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Coronary Artery Disease I: Introduction

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Coronary Artery Disease (CAD): An Overview with Scientific InsightsCoronary Artery Disease (CAD), often referred to as C-A-D, is a prevalent blood vessel disorder classified under the broader category of atherosclerosis. Atherosclerosis is a pathological process characterized by the hardening and narrowing of arteries due to the accumulation of atherosclerotic plaques. These plaques are composed of cholesterol, fatty substances, inflammatory cells, calcium, and fibrin, reducing blood flow to...

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Updated: Sep 15, 2025

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

Machine Learning based Model Reveals the Metabolites Involved in Coronary Artery Disease.

Fathima Lamya¹, Muhammad Arif¹, Mahbuba Rahman^2,3

¹College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

Biomedical Engineering and Computational Biology

|July 14, 2025

Summary

This summary is machine-generated.

Machine learning models can detect coronary artery disease (CAD) by identifying specific metabolites in the Qatari population. An artificial neural network achieved 91.67% accuracy, highlighting key metabolic biomarkers for early CAD diagnosis.

Keywords:

Qatar Biobank coronary artery disease machine learning metabolites metabolomics

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Last Updated: Sep 15, 2025

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

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Published on: November 29, 2024

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Area of Science:

Biochemistry
Computational Biology
Cardiovascular Medicine

Background:

Coronary artery disease (CAD) is a leading cause of death globally, necessitating improved early detection and personalized treatment strategies.
Metabolomic profiling offers a promising avenue for identifying biomarkers associated with complex diseases like CAD.

Purpose of the Study:

To develop and validate a machine learning (ML) model for early detection of CAD in the Qatari population using metabolomic data.
To identify statistically significant metabolic profiles and potential biomarkers associated with CAD.

Main Methods:

Utilized machine learning algorithms, including artificial neural networks (ANN), to analyze metabolomic data from the Qatari population.
Performed statistical analysis to identify metabolites significantly associated with CAD.

Main Results:

An artificial neural network (ANN) model demonstrated high performance with 91.67% accuracy, 80.0% recall, and 100% specificity.
Identified 173 metabolites significantly associated with CAD (P < .05).
Highlighted specific metabolites, such as 2-hydroxyhippurate and salicylate, as potentially elevated in CAD patients, while others like cholate and 3-hydroxybutyrate were higher in controls.

Conclusions:

The developed ML model shows potential for advancing personalized diagnostic plans for CAD patients.
Metabolomic signatures identified in this study can contribute to earlier and more accurate CAD detection.