Precision medicine in nephrology

Sullivan and Susztak examine the process of translating data on genetic variants associated with common kidney diseases into information about the underlying disease mechanisms. The authors propose that identification of causal variants, genetic regulatory mechanisms, target-gene products and disease-associated phenotypes is crucial to this process.

An increasing body of evidence suggests that genomic disorders and monogenic aetiologies contribute meaningfully to seemingly complex forms of chronic kidney disease (CKD). This Review describes rare genetic causes of CKD and the genetic and phenotypic complexity of this group of disorders, and discusses novel approaches to help to address the challenges posed by the complexity of CKD.

Classification of kidney diseases according to their molecular mechanisms has potential to improve patient outcomes through the identification of targeted therapeutic approaches. This Review provides an overview of the ways in which omics and other data types can be integrated to enhance our understanding of the mechanisms underlying kidney function and failure.

Developments in digital pathology and computational image analysis have the potential to identify new disease mechanisms, improve disease classification and prognostication, and ultimately aid the identification of targeted therapies. In this Review, the authors provide an outline of the digital ecosystem in nephropathology and describe potential applications and challenges associated with the emerging armamentarium of technologies for image analysis.

Ontologies are powerful tools that facilitate the integration of large and disparate data sets. Here, researchers from the Kidney Precision Medicine Project provide an introduction to ontologies, including those developed by the consortium, describing how these will be used to improve the annotation of kidney-relevant data, eventually leading to new definitions of kidney disease in support of precision medicine.

Insights into the heterogeneity of processes underlying kidney diseases and their relationship with disease phenotype could redefine classifications of disease and improve patient outcomes.

The increasing availability of sequencing has accelerated the discovery of genetic causes of kidney disease, with clear benefits for patients. However, insufficient or contradictory evidence exists for numerous variants that were previously reported to be pathogenic, calling into question some proposed gene–disease associations. Rigorous re-appraisal of evidence is needed to ensure diagnostic accuracy.

Growing genomic knowledge has provided immense insight into the aetiology and mechanisms of kidney diseases but raises ethical issues that risk the successful implementation of genomic medicine. We highlight such issues in two contexts: the return of individual genetic results from nephrology research and preimplantation genetic diagnosis for heritable kidney diseases.

New exposome-based approaches permit omic-scale characterization of the non-genetic contributors to kidney disease. High-resolution mass spectrometry analysis of plasma and urine samples captures a wide range of exogenous and endogenous metabolites that can be used in combination with genetic risk factors to identify new biomarkers of exposure and therapeutic approaches.

The drug development pipeline for kidney diseases is plagued with challenges ranging from an insufficient understanding of disease mechanisms to a lack of robust preclinical models. Bioengineering approaches have the potential to streamline preclinical drug discovery efforts and improve the success of clinical trials for kidney disease.

In this Viewpoint, we asked three individuals who have been affected by kidney failure for their views on the importance of understanding the drivers of kidney disease and, on a personal level, what they hope might be achieved with this information.