Background

Impaired left ventricular diastolic filling is common in chronic thromboembolic pulmonary hypertension (CTEPH), and recent studies support left ventricular underfilling as a cause. To investigate this further, we assessed left atrial volume index (LAVI) in patients with CTEPH before and after pulmonary thromboendarterectomy (PTE).

Methods

Forty-eight consecutive CTEPH patients had pre- & post-PTE echocardiograms and right heart catheterizations. Parameters included mean pulmonary artery pressure (mPAP), pulmonary vascular resistance (PVR), cardiac index, LAVI, & mitral E/A ratio. Echocardiograms were performed 6 ± 3 days pre-PTE and 10 ± 4 days post-PTE. Regression analyses compared pre- and post-PTE LAVI with other parameters.

Results

Pre-op LAVI (mean 19.0 ± 7 mL/m2) correlated significantly with pre-op PVR (R = -0.45, p = 0.001), mPAP (R = -0.28, p = 0.05) and cardiac index (R = 0.38, p = 0.006). Post-PTE, LAVI increased by 18% to 22.4 ± 7 mL/m2 (p = 0.003). This change correlated with change in PVR (765 to 311 dyne-s/cm5, p = 0.01), cardiac index (2.6 to 3.2 L/min/m2, p = 0.02), and E/A (.95 to 1.44, p = 0.002).

Conclusion

In CTEPH, smaller LAVI is associated with lower cardiac output, higher mPAP, and higher PVR. LAVI increases by ~20% after PTE, and this change correlates with changes in PVR and mitral E/A. The rapid increase in LAVI supports the concept that left ventricular diastolic impairment and low E/A pre-PTE are due to left heart underfilling rather than inherent left ventricular diastolic dysfunction.

Objectives

Chest pain is a common complaint to emergency departments (EDs) and clinical risk factors are used to predict which patients are at risk for worse outcomes and mortality. The goal was to assess the novel biomarker midregional proadrenomedullin (MR-proADM) in prediction of mortality and major adverse cardiac events (MACE).

Methods

This was a subanalysis of the CHOPIN study, a 16-center prospective trial that enrolled 2,071 patients presenting with chest pain within 6 hours of onset. The primary endpoint was 6-month all-cause mortality and the secondary endpoint was 30-day and 6-month MACE: ED visits or hospitalization for acute myocardial infarction, unstable angina, reinfarction, revascularization, and heart failure.

Results

MR-proADM performed similarly to troponin (cTnI; c-statistic = 0.845 and 0.794, respectively) for mortality prediction in all subjects and had similar results in those with noncardiac diagnoses. MR-proADM concentrations were stratified by decile, and the cohort in the top decile had a 9.8% 6-month mortality risk versus 0.9% risk for those in the bottom nine deciles (p < 0.0001). MR-proADM, history of coronary artery disease (CAD), and hypertension were predictors of short-term MACE, while history of CAD, hypertension, cTnI, and MR-proADM were predictors of long-term MACE.

Conclusions

In patients with chest pain, MR-proADM predicts mortality and MACE in all-comers with chest pain and has similar prediction in those with a noncardiac diagnosis. This exploratory analysis is primarily hypotheses-generating and future prospective studies to identify its utility in risk stratification should be considered.

Accurate and efficient classification of variant pathogenicity is critical for research and clinical care. Using data from three large studies, we demonstrate that population-based associations between rare variants and quantitative endophenotypes for three monogenic diseases (low-density-lipoprotein cholesterol for familial hypercholesterolemia, electrocardiographic QTc interval for long QT syndrome, and glycosylated hemoglobin for maturity-onset diabetes of the young) provide evidence for variant pathogenicity. Effect sizes are associated with pathogenic ClinVar assertions (P < 0.001 for each trait) and discriminate pathogenic from non-pathogenic variants (area under the curve 0.82-0.84 across endophenotypes). An effect size threshold of ≥ 0.5 times the endophenotype standard deviation nominates up to 35% of rare variants of uncertain significance or not in ClinVar in disease susceptibility genes with pathogenic potential. We propose that variant associations with quantitative endophenotypes for monogenic diseases can provide evidence supporting pathogenicity.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.