Genetic relations between type 1 diabetes, coronary artery disease and leukocyte counts

Jolade Adebekun1,2, Ajay Nadig3, Priscilla Saarah1,2

  • 1Yale Center for Molecular and Systems Metabolism, Yale University School of Medicine, New Haven, CT, USA.

Diabetologia
|August 14, 2024
PubMed

Insights

Type 1 diabetes and coronary artery disease share genetic links, potentially mediated by leukocyte counts and gene expression. This finding offers new targets for predicting and treating these conditions.

Area of Science:

  • Genetics
  • Cardiovascular Disease
  • Immunology

Background:

  • Type 1 diabetes (T1D) is linked to increased coronary artery disease (CAD) risk.
  • Genetic factors influencing leukocyte production may independently affect CAD risk.
  • Shared genetic determinants between T1D, CAD, and leukocyte counts require investigation.

Purpose of the Study:

  • To examine shared genetic determinants between T1D, CAD, and leukocyte counts.
  • To investigate causal relationships between T1D, CAD, and leukocyte traits.
  • To identify genetic links contributing to the comorbidity of T1D and CAD.

Main Methods:

  • Genome-wide association study (GWAS) summary statistics were analyzed.
  • Linkage disequilibrium score regression (LDSC) estimated genetic correlations.
  • Two-sample Mendelian randomization (MR) assessed causal relationships.
  • Latent causal variable (LCV) models evaluated genetic causality proportion.

Main Results:

  • Significant genetic correlation found between T1D and CAD (rg=0.088).
  • Shared genetic determinants identified with eosinophil and lymphocyte counts.
  • Genetically predicted leukocyte counts (lymphocyte, neutrophil, eosinophil) associated with T1D and CAD.
  • LCV model suggested a genetic causality proportion of 0.36 between T1D and CAD.

Conclusions:

  • Shared genetic mechanisms link T1D and CAD, potentially involving leukocyte counts and gene expression.
  • Specific genes (e.g., SH2B3, IFIH1) implicated in shared loci.
  • Findings highlight potential cellular and molecular targets for disease prediction and drug discovery.
Abstract

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