The phosphodiesterase 5 inhibitor sildenafil decreases the proinflammatory chemokine IL-8 in diabetic cardiomyopathy: in vivo and in vitro evidence

Purpose: Interleukin (IL)-8 is a proinflammatory C-X-C chemokine involved in inflammation underling cardiac diseases, primary or in comorbid condition, such diabetic cardiomyopathy (DCM). The phosphodiesterase type 5 inhibitor sildenafil can ameliorate cardiac conditions by counteracting inflammation. The study aim is to evaluate the effect of sildenafil on serum IL-8 in DCM subjects vs. placebo, and on IL-8 release in human endothelial cells (Hfaec) and peripheral blood mononuclear cells (PBMC) under inflammatory stimuli. Methods: IL-8 was quantified: in sera of (30) DCM subjects before (baseline) and after sildenafil (100 mg/day, 3-months) vs. (16) placebo and (15) healthy subjects, by multiplatform array; in supernatants from inflammation-challenged cells after sildenafil (1 µM), by ELISA. Results: Baseline IL-8 was higher in DCM vs. healthy subjects (149.14 ± 46.89 vs. 16.17 ± 5.38 pg/ml, p < 0.01). Sildenafil, not placebo, significantly reduced serum IL-8 (23.7 ± 5.9 pg/ml, p < 0.05 vs. baseline). Receiver operating characteristic (ROC) curve for IL-8 was 0.945 (95% confidence interval of 0.772 to 1.0, p < 0.01), showing good capacity of discriminating the response in terms of drug-induced IL-8 decrease (sensitivity of 0.93, specificity of 0.90). Sildenafil significantly decreased IL-8 protein release by inflammation-induced Hfaec and PBMC and downregulated IL-8 mRNA in PBMC, without affecting cell number or PDE5 expression. Conclusion: Sildenafil might be suggested as potential novel pharmacological tool to control DCM progression through IL-8 targeting at systemic and cellular level

Hypermethylated 14-3-3-σ and ESR1 gene promoters in serum as candidate biomarkers for the diagnosis and treatment efficacy of breast cancer metastasis

9 páginas, 5 figuras, 2 tablas.-- et al.[Methods]: We studied two cohorts of patients: 77 patients treated for breast cancer with no signs of disease, and 34 patients with metastatic breast cancer. DNA was obtained from serum samples, and promoter methylation status was determined by using DNA bisulfite modification and quantitative methylation-specific PCR. [Results]: Serum levels of methylated gene promoter 14-3-3-σ significantly differed between Control and Metastatic Breast Cancer groups (P < 0.001), and between Disease-Free and Metastatic Breast Cancer groups (P < 0.001). The ratio of the 14-3-3-σ level before the first chemotherapy cycle to the level just before administration of the second chemotherapy cycle was defined as the Biomarker Response Ratio [BRR]. We calculated BRR values for the "continuous decline" and "rise-and-fall" groups. Subsequent ROC analysis showed a sensitivity of 75% (95% CI: 47.6 - 86.7) and a specificity of 66.7% (95% CI: 41.0 - 86.7) to discriminate between the groups for a cut-off level of BRR = 2.39. The area under the ROC curve (Z = 0.804 ± 0.074) indicates that this test is a good approach to post-treatment prognosis. [Conclusions]: The relationship of 14-3-3-σ with breast cancer metastasis and progression found in this study suggests a possible application of 14-3-3-σ as a biomarker to screen for metastasis and to follow up patients treated for metastatic breast cancer, monitoring their disease status and treatment response.This study was supported by a grant from the Ministerio de Ciencia e Innovación: SAF 2004-00889; JL Linares is supported by the Junta de Andalucía (P06-CTS-1385).Peer reviewe

Hypermethylated 14-3-3-σ and ESR1 gene promoters in serum as candidate biomarkers for the diagnosis and treatment efficacy of breast cancer metastasis

Background: Numerous hypermethylated genes have been reported in breast cancer, and the silencing of these genes plays an important role in carcinogenesis, tumor progression and diagnosis. These hypermethylated promoters are very rarely found in normal breast. It has been suggested that aberrant hypermethylation may be useful as a biomarker, with implications for breast cancer etiology, diagnosis, and management. The relationship between primary neoplasm and metastasis remains largely unknown. There has been no comprehensive comparative study on the clinical usefulness of tumor-associated methylated DNA biomarkers in primary breast carcinoma and metastatic breast carcinoma. The objective of the present study was to investigate the association between clinical extension of breast cancer and methylation status of Estrogen Receptor1 (ESR1) and Stratifin (14-3-3-σ) gene promoters in disease-free and metastatic breast cancer patients. Methods: We studied two cohorts of patients: 77 patients treated for breast cancer with no signs of disease, and 34 patients with metastatic breast cancer. DNA was obtained from serum samples, and promoter methylation status was determined by using DNA bisulfite modification and quantitative methylation-specific PCR. Results: Serum levels of methylated gene promoter 14-3-3-σ significantly differed between Control and Metastatic Breast Cancer groups (P < 0.001), and between Disease-Free and Metastatic Breast Cancer groups (P < 0.001). The ratio of the 14-3-3-σ level before the first chemotherapy cycle to the level just before administration of the second chemotherapy cycle was defined as the Biomarker Response Ratio [BRR]. We calculated BRR values for the "continuous decline" and "rise-and-fall" groups. Subsequent ROC analysis showed a sensitivity of 75% (95% CI: 47.6 - 86.7) and a specificity of 66.7% (95% CI: 41.0 - 86.7) to discriminate between the groups for a cut-off level of BRR = 2.39. The area under the ROC curve (Z = 0.804 ± 0.074) indicates that this test is a good approach to post-treatment prognosis. Conclusions: The relationship of 14-3-3-σ with breast cancer metastasis and progression found in this study suggests a possible application of 14-3-3-σ as a biomarker to screen for metastasis and to follow up patients treated for metastatic breast cancer, monitoring their disease status and treatment response.This study was supported by a grant from the Ministerio de Ciencia e Innovación: SAF 2004-00889; JL Linares is supported by the Junta de Andalucía (P06-CTS-1385)

Blood Biomarker Panels for the Early Prediction of Stroke‐Associated Complications

Background Acute decompensated heart failure (ADHF) and respiratory tract infections (RTIs) are potentially life-threatening complications in patients experiencing stroke during hospitalization. We aimed to test whether blood biomarker panels might predict these complications early after admission. Methods and Results Nine hundred thirty-eight patients experiencing ischemic stroke were prospectively recruited in the Stroke-Chip study. Post-stroke complications during hospitalization were retrospectively evaluated. Blood samples were drawn within 6 hours after stroke onset, and 14 biomarkers were analyzed by immunoassays. Biomarker values were normalized using log-transformation and Z score. PanelomiX algorithm was used to select panels with the best accuracy for predicting ADHF and RTI. Logistic regression models were constructed with the clinical variables and the biomarker panels. The additional predictive value of the panels compared with the clinical model alone was evaluated by receiver operating characteristic curves. An internal validation through a 10-fold cross-validation with 3 repeats was performed. ADHF and RTI occurred in 19 (2%) and 86 (9.1%) cases, respectively. Three-biomarker panels were developed as predictors: vascular adhesion protein-1 >5.67, NT-proBNP (N-terminal pro-B-type natriuretic peptide) >4.98 and d-dimer >5.38 (sensitivity, 89.5%; specificity, 71.7%) for ADHF; and interleukin-6 >3.97, von Willebrand factor >3.67, and d-dimer >4.58 (sensitivity, 82.6%; specificity, 59.8%) for RTI. Both panels independently predicted stroke complications (panel for ADHF: odds ratio [OR] [95% CI], 10.1 [3-52.2]; panel for RTI: OR, 3.73 [1.95-7.14]) after adjustment by clinical confounders. The addition of the panel to clinical predictors significantly improved areas under the curve of the receiver operating characteristic curves in both cases. Conclusions Blood biomarkers could be useful for the early prediction of ADHF and RTI. Future studies should assess the usefulness of these panels in front of patients experiencing stroke with respiratory symptoms such as dyspnea

Blood Biomarker Panels for the Early Prediction of Stroke‐Associated Complications

Biomarkers; Stroke; Stroke‐associated infectionBiomarcadors; Ictus; Infecció associada a un ictusBiomarcadores; Ictus; Infección asociada a un ictusBackground Acute decompensated heart failure (ADHF) and respiratory tract infections (RTIs) are potentially life‐threatening complications in patients experiencing stroke during hospitalization. We aimed to test whether blood biomarker panels might predict these complications early after admission. Methods and Results Nine hundred thirty‐eight patients experiencing ischemic stroke were prospectively recruited in the Stroke‐Chip study. Post‐stroke complications during hospitalization were retrospectively evaluated. Blood samples were drawn within 6 hours after stroke onset, and 14 biomarkers were analyzed by immunoassays. Biomarker values were normalized using log‐transformation and Z score. PanelomiX algorithm was used to select panels with the best accuracy for predicting ADHF and RTI. Logistic regression models were constructed with the clinical variables and the biomarker panels. The additional predictive value of the panels compared with the clinical model alone was evaluated by receiver operating characteristic curves. An internal validation through a 10‐fold cross‐validation with 3 repeats was performed. ADHF and RTI occurred in 19 (2%) and 86 (9.1%) cases, respectively. Three‐biomarker panels were developed as predictors: vascular adhesion protein‐1 >5.67, NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) >4.98 and d‐dimer >5.38 (sensitivity, 89.5%; specificity, 71.7%) for ADHF; and interleukin‐6 >3.97, von Willebrand factor >3.67, and d‐dimer >4.58 (sensitivity, 82.6%; specificity, 59.8%) for RTI. Both panels independently predicted stroke complications (panel for ADHF: odds ratio [OR] [95% CI], 10.1 [3–52.2]; panel for RTI: OR, 3.73 [1.95–7.14]) after adjustment by clinical confounders. The addition of the panel to clinical predictors significantly improved areas under the curve of the receiver operating characteristic curves in both cases. Conclusions Blood biomarkers could be useful for the early prediction of ADHF and RTI. Future studies should assess the usefulness of these panels in front of patients experiencing stroke with respiratory symptoms such as dyspnea.This project received funding from Instituto de Salud Carlos III (ISCIII) [DTS14/00004, PI17/02130], co‐financed by the European Regional Development Fund (FEDER), and from Fundació La Marató de TV3 [201706] and the European Union's Horizon 2020 research and innovation program [754517]. Neurovascular Research Laboratory takes part into the Spanish stroke research network INVICTUS+ (RD16/0019/0021). The funders had no role in the study design and conduction

Net Reclassification Indices for Evaluating Risk Prediction Instruments: A Critical Review

Background Net Reclassification Indices (NRI) have recently become popular statistics for measuring the prediction increment of new biomarkers. Methods In this review, we examine the various types of NRI statistics and their correct interpretations. We evaluate the advantages and disadvantages of the NRI approach. For pre-defined risk categories, we relate NRI to existing measures of the prediction increment. We also consider statistical methodology for constructing confidence intervals for NRI statistics and evaluate the merits of NRI-based hypothesis testing. Conclusions Investigators using NRI statistics should report them separately for events (cases) and nonevents (controls). When there are two risk categories, the NRI components are the same as the changes in the true and false positive rates. We advocate use of true and false positive rates and suggest it is more useful for investigators to retain the existing, descriptive terms. When there are three or more risk categories, we recommend against NRI statistics because they do not adequately account for clinically important differences in movements among risk categories. The category-free NRI is a new descriptive device designed to avoid pre-defined risk categories. The category-free NRI suffers from many of the same problems as other measures such as the area under the receiver operating characteristic curve. In addition, the category-free NRI can mislead investigators by overstating the incremental value of a biomarker, even in independent validation data. When investigators want to test a null hypothesis of no prediction increment, the well-established tests for coefficients in the regression model are superior to the NRI. If investigators want to use NRI measures, their confidence intervals should be calculated using bootstrap methods rather than published variance formulas. The preferred single-number summary of the prediction increment is the improvement in the Net Benefit