Staff member

Biological sex significantly influences the prevalence, incidence, and severity of numerous human diseases, yet it remains an underappreciated variable in biomedical research. Although sexually dimorphic genes contribute to sex-specific traits and disease manifestations, their regulation under metabolic stress is poorly understood. To explore sex-specific metabolic adaptations, we analyzed responses to high-fat diet (HFD)-induced obesity in male and female mice, focusing on the regulation of sex-biased genes. Despite similar adiposity, HFD-fed males displayed more severe metabolic impairments than females, highlighting divergent metabolic outcomes. To investigate the basis for these sex-specific differences, we performed whole transcriptomic profiling of liver and white adipose tissue (WAT) at early (2 wk) and late (12 wk) stages of HFD exposure. Our analysis revealed marked sex-specific gene expression changes across multiple categories, particularly pronounced in male WAT after prolonged HFD feeding. Strikingly, genes exhibiting sexual dimorphism under normal conditions were preferentially modulated in both sexes, comprising up to 46% of all HFD-regulated genes. This led to a substantial loss of sex-biased gene expression in both liver and WAT after HFD exposure, correlating with metabolic dysfunction. Male-biased genes associated with cilia function and estrogen response were among the most affected, showing significant downregulation in male WAT under HFD. Our findings provide a novel perspective on how obesity disrupts sex-specific gene expression in key metabolic tissues, particularly targeting sex-biased genes. By revealing that a considerable proportion of sex-biased genes exhibit HFD-regulated modulation, our study highlights the critical role of these genes in maintaining metabolic health. NEW & NOTEWORTHY Biological sex shapes metabolic tissue physiology, largely through sex-biased gene regulation. Our comprehensive transcriptomic analysis reveals that sex-biased genes in liver and white adipose tissue undergo the most significant regulation during obesity-driven metabolic dysfunction, resulting in a loss of their bias. This disruption highlights a previously unrecognized role of sex-biased genes in maintaining metabolic health in both males and females.

JTD

Home hospitalization (HH) is presented as a healthcare alternative capable of providing high standards of care when patients no longer need hospital facilities. Although HH seems to lower healthcare costs by shortening hospital stays and improving patient's quality of life, the lack of continuous observation at home may lead to complications in some patients. Since blood tests have been proven to provide relevant prognosis information in many diseases, this paper analyzes the impact of different sampling methods on the prediction of HH outcomes. After a first exploratory analysis, some variables extracted from routine blood tests performed at the moment of HH admission, such as hemoglobin, lymphocytes or creatinine, were found to unmask statistically significant differences between patients undergoing successful and unsucessful HH stays. Then, predictive models were built with these data, in order to identify unsuccessful cases eventually needing hospital facilities. However, since these hospital admissions during HH programs are rare, their identification through conventional machine-learning approaches is challenging. Thus, several sampling strategies designed to face class imbalance were herein overviewed and compared. Among the analyzed approaches, over-sampling strategies, such as ROSE (Random Over-Sampling Examples) and conventional random over-sampling, showed the best performances. Nevertheless, further improvements should be proposed in the future so as to better identify those patients not benefiting from HH.

JTD Keywords: Hospitals, Blood, Training, Standards, Diseases, Prognostics and health management