Predictor Of Cardiovascular Disease Research Articles

Heart rate from intermittent home measurements within an atrial fibrillation screening program is associated with mortality

Abstract Introduction A high resting heart rate is a predictor of cardiovascular disease and cardiovascular mortality. Studies on the association between heart rate and outcomes often measure heart rate at a single time point, even though multiple measurements may be a stronger predictor of cardiovascular risk. Indeed, the association between short-term home heart rate measurements and outcomes has not been extensively studied. Purpose This sub study of the STROKESTOP trial explores the association of baseline and home resting heart rate measurements with all-cause and cardiovascular mortality. Methods The STROKESTOP randomised controlled trial consisted of an atrial fibrillation (AF) screening program among the 75- and 76-year-old inhabitants of two Swedish regions. Participants in the intervention group were invited to a two week intermittent screening program, consisting of twice daily measurements with an ECG-based handheld device. For this sub analysis, baseline heart rate, the median of 5 home morning heart rates and the median of 5 home evening heart rates were evaluated. Associations were evaluated by heart rate quintiles (41-63 bpm, 64-70 bpm, 71-76 bpm, 77-84 bpm and ≥85 bpm), with 64-70 bpm as the reference category. Hazard rations were obtained by Cox regression analysis. The full multivariable model included demograhic factors such as sex and educational level, medical history of several diseases and the use of cardiovascular medication. Both all-cause mortality and cardiovascular mortality were evaluated. Results In total, 5873 individuals were included in this analysis. Overall, 55.6% were women and the median baseline heart rate was 73 [65-81] bpm, whereas the median of morning heart rates was 71 [64-78] bpm and that of evening heart rates 71 [65-79] bpm. Having a baseline heart rate in the highest quintile compared to the reference quintile, showed a hazard ratio (HR) for all-cause mortality in unadjusted analysis of 1.39 (1.11-1.73) (p=0.004), and of 1.51 (1.20-1.90) in the fully adjusted analysis (p<.001). For cardiovascular mortality, HRs were 1.78 (1.21-2.63) (p=0.004) and 2.13 (1.42-3.20) (p=<.001), respectively. The association between the highest heart rate and all-cause mortality and cardiovascular mortality were substantially strengthened when median morning heart rates were considered (HR 2.03 (1.61-2.56) and 2.18 (1.72-2.76), p<.001 for all-cause mortality on univariable and multivariable analysis, HR 2.35 (1.57-3.51) and 2.59 (1.72-3.92), p<.001) for cardiovascular mortality). Associations between home evening heart rates and all-cause and cardiovascular mortality were not substantially stronger compared to the associations with baseline heart rate. Conclusion Our study shows that home-measured morning heart rate is more strongly associated with all-cause and cardiovascular mortality compared to a single time-point office heart rate.Hazard ratios for mortality

Relationship between serum apolipoprotein B and risk of all-cause and cardiovascular disease mortality in individuals with hypertension: a prospective cohort study

BackgroundDyslipidemia frequently coexists with hypertension in the population. Apolipoprotein B (ApoB) is increasingly considered a more potent predictor of cardiovascular disease (CVD). Abnormal levels of serum ApoB can potentially impact the mortality risk.MethodsThe prospective cohort study employed data from the National Health and Nutrition Examination Survey (NHANES), which was performed between 2005 and 2016, with follow-ups extended until December 2019. Serum ApoB concentrations were quantified using nephelometry. In line with the NHANES descriptions and recommendations, the reference ranges for ApoB concentrations are 55–140 and 55–125 mg/dL for men and women, respectively. Participants were categorized into low, normal, and high ApoB levels. The low and high groups were combined into the abnormal group. In this study, all-cause mortality (ACM) and CVD mortality (CVM) were the endpoints. Survey-weighted cox hazards models were used for evaluating the correlation between serum ApoB levels and ACM and CVM. A generalized additive model (GAM) was employed to examine the dose-dependent relationship between ApoB levels and mortality risk.ResultsAfter a median of 95 (interquartile range: 62–135) months of follow-up, 986 all-cause and 286 CVD deaths were recorded. The abnormal ApoB group exhibited a trend toward an elevated risk of ACM in relative to the normal group (HR 1.22, 95% CI: 0.96–1.53). The risk of CVM was elevated by 76% in the ApoB abnormal group (HR 1.76, 95% CI: 1.28–2.42). According to the GAM, there existed a nonlinear association between serum ApoB levels and ACM (P = 0.005) and CVM (P = 0.009).ConclusionsIn the US hypertensive population, serum Apo B levels were U-shaped and correlated with ACM and CVM risk, with the lowest risk at 100 mg/dL. Importantly, abnormal Apo B levels were related to an elevated risk of ACM and CVM. These risks were especially high at lower Apo B levels. The obtained findings emphasize the importance of maintaining appropriate Apo B levels to prevent adverse outcomes in hypertensive individuals.

Hybrid Feature Selection with Chaotic Rat Swarm Optimization-Based Convolutional Neural Network for Heart Disease Prediction from Imbalanced Datasets

Introduction: Early diagnosis of Cardiovascular Disease (CVD) is vital in reducing mortality rates. Artificial intelligence and machine learning algorithms have increased the CVD prediction capability of clinical decision support systems. However, the shallow feature learning in machine learning and incompetent feature selection methods still pose a greater challenge. Consequently, deep learning algorithms are needed to improvise the CVD prediction frameworks. Methods: This paper proposes an advanced CDSS for CVD detection using a hybrid DL method. Initially, the Improved Hierarchical Density-based Spatial Clustering of Applications with Noise (IHDBSCAN), Adaptive Class Median-based Missing Value Imputation (ACMMVI) and Clustering Using Representatives-Adaptive Synthetic Sampling (CURE-ADASYN) approaches are introduced in the pre-processing stage for enhancing the input quality by solving the problems of outliers, missing values and class imbalance, respectively. Then, the features are extracted, and optimal feature subsets are selected using the hybrid model of Information gain with Improved Owl Optimization algorithm (IG-IOOA), where OOA is improved by enhancing the search functions of the local search process. These selected features are fed to the proposed Chaotic Rat Swarm Optimization-based Convolutional Neural Networks (CRSO-CNN) classifier model for detecting heart disease. Results: Four UCI datasets are used to validate the proposed framework, and the results showed that the OOA-DLSO-ELM-based approach provides better heart disease prediction with high accuracy of 97.57%, 97.32%, 96.254% and 97.37% for the four datasets. Conclusions: Therefore, this proposed CRSO-CNN model improves the heart disease classification with reduced time complexity for all four UCI datasets.

Efficient Machine Learning Techniques to Classifying Cardiovascular Disease and Improve Prediction Analysis

Cardiovascular Disease (CVD) account for a large portion of the global health burden and are one of the main causes of decease worldwide. In the classification and forecasting of CVDs, Machine Learning (ML) techniques have demonstrated encouraging outcomes. In this research report, a comparative analysis of classification and prediction models for CVD is presented, including both linear and ensemble ML approaches. The paper compares ensemble models like Catboost, Histogram Gradient Boosting Machine (HGBM), and Extra Trees against linear models like Gaussian Nave Bayes, SVM, and KNN. The objective is to identify the most effective CVD prediction model by assessing its performance through accuracy, precision, sensitivity and F1 score as key evaluation metrics. Moreover, results show that ensemble models outperform linear models using advanced techniques such as boosting and histogram-based algorithms. The results underscore the critical role ensemble models play in accurately diagnosing and predicting cardiovascular disease and provide important new information to researchers and healthcare providers. Using these models has the potential to significantly improve patient outcomes and health management by enabling early detection and intervention

The Role of Angiopoietins in Cardiovascular Outcomes of Kidney Transplant Recipients- An Ancillary Study From the FAVORIT.

Kidney transplant recipients (KTRs) have increased risk of cardiovascular disease (CVD) mortality. We investigated vascular biomarkers, angiopoietin-1 and angiopoietin-2 (angpt-1, -2), in CVD development in KTRs. This ancillary study from the FAVORIT, evaluates the associations of baseline plasma angpt-1,-2 levels in CVD development (primary outcome) and graft failure (GF) and death (secondary outcomes) in 2000 deceased donor KTRs. We used Cox regression to analyze the association of biomarker quartiles with outcomes. We adjusted for demographic, CVD and transplant-related variables; medications; urine albumin-to-creatinine ratio and randomization status. We calculated areas under the curves (AUC) to predict CVD or death, and GF or death by incorporating biomarkers alongside clinical variables. Participants' median age was 52 IQR [45, 59] years: with 37% women and 73% identifying as white. Median time from transplantation was 3.99 IQR [1.58, 7.93] years and to CVD development was 2.54 IQR [1.11-3.80] years. Quartiles of angpt-1 were not associated with outcomes. Whereas higher levels of angpt-2 (quartile 4) were associated with about 2 times the risk of CVD, GF and death [aHR 1.85 (1.25 - 2.73), P<.01; 2.24 (1.36 - 3.70), P<.01; 2.30 (1.48 - 3.58), P<.01, respectively] as compared to quartile 1. Adding angiopoietins to pre-existing clinical variables improved prediction of CVD or death (AUC improved from 0.70 to 0.72, P=0.005) and GF or death (AUC improved from 0.68 to 0.70, P =0.005). Angpt-2 may partially explain the increased risk of future CVD in KTRs. Further research is needed to assess the utility of using angiopoietins in the clinical care of KTRs. Angpt-2 may be a useful prognostic tool for future CVD in KTRs. Combining angiopoietins with clinical markers may tailor follow-up to mitigate CVD risk.

Non-Invasive Retinal Vessel Analysis as a Predictor for Cardiovascular Disease.

Cardiovascular disease (CVD) is the most frequent cause of death worldwide. The alterations in the microcirculation may predict the cardiovascular mortality. The retinal vasculature can be used as a model to study vascular alterations associated with cardiovascular disease. In order to quantify microvascular changes in a non-invasive way, fundus images can be taken and analysed. The central retinal arteriolar (CRAE), the venular (CRVE) diameter and the arteriolar-to-venular diameter ratio (AVR) can be used as biomarkers to predict the cardiovascular mortality. A narrower CRAE, wider CRVE and a lower AVR have been associated with increased cardiovascular events. Dynamic retinal vessel analysis (DRVA) allows the quantification of retinal changes using digital image sequences in response to visual stimulation with flicker light. This article is not just a review of the current literature, it also aims to discuss the methodological benefits and to identify research gaps. It highlights the potential use of microvascular biomarkers for screening and treatment monitoring of cardiovascular disease. Artificial intelligence (AI), such as Quantitative Analysis of Retinal vessel Topology and size (QUARTZ), and SIVA-deep learning system (SIVA-DLS), seems efficient in extracting information from fundus photographs and has the advantage of increasing diagnosis accuracy and improving patient care by complementing the role of physicians. Retinal vascular imaging using AI may help identify the cardiovascular risk, and is an important tool in primary cardiovascular disease prevention. Further research should explore the potential clinical application of retinal microvascular biomarkers, in order to assess systemic vascular health status, and to predict cardiovascular events.

Association between triglyceride glucose-body mass index and long-term adverse outcomes of heart failure patients with coronary heart disease

BackgroundThe triglyceride glucose-body mass index (TyG-BMI) is recognized as a reliable surrogate for evaluating insulin resistance and an effective predictor of cardiovascular disease. However, the link between TyG-BMI index and adverse outcomes in heart failure (HF) patients remains unclear. This study examines the correlation of the TyG-BMI index with long-term adverse outcomes in HF patients with coronary heart disease (CHD).MethodsThis single-center, prospective cohort study included 823 HF patients with CHD. The TyG-BMI index was calculated as follows: ln [fasting triglyceride (mg/dL) × fasting blood glucose (mg/dL)/2] × BMI. To explore the association between the TyG-BMI index and the occurrences of all-cause mortality and HF rehospitalization, we utilized multivariate Cox regression models and restricted cubic splines with threshold analysis.ResultsOver a follow-up period of 9.4 years, 425 patients died, and 484 were rehospitalized due to HF. Threshold analysis revealed a significant reverse “J”-shaped relationship between the TyG-BMI index and all-cause mortality, indicating a decreased risk of all-cause mortality with higher TyG-BMI index values below 240.0 (adjusted model: HR 0.90, 95% CI 0.86–0.93; Log-likelihood ratio p = 0.003). A distinct “U”-shaped nonlinear relationship was observed with HF rehospitalization, with the inflection point at 228.56 (adjusted model: below: HR 0.95, 95% CI 0.91–0.98; above: HR 1.08, 95% CI 1.03–1.13; Log-likelihood ratio p < 0.001).ConclusionsThis study reveals a nonlinear association between the TyG-BMI index and both all-cause mortality and HF rehospitalization in HF patients with CHD, positioning the TyG-BMI index as a significant prognostic marker in this population.

Association of 1,25 dihydroxyvitamin D with left ventricular hypertrophy and left ventricular diastolic dysfunction in patients with chronic kidney disease.

Left ventricular hypertrophy (LVH) and left ventricular diastolic dysfunction (LVDD) are highly prevalent predictors of cardiovascular disease in individuals with chronic kidney disease (CKD). Vitamin D, particularly 25-hydroxyvitamin D [25(OH)D], deficiency has been reported to be associated with cardiac structure and function in CKD patients. In the current study, we investigated the association between 1,25-dihydroxyvitamin D [1,25(OH)2D], the active form of 25(OH)D, and LVH/LVDD in CKD patients. We enrolled 513 non-dialysis CKD patients. The presence of LVH and LVDD was determined using transthoracic echocardiography. In multivariable analysis, serum 1,25(OH)2D levels, but not serum 25(OH)D, were independently associated with LVH [odds ratio (OR): 0.90, 95% confidential interval (CI): 0.88-0.93, P < 0.001]. Additionally, age, systolic blood pressure, and intact parathyroid hormone levels were independently associated with LVH. Similarly, multivariable analysis demonstrated that serum 1,25(OH)2D levels, but not 25(OH)D levels, were independently associated with LVDD (OR: 0.88, 95% CI: 0.86-0.91, P < 0.001) with systolic blood pressure showing independent association with LVDD. The optimal cut-off values for serum 1,25(OH)2D levels for identifying LVH and LVDD were determined as ≤ 12.7 pg/dl and ≤ 18.1 pg/dl, respectively. Our findings suggest that serum 1,25(OH)2D levels have independent association with LVH and LVDD in CKD patients, underscoring their potential as biomarkers for these conditions in this patient population.

Joint association of TyG index and high sensitivity C-reactive protein with cardiovascular disease: a national cohort study

BackgroundBoth the triglyceride-glucose (TyG) index, as a surrogate marker of insulin resistance, and systemic inflammation are predictors of cardiovascular diseases; however, little is known about the coexposures and relative contributions of TyG index and inflammation to cardiovascular diseases. Using the nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS), we conducted longitudinal analyses to evaluate the joint and mutual associations of the TyG index and high-sensitivity C-reactive protein (hsCRP) with cardiovascular events in middle-aged and older Chinese population.MethodsThis study comprised 8 658 participants aged at least 45 years from the CHARLS 2011 who are free of cardiovascular diseases at baseline. The TyG index was calculated as Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Cardiovascular events were defined as the presence of physician-diagnosed heart disease and/or stroke followed until 2018.We performed adjusted Cox proportional hazards regression and mediation analyses.ResultsThe mean age of the participants was 58.6 ± 9.0 years, and 3988 (46.1%) were females. During a maximum follow-up of 7.0 years, 2606 (30.1%) people developed cardiovascular diseases, including 2012 (23.2%) cases of heart diseases and 848 (9.8%) cases of stroke. Compared with people with a lower TyG index (< 8.6 [median level]) and hsCRP < 1 mg/L, those concurrently with a higher TyG and hsCRP had the highest risk of overall cardiovascular disease (adjusted hazard ratio [aHR], 1.300; 95% CI 1.155–1.462), coronary heart disease (aHR, 1.294; 95% CI 1.130–1.481) and stroke (aHR, 1.333; 95% CI 1.093–1.628), which were predominant among those aged 70 years or below. High hsCRP significantly mediated 13.4% of the association between the TyG index and cardiovascular disease, while TyG simultaneously mediated 7.9% of the association between hsCRP and cardiovascular risk.ConclusionsThe findings highlight the coexposure effects and mutual mediation between the TyG index and hsCRP on cardiovascular diseases. Joint assessments of the TyG index and hsCRP should be underlined for the residual risk stratification and primary prevention of cardiovascular diseases, especially for middle-aged adults.

Circulating interleukin 17A and other inflammatory proteins may predict cardiovascular disease in early rheumatoid arthritis.

The objective of this study was to investigate the impact of 92 inflammatory proteins on the risk of cardiovascular disease (CVD) in patients with early RA. This study included consecutive patients with early RA recruited 1995-2002. Stored plasma samples were analyzed for 92 inflammatory proteins. CVD diagnoses were retrieved from national in-patient and cause-of-death registries. Statistical analyses were pre-designated as hypothesis-driven or exploratory. For the latter, proteins were selected based on principal component analysis (i.e., factor loading >0.5 within main components). Potential predictors of CVD and coronary artery disease (CAD) were assessed using Cox regression. Data on baseline levels of proteins and CVD were available for 163 patients. As hypothesized, levels of IL-17A were associated with CVD [hazard ratio 1.35; 95% confidence interval 1.02-1.78, adjusted for age, sex, hypertension, diabetes, smoking and ESR], while not significantly with CAD. Osteoprotegerin (OPG) levels were significantly associated with both outcomes but only in crude models. No associations were observed for IL-6, TNF-α, monocyte chemotactic protein (MCP)-1 or IL-8. In the exploratory analyses, particularly MCP-3 had significant associations with both outcomes in crude models. Circulating IL-17A at RA diagnosis predicted future CVD, although we cannot exclude that this finding is due to multiple testing. The association was independent of traditional CVD risk factors, and of ESR at the time of diagnosis. Furthermore, OPG may be a predictor of CVD. We also identified some novel potential biomarkers for CVD in RA.

Determinants of cardiovascular disease among type 2 diabetic patients attending diabetic follow-up clinic in Arba Minch general hospital, southern Ethiopia: an unmatched case-control study.

Cardiovascular disease (CVD) is a major cause of death and disability among patients with type 2 diabetes, especially in low-income and middle-income countries. Type 2 diabetes mellitus (T2DM) patients have a 2-4-fold increased risk of CVD. There is limited data about cardiovascular disease risks and its determinants among T2DM patients in Ethiopia. This study aimed to identify possible predictors of cardiovascular diseases among adults with T2DM in southern Ethiopia. A hospital-based unmatched case-control study was conducted at southern Ethiopia Arbaminch Hospital on 196 randomly selected patients with type 2 diabetes on follow-up (98 cases and 98 controls). The authors collected data using a structured interviewer-administered questionnaire, laboratory checklist, and additional document review of T2DM patients. A multivariable binary logistic regression was fitted to identify cardiovascular disease determinants, and the findings were presented using an adjusted odds ratio (AOR) with a 95% CI. The mean reported age (±SD) of the cases and the controls was 56.3.3 (±8.9) and 52.3 (±9.3) years, respectively. The two identified independent determinants of cardiovascular disease with AOR [95% CI] were hypertension [AOR=4.953, 95% CI (2.47, 9.93) and persistent urine albuminuria [AOR=12.9, 95% CI (3.98, 41.7)]. This study showed that having high blood pressure and persistent urine albuminuria are independent predictors of cardiovascular disease in T2DM patients. The current study setting needs an intervention for mitigating these cardiovascular disease determinants.

Cardiovascular Health and Colorectal Cancer Screening in Black Men: A Cross-Sectional Analysis.

African American/Black (henceforth Black) men face disproportionate risks of morbidity and mortality from both cardiovascular disease (CVD) and colorectal cancer (CRC). The American Heart Association's Life's Simple 7 (LS7) tool was designed to examine predictors of CVD with included behaviors also linked to CRC risk (i.e., smoking status, weight, diet, and physical activity). However, no studies have combined LS7 assessment alongside CRC screening history, which serves as a proxy for assessing CRC risk, in Black men. In this study, Black men aged 45-75 participating in annual community wellness events were screened for 6 of 7 LS7 measures (excluding diet, LS6) and self-reported CRC screening. Analyses conducted using R 4.0.5. revealed that Black men (N = 680), with an average age of 57.3 years (SD = 7.5), reported poor (39.7%), intermediate (34.6%), or ideal (25.7%) LS6 scores with higher scores corresponding to lower risk for CVD. However, for every 1-point increase in LS6 scores (0-6), there was a 26% lower odds of reported CRC screening (p = .001). In the fully adjusted model, men with 4+ ideal LS6 behaviors had a 60% lower odds of self-reported CRC screening compared to those with two ideal LS6 behaviors (p < .001). These findings underscore the need for culturally relevant interventions for Black men across all levels of cardiovascular health (CVH) to increase CRC screening uptake.

THE METABOLIC SCORE FOR INSULIN RESISTANCE (METS-IR) PREDICTS CARDIOVASCULAR DISEASE AND ITS SUBTYPES IN PATIENTS WITH HYPERTENSION AND OBSTRUCTVE SLEEP APNEA

Objective: We aimed to evaluate the METS-IR (metabolic score for insulin resistance) index for the prediction of incident cardiovascular disease (CVD) and its subtypes (coronary artery disease and stroke) in patients with hypertension and obstructive sleep apnea (OSA). Design and method: A retrospective cohort study was conducted with 2031 adults with hypertension and OSA, participants from the Urumqi Research on Sleep Apnea and Hypertension study (UROSAH). The hazard ratios and 95% CIs (credibility interval) for CVD and its subtypes were estimated using multivariate Cox proportional hazards regression models. Results: After a median follow-up of 6.80 years (interquartile range: 5.90–8.00 years), a total of 317 (15.61%) participants developed new-onset CVD, including 198 (9.75%) incident coronary heart disease (CHD) and 119 (5.86%) incident stroke. After adjusting for as many relevant confounding factors as possible, each SD increase in METS-IR was associated with a 30% increased risk of new onset overall CVD events, a 32% increased risk of new onset CHD, and a 27% increased risk of new onset stroke. When METS-IR was assessed as tertiles, after adjustment for fully confounding factors, the highest tertiles versus the lowest tertiles were associated with a greater hazard of CVD (HR 2.05; 95% CI 1.52,-2.77), CHD (HR 1.96; 95% CI 1.35–2.84), and stroke (HR 2.24; 95% CI 1.35–3.72). The results of various subgroups and sensitivity analyses were similar. When METS-IR was added, CVD predictions were reclassified and identified more accurately than baseline models for the C-index, continuous net reclassification improvement, and integrated discrimination index. CHD and stroke showed similar results. Conclusions: METS-IR is a powerful predictor of CVD and its subtypes in patients with hypertension and OSA, which can facilitate the identification of high-risk individuals and provide individualized CVD prevention.

Age and Handgrip Strength Induce Differences in Micro- and Macro-Vascular Responses

Purpose: Handgrip (HG) strength is a potent predictor of cardiovascular disease (CVD) and all-cause mortality. Advances in technology have provided novel assessments of peripheral microvascular and macrovascular health and function. These advances include measurements of skeletal muscle tissue oxygenation (StO2, %) with near-infrared spectroscopy (NIRS) during vascular occlusion tests (VOT) as well as carotid-femoral pulse-wave velocity (cfPWV), which is the current gold-standard to measure aortic stiffness. Although NIRS-VOT and cfPWV have been shown to change across the lifespan, it is unknown if these variables are sensitive to group differences as defined by HG strength. Therefore, our purpose was to examine mean differences in NIRS-VOT parameters and cfPWV between groups of low and high HG strength. We hypothesized that individuals with lower HG strength would exhibit lower vascular function. Methods: 67 adults were separated into age groups: young (19 – 40 yr), midlife (41 – 64 yr), and older (≥65 yr). Groups were divided into low and high strength at the median for HG strength, and quasi-balanced based on biological sex. This resulted in six participant groups (e.g., midlife low strength). The VOT included 3 min of rest, 5 min of ischemia, and 3 min of reperfusion to determine indices of reactive hyperemia. Indices included the initial (i.e., first 10 s) rate of re-saturation (upslope) and the maximal StO2 (StO2max) percent following ischemia. Aortic stiffness was defined as cfPWV using the time difference between the appearance of the carotid and femoral pulses. Separate 2×3 between factor (HG by Age Group) ANOVAs were conducted for each outcome. A p≤0.05 was considered significant, and data were presented as mean ± SD. Results: For upslope, there was a significant interaction (p=0.042, ηp2=0.078), and follow-up analyses revealed for young and midlife adults the high HG group exhibited greater upslopes than the low strength group (young: 2.24±0.9 vs. 1.64±0.7 and midlife: 2.21±0.9 vs. 1.59±0.7%∙s−1). There was no significant interaction (p=0.360) for StO2max, but there was a significant main effect (p&lt;0.001) of Age such that it progressively decreased across the lifespan (83.9 ± 2.0 &gt; 80.7 ± 2.3 &gt; 77.3 ± 2.8%). Similarly, cfPWV exhibited a significant main effect (p&lt;0.001; ηp2 =0.429) of Age indicating aortic stiffness worsened with age, independent of HG strength. Conclusions: These results indicated that upslope was sensitive to group differences in HG strength, but only in the young and midlife age groups. For StO2max and cfPWV, age differences appeared to elicit a stronger effect than HG strength. The current findings suggested that HG strength may be most reflective of a specific portion of the vascular tree, while the initial rate of re-saturation and StO2max may be influenced by different mechanisms associated with reactive hyperemia. Notably, the participants in this study were relatively healthy based on being free of any chronic disease. Future work is necessary to determine the predictive power of NIRS-VOT parameters for future onset of CVD. We would like to thank the American Physiology Society for support and the University of South Alabama for allowing us to use its facilities. We would also like to thank all of the participants who agreed to adhere to the protocol and procedures of this study. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study.

Electrocardiography (ECG) is a non-invasive tool for predicting cardiovascular diseases (CVDs). Current ECG-based diagnosis systems show promising performance owing to the rapid development of deep learning techniques. However, the label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets greatly hinder the widespread application of deep learning-based models. Addressing them in a unified framework remains a significant challenge. To this end, we propose a multi-label semi-supervised model (ECGMatch) to recognize multiple CVDs simultaneously with limited supervision. In the ECGMatch, an ECGAugment module is developed for weak and strong ECG data augmentation, which generates diverse samples for model training. Subsequently, a hyperparameter-efficient framework with neighbor agreement modeling and knowledge distillation is designed for pseudo-label generation and refinement, which mitigates the label scarcity problem. Finally, a label correlation alignment module is proposed to capture the co-occurrence information of different CVDs within labeled samples and propagate this information to unlabeled samples. Extensive experiments on four datasets and three protocols demonstrate the effectiveness and stability of the proposed model, especially on unseen datasets. As such, this model can pave the way for diagnostic systems that achieve robust performance on multi-label CVDs prediction with limited supervision.

Identifying Risk Indicators of Cardiovascular Disease in Fasa Cohort Study (FACS): An Application of Generalized Linear Mixed-Model Tree.

Today, cardiovascular disease (CVD) is the most important cause of death around the world. In this study, our main aim was to predict CVD using some of the most important indicators of this disease and present a tree-based statistical framework for detecting CVD patients according to these indicators. We used data from the baseline phase of the Fasa Cohort Study (FACS). The outcome variable was the presence of CVD. The ordinary Tree and generalized linear mixed models (GLMM) were fitted to the data and their predictive power for detecting CVD was compared with the obtained results from the GLMM tree. Statistical analysis was performed using the RStudio software. Data of 9499 participants aged 35‒70 years were analyzed. The results of the multivariable mixed-effects logistic regression model revealed that participants' age, total cholesterol, marital status, smoking status, glucose, history of cardiac disease or myocardial infarction (MI) in first- and second-degree relatives, and presence of other diseases (like hypertension, depression, chronic headaches, and thyroid disease) were significantly related to the presence of CVD (P<0.05). Fitting the ordinary tree, GLMM, and GLMM tree resulted in area under the curve (AUC) values of 0.58 (0.56, 0.61), 0.81 (0.77, 0.84), and 0.80 (0.76, 0.83), respectively, among the study population. In addition, the tree model had the best specificity at 81% but the lowest sensitivity at 65% compared to the other models. Given the superior performance of the GLMM tree compared with the standard tree and the lack of significant difference with the GLMM, using this model is suggested due to its simpler interpretation and fewer assumptions. Using updated statistical models for more accurate CVD prediction can result in more precise frameworks to aid in proactive patient detection planning.

Dental caries prevalence in relation to the cardiovascular diseases: cross-sectional findings from the Iranian kurdish population.

Oral and dental health (ODH) is a significant public health concern globally, affecting billions of people. This cross-sectional study aimed to examine the prevalence of dental caries using the DMFT index and investigate its relationship with cardiovascular diseases in the Iranian Kurdish population. A total of 3,996 individuals aged 35-70 years were included from the Dehgolan prospective cohort study (DehPCS). Clinical examinations were performed to assess decayed (DT), missing (MT) and filled (FT) teeth (DMFT index). Data on demographics, socioeconomic status, medical history, oral hygiene practices were also collected. Cardiovascular disease (CVD) history was self-reported via questionnaires assessing heart disease, heart attack and stroke. Logistic regression was used to assess associations between oral health indicators and self-reported CVD, adjusting for potential confounders. The mean DMFT score was 18.18 ± 19, indicating a high oral disease burden. Specifically, 60.04% had DT, 37.82% had more than 16 MT, and 38.83% had FT. Only 13.21% reported flossing regularly and 43.17% brushed less than daily, showing suboptimal oral hygiene. The overall CVD prevalence was 9.21%. Individuals with high DMFT (≥ 14) scores had approximately two times higher CVD prevalence than those with low DMFT. DMFT decreased by 10.23% with increase in education level, and by 5.87% as economic status increased In adjusted analyses, high DMFT scores (OR = 1.5, 95%CI: 1.2-1.9) and MT (OR = 1.5, 95%CI: 1.1-2.1) were associated with 50-150% increased odds of CVD, though associations weakened after adjusting for age. Among men, DMFT remained a significant predictor for CVD after age adjustment, with an odds ratio of 2.37 (95% CI: 1.22-4.60). This population had substantial oral disease and poor oral hygiene. Higher DMFT scores and MT positively correlated with increased CVD prevalence. Promoting preventive oral care and health education could help reduce dental issues and potentially lower CVD risk. Further research is needed to clarify biological mechanisms linking oral and systemic health.

Association of the trimethylamine N-oxide with cardiovascular risk and vascular alterations in middle-aged patients with risk factors for cardiovascular diseases.

Trimethylamine N-oxide (TMAO) is synthesized by the intestinal microbiota and is an independent predictor of cardiovascular disease (CVD). However, its underlying mechanisms remain unclear. We investigated TMAO levels across different CVD-risk patient groups, and evaluated associations between TMAO and vascular alterations (e.g., arterial stiffness, intima-media thickness (IMT), and the presence and grade of carotid artery plaques (CAPs)).Methods: We examined 95 patients (58.5 ± 7.3 years): 40 with clinical atherosclerotic cardiovascular disease (ASCVD), 40 with atherosclerosis risk factors (RF), and 15 controls. Arterial stiffness was measured by Carotid-Femoral Pulse Wave Velocity (C-F PWV). B-mode ultrasound was used to evaluate the presence and grade of CAPs and carotid IMT (CIMT). TMAO was measured by high performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) and results were presented as the median (interquartile range).Results: TMAO levels were higher in patients with ASCVD (251.5 (164.5) µg/l) when compared to patients with RFs (194.0 (174) µg/l, p = 0.04) and controls (122.0 (77) µg/l, p < 0.001). A significant correlation was observed between TMAO and PWV (r = 0.31, p = 0.003), which was not confirmed after adjustment for RFs. TMAO levels were significantly correlated with plaque score (r = 0.46, p < 0.001) and plaque height (r = 0.41, p = 0.003), and were independent predictors for grade III plaques (odds ratio (OR) = 1.002, confidence interval (CI) 95%: 1.000047-1.003, p = 0.044).Conclusions: TMAO levels are increased with expanded CVD risk. Across different types of vascular damage, TMAO is associated with atherosclerotic changes.

Relationship between arginine methylation and vascular calcification

In patients on maintenance hemodialysis (MHD), vascular calcification (VC) is an independent predictor of cardiovascular disease (CVD), which is the primary cause of death in chronic kidney disease (CKD). The main component of VC in CKD is the vascular smooth muscle cells (VSMCs). VC is an ordered, dynamic activity. Under the stresses of oxidative stress and calcium‐‑phosphorus imbalance, VSMCs undergo osteogenic phenotypic transdifferentiation, which promotes the formation of VC. In addition to traditional epigenetics like RNA and DNA control, post-translational modifications have been discovered to be involved in the regulation of VC in recent years. It has been reported that the process of osteoblast differentiation is impacted by catalytic histone or non-histone arginine methylation. Its function in the osteogenic process is comparable to that of VC. Thus, we propose that arginine methylation regulates VC via many signaling pathways, including as NF-B, WNT, AKT/PI3K, TGF-/BMP/SMAD, and IL-6/STAT3. It might also regulate the VC-related calcification regulatory factors, oxidative stress, and endoplasmic reticulum stress. Consequently, we propose that arginine methylation regulates the calcification of the arteries and outline the regulatory mechanisms involved.

TPTM-HANN-GA: A Novel Hyperparameter Optimization Framework Integrating the Taguchi Method, an Artificial Neural Network, and a Genetic Algorithm for the Precise Prediction of Cardiovascular Disease Risk

The timely and precise prediction of cardiovascular disease (CVD) risk is essential for effective prevention and intervention. This study proposes a novel framework that integrates the two-phase Taguchi method (TPTM), the hyperparameter artificial neural network (HANN), and a genetic algorithm (GA) called TPTM-HANN-GA. This framework efficiently optimizes hyperparameters for an artificial neural network (ANN) model during the training stage, significantly enhancing prediction accuracy for cardiovascular disease (CVD) risk. The proposed TPTM-HANN-GA framework requires far fewer experiments than a traditional grid search, making it highly suitable for application in resource-constrained, low-power computers, and edge artificial intelligence (edge AI) devices. Furthermore, the proposed TPTM-HANN-GA framework successfully identified the optimal configurations for the ANN model’s hyperparameters, resulting in a hidden layer of 4 nodes, a tan h activation function, an SGD optimizer, a learning rate of 0.23425849, a momentum rate of 0.75462782, and seven hidden nodes. This optimized ANN model achieves 74.25% accuracy in predicting the risk of cardiovascular disease, which exceeds the existing state-of-the-art GA-ANN and TSTO-ANN models. The proposed TPTM-HANN-GA framework enables personalized CVD prediction to be efficiently conducted on low-power computers and edge-AI devices, achieving the goal of point-of-care testing (POCT) and empowering individuals to manage their heart health effectively.