Inflammatory Protein Signatures as Predictive Disease-Specific Markers for Non-Alcoholic Steatohepatitis (NASH).

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic disease worldwide, consisting of a broad spectrum of diseases such as simple steatosis (NAFL), non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and hepatocellular carcinoma. Hepatic inflammation plays a key role in the pathophysiology of NAFLD. Inflammatory mediators such as cytokines and chemokines are considered as contributing factors to NAFLD development and progression. In the present study, we aimed to investigate the inflammatory protein signatures as predictive disease-specific markers for non-alcoholic fatty liver disease (NAFLD). This cross-sectional study included healthy control (n = 64), NAFL (n = 109), and NASH (n = 60) human subjects. Serum concentrations of various cytokines and chemokines were evaluated using sensitive multiplex assays. We used principal component analysis (PCoA) to reveal distinct differences in the levels of cytokines and chemokines between each of the study groups. Further, a random forest classification model was developed to identify the panel of markers that could predict diseases. The protein-protein network analysis was performed to determine the various signaling pathways associated with the disease-specific panel of markers. Serum concentrations of TNF-α, IL-1ß, IL-1ra, G-CSF, PDGF-BB, MCP-1, MIP-1a, MIP-1b, RANTES, eotaxin, IL-8 and IP-10 were significantly increased in NASH group as compared to control group. Furthermore, serum concentrations of IL-9 and IL-13 were significantly lower in the NASH group, whereas IL-2 levels were significantly decreased in the NAFL group when compared to the control group. PCoA results demonstrated statistically significant differences in cytokines and chemokines between each of the study groups (PERMANOVA p = 0.001; R2 = 0.102). RANTES, IL-1ra, MIP-1b, IL-2, and G-CSF could differentiate the NAFL group from the controls; G-CSF, IL-1ra, TNF-α, RANTES, and IL-9 could differentiate the NASH group from the controls; and G-CSF, IL-9, IL-13, eotaxin, and TNF- α could differentiate the NASH group from the NAFL group. Our protein-protein network revealed that these markers are involved in cytokine-cytokine receptor interaction, Th1 and Th2 cell differentiation, TNF, chemokine, JAK/STAT, P13K/Akt, TLR, NOD-like receptor, NF-kB, and adipocytokine signaling pathways which might be responsible for disease pathogenesis. Our study findings revealed a set of distinct cytokine and chemokine markers and they might be considered as biomarkers in distinguishing NASH from NAFL. Future multicentre studies with larger sample size are recommended to determine the potential utility of these panels of markers.

Platelet-derived sTLT-1 is associated with platelet-mediated inflammation in coronary artery disease patients.

The development of coronary artery disease (CAD) depends heavily on platelet activation, and inflammation plays a major role in all stages of atherosclerosis. Platelet-specific soluble triggering receptor expressed on myeloid cells like transcript 1 (sTLT-1) facilitate clot formation and have been linked to chronic inflammation. In this study, we explored the role of platelet-derived sTLT-1 in platelet-mediated inflammation in CAD patients. Plasma levels of sTLT-1 were measured using enzyme-linked immunosorbent assay in CAD patients (n = 163) and healthy controls (n = 99). Correlation analysis was performed to determine the circulatory sTLT-1 levels with platelet activation markers, immune cells, and inflammatory cytokines/chemokines. Increased plasma sTLT-1 levels were observed in CAD patients compared with those in healthy controls (p < 0.0001). A positive correlation was observed between sTLT-1 and platelet activation markers (P-selectin, PAC-1), CD14++ CD16- cells (classical monocytes), Natural killer T (NKT) cells, and platelet-immune cell aggregates with monocytes, neutrophils, dendritic cells, CD11c+ cells, and NKT cells. In contrast, a significant negative correlation was observed with CD8 cells. Furthermore, a significant positive correlation was observed between sTLT-1 and inflammatory markers (TNF-α, IL-1ß, IL-2, IL-6, IL-12p70, IL-18, CXCL-12, and CCL-11). Logistic regression analysis identified sTLT-1 and triglycerides as predictors of CAD. Receiver operating characteristic curve (ROC) analysis showed that sTLT-1 had a higher sensitivity and specificity for predicting CAD. Our findings suggest that platelet activation induces the release of sTLT-1 into the circulation in CAD patients, which aggregates with immune cells and enhances inflammatory responses.

Circulatory bone morphogenetic protein (BMP) 8B is a non-invasive predictive biomarker for the diagnosis of non-alcoholic steatohepatitis (NASH).

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is a complex disease which is characterized by the deposition of fats in the hepatocytes. Further, it progresses to nonalcoholic steatohepatitis (NASH), fibrosis, and hepatocellular carcinoma. The increasing prevalence of NAFLD urges to find the non-invasive predictive biomarkers. In this study, we sought to determine increased BMP8B levels as predictors for the progression of NAFLD. METHODS: In the present cross-sectional study, circulatory BMP8B levels were measured in healthy controls (n = 56), NAFL patients (n = 72) and NASH patients (n = 77) by using an ELISA kit. Human hepatic BMP8B mRNA expression was measured in the liver tissue of control and NASH patients. In addition, BMP8B expression was confirmed by immunohistochemistry analysis. Furthermore, hepatic BMP8B mRNA expression was measured in wild type (WT) mice, WT mice fed with choline deficient high fat diet (WT+CDHF), iNOS (inducible nitric oxide synthase) knockout (iNOS-/-) mice, iNOS-/- fed with CDHF diet (iNOS-/-+CDHF). RESULTS: Increased circulatory BMP8B levels and BMP8B mRNA expression in hepatic tissue were significantly higher in NASH patients as compared with the control subjects. BMP8B expression was increased parallel to the fibrosis score in the hepatic tissues of NASH patients. It was observed that increased BMP8B levels have shown a significant positive correlation between aspartate aminotransferase (r = 0.31, p = 0.005), alanine aminotransferase (r = 0.23, p = 0.045), APRI (r = 0.30, p = 0.009), and Fib-4 score (r = 0.25, p = 0.036) in NASH patients. BMP8B has maintained a significant association with NASH and shown high sensitivity (92.91%) and specificity (92.73%) in NASH patients. Furthermore, increased BMP8B mRNA expression levels were observed in iNOS-/-+CDHF mice. CONCLUSION: Our study findings confirmed that BMP8B increases with the severity of the disease and BMP8B shows potential as a non-invasive predictive biomarker to identify NAFLD progression. However, future studies should investigate circulatory BMP8B levels in a large number of patients and also its impact on liver during NAFLD progression.

Efficacy and Safety of Saroglitazar in Patients with Cardiometabolic Diseases: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND AND OBJECTIVE: The incidence of cardiometabolic diseases is increasing because of an increase in the standard of living. Currently, clinical treatment strategies for cardiometabolic diseases mainly focus on maintaining glycemic and lipid profiles. The objective of this systematic review and meta-analysis was to evaluate the efficacy and safety of saroglitazar in patients with metabolic disease and provide evidence for clinical decision making. METHODS: We searched electronic databases (PubMed, Cochrane Central Register of Controlled Trials [CENTRAL], and Google Scholar) for randomized controlled trials that examined saroglitazar for the treatment of patients with cardiometabolic disease. A total of seven randomized controlled trials were included for the qualitative and quantitative synthesis. Mean difference (MD) and risk ratio with a 95% confidence interval (CI) were applied for continuous and dichotomous data, respectively. RESULTS: The overall effect of saroglitazar showed significant changes in triglycerides, total cholesterol, low-density lipoprotein, non-high-density lipoprotein, high-density lipoprotein, very low-density lipoprotein, alkaline phosphatase, and gamma-glutamyl transferase levels [MD: - 40.50; 95% CI - 58.09 to - 22.92; p < 0.00001; I2 = 78%], [MD: - 7.49; 95% CI - 11.33 to - 3.65; p = 0.0001; I2 = 41%], [MD: - 3.53; 95% CI - 6.91 to - 0.15; p = 0.04; I2 = 19%], [MD: - 8.08; 95% CI - 15.63 to - 0.54; p = 0.04; I2 = 58%], [MD: 2.04; 95% CI 0.17 to 3.92; p = 0.03; I2 = 69%], [MD: - 6.10; 95% CI - 9.40 to - 2.80; p = 0.0003; I2 = 65%], [MD: - 5.89; 95% CI - 7.50 to - 4.28; p < 0.00001; I2 = 98%], and [MD: - 1.64; 95% CI - 2.83 to - 0.45; p = 0.007; I2 = 95%], respectively. A subgroup analysis showed favorable outcomes with sarogiltazar 4 mg. There was a statistically non-significant reduced risk of adverse event occurrence in the saroglitazar treatment group. CONCLUSIONS: Our study results conclude that the overall effect of saroglitazar was beneficial only in terms of lipid profiles and liver function parameters, whereas saroglitazar 4 mg showed a better therapeutic role in maintaining lipid and glycemic parameters in patients with cardiometabolic disease.