Kidney disease affects over 1 in 10 people in the UK and is associated with significant morbidity and mortality. It can progress to end-stage renal disease (ESRD), requiring dialysis or a kidney transplant to survive. Importantly, even those with early chronic kidney disease (CKD) have a significantly increased risk of cardiovascular (CV) events.
Using the UK Biobank proteomic data, we aim to:
1. Evaluate whether serum protein biomarkers linked with CKD progression in advanced CKD are also predictive in early CKD.
*Include participants with an eGFR 45-60mls/min and/or albumin-to-creatinine ratio (ACR)> 3mg/mmol.
*Established biomarkers of CKD progression such as Kidney-injury molecule-1 (KIM-1) will be tested to determine if they predict kidney failure (defined as onset of CKD 5, ESRD or initiation of dialysis or a renal transplant), after adjusting for clinical variables linked to CKD progression including eGFR, ACR, age and blood pressure.
2. Develop a protein signature combining established and novel biomarkers of kidney disease available in the Olink platform to predict CKD progression.
*The signature will be tested in those with no CKD, early CKD and advanced CKD at baseline.
*The primary endpoint will be kidney failure. Incident CKD will be an additional endpoint in those with no CKD at baseline.
3. Identify protein biomarkers predictive of CV events in CKD patients.
*We will analyse those with early and advanced CKD separately.
*Olink data will be integrated with other UKB data linked to CV risk including troponin, cystatin C and lipid profiles.
*The primary endpoint will be major adverse CV events (composite of myocardial infarction, stroke and CV death) and we will adjust for established risk factors of CV disease.
Time-to-event analysis (Cox Proportional Hazards) will be used to determine if biomarkers predict outcomes and to develop outcome models. Model performance will be evaluated by calculating the c-index and area under the curve.
