Stephanie Feleus

Background and objectives: The total functioning capacity (TFC) assessment has been integral to Huntington's disease (HD) research and clinical trials, measuring disease stage and progression. This study investigates the natural progression of function in HD, focusing on changes in TFC scores related to age and CAG-repeat length, and evaluates TFC's strengths and weaknesses in longitudinal studies. Methods: Using Enroll-HD platform's clinical dataset version 5, including Registry-3, we analysed data from 21,079 participants, with 16,083 having an expanded CAG repeat. Our final analysis encompassed 15,527 patients and 52,457 visits, with TFC scores ranging from 0 to 13. Results: Alluvial charts show that most individuals maintain maximum functional capacity over time. 3505 individuals experienced change in TFC scores, over the subsequent 4 years, 2224 (64.1%) experienced declining TFC scores, while 661 (18.6%) showed improvement within a year. The remaining 17.3% exhibited stable TFC scores. Age-related changes followed a specific sequence: occupation, household chores/finances, daily living, and care. Longer CAG-repeat lengths were linked to earlier functional decline, with some geographic regions showing earlier losses in specific domains. Reduced penetrance CAG-repeat groups exhibited different trajectories from full penetrance HD participants. Discussion: When we focus on those who experienced a change in TFC score, the number of HD patients with regained functional capacity is substantial, even considering interrater variability, which may influence outcome assessments in clinical trials. The TFC effectively reflects changes in functional domains as intended. Analysis of the reduced penetrance group suggests potential selection biases in seeking medical attention earlier and for reasons unrelated to HD.

Background Proper medication reconciliation (= comparing the accuracy of patient-reported medication use with pharmacy records) could prevent potentially dangerous situations such as drug–drug interactions and hospitalization. This is particularly important when patients rely on multiple medications, such as in neurodegenerative disorders like Huntington’s Disease (HD). Currently, it is unknown how often medication discrepancies occur in HD patients and which factors contribute to the discrepancies. Objective Identify prognostic factors of medication discrepancies in HD, using patient-reported medication use and local pharmacy records. Methods With 134 pre- and manifest HD patients, we performed a multivariable logistic regression analysis with medication discrepancy as dependent variable (pharmacy records as reference value) and sex, CAP score, disease status (pre- or manifest HD), number of concomitant medications taken, presence of an informal caregiver, Unified Huntington’s Disease Rating Scale-Total Functioning Capacity, unified cognitive Z-score and Problem Behaviors Assessment-short characteristic scores for depression, anxiety, and apathy as independent variables. Results Medication discrepancies were reported frequently, both in premanifest (43.2%) and manifest HD subjects (36.7%). Impaired cognition significantly predicted medication discrepancies (beta = −0.688, SE 0.27, p = 0.011). All other variables were non-significant. Conclusions Regardless of HD disease status and stage, patient self-reported medication use is not a reliable source, especially in those with impaired cognitive function. The presence of an informal caregiver and the absence of polypharmacy, depression, anxiety and apathy do not influence self-reported medication accuracy. Objective verification of medication use with HD patients’ local pharmacy is recommended. Trial registration number ICTRP-NL-OMON55123, HD-med, registered 26-Nov-2019.

Background: Huntington's Disease is a rare neurodegenerative disorder in which appropriate medication management is essential. While many medications are prescribed based on expert knowledge, overviews of actual medication use in HD are sparse.Objectives: We provide a detailed overview of medication use and associated indications across HD disease stages, considering sex and regional differences.Methods: Data from the largest observational HD study, ENROLL‐HD, were used. We created HD‐related medication and indication classes to identify medication trends in manifest, premanifest and control subjects. We studied medication use in adult, childhood‐ and adolescent‐onset HD, incorporating disease stage (including phenoconverters), sex and regional differences.Results: In 8546 manifest HD patients, 84.6% used medication (any type), with the average number of medications per user rising from 2.5 in premanifest HD to 5.2 in end stage disease. Antipsychotics (29.2%), SSRIs (27.5%) and painkillers (21.8%) were most often used. Medication use varied with disease progression. Several differences were observed between the sexes, and notably between Europe and Northern America as well. Medication use increased after phenoconversion (from 64.8% to 70.6%, P < 0.05), with the largest difference in antipsychotic use (4.4%–7.8%, P < 0.05). Medication patterns were different in childhood‐onset HD, with no use of painkillers, less use of anti‐chorea and antidepressant drugs, and more for aggression and irritability.Conclusions: Medication use in HD increases with disease progression, with varying types of medications prescribed based on disease stage, sex, and region of living. Recognizing these medication trends is vital for further personalized HD management.

Background: In 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. Results: Simple 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. Conclusions: Our 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.

Background: Huntington’s disease (HD) is a hereditary, neurodegenerative disorder characterized by motor, cognitive, and psychiatric symptoms. Currently, HD can only be managed symptomatically, including a large variety of prescribed drugs. Many HD patients experience negative medication effects (e.g. side effects or non-response). Pharmacogenetic (PGx) studies show how genetic variation affects both medication efficacy and toxicity and holds the potential to improve these outcomes of drug treatment. Primary objective: To classify the effect of the PGx profile of CYP2C19 and CYP2D6 in HD gene expansion carriers on negative medication effects of HD-related medication. Design: Multicenter, observational study with 1-year follow-up. Adult HD gene expansion carriers who use one or more HD-related medications are eligible to participate. Methods and analysis: A detailed overview of medication use, medication efficacy, and side effects is retrospectively and prospectively collected via medication diaries, questionnaires, phone calls, and pharmacy medication verification schemes. PGx analysis on whole blood-extracted DNA is performed with Agena Bioscience VeriDose^® Core Panel and long-range polymerase chain reaction copy number variation analysis. Per the study protocol-defined negative medication effects in HD gene expansion carriers with a genotype predicted poor or ultrarapid metabolizer phenotype will be compared with HD gene expansion carriers with a predicted intermediate and normal metabolizer phenotype. Frequencies will be analyzed via χ² and logistic multivariate regression analysis. In addition, we summarize in this manuscript HD-relevant PGx prescription recommendations to improve drug therapy. Ethics: The original study protocol was approved by the medical research ethics committee Leiden Den Haag Delft on 26 November 2019. Discussion: HD-MED is a low-risk study that will generate personalized PGx results that can immediately be implemented in clinical practice, thus potentially improving pharmacovigilance and patients’ quality of life. Registration: This study is registered in the International Clinical Trial Registry Platform under registration number NL8251, URL https://trialsearch.who.int/Trial2.aspx?TrialID=NL8251 .

Huntington’s Disease (HD) is a rare, neurodegenerative disorder characterized by chorea, cognitive decline, and behavioral changes. Despite wide clinical use since the mid-1980s, tiapride was recently withdrawn from the Dutch market without rationale. Although alternatives are available, many patients experienced dysregulation after this unwanted change. We provide insight into the impact of sudden tiapride withdrawal by reviewing medical records of HD patients who were using tiapride at the time of withdrawal. In addition, we performed a systematic search in five databases on tiapride efficacy and its safety profile in HD. Original research and expert opinions were included. In our patient group on tiapride, 50% required tiapride import from abroad. Regarding the review, 12 articles on original datasets and three expert opinions were included. The majority of studies showed an improvement in chorea while patients were on tiapride. Due to limited sample sizes, not all studies performed statistical tests on their results. Fifty percent of clinical experts prefer tiapride as initial chorea monotherapy, especially when comorbid behavioral symptoms are present. Side effects are often rare and mild. No safety concerns were reported. In conclusion, tiapride is almost irreplaceable for some patients and is an effective and safe chorea treatment in HD.

Introduction: Cardiac function is a key player in maintaining energy homeostasis in the brain. Heart failure is closely related to higher risk of neurocognitive disorders. Recent evidence shows that this relationship might not be limited to patients with advanced heart failure, and even suboptimal cardiac functioning is associated with accelerated brain aging. Hence, hemodynamic and serum cardiac markers may provide valuable information about the risk of dementia.Methods: We provide an overview on the link between cardiac markers and cognitive function by a systematic search in five databases. Furthermore, we discuss the pathophysiological aspects of this link and highlight the pertinent clinical and public health implications.Results: Increasing evidence supports the associations of hemodynamic and serum cardiac markers with accelerated cognitive decline.Discussion: Hemodynamic and serum cardiac markers are closely linked with risk of cognitive impairment. This highlights the significance of the heart–brain connection in reducing the burden of dementia.