Current progress in systemic therapy for biliary tract cancers.

BACKGROUND: Biliary tract cancers (BTCs) are heterogeneous cancers that include cancers of the bile duct and gallbladder. Although they are relatively uncommon, most patients with BTC are diagnosed at advanced-stage disease with high mortality rates. Recently, systemic therapy options for patients with BTC have evolved. This paper reviews recent advancements in systemic therapy and the results of key clinical trials in BTC. METHODS: A literature search in PubMed and Google Scholar was performed using keywords related to BTC and systemic therapy. Studies that were presented in major international cancer research conferences were also included. RESULTS: The evidence shows that adjuvant capecitabine has been associated with a lower relapse rate in early-stage BTC. In unselected patients with advanced BTC, combination chemotherapy is a standard treatment option. However, with a better understanding of the molecular profile of BTC, there has been a shift toward targeted agents in BTC that have shown promising responses. The evolving data also support the evolving role of immunotherapy in patients with deficient DNA mismatch repair or PD-L1-positive BTC. DISCUSSION: Systemic treatment options for BTC have improved. The future identification of new targets, novel compounds, and predictive markers is a key step toward the use of personalized medicine in BTC.

What Happens to the Worried Well? Follow-Up of Subjective Cognitive Impairment.

BACKGROUND: Increasing concern around perceived neurocognitive decline is increasing the number of referrals to specialists and anxiety for patients. We aimed to explore the likelihood of the "worried well" experiencing neurocognitive decline and developing a neurological diagnosis. METHODS: A total of 166 "worried well" patients who attended the Rural and Remote Memory Clinic (RRMC) between 2004 and 2019 were included in this study. Demographic, health, social, and behavioral factors were measured at the initial visit. Mini-Mental State Examination (MMSE), Center for Epidemiologic Studies Depression Scale (CESD), and Functional Activities Questionnaire (FAQ) scores were measured and compared at initial assessment and at 1-year follow-up. MMSE scores over time were assessed with a mean follow-up of 2.95 years (SD 2.87). RESULTS: No statistically significant difference was seen in MMSE, CESD, or FAQ scores when comparing clinic day to 1-year follow-up, and no consistent pattern of MMSE score over time was seen. Of the 166 patients with subjective cognitive impairment (SCI) on initial assessment, 5 were diagnosed with Alzheimer's disease (AD) at 8.5, 3.5, 5, 3, and 1.75 years; 2 were diagnosed with MCI at 1 and 2 years; 1 was diagnosed with vascular cognitive impairment at 5 years; and 1 was diagnosed with frontotemporal dementia (FTD) at 0.5 years. CONCLUSION: The likelihood of a patient with SCI developing a neurological diagnosis is reassuringly low (9/166), but not irrelevant. This, along with the benefits of early diagnosis and treatment for dementia, leads us to believe that patients with SCI should still be seen in follow-up at least at the 1-year mark.

Incongruence effects in cross-modal emotional processing in autistic traits: An fMRI study.

In everyday life, emotional information is often conveyed by both the face and the voice. Consequently, information presented by one source can alter the way in which information from the other source is perceived, leading to emotional incongruence. Here, we used functional magnetic resonance imaging (fMRI) to examine neutral correlates of two different types of emotional incongruence in audiovisual processing, namely incongruence of emotion-valence and incongruence of emotion-presence. Participants were in two groups, one group with a low Autism Quotient score (LAQ) and one with a high score (HAQ). Each participant experienced emotional (happy, fearful) or neutral faces or voices while concurrently being exposed to emotional (happy, fearful) or neutral voices or faces. They were instructed to attend to either the visual or auditory track. The incongruence effect of emotion-valence was characterized by activation in a wide range of brain regions in both hemispheres involving the inferior frontal gyrus, cuneus, superior temporal gyrus, and middle frontal gyrus. The incongruence effect of emotion-presence was characterized by activation in a set of temporal and occipital regions in both hemispheres, including the middle occipital gyrus, middle temporal gyrus and inferior temporal gyrus. In addition, the present study identified greater recruitment of the right inferior parietal lobule in perceiving audio-visual emotional expressions in HAQ individuals, as compared to the LAQ individuals. Depending on face or voice-to-be attended, different patterns of emotional incongruence were found between the two groups. Specifically, the HAQ group tend to show more incidental processing to visual information whilst the LAQ group tend to show more incidental processing to auditory information during the crossmodal emotional incongruence decoding. These differences might be attributed to different attentional demands and different processing strategies between the two groups.

Organs-on-a-Chip.

Organs-on-chips, also known as "tissue chips" or microphysiological systems (MPS), are bioengineered microsystems capable of recreating aspects of human organ physiology and function and are in vitro tools with multiple applications in drug discovery and development. The ability to recapitulate human and animal tissues in physiologically relevant three-dimensional, multi-cellular environments allows applications in the drug development field, including; (1) use in assessing the safety and toxicity testing of potential therapeutics during early-stage preclinical drug development; (2) confirmation of drug/therapeutic efficacy in vitro; and (3) disease modeling of human tissues to recapitulate pathophysiology within specific subpopulations and even individuals, thereby advancing precision medicine efforts. This chapter will discuss the development and evolution of three-dimensional organ models over the past decade, and some of the opportunities offered by MPS technology that are not available through current standard two-dimensional cell cultures, or three-dimensional organoid systems. This chapter will outline future avenues of research in the MPS field, how cutting-edge biotechnology advances are expanding the applications for these systems, and discuss the current and future potential and challenges remaining for the field to address.

New directions in clinical trials for frontotemporal lobar degeneration: Methods and outcome measures.

INTRODUCTION: Frontotemporal lobar degeneration (FTLD) is the most common form of dementia for those under 60 years of age. Increasing numbers of therapeutics targeting FTLD syndromes are being developed. METHODS: In March 2018, the Association for Frontotemporal Degeneration convened the Frontotemporal Degeneration Study Group meeting in Washington, DC, to discuss advances in the clinical science of FTLD. RESULTS: Challenges exist for conducting clinical trials in FTLD. Two of the greatest challenges are (1) the heterogeneity of FTLD syndromes leading to difficulties in efficiently measuring treatment effects and (2) the rarity of FTLD disorders leading to recruitment challenges. DISCUSSION: New personalized endpoints that are clinically meaningful to individuals and their families should be developed. Personalized approaches to analyzing MRI data, development of new fluid biomarkers and wearable technologies will help to improve the power to detect treatment effects in FTLD clinical trials and enable new, clinical trial designs, possibly leveraged from the experience of oncology trials. A computational visualization and analysis platform that can support novel analyses of combined clinical, genetic, imaging, biomarker data with other novel modalities will be critical to the success of these endeavors.

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies.

BACKGROUND: Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. METHODS: We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. FINDINGS: Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16-36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10-7). INTERPRETATION: These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. FUNDING: The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources).

Motor phenotype classification in moderate to advanced PD in BioFIND study.

BACKGROUND: Three motor phenotypes have been described in PD: postural instability and gait difficulty (PIGD) dominant, tremor-dominant (TD), and indeterminate (IND) subtype. These phenotypes have been associated with different cognitive trajectories, motor outcomes, and biomarkers profiles. However, whether motor subtype classifications change with treatment and disease progression is not well established. METHODS: To evaluate motor subtype ratio changes, we used the chi-square test for the off and on state motor subtypes for 115 PD participants in the BioFIND study and used repeated-measures analyses to evaluate longitudinal changes in 162 PD participants with five-year follow-up in the PPMI study. RESULTS: PIGD and TD subtypes in moderate to advanced PD participants change with dopaminergic agents. For those who shifted subtypes, improvement in tremor accounted for the transition of 15 (25.4%) TD participants, while the lack of tremor improvement along with minimal changes in PIGD score resulted in changes for eight (19.0%) PIGD individuals. Analyses of PPMI data revealed that all three subgroups had a significant decrease in subtype ratio with disease progression and a significant decline in subtype ratio occurred only in the TD subgroup with dopaminergic agents. The impact of dopaminergic medication effect on subtype shift for each visit was also more notable with disease advancement. CONCLUSIONS: Motor subtypes are not fixed but change with progression of the disease and with treatment. Improvement in tremor was the main contributor to motor phenotype transitions in the BioFIND cohort. A more stable classification system for subtypes based on underlying biological differences is desirable.

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.

Cerebrospinal fluid, plasma, and saliva in the BioFIND study: Relationships among biomarkers and Parkinson's disease Features.

OBJECTIVE: Examine relationships among neurodegenerative biomarkers and PD motor and nonmotor symptoms. BACKGROUND: CSF alpha-synuclein is decreased in PD versus healthy controls, but whether plasma and saliva alpha-synuclein differentiate these groups is controversial. Correlations of alpha-synuclein among biofluids (CSF, plasma, saliva) or biomarkers (eg, beta-amyloid, tau [total, phosphorylated]) are not fully understood. The relationships of these biomarkers with PD clinical features remain unclear. METHODS: BioFIND, a cross-sectional, observational study, examines clinical and biomarker characteristics in moderate-advanced PD and matched healthy controls. We compared alpha-synuclein concentrations across diagnosis, biofluids, and CSF biomarkers. Correlations of CSF biomarkers and MDS-UPDRS, motor phenotype, MoCA, and rapid eye movement sleep behavior disorder questionnaire scores in PD were examined. RESULTS: CSF alpha-synuclein was lower in PD versus controls (P = .01), controlling for age, gender, and education. Plasma and saliva alpha-synuclein did not differ between PD and controls, and alpha-synuclein did not significantly correlate among biofluids. CSF beta-amyloid1-42 was lower in PD versus controls (P < .01), and correlated weakly with MoCA recall scores (r = 0.23, P = .02). CSF alpha-synuclein was lower in the postural instability/gait difficulty phenotype than other motor phenotypes (P < .01). No CSF biomarkers predicted or correlated with total motor or rapid eye movement sleep behavior disorder scores. CSF alpha-synuclein correlated with beta-amyloid1-42 , total-tau, and phosphorylated-tau (r = 0.41, 0.81, 0.43, respectively; Ps < .001). CONCLUSION: Lower CSF alpha-synuclein is associated with diagnosis and motor phenotype in moderate-advanced PD. Plasma and saliva alpha-synuclein neither correlate with CSF alpha-synuclein, nor distinguish PD from controls. CSF beta-amyloid1-42 remains a potential biomarker for cognitive impairment in PD. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Alzheimer's Disease-Related Dementias Summit 2016: National research priorities.

Goal 1 of the National Plan to Address Alzheimer's Disease is to prevent and effectively treat Alzheimer disease and Alzheimer disease-related dementias by 2025. To help inform the research agenda toward achieving this goal, the NIH hosts periodic summits that set and refine relevant research priorities for the subsequent 5 to 10 years. This proceedings article summarizes the 2016 Alzheimer's Disease-Related Dementias Summit, including discussion of scientific progress, challenges, and opportunities in major areas of dementia research, including mixed-etiology dementias, Lewy body dementia, frontotemporal degeneration, vascular contributions to cognitive impairment and dementia, dementia disparities, and dementia nomenclature.

Parkinson's disease biomarkers: perspective from the NINDS Parkinson's Disease Biomarkers Program.

Biomarkers for Parkinson's disease (PD) diagnosis, prognostication and clinical trial cohort selection are an urgent need. While many promising markers have been discovered through the National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP) and other mechanisms, no single PD marker or set of markers are ready for clinical use. Here we discuss the current state of biomarker discovery for platforms relevant to PDBP. We discuss the role of the PDBP in PD biomarker identification and present guidelines to facilitate their development. These guidelines include: harmonizing procedures for biofluid acquisition and clinical assessments, replication of the most promising biomarkers, support and encouragement of publications that report negative findings, longitudinal follow-up of current cohorts including the PDBP, testing of wearable technologies to capture readouts between study visits and development of recently diagnosed (de novo) cohorts to foster identification of the earliest markers of disease onset.

Using a logic model to evaluate the Kids Together early education inclusion program for children with disabilities and additional needs.

Despite clear evidence that learning and social opportunities for children with disabilities and special needs are more effective in inclusive not segregated settings, there are few known effective inclusion programs available to children with disabilities, their families or teachers in the early years within Australia. The Kids Together program was developed to support children with disabilities/additional needs aged 0-8 years attending mainstream early learning environments. Using a key worker transdisciplinary team model, the program aligns with the individualised package approach of the National Disability Insurance Scheme (NDIS). AIM: This paper reports on the use of a logic model to underpin the process, outcomes and impact evaluation of the Kids Together program. METHODS: The research team worked across 15 Early Childhood Education and Care (ECEC) centres and in home and community settings. A realist evaluation using mixed methods was undertaken to understand what works, for whom and in what contexts. The development of a logic model provided a structured way to explore how the program was implemented and achieved short, medium and long term outcomes within a complex community setting. DISCUSSION AND CONCLUSION: Kids Together was shown to be a highly effective and innovative model for supporting the inclusion of children with disabilities/additional needs in a range of environments central for early childhood learning and development. The use of a logic model provided a visual representation of the Kids Together model and its component parts and enabled a theory of change to be inferred, showing how a coordinated and collaborative approached can work across multiple environments.

Motivational orientations of high-achieving students as mediators of a positive perception of a high-achieving classmate: Results from a cross-national study / Orientaciones motivacionales de los estudiantes de alto rendimiento como mediadoras de una percepción positiva por parte de sus compañeros: Resultados de un estudio transnacional

The purpose of this study was to explore whether and in what ways high-achieving school students’ motivational orientations influence their perceptions of a fictitious future high-achieving classmate. The final sample consisted of the 396 highest achieving students out of a sample from 1794 seventh and tenth graders from five countries: Australia, Peru, South Korea, Spain, and Vietnam. A series of stepwise regression models were used to test the hypothesis that positive perceptions of a highachieving classmate might be mediated by an approach motivation, but not by an avoidance motivational orientation. The hypothesis was generally confirmed. Learning goal orientation and performance approach motivation predicted positive perceptions of a high-achieving classmate’s intellectual ability, social qualities and popularity among peers, whereas a performance avoidance orientation was usually uncorrelated. However, sporadic exceptions have been found among the participants from Vietnam, South Korea, and Peru

El propósito de este estudio es explorar si las motivaciones de alto logro influencian las percepciones de los alumnos sobre un futuro compañero de clase ficticio que se caracteriza por tener un alto rendimiento; y si es así de qué modo ocurre. La muestra final consistió en 396 alumnos de alto rendimiento que fueron seleccionados de un total de 1794 alumnos de séptimo y decimo grado de cinco países: Australia, Perú, Corea del sur, España y Vietnam. Se llevaron a cabo una serie de modelos de regresión por pasos sucesivos para probar la hipótesis de que las percepciones positivas sobre un compañero de alto rendimiento podrían estar mediadas por el enfoque motivacional, pero no por la orientación a la evitación del trabajo. La hipótesis fue confirmada en su generalidad. Las metas orientadas al manejo del aprendizaje y las orientadas al alto rendimiento predijeron percepciones positivas de la habilidad intelectual, cualidades sociales y popularidad entre los compañeros del alumno de alto rendimiento, mientras que las metas de evitación del trabajo no obtuvieron correlaciones significativas. Sin embargo, se dieron algunas excepciones esporádicas entre los participantes de Vietnam, Corea del sur y Perú

The BioFIND study: Characteristics of a clinically typical Parkinson's disease biomarker cohort.

BACKGROUND: Identifying PD-specific biomarkers in biofluids will greatly aid in diagnosis, monitoring progression, and therapeutic interventions. PD biomarkers have been limited by poor discriminatory power, partly driven by heterogeneity of the disease, variability of collection protocols, and focus on de novo, unmedicated patients. Thus, a platform for biomarker discovery and validation in well-characterized, clinically typical, moderate to advanced PD cohorts is critically needed. METHODS: BioFIND (Fox Investigation for New Discovery of Biomarkers in Parkinson's Disease) is a cross-sectional, multicenter biomarker study that established a repository of clinical data, blood, DNA, RNA, CSF, saliva, and urine samples from 118 moderate to advanced PD and 88 healthy control subjects. Inclusion criteria were designed to maximize diagnostic specificity by selecting participants with clinically typical PD symptoms, and clinical data and biospecimen collection utilized standardized procedures to minimize variability across sites. RESULTS: We present the study methodology and data on the cohort's clinical characteristics. Motor scores and biospecimen samples including plasma are available for practically defined off and on states and thus enable testing the effects of PD medications on biomarkers. Other biospecimens are available from off state PD assessments and from controls. CONCLUSION: Our cohort provides a valuable resource for biomarker discovery and validation in PD. Clinical data and biospecimens, available through The Michael J. Fox Foundation for Parkinson's Research and the National Institute of Neurological Disorders and Stroke, can serve as a platform for discovering biomarkers in clinically typical PD and comparisons across PD's broad and heterogeneous spectrum. © 2016 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

The NINDS Parkinson's disease biomarkers program.

BACKGROUND: Neuroprotection for Parkinson's disease (PD) remains elusive. Biomarkers hold the promise of removing roadblocks to therapy development. The National Institute of Neurological Disorders and Stroke has therefore established the Parkinson's Disease Biomarkers Program to promote discovery of PD biomarkers for use in phase II and III clinical trials. METHODS: Using a novel consortium design, the Parkinson's Disease Biomarker Program is focused on the development of clinical and laboratory-based biomarkers for PD diagnosis, progression, and prognosis. Standardized operating procedures and pooled reference samples were created to allow cross-project comparisons and assessment of batch effects. A web-based Data Management Resource facilitates rapid sharing of data and biosamples across the research community for additional biomarker projects. RESULTS: Eleven consortium projects are ongoing, seven of which recruit participants and obtain biosamples. As of October 2014, 1,082 participants have enrolled (620 PD, 101 with other causes of parkinsonism, 23 essential tremor, and 338 controls), 1,040 of whom have at least one biosample. Six thousand eight hundred ninety-eight total biosamples are available from baseline, 6-, 12-, and 18-month visits: 1,006 DNA, 1,661 RNA, 1,419 whole blood, 1,382 plasma, 1,200 serum, and 230 cerebrospinal fluid (CSF). Quality control analysis of plasma, serum, and CSF samples indicates that almost all samples are high quality (24 of 2,812 samples exceed acceptable hemoglobin levels). CONCLUSIONS: By making samples and data widely available, using stringent operating procedures based on existing standards, hypothesis testing for biomarker discovery, and providing a resource that complements existing programs, the Parkinson's Disease Biomarker Program will accelerate the pace of PD biomarker research. © 2015 International Parkinson and Movement Disorder Society.

The NIH Extracellular RNA Communication Consortium.

The Extracellular RNA (exRNA) Communication Consortium, funded as an initiative of the NIH Common Fund, represents a consortium of investigators assembled to address the critical issues in the exRNA research arena. The overarching goal is to generate a multi-component community resource for sharing fundamental scientific discoveries, protocols, and innovative tools and technologies. The key initiatives include (a) generating a reference catalogue of exRNAs present in body fluids of normal healthy individuals that would facilitate disease diagnosis and therapies, (b) defining the fundamental principles of exRNA biogenesis, distribution, uptake, and function, as well as development of molecular tools, technologies, and imaging modalities to enable these studies,

Precompetitive Data Sharing as a Catalyst to Address Unmet Needs in Parkinson's Disease.

Parkinson's disease is a complex heterogeneous disorder with urgent need for disease-modifying therapies. Progress in successful therapeutic approaches for PD will require an unprecedented level of collaboration. At a workshop hosted by Parkinson's UK and co-organized by Critical Path Institute's (C-Path) Coalition Against Major Diseases (CAMD) Consortiums, investigators from industry, academia, government and regulatory agencies agreed on the need for sharing of data to enable future success. Government agencies included EMA, FDA, NINDS/NIH and IMI (Innovative Medicines Initiative). Emerging discoveries in new biomarkers and genetic endophenotypes are contributing to our understanding of the underlying pathophysiology of PD. In parallel there is growing recognition that early intervention will be key for successful treatments aimed at disease modification. At present, there is a lack of a comprehensive understanding of disease progression and the many factors that contribute to disease progression heterogeneity. Novel therapeutic targets and trial designs that incorporate existing and new biomarkers to evaluate drug effects independently and in combination are required. The integration of robust clinical data sets is viewed as a powerful approach to hasten medical discovery and therapies, as is being realized across diverse disease conditions employing big data analytics for healthcare. The application of lessons learned from parallel efforts is critical to identify barriers and enable a viable path forward. A roadmap is presented for a regulatory, academic, industry and advocacy driven integrated initiative that aims to facilitate and streamline new drug trials and registrations in Parkinson's disease.

Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study.

BACKGROUND: Accurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts. METHODS: We developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinson's disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD). FINDINGS: In the population from PPMI, our initial model correctly distinguished patients with Parkinson's disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900-0·946) with high sensitivity (0·834, 95% CI 0·711-0·883) and specificity (0·903, 95% CI 0·824-0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson's disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896-0·962) in LABS-PD, and 0·939 (0·891-0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinson's disease converted to Parkinson's disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinson's disease underwent conversion (test of proportions, p=0·003). INTERPRETATION: Our model provides a potential new approach to distinguish participants with Parkinson's disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinson's disease in prospective cohorts, it could facilitate identification of biomarkers and interventions. FUNDING: National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation.

NeuroX, a fast and efficient genotyping platform for investigation of neurodegenerative diseases.

Our objective was to design a genotyping platform that would allow rapid genetic characterization of samples in the context of genetic mutations and risk factors associated with common neurodegenerative diseases. The platform needed to be relatively affordable, rapid to deploy, and use a common and accessible technology. Central to this project, we wanted to make the content of the platform open to any investigator without restriction. In designing this array we prioritized a number of types of genetic variability for inclusion, such as known risk alleles, disease-causing mutations, putative risk alleles, and other functionally important variants. The array was primarily designed to allow rapid screening of samples for disease-causing mutations and large population studies of risk factors. Notably, an explicit aim was to make this array widely available to facilitate data sharing across and within diseases. The resulting array, NeuroX, is a remarkably cost and time effective solution for high-quality genotyping. NeuroX comprises a backbone of standard Illumina exome content of approximately 240,000 variants, and over 24,000 custom content variants focusing on neurologic diseases. Data are generated at approximately $50-$60 per sample using a 12-sample format chip and regular Infinium infrastructure; thus, genotyping is rapid and accessible to many investigators. Here, we describe the design of NeuroX, discuss the utility of NeuroX in the analyses of rare and common risk variants, and present quality control metrics and a brief primer for the analysis of NeuroX derived data.