The Role of Appearance in Adolescents’ Experiences of Neurofibromatosis Type 1: A Survey of Young People and Parents

© 2016, National Society of Genetic Counselors, Inc. Neurofibromatosis type 1 (NF1) is a genetic condition which can result in varying degrees of visible difference (disfigurement). Adolescence is a time when appearance concerns become more salient for many young people and is acknowledged as a particularly challenging time for individuals with NF1. There is currently little research into the psychosocial impact of the appearance changes associated with NF1 during this stage of life. In order to address this, surveys of young people with NF1 aged 14–24years (n=73), and parents of young people with NF1 (n=55) were developed following interview studies with these groups. The surveys included the Perceived Stigma Questionnaire, Social Comfort Questionnaire, Body Esteem Scale (appearance subscale) and the Subjective Happiness Scale. Young people and parents identified appearance as central to young peoples’ experience of NF1, however no significant difference was found on measures of body esteem, happiness, stigma or social comfort between those young people who reported their NF1 was noticeable to others and those who reported it was not. Findings from the parent survey indicated that their reports of greater perceived noticeability did relate to greater perceived stigma and lower levels of social comfort. Findings highlight the importance of attending to young people’s concerns around appearance in general and managing the possibility of future appearance changes, rather than the current noticeability of NF1

Expression of FBN1 during adipogenesis:relevance to the lipodystrophy phenotype in Marfan syndrome and related conditions

Fibrillin-1 is a large glycoprotein encoded by the FBN1 gene in humans. It provides strength and elasticity to connective tissues and is involved in regulating the bioavailability of the growth factor TGF beta. Mutations in FBN1 may be associated with depleted or abnormal adipose tissue, seen in some patients with Marfan syndrome and lipodystrophies. As this lack of adipose tissue does not result in high morbidity or mortality, it is generally under-appreciated, but is a cause of psychosocial problems particularly to young patients. We examined the role of fibrillin-1 in adipogenesis. In inbred mouse strains we found significant variation in the level of expression in the Fbn1 gene that correlated with variation in several measures of body fat, suggesting that mouse fibrillin-1 is associated with the level of fat tissue. Furthermore, we found that FBN1 mRNA was up-regulated in the adipose tissue of obese women compared to non-obese, and associated with an increase in adipocyte size. We used human mesenchymal stem cells differentiated in culture to adipocytes to show that fibrillin-1 declines after the initiation of differentiation. Gene expression results from a similar experiment (available through the FANTOM5 project) revealed that the decline in fibrillin-1 protein was paralleled by a decline in FBN1 mRNA. Examination of the FBN1 gene showed that the region commonly affected in FBN1-associated lipodystrophy is highly conserved both across the three human fibrillin genes and across genes encoding fibrillin-1 in vertebrates. These results suggest that fibrillin-1 is involved as the undifferentiated mesenchymal stem cells transition to adipogenesis but then declines as the developing adipocytes take on their final phenotype. Since the C-terminal peptide of fibrillin-1 is a glucogenic hormone, individuals with low fibrillin-1 (for example with FBN1 mutations associated with lipodystrophy) may fail to differentiate adipocytes and/or to accumulate adipocyte lipids, although this still needs to be shown experimentally. (C) 2016 The Authors. Published by Elsevier Inc

Recurrence quantification analysis of resting state EEG signals in autism spectrum disorder - a systematic methodological exploration of technical and demographic confounders in the search for biomarkers

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a worldwide prevalence of 1-2%. In low-resource environments, in particular, early identification and diagnosis is a significant challenge. Therefore, there is a great demand for 'language-free, culturally fair' low-cost screening tools for ASD that do not require highly trained professionals. Electroencephalography (EEG) has seen growing interest as an investigational tool for biomarker development in ASD and neurodevelopmental disorders. One of the key challenges is the identification of appropriate multivariate, next-generation analytical methodologies that can characterise the complex, nonlinear dynamics of neural networks in the brain, mindful of technical and demographic confounders that may influence biomarker findings. The aim of this study was to evaluate the robustness of recurrence quantification analysis (RQA) as a potential biomarker for ASD using a systematic methodological exploration of a range of potential technical and demographic confounders. Methods: RQA feature extraction was performed on continuous 5-second segments of resting state EEG (rsEEG) data and linear and nonlinear classifiers were tested. Data analysis progressed from a full sample of 16 ASD and 46 typically developing (TD) individuals (age 0-18 years, 4802 EEG segments), to a subsample of 16 ASD and 19 TD children (age 0-6 years, 1874 segments), to an age-matched sample of 7 ASD and 7 TD children (age 2-6 years, 666 segments) to prevent sample bias and to avoid misinterpretation of the classification results attributable to technical and demographic confounders. A clinical scenario of diagnosing an unseen subject was simulated using a leave-one-subject-out classification approach. Results: In the age-matched sample, leave-one-subject-out classification with a nonlinear support vector machine classifier showed 92.9% accuracy, 100% sensitivity and 85.7% specificity in differentiating ASD from TD. Age, sex, intellectual ability and the number of trainingandtest segments per group were identified as possible demographic and technical confounders. Consistent repeatability, i.e. the correct identification of all segments per subject, was found to be a challenge. Conclusions: RQA of rsEEG was an accurate classifier of ASD in an age-matched sample, suggesting the potential of this approach for global screening in ASD. However, this study also showed experimentally how a range of technical challenges and demographic confounders can skew results, and highlights the importance of probing for these in future studies. We recommend validation of this methodology in a large and well-matched sample of infants and children, preferably in a low- and middle-income setting