Background Patients with chronic kidney disease ( CKD ) are at high risk of myocardial infarction. Cardiac troponins are the biomarkers of choice for the diagnosis of acute myocardial infarction ( AMI ) without ST ‐segment elevation ( NSTE ). In patients with CKD , troponin levels are often chronically elevated, which reduces their diagnostic utility when NSTE ‐ AMI is suspected. The aim of this study was to derive a diagnostic algorithm for serial troponin measurements in patients with CKD and suspected NSTE ‐ AMI . Methods and Results Two cohorts, 1494 patients from a prospective cohort study with high‐sensitivity troponin I (hs‐ cTnI ) measurements and 7059 cases from a clinical registry with high‐sensitivity troponin T (hs‐ cTnT ) measurements, were analyzed. The prospective cohort comprised 280 CKD patients (estimated glomerular filtration rate <60 mL/min/1.73 m 2 ). The registry data set contained 1581 CKD patients. In both cohorts, CKD patients were more likely to have adjudicated NSTE ‐ AMI than non‐ CKD patients. The specificities of hs‐ cTnI and hs‐ cTnT to detect NSTE ‐ AMI were reduced with CKD (0.82 versus 0.91 for hs‐ cTnI and 0.26 versus 0.73 for hs‐ cTnT ) but could be restored by applying optimized cutoffs to either the first or a second measurement after 3 hours. The best diagnostic performance was achieved with an algorithm that incorporates serial measurements and rules in or out AMI in 69% (hs‐ cTnI ) and 55% (hs‐ cTnT ) of CKD patients. Conclusions The diagnostic performance of high‐sensitivity cardiac troponins in patients with CKD with suspected NSTE ‐ AMI is improved by use of an algorithm based on admission troponin and dynamic changes in troponin concentration.

BackgroundAdipokines mediate cardiometabolic risk associated with obesity but their role in the pathogenesis of obesity‐associated heart failure remains uncertain. We investigated the associations between circulating adipokine concentrations and echocardiographic measures in a community‐based sample.Methods and ResultsWe evaluated 3514 Framingham Heart Study participants (mean age 40 years, 53.8% women) who underwent routine echocardiography and had select circulating adipokines measured, ie, leptin, soluble leptin receptor, fatty acid–binding protein 4, retinol‐binding protein 4, fetuin‐A, and adiponectin. We used multivariable linear regression, adjusting for known correlates (including weight), to relate adipokine concentrations (independent variables) to the following echocardiographic measures (dependent variables): left ventricular mass index, left atrial diameter in end systole, fractional shortening, and E/e′. In multivariable‐adjusted analysis, left ventricular mass index was inversely related to circulating leptin and fatty acid–binding protein 4 concentrations but positively related to retinol‐binding protein 4 and leptin receptor levels (P≤0.002 for all). Left atrial end‐systolic dimension was inversely related to leptin but positively related to retinol‐binding protein 4 concentrations (P≤0.0001). E/e′ was inversely related to leptin receptor levels (P=0.0002). We observed effect modification by body weight for select associations (leptin receptor and fatty acid–binding protein 4 with left ventricular mass index, and leptin with left atrial diameter in end systole; P<0.05 for interactions). Fractional shortening was not associated with any of the adipokines. No echocardiographic trait was associated with fetuin‐A or adiponectin concentrations.ConclusionsIn our cross‐sectional study of a large, young to middle‐aged, relatively healthy community‐based sample, key indices of subclinical cardiac remodeling were associated with higher or lower circulating concentrations of prohypertrophic and antihypertrophic adipokines in a context‐specific manner. These observations may offer insights into the pathogenesis of the cardiomyopathy of obesity.

To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel ‘rain plot’ approach to display the results of these analyses. The ‘rain plot’ combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically.