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Record W2887162912 · doi:10.1177/2047487318791005

Healthy lifestyle and prevention of atrial fibrillation: Weighty matters

2018· review· en· W2887162912 on OpenAlexaff
Stefanie Aeschbacher, David Conen

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2018
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicineAtrial fibrillationSecondary preventionPrimary preventionInternal medicineCardiologyIntensive care medicineDisease

Abstract

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Atrial fibrillation (AF) is the most common arrhythmia, and is associated with an increased risk of death, cardiovascular events and cognitive dysfunction.1,2 Preventing AF and its adverse consequences may therefore have a huge socioeconomic impact. In order to implement AF prevention programs in the future, a detailed understanding is needed on the effects of potentially modifiable risk factors on the development of AF. Many studies have investigated the effect of individual risk factors on the incidence of AF. These studies consistently show that elevated blood pressure and obesity are the strongest modifiable risk factors for new-onset AF.3–5 Both factors have a similar population attributable risk of around 20%, suggesting that the elimination of each of the two factors would reduce the occurrence of new-onset AF by approximately 20%.6 Randomized trials in this setting are scarce, but do support this concept. A relatively small randomized trial showed that intense blood pressure control reduced the risk of new-onset AF compared with conventional blood pressure control, independent of the type of antihypertensive medication used.7 Patients with established AF who successfully lose weight have less arrhythmia burden, AF-related symptoms8 and a higher long-term success rate after AF ablation.9 The relationship between physical activity and AF is more complex. Light-to-moderate physical activity has been associated with a lower risk of AF.10 However, whether this effect is independent of changes in body mass index (BMI) has been questioned in another study.11 Extreme levels of exercise may actually increase the risk of AF.12 With regard to alcohol consumption, women consuming ≥2 drinks of alcohol per day had a 1.49-fold increased risk of AF compared with non-drinkers.13 Recently, a comprehensive meta-analysis suggested a linear association between alcohol and AF occurrence, with an 8% increase in risk per additional drink consumed, without evidence of a lower threshold.14 With regard to smoking, there seems to be a strong dose-response relationship, providing an additional reason to quit smoking.15,16 In this context, Di Benedetto and colleagues17 publish their work on the association of a healthy lifestyle score and incident AF using data from the European Prospective Investigation into Cancer and Nutrition (EPIC) study, a population-based cohort of individuals aged 39–79 years at baseline. Study participants with suspected AF, known AF and cardiovascular disease or other chronic illnesses were excluded, leaving 21,499 patients for the current analysis. Incident AF was defined as hospitalization due to AF, and case ascertainment was based on a linkage with the National Health Service hospital information system. To quantify healthy lifestyle, the authors developed a scoring-system that is scaled from zero (unhealthy) to four (healthy). Two points were given for BMI<25 kg/m2 and one point was given for each of the following items: being a nonsmoker, drinking <14 units a week and having a BMI of 25–27.5 kg/m2. There were 2155 incident AF cases during an average follow-up of 17.1 years (1195 (12.3%) men and 960 (8.1%) women). Current smoking, alcohol intake ≥14 units a week and a BMI ≥ 25 kg/m2 were independently associated with hospitalizations for AF with an age- and sex adjusted hazard ratio (HR) (95% confidence interval (CI)) of 1.27 (1.11–1.45), p = 0.001, 1.13 (1.01–1.27), p = 0.03 and 1.43 (1.30–1.57), p < 0.0001, respectively. There was no significant association between physical activity and AF (HR (95% CI) 1.05 (0.96–1.15), p = 0.33). The HR (95% CI) of AF for individuals with a lifestyle score of zero was 2.82 (1.85–4.29), and the cumulative survival free of AF at 20 years in these individuals was 61%. The combined population attributable fraction (PAF) of an unhealthy behavior was 24.9%, with overweight being the dominant factor (PAF 21.6%). The study of Di Benedetto and colleagues has several strengths. First, the sample size is large, the follow-up complete, and the number of incident cases high, allowing for meaningful analyses. Second, based on the study design, the distribution of the lifestyle factors in the study population is a reflection of the distribution in a general population and can be used to implement AF prevention programs. This study also has some limitations. First, lifestyle factors were assessed at baseline and not updated over time. Given the long follow-up time, at least some of the predictors have changed over time, which could have introduced regression towards the null. Second, the authors used their own, not previously validated scoring system based on associations found in their cohort. For example, the authors decided to double the impact of a normal BMI, rather than empirically derive these weights. Also, it remains unclear why the authors did not use conventionally established BMI categories instead of using a cut-off of 27.5 kg/m2. Since physical activity was not significantly associated with incident AF in their data, this variable was not included in the score, even though other studies suggest a significant effect of light-to-moderate physical activity. It is therefore unclear whether these data can be replicated in other cohorts, and whether the authors truly derived the best score for AF prevention. Third, diet is an important modifiable lifestyle factor. Unfortunately, information on dietary habits other than alcohol consumption was not available. Data about diet would have been particularly welcome as the effect of diet on incident AF is relatively unknown. Fourth, the primary endpoint was hospitalization for AF, and the real number of individuals with AF is most likely higher as not all AF patients need hospitalization. Finally, although the lifestyle score was associated with incident AF, BMI alone explained the great majority of the effect. It is therefore unclear whether the other lifestyle factors provided any independent information over and above BMI alone. In summary, the current study underscores the importance of a healthy lifestyle in the prevention of AF. Overweight and obesity have by far the biggest impact, suggesting that programs targeting overweight in the population will have a major impact on the occurrence of AF. While moderation of alcohol intake, smoking cessation and physical exercise are crucial components of a healthy lifestyle, their effect on AF prevention seems to be more modest after taking into account the effect of overweight. 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.091
GPT teacher head0.383
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

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Citations2
Published2018
Admission routes1
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