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Updated: Aug 13, 2026
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
CVD-predictive performances of "a body shape index" versus simple anthropometric measures: Tehran lipid and glucose
Mohammadreza Bozorgmanesh1, Mahsa Sardarinia1, Farhad Hajsheikholeslami1
1Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences (RIES), Shahid Beheshti University of Medical Sciences, No 24, Parvaneh AVE, Velenjak St, Chamran HWY, P.O. Box 19395-4763, Tehran, Iran.
Purpose:
To examine whether a body shape index (ABSI) calculated by using waist circumference (WC) adjusted for height and weight could improve the predictive performances for cardiovascular disease (CVD) of the Framingham's general CVD algorithm and to compare its predictive performances with other anthropometric measures.
Methods:
We analyzed data on a 10-year population-based follow-up of 8,248 (4,471 women) individuals aged ≥30 years, free of CVD at baseline. CVD risk was estimated for a 1 SD increment in ABSI, body mass index (BMI), waist-to-hip ratio (WHpR) and waist-to-height ratio (WHtR), by incorporating them, one at a time, into multivariate accelerated failure time models.
Results:
ABSI was associated with multivariate-adjusted increased risk of incident CVD among both men (1.26, 95% CI 1.09-1.46) and women (1.17, 1.03-1.32). Among men, for a one-SD increment, ABSI conferred a greater increase in the hazard of CVD [1.26 (1.09-1.46)] than did BMI [1.06 (0.94-1.20)], WC [1.15(1.03-1.28)], WHpR [1.02 (1.01-1.03)] and WHtR [1.16 (1.02-1.31)], and the corresponding figures among women were 1.17 (1.03-1.32), 1.02 (0.90-1.16), 1.11 (0.98-1.27), 1.03 (1.01-1.05) and 1.14 (0.99-1.03), respectively. ABSI as well as other anthropometric measures failed to add to the predictive ability of the Framingham general CVD algorithm either.
Conclusions:
Although ABSI could not improve the predictability of the Framingham algorithm, it provides more information than other traditional anthropometric measures in settings where information on traditional CVD risk factors are not available, and it can be used as a practical criterion to predict adiposity-related health risks in clinical assessments.
