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

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2021 · $770,230

## Abstract

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.

## Key facts

- **NIH application ID:** 10101075
- **Project number:** 1R01HL155915-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Ron Do
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $770,230
- **Award type:** 1
- **Project period:** 2021-02-15 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10101075

## Citation

> US National Institutes of Health, RePORTER application 10101075, Elucidating hereditary transthyretin-mediated heart failure risk using machine learning, polygenic risk and recall by genotype approaches in African ancestry individuals (1R01HL155915-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10101075. Licensed CC0.

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