Abstract 4364012: Machine Learning-Enabled Papillary Muscle Fibrosis Assessment in 51,000 Individuals Reveals Independent Links to Cardiac Structure, Function, and Disease and Identifies Genetic Susceptibility Loci

V Victor Nauffal (Brigham and Women's Hospital, Boston, Massachusetts, United States) D Danielle Pace (Broad Institute, Cambridge, Massachusetts, United States) A Aadhi Balasubramanian (Broad Institute, Cambridge, Massachusetts, United States) J Jad Kassir (Broad Institute, Cambridge, Massachusetts, United States) K Katherine Danik (Broad Institute, Cambridge, Massachusetts, United States) S Sam Friedman (Broad Institute, Cambridge, Massachusetts, United States) B Bridget Simonson (Broad Institute, Cambridge, Massachusetts, United States) M Michael Chilazi (Broad Institute, Cambridge, Massachusetts, United States) M Mahnaz Maddah R Raymond Kwong (Department of Medicine, Brigham and Women’s Hospital, Boston) P Patrick Ellinor (The Broad Institute, Cambridge, Massachusetts, United States)

Abstract

Background: Mitral valve prolapse (MVP) affects 1–3% of the population and is linked to sudden cardiac death (SCD). Papillary muscle (PM) fibrosis, detectable by cardiac magnetic resonance imaging (MRI), is a recognized risk factor for SCD in MVP. However, the prevalence, clinical relevance, and genetic underpinnings of PM fibrosis in the general population—beyond the context of MVP—remain poorly understood. We hypothesized that PM T1, a measure of interstitial fibrosis, is associated with cardiovascular (CV) disease independent of left ventricular (LV) myocardial fibrosis and has a unique genetic architecture. Methods: We trained a deep learning model to segment PMs using 447 manually labelled cardiac T1 maps in the UK Biobank. In a test set (n=49), we demonstrated excellent correlation between model- and manually-derived PM T1 (r=0.94, 95% CI 0.89-0.97). We then applied the model to our full dataset (n=51,316) to segment PMs and measure PM T1 ( Fig. 1 ). Using multivariable models, adjusting for age, sex, body mass index, and LV T1, we examined the association of PM T1, independent of LV myocardial fibrosis, with relevant prevalent CV diseases and MRI measures of atrial/ventricular structure and function. Lastly, we performed a genome-wide association study (GWAS) of PM T1 across 9,855,505 imputed common variants. Results: Mean age was 65.4 ± 7.7 years and 51.2% were women. PM T1 was 83.6 ms (95% CI 82.9-84.1 ms) higher than LV T1, with mean values of 1,008±57.9 ms and 924.8±34.6 ms, respectively. After adjustment for LV T1, PM T1 (/100 ms) was significantly associated with a 39%, 22%, 31% and 43% increase in the odds of prevalent MVP/mitral regurgitation, heart failure, atrial fibrillation and ventricular arrhythmias, respectively ( Fig. 2a ). PM T1 was also independently associated with increased atrial and ventricular volumes and lower atrial and left ventricular ejection fraction ( Fig. 2b ). In our GWAS, we identified 6 genome-wide significant loci associated with PM T1 implicating genes linked to MVP ( DIRC3/TNS1 and TBX3 ), mitral annular diameter ( GOSR2 ), arrhythmogenic cardiomyopathy ( PLN ), cardiac conduction/arrhythmias ( NFIA ), and cardiac hypertrophy ( FOXO1 ) ( Fig. 3 ). Conclusions: In this first large-scale study of PM fibrosis, we highlight PM T1 as an imaging biomarker independently associated with CV disease and adverse cardiac remodeling. Furthermore, our genetic analysis yields new insights into biologically relevant pathways underlying PM fibrosis.

Article Details

Journal Circulation
Volume / Issue Vol. 152, Issue Suppl_3
Published November 04, 2025
ISSN 0009-7322
Publisher Lippincott Williams & Wilkins

Journal Info

Circulation

Lippincott Williams & Wilkins

ISSN: 0009-7322 Health Sciences

Authors (11)

V

Victor Nauffal

Brigham and Women's Hospital, Boston, Massachusetts, United States

D

Danielle Pace

Broad Institute, Cambridge, Massachusetts, United States

A

Aadhi Balasubramanian

Broad Institute, Cambridge, Massachusetts, United States

J

Jad Kassir

Broad Institute, Cambridge, Massachusetts, United States

K

Katherine Danik

Broad Institute, Cambridge, Massachusetts, United States

S

Sam Friedman

Broad Institute, Cambridge, Massachusetts, United States

B

Bridget Simonson

Broad Institute, Cambridge, Massachusetts, United States

M

Michael Chilazi

Broad Institute, Cambridge, Massachusetts, United States

M

Mahnaz Maddah

R

Raymond Kwong

Department of Medicine, Brigham and Women’s Hospital, Boston

P

Patrick Ellinor

The Broad Institute, Cambridge, Massachusetts, United States