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Abstract Background Evidence links gamma‐glutamyl transferase (GGT) to mortality in the general population. However, the relationship of GGT with all‐cause and cause‐specific mortality risk has been little explored in type 2 diabetes mellitus (T2DM) patients. Methods We recruited 20 340 community‐dwelling T2DM patients between 2013 and 2014 in Jiangsu, China. Cox regression models were used to assess associations of GGT with all‐cause and specific‐cause mortality. Restricted cubic splines were used to analyze dose–response relationships between GGT and mortality. Stratified analysis was conducted to examine potential interaction effects by age, sex, smoking status, body mass index (BMI), diabetes duration, and dyslipidemia. Results During a median follow‐up period of 7.04 years (interquartile range: 6.98–7.08), 2728 deaths occurred, including 902 (33.09%) due to cardiovascular disease (CVD), and 754 (27.58%) due to cancer. GGT concentrations were positively associated with all‐cause, CVD, and cancer mortality. Multivariable hazard ratios (HRs) for the highest (Q5) vs. the lowest quintile (Q1) were 1.63 (95% confidence intervals [CI]: 1.44–1.84) for all‐cause mortality, 1.87 (95% CI: 1.49–2.35) for CVD mortality, and 1.43 (95% CI: 1.13–1.81) for cancer mortality. Effect modification by BMI and dyslipidemia was observed for all‐cause mortality (both p for interaction

Objective Investigating the associations of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) with all-cause, cardiovascular disease (CVD) and cancer mortality in a large cohort of community-dwelling patients with type 2 diabetes mellitus (T2DM).Design Community-based prospective cohort study conducted between 2013 and 2014.Setting 44 selected townships in Changshu and Huai’an City, Jiangsu province, China.Participants 20340 participants with T2DM were recruited in Jiangsu province, China.Methods We use Cox proportional hazard models to estimate the HR and 95% CIs of associations of serum ALT and AST levels with all-cause and cause-specific mortality. Restricted cubic splines were used to explore the dose-response relationships between ALT and AST levels with mortality.Results ALT and AST levels were inversely associated with CVD mortality, compared with the lowest quintile (Q1), the multivariable HRs of the highest quintile (Q5) was 0.82 (95% CI: 0.66 to 1.01, p for trend=0.022) and 0.78 (95% CI: 0.63 to 0.96, p for trend=0.022), respectively. Furthermore, the HRs for ALT levels in all-cause mortality were 0.90 (95% CI: 0.79 to 1.01, p for trend=0.018), and the HRs for AST levels in cancer mortality were 1.29 (95% CI: 1.02 to 1.63, p for trend=0.023). Stronger inverse effects of ALT and AST levels on all-cause mortality were observed in the older subgroup and in those with dyslipidaemia (all p for interaction

ObjectiveTo investigate the associations of circulating liver function marker levels with the risk of chronic obstructive pulmonary disease (COPD).MethodsWe leveraged the data of 372,056 participants from the UK Biobank between 2006 and 2010. The assessed circulating liver function markers included alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), total bilirubin (TBIL), albumin (ALB), and total protein (TP). Incident COPD was identified through linkage to the National Health Service registries. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs).ResultsDuring a median follow-up period of 12.3 (interquartile range:11.4-13.2) years, we documented 10,001 newly diagnosed COPD cases. Lower levels of ALT, TBIL, ALB, and TP and higher levels of GGT and ALP were nonlinearly associated with elevated COPD risk. The HR (95% CI) for decile 10 vs. 1 was 0.92 (0.84-1.01) for ALT, 0.82 (0.75-0.89) for TBIL, 0.74 (0.67-0.81) for ALB, 0.96 (0.88-1.04) for TP, 1.45 (1.31-1.62) for GGT, and 1.31 (1.19-1.45) for ALP. Restricted cubic spline analyses suggested a U-shaped relationship between AST levels and COPD risk (P for nonlinearity

IntroductionGastric cancer (GC) remains the major constituent of cancer-related deaths and a global public health challenge with a high incidence rate. Helicobacter pylori (HP) plays an essential role in promoting the occurrence and progression of GC. Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the tumor microenvironment (TME), which is related to the metastasis of GC. However, the regulation mechanisms of CAFs in HP-related GC are not elucidated thoroughly.MethodsHP-related genes (HRGs) were downloaded from the GSE84437 and TCGA-GC databases. The two databases were combined into one cohort for training. Furthermore, the consensus unsupervised clustering analysis was obtained to sort the training cohort into different groups for the identification of differential expression genes (DEGs). Weighted correlation network analysis (WGCNA) was performed to verify the correlation between the DEGs and cancer-associated fibroblasts which were key components in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) was executed to find cancer-associated fibroblast-related differential expression genes (CDEGs) for the further establishment of a prognostic model.Results and discussionIn this study, 52 HP-related genes (HRGs) were screened out based on the GSE84437 and TCGA-GC databases. A total of 804 GC samples were analyzed, respectively, and clustered into two HP-related subtypes. The DEGs identified from the two subtypes were proved to have a relationship with TME. After WGCNA and LASSO, the CAFs-related module was identified, from which 21 gene signatures were confirmed. Then, a CDEGs-Score was constructed and its prediction efficiency in GC patients was conducted for validation. Overall, a highly precise nomogram was established for enhancing the adaptability of the CDEGs-Score. Furthermore, our findings revealed the applicability of CDEGs-Score in the sensitivity of chemotherapeutic drugs. In general, our research provided brand-new possibilities for comprehending HP-related GC, evaluating survival, and more efficient therapeutic strategies.

Background: Ferroptosis, a form of regulatory cell death, has been linked to the development of various tumors. Peripheral neuroblastoma (NB) is one of the most common extracranial solid tumors in children, and it has been proposed that regulating tumor cell ferroptosis may be a future treatment for NB. However, it is unclear how ferroptosis contributes to NB development.Methods: Expression data were collected from two independent cohorts (GEO and Arrayexpress databases). Univariate Cox analysis, multivariate Cox analysis, and the least absolute shrinkage and selection operator (Lasso) algorithm were applied to create a prognostic signature, whose performance was quantified using the area under the receiver operating characteristic curve (AUC) and Kaplan–Meier curves. A prognostic meta-analysis was used to test the suitability and stability of the FRG signature. Drug sensitivity analyses were performed using the data collected from Cell Miner™.Results:PROM2, AURKA, STEAP3, CD44, ULK2, MAP1LC3A, ATP6V1G2, and STAT3 are among the eight genes in the FRG prognostic signature, all of which were highly expressed in stage 1 NB, except AURKA. Furthermore, the high-risk group, which was stratified by signature, had a lower overall survival rate than the low-risk group. GSEA revealed that high-risk groups have more biological processes related to ferroptosis.Conclusion: Ferroptosis-related genes are expressed differently between stages 1 and 4 NB. The FRG signature successfully stratified NB patients into two risk groups and can accurately predict the overall survival in NB. In addition, we found that the gene AURKA might have the potential to be a prognostic marker in NB.

Computation of a large group of interior eigenvalues at the middle spectrum is an important problem for quantum many-body systems, where the level statistics provides characteristic signatures of quantum chaos. We propose an exact numerical method, dual applications of Chebyshev polynomials (DACP), to simultaneously find thousands of central eigenvalues, where the level space decreases exponentially with the system size. To disentangle the near-degenerate problem, we employ twice the Chebyshev polynomials, to construct an exponential semicircle filter as a preconditioning step and to generate a large set of proper basis states in the desired subspace. Numerical calculations on Ising spin chain and spin glass shards confirm the correctness and efficiency of DACP. As numerical results demonstrate, DACP is $30$ times faster than the state-of-the-art shift-invert method for the Ising spin chain while $8$ times faster for the spin glass shards. In contrast to the shift-invert method, the computation time of DACP is only weakly influenced by the required number of eigenvalues, which renders it a powerful tool for large scale eigenvalues computations. Moreover, the consumed memory also remains a small constant (5.6 GB) for spin-$1/2$ systems consisting of up to $20$ spins, making it desirable for parallel computing.