AIMC Topic: Metabolic Syndrome

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Multi-task deep learning for predicting metabolic syndrome from retinal fundus images in a Japanese health checkup dataset.

PloS one
BACKGROUND: Retinal fundus images provide a noninvasive window into systemic health, offering opportunities for early detection of metabolic disorders such as metabolic syndrome (METS).

Metabolomics and nutrient intake reveal metabolite-nutrient interactions in metabolic syndrome: insights from the Korean Genome and Epidemiology Study.

Nutrition journal
BACKGROUND: Despite advances in metabolomics, the complex relationship between metabolites and nutrient intake in metabolic syndrome (MetS) remains poorly understood in the Korean population.

Incorporation of Metabolic Dysfunction-Associated Steatotic Liver Disease in the Health Stage of Cardiovascular-Kidney-Metabolic Syndrome Improves Predictive Ability for Coronary Artery Disease in a Japanese General Population.

Journal of the American Heart Association
BACKGROUND: Cardiovascular-kidney-metabolic (CKM) syndrome is a recently proposed condition encompassing metabolic dysfunction, chronic kidney disease, and cardiovascular diseases including coronary artery disease (CAD). Although concomitant metaboli...

NAFLD progression in metabolic syndrome: a Raman spectroscopy and machine learning approach in an animal model.

The Analyst
Nonalcoholic fatty liver disease (NAFLD) is emerging as the leading cause of chronic liver disease in many regions, particularly in association with the rising prevalence of Metabolic syndrome (MetS), affecting more than 30% of the population worldwi...

METS-VF as a novel predictor of gallstones in U.S. adults: a cross-sectional analysis (NHANES 2017-2020).

BMC gastroenterology
BACKGROUND AND AIMS: Obesity is a well-established risk factor for gallstone formation, but traditional anthropometric measures (e.g., BMI, waist circumference) inadequately assess metabolically active visceral adiposity. The novel Metabolic Score fo...

Detection and Analysis of Circadian Biomarkers for Metabolic Syndrome Using Wearable Data: Cross-Sectional Study.

JMIR medical informatics
BACKGROUND: Wearable devices are increasingly used for monitoring health and detecting digital biomarkers related to chronic diseases such as metabolic syndrome (MetS). Although circadian rhythm disturbances are known to contribute to MetS, few studi...

A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein.

Scientific reports
Metabolic Syndrome (MetS) comprises a clustering of conditions that significantly increase the risk of heart disease, stroke, and diabetes. Timely detection and intervention are crucial in preventing severe health outcomes. In this study, we implemen...

The utility of an artificial intelligence model based on decision tree and evolution algorithm to evaluate steatotic liver disease in a primary care setting.

Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas
Many ways of classifying steatotic liver disease (SLD) with metabolic conditions have been proposed. Thus, SLD-related variables were verified using a decision tree. We tested if the suggested components of the actual classification (metabolic dysfun...

Integrated muti-omics data and machine learning reveal CD151 as a key biomarker inducing chemoresistance in metabolic syndrome-related early-onset left-sided colorectal cancer.

Functional & integrative genomics
Emerging evidence has suggested a potential pathological association between early-onset left-sided colorectal cancer (EOLCC) and metabolic syndrome (MetS). However, the underlying genetic and molecular mechanisms remain insufficiently elucidated. Th...