Personalized Diabetic Kidney Disease Care
Looking to advance your understanding of diabetic kidney disease? Check out this new article published in Nephron journal on a Systems Nephrology Approach to Diabetic Kidney Disease Research and Practice, which provides valuable insights into the diagnosis and treatment of DKD using a personalized and targeted approach.
We provide an insight into the key findings! Let's go.
Integration of data from multiple model systems (in vitro, animal models, and patients) and from diverse domains (clinical phenotypic, imaging, histopathological/ultrastructural, and molecular omics) offers potential to create a precision medicine approach to DKD care wherein the right treatments are offered to the right patients at the right time.
At a glance
A systems nephrology approach can help in resolving the heterogeneity of DKD by identifying diagnostic and prognostic biomarkers of DKD, and by stratifying patients based on their molecular characteristics.
Omics modalities such as genome, epigenome, transcriptome, proteome, metabolome, lipidome, and microbiome assessment across in vitro, preclinical, and human studies are key technologies used in a systems nephrology approach.
Machine and deep learning are also used for automated feature extraction in imaging and histopathological studies, clustering and multivariate outcome analyses, and classification of patient response according to molecular, imaging, histopathological, or other biomarkers.
Multiple model systems, including in vitro, animal models, and human consortia and biobanks, are necessary for a comprehensive systems nephrology approach to advance DKD care.
The current approach to diagnosing and staging DKD relies on serial assessment of a limited number of biochemical parameters, such as estimated glomerular filtration rate (eGFR) derived from serum creatinine and the urinary albumin-to-creatinine ratio (uACR), to diagnose and stage the disease. However, both eGFR and uACR have particular limitations in the identification of early DKD.
A Systems Nephrology Approach to Diabetic Kidney Disease Research and Practice
Diabetic kidney disease (DKD) is a serious complication of diabetes mellitus and the leading cause of end-stage kidney disease (ESKD) worldwide. Despite advances in treatment, DKD remains a major public health challenge, with significant morbidity and mortality.
Current approaches to DKD management are based on the control of blood sugar and blood pressure, but these measures are not always sufficient to prevent disease progression. There is a need for new and more effective treatments for DKD, and a personalized approach to care that takes into account the individual patient's molecular and clinical characteristics.
A systems nephrology approach to DKD research and practice has the potential to address these challenges. This approach integrates data from multiple sources, including clinical phenotypic data, imaging biomarkers, histopathological data, and molecular omics data, to gain a deeper understanding of the complex pathophysiology of DKD and to identify new targets for therapy.
The promise of personalised DKD care
As well as identifying diagnostic and prognostic biomarkers of DKD, a systems nephrology approach may facilitate the translation of novel therapies for DKD. This may be achieved through the identification of novel drug targets or indeed by drug repurposing using databases of perturbagen-driven gene expression profiles, such as Connectivity Map.
A systems nephrology approach to DKD care could lead to the following benefits:
Earlier diagnosis and intervention: By identifying biomarkers of early DKD, clinicians could intervene earlier to prevent disease progression.
More accurate risk stratification: By understanding the molecular and clinical characteristics of patients with DKD, clinicians could more accurately stratify patients according to their risk of developing complications.
More targeted and personalized treatment: By matching patients to the most effective treatments based on their molecular and clinical characteristics, clinicians could improve outcomes and reduce side effects.
Development of new and more effective treatments: By identifying new drug targets and repurposing existing drugs, systems nephrology research could lead to the development of more effective and less toxic treatments for DKD.
Key technologies and techniques
A systems nephrology approach to DKD research and practice makes use of a range of key technologies and techniques, including:
Omics technologies: Omics technologies allow for the high-throughput analysis of large datasets of molecular data, such as gene expression, protein levels, and metabolite levels. This data can be used to identify biomarkers of DKD, to understand the molecular mechanisms of disease progression, and to develop new therapeutic strategies.
Imaging biomarkers: Imaging biomarkers, such as ultrasound and MRI, can be used to assess kidney structure and function. This information can be used to diagnose DKD, to monitor disease progression, and to assess treatment response.
Histopathological data: Histopathological data, obtained from kidney biopsies, can provide detailed information about the cellular and structural changes that occur in DKD. This information can be used to diagnose DKD, to determine the severity of disease, and to identify potential targets for therapy.
Machine learning and deep learning: Machine learning and deep learning algorithms can be used to analyze large datasets of omics, imaging, and histopathological data to identify patterns and associations that would be difficult to detect by human eye. This information can be used to develop biomarkers, to predict disease progression, and to identify new therapeutic targets.
Challenges and opportunities
While a systems nephrology approach to DKD research and practice has the potential to revolutionize the management of this disease, there are some challenges that need to be addressed.
One challenge is the complexity of DKD. DKD is a complex disease with multiple contributing factors, including genetic susceptibility, environmental exposures, and metabolic derangements. This complexity makes it difficult to develop a comprehensive understanding of the disease and to identify effective treatments.
Another challenge is the need for large datasets. To develop and validate predictive models and to identify new drug targets, large datasets of omics, imaging, and histopathological data are needed. These datasets can be difficult to collect and to manage.
Despite these challenges, there are a number of opportunities to advance a systems nephrology approach to DKD research and practice. Technological advances, such as the development of new omics technologies and machine learning algorithms, are making it possible to analyze large datasets of complex data in new ways. Additionally, there is a growing recognition of the importance of personalized medicine, and DKD is an ideal disease for this approach.
Future directions
The future of systems nephrology is bright, with the potential to revolutionize the diagnosis, prognosis, and treatment of DKD. Some key areas for future research include:
Development of model systems that reliably recapitulate progressive and advanced human DKD. This would allow for the preclinical testing of new therapies in a more realistic setting. One promising approach is to develop 3D kidney organoids from induced pluripotent stem cells (iPSCs) from patients with DKD. These organoids can be cultured to recapitulate the key cellular and structural features of human kidney tissue, and they can be used to study disease mechanisms and to test new therapies.
Identification of biomarkers that predict response to RAAS blockade, SGLT2is, and other emerging disease-modifying treatments for DKD. This would enable clinicians to personalize treatment and improve outcomes. Machine learning and deep learning algorithms can be used to analyze large datasets of patient data to identify patterns and associations that can be used to develop predictive biomarkers.
Delineation of mechanisms of DKD progression in the face of combined therapy with RAAS blockade and an SGLT2i, the current backbone of treatment. This could lead to the identification of novel targets for therapy and the development of more effective combination therapies. One approach is to use single-cell RNA sequencing (scRNA-seq) to analyze kidney tissue from patients with DKD who are receiving combined RAAS blockade and SGLT2i therapy. This would allow for the identification of specific cell populations and molecular pathways that are associated with disease progression.
Integration of data from multiple model systems and domains. This is essential to capture the complex and heterogeneous nature of DKD. New computational methods are being developed to integrate data from different sources, such as omics data, imaging data, and clinical data. This will allow for the development of more comprehensive and informative models of DKD.
In addition to these research priorities, there is a need to translate the findings of systems nephrology research into clinical practice. This will require collaboration between researchers, clinicians, and industry. For example, new diagnostic tests and risk assessment tools based on systems nephrology research could be developed and deployed in clinical settings. Additionally, new therapeutic strategies based on systems nephrology research could be tested in clinical trials.
The implementation of a systems nephrology approach to DKD care will take time and effort, but the potential benefits are enormous. By understanding the complex molecular basis of DKD and developing personalized treatment strategies, we can hope to improve the lives of millions of people living with this disease.
Conclusion
The current one-size-fits-all approach to DKD care ignores the clinically apparent heterogeneity in disease prognosis and treatment-responsiveness.
A systems nephrology approach to DKD research and practice has the potential to revolutionize the management of this disease. By integrating data from multiple sources, systems nephrology can help us to better understand the molecular mechanisms of DKD, to identify new biomarkers, and to develop more targeted and effective treatments. While there are some challenges that need to be addressed, the future of systems nephrology is bright.
Omics-Driven Systems Nephrology
Molecular omics has played a major role in advancing our understanding of DKD pathogenesis and heterogeneity, leading to the identification of new biomarkers and treatment targets. The integration of data from multiple omics modalities, including genome, epigenome, transcriptome, proteome, metabolome, lipidome, and microbiome, has revealed consistent insights into DKD pathogenesis across in vitro, preclinical, and human studies.
For example, omics analyses have identified specific genetic variants, epigenetic modifications, and transcriptomic signatures that are associated with DKD susceptibility and progression. Omics has also been used to identify novel protein biomarkers and metabolic pathways that are involved in DKD pathogenesis. This information is being used to develop new diagnostic tests, risk assessment tools, and therapeutic strategies for DKD.
Here are some specific examples of how omics has been used to advance our understanding of DKD:
Genome-wide association studies (GWAS) have identified hundreds of genetic variants that are associated with DKD risk. These variants are located in genes that are involved in a variety of biological processes, including kidney development, metabolism, and inflammation.
Epigenomic studies have shown that DNA methylation and histone modifications play a role in DKD pathogenesis. For example, DNA methylation of the promoter region of the podocin gene has been shown to be associated with DKD progression.
Transcriptomic studies have identified gene expression signatures that are associated with DKD susceptibility and progression. For example, a recent study identified a 10-gene signature that can predict the risk of developing DKD in patients with prediabetes.
Proteomic studies have identified novel protein biomarkers of DKD. For example, a recent study identified a panel of five proteins that can predict the risk of developing DKD complications in patients with type 2 diabetes.
Metabolomic studies have identified metabolic pathways that are involved in DKD pathogenesis. For example, a recent study showed that increased levels of circulating branched-chain amino acids are associated with DKD progression.
The integration of data from multiple omics modalities is essential to capture the complex and heterogeneous nature of DKD. By understanding the molecular basis of DKD, we can develop more targeted and effective treatments for this devastating disease.