Racial Differences in Age-Related Changes in Left Ventricular Structural and Functional Echocardiographic Measurements Among Healthy Japanese and American Participants　- A Subanalysis of the World Alliance Society of Echocardiography Normal Values Study.

BACKGROUND: Age-related changes in left ventricular (LV) structure and function lower the threshold for the onset of heart failure with preserved ejection fraction (HFpEF). LV parameters change also with race; however, the racial differences in age-related changes in LV parameters with and without adjustment for body mass index (BMI), heart rate (HR), and blood pressure (BP) remain unclear. METHODS AND RESULTS: We performed a subanalysis of the World Alliance Society of Echocardiography Normal Values Study, an international cross-sectional study that examined normal echocardiographic values in 15 countries. The age-related changes in 2-dimensional echocardiographic derived parameters including LV size, systolic function, and mass, were compared between healthy Japanese (n=227) and healthy White (n=98) and Black (n=69) American participants. In men, age-related changes in all parameters did not differ significantly among races. However, compared with Japanese women, White American women had a smaller body surface area (BSA)-indexed LV volume, BSA-indexed LV internal dimension at end-systole, BSA-indexed LV stroke volume, and LV mass index to BSA, and a larger LV ejection fraction with age, even after adjusting for BMI, HR, and BP. CONCLUSIONS: Age-related changes in LV structure and function, which are important for the pathophysiology of HFpEF, may differ by race. Therefore, future studies examining echocardiographic reference values for each age group in each race are needed.

Three-Dimensional Geometry Values and Clinical Determinants for the Annulus of the Tricuspid Valve in a Japanese Population.

The tricuspid annulus (TA) is the primary target of tricuspid valve (TV) surgery for tricuspid regurgitation (TR). However, the reference values for TA geometry in the Japanese population is currently unavailable. We aimed to elucidate the geometric reference values of the TA in Japanese individuals using 3-dimensional (3D) echocardiography.We conducted a prospective study using transthoracic 3D echocardiography on 142 healthy Japanese subjects aged between 20 and 79 years. The tricuspid geometric parameters in the late-diastole and the mid-systole were analyzed using custom 3D software (Realview™).After excluding 46 subjects with poor images, data from 96 subjects (67.6%) were analyzed. TA area and circumference showed strong correlations with body surface area (BSA) (P < 0.001 for all), while some of these parameters exhibited weak correlations with age. Gender differences in TV geometry were assessed across 3 age groups: 20-39 years (42 subjects), 40-59 years (28 subjects), and 60-79 years (26 subjects). In the youngest subjects (20-39 years), males had a significantly larger TA area and smaller anterior-posterior and medial-lateral diameters (P < 0.001 for all), even after adjusting for BSA, indicating gender differences of TA geometry. These differences diminished with age.We present reference values for TA geometry by age and gender in a Japanese cohort. BSA may be a suitable metric for indexing the TA parameters. While age-related changes in TA parameters may not be significant, gender differences, particularly in younger individuals, persist even after adjusting for BSA.

Efficacy and Tolerability of Ivabradine for Cardiomyopathy in Patients with Duchenne Muscular Dystrophy.

Duchenne muscular dystrophy (DMD) is an intractable X-linked myopathy caused by dystrophin gene mutations. Patients with DMD suffer from progressive muscle weakness, inevitable cardiomyopathy, increased heart rate (HR), and decreased blood pressure (BP). The aim of this study was to clarify the efficacy and tolerability of ivabradine treatment for DMD cardiomyopathy.A retrospective analysis was performed in 11 patients with DMD, who received ivabradine treatment for more than 1 year. Clinical results were analyzed before (baseline), 6 months after, and 12 months after the ivabradine administration.The initial ivabradine dose was 2.0 ± 1.2 mg/day and the final dose was 5.6 ± 4.0 mg/day. The baseline BP was 95/64 mmHg. A non-significant BP decrease to 90/57 mmHg was observed at 1 month but it recovered to 97/62 mmHg at 12 months after ivabradine administration. The baseline HR was 93 ± 6 bpm and it decreased to 74 ± 12 bpm at 6 months (P = 0.011), and to 77 ± 10 bpm at 12 months (P = 0.008). A linear correlation (y = 2.2x + 5.1) was also observed between the ivabradine dose (x mg/day) and HR decrease (y bpm). The baseline LVEF was 38 ± 12% and it significantly increased to 42 ± 9% at 6 months (P = 0.011) and to 41 ± 11% at 12 months (P = 0.038). Only 1 patient with the lowest BMI of 11.0 kg/m2 and BP of 79/58 mmHg discontinued ivabradine treatment at 6 months, while 1-year administration was well-tolerated in the other 10 patients.Ivabradine decreased HR and increased LVEF without lowering BP, suggesting it can be a treatment option for DMD cardiomyopathy.

Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates.

Microvascular thrombosis is a typical symptom of COVID-19 and shows similarities to thrombosis. Using a microfluidic imaging flow cytometer, we measured the blood of 181 COVID-19 samples and 101 non-COVID-19 thrombosis samples, resulting in a total of 6.3 million bright-field images. We trained a convolutional neural network to distinguish single platelets, platelet aggregates, and white blood cells and performed classical image analysis for each subpopulation individually. Based on derived single-cell features for each population, we trained machine learning models for classification between COVID-19 and non-COVID-19 thrombosis, resulting in a patient testing accuracy of 75%. This result indicates that platelet formation differs between COVID-19 and non-COVID-19 thrombosis. All analysis steps were optimized for efficiency and implemented in an easy-to-use plugin for the image viewer napari, allowing the entire analysis to be performed within seconds on mid-range computers, which could be used for real-time diagnosis.

Prevalence of glucose metabolism disorders and its association with left atrial remodelling before and after catheter ablation in patients with atrial fibrillation.

AIMS: To investigate the exact prevalence of glucose metabolism disorders, and their impact on left atrial (LA) remodelling and reversibility in patients with atrial fibrillation (AF). METHODS AND RESULTS: We examined 204 consecutive patients with AF who underwent their first catheter ablation (CA). Oral glucose tolerance test was used to evaluate glucose metabolism disorders in 157 patients without known diabetes mellitus (DM). Echocardiography was performed before and 6 months after CA. Oral glucose tolerance test identified abnormal glucose metabolism in 86 patients [11 with newly diagnosed DM, 74 with impaired glucose tolerance (IGT) and 1 with impaired fasting glucose (IFG)]. Ultimately, 65.2% of patients had abnormal glucose metabolism. Diabetes mellitus group had the worst LA reservoir strain and LA stiffness (both P < 0.05), while there was no significant difference in baseline LA parameters between normal glucose tolerance (NGT) group and IGT/IFG group. The prevalence of LA reverse remodelling (≥15% decrease in the LA volume index at 6 months after CA) was significantly higher in NGT group compared with IGT/IFG and DM group (64.1 vs. 38.6 vs. 41.5%, P = 0.006). Both DM and IFG/IGT carry a significant risk of lack of LA reverse remodelling independent of baseline LA size and AF recurrence. CONCLUSION: Approximately 65% of patients with AF who underwent their first CA had abnormal glucose metabolism. Patients with DM had significantly impaired LA function compared with non-DM patients. Impaired glucose tolerance/IFG as well as DM carries significant risk of unfavourable LA reverse remodelling. Our observations may provide valuable information regarding the mechanisms and therapeutic strategies of glucose metabolism-related AF.

Deep Learning Models for Predicting Left Heart Abnormalities From Single-Lead Electrocardiogram for the Development of Wearable Devices.

BACKGROUND: Left heart abnormalities are risk factors for heart failure. However, echocardiography is not always available. Electrocardiograms (ECGs), which are now available from wearable devices, have the potential to detect these abnormalities. Nevertheless, whether a model can detect left heart abnormalities from single Lead I ECG data remains unclear.Methods and Results: We developed Lead I ECG models to detect low ejection fraction (EF), wall motion abnormality, left ventricular hypertrophy (LVH), left ventricular dilatation, and left atrial dilatation. We used a dataset comprising 229,439 paired sets of ECG and echocardiography data from 8 facilities, and validated the model using external verification with data from 2 facilities. The area under the receiver operating characteristic curves of our model was 0.913 for low EF, 0.832 for wall motion abnormality, 0.797 for LVH, 0.838 for left ventricular dilatation, and 0.802 for left atrial dilatation. In interpretation tests with 12 cardiologists, the accuracy of the model was 78.3% for low EF and 68.3% for LVH. Compared with cardiologists who read the 12-lead ECGs, the model's performance was superior for LVH and similar for low EF. CONCLUSIONS: From a multicenter study dataset, we developed models to predict left heart abnormalities using Lead I on the ECG. The Lead I ECG models show superior or equivalent performance to cardiologists using 12-lead ECGs.

Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning.

Left ventricular dilatation (LVD) and left ventricular hypertrophy (LVH) are risk factors for heart failure, and their detection improves heart failure screening. This study aimed to investigate the ability of deep learning to detect LVD and LVH from a 12-lead electrocardiogram (ECG). Using ECG and echocardiographic data, we developed deep learning and machine learning models to detect LVD and LVH. We also examined conventional ECG criteria for the diagnosis of LVH. We calculated the area under the receiver operating characteristic (AUROC) curve, sensitivity, specificity, and accuracy of each model and compared the performance of the models. We analyzed data for 18,954 patients (mean age (standard deviation): 64.2 (16.5) years, men: 56.7%). For the detection of LVD, the value (95% confidence interval) of the AUROC was 0.810 (0.801-0.819) for the deep learning model, and this was significantly higher than that of the logistic regression and random forest methods (P < 0.001). The AUROCs for the logistic regression and random forest methods (machine learning models) were 0.770 (0.761-0.779) and 0.757 (0.747-0.767), respectively. For the detection of LVH, the AUROC was 0.784 (0.777-0.791) for the deep learning model, and this was significantly higher than that of the logistic regression and random forest methods and conventional ECG criteria (P < 0.001). The AUROCs for the logistic regression and random forest methods were 0.758 (0.751-0.765) and 0.716 (0.708-0.724), respectively. This study suggests that deep learning is a useful method to detect LVD and LVH from 12-lead ECGs.

Impact of insulin resistance on subclinical left ventricular dysfunction in normal weight and overweight/obese japanese subjects in a general community.

BACKGROUND: Insulin resistance carries increased risk of heart failure, although the pathophysiological mechanisms remain unclear. LV global longitudinal strain (LVGLS) assessed by speckle-tracking echocardiography has emerged as an important tool to detect early LV systolic abnormalities. This study aimed to investigate the association between insulin resistance and subclinical left ventricular (LV) dysfunction in a sample of the general population without overt cardiac disease. METHODS: We investigated 539 participants who voluntarily underwent extensive cardiovascular health check including laboratory test and speckle-tracking echocardiography. Glycemic profiles were categorized into 3 groups according to homeostatic model assessment of insulin resistance (HOMA-IR): absence of insulin resistance (HOMA-IR < 1.5), presence of insulin resistance (HOMA-IR ≥ 1.5) and diabetes mellitus (DM). Multivariable logistic regression models were conducted to evaluate the association between abnormal glucose metabolism and impaired LVGLS (> - 16.65%). RESULTS: Forty-five (8.3%) participants had DM and 66 (12.2%) had abnormal HOMA-IR. LV mass index and E/e' ratio did not differ between participants with and without abnormal HOMA-IR, whereas abnormal HOMA-IR group had significantly decreased LVGLS (- 17.6 ± 2.6% vs. - 19.7 ± 3.1%, p < 0.05). The prevalence of impaired LVGLS was higher in abnormal HOMA-IR group compared with normal HOMA-IR group (42.4% vs. 14.0%) and similar to that of DM (48.9%). In multivariable analyses, glycemic abnormalities were significantly associated with impaired LVGLS, independent of traditional cardiovascular risk factors and pertinent laboratory and echocardiographic parameters [adjusted odds ratio (OR) 2.38, p = 0.007 for abnormal HOMA-IR; adjusted OR 3.02, p = 0.003 for DM]. The independent association persisted even after adjustment for waist circumference as a marker of abdominal adiposity. Sub-group analyses stratified by body mass index showed significant association between abnormal HOMA-IR and impaired LVGLS in normal weight individuals (adjusted OR 4.59, p = 0.001), but not in overweight/obese individuals (adjusted OR 1.62, p = 0.300). CONCLUSIONS: In the general population without overt cardiac disease, insulin resistance carries independent risk for subclinical LV dysfunction, especially in normal weight individuals.

Deep Learning Algorithm to Detect Cardiac Sarcoidosis From Echocardiographic Movies.

BACKGROUND: Because the early diagnosis of subclinical cardiac sarcoidosis (CS) remains difficult, we developed a deep learning algorithm to distinguish CS patients from healthy subjects using echocardiographic movies.Methods and Results:Among the patients who underwent echocardiography from January 2015 to December 2019, we chose 151 echocardiographic movies from 50 CS patients and 151 from 149 healthy subjects. We trained two 3D convolutional neural networks (3D-CNN) to identify CS patients using a dataset of 212 echocardiographic movies with and without a transfer learning method (Pretrained algorithm and Non-pretrained algorithm). On an independent set of 41 echocardiographic movies, the area under the receiver-operating characteristic curve (AUC) of the Pretrained algorithm was greater than that of Non-pretrained algorithm (0.842, 95% confidence interval (CI): 0.722-0.962 vs. 0.724, 95% CI: 0.566-0.882, P=0.253). The AUC from the interpretation of the same set of 41 echocardiographic movies by 5 cardiologists was not significantly different from that of the Pretrained algorithm (0.855, 95% CI: 0.735-0.975 vs. 0.842, 95% CI: 0.722-0.962, P=0.885). A sensitivity map demonstrated that the Pretrained algorithm focused on the area of the mitral valve. CONCLUSIONS: A 3D-CNN with a transfer learning method may be a promising tool for detecting CS using an echocardiographic movie.

Independent effect of visceral fat on left atrial phasic function in the general population.

BACKGROUND AND AIMS: Obesity increases the risk for atrial fibrillation (AF), although the impact of abdominal fat distribution on left atrial (LA) morphology and functional remodeling remains unclear. This study aimed to investigate whether increased abdominal adiposity is independently associated with impaired LA function and/or LA enlargement in a sample of the general population and to evaluate the role of adipokines in this association. METHODS AND RESULTS: The study cohort consisted of 527 participants (362 men; 57 ± 10 years) without overt cardiac disease who underwent laboratory testing, abdominal computed tomographic examination and echocardiography. Abdominal adiposity was quantitatively assessed as visceral fat area (VFA) and subcutaneous fat area (SFA) at the level of the umbilicus. Speckle-tracking echocardiography was performed to assess LA phasic function including reservoir, conduit and pump strain. LA reservoir and conduit strain decreased with increasing VFA quartiles (both p < 0.05), whereas no significant differences were observed in LA volume index and pump strain. When stratified by SFA, there were no significant differences in LA size and function across the quartiles. In multivariable analysis, VFA was significantly associated with LA conduit strain independent of cardiovascular risk factors, and pertinent laboratory and echocardiographic parameters (standardized ß = -0.136, p = 0.019). VFA was correlated with serum adiponectin level (r = -0.51, p < 0.001), but there was no association between adiponectin level and three LA phasic strains. CONCLUSION: In a sample of the general population, VFA accumulation was independently associated with worse LA conduit strain, which may be involved in the pathophysiological mechanism of obesity-related AF.

Percutaneous Mitral Valve Intervention Using MitraClip for Functional Mitral Regurgitation and Heart Failure.

Functional mitral regurgitation (FMR) frequently coexists with left ventricular systolic dysfunction and advanced heart failure, and typically has poor clinical outcomes. Although various therapeutic options including cardiac resynchronization therapy and surgical mitral intervention, have been proposed, an optimal treatment strategy for functional mitral regurgitation has not yet been established. Over the last decade, transcatheter mitral valve repair using MitraClip has emerged as a novel alternative therapeutic option for functional mitral regurgitation. In 2018, the COAPT trial demonstrated that MitraClip treatment reduced rehospitalization due to heart failure and all-cause death in patients with functional mitral regurgitation and heart failure. As a consequence, the MitraClip has become a very promising potential treatment for functional mitral regurgitation. In this review, we discuss and summarize the current status and future perspectives of the treatment for functional mitral regurgitation and heart failure.

Mitral Valvular Coaptation-Zone Area Is Associated with the Severity of Atherosclerosis Assessed by Cardio-Ankle Vascular Index.

Preservation of the mitral valve (MV) size is essential for valve function, and a reduced MV coaptation-zone area increases the risk of developing functional mitral regurgitation (FMR). We aimed to determine if the MV leaflet and coaptation-zone areas were associated with the severity of atherosclerosis assessed by cardio-ankle vascular index (CAVI) in patients with normal left ventricle (LV) systolic function and size by real-time 3D echocardiography (RT3DE).We performed RT3DE analysis in 66 patients with normal LV size and ejection fraction who underwent 2D echocardiography and CAVI. MV coaptation-zone areas were measured by custom 3D software and indexed by body surface area (BSA). The associations of clinical factors and mean CAVI with MV leaflet and coaptation-zone areas were evaluated by univariable and multivariable linear regression analyses.On univariable analysis, MV leaflet area/BSA was significantly associated with age (r = -0.335, P = 0.0069) and mean CAVI (r = -0.464, P < 0.001), and MV coaptation-zone area was significantly associated with age (r = -0.626, P < 0.001), hypertension (r = -0.626, P < 0.001), dyslipidemia (r = -0.626, P < 0.001), E/e' (r = -0.626, P < 0.001), and CAVI (r = -0.740, P < 0.001). On multivariable analysis, mean CAVI was independently associated only with MV leaflet area/BSA (standardized coefficient = -0.611, P < 0.001) and MV coaptation-zone area/BSA (standardized coefficient = -0.74, P < 0.001).In patients with normal LV systolic function and size, MV leaflet and coaptation-zone areas might be reduced according to advancing atherosclerosis. Patients with atherosclerosis might be at increased risk of developing FMR.

Relationship Between Mitral Leaflet Size and Coaptation and Their Associated Factors in Patients with Normal Left Ventricular Size and Systolic Function.

Enlargement of the mitral valve (MV) has gained attention as a compensatory mechanism for functional mitral regurgitation (FMR). We aimed to determine if MV leaflet area is associated with MV coaptation-zone area and identify the clinical factors associated with MV leaflet size and coaptation-zone area in patients with normal left ventricle (LV) systolic function and size using real-time 3D echocardiography (RT3DE).We performed RT3DE in 135 patients with normal LV size and ejection fraction. MV leaflet and coaptation-zone areas were measured using custom 3D software. The clinical factors associated with MV leaflet and coaptation-zone areas were evaluated using univariate and multivariate linear regression analyses.There was a significant relationship between MV leaflet and coaptation-zone areas (r = 0.499, P < 0.001). MV leaflet area was strongly associated with body surface area (BSA) (r = 0.905, P < 0.001) rather than LV size and age. MV leaflet area/BSA was independently associated with male gender (P = 0.002), lower diastolic blood pressure (P = 0.042), and LV end-diastolic volume (LVEDV) index (P = 0.048); MV coaptation-zone area/BSA was independently associated with lower LVEDV index (P = 0.01).In patients with normal LV systolic function and size, MV leaflet size has a significant impact on competent MV coaptation. MV leaflet area might be intrinsically determined by body size rather than age and LV size, and the MV leaflet area/BSA is relatively constant. On the other hand, some clinical factors might also influence MV leaflet and coaptation-zone area.

The Effectiveness of a Deep Learning Model to Detect Left Ventricular Systolic Dysfunction from Electrocardiograms.

Deep learning models can be applied to electrocardiograms (ECGs) to detect left ventricular (LV) dysfunction. We hypothesized that applying a deep learning model may improve the diagnostic accuracy of cardiologists in predicting LV dysfunction from ECGs. We acquired 37,103 paired ECG and echocardiography data records of patients who underwent echocardiography between January 2015 and December 2019. We trained a convolutional neural network to identify the data records of patients with LV dysfunction (ejection fraction < 40%) using a dataset of 23,801 ECGs. When tested on an independent set of 7,196 ECGs, we found the area under the receiver operating characteristic curve was 0.945 (95% confidence interval: 0.936-0.954). When 7 cardiologists interpreted 50 randomly selected ECGs from the test dataset of 7,196 ECGs, their accuracy for predicting LV dysfunction was 78.0% ± 6.0%. By referring to the model's output, the cardiologist accuracy improved to 88.0% ± 3.7%, which indicates that model support significantly improved the cardiologist diagnostic accuracy (P = 0.02). A sensitivity map demonstrated that the model focused on the QRS complex when detecting LV dysfunction on ECGs. We developed a deep learning model that can detect LV dysfunction on ECGs with high accuracy. Furthermore, we demonstrated that support from a deep learning model can help cardiologists to identify LV dysfunction on ECGs.

Comparison Between Healthcare-Associated and Community-Acquired Infective Endocarditis at Tertiary Care Hospitals in Japan.

BACKGROUND: Healthcare-associated infective endocarditis (HAIE) has become increasingly recognized worldwide because the underlying patient conditions are completely different from those of community-acquired infective endocarditis (CIE). However, data on HAIE in the Japanese population is lacking. We sought to clarify the patient characteristics and prognosis of HAIE in a Japanese population.Methods and Results:A retrospective study was conducted in 158 patients who were diagnosed with infective endocarditis, 53 of whom (33.5%) were classified as HAIE. Compared with patients with CIE, those with HAIE were older (median age 72 vs. 61 years; P=0.0002) and received surgical treatment less frequently (41.5% vs. 62.9%; P=0.01). Regarding causative microorganisms, staphylococci,including methicillin-resistant pathogens, were more common in patients with HAIE (32.1% vs. 14.3%; P=0.01). Patients with HAIE had higher in-hospital mortality (32.1% vs. 4.8%; P<0.0001) and Kaplan-Meier analysis showed worse prognosis for patients with HAIE than CIE (P<0.0001, log-rank test). On multivariate Cox analysis, HAIE (hazard ratio 3.26; 95% confidence interval 1.49-7.14), age ≥60 years, surgical treatment, stroke, and heart failure were independently associated with mortality. CONCLUSIONS: HAIE has different clinical characteristics and causative microorganisms, as well as worse prognosis, than CIE. Preventive strategies, and the prompt and appropriate identification of HAIE may improve the outcome of infective endocarditis.

High Prevalence of Left Ventricular Non-Compaction and Its Effect on Chemotherapy-Related Cardiac Dysfunction in Patients With Hematological Diseases.

BACKGROUND: Recent progress in chemotherapy has prolonged the survival of patients with hematological diseases, but has also increased the number of patients with chemotherapy-related cardiac dysfunction (CTRCD). However, the causes of individual variations and risk factors for CTRCD have yet to be fully elucidated.Methods and Results:Consecutive echocardiograms of 371 patients were retrospectively evaluated for the presence of left ventricular (LV) non-compaction (LVNC). Individual LV ejection fraction (LVEF) outcome estimates were made using bivariate linear regression with log-transformed duration Akaike information criterion (AIC) model fitting. The prevalence of LVNC was 6-fold higher in patients with hematological diseases than in those with non-hematological diseases (12% vs. 2%; risk ratio 6.1; 95% confidence interval [CI] 2.0, 18.2). Among patients with hematological diseases, the ratio of myeloid diseases was significantly higher in the group with LVNC (P=0.031). Deterioration of LVEF was more severe in patients with than without LVNC (-14.4 percentage points/year [95% CI -21.0, -7.9] vs. -4.6 percentage points/year [95% CI -6.8, -2.4], respectively), even after multivariate adjustment for baseline LVEF, background disease distributions, cumulative anthracycline dose, and other baseline factors. CONCLUSIONS: LVNC is relatively prevalent in patients with hematological diseases (particularly myeloid diseases) and can be one of the major risk factors for CTRCD. Detailed cardiac evaluations including LVNC are recommended for patients undergoing chemotherapy.

Effect of cigarette smoking on carotid artery atherosclerosis: a community-based cohort study.

Cigarette smoking is closely associated with the development of cardiovascular diseases. However, the relationship between cigarette smoking and subclinical atherosclerosis has not been fully studied. We sought to clarify the association between cigarette smoking and carotid intima-media thickness (cIMT) in a general Japanese population. Among 1,209 participants who received a medical check-up with cardiovascular examination at our institution, 450 participants (37.2%) were smokers (including both past and current smokers). We evaluated cIMT as a marker of subclinical atherosclerosis. The value of cIMT and rate of carotid plaque defined as IMT ≥ 1.1 mm did not differ between smokers and never smokers. However, the rate of carotid high-risk atheroma, defined as carotid artery atheroma including hypoechoic dominant and ulceration, was significantly higher among smokers than never smokers (30.4%, vs 23.6%, p = 0.009). Even after adjustment for covariates, cigarette smoking was independently associated with high-risk atheroma formation (odds ratio 1.384, 95% CI 1.019-1.880; p = 0.038). The value of cIMT and the rate of high-risk atheroma were significantly higher in smokers than never smokers in the subgroup of participants aged ≥ 60 years, whereas the rate of high-risk atheroma only was higher in smokers than never smokers in the subgroup of participants aged < 60 years. In conclusion, the development of high-risk carotid artery atheroma may precede the thickening of cIMT in cigarette smokers, which suggests the novel insight for the pathological mechanism underlying cardiovascular events and cigarette smoking.

Are there gender differences in the association between body mass index and left ventricular diastolic function? A clinical observational study in the Japanese general population.

BACKGROUND: Increased body mass index (BMI) is a major risk factor for heart failure with preserved ejection fraction (HFpEF), and HFpEF is more prevalent in elderly females than males. We hypothesized that there may be gender differences in the association between BMI and echocardiographic left ventricular (LV) diastolic parameters. METHODS: We enrolled 456 subjects (243 males) without overt cardiac diseases, all of whom underwent a health checkup. Early (E) and late (A) diastolic transmitral flow velocity, early diastolic mitral annular velocity (e'), and left atrial (LA) volume index were measured by echocardiography to assess LV diastolic function. To examine gender differences in the association between BMI and LV diastolic function, we analyzed the interaction effects of gender on the association between BMI and echocardiographic LV diastolic parameters. RESULTS: Although there were significant gender differences in the association between BMI and E/A and e' in the crude model (interaction effect 0.037 and 0.173, respectively; P = .006 and .022, respectively), these differences were not statistically significant after adjustment for factors related to LV diastolic function. On the other hand, there were significant associations between BMI and LV diastolic parameters in each gender, even after adjustment. CONCLUSIONS: Our findings suggest there is no gender difference in the association between BMI and echocardiographic LV diastolic parameters. However, the association between BMI and LV diastolic parameters was significant in both genders. Controlling body weight might be beneficial for both women and men to prevent progression of LV diastolic dysfunction and development of HFpEF.

Time-Dependent Changes in Porcelain Aorta and Aortic Stenosis in a Patient with Systemic Lupus Erythematosus.

Porcelain aorta, defined as extensive calcification of the ascending aorta or aortic arch, is a reported risk factor for embolic stroke during cardiac surgery. However, the time course of the progression of aortic calcification leading to porcelain aorta has not been elucidated. We herein describe a 70-year-old woman who was followed up for systemic lupus erythematosus and antiphospholipid syndrome for approximately 20 years. A routine computed tomography scan revealed progression of ascending aortic calcification to porcelain aorta. The calcification was absent during the preceding 12 years, partial 6 years later, and total after another 3 years. Computed tomography also demonstrated aortic and mitral valve calcification in the development of porcelain aorta. During the 3 years prior to the last admission, annual echocardiography examinations showed progression of calcific aortic stenosis with symptoms. The patient was admitted to our institution for aortic valve replacement. Considering the high risk of perioperative stroke associated with porcelain aorta, transcatheter aortic valve implantation was performed. Postoperative transthoracic echocardiography revealed improvement of the aortic stenosis with no symptoms. The present case revealed aortic calcific progression to porcelain aorta during an approximately 10-year period with deterioration of aortic stenosis within a short time. The aortic and valvular calcification could be attributed to the inflammatory process of systemic lupus erythematosus and antiphospholipid syndrome. The presence of aortic and mitral annular calcification in the serial imaging can provide information on aortic and valvular atherosclerotic progression, which may be modifiable by early steroid-lowering therapy.

Age-Dependent Echocardiographic and Pathologic Findings in a Rat Model with Duchenne Muscular Dystrophy Generated by CRISPR/Cas9 Genome Editing.

Duchenne muscular dystrophy (DMD) is X-linked recessive myopathy caused by mutations in the dystrophin gene. Although conventional treatments have improved their prognosis, inevitable progressive cardiomyopathy is still the leading cause of death in patients with DMD. To explore novel therapeutic options, a suitable animal model with heart involvement has been warranted.We have generated a rat model with an out-of-frame mutation in the dystrophin gene using CRISPR/Cas9 genome editing (DMD rats). The aim of this study was to evaluate their cardiac functions and pathologies to provide baseline data for future experiments developing treatment options for DMD.In comparison with age-matched wild rats, 6-month-old DMD rats showed no significant differences by echocardiographic evaluations. However, 10-month-old DMD rats showed significant deterioration in left ventricular (LV) fractional shortening (P = 0.024), and in tissue Doppler peak systolic velocity (Sa) at the LV lateral wall (P = 0.041) as well as at the right ventricular (RV) free-wall (P = 0.004). These functional findings were consistent with the fibrotic distributions by histological analysis.Although the cardiac phenotype was milder than anticipated, DMD rats showed similar distributions and progression of heart involvement to those of patients with DMD. This animal may be a useful model with which to develop effective drugs and to understand the underlying mechanisms of progressive heart failure in patients with DMD.