BACKGROUND AND AIMS:Atrial cardiomyopathy (AtCM) is increasingly recognized as an important substrate for atrial fibrillation (AF). This study aimed to examine potential markers and risk factors of AtCM, and associations with incident AF, heart failure (HF), and stroke. METHODS:Individuals from the UK Biobank with cardiac magnetic resonance imaging and electrocardiographic information were included. Atrial cardiomyopathy markers included left atrial dilation, left atrial mechanical dysfunction, P-wave prolongation, and abnormal P-wave terminal force. Risk factors for AtCM were assessed using logistic regressions. Incident AF, HF, and stroke according to AtCM markers were assessed in multivariable Cox-regression and cumulative incidence models. AF risk according to AtCM markers, clinical and genetic risk factors was evaluated by integrating the HARMS2-AF score and a polygenic risk score for AF. We used net reclassification improvement (NRI) to evaluate reclassification of risk when considering AtCM markers. RESULTS:Among 26 467 individuals, 4145 (15.7%) had ≥1 marker and 619 (2.3%) had ≥2 markers of AtCM. Age, coronary artery disease, and hypertension were consistently associated with AtCM. Having one AtCM marker conferred a hazard ratio (HR) for AF of 1.88 [95% confidence interval (CI): 1.54-2.31; P < .001], with higher rates observed in individuals with ≥2 markers (HR: 4.59; 95% CI: 3.52-5.99; P < .001). Addition of AtCM markers was associated with an NRI of 13.7% (95% CI: 9.2%-18.3%). Integration of clinical and genetic risk factors indicated an additive effect on AF rates. Having ≥2 markers associated with HF (HR: 3.08, 95% CI: 2.03-4.66, P < .001), and stroke (HR: 3.07, 95% CI: 1.78-5.28, P < .001). CONCLUSIONS:One in seven individuals had at least one marker of AtCM. Atrial cardiomyopathy markers were associated with AF, HF, and stroke, supporting AtCM as a common substrate for all three outcomes.

BACKGROUND:It is unclear, whether photoplethysmography (PPG) waveforms from wearable devices can differentiate between supraventricular and ventricular arrhythmias. We assessed, whether a neural network-based classifier can distinguish the origin of PPG pulse waveforms. METHODS:In thirty patients undergoing invasive electrophysiological (EP) studies for narrow complex tachycardia, PPG waveforms were recorded using a PPG wristband (Empatica E4) in parallel to 12-lead surface electrocardiograms (ECGs) and intracardiac bipolar electrograms. PPG waveforms were annotated to either atrial (AP, supraventricular) or ventricular pacing (VP) based on bipolar electrograms, ECGs and stimulation protocols. 25 221 samples were split into training, testing, and validation data sets and used to develop, optimize and validate a residual network based on convolutional layers for classifying PPG waveforms according to their origin into AP or VP. RESULTS:Datasets were complete for 27 patients. 74 % were female, median age was 53 (range 18, 78) years and median BMI was 27±5 kg/m². The electrophysiological study revealed typical atrioventricular nodal re-entrant tachycardias in 63 %, atrial tachycardias in 15 % and no inducible tachyarrhythmias in 12 % of patients. On an independent patient level, correct prediction was possible in ∼73 % for AP and ∼59 % for VP. With adaptive performance built on previous patient-specific annotations, the classifier correctly predicted the origins of PPG-derived pulse waves in ∼97 % for AP and ∼95 % for VP. CONCLUSIONS:A neural network trained on ground truth PPG data collected during EP studies could distinguish between supraventricular or ventricular origin from PPG waveforms alone.

OBJECTIVE:Evaluate popular explanation methods using heatmap visualizations to explain the predictions of deep neural networks for electrocardiogram (ECG) analysis and provide recommendations for selection of explanations methods. MATERIALS AND METHODS:A residual deep neural network was trained on ECGs to predict intervals and amplitudes. Nine commonly used explanation methods (Saliency, Deconvolution, Guided backpropagation, Gradient SHAP, SmoothGrad, Input × gradient, DeepLIFT, Integrated gradients, GradCAM) were qualitatively evaluated by medical experts and objectively evaluated using a perturbation-based method. RESULTS:No single explanation method consistently outperformed the other methods, but some methods were clearly inferior. We found considerable disagreement between the human expert evaluation and the objective evaluation by perturbation. DISCUSSION:The best explanation method depended on the ECG measure. To ensure that future explanations of deep neural networks for medical data analyses are useful to medical experts, data scientists developing new explanation methods should collaborate tightly with domain experts. Because there is no explanation method that performs best in all use cases, several methods should be applied. CONCLUSION:Several explanation methods should be used to determine the most suitable approach.

Background: Synthetic data can be the solution to privacy requirements, can enrich datasets limited by underrepresentation of certain subgroups/minorities, combat data shortage, and reduce annotation costs, to facilitate the development of data-hungry machine-learning applications. An important limitation of current synthetic data is the missing link to patient characteristics and outcomes. This metadata is essential for synthetic data to be useful for solving real-world problems, and we aimed to generate novel synthetic data with clinical characteristics and outcomes. Methods: We designed a novel generative method for generating 1:1 personalized synthetic electrocardiograms (ECGs) with associated patient characteristics and outcomes. The architecture of the model is a U-net neural network, which creates patient-specific ECGs to allow attachment of patient characteristics, comorbidities, and outcomes. We developed the model on the General Suburban Population Study and the Lolland-Falster Study cohorts and validated the model on the Danish Inter99 cohort. We compared original and synthetic ECGs usings Bland-Altman plots, by comparing associations with sex, age, and body mass index using linear models, and by comparing associations with all-cause mortality using Cox models. We defined that at most 5% of synthetic ECGs should have their paired original ECG as nearest neighbor in Euclidean space. Results: We generated 6,612 novel, personalized electrocardiograms. Although Bland-Altman plots showed a high level of agreement between synthetic and original ECGs, only 3.7% of synthetic ECGs had their original paired ECG as their nearest neighbor. Synthetic ECGs had preserved relations between heart rate, R-wave amplitude, and T-wave amplitude and age, sex, and body mass index. Corrected QT interval was about 9.5 ms shorter in men compared to women in both the original and synthetic cohorts. The associations between PR interval and clinical characteristics were attenuated in the synthetic cohort. Heart rate and corrected QT interval were each associated with increased mortality with similar hazard ratios in the original and synthetic populations. Conclusions: We demonstrated the ability of a novel neural network method to generate personalized synthetic ECGs with preserved associations with many patient characteristics and with all-cause mortality. This method may facilitate data sharing for the development of better risk prediction models. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement JLI is supported by a grant from the Danish Cardiovascular Academy (PD2Y-2023004-DCA). The Danish Cardiovascular Academy is funded by the Novo Nordisk Foundation and the Danish Heart Foundation, grant number NNF20SA0067242. MN is supported by a grant from the Danish Data Science Academy and the Danish Cardiovascular Academy (PhD2024015-DCA-DDSA). The Danish Data Science Academy is funded by the Novo Nordisk Foundation (NNF21SA0069429) and the VILLUM FONDEN (40516). DL is supported by a Novo Nordisk Foundation Young Investigator Awards 2021 (NNF21OC0066480). CE is partly funded from the Laboratory Endowment Fund at Boston Children?s Hospital, USA. GESUS was funded by the Region Zealand Research Foundation, Naestved Hospital Foundation, Naestved Municipality, Johan and Lise Boserup Foundation, TrygFonden, Johannes Fog's Foundation, Region Zealand, Naestved Hospital, The National Board of Health, The Local Government Denmark Foundation ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The present study used already collected data from population studies, which had each been approved by the relevant IRB and the use of the data was approved by the Danish Data Protection Agency. The data used in our study was de-identified individual-level data. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes We are prohibited by law from sharing the underlying patient data.

BACKGROUND:Wearables with integrated electrocardiogram (ECG) acquisition have made single-lead ECGs widely accessible to patients and consumers. However, the 12-lead ECG remains the gold standard for most clinical cardiac assessments. In this study, we developed a neural network to reconstruct 12-lead ECGs from single-lead and dual-lead ECGs, and evaluated the mathematical accuracy. METHODS:We used lead I or leads I and II from 9514 individuals from the Physikalisch-Technische Bundesanstalt (PTB-XL) cohort and a generative adversarial network, with the aim of recreating the missing leads from the 12-lead ECG. ECGs were divided into training, validation, and testing (10%). Original and recreated leads were measured with a commercially available algorithm. Differences in means and variances were assessed with Student's t-tests and F-tests, respectively. Calibration and bias were assessed with Bland-Altman plots. Inter-lead correlations were compared in original and recreated ECGs. RESULTS:The variability of precordial ECG amplitudes is significantly reduced in recreated ECGs compared to real ECGs (all p < 0.05), indicating regression-to-the-mean. Amplitude averages are recreated with bias (p < 0.05 for most leads). Reconstruction errors depend on the real amplitudes, suggesting regression-to-the-mean (R2 between target and error in R-peak amplitude in lead V3: 0.92). The relations between lead markers have a similar slope but are much stronger due to reduced variance (R-peak amplitude R2 between leads I and V3, real ECGs: 0.04, recreated ECGs: 0.49). Using two leads does not significantly improve 12-lead recreation. CONCLUSIONS:AI-based 12-lead ECG reconstruction results in a regression-to-the-mean effect rather than personalized output, rendering it unsuitable for clinical use.

Machine learning methods are increasingly used in cardiovascular research. In order to highlight opportunities and challenges of the evaluation of studies applying machine learning, we use examples from cardiac electrophysiology, a field characterized by large and often imbalanced amounts of data. We provide recommendations and guidance on evaluating and presenting supervised machine learning studies. We recommend proper cohort selection, keeping training and testing data strictly separate, and comparing results to a reference model without machine learning as basic principles to ensure the quality of studies using machine learning methods. We furthermore recommend specific metrics and plots when reporting on machine learning including on models for multi-channel time series or images. This Best Practice paper represents a possible blueprint to help evaluate machine learning-based medical tests in cardiac electrophysiology and beyond.

Background: The association between type 2 diabetes and electrocardiographic (ECG) markers are incompletely explored and the dependence on diabetes duration is largely unknown. We aimed to investigate the electrocardiographic (ECG) changes associated with type 2 diabetes over time. Methods: In this cross-sectional study, we matched people with type 2 diabetes 1:1 on sex, age, and body mass index with people without diabetes from the general population. We regressed ECG markers with the presence of diabetes and the duration of clinical diabetes, respectively, adjusted for sex, age, body mass index, smoking, heart rate, diabetes medication, renal function, hypertension, and myocardial infarction. Results: We matched 988 people with type 2 diabetes (332, 34% females) with as many controls. Heart rate was 8 bpm higher ( p < 0.001) in people with vs. without type 2 diabetes, but the difference declined with increasing diabetes duration. For most depolarization markers, the difference between people with and without type 2 diabetes increased progressively with diabetes duration. On average, R-wave amplitude was 6 mm lower in lead V5 ( p < 0.001), P-wave duration was 5 ms shorter ( p < 0.001) and QRS duration was 3 ms ( p = 0.03). Among repolarization markers, T-wave amplitude (measured in V5) was lower in patients with type 2 diabetes (1 mm lower, p < 0.001) and the QRS-T angle was 10 degrees wider ( p = 0.002). We observed no association between diabetes duration and repolarization markers. Conclusions: Type 2 diabetes was independently associated with electrocardiographic depolarization and repolarization changes. Differences in depolarization markers, but not repolarization markers, increased with increasing diabetes duration.

To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.

Context Some evidence suggests gene-treatment interactions might cause persistent symptoms in individuals receiving levothyroxine (LT4) treatment.Objective We investigated, as previously hypothesized, if single-nucleotide variations (SNVs; formerly single-nucleotide polymorphisms) in rs225014 (Thr92Ala), rs225015, or rs12885300 (ORFa-Gly3Asp) in the deiodinase 2 gene (DIO2), or rs17606253 in the monocarboxylate transporter 10 gene (MCT10) were associated with outcomes indicative of local tissue hypothyroidism in LT4-treated patients and controls.Methods We included 18 761 LT4-treated patients and 360 534 controls in a population-based cross-sectional study in the UK Biobank. LT4 treatment was defined as a diagnosis of hypothyroidism and self-reported use of LT4 without use of 3,5,3 '-triiodothyronine. Outcomes were psychological well-being, cognitive function, and cardiovascular risk factors. Associations were evaluated by linear, logistic, or ordinal logistic multiple regression. Adjustments included sex, age, sex-age interaction, and genetic principal components 1 to 10.Results Compared to controls, LT4 treatment was adversely associated with almost all outcomes, most noteworthy: Increased frequency of tiredness (P < .001), decreased well-being factor score (P < .001), increased reaction-time (P < .001), and increased body mass index (P < .001). Except for a significant association between the minor rs225015 A allele and financial dissatisfaction, there was no association of rs225014, rs225015, rs12885300, or rs17606253 with any outcomes in LT4-treated patients. For all outcomes, carrying the risk allele at these 4 SNVs did not amplify symptoms associated with LT4 treatment compared to controls.Conclusion rs225014, rs225015, rs12885300, and rs17606253 could not explain changed psychological well-being, cognitive function, or cardiovascular risk factors in LT4-treated patients. Our findings do not support a gene-treatment interaction between these SNVs and LT4 treatment.

Background: Ventricular repolarization time (ECG QT and JT intervals) is associated with malignant arrhythmia. Genome-wide association studies have identified 230 independent loci for QT and JT; however, 50% of their heritability remains unexplained. Previous work supports a causal effect of lower serum calcium concentrations on longer ventricular repolarization time. We hypothesized calcium interactions with QT and JT variant associations could explain a proportion of the missing heritability. Methods and Results :We performed genome-wide calcium interaction analyses for QT and JT intervals. Participants were stratified by their calcium level relative to the study distribution (top or bottom 20%). We performed a 2-stage analysis (genome-wide discovery [N=62 532] and replication [N=59 861] of lead variants) and a single-stage genome-wide meta-analysis (N=122 393, [European ancestry N=117 581, African ancestry N=4812]). We also calculated 2-degrees of freedom joint main and interaction and 1-degree of freedom interaction P values. In 2-stage and single-stage analyses, 50 and 98 independent loci, respectively, were associated with either QT or JT intervals (2-degrees of freedom joint main and interaction P value <5x10(-8)). No lead variant had a significant interaction result after correcting for multiple testing and sensitivity analyses provided similar findings. Two loci in the single-stage meta-analysis were not reported previously (SPPL2B and RFX6). Conclusions: We have found limited support for an interaction effect of serum calcium on QT and JT variant associations despite sample sizes with suitable power to detect relevant effects. Therefore, such effects are unlikely to explain a meaningful proportion of the heritability of QT and JT, and factors including rare variation and other environmental interactions need to be considered.

Introduction The population-based Inter99 cohort has contributed extensively to our understanding of effects of a systematic screening and lifestyle intervention, as well as the multifactorial aetiology of type 2 diabetes (T2D) and cardiovascular disease. To understand causes, trajectories and patterns of early and overt cardiometabolic disease manifestations, we will perform a combined clinical deep phenotyping and registry follow-up study of the now 50–80 years old Inter99 participants.Methods and analysis The Inter99 cohort comprises individuals aged 30–60 years, who lived in a representative geographical area of greater Copenhagen, Denmark, in 1999. Age-stratified and sex-stratified random subgroups were invited to participate in either a lifestyle intervention (N=13 016) or questionnaires (N=5264), while the rest served as a reference population (N=43 021). Of the 13 016 individuals assigned to the lifestyle intervention group, 6784 (52%) accepted participation in a baseline health examination in 1999, including screening for cardiovascular risk factors and prediabetic conditions. In total, 6004 eligible participants, who participated in the baseline examination, will be invited to participate in the deep phenotyping 20-year follow-up clinical examination including measurements of anthropometry, blood pressure, arterial stiffness, cardiometabolic biomarkers, coronary artery calcification, heart rate variability, heart rhythm, liver stiffness, fundus characteristics, muscle strength and mass, as well as health and lifestyle questionnaires. In a subsample, 10-day monitoring of diet, physical activity and continuous glucose measurements will be performed. Fasting blood, urine and faecal samples to be stored in a biobank. The established database will form the basis of multiple analyses. A main purpose is to investigate whether low birth weight independent of genetics, lifestyle and glucose tolerance predicts later common T2D cardiometabolic comorbidities.Ethics and dissemination The study was approved by the Medical Ethics Committee, Capital Region, Denmark (H-20076231) and by the Danish Data Protection Agency through the Capital Region of Denmark’s registration system (P-2020-1074). Informed consent will be obtained before examinations. Findings will be disseminated in peer-reviewed journals, at conferences and via presentations to stakeholders, including patients and public health policymakers.Trial registration number NCT05166447.

PURPOSE:The association between thyroid dysfunction and exudative age-related macular degeneration (AMD) is unknown. METHODS:In this Danish longitudinal nationwide registry-based cohort study we included all Danish residents aged 50-100 between 2008 and 2018. Using the Danish national registries, we studied the association between thyroid dysfunction and exudative AMD. Thyroid dysfunction was classified as two consecutive redeemed prescriptions of thyroid hormones (hypothyroidism) or anti-thyroid medication (hyperthyroidism). Exudative AMD was classified as an ICD diagnosis of AMD and a code for anti-VEGF treatment. All patients are treated for exudative AMD in a hospital in Denmark, and we therefore have complete registration of this patient group. RESULTS:We included 2 087 305 individuals, of which 1 072 567 (51.4%) were women; 59 318 (2.8%) had hypothyroidism, and 33 922 (1.6%) had hyperthyroidism. During a median follow-up of 11 years, 26 998 (1.3%) people developed exudative AMD. Hypothyroidism (adjusted hazard ratio [HR]: 1.17; 95% confidence interval [CI] 1.10-1.25; p < 0.001) and hyperthyroidism (HR: 1.23; 95% CI:1.13-1.34; p < 0.001) were both associated with the development of exudative AMD. The age-stratified analyses yielded similar results to the main analyses, except that the risks were exaggerated in the older part of the population. CONCLUSION:This is the first longitudinal nationwide study showing that both hypo- and hyperthyroidism are associated with an increased risk of exudative AMD. AMD is a quantitative problem in the population and our findings could have a public health impact. Further studies are needed to study the underlying mechanisms of the association.

Background Machine learning-based analysis can accurately detect atrial fibrillation (AF) from photoplethysmograms (PPGs), however the computational requirements for analyzing raw PPG waveforms can be significant. The analysis of PPG-derived peak-to-peak intervals may offer a more feasible solution for smartphone deployment, provided the diagnostic utility is comparable. Aims To compare raw PPG waveforms and PPG-derived peak-to-peak intervals as input signals for machine learning detection of AF. Methods We developed specialized neural networks for raw waveform and peak-to-peak interval analyses and trained them on 7,704 PPGs from 106 patients from the TeleCheck-AF project. We evaluated the neural networks on 48,912 PPGs from 416 patients from the VIRTUAL-SAFARI project. We recorded computational requirements, sensitivity, positive predictive value (PPV), and F1 score. Results With 1.6 million trainable parameters, the waveform model was more than 100 times as complex as the interval model (15,513 parameters) and required 19 times more computational power. In external validation, metrics were comparable between the interval and waveform models. For the interval model vs. the waveform model, sensitivity was 91.7% vs. 81.9% (p=0.4), PPV was 80.5% vs. 84.5% (p=0.3), and F1 score was 85.6% vs. 81.3% (p=0.5), respectively. Conclusion PPG-derived peak-to-peak intervals and PPG waveforms were equivalent as input signals to neural networks in terms of accurate AF detection. The reduced computational requirements of the interval model make it a more suitable option for deployment on digital end-user devices such as smartphones.