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Platelets participate in the development of liver fibrosis in animal models, but little is known about the benefit of antiplatelet agents in preventing liver fibrosis in humans. We therefore explored the relationship between the use of antiplatelet agents and liver fibrosis in a prospective cohort study of patients at high risk of liver fibrosis and cardiovascular events. Consecutive patients undergoing elective coronary angiography at the University Hospital Frankfurt were prospectively included in the present study. Associations between use of antiplatelet agents (acetyl salicylic acid, P2Y12 receptor antagonists) and liver fibrosis were assessed in regression models, and the relationship between platelet‐derived growth factor beta (PDGF‐β) serum concentration, platelets, liver fibrosis, and use of antiplatelet agents was characterized. Out of 505 included patients, 337 (67%) received antiplatelet agents and 134 (27%) had liver fibrosis defined as a FibroScan transient elastography (TE) value ≥7.9 kPa. Use of antiplatelet agents was inversely associated with the presence of liver fibrosis in univariate and multivariate analyses (multivariate odds ratio [OR], 0.67; 95% confidence interval [CI], 0.51‐0.89; P = 0.006). Use of antiplatelet agents was also inversely associated with FibroTest values (beta, –0.38; SD beta, 0.15; P = 0.02). Furthermore, there was a significant correlation between platelet counts and PDGF‐β serum concentration (rho, 0.33; P < 0.0001), but PDGF‐β serum levels were not affected by antiplatelet agents. Conclusion: There is a protective association between the use of antiplatelet agents and occurrence of liver fibrosis. A randomized controlled trial is needed to explore causality and the potential of antiplatelet agents as antifibrotic therapy in patients at risk for liver fibrosis progression.
Background: Liver cirrhosis is associated with profound immunodysfunction, i.e. a parallel presence of chronic systemic inflammation and immunosuppression, which can result in acute-on-chronic liver failure (ACLF). Omega-3 fatty acids are precursors of pro-resolving mediators and support the resolution of inflammation.
Objective: The aim of this study was to determine plasma levels of omega-3 fatty acids in patients with liver cirrhosis and ACLF.
Methods: Patients with liver cirrhosis with and without ACLF were enrolled in a prospective cohort study and analyzed post-hoc for the present sub-study. Clinical data and biomaterials were collected at baseline and at day 7, 28 and after 3 months of follow-up. Plasma concentrations of arachidonic acid (ARA) and docosahexaenoic acid (DHA), which represent key omega-6 and -3 fatty acids, respectively, were quantified and associated with markers of systemic inflammation and severity of liver cirrhosis.
Results: A total of 117 patients were included in the present analyses. Of those, 26 (22.2%), 51 (43.6%) and 40 (34.2%) patients had compensated or decompensated liver cirrhosis, and ACLF. Plasma levels of ARA and DHA were similar in patients with compensated cirrhosis, decompensated cirrhosis, and ACLF. Furthermore, no significant association between plasma ARA or DHA and C-reactive protein or peripheral blood leukocytes were observed (P>0.05).
Conclusion: In our study plasma levels of key omega-3 and omega-6 fatty acid are neither associated with the severity of liver cirrhosis nor with liver-cirrhosis-associated systemic inflammation.
Most studies in the life sciences and other disciplines involve generating and analyzing numerical data of some type as the foundation for scientific findings. Working with numerical data involves multiple challenges. These include reproducible data acquisition, appropriate data storage, computationally correct data analysis, appropriate reporting and presentation of the results, and suitable data interpretation.
Finding and correcting mistakes when analyzing and interpreting data can be frustrating and time-consuming. Presenting or publishing incorrect results is embarrassing but not uncommon. Particular sources of errors are inappropriate use of statistical methods and incorrect interpretation of data by software. To detect mistakes as early as possible, one should frequently check intermediate and final results for plausibility. Clearly documenting how quantities and results were obtained facilitates correcting mistakes. Properly understanding data is indispensable for reaching well-founded conclusions from experimental results. Units are needed to make sense of numbers, and uncertainty should be estimated to know how meaningful results are. Descriptive statistics and significance testing are useful tools for interpreting numerical results if applied correctly. However, blindly trusting in computed numbers can also be misleading, so it is worth thinking about how data should be summarized quantitatively to properly answer the question at hand. Finally, a suitable form of presentation is needed so that the data can properly support the interpretation and findings. By additionally sharing the relevant data, others can access, understand, and ultimately make use of the results.
These quick tips are intended to provide guidelines for correctly interpreting, efficiently analyzing, and presenting numerical data in a useful way.