ApoRII expression correlates with serum adiponectin, steatosis, and liver fibrosis. Increased adiponectin levels, without an actual protective effect might even indicate a crosstalk from liver to adipose tissue, initiating a compensatory mechanism. Further studies are warranted to establish adiponectin as possible marker for monitoring of metabolic liver diseases. Furthermore, the current lack in mechanistical understanding of adiponectin signaling within the liver and the regulatory mechanisms in adipose tissue for adiponectin production should be targeted to evaluate this axis as drug target for ALD or NAFLD. A crucial result of the presented work is the ability of a small set of non-invasive parameters to discern NAFLD and ALD, as shown by the calculated machine learning methods. One major advantage of the presented algorithms is the wide availability of the used parameters. Self reported consumption of alcohol is not always reliable to establish either NAFLD or ALD. From a Gomisin-D clinical perspective it would be highly valuable to confirm or exclude ALD with high probability, without the need to rely on information given by the patient. Similarly RFs and DTs were able to discriminate between ALDC and ALDNC with very high accuracy. This was mainly due to inclusion of transient elastography, which can detect cirrhosis reliably when ascites or other disturbing factors are absent. Unfortunately this simple and highly informative method is not widely available, as a special ultra-sound head is needed to perform transient elastography measurements on tissue. Moreover, during patient recruitment cirrhosis was assessed by conventional ultra-sonography and transient elastography was performed as additional parameter. Though, to avoid confirmation bias from two sonographic methods, a second model to discriminate ALDC and ALDNC was calculated without transient elastography, which again yielded significant results. RFs and DTs offer the ability to assess importance of variables used for classification in a specific model. These importance Diacerein values can be used to find a minimal set of variables for the classification, thus reducing the amount of parameters which need to be determined and thus cost of a possible clinical application. Furthermore, assessment of importance enables more insights into the classification process and might even suggest underlying biological interactions, identifying interesting targets for disease monitoring or therapy. This is a clear advantage of DTs and RFs compared to other machine learning techniques that are rather black boxes, such as SVMs. It is noteworthy that in this model serum parameters of cell death and cytokines were the most important parameters for decision making. A previous approach for non-invasive fibrosis assessment in NAFLD yielded similar results. While the classic liver serum parameters are still important, as seen for discerning NAFLD and ALD, additional parameters as cell death markers, cytokines and adipokines should be collected routinely to monitor disease progression or for diagnostic purposes. Broad usage of those parameters may confirm current data in larger proportions of the general population. Limitations of the current study are the unavailability of liver tissue biopsies from the majority of patients. This unfortunately not only restricts exact pathological assessmentbut also excludes studies on cellular or molecular processes.