Relevance of body composition in phenotyping the obesities.

Obesity is the most extended metabolic alteration worldwide increasing the risk for the development of cardiometabolic alterations such as type 2 diabetes, hypertension, and dyslipidemia. Body mass index (BMI) remains the most frequently used tool for classifying patients with obesity, but it does not accurately reflect body adiposity. In this document we review classical and new classification systems for phenotyping the obesities. Greater accuracy of and accessibility to body composition techniques at the same time as increased knowledge and use of cardiometabolic risk factors is leading to a more refined phenotyping of patients with obesity. It is time to incorporate these advances into routine clinical practice to better diagnose overweight and obesity, and to optimize the treatment of patients living with obesity.

Cardiometabolic risk stratification using a novel obesity phenotyping system based on body adiposity and waist circumference.

BACKGROUND: The estimation of obesity-associated cardiometabolic risk does not usually take into account body composition or the distribution of adiposity. The aim of the present study was to assess the clinical usefulness of a novel obesity phenotyping system based on the combination of actual body fat percentage (BF%) and waist circumference (WC) according to the cardiometabolic risk estimation. METHODS: A classification matrix combining BF% and WC as measures of both amount and distribution of adiposity establishing nine body phenotypes (3 BF% x 3 WC) was developed. Individuals were grouped in five different cardiometabolic risk phenotypes. We conducted a validation study in a large cohort of White subjects from both genders representing a wide range of ages and adiposity (n = 12,754; 65 % females, aged 18-88 years). RESULTS: The five risk groups using the matrix combination of BF% and WC exhibited a robust linear distribution regarding cardiometabolic risk, estimated by the Metabolic Syndrome Severity Score, showing a continuous increase between groups with significant differences (P < 0.001) among them, as well as in other cardiometabolic risk factors. An additional 24 % of patients at very high risk was detected with the new classification system proposed (P < 0.001) as compared to an equivalent matrix using BMI and WC instead of BF% and WC. CONCLUSIONS: A more detailed phenotyping should be a priority in the diagnosis and management of patients with obesity. Our classification system allows to gradually estimate the cardiometabolic risk according to BF% and WC, thus representing a novel and useful tool for both research and clinical practice.