomarkers for PD has made great progress but much remains to be done in translating putative biomarkers into tools useful in the clinic and for research. Multimodal biomarker platforms have the potential to capitalize on the utility and strengths of individual biomarkers. Rigorous methodology and standards for replication of findings will be key to meaningful progress in the field.

Analysis of blood-based gene expression in idiopathic Parkinson disease

OBJECTIVE

To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples).

METHODS

Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks.

RESULTS

A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E-6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E-4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1, ATP5A1, and VDAC3.

CONCLUSIONS

We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.

Unlocking the mystery of biomarkers: A brief introduction, challenges and opportunities in Parkinson Disease

First described 200 years ago, Parkinson Disease (PD) exhibits considerable heterogeneity in clinical presentation, as well as trajectory of motor and non-motor decline. This heterogeneity, in turn, complicates the planning of clinical research, particularly trials of disease-modifying therapies, as well as the care of PD patients. While clinical features have been used to delineate subgroups of PD patients, clinical subtyping is hampered by change in features over time, and clinical subtyping may fail to capture the biological processes underlying heterogeneity. In contrast, biomarkers - objective measures that serve as indicators of normal biological processes, pathogenic processes, or pharmacologic responses to therapeutic interventions - have promise to delineate molecularly-defined subgroups of PD patients who may be most likely to benefit from specific therapeutic interventions. Here we review the present role of genetic and biochemical biomarkers in PD. Moreover, we highlight areas where the use of biomarkers may benefit clinical trial planning, as well as clinical care through the application of a "precision medicine" approach, in the near term.

Parkinson disease

Parkinson disease is the second-most common neurodegenerative disorder that affects 2-3% of the population ≥65 years of age. Neuronal loss in the substantia nigra, which causes striatal dopamine deficiency, and intracellular inclusions containing aggregates of α-synuclein are the neuropathological hallmarks of Parkinson disease. Multiple other cell types throughout the central and peripheral autonomic nervous system are also involved, probably from early disease onwards. Although clinical diagnosis relies on the presence of bradykinesia and other cardinal motor features, Parkinson disease is associated with many non-motor symptoms that add to overall disability. The underlying molecular pathogenesis involves multiple pathways and mechanisms: α-synuclein proteostasis, mitochondrial function, oxidative stress, calcium homeostasis, axonal transport and neuroinflammation. Recent research into diagnostic biomarkers has taken advantage of neuroimaging in which several modalities, including PET, single-photon emission CT (SPECT) and novel MRI techniques, have been shown to aid early and differential diagnosis. Treatment of Parkinson disease is anchored on pharmacological substitution of striatal dopamine, in addition to non-dopaminergic approaches to address both motor and non-motor symptoms and deep brain stimulation for those developing intractable L-DOPA-related motor complications. Experimental therapies have tried to restore striatal dopamine by gene-based and cell-based approaches, and most recently, aggregation and cellular transport of α-synuclein have become therapeutic targets. One of the greatest current challenges is to identify markers for prodromal disease stages, which would allow novel disease-modifying therapies to be started earlier.

α-synuclein toxicity in neurodegeneration: mechanism and therapeutic strategies

Alterations in α-synuclein dosage lead to familial Parkinson's disease (PD), and its accumulation results in synucleinopathies that include PD, dementia with Lewy bodies (DLB) and multiple system atrophy (MSA). Furthermore, α-synuclein contributes to the fibrilization of amyloid-b and tau, two key proteins in Alzheimer's disease, which suggests a central role for α-synuclein toxicity in neurodegeneration. Recent studies of factors contributing to α-synuclein toxicity and its disruption of downstream cellular pathways have expanded our understanding of disease pathogenesis in synucleinopathies. In this Review, we discuss these emerging themes, including the contributions of aging, selective vulnerability and non-cell-autonomous factors such as α-synuclein cell-to-cell propagation and neuroinflammation. Finally, we summarize recent efforts toward the development of targeted therapies for PD and related synucleinopathies.

Alterations of the circadian system in Parkinson's disease patients

Alterations of circadian rhythms are among the most debilitating non-motor symptoms in Parkinson's Disease (PD). Although a growing awareness towards these symptoms has occurred during the last decade, their underlying neuropathophysiology remains poorly understood and consequently no effective therapeutic strategies are available to alleviate these problems. Recent studies have investigated multiple circadian rhythms at different stages of PD. The advances made have allowed an accurate evaluation of the affected underlying pathways and mechanisms. Here I dissect, over disease progression, the relative causal contribution to health impairments in PD patients of dysfunctions in the different components of the neural network governing circadian rhythms. A deeper understanding of these mechanisms will provide not only a greater understanding of disease neuropathology, but also hold the promise for effective therapies. © 2016 International Parkinson and Movement Disorder Society.

Ser(P)-1292 LRRK2 in urinary exosomes is elevated in idiopathic Parkinson's disease

BACKGROUND

Mutations in Leucine-rich repeat kinase 2 (LRRK2) enhance levels of the autophosphorylated LRRK2 protein and are the most common known cause of inherited Parkinson's disease (PD). LRRK2 has been further implicated in susceptibility to idiopathic PD in genetic association studies.

OBJECTIVE

The objective of this study was to compare autophosphorylated Ser(P)-1292 LRRK2 levels from biobanked urine samples with clinical data in PD patients and controls.

METHODS

Ser(P)-1292 LRRK2 levels were measured from urine exosome fractions from 79 PD patients and 79 neurologically healthy controls enrolled in the Parkinson Disease Biomarker Program at the University of Alabama at Birmingham.

RESULTS

Ser(P)-1292 LRRK2 levels were higher in men than women (P < .0001) and elevated in PD patients when compared with controls (P = .0014). Ser(P)-1292 LRRK2 levels were higher in PD cases with worse cognition and correlated with poor performance in MoCA (r = -0.2679 [-0.4628 to -0.0482]), MDS-UPDRS subscales 1 and 2 (r = 0.2239 [0.0014-0.4252], 0.3404 [0.1276-0.5233], respectively), Epworth Sleepiness Scale (r = 0.3215 [0.1066-0.5077]), and Modified Schwab and England Activities of Daily Living Scales (r = -0.4455 [-0.6078 to -0.2475]). Ser(P)-1292 LRRK2 levels predicted those with worse cognitive impairment in PD patients with some success (c = 0.73).

CONCLUSIONS

Urinary exosome Ser(P)-1292 LRRK2 levels are elevated in idiopathic PD and correlated with the severity of cognitive impairment and difficultly in accomplishing activities of daily living. These results implicate biochemical changes in LRRK2 in idiopathic PD. © 2016 International Parkinson and Movement Disorder Society.

Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD-abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions.

α-synuclein genetic variability: A biomarker for dementia in Parkinson disease

OBJECTIVE

The relationship between Parkinson disease (PD), PD with dementia (PDD), and dementia with Lewy bodies (DLB) has long been debated. Although PD is primarily considered a motor disorder, cognitive impairment is often present at diagnosis, and only ∼20% of patients remain cognitively intact in the long term. Alpha-synuclein (SNCA) was first implicated in the pathogenesis of the disease when point mutations and locus multiplications were identified in familial parkinsonism with dementia. In worldwide populations, SNCA genetic variability remains the most reproducible risk factor for idiopathic PD. However, few investigators have looked at SNCA variability in terms of cognitive outcomes.

METHODS

We have used targeted high-throughput sequencing to characterize the 135kb SNCA locus in a large multinational cohort of patients with PD, PDD, and DLB and healthy controls.

RESULTS

An analysis of 43 tagging single nucleotide polymorphisms across the SNCA locus shows 2 distinct association profiles for symptoms of parkinsonism and/or dementia, respectively, toward the 3' or the 5' of the SNCA gene. In addition, we define a specific haplotype in intron 4 that is directly associated with PDD. The PDD risk haplotype has been interrogated at single nucleotide resolution and is uniquely tagged by an expanded TTTCn repeat.

INTERPRETATION

Our data show that PD, PDD, and DLB, rather than a disease continuum, have distinct genetic etiologies albeit within one genomic locus. Such results may serve as prognostic biomarkers to these disorders, to inform physicians and patients, and to assist in the design and stratification of clinical trials aimed at disease modification. Ann Neurol 2016;79:991-999.

Plasma EGF and cognitive decline in Parkinson's disease and Alzheimer's disease

Objective

Cognitive decline occurs in multiple neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD). Shared underlying mechanisms may exist and manifest as shared biomarker signatures. Previously, we nominated plasma epidermal growth factor (EGF) as a biomarker predicting cognitive decline in patients with established PD. Here, we investigate EGF as a predictive biomarker in prodromal PD, as well as AD.

Methods

A cohort of PD patients (n = 236) was recruited to replicate our finding that low baseline EGF levels predict future cognitive decline. Additionally, plasma EGF and cognitive outcome measures were obtained from individuals with normal cognition (NC, n = 58), amnestic mild cognitive impairment (AD‐MCI, n = 396), and Alzheimer's disease (AD, n = 112) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to investigate whether low EGF levels correlate with cognitive status and outcome in AD‐MCI and AD. Third, plasma EGF and cognitive measures were evaluated in the high‐risk asymptomatic Parkinson's Associated Risk Study (PARS) cohort (n = 165) to investigate the association of EGF and cognitive performance in a PD prodromal context.

Results

In both PD and AD‐MCI, low baseline plasma EGF predicted poorer long‐term cognitive outcomes. In asymptomatic individuals at highest risk for developing PD from the PARS cohort, low baseline plasma EGF associated with poorer performance in the visuospatial domain but not in other cognitive domains.

Interpretation

Low plasma EGF at baseline predicts cognitive decline in both AD and PD. Evidence for this signal may exist in prodromal stages of both diseases.

An Alzheimer's Disease-Derived Biomarker Signature Identifies Parkinson's Disease Patients with Dementia

Biomarkers from multiple modalities have been shown to correlate with cognition in Parkinson's disease (PD) and in Alzheimer's disease (AD). However, the relationships of these markers with each other, and the use of multiple markers in concert to predict an outcome of interest, are areas that are much less explored. Our objectives in this study were (1) to evaluate relationships among 17 biomarkers previously reported to associate with cognition in PD or AD and (2) to test performance of a five-biomarker classifier trained to recognize AD in identifying PD with dementia (PDD). To do this, we evaluated a cross-sectional cohort of PD patients (n = 75) across a spectrum of cognitive abilities. All PD participants had 17 baseline biomarkers from clinical, genetic, biochemical, and imaging modalities measured, and correlations among biomarkers were assessed by Spearman's rho and by hierarchical clustering. We found that internal correlation among all 17 candidate biomarkers was modest, showing a maximum pairwise correlation coefficient of 0.51. However, a five-marker subset panel derived from AD (CSF total tau, CSF phosphorylated tau, CSF amyloid beta 42, APOE genotype, and SPARE-AD imaging score) discriminated cognitively normal PD patients vs. PDD patients with 80% accuracy, when employed in a classifier originally trained to recognize AD. Thus, an AD-derived biomarker signature may identify PDD patients with moderately high accuracy, suggesting mechanisms shared with AD in some PDD patients. Based on five measures readily obtained during life, this AD-derived signature may prove useful in identifying PDD patients most likely to respond to AD-based crossover therapies.

Blood Biomarkers Associated with Cognitive Decline in Early Stage and Drug-Naive Parkinson's Disease Patients

Early diagnosis of Parkinson's disease (PD) continues to be a major challenge in the field. The lack of a robust biomarker to detect early stage PD patients has considerably slowed the progress toward the development of potential therapeutic agents. We have previously evaluated several RNA biomarkers in whole blood from participants enrolled in two independent clinical studies. In these studies, PD patients were medicated, thus, expression of these biomarkers in de novo patients remains unknown. To this end, we tested ten RNA biomarkers in blood samples from 99 untreated PD patients and 101 HC nested in the cross-sectional Parkinson's Progression Markers Initiative by quantitative real-time PCR. One biomarker out of ten, COPZ1 trended toward significance (nominal p = 0.009) when adjusting for age, sex, and educational level. Further, COPZ1, EFTUD2 and PTBP1 mRNAs correlated with clinical features in PD patients including the Hoehn and Yahr scale, Movement Disorder Society revision of Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Montreal Cognitive Assessment (MoCA) score. Levels of EFTUD2 and PTBP1 were significantly higher in cognitively normal PD patients (PD-CN) compared to cognitively impaired PD patients (PD-MCI). Interestingly, blood glucose levels were significantly higher in PD and PD-MCI patients (≥ 100 mg/dL, pre-diabetes) compared to HC. Collectively, we report the association of three RNA biomarkers, COPZ1, EFTUD2 and PTBP1 with clinical features including cognitive decline in early drug-naïve PD patients. Further, our results show that drug-naïve PD and PD-MCI patients have glucose levels characteristic of pre-diabetes patients, suggesting that impaired glucose metabolism is an early event in PD. Evaluation of these potential biomarkers in a larger longitudinal study is warranted.

Association between α-synuclein blood transcripts and early, neuroimaging-supported Parkinson's disease

There are no cures for neurodegenerative diseases and this is partially due to the difficulty of monitoring pathogenic molecules in patients during life. The Parkinson's disease gene α-synuclein (SNCA) is selectively expressed in blood cells and neurons. Here we show that SNCA transcripts in circulating blood cells are paradoxically reduced in early stage, untreated and dopamine transporter neuroimaging-supported Parkinson's disease in three independent regional, national, and international populations representing 500 cases and 363 controls and on three analogue and digital platforms with P < 0.0001 in meta-analysis. Individuals with SNCA transcripts in the lowest quartile of counts had an odds ratio for Parkinson's disease of 2.45 compared to individuals in the highest quartile. Disease-relevant transcript isoforms were low even near disease onset. Importantly, low SNCA transcript abundance predicted cognitive decline in patients with Parkinson's disease during up to 5 years of longitudinal follow-up. This study reveals a consistent association of reduced SNCA transcripts in accessible peripheral blood and early-stage Parkinson's disease in 863 participants and suggests a clinical role as potential predictor of cognitive decline. Moreover, the three independent biobank cohorts provide a generally useful platform for rapidly validating any biological marker of this common disease.

The Role of Metabolomics in Brain Metabolism Research

This special edition of the Journal of Neuroimmune Pharmacology focuses on the leading edge of metabolomics in brain metabolism research. The topics covered include a metabolomic field overview and the challenges in neuroscience metabolomics. The workflow and utility of different analytical platforms to profile complex biological matrices that include biofluids, brain tissue and cells, are shown in several case studies. These studies demonstrate how global and targeted metabolite profiling can be applied to distinguish disease stages and to understand the effects of drug action on the central nervous system (CNS). Finally, we discuss the importance of metabolomics to advance the understanding of brain function that includes ligand-receptor interactions and new insights into the mechanisms of CNS disorders.