Publications

IMPORTANCE: Parkinsonism and Parkinson disease (PD) are known to result from repetitive head impacts from boxing. Repetitive head impacts from American football may also be associated with increased risk of neurodegenerative pathologies that cause parkinsonism, yet in vivo research on the association between football play and PD is scarce and limited by small samples and equivocal findings.

OBJECTIVE: To evaluate the association between football participation and self-reported parkinsonism or PD diagnosis.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study leveraged data from the online Fox Insight study. Participants completed online questionnaires and self-reported whether they currently had a diagnosis of Parkinson disease or parkinsonism by a physician or other health care professional. In November 2020, the Boston University Head Impact Exposure Assessment was launched for data collection on repetitive head impacts. Data used for this manuscript were obtained from the Fox Insight database on June 9, 2022. A total of 1875 men who endorsed playing any organized sport were included. Former athletes were divided into those who participated in football (n = 729 [38.9%]) and those who participated in other sports (reference group).

EXPOSURES: Self-reported participation in football, duration and level of football play, age at first exposure.

MAIN OUTCOMES AND MEASURES: Logistic regression tested associations between PD status and history of football play, duration of football play, highest level played, and age at first exposure, controlling for age, education, history of diabetes or heart disease, body mass index, history of traumatic brain injury with loss of consciousness, and family history of PD.

RESULTS: In this sample of 1875 men (mean [SD] age, 67.69 [9.84] years) enriched for parkinsonism or PD (n = 1602 [85.4%]), 729 (38.9%) played football (mean [SD] duration, 4.35 [2.91] years). History of playing football was associated with higher odds of having a parkinsonism or PD diagnosis (odds ratio [OR], 1.61; 95% CI, 1.19-2.17). Among the entire sample, longer duration of play was associated with higher odds of having a parkinsonism or PD diagnosis (OR, 1.12; 95% CI, 1.06-1.19). Among football players, longer duration of football play (OR, 1.12; 95% CI, 1.02-1.23) and higher level of play (OR, 2.93; 95% CI, 1.28-6.73) were associated with higher odds of having parkinsonism or PD.

CONCLUSIONS AND RELEVANCE: In this cross-sectional study of participants enriched for PD, participation in football was associated with higher odds of having a reported parkinsonism or PD diagnosis.

We reviewed foundational concepts in artificial intelligence (AI) and machine learning (ML) and discussed ways in which these methodologies may be employed to enhance progress in clinical trials and research, with particular attention to applications in the design, conduct, and interpretation of clinical trials for neurologic diseases. We discussed ways in which ML may help to accelerate the pace of subject recruitment, provide realistic simulation of medical interventions, and enhance remote trial administration via novel digital biomarkers and therapeutics. Lastly, we provide a brief overview of the technical, administrative, and regulatory challenges that must be addressed as ML achieves greater integration into clinical trial workflows.

In this manuscript, we review the epidemiology of movement disorders including Parkinson's disease (PD), atypical parkinsonism, essential tremor, dystonia, functional movement disorders, tic disorders, chorea, and ataxias. We emphasize age-, sex-, and geography-based incidence and prevalence, as well as notable trends including the rising incidence and prevalence of PD. Given the growing global interest in refining clinical diagnostic skills in recognizing movement disorders, we highlight some key epidemiological findings that may be of interest to clinicians and health systems tasked with diagnosing and managing the health of patients with movement disorders.

PURPOSE OF REVIEW: The purpose is to review the results and impact of recent studies for current and future treatment of both motor and non-motor symptoms in Parkinson's disease (PD).

RECENT FINDINGS: New formulations of levodopa further optimize motor fluctuations, allowing for more on-time and less dyskinesia. On demand apomorphine continues to showcase itself as an effective and tolerable tool for treating motor off-periods. Though there are no clear treatment guidelines for PD-related constipation and sleep related disorders, several new agents for these non-motor symptoms show promising preliminary data. Expiratory muscle strength training may represent a useful and cost-effective strategy to alleviate oropharyngeal dysphagia associated with PD. There is evidence to suggest that the use of shorter pulse width and directional deep brain stimulation leads can results in a greater therapeutic window.

SUMMARY: Though no interventions currently exist to significantly modify the disease progression of PD, new studies continue to give insight into optimal symptomatic management. Clinicians should be familiar with expanding the repertoire of tools available to treat the diverse range of symptoms and challenges associated with PD.

BACKGROUND: Initiation of symptomatic therapy in Parkinson disease is a disease progression milestone, and its prediction is important. Previous studies were limited in duration and number of variables included in their predictive models.

OBJECTIVES: To identify predictors of time to initiation of symptomatic therapy in patients with PD not on treatment, using a large pool of candidate variables from the Parkinson's Progression Markers Initiative dataset, analyzed at ten years.

METHODS: Kaplan Meier survival curve was used to estimate time to initiation of symptomatic treatment. Potential predictors included 33 baseline clinical, imaging, biofluid, and genetic biomarkers. Univariate Cox regression was used for variable selection, significant predictors subsequently entering a multivariate Cox proportional hazard model, which was further reduced using the Akaike Information Criterion into a final reduced model.

RESULTS: Of 425 participants with Parkinson's Disease, 406 initiated symptomatic therapy at last follow up. The outcome was censored for 4.5% of the sample. The risk of initiating symptomatic therapy was 65% (95%CI 60-70%) within the first year from enrollment. Predictors included dopamine transporter SPECT, the Movement Disorders Society Unified Parkinson Disease Rating Scale, and anxiety (State Trait Anxiety Inventory).

CONCLUSIONS: Baseline dopamine transporter SPECT specific binding ratio was found to be the most impactful predictor for time to initiation of symptomatic therapy in this 10-year follow up analysis of the Progressive Parkinson Markers Initiative cohort, when treatment status was known for 95.5% of the sample.

Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records and gain insights on specific topics in neurology. In addition, we explore use cases for LLMs, such as early diagnosis, supporting patient and caregivers, and acting as an assistant for clinicians. We point to the potential ethical and technical challenges raised by LLMs, such as concerns about privacy and data security, potential biases in the data for model training, and the need for careful validation of results. Researchers must consider these challenges and take steps to address them to ensure that their work is conducted in a safe and responsible manner. Despite these challenges, LLMs offer promising opportunities for improving care and treatment of various neurologic disorders.

IMPORTANCE: Parkinsonism and Parkinson disease (PD) are known to result from repetitive head impacts from boxing. Repetitive head impacts from American football may also be associated with increased risk of neurodegenerative pathologies that cause parkinsonism, yet in vivo research on the association between football play and PD is scarce and limited by small samples and equivocal findings.

OBJECTIVE: To evaluate the association between football participation and self-reported parkinsonism or PD diagnosis.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study leveraged data from the online Fox Insight study. Participants completed online questionnaires and self-reported whether they currently had a diagnosis of Parkinson disease or parkinsonism by a physician or other health care professional. In November 2020, the Boston University Head Impact Exposure Assessment was launched for data collection on repetitive head impacts. Data used for this manuscript were obtained from the Fox Insight database on June 9, 2022. A total of 1875 men who endorsed playing any organized sport were included. Former athletes were divided into those who participated in football (n = 729 [38.9%]) and those who participated in other sports (reference group).

EXPOSURES: Self-reported participation in football, duration and level of football play, age at first exposure.

MAIN OUTCOMES AND MEASURES: Logistic regression tested associations between PD status and history of football play, duration of football play, highest level played, and age at first exposure, controlling for age, education, history of diabetes or heart disease, body mass index, history of traumatic brain injury with loss of consciousness, and family history of PD.

RESULTS: In this sample of 1875 men (mean [SD] age, 67.69 [9.84] years) enriched for parkinsonism or PD (n = 1602 [85.4%]), 729 (38.9%) played football (mean [SD] duration, 4.35 [2.91] years). History of playing football was associated with higher odds of having a parkinsonism or PD diagnosis (odds ratio [OR], 1.61; 95% CI, 1.19-2.17). Among the entire sample, longer duration of play was associated with higher odds of having a parkinsonism or PD diagnosis (OR, 1.12; 95% CI, 1.06-1.19). Among football players, longer duration of football play (OR, 1.12; 95% CI, 1.02-1.23) and higher level of play (OR, 2.93; 95% CI, 1.28-6.73) were associated with higher odds of having parkinsonism or PD.

CONCLUSIONS AND RELEVANCE: In this cross-sectional study of participants enriched for PD, participation in football was associated with higher odds of having a reported parkinsonism or PD diagnosis.

We reviewed foundational concepts in artificial intelligence (AI) and machine learning (ML) and discussed ways in which these methodologies may be employed to enhance progress in clinical trials and research, with particular attention to applications in the design, conduct, and interpretation of clinical trials for neurologic diseases. We discussed ways in which ML may help to accelerate the pace of subject recruitment, provide realistic simulation of medical interventions, and enhance remote trial administration via novel digital biomarkers and therapeutics. Lastly, we provide a brief overview of the technical, administrative, and regulatory challenges that must be addressed as ML achieves greater integration into clinical trial workflows.