Eoin O'Sullivan

Dr Eoin O’Sullivan is a consultant nephrologist and clinician scientist whose research focuses on understanding kidney disease through the integration of diverse biological, clinical and population-level data.

His work spans molecular biology and multi-omics, large-scale clinical datasets, geospatial epidemiology and environmental health. A central theme is connecting information across different biological and population scales to better understand disease mechanisms, identify unmet need and develop more targeted approaches to healthcare.

His research interests include kidney ageing and senescence, autoimmune kidney disease, dialysis outcomes, and the influence of geography and environmental exposures on kidney health. An emerging focus is cardiovascular–kidney–metabolic (CKM) health, exploring how linked clinical, pathology, genomic and population datasets can identify overlapping disease burden, improve risk stratification and inform precision screening.

Through this work, he aims to translate insights from diverse datasets into practical, equitable and data-driven models of care that bridge hospital, primary care and community settings.

Alongside his research, he practises clinical nephrology at Metro North Health, with clinical leadership roles in haemodialysis and autoimmune kidney disease.

In addition to his research, Dr. O'Sullivan is actively involved in medical education and mentorship, involved with ANZSN Educational an scientific comittees, with national level educational experiences both in Australia and in UK.

Current Research Focus

Theme 1: Kidney Ageing and Cellular Senescence

Investigating the molecular mechanisms of kidney ageing and cellular senescence using single-cell RNA sequencing and multi-omics approaches, with a focus on identifying novel therapeutic targets and translating biological discoveries into clinical interventions.

Theme 2: Translational Data Science and Precision Medicine

Integrating diverse molecular, genomic, pathology and clinical datasets to better understand kidney disease. Applying advanced statistical methods, machine learning and artificial intelligence to improve disease classification, prognostication, biomarker discovery and clinical decision support.

Theme 3: Population Health and Cardiovascular–Kidney–Metabolic Disease

Using large-scale linked health datasets, geospatial epidemiology and environmental exposure data to investigate patterns of kidney and cardiovascular–kidney–metabolic (CKM) disease. Developing data-driven approaches to identify unmet need, inform precision screening and improve equitable access to healthcare through integrated models of care.