Hemodynamic and serum cardiac markers and risk of cognitive impairment and dementia.

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.

Study protocol of the HD-MED study aiming to personalize drug treatment in Huntington's disease: a longitudinal, observational study to assess medication use and efficacy in relation to pharmacogenetics.

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.

Machine learning in Huntington's disease: exploring the Enroll-HD dataset for prognosis and driving capability prediction.

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.

The Many Faces of Huntington's Chorea Treatment: The Impact of Sudden Withdrawal of Tiapride after 40 Years of Use and a Systematic Review.

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.