AIMC Topic: Cholesterol, HDL

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Association between atherogenic index of plasma and hypertension combined with diabetes mellitus in United States adults: an analysis of the NHANES surveys from 2011 to 2016.

Journal of health, population, and nutrition Jul 30, 2025
INTRODUCTION: Observational studies have indicated that individuals with hypertension (HTN) and diabetes mellitus (DM) tend to exhibit elevated plasma atherogenic index of plasma (AIP), defined as log (triglyceride [TG]/high-density lipoprotein chole...
Diabetes Mellitus Atherosclerosis Cholesterol, HDL Triglycerides ROC Curve Humans Nutrition Surveys Female United States Risk Factors Hypertension Aged Adult Cross-Sectional Studies Male Middle Aged
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IDENTIFYING A SEPSIS SUBPHENOTYPE CHARACTERIZED BY DYSREGULATED LIPOPROTEIN METABOLISM USING A SIMPLIFIED CLINICAL DATA ALGORITHM.

Shock (Augusta, Ga.) Apr 23, 2025
Background: Cholesterol metabolism is dysregulated in sepsis contributing to patient heterogeneity. Subphenotypes displaying lower lipoprotein levels and higher mortality (previously subphenotyped hypolipoprotein phenotype [HYPO]) or higher lipoprote...
Humans Male Middle Aged Algorithms Sepsis Phenotype Lipoproteins Prospective Studies Cholesterol, HDL Female Aged
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Machine learning for predicting metabolic-associated fatty liver disease including NHHR: a cross-sectional NHANES study.

PloS one Mar 18, 2025
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...
Non-alcoholic Fatty Liver Disease Cholesterol, HDL Cross-Sectional Studies Male Middle Aged Fatty Liver Nutrition Surveys Humans Machine Learning Female Adult
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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 Mar 15, 2025
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 ...
Machine Learning Biomarkers Female Aged Male Humans Cholesterol, HDL Triglycerides Middle Aged Insulin Resistance Blood Glucose Logistic Models ROC Curve Body Mass Index Diabetes Mellitus, Type 2 Coronary Artery Disease Waist Circumference
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Relationship between atherogenic index of plasma and length of stay in critically ill patients with atherosclerotic cardiovascular disease: a retrospective cohort study and predictive modeling based on machine learning.

Cardiovascular diabetology Feb 28, 2025
BACKGROUND: The atherogenic index of plasma (AIP) is considered an important marker of atherosclerosis and cardiovascular risk. However, its potential role in predicting length of stay (LOS), especially in patients with atherosclerotic cardiovascular...
Male Middle Aged Risk Assessment Length of Stay Time Factors Intensive Care Units Female Risk Factors Aged Predictive Value of Tests Atherosclerosis Cholesterol, HDL Triglycerides Humans Machine Learning Decision Support Techniques Databases, Factual Biomarkers Prognosis Retrospective Studies Critical Illness
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Prediction of insulin resistance using multiple adaptive regression spline in Chinese women.

Endocrine journal Feb 1, 2025
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...
East Asian People Cholesterol, HDL Humans Adult C-Reactive Protein Bilirubin Machine Learning Middle Aged Triglycerides Blood Pressure Female China Waist-Hip Ratio Uric Acid Young Adult Blood Glucose Asian People Insulin Resistance
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A machine learning analysis of predictors of future hypertension in a young population.

Minerva cardiology and angiology May 27, 2024
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.
Logistic Models Hypertension Follow-Up Studies Age Factors Predictive Value of Tests Blood Pressure Cholesterol, HDL Early Diagnosis Risk Factors Triglycerides Humans Young Adult Blood Pressure Monitoring, Ambulatory Machine Learning Adult Female Male Adolescent
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Estimation of low-density lipoprotein cholesterol levels using machine learning.

International journal of cardiology Jan 20, 2022
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...
Humans Machine Learning Algorithms Cholesterol, LDL Cholesterol, HDL Dyslipidemias Triglycerides
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Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.

Lipids in health and disease May 6, 2021
BACKGROUND: Numerous studies have revealed the relationship between lipid expression and increased cardiovascular risk in ST-segment elevation myocardial infarction (STEMI) patients. Nevertheless, few investigations have focused on the risk stratific...
Triglycerides Kaplan-Meier Estimate Apolipoprotein B-100 Lipoprotein(a) Cholesterol, LDL Lipid Metabolism Patient Selection Aged Patient Readmission Male Cholesterol, HDL Middle Aged Principal Component Analysis Risk Assessment Humans ST Elevation Myocardial Infarction Unsupervised Machine Learning Prognosis Apolipoprotein A-I Female Percutaneous Coronary Intervention
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Comparing a novel machine learning method to the Friedewald formula and Martin-Hopkins equation for low-density lipoprotein estimation.

PloS one Sep 30, 2020
BACKGROUND: Low-density lipoprotein cholesterol (LDL-C) is a target for cardiovascular prevention. Contemporary equations for LDL-C estimation have limited accuracy in certain scenarios (high triglycerides [TG], very low LDL-C).
Male Middle Aged Data Interpretation, Statistical Cholesterol, LDL Hyperlipidemias Cholesterol, HDL Machine Learning Humans Triglycerides Female Aged Adult
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