Defining Clinical Meaningfulness in Huntington's Disease.

BACKGROUND: Minimal clinically important difference (MCID) represents the smallest within-person change on an outcome measure considered meaningful to the patient. Anchor-based MCID methods evaluate the relationship between changes in an outcome measure and the patient-reported clinical importance of that change. OBJECTIVE: This study aims to estimate longitudinal MCID for clinically relevant outcome measures for individuals who have Stages 2 or 3 disease as measured by the Huntington's Disease Integrated Staging System (HD-ISS). METHODS: Data were drawn from Enroll-HD, a large global longitudinal, observational study and clinical research platform for HD family members. We analyzed HD participants (N = 11,070) by staging group using time frames ranging from 12 to 36 months. The anchor was the physical component summary score of the 12-item short-form health survey. HD-relevant motor, cognitive, and functional outcome measures were independent, external criterion outcomes. Complex analysis was conducted using multiple, independent, linear mixed effect regression models with decomposition to calculate MCID for each external criterion by group. RESULTS: MCID estimates varied by progression stage. MCID estimates increased as stage progression increased and as the time frame increased. MCID values for key HD measures are provided. For example, starting in HD-ISS stage 2, meaningful group change over 24 months equals an average increase of 3.6 or more points on the Unified Huntington's Disease Rating Scale Total Motor Score. CONCLUSIONS: This is the first study to examine MCID estimation thresholds for HD. The results can be used to improve clinical interpretation of study outcomes and enable treatment recommendations to support clinical decision-making and clinical trial methodology. © 2023 International Parkinson and Movement Disorder Society.

Economic burden of Huntington disease in Europe and the USA: Results from the Huntington's Disease Burden of Illness study.

BACKGROUND AND PURPOSE: The prevalence of Huntington disease (HD) has increased over time; however, there is a lack of up-to-date evidence documenting the economic burden of HD by disease stage. This study provides an estimate of the annual direct medical, nonmedical, and indirect costs associated with HD from participants in the Huntington's Disease Burden of Illness (HDBOI) study in five European countries and the USA. METHODS: The HDBOI is a retrospective, cross-sectional study. Data collection was conducted between September 2020 and May 2021. Participants were recruited by their HD-treating physicians and categorized as early stage (ES), mid stage (MS), or advanced stage (AS) HD. Data were collected via three questionnaires: a case report form, completed by physicians who collected health care resource use associated with HD to compute direct medical cost, and optional patient and caregiver questionnaires, which included information used to compute nondirect medical and indirect costs. Country-specific unit cost sources were used. RESULTS: HDBOI cost estimates were €12,663 (n = 2094) for direct medical costs, €2984 (n = 359) for nondirect medical costs, and €47,576 (n = 436) for indirect costs. Costs are higher in patients who are at later stages of disease; for example, direct medical costs estimates were €9220 (n = 846), €11,885 (n = 701), and €18,985 (n = 547) for ES, MS, and AS, respectively. Similar trends were observed for nondirect and indirect costs. Costs show large variations between patients and countries. CONCLUSIONS: Cost estimates from the HDBOI study show that people with HD and their caregivers bear a large economic burden that increases as disease progresses.

Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning.

BACKGROUND: It is not clear how specific gait measures reflect disease severity across the disease spectrum in Parkinson's disease (PD). OBJECTIVE: To identify the gait and mobility measures that are most sensitive and reflective of PD motor stages and determine the optimal sensor location in each disease stage. METHODS: Cross-sectional wearable-sensor records were collected in 332 patients with PD (Hoehn and Yahr scale I-III) and 100 age-matched healthy controls. Sensors were adhered to the participant's lower back, bilateral ankles, and wrists. Study participants walked in a ~15-meter corridor for 1 minute under two walking conditions: (1) preferred, usual walking speed and (2) walking while engaging in a cognitive task (dual-task). A subgroup (n = 303, 67% PD) also performed the Timed Up and Go test. Multiple machine-learning feature selection and classification algorithms were applied to discriminate between controls and PD and between the different PD severity stages. RESULTS: High discriminatory values were found between motor disease stages with mean sensitivity in the range 72%-83%, specificity 69%-80%, and area under the curve (AUC) 0.76-0.90. Measures from upper-limb sensors best discriminated controls from early PD, turning measures obtained from the trunk sensor were prominent in mid-stage PD, and stride timing and regularity were discriminative in more advanced stages. CONCLUSIONS: Applying machine-learning to multiple, wearable-derived features reveals that different measures of gait and mobility are associated with and discriminate distinct stages of PD. These disparate feature sets can augment the objective monitoring of disease progression and may be useful for cohort selection and power analyses in clinical trials of PD. © 2021 International Parkinson and Movement Disorder Society.

Current and projected future economic burden of Parkinson's disease in the U.S.

Parkinson's disease (PD) is one of the world's fastest growing neurological disorders. Much is unknown about PD-associated economic burdens in the United States (U.S.) and other high-income nations. This study provides a comprehensive analysis of the economic burdens of PD in the U.S. (2017) and projections for the next two decades. Multiple data sources were used to estimate the costs of PD, including public and private administrative claims data, Medicare Current Beneficiary Survey, Medical Expenditure Panel Survey, and a primary survey (n = 4,548) designed for this study. We estimated a U.S. prevalence of approximately one million individuals with diagnosed Parkinson's disease in 2017 and a total economic burden of $51.9 billion. The total burden of PD includes direct medical costs of $25.4 billion and $26.5 billion in indirect and non-medical costs, including an indirect cost of $14.2 billion (PWP and caregiver burden combined), non-medical costs of $7.5 billion, and $4.8 billion due to disability income received by PWPs. The Medicare program bears the largest share of excess medical costs, as most PD patients are over age 65. Projected PD prevalence will be more than 1.6 million with projected total economic burden surpassing $79 billion by 2037. The economic burden of PD was previously underestimated. Our findings underscore the substantial burden of PD to society, payers, patients, and caregivers. Interventions to reduce PD incidence, delay disease progression, and alleviate symptom burden may reduce the future economic burden of PD.

Role of data measurement characteristics in the accurate detection of Parkinson's disease symptoms using wearable sensors.

BACKGROUND: Parkinson's disease (PD) is a progressive neurological disease, with characteristic motor symptoms such as tremor and bradykinesia. There is a growing interest to continuously monitor these and other symptoms through body-worn sensor technology. However, limited battery life and memory capacity hinder the potential for continuous, long-term monitoring with these devices. There is little information available on the relative value of adding sensors, increasing sampling rate, or computing complex signal features, all of which may improve accuracy of symptom detection at the expense of computational resources. Here we build on a previous study to investigate the relationship between data measurement characteristics and accuracy when using wearable sensor data to classify tremor and bradykinesia in patients with PD. METHODS: Thirteen individuals with PD wore a flexible, skin-mounted sensor (collecting tri-axial accelerometer and gyroscope data) and a commercial smart watch (collecting tri-axial accelerometer data) on their predominantly affected hand. The participants performed a series of standardized motor tasks, during which a clinician scored the severity of tremor and bradykinesia in that limb. Machine learning models were trained on scored data to classify tremor and bradykinesia. Model performance was compared when using different types of sensors (accelerometer and/or gyroscope), different data sampling rates (up to 62.5 Hz), and different categories of pre-engineered features (up to 148 features). Performance was also compared between the flexible sensor and smart watch for each analysis. RESULTS: First, there was no effect of device type for classifying tremor symptoms (p > 0.34), but bradykinesia models incorporating gyroscope data performed slightly better (up to 0.05 AUROC) than other models (p = 0.01). Second, model performance decreased with sampling frequency (p < 0.001) for tremor, but not bradykinesia (p > 0.47). Finally, model performance for both symptoms was maintained after substantially reducing the feature set. CONCLUSIONS: Our findings demonstrate the ability to simplify measurement characteristics from body-worn sensors while maintaining performance in PD symptom detection. Understanding the trade-off between model performance and data resolution is crucial to design efficient, accurate wearable sensing systems. This approach may improve the feasibility of long-term, continuous, and real-time monitoring of PD symptoms by reducing computational burden on wearable devices.

Gait impairments in Parkinson's disease.

Gait impairments are among the most common and disabling symptoms of Parkinson's disease. Nonetheless, gait is not routinely assessed quantitatively but is described in general terms that are not sensitive to changes ensuing with disease progression. Quantifying multiple gait features (eg, speed, variability, and asymmetry) under natural and more challenging conditions (eg, dual-tasking, turning, and daily living) enhanced sensitivity of gait quantification. Studies of neural connectivity and structural network topology have provided information on the mechanisms of gait impairment. Advances in the understanding of the multifactorial origins of gait changes in patients with Parkinson's disease promoted the development of new intervention strategies, such as neurostimulation and virtual reality, aimed at alleviating gait impairments and enhancing functional mobility. For clinical applicability, it is important to establish clear links between specific gait impairments, their underlying mechanisms, and disease progression to foster the acceptance and usability of quantitative gait measures as outcomes in future disease-modifying clinical trials.

Cognition among individuals along a spectrum of increased risk for Parkinson's disease.

INTRODUCTION: Several characteristics associated with increased risk for Parkinson's disease (PD) have been identified, including specific genotypes and various non-motor symptoms. Characterizing non-motor features, such as cognitive abilities, among individuals considered at-risk for PD is essential to improving prediction of future neurodegeneration. METHODS: Participants belonging to the following cohorts of the Parkinson Progression Markers Initiative (PPMI) study were included: de novo PD with dopamine transporter binding deficit (n = 423), idiopathic REM sleep behavior disorder (RBD, n = 39), hyposmia (n = 26) and non-PD mutation carrier (NMC; Leucine-rich repeat kinase 2 (LRRK2) G2019S (n = 88) and glucocerebrosidase (GBA) gene (n = 38) mutations)). Inclusion criteria enriched the RBD and hyposmia cohorts, but not the NMC cohort, with individuals with dopamine transporter binding deficit. Baseline neuropsychological performance was compared, and analyses were adjusted for age, sex, education, and depression. RESULTS: The RBD cohort performed significantly worse than the hyposmia and NMC cohorts on Symbol Digit Modality Test (mean (SD) 32.4 (9.16) vs. 41.8 (9.98), p = 0.002 and vs. 45.2 (10.9), p<0.001) and Judgment of Line Orientation (11.3 (2.36) vs.12.9 (1.87), p = 0.004 and vs. 12.9 (1.87), p<0.001). The RBD cohort also performed worse than the hyposmia cohort on the Montreal Cognitive Assessment (25.5 (4.13) vs. 27.3 (1.71), p = 0.02). Hyposmics did not differ from PD or NMC cohorts on any cognitive test score. CONCLUSION: Among individuals across a spectrum of risk for PD, cognitive function is worse among those with the characteristic most strongly associated with future risk of PD or dementia with Lewy bodies, namely RBD.

Freezing of gait and fall detection in Parkinson's disease using wearable sensors: a systematic review.

Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson's disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73-100% for sensitivity and 67-100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets.

The effect of white matter hyperintensities on neurodegeneration in mild cognitive impairment.

INTRODUCTION: It is unclear whether white matter hyperintensities (WMHs), magnetic resonance imaging markers of small-vessel cerebrovascular disease, promote neurodegeneration and associated clinical decline in Alzheimer's disease (AD), or simply co-occur with recognized pathogenic processes. METHODS: In 169 patients with mild cognitive impairment, followed for 3 years, we examined the association of (1) baseline regional WMH and cerebral spinal fluid-derived t-tau (total tau) with entorhinal cortex atrophy rates, as a marker of AD-related neurodegeneration, and conversion to AD; and (2) baseline regional WMH with change in t-tau level. RESULTS: In participants with low baseline t-tau, higher regional WMH volumes were associated with faster entorhinal cortex atrophy. Higher parietal WMH volume predicted conversion to AD in those with high t-tau. Higher parietal and occipital WMH volumes predicted increasing t-tau. DISCUSSION: WMHs affect AD clinical and pathologic processes both directly and interacting with tau.

Examining the association between late-life depressive symptoms, cognitive function, and brain volumes in the context of cognitive reserve.

OBJECTIVE: The present study aimed to investigate whether cognitive reserve moderated the association between depressive symptoms and cognition, as well as brain volumes in a sample of older adults. METHODS: Non-demented participants (n = 3484) were selected from the Washington Heights/Hamilton Heights Inwood Columbia Aging Project (Northern Manhattan). A subsample of these participants without dementia (n = 703), who had brain imaging data, was also selected for a separate analysis. Depressive symptomatology was assessed with the 10-item Center for Epidemiologic Studies Depression Scale. Reading level and years of education were used as measures of cognitive reserve. Four distinct cognitive composite scores were calculated: executive function, memory, visual-spatial, and language. RESULTS: Multiple regression analysis revealed interaction effects between both measures of cognitive reserve and depressive symptoms on all the cognitive outcome measures except for visual-spatial ability. Those with greater reserve showed greater cognitive decrements than those with lower levels of reserve as depressive symptoms increased. A borderline interaction effect was revealed between reading level and depressive symptoms on total brain volumes. Those with lower reading scores showed greater volume loss as depressive symptoms increased than those with higher reading scores. CONCLUSIONS: Our findings indicate that the association between late-life depressive symptoms and core aspects of cognition varies depending on one's level of cognitive reserve. Those that had greater levels of education and/or reading ability showed a greater decrease in memory, executive, and language performances as depressive symptoms increased than those with lower years of education and reading ability.

Relationship between depressive symptoms and cognition in older, non-demented African Americans.

Knowledge of the relationship between depressive symptoms and cognition in older adults has primarily come from studies of clinically depressed, functionally impaired or cognitively impaired individuals, and in predominately White samples. Limited minority representation in depression research exposes the need to examine these associations in more ethnic/racially diverse populations. We sought to examine the relationship between depressive symptoms and cognition in a sample of non-demented older African Americans recruited from surrounding U.S. cities of New York, Greensboro, Miami, and Nashville (N=944). Depressive symptoms were evaluated with the Geriatric Depression Scale (GDS). Cognition was evaluated with a comprehensive neuropsychological battery. Test scores were summarized into attention, executive function, memory, language, and processing speed composites. Controlling for age, education, reading level, and sex, African American older adults who endorsed more symptoms obtained significantly lower scores on measures of memory, language, processing speed, and executive functioning. Further investigation of the causal pathway underlying this association, as well as potential mediators of the relationship between depressive symptoms and cognitive test performance among older African Americans, such as cardiovascular and cerebrovascular disease, may offer potential avenues for intervention.