Improving DEIB in Addiction Medicine Training Through Interdisciplinary Collaboration and Program Evaluation.

Developing a diverse Addiction Medicine (AM) workforce will improve medical and public health responses to the increasing health risks created by substance use disorders (SUDs). A workforce that embraces diversity, equity, inclusion, and belonging (DEIB) principles may foster novel responses to address the disparities in treatment and outcomes experienced by Black, Indigenous, and People of Color (BIPOC) who are impacted by SUDs. However, experiences of bias and discrimination in the workplace and a lack of exposure to addiction-related content in educational settings limit opportunities to develop and retain a diverse workforce. In this commentary, we describe the creation of the Inclusion, Diversity, and Equity in Addiction medicine, Addiction research, and Addiction health professions (IDEAAA) initiative, a strategy to foster diversity in the field of addiction through efforts targeting learners at different stages of the biomedical education pipeline. Now in its second year, the IDEAAA Program is focused on programmatic evaluation through a qualitative interview study of AM training programs to improve the understanding of experiences of participants who are self-identified members of underrepresented groups (URGs). Interdisciplinary programs with multi-faceted approaches are a strategy to improve DEIB in the AM workforce; IDEAAA's design and methods can inform other AM programs who have the desire to improve DEIB through novel approaches.

Integrating Mouse and Human Genetic Data to Move beyond GWAS and Identify Causal Genes in Cholesterol Metabolism.

Identifying the causal gene(s) that connects genetic variation to a phenotype is a challenging problem in genome-wide association studies (GWASs). Here, we develop a systematic approach that integrates mouse liver co-expression networks with human lipid GWAS data to identify regulators of cholesterol and lipid metabolism. Through our approach, we identified 48 genes showing replication in mice and associated with plasma lipid traits in humans and six genes on the X chromosome. Among these 54 genes, 25 have no previously identified role in lipid metabolism. Based on functional studies and integration with additional human lipid GWAS datasets, we pinpoint Sestrin1 as a causal gene associated with plasma cholesterol levels in humans. Our validation studies demonstrate that Sestrin1 influences plasma cholesterol in multiple mouse models and regulates cholesterol biosynthesis. Our results highlight the power of combining mouse and human datasets for prioritization of human lipid GWAS loci and discovery of lipid genes.