Major Adverse Cardiac Events Clinical Trial
Official title:
Novel Cardiovascular Biomarkers in Patients With Kidney Disease
Chronic kidney disease (CKD) is a long-term condition where the kidneys do not work as well as they should. End-stage kidney failure (ESKD) is the final, irreparable stage of chronic kidney disease (CKD), where kidney function has worsened, so the kidneys can no longer function independently. At this stage, dialysis is required to remove waste products and excess fluid from the blood. There are two types of dialysis. In haemodialysis (HD), blood is pumped out of the body to an artificial kidney machine and returned to the body by tubes that connect a person to the machine. In peritoneal dialysis (PD), the inside lining of the belly acts as a natural filter. PD has the advantage of being gentler on the heart. HD causes significant stress to the heart by reducing the blood flow to the heart muscle, resulting in heart failure, irregular rhythms, and eventually sudden heart death. A large observational study showed that HD patients had 48% worse survival in the first two years than PD patients. Several molecules ('biomarkers') can be detected in blood and inform doctors of heart damage. Studying the form and function of proteins (Proteomics), including how they work and interact with each other inside cells in patients, could help identify the onset of heart problems. HD patients are also prone to body fat changes (cholesterol/lipids). Due to high cholesterol, there is build-up on the walls of arteries, causing their hardening. In HD patients, this process is faster due to abnormalities in lipid structure. Therefore, studying the heart biomarkers, protein, and lipid makeup of HD patients may help to find people at substantial risk of heart and vascular problems and if they are likely to become unwell due to these heart problems.
Currently, there is no specific approach to stratify CV risk in HD patients; therefore, patients are not offered targeted preventative interventions. This novel project will characterise circulating biomarkers and proteomics of myocyte damage, cardiac stress, fibrosis, and inflammation, including lipid composition. Understanding the cardiac biomarkers, targeted proteomics and lipidomics in HD patients as early predictors of CV outcomes will help better decisions on treatment choices and earlier interventions to improve outcomes in these patients. Proteomics and lipidomics, analysed with machine learning techniques, may offer new opportunities to improve risk stratification in these patients. The successful introduction of novel agents, comprising proprotein convertase subtilisin-like/kexin type 9 inhibitors, low-dose oral anticoagulants, sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide-1 agonists, anti-inflammatory agents, and icosapent ethyl, offers an opportunity to reduce the burden of recurrent CV risk further based on their risk stratification. This study aims to obtain pilot data for promising cardiac biomarkers, proteomics and lipidomics, validating them against control groups and establishing prospective changes of the new markers and their relation to the major adverse cardiac events (MACE). Study Objectives 1. To determine circulating plasma levels of new cardiac biomarkers (cMyC, galectin 3) in incident HD patients by comparing them to control groups (PD and CKD 3-4 (not on dialysis)) and to correlate with traditional biomarkers (hsTnT). 2. To evaluate the lipidome as a marker of CVD risk in incident HD patients by studying the indices of HDL quality and quantity, including HDL-particle number, HDL cholesterol, apolipoprotein (apo) A-I levels, serum amyloid A (SAA) content and HDL-cholesterol efflux capacity, and paraoxonase activity of apoB-depleted serum. 3. To evaluate proteomic signature as a marker of cardiac disease risk in incident HD patients by studying the proteomic platform of untargeted high-value proteins for CVD risk (as an exploratory analysis). 4. To explore the association between proposed cardiac biomarkers, proteomics, lipidome and MACE (as an exploratory analysis). ;
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