Metabolic Syndrome

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 dyslipidemia. However, the molecular mechanisms linking lipid metabolism to OSA remain incompletely understood. This study aimed to investigate the association between blood lipid levels and OSA and explore potential underlying molecular pathways. METHODS: ...

Phenotyping of pulmonary arterial hypertension associated with congenital heart disease using latent class analysis: insights from a national prospective registry.

BACKGROUND: Pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD) is characterized by substantial prognostic and therapeutic heterogeneity. Conventional clinical and anatomic-pathophysiological classifications fail to fully capture these intricacies. Unsupervised clustering approaches such as latent class analysis (LCA) have been used to address this limitation. RESEAR...

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...

Biases of large language models in diagnosing Cushing's syndrome.

The diagnosis of endogenous Cushing's syndrome (CS) can be complicated and often delayed, given its low incidence (estimated globally at 1.8 cases to ...

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...

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...

Retinal Microvascular Signatures as Early Predictors of Cardiovascular Risk: Integrating Pathophysiology, Molecular Insights, and Artificial Intelligence.

Cardiovascular diseases (CVDs) continue to be a significant public health burden and public health emergency in the world, underscoring the importance...

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...

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...

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...

Validation of latent health behaviour subtypes in young and middle-aged adults with hypertension: a machine learning-augmented LCA approach with external cohort replication.

AIMS: To validate behavioural subtypes among young and middle-aged hypertensive patients using latent class analysis (LCA) and assess their generaliza...

Persistent social connection over time is associated with attenuated biological ageing assessed by AI-enabled electrocardiography.

AIMS: Social isolation (SI) is associated with a higher risk of cardiovascular disease (CVD). One mechanism linking SI and CVD is accelerated biologic...

nnU-Net-based CT segmentation of perigastric varices in sinistral portal hypertension: a multicenter study.

PURPOSE: To develop a deep learning model based on nnU-Net for automated segmentation of all perigastric veins on contrast-enhanced CT images in patie...

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...

Multimodal Machine Learning Integrating Clinical and Proteomic Data for Early Prediction of Hypertensive Complications: A UKB Longitudinal Study.

BACKGROUND: Hypertension is a leading risk factor for cardiovascular, cerebrovascular, and renal diseases, significantly worsening prognosis and quali...

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 ...

Comparison of ChatGPT and dietitians in formulating diet plans and recommendations for patients with cardiometabolic diseases.

BACKGROUND: Artificial Intelligence (AI) is transforming personalized medicine, yet its efficacy constitutes a dynamic factor in the field of health a...

Novel method for risk stratification of radiation-induced breast fibrosis: subgroup hypothesis verified by machine learning.

Breast fibrosis (BF) after radiotherapy remains one of the most dreaded late toxicities in breast cancer care, yet multiple additive predictors strugg...