Kidney Function and Cardiovascular Disease: Evidence from Observational Studies and Mendelian Randomization Analyses

Wenjun Yang, Xuemei Wu, Manying Zhao, Jianying Hu, Chenhao Lin, Zhendong Mei, Jing Chen, Xu Jie Zhou, Sheng Nie, Jing Nie, Xiang Gao, Yan Zheng, Zhonghan Sun

Research output: Contribution to journalArticlepeer-review

Abstract

Observational studies have identified that declined kidney function was associated with a higher risk of cardiovascular disease (CVD). However, the causation of such associations requires further exploration. Here, we compared the observational associations of various kidney function measurements with CVD and its major subtypes and further assessed the potential causality using Mendelian randomization (MR) analyses with individual-level data of 306,246 participants from the UK Biobank and genome-wide association study summary-level data from FinnGen consortium, Coronary ARtery DIsease Genome wide Replication and Meta-analysis plus The Coronary Artery Disease Genetics (CARDIoGRAMplusC4D) consortium, and MEGASTROKE Consortium, respectively. Kidney function measurements included circulating levels of cystatin C and creatinine, urine albumin-to-creatinine ratio (uACR), and estimated glomerular filtration rates (eGFR) from three chronic kidney disease epidemiology collaboration formulae (i.e., that using cystatin C, creatinine, and both). In the observational analyses, decreased kidney function measurements were associated with a higher risk of CVD in the UK Biobank study. For example, per one standard deviation decrease in baseline estimated glomerular filtration rates using both cystatin C and creatinine (eGFRcys + cre) was associated with 11% higher risk of CVD (hazard ratio = 0.89; 95% confidence interval, 0.87–0.90). In contrast, two-sample MR analyses did not suggest significant associations of any genetically instrumented kidney function measurement with the risk of CVD or its subtypes. Our findings suggested the inverse associations of kidney function measurements with CVD risk from observational studies were not supported by large MR analyses.

Original languageEnglish (US)
JournalPhenomics
DOIs
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics
  • Biotechnology
  • Cell Biology

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