Abnormal blood lipid and electrocardiogram characteristics in common mental disorders

Li, Yan; Tang, Chaohua; Wu, Wen; Z, Li; Li, Xuesong; Huang, Wei; Chen, Wensheng; Mai, Xiancong; Li, Xiaoling; Xu, Caixia; Xie, Guojun; Liang, Jiaquan

doi:10.1186/s12888-023-04965-9

Cited by 1 publication

(1 citation statement)

References 48 publications

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“…In our study, all sugars/lipids/BP were associated with heart rate except LDL-C. An increase in these traits was associated with an increase in heart rate, except for HDL-C, which was negatively correlated with heart rate. In a recent study with a relatively small sample size, a positive correlation was observed between heart rate and HDL-C, LDL-C, BMI, and TG [ 29 ]. Our results should be more accurate because of our larger sample size (40× higher) and thus higher statistical power.…”

Section: Discussionmentioning

confidence: 99%

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

Qafoud,

Kunji,

Elshrif

et al. 2024

JCM

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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.

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Section: Discussionmentioning

confidence: 99%