Dyslipidemia

Non-Traditional Lipid Ratios Predict Cardiovascular-Kidney-Metabolic Syndrome: Insights from Machine Learning Model Using NHANES Data

Cardiovascular-kidney-metabolic (CKM) syndrome is a newly defined multisystem disease continuum characterized by the coexistence of metabolic dysfunction, cardiovascular disease, and chronic kidney disease (CKD). This study aimed to explore the relationship between the non-traditional lipid ratios and CKM syndrome. A cross-sectional analysis was performed using data from the 2007-2018 National Hea...

Plasma Proteomics Linking Primary and Secondary diseases: Insights into Molecular Mediation from UK Biobank Data

Diabetes, hypertension, and dyslipidemia are major risk factors for cardiovascular (CVD), cerebral, and renal diseases (RD). However, the underlying molecular mechanisms, particularly the biological paths linking these primary conditions to downstream diseases remain incompletely understood. In this study, we investigated the role of plasma proteins as mediators of secondary disease development us...

A Self-Explainable Dynamic Risk Monitoring Framework for Predicting Alzheimer’s Disease and Related Dementias

Alzheimer’s Disease and Related Dementias (ADRD) affect millions worldwide and can begin over a decade before symptoms appear. ADRD are generally irre...

Long-Term Carotid Plaque Progression and the Role of Intraplaque Hemorrhage: A Deep Learning-Based Analysis of Longitudinal Vessel Wall Imaging

Carotid atherosclerosis is a major contributor in the etiology of ischemic stroke. Although intraplaque hemorrhage (IPH) is known to increase stroke r...

Foundation model embeddings enable cardiovascular screening for people living with HIV in Vietnam using wearable signals

Cardiovascular disease (CVD) screening faces significant challenges in resource-limited settings, where infrastructure and computational constraints p...

Machine Learning Risk Prediction for Prolonged Hospitalization in Frail Older Adults with Multimorbidity

Frailty and multimorbidity are common in older adults and contribute substantially to prolonged hospitalizations, readmissions, and mortality. Yet, ex...

A photoplethysmography-based aging clock reveals genetic determinants of arterial aging

Arterial aging, marked by progressive vascular stiffening, is a contributor to cardiovascular disease. Photoplethysmography (PPG) waveforms offer an e...

Circulating Metabolites are Linked to Dementia and Brain Imaging Phenotypes, and Mediate Modifiable Risk Pathways

Dementia poses an escalating global health burden, yet its underlying mechanisms remain incompletely understood. In this large-scale, targeted metabol...

Medication-Stratified Analysis of LDL-C Equation Miscalibration in Diabetes: Evidence from the All of Us Research Program and a Medication-Agnostic Machine-Learning Correction

Standard LDL-C equations were derived in cohorts largely untreated with modern combination diabetes therapies. With medication-treated patients compri...

A Zero-Burden Sleep Foundation Model Built on Cardiorespiratory Signals from 800,000+ Hours of Multi-Ethnic Sleep Recordings

Sleep disorders pose a major global health burden and are associated with a wide range of adverse health outcomes. Polysomnography (PSG) is the gold s...

Accurate, Race-Free LDL-C Estimation in Non-Fasting Settings: A Machine-Learning Study in 3,477 Adults

Traditional LDL-C testing barriers—mandatory 9–12 hour fasting and inperson visits—disproportionately limit access for rural populations (60% of US co...

Characterize Disease Progression Subphenotypes in Real World Populations with Overweight and Obesity using a Graph-based Neural Network Framework

Obesity is a chronic, heterogeneous condition, with risks, trajectories, and treatment responses that vary widely among individuals. However, research...

Genetic and Etiological Insights from Automated Lumen Diameter Measurements in Carotid Ultrasounds of the UK Biobank

Carotid ultrasound is routinely used in clinical practice for non-invasive vascular anatomical and functional assessment. In particular, the carotid i...

Neuroimaging-derived brain endophenotypes link molecular mechanisms to Alzheimer’s disease and aging

Alzheimer’s disease (AD) genome-wide association studies (GWAS), typically based on clinical phenotypes, have identified numerous risk loci, yet linki...

Machine learning model predicts new-onset lower extremity deep vein thrombosis after pelvic fracture surgery and targeted diagnosis

Postoperative new-onset deep vein thrombosis (PNO-DVT) of the lower extremities represents a prevalent and serious clinical complication following pel...

A Pathway-Based Machine Learning Approach Identifies Region-Specific Markers and Patterns in Alzheimer’s Disease Patients Based on Spatial and Severity Metadata

Alzheimer’s disease (AD) exhibits profound spatial heterogeneity in its molecular and pathological features, yet the basis of this regional selectivit...

Leveraging Pretrained Large Language Model for Prognosis of Type 2 Diabetes Using Longitudinal Medical Records

Timely prognosis of type 2 diabetes (T2D) is critical for effective interventions and reducing economic burden. Longitudinal medical records offer pot...

Temporal deep learning with clinically engineered biomarkers for the early prediction of type 2 diabetes

Diabetes mellitus remains a major global health burden, causing an estimated 3.4 million deaths in 2024 and highlighting the need for accurate early i...

Comparing the Treatment and Side Effects of Existing Bariatric Surgery Procedures: An Observational Study

Bariatric surgery is an established treatment for obesity and its associated comorbidities, including diabetes, hypertension, sleep apnea, and hyperch...

Characterizing hub biomarkers for metabolic-induced endothelial dysfunction and unveiling their regulatory roles in EndMT through RNA sequencing and machine learning approaches.

BACKGROUND: Metabolic disorder and endothelial dysfunction (ED) are key events in the development and pathophysiology of atherosclerosis and are assoc...