Evaluation of the Somatotype of Patients with Class 1, 2 and 3 Obesity According to the Heath-Carter Scheme Using Various Formulas

AIM. The purpose of this work was to study the somatotypological characteristics of patients with non-communicable diseases and obesity of class 1, 2 and 3; compare three methods to evaluate the somatotype using three types of complex formulas according to the Heath-Carter scheme; to check the reliability and informativeness of the method of bioimpedance evaluation of somatotype components by regression formulas used in bioimpedance analysis. MATERIAL AND METHODS. 145 patients (67 men, mean age 41.4±10.3 years and 78 women, mean age 40.6±9.4 years) with class 1, class 2 and class 3 obesity, were examined at the clinic of the Federal Research Center of Nutrition and Biotechnology. Anthropometric measurements were taken. Bioimpedance evaluation of body composition was performed using the analyzer ABC-01 "Medas". The somatotype was determined according to the Heath-Carter scheme using three types of complex formulas – based on anthropometry and based on a bioimpedance study of body composition. RESULTS AND DISCUSSION. Based on anthropometric and bioimpedance studies, a characterization of somatotypes according to the Heath-Carter scheme in patients with alimentary-dependent pathologies and class 1, class 2 and class 3 obesity is presented. Significant differences were shown in the values of the somatotype components ENDO and MESO, obtained by calculation using the formulas implemented in the software of the bioimpedance analyzer, from the values obtained by calculating by formulas based on anthropometry. CONCLUSION. The degree of gender dimorphism was different when determining the somatotype according to the Heath-Carter scheme in patients with class 1, class 2 and class 3 obesity, and it depended on what particular formulas were used to calculate the scores. Pronounced gender dimorphism was noted when using both versions of the regression formulas, because they take into account the gender of the individual being examined. It was shown that these formulas are not applicable for evaluation of the components of the somatotype in persons with obesity of class 1, class 2 and class, because the coefficients of determination do not correspond to those previously obtained for a group of people with normal BMI values. We consider it expedient to develop new regression equations for evaluation of the somatotype of the above category of patients

Does Proteomic Mirror Reflect Clinical Characteristics of Obesity?

Obesity is a frightening chronic disease, which has tripled since 1975. It is not expected to slow down staying one of the leading cases of preventable death and resulting in an increased clinical and economic burden. Poor lifestyle choices and excessive intake of “cheap calories” are major contributors to obesity, triggering type 2 diabetes, cardiovascular diseases, and other comorbidities. Understanding the molecular mechanisms responsible for development of obesity is essential as it might result in the introducing of anti-obesity targets and early-stage obesity biomarkers, allowing the distinction between metabolic syndromes. The complex nature of this disease, coupled with the phenomenon of metabolically healthy obesity, inspired us to perform data-centric, hypothesis-generating pilot research, aimed to find correlations between parameters of classic clinical blood tests and proteomic profiles of 104 lean and obese subjects. As the result, we assembled patterns of proteins, which presence or absence allows predicting the weight of the patient fairly well. We believe that such proteomic patterns with high prediction power should facilitate the translation of potential candidates into biomarkers of clinical use for early-stage stratification of obesity therapy

Does Proteomic Mirror Reflect Clinical Characteristics of Obesity?

Obesity is a frightening chronic disease, which has tripled since 1975. It is not expected to slow down staying one of the leading cases of preventable death and resulting in an increased clinical and economic burden. Poor lifestyle choices and excessive intake of “cheap calories” are major contributors to obesity, triggering type 2 diabetes, cardiovascular diseases, and other comorbidities. Understanding the molecular mechanisms responsible for development of obesity is essential as it might result in the introducing of anti-obesity targets and early-stage obesity biomarkers, allowing the distinction between metabolic syndromes. The complex nature of this disease, coupled with the phenomenon of metabolically healthy obesity, inspired us to perform data-centric, hypothesis-generating pilot research, aimed to find correlations between parameters of classic clinical blood tests and proteomic profiles of 104 lean and obese subjects. As the result, we assembled patterns of proteins, which presence or absence allows predicting the weight of the patient fairly well. We believe that such proteomic patterns with high prediction power should facilitate the translation of potential candidates into biomarkers of clinical use for early-stage stratification of obesity therapy