AI Medical Compendium Topic

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Cholesterol, HDL

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Leisure time physical activity is associated with improved HDL functionality in high cardiovascular risk individuals: a cohort study.

European journal of preventive cardiology
AIMS: Physical activity has consistently been shown to improve cardiovascular health and high-density lipoprotein-cholesterol levels. However, only small and heterogeneous studies have investigated the effect of exercise on high-density lipoprotein f...

Prediction of insulin resistance using multiple adaptive regression spline in Chinese women.

Endocrine journal
Insulin resistance (IR) is the core for type 2 diabetes and metabolic syndrome. The homeostasis assessment model is a straightforward and practical tool for quantifying insulin resistance (HOMA-IR). Multiple adaptive regression spline (MARS) is a mac...

Estimation of low-density lipoprotein cholesterol levels using machine learning.

International journal of cardiology
BACKGROUND: Low-density lipoprotein-cholesterol (LDL-C) is used as a threshold and target for treating dyslipidemia. Although the Friedewald equation is widely used to estimate LDL-C, it has been known to be inaccurate in the case of high triglycerid...

Estimation of Low-Density Lipoprotein Cholesterol Concentration Using Machine Learning.

Laboratory medicine
OBJECTIVE: Low-density lipoprotein cholesterol (LDL-C) can be estimated using the Friedewald and Martin-Hopkins formulas. We developed LDL-C prediction models using multiple machine learning methods and investigated the validity of the new models alo...

Mechanism of liver X receptor α and ATP binding cassette transporter A1 involved in preeclampsia using an optimized deep learning model.

European review for medical and pharmacological sciences
OBJECTIVE: Preeclampsia (PE) is a complex disease-causing multisystem damage. Many genes, environmental factors, and their interactions are involved in the development and progression of PE. The pathogenesis of PE is not fully understood, limiting th...

A machine learning analysis of predictors of future hypertension in a young population.

Minerva cardiology and angiology
BACKGROUND: Early diagnosis of hypertension (HT) is crucial for preventing end-organ damage. This study aims to identify the risk factors for future HT in young individuals through the application of machine learning (ML) models.

Biomarker signatures associated with ageing free of major chronic diseases: results from a population-based sample of the EPIC-Potsdam cohort.

Age and ageing
BACKGROUND: A number of biomarkers denoting various pathophysiological pathways have been implicated in the aetiology and risk of age-related diseases. Hence, the combined impact of multiple biomarkers in relation to ageing free of major chronic dise...

Machine learning for predicting metabolic-associated fatty liver disease including NHHR: a cross-sectional NHANES study.

PloS one
OBJECTIVE: Metabolic - associated fatty liver disease (MAFLD) is a common hepatic disorder with increasing prevalence, and early detection remains inadequately achieved. This study aims to explore the relationship between the non-high-density lipopro...

Surrogate markers of insulin resistance and coronary artery disease in type 2 diabetes: U-shaped TyG association and insights from machine learning integration.

Lipids in health and disease
BACKGROUND: Surrogate insulin resistance (IR) indices are simpler and more practical alternatives to insulin-based IR indicators for clinical use. This study explored the association between surrogate IR indices, including triglyceride-glucose index ...