Small dense LDL: An underestimated driver of atherosclerosis (Review)

Makhabbat Bekbossynova1, Timur Saliev2, Tatyana Ivanova-Razumova1

  • 1Heart Center, University Medical Center, Nazarbayev University, Astana 010000, Kazakhstan.

Molecular Medicine Reports
|September 26, 2025
PubMed

Insights

Small, dense low-density lipoprotein (sdLDL) particles significantly contribute to cardiovascular disease risk, especially in metabolic disorders. Monitoring sdLDL is crucial for better cardiovascular risk assessment and management.

Area of Science:

  • Cardiology
  • Lipidology
  • Preventive Medicine

Background:

  • Cardiovascular disease is a leading global cause of death.
  • Small, dense low-density lipoprotein (sdLDL) particles are highly atherogenic, contributing to residual cardiovascular risk.
  • Elevated sdLDL is common in metabolic disorders like insulin resistance, type 2 diabetes, and obesity.

Purpose of the Study:

  • To review the pathophysiological mechanisms rendering sdLDL atherogenic.
  • To explore sdLDL's role in endothelial dysfunction, inflammation, and plaque instability.
  • To discuss diagnostic challenges, clinical relevance, and limitations of current lipid panels for sdLDL.

Main Methods:

  • Critical examination of pathophysiological characteristics of sdLDL.
  • Review of epidemiological studies linking sdLDL to cardiovascular events.
  • Analysis of lifestyle and pharmacological strategies for sdLDL reduction.

Main Results:

  • sdLDL exhibits increased permeability, prolonged circulation, and heightened oxidative susceptibility.
  • sdLDL promotes endothelial dysfunction, foam cell formation, inflammation, and plaque instability.
  • Elevated sdLDL is an independent predictor of cardiovascular events, particularly in high-risk populations.

Conclusions:

  • sdLDL plays a significant role in atherogenesis and residual cardiovascular risk.
  • Current lipid panels often fail to capture sdLDL's contribution to disease.
  • Enhanced sdLDL testing and advanced diagnostics, including AI, are recommended for improved risk assessment and management.

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