Modified risk associations of lipoproteins and apolipoproteins by chronic low-grade inflammation

Altan Onat1, Aysem Kaya2, Evin Ademoglu3

  • 1a Department of Cardiology, Cerrahpasa Medical Faculty , Istanbul University , Istanbul , Turkey.

Insights

Cardiovascular disease risk from lipoproteins like LDL-C and HDL-C is altered by chronic inflammation and oxidative stress. This review examines the complex, non-linear relationship of lipoprotein(a) [Lp(a)] in these conditions.

Area of Science:

  • Cardiovascular Medicine
  • Metabolic Disorders
  • Inflammation Research

Background:

  • Lipoproteins and apolipoproteins are key factors in cardiovascular diseases (CVD), metabolic, renal, and inflammatory disorders.
  • Traditional associations of LDL-C, apoB (risk), and HDL-C, apoA-I (protection) with CVD are being re-evaluated.
  • Factors like age, adiposity, ethnicity, and impaired glucose intolerance can trigger autoimmune activation in a pro-inflammatory state, disrupting linear lipoprotein-CVD risk associations.

Purpose of the Study:

  • To review modified risk associations of lipoproteins and apolipoproteins in chronic low-grade inflammation.
  • To emphasize the non-linear relationship of lipoprotein(a) [Lp(a)] as a significant cardiometabolic risk biomarker.

Main Methods:

  • Literature review summarizing current research on lipoprotein-CVD risk.
  • Focus on studies investigating the impact of inflammation and oxidative stress.
  • Analysis of the role of lipoprotein(a) [Lp(a)] in complex risk environments.

Main Results:

  • Chronic systemic inflammation and oxidative stress modify the established risk associations of lipoproteins and apolipoproteins.
  • The relationship between lipoprotein(a) [Lp(a)] and cardiometabolic risk is non-linear and warrants further investigation.
  • Autoimmune activation in inflammatory states may interfere with immunoassay accuracy.

Conclusions:

  • Inflammation and oxidative stress disrupt linear lipoprotein-CVD risk associations.
  • Further research on susceptible populations and methodological improvements in immunoassays are crucial.
  • Understanding these complex interactions is vital for advancing CVD risk prediction and management.
Abstract

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