Socioeconomic status can help predict whether a person will die from heart disease, according to a study published August 25 in the Journal of Cardiac Failure.
Counties in the U.S. that have high rates of poverty and other measures of social deprivation also have higher rates of death from heart failure, indicating that socioeconomic status plays an important role in disease mortality, according to researchers from University Hospitals in Cleveland.
More than 1.25 million heart failure deaths across 3,048 counties that occurred between 1999
and 2018 were analyzed for the study. Researchers used multiple indicators of employment, poverty, income, housing, and education to determine a person's level of socioeconomic deprivation.
People's addresses may affect their mortality because poorer communities often have less access to expensive medications, therapies, and quality healthcare. This highlights the need to address these factors to improve heart failure outcomes, according to the study.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)