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To assess the neurobiological substrate of initial cognitive decline in Parkinson's disease (PD) to inform patient management, clinical trial design, and development of treatments. We longitudinally assessed, up to 3 years, 423 newly diagnosed patients with idiopathic PD, untreated at baseline, from 33 international movement disorder centers. Study outcomes were four determinations of cognitive impairment or decline, and biomarker predictors were baseline dopamine transporter (DAT) single photon emission computed tomography (SPECT) scan, structural magnetic resonance imaging (MRI; volume and thickness), diffusion tensor imaging (mean diffusivity and fractional anisotropy), cerebrospinal fluid (CSF; amyloid beta [Aβ], tau and alpha synuclein), and 11 single nucleotide polymorphisms (SNPs) previously associated with PD cognition. Additionally, longitudinal structural MRI and DAT scan data were included. Univariate analyses were run initially, with false discovery rate = 0.2, to select biomarker variables for inclusion in multivariable longitudinal mixed-effect models. By year 3, cognitive impairment was diagnosed in 15-38% participants depending on the criteria applied. Biomarkers, some longitudinal, predicting cognitive impairment in multivariable models were: (1) dopamine deficiency (decreased caudate and putamen DAT availability); (2) diffuse, cortical decreased brain volume or thickness (frontal, temporal, parietal, and occipital lobe regions); (3) co-morbid Alzheimer's disease Aβ amyloid pathology (lower CSF Aβ 1-42); and (4) genes (COMT val/val and BDNF val/val genotypes). Cognitive impairment in PD increases in frequency 50-200% in the first several years of disease, and is independently predicted by biomarker changes related to nigrostriatal or cortical dopaminergic deficits, global atrophy due to possible widespread effects of neurodegenerative disease, co-morbid Alzheimer's disease plaque pathology, and genetic factors.

Aging is the main risk factor for Alzheimer's disease (AD); however, the aspects of the aging process that predispose the brain to the development of AD are largely unknown. Astrocytes perform a myriad of functions in the central nervous system to maintain homeostasis and support neuronal function. In vitro, human astrocytes are highly sensitive to oxidative stress and trigger a senescence program when faced with multiple types of stress. In order to determine whether senescent astrocytes appear in vivo, brain tissue from aged individuals and patients with AD was examined for the presence of senescent astrocytes using p16(INK4a) and matrix metalloproteinase-1 (MMP-1) expression as markers of senescence. Compared with fetal tissue samples (n = 4), a significant increase in p16(INK4a)-positive astrocytes was observed in subjects aged 35 to 50 years (n = 6; P = 0.02) and 78 to 90 years (n = 11; P<10(-6)). In addition, the frontal cortex of AD patients (n = 15) harbored a significantly greater burden of p16(INK4a)-positive astrocytes compared with non-AD adult control subjects of similar ages (n = 25; P = 0.02) and fetal controls (n = 4; P<10(-7)). Consistent with the senescent nature of the p16(INK4a)-positive astrocytes, increased metalloproteinase MMP-1 correlated with p16(INK4a). In vitro, beta-amyloid 1-42 (Aβ(1-42)) triggered senescence, driving the expression of p16(INK4a) and senescence-associated beta-galactosidase. In addition, we found that senescent astrocytes produce a number of inflammatory cytokines including interleukin-6 (IL-6), which seems to be regulated by p38MAPK. We propose that an accumulation of p16(INK4a)-positive senescent astrocytes may link increased age and increased risk for sporadic AD.

In 2010, we put forward a hypothetical model of the major biomarkers of Alzheimer's disease (AD). The model was received with interest because we described the temporal evolution of AD biomarkers in relation to each other and to the onset and progression of clinical symptoms. Since then, evidence has accumulated that supports the major assumptions of this model. Evidence has also appeared that challenges some of our assumptions, which has allowed us to modify our original model. Refinements to our model include indexing of individuals by time rather than clinical symptom severity; incorporation of interindividual variability in cognitive impairment associated with progression of AD pathophysiology; modifications of the specific temporal ordering of some biomarkers; and recognition that the two major proteinopathies underlying AD biomarker changes, amyloid β (Aβ) and tau, might be initiated independently in sporadic AD, in which we hypothesise that an incident Aβ pathophysiology can accelerate antecedent limbic and brainstem tauopathy.

Alzheimer’s disease (AD) is a progressive neurodegenerative disease with a complex and heterogeneous pathophysiology. The number of people living with AD is predicted to increase; however, there are no disease-modifying therapies currently available and none have been successful in late-stage clinical trials. Fluid biomarkers measured in cerebrospinal fluid (CSF) or blood hold promise for enabling more effective drug development and establishing a more personalized medicine approach for AD diagnosis and treatment. Biomarkers used in drug development programmes should be qualified for a specific context of use (COU). These COUs include, but are not limited to, subject/patient selection, assessment of disease state and/or prognosis, assessment of mechanism of action, dose optimization, drug response monitoring, efficacy maximization, and toxicity/adverse reactions identification and minimization. The core AD CSF biomarkers Aβ42, t-tau, and p-tau are recognized by research guidelines for their diagnostic utility and are being considered for qualification for subject selection in clinical trials. However, there is a need to better understand their potential for other COUs, as well as identify additional fluid biomarkers reflecting other aspects of AD pathophysiology. Several novel fluid biomarkers have been proposed, but their role in AD pathology and their use as AD biomarkers have yet to be validated. In this review, we summarize some of the pathological mechanisms implicated in the sporadic AD and highlight the data for several established and novel fluid biomarkers (including BACE1, TREM2, YKL-40, IP-10, neurogranin, SNAP-25, synaptotagmin, α-synuclein, TDP-43, ferritin, VILIP-1, and NF-L) associated with each mechanism. We discuss the potential COUs for each biomarker.

Key Points Many neurodegenerative diseases share a common pathological hallmark: the accumulation of characteristic proteins into insoluble aggregates in vulnerable neurons and glial cells. Converging lines of evidence from cell culture studies and animal models indicate that progression of these diseases is driven by the template-directed misfolding, seeded aggregation and cell-to-cell transmission of characteristic disease-related proteins. Although such mechanisms of propagation are similar to prions, important differences to prions exists, namely the lack of inter-individual infectivity and the lack of zoonoses. Neuropathological studies in humans identified stereotypical patterns of pathology in various neurodegenerative diseases, and progression of these patterns can be correlated with increasing severity of the clinical phenotype, enabling the development of staging systems for these diseases. Human tissue pathology-staging studies are limited by the relative lack of early (prodromal) cases and by the fact that the resulting neuropathological data are, by definition, cross-sectional. Imaging biomarkers specific to the different disease proteins are necessary to validate the sequential involvement of different CNS regions proposed by human autopsy studies. Currently, the most promising markers are positron emission tomography ligands that should enable the in vivo detection and monitoring of the spread of protein pathology in longitudinal studies. Various neurodegenerative diseases are characterized by aggregates of pathological proteins, and increasing evidence suggests these disease-associated proteins may 'spread' via neuronal connections. Trojanowski and colleagues describe the molecular mechanisms of such spreading, and present the findings from neuropathological and imaging studies in humans that support this process. The progression of many neurodegenerative diseases is thought to be driven by the template-directed misfolding, seeded aggregation and cell–cell transmission of characteristic disease-related proteins, leading to the sequential dissemination of pathological protein aggregates. Recent evidence strongly suggests that the anatomical connections made by neurons — in addition to the intrinsic characteristics of neurons, such as morphology and gene expression profile — determine whether they are vulnerable to degeneration in these disorders. Notably, this common pathogenic principle opens up opportunities for pursuing novel targets for therapeutic interventions for these neurodegenerative disorders. We review recent evidence that supports the notion of neuron–neuron protein propagation, with a focus on neuropathological and positron emission tomography imaging studies in humans.

Most neurodegenerative diseases are characterized by the intracellular or extracellular aggregation of misfolded proteins such as amyloid-β and tau in Alzheimer disease, α-synuclein in Parkinson disease, and TAR DNA-binding protein 43 in amyotrophic lateral sclerosis. Accumulating evidence from both human studies and disease models indicates that intercellular transmission and the subsequent templated amplification of these misfolded proteins are involved in the onset and progression of various neurodegenerative diseases. The misfolded proteins that are transferred between cells are referred to as ‘pathological seeds’. Recent studies have made exciting progress in identifying the characteristics of different pathological seeds, particularly those isolated from diseased brains. Advances have also been made in our understanding of the molecular mechanisms that regulate the transmission process, and the influence of the host cell on the conformation and properties of pathological seeds. The aim of this Review is to summarize our current knowledge of the cell-to-cell transmission of pathological proteins and to identify key questions for future investigation.In this Review, Peng et al. summarize the evidence for cell-to-cell transmission of pathological proteins in neurodegenerative diseases such as Alzheimer disease and Parkinson disease, and identify key questions for future investigation.

Tauopathies are neurodegenerative disorders characterized by the accumulation of abnormal tau protein leading to cognitive and/or motor dysfunction. To understand the relationship between tau pathology and behavioral impairments, we comprehensively assessed behavioral abnormalities in a mouse tauopathy model expressing the human P301S mutant tau protein in the early stage of disease to detect its initial neurological manifestations. Behavioral abnormalities, shown by open field test, elevated plus-maze test, hot plate test, Y-maze test, Barnes maze test, Morris water maze test, and/or contextual fear conditioning test, recapitulated the neurological deficits of human tauopathies with dementia. Furthermore, we discovered that prepulse inhibition (PPI), a marker of sensorimotor gating, was enhanced in these animals concomitantly with initial neuropathological changes in associated brain regions. This finding provides evidence that our tauopathy mouse model displays neurofunctional abnormalities in prodromal stages of disease, since enhancement of PPI is characteristic of amnestic mild cognitive impairment, a transitional stage between normal aging and dementia such as Alzheimer's disease (AD), in contrast with attenuated PPI in AD patients. Therefore, assessment of sensorimotor gating could be used to detect the earliest manifestations of tauopathies exemplified by prodromal AD, in which abnormal tau protein may play critical roles in the onset of neuronal dysfunctions.

Progressive supranuclear palsy (PSP) is a 4R-tauopathy predominated by subcortical pathology in neurons, astrocytes, and oligodendroglia associated with various clinical phenotypes. In the present international study, we addressed the question of whether or not sequential distribution patterns can be recognized for PSP pathology. We evaluated heat maps and distribution patterns of neuronal, astroglial, and oligodendroglial tau pathologies and their combinations in different clinical subtypes of PSP in postmortem brains. We used conditional probability and logistic regression to model the sequential distribution of tau pathologies across different brain regions. Tau pathology uniformly predominates in the neurons of the pallido-nigro-luysian axis in different clinical subtypes. However, clinical subtypes are distinguished not only by total tau load but rather cell-type (neuronal versus glial) specific vulnerability patterns of brain regions suggesting distinct dynamics or circuit-specific segregation of propagation of tau pathologies. For Richardson syndrome ( n  = 81) we recognize six sequential steps of involvement of brain regions by the combination of cellular tau pathologies. This is translated to six stages for the practical neuropathological diagnosis by the evaluation of the subthalamic nucleus, globus pallidus, striatum, cerebellum with dentate nucleus, and frontal and occipital cortices. This system can be applied to further clinical subtypes by emphasizing whether they show caudal (cerebellum/dentate nucleus) or rostral (cortical) predominant, or both types of pattern. Defining cell-specific stages of tau pathology helps to identify preclinical or early-stage cases for the better understanding of early pathogenic events, has implications for understanding the clinical subtype-specific dynamics of disease-propagation, and informs tau-neuroimaging on distribution patterns.

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. 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. 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.

Whilst cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers for amyloid-β (Aβ) and tau pathologies are accurate for the diagnosis of Alzheimer’s disease (AD), their broad implementation in clinical and trial settings are restricted by high cost and limited accessibility. Plasma phosphorylated-tau181 (p-tau181) is a promising blood-based biomarker that is specific for AD, correlates with cerebral Aβ and tau pathology, and predicts future cognitive decline. In this study, we report the performance of p-tau181 in >1000 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including cognitively unimpaired (CU), mild cognitive impairment (MCI) and AD dementia patients characterized by Aβ PET. We confirmed that plasma p-tau181 is increased at the preclinical stage of Alzheimer and further increases in MCI and AD dementia. Individuals clinically classified as AD dementia but having negative Aβ PET scans show little increase but plasma p-tau181 is increased if CSF Aβ has already changed prior to Aβ PET changes. Despite being a multicenter study, plasma p-tau181 demonstrated high diagnostic accuracy to identify AD dementia (AUC = 85.3%; 95% CI, 81.4–89.2%), as well as to distinguish between Aβ− and Aβ+ individuals along the Alzheimer’s continuum (AUC = 76.9%; 95% CI, 74.0–79.8%). Higher baseline concentrations of plasma p-tau181 accurately predicted future dementia and performed comparably to the baseline prediction of CSF p-tau181. Longitudinal measurements of plasma p-tau181 revealed low intra-individual variability, which could be of potential benefit in disease-modifying trials seeking a measurable response to a therapeutic target. This study adds significant weight to the growing body of evidence in the use of plasma p-tau181 as a non-invasive diagnostic and prognostic tool for AD, regardless of clinical stage, which would be of great benefit in clinical practice and a large cost-saving in clinical trial recruitment.