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posted on 15.03.2022, 14:28 authored by Chirag PatelChirag Patel
Abdomen Age Acceleration GWAS

With age, the prevalence of diseases such as fatty liver disease, cirrhosis, and type two diabetes increases. Approaches to both determine abdominal age and identify risk factors for accelerated abdominal age will help delay the onset of these diseases. We build the first abdominal age predictor by training convolutional neural networks to predict abdominal age (or “AbdAge”) from 45,552 liver magnetic resonance images [MRIs] and 36,784 pancreas MRIs (R-Squared=73.3±0.6; mean absolute error=2.94±0.03 years). Attention maps show that the prediction is driven by both liver and pancreas anatomical features, and surrounding organs and tissue. Abdominal aging is a complex trait, partially heritable (h_g2=26.3±1.9%), and associated with 16 genetic loci (e.g. PLEKHA1 and EFEMP1), biomarkers (e.g body impedance), clinical phenotypes (e.g, chest pain), diseases (e.g. hypertension), environmental (e.g smoking), and socioeconomic (e.g education, income) factors.


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