104 research outputs found

    Acylcarnitines serum concentration in individuals with normal glucose tolerance (NGT), isolated impaired fasting glycaemia (IFG), impaired glucose tolerance (IGT) and type 2 diabetes (T2D).

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    <p>± standard deviation and ranges. P-values are corrected for age, gender and BMI. Tukey-HSD post-hoc test was performed only when the adjusted ANOVA showed significant differences.<sup></sup> Data present means </p

    Association between acylcarnitines and body fat or waist to hip ratio.

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    <p><sup></sup> Associations were assessed in a linear regression model adjusted for age and gender. P-values <0.05 were considered as statistically significant and marked in bold italic.</p

    Serum concentration for C14:1-carnitine (A) and C3DC+C4OH-carnitine (B) in the different groups.

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    <p>Shown are means + SEM for NGT (normal glucose tolerance), IFG (impaired fasting glycaemia), IGT (impaired glucose tolerance) and T2D (type 2 diabetes); * p<0.05 vs. T2D, # p<0.01 vs. IGT</p

    Identification of Adipokine Clusters Related to Parameters of Fat Mass, Insulin Sensitivity and Inflammation

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    <div><p>In obesity, elevated fat mass and ectopic fat accumulation are associated with changes in adipokine secretion, which may link obesity to inflammation and the development of insulin resistance. However, relationships among individual adipokines and between adipokines and parameters of obesity, glucose metabolism or inflammation are largely unknown. Serum concentrations of 20 adipokines were measured in 141 Caucasian obese men (n = 67) and women (n = 74) with a wide range of body weight, glycemia and insulin sensitivity. Unbiased, distance-based hierarchical cluster analyses were performed to recognize patterns among adipokines and their relationship with parameters of obesity, glucose metabolism, insulin sensitivity and inflammation. We identified two major adipokine clusters related to either (1) body fat mass and inflammation (leptin, ANGPTL3, DLL1, chemerin, Nampt, resistin) or insulin sensitivity/hyperglycemia, and lipid metabolism (vaspin, clusterin, glypican 4, progranulin, ANGPTL6, GPX3, RBP4, DLK1, SFRP5, BMP7, adiponectin, CTRP3 and 5, omentin). In addition, we found distinct adipokine clusters in subgroups of patients with or without type 2 diabetes (T2D). Logistic regression analyses revealed ANGPTL6, DLK1, Nampt and progranulin as strongest adipokine correlates of T2D in obese individuals. The panel of 20 adipokines predicted T2D compared to a combination of HbA1c, HOMA-IR and fasting plasma glucose with lower sensitivity (78% versus 91%) and specificity (76% versus 94%). Therefore, adipokine patterns may currently not be clinically useful for the diagnosis of metabolic diseases. Whether adipokine patterns are relevant for the predictive assessment of intervention outcomes needs to be further investigated.</p></div

    Hierarchical clustering of 20 serum adipokine concentrations in obese patients (n = 141).

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    <p>We provide approximately unbiased (AU, numbers in red) p-values and bootstrap probability (numbers in green) values as measures of certainty for clusters. Values are based on 10,000 bootstrapping replicates. AU≄90% was considered as strong evidence for the cluster and is marked by red rectangles (only largest possible clusters are marked). Abbreviations: CRP, high sensitive C-reactive protein; ANGPTL 3, angiopoietin-like protein 3; ANGPTL 6, angiopoietin-like protein 6; BMP7, bone morphogenetic protein 7; CTRP3, complement C1q tumor necrosis factor-related protein 3; CTRP5, complement C1q tumor necrosis factor-related protein 5; LPS, Lipopolysacharid (Endotoxin); GPX3, glutathione peroxidase 3; DLL1, delta-like protein 1; DLK1, preadipocyte factor 1; NAMPT, nicotinamide phosphoribosyltransferase (visfatin); RBP4, retinol binding protein 4; SFRP5, secreted frizzled-related protein-5; TG, triglycerides.</p
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