| Title: | Human genetics suggests differing causal pathways from HMGCR inhibition to coronary artery disease and type 2 diabetes |
| Journal: | International Journal of Epidemiology |
| Published: | 2 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41489590/ |
| DOI: | https://doi.org/10.1093/ije/dyaf223 |
Publication 16893
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Abstract
BACKGROUND: Statins lower low-density lipoprotein cholesterol (LDL-C) and reduce the risk of coronary artery disease (CAD). However, they also increase the risk of type 2 diabetes (T2D).</p>
METHODS: We consider genetic variants in the region of the HMGCR gene, which encodes the target of statins, and their associations with downstream consequences of statins. We use various statistical methods to identify causal pathways influencing CAD and T2D, and investigate whether these are the same or different for the two diseases.</p>
RESULTS: Colocalization analyses indicate that LDL-C and body mass index (BMI) have distinct genetic predictors in this gene region, suggesting that they do not lie on the same causal pathway. Multivariable Mendelian randomization analyses restricted to variants in the HMGCR gene region revealed LDL-C and BMI as causal risk factors for CAD, and BMI as a causal risk factor for T2D, but not LDL-C. A Bayesian model averaging method prioritized BMI as the most likely causal risk factor for T2D, and LDL-C as the second most likely causal risk factor for CAD (behind ubiquinone). Colocalization analyses provided consistent evidence of LDL-C colocalizing with CAD, and BMI colocalizing with T2D; evidence was inconsistent for colocalization of LDL-C with T2D, and BMI with CAD.</p>
CONCLUSIONS: Our analyses suggest cardiovascular and metabolic consequences of statin usage are on different causal pathways, and hence could be influenced separately by targeted interventions. More broadly, our analysis workflow offers potential insights to identify pathway-specific causal risk factors that could provide possible repositioning or refinement opportunities for existing drug targets.</p>
13 Keywords
- Bayes Theorem
- Body Mass Index
- Cholesterol, LDL
- Coronary Artery Disease
- Diabetes Mellitus, Type 2
- Female
- Humans
- Hydroxymethylglutaryl CoA Reductases
- Hydroxymethylglutaryl-CoA Reductase Inhibitors
- Male
- Mendelian Randomization Analysis
- Polymorphism, Single Nucleotide
- Risk Factors
7 Authors
- Seongwon Hwang
- Ville Karhunen
- Ashish Patel
- Sam M Lockhart
- Paul Carter
- John C Whittaker
- Stephen Burgess
1 Application
| Application ID | Title |
|---|---|
| 98032 | Development, benchmarking, and application of biostatistical methods for population health |
Enabling scientific discoveries that improve human health