Pioglitazone Ameliorates Acute Endotoxemia-Induced Acute on Chronic Renal Dysfunction in Cirrhotic Ascitic Rats

Prediction models combining zonulin, LPS, and LBP predict acute kidney injury and hepatorenal syndrome-acute kidney injury in cirrhotic patients

The development of acute kidney injury (AKI) and hepatorenal syndrome-acute kidney injury (HRS-AKI) in cirrhosis has been associated with intestinal barrier dysfunction and gut-kidney crosstalk. We use the related markers such as zonulin, lipopolysaccharides (LPS), and lipopolysaccharide-binding protein (LBP) to predict AKI and HRS-AKI in cirrhotic patients and evaluate their in vitro effects on intestinal (Caco-2) cells and renal tubular (HK-2) cells. From 2013 to 2020, we enrolled 70 cirrhotic patients and developed prediction models for AKI and HRS-AKI over a six-month period. There were 13 (18.6%) and 8 (11.4%) cirrhotic patients developed AKI and HRS-AKI. The prediction models incorporated zonulin, LPS, LBP, C-reactive protein, age, and history of hepatitis B for AKI, and zonulin, LPS, LBP, total bilirubin, and Child-Pugh score for HRS-AKI. The area under curve (AUC) for the prediction of AKI and HRS-AKI was 0.94 and 0.95, respectively. Furthermore, the conditioned medium of LPS+hrLBP pre-treated Caco-2 cells induced apoptosis, necrosis, and zonulin release in HK-2 cells, demonstrating the communication between them. This study found that zonulin, LPS, and LBP are potential practical markers for predicting AKI and HRS-AKI in cirrhotic patients, which may serve as potential targets for renal outcomes in cirrhotic patients.

Pioglitazone Modifies Kupffer Cell Function and Protects against Escherichia coli-Induced Bacteremia in Burned Mice

Infectious complications and subsequent sepsis in severely burned patients lead to high morbidity and mortality in response to uncontrolled innate immune responses mediated by macrophages. Peroxisome proliferator-activated receptor gamma (PPARγ) has anti-inflammatory activity and acts as a master regulator of macrophage polarization. In this study, we investigated whether the administration of a PPARγ agonist could modulate the Kupffer cell phenotype and thereby ameliorate the dysregulated innate response during post-burn bacterial infection. C57BL/6 mice were subjected to severe burns and randomized to receive either the PPARγ agonist, pioglitazone, or the vehicle control five days after injury, followed by the subsequent analysis of hepatic macrophages. Survival from the bacterial infection was monitored for seven days. Pioglitazone protected burned mice against bacterial infection. A single treatment with pioglitazone significantly enhanced phagocytosis, phagosome acidification, bacterial clearance, and reduction in inflammatory mediators in Kupffer cells. In conclusion, PPARγ activation by pioglitazone prevents clinical deterioration due to post-burn bacterial infection and improves survival. Our findings suggest that pioglitazone may be an effective therapeutic candidate for post-burn infectious complications.

Identification of Serum Metabolomics Characteristics in Patients with Stable Angina Pectoris Using UHPLC-QE-MS

Background

Stable angina pectoris (SAP) is one of the main types of coronary heart disease (CHD). To improve treatment outcomes, more effective biomarkers are needed. Currently, studies on the metabolic characteristics of SAP are lacking. Here, we explored the serum metabolomic profile of SAP and identified potential biomarkers and related pathways to assist the clinical diagnosis and treatment of SAP.

Method

Thirty patients with SAP patients and 30 healthy controls (CON) without stenosis were selected for this study. All patients underwent coronary angiography. The metabolites of the two groups' serum samples were investigated using UHPLC-QE-MS. Changes in serum metabolic profile were evaluated using multivariate statistical analysis and pathway analysis.

Result

OPLS-DA analysis identified significant differences in the serum metabolic profiles between patients with SAP and CON. Twenty-five differential metabolites were identified between patients from SAP and CON groups, including choline, creatine, L-arginine, beta-guanidinopropionic acid, isopalmitic acid, xanthine, LysoPC (18 : 1), and LysoPC (20 : 3). Pathway analysis found that these differential metabolites were involved in energy metabolism, oxidative stress, purine metabolism, and other metabolic pathways.

Conclusion

By comparing the serum metabolic profiles of SAP patients with a control group, we identified 25 potential biomarkers that could improve the clinical diagnosis and treatment of SAP.