AIMC Topic: Metabolic Syndrome

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Deep-learning analysis of greenspace and metabolic syndrome: A street-view and remote-sensing approach.

Environmental research
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...

Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning.

Artificial cells, nanomedicine, and biotechnology
Ageing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets an...

Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.

Scientific reports
Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but...

Nonlinear relationship between serum Klotho and chronic kidney disease in US adults with metabolic syndrome.

Frontiers in endocrinology
BACKGROUND: Current evidence regarding the effects of serum Klotho among patients with metabolic syndrome (MetS) is scarce. This study explored the relationship between serum Klotho levels and the odds of chronic kidney disease (CKD) in middle-aged a...

Machine learning algorithms that predict the risk of prostate cancer based on metabolic syndrome and sociodemographic characteristics: a prospective cohort study.

BMC public health
BACKGROUND: Given the rapid increase in the prevalence of prostate cancer (PCa), identifying its risk factors and developing suitable risk prediction models has important implications for public health. We used machine learning (ML) approach to scree...

Metabolic syndrome predictive modelling in Bangladesh applying machine learning approach.

PloS one
Metabolic syndrome (MetS) is a cluster of interconnected metabolic risk factors, including abdominal obesity, high blood pressure, and elevated fasting blood glucose levels, that result in an increased risk of heart disease and stroke. In this resear...

Prediction of metabolic syndrome using machine learning approaches based on genetic and nutritional factors: a 14-year prospective-based cohort study.

BMC medical genomics
INTRODUCTION: Metabolic syndrome is a chronic disease associated with multiple comorbidities. Over the last few years, machine learning techniques have been used to predict metabolic syndrome. However, studies incorporating demographic, clinical, lab...

A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data.

Scientific reports
Metabolic syndrome (MetS) is a complex disorder characterized by a cluster of metabolic abnormalities, including abdominal obesity, hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, and impaired glucose tolerance. It...