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. 2025 Dec 28;31(1):e70146. doi: 10.1111/anec.70146

Left Ventricular Ejection Fraction and Incident Cardiac Conduction Dysfunction: Exploring the Mediating Effects of Electrophysiological Parameters

Qie Zhang 1, Feilong Zhang 1, Yuhao Hu 1, Jinfeng Wang 1, Xinlin Yang 1, Zhou Du 1, Yuting Chen 2, Jiran Shen 1, Ronghui Yu 1,✉
PMCID: PMC12744959  PMID: 41456925

ABSTRACT

Background

Population‐based evidence on the predictive role of left ventricular ejection fraction (LVEF) in incident Cardiac Conduction Dysfunction (CCD) and the mediating effects of electrophysiological parameters remains understudied. This study aimed to characterize the relationship between LVEF and incident CCD and explore the potential mediating effects of electrophysiological parameters.

Methods

This prospective cohort study included 32,398 participants (96.6% White ethnicity) from the UK Biobank with analyzable LVEF and electrocardiogram data. Incident CCD was defined as the first occurrence of atrioventricular block, left bundle branch block, or other conduction disorders. Stepwise backward Cox regression and sensitivity analyses evaluated the association between LVEF and CCD. Additionally, mediation analysis was performed to examine QRS duration, PQ interval, and corrected QT interval as potential mediators.

Results

During a mean follow‐up of 6.96 ± 1.63 years, 484 incident CCD cases were identified. LVEF was an independent predictor of incident CCD, with each 1‐standard deviation increase in LVEF associated with a 17% reduction in risk (adjusted hazard ratio, 0.83; 95% confidence interval, 0.77–0.89; p < 0.001). Sensitivity analyses across LVEF thresholds, competing risks, and exclusion of early events confirmed the robustness of these findings. Mediation analysis showed that PQ interval mediated 6% (p < 0.001), QRS duration mediated 17% (p < 0.001), and corrected QT interval mediated −3% (p = 0.002) of the total effect.

Conclusion

LVEF is independently associated with incident CCD, with electrophysiological parameters potentially explaining part of this association. These findings underscore the clinical relevance of myocardial mechano‐electrical coupling in large‐scale population settings.

Keywords: electrophysiological parameters, incident cardiac conduction dysfunction, left ventricular ejection fraction, mediation factors, predictive value

Left ventricular ejection fraction (LVEF) and incident cardiac conduction dysfunction (CCD). Models were adjusted for age, waist‐to‐hip ratio, hypertension, diabetes mellitus, ischemic heart diseases, atrial fibrillation, left ventricular myocardial mass, left ventricular end‐diastolic volume, PQ interval, QRS duration and corrected QT interval.

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1. Introduction

Cardiac conduction dysfunction (CCD), encompassing atrioventricular block (AVB) and left bundle branch block (LBBB), is strongly linked to a wide array of cardiovascular diseases and elevated mortality risk (Cheng et al. 2009; Higuchi et al. 2020; Dideriksen et al. 2021; Huizar et al. 2023; Yang et al. 2024), notably serving as a risk factor of syncope (Saklani et al. 2013). Pathophysiologically, CCD impairs the feedforward excitation‐contraction coupling and induces asynchronous myocardial contraction, ultimately leading to progressive myocardial dysfunction (Bers 2002; Vernooy et al. 2005; Viskin et al. 2021).

Concurrently, existing evidence from myocardial mechanoelectrical feedback study highlights that the alterations of cardiac mechanics exert detrimental effects on myocardial electrophysiological properties, mainly manifesting as changes in conduction velocity, excitability, and refractoriness (Quinn and Kohl 2021). Preclinical investigations have demonstrated that acute left ventricle (LV) overload disrupts ventricular conduction, predisposing to prolonged QRS duration and decelerated impulse propagation (Sideris et al. 1994; Sung et al. 2003). Clinically, myocardial stretching induced by infarct‐related scar stiffness exacerbates mechanical heterogeneity, further impairing conduction velocity (Ashikaga et al. 2005; Saffitz and Kléber 2012). Conversely, percutaneous transvenous mitral balloon valvotomy normalizes left atrial pressure, resulting in immediate reversal of arrhythmogenic dispersion and conduction delay in the pulmonary veins (Walters et al. 2014). Notably, while these findings underscore the mechanistic interplay between cardiac mechanics and electrical conduction, a critical gap remains: the association between left ventricular ejection fraction (LVEF) and incident CCD has not been systematically evaluated in population‐based cohorts. Current risk evaluations for CCD primarily focus on demographic features, lifestyle factors, and preexisting cardiovascular comorbidities (Kerola et al. 2019; Frimodt‐Møller et al. 2023; Zhao et al. 2024; Zhang et al. 2024), overlooking the potential role of LV systolic dysfunction in predisposing to conduction abnormalities. Given that LVEF reflects global cardiac mechanical performance and is intricately linked to mechanoelectrical feedback pathways, elucidating its independent association with CCD incidence is essential for proactive risk stratification.

Against this backdrop, we conducted a prospective analysis using data from the UK Biobank (UKB) cohort to address two primary objectives: (1) to examine whether baseline LVEF is independently associated with the incidence of CCD in a large general population; and (2) to characterize the potential mediating effects of PQ interval (PQ), QRS duration (QRS), and corrected QT interval (QTC) in the hypothesized LVEF–CCD pathway.

2. Methods

2.1. Study Population and Design

Data were derived from the UKB resource, a prospective cohort of over 500,000 volunteers recruited between 2006 and 2010 (Caleyachetty et al. 2021). Baseline was defined as the date of participants' first cardiac magnetic resonance (CMR) imaging, conducted between 2015 and 2020 (Raisi‐Estabragh et al. 2021).

Initially, 32,704 individuals with available baseline LVEF and electrophysiological parameters (PQ, QRS and QTC) were included. We excluded 300 participants with prevalent CCD at baseline and 6 with missing follow‐up data, resulting in a final analytic cohort of 32,398 participants. The inclusion and exclusion criteria are detailed in Figure S1. This study utilized UK Biobank datasets under approved application (number: 280417). All participants provided written informed consent. Ethical approval was obtained from the UK Biobank Research Ethics Committee (11/NW/0382, 21/NW/0157) and the Ethics Committee of The First Affiliated Hospital of Anhui Medical University (PJ 2024‐13‐46).

2.2. Ascertainment of CMR Metrics

Baseline CMR‐derived metrics included LVEF, left ventricular end‐diastolic volume (LVEDV), left ventricular end‐systolic volume (LVESV), left ventricular wall thickness (LVWT), and left ventricular myocardial mass (LVMM). CMR imaging was performed using 1.5‐T scanners (MAGNETOM Aera, Syngo Platform VD13A, Siemens Healthcare), as previously described (Petersen et al. 2016). Briefly, cardiac function assessment involved three long‐axis cine acquisitions and a full short‐axis slice stack to comprehensively visualize both ventricles. Image acquisition utilized balanced steady state free precession sequences with single‐slice per breath‐hold.

2.3. Ascertainment of Covariables

Baseline covariables included established risk factors for incident CCD (Zhao et al. 2024; Zhang et al. 2024), encompassing demographics, electrophysiological parameters, and clinical features. Age was calculated as the time elapsed from birth to the CMR scan date, with participants ≥ 65 years categorized as the older subgroup. Self‐reported data on sex, ethnicity, smoking status, and alcohol consumption were collected via touch screen questionnaires. Electrophysiological metrics were extracted from 12‐lead resting electrocardiograms in the UK Biobank physical measures dataset. Baseline clinical diseases were identified using self‐report, death registry records, or hospital diagnoses based on International Classification of Diseases, 10th edition (ICD‐10) codes. Detailed definitions of all covariates are provided in Table S1.

2.4. Outcome Definition

The primary outcome was incident CCD, defined as the first diagnosis of any condition under the ICD‐10 chapters I44 (Atrioventricular and left bundle branch block) or I45 (Other conduction disorders). This composite endpoint encompasses a broad spectrum of disorders, including but not limited to first‐degree atrioventricular block (I44.0), second‐degree atrioventricular block (I44.1), complete atrioventricular block (I44.2), and left or right bundle branch blocks (e.g., I44.7, I45.1). The full case definition for CCD and its subtypes is detailed in Table S1.

2.5. Statistical Analysis

Baseline characteristics were summarized using Student's t‐test or Wilcoxon rank‐sum test for continuous variables (reported as mean ± standard deviation [SD] or median [interquartile range, IQR]) and chi‐square or Fisher's exact test for categorical variables (reported as counts and percentages). Spearman's rank correlation evaluated the correlation between variables. Missing data (≤ 2.33% for all covariables) were imputed using multiple imputation with the mice package (Table S2).

Univariable Cox proportional hazards models were first used to assess associations between individual covariables and incident CCD. To address multicollinearity (Figure S2) and optimize model parsimony, a backward stepwise selection procedure was applied to a comprehensive multivariable model containing all candidate variables. Independent predictors were defined as variables that remained significantly associated with incident CCD in the final model. Proportional hazards assumptions were verified via Schoenfeld residual tests for all retained variables.

A restricted cubic spline (RCS) analysis with five knots was used to characterize the nonlinear dose–response relationship between LVEF and CCD risk. LVEF was also categorized into quartiles (Q1–Q4) for multivariable Cox regression and adjusted Kaplan–Meier cumulative incidence analysis, with Q1 as the reference group. Continuous predictors (excluding age) in the final model were dichotomized using optimal cutoffs derived from receiver operating characteristic (ROC) curve analysis (Table S3) for subgroup analyses, which were corrected for multiple comparisons using the Bonferroni method.

Four sensitivity analyses were performed to assess result robustness. First, we conducted multivariable Cox regression using LVEF cutoff values derived from four methods: (1) ROC curve analysis; (2) 2022 American Heart Association (AHA) guidelines (Heidenreich et al. 2022); (3) median split; and (4) the lowest 5th percentile of the LVEF distribution. Second, the competing risk of all‐cause mortality was performed with Fine‐Gray sub‐distribution hazards models. Third, we excluded the events occurring within the first 1 and 2 years of follow‐up for reverse causality mitigation. Fourth, because previous studies have demonstrated a significant association between ischemic heart disease (IHD) and CCD (Frimodt‐Møller et al. 2023; Haxha et al. 2023), we excluded participants with a confirmed diagnosis of IHD at baseline.

Mediation analysis was conducted to evaluate whether electrophysiological parameters mediated the association between LVEF and incident CCD. First, linear regression confirmed the association between LVEF and each mediator. Then, the Bootstrap method (5000 resamples) was used to estimate the indirect effect, with 95% confidence intervals (CIs) not including zero indicating significant mediation. All analyses were performed on the UK Biobank Research Analysis Platform (RAP) using R (v4.4.0). Statistical significance was defined as two‐tailed p < 0.05.

3. Results

3.1. Baseline Characteristics

Baseline characteristics of 32,398 participants are presented in Table 1. The cohort had a mean age of 63.4 ± 7.55 years, with 47.4% male and 96.6% White race. During a mean follow‐up of 6.96 ± 1.63 years, 484 incident CCD events occurred. Compared to nonevent participants, those with incident CCD were older, more frequently male, and had higher body mass index (BMI), waist‐to‐hip ratio (WHR), and prevalence of clinical comorbidities. Additionally, CCD cases exhibited significantly lower LVEF (Figure S6) and higher LVEDV, LVESV, LVWT, LVMM, and prolonged PQ, QRS, and QTC.

TABLE 1.

Baseline characteristics.

[ALL] N = 32,398 CCD N = 484 No CCD N = 31,914 p for overall
Age 64.0 [58.0; 69.0] 69.0 [64.0; 72.0] 64.0 [58.0; 69.0] < 0.001
Sex (male) 15,359 (47.4%) 326 (67.4%) 15,033 (47.1%) < 0.001
Ethnic (White) 31,304 (96.6%) 473 (97.7%) 30,831 (96.6%) 0.219
BMI 25.9 [23.5; 28.8] 27.0 [24.2; 29.9] 25.9 [23.5; 28.8] < 0.001
WHR 0.87 [0.80; 0.93] 0.91 [0.84; 0.97] 0.87 [0.80; 0.93] < 0.001
Smoking (ever) 17,187 (53.0%) 282 (58.3%) 16,905 (53.0%) 0.023
Alcohol (ever) 31,354 (96.8%) 469 (96.9%) 30,885 (96.8%) 0.980
VD 321 (0.99%) 13 (2.69%) 308 (0.97%) 0.001
CM 53 (0.16%) 4 (0.83%) 49 (0.15%) 0.008
IHD 1751 (5.40%) 69 (14.3%) 1682 (5.27%) < 0.001
Stroke 435 (1.34%) 13 (2.69%) 422 (1.32%) 0.017
HTS 9885 (30.5%) 258 (53.3%) 9627 (30.2%) < 0.001
DM 1678 (5.18%) 55 (11.4%) 1623 (5.09%) < 0.001
AF 611 (1.89%) 24 (4.96%) 587 (1.84%) < 0.001
HF 120 (0.37%) 9 (1.86%) 111 (0.35%) < 0.001
LVEF 59.8 [56.0; 63.7] 57.4 [52.2; 61.7] 59.9 [56.1; 63.7] < 0.001
LVEDV 143 [123; 168] 160 [133; 186] 143 [123; 167] < 0.001
LVESV 57.1 [46.7; 70.2] 68.2 [52.8; 84.4] 57.0 [46.6; 70.0] < 0.001
LVWT 5.62 [5.11; 6.18] 6.11 [5.55; 6.63] 5.61 [5.11; 6.17] < 0.001
LVMM 82.5 [68.3; 100] 99.0 [81.2; 116] 82.3 [68.2; 100] < 0.001
PQ 162 [146; 180] 180 [158; 206] 162 [146; 178] < 0.001
QRS 86.0 [80.0; 94.0] 98.0 [88.0; 126] 86.0 [80.0; 94.0] < 0.001
QTC 420 [404; 435] 430 [411; 450] 419 [404; 435] < 0.001
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Note: Data are presented as mean ± SD or median [IQR] for continuous variables, and as n (%) for categorical variables. PQ represents PQ interval, QRS represents QRS duration, QTC represents corrected QT interval.

Abbreviations: AF, atrial fibrillation; BMI, body mass index; CCD, cardiac conduction dysfunction; CM, cardiomyopathy; DM, diabetes mellitus; HF, heart failure; HTS, hypertension; IHD, ischemic heart diseases; LVEDV, left ventricular end‐diastolic volume; LVEF, left ventricular ejection fraction; LVESV, left ventricular end‐systolic volume; LVMM, left ventricular myocardial mass; LVWT, left ventricular wall thickness; VD, valve disease; WHR, waist‐to‐hip ratio.

3.2. Associations of Baseline Covariables and Incident CCD

Univariate COX regression identified baseline variables associated with incident CCD, including LVEF, age, sex, BMI, WHR, smoking, hypertension (HTS), diabetes mellitus (DM), valve disease (VD), cardiomyopathy (CM), IHD, atrial fibrillation (AF), heart failure (HF), stroke, LVWT, LVMM, LVEDV, LVESV, and electrophysiological parameters (PQ, QRS, and QTC) (Table S4).

A stepwise backward multivariable Cox model revealed that higher LVEF (per standard deviation [SD] increase: hazard ratio [HR] 0.83, 95% confidence interval [CI] 0.77–0.89, p < 0.001), greater WHR, and larger LVEDV were significantly associated with lower CCD risk. Conversely, older age, HTS, DM, IHD, AF, higher LVMM, and prolonged PQ, QRS, or QTC increased risk (Figure 1). Notable discrepancies in the direction of associations for WHR and LVEDV emerged between univariate and multivariable analyses (Table S4, Figure 1).

FIGURE 1.

FIGURE 1

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Stepwise backward multivariable Cox regression analysis. Variables included in the full model of stepwise backward Cox regression analysis were LVEF, age, sex, ethnicity, BMI, WHR, smoking status, alcohol status, HTS, DM, VD, CM, IHD, AF, HF, stroke, LVWT, LVMM, LVEDV, LVESV, PQ interval, QRS duration, and QTC. HRs represented the associated risk for per SD increase in continuous variables. SD values for continuous variables were as follows: LVEF (%): 5.91; age (years): 7.55; WHR: 0.09; LVMM (g): 22.14; LVEDV (mL): 33.59.

3.3. Association of LVEF and Incident CCD

LVEF was independently associated with incident CCD (Figure 1). Subgroup analyses using an LVEF cutoff of ≤ 56.14% (derived from ROC curve analysis) revealed synergistic increases in risk when combined with DM (HR 2.37, 95% CI 1.54–3.66, Bonferroni‐corrected p < 0.001), higher LVMM (HR 2.17, 95% CI 1.59–2.96, p < 0.001), and prolonged PQ (HR 3.33, 95% CI 2.54–4.37, p < 0.001) or QTC (HR 2.69, 95% CI 2.02–3.58, p < 0.001) (Table S5). Quartile‐based analysis of LVEF showed a dose‐dependent reduction in risk with increasing LVEF: compared to the lowest quartile (Q1, ≤ 56.0%), risk was reduced by 27% in Q2 (56.0%–59.8%, Bonferroni‐corrected p = 0.030), 41% in Q3 (59.8%–63.7%, p < 0.001), and 29% in Q4 (> 63.7%, p = 0.035) (Figure S3). RCS and Kaplan–Meier analyses consistently demonstrated this inverse, dose‐responsive association (Figures S4 and S5). Taken together, these results show that higher LVEF is generally associated with a reduced risk of CCD, with the most pronounced risk reduction observed at moderate LVEF levels (Q3), and a slight uptick in risk at the highest LVEF quartile (Q4) compared to Q3, yet still maintaining a lower risk than the lowest LVEF group.

3.4. Sensitivity Analyses of LVEF for Incident CCD

3.4.1. Threshold Effect Analyses

Using LVEF cutoffs from ROC analysis (56.14%), median split (59.85%), 2022 AHA guidelines (50%), and the lowest 5th percentile (50.24%), LVEF ≤ cutoff was consistently associated with higher risk: HRs 1.50 (1.24–1.81, p < 0.001), 1.37 (1.13–1.66, p = 0.002), 2.04 (1.58–2.63, p < 0.001), and 1.94 (1.51–2.50, p < 0.001), respectively (Figure 2, Table S6).

FIGURE 2.

FIGURE 2

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Categorization of LVEF using different cutoff points. Cox regression analyses were performed using four LVEF categorization cutoff points: (A) ROC‐derived cutoff value: 56.14%; (B) median: 59.85%; (C) American Heart Association (AHA)‐recommended cutoff: 50%; (D) lower 5th percentile of LVEF distribution: 50.24%. Models were adjusted for all covariables listed. HRs represented the risk associated with per SD increase in continuous variables. The red lines indicated p values < 0.05, while the gray lines represented p values ≥ 0.05.

3.4.2. Competing Risks Analysis

Accounting for 592 deaths during follow‐up, LVEF remained a significant predictor in the Fine‐Gray model (HR 0.84, 95% CI 0.77–0.91, p < 0.001) (Table 2).

TABLE 2.

Sensitivity analysis.

Variable Competing risk of all‐cause mortality (N = 592) Exclude the events during the first 1 year of follow‐up (N = 69) Exclude the events during the first 2 years of follow‐up (N = 152) Exclude baseline IHD (N = 1751)
Events/population 415/32,329 Events/population 332/32,246 Events/population 415/30,647
HR (95% CI) p HR (95% CI) p HR (95% CI) p HR (95% CI) p
LVEF 0.84 (0.77–0.91) < 0.001 0.83 (0.76–0.90) < 0.001 0.86 (0.78–0.95) 0.003 0.82 (0.76–0.90) < 0.001
Age 1.57 (1.41–1.74) < 0.001 1.55 (1.38–1.74) < 0.001 1.54 (1.36–1.75) < 0.001 1.64 (1.47–1.84) < 0.001
WHR 0.88 (0.78–0.98) 0.027 0.87 (0.78–0.99) 0.029 0.91 (0.79–1.04) 0.170 0.88 (0.78–0.99) 0.032
HTS 1.36 (1.10–1.67) 0.004 1.49 (1.20–1.85) < 0.001 1.52 (1.19–1.93) < 0.001 1.36 (1.10–1.68) 0.004
DM 1.64 (1.21–2.23) 0.002 1.64 (1.20–2.25) 0.002 1.57 (1.10–2.24) 0.013 1.76 (1.27–2.43) < 0.001
IHD 1.35 (1.01–1.80) 0.040 1.32 (0.98–1.78) 0.065 1.18 (0.83–1.66) 0.363 NA NA
AF 1.65 (1.10–2.49) 0.017 1.63 (1.03–2.56) 0.036 1.64 (0.99–2.73) 0.055 1.18 (0.66–2.10) 0.577
LVMM 1.40 (1.20–1.64) < 0.001 1.41 (1.20–1.65) < 0.001 1.36 (1.13–1.62) < 0.001 1.35 (1.15–1.58) < 0.001
LVEDV 0.85 (0.74–0.98) 0.021 0.82 (0.71–0.94) 0.005 0.85 (0.72–1.00) 0.054 0.92 (0.79–1.06) 0.232
PQ 1.36 (1.27–1.45) < 0.001 1.37 (1.31–1.43) < 0.001 1.37 (1.30–1.44) < 0.001 1.36 (1.30–1.42) < 0.001
QRS 1.58 (1.48–1.67) < 0.001 1.56 (1.47–1.66) < 0.001 1.51 (1.41–1.62) < 0.001 1.57 (1.48–1.66) < 0.001
QTC 1.16 (1.07–1.26) < 0.001 1.18 (1.09–1.27) < 0.001 1.15 (1.05–1.26) 0.003 1.17 (1.08–1.27) < 0.001
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Note: Models were adjusted for all covariables listed. HRs represented the risk associated with per SD increase in continuous variables. Bold values indicate statistical significance (p < 0.05).

3.4.3. Reverse Causality Mitigation

Excluding events within the first 1 year (n = 69) and 2 years (n = 152) yielded consistent results: HRs 0.83 (0.76–0.90, p < 0.001) and 0.86 (0.78–0.95, p = 0.003), respectively (Table 2).

3.4.4. IHD Exclusion

Among 30,647 participants without baseline IHD, LVEF remained a strong predictor (HR 0.82, 95% CI 0.76–0.90, p < 0.001) (Table 2).

Collectively, these analyses confirmed the robust and independent association between LVEF and CCD risk across multiple methodological frameworks.

3.5. Mediation Analysis

Before advancing to mediation analysis, correlations between LVEF and electrophysiological parameters were evaluated. Multiple linear regression (Table S7) showed that LVEF was negatively correlated with PQ (β = −1.21, p < 0.001) and QRS (β = −1.25, p < 0.001)—higher LVEF was associated with shorter PQ and QRS—and positively correlated with QTC (β = 0.93, p < 0.001). Mediation analysis revealed distinct patterns.

3.5.1. PQ and QRS Strengthened the Inverse LVEF–CCD Risk Association

PQ mediated 6% of the total effect (indirect HR: 0.99, 95% CI: 0.98–0.99, p < 0.001, Figure 3A), and QRS mediated 17% (indirect HR: 0.96, 95% CI: 0.95–0.96, p < 0.001, Figure 3B). Shorter PQ/QRS (driven by higher LVEF) was independently linked to lower CCD risk, amplifying LVEF's protective effect.

FIGURE 3.

FIGURE 3

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Mediating Effects of Electrophysiological Parameters. HRs represented the risk associated with per SD increase in LVEF (5.91%). Models were adjusted for age, WHR, HTS, DM, IHD, AF, LVMM, LVEDV, PQ, QRS, and QTC.

3.5.2. QTC Weakened the Association

Despite a significant indirect effect (HR: 1.01, 95% CI: 1.00–1.01, p < 0.001), QTC exhibited a mediated proportion of −3% (p = 0.002, Figure 3C). Longer QTC (associated with higher LVEF) was moderately linked to increased CCD risk, counteracting part of LVEF's protective pathway.

These findings indicate that PQ and QRS may act as facilitatory mediators, while QTC serves as a minor inhibitory mediator in the LVEF–CCD risk relationship.

4. Discussion

4.1. Summary of Findings

This prospective observational study of 32,398 participants from the UK Biobank establishes a robust inverse association between LVEF and incident CCD. After adjusting for demographic, clinical, and electrophysiological covariates, multivariable Cox regression revealed that a one‐SD increase in LVEF (5.91%) was associated with a 17% lower risk of CCD (HR = 0.83, 95% CI: 0.77–0.89, p < 0.001). Electrophysiological parameters may mediate this relationship: PQ and QRS accounted for 6% and 17% of the total effect, respectively, whereas QTC exerted a minimal inverse mediation effect (−3%, p = 0.002), as visualized in the Central Illustration.

4.2. Interpretation in Context of Prior Literature

Our univariate analysis initially identified WHR and LVEDV as risk factors for CCD, consistent with prior reports linking adiposity and ventricular remodeling to conduction system dysfunction (Liu et al. 2023). However, after multivariable adjustment, both variables emerged as protective factors, underscoring the critical need for rigorous covariate control in observational studies of complex phenotypes like CCD. This reversal highlights how cardiovascular comorbidities can confound associations, necessitating comprehensive adjustment to avoid spurious risk factor identification.

Aligning with prior literature, aging in particular has been consistently proved to be an independent risk factor of CCD occurrence (Zhao et al. 2024; Schneider et al. 1979). HTS, DM, IHD, and AF emerged as independent CCD risk factors, also aligning with established literature (Frimodt‐Møller et al. 2023; Haxha et al. 2023). These associations likely reflect shared pathophysiological pathways, such as myocardial fibrosis, electrical remodeling, and structural degeneration of the conduction system (Brilla and Weber 1992; Wijesurendra and Casadei 2015; Mohammed et al. 2015; Jia et al. 2018; Thompson et al. 2011; Centurión et al. 2019). Moreover, the interactive effects of multiple cardiovascular conditions may exacerbate structural remodeling, accelerating conduction system dysfunction (Said et al. 2018; Cruz‐Ávila et al. 2020).

LVMM—a marker of ventricular hypertrophy—was associated with a 41% higher CCD risk per SD increase, consistent with fibrotic remodeling of the myocardial interstitium that impairs electrical conduction (Thompson et al. 2011; Halliday and Prasad 2019). Besides, prolonged PQ, QRS, and QTC, hallmarks of conduction delay, also strongly predicted CCD, corroborating electrophysiological mechanisms of arrhythmogenesis described in prior studies (Kerola et al. 2019; Zhang et al. 2024).

Linear regression analysis revealed a negative correlation between LVEF and PQ/QRS, while a positive correlation between QTC and LVEF. While the inverse LVEF‐PQ/QRS association aligns with prior findings (Husby et al. 2015; Shah et al. 2016), the positive LVEF‐QTc relationship contrasts with some literature (Dahou et al. 2016). This heterogeneous result may partly be explained by the coexistence of higher LVEF and longer QTC in a hyperkinetic circulatory state, especially in patients with hyperdynamic conditions such as hypertrophic cardiomyopathy and sympathetic hyperactivity (Johnson et al. 2011; May et al. 2017), though this warrants further mechanistic exploration.

4.3. Novel Contributions and Mechanistic Implications

Beyond their predictive utility, the results highlight the complex interplay of electrophysiological parameters in mediating the LVEF‐CCD association: PQ and QRS may act as key positive mediators, whereas QTc exerts negligible inverse mediation, playing only a minor role in this pathway—markedly distinct from PQ/QRS's robust facilitatory effects. These findings underscore the differential mechanistic roles of electrophysiological parameters, with PQ and QRS partially explaining the link between ventricular function and conduction dysfunction. Even though a reversal causality may occur due to no time sequence between LVEF and electrophysiological parameters.

To our knowledge, this is the first population‐based study to characterize LVEF as a predictor of incident CCD. Our results identify the robust independence of the LVEF‐CCD association across diverse sensitivity analyses. The mechanistic framework likely involves multiple pathways: (1) One may be attributed to the lower risk of larger LVEF on mediating risk factors, including IHD and AF (Shah et al. 2022), both of which presented an increased risk of CCD in our study. (2) Low LVEF often coexists with CCD risk factors, facilitating disease progression through shared risk exposure and structural remodeling (Davis et al. 2002; Wang et al. 2003; Rosmini et al. 2017). (3) LVEF impairment is associated with myocardial fibrosis, activating mechanosensitive ion channels and disrupting electrophysiological properties (Thompson et al. 2011; Segura et al. 2014; Miragoli et al. 2006). (4) PQ and QRS mediate 22.93% of the total effect, linking ventricular mechanics to electrical conduction through established mechano‐electrical coupling (Thompson et al. 2011). Abnormal mechanical coupling between myofibroblast and adjacent myocardium transmits the intercellular interaction to activate mechanosensitive ion channels of myocyte and subsequently causes alteration of electrophysiological function and slow conduction, especially in cardiac injury reflected in LVEF impairment. Meanwhile, we identified the optimal interval range of LVEF (Q3, 59.8%–63.7%) associated with the lowest risk of CCD event, which exhibited a similar threshold range (60%–65%) on the lowest mortality (Wehner et al. 2020).

4.4. Clinical Implications

Our finding identifies that LVEF emerges as a readily available, actionable biomarker for CCD risk, particularly in asymptomatic individuals. Clinicians can integrate LVEF with electrophysiological parameters to enhance risk prediction, potentially improving early detection of conduction disorders in patients with reduced systolic function. The identification of PQ and QRS as potential mediators supports the concept that interventions improving LV mechanics may indirectly reduce CCD risk by normalizing electrophysiological parameters. This highlights the clinical relevance of “mechano‐electrical coupling” as a therapeutic target. Subgroup analyses reveal synergistic risks in individuals with low LVEF combined with DM, LV hypertrophy, or prolonged PQ/QRS. Intensified monitoring in these populations may enable early detection of conduction disorders, potentially reducing complications like syncope or sudden cardiac death—key priorities in precision cardiovascular care. In summary, this work establishes LVEF as a critical predictor of CCD and underscores the importance of integrating ventricular function and electrophysiological indices in clinical practice. While mechanistic and causal questions remain, the findings provide a robust foundation for future interventional studies and risk prediction models.

4.5. Strengths

The study leverages data from 32,398 participants in the UK Biobank, a well‐characterized prospective cohort, offering several innovations: (1) The long mean follow‐up of 6.96 years minimizes time‐dependent bias, providing reliable evidence on the association between LVEF and incident CCD. (2) By integrating demographic, clinical, structural, and electrophysiological parameters, the study controls for a wide range of potential confounders. Stepwise backward regression and multiple sensitivity analyses—including competing risk modeling, exclusion of early events, and validation across LVEF thresholds—demonstrate the robustness of LVEF as an independent predictor of CCD (adjusted HR: 0.83, 95% CI: 0.77–0.89, p < 0.001). (3) The study is the first to quantify the mediating effects of electrophysiological parameters in the LVEF‐CCD association. PQ and QRS account for 6% and 17% of the total effect, respectively, linking myocardial mechanics to electrical conduction through established mechano‐electrical feedback pathways. This bridges clinical observations with preclinical evidence of ventricular stretch/fibrosis affecting conduction velocity and refractoriness. (4) The identification of an optimal LVEF range (59.8%–63.7%, Q3) reinforcing LVEF as a pragmatic biomarker for integrated CCD risk assessment.

4.6. Study Limitations and Future Directions

Several limitations merit consideration. First, while the study demonstrates a robust statistical association, the cross‐sectional nature of baseline LVEF and electrophysiological measurements precludes definitive causal inference. Reverse causality—where prolonged QRS/PQ contribute to ventricular dyssynchrony and subsequent LVEF decline—cannot be fully excluded, requiring longitudinal studies to validate temporal relationships. Second, because LVEF and the proposed mediators (PQ, QRS, QTc) were measured contemporaneously at baseline, the direction of causality in the LVEF‐mediator‐CCD pathway cannot be definitively established. It is plausible that prolonged PQ or QRS intervals are not merely mediators but also confounders. For instance, underlying conditions such as diffuse myocardial fibrosis or inherent conduction system disease could independently lead to both a reduction in LVEF and a prolongation of conduction intervals. Third, regarding outcome ascertainment, our study was constrained by the use of predefined, broad ICD‐10 codes (I44, I45) for CCD that were directly extracted from the UK Biobank. While the use of a broad, code‐based definition for CCD enhanced statistical power, it may underdiagnose mild or subclinical conduction disorders and inherently groups together conditions with distinct pathophysiologies and clinical severities (e.g., asymptomatic first‐degree AV block vs. high‐grade AV block). Consequently, the association we observed between LVEF and the composite CCD endpoint might not uniformly apply to all specific subtypes, a nuance that future studies with adjudicated outcomes should explore. Fourth, our study population lacked ethnic diversity, with over 96% of participants being of White ethnicity. This severely compromises the generalizability of our findings to other ethnic groups.

Future studies should: (1) Use longitudinal data to validate the temporal sequence of LVEF and electrophysiological changes; (2) Elucidate the specific relationships between CCD subtypes and LVEF by incorporating validated outcomes or continuous electrocardiographic monitoring data; (3) Investigate ethnicity‐specific effects, given the study's predominantly White sample; (4) Explore therapeutic interventions targeting mechano‐electrical coupling to mitigate CCD risk in low‐LVEF populations.

5. Conclusions

In this large prospective cohort study, LVEF was independently associated with incident CCD. Sensitivity analyses across multiple LVEF thresholds and clinical scenarios consistently supported this association. PQ and QRS mediated 6% and 17% of the total effect, respectively, highlighting their role in the mechanistic link between myocardial mechanics and electrical conduction. Further studies are warranted to explore causal pathways and validate these results in diverse populations.

Author Contributions

F.Z., R.Y., and Q.Z. designed the study. Q.Z. and J.S. developed the methodology. Q.Z., Z.D., and J.W. acquired the data. Y.H., Y.C., and X.Y. performed the statistical analysis; and Q.Z. and F.Z. drafted the manuscript. All authors critically reviewed the manuscript, and Q.Z. revised the manuscript for final submission. All authors read and approved the final manuscript.

Funding

This study was supported by Anhui Provincial Department of Science and Technology (Grants 202427b10020101 to Prof. R.Y.), Anhui Provincial Department of Education (Grant 2024AH040113 to Prof. R.Y.), and Lvliang Municipal Bureau of Science and Technology, Shanxi Province, China. (Grant 2024RC23 to Prof. R.Y.).

Ethics Statement

The UK Biobank study received ethical approval from the North West Research Ethics Committee (reference numbers: 11/NW/0382, 21/NW/0157). All participants provided written informed consent prior to their enrollment. The utility of UK Biobank data was approved by the Ethics Committee of The First Affiliated Hospital of Anhui Medical University (approval number: PJ 2024‐13‐46).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1: Summary of approach to selection of participants.

Figure S2: Spearman's rank correlation analysis between all variables.

Figure S3: Association between LVEF quartiles and incident CCD risk.

Figure S4: Cumulative incidence of CCD by LVEF.

Figure S5: The relationship between LVEF and incident CCD.

Figure S6: Distribution of baseline LVEF by incident CCD status.

Table S1: Variable definitions in UK Biobank.

Table S2: The numbers and proportions of missing baseline data.

Table S3: Cutoff values for continuous variables among independent predictors.

Table S4: Univariable Cox regression analysis for all variables.

Table S5: Subgroup analysis for LVEF and other risk factors.

Table S6: Categorization of LVEF using different cutoff points.

Table S7: Linear regression analysis between LVEF and electrophysiological parameters.

ANEC-31-e70146-s001.docx (930.5KB, docx)

Acknowledgments

This research has been conducted using the UK Biobank Resource under Application Number 280417. We are grateful to the participants and investigators of UK Biobank. The target and heart elements in Central Illustration were created on figdraw.com.

Qie Zhang and Feilong Zhang are first co‐authors.

Data Availability Statement

The data that support the findings of this study are available from UK Biobank. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from https://www.ukbiobank.ac.uk/ with the permission of UK Biobank.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1: Summary of approach to selection of participants.

Figure S2: Spearman's rank correlation analysis between all variables.

Figure S3: Association between LVEF quartiles and incident CCD risk.

Figure S4: Cumulative incidence of CCD by LVEF.

Figure S5: The relationship between LVEF and incident CCD.

Figure S6: Distribution of baseline LVEF by incident CCD status.

Table S1: Variable definitions in UK Biobank.

Table S2: The numbers and proportions of missing baseline data.

Table S3: Cutoff values for continuous variables among independent predictors.

Table S4: Univariable Cox regression analysis for all variables.

Table S5: Subgroup analysis for LVEF and other risk factors.

Table S6: Categorization of LVEF using different cutoff points.

Table S7: Linear regression analysis between LVEF and electrophysiological parameters.

ANEC-31-e70146-s001.docx (930.5KB, docx)

Data Availability Statement

The data that support the findings of this study are available from UK Biobank. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from https://www.ukbiobank.ac.uk/ with the permission of UK Biobank.

Articles from Annals of Noninvasive Electrocardiology are provided here courtesy of International Society for Holter and Noninvasive Electrocardiology, Inc. and Wiley Periodicals, Inc.

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