Propionic Acid Shapes the Multiple Sclerosis Disease Course by an Immunomodulatory Mechanism.

Short-chain fatty acids are processed from indigestible dietary fibers by gut bacteria and have immunomodulatory properties. Here, we investigate propionic acid (PA) in multiple sclerosis (MS), an autoimmune and neurodegenerative disease. Serum and feces of subjects with MS exhibited significantly reduced PA amounts compared with controls, particularly after the first relapse. In a proof-of-concept study, we supplemented PA to therapy-naive MS patients and as an add-on to MS immunotherapy. After 2 weeks of PA intake, we observed a significant and sustained increase of functionally competent regulatory T (Treg) cells, whereas Th1 and Th17 cells decreased significantly. Post-hoc analyses revealed a reduced annual relapse rate, disability stabilization, and reduced brain atrophy after 3 years of PA intake. Functional microbiome analysis revealed increased expression of Treg-cell-inducing genes in the intestine after PA intake. Furthermore, PA normalized Treg cell mitochondrial function and morphology in MS. Our findings suggest that PA can serve as a potent immunomodulatory supplement to MS drugs.

Grey Matter Atrophy and its Relationship with White Matter Lesions in Patients with Myelin Oligodendrocyte Glycoprotein Antibody-associated Disease, Aquaporin-4 Antibody-Positive Neuromyelitis Optica Spectrum Disorder, and Multiple Sclerosis.

OBJECTIVE: To evaluate: (1) the distribution of gray matter (GM) atrophy in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder (AQP4+NMOSD), and relapsing-remitting multiple sclerosis (RRMS); and (2) the relationship between GM volumes and white matter lesions in various brain regions within each disease. METHODS: A retrospective, multicenter analysis of magnetic resonance imaging data included patients with MOGAD/AQP4+NMOSD/RRMS in non-acute disease stage. Voxel-wise analyses and general linear models were used to evaluate the relevance of regional GM atrophy. For significant results (p < 0.05), volumes of atrophic areas are reported. RESULTS: We studied 135 MOGAD patients, 135 AQP4+NMOSD, 175 RRMS, and 144 healthy controls (HC). Compared with HC, MOGAD showed lower GM volumes in the temporal lobes, deep GM, insula, and cingulate cortex (75.79 cm3); AQP4+NMOSD in the occipital cortex (32.83 cm3); and RRMS diffusely in the GM (260.61 cm3). MOGAD showed more pronounced temporal cortex atrophy than RRMS (6.71 cm3), whereas AQP4+NMOSD displayed greater occipital cortex atrophy than RRMS (19.82 cm3). RRMS demonstrated more pronounced deep GM atrophy in comparison with MOGAD (27.90 cm3) and AQP4+NMOSD (47.04 cm3). In MOGAD, higher periventricular and cortical/juxtacortical lesions were linked to reduced temporal cortex, deep GM, and insula volumes. In RRMS, the diffuse GM atrophy was associated with lesions in all locations. AQP4+NMOSD showed no lesion/GM volume correlation. INTERPRETATION: GM atrophy is more widespread in RRMS compared with the other two conditions. MOGAD primarily affects the temporal cortex, whereas AQP4+NMOSD mainly involves the occipital cortex. In MOGAD and RRMS, lesion-related tract degeneration is associated with atrophy, but this link is absent in AQP4+NMOSD. ANN NEUROL 2024;96:276-288.

Minor Phenomena in Parkinson's Disease-Prevalence, Associations, and Risk of Developing Psychosis.

BACKGROUND: Minor phenomena, including passage phenomena, feeling of presence, and illusions, are common and may represent a prodromal form of psychosis in Parkinson's disease (PD). We examined the prevalence and clinical correlates of minor phenomena, and their potential role as a risk factor for PD psychosis. METHODS: A novel questionnaire, the Psychosis and Mild Perceptual Disturbances Questionnaire for PD (PMPDQ), was completed by Fox Insight cohort participants with and without PD. Additional assessments included the Non-Motor Symptoms Questionnaire (NMSQuest), REM Sleep Behavior Disorder Single Question Screen (RBD1Q), Movement Disorder Society-Unified Parkinson Disease Rating Scale Part II, demographic features, and medication usage. For participants with PD, we used regression models to identify clinical associations and predictors of incident psychosis over one year of follow-up. RESULTS: Among participants with PD (n = 5950) and without PD (n = 1879), the prevalence of minor phenomena was 43.1% and 31.7% (P < .001). Of the 3760 participants with PD and no baseline psychosis, independent correlates of minor phenomena included positive responses on the NMSQuest apathy/attention/memory (OR 1.7, 95% CI 1.3-2.1, P < .001) or sexual function domain (OR 1.3, 95% CI 1.1-1.6, P = .01) and positive RBD1Q (OR 1.3, 95% CI 1.05-1.5, P = .01). Independent risk factors for incident PD psychosis included the presence of minor phenomena (HR 3.0, 95% CI 2.4-3.9, P < .001), positive response on the NMSQuest apathy/attention/memory domain (HR 1.8, 95% CI 1.3-2.6, P < .001), and positive RBD1Q (HR 1.5, 95% CI 1.1-1.9, P = .004). CONCLUSIONS: Minor phenomena are common, associated with specific non-motor symptoms, and an independent predictor of incident psychosis in PD.

Clinical and MRI measures to identify non-acute MOG-antibody disease in adults.

MRI and clinical features of myelin oligodendrocyte glycoprotein (MOG)-antibody disease may overlap with those of other inflammatory demyelinating conditions posing diagnostic challenges, especially in non-acute phases and when serologic testing for MOG antibodies is unavailable or shows uncertain results. We aimed to identify MRI and clinical markers that differentiate non-acute MOG-antibody disease from aquaporin 4 (AQP4)-antibody neuromyelitis optica spectrum disorder and relapsing remitting multiple sclerosis, guiding in the identification of patients with MOG-antibody disease in clinical practice. In this cross-sectional retrospective study, data from 16 MAGNIMS centres were included. Data collection and analyses were conducted from 2019 to 2021. Inclusion criteria were: diagnosis of MOG-antibody disease; AQP4-neuromyelitis optica spectrum disorder and multiple sclerosis; brain and cord MRI at least 6 months from relapse; and Expanded Disability Status Scale (EDSS) score on the day of MRI. Brain white matter T2 lesions, T1-hypointense lesions, cortical and cord lesions were identified. Random forest models were constructed to classify patients as MOG-antibody disease/AQP4-neuromyelitis optica spectrum disorder/multiple sclerosis; a leave one out cross-validation procedure assessed the performance of the models. Based on the best discriminators between diseases, we proposed a guide to target investigations for MOG-antibody disease. One hundred and sixty-two patients with MOG-antibody disease [99 females, mean age: 41 (±14) years, median EDSS: 2 (0-7.5)], 162 with AQP4-neuromyelitis optica spectrum disorder [132 females, mean age: 51 (±14) years, median EDSS: 3.5 (0-8)], 189 with multiple sclerosis (132 females, mean age: 40 (±10) years, median EDSS: 2 (0-8)] and 152 healthy controls (91 females) were studied. In young patients (<34 years), with low disability (EDSS < 3), the absence of Dawson's fingers, temporal lobe lesions and longitudinally extensive lesions in the cervical cord pointed towards a diagnosis of MOG-antibody disease instead of the other two diseases (accuracy: 76%, sensitivity: 81%, specificity: 84%, P < 0.001). In these non-acute patients, the number of brain lesions < 6 predicted MOG-antibody disease versus multiple sclerosis (accuracy: 83%, sensitivity: 82%, specificity: 83%, P < 0.001). An EDSS < 3 and the absence of longitudinally extensive lesions in the cervical cord predicted MOG-antibody disease versus AQP4-neuromyelitis optica spectrum disorder (accuracy: 76%, sensitivity: 89%, specificity: 62%, P < 0.001). A workflow with sequential tests and supporting features is proposed to guide better identification of patients with MOG-antibody disease. Adult patients with non-acute MOG-antibody disease showed distinctive clinical and MRI features when compared to AQP4-neuromyelitis optica spectrum disorder and multiple sclerosis. A careful inspection of the morphology of brain and cord lesions together with clinical information can guide further analyses towards the diagnosis of MOG-antibody disease in clinical practice.

Automatic estimation of brain parenchymal fraction in patients with multple sclerosis: a comparison between synthetic MRI and an established automated brain segmentation software based on FSL.

PURPOSE: We aimed to validate the estimation of the brain parenchymal fraction (BPF) in patients with multiple sclerosis (MS) using synthetic magnetic resonance imaging (SyMRI) by comparison with software tools of the FMRIB Software Library (FSL). In addition to a cross-sectional method comparison, longitudinal volume changes were assessed to further elucidate the suitability of SyMRI for quantification of disease-specific changes. METHODS: MRI data from 216 patients with MS and 28 control participants were included for volume estimation by SyMRI and FSL-SIENAX. Moreover, longitudinal data from 35 patients with MS were used to compare registration-based percentage brain volume changes estimated using FSL-SIENA to difference-based calculations of volume changes using SyMRI. RESULTS: We observed strong correlations of estimated brain volumes between the two methods. While SyMRI overestimated grey matter and BPF compared to FSL-SIENAX, indicating a systematic bias, there was excellent agreement according to intra-class correlation coefficients for grey matter and good agreement for BPF and white matter. Bland-Altman plots suggested that the inter-method differences in BPF were smaller in patients with brain atrophy compared to those without atrophy. Longitudinal analyses revealed a tendency for higher atrophy rates for SyMRI than for SIENA, but SyMRI had a robust correlation and a good agreement with SIENA. CONCLUSION: In summary, BPF based on data from SyMRI and FSL-SIENAX is not directly transferable because an overestimation and higher variability of SyMRI values were observed. However, the consistency and correlations between the two methods were satisfactory, and SyMRI was suitable to quantify disease-specific atrophy in MS.

Predictors of atrial fibrillation after embolic stroke of undetermined source in patients with implantable loop recorders.

BACKGROUND: In patients with embolic stroke of undetermined source (ESUS), underlying subclinical atrial fibrillation (AF) is often suspected. Previous studies identifying predictors of AF have been limited in their ability to diagnose episodes of AF. Implantable loop recorders enable prolonged, continuous, and therefore more reliable detection of AF. The aim of this study was to identify clinical and ECG parameters as predictors of AF in ESUS patients with implantable loop recorders. METHODS: 101 ESUS patients who received an implantable loop recorder between 2012 and 2020 were included in this study. Patients were followed up regularly on a three-monthly outpatient interval. RESULTS: During a mean follow-up of 647 ± 385 days, AF was detected in 26 patients (26%). Independent risk factors of AF were age ≥ 60 years (HR 2.753, CI 1.129-6.713, p = 0.026), P-wave amplitude in lead II ≤ 0.075 mV (HR 3.751, CI 1.606-8.761, p = 0.002), and P-wave duration ≥ 125 ms (HR 4.299, CI 1.844-10.021, p < 0.001). In patients without risk factors, the risk of developing AF was 16%. In the presence of one risk factor, the probability increased only slightly to 18%. With two or three risk factors, the risk of AF increased to 70%. CONCLUSION: AF was detected in about one in four patients after ESUS in this study. A comprehensive evaluation involving multiple parameters and the existence of multiple risk factors yields the highest predictive accuracy for detecting AF in patients with ESUS.

Early spinal cord pseudoatrophy in interferon-beta-treated multiple sclerosis.

BACKGROUND AND PURPOSE: Brain pseudoatrophy has been shown to play a pivotal role in the interpretation of brain atrophy measures during the first year of disease-modifying therapy in multiple sclerosis. Whether pseudoatrophy also affects the spinal cord remains unclear. The aim of this study was to analyze the extent of pseudoatrophy in the upper spinal cord during the first 2 years after therapy initiation and compare this to the brain. METHODS: A total of 129 patients from a prospective longitudinal multicentric national cohort study for whom magnetic resonance imaging scans at baseline, 12 months, and 24 months were available were selected for brain and spinal cord volume quantification. Annual percentage brain volume and cord area change were calculated using SIENA (Structural Image Evaluation of Normalized Atrophy) and NeuroQLab, respectively. Linear mixed model analyses were performed to compare patients on interferon-beta therapy (n = 84) and untreated patients (n = 45). RESULTS: Patients treated with interferon-beta demonstrated accelerated annual percentage brain volume and cervical cord area change in the first year after treatment initiation, whereas atrophy rates stabilized to a similar and not significantly different level compared to untreated patients during the second year. CONCLUSIONS: These results suggest that pseudoatrophy occurs not only in the brain, but also in the spinal cord during the first year of interferon-beta treatment.

Course of neuropsychological impairment during natalizumab-associated progressive multifocal leukoencephalopathy.

BACKGROUND AND PURPOSE: Progressive multifocal leukoencephalopathy (PML), an opportunistic infection of the central nervous system from the John Cunningham virus (JCV), is a side effect of natalizumab (NTZ) treatment for relapsing-remitting multiple sclerosis (RRMS), potentially leading to a substantial increase of physical and mental disability. Nevertheless, data of neuropsychological impairment during the NTZ-PML disease course are missing. Our objective was to evaluate the neuropsychological disease course of NTZ-PML patients and to compare neuropsychological deficits of NTZ-PML patients with two different non-PML multiple sclerosis (MS) cohorts. METHODS: Neuropsychological examinations of 28 NTZ-PML patients performed during different phases of the disease ([i] at PML diagnosis, [ii] during immune reconstitution inflammatory syndrome [IRIS], and [iii] post-IRIS/PML) were retrospectively analyzed and compared to those of NTZ-treated RRMS or secondary progressive MS patients with and without immunotherapy. RESULTS: Compared to controls, NTZ-PML patients performed worse in neuropsychological examinations during all stages of disease, mainly affecting visuospatial ability and working memory. Furthermore, failure to eliminate the JCV from the central nervous system was associated with a progredient decline of cognition, especially working memory. CONCLUSIONS: Working memory and visuospatial abilities are the core neuropsychological deficits of NTZ-PML patients in long-term follow-up. Our findings should be implemented in neurorehabilitation strategies.

Digital Technology in Movement Disorders: Updates, Applications, and Challenges.

PURPOSE OF REVIEW: Digital technology affords the opportunity to provide objective, frequent, and sensitive assessment of disease outside of the clinic environment. This article reviews recent literature on the application of digital technology in movement disorders, with a focus on Parkinson's disease (PD) and Huntington's disease. RECENT FINDINGS: Recent research has demonstrated the ability for digital technology to discriminate between individuals with and without PD, identify those at high risk for PD, quantify specific motor features, predict clinical events in PD, inform clinical management, and generate novel insights. Digital technology has enormous potential to transform clinical research and care in movement disorders. However, more work is needed to better validate existing digital measures, including in new populations, and to develop new more holistic digital measures that move beyond motor features.

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.

Parkinson disease with mild cognitive impairment: Domain-specific cognitive complaints predict dementia.

BACKGROUND: The presence of subjective cognitive complaints (SCC) as a predictor of cognitive impairment in Parkinson´s disease (PD) has shown conflicting results. Most previous studies only assessed complaints in the memory domain. We investigate the association of SCCs across cognitive domains with development of mild cognitive impairment (PD-MCI) and dementia (PDD) in PD and to assess agreement between SCCs and objective cognitive impairments in this population. METHODS: This is a retrospective analysis of a prospective cohort study. Participants were enrolled at six North-American movement disorders centers. They underwent neuropsychological and non-cognitive clinical evaluations, including the modified Neurobehavioral Inventory to elicit SCC (rated by each patient and independently by their close contact (CC)). Associations between SCCs and development of future cognitive impairment were assessed. Agreement between SCCs and objective impairment within the same domain was also calculated. RESULTS: Of 138 included PD patients, 42% fulfilled criteria for PD-MCI. None of the NBI items predicted development of cognitive impairment after one and two years in PD with normal cognition. In PD-MCI patients, SCCs related to attention predicted dementia at year one. CC ratings of SCCs related to memory and language problems predicted PDD in PD-MCI patients. According to CC reported patients' complaints, there was a significant agreement between SCCs and objective cognitive test scores on attention. CONCLUSIONS: Eliciting SCCs including cognitive domains other than memory is crucial for a complete evaluation, including both patient and CC report. Memory, language, and especially attention SCCs in PD-MCI may predict progression to dementia.

Responsiveness to Change of the Montreal Cognitive Assessment, Mini-Mental State Examination, and SCOPA-Cog in Non-Demented Patients with Parkinson's Disease.

BACKGROUND: Clinical monitoring of patients with Parkinson's disease (PD) for cognitive decline is an important element of care. The Montreal Cognitive Assessment (MoCA) has been proposed to be a sensitive tool for assessing cognitive impairment in PD. The aim of our study was to compare the responsiveness of the MoCA to decline in cognition to the responsiveness of the Mini Mental State Examination (MMSE) and the Scales for Outcomes of Parkinson's disease-cognition (SCOPA-Cog). METHODS: PD patients without dementia were enrolled at 6 North American movement disorders centers between 2008 and 2011. Participants received annual evaluations including the MoCA, MMSE, and SCOPA-Cog followed by formal neuropsychological testing. The gold standard for change in cognition was defined as the change on the neuropsychological test scores over the annual assessments. The Reliable Change Method was used to provide an estimate of the probability that a given difference score would be obtained by chance. The sensitivity of the MoCA, MMSE, and SCOPA-Cog to change was quantified using receiver operating characteristics (ROC) curves. RESULTS: One hundred seventeen patients were included in the analysis. Participants were followed at mean intervals of 11 ± 2 months for a median of 2 (maximum 5) visits. According to the reliable change index, 56 intervals of cognitive testing showed a decline in global cognition. ROC analysis of change in MoCA, MMSE, and SCOPA-Cog global scores compared to gold standard testing found an area under the curve (AUC) of 0.55 (95% CI 0.48-0.62), 0.56 (0.48-0.63), and 0.63 (0.55-0.70) respectively. There were no significant differences in the AUCs across the tests. The sensitivity of the MoCA, MMSE, and SCOPA-Cog to change at various thresholds for decline in scores reached a maximum of 71% for a cut-off of 1 point change on the SCOPA-Cog. CONCLUSION: Using neuropsychological testing as a gold standard comparator, the performance of the MoCA, MMSE, and SCOPA-Cog for detecting decline in non-demented PD patients over a 1-year interval is poor. This has implications for clinical practice; stable scores may not be taken as reassurance of the absence of cognitive decline.

Novel variants in a patient with late-onset hyperprolinemia type II: diagnostic key for status epilepticus and lactic acidosis.

BACKGROUND: Hyperprolinemia type 2 (HPII) is a rare autosomal recessive disorder of the proline metabolism, that affects the ALDH4A1 gene. So far only four different pathogenic mutations are known. The manifestation is mostly in neonatal age, in early infancy or early childhood. CASE PRESENTATION: The 64-years female patient had a long history of abdominal pain, and episode of an acute neuritis. Ten years later she was admitted into the neurological intensive-care-unit with acute abdominal pain, multiple generalized epileptic seizures, a vertical gaze palsy accompanied by extensive lactic acidosis in serum 26.0 mmol/l (reference: 0.55-2.2 mmol/l) and CSF 12.01 mmol/l (reference: 1.12-2.47 mmol/l). Due to repeated epileptic seizures and secondary complications a long-term sedation with a ventilation therapy over 20 days was administered. A diagnostic work-up revealed up to 400-times increased prolin-level in urine CSF and blood. Furthermore, a low vitamin-B6 serum value was found, consistent with a HPII causing secondary pyridoxine deficiency and seizures. The ALDH4A1 gene sequencing confirmed two previously unknown compound heterozygous variants (ALDH4A1 gene (NM_003748.3) Intron 1: c.62 + 1G > A - heterozygous and ALDH4A1 gene (NM_003748.3) Exon 5 c.349G > C, p.(Asp117His) - heterozygous). Under high-dose vitamin-B6 therapy no further seizures occurred. CONCLUSION: We describe two novel ALDH4A1-variants in an adult patient with hyperprolinemia type II causing secondary pyridoxine deficiency and seizures. Severe and potentially life-threatening course of this treatable disease emphasizes the importance of diagnostic vigilance and thorough laboratory work-up including gene analysis even in cases with atypical late manifestation.

Predictors of severity and functional outcome in natalizumab-associated progressive multifocal leukoencephalopathy.

OBJECTIVE: Progressive multifocal leukoencephalopathy (PML) is an emerging complication of immunosuppressive therapies, especially natalizumab in multiple sclerosis (MS). Factors associated with functional outcome of natalizumab-associated PML (natalizumab-PML) have not been sufficiently described. METHODS: We retrospectively analyzed medical records of all patients with natalizumab-PML ( n = 32) treated in our hospital since 2009. Disability measured by Expanded Disability Status Scale (EDSS) at two different time points (highest available EDSS during PML and last available EDSS after PML diagnosis) served as functional outcome parameters. Clinical, laboratory, and imaging data were analyzed for association with functional outcome by applying Spearman's rho and multivariate regression analysis. RESULTS: In all, 31/32 patients survived PML. A poor functional outcome was associated with higher age, higher initial John Cunningham virus (JCV) copy number in cerebrospinal fluid (CSF), and more extensive PML lesions on initial magnetic resonance imaging (MRI). No association between functional outcome and the duration of natalizumab therapy or a delayed PML diagnosis was observed. CONCLUSION: This study will be useful for neurological practice to estimate functional outcome or disease severity of natalizumab-PML in primary care settings.

The Importance of Digital Health Literacy in an Evolving Parkinson's Disease Care System.

Digital health technologies are growing at a rapid pace and changing the healthcare landscape. Our current understanding of digital health literacy in Parkinson's disease (PD) is limited. In this review, we discuss the potential challenges of low digital health literacy in PD with particular attention to telehealth, deep brain stimulation, wearable sensors, and smartphone applications. We also highlight inequities in access to digital health technologies. Future research is needed to better understand digital health literacy among individuals with PD and to develop effective solutions. We must invest resources to evaluate, understand, and enhance digital health literacy for individuals with PD.

Using Wearable Sensors and Machine Learning to Assess Upper Limb Function in Huntington's Disease.

Huntington's disease (HD), like many other neurological disorders, affects both lower and upper limb function that is typically assessed in the clinic - providing a snapshot of disease symptoms. Wearable sensors enable the collection of real-world data that can complement such clinical assessments and provide a more comprehensive insight into disease symptoms. In this context, almost all studies are focused on assessing lower limb function via monitoring of gait, physical activity and ambulation. In this study, we monitor upper limb function during activities of daily living in individuals with HD (n = 16), prodromal HD (pHD, n = 7), and controls (CTR, n = 16) using a wrist-worn wearable sensor, called PAMSys ULM, over seven days. The participants were highly compliant in wearing the sensor with an average daily compliance of 99% (100% for HD, 98% for pHD, and 99% for CTR). Goal-directed movements (GDM) of the hand were detected using a deep learning model, and kinematic features of each GDM were estimated. The collected data was used to predict disease groups (i.e., HD, pHD, and CTR) and clinical scores using a combination of statistical and machine learning-based models. Significant differences in GDM features were observed between the groups. HD participants performed fewer GDMs with long duration (> 7.5 seconds) compared to CTR (p-val = 0.021, d = -0.86). In velocity and acceleration metrics, the highest effect size feature was the entropy of the velocity zero-crossing length segments (HD vs CTR p-val <0.001, d = -1.67; HD vs pHD p-val = 0.043, d=-0.98; CTR vs pHD p-val = 0.046, d=0.96). In addition, this same variable showed a strongest correlation with clinical scores. Classification models achieved good performance in distinguishing HD, pHD and CTR individuals with a balanced accuracy of 67% and a 0.72 recall for the HD group, while regression models accurately predicted clinical scores. Notably the explained variance for the upper extremity function subdomain scale of Unified Huntington's Disease Rating Scale (UHDRS) was the highest, with the model capturing 60% of the variance. Our findings suggest the potential of wearables and machine learning for early identification of phenoconversion, remote monitoring in HD, and evaluating new treatments efficacy in clinical trials and medicine.