Meta-Analysis of Percutaneous Endomyocardial Cell Therapy in Patients with Ischemic Heart Failure by Combination of Individual Patient Data (IPD) of ACCRUE and Publication-Based Aggregate Data

Individual patient data (IPD)-based meta-analysis (ACCRUE, meta-analysis of cell-based cardiac studies, NCT01098591) revealed an insufficient effect of intracoronary cell-based therapy in acute myocardial infarction. Patients with ischemic heart failure (iHF) have been treated with reparative cells using percutaneous endocardial, surgical, transvenous or intracoronary cell delivery methods, with variable effects in small randomized or cohort studies. The objective of this meta-analysis was to investigate the safety and efficacy of percutaneous transendocardial cell therapy in patients with iHF. Two investigators extracted the data. Individual patient data (IPD) (n = 8 studies) and publication-based (n = 10 studies) aggregate data were combined for the meta-analysis, including patients (n = 1715) with chronic iHF. The data are reported in accordance with PRISMA guidelines. The primary safety and efficacy endpoints were all-cause mortality and changes in global ejection fraction. The secondary safety and efficacy endpoints were major adverse events, hospitalization and changes in end-diastolic and end-systolic volumes. Post hoc analyses were performed using the IPD of eight studies to find predictive factors for treatment safety and efficacy. Cell therapy was significantly (p < 0.001) in favor of survival, major adverse events and hospitalization during follow-up. A forest plot analysis showed that cell therapy presents a significant benefit of increasing ejection fraction with a mean change of 2.51% (95% CI: 0.48; 4.54) between groups and of significantly decreasing end-systolic volume. The analysis of IPD data showed an improvement in the NYHA and CCS classes. Cell therapy significantly decreased the end-systolic volume in male patients; in patients with diabetes mellitus, hypertension or hyperlipidemia; and in those with previous myocardial infarction and baseline ejection fraction ≤ 45%. The catheter-based transendocardial delivery of regenerative cells proved to be safe and effective for improving mortality and cardiac performance. The greatest benefit was observed in male patients with significant atherosclerotic co-morbidities

Sequential activation of different pathway networks in ischemia-affected and non-affected myocardium, inducing intrinsic remote conditioning to prevent left ventricular remodeling

We have analyzed the pathway networks of ischemia-affected and remote myocardial areas after repetitive ischemia/reperfusion (r-I/R) injury without ensuing myocardial infarction (MI) to elaborate a spatial- and chronologic model of cardioprotective gene networks to prevent left ventricular (LV) adverse remodeling. Domestic pigs underwent three cycles of 10/10 min r-I/R by percutaneous intracoronary balloon inflation/deflation in the mid left anterior descending artery, without consecutive MI. Sham interventions (n = 8) served as controls. Hearts were explanted at 5 h (n = 6) and 24 h (n = 6), and transcriptomic profiling of the distal (ischemia-affected) and proximal (non-affected) anterior myocardial regions were analyzed by next generation sequencing (NGS) and post-processing with signaling pathway impact and pathway network analyses. In ischemic region, r-I/R induced early activation of Ca-, adipocytokine and insulin signaling pathways with key regulator STAT3, which was also upregulated in the remote areas together with clusterin (CLU) and TNF-alpha. During the late phase of cardioprotection, antigen immunomodulatory pathways were activated with upregulation of STAT1 and CASP3 and downregulation of neprilysin in both zones, suggesting r-I/R induced intrinsic remote conditioning. The temporo-spatially differently activated pathways revealed a global myocardial response, and neprilysin and the STAT family as key regulators of intrinsic remote conditioning for prevention of adverse remodeling

Integration of imaging and circulating biomarkers in heart failure: a consensus document by the Biomarkers and Imaging Study Groups of the Heart Failure Association of the European Society of Cardiology

Circulating biomarkers and imaging techniques provide independent and complementary information to guide management of heart failure (HF). This consensus document by the Heart Failure Association (HFA) of the European Society of Cardiology (ESC) presents current evidence-based indications relevant to integration of imaging techniques and biomarkers in HF. The document first focuses on application of circulating biomarkers together with imaging findings, in the broad domains of screening, diagnosis, risk stratification, guidance of treatment and monitoring, and then discusses specific challenging settings. In each section we crystallize clinically relevant recommendations and identify directions for future research. The target readership of this document includes cardiologists, internal medicine specialists and other clinicians dealing with HF patients

OBSERVATORIO TERRITORIAL Y AMBIENTAL ALENTEJO, EXTREMADURA, CENTRO (OTALEX C): DE GIS A IDE.

In the scope of the Spain-Portugal INTERREG projects and FEDER funded POCTEP program, OTALEX C (Territorial and Environmental Monitoring Alentejo Extremadura Center) project aims at studying of various territorial, socioeconomic and environmental indicators. It is the fundamental objective of this project, to develop a geo-portal accessible via internet, for anyone, so that the information will be useful in making decisions related to land use and therefore sustainable development of the environment. Under this general framework over the past fifteen years, we have developed different projects that have set the standardization of data between Portugal and Spain, also was designed GIS systems, and developed regional models and indicator systems, culminating in the current Spatial Data Infrastructure SDI-OTALEX C

Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes:An IMI-DIRECT study

AIM: Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D. METHODS: The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders. RESULTS: At baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1-3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18-1.92) for subgroup 2 and 1.88 (-0.08-3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose. CONCLUSIONS: Different glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk

Distinct molecular signatures of clinical clusters in people with type 2 diabetes:an IMI-RHAPSODY study

Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity a previous study clustered people with diabetes into five diabetes subtypes. The aim of the current study is to investigate the aetiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic- (N=12828), metabolomic- (N=2945), lipidomic- (N=2593) and proteomic (N=1170) data were obtained in plasma. In each datatype each cluster was compared with the other four clusters as the reference. The insulin resistant cluster showed the most distinct molecular signature, with higher BCAAs, DAG and TAG levels and aberrant protein levels in plasma enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher cytokines. A subset of the mild diabetes cluster with high HDL showed the most beneficial molecular profile with opposite effects to those seen in the insulin resistant cluster. This study showed that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous diseas

Profiles of glucose metabolism in different prediabetes phenotypes, classified by fasting glycemia, 2-hour OGTT, glycated hemoglobin, and 1-hour OGTT:An IMI DIRECT study

Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA1c indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). β-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (P &lt; 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISRr (P &lt; 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio &gt;2, P &lt; 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence.</p

Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study

Objective We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D). Research Design and Methods 732 recently diagnosed T2D patients from the IMI-DIRECT study were extensively phenotyped over three years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS) and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression. Results Faster HbA1c progression was independently associated with faster deterioration of OGIS and GS, and increasing CLIm; visceral or liver fat, HDL-cholesterol and triglycerides had further independent, though weaker, roles (R2=0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from AUROC=0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS and CLIm was relatively stable (odds ratios 0.07 to 0.09). T2D polygenic risk score and baseline pancreatic fat, GLP-1, glucagon, diet, and physical activity did not show an independent role. Conclusions Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of T2D patients in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression

Predicting and elucidating the etiology of fatty liver disease : A machine learning modeling and validation study in the IMI DIRECT cohorts

Background Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n= 795) or at high risk of developing the disease (n= 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (= 5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86;p = 5%) rather than a continuous one. Conclusions In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see:) and made it available to the community.Peer reviewe