The Inflamm-Aging Model Identifies Key Risk Factors in Atherosclerosis

Area of Science:

• Cardiovascular Research
• Aging Biology
• Inflammation Studies

Background:

• Atherosclerosis is a major health threat, significantly influenced by abnormal inflammation and oxidative stress during accelerated aging.
• Inflamm-aging, characterized by immune homeostasis imbalance, is increasingly recognized as a key contributor to atherosclerosis development.
• The precise mechanisms linking inflamm-aging to atherosclerosis and effective prediction models remain underexplored.

Purpose of the Study:

• To develop and validate an improved machine learning-based prediction model for inflamm-aging.
• To investigate the causal relationship between inflamm-aging and atherosclerosis.
• To identify critical risk factors and underlying mechanisms driving atherosclerosis in the context of inflamm-aging.

Main Methods:

• Construction of an inflamm-aging prediction model integrating aging, inflammation, and disease markers using machine learning.
• Calculation of inflamm-aging scores for individuals.
• Application of Mendelian randomization, causal analysis, sensitivity analysis, and network analysis to identify causal relationships and risk factors.

Main Results:

• An accelerated inflamm-aging pattern was identified in atherosclerosis.
• A causal relationship between inflamm-aging and atherosclerosis was established.
• Key driving factors for atherosclerosis in inflamm-aging include inflammation, nutritional imbalance, vascular homeostasis disruption, and oxidative stress.

Conclusions:

• A novel computational model integrating crucial risk factors for inflamm-aging and atherosclerosis has been developed.
• The developed pipeline offers a valuable tool for exploring the mechanisms of atherosclerosis and related diseases.
• This research highlights the critical role of inflamm-aging in atherosclerosis pathogenesis and provides a framework for risk prediction and mechanistic understanding.