Obesity

Simplified and accurate approach to sleep disorder prediction using body composition metrics: a development and validation study.

Predicting the risk of sleep disorders such as insomnia, obstructive sleep apnea (OSA), and comorbid insomnia and sleep apnea (COMISA) typically requires costly and time-consuming assessments. The SLEEPS algorithm simplifies this process using only nine questions, including body mass index (BMI). However, BMI alone cannot capture differences in body composition, as individuals with the same BMI ma...

Integrated multi-omics analysis identifies TBC1D10C as an NK- and T cell-associated diagnostic biomarker in childhood obesity.

Childhood obesity is a growing global health crisis associated with an increased risk of metabolic and cardiovascular diseases in adulthood. While accumulating evidence implicates immune dysregulation in obesity pathogenesis, the specific transcriptional alterations of immune cells, particularly NK cells and T cells in the context of childhood obesity, and their potential as non-invasive diagnosti...

Artificial Intelligence in Cardiovascular Disease Prevention: Current Applications and Future Perspectives.

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, emphasizing the ongoing need for effective and scalable ...

Early detection of heart failure using trend similarity metrics combined with hybrid deep learning based on real-world health monitoring data.

Heart failure (HF) is a global health crisis, with over 18 million annual deaths attributed to cardiovascular diseases. Early detection is critical to...

Identification and Validation of Transcriptomic Signatures in Inflammatory Bowel Disease with Metabolic Syndrome via Bioinformatics and Machine Learning.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic, nonspecific inflammatory disorder affecting the gastrointestinal tract. The condition's pat...

Dietary nutritional strategies for circadian rhythm regulation: advances, challenges and future directions.

The escalation of urban light pollution and the trend toward a globalized lifestyle have led to a notable increase in circadian rhythm disruption (CRD...

Use of Continuous Glucose Monitoring With Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes.

The classification of diabetes and prediabetes by static glucose thresholds obscures the pathophysiological dysglycemia heterogeneity, primarily drive...

Long-Term Exposure to Fine Particulate Matter and Kidney Function among Patients with Polycystic Kidney Disease.

BACKGROUND: Polycystic kidney disease (PKD) is the fourth leading cause of end-stage kidney disease. Although the condition is primarily genetic, envi...

Development of artificially intelligent tool for analysis and prediction of myopia progression among school-going children.

BACKGROUND: Myopia is a growing public health concern among school-going children, with increased screen time and reduced outdoor activities contribut...

Analysis of B-Scan Ultrasonography Using Neural Networks to Predict Risk of Fibrosis in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD).

OBJECTIVE: The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, underscoring the need for tools to st...

Generative AI in a new era of computer model-informed tobacco research: a short report.

BACKGROUND: Social media influencers who promote e-cigarettes on Instagram or TikTok for tobacco brands use marketing tactics to increase the appeal o...

Digital Monitoring of Pre-Exposure Prophylaxis Users Through Electronic Patient-Reported Outcome Measures and Electronic Patient-Reported Experience Measures: Multicenter Prospective Study on Feasibility, Safety, and Predictive Modeling of Digital Engagement.

BACKGROUND: Digital health tools integrating electronic patient-reported outcome and experience measures (ePROMs/ePREMs) enable longitudinal monitorin...

Machine Learning to Predict Implant-Based Breast Reconstruction Failure: A Bootstrap-Validated Elastic Net Model.

BACKGROUND: Implant-based reconstruction failure remains a significant complication following breast reconstruction, with substantial implications for...

Comorbidity-aware transfer learning for neuro-developmental disorder diagnosis.

Neurodevelopmental disorders (NDDs) are characterized by impairments in cognition, communication, behavior, and adaptive functioning, placing increasi...

Incremental value of deep learning denoising in low-dose coronary CT angiography in predominantly obese patients.

OBJECTIVE: To determine the added value of post-hoc convolutional neural network (CNN)-based denoising for low-radiation coronary CT angiography (CCTA...

Intelligence-driven Mechanomedicine for Weight Rebound in Obesity.

From Other Journals: A Review of Recent Articles by Our Editorial Team - 2026 First Edition.

In this review we provide a brief description of recently published articles addressing topics relevant to pediatric cardiologists. Our hope is to pro...

Using causal machine learning and real world data to improve dose response decision making for secukinumab in psoriatic arthritis.

Personalized treatment in psoriatic arthritis (PsA) remains challenging, particularly in guiding dose escalation decisions. We applied a causal machin...

Baseline pain, fatigue, and sleep quality predict 12-week pain improvement in inflammatory arthritis: retrospective real-world analysis of a digital health application cohort.

UNLABELLED: Despite advances in pharmacotherapy, many patients with inflammatory arthritis (IA) experience residual pain even in remission. Digital he...

ChatGPT for obesity management: a review of evidence, potential challenges, and clinical implications.

The global rise in the prevalence of obesity highlights the need for accessible and effective solutions for obesity management. ChatGPT, one of the fa...