Obesity

Artificial intelligence in cardio-kidney-metabolic care: Transforming integrated disease management through data-informed innovation.

Artificial intelligence (AI) is rapidly transforming the landscape of chronic medical conditions, such as cardio-kidney-metabolic (CKM) issues linked to type 2 diabetes and obesity. It creates new opportunities to shift from reactive to proactive, data-driven care. Recent advances include predictive algorithms for hypoglycemia and hyperglycemia, decision-support tools for insulin titration, and ge...

Using Machine Learning to Analyze the Predictors of Life Satisfaction: Focus on Lifestyle Attitudes and Psychological Factors.

OBJECTIVES: Life satisfaction is an essential indicator of quality of life, and enhancing it can contribute to individual well-being strategies. Because it is a complex concept, a comprehensive approach is needed to address it effectively. Machine learning offers a unique statistical opportunity to address this challenge effectively. In this study, we examined how lifestyle parameters, psychologic...

Triglyceride-Glucose-Waist-to-Weight Index: Novel Biomarker for Mild Cognitive Impairment in Older People With Sarcopenic Obesity.

BACKGROUND: Sarcopenic obesity (SO) is closely associated with mild cognitive impairment (MCI). However, traditional body composition-based diagnostic...

Predicting risk of mental health deterioration using multimodal data from the UK biobank.

INTRODUCTION: Prospective prediction of mental health risk is critical for early intervention to reduce the burden of depression, anxiety, and cogniti...

Deep learning-based CT-derived vertebral bone mineral density and metformin therapy: a longitudinal study in the Multi-Ethnic Study of Atherosclerosis.

OBJECTIVE: Evidence on metformin's skeletal effects remains conflicting. We emulated a target trial to evaluate associations between metformin therapy...

Probiotics and Postbiotics in Life-Style Disease Management: A Comprehensive Review on the Technologies in the Era of Omics and Artificial Intelligence.

Both conventional and next-generation probiotics play a crucial role in maintaining host homeostasis. They modulate insulin sensitivity, immune-regula...

Assessing Image Quality and Diagnostic Performance of Quadruple-Low Coronary CT Angiography with Deep Learning in High-BMI Patients.

RATIONALE AND OBJECTIVES: To examine the feasibility of a quadruple-low protocol in coronary computed tomography angiography (CCTA) assisted by the de...

Machine learning and SHAP interpretation for predicting coronary heart disease-diabetes comorbidity with dietary antioxidants.

Coronary heart disease (CHD) and diabetes mellitus frequently co-occur through shared mechanisms such as oxidative stress and inflammation. Whether sp...

Machine learning-based screening model for Tic disorders comorbid with attention-deficit/hyperactivity disorder in children and SHAP value interpretation: a retrospective observational study.

Tic disorders (TD) are common neurodevelopmental conditions in children and adolescents, characterized primarily by motor and vocal tics, with heterog...

Explainable ensemble machine learning for predicting diabetes mellitus and identifying key risk factors: a population-based study in northern Bangladesh.

Diabetes mellitus (DM) is an escalating global public health concern, with a rapidly increasing burden in low- and middle-income countries, including ...

Machine-learning prediction of 30-day infection-related hospitalization in advanced CKD.

BACKGROUND: Patients with advanced chronic kidney disease (CKD), defined as having an estimated glomerular filtration rate (eGFR) < 45 ml/min/1.73 m²,...

Predicting depressive symptoms in older adults with hearing impairment development and validation of NHANES-based machine-learning models: A cross-sectional study.

Depressive symptoms frequently co-occur with hearing impairment in older adults, yet available prediction tools for this high-risk subgroup remain lim...

Integration of network pharmacology, deep learning, and molecular biology reveals the efficacy of Citrus aurantium L. var. amara Engl. blossom extract in ameliorating diabetic osteoporosis.

ETHNOPHARMACOLOGICAL RELEVANCE: Diabetic osteoporosis (DOP) is a type of metabolic bone disease. Blossom of Citrus aurantium L. var. amara Engl. (CAVA...

Artificial Intelligence for Sleep Instability and Motor Phenotyping: Clinical Translation Beyond Sleep Staging.

Sleep medicine has rapidly adopted artificial intelligence, but most applications still prioritize automated sleep staging or single summary indices, ...

Integrative multi-omics, machine learning, and experimental validation reveal that TPM1 suppresses M2 macrophage polarization and enhances chemosensitivity in acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) remains a therapeutic challenge due to chemoresistance and immunosuppression. This study aimed to discover no...

Associations of body mass index and metabolic health with stroke risk in a large prospective cohort with time updated covariates.

The global prevalence of overweight and obesity is rising, and recent studies have established an independent contribution of adiposity to stroke risk...

Early-life proteomic and microbiome features signal obesity risk across 26 years of follow-up.

UNLABELLED: Childhood obesity is rising globally. Yet, few studies have examined the microbiome and proteome in early childhood in relation to this ou...

Impact of Unfractionated Heparin Use on Maternal Bone Mineral Density During Pregnancy: A Retrospective Study Using AI-Assisted Radiographic Analysis.

PURPOSE: This study aimed to evaluate the impact of unfractionated heparin (UFH) use during pregnancy on maternal bone mineral density (BMD) by assess...

Machine learning-based prediction of hepatocellular carcinoma risk in steatotic liver disease: A nationwide cohort study.

BACKGROUND AND AIMS: Steatotic liver disease (SLD) has emerged as an important risk factor for hepatocellular carcinoma (HCC), often in the absence of...

Importance ranking and predictive model construction of WMSDs risk factors among shift-working nurses based on multiple machine-learning algorithms.

BACKGROUND AND OBJECTIVE: Nurses are essential for safeguarding public health, and their physical condition directly affects care quality and patient ...