Dyslipidemia

Deep Learning Radiomics Signature from Multicontrast MRI for Automated Identification of Symptomatic Carotid Plaques: A Multicenter Study.

BACKGROUND AND PURPOSE: Ischemic stroke poses a significant global health burden. Accurately identifying symptomatic carotid atherosclerotic plaques, beyond relying solely on stenosis degree, remains a critical challenge for precise stroke risk stratification. We aimed to develop and validate a deep learning radiomics (DLR) signature based on multicontrast MRI to identify symptomatic carotid plaqu...

The association between triglyceride glucose-frailty index and cardiometabolic multimorbidity among Chinese middle-aged and older adults: a national prospective cohort study.

BACKGROUND: Cardiometabolic multimorbidity (CMM) poses a growing global health burden, yet few studies have combined the Triglyceride-Glucose (TyG) index, which reflects metabolic dysfunction, with the Frailty Index (FI), which captures physiological reserve and aging-related vulnerability, to assess CMM risk. Given their complementary biological information, this study examines whether a composit...

Deep Learning-based Automated Coronary Plaque Quantification: First Demonstration With Ultra-high Resolution Photon-counting Detector CT at Different Temporal Resolutions.

OBJECTIVES: The aim of this study was to evaluate the feasibility and reproducibility of a novel deep learning (DL)-based coronary plaque quantificati...

Network pharmacology and machine learning reveal multi-target mechanisms of poly herbal formulation against atherosclerosis.

BACKGROUND: Herbs, like Allium sativum, Ginkgo biloba and Nerium oleander are traditional medicinal plants that have been used to treat atherosclerosi...

Integrated plasma proteomics and metabolomics reveal immunometabolic pathways and predictive signatures for age-related eye diseases.

BACKGROUND AND AIMS: Age-related eye diseases (AREDs) share aging as a major risk factor, but the systemic molecular changes preceding disease onset r...

A novel serum metabolite classifier for identifying Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) integrating metabolomics and machine learning.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) presents a growing global health burden, while reliable non-invasive biom...

The impact of lipoprotein(a) on coronary atherosclerotic plaque phenotype in primary prevention.

AIMS: Lipoprotein(a) (Lp[a]) is a causal risk factor for cardiovascular events. However, the effect of Lp(a) on coronary plaque composition and high-r...

Using machine learning to identify the most important predictors of fatty liver index in healthy young Taiwanese men.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide. While many factors have been associated with NAFLD, their...

Predicting Acute Myocardial Infarction in the Absence of Detectable Atherosclerotic Plaque With Perivascular Coronary Inflammation.

BACKGROUND: Coronary computed tomography angiography (CTA) with analysis by artificial intelligence (AI) can personalize coronary artery disease risk ...

1H NMR-Based Plasma Metabolomics and Machine Learning Reveal Distinct Metabolic Signatures of Breast Cancer in Nigerian Women.

Untargeted 1H NMR metabolomics offers a noninvasive means to identify biomarkers in breast cancer (BC) patients; however, metabolic signatures specifi...

A Novel Breast Arterial Calcification Age-Based Percentile Nomogram for the Incremental Prediction of Incidental Cardiovascular Events.

BACKGROUND: Breast arterial calcification (BAC) detected on routine mammography is an emerging marker of cardiovascular risk in women. However, substa...

Identification of Mitochondrial Signature Biomarkers and Molecular Mechanisms in Atherosclerotic Tissues and Blood: Combined Single-Cell and Bulk RNA Sequencing Analysis.

Atherosclerosis (AS) is a leading cause of cardiovascular disease and stroke. Although mitochondria's role in AS is recognized, effective molecular ta...

Identification of lipid metabolism-related biomarkers in familial hypercholesterolemia via integrated bioinformatics and machine learning approaches.

Comparative Analysis of Artificial Intelligence-Based Quantification versus Visual Rating of Enlarged Perivascular Spaces in the Multi-Ethnic Study of Atherosclerosis Cohort.

BACKGROUND AND PURPOSE: Enlarged perivascular spaces (PVS) are fluid-filled spaces surrounding small cerebral vessels. Current PVS research has been l...

Leveraging routine clinical data for dementia risk prediction using machine learning.

BackgroundEarly diagnosis of dementia is essential for enabling timely interventions that may slow disease progression, improve patient outcomes, and ...

The relationship between triglyceride-glucose index and chronic lung diseases: a nationwide cohort study.

Prevalence of Lipoprotein(a) Testing in Patients With Atherosclerotic Cardiovascular Disease Within a Large Australian Cardiology Network.

BACKGROUND: Increased lipoprotein(a) (Lp[a]) is a genetically determined causal risk factor for atherosclerotic cardiovascular disease (ASCVD). Histor...

Deep learning-based early prediction of carotid plaque response to lipid-lowering therapy using longitudinal multimodal ultrasound imaging.

OBJECTIVE: This study aimed to develop and validate a deep learning prediction model using longitudinal multimodal ultrasound imaging for early identi...

Risk prediction models for incident heart failure: a systematic review and meta-analysis.

BACKGROUND: Early identification of patients at risk of heart failure (HF) provides opportunities for preventative management. Though models have been...

The role of triglyceride-glucose (TyG) index in predicting mortality risk in critically ill patients with sepsis: a machine learning model in a cohort study.