Serum insulin levels are associated with vulnerable plaque components in the carotid artery: the Rotterdam Study

Background: To investigate the association between fasting serum insulin and glucose levels with atherosclerotic plaque composition in the carotid artery. Impaired insulin and glucose levels are implicated in the etiology of cardiovascular disease; however, their influence on the formation and composition of atherosclerotic plaqu

Comparison of CT and CMR for detection and quantification of carotid artery calcification: the Rotterdam Study

Background: Carotid artery atherosclerosis is an important risk factor for stroke. As such, quantitative imaging of carotid artery calcification, as a proxy of atherosclerosis, has become a cornerstone of current stroke research. Yet, population-based data comparing the computed tomography (CT) and cardiovascular magnetic resonance (CMR) for the detection and quantification of calcification remain scarce. Methods: A total of 684 participants from the population-based Rotterdam Study underwent both a CT and CMR of the carotid artery bifurcation to quantify the amount of carotid artery calcification (mean interscan interval: 4.9 ± 1.2 years). We investigated the correlation between the amount of calcification measured on CT and CMR using Spearman's correlation coefficient, Bland-Altman plots, and linear regression. In addition, using logistic regression modeling, we assessed the association of CT and CMR based calcification volumes with a history of stroke. Results: We found a strong correlation between CT and CMR based calcification volumes (Spearman's correlation coefficient:0.86, p-value ≤0.01). Bland-Altman analyses showed a good agreement, though CT based calcification volumes were systematically larger. Finally, calcification volume assessed with either imaging modality was associated with a history of stroke with similar effect estimates (odds ratio (OR) per 1-SD increase in calcification volume: 1.52 (95% CI:1.00;2.30) for CT, and 1.47 (95% CI:1.01;2.14) for CMR. Conclusion: CT based and CMR based volumes of carotid artery calcificatio

Conventional and ambulatory blood pressure as predictors of diastolic left ventricular function in a Flemish population

Background--No longitudinal study compared associations of echocardiographic indexes of diastolic left ventricular function studies with conventional (CBP) and daytime ambulatory (ABP) blood pressure in the general population. Methods and Results--In 780 Flemish (mean age, 50.2 years; 51.7% women), we measured left atrial volume index (LAVI), peak velocities of the transmitral blood flow (E) and mitral annular movement (e0) in early diastole and E/e0 9.6 years (median) after CBP and ABP. In adjusted models including CBP and ABP, we expressed associations per 10/5-mm Hg systolic/diastolic blood pressure increments. LAVI and E/e0 were 0.65/0.40 mL/m2 and 0.17/0.09 greater with higher systolic/diastolic ABP (P≤0.028), but not with higher baseline CBP (P≤0.086). e0 was lower (P≤0.032) with higher diastolic CBP (-0.09 cm/s) and ABP (-0.19 cm/s). When we substituted baseline CBP by CBP recorded concurrently with echocardiography, LAVI and E/e0 remained 0.45/0.38 mL/m2 and 0.15/0.08 greater with baseline ABP (P≤0.036), while LAVI (+0.53 mL/m2) and E/e0 (+0.19) were also greater (P < 0.001) in relation to concurrent systolic CBP. In categorized analyses of baseline data, sustained hypertension or masked hypertension compared with normotension or white-

Reproducibility of Retinal Microvascular Traits Decoded by the Singapore i Vessel Assessment Software Across the Human Age Range

Retinal microvascular traits predict adverse health outcomes. The Singapore I Vessel Assessment (SIVA) software improved automated postprocessing of retinal photographs. In addition to microvessel caliber, it generates measure

Bacteriology testing of cardiovascular tissues: comparison of transport solution versus tissue testing

Bacteriology testing is mandatory for quality control of recovered cardiovascular allografts (CVA). In this paper, two different bacteriology examinations (A tests) performed before tissue antibiotic decontamination were compared: transport solution filtration analysis (A1) and tissue fragment direct incubation (A2). For this purpose, 521 CVA (326 heart and 195 artery tissues) from 280 donors were collected and analyzed by the European Homograft Bank (EHB). Transport solution (A1) tested positive in 43.25 % of hearts and in 48.21 % of arteries, whereas the tissue samples (A2) tested positive in 38.34 % of hearts and 33.85 % of arteries. The main species identified in both A1 and A2 were Staphylococcus spp. in 55 and 26 % of cases, and Propionibacterium spp. in 8 and 19 %, respectively. Mismatches in bacteriology results between both initial tests A1 and A2 were found. 18.40 % of the heart valves were identified as positive by A1 whilst 13.50 % were considered positive by A2. For arteries, 20.51 % of cases were positive in A1 and negative in A2, and just 6.15 % of artery allografts presented contamination in the A2 test but were considered negative for the A1 test. Comparison between each A test with the B and C tests after antibiotic treatment of the allograft was also performed. A total decontamination rate of 70.8 % of initial positive A tests was obtained. Due to the described mismatches and different bacteria identification percentage, utilization of both A tests should be implemented in tissue banks in order to avoid false negatives.status: publishe

A novel urinary biomarker predicts 1-year mortality after discharge from intensive care

Rationale The urinary proteome reflects molecular drivers of disease. Objectives To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality. Methods In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses. Measurements and main results In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708-0.798) and 0.688 (0.656-0.719), respectively. While accounting for study site and clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00-2.91) for ACM128 (+ 1 SD), 1.24 (1.16-1.32) for the Charlson Comorbidity Index (+ 1 point), and >= 1.19 (P = + 0.50), NRI (>= + 53.7), and AUC (>= + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity, and apoptosis. Conclusions The urinary proteomic classifier ACM128 predicts the 1-year post-ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome

A novel urinary biomarker predicts 1-year mortality after discharge from intensive care

Rationale The urinary proteome reflects molecular drivers of disease. Objectives To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality. Methods In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses. Measurements and main results In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708-0.798) and 0.688 (0.656-0.719), respectively. While accounting for study site and clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00-2.91) for ACM128 (+ 1 SD), 1.24 (1.16-1.32) for the Charlson Comorbidity Index (+ 1 point), and >= 1.19 (P = + 0.50), NRI (>= + 53.7), and AUC (>= + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity, and apoptosis. Conclusions The urinary proteomic classifier ACM128 predicts the 1-year post-ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome

A novel urinary biomarker predicts 1-year mortality after discharge from intensive care

RATIONALE: The urinary proteome reflects molecular drivers of disease. OBJECTIVES: To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality. METHODS: In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses. MEASUREMENTS AND MAIN RESULTS: In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708-0.798) and 0.688 (0.656-0.719), respectively. While accounting for study site and clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00-2.91) for ACM128 (+ 1 SD), 1.24 (1.16-1.32) for the Charlson Comorbidity Index (+ 1 point), and ≥ 1.19 (P ≤ 0.022) for other biomarkers (+ 1 SD). ACM128 improved (P ≤ 0.0001) IDI (≥ + 0.50), NRI (≥ + 53.7), and AUC (≥ + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity, and apoptosis. CONCLUSIONS: The urinary proteomic classifier ACM128 predicts the 1-year post-ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome.status: publishe