Dilated cardiomyopathy (DCM) is a common cause of heart failure and is the primary indication for heart transplantation. A genetic etiology can be found in 20-35% of patients with DCM, especially in those with a family history of cardiomyopathy or sudden cardiac death at an early age. With advancements in genome sequencing, the understanding of genotype-phenotype relationships in DCM has expanded with over 60 genes implicated in the disease. Subsequently, these findings have increased adoption of genetic testing in the management of DCM, which has allowed for improved risk stratification and identification of at risk family members. In this review, we discuss the genetic evaluation of DCM with a focus on practical genetic testing considerations, genotype-phenotype associations, and insights into upcoming personalized therapies.

Background

Breast arterial calcification (BAC) on mammograms has emerged as a biomarker of women's cardiovascular disease (CVD) risk, but there is a lack of quantification tools and clinical outcomes studies.

Objectives

This study assessed the association of BAC (both presence and quantity) with CVD outcomes.

Methods

This single-center, retrospective study included women with a screening mammogram from 2007 to 2016. BAC was quantified using an artificial intelligence-generated score, which was assessed as both a binary and continuous variable. Regression analyses evaluated the association between BAC and mortality and a composite of acute myocardial infarction, heart failure, stroke, and mortality. Analyses were adjusted for age, race, diabetes, smoking, blood pressure, cholesterol, and history of CVD and chronic kidney disease.

Results

A total of 18,092 women were included in this study (mean age 56.8 ± 11.0 years; diabetes [13%], hypertension [36%], hyperlipidemia [40%], and smoking [5%]). BAC was present in 4,223 (23%). Over a median follow-up of 6 years, death occurred in 7.8% and 2.3% of women with and without BAC, respectively. The composite occurred in 12.4% and 4.3% of women with and without BAC, respectively. Compared to those without, women with BAC had adjusted HRs of 1.49 (95% CI: 1.33-1.67) for mortality and 1.56 (95% CI: 1.41-1.72) for the composite. Each 10-point increase in the BAC score was associated with higher risk of mortality (HR: 1.08 [95% CI: 1.06-1.11]) and the composite (HR: 1.08 [95% CI: 1.06-1.10]). BAC was especially predictive of future events among younger women.

Conclusions

BAC is independently associated with mortality and CVD, especially among younger women. Measurement of BAC beyond presence adds incremental risk stratification. Quantifying BAC using an artificial intelligence algorithm is feasible, clinically relevant, and may improve personalized CVD risk stratification.

The impact of statins, angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers (ARBs) on coronavirus disease 2019 (COVID-19) severity and recovery is important given their high prevalence of use among individuals at risk for severe COVID-19. We studied the association between use of statin/angiotensin-converting enzyme inhibitors/ARB in the month before hospital admission, with risk of severe outcome, and with time to severe outcome or disease recovery, among patients hospitalized for COVID-19. We performed a retrospective single-center study of all patients hospitalized at University of California San Diego Health between February 10, 2020 and June 17, 2020 (n = 170 hospitalized for COVID-19, n = 5,281 COVID-negative controls). Logistic regression and competing risks analyses were used to investigate progression to severe disease (death or intensive care unit admission), and time to discharge without severe disease. Severe disease occurred in 53% of COVID-positive inpatients. Median time from hospitalization to severe disease was 2 days; median time to recovery was 7 days. Statin use prior to admission was associated with reduced risk of severe COVID-19 (adjusted OR 0.29, 95%CI 0.11 to 0.71, p < 0.01) and faster time to recovery among those without severe disease (adjusted HR for recovery 2.69, 95%CI 1.36 to 5.33, p < 0.01). The association between statin use and severe disease was smaller in the COVID-negative cohort (p for interaction = 0.07). There was potential evidence of faster time to recovery with ARB use (adjusted HR 1.92, 95%CI 0.81 to 4.56). In conclusion, statin use during the 30 days prior to admission for COVID-19 was associated with a lower risk of developing severe COVID-19, and a faster time to recovery among patients without severe disease.

Home health monitoring has the potential to improve outpatient management of chronic cardiopulmonary diseases such as heart failure. However, it is often limited by the need for adherence to self-measurement, charging and self-application of wearables, or usage of apps. Here, we describe a non-contact, adherence-independent sensor, that when placed beneath the legs of a patient's home bed, longitudinally monitors total body weight, detailed respiratory signals, and ballistocardiograms for months, without requiring any active patient participation. Accompanying algorithms separate weight and respiratory signals when the bed is shared by a partner or a pet. Validation studies demonstrate quantitative equivalence to commercial sensors during overnight sleep studies. The feasibility of detecting obstructive and central apneas, cardiopulmonary coupling, and the hemodynamic consequences of non-sustained ventricular tachycardia is also established. Real-world durability is demonstrated by 3 months of in-home monitoring in an example patient with heart failure and ischemic cardiomyopathy as he recovers from coronary artery bypass grafting surgery. BedScales is the first sensor to measure adherence-independent total body weight as well as longitudinal cardiopulmonary physiology. As such, it has the potential to create a multidimensional picture of chronic disease, learn signatures of impending hospitalization, and enable optimization of care in the home.

Pulmonary arterial hypertension (PAH) is characterized by increased pulmonary artery pressure and vascular resistance, typically leading to right heart failure and death. Current therapies improve quality of life of the patients but have a modest effect on long-term survival. A detailed transcriptomics and systems biology view of the PAH lung is expected to provide new testable hypotheses for exploring novel treatments. We completed transcriptomics analysis of PAH and control lung tissue to develop disease-specific and clinical data/tissue pathology gene expression classifiers from expression datasets. Gene expression data were integrated into pathway analyses. Gene expression microarray data were collected from 58 PAH and 25 control lung tissues. The strength of the dataset and its derived disease classifier was validated using multiple approaches. Pathways and upstream regulators analyses was completed with standard and novel graphical approaches. The PAH lung dataset identified expression patterns specific to PAH subtypes, clinical parameters, and lung pathology variables. Pathway analyses indicate the important global role of TNF and transforming growth factor signaling pathways. In addition, novel upstream regulators and insight into the cellular and innate immune responses driving PAH were identified. Finally, WNT-signaling pathways may be a major determinant underlying the observed sex differences in PAH. This study provides a transcriptional framework for the PAH-diseased lung, supported by previously reported findings, and will be a valuable resource to the PAH research community. Our investigation revealed novel potential targets and pathways amenable to further study in a variety of experimental systems.