Objective: Obesity is associated with a chronic low-grade inflammation and an increased abundance of macrophages in adipose tissue. Adipose tissue macrophages (ATMs) are assumed to interfere with adipocyte function leading to insulin resistance, thereby contributing to the pathogenesis of type 2 diabetes mellitus. Macrophages exist in separate types of differentiation, but the nature of ATMs is largely unknown. Design and measurements: Stromal vascular cells (SVCs) and ATMs were isolated from human adipose tissues from different locations. We characterized ATMs phenotypically and functionally by flow cytometry, endocytosis assay and determination of secreted cytokines. For comparison, we used macrophages of the 'classical' (M1) and the 'alternative', anti-inflammatory (M2) type differentiated in vitro from peripheral blood monocytes. Results: Like prototypic M2 macrophages, ATMs expressed considerable amounts of mannose receptor, haemoglobin scavenger receptor CD163 and integrin avb5. The number of cells expressing these molecules correlated significantly with the donors' body mass indices (BMIs). Notably, SVCs positive for the common monocyte/macrophage marker CD14 contained a considerable fraction of blood monocytes, the abundance of which did not correlate with the BMIs, pointing to the requirement of the surface markers identified here for the identification of ATMs. ATMs showed endocytic activities similar to M2 macrophages and accordingly secreted high amounts of IL-10 and IL-1 receptor antagonist. However, basal and induced secretion of pro-inflammatory mediators TNF-a, IL-6, IL-1, MCP-1 and MIP-1a was even higher in ATMs than in proinflammatory M1 macrophages. Conclusion: ATMs comprise a particular macrophage type that is M2-like by surface marker expression, but they are competent to produce extensive amounts of inflammatory cytokines, which could considerably contribute to the development of insulin resistance.

Allocation of liver grafts triggers emotional debates, as those patients, not receiving an organ, are prone to death. We analyzed a high-Model of End-stage Liver Disease (MELD) cohort (laboratory MELD score ≥30, n = 100, median laboratory MELD score of 35; interquartile range 31-37) of liver transplant recipients at our center during the past 10 years and compared results with a low-MELD group, matched by propensity scoring for donor age, recipient age, and cold ischemia time. End points of our study were cumulative posttransplantation morbidity, cost, and survival. Six different prediction models, including donor age x recipient MELD (D-MELD), Difference between listing MELD and MELD at transplant (Delta MELD), donor-risk index (DRI), Survival Outcomes Following Liver Transplant (SOFT), balance-of-risk (BAR), and University of California Los Angeles-Futility Risk Score (UCLA-FRS), were applied in both cohorts to identify risk for poor outcome and high cost. All score models were compared with a clinical-oriented decision, based on the combination of hemofiltration plus ventilation. Median intensive care unit and hospital stays were 8 and 26 days, respectively, after liver transplantation of high-MELD patients, with a significantly increased morbidity compared with low-MELD patients (median comprehensive complication index 56 vs. 36 points [maximum points 100] and double cost [median US$179 631 vs. US$80 229]). Five-year survival, however, was only 8% less than that of low-MELD patients (70% vs. 78%). Most prediction scores showed disappointing low positive predictive values for posttransplantation mortality, such as mortality above thresholds, despite good specificity. The clinical observation of hemofiltration plus ventilation in high-MELD patients was even superior in this respect compared with D-MELD, DRI, Delta MELD, and UCLA-FRS but inferior to SOFT and BAR models. Of all models tested, only the BAR score was linearly associated with complications. In conclusion, the BAR score was most useful for risk classification in liver transplantation, based on expected posttransplantation mortality and morbidity. Difficult decisions to accept liver grafts in high-risk recipients may thus be guided by additional BAR score calculation, to increase the safe use of scarce organs.

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Background and Aims The Model for End‐Stage Liver Disease (MELD) is used for clinical decision‐making and organ allocation for orthotopic liver transplantation (OLT) and was previously upgraded through inclusion of serum sodium (Na) concentrations (MELD‐Na). However, MELD‐Na may underestimate complications arising from portal hypertension or infection. The von Willebrand factor (vWF) antigen (vWF‐Ag) correlates with portal pressure and seems capable of predicting complications in patients with cirrhosis. Accordingly, this study aimed to evaluate vWF‐Ag as an adjunct surrogate marker for risk stratification on the waiting list for OLT. Approach and Results Hence, WF‐Ag at time of listing was assessed in patients listed for OLT. Clinical characteristics, MELD‐Na, and mortality on the waiting list were recorded. Prediction of 3‐month waiting‐list survival was assessed by receiver operating characteristics and net reclassification improvement. Interestingly, patients dying within 3 months on the waiting list displayed elevated levels of vWF‐Ag (P < 0.001). MELD‐Na and vWF‐Ag were comparable and independent in their predictive potential for 3‐month mortality on the waiting list (area under the curve [AUC], vWF‐Ag = 0.739; MELD‐Na = 0.764). Importantly, a vWF‐Ag cutoff at 413% identified patients at risk for death within 3 months of listing with a higher odds ratio (OR) than the previously published cutoff at a MELD‐Na of 20 points (vWF‐Ag, OR = 10.873, 95% confidence interval [CI], 3.160, 36.084; MELD‐Na, OR = 7.594, 95% CI, 2.578, 22.372; P < 0.001, respectively). Ultimately, inclusion of vWF‐Ag into the MELD‐Na equation significantly improved prediction of 3‐month waiting‐list mortality (AUC, MELD‐Na–vWF = 0.804). Conclusions A single measurement of vWF‐Ag at listing for OLT predicts early mortality. Combining vWF‐Ag levels with MELD‐Na improves risk stratification and may help to prioritize organ allocation to decrease waiting‐list mortality.