Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Jan 3;13(1):276.
doi: 10.3390/jcm13010276.

Correlations between Resting Electrocardiogram Findings and Disease Profiles: Insights from the Qatar Biobank Cohort

Fatima Qafoud  1 , Khalid Kunji  2 , Mohamed Elshrif  2 , Asma Althani  1 , Amar Salam  3 , Jassim Al Suwaidi  4 , Dawood Darbar  5 , Nidal Asaad  4 , Mohamad Saad  2
Affiliations

Correlations between Resting Electrocardiogram Findings and Disease Profiles: Insights from the Qatar Biobank Cohort

Fatima Qafoud et al. J Clin Med. .

Abstract

Background: Resting electrocardiogram (ECG) is a valuable non-invasive diagnostic tool used in clinical medicine to assess the electrical activity of the heart while the patient is resting. Abnormalities in ECG may be associated with clinical biomarkers and can predict early stages of diseases. In this study, we evaluated the association between ECG traits, clinical biomarkers, and diseases and developed risk scores to predict the risk of developing coronary artery disease (CAD) in the Qatar Biobank.

Methods: This study used 12-lead ECG data from 13,827 participants. The ECG traits used for association analysis were RR, PR, QRS, QTc, PW, and JT. Association analysis using regression models was conducted between ECG variables and serum electrolytes, sugars, lipids, blood pressure (BP), blood and inflammatory biomarkers, and diseases (e.g., type 2 diabetes, CAD, and stroke). ECG-based and clinical risk scores were developed, and their performance was assessed to predict CAD. Classical regression and machine-learning models were used for risk score development.

Results: Significant associations were observed with ECG traits. RR showed the largest number of associations: e.g., positive associations with bicarbonate, chloride, HDL-C, and monocytes, and negative associations with glucose, insulin, neutrophil, calcium, and risk of T2D. QRS was positively associated with phosphorus, bicarbonate, and risk of CAD. Elevated QTc was observed in CAD patients, whereas decreased QTc was correlated with decreased levels of calcium and potassium. Risk scores developed using regression models were outperformed by machine-learning models. The area under the receiver operating curve reached 0.84 using a machine-learning model that contains ECG traits, sugars, lipids, serum electrolytes, and cardiovascular disease risk factors. The odds ratio for the top decile of CAD risk score compared to the remaining deciles was 13.99.

Conclusions: ECG abnormalities were associated with serum electrolytes, sugars, lipids, and blood and inflammatory biomarkers. These abnormalities were also observed in T2D and CAD patients. Risk scores showed great predictive performance in predicting CAD.

Keywords: ECG; Middle East; Qatar Biobank; arrythmia; cardiovascular diseases; diverse populations; risk scores; type 2 diabetes.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Variability in ECG traits by ancestry. Q-AFR: Qatari citizens with African origins; Q-SAS: Qatari citizens with South Asian origins (Iran and India); Arab: Qatari citizens with Arab origins spanning the Gulf and Middle East region.
Figure 2
Figure 2
Associations between ECG traits and serum electrolytes. Colors are proportional to the effect size of each regression model. Red colors represent a negative correlation, while green colors represent positive ones. The numbers in each cell are the p-values. The underlined p-values are significant with Bonferroni threshold.
Figure 3
Figure 3
Associations between ECG traits and sugars/lipids. Colors are proportional to the effect size of each regression model. Red colors represent a negative correlation, while green colors represent positive ones. The numbers in each cell are the p-values. HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; TC: total cholesterol; TG: triglyceride. The underlined p-values are significant with Bonferroni threshold.
Figure 4
Figure 4
Associations between ECG traits and blood and inflammatory biomarkers. Colors are proportional to the effect size of each regression model. Red colors represent a negative correlation, while green colors represent positive ones. The numbers in each cell are the p-values. The underlined p-values are significant with Bonferroni threshold.
Figure 5
Figure 5
Associations between ECG traits and clinical/disease traits. Colors are proportional to the effect size of each regression model. Red colors represent a negative association, while green colors represent positive ones. The numbers in each cell are the p-values. Cases were coded as 1 and controls as 0. AF: atrial fibrillation; AR: arrhythmia; CAD: coronary artery disease; T2D: type 2 diabetes; HyperThyroid: hyperthyroidism. The underlined p-values are significant with Bonferroni threshold.
Figure 6
Figure 6
Summary of all Bonferroni-significant associations. Circle size is proportional to the effect size. The value of effect sizes is shown above the circles.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Hampton J.R. The importance of minor abnormalities in the resting electrocardiogram. Eur. Heart J. 1984;5((Suppl. SA)):61–63. doi: 10.1093/eurheartj/5.suppl_A.61. - DOI - PubMed
    1. Surber R., Schwarz G., Figulla H.R., Werner G.S. Resting 12-lead electrocardiogram as a reliable predictor of functional recovery after recanalization of chronic total coronary occlusions. Clin. Cardiol. 2005;28:293–297. doi: 10.1002/clc.4960280608. - DOI - PMC - PubMed
    1. Yue W., Wang G., Zhang X., Chen B., Wang X., Huangfu F., Jia R. Electrocardiogram for predicting cardiac functional recovery. Cell Biochem. Biophys. 2014;70:87–91. doi: 10.1007/s12013-014-9862-2. - DOI - PubMed
    1. Perez M.V., Dewey F.E., Marcus R., Ashley E.A., Al-Ahmad A.A., Wang P.J., Froelicher V.F. Electrocardiographic predictors of atrial fibrillation. Am. Heart J. 2009;158:622–628. doi: 10.1016/j.ahj.2009.08.002. - DOI - PubMed
    1. Shah A.M., Jefferies J.L., Rossano J.W., Decker J.A., Cannon B.C., Kim J.J. Electrocardiographic abnormalities and arrhythmias are strongly associated with the development of cardiomyopathy in muscular dystrophy. Heart Rhythm. 2010;7:1484–1488. doi: 10.1016/j.hrthm.2010.06.022. - DOI - PubMed