Association of Sleep Deprivation and the Risk of Developing Systemic Lupus Erythematosus Among Women

Risk factors and consequences of mental health problems in nurses: A scoping review of cohort studies

Mental health problems in nurses are prevalent and impairing. To date, no literature has comprehensively synthesised cohort evidence on mental health among nurses. This scoping review aimed to synthesise the existing literature on the risk factors and consequences of mental health problems in nurses. A systematic search was conducted on PubMed, EMBASE, Epistemonikos database, Web of Science, CINAHL, and PsycINFO from inception to March 2023. We identified 171 cohort studies from 16 countries, mostly (95.3%) from high-income economies. This review indicated that nurses worldwide encountered significant mental health challenges, including depression, cognitive impairment, anxiety, trauma/post-traumatic stress disorder, burnout, sleep disorder, and other negative mental health problems. These problems were closely related to various modifiable risk factors such as nurses' behaviours and lifestyles, social support, workplace bullying and violence, shift work, job demands, and job resources. Moreover, nurses' mental health problems have negative effects on their physical health, behaviour and lifestyle, occupation and organisation, and intrapersonal factors. These findings provided an enhanced understanding of mental health complexities among nurses, and shed light on policy enactment to alleviate the negative impact of mental health problems on nurses. Addressing mental health among nurses should be a top priority.

Systematic Metabolic Profiling of Mice with Sleep-Deprivation

Adequate sleep is essential for the biological maintenance of physical energy. Lack of sleep can affect thinking, lead to emotional anxiety, reduce immunity, and interfere with endocrine and metabolic processes, leading to disease. Previous studies have focused on long-term sleep deprivation and the risk of cancer, heart disease, diabetes, and obesity. However, systematic metabolomics analyses of blood, heart, liver, spleen, kidney, brown adipose tissue, and fecal granules have not been performed. This study aims to systematically assess the metabolic changes in the target organs caused by sleep deprivation in vivo, to search for differential metabolites and the involved metabolic pathways, to further understand the impact of sleep deprivation on health, and to provide strong evidence for the need for early intervention.

Chlorogenic acid improves the cognitive deficits of sleep-deprived mice via regulation of immunity function and intestinal flora

BACKGROUND

Sleep deprivation (SD) has become a global health concern with serious consequences containing memory deficits and gastrointestinal dysfunctions. The gut-brain axis serves as a crucial link between the brain and gut, and the utilization of chlorogenic acid (CGA) presents a compelling strategy for mitigating or potentially resolving various neuroinflammation-associated disorders. However, it is still unknown how CGA may interact with the gut, microbiota and the brain during SD.

PURPOSE

This study aims to explore the therapeutic effect and underlying mechanism of microbiota-gut-brain axis by which CGA prevents SD-induced cognitive deficits.

STUDY DESIGN AND METHODS

CGA (30, 60 mg/kg.bw.) was gavaged to C57BL/6 mice, and then they were submitted to 48-h SD. The cognitive and spatial learning abilities were investigated through behavioral tests. Furthermore, we explored the action mechanism of this compound with haematological analysis, histopathological examination, Western blot, ELISA and 16S rRNA gene pyrosequencing from colonic contents.

RESULTS

The cognitive deficits induced by SD were significantly relieved by administration of CGA in a dose-dependent manner. The hematoxylin and eosin staining of hippocampus and colon tissues indicated that pretreatment of CGA not only protected brain tissue from SD, but also maintained intestinal integrity. In the hippocampus, the increased pro-inflammatory neurometabolites were significantly prevented by CGA, and an immune profile capable of hippocampal-dependent spatial memory was improved via Nrf2/PPAR signaling pathways. The observed immunomodulatory effect was concomitant with augmentation of the intestinal barrier, as evidenced by the heightened expressions of tight junction proteins. 16S rRNA analysis of colonic contents revealed that levels of Clostridia_UCG-014 and lipopolysaccharide were significantly inhibited, and those of Lactobacillus and intestinal tight junction proteins were upregulated in the CGA group. Pathways of ko05322 (immune disease) and ko04610 (immune system) were significantly regulated by CGA. Based on PICRUSt2 algorithm, CGA probably influenced gut microbial functions via several metabolism pathways, such as arginine biosynthesis, pyrimidine metabolism and purine metabolism.

CONCLUSION

The present study first proved the efficacy and mechanism of CGA in alleviating SD-induced cognitive impairment and neuroinflammation via creating a systemic protection, a bidirectional communication system connecting the gut with the brain. The intestinal barrier improvement and the reshaped "SD microbiota" profiles restored immunity functions, which were probably the main contributors to Nrf2/PPAR activation and the neuroprotective effect of CGA. Overall, this work provided novel insights of CGA, which might guide the more reasonable clinical use of CGA in the pathogenesis of sleep-related disorders.

Risk prediction models for incident systemic lupus erythematosus among women in the Nurses' health study cohorts using genetics, family history, and lifestyle and environmental factors

OBJECTIVE

Systemic lupus erythematosus (SLE) is a severe multisystem autoimmune disease that predominantly affects women. Its etiology is complex and multifactorial, with several known genetic and environmental risk factors, but accurate risk prediction models are still lacking. We developed SLE risk prediction models, incorporating known genetic, lifestyle and environmental risk factors, and family history.

METHODS

We performed a nested case-control study within the Nurses' Health Study cohorts (NHS). NHS began in 1976 and enrolled 121,700 registered female nurses ages 30-55 from 11 U.S. states; NHSII began in 1989 and enrolled 116,430 registered female nurses ages 25-42 from 14 U.S. states. Participants were asked about lifestyle, reproductive and environmental exposures, as well as medical information, on biennial questionnaires. Incident SLE cases were self-reported and validated by medical record review (Updated 1997 American College of Rheumatology classification criteria). Those with banked blood samples for genotyping (∼25% of each cohort), were selected and matched by age (± 4 years) and race/ethnicity to women who had donated a blood sample but did not develop SLE. Lifestyle and reproductive variables, including smoking, alcohol use, body mass index, sleep, socioeconomic status, U.S. region, menarche age, oral contraceptive use, menopausal status/postmenopausal hormone use, and family history of SLE or rheumatoid arthritis (RA) were assessed through the questionnaire prior to SLE diagnosis questionnaire cycle (or matched index date). Genome-wide genotyping results were used to calculate a SLE weighted genetic risk score (wGRS) using 86 published single nucleotide polymorphisms (SNPs) and 10 classical HLA alleles associated with SLE. We compared four sequential multivariable logistic regression models of SLE risk prediction, each calculating the area under the receiver operating characteristic curve (AUC): 1) SLE wGRS, 2) SLE/RA family history, 3) lifestyle, environmental and reproductive factors and 4) combining model 1-3 factors. Models were internally validated using a bootstrapped estimate of optimism of the AUC. We also examined similar sequential models to predict anti-dsDNA positive SLE risk.

RESULTS

We identified and matched 138 women who developed incident SLE to 1136 women who did not. Models 1-4 yielded AUCs 0.63 (95%CI 0.58-0.68), 0.64 (95%CI 0.59-0.68), 0.71(95% CI 0.66-0.75), and 0.76 (95% CI 0.72-0.81). Model 4 based on genetics, family history and eight lifestyle and environmental factors had best discrimination, with an optimism-corrected AUC 0.75. AUCs for similar models predicting anti-dsDNA positive SLE risk, were 0.60, 0.63, 0.81 and 0.82, with optimism corrected AUC of 0.79 for model 4.

CONCLUSION

A final model including SLE weighted genetic risk score, family history and eight lifestyle and environmental SLE risk factors accurately classified future SLE risk with optimism corrected AUC of 0.75. To our knowledge, this is the first SLE prediction model based on known risk factors. It might be feasibly employed in at-risk populations as genetic data are increasingly available and the risk factors easily assessed. The NHS cohorts include few non-White women and mean age at incident SLE was early 50s, calling for further research in younger and more diverse cohorts.