NEW YORK (Reuters Health), Nov. 19 - Calf circumference shows an inverse association with carotid plaques, according to French researchers who report the findings of the "Three-City Study" in the November issue of Stroke.
Dr. Mahmoud Zureik of INSERM 700, Paris, and colleagues note there is growing evidence that body composition and fat distribution are of major importance in determining vascular risk, but these associations are poorly understood.
To gain further information on the relationship between calf circumference, a marker of peripheral fat and lean mass, and carotid plaques, the researchers studied more than 6,200 residents of Dijon, Montpellier, or Bordeaux. None of these participants were institutionalized and all were between the ages of 65 and 84 years.
Increasing calf circumference was associated with fewer carotid plaques. Compared to those with the lowest calf circumference, the odds ratio in those with the highest was 0.71. The effect was independent of age, gender, body mass index, and other vascular risk factors.
The team found an additional effect of waist-to-hip ratio (WHR). Those with highest WHR and the lowest calf circumference had the highest frequency of carotid plaques (55.1%). In subjects with the lowest WHR and highest calf circumference, the frequency was 31.8%.
The investigators acknowledge the need for validation, but suggest that "calf circumference may be a new anthropometric marker to take into account when assessing the risk of carotid atherosclerosis."
Stroke 2008;39:2958-2965.
Last Updated: 2008-11-18 13:31:19 -0400 (Reuters Health)
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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)
