Body shape: Implications in the study of obesity and related traits. Issue 2 (10th September 2019)

• Record Type: Journal Article
• Authors: Navarro, Pablo; Ramallo, Virginia; Cintas, Celia; Ruderman, Anahí; de Azevedo, Soledad; Paschetta, Carolina; Pérez, Orlando; Pazos, Bruno; Delrieux, Claudio; González‐José, Rolando
• Is Part Of: American journal of human biology. Volume 32:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-09-10
• Subjects: Human biology -- Periodicals; Physical anthropology -- Periodicals; Biologie humaine -- Périodiques; Anthropologie physique -- Périodiques; 612
• DOI: 10.1002/ajhb.23323
• Languages: English
• ISSNs: 1042-0533

Abstract

Abstract: Objectives: The diagnosis and treatment of obesity are usually based on traditional anthropometric variables including weight, height, and several body perimeters. Here we present a three‐dimensional (3D) image‐based computational approach aimed to capture the distribution of abdominal adipose tissue as an aspect of shape rather than a relationship among classical anthropometric measures. Methods: A morphometric approach based on landmarks and semilandmarks placed upon the 3D torso surface was performed in order to quantify abdominal adiposity shape variation and its relation to classical indices. Specifically, we analyzed sets of body cross‐sectional circumferences, collectively defining each, along with anthropometric data taken on 112 volunteers. Principal Component Analysis (PCA) was performed on 250 circumferences located along the abdominal region of each volunteer. An analysis of covariance model was used to compare shape variables (PCs) against anthropometric data (weight, height, and waist and hip circumferences). Results: The observed shape patterns were mainly related to nutritional status, followed by sexual dimorphism. PC1 (12.5%) and PC2 (7.5%) represented 20% of the total variation. In PCAs calculated independently by sex, linear regression analyses provide statistically significant associations between PC1 and the three classical indexes: body mass index, waist‐to‐height ratio, and waist‐hip ratio. Conclusion: Shape indicators predict well the behavior of classical markers, but also evaluate 3D and geometric features with more accuracy as related to the body shape under study. This approach also facilitates diagnosis and follow‐up of therapies by using accessible 3D technology.