AIMC Topic: Metabolic Syndrome

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Prediction of depression risk in middle-aged and elderly Cardiovascular-Kidney-Metabolic syndrome patients by social and environmental determinants of health: an interpretable machine learning approach using longitudinal data from China.

Journal of health, population, and nutrition Jun 4, 2025
BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic disease characterized by pathophysiological interactions between the cardiovascular system, chronic kidney disease, and metabolic risk factors. In China, the prevalence of CKM i...
Metabolic Syndrome Social Determinants of Health China Depression Renal Insufficiency, Chronic Longitudinal Studies Humans Machine Learning Female Risk Factors Cardiovascular Diseases Middle Aged Risk Assessment Aged Male
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Effect of visceral fat on onset of metabolic syndrome.

Scientific reports May 30, 2025
This study analysed the effects of visceral fat on metabolic syndrome (MetS) and developed an algorithm to predict its onset using health examination data from the Iwaki Health Promotion Project in Japan. The dataset included 213 cases of MetS onset ...
Aged Metabolic Syndrome Humans Machine Learning Adult Japan Algorithms Male Intra-Abdominal Fat Female Middle Aged
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Identification of diagnostic genes for myocardial ischemia reperfusion injury associated with metabolic syndrome through the integration of bioinformatics analysis, molecular docking and experimental validation.

Frontiers in immunology May 29, 2025
BACKGROUND: Metabolic dysregulation in metabolic syndrome (MetS) exacerbates myocardial ischemia-reperfusion injury (MIRI). This study aimed to identify diagnostic biomarkers and therapeutic candidates for MetS-associated MIRI.
Biomarkers Animals Molecular Docking Simulation Gene Expression Profiling Humans Metabolic Syndrome Cell Line Myocardial Reperfusion Injury Dexamethasone Gene Expression Regulation Protein Interaction Maps Gene Regulatory Networks Computational Biology Rats
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Development and validation of a predictive model for depression in patients with advanced stage of cardiovascular-kidney-metabolic syndrome.

Journal of affective disorders Apr 23, 2025
Depression is highly prevalent among patients with chronic disease advanced and with poor clinical outcomes. However, effective tools for identifying individuals at risk remain limited. This study aimed to develop and validate a predictive model for ...
Humans Female Logistic Models Nutrition Surveys Metabolic Syndrome Predictive Learning Models Cardio-Renal Syndrome Aged Male Middle Aged Depression
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Assessing the association of multi-environmental chemical exposures on metabolic syndrome: A machine learning approach.

Environment international Apr 17, 2025
Metabolic syndrome (MetS) is a major global public health concern due to its rising prevalence and association with increased risks of cardiovascular disease and type 2 diabetes. Emerging evidence suggests that environmental chemical exposures may pl...
Environmental Pollutants Environmental Exposure Fluorocarbons Republic of Korea Female Humans Aged Machine Learning Adult Male Middle Aged Risk Assessment Metabolic Syndrome
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Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0-3 and the development of a machine learning prediction model: a nationwide prospective cohort study.

Cardiovascular diabetology Apr 16, 2025
BACKGROUND: The American Heart Association recently introduced the concept of cardiovascular-kidney-metabolic (CKM) syndrome, highlighting the increasing importance of the complex interplay between metabolic, renal, and cardiovascular diseases (CVD)....
Time Factors Insulin Resistance Longitudinal Studies Metabolic Syndrome Kidney Diseases Predictive Value of Tests Biomarkers Risk Assessment Humans Prognosis China Aged Machine Learning Female Blood Glucose Male Decision Support Techniques Risk Factors Incidence Cardiovascular Diseases Prospective Studies Middle Aged
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Development and validation of a machine learning model for predicting pediatric metabolic syndrome using anthropometric and bioelectrical impedance parameters.

International journal of obesity (2005) Apr 1, 2025
OBJECTIVE: Metabolic syndrome (MS) is a risk factor for cardiovascular diseases, and its prevalence is increasing among children and adolescents. This study developed a machine learning model to predict MS using anthropometric and bioelectrical imped...
Anthropometry Electric Impedance Adolescent Child Nutrition Surveys Humans Metabolic Syndrome Machine Learning Republic of Korea Female Male
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Gender Differences in Predicting Metabolic Syndrome Among Hospital Employees Using Machine Learning Models: A Population-Based Study.

The journal of nursing research : JNR Apr 1, 2025
BACKGROUND: Metabolic syndrome (MetS) is a complex condition that captures several markers of dysregulation, including obesity, elevated blood glucose levels, dyslipidemia and hypertension. Using an approach to early prediction of MetS risk in hospit...
Humans Machine Learning Middle Aged Metabolic Syndrome Female Sex Factors Aged Personnel, Hospital Adult Male
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Deep-learning analysis of greenspace and metabolic syndrome: A street-view and remote-sensing approach.

Environmental research Mar 7, 2025
Evidence linking greenspace exposure to metabolic syndrome (MetS) remains sparse and inconsistent. This exploratory study evaluate the relationship between green visibility index (GVI) and normalized difference vegetation index (NDVI) with MetS preva...
Deep Learning China Metabolic Syndrome Remote Sensing Technology Adult Male Middle Aged Prevalence Humans Female Aged
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Role of oxidative balance score in staging and mortality risk of cardiovascular-kidney-metabolic syndrome: Insights from traditional and machine learning approaches.

Redox biology Mar 7, 2025
OBJECTIVES: To evaluate the roles of oxidative balance score (OBS) in staging and mortality risk of cardiovascular-kidney-metabolic syndrome (CKM).
Cardiovascular Diseases Middle Aged Oxidative Stress Aged Nutrition Surveys Humans Metabolic Syndrome Adult Machine Learning Cross-Sectional Studies Female Male Risk Factors
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