Perinatal Licensing of Thermogenesis by IL-33 and ST2.

For placental mammals, the transition from the in utero maternal environment to postnatal life requires the activation of thermogenesis to maintain their core temperature. This is primarily accomplished by induction of uncoupling protein 1 (UCP1) in brown and beige adipocytes, the principal sites for uncoupled respiration. Despite its importance, how placental mammals license their thermogenic adipocytes to participate in postnatal uncoupled respiration is not known. Here, we provide evidence that the "alarmin" IL-33, a nuclear cytokine that activates type 2 immune responses, licenses brown and beige adipocytes for uncoupled respiration. We find that, in absence of IL-33 or ST2, beige and brown adipocytes develop normally but fail to express an appropriately spliced form of Ucp1 mRNA, resulting in absence of UCP1 protein and impairment in uncoupled respiration and thermoregulation. Together, these data suggest that IL-33 and ST2 function as a developmental switch to license thermogenesis during the perinatal period. PAPERCLIP.

Type 2 innate signals stimulate fibro/adipogenic progenitors to facilitate muscle regeneration.

In vertebrates, activation of innate immunity is an early response to injury, implicating it in the regenerative process. However, the mechanisms by which innate signals might regulate stem cell functionality are unknown. Here, we demonstrate that type 2 innate immunity is required for regeneration of skeletal muscle after injury. Muscle damage results in rapid recruitment of eosinophils, which secrete IL-4 to activate the regenerative actions of muscle resident fibro/adipocyte progenitors (FAPs). In FAPs, IL-4/IL-13 signaling serves as a key switch to control their fate and functions. Activation of IL-4/IL-13 signaling promotes proliferation of FAPs to support myogenesis while inhibiting their differentiation into adipocytes. Surprisingly, type 2 cytokine signaling is also required in FAPs, but not in myeloid cells, for rapid clearance of necrotic debris, a process that is necessary for timely and complete regeneration of tissues.

Multinational Federated Learning Approach to Train ECG and Echocardiogram Models for Hypertrophic Cardiomyopathy Detection.

BACKGROUND: Novel targeted treatments increase the need for prompt hypertrophic cardiomyopathy (HCM) detection. However, its low prevalence (0.5%) and resemblance to common diseases present challenges that may benefit from automated machine learning-based approaches. We aimed to develop machine learning models to detect HCM and to differentiate it from other cardiac conditions using ECGs and echocardiograms, with robust generalizability across multiple cohorts. METHODS: Single-institution HCM ECG models were trained and validated on external data. Multi-institution models for ECG and echocardiogram were trained on data from 3 academic medical centers in the United States and Japan using a federated learning approach, which enables training on distributed data without data sharing. Models were validated on held-out test sets for each institution and from a fourth academic medical center and were further evaluated for discrimination of HCM from aortic stenosis, hypertension, and cardiac amyloidosis. Last, automated detection was compared with manual interpretation by 3 cardiologists on a data set with a realistic HCM prevalence. RESULTS: We identified 74 376 ECGs for 56 129 patients and 8392 echocardiograms for 6825 patients at the 4 academic medical centers. Although ECG models trained on data from each institution displayed excellent discrimination of HCM on internal test data (C statistics, 0.88-0.93), the generalizability was limited, most notably for a model trained in Japan and tested in the United States (C statistic, 0.79-0.82). When trained in a federated manner, discrimination of HCM was excellent across all institutions (C statistics, 0.90-0.96 and 0.90-0.96 for ECG and echocardiogram model, respectively), including for phenotypic subgroups. The models further discriminated HCM from hypertension, aortic stenosis, and cardiac amyloidosis (C statistics, 0.84, 0.83, and 0.88, respectively, for ECG and 0.93, 0.94, 0.85, respectively, for echocardiogram). Analysis of electrocardiography-echocardiography paired data from 11 823 patients from an external institution indicated a higher sensitivity of automated HCM detection at a given positive predictive value compared with cardiologists (0.98 versus 0.81 at a positive predictive value of 0.01 for ECG and 0.78 versus 0.59 at a positive predictive value of 0.24 for echocardiogram). CONCLUSIONS: Federated learning improved the generalizability of models that use ECGs and echocardiograms to detect and differentiate HCM from other causes of hypertrophy compared with training within a single institution.

Recommendations for Statistical Reporting in Cardiovascular Medicine: A Special Report From the American Heart Association.

Statistical analyses are a crucial component of the biomedical research process and are necessary to draw inferences from biomedical research data. The application of sound statistical methodology is a prerequisite for publication in the American Heart Association (AHA) journal portfolio. The objective of this document is to summarize key aspects of statistical reporting that might be most relevant to the authors, reviewers, and readership of AHA journals. The AHA Scientific Publication Committee convened a task force to inventory existing statistical standards for publication in biomedical journals and to identify approaches suitable for the AHA journal portfolio. The experts on the task force were selected by the AHA Scientific Publication Committee, who identified 12 key topics that serve as the section headers for this document. For each topic, the members of the writing group identified relevant references and evaluated them as a resource to make the standards summarized herein. Each section was independently reviewed by an expert reviewer who was not part of the task force. Expert reviewers were also permitted to comment on other sections if they chose. Differences of opinion were adjudicated by consensus. The standards presented in this report are intended to serve as a guide for high-quality reporting of statistical analyses methods and results.

Research Priorities for Heart Failure With Preserved Ejection Fraction: National Heart, Lung, and Blood Institute Working Group Summary.

Heart failure with preserved ejection fraction (HFpEF), a major public health problem that is rising in prevalence, is associated with high morbidity and mortality and is considered to be the greatest unmet need in cardiovascular medicine today because of a general lack of effective treatments. To address this challenging syndrome, the National Heart, Lung, and Blood Institute convened a working group made up of experts in HFpEF and novel research methodologies to discuss research gaps and to prioritize research directions over the next decade. Here, we summarize the discussion of the working group, followed by key recommendations for future research priorities. There was uniform recognition that HFpEF is a highly integrated, multiorgan, systemic disorder requiring a multipronged investigative approach in both humans and animal models to improve understanding of mechanisms and treatment of HFpEF. It was recognized that advances in the understanding of basic mechanisms and the roles of inflammation, macrovascular and microvascular dysfunction, fibrosis, and tissue remodeling are needed and ideally would be obtained from (1) improved animal models, including large animal models, which incorporate the effects of aging and associated comorbid conditions; (2) repositories of deeply phenotyped physiological data and human tissue, made accessible to researchers to enhance collaboration and research advances; and (3) novel research methods that take advantage of computational advances and multiscale modeling for the analysis of complex, high-density data across multiple domains. The working group emphasized the need for interactions among basic, translational, clinical, and epidemiological scientists and across organ systems and cell types, leveraging different areas or research focus, and between research centers. A network of collaborative centers to accelerate basic, translational, and clinical research of pathobiological mechanisms and treatment strategies in HFpEF was discussed as an example of a strategy to advance research progress. This resource would facilitate comprehensive, deep phenotyping of a multicenter HFpEF patient cohort with standardized protocols and a robust biorepository. The research priorities outlined in this document are meant to stimulate scientific advances in HFpEF by providing a road map for future collaborative investigations among a diverse group of scientists across multiple domains.

Coronary Microvascular Dysfunction, Left Ventricular Remodeling, and Clinical Outcomes in Patients With Chronic Kidney Impairment.

BACKGROUND: Cardiac dysfunction and cardiovascular events are prevalent among patients with chronic kidney disease without overt obstructive coronary artery disease, but the mechanisms remain poorly understood. Coronary microvascular dysfunction has been proposed as a link between abnormal renal function and impairment of cardiac function and cardiovascular events. We aimed to investigate the relations between chronic kidney disease, coronary microvascular dysfunction, cardiac dysfunction, and adverse cardiovascular outcomes. METHODS: Patients undergoing cardiac stress positron emission tomography, echocardiogram, and renal function ascertainment at Brigham and Women's Hospital were studied longitudinally. Patients free of overt coronary (summed stress score <3 and without a history of ischemic heart disease), valvular, and end-organ disease were followed up for the adverse composite outcome of death or hospitalization for myocardial infarction or heart failure. Coronary flow reserve (CFR) was determined from positron emission tomography. Echocardiograms were used to measure cardiac mechanics: diastolic (lateral and septal E/e') and systolic (global longitudinal, radial, and circumferential strain). Image analyses and event adjudication were blinded. The associations between estimated glomerular filtration rate (eGFR), CFR, diastolic and systolic indices, and adverse cardiovascular outcomes were assessed in adjusted models and mediation analyses. RESULTS: Of the 352 patients (median age, 65 years; 63% female; 22% black) studied, 35% had an eGFR <60 mL·min-1·1.73 m-2, a median left ventricular ejection fraction of 62%, and a median CFR of 1.8. eGFR and CFR were associated with diastolic and systolic indices, as well as future cardiovascular events (all P<0.05). In multivariable models, CFR, but not eGFR, was independently associated with cardiac mechanics and cardiovascular events. The associations between eGFR, cardiac mechanics, and cardiovascular events were partly mediated via CFR. CONCLUSIONS: Coronary microvascular dysfunction, but not eGFR, was independently associated with abnormal cardiac mechanics and an increased risk of cardiovascular events. Coronary microvascular dysfunction may mediate the effect of chronic kidney disease on abnormal cardiac function and cardiovascular events in those without overt coronary artery disease.

An International Multicenter Evaluation of Inheritance Patterns, Arrhythmic Risks, and Underlying Mechanisms of CASQ2-Catecholaminergic Polymorphic Ventricular Tachycardia.

BACKGROUND: Genetic variants in calsequestrin-2 (CASQ2) cause an autosomal recessive form of catecholaminergic polymorphic ventricular tachycardia (CPVT), although isolated reports have identified arrhythmic phenotypes among heterozygotes. Improved insight into the inheritance patterns, arrhythmic risks, and molecular mechanisms of CASQ2-CPVT was sought through an international multicenter collaboration. METHODS: Genotype-phenotype segregation in CASQ2-CPVT families was assessed, and the impact of genotype on arrhythmic risk was evaluated using Cox regression models. Putative dominant CASQ2 missense variants and the established recessive CASQ2-p.R33Q variant were evaluated using oligomerization assays and their locations mapped to a recent CASQ2 filament structure. RESULTS: A total of 112 individuals, including 36 CPVT probands (24 homozygotes/compound heterozygotes and 12 heterozygotes) and 76 family members possessing at least 1 presumed pathogenic CASQ2 variant, were identified. Among CASQ2 homozygotes and compound heterozygotes, clinical penetrance was 97.1% and 26 of 34 (76.5%) individuals had experienced a potentially fatal arrhythmic event with a median age of onset of 7 years (95% CI, 6-11). Fifty-one of 66 CASQ2 heterozygous family members had undergone clinical evaluation, and 17 of 51 (33.3%) met diagnostic criteria for CPVT. Relative to CASQ2 heterozygotes, CASQ2 homozygote/compound heterozygote genotype status in probands was associated with a 3.2-fold (95% CI, 1.3-8.0; P=0.013) increased hazard of a composite of cardiac syncope, aborted cardiac arrest, and sudden cardiac death, but a 38.8-fold (95% CI, 5.6-269.1; P<0.001) increased hazard in genotype-positive family members. In vitro turbidity assays revealed that p.R33Q and all 6 candidate dominant CASQ2 missense variants evaluated exhibited filamentation defects, but only p.R33Q convincingly failed to dimerize. Structural analysis revealed that 3 of these 6 putative dominant negative missense variants localized to an electronegative pocket considered critical for back-to-back binding of dimers. CONCLUSIONS: This international multicenter study of CASQ2-CPVT redefines its heritability and confirms that pathogenic heterozygous CASQ2 variants may manifest with a CPVT phenotype, indicating a need to clinically screen these individuals. A dominant mode of inheritance appears intrinsic to certain missense variants because of their location and function within the CASQ2 filament structure.

Fully Automated Echocardiogram Interpretation in Clinical Practice.

BACKGROUND: Automated cardiac image interpretation has the potential to transform clinical practice in multiple ways, including enabling serial assessment of cardiac function by nonexperts in primary care and rural settings. We hypothesized that advances in computer vision could enable building a fully automated, scalable analysis pipeline for echocardiogram interpretation, including (1) view identification, (2) image segmentation, (3) quantification of structure and function, and (4) disease detection. METHODS: Using 14 035 echocardiograms spanning a 10-year period, we trained and evaluated convolutional neural network models for multiple tasks, including automated identification of 23 viewpoints and segmentation of cardiac chambers across 5 common views. The segmentation output was used to quantify chamber volumes and left ventricular mass, determine ejection fraction, and facilitate automated determination of longitudinal strain through speckle tracking. Results were evaluated through comparison to manual segmentation and measurements from 8666 echocardiograms obtained during the routine clinical workflow. Finally, we developed models to detect 3 diseases: hypertrophic cardiomyopathy, cardiac amyloid, and pulmonary arterial hypertension. RESULTS: Convolutional neural networks accurately identified views (eg, 96% for parasternal long axis), including flagging partially obscured cardiac chambers, and enabled the segmentation of individual cardiac chambers. The resulting cardiac structure measurements agreed with study report values (eg, median absolute deviations of 15% to 17% of observed values for left ventricular mass, left ventricular diastolic volume, and left atrial volume). In terms of function, we computed automated ejection fraction and longitudinal strain measurements (within 2 cohorts), which agreed with commercial software-derived values (for ejection fraction, median absolute deviation=9.7% of observed, N=6407 studies; for strain, median absolute deviation=7.5%, n=419, and 9.0%, n=110) and demonstrated applicability to serial monitoring of patients with breast cancer for trastuzumab cardiotoxicity. Overall, we found automated measurements to be comparable or superior to manual measurements across 11 internal consistency metrics (eg, the correlation of left atrial and ventricular volumes). Finally, we trained convolutional neural networks to detect hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary arterial hypertension with C statistics of 0.93, 0.87, and 0.85, respectively. CONCLUSIONS: Our pipeline lays the groundwork for using automated interpretation to support serial patient tracking and scalable analysis of millions of echocardiograms archived within healthcare systems.

Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins.

Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer. However, it remains difficult to distinguish background, or 'passenger', cancer mutations from causal, or 'driver', mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer.

RNA sequencing of mouse sinoatrial node reveals an upstream regulatory role for Islet-1 in cardiac pacemaker cells.

RATIONALE: Treatment of sinus node disease with regenerative or cell-based therapies will require a detailed understanding of gene regulatory networks in cardiac pacemaker cells (PCs). OBJECTIVE: To characterize the transcriptome of PCs using RNA sequencing and to identify transcriptional networks responsible for PC gene expression. METHODS AND RESULTS: We used laser capture microdissection on a sinus node reporter mouse line to isolate RNA from PCs for RNA sequencing. Differential expression and network analysis identified novel sinoatrial node-enriched genes and predicted that the transcription factor Islet-1 is active in developing PCs. RNA sequencing on sinoatrial node tissue lacking Islet-1 established that Islet-1 is an important transcriptional regulator within the developing sinoatrial node. CONCLUSIONS: (1) The PC transcriptome diverges sharply from other cardiomyocytes; (2) Islet-1 is a positive transcriptional regulator of the PC gene expression program.

Identification of adult nephron progenitors capable of kidney regeneration in zebrafish.

Loss of kidney function underlies many renal diseases. Mammals can partly repair their nephrons (the functional units of the kidney), but cannot form new ones. By contrast, fish add nephrons throughout their lifespan and regenerate nephrons de novo after injury, providing a model for understanding how mammalian renal regeneration may be therapeutically activated. Here we trace the source of new nephrons in the adult zebrafish to small cellular aggregates containing nephron progenitors. Transplantation of single aggregates comprising 10-30 cells is sufficient to engraft adults and generate multiple nephrons. Serial transplantation experiments to test self-renewal revealed that nephron progenitors are long-lived and possess significant replicative potential, consistent with stem-cell activity. Transplantation of mixed nephron progenitors tagged with either green or red fluorescent proteins yielded some mosaic nephrons, indicating that multiple nephron progenitors contribute to a single nephron. Consistent with this, live imaging of nephron formation in transparent larvae showed that nephrogenic aggregates form by the coalescence of multiple cells and then differentiate into nephrons. Taken together, these data demonstrate that the zebrafish kidney probably contains self-renewing nephron stem/progenitor cells. The identification of these cells paves the way to isolating or engineering the equivalent cells in mammals and developing novel renal regenerative therapies.

Machine Learning in Medicine.

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.

Phenomapping for novel classification of heart failure with preserved ejection fraction.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome in need of improved phenotypic classification. We sought to evaluate whether unbiased clustering analysis using dense phenotypic data (phenomapping) could identify phenotypically distinct HFpEF categories. METHODS AND RESULTS: We prospectively studied 397 patients with HFpEF and performed detailed clinical, laboratory, ECG, and echocardiographic phenotyping of the study participants. We used several statistical learning algorithms, including unbiased hierarchical cluster analysis of phenotypic data (67 continuous variables) and penalized model-based clustering, to define and characterize mutually exclusive groups making up a novel classification of HFpEF. All phenomapping analyses were performed by investigators blinded to clinical outcomes, and Cox regression was used to demonstrate the clinical validity of phenomapping. The mean age was 65±12 years; 62% were female; 39% were black; and comorbidities were common. Although all patients met published criteria for the diagnosis of HFpEF, phenomapping analysis classified study participants into 3 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, invasive hemodynamics, and outcomes (eg, phenogroup 3 had an increased risk of HF hospitalization [hazard ratio, 4.2; 95% confidence interval, 2.0-9.1] even after adjustment for traditional risk factors [P<0.001]). The HFpEF phenogroup classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort (n=107). CONCLUSIONS: Phenomapping results in a novel classification of HFpEF. Statistical learning algorithms applied to dense phenotypic data may allow improved classification of heterogeneous clinical syndromes, with the ultimate goal of defining therapeutically homogeneous patient subclasses.

Phenotypic spectrum of heart failure with preserved ejection fraction.

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome, with several underlying etiologic and pathophysiologic factors. The heterogeneity of the HFpEF syndrome may explain why (1) diagnosing and treating HFpEF is so challenging and (2) clinical trials in HFpEF have failed thus far. Here we describe 4 ways of categorizing HFpEF based on pathophysiology, clinical/etiologic subtype, type of clinical presentation, and quantitative phenomics (phenomapping analysis). Regardless of the classification method used, improved phenotypic characterization of HFpEF, and matching targeted therapies with specific HFpEF subtypes, will be a critical step towards improving outcomes in this increasingly prevalent syndrome.

Artificial intelligence-enabled prediction of chemotherapy-induced cardiotoxicity from baseline electrocardiograms.

Anthracyclines can cause cancer therapy-related cardiac dysfunction (CTRCD) that adversely affects prognosis. Despite guideline recommendations, only half of the patients undergo surveillance echocardiograms. An AI model detecting reduced left ventricular ejection fraction from 12-lead electrocardiograms (ECG) (AI-EF model) suggests ECG features reflect left ventricular pathophysiology. We hypothesized that AI could predict CTRCD from baseline ECG, leveraging the AI-EF model's insights, and developed the AI-CTRCD model using transfer learning on the AI-EF model. In 1011 anthracycline-treated patients, 8.7% experienced CTRCD. High AI-CTRCD scores indicated elevated CTRCD risk (hazard ratio (HR), 2.66; 95% CI 1.73-4.10; log-rank p < 0.001). This remained consistent after adjusting for risk factors (adjusted HR, 2.57; 95% CI 1.62-4.10; p < 0.001). AI-CTRCD score enhanced prediction beyond known factors (time-dependent AUC for 2 years: 0.78 with AI-CTRCD score vs. 0.74 without; p = 0.005). In conclusion, the AI model robustly stratified CTRCD risk from baseline ECG.

Perturbational phenotyping of human blood cells reveals genetically determined latent traits associated with subsets of common diseases.

Although genome-wide association studies (GWAS) have successfully linked genetic risk loci to various disorders, identifying underlying cellular biological mechanisms remains challenging due to the complex nature of common diseases. We established a framework using human peripheral blood cells, physical, chemical and pharmacological perturbations, and flow cytometry-based functional readouts to reveal latent cellular processes and performed GWAS based on these evoked traits in up to 2,600 individuals. We identified 119 genomic loci implicating 96 genes associated with these cellular responses and discovered associations between evoked blood phenotypes and subsets of common diseases. We found a population of pro-inflammatory anti-apoptotic neutrophils prevalent in individuals with specific subsets of cardiometabolic disease. Multigenic models based on this trait predicted the risk of developing chronic kidney disease in type 2 diabetes patients. By expanding the phenotypic space for human genetic studies, we could identify variants associated with large effect response differences, stratify patients and efficiently characterize the underlying biology.