Exercise‐Dependent Modulation of Immunological Response Pathways in Endurance Athletes With and Without Atrial Fibrillation
Bibliographic record
Abstract
Background Atrial fibrillation (AF) is a common arrhythmia characterized by uncoordinated atrial electrical activity. Lone AF occurs in the absence of traditional risk factors and is frequently observed in male endurance athletes, who face a 2‐ to 5‐fold higher risk of AF compared with healthy, moderately active males. Our understanding of how endurance exercise contributes to the pathophysiology of lone AF remains limited. This study aimed to characterize the circulating protein fluctuations during high‐intensity exercise as well as explore potential biomarkers of exercise‐associated AF. Methods and Results A prospective cohort of 12 male endurance cyclists between the ages of 40 and 65 years, 6 of whom had a history of exercise‐associated AF, were recruited to participate using a convenience sampling method. The circulating proteome was subsequently analyzed using multiplex immunoassays and aptamer‐based proteomics before, during, and after an acute high‐intensity endurance exercise bout to assess temporality and identify potential markers of AF. The endurance exercise bout resulted in significant alterations to proteins involved in immune modulation (eg, growth/differentiation factor 15), skeletal muscle metabolism (eg, α‐actinin‐2), cell death (eg, histones), and inflammation (eg, interleukin‐6). Subjects with AF differed from those without, displaying modulation of proteins previously known to have associations with incident AF (eg, C‐reactive protein, insulin‐like growth factor‐1, and angiopoietin‐2), and also with proteins having no previous association (eg, tapasin‐related protein and α 2 ‐Heremans‐Schmid glycoprotein). Conclusions These findings provide insights into the proteomic response to acute intense exercise, provide mechanistic insights into the pathophysiology behind AF in athletes, and identify targets for future study and validation.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".