Digital assessment of speech in Huntington disease.

Background: Speech changes are an early symptom of Huntington disease (HD) and may occur prior to other motor and cognitive symptoms. Assessment of HD commonly uses clinician-rated outcome measures, which can be limited by observer variability and episodic administration. Speech symptoms are well suited for evaluation by digital measures which can enable sensitive, frequent, passive, and remote administration. Methods: We collected audio recordings using an external microphone of 36 (18 HD, 7 prodromal HD, and 11 control) participants completing passage reading, counting forward, and counting backwards speech tasks. Motor and cognitive assessments were also administered. Features including pausing, pitch, and accuracy were automatically extracted from recordings using the BioDigit Speech software and compared between the three groups. Speech features were also analyzed by the Unified Huntington Disease Rating Scale (UHDRS) dysarthria score. Random forest machine learning models were implemented to predict clinical status and clinical scores from speech features. Results: Significant differences in pausing, intelligibility, and accuracy features were observed between HD, prodromal HD, and control groups for the passage reading task (e.g., p < 0.001 with Cohen'd = -2 between HD and control groups for pause ratio). A few parameters were significantly different between the HD and control groups for the counting forward and backwards speech tasks. A random forest classifier predicted clinical status from speech tasks with a balanced accuracy of 73% and an AUC of 0.92. Random forest regressors predicted clinical outcomes from speech features with mean absolute error ranging from 2.43-9.64 for UHDRS total functional capacity, motor and dysarthria scores, and explained variance ranging from 14 to 65%. Montreal Cognitive Assessment scores were predicted with mean absolute error of 2.3 and explained variance of 30%. Conclusion: Speech data have the potential to be a valuable digital measure of HD progression, and can also enable remote, frequent disease assessment in prodromal HD and HD. Clinical status and disease severity were predicted from extracted speech features using random forest machine learning models. Speech measurements could be leveraged as sensitive marker of clinical onset and disease progression in future clinical trials.

Dry-Cleaning Chemicals and a Cluster of Parkinson's Disease and Cancer: A Retrospective Investigation.

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.

Trichloroethylene: An Invisible Cause of Parkinson's Disease?

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.

Monitoring gait at home with radio waves in Parkinson's disease: A marker of severity, progression, and medication response.

Parkinson's disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people's bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients' daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.

A Remote Longitudinal Observational Study of Individuals at Genetic Risk for Parkinson Disease: Baseline Results.

Background and Objectives: To recruit and characterize a national cohort of individuals who have a genetic variant (LRRK2 G2019S) that increases risk of Parkinson disease (PD), assess participant satisfaction with a decentralized, remote research model, and evaluate interest in future clinical trials. Methods: In partnership with 23andMe, Inc., a personal genetics company, LRRK2 G2019S carriers with and without PD were recruited to participate in an ongoing 36-month decentralized, remote natural history study. We examined concordance between self-reported and clinician-determined PD diagnosis. We applied the Movement Disorder Society Prodromal Parkinson's Disease Criteria and asked investigators to identify concern for parkinsonism to distinguish participants with probable prodromal PD. We compared baseline characteristics of LRRK2 G2019S carriers with PD, with prodromal PD, and without PD. Results: Over 15 months, we enrolled 277 LRRK2 G2019S carriers from 34 states. At baseline, 60 had self-reported PD (mean [SD] age 67.8 years [8.4], 98% White, 52% female, 80% Ashkenazi Jewish, and 67% with a family history of PD), and 217 did not (mean [SD] age 53.7 years [15.1], 95% White, 59% female, 73% Ashkenazi Jewish, and 57% with a family history of PD). Agreement between self-reported and clinician-determined PD status was excellent (κ = 0.94, 95% confidence interval 0.89-0.99). Twenty-four participants had prodromal PD; 9 met criteria for probable prodromal PD and investigators identified concern for parkinsonism in 20 cases. Compared with those without prodromal PD, participants with prodromal PD were older (63.9 years [9.0] vs 51.9 years [15.1], p < 0.001), had higher modified Movement Disorders Society-Unified Parkinson's Disease Rating Scale motor scores (5.7 [4.3] vs 0.8 [2.1], p < 0.001), and had higher Scale for Outcomes in PD for Autonomic Symptoms scores (11.5 [6.2] vs 6.9 [5.7], p = 0.002). Two-thirds of participants enrolled were new to research, 97% were satisfied with the overall study, and 94% of those without PD would participate in future preventive clinical trials. Discussion: An entirely remote national cohort of LRRK2 G2019S carriers was recruited from a single site. This study will prospectively characterize a large LRRK2 G2019S cohort, refine a new model of clinical research, and engage new research participants willing to participate in future therapeutic trials.

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study.

BACKGROUND: Access to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases-fueled mostly by environmental pollution and an aging population-can be seen worldwide. The current projection of more than 12 million patients with PD worldwide by 2040 is only part of the picture given that more than 20% of patients with PD remain undiagnosed. Timely diagnosis and frequent assessment are key to ensure timely and appropriate medical intervention, thus improving the quality of life of patients with PD. OBJECTIVE: In this paper, we propose a web-based framework that can help anyone anywhere around the world record a short speech task and analyze the recorded data to screen for PD. METHODS: We collected data from 726 unique participants (PD: 262/726, 36.1% were women; non-PD: 464/726, 63.9% were women; average age 61 years) from all over the United States and beyond. A small portion of the data (approximately 54/726, 7.4%) was collected in a laboratory setting to compare the performance of the models trained with noisy home environment data against high-quality laboratory-environment data. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet, "the quick brown fox jumps over the lazy dog." We extracted both standard acoustic features (mel-frequency cepstral coefficients and jitter and shimmer variants) and deep learning-based embedding features from the speech data. Using these features, we trained several machine learning algorithms. We also applied model interpretation techniques such as Shapley additive explanations to ascertain the importance of each feature in determining the model's output. RESULTS: We achieved an area under the curve of 0.753 for determining the presence of self-reported PD by modeling the standard acoustic features through the XGBoost-a gradient-boosted decision tree model. Further analysis revealed that the widely used mel-frequency cepstral coefficient features and a subset of previously validated dysphonia features designed for detecting PD from a verbal phonation task (pronouncing "ahh") influence the model's decision the most. CONCLUSIONS: Our model performed equally well on data collected in a controlled laboratory environment and in the wild across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with an audio-enabled device and help the participants screen for PD remotely, contributing to equity and access in neurological care.