Early Tracheostomy Is Associated With Shorter Ventilation Time and Duration of ICU Stay in Patients With Myasthenic Crisis-A Multicenter Analysis.

BACKGROUND: Myasthenic crisis (MC) requiring mechanical ventilation (MV) is a rare and serious complication of myasthenia gravis. Here we analyzed the frequency of performed tracheostomies, risk factors correlating with a tracheostomy, as well as the impact of an early tracheostomy on ventilation time and ICU length of stay (LOS) in MC. METHODS: Retrospective chart review on patients treated for MC in 12 German neurological departments between 2006 and 2015 to assess demographic/diagnostic data, rates and timing of tracheostomy and outcome. RESULTS: In 107 out of 215 MC (49.8%), a tracheostomy was performed. Patients without tracheostomy were more likely to have an early-onset myasthenia gravis (27 [25.2%] vs 12 [11.5%], p = 0.01). Patients receiving a tracheostomy, however, were more frequently suffering from multiple comorbidities (20 [18.7%] vs 9 [8.3%], p = 0.03) and also the ventilation time (34.4 days ± 27.7 versus 7.9 ± 7.8, p < 0.0001) and ICU-LOS (34.8 days ± 25.5 versus 12.1 ± 8.0, p < 0.0001) was significantly longer than in non-tracheostomized patients. Demographics and characteristics of the course of the disease up to the crisis were not significantly different between patients with an early (within 10 days) compared to a late tracheostomy. However, an early tracheostomy correlated with a shorter duration of MV at ICU (26.2 days ± 18.1 versus 42.0 ± 33.1, p = 0.006), and ICU-LOS (26.2 days ± 14.6 versus 42.3 ± 33.0, p = 0.003). CONCLUSION: Half of the ventilated patients with MC required a tracheostomy. Poorer health condition before the crisis and late-onset MG were associated with a tracheostomy. An early tracheostomy (≤ day 10), however, was associated with a shorter duration of MV and ICU-LOS by 2 weeks.

[Inpatient Schema Therapy for School Avoidance - A Case Study]. / Stationäre Schematherapie bei Schulvermeidung ­ ein Fallbeispiel.

Inpatient Schema Therapy for School Avoidance - A Case Study Abstract. After a short overview of the basic concepts and methods of schema therapy, we report a case study on the inpatient treatment of a 16-year-old youth with depression and chronic school avoidance. The scores on the Schema inventories and Depression questionnaires are compared before vs. after the treatment, and we then discuss the opportunities and limitations of schema therapy with school-avoiding youths.

[Course of School Absenteeism 1.5-3 Years After Initial Evaluation: Symptoms, Psychosocial Functioning, and Help-Seeking Behavior]. / Verlauf von Schulabsentismus 1.5 bis 3 Jahre nach Erstvorstellung: Prädiktoren, psychosoziales Funktionsniveau und Inanspruchnahme von Hilfen.

Course of School Absenteeism 1.5-3 Years After Initial Evaluation: Symptoms, Psychosocial Functioning, and Help-Seeking Behavior Abstract. Objective: To explore the course of patients with school absenteeism 1.5-3 years after their initial evaluation, including symptoms, school attendance, psychosocial functioning, and help-seeking behavior. Method: Of the 237 patients from specialized psychiatric units for youths displaying school absenteeism, we successfully contacted 108 of them 1.5-3 years after initial admission. We conducted a telephone interview with their parents (SDQ, school situation, help-seeking behavior). Among others, we analyzed the extent of school absenteeism, the scales of the Inventory of School Attendance Problems (ISAP; Knollmann et al., 2019), and the quality of school absenteeism (school refusal vs. truancy vs. mixed group) at admission as possible predictors. Results: The patients had received an extensive amount of youth-welfare measures and inpatient, daycare, and outpatient therapy. 46.3 % had elevated values in the SDQ total score at follow-up, mostly because of emotional problems or problems with peers. Psychosocial functioning, including school attendance, was described as poor for only about 30 %. School attendance problems were significantly predicted by having a conduct disorder and elevated scores on the ISAP-scales Aggression, Teacher Problems, and Peer Problems, though the effect sizes were weak. Conclusions: Externalizing symptoms and associated psychosocial problems seem to be predictive of a negative course of school absenteeism. Implications for prospective longitudinal studies are discussed.

Leptin contributes to the beneficial effects of insulin treatment in streptozotocin-diabetic male mice.

It was long thought that the only hormone capable of reversing the catabolic consequences of diabetes was insulin. However, various studies have demonstrated that the adipocyte-derived hormone leptin can robustly lower blood glucose levels in rodent models of insulin-deficient diabetes. In addition, it has been suggested that some of the metabolic manifestations of insulin-deficient diabetes are due to hypoleptinemia as opposed to hypoinsulinemia. Because insulin therapy increases leptin levels, we sought to investigate the contribution of leptin to the beneficial effects of insulin therapy. To do this, we tested insulin therapy in streptozotocin (STZ) diabetic mice that were either on an ob/ ob background or that were given a leptin antagonist to determine if blocking leptin action would blunt the glucose-lowering effects of insulin therapy. We found that STZ diabetic ob/ ob mice have a diminished blood glucose-lowering effect in response to insulin therapy compared with STZ diabetic controls and exhibited more severe weight loss post-STZ injection. In addition, STZ diabetic mice administered a leptin antagonist through daily injection or plasmid expression respond less robustly to insulin therapy as assessed by both fasting blood glucose levels and blood glucose levels during an oral glucose tolerance test. However, leptin antagonism did not prevent the insulin-induced reduction in ß-hydroxybutyrate and triglyceride levels. Therefore, we conclude that elevated leptin levels can contribute to the glucose-lowering effect of insulin therapy in insulin-deficient diabetes.

Leptin induces fasting hypoglycaemia in a mouse model of diabetes through the depletion of glycerol.

AIMS/HYPOTHESIS: Leptin has profound glucose-lowering effects in rodent models of type 1 diabetes, and is currently being tested clinically to treat this disease. In addition to reversing hyperglycaemia, leptin therapy corrects multiple lipid, energy and neuroendocrine imbalances in rodent models of type 1 diabetes, yet the precise mechanism has not been fully defined. Thus, we performed metabolic analyses to delineate the downstream metabolic pathway mediating leptin-induced glucose lowering in diabetic mice. METHODS: Mice were injected with streptozotocin (STZ) to induce insulin-deficient diabetes, and were subsequently treated with 20 µg/day recombinant murine leptin or vehicle for 5 to 14 days. Energy-yielding substrates were measured in the liver and plasma, and endogenous glucose production was assessed by tolerance to extended fasting. RESULTS: STZ-leptin-treated mice developed severe hypoketotic hypoglycaemia during prolonged fasting, indicative of suppressed endogenous ketone and glucose production. STZ-leptin mice displayed normal gluconeogenic and glycogenolytic capacity, but had depleted circulating glycerol and NEFA. The depletion of glycerol and NEFA correlated tightly with the kinetics of glucose lowering in response to chronic leptin administration, and was not mimicked by single leptin injection. Administration of glycerol acutely reversed fasting-induced hypoglycaemia in leptin-treated mice. CONCLUSIONS/INTERPRETATION: The findings of this study suggest that the diminution of circulating glycerol reduces endogenous glucose production, contributing to severe fasting-induced hypoglycaemia in leptin-treated rodent models of type 1 diabetes, and support that depletion of glycerol contributes to the glucose-lowering action of leptin.

A role for hepatic leptin signaling in lipid metabolism via altered very low density lipoprotein composition and liver lipase activity in mice.

UNLABELLED: Obesity is highly associated with dyslipidemia and cardiovascular disease. However, the mechanism behind this association is not completely understood. The hormone leptin may be a molecular link between obesity and dysregulation of lipid metabolism. Leptin can affect lipid metabolism independent of its well-known effects on food intake and energy expenditure, but exactly how this occurs is ill-defined. We hypothesized that since leptin receptors are found on the liver and the liver plays an integral role in regulating lipid metabolism, leptin may affect lipid metabolism by acting directly on the liver. To test this hypothesis, we generated mice with a hepatocyte-specific loss of leptin signaling. We previously showed that these mice have increased insulin sensitivity and elevated levels of liver triglycerides compared with controls. Here, we show that mice lacking hepatic leptin signaling have decreased levels of plasma apolipoprotein B yet increased levels of very low density lipoprotein (VLDL) triglycerides, suggesting alterations in triglyceride incorporation into VLDL or abnormal lipoprotein remodeling in the plasma. Indeed, lipoprotein profiles revealed larger apolipoprotein B-containing lipoprotein particles in mice with ablated liver leptin signaling. Loss of leptin signaling in the liver was also associated with a substantial increase in lipoprotein lipase activity in the liver, which may have contributed to increased lipid droplets in the liver. CONCLUSION: Lack of hepatic leptin signaling results in increased lipid accumulation in the liver and larger, more triglyceride-rich VLDL particles. Collectively, these data reveal an interesting role for hepatic leptin signaling in modulating triglyceride metabolism.

Evaluating the impact of multivariate imputation by MICE in feature selection.

Handling missing values is a crucial step in preprocessing data in Machine Learning. Most available algorithms for analyzing datasets in the feature selection process and classification or estimation process analyze complete datasets. Consequently, in many cases, the strategy for dealing with missing values is to use only instances with full data or to replace missing values with a mean, mode, median, or a constant value. Usually, discarding missing samples or replacing missing values by means of fundamental techniques causes bias in subsequent analyzes on datasets. AIM: Demonstrate the positive impact of multivariate imputation in the feature selection process on datasets with missing values. RESULTS: We compared the effects of the feature selection process using complete datasets, incomplete datasets with missingness rates between 5 and 50%, and imputed datasets by basic techniques and multivariate imputation. The feature selection algorithms used are well-known methods. The results showed that the datasets imputed by multivariate imputation obtained the best results in feature selection compared to datasets imputed by basic techniques or non-imputed incomplete datasets. CONCLUSIONS: Considering the results obtained in the evaluation, applying multivariate imputation by MICE reduces bias in the feature selection process.

Correction: Evaluating the impact of multivariate imputation by MICE in feature selection.

[This corrects the article DOI: 10.1371/journal.pone.0254720.].

Liver parameters as part of a non-invasive model for prediction of all-cause mortality after myocardial infarction.

INTRODUCTION: Liver parameters are associated with cardiovascular disease risk and severity of stenosis. It is unclear whether liver parameters could predict the long-term outcome of patients after acute myocardial infarction (AMI). We performed an unbiased analysis of the predictive value of serum parameters for long-term prognosis after AMI. MATERIAL AND METHODS: In a retrospective, observational, single-center, cohort study, 569 patients after AMI were enrolled and followed up until 6 years for major adverse cardiovascular events, including cardiac death. Patients were classified into non-survivors (n = 156) and survivors (n = 413). Demographic and laboratory data were analyzed using ensemble feature selection (EFS) and logistic regression. Correlations were performed for serum parameters. RESULTS: Age (73; 64; p < 0.01), alanine aminotransferase (ALT; 93 U/l; 40 U/l; p < 0.01), aspartate aminotransferase (AST; 162 U/l; 66 U/l; p < 0.01), C-reactive protein (CRP; 4.7 U/l; 1.6 U/l; p < 0.01), creatinine (1.6; 1.3; p < 0.01), γ-glutamyltransferase (GGT; 71 U/l; 46 U/l; p < 0.01), urea (29.5; 20.5; p < 0.01), estimated glomerular filtration rate (eGFR; 49.6; 61.4; p < 0.01), troponin (13.3; 7.6; p < 0.01), myoglobin (639; 302; p < 0.01), and cardiovascular risk factors (hypercholesterolemia p < 0.02, family history p < 0.01, and smoking p < 0.01) differed significantly between non-survivors and survivors. Age, AST, CRP, eGFR, myoglobin, sodium, urea, creatinine, and troponin correlated significantly with death (r = -0.29; 0.14; 0.31; -0.27; 0.20; -0.13; 0.33; 0.24; 0.12). A prediction model was built including age, CRP, eGFR, myoglobin, and urea, achieving an AUROC of 77.6% to predict long-term survival after AMI. CONCLUSIONS: Non-invasive parameters, including liver and renal markers, can predict long-term outcome of patients after AMI.

Myasthenic crisis demanding mechanical ventilation: A multicenter analysis of 250 cases.

OBJECTIVE: To determine demographic characteristics, clinical features, treatment regimens, and outcome of myasthenic crisis (MC) requiring mechanical ventilation (MV). METHODS: Analysis of patients who presented with MC between 2006 and 2015 in a German multicenter retrospective study. RESULTS: We identified 250 cases in 12 participating centers. Median age at crisis was 72 years. Median duration of MV was 12 days. Prolonged ventilation (>15 days) depended on age (p = 0.0001), late-onset myasthenia gravis (MG), a high Myasthenia Gravis Foundation of America Class before crisis (p = 0.0001 for IVb, odds ratio [OR] = infinite), number of comorbidities (>3 comorbidities: p = 0.002, OR 2.99), pneumonia (p = 0.0001, OR 3.13), and resuscitation (p = 0.0008, OR 9.15). MV at discharge from hospital was necessary in 20.5% of survivors. Patients with early-onset MG (p = 0.0001, OR 0.21), thymus hyperplasia (p = 0.002, OR 0), and successful noninvasive ventilation trial were more likely to be ventilated for less than 15 days. Noninvasive ventilation in 92 cases was sufficient in 38%, which was accompanied by a significantly shorter duration of ventilation (p = 0.001) and intensive care unit (ICU) stay (p = 0.01). IV immunoglobulins, plasma exchange, and immunoadsorption were more likely to be combined sequentially if the duration of MV and the stay in an ICU extended (p = 0.0503, OR 2.05). Patients who received plasma exchange or immunoadsorption as first-line therapy needed invasive ventilation significantly less often (p = 0.003). In-hospital mortality was 12%, which was significantly associated with the number of comorbidities (>3) and complications such as acute respiratory distress syndrome and resuscitation. Main cause of death was multiorgan failure, mostly due to sepsis. CONCLUSION: Mortality and duration of MC remained comparable to previous reports despite higher age and a high disease burden in our study. Prevention and treatment of complications and specialized neurointensive care are the cornerstones in order to improve outcome.

Non-invasive assessment of NAFLD as systemic disease-A machine learning perspective.

BACKGROUND & AIMS: Current 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. METHODS: Non-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). RESULTS: EFS 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. CONCLUSIONS: A 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.

NAFLD-Associated Comorbidities in Advanced Stage HCC Do Not Alter the Safety and Efficacy of Yttrium-90 Radioembolization.

BACKGROUND: Patients with advanced hepatocellular carcinoma (HCC) arising in nonalcoholic fatty liver disease (NAFLD) may not be suitable for systemic therapy due to metabolic syndrome-related diseases. Recent trials did not show a survival benefit of radioembolization (RE) compared to sorafenib in advanced stage HCC but RE may represent an adequate alternative in patients with contraindications to systemic therapy due to its favorable safety profile. AIM: To investigate the impact of NAFLD-related comorbidities on safety and efficacy of RE for HCC treatment in a retrospective monocentric cohort study. PATIENTS AND METHODS: Safety and efficacy of RE were evaluated in patients with NAFLD-associated HCC. Hepatitis B virus (HBV)-related HCC patients served as controls, exhibiting matching Barcelona Liver Cancer Clinic (BCLC) stages while showing significantly fewer metabolic comorbidities. RESULTS: Overall, 87 HCC patients with NAFLD (mean age 71.3 ± 6.9 years) and 62 HCC patients with HBV (mean age 58.8 ± 10.9 years) not amenable to surgical or conventional locoregional treatments were included. Patients with HBV-related HCC had a comparable liver function to HCC patients with NAFLD. RE treatment-related toxicity did not differ between the two groups (increase in bilirubin Common Terminology Criteria for Adverse Events grade in 29 [38.7%] NAFLD and 20 [39.2%] HBV patients, p = 0.91). Overall survival was similar in HCC patients with NAFLD and HBV (11.1 [interquartile range, IQR, 18.27] vs. 9.3 months [IQR 14.73], p = 0.38), also in the subgroup analyses of BCLC B and C stages. CONCLUSION: RE showed similar survival outcomes at a comparable toxicity profile in HCC patients with NAFLD and HBV. NAFLD-associated metabolic comorbidities did not exhibit limitations for RE while offering comparable therapeutic efficacy as compared to HBV patients.

EFS: an ensemble feature selection tool implemented as R-package and web-application.

BACKGROUND: Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, whereas an ensemble feature selection has the advantage to alleviate and compensate for these biases. RESULTS: The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble. CONCLUSION: EFS identifies relevant features while compensating specific biases of single methods due to an ensemble approach. Thereby, EFS can improve the prediction accuracy and interpretability in subsequent binary classification models. AVAILABILITY: EFS can be downloaded as an R-package from CRAN or used via a web application at http://EFS.heiderlab.de.

The glucoregulatory actions of leptin.

BACKGROUND: The hormone leptin is an important regulator of metabolic homeostasis, able to inhibit food intake and increase energy expenditure. Leptin can also independently lower blood glucose levels, particularly in hyperglycemic models of leptin or insulin deficiency. Despite significant efforts and relevance to diabetes, the mechanisms by which leptin acts to regulate blood glucose levels are not fully understood. SCOPE OF REVIEW: Here we assess literature relevant to the glucose lowering effects of leptin. Leptin receptors are widely expressed in multiple cell types, and we describe both peripheral and central effects of leptin that may be involved in lowering blood glucose. In addition, we summarize the potential clinical application of leptin in regulating glucose homeostasis. MAJOR CONCLUSIONS: Leptin exerts a plethora of metabolic effects on various tissues including suppressing production of glucagon and corticosterone, increasing glucose uptake, and inhibiting hepatic glucose output. A more in-depth understanding of the mechanisms of the glucose-lowering actions of leptin may reveal new strategies to treat metabolic disorders.

Lipid nanoparticle delivery of glucagon receptor siRNA improves glucose homeostasis in mouse models of diabetes.

OBJECTIVE: Hyperglucagonemia is present in many forms of diabetes and contributes to hyperglycemia, and glucagon suppression can ameliorate diabetes in mice. Leptin, a glucagon suppressor, can also reverse diabetes in rodents. Lipid nanoparticle (LNP) delivery of small interfering RNA (siRNA) effectively targets the liver and is in clinical trials for the treatment of various diseases. We compared the effectiveness of glucagon receptor (Gcgr)-siRNA delivered via LNPs to leptin in two mouse models of diabetes. METHODS: Gcgr siRNA encapsulated into LNPs or leptin was administered to mice with diabetes due to injection of the ß-cell toxin streptozotocin (STZ) alone or combined with high fat diet (HFD/STZ). RESULTS: In STZ-diabetic mice, a single injection of Gcgr siRNA lowered blood glucose levels for 3 weeks, improved glucose tolerance, and normalized plasma ketones levels, while leptin therapy normalized blood glucose levels, oral glucose tolerance, and plasma ketones, and suppressed lipid metabolism. In contrast, in HFD/STZ-diabetic mice, Gcgr siRNA lowered blood glucose levels for 2 months, improved oral glucose tolerance, and reduced HbA1c, while leptin had no beneficial effects. CONCLUSIONS: While leptin may be more effective than Gcgr siRNA at normalizing both glucose and lipid metabolism in STZ diabetes, Gcgr siRNA is more effective at reducing blood glucose levels in HFD/STZ diabetes.

Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification.

BACKGROUND: Antiretroviral therapy is essential for human immunodeficiency virus (HIV) infected patients to inhibit viral replication and therewith to slow progression of disease and prolong a patient's life. However, the high mutation rate of HIV can lead to a fast adaptation of the virus under drug pressure and thereby to the evolution of resistant variants. In turn, these variants will lead to the failure of antiretroviral treatment. Moreover, these mutations cannot only lead to resistance against single drugs, but also to cross-resistance, i.e., resistance against drugs that have not yet been applied. METHODS: 662 protease sequences and 715 reverse transcriptase sequences with complete resistance profiles were analyzed using machine learning techniques, namely binary relevance classifiers, classifier chains, and ensembles of classifier chains. RESULTS: In our study, we applied multi-label classification models incorporating cross-resistance information to predict drug resistance for two of the major drug classes used in antiretroviral therapy for HIV-1, namely protease inhibitors (PIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs). By means of multi-label learning, namely classifier chains (CCs) and ensembles of classifier chains (ECCs), we were able to improve overall prediction accuracy for all drugs compared to hitherto applied binary classification models. CONCLUSIONS: The development of fast and precise models to predict drug resistance in HIV-1 is highly important to enable a highly effective personalized therapy. Cross-resistance information can be exploited to improve prediction accuracy of computational drug resistance models.

Insulin Knockout Mice Have Extended Survival but Volatile Blood Glucose Levels on Leptin Therapy.

Leptin can reverse hyperglycemia in rodent models of type 1 diabetes. However, these models have used chemical or immune mediated ß-cell destruction where insulin depletion is incomplete. Thus it is unknown which actions of leptin are entirely insulin independent, versus those which require insulin. To directly assess this we maximized blockage of insulin action using an insulin receptor antagonist in combination with streptozotocin-diabetic mice; leptin treatment was still able to reduce blood glucose. Next, we leptin-treated adult insulin knockout (InsKO) mice. Remarkably, leptin-treated InsKO mice were viable for up to 3 weeks without insulin therapy. Leptin treatment reduced plasma corticosterone, glucagon, ß-hydroxybutyrate, triglycerides, cholesterol, fatty acids and glycerol. However, leptin-treated InsKO mice exhibited overt fed hyperglycemia and severe fasting hypoglycemia. Therefore, leptin can normalize many metabolic parameters in the complete absence of insulin, but blood glucose levels are volatile and the length of survival finite.

Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach.

MOTIVATION: Biomarker 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. RESULTS: By 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.

In Acute Myocardial Infarction Liver Parameters Are Associated With Stenosis Diameter.

Detection of high-risk subjects in acute myocardial infarction (AMI) by noninvasive means would reduce the need for intracardiac catheterization and associated complications. Liver enzymes are associated with cardiovascular disease risk. A potential predictive value for liver serum markers for the severity of stenosis in AMI was analyzed.Patients with AMI undergoing percutaneous coronary intervention (PCI; n = 437) were retrospectively evaluated. Minimal lumen diameter (MLD) and percent stenosis diameter (SD) were determined from quantitative coronary angiography. Patients were classified according to the severity of stenosis (SD ≥ 50%, n = 357; SD < 50%, n = 80). Routine heart and liver parameters were associated with SD using random forests (RF). A prediction model (M10) was developed based on parameter importance analysis in RF.Age, alkaline phosphatase (AP), aspartate aminotransferase (AST), and MLD differed significantly between SD ≥ 50 and SD < 50. Age, AST, alanine aminotransferase (ALT), and troponin correlated significantly with SD, whereas MLD correlated inversely with SD. M10 (age, BMI, AP, AST, ALT, gamma-glutamyltransferase, creatinine, troponin) reached an AUC of 69.7% (CI 63.8-75.5%, P < 0.0001).Routine liver parameters are associated with SD in AMI. A small set of noninvasively determined parameters can identify SD in AMI, and might avoid unnecessary coronary angiography in patients with low risk. The model can be accessed via http://stenosis.heiderlab.de.

Glucagon receptor gene deletion in insulin knockout mice modestly reduces blood glucose and ketones but does not promote survival.

OBJECTIVE: It has been thought that the depletion of insulin is responsible for the catabolic consequences of diabetes; however, evidence suggests that glucagon also plays a role in diabetes pathogenesis. Glucagon suppression by glucagon receptor (Gcgr) gene deletion, glucagon immunoneutralization, or Gcgr antagonist can reverse or prevent type 1 diabetes in rodents suggesting that dysregulated glucagon is also required for development of diabetic symptoms. However, the models used in these studies were rendered diabetic by chemical- or immune-mediated ß-cell destruction, in which insulin depletion is incomplete. Therefore, it is unclear whether glucagon suppression could overcome the consequence of the complete lack of insulin. METHODS: To directly test this we characterized mice that lack the Gcgr and both insulin genes (GcgrKO/InsKO). RESULTS: In both P1 pups and mice that were kept alive to young adulthood using insulin therapy, blood glucose and plasma ketones were modestly normalized; however, mice survived for only up to 6 days, similar to GcgrHet/InsKO controls. In addition, Gcgr gene deletion was unable to normalize plasma leptin levels, triglycerides, fatty acids, or hepatic cholesterol accumulation compared to GcgrHet/InsKO controls. CONCLUSION: Therefore, the metabolic manifestations associated with a complete lack of insulin cannot be overcome by glucagon receptor gene inactivation.