Normal Infrarenal Aortic Diameter in Men and Women in a Mediterranean Area

Infrarenal aortic diameter (AD) values currently considered normal are based on measurements from epidemiologic studies performed over 20 years ago. Knowledge of expected normal AD is important for understanding the relevance of abdominal aortic dilatation. The aim of this study was to define contemporary reference values for normal infrarenal AD and build a predictive model based on individual features.

A cross-sectional study of participants in a population-based screening program for abdominal aortic aneurysm (AAA) was performed in a healthcare district with 400,000 inhabitants. Men and women aged 65 years were invited to participate. Cardiovascular (CV) risk factors, family history of AAA, personal history of other aneurysms, CV disease, and anthropometric parameters were evaluated. The largest anteroposterior inner-inner diameter of the infrarenal aorta was measured by ultrasound. Multiple linear regressions were used to determine independent predictors of AD. The best-fit model was obtained by randomly selecting 70% of the sample and validating the results in the remaining 30%.

A total of 4,730 people (2,089 men and 2,641 women) were invited. The participation rate was 50.4% for men and 44.0% for women. Mean AD (standard deviation, SD) was 16.51 (3.2) mm in the overall group, 17.91 (3.51) mm in men, and 15.25 (2.32) mm in women (P < 0.001). Male sex (P < 0.001), body surface area (P < 0.001), smoking habit (P = 0.012), and history of arterial aneurysms (P = 0.013) were independently associated with increased AD. Dyslipidemia was associated with decreased AD (P < 0.001). The findings were used to build a model for predicting AD based on individual characteristics.

ADs in our study population are smaller than those described in classic epidemiological studies. Men have a significantly larger diameter than women and the strongest predictor of increased AD is body surface area. A greater understanding of factors associated with AD will help predict expected sizes in individual members of the population.