Enhanced Detection of Genetic Factors in Chronic Kidney Disease Using Multi-Marker Approach

Recent research from Queensland University of Technology (QUT) and the University of Queensland has demonstrated that a novel multi-marker method significantly improves the identification of genetic factors associated with chronic kidney disease (CKD). This comprehensive study leverages the integration of multiple health measurements to better understand kidney function and aid in the development of targeted therapies. CKD encompasses various kidney conditions with different causes, including diabetes, high blood pressure, infections, and lifestyle factors, leading to a gradual deterioration of kidney function.

Traditional assessments primarily rely on single biomarkers, such as estimated glomerular filtration rate (eGFR), which often fail to capture the full complexity of kidney health. Recognizing these limitations, the research team developed an innovative approach that combines 21 kidney-related health measures into two million composite traits, analyzing data from over 300,000 individuals in the UK Biobank.

This method differs from conventional multivariate approaches by systematically exploring and selecting optimal combinations of biomarkers, resulting in more powerful traits for disease detection. Notably, approximately 50,000 of these composite traits outperformed eGFR in identifying CKD, with one trait combining albumin, cystatin C, eGFR, gamma-glutamyltransferase, HbA1c, low-density lipoprotein, and microalbuminuria showing particularly high effectiveness.

This enhanced trait sensitivity enabled researchers to uncover genetic signals missed by traditional metrics. For example, they identified a variant in the SH2B3 gene, previously linked to kidney function in larger studies, which was not detectable with single biomarkers in this dataset, confirming the approach's potential to reveal critical genetic insights even in moderate-sized cohorts.

Importantly, these findings were validated in an independent group of Irish participants, underscoring the robustness of the method. This research underscores that employing such comprehensive, interpretable multi-phenotype strategies can significantly advance the understanding of CKD genetics and could be adapted for use across various biobank resources and complex diseases.