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Alcohol and Cardiovascular Health: The Dose Makes the Poison…or the Remedy
Abstract
Habitual light to moderate alcohol intake (up to 1 drink per day for women and 1 or 2 drinks per day for men) is associated with decreased risks for total mortality, coronary artery disease, diabetes mellitus, congestive heart failure, and stroke. However, higher levels of alcohol consumption are associated with increased cardiovascular risk. Indeed, behind only smoking and obesity, excessive alcohol consumption is the third leading cause of premature death in the United States. Heavy alcohol use (1) is one of the most common causes of reversible hypertension, (2) accounts for about one-third of all cases of nonischemic dilated cardiomyopathy, (3) is a frequent cause of atrial fibrillation, and (4) markedly increases risks of stroke-both ischemic and hemorrhagic. The risk-to-benefit ratio of drinking appears higher in younger individuals, who also have higher rates of excessive or binge drinking and more frequently have adverse consequences of acute intoxication (for example, accidents, violence, and social strife). In fact, among males aged 15 to 59 years, alcohol abuse is the leading risk factor for premature death. Of the various drinking patterns, daily low- to moderate-dose alcohol intake, ideally red wine before or during the evening meal, is associated with the strongest reduction in adverse cardiovascular outcomes. Health care professionals should not recommend alcohol to nondrinkers because of the paucity of randomized outcome data and the potential for problem drinking even among individuals at apparently low risk. The findings in this review were based on a literature search of PubMed for the 15-year period 1997 through 2012 using the search terms alcohol, ethanol, cardiovascular disease, coronary artery disease, heart failure, hypertension, stroke, and mortality. Studies were considered if they were deemed to be of high quality, objective, and methodologically sound.
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“The relationship between alcohol use and cardiovascular disease outcomes is complex, as different dimensions play a role for different outcomes 238, 239, 240.”
From The relationship between different dimensions of alcohol use and the burden of disease—an update (2017) · cites this paper as [239]
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Metadata
- Authors
- O’Keefe, J. H., Bhatti, S. K., Bajwa, A., DiNicolantonio, J. J., & Lavie, C. J.
- Affiliations
- Saint Luke's Hospital (US)
- The University of Queensland (AU)
- Corresponding author
- O’Keefe, James H.
- Venue
- Mayo Clinic Proceedings
- Publisher
- Elsevier BV
- Published
- 2014-03
- Volume
- 89
- Issue
- 3
- Pages
- 382-393
- ISSN
- 0025-6196 (ISSN-L)
- OpenAlex
- W2042273266
- Semantic Scholar
- fb99861a75849bf01ecd526f770d9df083ecff23
- Cited by
- 406
- References
- 96
- Citations by year
- 2026: 20 · 2025: 20 · 2024: 27 · 2023: 41 · 2022: 31 · 2021: 33 · 2020: 43 · 2019: 22 · 2018: 39 · 2017: 35 · 2016: 40 · 2015: 42 · 2014: 13
- FWCI
- 6.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Mayo Clinic Proceedings (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Pathology and Forensic Medicine › Alcohol Consumption and Health Effects
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Health Sciences › Medicine › Pharmacology (medical) › Coffee research and impacts
- Keywords
- alcohol consumption, cardiovascular health, moderate drinking, heavy drinking, binge drinking, coronary artery disease, stroke, hypertension, congestive heart failure, diabetes mellitus, premature mortality, red wine, atrial fibrillation, non-ischemic dilated cardiomyopathy, risk-benefit ratio
- MeSH terms
- Alcohol Drinking, Ethanol, Alcoholic Beverages, Cardiotonic Agents, Cardiovascular Diseases, Dose-Response Relationship, Drug, Humans, Primary Prevention, Risk Factors, United States, Health Behavior, Secondary Prevention
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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