Relation of Overall and Abdominal Adiposity With Electrocardiogram Parameters of Subclinical Cardiovascular Disease in Individuals Aged 45 to 65 Years (from the Netherlands Epidemiology of Obesity Study)

Effect of the index of cardiac electrophysiological balance on major adverse cardiovascular events in patients with diabetes complicated with coronary heart disease

Purpose

To investigate the prognostic value of the index of cardio-electrophysiological balance (ICEB) and its association with major adverse cardiac events (MACE) and cardiovascular death in diabetic patients complicated with coronary heart disease.

Methods

A total of 920 diabetic patients were enrolled in this longitudinal study. Participants were categorized into three groups based on their ICEB levels: normal ICEB, low ICEB, and high ICEB. The primary outcome was the occurrence of MACE, and secondary outcomes included cardiovascular death, coronary heart disease (CHD), heart failure (HF), and sudden cardiac arrest (SCA). Patients were followed for a median period of 3.26 years, and the associations between ICEB levels and various outcomes were evaluated.

Results

Over the follow-up period, 46 (5.0%) MACE were observed in the normal ICEB group, 57 (6.2%) in the low ICEB group, and 62 (6.8%) in the high ICEB group. Elevated ICEB levels were found to be associated with a higher risk of MACE and cardiovascular death. A significant relationship between ICEB levels and the risk of MACE was observed for both genders. The risk of MACE increased with each unit increment in the ICEB index. However, the two-stage linear regression model did not outperform the single-line linear regression models in determining the threshold effect.

Conclusion

This study demonstrates the potential utility of ICEB, derived from a standard non-invasive ECG, as a prognostic tool for predicting MACE and cardiovascular death in diabetic patients complicated with CVD. The associations between ICEB levels and the risk of MACE highlight the importance of understanding cardiac electrophysiological imbalances and their implications in CVD.

Analysis of the determining factors of detectable P-wave and amplitude of QRS complex sensed by implantable loop recorder

BACKGROUND

Determining factors for sufficient QRS amplitude and discernible P-wave sensing in implantable loop recorder (ILR) are unknown. We aimed to investigate determining factors and ILR implantation angle that may improve QRS complex and P-wave sensing in ILR.

METHODS

We retrospectively reviewed 220 patients who underwent ILR implantation or follow-up analysis. Patient demographic, clinical, echocardiography, electrocardiography, heart angle, and ILR angle data were collected as predictor variables. Associations between ILR QRS amplitude/P-wave detectability and each predictor variable were investigated.

RESULTS

Univariate linear regression showed that ILR QRS amplitude was significantly associated with age, height, ILR angle, and QRS amplitudes of 12-lead electrocardiogram (ECG) (lead I, II, aVR [inverted aVR], aVF, V1-V6) and Holter ECG (lead V3, V5). Among discrete variables, only left ventricular hypertrophy (LVH) affected ILR QRS amplitude (P = .016). A multivariate linear regression analysis revealed that ILR angle (β = -0.008, P < .001), lead aVR amplitude (β = 0.469, P = .003), Holter lead V5 amplitude (β = 0.116, P = .049), Age (β = -0.005, P = .014), and LVH (β = 0.213, P = .031) were independent determinants of ILR QRS amplitude. Logistic regression revealed that heart angle significantly affected ILR P-wave detectability (β = 0.12, P = .008). Multiple logistic regression revealed that heart angle (β = 0.121, P = .013) and lead V1 amplitude (β = 28.1, P = .034) were independent determinants of ILR P-wave detectability.

CONCLUSION

ILR insertion angle, lead aVR QRS amplitude, Holter lead V5 QRS amplitude, age, and LVH are determinants of ILR QRS amplitude. Heart angle and lead V1 P-wave amplitude of 12-lead ECG are determinants of ILR P-wave detectability.

The specification of zebrafish (Danio rerio) heart electrocardiogram index characteristic responses to different types of pollutants

Water quality was highly affected by common pollutants. Metals, pesticides and small molecules are ubiquitous pollutants. Advancement in engineering technology (computer-based monitoring systems) increased the efficiency of quantifying toxicity of different chemicals in an organism. The cardiovascular system reflects internal and external stress of an organism, and electrocardiogram (ECG) data reliably measure external stress. As ECG data can accurately reflect the physiological conditions of organisms, and zebrafish (Danio rerio) are considered to be good models for cardiovascular research, it is hypothesized that ECG parameters of zebrafish could indicate the toxicity of water-borne chemicals. To achieve this, we treated zebrafish with different concentrations of target chemicals (CuSO₄, C₁₀H₁₉O₆PS₂ and NH₄Cl) for 48 h and ECG data were measured. P-wave, R-wave, T-wave, PR-interval, QRS-complex and QT-interval data were the focus of this study. The results of self-organizing maps and Pearson correlation analysis indicate that the QRS-complex can be used as an indicator for CuSO₄ stress. The QT-interval could be used to assess the C₁₀H₁₉O₆PS₂ stress. The QT-interval and P-wave can be used to evaluate the NH₄Cl stress. Responses of zebrafish ECG parameters were identical with other vertebrate model, and were specific to toxicant types. It is proved that zebrafish heart ECG index could be used as a potential indicator in early detection of environmental stress.

The Prediction of Human Abdominal Adiposity Based on the Combination of a Particle Swarm Algorithm and Support Vector Machine

Background: Abdominal adiposity is an important risk factor of chronic cardiovascular diseases, thus the prediction of abdominal adiposity and obesity can reduce the risks of contracting such diseases. However, the current prediction models display low accuracy and high sample size dependence. The purpose of this study is to put forward a new prediction method based on an improved support vector machine (SVM) to solve these problems. Methods: A total of 200 individuals participated in this study and were further divided into a modeling group and a test group. Their physiological parameters (height, weight, age, the four parameters of abdominal impedance and body fat mass) were measured using the body composition tester (the universal INBODY measurement device) based on BIA. Intelligent algorithms were used in the modeling group to build predictive models and the test group was used in model performance evaluation. Firstly, the optimal boundary C and parameter gamma were optimized by the particle swarm algorithm. We then developed an algorithm to classify human abdominal adiposity according to the parameter setup of the SVM algorithm and constructed the prediction model using this algorithm. Finally, we designed experiments to compare the performances of the proposed method and the other methods. Results: There are different abdominal obesity prediction models in the 1 KHz and 250 KHz frequency bands. The experimental data demonstrates that for the frequency band of 250 KHz, the proposed method can reduce the false classification rate by 10.7%, 15%, and 33% in relation to the sole SVM algorithm, the regression model, and the waistline measurement model, respectively. For the frequency band of 1 KHz, the proposed model is still more accurate. (4) Conclusions: The proposed method effectively improves the prediction accuracy and reduces the sample size dependence of the algorithm, which can provide a reference for abdominal obesity.

The Association of Metabolic Syndrome and Its Components with Electrocardiogram Parameters and Abnormalities Among an Iranian Rural Population: The Fasa PERSIAN Cohort Study

BACKGROUND

Metabolic syndrome (MetS) as a set of cardiac risk factors and its growing prevalence is one of the major concerns in different societies. In this study, we aimed to investigate the relationship between Mets and electrocardiogram (ECG) parameters and abnormalities as indicators for subclinical cardiovascular diseases (CVD).

METHODS

In this sub-analysis study, we used the data from Fasa PERSIAN Cohort Study which includes subjects age 35-70 years. Subjects with available ECG data included in the study (n=7002) and subjects with missing data on MetS components and non-sinus rhythm ECG were excluded (n=44). The MetS definition based on the Adult Treatment Panel (ATP) III guidelines and also a 12-lead ECG was obtained from all participants.

RESULTS

Our study population (n=6958) showed a mean age of 48.60±9.34 years and also 1656 (24.2%) subjects had MetS. Except for P duration, PR interval and S amplitude in men and P amplitude, S amplitude, Sokolow-Lyon Index, and QT interval in women, other ECG parameters differ significantly between subjects with and without Mets (P<0.05). Also among ECG abnormalities, prolonged P duration (≥120ms), QRS duration (≥100ms), and QTc interval (>450ms in male, >470ms in female) had a significant association with MetS in the total population. Waist circumferences (WC) showed the most count of significant relationship with ECG parameters in both genders. In males, WC more than ATP cut-points had significant associations with prolonged P and QRS duration, and also blood pressure (BP) had significant associations with prolonged P and QRS durations and QTc interval. In females, the MetS component except triglyceride had at least a significant relationship with prolonged P and/or QRS duration.

CONCLUSION

MetS and its component especially WC and BP were associated with ECG parameters and abnormalities. These associations with ECG as a marker of subclinical CVD showed the importance of MetS and each component in our population to monitor in the further longitudinal studies.