Understanding the Relationship between Activity and Neighbourhoods (URBAN) Study: research design and methodology

<p>Abstract</p> <p>Background</p> <p>Built environment attributes are recognized as being important contributors to physical activity (PA) engagement and body size in adults and children. However, much of the existing research in this emergent public health field is hindered by methodological limitations, including: population and site homogeneity, reliance on self-report measures, aggregated measures of PA, and inadequate statistical modeling. As an integral component of multi-country collaborative research, the Understanding the Relationship between Activity and Neighbourhoods (URBAN) Study seeks to overcome these limitations by determining the strengths of association between detailed measures of the neighborhood built environment with PA levels across multiple domains and body size measures in adults and children. This article outlines the research protocol developed for the URBAN Study.</p> <p>Methods and design</p> <p>The URBAN Study is a multi-centered, stratified, cross-sectional research design, collecting data across four New Zealand cities. Within each city, 12 neighborhoods were identified and selected for investigation based on higher or lower walkability and Māori demographic attributes. Neighborhoods were selected to ensure equal representation of these characteristics. Within each selected neighborhood, 42 households are being randomly selected and an adult and child (where possible) recruited into the study. Data collection includes: objective and self-reported PA engagement, neighborhood perceptions, demographics, and body size measures. The study was designed to recruit approximately 2,000 adults and 250 children into the project. Other aspects of the study include photovoice, which is a qualitative assessment of built environment features associated with PA engagement, an audit of the neighborhood streetscape environment, and an individualized neighborhood walkability profile centered on each participant's residential address. Multilevel modeling will be used to examine the individual-level and neighborhood-level relationships with PA engagement and body size.</p> <p>Discussion</p> <p>The URBAN Study is applying a novel scientifically robust research design to provide urgently needed epidemiological information regarding the associations between the built environment and health outcomes. The findings will contribute to a larger, international initiative in which similar neighborhood selection and PA measurement procedures are utilized across eight countries. Accordingly, this study directly addresses the international priority issues of increasing PA engagement and decreasing obesity levels.</p

Repeat expansions nested within tandem CNVs: a unique structural change in GLS exemplifies the diagnostic challenges of non-coding pathogenic variation

Glutaminase deficiency has recently been associated with ataxia and developmental delay due to repeat expansions in the 5'UTR of the glutaminase (GLS) gene. Patients with the described GLS repeat expansion may indeed remain undiagnosed due to the rarity of this variant, the challenge of its detection and the recency of its discovery. In this study, we combined advanced bioinformatics screening of ~3000 genomes and ~1500 exomes with optical genome mapping and long-read sequencing for confirmation studies. We identified two GLS families, previously intensely and unsuccessfully analyzed. One family carries an unusual and complex structural change involving a homozygous repeat expansion nested within a quadruplication event in the 5'UTR of GLS. Glutaminase deficiency and its metabolic consequences were validated by in-depth biochemical analysis. The identified GLS patients showed progressive early-onset ataxia, cognitive deficits, pyramidal tract damage and optic atrophy, thus demonstrating susceptibility of several specific neuron populations to glutaminase deficiency. This large-scale screening study demonstrates the ability of bioinformatics analysis-validated by latest state-of-the-art technologies (optical genome mapping and long-read sequencing)-to effectively flag complex repeat expansions using short-read datasets and thus facilitate diagnosis of ultra-rare disorders