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.</p

Bi-allelic <i>NIT1 </i>variants cause a brain small vessel disease characterized by movement disorders, massively dilated perivascular spaces, and intracerebral hemorrhage

Purpose: To describe a recessively inherited cerebral small vessel disease, caused by loss-of-function variants in Nitrilase1 (NIT1). Methods:We performed exome sequencing, brain magnetic resonance imaging, neuropathology, electron microscopy, western blotting, and transcriptomic and metabolic analyses in 7 NIT1-small vessel disease patients from 5 unrelated pedigrees. Results: The first identified patients were 3 siblings, compound heterozygous for the NIT1 c.727C&gt;T; (p.Arg243Trp) variant and the NIT1 c.198_199del; p.(Ala68∗) variant. The 4 additional patients were single cases from 4 unrelated pedigrees and were all homozygous for the NIT1 c.727C&gt;T; p.(Arg243Trp) variant. Patients presented in mid-adulthood with movement disorders. All patients had striking abnormalities on brain magnetic resonance imaging, with numerous and massively dilated basal ganglia perivascular spaces. Three patients had non-lobar intracerebral hemorrhage between age 45 and 60, which was fatal in 2 cases. Western blotting on patient fibroblasts showed absence of NIT1 protein, and metabolic analysis in urine confirmed loss of NIT1 enzymatic function. Brain autopsy revealed large electron-dense deposits in the vessel walls of small and medium sized cerebral arteries. Conclusion: NIT1-small vessel disease is a novel, autosomal recessively inherited cerebral small vessel disease characterized by a triad of movement disorders, massively dilated basal ganglia perivascular spaces, and intracerebral hemorrhage.</p

Bi-allelic <i>NIT1 </i>variants cause a brain small vessel disease characterized by movement disorders, massively dilated perivascular spaces, and intracerebral hemorrhage

Purpose: To describe a recessively inherited cerebral small vessel disease, caused by loss-of-function variants in Nitrilase1 (NIT1). Methods:We performed exome sequencing, brain magnetic resonance imaging, neuropathology, electron microscopy, western blotting, and transcriptomic and metabolic analyses in 7 NIT1-small vessel disease patients from 5 unrelated pedigrees. Results: The first identified patients were 3 siblings, compound heterozygous for the NIT1 c.727C&gt;T; (p.Arg243Trp) variant and the NIT1 c.198_199del; p.(Ala68∗) variant. The 4 additional patients were single cases from 4 unrelated pedigrees and were all homozygous for the NIT1 c.727C&gt;T; p.(Arg243Trp) variant. Patients presented in mid-adulthood with movement disorders. All patients had striking abnormalities on brain magnetic resonance imaging, with numerous and massively dilated basal ganglia perivascular spaces. Three patients had non-lobar intracerebral hemorrhage between age 45 and 60, which was fatal in 2 cases. Western blotting on patient fibroblasts showed absence of NIT1 protein, and metabolic analysis in urine confirmed loss of NIT1 enzymatic function. Brain autopsy revealed large electron-dense deposits in the vessel walls of small and medium sized cerebral arteries. Conclusion: NIT1-small vessel disease is a novel, autosomal recessively inherited cerebral small vessel disease characterized by a triad of movement disorders, massively dilated basal ganglia perivascular spaces, and intracerebral hemorrhage.</p

Study Protocol for the Development of a European eHealth Platform to Improve Quality of Life in Individuals With Huntington's Disease and Their Partners (HD-eHelp Study): A User-Centered Design Approach

Background: Huntington's disease (HD) is an autosomal dominant neurodegenerative disease that affects the quality of life (QoL) of HD gene expansion carriers (HDGECs) and their partners. Although HD expertise centers have been emerging across Europe, there are still some important barriers to care provision for those affected by this rare disease, including transportation costs, geographic distance of centers, and availability/accessibility of these services in general. eHealth seems promising in overcoming these barriers, yet research on eHealth in HD is limited and fails to use telehealth services specifically designed to fit the perspectives and expectations of HDGECs and their families. In the European HD-eHelp study, we aim to capture the needs and wishes of HDGECs, partners of HDGECs, and health care providers (HCPs) in order to develop a multinational eHealth platform targeting QoL of both HDGECs and partners at home.Methods: We will employ a participatory user-centered design (UCD) approach, which focusses on an in-depth understanding of the end-users' needs and their contexts. Premanifest and manifest adult HDGECs (n = 76), partners of HDGECs (n = 76), and HCPs (n = 76) will be involved as end-users in all three phases of the research and design process: (1) Exploration and mapping of the end-users' needs, experiences and wishes; (2) Development of concepts in collaboration with end-users to ensure desirability; (3) Detailing of final prototype with quick review rounds by end-users to create a positive user-experience. This study will be conducted in the Netherlands, Germany, Czech Republic, Italy, and Ireland to develop and test a multilingual platform that is suitable in different healthcare systems and cultural contexts.Discussion: Following the principles of UCD, an innovative European eHealth platform will be developed that addresses the needs and wishes of HDGECs, partners and HCPs. This allows for high-quality, tailored care to be moved partially into the participants' home, thereby circumventing some barriers in current HD care provision. By actively involving end-users in all design decisions, the platform will be tailored to the end-users' unique requirements, which can be considered pivotal in eHealth services for a disease as complex and rare as HD

Development of the Huntington Support App (HD-eHelp study): a human-centered and co-design approach

IntroductioneHealth seems promising in addressing challenges in the provision of care for Huntington’s disease (HD) across Europe. By harnessing information and communication technologies, eHealth can partially relocate care from specialized centers to the patients’ home, thereby increasing the availability and accessibility of specialty care services beyond regional borders. Previous research on eHealth (development) in HD is however limited, especially when it comes to including eHealth services specifically designed together with HD gene expansion carriers (HDGECs) and their partners to fit their needs and expectations.MethodsThis article describes the qualitative human-centered design process and first evaluations of the Huntington Support App prototype: a web-app aimed to support the quality of life (QoL) of HDGECs and their partners in Europe. Prospective end-users, i.e., HDGECs, their partners, and healthcare providers (HCPs), from different countries were involved throughout the development process. Through interviews, we captured people’s experiences with the disease, quality of life (QoL), and eHealth. We translated their stories into design directions that were further co-designed and subsequently evaluated with the user groups.ResultsThe resulting prototype centralizes clear and reliable information on the disease, HD-related news and events, as well as direct contact possibilities with HCPs via an online walk-in hour or by scheduling an appointment. The app’s prototype was positively received and rated as (very) appealing, pleasant, easy to use and helpful by both HDGECs and partners.DiscussionBy involving end-users in every step, we developed a healthcare app that meets relevant needs of individuals affected by HD and therefore may lead to high adoption and retention rates. As a result, the app provides low-threshold access to reliable information and specialized care for HD in Europe. A description of the Huntington Support App as well as implications for further development of the app’s prototype are provided

CSF studies facilitate DNA diagnosis in familial Alzheimer's disease due to a presenilin-1 mutation

In sporadic Alzheimer's disease (AD), cerebrospinal fluid (CSF) analysis is becoming increasingly relevant to establish an early diagnosis. We present a case of familial AD due to a presenilin-1 mutation in which CSF studies suggested appropriate DNA diagnostics. A 38 year old Dutch man presented with dementia, spastic paraparesis, and frontal executive function impairments, mimicking familial Creutzfeldt Jakob disease and frontotemporal dementia. CSF studies, revealing increased total tau and phosphorylated-tau levels with decreased amyloid-β42, distinguished familial AD from Creutzfeldt Jakob disease and frontotemporal dementia. A causative p.L424R PSEN1 mutation was subsequently identified

Pure adult-onset Spastic Paraplegia caused by a novel mutation in the KIAA0196 (SPG8) gene

<p>SPG8 is a rare autosomal dominant hereditary spastic paraplegia (AD-HSP), with only six SPG8 families described so far. Our purpose was to screen for KIAA0196 (SPG8) mutations in AD-HSP patients and to investigate their phenotype. Extensive family investigation was performed after positive KIAA0196 mutation analysis, which was part of an on-going mutation screening effort in AD-HSP patients. A novel pathogenic KIAA0196 mutation p.(Gly696Ala) was identified in two AD-HSP patients, who subsequently were shown to belong to a single large Dutch pedigree with more than 10 affected family members. The phenotype consisted of a pure HSP with ages at onset between 20 and 60 years, distally reduced vibration sense in the legs in all, and urinary urgency in seven out of 10 patients. Frequent features were exercise- or emotion-induced increase of spasticity and gait problems and chronic nonspecific lower back and joint pains. We have identified a fourth pathogenic KIAA0196 mutation in a Dutch HSP-family, the seventh family worldwide, with a less severe clinical course than described before.</p>