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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.
Full description
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).
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Inclusion criteria
Patients aged≥45 years (or ≥18 with a history of diabetes). b. Cases: Incident haemodialysis patients c. A comparative arm of peritoneal dialysis patients and CKD 3-4 (not on dialysis) with hypertension as a key risk factor for CVD.
d. Capable of understanding the purpose and risks of the study, fully informed, and given informed consent.
Exclusion criteria
100 participants in 2 patient groups
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Central trial contact
Anirudh Rao, PhD
Data sourced from clinicaltrials.gov
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