Diabetes mellitus as a risk factor for severe dengue fever and West Nile fever: A meta-analysis.

BACKGROUND: Dengue fever (DF) and West Nile fever (WNF) have become endemic worldwide in the last two decades. Studies suggest that individuals with diabetes mellitus (DM) are at a higher risk of developing severe complications from these diseases. Identifying the factors associated with a severe clinical presentation is crucial, as prompt treatment is essential to prevent complications and fatalities. This article aims to summarize and assess the published evidence regarding the link between DM and the risk of severe clinical manifestations in cases of DF and WNF. METHODOLOGY/PRINCIPAL FINDINGS: A systematic search was conducted using the PubMed and Web of Science databases. 27 studies (19 on DF, 8 on WNF) involving 342,873 laboratory-confirmed patients were included in the analysis. The analysis showed that a diagnosis of DM was associated with an increased risk for severe clinical presentations of both DF (OR 3.39; 95% CI: 2.46, 4.68) and WNF (OR 2.89; 95% CI: 1.89, 4.41). DM also significantly increased the risk of death from both diseases (DF: OR 1.95; 95% CI: 1.09, 3.52; WNF: OR 1.74; 95% CI: 1.40, 2.17). CONCLUSIONS/SIGNIFICANCE: This study provides strong evidence supporting the association between DM and an increased risk of severe clinical manifestations in cases of DF and WNF. Diabetic individuals in DF or WNF endemic areas should be closely monitored when presenting with febrile symptoms due to their higher susceptibility to severe disease. Early detection and appropriate management strategies are crucial in reducing the morbidity and mortality rates associated with DF and WNF in diabetic patients. Tailored care and targeted public health interventions are needed to address this at-risk population. Further research is required to understand the underlying mechanisms and develop effective preventive and therapeutic approaches.

Associations between dietary patterns and renal impairment in individuals with diabetes: a cross-sectional study.

BACKGROUND: A variety of chronic diseases are affected by diet. To our knowledge, few studies have investigated the relationship between dietary patterns and renal impairment in individuals with diabetes within an Asian population. This study aimed to assess the relationship between renal impairment and dietary patterns in individuals with diabetes within a Chinese population. METHODS: In this cross-sectional survey, we analysed data on 1522 participants with diabetes aged 18 years or older who took part in the China National Diabetic Chronic Complications Study. We utilised the Chinese Diabetes Complications Questionnaire, including the semiquantitative food frequency questionnaire (SQFFQ). We identified three dietary patterns using factor analysis: Chinese traditional, healthy and plant-based dietary patterns, and these dietary patterns were used to classify participants into four groups based on the quartiles of their scores. A decrease in the estimated glomerular filtration rate (eGFR; <60 mL/min/1.73 m2 ) and an increase in the albumin-to-creatinine ratio (ACR; ≥3 mg/mmol) were used as indicators of renal impairment. Binary logistic regression models were used to estimate the odds ratio (OR) of the highest quartile (Q4: high intake levels of each dietary pattern) for renal impairment compared to the lowest quartile (Q1: low intake levels of each dietary pattern). RESULTS: Among the 1522 participants, there was a 5.5% prevalence of low eGFR, with prevalence rates of 5.2% in men and 5.9% in women, yet the prevalence of albuminuria was as high as 47.9%. After adjusting for confounders, participants in Q4 of the plant-based dietary pattern had a smaller OR for renal impairment than those in Q1. CONCLUSIONS: Our findings demonstrated that a plant-based dietary pattern is associated with a reduced risk of renal impairment in a population with diabetes.

Effects of Internet Language Related to COVID-19 on Mental Health in College Students: The Mediating Effect of Cognitive Flexibility.

During the COVID-19 pandemic, Internet language (INL) has influenced daily life extensively. However, the process by which INL influences people's psychology and behavior is unclear. This study explored the effects of INL on mental health (anxiety and depression). A pilot study was conducted to develop a qualified scale for INL related to COVID-19 (CINL) in college students using an online questionnaire. The CINL scale was found to have two dimensions: frequency and comprehension, as well as good reliability and validity. A formal study explored the mediating effect of cognitive flexibility on the relationship between CINL and mental health. The results showed that CINL positively predicted mental health when it was mediated by cognitive flexibility. These results not only provide a new perspective on understanding the effects of cyber behavior on human mental health from a positive perspective, but also provide practitioners with new insights for interventions on college students' mental health.