Dyslipidemia

Age-related metabolomic signatures and stroke susceptibility in a population-based cohort.

Aging-related metabolic dysregulation and vascular vulnerability contribute substantially to stroke susceptibility, yet subtype-specific metabolic signatures remain incompletely characterized. Employing a nested case-control design within the Taizhou Longitudinal Study, we quantified 296 lipoprotein parameters and 54 metabolites in 1208 stroke-control pairs using nuclear magnetic resonance. Logist...

Trusting Generative AI for Health Advice: Preregistered Survey Experiment.

BACKGROUND: Generative artificial intelligence (AI) systems are increasingly used for health information seeking, yet it remains unclear how the public evaluates AI-generated health advice relative to guidance from credentialed clinicians in digital environments. Understanding the conditions under which AI is perceived as credible is critical as these systems become integrated into digital health ...

Burden trajectories of stroke attributable to lipid-glucose dysfunction and biomarker validation: evidence from the global burden of disease (GBD) 2023 and the China health and retirement longitudinal study (CHARLS).

BACKGROUND: Metabolic dysfunction is an important contributor to stroke burden and may also help identify individuals at higher future risk of stroke....

Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study.

BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage increases risk of vascular events...

Integrating epidemiological and transcriptomic data reveals novel lipid metabolic drivers of obstructive sleep apnea.

BACKGROUND: Obstructive sleep apnea (OSA) is characterized by recurrent upper airway obstruction during sleep and is frequently accompanied by dyslipi...

A machine learning-based framework for predicting hypertension using serum hematological factors.

Hypertension (HTN) is a leading global cause of cardiovascular disease (CVD) and all-cause mortality, underscoring the need for early detection and in...

Association between visceral adiposity index and delayed union/nonunion after tibial or femoral shaft fractures: a single-center retrospective cohort study.

BACKGROUND: Delayed union and nonunion remain clinically important complications after tibial and femoral shaft fractures. Although traditional risk f...

Integrating 2.5D multi-modal CT imaging with serum triglycerides for early risk stratification in hypertriglyceridemia-induced acute pancreatitis: A multicenter study.

OBJECTIVES: To develop and externally validate a clinical-radiological framework that fuses 2.5D deep learning features from dual-phase computed tomog...

Decoding chromatin regulator-LAIR1+ M2 like macrophage patterns in atherosclerosis and clinical outcomes of Lung adenocarcinoma patients: evidence from artificial intelligence-driven multi omics and in vitro validation.

Atherosclerosis (AS) is a common complication of lung adenocarcinoma (LUAD), but its underlying mechanisms in LUAD remain unclear. This study aimed to...

Hepatic Steatosis Severity Prediction in Nonobese Individuals: Machine Learning Model Development and Validation.

BACKGROUND: Steatotic liver disease affects 40% of nonobese individuals, but existing screening tools inadequately detect and stage disease severity i...

Interpretable machine learning and incremental prognostic model for all-cause and cardiovascular mortality in obesity with MASLD population: Comorbidity insights from triglyceride-glucose and its obesity-related derivatives.

OBJECTIVE: To investigate comorbid correlation and prognostic value of triglyceride-glucose (TyG) and its obesity-related derivatives for all-cause an...

The role of metabolic health in neurostructural and cognitive alterations in bipolar disorders.

BACKGROUND: Bipolar disorders (BD) rank among the most disabling conditions, affecting millions worldwide. Cognitive impairment in BD is linked with b...

UNICORN: a deep learning model for integrating multi-stain data in histopathology.

The integration of multi-stain histopathology images through deep learning poses a significant challenge. Current approaches struggle with data hetero...

Multiomics reveals fatty acid metabolism and immune remodeling in retinal artery occlusion.

Retinal artery occlusion (RAO), an acute ocular ischemic stroke, is critically linked to cardiovascular and metabolic dysfunction. To elucidate the in...

Interaction Between Air Pollution and Genetic Predisposition to Blood Pressure and LDL-C on Cardiovascular Events.

AIMS: Particulate matter ≤2.5μm (PM2.5) air pollution is a leading global environmental risk factor. We investigated the impact of PM2.5 on cardiovasc...

Determinants of childhood and adolescent obesity: An explainable AI approach using the ICAD database.

OBJECTIVES: To identify the biological, behavioural, and socio-demographic determinants of childhood and adolescent obesity and evaluate how their rel...

Fatty acid synthesis therapy-induced senescence (FASTIS) in cancer cells.

Therapy-induced senescence (TIS) in cancer cells can be triggered by radiotherapy, chemotherapy, and certain targeted therapeutics. Here, we demonstra...

Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications.

Heavy metals, including lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg), are pervasive environmental toxicants increasingly recognized as nont...

Machine learning versus deep learning for screening ischemic stroke among asymptomatic population.

Ischemic stroke puts great health burden in public. However, the diagnosis is based on head CT or MRI scanning. We aim to develop classifier models to...

Explainable artificial intelligence models in predicting major cardiovascular events: insights from the PolyIran and PolyPars prospective studies.

Cardiovascular diseases are among the leading causes of global mortality and morbidity, underscoring the need for accurate and explainable predictive ...