Dyslipidemia

Associations of Iron Indicators with Cognitive Impairment Risk in Postmenopausal Women: Evidence from a Matched Case-Control Study and Back Propagation Neural Network Model.

Iron is essential for normal cognitive function, and women have nearly twice the age-matched prevalence of cognitive impairment compared with men. The matched case-control study included 1344 postmenopausal women aged over 60 years, and cognitive status was assessed using Mini-Cog. Plasma ferritin and hepcidin were detected by enzyme-linked immunosorbent assay (ELISA), and total iron binding capac...

Identification of weight loss predictors using machine learning approaches in adolescents with obesity.

BACKGROUND: Despite the effectiveness of lifestyle multidisciplinary (LMD) weight loss interventions in pediatric obesity, outcomes remain variable between individuals. Machine learning (ML), capable of capturing complex relationships between variables, offer a promising avenue to better understand and predict this variability. METHODS: This study aimed to identify baseline predictors of 9-month L...

Metabolic signatures and machine learning identify gut-liver-heart axis dysfunction as a potential link to major adverse cardiovascular events in coronary artery disease.

BACKGROUND: Traditional risk factors do not fully account for the residual cardiometabolic risk of major adverse cardiovascular events (MACE) in coron...

MINPP1, HES4 and SLCO5A1 as core signature genes mediating the effects of type 2 diabetes on coronary artery bypass grafting.

PURPOSE: This study aimed to identify the signature genes that mediate the effects of type 2 diabetes (T2D) on coronary artery bypass grafting (CABG),...

Sex-Related Performance Disparities in Convolutional Neural Networks for Imaging-Based Assessment of Coronary Atherosclerosis and Ischemic Heart Disease: A Systematic Review.

Sex-related disparities persist in the diagnosis and management of ischemic heart disease (IHD), raising concern that convolutional neural networks (C...

A Cross-Sectional Analysis of Vascular Geometry in Adults: Associations of Age and Biological Sex with Iliofemoral Arterial and Venous Tortuosity.

Iliofemoral vascular tortuosity may reflect vascular aging, yet natural anatomic variation remains poorly characterized at scale. In this single-cente...

Interpretable machine learning for gallstone risk prediction: an ensemble stacking approach with SHAP analysis.

Gallstone disease is a gastrointestinal condition requiring accessible, low-cost screening tools. We developed an interpretable ensemble stacking mode...

Psychological stress-associated ceramide and diacylglyceride lipotoxicity as contributors to first-episode depression pathophysiology: A neuroimmune-metabolic-oxidative stress (NIMETOX) perspective.

BACKGROUND: Aberrations in neuro-immune, metabolic, and oxidative stress (NIMETOX) pathways are implicated in major depressive disorder (MDD). First-e...

Plasma proteomics linking primary and secondary diseases: Insights into molecular mediation from UK Biobank data.

BACKGROUND: Diabetes, hypertension, and dyslipidemia are major risk factors for cardiovascular, neurological, renal, and pulmonary diseases, yet clini...

MRI-based shear stress profiles in subclinical carotid atherosclerosis - A feasibility study.

Arterial wall shear stress (WSS) plays an important role in atherosclerosis, but its assessment is often limited to small, retrospective studies due t...

A machine learning-based framework for predicting type 2 diabetes mellitus using hematological indices.

OBJECTIVE: This study aimed to develop a machine learning (ML) framework to predict incident type 2 diabetes mellitus (T2DM) using routinely available...

Leveraging machine learning with real-world data for hypothesis generation by identifying exposomic predictors in Parkinson's disease among farmers.

BackgroundMachine learning offers new avenues for complementing traditional epidemiological approaches by analyzing routinely collected, population-ba...

Prediction of coronary atherosclerosis progression in type 2 diabetes mellitus based on AI-derived CCTA parameters and clinical factors: a follow-up study.

OBJECTIVE: To develop a predictive model for coronary atherosclerosis progression in patients with type 2 diabetes mellitus (T2DM) based on Artificial...

AI-quantified Myosteatosis at CAC CT for Prediction of Atrial Fibrillation and Heart Failure: The Multi-Ethnic Study of Atherosclerosis.

Purpose To evaluate whether the artificial intelligence (AI)-quantified mean thoracic skeletal muscle (TSM) attenuation from coronary artery calcium (...

Prediction Model for Mild Cognitive Impairment in Older Chinese Patients With Cerebral Small Vessel Disease Based on XGBoost Algorithms and Shapley Additive Explanations.

OBJECTIVE: This study aims to evaluate cognitive function in patients with Cerebral Small Vessel Disease (CSVD) and investigate its association with v...

Machine-Learning-Based Evaluation of the Prognostic Significance of the Non-High-Density Lipoprotein to High-Density Lipoprotein Cholesterol Ratio in Critical Ischemic Stroke.

BACKGROUND This study examined associations of the non-high-density lipoprotein to high-density lipoprotein cholesterol ratio (NHHR) with short-term (...

Development and validation of a nomogram for predicting hemoptysis risk in patients with bronchiectasis: A study based on Random Forest algorithm.

Hemoptysis is a severe and potentially life-threatening complication of bronchiectasis. There is currently a lack of reliable tools for the individual...

Modular RNA-seq Analytics for Exploratory Biomarker Discovery using Public Data.

Publicly available RNA sequencing (RNA-seq) data provide a cost-effective springboard for biomarker discovery. However, heterogeneity across studies o...

Cardiovascular risks of plastic-associated chemicals: Integrating mixture modeling and SHAP-explained machine learning to unravel linear and non-linear associations.

INTRODUCTION: Plastic-associated chemicals (PACs) are widely detected environmental contaminants, yet their cardiovascular relevance in the context of...

Development of a machine learning-based noninvasive diagnostic model for liver fibrosis in metabolic-associated steatotic liver disease.

BACKGROUND: To address the lack of simple tools for assessing fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD), this study...