Immunological and Cardiometabolic Risk Factors in the Prediction of Type 2 Diabetes and Coronary Events: MONICA/KORA Augsburg Case-Cohort Study

BACKGROUND: This study compares inflammation-related biomarkers with established cardiometabolic risk factors in the prediction of incident type 2 diabetes and incident coronary events in a prospective case-cohort study within the population-based MONICA/KORA Augsburg cohort. METHODS AND FINDINGS: Analyses for type 2 diabetes are based on 436 individuals with and 1410 individuals without incident diabetes. Analyses for coronary events are based on 314 individuals with and 1659 individuals without incident coronary events. Mean follow-up times were almost 11 years. Areas under the receiver-operating characteristic curve (AUC), changes in Akaike's information criterion (ΔAIC), integrated discrimination improvement (IDI) and net reclassification index (NRI) were calculated for different models. A basic model consisting of age, sex and survey predicted type 2 diabetes with an AUC of 0.690. Addition of 13 inflammation-related biomarkers (CRP, IL-6, IL-18, MIF, MCP-1/CCL2, IL-8/CXCL8, IP-10/CXCL10, adiponectin, leptin, RANTES/CCL5, TGF-β1, sE-selectin, sICAM-1; all measured in nonfasting serum) increased the AUC to 0.801, whereas addition of cardiometabolic risk factors (BMI, systolic blood pressure, ratio total/HDL-cholesterol, smoking, alcohol, physical activity, parental diabetes) increased the AUC to 0.803 (ΔAUC [95% CI] 0.111 [0.092-0.149] and 0.113 [0.093-0.149], respectively, compared to the basic model). The combination of all inflammation-related biomarkers and cardiometabolic risk factors yielded a further increase in AUC to 0.847 (ΔAUC [95% CI] 0.044 [0.028-0.066] compared to the cardiometabolic risk model). Corresponding AUCs for incident coronary events were 0.807, 0.825 (ΔAUC [95% CI] 0.018 [0.013-0.038] compared to the basic model), 0.845 (ΔAUC [95% CI] 0.038 [0.028-0.059] compared to the basic model) and 0.851 (ΔAUC [95% CI] 0.006 [0.003-0.021] compared to the cardiometabolic risk model), respectively. CONCLUSIONS: Inclusion of multiple inflammation-related biomarkers into a basic model and into a model including cardiometabolic risk factors significantly improved the prediction of type 2 diabetes and coronary events, although the improvement was less pronounced for the latter endpoint

Stearoyl-CoA Desaturase-1 (SCD1) Augments Saturated Fatty Acid-Induced Lipid Accumulation and Inhibits Apoptosis in Cardiac Myocytes

Mismatch between the uptake and utilization of long-chain fatty acids in the myocardium leads to abnormally high intracellular fatty acid concentration, which ultimately induces myocardial dysfunction. Stearoyl-Coenzyme A desaturase-1 (SCD1) is a rate-limiting enzyme that converts saturated fatty acids (SFAs) to monounsaturated fatty acids. Previous studies have shown that SCD1-deficinent mice are protected from insulin resistance and diet-induced obesity; however, the role of SCD1 in the heart remains to be determined. We examined the expression of SCD1 in obese rat hearts induced by a sucrose-rich diet for 3 months. We also examined the effect of SCD1 on myocardial energy metabolism and apoptotic cell death in neonatal rat cardiac myocytes in the presence of SFAs. Here we showed that the expression of SCD1 increases 3.6-fold without measurable change in the expression of lipogenic genes in the heart of rats fed a high-sucrose diet. Forced SCD1 expression augmented palmitic acid-induced lipid accumulation, but attenuated excess fatty acid oxidation and restored reduced glucose oxidation. Of importance, SCD1 substantially inhibited SFA-induced caspase 3 activation, ceramide synthesis, diacylglycerol synthesis, apoptotic cell death, and mitochondrial reactive oxygen species (ROS) generation. Experiments using SCD1 siRNA confirmed these observations. Furthermore, we showed that exposure of cardiac myocytes to glucose and insulin induced SCD1 expression. Our results indicate that SCD1 is highly regulated by a metabolic syndrome component in the heart, and such induction of SCD1 serves to alleviate SFA-induced adverse fatty acid catabolism, and eventually to prevent SFAs-induced apoptosis

The Yeast La Related Protein Slf1p Is a Key Activator of Translation during the Oxidative Stress Response

The mechanisms by which RNA-binding proteins control the translation of subsets of mRNAs are not yet clear. Slf1p and Sro9p are atypical-La motif containing proteins which are members of a superfamily of RNA-binding proteins conserved in eukaryotes. RIP-Seq analysis of these two yeast proteins identified overlapping and distinct sets of mRNA targets, including highly translated mRNAs such as those encoding ribosomal proteins. In paralell, transcriptome analysis of slf1Δ and sro9Δ mutant strains indicated altered gene expression in similar functional classes of mRNAs following loss of each factor. The loss of SLF1 had a greater impact on the transcriptome, and in particular, revealed changes in genes involved in the oxidative stress response. slf1Δ cells are more sensitive to oxidants and RIP-Seq analysis of oxidatively stressed cells enriched Slf1p targets encoding antioxidants and other proteins required for oxidant tolerance. To quantify these effects at the protein level, we used label-free mass spectrometry to compare the proteomes of wild-type and slf1Δ strains following oxidative stress. This analysis identified several proteins which are normally induced in response to hydrogen peroxide, but where this increase is attenuated in the slf1Δ mutant. Importantly, a significant number of the mRNAs encoding these targets were also identified as Slf1p-mRNA targets. We show that Slf1p remains associated with the few translating ribosomes following hydrogen peroxide stress and that Slf1p co-immunoprecipitates ribosomes and members of the eIF4E/eIF4G/Pab1p ‘closed loop’ complex suggesting that Slf1p interacts with actively translated mRNAs following stress. Finally, mutational analysis of SLF1 revealed a novel ribosome interacting domain in Slf1p, independent of its RNA binding La-motif. Together, our results indicate that Slf1p mediates a translational response to oxidative stress via mRNA-specific translational control

CD14 Deficiency Impacts Glucose Homeostasis in Mice through Altered Adrenal Tone

The toll-like receptors comprise one of the most conserved components of the innate immune system, signaling the presence of molecules of microbial origin. It has been proposed that signaling through TLR4, which requires CD14 to recognize bacterial lipopolysaccharide (LPS), may generate low-grade inflammation and thereby affect insulin sensitivity and glucose metabolism. To examine the long-term influence of partial innate immune signaling disruption on glucose homeostasis, we analyzed knockout mice deficient in CD14 backcrossed into the diabetes-prone C57BL6 background at 6 or 12 months of age. CD14-ko mice, fed either normal or high-fat diets, displayed significant glucose intolerance compared to wild type controls. They also displayed elevated norepinephrine urinary excretion and increased adrenal medullary volume, as well as an enhanced norepinephrine secretory response to insulin-induced hypoglycemia. These results point out a previously unappreciated crosstalk between innate immune- and sympathoadrenal- systems, which exerts a major long-term effect on glucose homeostasis

Loss of Toll-Like Receptor 4 Function Partially Protects against Peripheral and Cardiac Glucose Metabolic Derangements During a Long-Term High-Fat Diet

We would like to acknowledge Matt Priest for excellent technical assistance.Diabetes is a chronic inflammatory disease that carries a high risk of cardiovascular disease. However, the pathophysiological link between these disorders is not well known. We hypothesize that TLR4 signaling mediates high fat diet (HFD)-induced peripheral and cardiac glucose metabolic derangements. Mice with a loss-of-function mutation in TLR4 (C3H/HeJ) and age-matched control (C57BL/6) mice were fed either a high-fat diet or normal diet for 16 weeks. Glucose tolerance and plasma insulin were measured. Protein expression of glucose transporters (GLUT), AKT (phosphorylated and total), and proinflammatory cytokines (IL-6, TNF-α and SOCS-3) were quantified in the heart using Western Blotting. Both groups fed a long-term HFD had increased body weight, blood glucose and insulin levels, as well as impaired glucose tolerance compared to mice fed a normal diet. TLR4-mutant mice were partially protected against long-term HFD-induced insulin resistance. In control mice, feeding a HFD decreased cardiac crude membrane GLUT4 protein content, which was partially rescued in TLR4-mutant mice. TLR4-mutant mice fed a HFD also had increased expression of GLUT8, a novel isoform, compared to mice fed a normal diet. GLUT8 content was positively correlated with SOCS-3 and IL-6 expression in the heart. No significant differences in cytokine expression were observed between groups, suggesting a lack of inflammation in the heart following a HFD. Loss of TLR4 function partially restored a healthy metabolic phenotype, suggesting that TLR4 signaling is a key mechanism in HFD-induced peripheral and cardiac insulin resistance. Our data further suggest that TLR4 exerts its detrimental metabolic effects in the myocardium through a cytokine-independent pathway.Yeshttp://www.plosone.org/static/editorial#pee

Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting insubstantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions