Time-Dependent ECG-AI Prediction of Fatal Coronary Heart Disease: A Retrospective Study.

: Fatal coronary heart disease (FCHD) affects ~650,000 people yearly in the US. Electrocardiographic artificial intelligence (ECG-AI) models can predict adverse coronary events, yet their application to FCHD is understudied. : The study aimed to develop ECG-AI models predicting FCHD risk from ECGs.

Metabolites Link Intake of a Healthy Diet to Better Insulin and Glucose Homeostasis in the Microbiome and Insulin Longitudinal Evaluation Study (MILES).

Background: Dietary quality has been linked to better glycemic control, but the precise molecular mechanisms giving rise to these associations are not fully understood.

Objectives: To examine the association of metabolites associated with the intake of a healthy diet with measures of insulin/glucose homeostasis.

Methods: Using cross-sectional data from 295 United States adults, the associations between 3 diet pattern scores and metabolome-wide metabolites were estimated via linear regression models, which controlled for demographic factors and health behaviors.

Serum metabolomic profiling of incident type 2 diabetes mellitus in the Multi-Ethnic Study of Atherosclerosis and Rotterdam Study.

Objective: This study aimed to investigate serum metabolomic biomarkers associated with incident type 2 diabetes mellitus (T2DM) and evaluate their performance in improving T2DM risk prediction.

Methods: Untargeted proton nuclear magnetic resonance (1H NMR) spectroscopy-based metabolomics analyses were conducted in the Multi-Ethnic Study of Atherosclerosis (MESA; n=3460; discovery cohort) and Rotterdam Study (RS; n=1556; replication cohort). Multivariable cause-specific hazards models were used to analyze the associations between 23,571 serum metabolomic spectral variables and incident T2DM.

Embracing the informative missingness and silent gene in analyzing biologically diverse samples.

Bioinformatics software tools are essential to identify informative molecular features that define different phenotypic sample groups. Among the most fundamental and interrelated tasks are missing value imputation, signature gene detection, and differential pattern visualization. However, many commonly used analytics tools can be problematic when handling biologically diverse samples if either informative missingness possess high missing rates with mixed missing mechanisms, or multiple sample groups are compared and visualized in parallel.

Metagenomic Study of the MESA: Detection of and Association With Coronary Heart Disease.

Background: Inflammation is a feature of coronary heart disease (CHD), but the role of proinflammatory microbial infection in CHD remains understudied.

Methods And Results: CHD was defined in the MESA (Multi-Ethnic Study of Atherosclerosis) as myocardial infarction (251 participants), resuscitated arrest (2 participants), and CHD death (80 participants). We analyzed sequencing reads from 4421 MESA participants in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program using the PathSeq workflow of the Genome Analysis Tool Kit and a 65-gigabase microbial reference.

Metabolomic profiling identifies novel metabolites associated with cardiac dysfunction.

Metabolic comorbidities, such as obesity and diabetes, are associated with subclinical alterations in both cardiac structure/function and natriuretic peptides prior to the onset of heart failure (HF). Despite this, the exact metabolic pathways of cardiac dysfunction which precede HF are not well-defined. Among older individuals without HF in the Multi-Ethnic Study of Atherosclerosis (MESA), we evaluated the associations of 47 circulating metabolites measured by H-NMR with echocardiographic measures of cardiac structure and function.

Feasibility of remote monitoring for fatal coronary heart disease using Apple Watch ECGs.

Background: Fatal coronary heart disease (FCHD) is often described as sudden cardiac death (affects >4 million people/year), where coronary artery disease is the only identified condition. Electrocardiographic artificial intelligence (ECG-AI) models for FCHD risk prediction using ECG data from wearable devices could enable wider screening/monitoring efforts.

Objectives: To develop a single-lead ECG-based deep learning model for FCHD risk prediction and assess concordance between clinical and Apple Watch ECGs.

DDN3.0: determining significant rewiring of biological network structure with differential dependency networks.

Motivation: Complex diseases are often caused and characterized by misregulation of multiple biological pathways. Differential network analysis aims to detect significant rewiring of biological network structures under different conditions and has become an important tool for understanding the molecular etiology of disease progression and therapeutic response. With few exceptions, most existing differential network analysis tools perform differential tests on separately learned network structures that are computationally expensive and prone to collapse when grouped samples are limited or less consistent.

Eicosapentaenoic acid and Arachidonic acid Protection Against Left Ventricle Pathology: the Multi-Ethnic Study of Atherosclerosis.

Background: We have shown that ω3 polyunsaturated fatty acids (PUFAs) reduce risk for heart failure, regardless of ejection fraction status. Ventricular remodeling and reduced ventricular performance precede overt hear failure, however there is little insight into how PUFAs contribute to maladaptive signaling over time. PUFAs are agonists for regulatory activity at g-protein coupled receptors such as Ffar4, and downstream as substrates for monooxygenases (e.

Relationship Between Sarcopenia and Intensive Blood Pressure Control Efficacy and Safety: A Secondary Analysis of SPRINT.

Background: Sarcopenia and hypertension are independently associated with worse cardiovascular disease (CVD) risk and survival. While individuals with sarcopenia may benefit from intensive blood pressure (BP) control, the increased vulnerability of this population raises concerns for potential harm. This study aimed to evaluate clinical and safety outcomes with intensive (target <120 mm Hg) versus standard (<140 mm Hg) systolic BP targets in older hypertensive adults with sarcopenia compared with nonsarcopenic counterparts in the SPRINT (Systolic Blood Pressure Intervention Trial).

ABDS: a bioinformatics tool suite for analyzing biologically diverse samples.

Bioinformatics software tools are essential to identify informative molecular features that define different phenotypic sample groups. Among the most fundamental and interrelated tasks are missing value imputation, signature gene detection, and differential pattern visualization. However, many commonly used analytics tools can be problematic when handling biologically diverse samples if either informative missingness possess high missing rates with mixed missing mechanisms, or multiple sample groups are compared and visualized in parallel.

LXR signaling pathways link cholesterol metabolism with risk for prediabetes and diabetes.

BACKGROUNDPreclinical studies suggest that cholesterol accumulation leads to insulin resistance. We previously reported that alterations in a monocyte cholesterol metabolism transcriptional network (CMTN) - suggestive of cellular cholesterol accumulation - were cross-sectionally associated with obesity and type 2 diabetes (T2D). Here, we sought to determine whether the CMTN alterations independently predict incident prediabetes/T2D risk, and correlate with cellular cholesterol accumulation.

CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution.

Motivation: Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments.

Adipose tissue-derived metabolite risk scores and risk for type 2 diabetes in South Asians.

Background: South Asians are at higher risk for type 2 diabetes (T2D) than many other race/ethnic groups. Ectopic adiposity, specifically hepatic steatosis and visceral fat may partially explain this. Our objective was to derive metabolite risk scores for ectopic adiposity and assess associations with incident T2D in South Asians.

Assessment of Prevalence, Clinical Characteristics, and Risk Factors Associated With "Low Flow State" Using Cardiac Magnetic Resonance.

Objective: To assess prevalence, clinical characteristics, and risk factors associated with low flow state (LFS) in a multiethnic population with normal left ventricular ejection fraction (LVEF).

Patients And Methods: The study included 4398 asymptomatic participants undergoing cardiac magnetic resonance from July 17, 2000, to August 29, 2002. Left ventricular (LV) mass, volume, and myocardial contraction fraction were assessed.

Association of lipoprotein subfractions with incidence of type 2 diabetes among five U.S. Race and Ethnic groups: The Mediators of Atherosclerosis in South Asians Living in America (MASALA) and Multi-Ethnic study of Atherosclerosis (MESA).

Aims: We examined associations between lipoprotein subfractions and prevalent and incident T2D in two race/ethnically diverse cohort studies.

Methods: Adults self-identifying as White, Black, Chinese, Hispanic and South Asian-American without cardiovascular disease, with fasting serum, demographic, and clinical data at enrollment and after 5 years of follow-up were included. Lipoprotein subfractions were measured at enrollment using NMR spectrometry.

CLINICAL HETEROGENEITY IN THE AGE OF BIG DATA, ADVANCED ANALYTICS, AND COMPLEXITY THEORY.

Clinical heterogeneity remains a challenge in the practice of medicine and is an underlying motivation for much of biomedical research. Unfortunately, despite an abundance of technologies capable of producing millions of discrete data elements with information about a patient's health status or disease prognosis, our ability to translate those data into meaningful improvements in understanding of clinical heterogeneity is limited. To address this gap, we have applied newer approaches to manifold learning and developed additional and complementary techniques to interrogate and interpret complex, high dimensional omics data.

Untargeted metabolomic analysis investigating links between unprocessed red meat intake and markers of inflammation.

Background: Whether red meat consumption is associated with higher inflammation or confounded by increased adiposity remains unclear. Plasma metabolites capture the effects of diet after food is processed, digested, and absorbed, and correlate with markers of inflammation, so they can help clarify diet-health relationships.

Objective: To identify whether any metabolites associated with red meat intake are also associated with inflammation.

Associations between Metabolomic Biomarkers of Avocado Intake and Glycemia in the Multi-Ethnic Study of Atherosclerosis.

Background: Avocado consumption is linked to better glucose homeostasis, but small associations suggest potential population heterogeneity. Metabolomic data capture the effects of food intake after digestion and metabolism, thus accounting for individual differences in these processes.

Objectives: To identify metabolomic biomarkers of avocado intake and to examine their associations with glycemia.

ABDS: tool suite for analyzing biologically diverse samples.

Motivation: Analytics tools are essential to identify informative molecular features about different phenotypic groups. Among the most fundamental tasks are missing value imputation, signature gene detection, and expression pattern visualization. However, most commonly used analytics tools may be problematic for characterizing biologically diverse samples when either signature genes possess uneven missing rates across different groups yet involving complex missing mechanisms, or multiple biological groups are simultaneously compared and visualized.

Implications of Social Disadvantage Score in Cardiovascular Outcomes and Risk Assessment: Findings From the Multi-Ethnic Study of Atherosclerosis.

Background: Social determinants of health contribute to disparate cardiovascular outcomes, yet they have not been operationalized into the current paradigm of cardiovascular risk assessment.

Methods: Data from the Multi-Ethnic Study of Atherosclerosis, which includes participants from 6 US field centers, were used to create an index of baseline Social Disadvantage Score (SDS) to explore its association with incident atherosclerotic cardiovascular disease (ASCVD) and all-cause mortality and impact on ASCVD risk prediction. SDS, which ranges from 0 to 4, was calculated by tallying the following social factors: (1) household income less than the federal poverty level; (2) educational attainment less than a high school diploma; (3) single-living status; and (4) experience of lifetime discrimination.