Metabolic Syndrome

Analysis and prediction of cardiovascular research hotspots, trends and interdisciplinarity.

OBJECTIVE: Comprehensive data and analyses on cardiovascular research could clarify recent research trends for the academic community and facilitate policy development. We examined publications and reference data to identify research topics, trends and interdisciplinarity for cardiovascular disease (CVD). METHODS: We extracted and clustered text fragments from the titles and abstracts of 2 512 445...

Glucose Predictions Improve Glycemic Control: A Digital Twin Evaluation.

BACKGROUND: Glucose predictions aim to empower continuous glucose monitoring (CGM) users by enabling preventive actions to reduce adverse glycemic events. The Accu-Chek® SmartGuide Predict app offers several AI-enabled predictive features, driven by machine learning algorithms. These include notifications for a low glucose predict within 30 min (LGP) and for nighttime low glucose risk, as well as ...

Investigation of Fatty Acid Metabolism-Associated Molecular CPOX and the Underlying Mechanism in Follicular Lymphoma.

Dysregulated lipid metabolism is a key driver of follicular lymphoma (FL). This study aimed to explore the lipid metabolism-related genes (LMRGs) and ...

Comparison of conventional formulas and machine learning models for estimating serum low-density lipoprotein cholesterol.

BACKGROUND: Accurate estimation of low-density lipoprotein cholesterol (LDL-C) is essential for cardiovascular risk assessment and monitoring. Because...

Association of the atherogenic index of plasma and a novel triglyceride-glucose-body shape index with stroke: insights from NHANES and machine learning approaches.

Bridging Population Patterns and Individual Prediction: Framework for Prospective Multimorbidity Study.

BACKGROUND: Multimorbidity has become a major global public health challenge. However, existing research primarily emphasizes the identification of di...

Comprehensive evaluation of triglyceride glucose index-a body shape index (TyG-ABSI) for incident peripheral artery disease: data-driven phenotyping and machine learning-based risk prediction in the UK Biobank.

BACKGROUND: The prevalence of Peripheral Artery Disease (PAD) is rising globally, yet early risk stratification remains challenging due to the limitat...

Unveiling Endotypes in Systemic Lupus Erythematosus Through Multi-Omic Analysis: Insights into Cardiovascular and Renal Complications.

BACKGROUND: Systemic lupus erythematosus (SLE) shows clinical and molecular heterogeneity, and cardiovascular (CV) complications and lupus nephritis (...

Characterization of biomarker profiles in patients with coronary artery disease: A prospective coronary computed tomography angiography study.

BACKGROUND AND AIMS: Conventional biomarkers such as low-density lipoprotein (LDL) and high-density lipoprotein may fail to identify patients' risk fo...

Race/ethnicity, disability, and antenatal depression in the United States: population-level insights from machine learning.

OBJECTIVE: Women with intersecting identities, such as being both Black and disabled, face heightened risk of antenatal depression, yet few studies ex...

Association between the oxidative balance score and depressive symptoms in adults with metabolic syndrome: a cross-sectional study from NHANES.

OBJECTIVE: This study investigated the association between the Oxidative Balance Score (OBS) and the odds of depressive symptoms in adults with metabo...

Association of stress hyperglycemia ratio with the incidence of delirium in critically ill patients: a retrospective cohort study with exploratory machine-learning analyses.

Association between stress hyperglycemia ratio and all-cause mortality in neurocritical patients.

Fluctuations in blood glucose during acute neurocritical illness are associated with poor outcomes, but the role of stress hyperglycemia ratio (SHR) i...

PAH-former: Transfer learning for efficient discovery of pulmonary arterial hypertension-associated genes.

BACKGROUND AND AIMS: Pulmonary arterial hypertension (PAH) is a severe disease with limited effective therapies, making the discovery of new therapeut...

Phenotypic clustering of myocardial infarction complicated by out-of-hospital cardiac arrest using unsupervised machine learning.

BACKGROUND: Patients with myocardial infarction (MI) complicated by out-of-hospital cardiac arrest (OHCA) represent a heterogeneous population with va...

Identifying patients at risk for short-term adverse events after hip arthroscopy: a machine learning analysis of a national database.

PURPOSE: To develop and compare machine learning-based risk prediction models to identify patients at risk for short-term adverse outcomes (overnight ...

Serum Element-Driven Gut Microbial Disturbances and Their Predictive Implications for Gestational Dyslipidemia: A Prospective Birth Cohort Study.

Pregnancy-triggered shifts in maternal serum elements and gut microbiota may disrupt metabolic balance; yet, their interplay in lipid regulation remai...

Heterogeneous associations of a mobile health-based disease management program on uncontrolled hypertension: A target trial emulation study.

Long-term effectiveness of digital health interventions for hypertension remains unclear, particularly regarding individual variability in treatment r...

Harnessing the gut-heart axis for cardiovascular drug innovation: microbiome, metabolites, and personalized treatment strategies.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide despite major advances in pharmacotherapy. Emerging evidence reveals a p...

Impact of Deep Learning-Based Time-of-Flight PET Images of Small Tumors Using a Human Anatomic Phantom.

Time-of-flight (ToF) in PET improves image quality by enhancing the signal-to-noise ratio, and recent deep learning (DL)-based ToF (DL-ToF) methods fu...