Correlation between serum uric acid to high-density lipoprotein cholesterol ratio and atrial fibrillation in patients with NAFLD.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is independently associated with atrial fibrillation (AF) risk. The uric acid (UA) to high-density lipoprotein cholesterol (HDL-C) ratio (UHR) has been shown to be closely associated with cardiovascular disease (CVD) and NAFLD. The aim of this study is to clarify whether elevated UHR is associated with the occurrence of AF in patients with NAFLD and to determine whether UHR predicted AF. METHODS: Patients diagnosed with NAFLD in the Department of Cardiovascular Medicine of the Second Hospital of Shanxi Medical University from January 1, 2020, to December 31, 2021, were retrospectively enrolled in this study. The study subjects were categorized into AF group and non-AF group based on the presence or absence of combined AF. Logistic regression was performed to evaluate the correlation between UHR and AF. Sensitivity analysis and subgroup interaction analysis were performed to verify the robustness of the study results. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cutoff value for UHR to predict the development of AF in patients with NAFLD. RESULTS: A total of 421 patients with NAFLD were included, including 171 in the AF group and 250 in the non-AF group. In the univariate regression analysis, NAFLD patients with higher UHR were more likely to experience AF, and the risk of AF persisted after confounding factors were adjusted for (OR: 1.010, 95%CI: 1.007-1.013, P<0.001). AF risk increased with increasing UHR quartile (P for trend < 0.001). Despite normal serum UA and HDL-C, UHR was still connected with AF in patients with NAFLD. All subgroup variables did not interact significantly with UHR in the subgroup analysis. The ROC curve analysis showed that the areas under the curve for UA, HDL-C, and UHR were 0.702, 0.606, and 0.720, respectively, suggesting that UHR has a higher predictive value for AF occurrence in NAFLD patients compared to HDL-C or UA alone. CONCLUSION: Increased UHR level was independently correlated with a high risk of AF in NAFLD patients.

Dual therapy with phospholipase and metalloproteinase inhibitors from Sinonatrix annularis alleviated acute kidney and liver injury caused by multiple snake venoms.

Snakebite envenomation often induces acute kidney injury (AKI) and acute liver injury (ALI), leading to augmented injuries and poor rehabilitation. Phospholipase A2 (PLA2) and metalloproteinase (SVMP) present in venom are responsible for the envenomation-associated events. In this study, mice envenomed with Deinagkistrodon acutus, Naja atra, or Agkistrodon halys pallas venom exhibited typical AKI and ALI symptoms, including significantly increased plasma levels of myoglobin, free hemoglobin, uric acid, aspartate aminotransferase, and alanine aminotransferase and upregulated expression of kidney NGAL and KIM-1. These effects were significantly inhibited when the mice were pretreated with natural inhibitors of PLA2 and SVMP isolated from Sinonatrix annularis (SaPLIγ and SaMPI). The inhibitors protected the physiological structural integrity of the renal tubules and glomeruli, alleviating inflammatory infiltration and diffuse hemorrhage in the liver. Furthermore, the dual therapy alleviated oxidative stress and apoptosis in the kidneys and liver by mitigating mitochondrial damage, thereby effectively reducing the lethal effect of snake venom in the inhibitor-treated mouse model. This study showed that dual therapy with inhibitors of metalloproteinase and phospholipase can effectively prevent ALI and AKI caused by snake bites. Our findings suggest that intrinsic inhibitors present in snakes are prospective therapeutic agents for multi-organ injuries caused by snake envenoming.

A combined analysis of TyG index, SII index, and SIRI index: positive association with CHD risk and coronary atherosclerosis severity in patients with NAFLD.

Background: Insulin resistance(IR) and inflammation have been regarded as common potential mechanisms in coronary heart disease (CHD) and non-alcoholic fatty liver disease (NAFLD). Triglyceride-glucose (TyG) index is a novel biomarker of insulin resistance, System immune-inflammation index(SII) and Systemic inflammation response index(SIRI) are novel biomarkers of inflammation, these biomarkers have not been studied in CHD with NAFLD patients. This study investigated the correlation between the TyG index, SII index, and SIRI index and CHD risk among NAFLD patients. Methods: This cross-sectional study included 407 patients with NAFLD in the Department of Cardiology, The Second Hospital of Shanxi Medical University. Of these, 250 patients with CHD were enrolled in the NAFLD+CHD group and 157 patients without CHD were enrolled as NAFLD control. To balance covariates between groups, 144 patients were selected from each group in a 1:1 ratio based on propensity score matching (PSM). Potential influences were screened using Lasso regression analysis. Univariate and multivariate logistic regression analyses and the Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to assess independent risk and protective factors for CHD. Construction of nomogram using independent risk factors screened by machine learning. The receiver operating characteristic(ROC) curve was used to assess the ability of these independent risk factors to predict coronary heart disease. The relationship between the Gensini score and independent risk factors was reflected using the Sankey diagram. Results: The LASSO logistic regression analysis and Logistic regression analyses suggest that TyG index (OR, 2.193; 95% CI, 1.242-3.873; P = 0.007), SII index (OR, 1.002; 95% CI, 1.001-29 1.003; P <0.001), and SIRI index (OR,1.483;95%CI,1.058-2.079,P=0.022) are independent risk factors for CHD. At the same time, Neutrophils, TG, and LDL-C were also found to be independent risk factors in patients, HDL-C was a protective factor for CHD in patients with NAFLD. Further analysis using three machine learning algorithms found these independent risk factors to have good predictive value for disease diagnosis, SII index shows the highest predictive value. ROC curve analysis demonstrated that combining the SII index, SIRI index, and TyG index can improve the diagnostic ability of non-alcoholic liver cirrhosis patients with CHD.ROC curve analysis showed that the combined analysis of these independent risk factors improved the predictive value of CHD(AUC: 0.751; 95% CI: 0.704-0.798; P <0.001). Conclusion: TyG index, SII index, and SIRI index are all independent risk factors for CHD in patients with NAFLD and are strongly associated with prediction and the severity of CHD.