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Cell-Type Composition Affects Adipose Gene Expression Associations With Cardiometabolic Traits. | LitMetric

AI Article Synopsis

  • Understanding how different gene expressions in fat tissue relate to cardiometabolic diseases can help identify potential health risks.
  • Researchers analyzed 859 adipose tissue samples, revealing that specific cell types, like macrophages and adipocytes, influence traits like body mass index (BMI).
  • The study found that including both BMI and cell type in analysis models led to the identification of 2,664 significant gene-trait associations, highlighting the need to consider cell diversity in genetic assessments related to metabolism.

Article Abstract

Understanding differences in adipose gene expression between individuals with different levels of clinical traits may reveal the genes and mechanisms leading to cardiometabolic diseases. However, adipose is a heterogeneous tissue. To account for cell-type heterogeneity, we estimated cell-type proportions in 859 subcutaneous adipose tissue samples with bulk RNA sequencing (RNA-seq) using a reference single-nuclear RNA-seq data set. Cell-type proportions were associated with cardiometabolic traits; for example, higher macrophage and adipocyte proportions were associated with higher and lower BMI, respectively. We evaluated cell-type proportions and BMI as covariates in tests of association between >25,000 gene expression levels and 22 cardiometabolic traits. For >95% of genes, the optimal, or best-fit, models included BMI as a covariate, and for 79% of associations, the optimal models also included cell type. After adjusting for the optimal covariates, we identified 2,664 significant associations (P ≤ 2e-6) for 1,252 genes and 14 traits. Among genes proposed to affect cardiometabolic traits based on colocalized genome-wide association study and adipose expression quantitative trait locus signals, 25 showed a corresponding association between trait and gene expression levels. Overall, these results suggest the importance of modeling cell-type proportion when identifying gene expression associations with cardiometabolic traits.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10588284PMC
http://dx.doi.org/10.2337/db23-0365DOI Listing

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