Smoking causes atrial fibrillation? Further evidence on a debated issue
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common sustained arrhythmia with a prevalence of 1–2% in the general population and 15% in people above 80 years of age.1,2 In 2010, AF was estimated to affect 8.8 m and 6 m adults older than 55 years in Europe and USA, respectively, and its prevalence is expected to double by 2060 leading to an epidemic of AF worldwide. These rates are likely markedly underestimated because AF can be ‘silent’, i.e. totally asymptomatic, and therefore underdiagnosed.3 Patients with AF have a five-fold risk of cardioembolic stroke and a two-fold risk of heart failure.3 Moreover, AF doubles the risk of death independently of other known predictors of mortality and, by causing silent and recurring strokes, favours dementia and disability, worsening quality of life.1 Hence, due to the heavy burden imposed by AF on healthcare systems and communities, the identification of all of the factors that may cause AF is crucial. Conditions that were first identified as independent risk factors for AF were coronary heart disease, dysthyroidism, valvular heart diseases, diabetes mellitus and hypertension.3 Other conditions have also been recognised as risk factors and, in recent years, the list of the so-called emerging factors has become very long, including inflammation and chronic kidney disease (Table 1).4–6 However, while solid evidence supports a causative role of obesity and obstructive sleep apnoea, data are limited or inconsistent for factors such as inflammation. Risk factors or conditions associated with atrial fibrillation (AF) (modified from Wyse et al.).4 Risk factors or conditions associated with atrial fibrillation (AF) (modified from Wyse et al.).4 Whether smoking status is an independent risk factor for AF remains an open question. The Framingham Heart Study was the first large study that showed an association between cigarette smoking and AF.7 After age-adjustment, the only predictors of AF were high blood pressure, left ventricular hypertrophy, diabetes mellitus and cigarette smoking. However, increased odds of developing AF were found in women, but not in men.2 Data from the Framingham Heart Study were supported by the Rotterdam study, which reported 51% and 49% increased risk of incident AF among current and former smokers, respectively,8 and from the Manitoba Follow-Up Study, which showed a 37% increased risk of AF among ever smokers, compared with never smokers, with no gender difference.8 The association between smoking and AF was not confirmed by other studies, such as the large Danish Diet, Cancer, and Health Study9 and the Multifactor Primary Prevention Study.10 The relationship between smoking and other cardiovascular risk factors is very complex, making it difficult to dissect the impact of smoking on AF. Smokers tend to have a less healthy lifestyle and less physical activity than non-smokers, and often have more abdominal obesity, atherosclerosis and hypertension. Moreover, cigarette smoking increases the risk of diabetes,11 chronic obstructive pulmonary disease,12 coronary heart disease and heart failure,13 which are risk factors for AF, adding further complexity to the smoking-AF relationship. The large meta-analysis performed by Aune et al. adds another important piece of evidence, which sheds light on this debated issue.14 By analysing 30 prospective studies they found that current, former and ever smokers were at 32%, 9% and 21% increased risk of AF compared to never smokers, with a dose-dependent association between number of cigarettes smoked and/or pack-years and increased AF risk. The meta-analysis included only prospective cohort studies and nested case-control studies within cohorts that investigated the association between smoking and the risk of AF, thereby avoiding bias of retrospective case-control studies. The sensitivity analyses and the large sample size are further strengths of Aune and coworkers’ study that confer reliability to the results. Among the included studies, the Danish Diet, Cancer, and Health Study9 was the only study that showed no impact of smoking on AF. In this study, exclusion criteria were stringent, with exclusion of the patients hospitalised for endocrine diseases or cardiovascular diseases.9 Stringency of exclusion criteria possibly created a healthier cohort, in which the impact of smoking on AF could be masked. However, when the investigators of the Danish Diet, Cancer, and Health Study9 considered smoking habits in 3161 patients with incident AF, smoking was found to be associated with a higher risk of thromboembolism or death, even after controlling for other risk factors, such as hypertension, diabetes, prior stroke/thromboembolism, vascular disease and age.15 The relationship between smoking and risk of thromboembolism in AF patients is intriguing, and a modified CHA2DS2-VASc score including smoking among risk factors has been proposed, although not validated.16 Identification of smoking as an AF risk factor implies that a proportion of ‘lone’ AF can be mechanistically classified as AF caused by smoking. The term ‘lone AF’ was coined in 1954 by Evans and Swann17 to describe patients for whom subsequent investigation shows that heart disease is absent. Considered synonymous with idiopathic AF, the term ‘lone AF’ was widely used, comprising about 30% of AF for decades. In recent years, knowledge of ectopic foci and re-entry mechanisms, as well as identification of novel risk factors (Table 1), have restricted the term ‘lone AF’ to 3% of AF cases (Figure 1) and in the coming years this proportion could shrink more. Smoking status is a modifiable risk factor, and therefore a proportion of AF caused by smoking could be preventable. However, smoking cessation needs not only self-change efforts, but also social intervention programmes. Although it has declined globally by 25% in last three decades, the smoking habit remains highly prevalent and, more worrying, smoking in adolescents is increasing with rates of 5–10% in USA and 20–30% in Europe.18,19 Reinforcement of programmes for smoking prevention and cessation, as well as screening of subjects at high risk of AF, which includes smokers, could be a strategy to limit the exponential rise of AF in the coming decades. Prevalence of ‘lone’ atrial fibrillation with time. (Left) The pie chart shows the proportion of ‘lone’ or idiopathic atrial fibrillation in 1954, when the term ‘lone’ atrial fibrillation was coined by Evans and Swann.17 ‘Lone’ atrial fibrillation, defined as atrial fibrillation not associated with any structural heart disease, was found to have a 30% prevalence. (Right) With time, several factors have been found to be associated with atrial fibrillation, thereby leading to consistently blunting the proportion of ‘lone’ atrial fibrillation to 3% (modified from Wyse et al.).4 Prevalence of ‘lone’ atrial fibrillation with time. (Left) The pie chart shows the proportion of ‘lone’ or idiopathic atrial fibrillation in 1954, when the term ‘lone’ atrial fibrillation was coined by Evans and Swann.17 ‘Lone’ atrial fibrillation, defined as atrial fibrillation not associated with any structural heart disease, was found to have a 30% prevalence. (Right) With time, several factors have been found to be associated with atrial fibrillation, thereby leading to consistently blunting the proportion of ‘lone’ atrial fibrillation to 3% (modified from Wyse et al.).4 The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article. The author(s) received no financial support for the research, authorship and/or publication of this article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".