Scholarlink

<p>Genomics can provide insight into the etiology of type 2 diabetes and its comorbidities, but assigning functionality to non-coding variants remains challenging. Polygenic scores, which aggregate variant effects, can uncover mechanisms when paired with molecular data. Here, we test polygenic scores for type 2 diabetes and cardiometabolic comorbidities for associations with 2,922 circulating proteins in the UK Biobank. The genome-wide type 2 diabetes polygenic score associates with 617 proteins, of which 75% also associate with another cardiometabolic score. Partitioned type 2 diabetes scores, which capture distinct disease biology, associate with 342 proteins (20% unique). In this work, we identify key pathways (e.g., complement cascade), potential therapeutic targets (e.g., FAM3D in type 2 diabetes), and biomarkers of diabetic comorbidities (e.g., EFEMP1 and IGFBP2) through causal inference, pathway enrichment, and Cox regression of clinical trial outcomes. Our results are available via an interactive portal (<a href="https://public.cgr.astrazeneca.com/t2d-pgs/v1/">https://public.cgr.astrazeneca.com/t2d-pgs/v1/</a>).</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Identification of plasma proteomic markers underlying polygenic risk of type 2 diabetes and related comorbidities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月3日

查看全文
  • Douglas P. Loesch,
  • Manik Garg,
  • Dorota Matelska,
  • Dimitrios Vitsios,
  • Xiao Jiang,
  • Scott C. Ritchie,
  • Benjamin B. Sun,
  • Heiko Runz,
  • Christopher D. Whelan,
  • Rury R. Holman,
  • Robert J. Mentz,
  • Filipe A. Moura,
  • Stephen D. Wiviott,
  • Marc S. Sabatine,
  • Miriam S. Udler,
  • Ingrid A. Gause-Nilsson,
  • Slavé Petrovski,
  • Jan Oscarsson,
  • Abhishek Nag,
  • Dirk S. Paul,
  • Michael Inouye

摘要

Genomics can provide insight into the etiology of type 2 diabetes and its comorbidities, but assigning functionality to non-coding variants remains challenging. Polygenic scores, which aggregate variant effects, can uncover mechanisms when paired with molecular data. Here, we test polygenic scores for type 2 diabetes and cardiometabolic comorbidities for associations with 2,922 circulating proteins in the UK Biobank. The genome-wide type 2 diabetes polygenic score associates with 617 proteins, of which 75% also associate with another cardiometabolic score. Partitioned type 2 diabetes scores, which capture distinct disease biology, associate with 342 proteins (20% unique). In this work, we identify key pathways (e.g., complement cascade), potential therapeutic targets (e.g., FAM3D in type 2 diabetes), and biomarkers of diabetic comorbidities (e.g., EFEMP1 and IGFBP2) through causal inference, pathway enrichment, and Cox regression of clinical trial outcomes. Our results are available via an interactive portal (https://public.cgr.astrazeneca.com/t2d-pgs/v1/).