Metabolic Syndrome

Hypertension Screening via Awake-Sleep Differences in Photoplethysmogram Signals

Hypertension is a major risk factor for cardiovascular diseases. This study proposes a novel hypertension screening framework based on awake-sleep differences in photoplethysmography (PPG) indices, using machine learning. We hypothesised that normotensive individuals exhibit greater PPG variation between awake and sleep states than unmanaged hypertensive individuals. The Aurora-BP dataset (n=180; ...

DeepDrug2: A Germline-focused Graph Neural Network Framework for Alzheimer’s Drug Repurposing Validated by Electronic Health Records

Alzheimer’s disease (AD) is a complex neurodegenerative disorder with limited therapeutic options. The original DeepDrug framework by Li et al. (2025) relied on somatic mutation data and emphasized long genes to guide AD drug repurposing. However, emerging evidence suggests that germline genetic variants play a more central role in AD pathogenesis. In response, we develop DeepDrug2, an enhanced AI...

Automated Deep Learning Pipeline for Characterizing Left Ventricular Diastolic Function

Left ventricular diastolic dysfunction (LVDD) is most commonly evaluated by echocardiography. However, without a sole identifying metric, LVDD is asse...

Integrative Machine Learning Approach to Risk Prediction for Dementia and Alzheimer’s Disease

Dementia, especially Alzheimer’s disease (AD), is a major global health challenge marked by progressive cognitive impairment, behavioral changes, and ...

Target Trial Emulation Applications in Hypertension Research: A Scoping Review

Target Trial Emulation (TTE) has emerged as a rigorous framework for causal inference using observational data, but its application in hypertension re...

Deep Learning-Based Risk Prediction Model for Major Adverse Cardiovascular Events in Long-Term Breast Cancer Survivors

Clinical practice guidelines recommend cardiovascular toxicity risk restratification including evaluation of new cardiovascular risk factors and cardi...

AI-Derived Splenic Response in Cardiac PET Predicts Mortality: A Multi-Site Study

Inadequate pharmacologic stress may limit the diagnostic and prognostic accuracy of myocardial perfusion imaging (MPI). The splenic ratio (SR), a meas...

Application of Machine Learning Approaches to Develop Predictive Models for Diabetes and Hypertension among Bangladesh Adults

With rapid urbanization, lifestyle changes, and an aging population, non-communicable diseases (NCDs), including hypertension and diabetes, pose signi...

Direct causal variable discovery leveraging the invariance principle: application in biomedical studies

Accurate identification of direct causal (parental) variables for a target is of primary interest in many applications, especially in biomedical scien...

Myocardial Native T1 Mapping in the German National Cohort (NAKO): Associations with Age, Sex, and Cardiometabolic Risk Factors

In cardiovascular magnetic resonance (CMR), myocardial native T1 mapping enables quantitative, non-invasive tissue characterization and is sensitive t...

Differential Predictability of Preterm Birth Types: Strong Signals for Indicated Cases versus Limited Success in Spontaneous Preterm Birth

Preterm birth, defined as birth occurring before 37 weeks of gestation, poses a significant and enduring public health challenge, with substantial emo...

MedAdhereAI: An Interpretable Machine Learning Pipeline for Predicting Medication Non-Adherence in Chronic Disease Patients Using Real-World Refill Data

Medication non-adherence remains a significant challenge in managing chronic conditions like diabetes and hypertension, leading to increased morbidity...

AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center Study

Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary art...

Multiethnic Validation of Artificial Intelligence-Enhanced Electrocardiographic Image Analysis in Detecting Cardiac Structural and Functional Abnormalities: A UK Biobank Study

Although artificial intelligence–enhanced electrocardiography (AI-ECG) has shown promise in detecting cardiac abnormalities, large-scale validation ag...

Deep learning predicts cardiac output from seismocardiographic signals in heart failure

Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compromise. However, the invasiveness, procedural risks...

Using discrete- and continuous-time machine learning models (Nnet, CoxNet, GLMnet) to explore sex and age differences in stroke prediction among hypertensive individuals

Stroke is one of the leading causes of death and long-term disability globally. Several studies have investigated the incidence and predictors of stro...

Serum metabolic signatures are associated with anti-drug antibody development in rheumatoid arthritis patients treated with adalimumab

Development of anti-drug antibodies (ADAs) is a barrier to long-term efficacy of biologic therapies in rheumatoid arthritis (RA), but no biomarkers ex...

Metabolic Subphenotypes of Obstructive Sleep Apnea: NHANES 2017-2020 (pre-pandemic)

OSA and MetS have a bidirectional relationship but increasing evidence suggests metabolic heterogeneity in OSA, systematic phenotyping of metabolic dr...

Scalable Deep Learning of Histology Images Reveals Genetic and Phenotypic Determinants of Adipocyte Hypertrophy

White adipose tissue dysfunction has emerged as a critical factor in cardiometabolic disease development, yet the cellular microstructure and genetic ...

Deep learning-based prediction of cardiopulmonary disease in retinal images of premature infants

Bronchopulmonary dysplasia (BPD) and pulmonary hypertension (PH) are leading causes of morbidity and mortality in premature infants. To determine whet...