Association between blood urea nitrogen levels and the risk of diabetes mellitus in Chinese adults: secondary analysis based on a multicenter, retrospective cohort study.

Background: As one of the recognized indicators of kidney function, blood urea nitrogen (BUN) is a key marker of metabolic diseases and other diseases. Currently, data on the relationship of BUN levels with the risk of diabetes mellitus (DM) in Chinese adults are sparse. This study aimed to investigate the correlation between BUN levels and DM risk in Chinese adults. Data and methods: This study is a secondary analysis of a multicenter, retrospective cohort study with data from the Chinese health screening program in the DATADRYAD database. From 2010 to 2016, health screening was conducted on 211833 Chinese adults over the age of 20 in 32 locations and 11 cities in China, and there was no DM at baseline. Cox proportional hazards regression analysis assessed an independent correlation between baseline BUN levels and the risk of developing DM. The Generalized Sum Model (GAM) and smoothed curve fitting methods were used to explore the nonlinear relationship. In addition, subgroup analyses were performed to assess the consistency of correlations between different subgroups and further validate the reliability of the results. Results: After adjusting for potential confounding factors (age, sex, etc.), BUN levels were positively correlated with the occurrence of DM (HR=1.11, 95% CI (1.00~1.23)). BUN level had a nonlinear relationship with DM risk, and its inflection point was 4.2mmol/L. When BUN was greater than 4.2mmol/L, BUN was positively correlated with DM, and the risk of DM increased by 7% for every 1 mmol/L increase in BUN (P<0.05). Subgroup analysis showed that a more significant correlation between BUN levels and DM was observed in terms of sex, BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), alaninetransaminase (ALT), aspartate transaminase (AST), creatinine (Cr) and smoking status (interaction P<0.05). Conclusion: High levels of BUN are associated with an increased risk of DM in Chinese adults, suggesting that active control of BUN levels may play an important role in reducing the risk of DM in Chinese adults.

Clinical characteristics and prognostic nomograms of 12555 non-severe COVID-19 cases with Omicron infection in Shanghai.

BACKGROUND: Omicron variant of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has rapidly become a global threat to public health. Numerous asymptomatic and mild cases had been admitted in shelter hospitals to quickly win the fight against Omicron pandemic in Shanghai. However, little is known about influencing factors for deterioration and length of stay (LOS) in hospitals among these non-severe cases. METHODS: This study included 12,555 non-severe cases with COVID-19 in largest shelter hospital of Shanghai, aiming to explore prognostic factors and build effective models for prediction of LOS. RESULTS: Data showed that 75.0% of participants were initially asymptomatic. In addition, 94.6% were discharged within 10 days, only 0.3% with deterioration in hospitals. The multivariate analysis indicated that less comorbidities (OR = 1.792, P = 0.012) and booster vaccination (OR = 0.255, P = 0.015) was associated with the decreased risk of deterioration. Moreover, age (HR = 0.991, P < 0.001), number of symptoms (HR = 0.969, P = 0.005), time from diagnosis to admission (HR = 1.013, P = 0.001) and Cycle threshold (CT) values of N gene (HR = 1.081, P < 0.001) were significant factors associated with LOS. Based on these factors, a concise nomogram model for predicting patients discharged within 3 days or more than 10 days was built in the development cohort. In validation cohort, 0.75 and 0.73 of Areas under the curve (AUC) in nomograms, similar with AUC in models of simple machine learning, showed good performance in estimating LOS. CONCLUSION: Collectively, this study not only provides important evidence to deeply understand clinical characteristics and risk factors of short-term prognosis in Shanghai Omicron outbreaks, but also offers a concise and effective nomogram model to predict LOS. Our findings will play critical roles in screening high-risk groups, providing advice on duration of quarantine and helping decision-makers with better preparation in outbreak of COVID-19.

XGBoost-based machine learning test improves the accuracy of hemorrhage prediction among geriatric patients with long-term administration of rivaroxaban.

BACKGROUND: Hemorrhage is a potential and serious adverse drug reaction, especially for geriatric patients with long-term administration of rivaroxaban. It is essential to establish an effective model for predicting bleeding events, which could improve the safety of rivaroxaban use in clinical practice. METHODS: The hemorrhage information of 798 geriatric patients (over the age of 70 years) who needed long-term administration of rivaroxaban for anticoagulation therapy was constantly tracked and recorded through a well-established clinical follow-up system. Relying on the 27 collected clinical indicators of these patients, conventional logistic regression analysis, random forest and XGBoost-based machine learning approaches were applied to analyze the hemorrhagic risk factors and establish the corresponding prediction models. Furthermore, the performance of the models was tested and compared by the area under curve (AUC) of the receiver operating characteristic (ROC) curve. RESULTS: A total of 112 patients (14.0%) had bleeding adverse events after treatment with rivaroxaban for more than 3 months. Among them, 96 patients had gastrointestinal and intracranial hemorrhage during treatment, which accounted for 83.18% of the total hemorrhagic events. The logistic regression, random forest and XGBoost models were established with AUCs of 0.679, 0.672 and 0.776, respectively. The XGBoost model showed the best predictive performance in terms of discrimination, accuracy and calibration among all the models. CONCLUSION: An XGBoost-based model with good discrimination and accuracy was built to predict the hemorrhage risk of rivaroxaban, which will facilitate individualized treatment for geriatric patients.

Primary membranous nephrotic syndrome with chylothorax as first presentation: A case report and literature review.

BACKGROUND: Primary membranous nephrotic syndrome with chylothorax as the first manifestation is an unusual condition. To date, only a few cases have been reported in clinical practice. CASE SUMMARY: The clinical data of a 48-year-old man with primary nephrotic syndrome combined with chylothorax admitted to the Department of Respiratory and Critical Care Medicine of Shaanxi Provincial People's Hospital were retrospectively analysed. The patient was admitted to the hospital for 12 d due to shortness of breath. Imaging showed pleural effusion, laboratory tests confirmed true chylothorax, and renal biopsy revealed membranous nephropathy. After primary disease treatment and early active symptom treatment, the prognosis of the patient was good. This case suggests that chylothorax is a rare complication of primary membranous nephrotic syndrome in adults, and early lymphangiography and renal biopsy can assist in the diagnosis when there are no contraindications. CONCLUSION: Primary membranous nephrotic syndrome combined with chylothorax is rare in clinical practice. We report a relevant case to provide case information for clinicians and to improve diagnosis and treatment.

Association Between Blood Urea Nitrogen Levels and Length of Stay in Patients with Pneumonic Chronic Obstructive Pulmonary Disease Exacerbation: A Secondary Analysis Based on a Multicentre, Retrospective Cohort Study.

Purpose: High blood urea nitrogen (BUN) is associated with an elevated risk of mortality in various diseases, such as heart failure and pneumonia. Heart failure and pneumonia are common comorbidities of chronic obstructive pulmonary disease (COPD) exacerbation. However, data on the relationship of BUN levels with length of stay (LOS) in patients with pneumonic COPD exacerbation are sparse. The purpose of this study was to evaluate the correlation between BUN levels and LOS in a cohort of patients with pneumonic COPD exacerbation. Patients and Methods: The present study was a multicentre, retrospective cohort study. A total of 1226 patients with pneumonic COPD exacerbation were included through a validated algorithm derived from the 10th revision of the International Classification of Diseases and Related Health Problems (ICD-10). It should be noted that the entire study was completed by Shiroshita et al, who uploaded the data to the DATADRYAD website. The author only used these data for secondary analysis. Results: After adjusting for potential confounders (age, gender), a nonlinear relationship was detected between BUN levels less than 40 mg/dl and LOS. The effect sizes and the confidence intervals on the left and right sides of the inflection point were 0.27 (0.16, 0.39) and -0.17 (-0.34, 0.01), respectively. Conclusion: High levels of BUN in the hospital may be associated with increased LOS. BUN was positively related to LOS when BUN was less than 40 mg/dl.

Significance of Trask protein interactions in brain metastatic cohorts of lung cancers.

A class of adhesion protein that occurs in the membrane with both extracellular and intracellular domain and play vital role in maintaining multicellularity is TRASK, also called CUB-domain containing protein1, CD318 (CDCP1). Specifically, in the current study, documented aggressive grades of lung cancers and distant metastatic tissues were examined for protein interactions of Trask and compared with lung cancer variants in situ. The intracellular domain of Trask has the ability to undergo tyrosine phosphorylation and thereafter undergo increased genomic expression, as well as interact with cytoskeletal proteins in the cell periphery and other local signal transduction machinery to induce invadopodia formation and distant metastasis. We incorporated proximity ligation assay to examine protein interactions of Trask in metastatic lung cancer tissues and compare with advanced and low-grade lung cancers restricted to the primary site of origins. Here, we provide direct evidence that activated Trask, which is a phosphorylated form, binds with cytoskeletal proteins actin and spectrin. These interactions were not seen in locally growing lung cancer and cancer in situ. These interactions may be responsible for invadopodia formation and breaking free from a multicellular environment. Functional studies demonstrated interaction between Trask and the STOCs Orai1 and Stim1. Calcium release from internal stores was highest in metastatic lung cancers, suggesting this mechanism as an initial stimulus for the cells to respond chaotically to external growth factor stimulation, especially in aggressive metastatic variants of lung cancers. Recently, inhibitors of STOCs have been identified, and preclinical evidence may be obtained whether these drugs may be of benefit in preventing the deadly consequences of lung cancer.