ABSTRACT
Post-traumatic stress disorder (PTSD) may be a risk factor for Parkinson's disease (PD). We examined the relation between PTSD and PD in a cohort of 158,122 Veterans who had any Veterans Health Administration (VHA) or Medicare health care utilization between 10/1/1999- 2/17/2021. Using a nested case-control design we matched 10 controls to each Veteran with PD by sex, race, and rank. In conditional logistic regression models adjusted for camp and smoking, a PTSD diagnosis was significantly associated with PD (ORâ=â1.35; pâ=â0.0002); odds were higher if PTSD was coded before PD (ORâ=â1.53, pâ<â0.0001). PTSD may be a risk factor for PD.
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BACKGROUND: We recently reported an increased risk of Parkinson's disease (PD) in service members who resided at Marine Base Camp Lejeune, North Carolina, when water supplies were contaminated with trichloroethylene and other volatile organic compounds (VOCs). Prior studies suggest that environmental exposures may affect PD phenotype or progression, but this has not been reported for VOCs. OBJECTIVE: The objective of this study was to test whether PD progression is faster in individuals exposed to VOCs in water at Camp Lejeune. METHODS: A cohort of 172,128 marines residing at Camp Lejeune between 1975 and 1985 was previously assembled. We identified individuals with PD in Veterans Health Administration and Medicare databases between 2000 and 2021. Using estimates derived by the US Agency for Toxic Substances and Disease Registry, we classified individuals as exposed or unexposed to VOCs in residential water. We used Kaplan-Meier and Cox regression models to test differences between exposed and unexposed groups in the time from PD diagnosis until psychosis, fracture, fall, or death. RESULTS: Among 270 persons with PD, 177 (65.6%) were exposed to VOCs in residential water. Median cumulative exposure was 4970 µg/L-months, >50-fold the permissible level. Time until psychosis, fracture, and fall were all shorter in the exposed group, with adjusted hazard ratios (HRs) exceeding 2: psychosis HR, 2.19 (95% confidence interval [CI]: 0.99-4.83); fracture HR, 2.44 (95% CI: 0.91-6.55); and fall HR, 2.64 (95% CI: 0.97-7.21). A significant dose response was observed for time to fall (P trend, 0.032). No differences were observed for time until death. CONCLUSIONS: PD progression may be faster in persons exposed to trichloroethylene and other VOCs in water decades earlier. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
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Background: The penetrance of common genetic risk variants for Parkinson's disease (PD) is low. Pesticide exposure increases PD risk, but how exposure affects penetrance is not well understood. Objective: To determine the relationship between occupational pesticide exposure and PD in people with LRRK2 and GBA risk variants. Methods: Participants of the Parkinson's Progression Markers Initiative (PPMI) with a LRRK2-G2019âS or GBA risk variant provided information about occupational pesticide exposure. We compared exposure in carriers with and without PD. Among carriers with PD, we used Cox proportional hazard models to compare time-to impairment in balance, cognition, and activities of daily living (ADLs) between participants with and without prior occupational pesticide exposure. Results: 378 participants with a risk variant provided exposure information; 176 with LRRK2-G2019âS (54 with and 122 without PD) and 202 with GBA variants (47 with and 155 without PD). Twenty-six participants reported pesticide exposure. People with a GBA variant and occupational pesticide exposure had much higher odds of PD (aOR: 5.4, 95% CI 1.7-18.5, pâ<â0.01). People with a LRRK2 variant and a history of occupational pesticide exposure had non-significantly elevated odds of PD (aOR 1.3, 95% CI 0.4-4.6, pâ=â0.7). Among those with PD, pesticide exposure was associated with a higher risk of balance problems and cognitive impairment in LRRK2-PD and functional impairment in GBA-PD, although associations were not statistically significant. Conclusions: Occupational pesticide exposure may increase penetrance of GBA-PD and may be associated with faster symptom progression. Further studies in larger cohorts are necessary.
Subject(s)
Glucosylceramidase , Leucine-Rich Repeat Serine-Threonine Protein Kinase-2 , Occupational Exposure , Parkinson Disease , Pesticides , Humans , Leucine-Rich Repeat Serine-Threonine Protein Kinase-2/genetics , Female , Parkinson Disease/genetics , Male , Glucosylceramidase/genetics , Occupational Exposure/adverse effects , Pesticides/adverse effects , Aged , Middle Aged , Penetrance , Activities of Daily Living , Cognitive Dysfunction/etiology , Cognitive Dysfunction/genetics , Cognitive Dysfunction/chemically inducedABSTRACT
INTRODUCTION: Between 1953 and 1987, over one million Veterans were exposed to contaminated water at Marine Corps Base Camp Lejeune, North Carolina. We examined the relationship between toxicant exposure and subsequent disability ratings in female veterans. MATERIALS AND METHODS: Comparisons were made between females stationed at Camp Lejeune and from Marine Corps Base Camp Pendleton, California who were not known to have been exposed to these toxicants, between 1975 and 1985, using data from the Agency for Toxic Substances and Diseases Registry and VA data. RESULTS: A total of 4,491 (52%) females from Camp Lejeune and 2,811 (47%) from Camp Pendleton used VA health care between October 1, 1999 and February 17, 2021. Approximately 51% of Camp Lejeune females were exposed to toxicants. More than half (50.6% and 53.9% from Lejeune and Pendleton, respectively) had a disability rating ≥10%. Females who were Black, Hispanic, officers, or had longer duration in camp were more likely to have a disability rating, whereas females exposed to toxicants were less likely to have a disability rating. When the regression was redone examining the predictors of disability due to any of 8 presumptive conditions associated with toxicant exposure, the only significant variable was having been at Camp Lejeune (odds ratio [OR], 2.5, 95% CI, 1.3-4.7). Toxicant exposure was not significant when only Camp Lejeune females were included in the model. CONCLUSION: Little attention has been given to female veterans exposed to toxicants at Camp Lejeune. Although we did not find an association between exposure and disability ratings, reliance on service-connected disability codes and small numbers were limitations. Further examination using international code of diseases diagnostic codes may be warranted.
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BACKGROUND: Environmental exposure to trichloroethylene (TCE), a carcinogenic dry-cleaning chemical, may be linked to Parkinson's disease (PD). OBJECTIVE: The objective of this study was to determine whether PD and cancer were elevated among attorneys who worked near a contaminated site. METHODS: We surveyed and evaluated attorneys with possible exposure and assessed a comparison group. RESULTS: Seventy-nine of 82 attorneys (96.3%; mean [SD] age: 69.5 [11.4] years; 89.9% men) completed at least one phase of the study. For comparison, 75 lawyers (64.9 [10.2] years; 65.3% men) underwent clinical evaluations. Four (5.1%) of them who worked near the polluted site reported PD, more than expected based on age and sex (1.7%; P = 0.01) but not significantly higher than the comparison group (n = 1 [1.3%]; P = 0.37). Fifteen (19.0%), compared to four in the comparison group (5.3%; P = 0.049), had a TCE-related cancer. CONCLUSIONS: In a retrospective study, diagnoses of PD and TCE-related cancers appeared to be elevated among attorneys who worked next to a contaminated dry-cleaning site. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Subject(s)
Neoplasms , Parkinson Disease , Trichloroethylene , Male , Humans , Aged , Female , Parkinson Disease/epidemiology , Parkinson Disease/etiology , Parkinson Disease/diagnosis , Retrospective Studies , Trichloroethylene/analysisABSTRACT
Little is known about electrocardiogram (ECG) markers of Parkinson's disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before disease diagnosis. This case-control study included samples from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). Cases and controls were matched according to specific characteristics (date, age, sex and race). Clinical data were available from May, 2014 onward at LUC and from January, 2015 onward at MLH, while the ECG data were available as early as 1990 in both institutes. PD was denoted by at least two primary diagnostic codes (ICD9 332.0; ICD10 G20) at least 30 days apart. PD incidence date was defined as the earliest of first PD diagnostic code or PD-related medication prescription. ECGs obtained at least 6 months before PD incidence date were modeled to predict a subsequent diagnosis of PD within three time windows: 6 months-1 year, 6 months-3 years, and 6 months-5 years. We applied a novel deep neural network using standard 10-s 12-lead ECGs to predict PD risk at the prodromal phase. This model was compared to multiple feature engineering-based models. Subgroup analyses for sex, race and age were also performed. Our primary prediction model was a one-dimensional convolutional neural network (1D-CNN) that was built using 131 cases and 1058 controls from MLH, and externally validated on 29 cases and 165 controls from LUC. The model was trained on 90% of the MLH data, internally validated on the remaining 10% and externally validated on LUC data. The best performing model resulted in an external validation AUC of 0.67 when predicting future PD at any time between 6 months and 5 years after the ECG. Accuracy increased when restricted to ECGs obtained within 6 months to 3 years before PD diagnosis (AUC 0.69) and was highest when predicting future PD within 6 months to 1 year (AUC 0.74). The 1D-CNN model based on raw ECG data outperformed multiple models built using more standard ECG feature engineering approaches. These results demonstrate that a predictive model developed in one cohort using only raw 10-s ECGs can effectively classify individuals with prodromal PD in an independent cohort, particularly closer to disease diagnosis. Standard ECGs may help identify individuals with prodromal PD for cost-effective population-level early detection and inclusion in disease-modifying therapeutic trials.
Subject(s)
Deep Learning , Parkinson Disease , Humans , Artificial Intelligence , Case-Control Studies , Parkinson Disease/diagnosis , Prodromal Symptoms , ElectrocardiographyABSTRACT
Importance: An increased risk of Parkinson disease (PD) has been associated with exposure to the solvent trichloroethylene (TCE), but data are limited. Millions of people in the US and worldwide are exposed to TCE in air, food, and water. Objective: To test whether the risk of PD is higher in veterans who served at Marine Corps Base Camp Lejeune, whose water supply was contaminated with TCE and other volatile organic compounds (VOCs), compared with veterans who did not serve on that base. Design, Setting, and Participants: This population-based cohort study examined the risk for PD among all Marines and Navy personnel who resided at Camp Lejeune, North Carolina (contaminated water) (n = 172â¯128), or Camp Pendleton, California (uncontaminated water) (n = 168â¯361), for at least 3 months between 1975 and 1985, with follow-up from January 1, 1997, until February 17, 2021. Veterans Health Administration and Medicare databases were searched for International Classification of Diseases diagnostic codes for PD or other forms of parkinsonism and related medications and for diagnostic codes indicative of prodromal disease. Parkinson disease diagnoses were confirmed by medical record review. Exposures: Water supplies at Camp Lejeune were contaminated with several VOCs. Levels were highest for TCE, with monthly median values greater than 70-fold the permissible amount. Main Outcome and Measures: Risk of PD in former residents of Camp Lejeune relative to residents of Camp Pendleton. In those without PD or another form of parkinsonism, the risk of being diagnosed with features of prodromal PD were assessed individually and cumulatively using likelihood ratio tests. Results: Health data were available for 158â¯122 veterans (46.4%). Demographic characteristics were similar between Camp Lejeune (5.3% women, 94.7% men; mean [SD] attained age of 59.64 [4.43] years; 29.7% Black, 6.0% Hispanic, 67.6% White; and 2.7% other race and ethnicity) and Camp Pendleton (3.8% women, 96.2% men; mean [SD] age, 59.80 [4.62] years; 23.4% Black, 9.4% Hispanic, 71.1% White, and 5.5% other race and ethnicity). A total of 430 veterans had PD, with 279 from Camp Lejeune (prevalence, 0.33%) and 151 from Camp Pendleton (prevalence, 0.21%). In multivariable models, Camp Lejeune veterans had a 70% higher risk of PD (odds ratio, 1.70; 95% CI, 1.39-2.07; P < .001). No excess risk was found for other forms of neurodegenerative parkinsonism. Camp Lejeune veterans also had a significantly increased risk of prodromal PD diagnoses, including tremor, anxiety, and erectile dysfunction, and higher cumulative prodromal risk scores. Conclusions and Relevance: The study's findings suggest that the risk of PD is higher in persons exposed to TCE and other VOCs in water 4 decades ago. Millions worldwide have been and continue to be exposed to this ubiquitous environmental contaminant.
Subject(s)
Military Personnel , Parkinson Disease , Trichloroethylene , Aged , Male , Humans , Female , United States , Middle Aged , Child, Preschool , Parkinson Disease/epidemiology , Parkinson Disease/etiology , Cohort Studies , Environmental Exposure/adverse effects , MedicareABSTRACT
Introduction: Early diagnosis of Parkinson's disease (PD) is important to identify treatments to slow neurodegeneration. People who develop PD often have symptoms before the disease manifests and may be coded as diagnoses in the electronic health record (EHR). Methods: To predict PD diagnosis, we embedded EHR data of patients onto a biomedical knowledge graph called Scalable Precision medicine Open Knowledge Engine (SPOKE) and created patient embedding vectors. We trained and validated a classifier using these vectors from 3,004 PD patients, restricting records to 1, 3, and 5 years before diagnosis, and 457,197 non-PD group. Results: The classifier predicted PD diagnosis with moderate accuracy (AUC = 0.77 ± 0.06, 0.74 ± 0.05, 0.72 ± 0.05 at 1, 3, and 5 years) and performed better than other benchmark methods. Nodes in the SPOKE graph, among cases, revealed novel associations, while SPOKE patient vectors revealed the basis for individual risk classification. Discussion: The proposed method was able to explain the clinical predictions using the knowledge graph, thereby making the predictions clinically interpretable. Through enriching EHR data with biomedical associations, SPOKE may be a cost-efficient and personalized way to predict PD diagnosis years before its occurrence.
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The etiologies of Parkinson's disease (PD) remain unclear. Some, such as certain genetic mutations and head trauma, are widely known or easily identified. However, these causes or risk factors do not account for the majority of cases. Other, less visible factors must be at play. Among these is a widely used industrial solvent and common environmental contaminant little recognized for its likely role in PD: trichloroethylene (TCE). TCE is a simple, six-atom molecule that can decaffeinate coffee, degrease metal parts, and dry clean clothes. The colorless chemical was first linked to parkinsonism in 1969. Since then, four case studies involving eight individuals have linked occupational exposure to TCE to PD. In addition, a small epidemiological study found that occupational or hobby exposure to the solvent was associated with a 500% increased risk of developing PD. In multiple animal studies, the chemical reproduces the pathological features of PD.Exposure is not confined to those who work with the chemical. TCE pollutes outdoor air, taints groundwater, and contaminates indoor air. The molecule, like radon, evaporates from underlying soil and groundwater and enters homes, workplaces, or schools, often undetected. Despite widespread contamination and increasing industrial, commercial, and military use, clinical investigations of TCE and PD have been limited. Here, through a literature review and seven illustrative cases, we postulate that this ubiquitous chemical is contributing to the global rise of PD and that TCE is one of its invisible and highly preventable causes. Further research is now necessary to examine this hypothesis.
Subject(s)
Parkinson Disease , Trichloroethylene , Animals , Trichloroethylene/toxicity , Trichloroethylene/analysis , Parkinson Disease/epidemiology , Parkinson Disease/etiology , Solvents/toxicity , Risk FactorsABSTRACT
BACKGROUND: Parkinson's disease (PD) is a chronic, disabling neurodegenerative disorder. OBJECTIVE: To predict a future diagnosis of PD using questionnaires and simple non-invasive clinical tests. METHODS: Participants in the prospective Kuakini Honolulu-Asia Aging Study (HAAS) were evaluated biannually between 1995-2017 by PD experts using standard diagnostic criteria. Autopsies were sought on all deaths. We input simple clinical and risk factor variables into an ensemble-tree based machine learning algorithm and derived models to predict the probability of developing PD. We also investigated relationships of predictive models and neuropathologic features such as nigral neuron density. RESULTS: The study sample included 292 subjects, 25 of whom developed PD within 3 years and 41 by 5 years. 116 (46%) of 251 subjects not diagnosed with PD underwent autopsy. Light Gradient Boosting Machine modeling of 12 predictors correctly classified a high proportion of individuals who developed PD within 3 years (area under the curve (AUC) 0.82, 95%CI 0.76-0.89) or 5 years (AUC 0.77, 95%CI 0.71-0.84). A large proportion of controls who were misclassified as PD had Lewy pathology at autopsy, including 79%of those who died within 3 years. PD probability estimates correlated inversely with nigral neuron density and were strongest in autopsies conducted within 3 years of index date (râ=â-0.57, pâ<â0.01). CONCLUSION: Machine learning can identify persons likely to develop PD during the prodromal period using questionnaires and simple non-invasive tests. Correlation with neuropathology suggests that true model accuracy may be considerably higher than estimates based solely on clinical diagnosis.
Subject(s)
Parkinson Disease , Humans , Machine Learning , Parkinson Disease/diagnosis , Parkinson Disease/pathology , Prodromal Symptoms , Prospective Studies , Risk FactorsABSTRACT
Remote health assessments that gather real-world data (RWD) outside clinic settings require a clear understanding of appropriate methods for data collection, quality assessment, analysis and interpretation. Here we examine the performance and limitations of smartphones in collecting RWD in the remote mPower observational study of Parkinson's disease (PD). Within the first 6 months of study commencement, 960 participants had enrolled and performed at least five self-administered active PD symptom assessments (speeded tapping, gait/balance, phonation or memory). Task performance, especially speeded tapping, was predictive of self-reported PD status (area under the receiver operating characteristic curve (AUC) = 0.8) and correlated with in-clinic evaluation of disease severity (r = 0.71; P < 1.8 × 10-6) when compared with motor Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Although remote assessment requires careful consideration for accurate interpretation of RWD, our results support the use of smartphones and wearables in objective and personalized disease assessments.
Subject(s)
Parkinson Disease , Smartphone , Gait , Humans , Movement , Parkinson Disease/diagnosis , Severity of Illness IndexABSTRACT
Fueled by aging populations and continued environmental contamination, the global burden of Parkinson's disease (PD) is increasing. The disease, or more appropriately diseases, have multiple environmental and genetic influences but no approved disease modifying therapy. Additionally, efforts to prevent this debilitating disease have been limited. As numerous environmental contaminants (e.g., pesticides, metals, industrial chemicals) are implicated in PD, disease prevention is possible. To reduce the burden of PD, we have compiled preclinical and clinical research priorities that highlight both disease prediction and primary prevention. Though not exhaustive, the "PD prevention agenda" builds upon many years of research by our colleagues and proposes next steps through the lens of modifiable risk factors. The agenda identifies ten specific areas of further inquiry and considers the funding and policy changes that will be necessary to help prevent the world's fastest growing brain disease.
Subject(s)
Parkinson Disease , Pesticides , Humans , Parkinson Disease/etiology , Parkinson Disease/prevention & controlABSTRACT
PURPOSE: Cardiac autonomic dysfunction in idiopathic Parkinson's disease (PD) manifests as reduced heart rate variability (HRV). In the present study, we explored the deceleration capacity of heart rate (DC) in patients with idiopathic PD, an advanced HRV marker that has proven clinical utility. METHODS: Standard and advanced HRV measures derived from 7-min electrocardiograms in 20 idiopathic PD patients and 27 healthy controls were analyzed. HRV measures were compared using regression analysis, controlling for age, sex, and mean heart rate. RESULTS: Significantly reduced HRV was found only in the subcohort of PD patients older than 60 years. Low- frequency power and global HRV measures were lower in patients than in controls, but standard beat-to-beat HRV markers (i.e., rMSSD and high-frequency power) were not significantly different between groups. DC was significantly reduced in the subcohort of PD patients older than 60 years compared to controls. CONCLUSIONS: Deceleration-related oscillations of HRV were significantly reduced in the older PD patients compared to healthy controls, suggesting that short-term DC may be a sensitive marker of cardiac autonomic dysfunction in PD. DC may be complementary to traditional markers of short-term HRV for the evaluation of autonomic modulation in PD. Further study to examine the association between DC and cardiac adverse events in PD is needed to clarify the clinical relevance of DC in this population.
Subject(s)
Parkinson Disease , Primary Dysautonomias , Autonomic Nervous System , Deceleration , Heart Rate , Humans , Parkinson Disease/complicationsABSTRACT
The Trial of Parkinson's And Zoledronic acid (TOPAZ, https://clinicaltrials.gov/ct2/show/NCT03924414 ) is a unique collaboration between experts in movement disorders and osteoporosis to test the efficacy of zoledronic acid, an FDA-approved parenteral treatment for osteoporosis, for fracture prevention in people with neurodegenerative parkinsonism. Aiming to enroll 3,500 participants age 65 years or older, TOPAZ is one of the largest randomized, placebo-controlled clinical trials ever attempted in parkinsonism. The feasibility of TOPAZ is enhanced by its design as a U.S.- wide home-based trial without geographical limits. Participants receive information from multiple sources, including specialty practices, support groups and websites. Conducting TOPAZ in participants' homes takes advantage of online consent technology, the capacity to confirm diagnosis using telemedicine and the availability of research nursing to provide screening and parenteral therapy in homes. Home-based clinical research may provide an efficient, convenient, less expensive method that opens participation in clinical trials to almost anyone with parkinsonism.
ABSTRACT
The gastrointestinal microbiome is altered in Parkinson's disease and likely plays a key role in its pathophysiology, affecting symptoms and response to therapy and perhaps modifying progression or even disease initiation. Gut dysbiosis therefore has a significant potential as a therapeutic target in Parkinson's disease, a condition elusive to disease-modifying therapy thus far. The gastrointestinal environment hosts a complex ecology, and efforts to modulate the relative abundance or function of established microorganisms are still in their infancy. Still, these techniques are being rapidly developed and have important implications for our understanding of Parkinson's disease. Currently, modulation of the microbiome can be achieved through non-pharmacologic means such as diet, pharmacologically through probiotic, prebiotic, or antibiotic use and procedurally through fecal transplant. Novel techniques being explored include the use of small molecules or genetically engineered organisms, with vast potential. Here, we review how some of these approaches have been used to date, important areas of ongoing research, and how microbiome modulation may play a role in the clinical management of Parkinson's disease in the future.
Subject(s)
Antiparkinson Agents/administration & dosage , Diet, Vegetarian/methods , Fecal Microbiota Transplantation/methods , Gastrointestinal Microbiome/physiology , Parkinson Disease/therapy , Animals , Gastrointestinal Microbiome/drug effects , Humans , Parkinson Disease/diagnosis , Parkinson Disease/physiopathology , Prebiotics/administration & dosage , Probiotics/administration & dosageABSTRACT
BACKGROUND: Parkinson's Disease (PD) is a clinically diagnosed neurodegenerative disorder that affects both motor and non-motor neural circuits. Speech deterioration (hypokinetic dysarthria) is a common symptom, which often presents early in the disease course. Machine learning can help movement disorders specialists improve their diagnostic accuracy using non-invasive and inexpensive voice recordings. METHOD: We used "Parkinson Dataset with Replicated Acoustic Features Data Set" from the UCI-Machine Learning repository. The dataset included 44 speech-test based acoustic features from patients with PD and controls. We analyzed the data using various machine learning algorithms including Light and Extreme Gradient Boosting, Random Forest, Support Vector Machines, K-nearest neighborhood, Least Absolute Shrinkage and Selection Operator Regression, as well as logistic regression. We also implemented a variable importance analysis to identify important variables classifying patients with PD. RESULTS: The cohort included a total of 80 subjects: 40 patients with PD (55% men) and 40 controls (67.5% men). Disease duration was 5 years or less for all subjects, with a mean Unified Parkinson's Disease Rating Scale (UPDRS) score of 19.6 (SD 8.1), and none were taking PD medication. The mean age for PD subjects and controls was 69.6 (SD 7.8) and 66.4 (SD 8.4), respectively. Our best-performing model used Light Gradient Boosting to provide an AUC of 0.951 with 95% confidence interval 0.946-0.955 in 4-fold cross validation using only seven acoustic features. CONCLUSIONS: Machine learning can accurately detect Parkinson's disease using an inexpensive and non-invasive voice recording. Light Gradient Boosting outperformed other machine learning algorithms. Such approaches could be used to inexpensively screen large patient populations for Parkinson's disease.
Subject(s)
Parkinson Disease , Voice Disorders , Algorithms , Cohort Studies , Female , Humans , Machine Learning , Male , Parkinson Disease/complications , Parkinson Disease/diagnosis , Support Vector Machine , Voice Disorders/etiologyABSTRACT
BACKGROUND: The effect of the COVID-19 pandemic on people with Parkinson's disease (PD) is poorly understood. OBJECTIVE: To rapidly identify areas of need and improve care in people with PD during the COVID-19 pandemic, we deployed a survey to assess COVID-19 symptoms and the pandemic's effect among those with and without COVID-19. METHODS: People with and without PD participating in the online study Fox Insight (FI) were invited to complete a survey between April 23 and May 23, 2020. Among people reporting COVID-19 diagnoses, we compared symptoms and outcomes in people with and without PD. Among people not reporting COVID-19, we assessed access to healthcare and services and PD symptoms. RESULTS: 7,209/9,762 active FI users responded (approximately 74% response rate), 5,429 people with PD and 1,452 without PD. COVID-19 diagnoses were reported by 51 people with and 26 without PD. Complications were more frequent in people with longer PD duration. People with PD and COVID-19 experienced new or worsening motor (63%) and nonmotor (75%) symptoms. People with PD not diagnosed with COVID-19 reported disrupted medical care (64%), exercise (21%), and social activities (57%), and worsened motor (43%) and non-motor (52%) symptoms. Disruptions were more common for those living alone, with lower income and non-White race. CONCLUSIONS: The COVID-19 pandemic is associated with wide-ranging effects on people with PD, and certain groups may be at particular risk. FI provides a rapid, patient-centered means to assess these effects and identify needs that can be used to improve the health of people with PD.
Subject(s)
Coronavirus Infections/epidemiology , Parkinson Disease/epidemiology , Pneumonia, Viral/epidemiology , Adult , Aged , Aged, 80 and over , COVID-19 , Cross-Sectional Studies , Female , Health Services Accessibility , Humans , Male , Middle Aged , Pandemics , Parkinson Disease/virology , Surveys and Questionnaires , Young AdultABSTRACT
BACKGROUND: The penetrance of leucine rich repeat kinase 2 (LRRK2) mutations is incomplete and may be influenced by environmental and/or other genetic factors. Nonsteroidal anti-inflammatory drugs (NSAIDs) are known to reduce inflammation and may lower Parkinson's disease (PD) risk, but their role in LRRK2-associated PD is unknown. OBJECTIVES: The objective of this study is to evaluate the association of regular NSAID use and LRRK2-associated PD. METHODS: Symptomatic ("LRRK2-PD") and asymptomatic ("LRRK2-non-PD") participants with LRRK2 G2019S, R1441X, or I2020T variants (definitely pathogenic variant carriers) or G2385R or R1628P variants (risk variant carriers) from 2 international cohorts provided information on regular ibuprofen and/or aspirin use (≥2 pills/week for ≥6 months) prior to the index date (diagnosis date for PD, interview date for non-PD). Multivariate logistic regression was used to evaluate the relationship between regular NSAID use and PD for any NSAID, separately for ibuprofen and aspirin in all carriers and separately in pathogenic and risk variant groups. RESULTS: A total of 259 LRRK2-PD and 318 LRRK2-non-PD participants were enrolled. Regular NSAID use was associated with reduced odds of PD in the overall cohort (odds ratio [OR], 0.34; 95% confidence interval [CI], 0.21-0.57) and in both pathogenic and risk variant carriers (ORPathogenic , 0.38; 95% CI, 0.21-0.67 and ORRiskVariant , 0.19; 95% CI, 0.04-0.99). Similar associations were observed for ibuprofen and aspirin separately (ORIbuprofen , 0.19; 95% CI, 0.07-0.50 and ORAspirin , 0.51; 95% CI, 0.28-0.91). CONCLUSIONS: Regular NSAID use may be associated with reduced penetrance in LRRK2-associated PD. The LRRK2 protein is involved in inflammatory pathways and appears to be modulated by regular anti-inflammatory use. Longitudinal observational and interventional studies of NSAID exposure and LRRK2-PD are needed to confirm this association. © 2020 International Parkinson and Movement Disorder Society.
Subject(s)
Parkinson Disease , Anti-Inflammatory Agents, Non-Steroidal/therapeutic use , Genetic Predisposition to Disease , Humans , Leucine-Rich Repeat Serine-Threonine Protein Kinase-2/genetics , Mutation/genetics , Parkinson Disease/drug therapy , Parkinson Disease/genetics , PenetranceABSTRACT
Autonomic nervous system involvement precedes the motor features of Parkinson's disease (PD). Our goal was to develop a proof-of-concept model for identifying subjects at high risk of developing PD by analysis of cardiac electrical activity. We used standard 10-s electrocardiogram (ECG) recordings of 60 subjects from the Honolulu Asia Aging Study including 10 with prevalent PD, 25 with prodromal PD, and 25 controls who never developed PD. Various methods were implemented to extract features from ECGs including simple heart rate variability (HRV) metrics, commonly used signal processing methods, and a Probabilistic Symbolic Pattern Recognition (PSPR) method. Extracted features were analyzed via stepwise logistic regression to distinguish between prodromal cases and controls. Stepwise logistic regression selected four features from PSPR as predictors of PD. The final regression model built on the entire dataset provided an area under receiver operating characteristics curve (AUC) with 95% confidence interval of 0.90 [0.80, 0.99]. The five-fold cross-validation process produced an average AUC of 0.835 [0.831, 0.839]. We conclude that cardiac electrical activity provides important information about the likelihood of future PD not captured by classical HRV metrics. Machine learning applied to ECGs may help identify subjects at high risk of having prodromal PD.