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.