Key features of the metabolic syndrome are insulin resistance and diabetes. The liver as central metabolic organ is not only affected by the metabolic syndrome as non-alcoholic fatty liver disease (NAFLD), but may contribute to insulin resistance and metabolic alterations. We aimed to identify potential associations between liver injury markers and diabetes in the population-based Heinz Nixdorf RECALL Study. Demographic and laboratory data were analyzed in participants (n = 4814, age 45 to 75y). ALT and AST values were significantly higher in males than in females. Mean BMI was 27.9 kg/m2 and type-2-diabetes (known and unkown) was present in 656 participants (13.7%). Adiponectin and vitamin D both correlated inversely with BMI. ALT, AST, and GGT correlated with BMI, CRP and HbA1c and inversely correlated with adiponectin levels. Logistic regression models using HbA1c and adiponectin or HbA1c and BMI were able to predict diabetes with high accuracy. Transaminase levels within normal ranges were closely associated with the BMI and diabetes risk. Transaminase levels and adiponectin were inversely associated. Re-assessment of current normal range limits should be considered, to provide a more exact indicator for chronic metabolic liver injury, in particular to reflect the situation in diabetic or obese individuals.
MotivationBiomarker discovery methods are essential to identify a minimal subset of features (e.g., serum markers in predictive medicine) that are relevant to develop prediction models with high accuracy. By now, there exist diverse feature selection methods, which either are embedded, combined, or independent of predictive learning algorithms. Many preceding studies showed the defectiveness of single feature selection results, which cause difficulties for professionals in a variety of fields (e.g., medical practitioners) to analyze and interpret the obtained feature subsets. Whereas each of these methods is highly biased, an ensemble feature selection has the advantage to alleviate and compensate for such biases. Concerning the reliability, validity, and reproducibility of these methods, we examined eight different feature selection methods for binary classification datasets and developed an ensemble feature selection system.ResultsBy using an ensemble of feature selection methods, a quantification of the importance of the features could be obtained. The prediction models that have been trained on the selected features showed improved prediction performance.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-016-0114-4) contains supplementary material, which is available to authorized users.
Background & aimsCurrent non-invasive scores for the assessment of severity of non-alcoholic fatty liver disease (NAFLD) and identification of patients with non-alcoholic steatohepatitis (NASH) have insufficient performance to be included in clinical routine. In the current study, we developed a novel machine learning approach to overcome the caveats of existing approaches.MethodsNon-invasive parameters were selected by an ensemble feature selection (EFS) from a retrospectively collected training cohort of 164 obese individuals (age: 43.5±10.3y; BMI: 54.1±10.1kg/m2) to develop a model able to predict the histological assessed NAFLD activity score (NAS). The model was evaluated in an independent validation cohort (122 patients, age: 45.2±11.75y, BMI: 50.8±8.61kg/m2).ResultsEFS identified age, γGT, HbA1c, adiponectin, and M30 as being highly associated with NAFLD. The model reached a Spearman correlation coefficient with the NAS of 0.46 in the training cohort and was able to differentiate between NAFL (NAS≤4) and NASH (NAS>4) with an AUC of 0.73. In the independent validation cohort, an AUC of 0.7 was achieved for this separation. We further analyzed the potential of the new model for disease monitoring in an obese cohort of 38 patients under lifestyle intervention for one year. While all patients lost weight under intervention, increasing scores were observed in 15 patients. Increasing scores were associated with significantly lower absolute weight loss, lower reduction of waist circumference and basal metabolic rate.ConclusionsA newly developed model (http://CHek.heiderlab.de) can predict presence or absence of NASH with reasonable performance. The new score could be used to detect NASH and monitor disease progression or therapy response to weight loss interventions.
High ferritin and low transferrin levels are associated with worse outcome in patients with acute liver failure. A model incorporating age, MELD score and transferrin outperformed MELD score for 90-day overall survival of non-transplanted patients.
Krüppel-like factor 6 (KLF6) is a transcription factor and tumor suppressor. We previously identified KLF6 as mediator of hepatocyte glucose and lipid homeostasis. The loss or reduction of KLF6 is linked to the progression of hepatocellular carcinoma, but its contribution to liver regeneration and repair in acute liver injury are lacking so far. Here we explore the role of KLF6 in acute liver injury models in mice, and in patients with acute liver failure (ALF). KLF6 was induced in hepatocytes in ALF, and in both acetaminophen (APAP)- and carbon tetrachloride (CCl4)-treated mice. In mice with hepatocyte-specific Klf6 knockout (DeltaKlf6), cell proliferation following partial hepatectomy (PHx) was increased compared to controls. Interestingly, key autophagic markers and mediators LC3-II, Atg7 and Beclin1 were reduced in DeltaKlf6 mice livers. Using luciferase assay and ChIP, KLF6 was established as a direct transcriptional activator of ATG7 and BECLIN1, but was dependent on the presence of p53. Here we show, that KLF6 expression is induced in ALF and in the regenerating liver, where it activates autophagy by transcriptional induction of ATG7 and BECLIN1 in a p53-dependent manner. These findings couple the activity of an important growth inhibitor in liver to the induction of autophagy in hepatocytes.
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