Understanding why cardiovascular complications are the number one cause of death in patients with End-Stage Kidney Disease

Description

End-stage Kidney failure, also known as End-Stage Kidney Disease (ESKD), is the final, irreversible stage of chronic kidney disease (CKD), where kidney function has worsened to the point that the kidneys can no longer function independently. In 2009, an estimated 7,000 extra strokes and 12,000 extra myocardial infarctions per year were due to CKD in the UK, costing the NHS over £170 million. However, cardiovascular complications rather than impaired renal function are the leading cause of death in ESKD. Several mechanisms contribute to this dysfunction, including vascular and myocardial remodelling, atherosclerosis, vascular calcification, senescence, cardiac fibrosis, valve calcification and complex dyslipidaemia. As a result, these patients are at increased risk of developing heart failure, irregular heart rhythms (arrhythmias) and sudden heart death.

Circulating blood cardiac biomarkers provide insight into various aspects of cardiovascular health, including injury, inflammation, and fibrosis. There are several promising new cardiovascular biomarkers (soluble suppression of tumorigenesis-2 (ST-2), galectin-3, and Cardiac myosin-binding protein C (cMyC)), but their role in risk stratification in ESKD is yet unknown. This project aims to take a multi-omics approach to evaluate existing and novel markers from patients diagnosed with ESKD, which could aid in early detection of heart disease/damage. We further aim to apply advanced machine and integration learning to the multi-omics datasets (genomics, proteomics, and metabolomics) to develop a prediction tool to aid the diagnosis of cardiovascular disease in ESKD patients.

Aim and Objectives

  • To identify proteomics, metabolomic and microbiome patterns associated with cardiovascular disease in ESKD.
  • Assess the utility of putative proteomic, metabolomic and genomic patterns in predicting deterioration in cardiac function and incidence of major adverse cardiac events (MACE) in patients with ESKD.
  • Determine the utility of existing and new cardiac biomarkers in predicting deterioration in cardiac function and incident MACE in patients with ESKD.
  • To characterise the microbiome in patients with ESKD and its association with incident MACE
  • To integrate proteomic, metabolomic, microbiome and clinical data using advanced machine learning techniques.

 

Methods

Blood samples and clinical data will be obtained from dialysis patients at Royal Liverpool University Hospital to conduct proteomics, mass-spectrometry-based metabolomics and microbiome assessment. We will use a targeted approach to understand the function of the current biomarkers listed above but also a global approach to identify new biomarkers. Students will gain experience in phlebotomy, SWATH-based mass spectrometry that will be used to analyse patient serum samples, metabolomic analysis and large-scale data handling. Proteomic analysis using tools such as DAVID and Reactome will be used to evaluate global changes and biomarker release. Validation of biomarkers and differential expressed proteins will establish skills including cell culture, western blotting, immunofluorescence and ELISA. The student will also gain some experience in genomic analysis (Amplification of genomic DNA analysed by 16S ribosomal RNA gene sequencing).

 

How to apply

Please contact the project primary supervisor directly to apply to this project, including your CV and covering letter. No other application formats will be accepted.