Correlation between Cardiac Electrophysiological Indicators and Lipid Parameters in Coronary Heart Disease with Arrhythmia and Heart Failure

Summary

This protocol aims to evaluate the correlation between cardiac electrophysiological markers and blood lipid parameters in patients with coronary artery disease complicated by arrhythmia, with a focus on their predictive value for heart failure risk. The study integrates multiple indicators to enhance early detection and clinical risk assessment.

Abstract

This study assessed the relationship between cardiac electrophysiological indicators and blood lipid parameters in patients with coronary artery disease complicated by arrhythmia and evaluated their combined influence on the development of heart failure. A cohort of 240 patients admitted to the Cardiac Center of The First Affiliated Hospital of Xiamen University between April 2023 and April 2025 was screened; 80 met the inclusion criteria and were included in the analysis. Participants were categorized according to the occurrence of heart failure. Clinical characteristics, including lipid profiles (triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and total cholesterol), were analyzed alongside echocardiographic parameters, including left ventricular ejection fraction (LVEF) and carotid intima-media thickness (CIMT). Statistical analyses included correlation testing, multivariable logistic regression, and receiver operating characteristic (ROC) curve analysis.

Impaired cardiac function, increased CIMT, and abnormal lipid concentrations were significantly associated with heart failure risk. QTc interval showed positive correlations with LDL-C (r = 0.342, P < 0.01) and triglycerides (r = 0.366, P < 0.01), whereas HDL-C showed inverse correlations with electrocardiographic parameters. In multivariable logistic regression analysis, prolonged QTc interval (OR = 1.08, 95% CI: 1.02–1.15), widened QRS duration (OR = 1.05, 95% CI: 1.01–1.11), elevated LDL-C (OR = 1.74, 95% CI: 1.12–2.63), increased triglycerides (OR = 1.62, 95% CI: 1.05–2.48), reduced HDL-C (OR = 0.68, 95% CI: 0.50–0.91), and decreased LVEF (OR = 0.89, 95% CI: 0.83–0.94) were independent predictors of heart failure. ROC analysis confirmed the predictive value of multiple risk factors, with AUCs ranging from 0.68 to 0.75. HDL-C had the highest predictive accuracy individually (AUC = 0.75), while QTc interval and LVEF also demonstrated strong discrimination (AUC = 0.73).

Introduction

Worldwide, non-communicable disease mortality is predominantly driven by cardiovascular diseases (CVDs), which are associated with roughly 18 million deaths per year¹. Even though hypertension, diabetes mellitus, smoking, and obesity are considered the classical risk factors when it comes to the development of the disease, a large percentage of the cardiovascular incidents take place in the absence of the mentioned risk factors in person². The observation underscores the need to identify additional biological and functional determinants and to address the ongoing progression of the disease and its poor clinical outcomes. Among them, cardiac arrhythmia complicated coronary artery disease (CAD) is a highly risky clinical manifestation that is often accompanied by sudden cardiac arrest and sudden cardiac death³.

Electrocardiography (ECG) is a non-invasive diagnostic test that provides vital information about the electrical activity and conduction anomalies of the heart. Ventricular arrhythmias, reduced myocardial conduction, and elevated cardiovascular mortality have consistently been linked to prolongation of electrophysiological parameters, including corrected QT (QTc) interval, QRS duration, and PR interval, which are direct measurements of the ventricular repolarization waveform⁴. Arrhythmias such as atrial fibrillation, premature contraction of the ventricles, and ventricular tachyarrhythmias disturb the coordinated contraction of the myocardium and are linked with raised morbidity, heart failure rates, and mortality⁵.

Dyslipidemia is a comorbidity of CAD, and both cause atherosclerotic plaque and lead to myocardial ischemia, inflammation, and poor cardiac remodelling⁶. Both experimental and clinical evidence show that lipid abnormalities can directly influence cardiac electrophysiology by modifying ion channels, membrane fluidity, and the integrity of gap junctions, leading to greater vulnerability to arrhythmogenic events⁷. Uncharacteristic lipid levels have been demonstrated to affect ECG-based measures of QT interval, heart rate variability, and QRS duration, which are all proven predictors of arrhythmia risk and poor cardiovascular events⁸. Simultaneously, structural and functional cardiac markers, such as left ventricular ejection fraction (LVEF) and carotid intima-media thickness (CIMT) are well-established heart failure predictors and risk factors of global cardiovascular condition⁹. Despite these developments, there remains a research gap.

The majority of studies on the subject consider electrophysiological, lipid, or echocardiographic parameters separately, which makes them incapable of adequately modeling the intricate interactions among metabolic, electrical, and structural factors in disease evolution^9,10,11. In addition, the joint predictive ability of routinely available ECG and lipid biomarker measures for identifying CAD patients with arrhythmias at increased risk of heart failure has not been adequately standardized within a reproducible methodological framework. Since this gap exists, the current protocol outlines a unified approach uniting cardiac electrophysiological indicators (QTc interval, QRS duration, PR interval, and echocardiographic heart rate variability), with serum lipid measurements (triglycerides, LDL-C, HDL-C, and total cholesterol) and echocardiographic measures (LVEF and CIMT)^12,13,14,15. This approach will revise risk stratification in patients with CAD complicated by arrhythmia using non-invasive, cost-effective, and readily available clinical assessments. This strategy is particularly applicable in clinical and research environments, where early detection of individuals at elevated risk of heart failure can inform preventive and treatment measures.

Although previous studies have examined electrocardiographic parameters, lipid metabolism, or structural cardiac markers individually in patients with coronary artery disease, few investigations have integrated these domains within a unified predictive framework. Most available evidence evaluates ECG abnormalities or lipid biomarkers independently, limiting understanding of the complex interaction between metabolic dysregulation, electrical remodeling, and structural cardiac impairment. Additionally, the combined predictive value of routinely available electrocardiographic and lipid biomarkers for identifying patients at high risk of developing heart failure has not been systematically evaluated in prospective clinical studies. Addressing this gap may facilitate the development of a practical, cost-effective risk-stratification model to identify patients at elevated risk of heart failure.

Based on the above considerations, we hypothesized that abnormalities in cardiac electrophysiological parameters, particularly prolonged QTc interval, QRS duration, and PR interval, are significantly associated with adverse lipid profiles in patients with coronary artery disease complicated by arrhythmia. Furthermore, we hypothesized that combining electrophysiological indicators, lipid parameters, and structural cardiac markers (LVEF and CIMT) would yield superior predictive accuracy for heart failure development compared with evaluating these parameters individually.

Protocol

All procedures were conducted in accordance with the institutional guidelines approved by the Ethics Committee of The First Affiliated Hospital of Xiamen University (Fujian Province, China). Written informed consent was obtained from all participants prior to enrollment. Participant confidentiality was ensured, and participants were allowed to withdraw from the study at any stage without consequence.

Study design and patient enrolment
Definition of the study cohort
A prospective observational study was conducted at the Cardiac Centre of The First Affiliated Hospital of Xiamen University between April 2023 and April 2025. Consecutive patients admitted during the study period were reviewed for eligibility. Diagnoses were confirmed using established international clinical guidelines and hospital diagnostic protocols.

Inclusion and exclusion criteria
Adult patients (≥18 years) with documented arrhythmia and frequent ectopic activity, defined as >10,000 premature ventricular contractions (PVCs) within a 24 h Holter ECG recording (Figure 1), were included. Patients with structural heart disease (e.g., cardiomyopathy or significant valvular disease), left ventricular ejection fraction (LVEF) < 35%, severe renal or hepatic failure, malignancy or other life-limiting comorbidities, incomplete clinical or laboratory data, or withdrawal of informed consent were excluded.

Classified patients
Baseline demographic data, medical history, medication use, and physical examination findings were recorded at enrollment. Patients who developed HF were assigned to the HF group, and those without HF were assigned to the non-HF group.

Blood collection and biochemical analysis
Blood samples
A total of 2 mL of fasting venous blood was collected from each participant using sterile single-use needles and vacutainer tubes between 07:00 and 09:00 AM. All samples were labelled with anonymized study identification numbers immediately after collection.

Process samples
Blood samples were centrifuged at 1,500 × g for 10 min at 4 °C to separate serum. Biochemical analyses were performed within 4 h of serum separation. If immediate analysis was not feasible, serum samples were aliquoted and stored at −20 °C until testing. Serum samples were stored at −20 °C for up to 4 weeks prior to analysis.

Biochemical parameters
Total cholesterol (TC), triglycerides (TG), LDL-cholesterol (LDL-C), and HDL-cholesterol (HDL-C) were measured using standardized enzymatic colorimetric assays on an automated biochemical analyzer, according to the manufacturer's instructions. High-sensitivity C-reactive protein (hs-CRP) was measured using an immunoturbidimetric latex agglutination method. Serum creatinine and B-type natriuretic peptide (BNP) were quantified using validated immunoassay techniques. Total cholesterol (TC), triglycerides (TG), LDL-cholesterol (LDL-C), and HDL-cholesterol (HDL-C) were also measured using enzymatic colorimetric assays on an automated biochemical analyzer according to the manufacturer’s protocol.

Electrocardiographic assessment
A resting 12-lead ECG was recorded using a standard ECG machine (paper speed 25 mm/s, calibration 10 mm/mV) after at least 10 min of patient rest in the supine position.

Standard 12-lead ECG
A resting 12-lead ECG was recorded with the patient in the supine position after at least 10 min of rest. ECG signals were recorded at a paper speed of 25 mm/s and a voltage calibration of 10 mm/mV. The QT, QTc, PR interval, QRS duration, and heart rate were measured using automated software with manual verification by two independent cardiologists.

24 h Holter monitoring
Disposable electrodes were attached according to standard lead placement, and continuous ECG data were recorded for 24 h using a portable Holter monitoring system. Recordings were analyzed using dedicated Holter analysis software to extract corrected QT interval (QTc), QT interval, PR interval, QRS duration, and mean heart rate. Arrhythmia burden was quantified, and PVC frequency was classified.

Diagnostic criteria
Coronary artery disease (CAD)
CAD was diagnosed based on at least one of the following: typical angina symptoms with ECG showing ≥0.1 mV ST-segment depression lasting <1 s, a positive exercise stress test, or coronary angiography demonstrating ≥50% luminal stenosis.

Left ventricular hypertrophy (LVH)
LVH was diagnosed by echocardiography using a left ventricular mass index >130 g/m² in males and >100 g/m² in females.

Congestive heart failure (CHF)
CHF was diagnosed based on clinical symptoms (dyspnea, fatigue, reduced exercise tolerance) and physical findings (pulmonary crackles, peripheral edema, jugular venous distension). Diagnosis was confirmed by echocardiographic evidence of structural or functional cardiac abnormalities.

Cerebrovascular accident (CVA)
CVA was diagnosed using CT or MRI, demonstrating acute ischemic infarction or intracranial hemorrhage.

Peripheral vascular atherosclerosis (PVA)
PVA was confirmed using arteriographic evidence of atherosclerotic lesions or ultrasound-measured increased carotid intima-media thickness.

Statistical analysis
Continuous variables were evaluated for normality using the Shapiro–Wilk test. Variables following a normal distribution were reported as mean ± standard deviation (SD) and compared between groups using the independent-samples t-test. In contrast, variables not conforming to normal distribution were presented as median with interquartile range and analyzed using the Mann–Whitney U test. Categorical data were summarized as frequencies and percentages and compared using the chi-square (χ²) test or Fisher’s exact test where appropriate. Associations between electrophysiological parameters (QT interval, corrected QT [QTc], PR interval, QRS duration) and lipid markers (total cholesterol, triglycerides, low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C]) were examined using Spearman’s rank correlation coefficient, given the potential non-normal distribution of biochemical variables.

To determine independent predictors of heart failure, univariate logistic regression analyses were initially performed. Variables demonstrating P < 0.10 in univariate analysis, along with clinically relevant factors (age, sex, lipid parameters, electrophysiological indices, and left ventricular ejection fraction [LVEF]), were subsequently included in a multivariable logistic regression model using a forward stepwise likelihood ratio method. The final model adjusted for potential confounders, including age, sex, renal function, and baseline cardiac function, to minimize residual confounding. Multicollinearity was evaluated using variance inflation factors (VIF), with values greater than 5 indicating significant collinearity. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were reported

To minimize confounding bias, clinically relevant covariates, including age, sex, baseline blood pressure, renal function, medication use (statins, antiarrhythmic drugs, and beta-blockers), and baseline cardiac function (LVEF) were included in the multivariable regression model where available. These variables were selected based on clinical relevance and previous literature linking them to cardiovascular risk and arrhythmia progression.

Model calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test, while discriminative performance was evaluated through receiver operating characteristic (ROC) curve analysis. Predictive accuracy was quantified by calculating the area under the ROC curve (AUC) along with corresponding 95% confidence intervals.Model performance was additionally evaluated using pseudo-R² statistics, including Cox & Snell R² and Nagelkerke R², to estimate the proportion of variance explained by the multivariable logistic regression model.

Optimal cutoff values were determined using the Youden index. Regarding sample size, this exploratory prospective study enrolled all eligible patients during the predefined recruitment period. Post hoc power analysis indicated that the sample size provided >80% statistical power to detect moderate effect sizes (OR ≥ 1.7 or r ≥ 0.30) at a two-sided α level of 0.05.

To identify independent predictors of heart failure, multivariable logistic regression analysis was performed using electrophysiological parameters, lipid variables, demographic factors, and echocardiographic indicators as candidate predictors. Because this exploratory prospective study included all eligible participants during the predefined recruitment period, a formal a priori sample size calculation was not performed. However, post hoc power analysis indicated that the final sample size (n = 80) provided approximately 80% statistical power to detect moderate effect sizes (r ≥ 0.30 or OR ≥ 1.7) at a two-sided α level of 0.05. Two-tailed statistical tests were applied throughout, with a P-value < 0.05 defined as statistically significant.

Results

Baseline variables
A total of 240 patients were screened for eligibility between April 2023 and April 2025. After applying the predefined inclusion and exclusion criteria, 80 patients were included in the final analysis. Among these participants, 60 patients had carotid intima–media thickness (CIMT) ≤ 0.9 mm and 20 patients had CIMT > 0.9 mm.

As shown in Table 1 and Figure 2, patients with CIMT > 0.9 mm were significantl...

Discussion

The present study evaluated the combined contribution of cardiac electrophysiological parameters, lipid abnormalities, and structural cardiovascular markers to cardiovascular disease (CVD) and heart failure risk in patients with coronary artery disease (CAD) complicated by arrhythmia. The findings demonstrate that prolonged QTc interval, widened QRS complex, extended PR interval, dyslipidemia (elevated total cholesterol, triglycerides, and LDL-C with reduced HDL-C), increased carotid intima-media thickness (CIMT), and re...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
12-lead Electrocardiograph (ECG) Machine	Philips Healthcare	ECG-12P-2025	Used for standard resting ECG to record cardiac electrical activity (QT, QRS, PR intervals).
24-h Holter Monitor System	GE Healthcare	HOLTER-24-01	Portable continuous ECG monitoring device for arrhythmia burden and QTc analysis.
Automated Hematology Analyzer	Sysmex	XN-1000	Performs leukocyte counts and calculates neutrophil-to-lymphocyte ratio.
Biochemical Analyzer	Roche Diagnostics	COBAS-8000	Automated analyzer for lipid profile (TC, TG, LDL-C, HDL-C) and hs-CRP measurement.
BNP Immunoassay Kit	Abbott Laboratories	BNP-ARCHITECT	Used for quantification of B-type natriuretic peptide for heart failure diagnosis.
Carotid Ultrasound Machine	Mindray	US-CIMT-900	High-resolution B-mode ultrasound system for carotid intima-media thickness measurement.
Centrifuge	Eppendorf	CEN-5810R	Used to separate serum from whole blood samples at 1,500 × g for 10 min at 4 °C.
Echocardiography System	Siemens Healthineers	ECHO-SIM-2025	Used to assess left ventricular ejection fraction and diagnose structural abnormalities.
Serum Storage Freezer (-20 °C)	Thermo Fisher Scientific	TSX-20F	For temporary storage of serum aliquots before analysis.
SPSS Software (Version 23.0)	IBM	SPSS-23	Statistical software used for correlation, regression, and ROC curve analyses.