A systematic review of Mendelian randomization evidence on alcohol-related phenotypes and multiple human diseases.
systematic_review · Level I
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- Also identified by DOI 10.1016/j.drugalcdep.2026.113330.
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Abstract
This systematic review synthesises Mendelian randomization (MR) studies to evaluate genetic evidence for the role of alcohol consumption across multiple health domains. We systematically searched PubMed, Embase, CINAHL, and PsycINFO for MR studies on alcohol phenotypes and health outcomes. Eligible studies used genetic variants as instruments for alcohol phenotypes such as alcohol consumption, alcohol use disorder, or Alcohol Use Disorder Identification Test scores. MR investigations were classified as single-variant or multiple-variant studies. We assessed the robustness of evidence as robust, probable, suggestive, insufficient, or non-evaluable. Due to mainly outcome sample overlap we conducted a qualitative synthesis of the evidence. We identified 88 MR studies covering 630 alcohol-disease investigations and up to 2433,011 participants. Most studies (84.4%) focused on European ancestry populations. Across 14 disease categories, 109 unique outcomes were assessed. We identified 30 robust, 98 probable, and 15 suggestive associations. Robust evidence linked alcohol consumption to increased risks of cardiovascular diseases (e.g., coronary artery disease, stroke, hypertension), several cancers (e.g., breast, colorectal, oropharyngeal, hepatocellular), alcohol-associated liver disease, chronic pancreatitis, Crohn's disease, Parkinson's disease, major depression, and all-cause mortality. An additional 113 probable and suggestive associations extended to other cancers, gastrointestinal, neurological, endocrine, eye, oral, and skin conditions. However, 487 (77%) investigations yielded insufficient or non-evaluable evidence. The reviewed MR evidence suggests associations between genetic liability to alcohol-related phenotypes and a range of disease and mortality outcomes. However, findings should be interpreted cautiously and supported by complementary causal inference approaches to strengthen causal interpretation.