King's College London

The APPLE-CKD study aims to investigate the genetic, epigenetic, and environmental factors contributing to chronic kidney disease (CKD) in people of African ancestry living in the UK. Specifically, it seeks to:

• Determine whether epigenetic DNA methylation patterns in the APOL1 gene promoter in renal tissue are reflected in peripheral blood mononuclear cells (PBMCs) and urine.
• Explore how APOL1 genetics and epigenetics influence kidney cell function.
• Develop a clinical, genetic, epigenetic, and inflammatory signature for CKD risk stratification and validation.

Participant Recruitment:

• CKD cases (≥720) and healthy controls (≥220), all self-reported African ancestry, recruited from NHS Trusts and community settings.
• Deceased kidney donors' renal tissue samples obtained from the Quality in Organ Donation (QUOD) Initiative.

Sample Collection:

• Blood and urine samples from all participants.
• Kidney tissue from CKD patients, nephrectomy samples, and deceased donor biopsies.

Laboratory Analyses: 

• DNA methylation profiling of the APOL1 gene promoter in kidney, blood, and urine samples.
• Inflammatory marker analysis to assess the role of systemic inflammation.
• Inducible pluripotent stem cell (iPSC) differentiation to study APOL1 gene expression in kidney cells.

Data Analysis:

• Statistical modeling to compare APOL1 methylation patterns across tissue types.
• Development of a CKD risk prediction model based on genetic and epigenetic data.

• Type: Multicenter observational cohort study.
• Duration: 36 months

Study Groups:

• CKD cases: Individuals with diagnosed CKD based on KDIGO criteria.
• Healthy controls: No evidence of CKD, matched by age and ethnicity.
• Deceased donor samples: Kidney tissue for comparison with living participants.

Recruitment Sites:

• King’s College Hospital NHS Foundation Trust
• Guy’s and St Thomas’ NHS Foundation Trust
• Barts and The Royal London Hospital

This study aims to advance understanding of CKD in African-ancestry populations, improve risk prediction, and identify potential therapeutic targets.