RESUMEN
Background The COVID-19 pandemic imposed unprecedented challenges on healthcare systems worldwide. The pandemic placed frontline nursing staff working in the ICU and ER at the epicenter of this global crisis. This study aimed to assess the multifaceted impact of sociodemographic characteristics on the quality of life (QOL) of nursing staff during the pandemic. Method A cross-sectional survey was conducted to evaluate the impact of sociodemographic characteristics on the QOL of 322 frontline nurses working in the ICU and ER of five Saudi hospitals from May to July 2022. Participants completed the electronic survey questionnaire including demographic characteristics and four domains of QOL from the World Health Organization Quality of Life Questionnaire (WHOQOL-BREFF). The data was evaluated using descriptive and inferential statistics. Results Among 322 nurse participants, the majority were female (84.8%), married (64.4%), and held a bachelor's degree (92.4%). Age (above 40 years), gender (male), and marital status (married) reported a higher individual domain and overall QOL scores which shows that these characteristics have a direct influence on QOL. Years of work experience, extra working hours, and direct contact with COVID-19 patients were additional significant factors. Pearson correlation coefficients among QOL domains ranged from 0.54 to 0.91, indicating a strong interrelation among these domains. The highest transformed score was in the social domain (70.10) while the lowest score was in the psychological domain (59.20). The overall QOL mean score (SD) was 3.49(0.14) and the mean score (SD) of general health was 3.46(0.15). Conclusion The findings of this study suggest that sociodemographic and work-related factors have a complex and multifaceted impact on the QOL of nurses during the COVID-19 pandemic in Saudi Arabia. It also presents an insight into developing specific interventions to enhance nurses' resilience and well-being amidst pandemic challenges and to improve their QOL.
RESUMEN
Plasma lipoproteins exist as several subpopulations with distinct particle number and size that are not fully reflected in the conventional lipid panel. In this study, we sought to quantify lipoprotein subpopulations in patients with type 2 diabetes mellitus (T2DM) to determine whether specific lipoprotein subpopulations are associated with insulin resistance and inflammation markers. The study included 57 patients with T2DM (age, 61.14 ± 9.99 years; HbA1c, 8.66 ± 1.60%; mean body mass index, 35.15 ± 6.65 kg/m2). Plasma lipoprotein particles number and size were determined by nuclear magnetic resonance spectroscopy. Associations of different lipoprotein subpopulations with lipoprotein insulin resistance (LPIR) score and glycoprotein acetylation (GlycA) were assessed using multi-regression analysis. In stepwise regression analysis, VLDL and HDL large particle number and size showed the strongest associations with LPIR (R2 = 0.960; p = 0.0001), whereas the concentrations of the small VLDL and HDL particles were associated with GlycA (R2 = 0.190; p = 0.008 and p = 0.049, respectively). In adjusted multi-regression analysis, small and large VLDL particles and all sizes of lipoproteins independently predicted LPIR, whereas only the number of small LDL particles predicted GlycA. Conventional markers HbA1c and Hs-CRP did not exhibit any significant association with lipoprotein subpopulations. Our data suggest that monitoring insulin resistance-induced changes in lipoprotein subpopulations in T2DM might help to identify novel biomarkers that can be useful for effective clinical intervention.
