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Associations of Circulating Biomarkers with Disease Risks: a Two-Sample Mendelian Randomization Study
Abdulkadir Elmas1, Kevin Spehar2,3,4,5, Ron Do1
1Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Medrxiv : the Preprint Server for Health Sciences
|July 15, 2024
Summary
Mendelian Randomization identified novel causal links between glucose and cystatin C with bipolar disorder. This study also confirmed known biomarker-disease relationships and highlighted others for cardiovascular conditions.
Area of Science:
- Genetics and Genomics
- Biomarkers and Diagnostics
- Personalized Medicine
Background:
- Circulating biomarkers are crucial for personalized medicine, aiding in disease screening, prevention, and treatment.
- Establishing causal relationships between biomarkers and diseases is complex.
- Mendelian Randomization (MR) utilizes genetic instruments to infer causality, with multiple methods enhancing reliability.
Purpose of the Study:
- To investigate causal relationships between 212 circulating biomarkers and 99 complex diseases using multiple MR methods.
- To identify novel and confirm known biomarker-disease associations.
Main Methods:
- Employed multiple Mendelian Randomization (MR) techniques: inverse variance weighted, simple mode, weighted mode, weighted median, and MR Egger.
- Utilized the MR-base resource, analyzing data from UK Biobank and other large-scale genetic studies.
- Assessed associations between 212 biomarkers and 99 complex diseases.
Main Results:
- Identified novel causal links between glucose and bipolar disorder (Mean Effect Size: 0.39) and cystatin C and bipolar disorder (Mean Effect Size: -0.31) supported by at least four MR methods.
- Confirmed known associations, including urate with gout and creatine with chronic kidney disease.
- Found potential causal links between several lipid biomarkers (apolipoprotein B, cholesterol, LDL, lipoprotein A, triglycerides) and cardiovascular conditions like coronary heart disease and myocardial infarction.
Conclusions:
- Corroborated existing causal relationships between biomarkers and diseases.
- Uncovered two novel potential causal biomarkers for bipolar disorder, warranting further research.
- Provided insights into disease etiology and potential for improved precision diagnostics and interventions.
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