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Deep learning to assess the role of early renal fibrosis on cardiac remodeling

Award Number: K08HL179467
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 09/04/2026
PERIOD OF PERFORMANCE END DATE: 04/30/2031

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Deep learning to assess the role of early renal fibrosis on cardiac remodeling - ABSTRACT Cardiovascular-Kidney-Metabolic (CKM) Syndrome is a major driver of cardiovascular disease, affecting hundreds of millions of Americans and costing over $500 billion annually in the United States. Multi-organ fibrosis is a central process in CKM, and individuals with renal fibrosis experience markedly higher rates of heart failure, arrhythmia, and cardiovascular death. Remarkably, therapies that act primarily in the kidney— such as SGLT2 inhibitors and mineralocorticoid receptor antagonists—produce substantial improvements in heart failure outcomes, underscoring a powerful kidney-heart axis. Still, the molecular mechanisms linking early kidney fibrosis to cardiac injury remain poorly defined. Elucidating how early kidney fibrosis drives cardiac disease and remodeling could identify novel therapeutic targets for cardiovascular disease. My goal in seeking a K08 Mentored Clinical Scientist Research Career Development Award is to gain the skills and training necessary to make major advances in defining how kidney fibrosis contributes to heart disease and cardiac remodeling. Advances in deep learning make it feasible to measure kidney fibrosis in a large population, capturing an earlier stage of fibrosis using magnetic resonance imaging (MRI). First, I will develop metrics of early kidney fibrosis using T1-mapped MRI images and evaluate their relationship to cardiovascular disease. Second, I will identify common and rare genetic variants associated with kidney fibrosis to dissect the shared mechanisms with myocardial fibrosis. Third, I will test whether early kidney fibrosis independently predicts future heart disease and cardiac remodeling longitudinally. To address these aims, I will combine deep learning, genome sequencing analysis, and longitudinal cardiac risk modeling. My long-term goal is to uncover metabolic pathways driving cardiovascular pathology to enable development of new therapeutic targets. My graduate training provided me with important experience in metabolic pathophysiology and statistical genetics, however my focus was on cardiac disease within endocrine disorders. I am now directing my efforts to studying how metabolic processes affect cardiovascular disease generally—an area of interest that emerged from my clinical endocrine training. My research mentor, Dr. Patrick Ellinor, has a long record of impactful discoveries using cutting-edge machine learning tools and genetic approaches. My training plan is specifically designed to provide me with mentorship and training in machine learning, MRI imaging, genome sequencing analysis, and longitudinal risk modeling of cardiac diseases. Completing the research and obtaining the skillsets outlined in this proposal will thoroughly prepare me to obtain an R01 or equivalent funding and begin my career as an independent investigator studying the metabolic processes acting in CKM and heart disease.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $170,640 )
2026	2026	THE GENERAL HOSPITAL CORPORATION	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Cardiovascular Diseases Research	000	1	9/4/2026	NEW	$170,640
														Subtotal = $170,640

Grand Total All Awards = $170,640

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Deep learning to assess the role of early renal fibrosis on cardiac remodeling

Award Number: K08HL179467

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 09/04/2026

PERIOD OF PERFORMANCE END DATE: 04/30/2031

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