Application of Polyacrylamide Gel Electrophoresis in Analyzing Lipoprotein Subfractions Relevant to Atherosclerotic Cardiovascular Disease

Low-density lipoprotein cholesterol (LDL-C) is a key factor in the development of atherosclerotic cardiovascular disease (ASCVD). Elevated levels of LDL-C are closely linked to the formation of atherosclerotic plaques and the risk of cardiovascular events. However, the heterogeneity of LDL significantly influences its ability to promote atherosclerosis. Research indicates that Low-density lipoprotein (LDL) particles can be further categorized into small dense LDL (sdLDL) and large buoyant LDL (lbLDL). Among these, sdLDL is more likely to penetrate vascular endothelium and has stronger oxidative modification activity, which is particularly associated with ASCVD. In patients with metabolic syndrome, diabetes, and hypertriglyceridemia, LDL-C levels may be normal, but the proportion of sdLDL is elevated, leading to an underestimation of cardiovascular risk. Therefore, accurately detecting LDL subfractions is crucial for stratifying ASCVD risk and optimizing intervention strategies. Traditional LDL-C detection methods, such as the Friedewald formula and direct measurement, only reflect total cholesterol levels and cannot differentiate between subfractions. Methods for detecting LDL-C subfractions include ultracentrifugation (UC), high-performance liquid chromatography (HPLC), and nuclear magnetic resonance (NMR) techniques. However, these methods have high technical requirements, complex operations, or expensive equipment, making them impractical for widespread clinical use. Polyacrylamide gel electrophoresis (PAGE) technology separates lipoprotein particles through the molecular sieve effect and charge differences. This technique uses non-denaturing gradient gel systems, combined with specific stains like Sudan black, offering high-resolution, strong reproducibility, and ease of operation. Lipoprotein typing using PAGE technology can provide a more comprehensive and precise assessment of patients' lipoprotein levels, aiding in clinical treatment decisions. This article has presented five representative results. The mean LDL-particle size of sample A, B, C, D, E are 275.3 Å, 266.6 Å, 258.2 Å , 257.4 Å, and 252.9 Å, indicating a low, marginal, mild, moderate, and severe risk of cardiovascular disease due to dyslipidemia, respectively.

Authors

Institutions

Publication Details

Journal
Journal of Visualized Experiments
Published
2025-10-24
DOI
https://doi.org/10.3791/68996
Primary Topic
Cancer, Lipids, and Metabolism
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Application of Polyacrylamide Gel Electrophoresis in Analyzing Lipoprotein Subfractions Relevant to Atherosclerotic Cardiovascular Disease

Yan‐an Yao, Huaquan Gan, Yun-xiong Li, Yingying Huang et al.
Journal of Visualized Experiments
Cancer, Lipids, and Metabolism
article

Application of Polyacrylamide Gel Electrophoresis in Analyzing Lipoprotein Subfractions Relevant to Atherosclerotic Cardiovascular Disease

Yan‐an Yao, Huaquan Gan, Yun-xiong Li, Yingying Huang, Yizhao Liao, Xuebing Liang
article en

Abstract

Low-density lipoprotein cholesterol (LDL-C) is a key factor in the development of atherosclerotic cardiovascular disease (ASCVD). Elevated levels of LDL-C are closely linked to the formation of atherosclerotic plaques and the risk of cardiovascular events. However, the heterogeneity of LDL significantly influences its ability to promote atherosclerosis. Research indicates that Low-density lipoprotein (LDL) particles can be further categorized into small dense LDL (sdLDL) and large buoyant LDL (lbLDL). Among these, sdLDL is more likely to penetrate vascular endothelium and has stronger oxidative modification activity, which is particularly associated with ASCVD. In patients with metabolic syndrome, diabetes, and hypertriglyceridemia, LDL-C levels may be normal, but the proportion of sdLDL is elevated, leading to an underestimation of cardiovascular risk. Therefore, accurately detecting LDL subfractions is crucial for stratifying ASCVD risk and optimizing intervention strategies. Traditional LDL-C detection methods, such as the Friedewald formula and direct measurement, only reflect total cholesterol levels and cannot differentiate between subfractions. Methods for detecting LDL-C subfractions include ultracentrifugation (UC), high-performance liquid chromatography (HPLC), and nuclear magnetic resonance (NMR) techniques. However, these methods have high technical requirements, complex operations, or expensive equipment, making them impractical for widespread clinical use. Polyacrylamide gel electrophoresis (PAGE) technology separates lipoprotein particles through the molecular sieve effect and charge differences. This technique uses non-denaturing gradient gel systems, combined with specific stains like Sudan black, offering high-resolution, strong reproducibility, and ease of operation. Lipoprotein typing using PAGE technology can provide a more comprehensive and precise assessment of patients' lipoprotein levels, aiding in clinical treatment decisions. This article has presented five representative results. The mean LDL-particle size of sample A, B, C, D, E are 275.3 Å, 266.6 Å, 258.2 Å , 257.4 Å, and 252.9 Å, indicating a low, marginal, mild, moderate, and severe risk of cardiovascular disease due to dyslipidemia, respectively.

Journal of Visualized Experiments(224)
Guangdong Academy of Medical Sciences (CN), Guangdong Provincial People's Hospital (CN), Southern Medical University (CN)
Openalex Percentile: Top 62%
Cancer, Lipids, and Metabolism
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.