Letter to the Editor, “Association Between Renal Function and Left Atrial Low‐Voltage Area Burden in Paroxysmal Atrial Fibrillation”

We read with great interest the recently published study by Deng et al. (2025) in Annals of Noninvasive Electrocardiology, which investigated the relationship between estimated glomerular filtration rate (eGFR) and left atrial low-voltage area (LVA) burden in elderly patients with paroxysmal atrial fibrillation (AF). By demonstrating an inverse association between renal function and atrial substrate remodeling, the authors provide important insights into the interplay between chronic kidney disease (CKD) and atrial fibrosis, an area of growing relevance in contemporary electrophysiology. Their findings contribute meaningfully to pre-ablation risk stratification and highlight the potential value of renal function in anticipating substrate complexity. We commend the authors for addressing this important and underexplored topic; however, several limitations warrant consideration. First, the retrospective single-center design increases susceptibility to selection and institutional procedural bias. Patients referred for ablation may not represent the broader CKD, AF population, and variations in mapping techniques, operator expertise, and voltage thresholds across centers may restrict external validity and generalizability. This may lead to over- or underestimation of the association between eGFR and LVA burden and limit applicability to other populations, ethnicities, and healthcare systems. Naruse et al. (2011) demonstrated that CKD predicted AF recurrence after ablation in a multicenter cohort, while Yanagisawa et al. (2017) emphasized the importance of multicenter validation in studies evaluating renal dysfunction and AF outcomes. Second, LVA burden was used as a surrogate for atrial fibrosis; however, voltage mapping is influenced by catheter contact, rhythm status, wall thickness, and mapping density, and does not always correspond directly to histologic fibrosis. Without late gadolinium enhancement cardiac MRI (LGE-MRI), substrate characterization remains incomplete, introducing measurement bias and limiting conclusions regarding true fibrosis burden. Marrouche et al. (2014) showed that MRI-quantified atrial fibrosis independently predicts AF recurrence and offers a more direct structural assessment. Third, limited mapping resolution and voltage variability may affect accuracy. LVA was defined as < 0.5 mV during sinus rhythm, yet bipolar voltage amplitude is influenced by electrode size, interelectrode spacing, catheter orientation, and contact force. Mapping density and rhythm variability further affect quantification. These methodological factors may result in misclassification of LVA burden, potentially attenuating or exaggerating its association with eGFR. Lin et al. (2014) demonstrated that atrial substrate abnormalities vary depending on rhythm during mapping, and Masuda et al. (2018) highlighted that voltage thresholds and mapping density critically influence LVA quantification. Fourth, residual confounding may persist due to incomplete adjustment for inflammatory and fibrotic biomarkers. CKD-associated atrial fibrosis is mechanistically linked to systemic inflammation, oxidative stress, renin–angiotensin–aldosterone system activation, and NLRP3 inflammasome signaling. The absence of inflammatory markers, fibrosis markers and RAAS activity indicators limits mechanistic interpretation and raises the possibility that the observed eGFR-LVA association is mediated by unmeasured inflammatory pathways. Ding et al. (2021) described mechanistic links between CKD and AF through inflammation and fibrosis pathways. Fifth, the predictive strength of eGFR was modest (AUC 0.611). ROC analysis demonstrated weak discrimination, indicating that eGFR alone is a limited standalone predictor of moderate LVA burden and has restricted clinical utility for guiding substrate modification strategies. Chao et al. (2011) similarly found that although reduced renal function correlates with abnormal atrial substrate, predictive models require incorporation of additional clinical variables beyond eGFR, highlighting the need for more comprehensive risk stratification. Future research should employ prospective multicenter designs, incorporate standardized high-density mapping and LGE-MRI for comprehensive substrate characterization, include broader CKD populations with biomarker assessment, and develop validated multivariable predictive models integrating clinical, biomarker, and imaging data. Such approaches will be essential to clarify causality, enhance clinical applicability, and refine the role of renal function in substrate-based ablation strategies. In conclusion, although this study offers important insights into the link between renal dysfunction and atrial substrate remodeling in paroxysmal AF, methodological and mechanistic limitations limit its strength and generalizability. Future prospective multicenter studies incorporating standardized high-density mapping, LGE-MRI, biomarker assessment, and validated multivariable models are needed to clarify causality, improve clinical applicability, and better define the role of renal function in substrate-based ablation strategies. All the authors meet the ICMJE authorship criteria and have made significant and equal contributions to this manuscript. All authors approved the final version and agree to be accountable for all aspects of the work, ensuring the accuracy and integrity of the data and interpretation. The authors have nothing to report. Guarantor Statement: All authors have read and approved the final version of the manuscript. They took complete responsibility for the data's integrity and the data analysis's accuracy. Transparency Statement: The authors affirm that this manuscript is an honest, accurate, and transparent account of the study being reported, that no important aspects of the study have been omitted, and that any discrepancies from the study as planned (and if relevant, registered) have been explained. The authors have nothing to report. The authors declare no conflicts of interest. Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.