Behavioral patterns that increase or decrease risk of abdominal adiposity in adults

Abstract

Introduction: The identification of risk or protective behavioral patterns associated with abdominal adiposity may aid in prevention and health promotion measures.

Objective: To identify and to associate behavioral patterns of risk and protection to abdominal adiposity in adults in a Brazilian city.

Material and methods: A population-based cross-sectional study was carried out in Viçosa, Brazil, with 1,226 adults of both sexes. Information on social-demographic characteristics, food intake, level of physical activity, alcohol consumption and smoking were collected by using a questionnaire. The anthropometric measurement of waist circumference and anthropometric indices waist/hip ratio and waist/height ratio were indicators of abdominal adiposity. To identify behavioral patterns, exploratory factor analysis was applied for the variables considered as risk or protective factors. The association of the identified patterns with abdominal adiposity was estimated by multiple linear regression, adjusted for gender, age and social economical class.

Results: Two patterns were obtained, "healthy" and "risk". The "healthy" pattern, comprised of the clustering of the variables food consumption, fruits, fresh fruit juices, raw and cooked vegetables and the appropriate level of physical activity, was negatively associated with abdominal adiposity identified by waist circumference (p = 0.048), waist/hip (p = 0.013) and waist/height (p = 0.018) indices. The "risk" pattern, composed of smoking, alcohol beverage abuse and habit of consuming visible fat in fat-rich red meat or poultry skin, was positively associated with abdominal adiposity identified by waist circumference (p = 0.002) and waist/hip (p = 0.007) and waist/height indices (p = 0.006).

Conclusions: Two behavioral patterns were identified, a risk pattern and a protective pattern for abdominal adiposity in the assessed population. The study shows the importance of conducting clustering of multiple risk and protective factors to better explain the health conditions of a group.