| Title: | Unravelling the complex causal effects of substance use behaviours on common diseases |
| Journal: | Communications Medicine |
| Published: | 12 Mar 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38472333/ |
| DOI: | https://doi.org/10.1038/s43856-024-00473-3 |
| URL: | https://www.nature.com/articles/s43856-024-00473-3.pdf |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 10137
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Abstract
BackgroundSubstance use behaviours (SUB) including smoking, alcohol consumption, and coffee intake are associated with many health outcomes. However, whether the health effects of SUB are causal remains controversial, especially for alcohol consumption and coffee intake.MethodsIn this study, we assess 11 commonly used Mendelian Randomization (MR) methods by simulation and apply them to investigate the causal relationship between 7 SUB traits and health outcomes. We also combine stratified regression, genetic correlation, and MR analyses to investigate the dosage-dependent effects.ResultsWe show that smoking initiation has widespread risk effects on common diseases such as asthma, type 2 diabetes, and peripheral vascular disease. Alcohol consumption shows risk effects specifically on cardiovascular diseases, dyslipidemia, and hypertensive diseases. We find evidence of dosage-dependent effects of coffee and tea intake on common diseases (e.g., cardiovascular disease and osteoarthritis). We observe that the minor allele effect of rs4410790 (the top signal for tea intake level) is negative on heavy tea intake (b̂GWAS=−0.091,s.e.=0.007,P=4.90×10−35)$$({\hat{b}}_{GWAS}=-0.091,s.e.=0.007,P=4.90\times {10}^{-35})$$but positive on moderate tea intake (b̂GWAS=0.034,s.e.=0.006,P=3.40×10−8)$$({\hat{b}}_{GWAS}=0.034,s.e.=0.006,P=3.40\times {10}^{-8})$$, compared to the non-tea-drinkers.ConclusionOur study reveals the complexity of the health effects of SUB and informs design for future studies aiming to dissect the causal relationships between behavioural traits and complex diseases.</p>
7 Authors
- Angli Xue
- Zhihong Zhu
- Huanwei Wang
- Longda Jiang
- Peter M. Visscher
- Jian Zeng
- Jian Yang
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