LGBTQ Inequality in Engineering Education

BackgroundResearchers over the past three decades have documented processes of gender and racial/ethnic inequality in engineering education but little is known about other axes of difference, including the experiences of lesbian, gay, bisexual, transgender, and queer (LGBTQ) persons in engineering. Despite growing interest in LGBTQ inequality generally, prior research has yet to systematically document day‐to‐day experiences of inequality in engineering education along LGBTQ status.Purpose/HypothesisIn this article, we use survey data from students enrolled in eight universities to examine LGBTQ inequality in engineering education. Specifically, we explore whether LGBTQ students experience greater marginalization than their classmates, whether their engineering work is more likely to be devalued, and whether they experience more negative health and wellness outcomes. We hypothesize that LGBTQ students experience greater marginalization and devaluation and more negative health and wellness outcomes compared to their non‐LGBTQ peers.Data/MethodWe analyzed novel survey data from 1,729 undergraduate students (141 of whom identify as LGBTQ) enrolled in eight U.S. engineering programs.ResultsWe found that LGBTQ students face greater marginalization, devaluation, and health and wellness issues relative to their peers, and that these health and wellness inequalities are explained in part by LGBTQ students’ experiences of marginalization and devaluation in their engineering programs. Furthermore, there is little variation in the climate for LGBTQ students across the eight schools, suggesting that anti‐LGBTQ bias may be widespread in engineering education.ConclusionsWe call for reflexive research on LGBTQ inequality in engineering education and the institutional and cultural shifts needed to mitigate these processes and better support LGBTQ students.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146822/1/jee20239.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146822/2/jee20239_am.pd

Effect of a reduction in glomerular filtration rate after nephrectomy on arterial stiffness and central hemodynamics: rationale and design of the EARNEST study

Background: There is strong evidence of an association between chronic kidney disease (CKD) and cardiovascular disease. To date, however, proof that a reduction in glomerular filtration rate (GFR) is a causative factor in cardiovascular disease is lacking. Kidney donors comprise a highly screened population without risk factors such as diabetes and inflammation, which invariably confound the association between CKD and cardiovascular disease. There is strong evidence that increased arterial stiffness and left ventricular hypertrophy and fibrosis, rather than atherosclerotic disease, mediate the adverse cardiovascular effects of CKD. The expanding practice of live kidney donation provides a unique opportunity to study the cardiovascular effects of an isolated reduction in GFR in a prospective fashion. At the same time, the proposed study will address ongoing safety concerns that persist because most longitudinal outcome studies have been undertaken at single centers and compared donor cohorts with an inappropriately selected control group.<p></p> Hypotheses: The reduction in GFR accompanying uninephrectomy causes (1) a pressure-independent increase in aortic stiffness (aortic pulse wave velocity) and (2) an increase in peripheral and central blood pressure.<p></p> Methods: This is a prospective, multicenter, longitudinal, parallel group study of 440 living kidney donors and 440 healthy controls. All controls will be eligible for living kidney donation using current UK transplant criteria. Investigations will be performed at baseline and repeated at 12 months in the first instance. These include measurement of arterial stiffness using applanation tonometry to determine pulse wave velocity and pulse wave analysis, office blood pressure, 24-hour ambulatory blood pressure monitoring, and a series of biomarkers for cardiovascular and bone mineral disease.<p></p> Conclusions: These data will prove valuable by characterizing the direction of causality between cardiovascular and renal disease. This should help inform whether targeting reduced GFR alongside more traditional cardiovascular risk factors is warranted. In addition, this study will contribute important safety data on living kidney donors by providing a longitudinal assessment of well-validated surrogate markers of cardiovascular disease, namely, blood pressure and arterial stiffness. If any adverse effects are detected, these may be potentially reversed with the early introduction of targeted therapy. This should ensure that kidney donors do not come to long-term harm and thereby preserve the ongoing expansion of the living donor transplant program.<p></p&gt

Choosing the target difference ('effect size') for a randomised controlled trial - DELTA(2) guidance protocol

BACKGROUND: A key step in the design of a randomised controlled trial (RCT) is the estimation of the number of participants needed. By far the most common approach is to specify a target difference and then estimate the corresponding sample size; this sample size is chosen to provide reassurance that the trial will have high statistical power to detect such a difference between the randomised groups (at the planned statistical significance level). The sample size has many implications for the conduct of the study, as well as carrying scientific and ethical aspects to its choice. Despite the critical role of the target difference for the primary outcome in the design of an RCT, the manner in which it is determined has received little attention. This article reports the protocol of the Difference ELicitation in TriAls (DELTA(2)) project, which will produce guidance on the specification and reporting of the target difference for the primary outcome in a sample size calculation for RCTs. METHODS/DESIGN: The DELTA(2) project has five components: systematic literature reviews of recent methodological developments (stage 1) and existing funder guidance (stage 2); a Delphi study (stage 3); a 2-day consensus meeting bringing together researchers, funders and patient representatives, as well as one-off engagement sessions at relevant stakeholder meetings (stage 4); and the preparation and dissemination of a guidance document (stage 5). DISCUSSION: Specification of the target difference for the primary outcome is a key component of the design of an RCT. There is a need for better guidance for researchers and funders regarding specification and reporting of this aspect of trial design. The aim of this project is to produce consensus based guidance for researchers and funders

Genetic architecture of white matter hyperintensities differs in hypertensive and nonhypertensive ischemic stroke.

BACKGROUND AND PURPOSE: Epidemiological studies suggest that white matter hyperintensities (WMH) are extremely heritable, but the underlying genetic variants are largely unknown. Pathophysiological heterogeneity is known to reduce the power of genome-wide association studies (GWAS). Hypertensive and nonhypertensive individuals with WMH might have different underlying pathologies. We used GWAS data to calculate the variance in WMH volume (WMHV) explained by common single nucleotide polymorphisms (SNPs) as a measure of heritability (SNP heritability [HSNP]) and tested the hypothesis that WMH heritability differs between hypertensive and nonhypertensive individuals. METHODS: WMHV was measured on MRI in the stroke-free cerebral hemisphere of 2336 ischemic stroke cases with GWAS data. After adjustment for age and intracranial volume, we determined which cardiovascular risk factors were independent predictors of WMHV. Using the genome-wide complex trait analysis tool to estimate HSNP for WMHV overall and within subgroups stratified by risk factors found to be significant in multivariate analyses. RESULTS: A significant proportion of the variance of WMHV was attributable to common SNPs after adjustment for significant risk factors (HSNP=0.23; P=0.0026). HSNP estimates were higher among hypertensive individuals (HSNP=0.45; P=7.99×10(-5)); this increase was greater than expected by chance (P=0.012). In contrast, estimates were lower, and nonsignificant, in nonhypertensive individuals (HSNP=0.13; P=0.13). CONCLUSIONS: A quarter of variance is attributable to common SNPs, but this estimate was greater in hypertensive individuals. These findings suggest that the genetic architecture of WMH in ischemic stroke differs between hypertensives and nonhypertensives. Future WMHV GWAS studies may gain power by accounting for this interaction

Identifying the science and technology dimensions of emerging public policy issues through horizon scanning

Public policy requires public support, which in turn implies a need to enable the public not just to understand policy but also to be engaged in its development. Where complex science and technology issues are involved in policy making, this takes time, so it is important to identify emerging issues of this type and prepare engagement plans. In our horizon scanning exercise, we used a modified Delphi technique [1]. A wide group of people with interests in the science and policy interface (drawn from policy makers, policy adviser, practitioners, the private sector and academics) elicited a long list of emergent policy issues in which science and technology would feature strongly and which would also necessitate public engagement as policies are developed. This was then refined to a short list of top priorities for policy makers. Thirty issues were identified within broad areas of business and technology; energy and environment; government, politics and education; health, healthcare, population and aging; information, communication, infrastructure and transport; and public safety and national security.Public policy requires public support, which in turn implies a need to enable the public not just to understand policy but also to be engaged in its development. Where complex science and technology issues are involved in policy making, this takes time, so it is important to identify emerging issues of this type and prepare engagement plans. In our horizon scanning exercise, we used a modified Delphi technique [1]. A wide group of people with interests in the science and policy interface (drawn from policy makers, policy adviser, practitioners, the private sector and academics) elicited a long list of emergent policy issues in which science and technology would feature strongly and which would also necessitate public engagement as policies are developed. This was then refined to a short list of top priorities for policy makers. Thirty issues were identified within broad areas of business and technology; energy and environment; government, politics and education; health, healthcare, population and aging; information, communication, infrastructure and transport; and public safety and national security

Practical help for specifying the target difference in sample size calculations for RCTs: the DELTA2 five-stage study, including a workshop

BACKGROUND: The randomised controlled trial is widely considered to be the gold standard study for comparing the effectiveness of health interventions. Central to its design is a calculation of the number of participants needed (the sample size) for the trial. The sample size is typically calculated by specifying the magnitude of the difference in the primary outcome between the intervention effects for the population of interest. This difference is called the 'target difference' and should be appropriate for the principal estimand of interest and determined by the primary aim of the study. The target difference between treatments should be considered realistic and/or important by one or more key stakeholder groups. OBJECTIVE: The objective of the report is to provide practical help on the choice of target difference used in the sample size calculation for a randomised controlled trial for researchers and funder representatives. METHODS: The Difference ELicitation in TriAls2 (DELTA2) recommendations and advice were developed through a five-stage process, which included two literature reviews of existing funder guidance and recent methodological literature; a Delphi process to engage with a wider group of stakeholders; a 2-day workshop; and finalising the core document. RESULTS: Advice is provided for definitive trials (Phase III/IV studies). Methods for choosing the target difference are reviewed. To aid those new to the topic, and to encourage better practice, 10 recommendations are made regarding choosing the target difference and undertaking a sample size calculation. Recommended reporting items for trial proposal, protocols and results papers under the conventional approach are also provided. Case studies reflecting different trial designs and covering different conditions are provided. Alternative trial designs and methods for choosing the sample size are also briefly considered. CONCLUSIONS: Choosing an appropriate sample size is crucial if a study is to inform clinical practice. The number of patients recruited into the trial needs to be sufficient to answer the objectives; however, the number should not be higher than necessary to avoid unnecessary burden on patients and wasting precious resources. The choice of the target difference is a key part of this process under the conventional approach to sample size calculations. This document provides advice and recommendations to improve practice and reporting regarding this aspect of trial design. Future work could extend the work to address other less common approaches to the sample size calculations, particularly in terms of appropriate reporting items. FUNDING: Funded by the Medical Research Council (MRC) UK and the National Institute for Health Research as part of the MRC-National Institute for Health Research Methodology Research programme

Focused HLA analysis in Caucasians with myositis identifies significant associations with autoantibody subgroups

Objectives: Idiopathic inflammatory myopathies (IIM) are a spectrum of rare autoimmune diseases characterised clinically by muscle weakness and heterogeneous systemic organ involvement. The strongest genetic risk is within the major histocompatibility complex (MHC). Since autoantibody presence defines specific clinical subgroups of IIM, we aimed to correlate serotype and genotype, to identify novel risk variants in the MHC region that co-occur with IIM autoantibodies. Methods: We collected available autoantibody data in our cohort of 2582 Caucasian patients with IIM. High resolution human leucocyte antigen (HLA) alleles and corresponding amino acid sequences were imputed using SNP2HLA from existing genotyping data and tested for association with 12 autoantibody subgroups. Results: We report associations with eight autoantibodies reaching our study-wide significance level of p<2.9x10(-5). Associations with the 8.1 ancestral haplotype were found with anti-Jo-1 (HLA-B*08:01, p=2.28x10(-53) and HLA-DRB1*03:01, p=3.25x10(-9)), anti-PM/Scl (HLA-DQB1*02:01, p=1.47x10(-26)) and anti-cN1A autoantibodies (HLA-DRB1*03:01, p=1.40x10(-11)). Associations independent of this haplotype were found with anti-Mi-2 (HLA-DRB1*07:01, p=4.92x10(-13)) and anti-HMGCR autoantibodies (HLA-DRB1*11, p=5.09x10(-6)). Amino acid positions may be more strongly associated than classical HLA associations; for example with anti-Jo-1 autoantibodies and position 74 of HLA-DRB1 (p=3.47x10(-64)) and position 9 of HLA-B (p=7.03x10(-11)). We report novel genetic associations with HLA-DQB1 anti-TIF1 autoantibodies and identify haplotypes that may differ between adult-onset and juvenile-onset patients with these autoantibodies. Conclusions: These findings provide new insights regarding the functional consequences of genetic polymorphisms within the MHC. As autoantibodies in IIM correlate with specific clinical features of disease, understanding genetic risk underlying development of autoantibody profiles has implications for future research

Genome-wide imputation identifies novel associations and localises signals in idiopathic inflammatory myopathies.

OBJECTIVES The idiopathic inflammatory myopathies (IIM) are heterogeneous diseases, thought to be initiated by immune activation in genetically predisposed individuals. In this study we imputed variants from the Immunochip array using a large reference panel to fine-map associations and identify novel associations in IIM. METHODS We analysed 2,565 Caucasian IIM samples collected through the Myositis Genetics Consortium (MYOGEN) and 10,260 ethnically-matched controls. We imputed 1,648,116 variants from the Immunochip array using the Haplotype Reference Consortium panel and conducted association analysis on IIM, and clinical and serological subgroups. RESULTS The human leukocyte antigen (HLA) locus was consistently the most significantly associated region. Four non-HLA regions reached genome-wide significance, three in the whole IIM cohort (SDK2 and LINC00924 - both novel, and STAT4), with evidence of independent variants in STAT4, and NAB1 in the polymyositis (PM) subgroup. We also found suggestive evidence of association with loci previously associated with other autoimmune rheumatic diseases (TEC and LTBR). We identified more significant associations than those previously reported in IIM, for STAT4 and DGKQ in the total cohort, for NAB1 and FAM167A-BLK loci in PM, and CCR5 in inclusion body myositis. We found enrichment of variants among DNase I hypersensitivity sites and histone marks associated with active transcription within blood cells. CONCLUSIONS We report novel and strong associations in IIM and PM, and localise signals to single genes and immune cell types. This article is protected by copyright. All rights reserved

Identification of Novel Associations and Localization of Signals in Idiopathic Inflammatory Myopathies Using Genome-Wide Imputation

OBJECTIVES: The idiopathic inflammatory myopathies (IIM) are heterogeneous diseases, thought to be initiated by immune activation in genetically predisposed individuals. In this study we imputed variants from the Immunochip array using a large reference panel to fine-map associations and identify novel associations in IIM. METHODS: We analysed 2,565 Caucasian IIM samples collected through the Myositis Genetics Consortium (MYOGEN) and 10,260 ethnically-matched controls. We imputed 1,648,116 variants from the Immunochip array using the Haplotype Reference Consortium panel and conducted association analysis on IIM, and clinical and serological subgroups. RESULTS: The human leukocyte antigen (HLA) locus was consistently the most significantly associated region. Four non-HLA regions reached genome-wide significance, three in the whole IIM cohort (SDK2 and LINC00924 - both novel, and STAT4), with evidence of independent variants in STAT4, and NAB1 in the polymyositis (PM) subgroup. We also found suggestive evidence of association with loci previously associated with other autoimmune rheumatic diseases (TEC and LTBR). We identified more significant associations than those previously reported in IIM, for STAT4 and DGKQ in the total cohort, for NAB1 and FAM167A-BLK loci in PM, and CCR5 in inclusion body myositis. We found enrichment of variants among DNase I hypersensitivity sites and histone marks associated with active transcription within blood cells. CONCLUSIONS: We report novel and strong associations in IIM and PM, and localise signals to single genes and immune cell types