Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease

Patient perspectives on the use of digital medical devices and health data for AI-driven personalised medicine in Parkinson's Disease

Introduction

Parkinson's Disease (PD) affects around 8.5 million people currently with numbers expected to rise to 12 million by 2040. PD is characterized by fluctuating motor and non-motor symptoms demanding accurate monitoring. Recent advancements in digital medical devices (DMDs) like wearables and AI offer promise in addressing these needs. However, the successful implementation of DMDs in healthcare relies on patients' willingness to adopt and engage with these digital tools.

Methods

To understand patient perspectives in individuals with PD, a cross-sectional study was conducted as part of the EU-wide DIGIPD project across France, Spain, and Germany. Multidisciplinary teams including neurodegenerative clinics and patient organizations conducted surveys focusing on (i) sociodemographic information, (ii) use of DMDs (iii) acceptance of using health data (iv) preferences for the DMDs use. We used descriptive statistics to understand the use of DMDs and patient preferences and logistic regression models to identify predictors of willingness to use DMDs and to share health data through DMDs.

Results

In total 333 individuals with PD participated in the study. Findings revealed a high willingness to use DMDs (90.3%) and share personal health data (97.4%,) however this differed across sociodemographic groups and was more notable among older age groups (under 65 = 17.9% vs. over 75 = 39.29%, p = 0.001) and those with higher education levels less willing to accept such use of data (university level = 78.6% vs. 21.43% with secondary level, p = 0.025). Providing instruction on the use of DMDs and receiving feedback on the results of the data collection significantly increased the willingness to use DMDs (OR = 3.57, 95% CI = 1.44-8.89) and (OR = 3.77, 95% CI = 1.01-14.12), respectively.

Conclusion

The study emphasizes the importance of considering patient perspectives for the effective deployment of digital technologies, especially for older and more advanced disease-stage patients who stand to benefit the most.

Predicting age of onset and progression of disease in late-onset genetic neurodegenerative diseases: An ethics review and research agenda

Currently, a prognostic biomarker-based model is being developed to predict the onset and disease progression of Huntington's Disease (HD) and Spinocerebellar Ataxia (SCA) types 1 and 3, both late-onset genetic neurodegenerative diseases lacking a disease-modifying treatment (DMT). The need for more accurate predictions of onset and disease progression arises in the context of clinical trials evaluating the effectiveness of potential DMTs and identifying the optimal time to initiate such a DMT. Moreover, such a prognostic model may provide mutation carriers with personal utility. The aim of this article is to anticipate the ethical issues raised by these new prognostic models and to formulate the ethical issues that need to be addressed to facilitate an ethically responsible development and implementation of such a model. Part one of this article describes the ethical issues raised by presymptomatic genetic testing for HD and evaluates whether and how these issues may also occur by predicting onset and disease progression. Part two presents the results of a critical interpretative review into the ethical issues raised by biomarker testing in other late-onset neurodegenerative diseases lacking a DMT. The review aims to identify new ethical issues related to biomarker testing for predicting the onset and disease progression of HD and SCA. Finally, based on parts one and two, part three proposes a research agenda for the near future regarding the most pressing ethical issues that need to be addressed to ensure an ethically responsible implementation of such a prognostic model in both research settings and clinical practice.

Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson's disease

Parkinson's disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data's utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83-92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection.

Levodopa-induced dyskinesia in Parkinson's disease: Insights from cross-cohort prognostic analysis using machine learning

BACKGROUND

Prolonged levodopa treatment in Parkinson's disease (PD) often leads to motor complications, including levodopa-induced dyskinesia (LID). Despite continuous levodopa treatment, some patients do not develop LID symptoms, even in later stages of the disease.

OBJECTIVE

This study explores machine learning (ML) methods using baseline clinical characteristics to predict the development of LID in PD patients over four years, across multiple cohorts.

METHODS

Using interpretable ML approaches, we analyzed clinical data from three independent longitudinal PD cohorts (LuxPARK, n = 356; PPMI, n = 484; ICEBERG, n = 113) to develop cross-cohort prognostic models and identify potential predictors for the development of LID. We examined cohort-specific and shared predictive factors, assessing model performance and stability through cross-validation analyses.

RESULTS

Consistent cross-validation results for single and multiple cohort analyses highlighted the effectiveness of the ML models and identified baseline clinical characteristics with significant predictive value for the LID prognosis in PD. Predictors positively correlated with LID include axial symptoms, freezing of gait, and rigidity in the lower extremities. Conversely, the risk of developing LID was inversely associated with the occurrence of resting tremors, higher body weight, later onset of PD, and visuospatial abilities.

CONCLUSIONS

This study presents interpretable ML models for dyskinesia prognosis with significant predictive power in cross-cohort analyses. The models may pave the way for proactive interventions against dyskinesia in PD by optimizing levodopa dosing regimens and adjunct treatments with dopamine agonists or MAO-B inhibitors, and by employing non-pharmacological interventions such as dietary adjustments affecting levodopa absorption for high-risk LID patients.

Regulatory mechanism of miR-20a-5p in neuronal damage and inflammation in lipopolysaccharide-induced BV2 cells and MPTP-HCl-induced Parkinson's disease mice

BACKGROUND

Parkinson's disease (PD) is a prevailing neurodegenerative disorder increasingly affecting the elderly population. The involvement of microRNAs (miRNAs) in PD has been confirmed. We sought to explore the molecular mechanism of miR-20a-5p in PD.

METHODS

Lipopolysaccharide (LPS)-induced BV2 cell model and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine hydrochloride (MPTP-HCl)-induced PD mouse model were established. miR-20a-5p, inducible nitric oxide synthase (iNOS), interleukin (IL)-6, tumour necrosis factor (TNF)-α, transforming growth factor (TGF)-β1, and IL-10 expression in BV2 cells was examined by reverse transcription - quantitative polymerase chain reaction. Cell viability was assessed by MTT assay. The apoptotic rate and levels of Bcl-2, Bax, cleaved caspase-3, and signal transducer and activator of transmission (STAT)3 were examined by flow cytometry and Western blot. Bioinformatics software predicted the potential binding sites of miR-20a-5p and STAT3. Dual-luciferase experiment verified the binding relationship. Iba1-positive and tyrosine hydroxylase (TH)-positive cell numbers in substantia nigra pars compacta were detected by immunohistochemistry. The effect of miR-20a-5p on motor function in MPTP-induced PD mice was detected by Rota-rod test, Pole test, Traction test and Beam-crossing task.

RESULTS

miR-20a-5p was under-expressed in LPS-induced BV2 cells. Overexpression of miR-20a-5p increased the viability of LPS-induced BV2 cells and reduced apoptosis rates. Moreover, overexpression of miR-20a-5p reduced cleaved caspase-3, Bax, iNOS, IL-6, and TNF-α and increased Bcl-2 and TGF-β1, and IL-10. miR-20a-5p targeted STAT3. STAT3 overexpression partially reversed miR-20a-5p overexpression-mediated effects on LPS-induced BV2 cell viability, apoptosis, and inflammatory responses. miR-20a-5p overexpression inhibited MPTP-induced STAT3 and α-synuclein levels, microglia activation, and inflammatory response, and reduced the loss of TH-positive cells in mice. miR-20a-5p overexpression ameliorated MPTP-induced dyskinesia in PD model mice.

CONCLUSION

miR-20a-5p alleviates neuronal damage and suppresses inflammation by targeting STAT3 in PD.

Digital biomarkers in Parkinson's disease

Digital biomarker (DB) assessments provide objective measures of daily life tasks and thus hold promise to improve diagnosis and monitoring of Parkinson's disease (PD) patients especially those with advanced stages. Data from DB studies can be used in advanced analytics such as Artificial Intelligence and Machine Learning to improve monitoring, treatment and outcomes. Although early development of inertial sensors as accelerometers and gyroscopes in smartphones provided encouraging results, the use of DB remains limited due to lack of standards, harmonization and consensus for analytical as well as clinical validation. Accordingly, a number of clinical trials have been developed to evaluate the performance of DB vs traditional assessment tools with the goal of monitoring disease progression, improving quality of life and outcomes. Herein, we update current evidence on the use of DB in PD and highlight potential benefits and limitations and provide suggestions for future research study.

Old problems, new solutions: harnessing technology and innovation in Parkinson's disease-evidence and experiences from Thailand

The prevalence of Parkinson's disease (PD) is increasing rapidly worldwide, but there are notable inequalities in its distribution and in the availability of healthcare resources across different world regions. Low- and middle-income countries (LMICs), including Thailand, bear the highest burden of PD so there is an urgent need to develop effective solutions that can overcome the many regional challenges associated with delivering high-quality, and equitable care to a diverse population with limited resources. This article describes the evolution of healthcare delivery for PD in Thailand, as a case example of a LMIC. The discussions reflect the author's presentation at the Yoshikuni Mizuno Lectureship Award given during the 8th Asian and Oceanian Parkinson's Disease and Movement Disorders Congress in March 2023 for which he was the 2023 recipient. The specific challenges faced in Thailand are reviewed along with new solutions that have been implemented to improve the knowledge and skills of healthcare professionals nationally, the delivery of care, and the outcomes for PD patients. Technology and innovation have played an important role in this process with many new tools and devices being implemented in clinical practice. Without any realistic prospect of a curative therapy in the near future that could halt the current PD pandemic, it will be necessary to focus on preventative lifestyle strategies that can help reduce the risk of developing PD such as good nutrition (EAT), exercise (MOVE), good sleep hygiene (SLEEP), and minimizing environmental risks (PROTECT), which should be initiated and continued (REPEAT) as early as possible.

Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics

Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson's disease. Sensor data collected from wearable gyroscopes located at the sole of the patient's shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson's as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen's Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.

Progression subtypes in Parkinson's disease identified by a data-driven multi cohort analysis

The progression of Parkinson's disease (PD) is heterogeneous across patients, affecting counseling and inflating the number of patients needed to test potential neuroprotective treatments. Moreover, disease subtypes might require different therapies. This work uses a data-driven approach to investigate how observed heterogeneity in PD can be explained by the existence of distinct PD progression subtypes. To derive stable PD progression subtypes in an unbiased manner, we analyzed multimodal longitudinal data from three large PD cohorts and performed extensive cross-cohort validation. A latent time joint mixed-effects model (LTJMM) was used to align patients on a common disease timescale. Progression subtypes were identified by variational deep embedding with recurrence (VaDER). In each cohort, we identified a fast-progressing and a slow-progressing subtype, reflected by different patterns of motor and non-motor symptoms progression, survival rates, treatment response, features extracted from DaTSCAN imaging and digital gait assessments, education, and Alzheimer's disease pathology. Progression subtypes could be predicted with ROC-AUC up to 0.79 for individual patients when a one-year observation period was used for model training. Simulations demonstrated that enriching clinical trials with fast-progressing patients based on these predictions can reduce the required cohort size by 43%. Our results show that heterogeneity in PD can be explained by two distinct subtypes of PD progression that are stable across cohorts. These subtypes align with the brain-first vs. body-first concept, which potentially provides a biological explanation for subtype differences. Our predictive models will enable clinical trials with significantly lower sample sizes by enriching fast-progressing patients.

Mapping the ethical landscape of digital biomarkers: A scoping review

In the evolving landscape of digital medicine, digital biomarkers have emerged as a transformative source of health data, positioning them as an indispensable element for the future of the discipline. This necessitates a comprehensive exploration of the ethical complexities and challenges intrinsic to this cutting-edge technology. To address this imperative, we conducted a scoping review, seeking to distill the scientific literature exploring the ethical dimensions of the use of digital biomarkers. By closely scrutinizing the literature, this review aims to bring to light the underlying ethical issues associated with the development and integration of digital biomarkers into medical practice.

The paradigm change from reactive medical services to 3PM in ischemic stroke: a holistic approach utilising tear fluid multi-omics, mitochondria as a vital biosensor and AI-based multi-professional data interpretation

Worldwide stroke is the second leading cause of death and the third leading cause of death and disability combined. The estimated global economic burden by stroke is over US$891 billion per year. Within three decades (1990-2019), the incidence increased by 70%, deaths by 43%, prevalence by 102%, and DALYs by 143%. Of over 100 million people affected by stroke, about 76% are ischemic stroke (IS) patients recorded worldwide. Contextually, ischemic stroke moves into particular focus of multi-professional groups including researchers, healthcare industry, economists, and policy-makers. Risk factors of ischemic stroke demonstrate sufficient space for cost-effective prevention interventions in primary (suboptimal health) and secondary (clinically manifested collateral disorders contributing to stroke risks) care. These risks are interrelated. For example, sedentary lifestyle and toxic environment both cause mitochondrial stress, systemic low-grade inflammation and accelerated ageing; inflammageing is a low-grade inflammation associated with accelerated ageing and poor stroke outcomes. Stress overload, decreased mitochondrial bioenergetics and hypomagnesaemia are associated with systemic vasospasm and ischemic lesions in heart and brain of all age groups including teenagers. Imbalanced dietary patterns poor in folate but rich in red and processed meat, refined grains, and sugary beverages are associated with hyperhomocysteinaemia, systemic inflammation, small vessel disease, and increased IS risks. Ongoing 3PM research towards vulnerable groups in the population promoted by the European Association for Predictive, Preventive and Personalised Medicine (EPMA) demonstrates promising results for the holistic patient-friendly non-invasive approach utilising tear fluid-based health risk assessment, mitochondria as a vital biosensor and AI-based multi-professional data interpretation as reported here by the EPMA expert group. Collected data demonstrate that IS-relevant risks and corresponding molecular pathways are interrelated. For examples, there is an evident overlap between molecular patterns involved in IS and diabetic retinopathy as an early indicator of IS risk in diabetic patients. Just to exemplify some of them such as the 5-aminolevulinic acid/pathway, which are also characteristic for an altered mitophagy patterns, insomnia, stress regulation and modulation of microbiota-gut-brain crosstalk. Further, ceramides are considered mediators of oxidative stress and inflammation in cardiometabolic disease, negatively affecting mitochondrial respiratory chain function and fission/fusion activity, altered sleep-wake behaviour, vascular stiffness and remodelling. Xanthine/pathway regulation is involved in mitochondrial homeostasis and stress-driven anxiety-like behaviour as well as molecular mechanisms of arterial stiffness. In order to assess individual health risks, an application of machine learning (AI tool) is essential for an accurate data interpretation performed by the multiparametric analysis. Aspects presented in the paper include the needs of young populations and elderly, personalised risk assessment in primary and secondary care, cost-efficacy, application of innovative technologies and screening programmes, advanced education measures for professionals and general population-all are essential pillars for the paradigm change from reactive medical services to 3PM in the overall IS management promoted by the EPMA.

Performance of the Dreem 2 EEG headband, relative to polysomnography, for assessing sleep in Parkinson's disease

GOAL AND AIMS

To pilot the feasibility and evaluate the performance of an EEG wearable for measuring sleep in individuals with Parkinson's disease.

FOCUS TECHNOLOGY

Dreem Headband, Version 2.

REFERENCE TECHNOLOGY

Polysomnography.

SAMPLE

Ten individuals with Parkinson's disease.

DESIGN

Individuals wore Dreem Headband during a single night of polysomnography.

CORE ANALYTICS

Comparison of summary metrics, bias, and epoch-by-epoch analysis.

ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES

Correlation of summary metrics with demographic and Parkinson's disease characteristics.

CORE OUTCOMES

Summary statistics showed Dreem Headband overestimated several sleep metrics, including total sleep, efficiency, deep sleep, and rapid eye movement sleep, with an exception in light sleep. Epoch-by-epoch analysis showed greater specificity than sensitivity, with adequate accuracy across sleep stages (0.55-0.82).

IMPORTANT SUPPLEMENTAL OUTCOMES

Greater Parkinson's disease duration and rapid eye movement behavior were associated with more wakefulness, and worse Parkinson's disease motor symptoms were associated with less deep sleep.

CORE CONCLUSION

The Dreem Headband performs similarly in Parkinson's disease as it did in non-Parkinson's disease samples and shows promise for improving access to sleep assessment in people with Parkinson's disease.

Matching science to reality: how to deploy a participant-driven digital brain health platform

Introduction

Advances in digital technologies for health research enable opportunities for digital phenotyping of individuals in research and clinical settings. Beyond providing opportunities for advanced data analytics with data science and machine learning approaches, digital technologies offer solutions to several of the existing barriers in research practice that have resulted in biased samples.

Methods

A participant-driven, precision brain health monitoring digital platform has been introduced to two longitudinal cohort studies, the Boston University Alzheimer's Disease Research Center (BU ADRC) and the Bogalusa Heart Study (BHS). The platform was developed with prioritization of digital data in native format, multiple OS, validity of derived metrics, feasibility and usability. A platform including nine remote technologies and three staff-guided digital assessments has been introduced in the BU ADRC population, including a multimodal smartphone application also introduced to the BHS population. Participants select which technologies they would like to use and can manipulate their personal platform and schedule over time.

Results

Participants from the BU ADRC are using an average of 5.9 technologies to date, providing strong evidence for the usability of numerous digital technologies in older adult populations. Broad phenotyping of both cohorts is ongoing, with the collection of data spanning cognitive testing, sleep, physical activity, speech, motor activity, cardiovascular health, mood, gait, balance, and more. Several challenges in digital phenotyping implementation in the BU ADRC and the BHS have arisen, and the protocol has been revised and optimized to minimize participant burden while sustaining participant contact and support.

Discussion

The importance of digital data in its native format, near real-time data access, passive participant engagement, and availability of technologies across OS has been supported by the pattern of participant technology use and adherence across cohorts. The precision brain health monitoring platform will be iteratively adjusted and improved over time. The pragmatic study design enables multimodal digital phenotyping of distinct clinically characterized cohorts in both rural and urban U.S. settings.

Mobile App Interventions for Parkinson's Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review

Central nervous system diseases (CNSDs) lead to significant disability worldwide. Mobile app interventions have recently shown the potential to facilitate monitoring and medical management of patients with CNSDs. In this direction, the characteristics of the mobile apps used in research studies and their level of clinical effectiveness need to be explored in order to advance the multidisciplinary research required in the field of mobile app interventions for CNSDs. A systematic review of mobile app interventions for three major CNSDs, i.e., Parkinson's disease (PD), multiple sclerosis (MS), and stroke, which impose significant burden on people and health care systems around the globe, is presented. A literature search in the bibliographic databases of PubMed and Scopus was performed. Identified studies were assessed in terms of quality, and synthesized according to target disease, mobile app characteristics, study design and outcomes. Overall, 21 studies were included in the review. A total of 3 studies targeted PD (14%), 4 studies targeted MS (19%), and 14 studies targeted stroke (67%). Most studies presented a weak-to-moderate methodological quality. Study samples were small, with 15 studies (71%) including less than 50 participants, and only 4 studies (19%) reporting a study duration of 6 months or more. The majority of the mobile apps focused on exercise and physical rehabilitation. In total, 16 studies (76%) reported positive outcomes related to physical activity and motor function, cognition, quality of life, and education, whereas 5 studies (24%) clearly reported no difference compared to usual care. Mobile app interventions are promising to improve outcomes concerning patient's physical activity, motor ability, cognition, quality of life and education for patients with PD, MS, and Stroke. However, rigorous studies are required to demonstrate robust evidence of their clinical effectiveness.

Genetics in parkinson's disease: From better disease understanding to machine learning based precision medicine

Parkinson's Disease (PD) is a neurodegenerative disorder with highly heterogeneous phenotypes. Accordingly, it has been challenging to robustly identify genetic factors associated with disease risk, prognosis and therapy response via genome-wide association studies (GWAS). In this review we first provide an overview of existing statistical methods to detect associations between genetic variants and the disease phenotypes in existing PD GWAS. Secondly, we discuss the potential of machine learning approaches to better quantify disease phenotypes and to move beyond disease understanding towards a better-personalized treatment of the disease.

Telemedicine in neurology: advances and possibilities

BACKGROUND

Telemedicine develops from technology that offers opportunities for knowledge transfer and information sharing and allows the provision of health services at a distance.

OBJECTIVE

To evaluate the number of publications on teleneurology in the last two decades in PubMed and the available evidence on the use of this technology in neurological clinical conditions.

METHODS

A quantitative assessment of publications related to telemedicine and neurology in the last two decades. A search was performed on the PubMed database for the descriptors ("Telemedicine"[Mesh]) AND "Neurology"[Mesh]). A review of the articles retrieved on the topic was carried out to evaluate the innovation processes used and applications in various clinical conditions involving teleneurology.

RESULTS

The search performed on March 14th 2022 resulted in 229 publications involving the topic of telemedicine and neurology between 1999 and 2022. Since 2000, there has been an increase in publications related to this topic, with a peak of 71 articles published in 2020, the year in which the World Health Organization defined the COVID-19 pandemic status.

CONCLUSION

In the last two decades, teleneurology has been developing through the expansion of technological resources and the COVID-19 pandemic intensified this process. Different modalities of teleneurology are studied in several neurology subfields and include teleconsultation (between healthcare professionals or between healthcare professionals and patients), telerehabilitation, telemonitoring and tele-education. The advances achieved by teleneurology in this period encouraged technological innovations and health processes that developed opportunities to improve the care provided in a mechanism of constant evolution.