Elsevier

Medical Hypotheses

Volume 71, Issue 2, August 2008, Pages 178-185
Medical Hypotheses

Running, esophageal acid reflux, and atrial fibrillation: A chain of events linked by evidence from separate medical literatures

https://doi.org/10.1016/j.mehy.2008.02.017Get rights and content

Summary

Using a previously described approach to generating novel medical hypotheses, this paper shows how two separate medical literatures taken together can suggest new information not apparent in either literature alone.

Many studies have demonstrated that aerobic exercise in healthy people can induce esophageal acidic reflux that increases with the duration and intensity of exercise. Separately, independently of exercise, it has been shown that, in patients with gastroesophageal reflux, esophageal acid exposure can lead to atrial fibrillation (AF) and to other heart dysrhythmias. The two arguments together suggest that a regimen of excessive exercise may be conducive to AF mediated by acid reflux, an implicit, but unpublished, hypothesis.

Proton pump inhibitors are widely used to treat gastroesophageal reflux. Remarkably, several small clinical trials of these drugs have been shown also to reduce symptoms and frequency of AF episodes in patients with comorbid acid reflux. Plausible mechanisms have been suggested. These small-scale tests in a highly restricted population may be of particular interest in the light of a possible exercise-reflux-AF causal chain of events in a broader population of athletes. Because the minimum degree of esophageal acidity exposure required to induce AF is unknown, further tests of proton pump inhibitors for that purpose are therefore merited without regard to any known prior reflux in a population of runners with lone AF.

The prospect of reducing AF burden with a relatively benign agent is attractive in view of the limited options for effective treatment otherwise available. The study of arrhythmia and esophageal reflux in athletes may offer insights on the origin and natural history of lone atrial fibrillation.

Section snippets

1. Introduction

An earlier analysis of atrial fibrillation (AF) in athletes combined arguments from separate medical literatures, one literature providing evidence that athletic overtraining can induce chronic systemic inflammation, and the other showing that inflammation may be important in the pathogenesis of AF [1]. The two arguments together suggest that inflammation as a mechanism might explain an unexpected susceptibility of athletes to AF, unexpected because the typical AF patient is much older (median

2.1. Finding novel connections between complementary literatures

The approach described here is based on the idea that two scientific articles, or two non-overlapping sets of articles, can be complementary, in that, taken together, they suggest new information not apparent in either set alone. To find such pairs of complementary literatures depends in general on finding pairs of statements from different articles that are linked by key words or phrases that they have in common. Sets of articles are identified by exploratory searching of Medline, and key

3.1. Results of Arrowsmith search

Among the top seven B-terms, “proton pump” is immediately of interest because of its possible clinical relevance. It is linked to an output display of the following two titles, each containing the phrase “proton pump inhibit––”.

  • A-title: Proton pump inhibition prevents gastrointestinal bleeding in ultramarathon runners: a randomized, double blinded, placebo controlled study [3].

  • C-title: Case of paroxysmal atrial fibrillation improved after the administration of proton pump inhibitor for

4. Discussion

The purpose of this article is not only to call attention to a perhaps previously unnoticed connection between running and AF via GER, as a basis for stimulating a new medical hypothesis, but also to suggest an approach to testing the hypothesis, particularly with respect to a suitable population of subjects and a clinical endpoint.

Acknowledgments

I thank Neil Smalheiser and Vetle Torvik for valuable suggestions. This work was supported in part by a subgrant from the University of Illinois at Chicago (PI Neil R. Smalheiser, M.D., Ph.D.) R01 LM007292-05, Arrowsmith data-mining techniques in neuro-informatics, co-sponsored by NLM and NIMH, 15/6/01–31/5/06. The contents of this article are solely the responsibility of the author and do not necessarily represent the views of NIH.

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