Myocardial Infarction Subjects Research Articles

A-005 Comparison of Performance of High Sensitive Troponin I (cTNIH) by “Point of care” (POC) and Automated cTNIH Immunoassays in Patients at Low Risk of Heart Attack in a Chilean Emergency Unit; Impact on E.R Department Efficiency

Abstract Background Most cases of death from Acute Myocardial Infarction (AMI) occur in the first hours after the manifestation of clinical symptoms, constituting 40 to 65% in the first hour and approximately 80% in the first 24 hours. For the safe, effective, and rapid diagnosis of a patient with suspected AMI, international guidelines recommend that it be done based on clinical symptoms, together with electrocardiogram and analysis of biomarkers in the context of suspected of non-ST-elevation myocardial infarction (NSTEMI). Troponins are the biomarkers of choice with level A level evidence and the ability to perform screening in patients complaining of chest pain and refer it to the correct clinical approach based on the 0h/1h algorithm. To date, the successful clinical implementation of these rapid algorithms is restricted to hospitals working with well-defined workflows and a robust central laboratory structure. The Atellica® vTLi analyzer is based on the chemiluminescence methodology for the quantification of hs-cTnI levels with low sample volume, low complexity, and may enable the rapid management of patients with chest pain and application of 0h/1h algorithm. Objectives The aim of the study is to evaluate clinical performance in effectiveness and efficiency of the hs-cTnI Atellica® vTLi point of care test to rule out acute myocardial infarction in subjects with low-risk chest pain established with a validated predictive rule, in an adult-Chilean emergency unit, compared to clinical evaluation and the commonly used ADVIA Centaur® troponin test. Methods Descriptive, observational, longitudinal study. Patients over 18 years of age, consulting with acute chest pain of at least one hour from symptom onset and a HEART SCORE less than or equal to 3 points were recruited. A venous blood sample was requested for hs-cTnI analysis by Atellica® vTLi Siemens Healthineers and ADVIA Centaur® Siemens Healthineers. The time difference between sample collection and start of sample analysis; the time difference between sample collection and release of sample results was recorded and the concordance between the two results was determined. Results 50 subjects were included in the study. The difference between blood sampling and start of sample analysis was 5 minutes with Atellica® vTLi POC and 68 minutes with ADVIA Centaur®. The concordance of hs-cTnI Atellica® vTLi POC and ADVIA Centaur® was 94% for predicting medical discharge and confirmed acute myocardial infarction. Conclusions The study findings support the safety and efficiency of the hs-cTnI POC test by providing faster results than central laboratory tests with the same analytical quality, with direct impact on reducing care times in patients consulting for low-risk chest pain.

Loss of y chromosome could delay the occurrence of myocardial infarction in subjects with clonal hematopoiesis

Loss of y chromosome could delay the occurrence of myocardial infarction in subjects with clonal hematopoiesis

Bone fracture is associated with incident myocardial infarction in long-term follow-up

BackgroundThe association between bone fracture and cardiovascular diseases is examined in this study. While basic research has established a connection between fractures and heart attacks through the linkage between bones and arteries, population studies have not provided clear evidence. The aim of the present study is to investigate the association between bone fracture and the occurrence of myocardial infarction in a natural population during long-term follow-up.MethodsA total of 13,196 adult participants with bone fracture history at baseline from the China Health and Nutrition Survey (CHNS) prospective cohort were included in this study. Baseline investigation was performed in 1997–2009 and the outcome was followed up till 2015. Hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were calculated using Cox proportional hazards models.ResultsFrom 1997 to 2015, a total of 329 incident myocardial infarction cases were identified. In univariate and multivariate Cox regression analysis, a history of bone fracture was associated with an increased risk of myocardial infarction incidence in the total population (for the crude model: HR = 2.56, 95% CI 1.83–3.53, P < 0.001; for the multivariate model: HR = 1.43, 95% CI 1.02–1.99, P = 0.036). In the stratified analysis, bone fracture was not associated with an increased risk of incident myocardial infarction in subjects with age < 50 years (HR = 0.71, 95% CI 0.34–1.47, P = 0.356), but significantly associated with an increased risk of incident myocardial infarction in subjects with age ≥ 50 years (HR = 1.80, 95% CI 1.23–2.63, P = 0.003).ConclusionsIt is suggested by the present study that bone fracture may be associated with an increased risk of incident myocardial infarction in the elderly population during long-term follow-up.

An interpretable shapelets-based method for myocardial infarction detection using dynamic learning and deep learning

Objective. Myocardial infarction (MI) is a prevalent cardiovascular disease that contributes to global mortality rates. Timely diagnosis and treatment of MI are crucial in reducing its fatality rate. Currently, electrocardiography (ECG) serves as the primary tool for clinical diagnosis. However, detecting MI accurately through ECG remains challenging due to the complex and subtle pathological ECG changes it causes. To enhance the accuracy of ECG in detecting MI, a more thorough exploration of ECG signals is necessary to extract significant features. Approach. In this paper, we propose an interpretable shapelet-based approach for MI detection using dynamic learning and deep learning. Firstly, the intrinsic dynamics of ECG signals are learned through dynamic learning. Then, a deep neural network is utilized to extract and select shapelets from ECG dynamics, which can capture locally specific ECG changes, and serve as discriminative features for identifying MI patients. Finally, the ensemble model for MI detection is built by integrating shapelets of multi-dimensional ECG dynamic signals. Main results. The performance of the proposed method is evaluated on the public PTB dataset with accuracy, sensitivity, and specificity of 94.11%, 94.97%, and 90.98%. Significance. The shapelets obtained in this study exhibit significant morphological differences between MI and healthy subjects.

Electronic Cigarette Use and Myocardial Infarction.

Current electronic cigarettes (e-cigarettes) is associated with myocardial infarction, controlling whether the subjects smoke cigarettes. However, no studies have been conducted on subjects who never smoked cigarettes. This study aimed to determine the association between e-cigarette use and myocardial infarction among subjects who have never smoked cigarettes. The National Health Interview Survey (NHIS) data from 2014 to 2021was used to evaluate the relationship between e-cigarette use and myocardial infarction in subjects who have never smoked cigarettes after adjusting for risk factors, including age, sex, diabetes, hypertension, hypercholesterolemia, and obesity/overweight,using logistic regression. A total of 139,697 subjects were never users, and 1,237 subjects were current e-cigarette users. E-cigarette users were significantly younger than never users.E-cigaretteusers were less likely to be female (40% vs. 60%, p <0.01), or have diabetes (3% vs. 10%, p <0.01), have hypertension (11% vs. 32%, p <0.01), have hypercholesterolemia (8% vs. 27%, p <0.01), or be overweight or obese (56% vs. 65%, p <0.01) compared to never users.The current e-cigarette users had a 2.6-fold increase in the odds of having a myocardial infarction (OR 2.62, 95% CI 1.44-4.77; p <0.01) after adjusting for sex, age, hypertension, diabetes, hypercholesterolemia, and obesity/overweight. Conclusions: This study suggests that current e-cigarette use increases the risks of cardiovascular disease, including myocardial infarction and stroke, in subjects who never smoked cigarettes. Further longitudinal studies are needed to confirm the results of this study.

Nonlinear Pulse Wave Dynamics in Prediction of Coronary Heart Disease and Myocardial Infarction

Atherosclerosis may induce coronary heart diseases (CHD) that progress to myocardial infarction (MCI), if untreated. The pulse plethysmogram (PPG) plot demonstrated the change in peripheral blood volume influence induced by the sympathetic branch of the autonomic nervous system that causes vasoconstriction. Thus, the present work has been aimed at proposing a noninvasive, low-cost, and wearable technology for the forecasting of CHD and MCI subjects using PPG-derived features with a support vector machine (SVM). A total of 70 volunteers (50-55 years) including MCI (n = 10), CHD (n = 30), and Control (n = 30) has been participated. Digital PPG was recorded for 10 min from the index finger of the left hand in the supine position. Ten samples from each subject were selected from the PPG signal for deriving nonlinear pulse rate variability (PRV) features using Kubios 2.0. The sensitivity, specificity, and accuracy were calculated from the obtained confusion matrix in the classification of control, CHD, and MCI classes. The findings suggested that the reduced value of PRV features in the Poincare plot, detrended fluctuation analysis recurrence plot, and correlation dimension while higher entropy measures in MCI than the CHD subjects. The best accuracy of 98.2% and 98.3% were observed with quadratic functions in the prediction of CHD and MCI subjects from the control group, respectively. The obtained results suggested the applicability of nonlinear PRV features in designing the low-cost real-time wearable technique in the prediction of CHD and MCI subjects.

Automatic diagnosis and localization of myocardial infarction using morphological features of ECG signal

Electrocardiogram (ECG) is a non-invasive and economical diagnostic tool for detecting myocardial infarction (MI). The occurrence of a heart attack causes distortions in the ECG waves. This article extracts morphological features from ECG signals to detect and localize MI. After preprocessing the ECG signal, its fiducial points are identified. Then morphological features such as the amplitude, interval, and angle between waves are extracted. A random forest classifier with 100 trees has been used for classification and feature selection. The method was evaluated using the PTB dataset, containing 52 healthy and 148 MI subjects. We tried to diagnose and localize MI in two schemes: interpatient and intrapatient. In this method, we obtained superior results with an accuracy of 80.98%, a sensitivity of 80.98%, a specificity of 96.32%, a positive predictive value of 79.72%, and an F-score of 79.53% for MI localization in the interpatient scheme compared to the state-of-the-art. Our model achieves an accuracy of 96.54%, a sensitivity of 99.74%, a positive predictive value of 96.09%, and an F-score of 97.88% in the interpatient scheme detection. In the interpatient domain, 96.68% accuracy was obtained using only 6 chest leads for detection. The proposed method is interpretable with low computational complexity and applies a new package of morphological features. Compared to recent studies, in this study, the results have been improved in the interpatient scheme which has more vital clinical significance.

Myocardial infarction detection using ITD, DWT and deterministic learning based on ECG signals.

Nowadays, cardiovascular diseases (CVD) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention issue. Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgement and several electrocardiographic (ECG) or vectorcardiographic (VCG) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study, we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector, which is decomposed into a series of proper rotation components (PRCs) by using the intrinsic time-scale decomposition (ITD) method. The novel cardiac vector may fully reflect the pathological alterations provoked by MI and may be correlated to the disparity of cardiac system dynamics between healthy and MI subjects. ITD is employed to measure the variability of cardiac vector and the first PRCs are extracted as predominant PRCs which contain most of the cardiac vector's energy. Second, four levels discrete wavelet transform with third-order Daubechies (db3) wavelet function is employed to decompose the predominant PRCs into different frequency bands, which combines with three-dimensional phase space reconstruction to derive features. The properties associated with the cardiac system dynamics are preserved. Since the frequency components above 40 Hz are lack of use in ECG analysis, in order to reduce the feature dimension, the advisable sub-band (D4) is selected for feature acquisition. Third, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the tenfold cross-validation style, the achieved average classification accuracy is reported to be 98.20%. Results verify the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.

Heart rate dynamics in the prediction of coronary artery disease and myocardial infarction using artificial neural network and support vector machine.

Atherosclerosis leads to coronary artery disease (CAD) and myocardial infarction (MI), a major cause of morbidity and mortality worldwide. The computer-aided prognosis of atherosclerotic events with the electrocardiogram (ECG) derived heart rate variability (HRV) can be a robust method in the prognosis of atherosclerosis events. A total of 70 male subjects aged 55 ± 5 years participated in the study. The lead-II ECG was recorded and sampled at 200 Hz. The tachogram was obtained from the ECG signal and used to extract twenty-five HRV features. The one-way Analysis of variance (ANOVA) test was performed to find the significant differences between the CAD, MI, and control subjects. Features were used in the training and testing of a two-class artificial neural network (ANN) and support vector machine (SVM). The obtained results revealed depressed HRV under atherosclerosis. Accuracy of 100% was obtained in classifying CAD and MI subjects from the controls using ANN. Accuracy was 99.6% with SVM, and in the classification of CAD from MI subjects using SVM and ANN, 99.3% and 99.0% accuracy was obtained respectively. Depressed HRV has been suggested to be a marker in the identification of atherosclerotic events. The good accuracy observed in classification between control, CAD, and MI subjects, revealed it to be a non-invasive cost-effective approach in the prognosis of atherosclerotic events.

Autonomic Features in Prediction of Coronary Artery Disease and Myocardial Infarction

Atherosclerosis is one of the causes of the progression of cardiovascular diseases that lead to coronary artery diseases (CAD) and myocardial infarction (MI). The disease severity causes electrocardiogram (ECG) shape deformation leads to difficulty in R-wave detection required for heart rate variability (HRV) analysis. This limitation in the diagnosis of atherosclerotic events using ECG leads to computer-aided ECG-derived features in the prediction of CAD and MI. In this study, the lead-II ECG was recorded from CAD (N = 30), MI (N = 10), and control (N = 30) subjects. The ECG-derived heart rate variability (HRV) features were extracted. The HRV features-based support vector machine (SVM) and the artificial neural network (ANN) models have been optimized in the prediction of CAD and MI subjects. The present study revealed an efficacy of time-domain HRV parameters with the ANN model in the classification of CAD and MI subjects with an accuracy of 100% and 99.6%, respectively. While SVM presented an accuracy of 75.5% and 98.9%, respectively. The time-domain HRV parameter-based automated computer-aided diagnostic approach can be used in developing low-cost technology that may provide aid to clinicians in the screening of CAD and MI subjects.

Nine-fold higher risk of acute myocardial infarction in subjects with type 1 diabetes compared to controls in Norway 1973\u20132017

BackgroundWe aimed to study the cumulative incidence and risk factors (sex, age, calendar year of diabetes onset, country of origin and educational level) of acute myocardial infarction (AMI) in subjects with type 1 diabetes and matched controls.MethodsA nationwide cohort of subjects with type 1 diabetes diagnosed at age < 15 years in Norway during 1973–2000 was followed until the first AMI event, emigration, death or 31st of December 2017. The Norwegian Childhood Diabetes Registry was linked to five nationwide registries, and up to ten sex- and age-matched controls per case were included.ResultsAmong 7086 subjects with type 1 diabetes, 170 (2.4%) were identified with incident AMI, compared to 193 (0.3%) of 69,356 controls. Mean age and diabetes duration at first AMI was 40.8 years and 30.6 years, respectively. The probability of AMI after 40 years of follow-up was 8.0% in subjects with type 1 diabetes and 1.1% in controls, aHR 9.05 (95% CI 7.18–11.41). In type 1 diabetes, male sex (aHR 1.45), higher age at onset of diabetes and lower education (higher compared to lower, aHR 0.38) were significantly associated with higher risk of AMI. There was no significant time trend in AMI by calendar year of diabetes onset.ConclusionsWe found nine-fold excess risk of AMI in subjects with type 1 diabetes, and three-fold higher risk in subjects with low versus high education. These results highlight a strengthened focus on prevention of cardiovascular disease, and diabetes education tailored to the subjects’ educational background.

Association of Myocardial Infarction with CDKN2B Antisense RNA 1 (CDKN2B-AS1) rs1333049 Polymorphism in Slovenian Subjects with Type 2 Diabetes Mellitus.

Background: We examined the role of rs1333049 polymorphism of the CDKN2B Antisense RNA 1 (CDKN2B-AS1) on the prevalence of myocardial infarction (MI) in Slovenian subjects with type 2 diabetes mellitus (T2DM). Methods: A total of 1071 subjects with T2DM were enrolled in this retrospective cross-sectional case-control study. Of the subjects, 334 had a history of recent MI, and 737 subjects in the control group had no clinical signs of coronary artery disease (CAD). With logistic regression, we performed a genetic analysis of rs1333049 polymorphism in all subjects. Results: The C allele of rs1333049 polymorphism was statistically more frequent in MI subjects (p = 0.05). Subjects with CC genotype had a higher prevalence of MI than the control group in the co-dominant (AOR 1.50, CI 1.02–2.21, p = 0.04) and recessive (AOR 1.38, CI 1.09–1.89, p = 0.04) genetic model. Conclusions: According to our study, the C allele and CC genotype of rs1333049 polymorphism of CDKN2B-AS1 are possible markers of MI in T2DM subjects in the Slovenian population.

Increased levels of low density neutrophils (LDNs) in myocardial infarction

Background: Recent evidence suggests that neutrophils are highly plastic cells that can display heterogeneous phenotypes. Low-density neutrophils (LDNs) have been described in many inflammatory conditions, and are thought to represent an immature, hyperactivated subtype of neutrophils. Neutrophils are significantly involved in the inflammatory response to myocardial infarction (MI), although we do not know the extent to which LDNs exist, or function, in MI. This study sought to determine the frequency and phenotype of LDNs in MI patients, compared to healthy subjects (HS). Methods: LDNs and normal-density neutrophils (NDNs) were isolated from the peripheral blood of MI subjects (n = 12) and HSs (n = 12) using density gradient centrifugation. LDNs and NDNs were analysed by flow cytometry to identify neutrophils (CD66b+CD15+CD14-CD3−CD19− cells) and examine neutrophil activation (CD11b, CD66b and CD15) and maturity (CD33 and CD16). Results: We identified LDNs within the peripheral blood mononuclear cell (PBMC) fraction of blood, and this population is significantly enriched in MI patients (1.04 ± 0.75% of PBMCs), compared to HS (0.29 ± 0.24%, p = .003). Across both cohorts, LDNs express significantly higher levels of CD66b and CD15, indicating a heightened state of activation compared to NDNs. In this study, LDNs were described as CD33highCD16low, compared to CD33lowCD16high NDNs, indicating the immaturity of this neutrophil subtype. Conclusions: An increase in the frequency of hyperactivated, immature LDNs is an immunological feature of MI. We highlight a potential pathological role of LDNs in MI, which underscores the need to expand our current understanding of this subtype in MI and other cardiovascular diseases (CVDs).

Effectiveness of incremental aerobic exercise program in patients with myocardial infarction using 6-min walk test

Background: Myocardial infarction (MI), which is also called as heart attack, happens as a phenomenon due to reduced or no blood supply to a certain part of heart, leading to damaging heart musculature. Aims and Objectives: To investigate the efficacy of aerobic exercise training in Phase II rehabilitation program of myocardial infarction (MI) patients, 40 MI subjects were randomly selected (n = 40), age ranging from 50 to 70 years. Materials and Methods: The subjects were prescribed an incremental aerobic exercise training program to be performed for 6 weeks with initial intensity on 50% maximum heart rate (HRmax); then, in Group B, we have asked the subjects to perform on 55% HRmax, then on 60% HRmax, and up to 75% of HRmax; then, at the end of 6 weeks, we have recorded the parameters again. Results: The data obtained were then analyzed using related t-test, and the results were found to be statistically significant (t = 0.300, P < 0.01). Conclusion: The study shows that incremental aerobic exercise can significantly improve the performance of MI subjects in 6-min walk test distance.

Parametric-based feature selection via spherical harmonic coefficients for the left ventricle myocardial infarction screening.

Computer-aided diagnosis (CAD) of heart diseases using machine learning techniques has recently received much attention. In this study, we present a novel parametric-based feature selection method using the three-dimensional spherical harmonic (SHs) shape descriptors of the left ventricle (LV) for intelligent myocardial infarction (MI) classification. The main hypothesis is that the SH coefficients of the parameterized endocardial shapes in MI patients are recognizable and distinguishable from healthy subjects. The SH parameterization, expansion, and registration of the LV endocardial shapes were performed, then parametric-based features were extracted. The proposed method performance was investigated by varying considered phases (i.e., the end-systole (ES) or the end-diastole (ED) frames), the spatial alignment procedures based on three modes (i.e., the center of the apical (CoA), the center of mass (CoM), and the center of the basal (CoB)), and considered orders of SH coefficients. After applying principal component analysis (PCA) on the feature vectors, support vector machine (SVM), K-nearest neighbors (K-NN), and random forest (RF) were trained and tested using the leave-one-out cross-validation (LOOCV). The proposed method validation was performed via a dataset containing healthy and MI subjects selected from the automated cardiac diagnosis challenge (ACDC) database. The promising results show the effectiveness of the proposed classification model. SVM reached the best performance with accuracy, sensitivity, specificity, and F-score of 97.50%, 95.00%, 100.00%, and 97.56%, respectively, using the introduced optimum feature set. This study demonstrates the robustness of combining the SH coefficients and machine learning techniques. We also quantify and notably highlight the contribution of different parameters in the classification and finally introduce an optimal feature set with maximum discriminant strength for the MI classification task. Moreover, the obtained results confirm that the proposed method performs more accurately than conventional point-based methods and also the current start-of-the-art, i.e., clinical measures. We showed our method's generalizability using employing it in dilated cardiomyopathy (DCM) detection and achieving promising results too. Parametric-based feature selection via spherical harmonics coefficients for the left ventricle myocardial infarction screening.

Temporal Feature-Based Classification Into Myocardial Infarction and Other CVDs Merging CNN and Bi-LSTM From ECG Signal

Heart attack else wise termed as myocardial infarction (MI) causes irreparable death of cardiac muscles yielding the focal reason for most casualties among all cardiovascular diseases (CVDs’). A 12-lead electrocardiogram (ECG) generally depicts cardiac abnormalities and so customary deep learning (DL) approaches use the whole signal for binary detection purposes, that is separating healthy control (HC), and MI classes. This paper proposes an alternative approach where 21 temporal features in lieu of the temporal signal are collected from the 12 lead data to reduce redundancy and class imbalance keeping the vital information intact. Then these extracted features are fed into a detection model consisting of a one dimensional (1-D) convolutional neural network (CNN) and a bidirectional long short-term memory (bi-LSTM) layer which classifies into three classes, namely: HC, MI, and non-myocardial infarction (non-MI) subjects for a realistic and reliable assessment. The model’s performance is evaluated using 517 records acquired from the Physikalisch-Technische Bundesanstalt (PTB) database and a state-of-art overall accuracy of 99.246%, kappa of 0.983, and macro averaged F1 score of 98.86% were achieved using stratified 5-fold cross-validation. DL methods suffer to make unbiased decisions in the case of class imbalance due to an insufficient amount of data for a particular class and thus temporal features are employed to inherently reduce this problem. The successful performance of the extracted features depends on the precise detection of fiducial points, and so multiple novel algorithms have been introduced in this paper.

The C allele of the reactive oxygen species modulator 1 (ROMO1) polymorphism rs6060566 is a biomarker predicting coronary artery stenosis in Slovenian subjects with type 2 diabetes mellitus

BackgroundWe aimed to examine the role of the rs6060566 polymorphism of the reactive oxygen species modulator 1 (ROMO1) gene in the development of myocardial infarction (MI) in Caucasians with type 2 diabetes (T2DM).MethodsA total of 1072 subjects with T2DM were enrolled in this cross-sectional case–control study: 335 subjects with MI and 737 subjects without clinical signs of coronary artery disease (CAD). The genetic analysis of the rs6060566 polymorphism was performed in all subjects. To assess the degree of coronary artery obstruction, a subpopulation of 128 subjects with T2DM underwent coronary computed tomography angiography. Next, endarterectomy samples were obtained during myocardial revascularization from diffusely diseased coronary arteries in 40 cases, which were analysed for ROMO1 expression according to their genotype.ResultsThere were no statistically significant associations between different genotypes or alleles of the rs6060566 polymorphism and MI in subjects with T2DM. The carriers of the C allele of the ROMO1 rs6060566 had a threefold increased likelihood of having 50–75% coronary artery stenosis (Adjusted OR = 3.27, 95% CI 1.16–9.20). Subjects with two affected coronary arteries had a 3.72 fold higher prevalence of MI (OR = 3.72, 95% CI 1.27–10.84). With CAD in LMCA or LAD, MI prevalence was about 3.5-fold higher (p = 0.07 for LMCA and p = 0.01 for LAD). Furthermore, the carriers of the rs6060566 C allele showed higher number of positive cells for ROMO1 expression in endarterectomy samples of coronary arteries.ConclusionsAccording to our study, the rs6060566 polymorphism of the ROMO1 gene is not a risk factor for MI in Caucasians with T2DM. However, we found that subjects carrying the C allele were at a 3.27-fold increased risk of developing severe CAD compared with those who had non-obstructive CAD. Moreover, C allele carriers showed a statistically higher number of cells positive for ROMO1 compared with T allele carriers in coronary endarterectomy samples.

Association between the phenotype of metabolically healthy obesity and the risk of myocardial infarction

Abstract Aim This study aimed to investigate the associations between the metabolically healthy obesity (MHO) and the risk of myocardial infarction (MI) in a Russian population sample. Materials and methods A random population sample of men and women aged 45–69 years old at baseline was examined in 2003–2005 in a Russian city; after that the cohort was followed-up for 12 years on the average (2003–2016). A sub-cohort of subjects with body mass index (BMI) ≥30 kg/m2 at baseline was selected for present study (3157 individuals, 73.2% women); of those, 3008 subjects free from baseline history of MI/acute CHD was included for analysis. The data on incident myocardial infarction (MI) were ascertained from register of MI, two repeated examinations and repeated postal interviews during 12-year follow-up of the cohort. We used two definitions of MHO: by NCEP ATPIII, 2001 (the presence of 2 or less components of the metabolic syndrome) and by IDF 2005 (waist circumference (WC) ≥94 cm in men and ≥80 cm in women regardless of risk factors). Multivariable Cox regression was conducted using the SPSS package (V. 13.0). Results The prevalence of MHO at baseline ranged from 20% (by IDF) to 45% (by NCEPATPIII) in obese sub-cohort. Among subjects with MHO at baseline, about one half of subjects developed metabolically unhealthy obesity phenotype (MUO) during 12-years. Women were more likely to retain MHO (32%) and more frequent transited from MUO to MHO (14%) compared to men (22% and 6% by IDF criteria, correspondently) during 12 years. The relative risk of incident MI in subjects with MUO at baseline was 1.9 times higher than in those with MHO (HR 1.9, 95% CI 1.2–2.9) by NCEPATP III. Among men, the relative risk of MI in those with MUO (by NCEP ATP III) was 2 times higher than in a group with MHO (HR 2.1, 95% CI 1.1–4.0). Among women, the relative risk of MI in those with MUO (by NCEP ATP III) was 2.2 (95% CI 1.2- 4.2) compared to MHO group. Using the IDF criteria, the relative risk of MI in MUO vs. MHO was 2.2 (95% CI 0.9–5.7) in men and 2.2 (95% CI: 0.9–5.7) in women. Conclusions In this study population, men aged 45–69 have more frequent progression from metabolically healthy to metabolically unhealthy obesity during 12 years compared to women. The 12-year risk of incident MI in subjects with MUO was approximately twice higher compared to MHO and the excess risk was similar in men and women. Funding Acknowledgement Type of funding source: Public grant(s) – EU funding. Main funding source(s): Wellcom Trust

Cardiac TRPV1‐Afferent Signaling Promotes Arrhythmogenic Ventricular Remodeling after Myocardial Infarction

Chronic sympathoexcitation is implicated in ventricular arrhythmogenesis (VAs) following myocardial infarction (MI), but the critical neural pathways involved are not well understood. Cardiac adrenergic function is partly regulated by sympathetic afferent reflexes, transduced by spinal afferent fibers expressing the TRPV1 channel. The role of chronic TRPV1 afferent signaling in VAs is not known. We hypothesized that persistent TRPV1 afferent neurotransmission promotes VAs after MI. Using epicardial Resiniferatoxin (RTX) to deplete cardiac TRPV1‐expressing fibers, we dissected the role of this neural circuit in VAs after chronic MI in a porcine model. We examined the underlying mechanisms using molecular approaches, immunohistochemistry, in vitro and in vivo cardiac electrophysiology, and simultaneous cardio‐neural mapping. Epicardial RTX depleted cardiac TRPV1 afferent fibers and abolished functional responses to TRPV1 agonists. Ventricular tachycardia/fibrillation (VT/VF) was readily inducible in MI subjects by programmed electrical stimulation or cesium chloride administration, however, TRPV1 afferent depletion prevented VT/VF induced by either method. Mechanistically, TRPV1 afferent depletion neither altered cardiomyocyte action potentials and calcium transients; nor the expression of ion channels and calcium handling proteins. However, it attenuated fibrosis and mitigated electrical instability in the scar‐border zone. In vivo recordings of cardiovascular‐related stellate ganglion neurons (SGNs) revealed that MI enhances SGN function and disrupts integrated neural processing. Depleting TRPV1 afferents normalized these processes. Taken together, these data indicate that after MI, TRPV1 afferent‐induced adrenergic dysfunction promotes fibrosis, adverse cardiac remodeling, and worsens border zone electrical heterogeneity, resulting in electrically unstable ventricular myocardium. We propose targeting TRPV1‐expressing afferent to reduce VT/VF following MI.Support or Funding InformationNIH/NHLBI DP2 OD024323‐01

Cardiac TRPV1 afferent signaling promotes arrhythmogenic ventricular remodeling after myocardial infarction.

Chronic sympathoexcitation is implicated in ventricular arrhythmogenesis (VAs) following myocardial infarction (MI), but the critical neural pathways involved are not well understood. Cardiac adrenergic function is partly regulated by sympathetic afferent reflexes, transduced by spinal afferent fibers expressing the transient receptor potential cation subfamily V member 1 (TRPV1) channel. The role of chronic TRPV1 afferent signaling in VAs is not known. We hypothesized that persistent TRPV1 afferent neurotransmission promotes VAs after MI. Using epicardial resiniferatoxin (RTX) to deplete cardiac TRPV1-expressing fibers, we dissected the role of this neural circuit in VAs after chronic MI in a porcine model. We examined the underlying mechanisms using molecular approaches, IHC, in vitro and in vivo cardiac electrophysiology, and simultaneous cardioneural mapping. Epicardial RTX depleted cardiac TRPV1 afferent fibers and abolished functional responses to TRPV1 agonists. Ventricular tachycardia/fibrillation (VT/VF) was readily inducible in MI subjects by programmed electrical stimulation or cesium chloride administration; however, TRPV1 afferent depletion prevented VT/VF induced by either method. Mechanistically, TRPV1 afferent depletion did not alter cardiomyocyte action potentials and calcium transients, the expression of ion channels, or calcium handling proteins. However, it attenuated fibrosis and mitigated electrical instability in the scar border zone. In vivo recordings of cardiovascular-related stellate ganglion neurons (SGNs) revealed that MI enhances SGN function and disrupts integrated neural processing. Depleting TRPV1 afferents normalized these processes. Taken together, these data indicate that, after MI, TRPV1 afferent-induced adrenergic dysfunction promotes fibrosis and adverse cardiac remodeling, and it worsens border zone electrical heterogeneity, resulting in electrically unstable ventricular myocardium. We propose targeting TRPV1-expressing afferent to reduce VT/VF following MI.