Venn diagram of prognostic and diagnostic miRNA predictors based on the univariate analysis with p<0.05.

<p>11 miRNAs were identified as differentially expressed at both time-points; 6 miRNAs were predictive of ß-cell glucose sensitivity at baseline only; and 16 only at follow-up.</p

Identification of novel biomarkers to monitor β-cell function and enable early detection of type 2 diabetes risk

<div><p>A decline in β-cell function is a prerequisite for the development of type 2 diabetes, yet the level of β-cell function in individuals at risk of the condition is rarely measured. This is due, in part, to the fact that current methods for assessing β-cell function are inaccurate, prone to error, labor-intensive, or affected by glucose-lowering therapy. The aim of the current study was to identify novel circulating biomarkers to monitor β-cell function and to identify individuals at high risk of developing β-cell dysfunction. In a nested case-control study from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) cohort (n = 1157), proteomics and miRNA profiling were performed on fasting plasma samples from 43 individuals who progressed to impaired glucose tolerance (IGT) and 43 controls who maintained normal glucose tolerance (NGT) over three years. Groups were matched at baseline for age, gender, body mass index (BMI), insulin sensitivity (euglycemic clamp) and β-cell glucose sensitivity (mathematical modeling). Proteomic profiling was performed using the SomaLogic platform (Colorado, USA); miRNA expression was performed using a modified RT-PCR protocol (Regulus Therapeutics, California, USA). Results showed differentially expressed proteins and miRNAs including some with known links to type 2 diabetes, such as adiponectin, but also novel biomarkers and pathways. In cross sectional analysis at year 3, the top differentially expressed biomarkers in people with IGT/ reduced β-cell glucose sensitivity were adiponectin, alpha1-antitrypsin (known to regulate adiponectin levels), endocan, miR-181a, miR-342, and miR-323. At baseline, adiponectin, cathepsin D and NCAM.L1 (proteins expressed by pancreatic β-cells) were significantly lower in those that progressed to IGT. Many of the novel prognostic biomarker candidates were within the epithelial-mesenchymal transition (EMT) pathway: for example, Noggin, DLL4 and miR-181a. Further validation studies are required in additional clinical cohorts and in patients with type 2 diabetes, but these results identify novel pathways and biomarkers that may have utility in monitoring β-cell function and/ or predicting future decline, allowing more targeted efforts to prevent and intercept type 2 diabetes.</p></div

Demographic and clinical characteristics of the RISC cohort^*.

<p>Demographic and clinical characteristics of the RISC cohort^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182932#t001fn001" target="_blank">*</a>}.</p

Scatterplot of diagnostic protein biomarkers.

<p>Circulating levels of top-ranked diagnostic biomarkers in IGT subjects (case, left) compared to healthy controls (right).</p

Características semióticas de una imagen publicitaria de Coca Cola que influyen en la interpretación que realizan 3 estudiantes universitarios de la ciudad de Cali

En la presente investigación se propone un análisis de las características semióticas de una imagen publicitaria de Coca Cola, que inciden en la interpretación que tres estudiantes universitarios realizan del mismo. Por medio del análisis semiótico de dicha imagen se busca conocer las diferentes opiniones, perspectivas, suposiciones e ideas que esta genera en los sujetos. Desde la psicología cognitiva se ha buscado responder a la inquietante pregunta de cómo los seres humanos construyen conocimiento, considerando este como un proceso dinámico y cambiante, que varía en cada sujeto; ya que cada persona realiza una interpretación y reinterpretación del mundo a su alrededor, la cual depende de los significados y sentidos que emergen fruto de la relación que establece con los otros y con su entorno

Venn diagram of prognostic and diagnostic miRNA predictors based on the univariate analysis with p<0.05.

<p>11 miRNAs were identified as differentially expressed at both time-points; 6 miRNAs were predictive of ß-cell glucose sensitivity at baseline only; and 16 only at follow-up.</p

Scatterplot of prognostic protein biomarkers.

<p>Circulating levels of top-ranked prognostic biomarkers in IGT subjects (case, left) compared to healthy controls (right).</p

Interleukin-6 and Cardiovascular and Kidney Outcomes in Patients with Type 2 Diabetes: New Insights from CANVAS

   Objective: The inflammatory cytokine interleukin-6 (IL-6) is associated with cardiovascular and kidney outcomes in various populations. However, data in patients with type 2 diabetes is limited. We assessed the association of IL-6 with cardiovascular and kidney outcomes in the CANVAS trial and determined the effect of canagliflozin on IL-6. Research Design and Methods: Patients with type 2 diabetes at high cardiovascular risk were randomly assigned to canagliflozin or placebo. Plasma IL-6 was measured at baseline and year 1, 3, and 6. The composite cardiovascular outcome was non-fatal myocardial infarction, non-fatal stroke, or cardiovascular death; the composite kidney outcome was sustained ≥40% eGFR decline, end-stage kidney disease, or kidney-related death. Multivariable adjusted Cox proportional hazard regression was used to estimate the associations between IL-6 and outcomes. The effect of canagliflozin on IL-6 over time was assessed with a repeated measures mixed effect model. Results: The geometric mean IL-6 at baseline, available in 3503 (80.2%) participants, was 1.7 pg/mL. Each doubling of baseline IL-6 was associated with 14% (95%CI 4, 24) and 21% (95%CI 1, 45) increased risk of cardiovascular and kidney outcomes, respectively. Over 6 years, IL-6 increased by 5.8% (95% CI 3.4, 8.3) in the placebo group. Canagliflozin modestly attenuated the IL-6 increase (absolute percentage difference versus placebo 4.4% [95% CI 1.3, 9.9; p=0.01]). At year 1, each 25% lower level of IL-6 compared to baseline was associated with 7% (95%CI 1, 22) and 14% (95%CI 5, 22) lower risks for the cardiovascular and kidney outcome, respectively.   Conclusions/interpretation: In patients with type 2 diabetes at high cardiovascular risk, baseline IL-6 and its 1-year change were associated with cardiovascular and kidney outcomes.  The effect of IL-6 lowering therapy on cardiovascular, kidney, and safety outcomes remains to be tested.  </p

Table_2_Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study.xlsx

IntroductionType 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised “bottom-up” approach, we attempt to group T2D patients based solely on -omics data generated from plasma.MethodsCirculating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics.ResultsFrom a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor.ConclusionsUsing an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.</p

Image_1_Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study.pdf

IntroductionType 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised “bottom-up” approach, we attempt to group T2D patients based solely on -omics data generated from plasma.MethodsCirculating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics.ResultsFrom a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor.ConclusionsUsing an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.</p