Background Little is known about the nature and reactions to sexual abuse of children with intellectual disability (ID). The aim was to fill this gap. Method Official reports of sexual abuse of children with ID in state care were examined (N = 128) and compared with children without ID (N = 48). Results Clear signs of penetration or genital touching by male (adolescent) peers or (step/foster) fathers were found in most ID reports. Victims often received residential care and disclosed themselves. Type of perpetrator seemed to affect the nature and reaction to the abuse. Cases of children with and without ID seemed to differ in location and reports to police. Conclusions Screening of (foster)homes seems crucial. Residential facilities should find a balance between independence of children and protection. Care providers should be trained in addressing sexual issues and sexual education, accounting for different types of perpetrators (peers/adults). Uniform reporting guidelines are needed.
Background: Persons with an intellectual disability are at a higher risk of experiencing adversities. The concept of resilience offers promising insights into facilitating personal growth after adversity. The current study aims at providing an overview of the current research on resilience and the way this can contribute to quality of life in people with intellectual disability. Method: A literature review was conducted in the databases PsycINFO and Web of Science. To evaluate the quality of the studies, the Mixed Method Appraisal Tool (MMAT) was used. Results: The themes, autonomy, self-acceptance and physical health, were identified as internal sources of resilience. External sources of resilience can be found within the social network and daily activities. Conclusion: The current overview shows promising results to address resilience in adults with intellectual disability. More research is needed to identify the full range of resiliency factors.
Heritable Connective Tissue Disorders (HCTD) show an overlap in the physical features that can evolve in childhood. It is unclear to what extent children with HCTD experience burden of disease. This study aims to quantify fatigue, pain, disability and general health with standardized validated questionnaires.METHODS: This observational, multicenter study included 107 children, aged 4-18 years, with Marfan syndrome (MFS), 58%; Loeys-Dietz syndrome (LDS), 7%; Ehlers-Danlos syndromes (EDS), 8%; and hypermobile Ehlers-Danlos syndrome (hEDS), 27%. The assessments included PROMIS Fatigue Parent-Proxy and Pediatric self-report, pain and general health Visual-Analogue-Scales (VAS) and a Childhood Health Assessment Questionnaire (CHAQ).RESULTS: Compared to normative data, the total HCTD-group showed significantly higher parent-rated fatigue T-scores (M = 53 (SD = 12), p = 0.004, d = 0.3), pain VAS scores (M = 2.8 (SD = 3.1), p < 0.001, d = 1.27), general health VAS scores (M = 2.5 (SD = 1.8), p < 0.001, d = 2.04) and CHAQ disability index scores (M = 0.9 (SD = 0.7), p < 0.001, d = 1.23). HCTD-subgroups showed similar results. The most adverse sequels were reported in children with hEDS, whereas the least were reported in those with MFS. Disability showed significant relationships with fatigue (p < 0.001, rs = 0.68), pain (p < 0.001, rs = 0.64) and general health (p < 0.001, rs = 0.59).CONCLUSIONS: Compared to normative data, children and adolescents with HCTD reported increased fatigue, pain, disability and decreased general health, with most differences translating into very large-sized effects. This new knowledge calls for systematic monitoring with standardized validated questionnaires, physical assessments and tailored interventions in clinical care.
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).
