| Title: | Discovery and validation of blood biomarkers from proteomics as predictors of kidney disease progression |
| Lead Institution: | University of Pennsylvania |
| Principal investigator: |
Application 273810
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About
Chronic kidney disease (CKD) is a growing public health problem worldwide, affecting millions of people and placing a significant burden on healthcare systems. This research project aims to identify and validate specific proteins in the blood (known as proteomic biomarkers) and genetic variants that can predict the progression of kidney disease. By leveraging the extensive data available in the UK Biobank, a large-scale biomedical database, we hope to improve the early detection and management of CKD. Our study will analyze the UK Biobank's proteomic and genetic datasets to discover novel biomarkers and genetic variants called protein quantitative trait loci (pQTLs) that are associated with kidney disease progression. We will also validate the predictive performance of previously identified biomarkers and pQTLs using this comprehensive dataset. Advanced computational methods, such as machine learning and neural networks, will be employed to develop accurate predictive models that incorporate these biomarkers and genetic variants. By investigating the relationship between genetic variants and protein expression levels, we aim to gain insights into the biological mechanisms underlying kidney disease progression. This knowledge could potentially lead to the identification of new therapeutic targets for CKD. The project is expected to span a duration of three years, during which time we will work closely with the UK Biobank to ensure the secure handling and analysis of participant data while adhering to strict confidentiality policies. The potential public health impact of this research is significant. Identifying accurate predictive biomarkers and genetic variants could enable the early detection of individuals at high risk of CKD progression, allowing for timely intervention and personalized treatment strategies. This, in turn, may slow disease progression, improve patient outcomes, and reduce the overall burden of CKD on healthcare systems. Furthermore, our findings could contribute to the development of novel diagnostic tools and targeted therapies for kidney disease. In summary, this project aims to harness the power of the UK Biobank's proteomic and genetic data to identify and validate predictive biomarkers and genetic variants in kidney disease progression. The outcomes of this research have the potential to significantly advance our understanding of CKD and improve the prevention, diagnosis, and treatment of this debilitating condition, ultimately benefiting public health in the UK and beyond.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 17996 | Proteomic risk score for early prediction of kidney disease progression in individuals with APOL1 high-risk genotypes | Chenyu Li (+23) | 2026 | Nature Medicine |
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