Large-scale pathway specific polygenic risk and transcriptomic community network analysis identifies novel functional pathways in Parkinson disease

Polygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological processes underlying PD using the largest currently available cohorts of genetic and gene expression data from International Parkinson’s Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership-Parkinson’s disease initiative (AMP-PD), among other sources. We applied large-scale gene-set specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk focusing on publicly annotated gene sets representative of curated pathways. We nominated specific molecular sub-processes underlying protein misfolding and aggregation, post-translational protein modification, immune response, membrane and intracellular trafficking, lipid and vitamin metabolism, synaptic transmission, endosomal–lysosomal dysfunction, chromatin remodeling and apoptosis mediated by caspases among the main contributors to PD etiology. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data and found evidence for a burden of rare damaging alleles in a range of processes, including neuronal transmission-related pathways and immune response. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for dopaminergic neurons, serotonergic neurons, hypothalamic GABAergic neurons, and neural progenitors. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of PD patients, which revealed functional enrichment in inflammatory signaling pathways, cell death machinery related processes, and dysregulation of mitochondrial homeostasis. Our analyses highlight several specific promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done

Parkinson’s disease determinants, prediction and gene-environment interactions in the UK Biobank

Objective: To systematically investigate the association of environmental risk factors and prodromal features with incident Parkinson’s disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate whether existing risk prediction algorithms are improved by the inclusion of genetic risk scores. Methods: We identified individuals with an incident diagnosis of PD (n=1276) and controls (n=500 406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. We constructed polygenic risk scores (PRSs) using external weights and selected the best PRS from a subset of the cohort (30%). The PRS was used in a separate testing set (70%) to examine gene–environment interactions and compare predictive models for PD. Results: Strong evidence of association (false discovery rate <0.05) was found between PD and a positive family history of PD, a positive family history of dementia, non-smoking, low alcohol consumption, depression, daytime somnolence, epilepsy and earlier menarche. Individuals with the highest 10% of PRSs had increased risk of PD (OR 3.37, 95% CI 2.41 to 4.70) compared with the lowest risk decile. A higher PRS was associated with earlier age at PD diagnosis and inclusion of the PRS in the PREDICT-PD algorithm led to a modest improvement in model performance. We found evidence of an interaction between the PRS and diabetes. Interpretation: Here, we used UK Biobank data to reproduce several well-known associations with PD, to demonstrate the validity of a PRS and to demonstrate a novel gene–environment interaction, whereby the effect of diabetes on PD risk appears to depend on background genetic risk for PD

Optimising classification of Parkinson’s disease based on motor, olfactory, neuropsychiatric and sleep features

Olfactory loss, motor impairment, anxiety/depression, and REM-sleep behaviour disorder (RBD) are prodromal Parkinson’s disease (PD) features. PD risk prediction models typically dichotomize test results and apply likelihood ratios (LRs) to scores above and below cut-offs. We investigate whether LRs for specific test values could enhance classification between PD and controls. PD patient data on smell (UPSIT), possible RBD (RBD Screening Questionnaire), and anxiety/depression (LADS) were taken from the Tracking Parkinson’s study (n = 1046). For motor impairment (BRAIN test) in PD cases, published data were supplemented (n = 87). Control data (HADS for anxiety/depression) were taken from the PREDICT-PD pilot study (n = 1314). UPSIT, RBDSQ, and anxiety/depression data were analysed using logistic regression to determine which items were associated with PD. Gaussian distributions were fitted to BRAIN test scores. LRs were calculated from logistic regression models or score distributions. False-positive rates (FPRs) for specified detection rates (DRs) were calculated. Sixteen odours were associated with PD; LRs for this set ranged from 0.005 to 5511. Six RBDSQ and seven anxiety/depression questions were associated with PD; LRs ranged from 0.35 to 69 and from 0.002 to 402, respectively. BRAIN test LRs ranged from 0.16 to 1311. For a 70% DR, the FPR was 2.4% for the 16 odours, 4.6% for anxiety/depression, 16.0% for the BRAIN test, and 20.0% for the RBDSQ. Specific selections of (prodromal) PD marker features rather than dichotomized marker test results optimize PD classification. Such optimized classification models could improve the ability of algorithms to detect prodromal PD; however, prospective studies are needed to investigate their value for PD-prediction models

The impact of COVID-19 on access to Parkinson’s disease medication

Background: Many countries have implemented drastic measures to fight the COVID‐19 pandemic. Restrictions and diversion of resources may have negatively affected patients with Parkinson's disease (PD). Our aim was to examine whether COVID‐19 had an impact on access to PD medication by region and income. Methods: This study was conducted as part of a survey sent to members of the Movement Disorders Society focusing on access to PD medication globally. Results: Of 346 responses, 157 (45.4%) agreed that COVID‐19 had affected access to PD medication, while 189 (54.6%) disagreed. 22.8% of high‐income and 88.9% of low‐income countries' respondents agreed that access to PD medication was affected by COVID‐19. 59% of all ‘yes' respondents reported increased disability of patients as an impact. Conclusions: Access to PD medication is likely to have been affected by COVID‐19 and result in deterioration of patients' symptomatic control. Resource‐poor countries appear to be disproportionately affected compared to more affluent countries. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Societ

Type 2 diabetes as a determinant of Parkinson's disease risk and progression

Background: Type 2 diabetes (T2DM) and Parkinson's disease (PD) are prevalent diseases that affect an aging population. Previous systematic reviews and meta-analyses have explored the relationship between diabetes and the risk of PD, but the results have been conflicting. Objective: The objective was to investigate T2DM as a determinant of PD through a meta-analysis of observational and genetic summary data. Methods: A systematic review and meta-analysis of observational studies was undertaken by searching 6 databases. We selected the highest-quality studies investigating the association of T2DM with PD risk and progression. We then used Mendelian randomization (MR) to investigate the causal effects of genetic liability toward T2DM on PD risk and progression, using summary data derived from genome-wide association studies. Results: In the observational part of the study, pooled effect estimates showed that T2DM was associated with an increased risk of PD (odds ratio [OR] 1.21, 95% confidence interval [CI] 1.07–1.36), and there was some evidence that T2DM was associated with faster progression of motor symptoms (standardized mean difference [SMD] 0.55, 95% CI 0.39–0.72) and cognitive decline (SMD −0.92, 95% CI −1.50 to −0.34). Using MR, we found supportive evidence for a causal effect of diabetes on PD risk (inverse-variance weighted method [IVW] OR 1.08, 95% CI 1.02–1.14; P = 0.010) and some evidence of an effect on motor progression (IVW OR 1.10, 95% CI 1.01–1.20; P = 0.032) but not on cognitive progression. Conclusions: Using meta-analyses of traditional observational studies and genetic data, we observed convincing evidence for an effect of T2DM on PD risk and new evidence to support a role in PD progression

Big data, machine learning and artificial intelligence: a neurologist’s guide

Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field

Evaluating lipid-lowering drug targets for Parkinson’s disease prevention with Mendelian randomization

Long‐term exposure to lipid‐lowering drugs might affect Parkinson's disease (PD) risk. We conducted Mendelian randomization analyses where genetic variants indexed expected effects of modulating lipid‐lowering drug targets on PD. Statin exposure was not predicted to increase PD risk, although results were not precise enough to support benefits for prevention clearly (odds ratio [OR] = 0.83; 95% confidence interval [CI] = 0.65, 1.07). Other target results were null, except for variants indicating Apolipoprotein‐A5 or Apolipoprotein‐C3 inhibition might confer protection. These findings suggest peripheral lipid variation may not have a prominent role in PD etiology, but some related drug targets could influence PD via alternate pathways

Domotics, smart homes and Parkinson’s disease

Technology has an increasing presence and role in the management of Parkinson’s disease. Whether embraced or rebuffed by patients and clinicians, this is an undoubtedly growing area. Wearable sensors have received most of the attention so far. This review will focus on technology integrated into the home setting; from fixed sensors to automated appliances, which are able to capture information and have the potential to respond in an unsupervised manner. Domotics also have the potential to provide ‘real world’ context to kinematic data and therapeutic opportunities to tackle challenging motor and non-motor symptoms. Together with wearable technology, domotics have the ability to gather long-term data and record discrete events, changing the model of the cross-sectional outpatient assessment. As clinicians, our ultimate goal is to maximise quality of life, promote autonomy, and personalisation of care. In these respects, domotics may play an essential role in the coming years

Tracking Driver Eye Movements at Permissive Left-Turns

The objective of this analysis was to identify sources of informationused by left-turning drivers. To complete the experiment, a virtual network ofsignalized intersections was created for use in a driving simulator equipped withhead and eye tracking equipment. Fourteen drivers were recruited to participate inthe experiment, which included two independent variables (permissive signalindication and presence of opposing traffic). The primary dependent variable wasthe associated eye movements at permissive left-turns, including the magnitude oftime focused on each potential cue and the pattern in which cues were detected.To complete the analysis, eye movements were tracked and the screen wasdivided into “areas of interest,” which coincided with potential cues used in thecompletion of a permissive left turn. For each permissive scenario, drivers usedmore total cues when no opposing traffic was present. Specifically, in theabsence of opposing traffic, drivers fixated on a wider array of availableinformation. When opposing traffic was present, drivers spent a majority of timefocused on opposing traffic and would use this as a base point from which theywould glance at other data sources. Overall, drivers looked at least once at theprotected/permissive left-turn (PPLT) signal display and the opposing trafficstream. Drivers tended to scan the intersection from right to left, after initiallylocating the PPLT signal display and opposing traffic and/or stop bar area. Theresults of the eye movement analysis are consistent with data obtained in afollow-up static evaluation