Mechanism-Guided Risk Prediction and Therapy for Aortic Stenosis Through an Lp(a)–Inflammation Two-Hit Framework
Description: Calcific aortic valve disease (CAVD) and aortic stenosis (AS) have no approved disease-modifying pharmacotherapy; care remains surveillance until valve replacement is indicated. Human genetics establish lipoprotein(a) (Lp(a)) as a leading causal risk factor, yet Lp(a) alone does not explain inter-individual variability or why lipid-lowering strategies (statins) fail to halt disease. We propose an Lp(a)–inflammation two-hit framework in which sustained elevated Lp(a) primes the valve toward a pro-calcific state, while inflammatory burden provides a second hit that accelerates osteogenic remodeling and hemodynamic obstruction. To anchor inflammation in a genetically defined exposure, we use clonal hematopoiesis of indeterminate potential (CHIP) as the primary inflammatory modifier and extend to broader inflammatory context (immune-disease history, circulating molecular signatures, and germline inflammatory genetic burden), explicitly testing which inflammatory signals potentiate Lp(a)-primed remodeling versus act independently across the full Lp(a) distribution. In Aim 1, we will quantify and validate Lp(a)×inflammation effects on continuous AS-related imaging-derived phenotypes (AS-related IDPs)—preclinical measures of valve hemodynamics/structure and downstream LV/LA remodeling—using a staged three-cohort design: UK Biobank cardiac MRI for discovery, TOPMed echocardiography/CT for external validation and expansion, and a prospective UPMC transthoracic echocardiogram/CT angiography cohort for clinical calibration. Within Lp(a)/CHIP strata, we will define resilience versus vulnerability and build machine-learning models that predict AS-related IDPs and preclinical risk from demographic and blood-based features without requiring imaging. In Aim 2, we will identify and classify circulating effectors as mediators, context-dependent amplifiers/buffers, or parallel pathways linked to AS-related IDPs; prioritize actionable targets using genetic and pathway evidence; and nominate preventive therapies via deep learning–based repurposing of FDA-approved drugs. Project 1 anchors the Center’s preclinical risk prediction and therapy nomination, synergizing with Project 2’s valve-on-a-chip mechanobiological “second hit” models and Project 3’s VIC-focused therapeutic mechanism testing by defining high-risk Lp(a)–inflammation strata and prioritizing circulating effectors/targets that enable blood-based stratification and mechanism-matched preventive drug shortlists.
Funder: American Heart Association
Amount: $897,400
Funding type: research
Funder scheme: Strategically Focused Research Network
Start date: 2026-04-01
End date: 2030-03-31
Investigators: Satoshi Okawa, Satoshi Okawa
Institution: University of Pittsburgh
Funded outputs count: 0
Funder award ID: 26SFRNPVHD1609644
OpenAlex ID: G381060044