Obesity

Exploration of an interpretable machine learning-based screening manner for low muscle mass among Chinese community-dwelling older adults using routine physical examination information.

BACKGROUND: With the accelerating aging of the global population, muscle health issue occurs commonly as an age-related process in older people. The conventional low muscle mass screening and diagnosis reliant on bulky and costly instruments, remain challenging for regular self-monitoring. If routine physical examination information from primary healthcare settings is integrated and analyzed using...

Abnormal Lipid Signaling Characterizes Diastolic Dysfunction in Pediatric Cardiomyopathy.

Pediatric cardiomyopathy is a major cause of diastolic heart failure; yet, its mechanisms remain unclear. Global myocardial transcriptomic and blood lipidomic profiling revealed a distinct metabolic signature of diastolic dysfunction marked by dysregulated lipid signaling. A machine learning model using these gene markers accurately classified diastolic dysfunction across cardiomyopathy subtypes. ...

Gut Microbiome as a Lifestyle Risk Factor Associated with Prostate Cancer.

BACKGROUND AND OBJECTIVE: Most prostate cancer prevention strategies suggest lifestyle modifications, which lack personalization. Gut microbiome is in...

Deep learning of wrist accelerometry from UK Biobank data identifies early movement signatures of knee osteoarthritis up to 5 years before diagnosis.

PURPOSE: This study aims to test whether week-long wrist accelerometry combined with deep learning can (i) distinguish healthy individuals from people...

An online machine learning model for predicting medication adherence in hypertensive patients: data from the China health and retirement longitudinal study (CHARLS).

INTRODUCTION: Hypertension, a leading global cause of death with high prevalence and poor control, faces a critical issue of poor medication adherence...

Enhancing health monitoring in smart offices: a multi-layered digital twin approach.

Sedentary nature of office work contributes to a range of physical health issues, including obesity, which can result from prolonged inactivity, and c...

Assessment and AI-Based Prediction of Complications in Reduction Mammoplasty: A Combined Statistical and Fuzzy Inference Approach in a 10-Year Cohort from Tehran Hospitals (2013-2023).

INTRODUCTION: Gigantomastia causes physical, psychological, and dermatological issues, often with ptosis; reduction mammoplasty effectively relieves s...

A machine learning model for optimizing treatment of patients with poorly controlled type 2 diabetes.

BACKGROUND: Cardiorenal-protective sodium-glucose cotransporter-2 inhibitors (SGLT-2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) lack se...

Application of machine learning approaches to develop predictive models for diabetes and hypertension among Bangladeshi Adults.

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

Vascular Age: A narrative review of assessment methods, clinical applications, and future directions.

Cardiovascular disease (CVD) is the leading cause of death and disability globally, highlighting the importance of effective risk assessment and early...

Epidemiological Trajectories and Quality of Care Disparities of MASLD in Asia, 1990-2023.

BACKGROUND: To characterize temporal and geographic patterns of metabolic dysfunction-associated steatotic liver disease (MASLD) across Asia from 1990...

Machine learning-predicted insulin resistance is a risk factor for 12 types of cancer.

Insulin resistance is suggested to be a risk factor for cancer; however, large-scale epidemiological evidence linking insulin resistance to cancer rem...

Estimating the odds ratio from the output scores of machine learning models: possibilities and limitations.

Estimation of exposure-response association is central to epidemiologic research. Although the advantages of machine learning (ML) techniques for mode...

Risk prediction for chronic atrophic gastritis using a random forest model: A multicenter study.

BACKGROUND: Chronic atrophic gastritis (CAG) is a significant precancerous condition of gastric cancer (GC). CAG often lacks typical symptoms in its e...

Evolving strategies in prostate cancer: Emerging approaches and unmet needs from the Bridging the Gaps in Prostate Cancer expert panel.

BACKGROUND: The expansion of treatment options for prostate cancer (PC) has improved disease-specific and overall survival outcomes but has also raise...

MRI-based Radiomics and volumetrics for predicting the onset of Alzheimer's Disease with explainable machine learning.

The detection of Alzheimer's Disease (AD) using structural Magnetic Resonance Imaging (MRI) and Machine Learning (ML) often focuses on late-stage atro...

Feasibility of BMI-based sub-milliSievert low-dose CT in individualized detection of lung nodules.

OBJECTIVES: To evaluate the performance of a body mass index (BMI)-based sub-milliSievert low-dose CT (LDCT) protocol with multiple reconstruction alg...

Disease-specific crosstalk of Alistipes with lipoprotein profiles in overweight individuals at high cardiometabolic risk.

Metabolic syndrome (MS) and systemic lupus erythematosus (SLE) represent two pathophysiologically distinct chronic conditions associated with elevated...

Toward practical screening of mortality risk: Insights from interpretable machine learning in NHANES.

BACKGROUND: Efficient community-based screening for individuals at high risk of mortality is a major public health challenge. While many predictors ha...

Predicting health-related quality of life two years post-diagnosis across seven cancer types: using machine learning to identify vulnerable patients.

PURPOSE: Cancer survivors often experience long-term consequences affecting their Health-Related Quality of Life (HRQoL). Sociodemographic factors, cl...