Objective To investigate the relationship between triglyceride glucose body mass index(TyG-BMI),high density lipoprotein cholesterol to Apolipoprotein A ratio(HAR),glycemic risk index(GRI)and diabetic retinopathy(DR).Methods A total of 159 patients with type 2 diabetes mellitus(T2DM)complicated with DR(the DR group)were divided into the non-proliferative retinopathy group(NPDR group,66 cases)and the proliferative retinopathy group(PDR group,93 cases)according to DR international clinical grading criteria,and 159 T2DM patients without DR were selected as the control group.Clinical information and baseline laboratory test results were recorded,and TyG-BMI,HAR and GRI were calculated.Multivariate Logistic regression analysis was conducted to analyze the related factors of the incidence of DR,and receiver operating characteristic(ROC)curve was used to analyze the diagnostic value of TyG-BMI,HAR and GRI for DR.Results The duration of T2DM,the proportion of hypertension,diabetic nephropathy and diabetic foot,levels of HbA1c,homeostasis model insulin resistance index(HOMA-IR),total cholesterol(TC),C-reactive protein(CRP),TyG-BMI,HAR and GRI were higher in the DR group than those in the control group(P<0.05).TyG-BMI,HAR and GRI were higher in the PDR group than those in the NPDR group(P<0.05).Multivariate Logistic regression analysis showed that the longer course of T2DM disease[OR(95%CI):2.781(1.398-5.534)],high TyG-BMI[2.036(1.169-3.546)],high HAR[1.890(1.090-3.280)]and high GRI[1.836(1.065-3.167)]were risk factors for DR(P<0.05).ROC curve analysis results showed that,the area under the curve(AUC)of combined TyG-BMI,HAR and GRI diagnosis of DR was higher than that of single diagnosis[0.940(0.908-0.964),0.864(0.821-0.900),0.796(0.747-0.839)and 0.836(0.790-0.875),all P<0.05].Conclusion The increased TyG-BMI,HAR and GRI in T2DM patients is associated with the onset and severity of DR,and which can be used to assess the risk of DR.

Objective To investigate the relationship between triglyceride glucose body mass index(TyG-BMI),high density lipoprotein cholesterol to Apolipoprotein A ratio(HAR),glycemic risk index(GRI)and diabetic retinopathy(DR).Methods A total of 159 patients with type 2 diabetes mellitus(T2DM)complicated with DR(the DR group)were divided into the non-proliferative retinopathy group(NPDR group,66 cases)and the proliferative retinopathy group(PDR group,93 cases)according to DR international clinical grading criteria,and 159 T2DM patients without DR were selected as the control group.Clinical information and baseline laboratory test results were recorded,and TyG-BMI,HAR and GRI were calculated.Multivariate Logistic regression analysis was conducted to analyze the related factors of the incidence of DR,and receiver operating characteristic(ROC)curve was used to analyze the diagnostic value of TyG-BMI,HAR and GRI for DR.Results The duration of T2DM,the proportion of hypertension,diabetic nephropathy and diabetic foot,levels of HbA1c,homeostasis model insulin resistance index(HOMA-IR),total cholesterol(TC),C-reactive protein(CRP),TyG-BMI,HAR and GRI were higher in the DR group than those in the control group(P<0.05).TyG-BMI,HAR and GRI were higher in the PDR group than those in the NPDR group(P<0.05).Multivariate Logistic regression analysis showed that the longer course of T2DM disease[OR(95%CI):2.781(1.398-5.534)],high TyG-BMI[2.036(1.169-3.546)],high HAR[1.890(1.090-3.280)]and high GRI[1.836(1.065-3.167)]were risk factors for DR(P<0.05).ROC curve analysis results showed that,the area under the curve(AUC)of combined TyG-BMI,HAR and GRI diagnosis of DR was higher than that of single diagnosis[0.940(0.908-0.964),0.864(0.821-0.900),0.796(0.747-0.839)and 0.836(0.790-0.875),all P<0.05].Conclusion The increased TyG-BMI,HAR and GRI in T2DM patients is associated with the onset and severity of DR,and which can be used to assess the risk of DR.

Objective To establish and validate a model that can predict the risk of death during hospitalization in elderly patients with sepsis. Methods A total of 238 hospitalized patients with sepsis in the Intensive Care Unit of the First Hospital Affiliated to Harbin Medical University from January 2019 to December 2022 were retrospectively included, and they were divided into death group with 68 cases (28.57%) and survival group with 170 cases (71.43%) according to the prognosis during hospitalization as the primary outcome indicator. Multivariate Logistic regression was used to screen the independent risk factors for death during hospitalization in sepsis patients, and a model for predicting the risk of death during hospitalization in sepsis patients was established based on these factors. The performance of the prediction model was evaluated by the receiver operating characteristic (ROC) curve, and the results were expressed by the area under the curve (AUC); external validation was performed based on the clinical data of 176 sepsis patients from January 2016 to December 2018. Results Univariate analysis showed that when compared with the survival group, the death group had higher ratios of patients aged over 70 years, ratio of injury (AKI) in stage Ⅲ, higher levels of red cell distribution width (RDW), fibrinogen, lactate, blood creatinine, the National Early Warning Score (NEWS) and the quick Sequential Organ Failure Assessment (qSOFA) score, but lower levels of oxygenation index and albumin, and the differences were statistically significant (P < 0.05). Multivariate Logistic regression analysis showed that age over 70 years old (OR=1.426, 95%CI, 1.055 to 1.928), lactate>6 mmol/L (OR=1.436, 95%CI, 1.105 to 1.867), RDW>16% (OR=1.354, 95%CI, 1.080 to 1.698), AKI in stage Ⅲ (OR=1.982, 95%CI, 1.407 to 2.791), and qSOFA score>2 points (OR=1.853, 95%CI, 1.255 to 2.738) were the independent risk factors for death during hospitalization in patients with sepsis (P < 0.05). Based on the regression analysis results, a risk equation for death in patients with sepsis was established, and consistency index (C_index)=-1.694+0.355×age+0.303×RDW+0.362×lactate+0.684×AKI in stage Ⅲ+0.617×qSOFA score. The ROC curve showed that the AUC of C_index for predicting death during hospitalization in patients with sepsis was 0.882 (95%CI, 0.834 to 0.929) with a sensitivity of 83.82% and a specificity of 77.06% by internal validation, and 0.823 (95%CI, 0.757 to 0.889) with a sensitivity of 74.13% and a specificity of 81.36% by external validation. Conclusion Age, lactate, RDW, AKI staging and qSOFA score are correlated with the risk of death in elderly patients with sepsis, and the model constructed based on these parameters may help predict the risk of all-cause death during hospitalization in elderly patients with sepsis.

Gliomas are one of the most common types of brain cancers. Numerous efforts have been devoted to studying the mechanisms of glioma genesis and identifying biomarkers for diagnosis and treatment. To help further investigations, we present a comprehensive database named GliomaDB. GliomaDB includes 21,086 samples from 4303 patients and integrates genomic, transcriptomic, epigenomic, clinical, and gene-drug association data regarding glioblastoma multiforme (GBM) and low-grade glioma (LGG) from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), the Memorial Sloan Kettering Cancer Center Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT), the US Food and Drug Administration (FDA), and PharmGKB. GliomaDB offers a user-friendly interface for two main types of functionalities. The first comprises queries of (i) somatic mutations, (ii) gene expression, (iii) microRNA (miRNA) expression, and (iv) DNA methylation. In addition, queries can be executed at the gene, region, and base level. Second, GliomaDB allows users to perform survival analysis, coexpression network visualization, multi-omics data visualization, and targeted drug recommendations based on personalized variations. GliomaDB bridges the gap between glioma genomics big data and the delivery of integrated information for end users, thus enabling both researchers and clinicians to effectively use publicly available data and empowering the progression of precision medicine in glioma. GliomaDB is freely accessible at http://bigd.big.ac.cn/gliomaDB.