Vitamin D Inadequacy and Its Relation to Body Fat and Muscle Mass in Adult Women of Childbearing Age.
Paula Moreira MagalhãesSabrina Pereira da CruzOrion Araújo CarneiroMichelle Teixeira TeixeiraAndréa RamalhoPublished in: Nutrients (2024)
To assess the correlation between vitamin D status and body composition variables in adult women of childbearing age, a cross-sectional study was conducted involving women aged 20-49 years. The participants were categorized based on their vitamin D status and further divided according to body mass index (BMI). Anthropometric and biochemical data were collected to compute body composition indices, specifically body fat and muscle mass. The sample included 124 women, with 63.70% exhibiting vitamin D inadequacy. Women with inadequate vitamin D status demonstrated a higher waist-to-height ratio (WHtR) and body adiposity index (BAI), along with a lower BMI-adjusted muscle mass index (SMI BMI ), compared to those with adequate levels of vitamin D ( p = 0.021; p = 0.019; and p = 0.039, respectively). A positive correlation was observed between circulating concentrations of 25(OH)D and SMI BMI , while a negative correlation existed between circulating concentrations of 25(OH)D and waist circumference (WC), WHtR, conicity index (CI), fat mass index (FMI), body fat percentage (% BF), and fat-to-muscle ratio (FMR). These findings suggest that inadequate vitamin D status may impact muscle tissue and contribute to higher body adiposity, including visceral adiposity. It is recommended that these variables be incorporated into clinical practice, with a particular emphasis on WHtR and SMI BMI , to mitigate potential metabolic consequences associated with vitamin D inadequacy.
Keyphrases
- body mass index
- body composition
- weight gain
- polycystic ovary syndrome
- insulin resistance
- resistance training
- bone mineral density
- physical activity
- adipose tissue
- skeletal muscle
- clinical practice
- cervical cancer screening
- metabolic syndrome
- type diabetes
- breast cancer risk
- fatty acid
- pregnant women
- childhood cancer
- risk factors
- deep learning
- data analysis