Elucidating hereditary transthyretin-mediated heart failure risk using machine learning, polygenic risk and recall by genotype approaches in African ancestry individuals

PROJECT SUMMARY / ABSTRACT Mutations in the Transthyretin (TTR) gene can lead to deposition of abnormal amyloid fibrils in the myocardium, resulting in hereditary transthyretin amyloid cardiomyopathy (hATTR-CM) and leading to heart failure. Targeted therapies for hATTR-CM have recently been developed and have shown to improve mortality

and hospitalization. Recently, we led a study (Journal of American Medical Association, Dec 2019) that showed that the TTR V122I mutation, commonly observed in racial/ethnic minorities (4% in African Americans (AAs) and 1% in Hispanic Americans (HAs)), confers two-fold increased risk of heart failure. Despite this strong effect, only 11%

of V122I carriers with heart failure were appropriately diagnosed with hATTR-CM, suggesting marked underdiagnosis and mis-diagnosis of the disease. We further showed subclinical evidence of echocardiographic derangements in young, asymptomatic V122I carriers, suggesting early signs can occur well before onset of disease.

We propose to extend our prior work by addressing knowledge gaps which are necessary for targeted therapies to attain their full potential. These include: understanding the incomplete penetrance of V122I; identifying V122I carriers in large health care systems where genotyping is not common; and understanding

subclinical disease burden. In Aim 1, we will examine the interplay between a polygenic risk score, which are comprised of millions of single nucleotide variants with small effects, and V122I, a monogenic mutation with a single strong effect, analyzed in conjunction with clinical risk factors on heart failure in in 6,609 AAs and 9,006

HAs in the BioMe biobank and 5,833 AAs in the Penn Medicine Biobank (PMBB). In Aim 2, we will apply machine learning tools to multi-modal electronic health record (EHR) data to identify V122I carriers in ~8 million patients from an electronic health record (EHR) data repository at Mount Sinai. In Aim 3, we will

evaluate subclinical effects of amyloid deposition on cardiac structural/functional traits in young, asymptomatic V122I carriers by recalling V122I carriers for imaging evaluation including research-grade echocardiograms, cardiac magnetic resonance and technetium nuclear scanning. The proposal is innovative because we are utilizing two large diverse ancestry EHR-linked biobanks from

academic health systems (BioMe at Mount Sinai, and PMBB at University of Pennsylvania), along with adopting cutting-edge methods including multi-ethnic polygenic risk scores, and machine learning approaches on multi-modal EHR data. We further propose patient recall based on genotypes and perform deep

phenotyping using comprehensive heart imaging scans. This proposal has the potential to realize the potential of precision medicine for heart failure in racial/ethnic minorities by informing clinical care, population management, risk stratification and clinical trials.