Recurrence After Atrial Fibrillation Ablation and Investigational Biomarkers of Cardiac Remodeling

BACKGROUND

Recurrence after atrial fibrillation (AF) ablation remains common. We evaluated the association between recurrence and levels of biomarkers of cardiac remodeling, and their ability to improve recurrence prediction when added to a clinical prediction model.

METHODS AND RESULTS

Blood samples collected before de novo catheter ablation were analyzed. Levels of bone morphogenetic protein-10, angiopoietin-2, fibroblast growth factor-23, insulin-like growth factor-binding protein-7, myosin-binding protein C3, growth differentiation factor-15, interleukin-6, N-terminal pro-brain natriuretic peptide, and high-sensitivity troponin T were measured. Recurrence was defined as ≥30 seconds of an atrial arrhythmia 3 to 12 months postablation. Multivariable logistic regression was performed using biomarker levels along with clinical covariates: APPLE score (Age >65 years, Persistent AF, imPaired eGFR [<60 ml/min/1.73m2], LA diameter ≥43 mm, EF <50%; which includes age, left atrial diameter, left ventricular ejection fraction, persistent atrial fibrillation, and estimated glomerular filtration rate), preablation rhythm, sex, height, body mass index, presence of an implanted continuous monitor, year of ablation, and additional linear ablation. A total of 1873 participants were included. A multivariable logistic regression showed an association between recurrence and levels of angiopoietin-2 (odds ratio, 1.08 [95% CI, 1.02-1.15], P=0.007) and interleukin-6 (odds ratio, 1.02 [95% CI, 1.003-1.03]; P=0.02). The area under the receiver operating characteristic curve of a model that only contained clinical predictors was 0.711. The addition of any of the 9 studied biomarkers to the predictive model did not result in a statistically significant improvement in the area under the receiver operating characteristic curve.

CONCLUSIONS

Higher angiopoietin-2 and interleukin-6 levels were associated with recurrence after atrial fibrillation ablation in multivariable modeling. However, the addition of biomarkers to a clinical prediction model did not significantly improve recurrence prediction.

Internationally standardized respiratory viral load testing with limited resources: A derivative-of-care calibration strategy for SARS-CoV-2

Introduction

SARS-CoV-2 has demonstrated that, in targeted circumstances, viral quantification within respiratory specimens can valuably inform patient management, as well as research. Nevertheless, the pandemic has illustrated concomitant challenges for obtaining high-quality (and broadly comparable) respiratory viral loads. This includes a critical need for standardization and calibration, even though the necessary resources may not always be available for emergent pathogens and non-bloodstream specimens.

Methods

To these ends, we describe a novel strategy for implementing quantitative SARS-CoV-2 testing with International Unit-based calibration. Earlier in the course of the pandemic-when analytic resources were far more limited-select residual SARS-CoV-2 positive specimens from routine care in our diagnostic laboratory were pooled to formulate a clinically realistic secondary standard of high volume and analyte concentration, which was cross-calibrated to the primary SARS-CoV-2 standard of the World Health Organization.

Results

The resultant calibrators were integrated into the original CDC RT-qPCR assay for SARS-CoV-2, whose (now broadened) performance characteristics were defined to generate a test appropriate for both clinical and research use. This test allowed for the quantification of virus in respiratory specimens down to a validated lower limit of quantification of 103.4 IU/ml.

Conclusions

By self-formulating calibrators from this derivative-of-care secondary standard, we successfully validated respiratory viral loads without the commercial availability (at that time) of quantitative assays or calibrators. As the SARS-CoV-2 pandemic continues to decline-and even beyond this pathogen-this strategy may be applicable for laboratories seeking to implement viral load testing for nontraditional specimen types despite limited resources.

Metabolism of parathyroid organoids

Introduction

We successfully developed a broad spectrum of patient-derived endocrine organoids (PDO) from benign and malignant neoplasms of thyroid, parathyroid, and adrenal glands. In this study, we employed functionally intact parathyroid PDOs from benign parathyroid tissues to study primary hyperparathyroidism (PHPT), a common endocrine metabolic disease. As proof of concept, we examined the utility of parathyroid PDOs for bioenergetic and metabolic screening and assessed whether parathyroid PDO metabolism recapitulated matched PHPT tissues.

Methods

Our study methods included a fine-needle aspiration (FNA)-based technique to establish parathyroid PDOs from human PHPT tissues (n=6) in semi-solid culture conditions for organoid formation, growth, and proliferation. Mass spectrometry metabolomic analysis of PHPT tissues and patient-matched PDOs, and live cell bioenergetic profiling of parathyroid PDOs with extracellular flux analyses, were performed. Functional analysis cryopreserved and re-cultured parathyroid PDOs for parathyroid hormone (PTH) secretion was performed using ELISA hormone assays.

Results and discussion

Our findings support both the feasibility of parathyroid PDOs for metabolic and bioenergetic profiling and reinforce metabolic recapitulation of PHPT tissues by patient-matched parathyroid PDOs. Cryopreserved parathyroid PDOs exhibited preserved, rapid, and sustained secretory function after thawing. In conclusion, successful utilization of parathyroid PDOs for metabolic profiling further affirms the feasibility of promising endocrine organoid platforms for future metabolic studies and broader multiplatform and translational applications for therapeutic advancements of parathyroid and other endocrine applications.

Obesity and Overweight: Probing Causes, Consequences, and Novel Therapeutic Approaches Through the American Heart Association's Strategically Focused Research Network

As the worldwide prevalence of overweight and obesity continues to rise, so too does the urgency to fully understand mediating mechanisms, to discover new targets for safe and effective therapeutic intervention, and to identify biomarkers to track obesity and the success of weight loss interventions. In 2016, the American Heart Association sought applications for a Strategically Focused Research Network (SFRN) on Obesity. In 2017, 4 centers were named, including Johns Hopkins University School of Medicine, New York University Grossman School of Medicine, University of Alabama at Birmingham, and Vanderbilt University Medical Center. These 4 centers were convened to study mechanisms and therapeutic targets in obesity, to train a talented cadre of American Heart Association SFRN-designated fellows, and to initiate and sustain effective and enduring collaborations within the individual centers and throughout the SFRN networks. This review summarizes the central themes, major findings, successful training of highly motivated and productive fellows, and the innovative collaborations and studies forged through this SFRN on Obesity. Leveraging expertise in in vitro and cellular model assays, animal models, and humans, the work of these 4 centers has made a significant impact in the field of obesity, opening doors to important discoveries, and the identification of a future generation of obesity-focused investigators and next-step clinical trials. The creation of the SFRN on Obesity for these 4 centers is but the beginning of innovative science and, importantly, the birth of new collaborations and research partnerships to propel the field forward.

Genetic Variants Associated With Systolic Blood Pressure in Children and Adolescents

Background Genetics, along with lifestyle and behavioral characteristics, play an important role in hypertension in adults. Our aim was to identify genetic variants associated with blood pressure in childhood and adolescence. Methods and Results We conducted a candidate single-nucleotide polymorphism (SNP) analysis and genome-wide association study among 9778 participants aged <18 years in BioVU, the Vanderbilt University Medical Center biobank. The outcome was childhood blood pressure percentile from age 0 to 18 years. For the candidate SNP analysis, a total of 457 previously identified SNPs were examined. Linear regression was used to test the association between genetic variants and median systolic blood pressure (SBP) percentile. Adjusted models included median age, self-reported sex, race, the first 4 principal components of ancestry, and median body mass index Z score. Analyses were conducted in the overall cohort and stratified by age group. A polygenic risk score was calculated for each participant, and the association between polygenic risk score and median SBP percentile in childhood was examined using linear regression. In the overall candidate SNP analysis, 2 SNPs reached significance: rs1018148 (FBN1; P=1.0×10-4) and rs11105354 (ATP2B1; P=1.4×10-4). In the postpuberty age group, 1 SNP reached significance: rs1018148 (FBN1; P=2.2×10-5). In the genome-wide association study of all participants, no SNPs reached genome-wide significance. Higher polygenic risk score was associated with higher SBP percentile (β, 0.35 [95% CI, 0.10-0.60)], and there was a significant interaction with age (P for interaction<0.01). Conclusions These findings suggest that genetic variants play an important role in SBP in childhood and adolescence and provide evidence for age-specific genetic associations with SBP.

Clinical presentations of adult and pediatric SARS-CoV-2-positive cases in a community cohort, Nashville, Tennessee

Compared to adults, the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) illness in children has been lower and less severe. However, reports comparing SARS-CoV-2 infection among children and adults are limited. As part of our longitudinal cohort study of adults and children with SARS-CoV-2 infection and their household contacts in Nashville, Tennessee, we compared the clinical characteristics and outcomes of SARS-CoV-2 infections between children and adults. Children were more likely to be asymptomatically infected and had a shorter illness duration compared to adults. The differences observed in clinical presentation across ages may inform symptom-specific testing, screening, and management algorithms.

Buffer glucose adjustment affects myocardial function after ischemia-reperfusion in long-term diabetic rat isolated hearts

Due to its comorbidities type 2 diabetes mellitus (T2DM) and hypertension, the Zucker Spontaneous Hypertensive Fatty (ZSF1) rat is a clinically relevant animal model when assessing ischemia-reperfusion (IR) injury. Most IR studies in hearts isolated from diabetic animals have been conducted at normal glucose concentrations, providing a different environment compared to in-vivo. We hypothesized IR injury to be attenuated in isolated hearts of diabetic ZSF1 rats when adjusting the Krebs-buffer (KB) to their in-vivo, i.e., elevated blood glucose (BG) levels. Diabetic and non-diabetic ZSF1 rats were anesthetized, hearts isolated and Langendorff-prepared. While standard KB was used for the non-diabetic and diabetic unadjusted groups, KB with glucose levels increased to each rat's prior BG level was used for the adjusted diabetic group. All hearts underwent 30 min ischemia and 120 min reperfusion. Diastolic contracture during ischemia and early reperfusion was delayed and temporarily attenuated in the adjusted compared to the unadjusted diabetic and the non-diabetic groups. The decrease in coronary flow on reperfusion was attenuated in diabetic animals. Left ventricular developed pressure and contractility were not different among the three groups. Infarct size was significantly lower in non-diabetic animals; buffer adjustment made no difference in diabetic animals. In our study, T2DM did not worsen myocardial function in ZSF1 rat isolated hearts. Since our results reveal that hearts with an adjusted glucose level exhibit an at least temporary improvement of function following IR, further studies should consider adapting glucose levels to create more realistic conditions in isolated, perfused hearts.

Exploration of an alternative to body mass index to characterize the relationship between height and weight for prediction of metabolic phenotypes and cardiovascular outcomes

OBJECTIVE

Body mass index (BMI) is the most commonly used predictor of weight-related comorbidities and outcomes. However, the presumed relationship between height and weight intrinsic to BMI may introduce bias with respect to prediction of clinical outcomes. A series of analyses comparing the performance of models representing weight and height as separate interacting variables to models using BMI were performed using Vanderbilt University Medical Center's deidentified electronic health records and landmark methodology.

METHODS

Use of BMI or height-weight interaction in prediction models for established weight-related cardiometabolic traits and metabolic syndrome was evaluated. Specifically, prediction models for hypertension, diabetes mellitus, low high-density lipoprotein, and elevated triglycerides, atrial fibrillation, coronary artery disease, heart failure, and peripheral artery disease were developed. Model performance was evaluated using likelihood ratio, R 2, and Somers' Dxy rank correlation. Differences in model predictions were visualized using heat maps.

RESULTS

Compared to BMI, the maximally flexible height-weight interaction model demonstrated improved prediction, higher likelihood ratio, R 2, and Somers' Dxy rank correlation, for event-free probability for all outcomes. The degree of improvement to the prediction model differed based on the outcome and across the height and weight range.

CONCLUSIONS

Because alternative measures of body composition such as waist-to-hip ratio are not routinely collected in the clinic clinical risk models quantifying risk based on height and weight measurements alone are essential to improve practice. Compared to BMI, modeling height and weight as independent, interacting variables results in less bias and improved predictive accuracy for all tested traits. Considering an individual's height and weight opposed to BMI is a better method for quantifying individual disease risk.