Efecto del metabolismo y de la composición corporal sobre la emisión termográfica de la piel en hombres sanos

Abstract

Introduction Increased subcutaneous body tissue can alter the skin’s ability to release heat. Anthropometric parameters for estimating body composition are variables commonly used to identify cardiovascular risk factors (CVRFs). Therefore, infrared thermography (IRT) is becoming a tool that provides relevant information in the assessment of CVRFs, body composition, and resting energy expenditure (REE). Objectives The overall objective of this study was to determine the relationships between skin thermographic emission, cardiovascular risk factors (CVRF), body composition, and resting energy expenditure (REE) in healthy, normal-weight subjects between 18 and 50 years of age. Methods To respond to this objective, two studies were conducted: a) In the first study, a total of 24 healthy men (age 33.9 ± 8.7 years, 85.8 ± 9.7 kg body weight, 172.5 ± 6.1 cm height, and 28.9 ± 3.4 kg/m2 of body mass index [BMI]) participated. B) The second study included 64 healthy men (age 26.4 ± 7.8 years, 171.3 ± 7.2 cm height, 76.1 ± 13.3 kg body mass, and 25.9 ± 3.7 kg/m2 [BMI]). REE was assessed using indirect calorimetry by gas analysis, and IRT and anthropometric assessments were performed. REE was estimated from the thermographic and skinfold data obtained from the second study, which was developed as a third study by applying a multiple regression model. Results Individual temperatures (supraspinal, thoracoabdominal, and abdominal) and the mean temperature of the six skinfolds showed negative correlations with BMI, waist circumference, waist-hip ratio, waist-height ratio, and the sum of the six skinfolds, all at p < 0.05. Negative correlations were found between REE and adipose mass percentage (r = -0.59, p = 0.002), as well as with BMI (r = -0.53, p = 0.006). The average temperature with IRT measured in the six folds showed a positive correlation with REE (r = 0.44, p = 0.02). However, the average temperature of the six skinfolds obtained by IRT, as well as all temperatures obtained by IRT for each of the folds, showed significant negative correlations with waist-height ratio, waist-hip ratio, waist circumference, BMI, and the sum of the six skinfolds (p < 0.05; r = - 0.35 to - 0.65). On the other hand, subjects with BMI ≤ 24.9 kg/m2 presented higher surface temperature values in all the areas studied, in contrast to subjects with BMI levels > 24.9 kg/m2, where body heat dissipation was lower. Multiple regression was used to select a set of variables that allowed us to formulate the following equation for REE estimation in Kcal/day: Model Y = - 715.11* + (1.39 * Weight Corp) + (65.36 * D - Thorax T) + (106.34 * D – femoral) + (- 62.64 * SKL - Subscap Te) + (-3.81* SKL - Supraspinal Te) + (- 53.84 * SKL- Calf Te) + (33.66 * SKL - Triceps Te) + (31.12 * SKL - Abdominal Te) + (43.61 * SKL -Thigh Te), with R2 = 0.68 and a value of p < 0,001. Where: D-Thorax-T = Transverse thoracic diameter, in cm. D-Femoral = Femoral or biepicondylar diameter, in cm. SKL-Subscap-Te = Temperature per IRT in the subscapular fold area, in ºC. SKL-Supraspinal-Te = Temperature per IRT in the supraspinal crease area, in ºC. SKL-Calf-Te = Temperature by IRT in the calf crease area, in ºC. SKL-Triceps-Te = Temperature by IRT in the triceps crease area, in ºC. SKL-Abdominal-Te = Temperature by IRT in the abdominal fold area, in ºC. SKL-Thigh-Te = Temperature by IRT in the thigh crease zone, in ºC. Conclusions Modifications of skin surface thermographic patterns caused by body composition and REE values are observed; these do not affect the mean total surface temperature. Therefore, these values could be used as predictive values. It is observed as well that some body composition parameters, associated with CVRFs, show an association with skin surface thermographic values. Subjects with a normal BMI showed higher heat dissipation and surface temperature values for all areas assessed, in contrast to subjects with a BMI above the normal limit. The findings above made it possible to create a predictive formula based on body composition parameters and thermographic values. New studies can be conducted based on this data.