Circulating tissue factor-positive procoagulant microparticles in patients with type 1 diabetes

Aim: To investigate the count of circulating tissue factor-positive (TF+) procoagulant microparticles (MPs) in patients with type 1 diabetes mellitus (T1DM). Methods: This case-control study included patients with T1DM and age and sex-matched healthy volunteers. The counts of phosphatidylserine-positive (PS+) MPs and TF(+)PS(+)MPs and the subgroups derived from different cell types were measured in the peripheral blood sample of the two groups using multicolor flow cytometric assay. We compared the counts of each MP between groups as well as the ratio of the TF(+)PS(+)MPs and PS(+)MPs (TF(+)PS(+)MPs/PS(+)MPs). Results: We recruited 36 patients with T1DM and 36 matched healthy controls. Compared with healthy volunteers, PS(+)MPs, TF(+)PS(+)MPs and TF(+)PS(+)MPs/PS(+)MPs were elevated in patients with T1DM (PS(+)MPs: 1078.5 +/- 158.08 vs 686.84 +/- 122.04/mu L, P &lt;0.001; TF(+)PS(+)MPs: 202.10 +/- 47.47 vs 108.33 +/- 29.42/mu L, P &lt;0.001; and TF(+)PS(+)MPs/PS(+)MPs: 0.16 +/- 0.04 vs 0.19 +/- 0.05, P = 0.004), mostly derived from platelet, lymphocytes and endothelial cells. In the subgroup analysis, the counts of total and platelet TF(+)PS(+)MPs were increased in patients with diabetic retinopathy (DR) and with higher HbA1c, respectively. Conclusion: Circulating TF(+)PS(+)MPs and those derived from platelet, lymphocytes and endothelial cells were elevated in patients with T1DM.De tre första författarna delar förstaförfattarskapet.</p

Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: Findings from the China Suboptimal Health Cohort

Background: Metabolic syndrome (MetS) has been proposed as a clinically identifiable high-risk state for the prediction and prevention of cardiovascular diseases and type 2 diabetes mellitus. As a promising “omics” technology, metabolomics provides an innovative strategy to gain a deeper understanding of the pathophysiology of MetS. The study aimed to systematically investigate the metabolic alterations in MetS and identify biomarker panels for the identification of MetS using machine learning methods. Methods: Nuclear magnetic resonance-based untargeted metabolomics analysis was performed on 1011 plasma samples (205 MetS patients and 806 healthy controls). Univariate and multivariate analyses were applied to identify metabolic biomarkers for MetS. Metabolic pathway enrichment analysis was performed to reveal the disturbed metabolic pathways related to MetS. Four machine learning algorithms, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression were used to build diagnostic models for MetS. Results: Thirteen significantly differential metabolites were identified and pathway enrichment revealed that arginine, proline, and glutathione metabolism are disturbed metabolic pathways related to MetS. The protein-metabolite-disease interaction network identified 38 proteins and 23 diseases are associated with 10 MetS-related metabolites. The areas under the receiver operating characteristic curve of the SVM, RF, KNN, and logistic regression models based on metabolic biomarkers were 0.887, 0.993, 0.914, and 0.755, respectively. Conclusions: The plasma metabolome provides a promising resource of biomarkers for the predictive diagnosis and targeted prevention of MetS. Alterations in amino acid metabolism play significant roles in the pathophysiology of MetS. The biomarker panels and metabolic pathways could be used as preventive targets in dealing with cardiometabolic diseases related to MetS

Water T2 as an early, global and practical biomarker for metabolic syndrome: an observational cross-sectional study

Background: Metabolic syndrome (MetS) is a highly prevalent condition that identifies individuals at risk for type 2 diabetes mellitus and atherosclerotic cardiovascular disease. Prevention of these diseases relies on early detection and intervention in order to preserve pancreatic β-cells and arterial wall integrity. Yet, the clinical criteria for MetS are insensitive to the early-stage insulin resistance, inflammation, cholesterol and clotting factor abnormalities that char- acterize the progression toward type 2 diabetes and atherosclerosis. Here we report the discovery and initial charac- terization of an atypical new biomarker that detects these early conditions with just one measurement. Methods: Water T2, measured in a few minutes using benchtop nuclear magnetic resonance relaxometry, is exqui- sitely sensitive to metabolic shifts in the blood proteome. In an observational cross-sectional study of 72 non-diabetic human subjects, the association of plasma and serum water T2 values with over 130 blood biomarkers was analyzed using bivariate, multivariate and logistic regression. Results: Plasma and serum water T2 exhibited strong bivariate correlations with markers of insulin, lipids, inflamma- tion, coagulation and electrolyte balance. After correcting for confounders, low water T2 values were independently and additively associated with fasting hyperinsulinemia, dyslipidemia and subclinical inflammation. Plasma water T2 exhibited 100% sensitivity and 87% specificity for detecting early insulin resistance in normoglycemic subjects, as defined by the McAuley Index. Sixteen normoglycemic subjects with early metabolic abnormalities (22% of the study population) were identified by low water T2 values. Thirteen of the 16 did not meet the harmonized clinical criteria for metabolic syndrome and would have been missed by conventional screening for diabetes risk. Low water T2 values were associated with increases in the mean concentrations of 6 of the 16 most abundant acute phase proteins and lipoproteins in plasma. Conclusions: Water T2 detects a constellation of early abnormalities associated with metabolic syndrome, provid- ing a global view of an individual’s metabolic health. It circumvents the pitfalls associated with fasting glucose and hemoglobin A1c and the limitations of the current clinical criteria for metabolic syndrome. Water T2 shows promise as an early, global and practical screening tool for the identification of individuals at risk for diabetes and atherosclerosis

Biomarkers, Obesity, and Cardiovascular Diseases

Obesity and overweight are among the major health problems in the world today. The excessive accumulation of fat in adipose tissue is accompanied by low‐grade inflammation, adipokine secretion dysregulation, oxidative stress, and an alteration of the secretion of gut hormones and food intake related to peptides. This is related to the development of cardiovascular diseases, which have been increased worldwide during the last 15 years approximately. The biomarkers are tremendously important to predict, diagnose, and observe the therapeutic success of common complex multifactorial metabolic diseases, such as obesity and cardiovascular diseases. This chapter presents a review of the most common biomarkers that have been used in the prevention, treatment, prognosis, and diagnosis of obesity and cardiovascular diseases

Graph-Theoretical Tools for the Analysis of Complex Networks

We are currently experiencing an explosive growth in data collection technology that threatens to dwarf the commensurate gains in computational power predicted by Moore’s Law. At the same time, researchers across numerous domain sciences are finding success using network models to represent their data. Graph algorithms are then applied to study the topological structure and tease out latent relationships between variables. Unfortunately, the problems of interest, such as finding dense subgraphs, are often the most difficult to solve from a computational point of view. Together, these issues motivate the need for novel algorithmic techniques in the study of graphs derived from large, complex, data sources. This dissertation describes the development and application of graph theoretic tools for the study of complex networks. Algorithms are presented that leverage efficient, exact solutions to difficult combinatorial problems for epigenetic biomarker detection and disease subtyping based on gene expression signatures. Extensive testing on publicly available data is presented supporting the efficacy of these approaches. To address efficient algorithm design, a study of the two core tenets of fixed parameter tractability (branching and kernelization) is considered in the context of a parallel implementation of vertex cover. Results of testing on a wide variety of graphs derived from both real and synthetic data are presented. It is shown that the relative success of kernelization versus branching is found to be largely dependent on the degree distribution of the graph. Throughout, an emphasis is placed upon the practicality of resulting implementations to advance the limits of effective computation

Oxylipins in triglyceride-rich lipoproteins of dyslipidemic subjects promote endothelial inflammation following a high fat meal.

Elevated triglyceride-rich lipoproteins (TGRL) in circulation is a risk factor for atherosclerosis. TGRL from subjects consuming a high saturated fat test meal elicited a variable inflammatory response in TNFα-stimulated endothelial cells (EC) that correlated strongly with the polyunsaturated fatty acid (PUFA) content. This study investigates how the relative abundance of oxygenated metabolites of PUFA, oxylipins, is altered in TGRL postprandially, and how these changes promote endothelial inflammation. Human aortic EC were stimulated with TNFα and treated with TGRL, isolated from subjects' plasma at fasting and 3.5 hrs postprandial to a test meal high in saturated fat. Endothelial VCAM-1 surface expression stimulated by TNFα provided a readout for atherogenic inflammation. Concentrations of esterified and non-esterified fatty acids and oxylipins in TGRL were quantified by mass spectrometry. Dyslipidemic subjects produced TGRL that increased endothelial VCAM-1 expression by ≥35%, and exhibited impaired fasting lipogenesis activity and a shift in soluble epoxide hydrolase and lipoxygenase activity. Pro-atherogenic TGRL were enriched in eicosapentaenoic acid metabolites and depleted in esterified C18-PUFA-derived diols. Abundance of these metabolites was strongly predictive of VCAM-1 expression. We conclude the altered metabolism in dyslipidemic subjects produces TGRL with a unique oxylipin signature that promotes a pro-atherogenic endothelial phenotype

Estrogen-mediated gut microbiome alterations influence sexual dimorphism in metabolic syndrome in mice

peer-reviewedBackground Understanding the mechanism of the sexual dimorphism in susceptibility to obesity and metabolic syndrome (MS) is important for the development of effective interventions for MS. Results Here we show that gut microbiome mediates the preventive effect of estrogen (17β-estradiol) on metabolic endotoxemia (ME) and low-grade chronic inflammation (LGCI), the underlying causes of MS and chronic diseases. The characteristic profiles of gut microbiome observed in female and 17β-estradiol-treated male and ovariectomized mice, such as decreased Proteobacteria and lipopolysaccharide biosynthesis, were associated with a lower susceptibility to ME, LGCI, and MS in these animals. Interestingly, fecal microbiota-transplant from male mice transferred the MS phenotype to female mice, while antibiotic treatment eliminated the sexual dimorphism in MS, suggesting a causative role of the gut microbiome in this condition. Moreover, estrogenic compounds such as isoflavones exerted microbiome-modulating effects similar to those of 17β-estradiol and reversed symptoms of MS in the male mice. Finally, both expression and activity of intestinal alkaline phosphatase (IAP), a gut microbiota-modifying non-classical anti-microbial peptide, were upregulated by 17β-estradiol and isoflavones, whereas inhibition of IAP induced ME and LGCI in female mice, indicating a critical role of IAP in mediating the effects of estrogen on these parameters. Conclusions In summary, we have identified a previously uncharacterized microbiome-based mechanism that sheds light upon sexual dimorphism in the incidence of MS and that suggests novel therapeutic targets and strategies for the management of obesity and MS in males and postmenopausal women

Increased plasma proneurotensin levels identify NAFLD in adults with and without type 2 diabetes

Context Neurotensin (NT), an intestinal peptide released by fat ingestion, promotes lipid absorption; higher circulating NT levels are associated with type 2 diabetes (T2D), obesity, and cardiovascular disease. Whether NT is related to nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) has not been fully investigated. Objective To study the relationship between plasma proneurotensin 1 to 117 (pro-NT), a stable fragment of the NT precursor hormone, and the presence/severity of NAFLD/NASH and to unravel correlates of increased pro-NT levels. Design/Setting/Participants For this cross-sectional study, 60 obese individuals undergoing bariatric surgery for clinical purposes were recruited. The association between pro-NT and NAFLD was further investigated in 260 consecutive subjects referred to our outpatient clinics for metabolic evaluations, including liver ultrasonography. The study population underwent complete metabolic characterization; in the obese cohort, liver biopsies were performed during surgery. Main Outcome Measures Plasma pro-NT levels in relation to NAFLD/NASH. Results Obese subjects with biopsy-proven NAFLD (53%) had significantly higher plasma pro-NT than those without NAFLD (183.6 ± 81.4 vs 86.7 ± 56.8 pmol/L, P &lt; 0.001). Greater pro-NT correlated with NAFLD presence (P &lt; 0.001) and severity (P &lt; 0.001), age, female sex, insulin resistance, and T2D. Higher pro-NT predicted NAFLD with an area under receiver operating characteristic curve of 0.836 [95% confidence interval (CI), 0.73 to 0.94; P &lt; 0.001]. Belonging to the highest pro-NT quartile correlated with increased NAFLD risk (odds ratio, 2.62; 95% CI, 1.08 to 6.40) after adjustment for confounders. The association between higher pro-NT and NAFLD was confirmed in the second cohort independently from confounders. Conclusions Increased plasma pro-NT levels identify the presence/severity of NAFLD; in dysmetabolic individuals, NT may specifically promote hepatic fat accumulation through mechanisms likely related to increased insulin resistance. © 2018 Endocrine Society