Evaluating the Causal Effect of Circulating Proteome on Glycemic Traits: Evidence From Mendelian Randomization

Exploring the mechanisms underlying abnormal glycemic traits is important for deciphering type 2 diabetes and characterizing novel drug targets. This study aimed to decipher the causal associations of circulating proteins with fasting glucose (FG), 2-h glucose after an oral glucose challenge (2hGlu), fasting insulin (FI), and glycated hemoglobin (HbA1c) using large-scale proteome-wide Mendelian randomization (MR) analyses. Genetic data on plasma proteomes were obtained from 10 proteomic genome-wide association studies. Both cis-protein quantitative trait loci (pQTLs) and cis + trans-pQTLs MR analyses were conducted. Bayesian colocalization, Steiger filtering analysis, assessment of protein-altering variants, and mapping expression QTLs to pQTLs were performed to investigate the reliability of the MR findings. Protein-protein interaction, pathway enrichment analysis, and evaluation of drug targets were performed. Thirty-three proteins were identified with causal effects on FG, FI, or HbA1c but not 2hGlu in the cis-pQTL analysis, and 93 proteins had causal effects on glycemic traits in the cis + trans-pQTLs analysis. Most proteins were either considered druggable or drug targets. In conclusion, many novel circulating protein biomarkers were identified to be causally associated with glycemic traits. These biomarkers enhance the understanding of molecular etiology and provide insights into the screening, monitoring, and treatment of diabetes. Article Highlights

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Publication Details

Journal
Diabetes
Published
2024-10-17
DOI
https://doi.org/10.2337/db24-0262
Citations
4
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
2.20
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article

Evaluating the Causal Effect of Circulating Proteome on Glycemic Traits: Evidence From Mendelian Randomization

Yining Wang, Guangfeng Ruan, Simin Wen, Guoqi Cai et al.
4 citations
Diabetes
Genetic Associations and Epidemiology
2.20
article

Evaluating the Causal Effect of Circulating Proteome on Glycemic Traits: Evidence From Mendelian Randomization

Yining Wang, Guangfeng Ruan, Simin Wen, Guoqi Cai, Ziyuan Shen, Xing Xing, Siqi Xu, Yan Zhang
article en
4 citations

Abstract

Exploring the mechanisms underlying abnormal glycemic traits is important for deciphering type 2 diabetes and characterizing novel drug targets. This study aimed to decipher the causal associations of circulating proteins with fasting glucose (FG), 2-h glucose after an oral glucose challenge (2hGlu), fasting insulin (FI), and glycated hemoglobin (HbA1c) using large-scale proteome-wide Mendelian randomization (MR) analyses. Genetic data on plasma proteomes were obtained from 10 proteomic genome-wide association studies. Both cis-protein quantitative trait loci (pQTLs) and cis + trans-pQTLs MR analyses were conducted. Bayesian colocalization, Steiger filtering analysis, assessment of protein-altering variants, and mapping expression QTLs to pQTLs were performed to investigate the reliability of the MR findings. Protein-protein interaction, pathway enrichment analysis, and evaluation of drug targets were performed. Thirty-three proteins were identified with causal effects on FG, FI, or HbA1c but not 2hGlu in the cis-pQTL analysis, and 93 proteins had causal effects on glycemic traits in the cis + trans-pQTLs analysis. Most proteins were either considered druggable or drug targets. In conclusion, many novel circulating protein biomarkers were identified to be causally associated with glycemic traits. These biomarkers enhance the understanding of molecular etiology and provide insights into the screening, monitoring, and treatment of diabetes. Article Highlights

DiabetesVol. 74(1)
University of Tasmania (AU), Anhui Medical University (CN), Guangzhou First People's Hospital (CN), Zhujiang Hospital (CN), Southern Medical University (CN), South China University of Technology (CN), Guangzhou Medical University (CN)
Good health and well-being
Openalex Percentile: Top 11%
Genetic Associations and Epidemiology
2.20
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