A new genetic study on the causes of chronic kidney disease that combined multiple health measurements has led to a more comprehensive view of kidney function and potential for targeted therapies, QUT and UQ researchers have found.
- Chronic kidney disease (CKD) is complex with diverse causes yet genetic studies often rely on only one or two biomarkers to investigate it.
- Novel approach to studying underlying genetic causes of CKD found multiple composite traits that outperformed traditional single-marker methods
- The researchers' novel approach combined 21-kidney related health measures into two million composite traits and found nearly 50,000 composite traits that were significantly more effective at identifying CKD than any single biomarker.
Dr Kim Ngan Tran, from QUT's School of Biomedical Sciences and the Centre for Genomics and Personalised Health, said chronic kidney disease (CKD) was an umbrella term for a range of kidney conditions with different causes that led to a gradual loss of kidney function.
The QUT researchers - Dr Tran, Dr Heidi Sutherland, Distinguished Professor Lyn Griffiths and Associate Professor Rodney Lea, all from the Centre for Genomics and Personalised Health, with Professor Andrew Mallet from The University of Queensland, published their findings in PLOS Genetics.
"Chronic kidney disease (CKD) can result from diverse underlying causes, such as diabetes, high blood pressure, infections, and lifestyle factors," Dr Tran said.
"This complexity makes it difficult to capture the full picture of kidney health using only a single biomarker, such as the commonly used eGFR, to assess kidney function, and therefore may miss important details.
"This incomplete understanding of the genetics for different CKD subtypes has hindered the identification of drug targets to treat the various subtypes."
The researchers' novel approach combined 21 kidney-related health measures, into two million new composite traits from a dataset of more than 300,000 people in the UK Biobank to gain a greater understanding of how the kidney functions.
"This approach differs from traditional multivariate methods that use predefined sets of traits, in that the algorithm, which we designed and applied, systematically explored and selected optimal biomarker combinations, making it a distinct and more flexible approach," Dr Tran said.
"We found around 50,000 composite traits outperformed eGFR in identifying people with CKD.
"Of these, one composite trait proved to be significantly more effective at identifying CKD than any single measurement and resulted from a combination of albumin, cystatin C, eGFR, gamma-glutamyltransferase, HbA1c, low-density lipoprotein, and microalbuminuria.
"These more informative traits enabled the discovery of genetic signals that traditional methods had missed.
"For example, we identified a variant in the SH2B3 gene, which was previously detected only in large-scale studies involving more than a million individuals, as significantly associated with kidney function using our composite traits.
"This locus was not detected using traditional single biomarkers like eGFR in our dataset but emerged consistently across many of our high-performing composite phenotypes, highlighting the power of our approach to uncover important genetic associations even in moderately sized cohorts.
"This demonstrates the benefit of using a more efficient multi-marker method applied to a much smaller dataset."
Dr Tran said the research team confirmed their findings in an independent group of Irish participants.
"This study demonstrates the value of an exhaustive yet interpretable multi-phenotype approach to understanding the genetics of CKD and could be applied across large biobanks, smaller deeply phenotyped cohorts, and potentially extended to uncover genetic signals in other complex diseases.
The study, New composite phenotypes enhance chronic kidney disease classification and genetic associations was published in PLOS Genetics.
(Main Image, from left: Dr Heidi Sutherland, Dr Kim Ngan Tran, inset top: Distinguished Professor Lyn Griffiths, Associate Professor Rodney Lea).
