NEW YORK (Reuters Health), Aug 4 - Levels of free fat acids are associated with mortality in patients with coronary artery disease, Austrian and German researchers report.
"Free fat acids are an independent predictor of cardiovascular death in subjects at intermediate risk," senior investigator Dr. Winfried Maerz, of the Medical University of Graz, told Reuters Health.
Dr. Maerz and colleagues came to this conclusion after examining data from 3,315 Caucasian subjects in an ongoing study of coronary artery disease. The patients were followed for a median of 5.38 years and all subjects underwent coronary angiography at baseline.
At follow-up, 513 patients had died, the researchers report in the July issue of the Journal of Clinical Endocrinology and Metabolism. Compared with subjects with the lowest levels of free fat acids, those with the highest levels had an adjusted hazard ratio for death from any cause of 1.58 and for death from cardiovascular causes of 1.83.
In the more than 2,500 subjects with stable or unstable cardiovascular disease, the predictive value of free fat acids was similar to that in the entire cohort. However, in the participants without cardiovascular disease, the association did not reach significance.
Free fat acids were higher in subjects with unstable cardiovascular disease and increased with the severity of heart failure.
"Although our study does not ultimately prove causality," concluded Dr. Maerz, "we suggest that looking at free fatty acids may hold a great potential for stratification and even for the treatment of coronary heart disease."
Last Updated: 2006-08-03 16:21:57 -0400 (Reuters Health)
J Clin Endocrinol Metab 2006;91:2542-2547.
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![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.auntminnieeurope.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)
