Background: Thyroid hormones are essential regulators of energy expenditure, thermogenesis, and body composition. Although overt thyroid dysfunction is well known to alter basal metabolic rate (BMR) and body mass, emerging evidence suggests that even hormonal variations within the reference range may exert measurable effects on metabolic and body composition profiles. Women of reproductive age represent a population particularly sensitive to hormonal oscillations due to the interplay between endocrine, reproductive, and cardiometabolic health. Objective: The study aimed to a) analyze thyroid hormone levels (TSH, FT3, FT4) alongside anthropometric and body composition parameters in women of reproductive age; b) examine thyroid hormone levels, BMR, and body composition parameters across age groups; and c) investigate associations of thyroid hormones with BMR, body composition components, and unfavorable body composition patterns (visceral adiposity, elevated metabolic age, obesity) as well as metabolic indicators.. Methods: A total of 117 women aged 18–45 years were included in this cross-sectional, observational study conducted in Bosnia and Herzegovina between September 2023 and November 2024. Thyroid hormone levels were measured using electrochemiluminescence assays, while body composition was assessed by bioelectrical impedance analysis. Statistical analyses included descriptive methods, Pearson’s correlation, and Chi-square testing, with significance set at p<0.05. Results: TSH showed significant positive associations with fat-free mass, muscle mass, and BMR (p<0.05). FT3 was inversely correlated with metabolic age and visceral fat, while FT4 demonstrated weak negative associations with fat-free mass and metabolic age (p<0.05). Significant age-related differences were observed in fat percentage, fat mass, BMI, visceral fat, and metabolic age, with the most unfavorable profiles in women aged 31–40 years. Conclusion: Thyroid hormones, even within the reference range, are associated with body composition and metabolic parameters in women of reproductive age. Their role as early indicators of unfavorable metabolic patterns highlights potential implications for reproductive and cardiovascular risk assessment.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has caused a worldwide emergency. The disease is characterized primarily by symptoms of the respiratory system, but also by systemic inflammation. Since the onset of the disease, there has been a need for biomarkers to predict the severity of the clinical picture and the outcome of the disease. The aim of this study is to evaluate systemic inflammatory markers for predicting severity of COVID-19. Methods: The study was conducted at the Sarajevo Canton Health Center on a total of 170 adults suffering from COVID-19. 70 subjects had mild clinical picture, while the control group consisted of 100 subjects with moderate clinical picture. The results of complete and differential blood counts, C-reactive protein (CRP), and systemic inflammatory indexes (SII) (neutrophil/lymphocyte ratio [NLR], derived NLR [dNLR], platelet/lymphocyte ratio [PLR], and SII) were used to compare the groups. IBM SPSS Ver. 23 was used for statistical analysis and data processing. Results: The proportion of male patients in the group with a milder clinical picture was higher than the proportion of male patients with a moderate clinical picture, p = 0.016. The values of leukocytes and neutrophils were higher in patients with a moderate clinical picture (p = 0.006 and p < 0.001, respectively). The values of all inflammatory indexes (NLR, dNLR, PLR and SII) were higher in patients with a moderate clinical picture of COVID-19 than in patients with a mild clinical picture (p < 0.001 for NLR, dNLR, and SII; p = 0.023 for PLR). In the research, patient age showed no correlation and CRP showed no correlation with SII. Conclusion: SII show higher values in patients with a moderate compared with a mild clinical picture of COVID-19. These parameters can be cost-effective and useful indicators in patient classification, diagnosis, and probably in monitoring patients with COVID-19.