Causal Graph Among Serum Lipids and Glycemic Traits: A Mendelian Randomization Study

Area of Science:

• Genetics and Epidemiology
• Metabolic Syndrome Research
• Cardiovascular Disease Etiology

Background:

• Lipid profiles (HDL-C, LDL-C, TGs) and glycemic markers (FI, HbA1c) are crucial for metabolic health.
• Bidirectional causal relationships between these markers are complex and not fully understood.
• Understanding these pathways is vital for preventing type 2 diabetes and cardiovascular disease.

Purpose of the Study:

• To systematically investigate the bidirectional causality among HDL cholesterol, LDL cholesterol, triglycerides, fasting insulin, and HbA1c.
• To identify causal pathways linking lipid metabolism and glycemic control using large-scale genetic data.
• To inform targeted interventions for metabolic and cardiovascular disease prevention.

Main Methods:

• Multivariable Mendelian randomization (MR) analysis utilizing genome-wide association summary statistics from large European cohorts.
• Application of inverse-variance weighting and other MR methods to assess causal effects.
• Construction of a causal graph to visualize significant causal relationships between traits.

Main Results:

• Identified 13 significant causal effects, highlighting complex interplays between lipids and glycemic traits.
• Triglycerides (TGs) and HDL cholesterol (HDL-C) influence glycemic traits indirectly through fasting insulin (FI).
• Fasting insulin (FI) strongly impacts HDL-C, TGs, and HbA1c; HbA1c affects LDL-C and TGs.

Conclusions:

• Elevated fasting insulin (FI) is a significant causal factor for high TGs and low HDL-C, creating a detrimental cycle.
• Early intervention to manage insulin resistance is critical for mitigating risks of type 2 diabetes, dyslipidemia, and cardiovascular complications.
• These findings underscore the importance of targeting insulin resistance in comprehensive metabolic health strategies.