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

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

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>

<i>KIF1A</i> variants are a frequent cause of autosomal dominant hereditary spastic paraplegia

Variants in the KIF1A gene can cause autosomal recessive spastic paraplegia 30, autosomal recessive hereditary sensory neuropathy, or autosomal (de novo) dominant mental retardation type 9. More recently, variants in KIF1A have also been described in a few cases with autosomal dominant spastic paraplegia. Here, we describe 20 KIF1A variants in 24 patients from a clinical exome sequencing cohort of 347 individuals with a mostly 'pure' spastic paraplegia. In these patients, spastic paraplegia was slowly progressive and mostly pure, but with a highly variable disease onset (0-57 years). Segregation analyses showed a de novo occurrence in seven cases, and a dominant inheritance pattern in 11 families. The motor domain of KIF1A is a hotspot for disease causing variants in autosomal dominant spastic paraplegia, similar to mental retardation type 9 and recessive spastic paraplegia type 30. However, unlike these allelic disorders, dominant spastic paraplegia was also caused by loss-of-function variants outside this domain in six families. Finally, three missense variants were outside the motor domain and need further characterization. In conclusion, KIF1A variants are a frequent cause of autosomal dominant spastic paraplegia in our cohort (6-7%). The identification of KIF1A loss-of-function variants suggests haploinsufficiency as a possible mechanism in autosomal dominant spastic paraplegia

Rapidly deteriorating course in Dutch hereditary spastic paraplegia type 11 patients

Item does not contain fulltextAlthough SPG11 is the most common complicated hereditary spastic paraplegia, our knowledge of the long-term prognosis and life expectancy is limited. We therefore studied the disease course of all patients with a proven SPG11 mutation as tested in our laboratory, the single Dutch laboratory providing SPG11 mutation analysis, between 1 January 2009 and 1 January 2011. We identified nine different SPG11 mutations, four of which are novel, in nine index patients. Eighteen SPG11 patients from these nine families were studied by means of a retrospective chart analysis and additional interview/examination. Ages at onset were between 4 months and 14 years; 39% started with learning difficulties rather than gait impairment. Brain magnetic resonance imaging showed a thin corpus callosum and typical periventricular white matter changes in the frontal horn region (known as the 'ears-of the lynx'-sign) in all. Most patients became wheelchair bound after a disease duration of 1 to 2 decades. End-stage disease consisted of loss of spontaneous speech, severe dysphagia, spastic tetraplegia with peripheral nerve involvement and contractures. Several patients died of complications between ages 30 and 48 years, 3-4 decades after onset of gait impairment. Other relevant features during the disease were urinary and fecal incontinence, obesity and psychosis. Our study of 18 Dutch SPG11-patients shows the potential serious long-term consequences of SPG11 including a possibly restricted life span