Patient- and caregiver-reported impact of symptoms in Duchenne muscular dystrophy.

INTRODUCTION/AIMS: To better understand the disease burden faced by individuals with Duchenne muscular dystrophy (DMD) of all ages and elucidate potential targets for therapeutics, this study determined the prevalence and relative importance of symptoms experienced by individuals with DMD and identified factors associated with a higher disease burden. METHODS: We conducted qualitative interviews with individuals with DMD and caregivers of individuals with DMD to identify potential symptoms of importance to those living with DMD. We subsequently performed a cross-sectional study to assess which symptoms have the highest prevalence and importance in DMD and to determine which factors are associated with a higher disease burden. RESULTS: Thirty-nine individuals, aged 11 years and above, provided 3262 quotes regarding the symptomatic burden of DMD. Two hundred participants (87 individuals with DMD and 113 caregivers) participated in a subsequent cross-sectional study. Individuals with DMD identified limitations with mobility or walking (100%), inability to do activities (98.9%), trouble getting around (97.6%), and leg weakness (97.6%) as the most prevalent and life altering symptomatic themes in DMD. The symptomatic themes with the highest prevalence, as reported by caregivers on behalf of those with DMD for whom they care, were limitations with mobility or walking (90.3%), leg weakness (89.2%), and emotional issues (79.6%). Steroid/glucocorticoid use (e.g., prednisone or deflazacort) was associated with a lower level of disease burden in DMD. DISCUSSION: There are many symptomatic themes that contribute to disease burden in individuals with DMD. These symptoms are identified by both individuals with DMD and their caregivers and have a variable level of importance and prevalence in the DMD population.

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.

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.

Inflammasomes: An Emerging Mechanism Translating Environmental Toxicant Exposure Into Neuroinflammation in Parkinson's Disease.

Evidence indicates that complex gene-environment interactions underlie the incidence and progression of Parkinson's disease (PD). Neuroinflammation is a well-characterized feature of PD widely believed to exacerbate the neurodegenerative process. Environmental toxicants associated with PD, such as pesticides and heavy metals, can cause cellular damage and stress potentially triggering an inflammatory response. Toxicant exposure can cause stress and damage to cells by impairing mitochondrial function, deregulating lysosomal function, and enhancing the spread of misfolded proteins. These stress-associated mechanisms produce sterile triggers such as reactive oxygen species (ROS) along with a variety of proteinaceous insults that are well documented in PD. These associations provide a compelling rationale for analysis of sterile inflammatory mechanisms that may link environmental exposure to neuroinflammation and PD progression. Intracellular inflammasomes are cytosolic assemblies of proteins that contain pattern recognition receptors, and a growing body of evidence implicates the association between inflammasome activation and neurodegenerative disease. Characterization of how inflammasomes may function in PD is a high priority because the majority of PD cases are sporadic, supporting the widely held belief that environmental exposure is a major factor in disease initiation and progression. Inflammasomes may represent a common mechanism that helps to explain the strong association between exposure and PD by mechanistically linking environmental toxicant-driven cellular stress with neuroinflammation and ultimately cell death.