The Use of Different Anthropometric Indices to Assess the Body Composition of Young Women in Relation to the Incidence of Obesity, Sarcopenia and the Premature Mortality Risk.
Martina GažarováMaroš BihariMarta LorkováPetra LenártováMarta HabanovaPublished in: International journal of environmental research and public health (2022)
The objective of the study was to evaluate the stratification of young women based on the assessment of body composition according to several currently recommended anthropometric indices and parameters, as well as the presence of obesity, sarcopenic obesity and the risk of premature death. Three hundred and three young Caucasian women aged 18-25 years were included in the cross-sectional observational study. For the purposes of the study, we used the bioelectrical impedance method and applied the obtained data to calculate indices defining obesity, sarcopenic obesity and premature mortality risk (ABSI z-score). We found significant differences between indicators of total and abdominal obesity when determining the rate of risk of premature death and diagnosis of obesity. Our results also suggest that FMI and FM/FFM indices correlate excellently with fat mass and visceral adipose tissue, better than BMI. Even in the case of abdominal obesity, FMI appears to correlate relatively strongly, more so than BMI. The results of the study support the opinion that in the assessment of body composition and health status, the presence of obesity (sarcopenic obesity) and the risk of premature death, anthropometric parameters and indices focusing not only on body weight (BMI, ABSI), but also on the proportionality and distribution of fat (WC, WHR, WHtR, VFA) and muscle tissue (FFMI, SMMI, FM/FFM ratio) should be used.
Keyphrases
- body composition
- insulin resistance
- metabolic syndrome
- weight loss
- weight gain
- adipose tissue
- high fat diet induced
- type diabetes
- resistance training
- skeletal muscle
- body mass index
- high fat diet
- polycystic ovary syndrome
- cross sectional
- machine learning
- magnetic resonance
- computed tomography
- pregnant women
- risk factors
- artificial intelligence
- data analysis
- pregnancy outcomes