Background: The relationship of cardiorespiratory fitness (CRF) with subclinical atherosclerosis affected by the body adiposity has been observed in children, whereas this relationship remains unclear in young adults. Methods and Results: A total of 1520 military recruits, aged 18–40 years, were included in Taiwan in 2018–2020. All subjects underwent detailed physical and blood laboratory examinations. CRF was evaluated by time for a 3000 m run, and subclinical atherosclerosis was evaluated by intima–media thickness of the bulb of the left common carotid artery (cIMT) utilizing high-resolution ultrasonography. Multivariable linear regression analysis with adjustments for age, sex, cigarette smoking, alcohol intake, systolic and diastolic blood pressure, high- and low-density lipoprotein cholesterols, fasting glucose, waist circumference, serum uric acid and serum triglycerides were utilized to determine the correlation between CRF and cIMT. CRF was independently correlated with cIMT (standardized β: 0.11, p < 0.001). Of the cardiometabolic risk markers, serum triglycerides were the only independent risk marker of cIMT (standardized β: 0.063, p = 0.03). In addition, the association of CRF with cIMT did not differ between those with a body mass index (BMI) ≥ 25 kg/m² and those with BMI < 25 kg/m² (standardized β: 0.103 and 0.117; p = 0.01 and 0.005, respectively). Conclusions: In physically active young men and women, there was an inverse association of cIMT with CRF, which was observed in both overweight/mild obesity and normal-weight individuals, highlighting the importance of endurance capacity on reducing risk of early atherosclerosis and implying that the moderation effect of body adiposity might not be present in this population.

Estrogen exerts beneficial cardiovascular effects by binding to specific receptors on various cells to activate nuclear and non-nuclear actions. Estrogen receptor α (ERα) non-nuclear signaling confers protection against heart failure remodeling, involving myocardial cyclic guanosine monophosphate (cGMP) - cGMP-dependent protein kinase G (PKG) activation; however, its tissue-specific role remains elusive. Herein, we examined the cell type-specific role of ERα non-nuclear signaling in estrogen-conferred protection against heart failure. We first assessed the tissue-specific impacts of ERα in estrogen’s cardiac benefits, utilizing endothelial ERα deletion (ERα^f/f/Tie2^Cre+) and myocyte ERα deletion (ERα^f/f/αMHC^Cre+) female mice. Female mice were ovariectomized and the effect of estradiol (E2) was assessed in hearts exposed to 3week pressure-overload (TAC). E2 failed to improve cardiac function in ERα^f/f/Tie2^Cre+ TAC hearts but provided benefits in ERα^f/f/αMHC^Cre+ TAC hearts, indicating that endothelial ERα is essential. We next assessed the role of non-nuclear signaling in endothelial cells, employing animals with endothelial-specific inactivation of ERα non-nuclear signaling (ERα^KI/KI/Tie2^Cre+). Female OVX mice were supplemented with E2 and subjected to 3-week TAC. ERα^KI/KI/Tie2^Cre+ TAC hearts revealed exacerbated cardiac dysfunction and reduced myocardial PKG activity as compared to littermate TAC hearts, which was associated with attenuated myocardial induction of vascular endothelial growth factor (VEGF) and angiogenesis as assessed with CD31-stained capillary density. This phenotype of ERα^KI/KI/Tie2^Cre+ was rescued by myocardial PKG activation from chronic treatment with soluble guanylate cyclase (sGC) stimulator. We performed co-culture experiments to determine endothelial-cardiomyocyte interactions. VEGF induction by E2 in cardiac myocytes required co-existence of intact endothelial ERα signaling in a NOS-dependent manner. On the other hand, VEGF was induced in myocytes directly with an sGC stimulator in the absence of endothelial cells. An endothelial estrogen - myocardial cGMP axis stimulates angiogenic response and improves cardiac performance during pressure-overload.

Background: Left atrial enlargement (LAE) is associated with cardiovascular events. Machine learning for ECG parameters to predict LAE has been performed in middle- and old-aged individuals but has not been performed in young adults.Methods: In a sample of 2,206 male adults aged 17–43 years, three machine learning classifiers, multilayer perceptron (MLP), logistic regression (LR), and support vector machine (SVM) for 26 ECG features with or without 6 biological features (age, body height, body weight, waist circumference, and systolic and diastolic blood pressure) were compared with the P wave duration of lead II, the traditional ECG criterion for LAE. The definition of LAE is based on an echocardiographic left atrial dimension > 4 cm in the parasternal long axis window.Results: The greatest area under the receiver operating characteristic curve is present in machine learning of the SVM for ECG only (77.87%) and of the MLP for all biological and ECG features (81.01%), both of which are superior to the P wave duration (62.19%). If the sensitivity is fixed to 70–75%, the specificity of the SVM for ECG only is up to 72.4%, and that of the MLP for all biological and ECG features is increased to 81.1%, both of which are higher than 48.8% by the P wave duration.Conclusions: This study suggests that machine learning is a reliable method for ECG and biological features to predict LAE in young adults. The proposed MLP, LR, and SVM methods provide early detection of LAE in young adults and are helpful to take preventive action on cardiovascular diseases.

Cardiovascular events such as myocarditis following mRNA COVID-19 vaccination are increasing. We present a 67-year-old postmenopausal woman with Takotsubo Syndrome and Graves’ disease after mRNA COVID-19 vaccination. She developed chest pain and shortness of breath one week after vaccination. An electrocardiogram revealed ST elevation in the precordial leads. Coronary angiography revealed the absence of obstructive coronary artery disease, and the left ventriculography showed a typical feature with apical ballooning. Laboratory workup showed the elevation of free T4 and thyrotropin receptor antibodies. It was presumed that Takotsubo Syndrome and Graves’ disease were probably related to the COVID-19 mRNA vaccination. The patient was treated with low-dose bisoprolol, diuretics, carbimazole, and steroid and discharged uneventfully. The mRNA COVID-19 vaccination is still safe and effective to defend against COVID-19 pandemic. However, clinicians should be aware of the possible cardiovascular adverse events other than myocarditis following vaccination.

BACKGROUND: The B-type natriuretic peptide (BNP) and N-terminal pro-brain natriuretic peptide (pBNP) are predictors of cardiovascular morbidity and mortality. Since the artificial intelligence (AI)-enabled electrocardiogram (ECG) system is widely used in the management of many cardiovascular diseases (CVDs), patients requiring intensive monitoring may benefit from an AI-ECG with BNP/pBNP predictions. This study aimed to develop an AI-ECG to predict BNP/pBNP and compare their values for future mortality. METHODS: The development, tuning, internal validation, and external validation sets included 47,709, 16,249, 4001, and 6042 ECGs, respectively. Deep learning models (DLMs) were trained using a development set for estimating ECG-based BNP/pBNP (ECG-BNP/ECG-pBNP), and the tuning set was used to guide the training process. The ECGs in internal and external validation sets belonging to nonrepeating patients were used to validate the DLMs. We also followed-up all-cause mortality to explore the prognostic value. RESULTS: The DLMs accurately distinguished mild (≥500 pg/mL) and severe (≥1000 pg/mL) an abnormal BNP/pBNP with AUCs of ≥0.85 in the internal and external validation sets, which provided sensitivities of 68.0–85.0% and specificities of 77.9–86.2%. In continuous predictions, the Pearson correlation coefficient between ECG-BNP and ECG-pBNP was 0.93, and they were both associated with similar ECG features, such as the T wave axis and correct QT interval. ECG-pBNP provided a higher all-cause mortality predictive value than ECG-BNP. CONCLUSIONS: The AI-ECG can accurately estimate BNP/pBNP and may be useful for monitoring the risk of CVDs. Moreover, ECG-pBNP may be a better indicator to manage the risk of future mortality.