Memantine Use and Cognitive Decline in Huntington's Disease: An Enroll-HD Study.
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ABSTRACT: Background
Memantine is an N-methyl-d-aspartate (NMDA) receptor antagonist that is used to treat moderate to severe Alzheimer's Dementia (AD) and has been speculated to provide clinical benefits in Huntington's disease (HD).Objective
To assess the effectiveness of memantine on the trajectory of cognitive decline in individuals with manifest HD.Methods
Using participants from the Enroll-HD study, the primary analysis compared trajectories in cognition over a 5-year period using linear mixed effect models of prevalent and incident memantine users who were propensity-score-matched with non-users on measures of disease progression and demographics.Results
In the primary analysis there were no significant differences in the trajectories between memantine users and non-users on any primary outcomes of interest.Conclusions
Memantine use was not associated with any clinical benefit for individuals with manifest HD. Further studies are warranted to assess the impact of memantine on clinical outcomes in HD.
SUBMITTER: Ogilvie AC
PROVIDER: S-EPMC10354618 | biostudies-literature | 2023 Jul
REPOSITORIES: biostudies-literature
Publications
Memantine Use and Cognitive Decline in Huntington's Disease: An Enroll-HD Study.
Ogilvie Amy C AC Schultz Jordan L JL
Movement disorders clinical practice 20230516 7
<h4>Background</h4>Memantine is an <i>N</i>-methyl-<i>d</i>-aspartate (NMDA) receptor antagonist that is used to treat moderate to severe Alzheimer's Dementia (AD) and has been speculated to provide clinical benefits in Huntington's disease (HD).<h4>Objective</h4>To assess the effectiveness of memantine on the trajectory of cognitive decline in individuals with manifest HD.<h4>Methods</h4>Using participants from the Enroll-HD study, the primary analysis compared trajectories in cognition over a ...[more]
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Project description:BackgroundCognitive decline in Huntington's disease (HD) begins early in the disease course, however the reported prevalence and severity of cognitive impairment varies based on diagnostic approach. A Movement Disorders Society Task Force recently endorsed the use of standardized DSM-5-based criteria to diagnose neurocognitive disorder (NCD) in Huntington's disease.ObjectivesTo determine the prevalence and severity of cognitive impairment across different stages of HD by applying NCD criteria (mild and major) to participant data from the Enroll-HD database.MethodsEnroll-HD participants were triaged into either premanifest (preHD), manifest or control groups. PreHD was further dichotomized into preHD near or preHD far based on predicted time to diagnosis using the scaled CAG-age product score (CAPs). Embedded cognitive performance and functional independence measures were used to determine prevalence of NCD (mild and major) for all groups.ResultsPrevalence of NCD-mild was 25.2%-38.4% for manifest HD, 22.8%-47.3% for preHD near, 11.5%-25.1% for preHD far, and 8.8%-19.1% for controls. Prevalence of NCD-major was 21.1%-57.7% for manifest HD, 0.5%-16.3% for preHD near, 0.0%-4.5% for preHD far, and 0.0%-3.0% for controls.ConclusionThe prevalence of NCD in HD is elevated in preHD and demonstrates a sharp rise prior to diagnosis. In manifest HD, the vast majority of participants meet criteria for NCD. These findings are important for optimizing clinical care and/or anticipating the need for supportive services.
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Project description:IntroductionJuvenile-onset Huntington's disease (JOHD) is characterized by a unique motor phenotype relative to patients with adult-onset Huntington's Disease (AOHD). This study characterized motor progression of JOHD to propose improved outcome measures for this group.MethodsWe used linear mixed effect regression models to compare progression of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score (TMS) and the chorea score between patients with JOHD and AOHD. We then evaluated all 31 subscales that make up the UHDRS over time within patients with JOHD to identify measures that may be used to track motor progression most reliably.ResultsThe JOHD cohort had faster TMS progression compared to AOHD (p = 0.006) but no group difference in the rate of change of chorea. Patients with JOHD did not show significant change in any of the chorea subscales. The subscales that changed most reliably over time amongst patients with JOHD were dysarthria, upper extremity dystonia, tandem walking, gait, bilateral pronate/supinate, bilateral finger-tapping, and tongue protrusion. When these subscales were summed, they progressed at a faster rate (7.07%, 95% CI [5.96-8.18]) than the TMS (4.92%, 95% CI [3.95-5.89]).ConclusionWhile the TMS changes at a significant rate in JOHD subjects, not all subscales that make up the TMS accurately represent the unique motor features of JOHD. A JOHD-specific scale performed better at tracking motor progression relative to the TMS. This scale may improve clinical care for patients with JOHD and allow for the development of more efficient clinical trials.
| S-EPMC10903276 | biostudies-literature
Project description:BackgroundThe study of complex neurodegenerative diseases is moving away from hypothesis-driven biological methods toward large scale multimodal approaches, requiring standardized collaborative efforts. Enroll-HD exemplifies such an integrated clinical research platform, designed and implemented to meet the research and clinical needs of Huntington's disease (HD). The aim of this study was to describe the unique organization of Enroll-HD and report baseline data analyses of its core study.MethodsThe Enroll-HD platform incorporates electronic data capture, biosampling, and a longitudinal observational study spanning four continents (ClinicalTrials.gov Identifier: NCT01574053). The primary study population includes HD gene expansion carriers (HDGECs; CAG expansion ≥36), subdivided into manifest/premanifest HD. The control population consists of genotype-negative first-degree relatives and family controls not genetically related. The study includes 10 core clinical assessments covering motor, cognitive, and behavioral domains.ResultsThis data set comprises 1,534 participants (HDGEC = 1,071; controls = 463). Participant retention was high; 42 participants prematurely withdrew from the study. Mean ± standard deviation SD CAG repeat size was 43.5 ± 3.5 for HDGECs and 19.8 ± 3.4 for controls. Motor and behavioral assessments identified numerical differences between controls and HDGECs (manifest > premanifest > controls). Functional and independence assessments were generally similar for the premanifest and control groups with overlap in range of scores obtained. For the majority of cognitive tests, there were large differences between participants with manifest HD and all other groups.ConclusionsThese first data from the Enroll-HD clinical research platform demonstrate the maturity and potential of the platform in collecting high-quality, clinically relevant data. Future data sets will be substantially larger as the platform expands longitudinally and regionally.
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Project description:ObjectivesHuntington's disease (HD) is a neurodegenerative disease in which cognitive and behavioural symptoms impair the performance of instrumental activities of daily living, including the handling of finances. We sought to determine the prevalence of financial dysfunction in HD, and the demographic and clinical predictors of such impairments.MethodsWe analysed longitudinal data for pre-manifest gene carriers and HD patients from the Enroll-HD dataset. Financial dysfunction was determined by finance-related items in the Total Functional Capacity (TFC) and Functional Assessment (FA) scales. A binary logistical regression model was used to investigate the predictive value of demographic and clinical factors for the development of financial dysfunction.ResultsFinancial impairment was found to be common in HD gene carriers, and over half required financial assistance within 5 years from diagnosis. Cognitive impairment, apathy, unemployment and disease severity predicted financial dysfunction in manifest patients. For pre-manifest patients, the predictors were proximity to disease onset and depression.ConclusionsLoss of financial autonomy is common in HD, and cognitive and psychiatric factors are important in its development. Clinicians must be vigilant to identify patients that may be vulnerable to financial exploitation.
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Project description:BackgroundIn biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of different diseases, including cancer and neurodegenerative disorders. For rare diseases, however, the requirement for large datasets often prevents this approach. Huntington's disease (HD) is a rare neurodegenerative disorder caused by a CAG repeat expansion in the coding region of the huntingtin gene. The world's largest observational study for HD, Enroll-HD, describes over 21,000 participants. As such, Enroll-HD is amenable to ML methods. In this study, we pre-processed and imputed Enroll-HD with ML methods to maximise the inclusion of participants and variables. With this dataset we developed models to improve the prediction of the age at onset (AAO) and compared it to the well-established Langbehn formula. In addition, we used recurrent neural networks (RNNs) to demonstrate the utility of ML methods for longitudinal datasets, assessing driving capabilities by learning from previous participant assessments.ResultsSimple pre-processing imputed around 42% of missing values in Enroll-HD. Also, 167 variables were retained as a result of imputing with ML. We found that multiple ML models were able to outperform the Langbehn formula. The best ML model (light gradient boosting machine) improved the prognosis of AAO compared to the Langbehn formula by 9.2%, based on root mean squared error in the test set. In addition, our ML model provides more accurate prognosis for a wider CAG repeat range compared to the Langbehn formula. Driving capability was predicted with an accuracy of 85.2%. The resulting pre-processing workflow and code to train the ML models are available to be used for related HD predictions at: https://github.com/JasperO98/hdml/tree/main .ConclusionsOur pre-processing workflow made it possible to resolve the missing values and include most participants and variables in Enroll-HD. We show the added value of a ML approach, which improved AAO predictions and allowed for the development of an advisory model that can assist clinicians and participants in estimating future driving capability.
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Project description:Background: Psychotic symptoms have been under-investigated in Huntington's disease (HD) and research is needed in order to elucidate the characteristics linked to the unique phenotype of HD patients presenting with psychosis. Objective: To evaluate the frequency and factors associated with psychosis in HD. Methods: Cross-sectional study including manifest individuals with HD from the Enroll-HD database. Both conventional statistical analysis (Stepwise Binary Logistic Regression) and five machine learning algorithms [Least Absolute Shrinkage and Selection Operator (LASSO); Elastic Net; Support Vector Machines (SVM); Random Forest; and class-weighted SVM] were used to describe factors associated with psychosis in manifest HD patients. Results: Approximately 11% of patients with HD presented history of psychosis. Logistic regression analysis indicated that younger age at HD clinical diagnosis, lower number of CAG repeats, history of [alcohol use disorders, depression, violent/aggressive behavior and perseverative/obsessive behavior], lower total functional capacity score, and longer time to complete trail making test-B were associated with psychosis. All machine learning algorithms were significant (chi-square p < 0.05) and capable of distinguishing individual HD patients with history of psychosis from those without a history of psychosis with prediction accuracy around 71-73%. The most relevant variables were similar to those found in the conventional analyses. Conclusions: Psychiatric and behavioral symptoms as well as poorer cognitive performance were related to psychosis in HD. In addition, psychosis was associated with lower number of CAG repeats and younger age at clinical diagnosis of HD, suggesting that these patients may represent a unique phenotype in the HD spectrum.
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Project description:IntroductionHuntington's disease (HD) is a rare neurodegenerative disease characterized by cognitive, behavioral and motor symptoms that progressively worsen with time. Cognitive and behavioral signs of HD are generally present in the years prior to a diagnosis; however, manifest HD is typically assessed by genetic confirmation and/or the presence of unequivocal motor symptoms. Nevertheless, there is a large variation in symptom severity and rate of progression among individuals with HD.MethodsIn this retrospective study, longitudinal natural history of disease progression was modeled in individuals with manifest HD from the global, observational Enroll-HD study (NCT01574053). Unsupervised machine learning (k-means; km3d) was used to jointly model clinical and functional disease measures simultaneously over time, based on one-dimensional clustering concordance such that individuals with manifest HD (N = 4,961) were grouped into three clusters: rapid (Cluster A; 25.3%), moderate (Cluster B; 45.5%) and slow (Cluster C; 29.2%) progressors. Features that were considered predictive of disease trajectory were then identified using a supervised machine learning method (XGBoost).ResultsThe cytosine adenine guanine-age product score (a product of age and polyglutamine repeat length) at enrollment was the top predicting feature for cluster assignment, followed by years since symptom onset, medical history of apathy, body mass index at enrollment and age at enrollment.ConclusionsThese results are useful for understanding factors that affect the global rate of decline in HD. Further work is needed to develop prognostic models of HD progression as these could help clinicians with individualized clinical care planning and disease management.
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Project description:Background and objectivesThe variable CAG repeat expansion in the huntingtin gene and its inverse relationship to motor dysfunction onset are fundamental features of Huntington disease (HD). However, the wider phenotype (including non-motor features) at particular CAG lengths, ages, and functional levels is less well-characterized. The large number of participants in the Enroll-HD observational study enables the development of a phenotype atlas that summarizes the range and distribution of HD phenotypes, including outliers and possible clusters, with respect to various CAG repeat lengths, age ranges, and declining functional levels.MethodsEnroll-HD is an ongoing prospective longitudinal observational study that collects natural history data, releasing periodic data sets, in people with HD (PwHD) and controls. Core assessments at annual visits focus on behavioral, cognitive, motor, and functional status. Periodic data set 5, used for the development of the first iteration of the Enroll-HD Phenotype Atlas (EHDPA), included all eligible data collected through October 31, 2020. The atlas is based on subsets (cells) of descriptive data for all motor, cognitive, psychiatric, and functional measures that are routinely collected at most Enroll-HD sites, analyzed by single CAG lengths and 5-year age blocks.ResultsData from 42,840 visits from 15,982 unique PwHD were available for analysis. At baseline, participants had a mean ± SD age of 48.9 ± 13.9 years and CAG repeat length of 43.4 ± 3.6 and 54.1% were female. The EHDPA includes 223 age-by-CAG subsets for CAG repeats between 36 and 69 with five-year age brackets starting from 20-24 years up to 85-89 years. The atlas can be browsed at enroll-hd.org/for-researchers/atlas-of-hd-phenotype/.DiscussionThe EHDPA summarizes the spectrum and distribution of HD phenotypes, including outliers and possible clusters, in all domains of disease involvement for the range of CAG repeat lengths, ages, and functional levels. Its availability in an easy-to-use online format will assist clinicians in tracking disease progression in PwHD by identifying phenotypic features most associated with loss of function and enabling conversations related to prognosis. The observable patterns in the EHDPA should also catalyze more formal multidomain characterization of motor, cognitive, and psychiatric progression and their relationships to functional decline and disease modifiers.Trial registration informationEnroll-HD is registered with clinicaltrials.gov: NCT01574053.
| S-EPMC10684052 | biostudies-literature
Project description:ObjectiveCognitive decline is a key characteristic of Huntington's disease (HD). This study aimed to investigate the diagnostic accuracy of a cognitive battery with six tests used by most HD research centers to assess cognitive impairment in HD.MethodIn total, 106 HD patients in different disease stages with more (HD-CD, N = 30) and less cognitive impairments (HD-NC, N = 70) and 100 healthy controls (NC) were matched by age, sex, and education and were examined using a standardized protocol including cognitive, motor, and functional assessments.ResultsOne-way between-groups analysis of variance showed that controls performed significantly better than HD patients and that HD-NC significantly outperformed HD-CD patients in all cognitive tests (NC > HD-NC > HD-CD), with all Games-Howell post-hoc tests p < .001. Analyses using area under the receiver-operating characteristic curve (AUC) disclosed the diagnostic accuracy of all tests included in the battery to discriminate between NC and HD patients with AUC ranging from 0.809 to 0.862 (all p < .001) and between HD-CD and HD-NC patients with AUC ranging from 0.833 to 0.899 (all p < .001). In both analysis, Stroop Color Naming Test showed the highest discriminative potential. Additional analyses showed that cognitive deficits in all domains progressed with disease duration. Moreover, cognitive performance correlated with the severity of motor and functional impairment (all p < .001) and with the Disease Burden Score regardless of disease duration and age.ConclusionOur results indicate that the cognitive battery is a suitable tool for assessing cognitive impairment in HD.
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Project description:ObjectiveTo assess the clinical effect of caudate-putaminal transplantation of fetal striatal tissue in Huntington's disease (HD).MethodsWe carried out a follow-up study on 10 HD transplanted patients and 16 HD not-transplanted patients. All patients were evaluated with the Unified HD Rating Scale (UHDRS) whose change in motor, cognitive, behavioural and functional capacity total scores were considered as outcome measures. Grafted patients also received morphological and molecular neuroimaging.ResultsPatients were followed-up from disease onset for a total of 309.3 person-years (minimum 5.3, median 11.2 years, maximum 21.6 years). UHDRS scores have been available since 2004 (median time of 5.7 years since onset, minimum zero, maximum 17.2 years). Median post-transplantation follow-up was 4.3 years, minimum 2.8, maximum 5.1 years. Adjusted post-transplantation motor score deterioration rate was reduced compared to the pretransplantation period, and to that of not-transplanted patients by 0.9 unit/years (95% CI 0.2 to 1.6). Cognitive score deterioration was reduced of 2.7 unit/years (95% CI 0.1 to 5.3). For grafted patients the 2-year post-transplantation [(18)F]fluorodeoxyglucose positron emission tomography (PET) showed striatal/cortical metabolic increase compared to the presurgical evaluation; 4-year post-transplantation PET values were slightly decreased, but remained higher than preoperatively. [(123)I]iodobenzamide single photon emission CT demonstrated an increase in striatal D2-receptor density during postgrafting follow-up.ConclusionsGrafted patients experienced a milder clinical course with less pronounced motor/cognitive decline and associated brain metabolism improvement. Life-time follow-up may ultimately clarify whether transplantation permanently modifies the natural course of the disease, allowing longer sojourn time at less severe clinical stage, and improvement of overall survival.
| S-EPMC4145428 | biostudies-literature