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.

This study investigated relationships between mood profiles, sports injuries, and countermovement jump with arm swing (CMJ) performance in a cohort study of 417 Brazilian athletes using a multi-methods approach during the period from January to November 2023. Six distinct mood profiles were identified, termed the shark fin (28.3%), iceberg (20.4%), submerged (18.7%), inverse iceberg (18.0%), surface (9.8%), and inverse Everest (4.8%). Athletes with the inverse Everest profile had a significantly higher risk of injury (OR = 2.90; 90% CI [1.09–7.55]) compared to those with the iceberg profile. Random forest models showed moderate predictive capability (AUC = 0.651), with vigour (12.7%) and anger (11.5%) as primary predictors. Bayesian analysis confirmed a higher injury probability in athletes with the inverse Everest profile (31.8%). Despite statistical power limitations, the results indicate that negative mood profiles, particularly those with elevated anger and fatigue, are associated with increased injury risk. Mood scores were not associated with CMJ performance variation. These findings highlight the importance of considering mood profiles in athlete monitoring systems, acknowledging the complex interplay between psychological and physical factors in injury risk.