Summary
The objective of ELIMINATE-CKD is to demonstrate that combining measures of kidney structure and function will strongly enhance detection of chronic kidney disease (CKD). To reach this objective, I will develop a new method to quantify the number of nephrons, the smallest functional units in the kidney.
Rationale:
- CKD affects ~10% of the global population and causes 2.6 million deaths/year. CKD-associated costs (140 billion euros/year in Europe) exceed diabetes and cancer. Limitations in CKD detection impede the success of emerging therapies.
- Current CKD definition is based on creatinine-based estimated glomerular filtration rate, which is often inaccurate due to interindividual differences in body composition and compensation for early kidney damage as remaining nephrons increase their function.
- Using a unique combination of multi-omics and machine learning approaches in human cohorts and innovative animal models, I will dissect pathways that drive nephron number and enable its quantification.
Concrete objectives are to:
1) Develop a high-throughput method to quantify nephron number in large human cohorts and a novel rat model of gradual nephron reduction
2) Construct and validate unbiased and hypothesis-driven nephron number algorithms based on kidney-derived proteins, lipids and metabolites in blood and urine
3) Demonstrate that nephron number algorithms enable earlier and more accurate CKD detection
4) Provide proof of principle that nephron number-triggered therapy prevents or halts CKD
Impact:
This project will lead to a new CKD classification, based on both structure and function. Identification of mechanisms driving nephron number will boost drug development and regenerative medicine towards nephron-preserving therapies. Finally, the concept of structure-function relationship is relevant in many other organs (brain, heart). My passion to beat kidney disease by innovative diagnostics drives me towards my ultimate goal: to eliminate CKD.
Rationale:
- CKD affects ~10% of the global population and causes 2.6 million deaths/year. CKD-associated costs (140 billion euros/year in Europe) exceed diabetes and cancer. Limitations in CKD detection impede the success of emerging therapies.
- Current CKD definition is based on creatinine-based estimated glomerular filtration rate, which is often inaccurate due to interindividual differences in body composition and compensation for early kidney damage as remaining nephrons increase their function.
- Using a unique combination of multi-omics and machine learning approaches in human cohorts and innovative animal models, I will dissect pathways that drive nephron number and enable its quantification.
Concrete objectives are to:
1) Develop a high-throughput method to quantify nephron number in large human cohorts and a novel rat model of gradual nephron reduction
2) Construct and validate unbiased and hypothesis-driven nephron number algorithms based on kidney-derived proteins, lipids and metabolites in blood and urine
3) Demonstrate that nephron number algorithms enable earlier and more accurate CKD detection
4) Provide proof of principle that nephron number-triggered therapy prevents or halts CKD
Impact:
This project will lead to a new CKD classification, based on both structure and function. Identification of mechanisms driving nephron number will boost drug development and regenerative medicine towards nephron-preserving therapies. Finally, the concept of structure-function relationship is relevant in many other organs (brain, heart). My passion to beat kidney disease by innovative diagnostics drives me towards my ultimate goal: to eliminate CKD.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101125516 |
| Start date: | 01-09-2024 |
| End date: | 31-08-2029 |
| Total budget - Public funding: | 2 000 000,00 Euro - 2 000 000,00 Euro |
Cordis data
Original description
The objective of ELIMINATE-CKD is to demonstrate that combining measures of kidney structure and function will strongly enhance detection of chronic kidney disease (CKD). To reach this objective, I will develop a new method to quantify the number of nephrons, the smallest functional units in the kidney.Rationale:
- CKD affects ~10% of the global population and causes 2.6 million deaths/year. CKD-associated costs (140 billion euros/year in Europe) exceed diabetes and cancer. Limitations in CKD detection impede the success of emerging therapies.
- Current CKD definition is based on creatinine-based estimated glomerular filtration rate, which is often inaccurate due to interindividual differences in body composition and compensation for early kidney damage as remaining nephrons increase their function.
- Using a unique combination of multi-omics and machine learning approaches in human cohorts and innovative animal models, I will dissect pathways that drive nephron number and enable its quantification.
Concrete objectives are to:
1) Develop a high-throughput method to quantify nephron number in large human cohorts and a novel rat model of gradual nephron reduction
2) Construct and validate unbiased and hypothesis-driven nephron number algorithms based on kidney-derived proteins, lipids and metabolites in blood and urine
3) Demonstrate that nephron number algorithms enable earlier and more accurate CKD detection
4) Provide proof of principle that nephron number-triggered therapy prevents or halts CKD
Impact:
This project will lead to a new CKD classification, based on both structure and function. Identification of mechanisms driving nephron number will boost drug development and regenerative medicine towards nephron-preserving therapies. Finally, the concept of structure-function relationship is relevant in many other organs (brain, heart). My passion to beat kidney disease by innovative diagnostics drives me towards my ultimate goal: to eliminate CKD.
Status
SIGNEDCall topic
ERC-2023-COGUpdate Date
25-11-2024
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