An assessment of obesity and hyperphagia in individuals with Smith-Magenis syndrome

Smith-Magenis syndrome (SMS;OMIM# 182290) is a multiple congenital anomalies and mental retardation syndrome caused by a 3.7- Mb deletion on chromosome 17p11.2 or a mutation in the RAI1 gene. Although the majority of the SMS phenotype has been well described, limited studies are available describing growth patterns in SMS. There is some evidence that individuals with SMS develop obesity. Thus, this study aims to characterize the growth and potential influence of hyperphagia in a cohort of individuals with SMS. A retrospective chart review was conducted of 78 individuals with SMS through Baylor College of Medicine (BCM) at Texas Children¡¯s Hospital (TCH.) All documented height and weight measurements were abstracted and Z-scores (SD units) for height-for-age, length-for-age and BMI-for-age were calculated. Mail-out questionnaires were provided to the corresponding parents of the cohort to assess for the presence of hyperphagia through a validated hyperphagia questionnaire (HQ). Analysis of this data demonstrates that by the age ¡Ý 20 years males with SMS have mean BMI¡¯s in the 85th-90th percentile corresponding to an overweight BMI, and females with SMS had mean BMI¡¯s in the 95th -97th percentile corresponding to an obese BMI. Parents indicated that hyperphagia is present in individuals with SMS as 76% of parent¡¯s report having to lock food away from their child. Females¡¯ age ¡Ý 20 years of age had the highest mean behavior, drive and severity scores as well as the highest BMI. Thus, this study concludes that it appears overweight and obesity, as well as hyperphagia, are present in this cohort of SMS individuals. The results of this study will hopefully enable parents and caregivers of children with SMS to take preventative measures in order to control food related behaviors present in their children as well as to prevent overweight and obesity and the associated negative health consequences.

A full pedigree based method for the statistical assessment of genetic anticipation

Genetic anticipation is defined as a decrease in age of onset or increase in severity as the disorder is transmitted through subsequent generations. Anticipation has been noted in the literature for over a century. Recently, anticipation in several diseases including Huntington's Disease, Myotonic Dystrophy and Fragile X Syndrome were shown to be caused by expansion of triplet repeats. Anticipation effects have also been observed in numerous mental disorders (e.g. Schizophrenia, Bipolar Disorder), cancers (Li-Fraumeni Syndrome, Leukemia) and other complex diseases. ^ Several statistical methods have been applied to determine whether anticipation is a true phenomenon in a particular disorder, including standard statistical tests and newly developed affected parent/affected child pair methods. These methods have been shown to be inappropriate for assessing anticipation for a variety of reasons, including familial correlation and low power. Therefore, we have developed family-based likelihood modeling approaches to model the underlying transmission of the disease gene and penetrance function and hence detect anticipation. These methods can be applied in extended families, thus improving the power to detect anticipation compared with existing methods based only upon parents and children. The first method we have proposed is based on the regressive logistic hazard model. This approach models anticipation by a generational covariate. The second method allows alleles to mutate as they are transmitted from parents to offspring and is appropriate for modeling the known triplet repeat diseases in which the disease alleles can become more deleterious as they are transmitted across generations. ^ To evaluate the new methods, we performed extensive simulation studies for data simulated under different conditions to evaluate the effectiveness of the algorithms to detect genetic anticipation. Results from analysis by the first method yielded empirical power greater than 87% based on the 5% type I error critical value identified in each simulation depending on the method of data generation and current age criteria. Analysis by the second method was not possible due to the current formulation of the software. The application of this method to Huntington's Disease and Li-Fraumeni Syndrome data sets revealed evidence for a generation effect in both cases. ^