Cardiovascular

Dyslipidemia

Latest AI and machine learning research in dyslipidemia for healthcare professionals.

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Exposome-Based Clustering of Urinary VOC and PAH Biomarkers Reveals Racially Patterned Cardiovascular Risk in a Nationally Representative US Cohort: A Machine Learning Analysis of NHANES 2017-2018

Background Polycyclic aromatic hydrocarbons (PAHs) and volatile organic compounds (VOCs) are combustion-derived pollutants linked to cardiovascular disease. Prior NHANES analyses have evaluated these chemicals individually, failing to capture the correlated co-exposure structures that characterize real-world environmental burden, thereby underscoring the need for application. In this study, we app...

Comparison of the Expert Guidelines With Artificial Intelligence-Driven Echocardiographic Assessment of Diastolic Function

Backgound: Accurate assessment of diastolic function and left ventricular (LV) filling pressure is central to heart failure diagnosis and risk stratification. Contemporary guideline algorithms rely on complex parameters that are not consistently available in routine clinical practice. Objective: To compare the diagnostic and prognostic performance of the 2016 American Society of Echocardiography/E...

Liver Biomarker Improves AHA/ACC 10-year ASCVD Risk Prediction in US and China Cohorts with ML

Introduction: Accurate stratification of hard atherosclerotic cardiovascular disease (ASCVD) risk remains challenging despite advances in prevention. ...

Cardiometabolic health trajectories from birth to old age based on multi-decadal series of biochemistry and anthropometry

Background and aims: Direct evidence to connect early life metabolism with cardiometabolic diseases in old age is limited due to the rarity of multi-d...

CardioAI: An Explainable Machine Learning System for Cardiovascular Risk Prediction and Patient Retention in Nigerian Healthcare Settings

Abstract Background: Cardiovascular disease is the leading cause of mortality in Nigeria and across sub-Saharan Africa, with rising incidence attribut...

Inter-individual variability in lipoprotein proteomics reveals distinct patient clusters informative for disease pathogenesis and severity

Lipoprotein composition is altered in sepsis, and supplementation with high-density lipoproteins has been reported to improve outcomes in experimental...

Machine learning-based advanced coronary artery disease pretest probability model: Comparison with conventional pretest probability models

Background: Pretest probability (PTP) models using clinical risk factors guide decision-making for coronary artery disease (CAD). Existing models (Upd...

When AI Shows Its Work, Is It Actually Working? Step-Level Evaluation Reveals Frontier Language Models Frequently Bypass Their Own Reasoning

Language models increasingly "show their work" by writing step-by-step reasoning before answering. But are these reasoning steps genuinely used, or de...

Mar 24 2026 2603.22816v1
CARDIAC-FM: A Multimodal Foundation Model for Cardiovascular Risk Prediction Using ECG and Cardiac MRI

Atrial fibrillation and heart failure impose substantial health burdens worldwide, yet existing prediction models lack sufficient accuracy and general...

Longitudinal immune transcriptomic signatures are associated with carotid intima-media thickness over 18 years

Background. Atherosclerosis is increasingly recognized as a chronic immunometabolic disorder involving complex interactions between circulating immune...

Phenotypic reversion and target prioritization for cellular inflammation via representation learning with foundation models

The identification of genetic perturbations that can reverse disease-associated cellular phenotypes toward a healthy state is a central challenge in e...

MRI Contrast Enhancement Kinetics World Model

Clinical MRI contrast acquisition suffers from inefficient information yield, which presents as a mismatch between the risky and costly acquisition pr...

Feb 22 2026 2602.19285v1
AI-Detected Asymptomatic Atrial Fibrillation and Risk of Incident Ischemic Stroke and Cardiovascular Events: A UK Biobank Study

Background: Advances in wearable devices and machine-learning-based ECG analysis enable highly accurate detection of atrial fibrillation (AF) outside ...

Spatial multi-omics identify an immunosuppressive lipid-laden macrophage niche in primary CNS lymphoma

Primary central nervous system lymphoma (PCNSL) is a subtype of diffuse large B-cell lymphoma (DLBCL) with confined CNS growth. We evaluated tumor mic...

Insulin resistance modifies longitudinal multi-omics responses to habitual diet

How habitual diet influences the gut microbiome and plasma metabolome across insulin resistance states remains unclear. We conducted year-long multi-o...

ESUS-AI:a machine learning framework to estimate the most likely embolic source in embolic stroke of undetermined source

Background and Purpose Embolic stroke of undetermined source (ESUS) emains a major diagnostic challenge in vascular neurology, as a substantial propor...

NIMETOX-informed Precision Nomothetic Models of Major Depressive Disorder: Group, Phenome, and Individual Signatures

Background: Major depressive disorder (MDD) is a neuro-immune-metabolic-oxidative (NIMETOX) disorder. Nevertheless, the effects of alterations in immu...

Development and Multinational Validation of Artificial Intelligence-Enabled ASCVD Risk Stratification Using Electrocardiograms

Aims: Despite the availability of clinical risk scores for atherosclerotic cardiovascular disease (ASCVD), their use is limited because the required p...

A genotype-phenotype transformer to assess and explain polygenic risk

Genome-wide association studies have linked millions of genetic variants to biomedical phenotypes, but their utility has been limited by lack of mecha...

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation

The analysis of carotid arteries, particularly plaques, in multi-sequence Magnetic Resonance Imaging (MRI) data is crucial for assessing the risk of...

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