Longitudinal risk factors for developing depressive symptoms in Parkinson's disease

BACKGROUND: Despite the established importance of identifying depression in Parkinson's disease, our understanding of the factors which place the Parkinson's disease patient at future risk of depression is limited. METHODS: Our sample consisted of 874 patients from two longitudinal cohorts, PPMI and PDBP, with median follow-up durations of 7 and 3 years respectively. Risk factors for depressive symptoms at baseline were determined using logistic regression. A Cox regression model was then used to identify baseline factors that predisposed the non-depressed patient to develop depressive symptoms that were sustained for at least one year, while adjusting for antidepressant use and cognitive impairment. Common predictors between the two cohorts were identified with a random-effects meta-analysis. RESULTS: We found in our analyses that the majority of baseline non-depressed patients would develop sustained depressive symptoms at least once during the course of the study. Probable REM sleep behavior disorder (pRBD), age, duration of diagnosis, impairment in daily activities, mild constipation, and antidepressant use were among the baseline risk factors for depression in either cohort. Our Cox regression model indicated that pRBD, impairment in daily activities, hyposmia, and mild constipation could serve as longitudinal predictors of sustained depressive symptoms. CONCLUSIONS: We identified several potential risk factors to aid physicians in the early detection of depression in Parkinson's disease patients. Our findings also underline the importance of adjusting for multiple covariates when analyzing risk factors for depression

Big Data: Astronomical or Genomical?

Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a "four-headed beast"-it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the "genomical" challenges of the next decade

Sight Distance Standards Based On Observational Data Risk Evaluation Of Passing

The paper presents an application of a reliability analysis for evaluating the risk associated with passing sight distance (PSD) standards in terms of the expected probability of noncompliance. Calculation of PSD is required to determine where drivers can safely execute passing maneuvers. Traditional PSD standards are based on deterministic, theoretical models, which are calibrated with conservative percentile values for uncertain design inputs to account for uncertainty. The PSD standards do not provide information about the risk of deviation from them. Reliability analysis is a technique that is based on limit state design and that accounts for the propagation of variability from input random parameters to the design outputs. A total of 1,098 passing maneuvers were observed on several two-lane highways in Spain; two data collection methodologies were used: external observations and an instrumented vehicle. The most significant factors affecting PSD were impeding-vehicle speed, passing-vehicle acceleration, and head-ways between impeding and passing vehicles. A uniform acceleration model described the passing-vehicle trajectory. The characterized input parameters and the passing model were used to perform a reliability analysis. The results showed the probability of noncompliance in different scenarios, defined as the proportion of cases in which the required PSD would exceed the available sight distance. American and Spanish PSD standards were evaluated. Geometric design standards presented a probability of noncompliance of about 0.15, whereas some marking standards had a probability of noncompliance exceeding 0.85. These standards may be associated with higher risk levels if they are followed by drivers. As well, PSD risk levels were not consistent for different design speeds, since they underestimated operating speed at some locationsThis paper was developed as a result of a mobility study at the University of British Columbia funded by the Erasmus Mundus Program of the European Commission under the Transatlantic Partnership for Excellence in Engineering project. The authors thank the Spanish Ministry of Economy and Competitiveness, which subsidized the research project, and the Spanish Directorate General of Traffic, Spanish Ministry of Public Works, Valencia Regional Department of Transport, and Valencia province road department for their collaboration during the field study.Llorca Garcia, C.; Moreno Chou, AT.; Sayed, T.; García García, A. (2014). Sight Distance Standards Based On Observational Data Risk Evaluation Of Passing. Transportation research record. 2404:18-26. doi:10.3141/2404-03S18262404Ismail, K., & Sayed, T. (2009). Risk-based framework for accommodating uncertainty in highway geometric design. Canadian Journal of Civil Engineering, 36(5), 743-753. doi:10.1139/l08-146Ismail, K., & Sayed, T. (2010). Risk-Based Highway Design. Transportation Research Record: Journal of the Transportation Research Board, 2195(1), 3-13. doi:10.3141/2195-01Richl, L., & Sayed, T. (2006). Evaluating the Safety Risk of Narrow Medians Using Reliability Analysis. Journal of Transportation Engineering, 132(5), 366-375. doi:10.1061/(asce)0733-947x(2006)132:5(366)Harwood, D. W., Gilmore, D. K., & Richard, K. R. (2010). Criteria for Passing Sight Distance for Roadway Design and Marking. Transportation Research Record: Journal of the Transportation Research Board, 2195(1), 36-46. doi:10.3141/2195-05Wang, Y., & Cartmell, M. P. (1998). New Model for Passing Sight Distance on Two-Lane Highways. Journal of Transportation Engineering, 124(6), 536-545. doi:10.1061/(asce)0733-947x(1998)124:6(536)Polus, A., Livneh, M., & Frischer, B. (2000). Evaluation of the Passing Process on Two-Lane Rural Highways. Transportation Research Record: Journal of the Transportation Research Board, 1701(1), 53-60. doi:10.3141/1701-07Llorca, C., & García, A. (2011). Evaluation of Passing Process on Two-Lane Rural Highways in Spain with New Methodology Based on Video Data. Transportation Research Record: Journal of the Transportation Research Board, 2262(1), 42-51. doi:10.3141/2262-05Carlson, P., Miles, J., & Johnson, P. (2006). Daytime High-Speed Passing Maneuvers Observed on Rural Two-Lane, Two-Way Highway: Findings and Implications. Transportation Research Record: Journal of the Transportation Research Board, 1961, 9-15. doi:10.3141/1961-02Llorca, C., Moreno, A. T., García, A., & Pérez-Zuriaga, A. M. (2013). Daytime and Nighttime Passing Maneuvers on a Two-Lane Rural Road in Spain. Transportation Research Record: Journal of the Transportation Research Board, 2358(1), 3-11. doi:10.3141/2358-01Easa, S. M. (1993). Reliability‐Based Design of Intergreen Interval at Traffic Signals. Journal of Transportation Engineering, 119(2), 255-271. doi:10.1061/(asce)0733-947x(1993)119:2(255)Selvanathan, E. A., & Selvanathan, S. (1994). The demand for transport and communication in the United Kingdom and Australia. Transportation Research Part B: Methodological, 28(1), 1-9. doi:10.1016/0191-2615(94)90027-2Easa, S. M. (2000). Reliability Approach to Intersection Sight Distance Design. Transportation Research Record: Journal of the Transportation Research Board, 1701(1), 42-52. doi:10.3141/1701-06Ibrahim, S. E.-B., & Sayed, T. (2011). Developing safety performance functions incorporating reliability-based risk measures. Accident Analysis & Prevention, 43(6), 2153-2159. doi:10.1016/j.aap.2011.06.006Khoury, J. E., & Hobeika, A. G. (2007). Assessing the Risk in the Design of Passing Sight Distances. Journal of Transportation Engineering, 133(6), 370-377. doi:10.1061/(asce)0733-947x(2007)133:6(370)Khoury, J. E., & Hobeika, A. G. (2012). Integrated Stochastic Approach for Risk and Service Estimation: Passing Sight Distance Application. Journal of Transportation Engineering, 138(5), 571-579. doi:10.1061/(asce)te.1943-5436.0000366Kim, S., & Choi, J. (2013). Effects of preceding geometric conditions on operating speed consistency of multilane highways. Canadian Journal of Civil Engineering, 40(6), 528-536. doi:10.1139/cjce-2012-005

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson's Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson's Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care

Multi-modality machine learning predicting Parkinson's disease

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available

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)

Physiological traits of the symbiotic bacterium Teredinibacter turnerae isolated from the mangrove shipworm Neoteredo reynei

Nutrition in the Teredinidae family of wood-boring mollusks is sustained by cellulolytic/nitrogen fixing symbiotic bacteria of the Teredinibacter clade. The mangrove Teredinidae Neoteredo reynei is popularly used in the treatment of infectious diseases in the north of Brazil. In the present work, the symbionts of N. reynei, which are strictly confined to the host's gills, were conclusively identified as Teredinibacter turnerae. Symbiont variants obtained in vitro were able to grow using casein as the sole carbon/nitrogen source and under reduced concentrations of NaCl. Furthermore, cellulose consumption in T. turnerae was clearly reduced under low salt concentrations. As a point of interest, we hereby report first hand that T. turnerae in fact exerts antibiotic activity. Furthermore, this activity was also affected by NaCl concentration. Finally, T. turnerae was able to inhibit the growth of Gram-negative and Gram-positive bacteria, this including strains of Sphingomonas sp., Stenotrophomonas maltophilia, Bacillus cereus and Staphylococcus sciuri. Our findings introduce new points of view on the ecology of T. turnerae, and suggest new biotechnological applications for this marine bacterium

Mitochondria function associated genes contribute to Parkinson's Disease risk and later age at onset

Mitochondrial dysfunction has been implicated in the etiology of monogenic Parkinson’s disease (PD). Yet the role that mitochondrial processes play in the most common form of the disease; sporadic PD, is yet to be fully established. Here, we comprehensively assessed the role of mitochondrial function-associated genes in sporadic PD by leveraging improvements in the scale and analysis of PD GWAS data with recent advances in our understanding of the genetics of mitochondrial disease. We calculated a mitochondrial-specific polygenic risk score (PRS) and showed that cumulative small effect variants within both our primary and secondary gene lists are significantly associated with increased PD risk. We further reported that the PRS of the secondary mitochondrial gene list was significantly associated with later age at onset. Finally, to identify possible functional genomic associations we implemented Mendelian randomization, which showed that 14 of these mitochondrial functionassociated genes showed functional consequence associated with PD risk. Further analysis suggested that the 14 identified genes are not only involved in mitophagy, but implicate new mitochondrial processes. Our data suggests that therapeutics targeting mitochondrial bioenergetics and proteostasis pathways distinct from mitophagy could be beneficial to treating the early stage of PD