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Background: The 12-lead electrocardiogram (ECG) has been adopted as an important component of preparticipation cardiovascular screening. However, there are still controversies in the screening and few studies with a large sample size have reported the results of ECGs of marathon runners. Therefore, the purpose of this study was to assess the prevalence of normal, borderline, and abnormal ECG changes in marathon runners., Methods: The 12-lead ECG data of 13,079 amateur marathon runners between the ages of 18 and 35 years were included for analysis. The prevalence of ECG abnormalities among different gender groups was compared with chi-square tests., Results: In terms of training-related changes, sinus bradycardia, sinus arrhythmia, and left ventricular high voltage were found in approximately 15, 5, and 3.28% of the participants, respectively. The incidence of right axis deviation in the marathon runners was 1.78%, which was slightly higher than the incidence of left axis deviation (0.88%). No more than 0.1% of the amateur marathon runners exhibited ST-segment depression, T wave inversion (TWI), premature ventricular contraction, pathologic Q waves, and prolonged QT interval., Conclusions: Training-related ECG changes, including sinus bradycardia, sinus arrhythmia, and left ventricular high voltage, were common in amateur marathon runners. Most abnormal ECG changes, including ST-segment depression, TWI, premature ventricular contraction, pathologic Q waves, and prolonged QT interval, were infrequently found in amateur marathon runners. The data also suggested Chinese amateur marathon runners may have a relatively lower prevalence of ECG abnormalities than black and white runners.

Many common and chronic diseases are influenced at some level by genetic variation. Research done in population genetics, specifically in the area of single nucleotide polymorphisms (SNPs) is critical to understanding human genetic variation. A key element in assessing role of a given SNP is determining if the variation is likely to result in change in function. The SNP Integration Tool (SNPit) is a comprehensive tool that integrates diverse, existing predictors of SNP functionality, providing the user with information for improved association study analysis. To evaluate the SNPit system, we developed an alternative gold standard to measure accuracy using sensitivity and specificity. The results of our evaluation demonstrated that our alternative gold standard produced encouraging results.