López-Jiménez, Francisco

Abstract Background Common genetic variation has been linked to an increased risk of developing dilated cardiomyopathy (DCM) in case-control studies. However, the phenotypic profile and biological mechanisms by which genetic background may predispose individuals to DCM and outcomes in the general population remain unclear. Aim The primary objectives of this study were twofold: (1) to validate a DCM polygenic score (PRS-1) based on a prior meta-analysis of case–control genetic association studies [Tadros R et al. Nat Genet (2021)]; and (2) To apply this PRS, along with two recently developed PRSs for DCM [PRS-2 from Zheng SL et al. Nat Genet (2024) and PRS-3 from Jurgens SJ et al. Nat Genet (2024)] to a general population that underwent comprehensive longitudinal phenotypic assessments and had long-term outcomes available. Methods While the PRS-1 was derived from only 13 SNPs, PRS-2 and PRS-3 were generated from 709,534 and 1,072,247 SNPs, respectively with significant overlap between them. These DCM-PRSs were applied to an unselected community-based cohort (n=1897) aged >45 years, who underwent a physical examination, echocardiogram, and blood sample at baseline. Subsequent clinical outcomes were collected over a median 16 year follow-up (IQR: 15-17). Results No significant differences between the quartiles were present in terms of age, sex (48% male), and cardiovascular risk factors (CV-RF) (such as hypertension, diabetes, and coronary artery disease). However, linear regression models demonstrated a significant correlation between all DCM-PRSs and left ventricular (LV) parameters, including LV ejection fraction, LV end-systolic diameter, and LV end-diastolic diameter (Table). Multivariable regression analysis confirmed that these correlations were independent of age, sex, CV-RF, and population stratification (adjusted for the first 10 principal components [PCs]). During follow-up, 47 individuals developed DCM. A logistic regression analysis, using a 3:1 matched cohort of non-DCM vs. DCM individuals based on age and sex and adjusted for the same 10 PCs, found a significant correlation between DCM development and both PRS-1 [OR=2.27 (1.06–4.85), p=0.03] and PRS-3 [OR=2.63 (1.20–5.75), p=0.02], while PRS-2 did not show a significant association [OR=1.85 (0.84–4.06), p=0.12]. No association was found between the DCM-PRSs and heart failure (HF) biomarkers (aldosterone, natriuretic peptides, ST2, Galectin-3) or HF outcomes (all-cause mortality/HF hospitalization). Conclusion PRS-1 for DCM despite being derived from only 13 risk alleles, performs at least as well as the newer and more extensive PRSs, in its correlation with LV function, dimensions, and DCM development (except for PRS-2) in a general population. The absence of correlation between these DCM-PRSs and the renin-angiotensin-aldosterone system or natriuretic peptides suggests that genetic background influences LV traits through mechanisms independent of these biological pathways.

Abstract Background Atherosclerotic cardiovascular disease (ASCVD) develops over a prolonged latent period, progressing from subclinical ASCVD to major cardiovascular events due to plaque accumulation. Subclinical ASCVD is a valid subrogate to assess potential risk factors or ASCVD. Dietary patterns are known to contribute to or prevent ASCVD. Dairy consumption has been associated with lower rates of obesity, hypertension, and diabetes, while saturated fats, present in most dairy products, have been associated with ASCVD. Studies assessing the association between subclinical ASCVD and dairy consumption have shown mixed results but have not been systematically analyzed Purpose This study aimed to systematically review and synthesize the existing evidence on the association between dairy product consumption and subclinical ASCVD Methods The study protocol was registered in the International Prospective Register of Systematic Reviews and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. We conducted a comprehensive search of PubMed, EMBASE, Scopus, Cochrane Central Register, Google Scholar, and Web of Science to identify experimental and observational studies that examined the association of dairy product consumption with measures of subclinical ASCVD assessed through coronary artery calcium (CAC) scanning, intima-media thickness (IMT), pulse pressure (PP) or pulse wave velocity (PWV), from inception to July 9, 2024. Titles and abstracts were screened in duplicate, followed by a full-text review and data abstraction from articles that met the study criteria. Dairy products were classified as: a) Dairy, according to the United States Department of Agriculture, encompassing milk, yogurt, cheese, lactose-free milk, and fortified soy milk/yogurt; and b) Dairy+, which further included higher fat content products such as cream cheese, sour cream, cream, and butter Results A total of 973 articles were identified; 22 met inclusion criteria representing 20 studies; being predominantly cross-sectional (55%) conducted in North America (50%); food frequency questionnaires were the most implemented tool for dietary assessment (86%), and 41% used PWV for subclinical ASCVD assessment; Fig. 1. Total Dairy and Dairy+ exhibited predominantly neutral effects with a trend toward a beneficial effect, similar to yogurt intake. Milk and cheese demonstrated contrasting effects, with studies reporting protective, neutral, and harmful associations, particularly for PWV and IMT. Limited data on CAC suggested no strong relationship with dairy consumption; Table 1 Conclusion Available evidence on the association between dairy consumption and subclinical ASCVD is complex with mixed effects across dairy products. No consistent evidence of harm or benefit was found; future research is needed to clarify the role of different dairy components in the development of subclinical ASCVD and incidence of cardiovascular eventsFig1.Summary of studies characteristics Table 1.Summary of effects

Abstract Background Left ventricular diastolic function (LVDF) and left bundle branch block (LBBB) are both linked with increased risk of LV systolic dysfunction, heart failure, and mortality. Assessing LVDF, which is typically done through echocardiography, becomes challenging in the presence of LBBB due to the intraventricular conduction delay that disrupts the reliability of tissue Doppler imaging. Recently, we validated an artificial intelligence-enabled electrocardiogram (AI-ECG) model to assess LVDF and filling pressures. Methods We performed a retrospective study among patients with ECG-confirmed LBBB who also underwent comprehensive echocardiography within 14 days of the index ECG between September 2001 and June 2022. Using multivariable survival models, we assessed the risk of all-cause mortality across AI-ECG-derived LVDF grades. Results Of 2,554 patients with LBBB (mean age 72.0+12.6, 54.6% women), 252 (9.8%), 656 (25.7%), and 1,436 (56.2%), and 210 (8.2%) had normal LVDF, G1 LVDF, G2 LVDF, and G3 LVDF, respectively. The median LV ejection fraction (LVEF) was 54% and 49.3% of the patients had heart failure. Worse LVDF was associated with older age, lower LVEF, and higher rates of diabetes, hypertension, chronic pulmonary disease, renal failure, cardiovascular disease, and heart failure (p-of-trend<0.001 for all). During a median follow-up of 2.6 years (interquartile range 0.5-4.0 years), 847 (33.2%) patients died. In multivariable survival analysis, adjusted to multiple risk factors including baseline LVEF and heart failure, worse LVDF was independently associated with a higher risk of death [G1 LVDF: adjusted hazard ratio (aHR) 2.15 (95% confidence interval (95%CI) 0.75-1.77), G2 LVDF: aHR 2.25 (95%CI 1.50-3.38) G3 LVDF: aHR 2.96 (95%CI 1.88-4.65); p<0.001 for both)]. These findings were consistent also in a subgroup of patients with baseline LVEF > 50%. Conclusion Among patients with LBBB, worse LVDF, as derived by a dedicated AI-ECG algorithm is independently associated with all-cause mortality beyond baseline LVEF and clinical heart failure.survival across AI-ECG LVDF grades

Abstract Background Artificial intelligence (AI) models trained on 12-lead ECGs effectively detect left ventricular systolic dysfunction (LVSD; left ventricular ejection fraction [LVEF] <=40%). Continuous ECG monitoring via Holter recordings provides an opportunity for opportunistic screening for structural heart disease beyond rhythm disorders. We hypothesized that a lead-invariant version of the 12-lead AI model would enable a Holter monitor to screen for both arrhythmias and ventricular dysfunction. Methods We retrospectively analyzed continuous Holter ECGs from 17,665 patients who underwent a Holter and transthoracic echocardiogram (TTE) within 30 days of each other at Mayo Clinic. From each Holter, a random 20-minute of valid (non-flatline/lead disconnect) ECG segment was extracted and analyzed for LVSD detection using the adapted lead-invariant AI model. To evaluate stability, we examined model performance across different time points of the day, presenting results as area under the receiver operating characteristic curve (AUC) over time. Moreover, we illustrated the model’s robustness to noisy data by comparing its performance on raw ECG signals with that on bandpass-filtered inputs. Results Among 17,665 patients (mean age 59 years, 48.57% female), 4.96% had an LVEF <=40%. The AI model demonstrated strong predictive performance (20-minute segment AUC 0.90, mean prediction of 24-hour AUC 0.92). Analysis of results over time (Figure) revealed temporal patterns in predictive accuracy, with specific time periods showing greater stability. Despite modest variability, model performance remained consistently high throughout the day, confirming robustness across different physiological states. The predictions remained robust with noisy input. We did not observe performance improvement when the baseline wander and high frequency noise are removed by the bandpass filter. Conclusion Applying a 12-lead AI ECG model with a lead-invariant framework to a continuous Holter ECG enables effective screening for left ventricular dysfunction. This suggests that AI-based analysis of Holter-monitors can facilitate opportunistic screening of ventricular dysfunction and may enable assessment of an arrhythmia’s impact on LVEF, as well as the relationship between arrhythmia burden and LVEF.Figure 1.Mean prediction AUC of the day Figure 2.AUC for different time point

Abstract Background Left ventricular ejection fraction (LVEF) and diastolic dysfunction (DD) are key prognostic factors in patients with ST-segment elevation myocardial infarction (STEMI). AI-enabled ECG (AI-ECG) has been shown to have excellent agreement with transthoracic echocardiography (TTE) assessment and incremental prognostic value to LVEF. Methods We retrospectively analyzed 1,005 adult acute STEMI patients referred for echocardiography (10/2007–01/2025) who had ECGs within seven days before and after the reference echocardiogram. The ECG closest to the TTE date was designated as the pre-STEMI and post-STEMI ECG, respectively. AI-ECG was used to classify diastolic function grades and grades 2 and 3 were considered as DD. Patients were stratified by LVEF (≤50% vs. >50%) at the time of TTE and the association between AI-ECG-determined DD and 10-year mortality was assessed using Cox regression and Kaplan-Meier curves. Results Among 1,005 patients (mean age 66.4 ± 13.4 years, 29.9% females), AI-ECG-determined DD prior to STEMI was present in 47.9% of patients with LVEF ≤50% and 30.3% of those with preserved LVEF (>50%). DD status remained unchanged post-STEMI in 79.3% of cases, while 10.2% showed worsening diastolic function. Regardless of LVEF, AI-ECG-determined DD prior to STEMI was associated with higher mortality risk (LVEF ≤50%: HR 3.03, 95% CI 2.16–4.24, p < 0.001; LVEF >50%: HR 2.32, 95% CI 1.58–3.41, p < 0.001). Post STEMI AI-ECG-determined DD remained a significant predictor of mortality (LVEF ≤50%: HR 2.54, 95% CI 1.82–3.53, p < 0.001; LVEF >50%: HR 2.43, 95% CI 1.66–3.58, p < 0.001). Echocardiographically determined DD also correlated with higher mortality (LVEF ≤50%: HR 4.91, 95% CI 2.95–8.15, p < 0.001; LVEF >50%: HR 2.72, 95% CI 1.68–4.41, p < 0.001). In patients with LVEF ≤50%, AI-ECG-determined DD remained associated with higher mortality even after adjusting for echocardiographically determined DD (pre-STEMI: HR 1.85, 95% CI 1.23–2.80, p < 0.001; post-STEMI: HR 1.90, 95% CI 1.23–2.91, p < 0.001). Conclusion AI-ECG-determined DD is a significant predictor of mortality in STEMI, independent of LVEF. The presence of normal filling pressure may indicate a better clinical outcome in patients with reduced LVEF patients. These findings underscore the clinical utility of AI-ECG in refining risk stratification beyond conventional LVEF assessment.

Abstract Background Elevated left ventricular filling pressure (LVFP) is associated with an increased risk of heart failure and mortality. Pulmonary artery wedge pressure (PAWP) is considered the gold standard for assessing LVFP. Recently, we validated an artificial intelligence-enabled electrocardiogram (AI-ECG) to assess LV diastolic function (LVDF) and LVFP. Herein, we compare AI-ECG with PAWP in predicting long-term mortality. Methods We included adults who had comprehensive echocardiography with available ECG within 14-days and right-heart-catheterization (RHC) within 30-days of echocardiography. Elevated-LVFP was defined as PAWP>15 mmHg by RHC, grades 2-3 LVDF by echocardiography, or as adjudicated by the AI-ECG algorithm. Prognosis by AI-ECG was compared to that by echocardiography and PAWP. Results Among 4,168 patients (median age 64 years, 47.7% female), 1,841 had normal-LVFP, and 2,327 had elevated-LVFP by RHC. Echocardiography classified 1,502 patients as normal-LVFP, 1,643 as elevated-LVFP, and 1,023 as indeterminate-LVFP. AI-ECG demonstrated an area-under-the-curve of 0.879 (95%-confidence-interval 0.867-0.891) for predicting elevated-LVFP as determined by echocardiography and 0.742 (95%-confidence-interval 0.724-0.759) for predicting elevated-LVFP as determined by RHC. Over a median follow-up of 7.7 years, 2,735 (34.4%) patients died. In multivariable models, elevated-LVFP by any modality was associated with an increased risk of death (AI-ECG: adjusted-hazard-ratio 1.80, 95% confidence-interval 1.55-2.09; echocardiography: adjusted-hazard-ratio 1.74, 95% confidence-interval 1.51-2.02; PAWP: adjusted-hazard-ratio 1.26, 95% confidence-interval 1.11-1.43). AI-ECG-derived LVFP provided incremental prognostic discrimination compared to echocardiography- or RHC-derived LVFP. Conclusions AI-ECG-derived LVFP offers an accessible, affordable, and noninvasive alternative for detecting elevated LVFP, with potential independent prognostic value beyond traditional echocardiographic and invasive assessments.Survival across LVFP concordance

Introduction: Semaglutide 2.4 mg has been shown to reduce the risk and severity of cardiometabolic comorbidities (CMCs) and to improve cardiometabolic biomarkers in patients with overweight or obesity in clinical trials. However, real-world evidence in these areas is limited. Research Question: This study compared the incidence rates of CMCs and 12-month changes in biomarkers between adults with overweight/obesity with and without once-weekly subcutaneous semaglutide 2.4 mg use. Methods: A retrospective cohort study using a large U.S. claims database linked with clinical data evaluated patients with semaglutide 2.4 mg use for ≥12 months (June 2021–June 2023) and those without. For each CMC, including hypertension, T2D, dyslipidemia, HF, prediabetes, ASCVD, and CVD, patients were excluded from analysis of each individual CMC if they had claims indicating the corresponding CMC during the 12-month baseline. For each biomarker, patients were required to have the corresponding biomarker measurements at both baseline and 12-month follow-up. For each CMC and biomarker, propensity score weighting was used to balance patient characteristics between semaglutide 2.4 mg users and non-users. Time to CMCs were compared using weighted Cox proportional hazards models and biomarker changes were compared using weighted linear regression. Results: Sample sizes ranged from 14,067 to 25,490 in semaglutide 2.4 mg users and 4,271,926 to 8,578,948 in non-users across CMCs. After balancing, baseline characteristics were similar between users and non-users. Mean age ranged from 44.4 to 47.2 years among users and 44.4 to 47.4 years among non-users; 77%–83% of users and 75%–82% of non-users were women. Compared to non-users, semaglutide 2.4 mg users had significantly lower risk of incident hypertension (Hazard ratio [HR]: 0.62, 95% confidence intervals [CI]: 0.57; 0.68), T2D (HR: 0.45, 95% CI: 0.39; 0.51), HF (HR: 0.48, 95% CI: 0.38; 0.61), prediabetes (HR: 0.75, 95% CI: 0.70; 0.80), ASCVD (HR: 0.65, 95% CI: 0.58; 0.72), and CVD (HR: 0.69, 95% CI: 0.64; 0.75) (all p<0.001). The risk of dyslipidemia was similar between users and non-users (HR: 1.05, 95% CI: 1.00; 1.11, p=0.05). Semaglutide 2.4 mg use was also associated with significant improvements in cardiometabolic biomarkers specified in Table 1 and slower decline in eGFR. Conclusion: In adults with overweight/obesity, semaglutide 2.4 mg was associated with lower risk of specified CMCs and greater improvements in cardiometabolic biomarkers.

Background: Disease-causative variants in LMNA -encoded lamin A/C cause the laminopathies, a heterogeneous group of diseases variably resulting in arrhythmogenic/dilated cardiomyopathy (ACM/DCM), lipodystrophy, muscular dystrophy, and progeria. The artificial intelligence (AI)-derived electrocardiographic age gap (AI-EAG), determined by the discrepancy between a patient’s artificial AI-enabled electrocardiogram (ECG) predicted biological age versus their chronological age, is accelerated in laminopathy. Thus, we sought to determine if the AI-EAG serves as a prognostic marker in patients with cardiac laminopathy. Methods: Retrospective analysis of 1,049 genotype-positive patients with genetic ACM/DCM was used to identify those with pathogenic/likely pathogenic (P/LP) variants in LMNA . After the exclusion of those who lacked a 12-lead ECG while in sinus rhythm, a previously trained AI-ECG age algorithm was used to determine the AI-EAG by subtracting the patient’s chronological age from the AI-ECG derived biological age. The AI-EAG was then correlated with a combined outcome of major ventricular arrhythmia [sudden cardiac arrest, sustained ventricular tachycardia, and appropriate implantable cardioverter-defibrillator shocks], heart transplantation, and cardiovascular death. Results: Overall, 147/1,049 (14%) patients with genetically-mediated ACM/DCM had a P/LP variant in LMNA . Of these, 80/147 (54%) LMNA variant-positive patients (52% female, mean chronological age 36 ± 15 years) had ECGs suitable for AI-EAG analysis. Most (52/80; 65%) had an AI-EAG >10 years with a mean AI-EAG of 17 ± 13 years. Of note, the AI-EAG was greater in those with a clinical cardiac phenotype (20 ± 14 vs. 13 ± 10 years; p = 0.011). As a continuous variable, an increased AI-EAG was associated with increased risk of the composite outcome at a median follow-up of 20 months (HR 1.036; 95% CI 1.004–1.068; p = 0.026) and AI-EAG > 10 and > 20 years were both associated with elevated risk (HR 4.272; 95% CI 1.388–13.147; p = 0.011 and HR 3.732; 95% CI 1.505–9.255; p = 0.004, respectively). Conclusion: In cardiac laminopathy, an increased AI-EAG was common and correlated with adverse cardiovascular outcomes. Non-invasive ascertainment of electrocardiographic aging by AI may provide a novel prognostic marker in cardiac laminopathy. Future studies are needed to validate this finding and further define the role of the AI-EAG in the risk-stratification of patients with cardiac laminopathy.

Background: Arrhythmogenic and dilated cardiomyopathies (ACM/DCM) are associated with adverse cardiovascular outcomes, including sudden death. Artificial intelligence (AI)-enabled electrocardiographic aging algorithms estimate biological age from 12-lead tracings and predict cardiovascular outcomes in the general population. However, their utility in genetic ACM/DCM has not been studied. Methods: Retrospective review of 1,045 genotype-positive ACM/DCM patients was used to identify those with a pathogenic/likely pathogenic variant in ClinGen-classified definitive/strong evidence ACM/DCM-susceptibility genes. Following exclusion of patients without a baseline ECG for analysis (e.g. paced rhythms), the AI-ECG age gap (AI-EAG) was calculated by subtracting chronological from AI-ECG age. Genes were grouped into functional categories: desmosomal ( PKP2, DSP, DSG2, DSC2, JUP ), nuclear envelope ( LMNA ), sarcomeric ( TTN, MYH7, TNNT2 ), cytoskeleton/Z-disc ( FLNC, DES, DMD, BAG3 ), and regulatory/ion channel ( PLN, RBM20, SCN5A ). Cardiac MRI data included left ventricular structure, function, and late gadolinium enhancement (LGE) presence and pattern. Mann-Whitney U was used for group comparisons; results are shown as median (IQR). Results: Overall, 824 patients (52.8% male; median age of 40.7 years, IQR 26.5–53.4; and median AI-EAG of 7.49 years, IQR 0.43–15.26) were included. DES variant-positive patients had the highest (19.98 years, IQR 9.66–23.21) and BAG3 variant-positive patients the lowest (–0.95 years, IQR -6.05–7.61) AI-EAGs. Among gene groups, nuclear envelope had the highest (15.12 years, IQR 6.79–25.17) and sarcomeric the lowest (5.75 years, IQR -0.69-14.01) AI-EAGs. Cardiac MRI was available for 538/824 (65%) of which 233/538 (43.3%) had LGE. No significant difference in AI-EAG was observed between LGE-positive and -negative patients (6.99 vs. 7.94 years; p=0.831). However, several LGE patterns were associated with significantly higher AI-EAGs: subepicardial (9.88 vs. 6.84; p=0.004), transmural (13.83 vs. 7.25; p=0.012), apical (12.89 vs. 7.17; p=0.002), and septal (8.91 vs. 7.19; p=0.016). Conclusion: AI-EAG varies by gene and functional group, possibly reflecting gene-specific myocardial remodeling. While certain LGE patterns were linked to increased biological aging, overall LGE status was not. These findings suggest AI-EAG may capture disease activity that is distinct from LGE and could serve as a complementary, non-invasive biomarker in genetic ACM/DCM.

ABSTRACT Aims Common genetic variants have been linked to an increased risk of non-ischaemic cardiomyopathy (NICM). The primary objective of this study was to evaluate the significance of these variants in a general population. Methods and results Thirteen NICM risk alleles, previously validated by meta-analyses of case-control genetic association studies, were used to determine genetic risk (GR) in a large, unselected cohort (n = 1897) of Olmsted County residents aged &gt;45 years. Clinical and echocardiographic data, with biomarkers, were correlated with this NICM-GR over a median of 16 years of follow-up (interquartile range [IQR] 15–17). There were no significant differences between the study population quartiles, based on NICM-GR, in terms of age (median 61.6 [IQR 53.6–70.4]), sex (48% male), and cardiovascular risk factors. A significant correlation was detected by specific linear-regression models between NICM-GR and left ventricular (LV) ejection-fraction (beta [standard error]: −2.68 [0.68], p &lt; 0.001), LV end-systolic-diameter (0.22 [0.06], p &lt; 0.001), and LV end-diastolic-diameter (0.14 [0.06], p = 0.01). During follow-up, 46 subjects developed NICM. Using a 3:1 matched-cohort of non-NICM versus NICM patients, linear-regression analysis found a significant correlation between NICM-GR and NICM development (odds ratio 2.27, 95% confidence interval 1.06–4.85, p = 0.03). There was no significant association between these variants and heart failure biomarkers (aldosterone, natriuretic peptides, ST2, galectin-3). Conclusions The NICM-GR correlates with LV function and dimension, and subsequent development of NICM in the general population. The lack of correlation of these variants with cardiovascular risk factors and the renin–angiotensin–aldosterone axis or natriuretic peptides suggest different biological mechanisms involved. These findings support the need to explore newer pathophysiologic pathways in the development of NICM.