Metabolic Syndrome

NATURal history of coronary PlaquE on cardiac computed tomography in individuals without MACE or lipid-lowering therapy: NATURE-CTstudy.

BACKGROUND: Coronary artery disease (CAD) progression has been examined mainly in cohorts enriched for major adverse cardiovascular events (MACE), a high burden of traditional risk factors, or prior exposure to risk-modifying therapies. In contrast, plaque progression is poorly described in patients without prior MACE and receiving no anti-atherosclerotic treatment who undergo cardiac computed tom...

Machine learning evaluation of TyG-based metrics for arteriosclerosis progression.

Insulin resistance (IR) is a significant risk factor for arteriosclerosis. The triglyceride-glucose (TyG) index and its obesity-related derivatives (TyG-BMI, TyG-WC, and TyG-WHtR) have emerged as reliable markers of IR. While diabetes, a consequence of IR, is a primary risk factor for arteriosclerosis, it is important to note that arteriosclerosis may develop prior to the onset of diabetes. Theref...

Gender disparities in random blood glucose levels among Pakistani adults with type 2 diabetes: a cross-sectional analysis.

This study investigated gender disparities in random blood glucose (RBS) levels among Pakistani adults with Type 2 Diabetes (T2D), examining biologica...

Optimization of artificial intelligence models for prediction of new-onset cardiovascular disease in patients with arterial hypertension.

Advanced preventive strategies are needed to decrease the burden of cardiovascular disease (CVD). We aimed to develop a predictive tool to identify in...

Integrated metabolic and inflammatory biomarkers for early association and prediction of adverse pregnancy outcomes: a multicenter validation study.

BACKGROUND: Adverse pregnancy outcomes (APOs) affect long-term maternal and offspring health. Conventional metabolic markers, including the triglyceri...

Predictive performance of CT-based artificial intelligence for predicting variceal bleeding in portal hypertension: a systematic review and meta-analysis.

OBJECTIVES: To systematically evaluate the predictive accuracy of computed tomography (CT)-based artificial intelligence (AI) for predicting variceal ...

Alignment Between Cardiologists and AI-Driven Diagnostic Systems: Mixed Methods Study.

BACKGROUND: The clinical value of artificial intelligence (AI)-based diagnostic systems depends not only on their accuracy but also on how well their ...

A Multimodal Learning Framework for Detecting Systemic Hypertension in Sleep Apnea Patients Using ECG and PPG Signals.

Systemic hypertension (HTN) is a major cardiovascular comorbidity in patients with obstructive sleep apnea (OSA), yet these conditions are often diagn...

Machine Learning Prediction Model for Dyslipidemia and Its Association With Atherothrombotic Events in 3 Independent Cohorts From South Korea, Japan, and the United Kingdom: Algorithm Development and Validation Study.

BACKGROUND: Dyslipidemia is a multifactorial and complex condition that warrants investigation through advanced analytical approaches such as machine ...

Machine learning approaches in identifying factors associated with hypertension and undiagnosed hypertension in adults in rural areas of Bangladesh.

BACKGROUND: Hypertension is a major cause of death and disability, and undiagnosed cases are particularly dangerous as they can cause severe damage wi...

Predicting metabolic syndrome in Iranian adults using artificial neural networks: a comparative analysis with logistic regression based on demographic, lifestyle, and clinical indicators.

OBJECTIVE: Metabolic syndrome (MetS) is a major risk factor for cardiovascular diseases and type 2 diabetes, imposing a substantial economic and publi...

Comparative prognostic value of TyG index and TyG-BMI for 90-day all-cause mortality in critically ill older adults with ASCVD: a machine learning analysis of the MIMIC-IV database.

BACKGROUND: The triglyceride-glucose (TyG) index and triglyceride-glucose-body mass index (TyG-BMI) are emerging surrogate markers of insulin resistan...

Explainable TabNet for gestational diabetes prediction with physician-in-the-loop and multi-site clinical validation.

BACKGROUND: Gestational diabetes mellitus (GDM) affects 15-25% of pregnancies worldwide and poses serious risks of macrosomia, preeclampsia, neonatal ...

Development and validation of an explainable machine learning model using routine laboratory biomarkers for identifying prevalent MASLD: Evidence from two observational studies.

Although many predictive models for metabolic dysfunction-associated steatotic liver disease (MASLD) have been developed, their performance remains su...

Construction of a predictive model for the risk of non-alcoholic fatty liver disease in patients with sleep apnea syndrome based on multiple machine learning algorithms: a multicenter study.

BACKGROUND: Sleep apnea syndrome (SAS) is closely related to an increased risk of non-alcoholic fatty liver disease (NAFLD), but current clinical tool...

Multimodal Fusion of Echocardiogram Images and Electronic Medical Records for Heart Disease Screening: Retrospective Algorithm Development and Validation Study.

BACKGROUND: Echocardiography is a fundamental imaging modality for the diagnosis of heart disease (HD), but its interpretation remains operator-depend...

Cuffless hemodynamic monitoring with physics-informed machine learning models.

Wearable technologies have the potential to transform ambulatory and at-home hemodynamic monitoring by providing continuous assessments of cardiovascu...

Non-traditional metabolic indices predict incident circadian syndrome in middle-aged and older Chinese adults: a nationwide prospective cohort study and machine learning analysis.

BACKGROUND: Circadian syndrome (CircS) augments the conventional metabolic syndrome construct by adding disturbed sleep and depressive features. Wheth...

Blood Pressure Assessment Across the Lifespan: Improving Clinical Research and Clinical Practice: A National Heart, Lung, and Blood Institute Workshop Report.

Hypertension is a modifiable risk factor for cardiovascular disease (CVD) and its prevalence is high in the United States and worldwide. Adequate char...

Thrombospondin-2 is a performant biomarker of at-risk MASH and advanced MASH fibrosis in a large multicentre European cohort.

BACKGROUND: Non-invasive biomarkers with biological rationale are needed for identifying patients with progressive metabolic dysfunction-associated st...