Keywords
Body Composition
Impedance
Body Mass Index
Anthropometry
Body Fat Distribution
How to Cite
Abstract
Background: The World Health Organization defines obesity as "an abnormal or excessive accumulation of fat that can be detrimental to health", the Spanish Consensus on Obesity of 2016 defines it "when the percentage of fat mass is greater than 33% in women". Bioelectrical impedance (BIA) quantifies fat mass from fat-free mass, it serves as a tool for the study, classification and diagnosis of obesity without the limitations of the Body Mass Index (BMI).
Purpose: To describe the body composition in women by means of the BIA, mainly the percentage of body fat (PGC) and the index of free mass of fat (IMLG) and to make a correlation between the classification of overweight and obesity according to the Body Mass Index (BMI) with the percentage of body fat (BFA) calculated by the BIA.
Methodology: Cross-sectional, retrospective study in a highly complex clinic. It included women between the ages of 18 and 60, evaluated in the endocrinology weight control consultation evaluated by BIA. A descriptive and correlation analysis was performed between BMI and PGC and IMLG through age groups. With non-parametric statistics, possible differences between age strata were established. Statistical difference was established with a p<0.05.
Results: 323 women with a mean age of 36.2 (± SD 9,578) years, mean weight of 73,351 kg (± SD 73,351), mean BMI 28,825 (± SD 4.69) were evaluated.
Mean PGC 40.98% (± SD 6.123), most of the patients were in the 29 to 39-year-old group (42.1%), 62 (19.2%) of the women had normal BMI, 158 (48.9%), 103 (31.8%) of the women were in some degree of obesity, by PGC 62 (19.19%) were overweight and 261 (80.80%) obese by PGC and like the BMI the number was higher in the group of 29-39 years, the IMLG was higher in the group of 29 to 39 years with important index levels.
Conclusions: Although the use of the BMI for the classification of obesity is the parameter that is currently most used both in the clinical and in the research field, we can see how this might not be the most accurate tool for said evaluation and should be gradually introduced. PGC use by methods such as BIA for classification.
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