Search Results (188)

Background: An important determinant of mortality in AS is the presence and quantity of myocardial scar. Scar quantification using late gadolinium enhancement (LGE) on cardiovascular magnetic resonance (CMR) imaging may be a useful risk-stratification tool for at-risk patients who do not meet current criteria for valve intervention. The incorporation of this tool into clinical practice is currently limited by a lack of consensus on the best LGE quantification technique to use. Methods: Fifteen patients with severe AS underwent LGE imaging on CMR. A reference estimate of the LGE mass was made using a semi-automatic quantitative visual method. An intensity slider (reporting software provided) was used to mark areas of enhanced signal in each short-axis slice that correlated with the reader’s visual assessment of LGE, which used predetermined imaging criteria. This visual slider method (VslM) of determining LGE mass was then used as a reference for establishing the accuracy of various semi- and fully automated methods for identifying and quantifying LGE burden. These included the signal threshold versus reference mean (STRM) method at thresholds of two-, three-, and five-standard deviations (2SD, 3SD, 5SD, respectively), the full width at half maximum (FWHM) method and the Otsu auto threshold (OAT) method. An intraclass correlation analysis was performed to establish and compare the inter-reader reliability for each method. Results: Three readers demonstrated 100% agreement on the presence of LGE in 12/15 (80%) of study cases. Accuracy determined by the Wilcoxon rank sum, Spearman correlation and Bland–Altman tests suggested that the 5SD method using remote myocardium reference regions of interest only in slices with visually detected LGE was best (Wilcoxon rank sum p-values ranged from 0.3 to 0.5 for the three readers, bias on Bland–Altman was <0.5 g for all three readers). This was followed by the FWHM method, but with wide minimum-maximum ranges observed. Inter-reader reliability was best for the 2SD STRM method (ICC = 0.9, p < 0.001), but accuracy using this method was clinically unacceptable. Inter-reader reliability was statistically acceptable for the VslM (ICC = 0.7, p < 0.001). The FWHM method yielded the best balance between accuracy and reliability but may be limited by the heterogeneity of scars observed from patient to patient. Conclusions: The FWHM appeared to offer a reasonable balance between accuracy and precision. However, it was not always the best fit, e.g., in patients with small or non-bright scars. There may be no additional benefit to using the semi- and fully automated methods in the context of AS, and visual estimation, when performed in the manner described in this study (i.e., the VslM), may be clinically sufficient. Full article

Background: Obesity is an independent and major risk factor for cardiovascular diseases. However, the presence of common comorbidities such as diabetes and hypertension makes it difficult to understand the direct impact of obesity on the myocardium. The aim of this study is to evaluate the isolated effects of weight loss achieved after bariatric surgery on left ventricular (LV) geometry and diastolic functions in individuals with the “Metabolically Healthy Obese” (MHO) phenotype. Materials and Methods: The study included 28 patients (Surgical Group) who underwent Laparoscopic Sleeve Gastrectomy (LSG) between January 2022 and December 2025, had a preoperative Body Mass Index (BMI) > 40 kg/m², and had no known cardiovascular or metabolic diseases. The control group consisted of 25 age- and gender-matched metabolically healthy morbidly obese patients who had not undergone surgery. Demographic and echocardiographic data of all participants were analyzed at baseline and at 6 months. Results: Weight Loss: In the surgical group, BMI decreased from 46.21 kg/m² to 37.11 kg/m² at the 6th month, while no significant change was observed in the control group. Cardiac Structure: In the surgical group, Left Ventricular Mass Index was significantly decreased from 51.11 g/m² to 44.57 g/m². Cardiac Function: The E/A ratio, an indicator of diastolic function, increased significantly from 1.19 to 1.34 in the surgical group, indicating notable improvement. No clinically meaningful change in systolic function was detected. Metabolic Parameters: The surgical group exhibited marked improvements in glucose and lipid profiles (decrease in Total Cholesterol, increase in HDL). Conclusions: The study demonstrates that bariatric surgery, independent of metabolic comorbidities, directly provides “reverse remodeling” of cardiac structure and improves function through reduction of adipose tissue and alleviation of hemodynamic load. These results support the effectiveness of surgery in reducing cardiovascular risk and preserving cardiac structure even in morbidly obese patients without comorbidities. Full article

Connexin-43 (CX43) lateralization in ventricular myocardium has been associated with abnormal impulse propagation and increased arrhythmia susceptibility. Its quantitative assessment in histological sections remains challenging because previous methods require segmentation of individual cardiomyocytes and rely on geometric rules applied to segmented cell profiles. Here, we present CLARISA, a segmentation-free, ROI-based deep learning framework that classifies CX43-positive regions as terminal or lateralized directly from fluorescence images. An expert-annotated dataset was generated from left-ventricular cryosections of Wistar rat hearts, in which CX43-positive regions were labeled according to their distribution pattern. A dual-stream EfficientNetV2-S classifier was trained to capture both local and contextual ROI morphology. We also developed a semi-automated whole-section inference module to generate spatial lateralization probability maps and global percent lateralization estimates. On the held-out test set, CLARISA achieved a ROC-AUC of 0.904 (95% bootstrap CI: 0.828–0.960) and a PR-AUC of 0.808 (95% bootstrap CI: 0.682–0.913), supporting the feasibility of automated ROI classification for CX43 lateralization assessment. When deployed on whole tissue sections, including an independently analyzed section not used during model development, CLARISA generated spatial maps that captured heterogeneous CX43 organization and produced a global percent lateralization estimate closely aligned with expert annotation, differing by only 1.30 percentage points over the same detected CX43-positive area. Comparison with a previously published segmentation-based method further indicated that ROI-based and cell-segmentation-based approaches provide related but non-equivalent readouts of CX43 lateralization. The ROI-based design additionally reduces annotation burden—requiring classification of discrete CX43-positive signal rather than complex cardiomyocyte delineation—and ensures that all detected CX43-positive signal contributes to the lateralization estimate regardless of cell boundaries. These results establish CLARISA as a proof-of-principle framework for scalable, segmentation-free CX43 lateralization assessment in cardiac tissue. Further validation across larger, independent, and more heterogeneous datasets will be required to assess robustness, portability across imaging conditions, and translational applicability. The complete codebase, pretrained model, image data, and expert annotation tool are publicly available. Full article

Dilated cardiomyopathy (DCM) is a leading cause of heart failure, but up to 50% of cases have no definitive etiology. Genetic susceptibility alone does not account for phenotypic inconsistency, so a ‘two-hit’ model has been proposed to explore the spectrum of gene-environment interactions. Certain triggers, such as alcohol, chemotherapy agents, and viral myocarditis, are well-established second hits in the pathogenesis of DCM. The exposome, which encompasses environmental and social exposures across the lifespan, provides a more comprehensive framework to understand these interactions. In patients with DCM, air pollution and heavy metals have already been associated with higher rates of mortality and heart failure hospitalization. Microplastics and nanoplastics (MNPs) are novel components of the exposome. They form from the degradation of plastics and enter the circulatory system primarily through ingestion and inhalation. They have recently been found in human cardiovascular tissue, including atherosclerotic plaques and the myocardium. In vivo and in vitro models consistently demonstrate that MNPs induce oxidative stress, mitochondrial dysfunction, and calcium dysregulation. These pathways are shared with established cardiotoxins and converge on cardiomyocyte death, fibrosis, and eccentric ventricular remodeling, which is consistent with the pathogenesis and phenotype of DCM. In genetically susceptible individuals, MNP exposure may therefore contribute to the progression from subclinical myocardial injury to overt systolic dysfunction. This narrative review synthesizes preclinical mechanistic evidence linking MNP exposure to myocardial injury, compares the underlying mechanisms with those of other environmental pollutants and cardiovascular toxins, and integrates these findings within the proposed ‘two-hit’ model of DCM. Whether MNP exposure contributes to DCM in humans remains to be established, but understanding the potential consequences of MNPs has important implications for prevention, therapeutic development and health policy. Standardization of detection methods, chronic low-dose exposure models, and prospective human studies using functional cardiac assessment are needed before translating these experimental findings into clinical practice. Full article

Micro- and nanoplastics (MNPs) are ubiquitous environmental pollutants recognized as emerging and relevant risk factors for numerous human diseases, including cardiovascular diseases. MNPs enter the human body through ingestion, inhalation, and dermal penetration, and their toxicity varies according to size, shape, and chemical composition, most notably between microplastics (>1 µm) and nanoplastics (<1 µm), which differ in cellular uptake mechanisms and biodistribution. Recent evidence has confirmed their presence in cardiac and vascular tissues, raising significant concerns about their potential impact on human health. This review summarizes current knowledge on MNP exposure sources, physicochemical properties, and systemic bioavailability, with a particular emphasis on the mechanisms of transport that facilitate their deposition within the myocardium and vasculature. It further addresses a broad spectrum of cardiotoxic effects, including oxidative stress, mitochondrial injury, immune activation, ion channel disruption, cell death, and fibrosis. Endothelial dysfunction, vascular injury, and pro-atherogenic activity are also discussed. In addition to outlining existing detection techniques and emerging in vitro models, the review highlights initial steps toward the development of preventive strategies. Concluding with key knowledge gaps and future research directions, this article underscores the urgent need for standardized measurement tools, deeper insights into damage mechanisms, and clinical interventions to prevent MNP-induced cardiovascular diseases. Full article

Background/Objective: Cardiac innervation plays a critical role in regulating myocardial function and enabling the heart to adapt to physiological and pathological conditions. Although the general features of sympathetic and parasympathetic innervation of the myocardium are well described, the spatial organisation of nerve fibres within the cardiac muscle remains incompletely characterised. This study aimed to develop and validate the SKUF (Slice–Keep–Unwrap–Fuse) protocol, a multimodal framework for mapping myocardial innervation through the integration of histological data and magnetic resonance imaging (MRI). Methods: The study was performed on the heart of a 7-year-old patient who died from rupture of a cerebral vascular malformation without evidence of cardiovascular disease. Prior to histological processing, post-mortem MRI was performed to provide a precise anatomical reference. The heart was sectioned into sequential transverse rings of 4 mm thickness, yielding 71 paraffin blocks. Histological sections (3 μm) were immunostained with antibodies against UCHL-1 to visualise nerve fibres and scanned using an Aperio AT2 system (20× magnification). Automated image analysis was conducted using the SVSSlide Processor module, which included tissue segmentation, colour-based nerve fibre detection, and sliding-window density mapping. Heatmaps were assembled into ring-based myocardial reconstructions and co-registered with MRI slices using combined rigid and deformable registration, followed by three-dimensional reconstruction of innervation patterns. Results: A higher density of nerve fibres was observed in the right ventricular myocardium compared with the left ventricle, whereas larger nerve trunks were identified in the epicardium of the left ventricle. Quantitative analysis revealed a pronounced longitudinal gradient of innervation, with minimal density in the apical region and progressive increases towards the mid-ventricular segments, where maximal density and spatial organisation of neural structures were observed. The atrioventricular groove exhibited the greatest heterogeneity of innervation due to the presence of large nerve trunks and ganglionated plexuses. Integration of histological maps with MRI enabled three-dimensional visualisation of spatial clusters of nerve fibres. Conclusions: The SKUF protocol provides a robust framework for integrating histological and MRI data to generate three-dimensional maps of myocardial innervation. This approach may facilitate the development of high-resolution anatomical atlases of cardiac innervation and support future studies of neurocardiac mechanisms of arrhythmogenesis and targeted neuromodulation. Full article

Cardiac sarcoidosis (CS) is a critical and frequently underdiagnosed phenotype of sarcoidosis, characterized by non-caseating granulomatous infiltration of the myocardium. This review synthesizes current knowledge regarding the pathogenesis, diagnosis, and management of CS. The disease manifests with a heterogeneous clinical spectrum ranging from asymptomatic conduction abnormalities to life-threatening ventricular arrhythmias and heart failure. Diagnosis remains challenging due to the patchy distribution of granulomas, which limits the sensitivity of endomyocardial biopsy. Consequently, a multimodal diagnostic approach is essential, integrating advanced imaging modalities such as cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE) and ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET). These tools not only facilitate detection but also enable the differentiation of active inflammation from chronic fibrosis. Histopathological assessment, supported by specific immunophenotyping and electron microscopy, remains the gold standard for confirming diagnosis and excluding mimics like giant cell myocarditis or infectious granulomatous diseases. Management requires a multidisciplinary strategy combining immunosuppressive therapy, primarily corticosteroids and steroid-sparing agents, with guideline-directed cardiac care, including implantable cardioverter-defibrillators for arrhythmia risk stratification. Emerging biomarkers and artificial intelligence-driven imaging analysis promise to further refine risk stratification and therapeutic monitoring, advancing precision medicine in this complex disorder. Full article

Myocarditis and inflammatory cardiomyopathy are inflammatory diseases of the heart muscle that can have both infectious and non-infectious causes. They can be caused by an unresolved viral infection or other infection, or they can be autoimmune, toxic, or allergic in nature. The specific identification of the pathogen and/or confirmation of inflammation can only be achieved through direct tissue analysis using endomyocardial biopsy (EMB), as neither detection of the virus nor assessment of the quality and intensity of the inflammation is possible using non-invasive methods. Accordingly, the removal and analysis of an EMB is considered the diagnostic gold standard in international guidelines and statements. The sudden onset of atypical angina pectoris and initially exertion-dependent dyspnea, as well as arrhythmias, pericardial effusion, and progressive symptoms of heart failure, indicate an acute inflammatory process of the myocardium. In addition, nonspecific symptoms such as fatigue and reduced physical performance may also occur. Diagnostic evaluation includes an electrocardiogram (ECG), cardiac imaging, and laboratory tests. The analysis of the EMB is crucial for a definitive diagnosis and thus for the initiation of an etiology-based, specific and personalized therapy. This includes histological and immunohistochemical inflammation diagnostics as well as molecular virological diagnostics. These enable both the detection of viruses and the assessment of transcriptional virus activity. New analyses using metagenomic next generation sequencing (NGS) techniques provide insights of enormous diagnostic and therapeutic relevance. This applies both to the spectrum of detectable pathogens and to the possibility of confirming transcriptional viral activity. In addition, gene expression profiling enables the differentiation of specific forms of myocardial inflammation (e.g., giant cell myocarditis, cardiac sarcoidosis, and eosinophilic myocarditis) and reduces the influence of “sampling errors” in focal inflammatory processes. The treatment of heart failure or ventricular arrhythmias is always symptomatic according to general evidence-based guidelines. In severe cases, mechanical circulatory support or even a heart transplant may be necessary. Patients with histologically confirmed myocardial inflammation or intramyocardial viral infection can be offered specific, causal, and personalized therapy. These patients can be successfully treated with immunosuppressive or antiviral therapy, which significantly improves the prognosis of the disease. Full article

Diagnosis of cardiomyopathies often depends on overt phenotypic manifestations, delaying patient management. This underscores the need for population-level opportunistic screening tools using clinically indicated CT scans to detect subclinical myocardial disease. This study developed an Ensemble Machine Learning (ML) model to automatically segment the left ventricular myocardium from CT data and estimate the probability of underlying myocardial disease using radiomic feature analysis. A total of 60 CT scans (~12,000 images) were used to train ML models for left ventricular myocardium segmentation, including scans from both healthy individuals and patients with myocardial disease. A novel Ensemble model was developed and externally validated on 10 independent CT scans. Subsequently, 100 unseen CT scans were segmented manually and automatically for radiomic feature analysis. After removing highly correlated features through intra-class variation and correlation filtering, the refined dataset was used for model training and testing. Key predictive features were identified, and model performance was evaluated. The four best-performing models (Unet++, ED w/ASC, FPN, and TresUNET) were combined to form an Ensemble model, achieving a final DICE score of 0.882 after hyperparameter optimization. External validation yielded a DICE score of 0.907. Radiomic feature analysis identified 15 key predictors of myocardial disease in both manual and automatic segmentation datasets. The model demonstrated strong performance in detecting underlying myocardial disease, with AUCs of 0.85 and 0.8, respectively. This study presents a fully automated CT-based framework for LV myocardial segmentation and radiomic phenotyping that accurately estimates the probability of underlying myocardial disease. The model demonstrates strong generalizability across different CT protocols and highlights the potential role of AI-driven CT analysis for early, non-invasive cardiomyopathy screening at a population level. Full article

Myocardial infarction (MI) is a life-threatening cardiovascular disorder caused by a partial or complete interruption of oxygenated blood flow to the myocardium, leading to high mortality rates if not diagnosed promptly. Although electrocardiogram (ECG) signals are widely used due to their non-invasive and low-cost nature, MI-specific abnormalities may be subtle and subject to inter-observer variability. Therefore, reliable artificial intelligence-based decision support systems are essential to enhance diagnostic classification accuracy. In this study, only the Lead II derivation from 12-lead ECG recordings of 52 healthy individuals and 148 MI patients was analyzed. To effectively characterize the non-stationary nature of ECG signals, a hybrid time–frequency feature extraction framework was employed. Five-level intrinsic mode functions and wavelet detail and approximation coefficients were obtained using Empirical Mode Decomposition and Discrete Wavelet Transform with a Daubechies-6 wavelet. From these components, 390 times, nonlinear and complexity-based features were extracted using 23 entropy-driven measures. Particle Swarm Optimization was applied to select the most discriminative feature subset, significantly enhancing classification performance. The optimized features were evaluated using Support Vector Machines, Artificial Neural Networks, k-Nearest Neighbors, and Bagged Tree classifiers. The Bagged Trees classifier achieved the best classification performance with an overall correct classification rate of 97.6%. The results demonstrate that the proposed hybrid feature representation combined with PSO-based selection provides a robust and reliable framework for MI detection, offering strong potential for clinical decision support applications. Full article

Background: Cardiovascular magnetic resonance (CMR) is the clinical gold standard for assessing cardiac anatomy and function. However, the manual segmentation of cardiac structures and myocardial infarction (MI) is time-consuming, prone to inter-observer variability, and often depends on contrast-enhanced imaging. Although deep learning (DL) has enabled substantial automation, challenges remain in generalizability, particularly for MI detection from non-contrast cine CMR. Objective: This study proposes a comprehensive DL-based framework for automatic segmentation of cardiac structures and myocardial infarction using contrast-free cine CMR. Methods: The framework integrates multiple convolutional neural network (CNN) architectures for cardiac structure segmentation with an attention-based deep learning model for MI localization. Post-processing refinement using stacked autoencoders and active contour modeling is applied to improve anatomical consistency. Segmentation performance is evaluated using overlap-based and boundary-based metrics, including the Dice Similarity Coefficient (DSC), Mean Contour Distance (MCD), and Hausdorff Distance (HD). Results: The best-performing model achieved Dice scores of 0.93 ± 0.05 for the left ventricular (LV) cavity, 0.89 ± 0.04 for the LV myocardium, and 0.91 ± 0.06 for the right ventricular (RV) cavity, with consistently low boundary errors across all structures. Myocardial infarction segmentation achieved a Dice score of 0.80 ± 0.02 with high recall, demonstrating reliable infarct localization without the use of contrast agents. Conclusions: By enabling accurate cardiac structure and myocardial infarction segmentation from contrast-free cine CMR, the proposed framework supports broader clinical applicability, particularly for patients with contraindications to gadolinium-based contrast agents and in emergency or resource-limited settings. This approach facilitates scalable, contrast-independent cardiac assessment. Full article

Sarcocystis species are apicomplexan protozoa infecting a wide range of domestic and wild animals, including cattle, in which several species are of zoonotic relevance. This study reports, for the first time, the detection and molecular identification of pathogenic and zoonotic Sarcocystis hominis in slaughtered cattle from Central Chile. A total of 200 muscle samples (100 = myocardium, 100 = diaphragm) were examined by macroscopic inspection and tissue homogenization. Selected samples were additionally analyzed by histology, scanning electron microscopy (SEM), transmission electron microscopy (TEM) and multiplex real-time PCR targeting the 18S rDNA. No macroscopic sarcocysts were observed, nonetheless microscopic sarcocysts were detected in 56% of assessed samples, with higher infection rates in the heart (91%) than in the diaphragm (21%). SEM and TEM analyses revealed thin-walled sarcocysts with finger-like protrusions in the diaphragm, as well as flattened hair-like projections in the myocardium. Molecular analysis identified Sarcocystis cruzi in all positive samples and detected additional DNA of Sarcocystis bovifelis/Sarcocystis rommeli and for the first time the zoonotic species S. hominis. These findings confirm the coexistence of canine-, feline-, and human-transmitted Sarcocystis species in Chilean cattle and highlight potential public health implications associated with consumption of raw or undercooked S. hominis-carrying beef meat. This constitutes the first molecular evidence of S. hominis in Chile, emphasizing the need for further surveillance and control measures in the meat production chain. These novel data on human S. hominis infections in Chile confirm the importance of initiating investigations on human sarcocystosis as this enteric parasitic disease is still sparsely considered by local public health authorities. Full article

Background and Objectives: Coronary heart disease is a leading cause of death in developed countries. While ECG-gated coronary CT is commonly used to detect coronary artery stenosis, the potential of non-ECG-gated CT (NECE-CT) to reveal incidental myocardial perfusion defects indicative of acute myocardial infarction (AMI) remains underexplored, particularly in emergency settings where rapid diagnosis is crucial. Materials and Methods: We retrospectively analyzed 22 suspected AMI patients from the emergency department who underwent NECE-CT without either an initial AMI diagnosis or available cardiac enzyme or ECG data. Results: AMI was confirmed in 45% (n = 10) of patients, with 30% (n = 3/10) showing elevated troponin I levels only after the CT exam. In the AMI group, all patients had perfusion defects, with 20% (n = 2) showing transmural defects and 80% (n = 8) showing endocardial defects. In contrast, all patients in the non-AMI group exhibited endocardial defects. Coronary artery calcification was significantly higher in the AMI group (70%) compared to the non-AMI group (25%, p < 0.05). Conclusions: These findings suggest that NECE-CT may reveal myocardial perfusion defects as an ancillary sign of AMI. While not a standalone diagnostic tool, careful evaluation of the myocardium in emergency CT scans may raise suspicion of AMI in patients with atypical presentations, offering more insight than standard methods. Further prospective studies with larger cohorts are needed to validate the clinical utility of these incidental findings. Full article

Background: Cardiovascular implications in systemic lupus erythematosus (SLE) are common and varied, including impacts on the pericardium, myocardium, valves, coronary arteries, and conduction system; all of these could be potential substrates or triggers of cardiac arrhythmias by interfering with disease severity and specific medication. Therefore, this narrative review aimed to assess the cardiac involvement in SLE underlying, mainly, cardiac arrhythmias. Methods: We analyzed studies, published between 2015 and 2025 on PubMed, which explore cardiovascular involvement with a focus on arrhythmias in SLE from the perspectives of epidemiology, underlying mechanisms, diagnostic techniques, and the impact of standard and biologic therapies. Results: The cardiac manifestation of LES (lupus pericarditis, lupus myocarditis, Libman–Sacks endocarditis, coronary artery disease, coronary vasculitis or myocardial fibrosis) represents a substrate for arrhythmia risk. These substrates, in association with other arrhythmias mechanisms considered as triggers or conduction abnormalities, determined arrhythmogenic conditions in these patients. In addition to structural heart disease, arrhythmias in SLE are caused by ongoing inflammation, immune system irregularities, microvascular problems, autonomic imbalance, oxidative stress, and side effects from treatments. Despite this complex background, arrhythmias are often overlooked and not routinely investigated in SLE care. Data that show how disease-modifying drugs may affect arrhythmias are limited and inconsistent, highlighting significant gaps in knowledge. Cardiac arrhythmias are a significant but, as yet, insufficiently underrecognized aspect of SLE, with serious implications for prognosis. Conclusions: Systemic lupus erythematosus causes cardiovascular involvement that is associated with arrhythmias through various and complexes mechanisms, mainly related to direct cardiovascular structural damage, systemic inflammation or specific therapies. Data on arrhythmias secondary to cardiovascular damage in patients with SLE in the literature are limited. Therefore, early detection of electrical issues, regular cardiovascular evaluation in high-risk patients, and careful management of treatment effects are vital. A coordinated, multidisciplinary cardio-rheumatology approach is essential to improving arrhythmia detection, tailoring treatments, and ultimately decreasing cardiovascular complications and deaths in SLE patients. Full article

Hypoglycaemia in patients with type 1 diabetes mellitus (T1DM) remains a major clinical burden and, beyond its metabolic complications, can cause serious cardiac arrhythmias. Multiple mechanisms lead to different types of arrhythmias during hypoglycaemia. However, existing studies often involve mixed diabetes populations, small cohorts, or limited monitoring during nocturnal periods, leaving a critical gap in understanding the links between glucose fluctuations and arrhythmic events. This review provides an updated combination of experimental and clinical evidence describing how autonomic dysfunction and ionic imbalances lead to electrophysiological instability and structural remodelling of the myocardium during hypoglycaemia. Continuous glucose monitoring (CGM) combined with electrocardiographic or wearable rhythm tracking may enable early detection of glycemic and cardiac disturbances and help identify high-risk individuals. Future prospective studies using combined CGM–ECG monitoring, particularly during sleep, are essential to clarify the relationship between hypoglycaemia and arrhythmic events. Full article