The psychosocial consequences of growing up with Heritable Connective Tissue Disorders (HCTD) are largely unknown. We aimed to assess Health-Related Quality of Life (HRQoL) and mental health of children and adolescents with HCTD. This observational multicenter study included 126 children, aged 4–18 years, with Marfan syndrome (MFS, n = 74), Loeys–Dietz syndrome (n = 8), molecular confirmed Ehlers–Danlos syndromes (n = 15), and hypermobile Ehlers–Danlos syndrome (hEDS, n = 29). HRQoL and mental health were assessed through the parent and child-reported Child Health Questionnaires (CHQ-PF50 and CHQ-CF45, respectively) and the parent-reported Strengths and Difficulties Questionnaire. Compared with a representative general population sample, parent-reported HRQoL of the HCTD-group showed significantly decreased Physical sum scores (p < 0.001, d = 0.9) and Psychosocial sum scores (p = 0.024, d = 0.2), indicating decreased HRQoL. Similar findings were obtained for child-reported HRQoL. The parent-reported mental health of the HCTD-group showed significantly increased Total difficulties sum scores (p = 0.01, d = 0.3), indicating decreased mental health. While the male and female MFS- and hEDS-subgroups both reported decreased HRQoL, only the hEDS-subgroup reported decreased mental health. In conclusion, children and adolescents with HCTD report decreased HRQoL and mental health, with most adverse outcomes reported in children with hEDS and least in those with MFS. These findings call for systematic monitoring and tailored interventions.
Due to considerable occupational challenges and stressors, classical musicians might face increased risk for mental health issues, compared to the general population. As such, scholars have highlighted the importance of developing psychological resilience in musicians. Nevertheless, this important psychological characteristic has remained understudied within music psychology. The present study therefore examined the relationship between mental health issues and resilience. Using a cross-sectional survey design, a total of 64 musicians (including both music students and professionals) participated in this study. Results highlight that symptoms of depression/anxiety were relatively high within the current population. Moreover, music students experienced significantly more symptoms compared to professional musicians. Both resilience and general physical health were found to be negatively associated with mental health issues. The results highlight the need for further research into mental health issues in music students and provide preliminary evidence for the importance of psychological resilience in classical musicians.
Background: Research into termination of long-term psychosocial treatment of mental disorders is scarce. Yearly 25% of people in Dutch mental health services receive long-term treatment. They account for many people, contacts, and costs. Although relevant in different health care systems, (dis)continuation is particularly problematic under universal health care coverage when secondary services lack a fixed (financially determined) endpoint. Substantial, unaccounted, differences in treatment duration exist between services. Understanding of underlying decisional processes may result in improved decision making, efficient allocation of scarce resources, and more personalized treatment.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
