A systematic review of Mendelian randomization evidence on alcohol-related phenotypes and multiple human diseases.
Nigussie Assefa Kassaw, David Stacey, Anwar Mulugeta et al.
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In 30 seconds
This systematic review synthesised Mendelian randomization studies using genetic variants as instruments for alcohol consumption, alcohol use disorder, or related measures to assess associations with health outcomes. It included 88 studies covering 630 alcohol–disease investigations and up to 2433,011 participants; most studies focused on European ancestry populations. The review identified 30 robust, 98 probable, and 15 suggestive associations. Robust evidence linked alcohol-related phenotypes with increased risks across cardiovascular and several cancer outcomes, as well as liver disease, chronic pancreatitis, Crohn’s disease, Parkinson’s disease, major depression, and all-cause mortality.
Key findings
- Across 630 investigations, 30 associations were classified as robust, 98 as probable, and 15 as suggestive.
- Robust evidence linked alcohol consumption to increased risks of cardiovascular diseases, including coronary artery disease, stroke, and hypertension.
- Robust associations also included several cancers, alcohol-associated liver disease, chronic pancreatitis, Crohn’s disease, Parkinson’s disease, major depression, and all-cause mortality.
- A total of 487 (77%) investigations had insufficient or non-evaluable evidence.
Why it matters
The review brings together genetic-instrument evidence across a broad range of outcomes, indicating that alcohol-related phenotypes may be associated with risks beyond commonly recognised cardiovascular and liver conditions. The large proportion of investigations with insufficient or non-evaluable evidence, and the authors’ caution about causal interpretation, underscore the need to distinguish stronger signals from uncertain findings.
What to keep in mind
Most studies focused on European ancestry populations. Outcome sample overlap led the authors to use qualitative synthesis, and they note that complementary causal inference approaches are needed to strengthen causal interpretation.