Diagnostic Performance of Serum Biomarkers Fibroblast Growth Factor 21, Galectin-3 and Copeptin for Heart Failure with Preserved Ejection Fraction in a Sample of Patients with Type 2 Diabetes Mellitus.

More than half of the patients with heart failure have preserved ejection fraction (HFpEF), however evidence shows a mortality rate comparable to those with reduced ejection fraction. The aim of this study was to evaluate whether FGF21, galectin-3 and copeptin can be used as biomarkers to identify HFpEF in patients with confirmed type 2 diabetes mellitus (DM). Sixty-nine diabetic patients were enrolled and divided into two groups: patients with HFpEF (n = 40) and those without HFpEF (n = 29). The ability of the studied biomarkers to discriminate HFpEF cases from non-HFpEF subjects were evaluated by the area under the Receiver Operating Characteristics (ROC) curve and the 95% confidence interval (CI). Compared to patients without heart failure, those with HFpEF had significantly higher levels of FGF21 (mean 146.79 pg/mL vs. 298.98 pg/mL). The AUC value of FGF21 was 0.88, 95% CI: [0.80, 0.96], Se = 85% [70.2, 94.3], Sp = 79.3% [60.3, 92.0], at an optimal cut-off value of 217.40 pg/mL. There was no statistical significance associated with galectin-3 and copeptin between patient cohorts. In conclusion, galectin-3 and copeptin levels were not effective for detecting HFpEF, while FGF21 is a promising biomarker for diagnosing HFpEF in DM patients.

Resistin and Cardiac Arrest-A Prospective Study.

. The systemic response to ischemia-reperfusion that occurs after a cardiac arrest (CA) followed by the return of spontaneous circulation leads to endothelial toxicity and cytokine production, both responsible for the subsequent occurrence of severe cardiocirculatory dysfunction and early death. Resistin is emerging as a biomarker of proinflammatory status and myocardial ischemic injury and as a mediator of endothelial dysfunction. The study aimed to analyze the possible associations between several clinical and biological variables and the serum levels of resistin in CA survivors. Forty patients with out-of-hospital resuscitated CA, were enrolled in the study. Demographic, clinical and laboratory data (including serum resistin measurements at admission and at 6, 12, 24, 48 and 72 h) were recorded. For resistin, we calculated the area under the curve (AUC) using the trapezoidal method with measurements from 0 to 12 h, 0 to 24 h, 0 to 48 h and 0 to 72 h. Fifteen (37.5%) patients died in the first 72 h after CA. Cardiovascular comorbidities were present in 65% of patients. The majority of patients had post-CA shock (29 (72.5%)). Resistin serum levels rose in the first 12-24 h and decreased in the next 48-72 h. In univariate analysis, advanced age, longer duration of resuscitation, high sequential organ failure assessment score, high lactate levels, presence of cardiovascular comorbidities and the post-CA shock were associated with higher resistin levels. In multivariate analysis, post-CA shock or cardiovascular comorbidities were independently associated with higher AUCs for resistin for 0-12 h and 0-24 h. The only identified variable to independently predict higher AUCs for resistin for 0-48 h and 0-72 h was the presence of post-CA shock. Our data demonstrate strong independent correlation between high serum resistin levels, cardiac comorbidities and post-CA shock. The impact of the post-CA shock on serum concentration of resistin was greater than that of cardiac comorbidities.

Multimarker Assessment of Diastolic Dysfunction in Metabolic Syndrome Patients.

BACKGROUND: Metabolic syndrome (MetS) has been associated with left ventricular diastolic dysfunction (LVDD) with preserved systolic function. This study aims at identifying the predictive factors for LVDD in MetS patients. METHODS: The studied group comprised 72 consecutive hospitalized patients (2010-2011) diagnosed with MetS based on AHA/NHLBI/IDF 2009 definition, free of cardiovascular disease (36.11% males, age 59.19 ± 5.26 years), who underwent echocardiographic examination. Laboratory measurements of high-sensitivity C-reactive protein (hs-CRP), fibrinogen (Fbg) and interleukin-6 (IL-6), 8-isoprostaglandin-F2alpa (8-isoPGF2α), uric acid, glutathione peroxidases, and N-terminal pro-B-type natriuretic peptide (NT-proBNP) were measured. RESULTS: LVDD was identified in 47 (65.27%) of the MetS patients. The diastolic blood pressure (DBP) was the strongest prediction factor for LVDD (areas under the receiver operating curve [AUC]: 0.73, odds ratios [OR]: 1.065). The number of MetS criteria was also significantly predictive for LVDD (AUC: 0.65, OR: 2.029, P < 0.04). IL-6, hs-CRP, Fbg, and NT-proBNP were predictive for LVDD when receiver operating curve (ROC) analyses were used. The multimarker model comprising age, sex, SBP and DBP, waist, circumference, triglycerides along with hs-CRP, IL-6, and NT-proBNP had the best predictive capacity (AUC: 0.88, P = 0.0001). In multivariate analysis, IL-6 remained an independent predictive biomarker for LVDD (OR: 2.045). CONCLUSION: Both MetS components and biomarkers of inflammation (IF) are predictive factors for LVDD. The best predictive multimarker model for LVDD in MetS patients is composed of waist, triglycerides (TGL), SBP, DBP, fasting glucose, IL-6, hs-CRP, and NT-proBNP. IL-6 remains an independent predictive biomarker for LVDD in MetS patients, underlining the importance of IF in the evolution of MetS to subclinical cardiac damage.

Intercellular adhesion molecule, plasma adiponectin and albuminuria in type 2 diabetic patients.

AIMS: Our study addressed the influence of early inflammatory stages of diabetic kidney disease: leukocyte adhesion and monocyte activation (as assessed by intercellular leukocyte adhesion molecule-ICAM-1 and monocyte chemoatractant protein-MCP-1) on the degree of albuminuria. Plasma levels of adiponectin, a possible anti-inflammatory counteracting mechanism, were also studied in correlation to the above-mentioned cytokines. METHODS: 79 consecutive type 2 diabetic outpatients and 46 controls were included. Routine laboratory analysis, urinary albumin to creatinine ratio (uACR), plasma adiponectin, plasma ICAM-1 and urinary MPC-1 were assessed. RESULTS: In multiple regression ICAM-1 (p=0.004) and adiponectin (p=0.04) were the main determinants of uACR. Plasma adiponectin positively correlated to ICAM-1 (p=0.03, r=0.24). In albuminuric patients (uACR ≥30 mg/g) plasma adiponectin was significantly higher compared to normoalbuminuric ones (uACR <30 mg/g). In albuminuric patients the main determinants of uACR were plasma ICAM-1 and adiponectin. In multiple regression ICAM-1 is the only one that retains statistical significance (p=0.02). Urinary MCP-1 did not correlate to uACR. CONCLUSIONS: In our type 2 diabetic patients, plasma levels of ICAM-1 and adiponectin are predictive for albuminuria. Urinary MCP-1 does not correlated to uACR. Plasma adiponectin positively correlates to adhesion molecule ICAM-1 in our cohort.