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Dried blood spot (DBS) sample collection has been suggested as a less invasive, cheaper and more convenient alternative to venepuncture, which requires trained personnel, making it a potentially viable approach for self-collection of blood on a large scale. We examine whether participants in a longitudinal survey were willing to provide a DBS sample in different interview settings, and how resulting cardiovascular risk biomarkers compared with those from venous blood to calculate clinical risk. Participants of the Understanding Society Innovation Panel, a representative sample of UK households, were randomly assigned to three modes of interview. Most participants (84%) were interviewed in their allocated mode. Participants (n = 2162) were interviewed by a nurse who collected both a blood sample by venepuncture and a DBS card ('nurse collection') or participants were seen by an interviewer or took part in the survey online to self-collect a DBS card ('self-collection'). All DBS cards were returned in the post after the sample had dried. Lipids (total cholesterol, HDL-cholesterol, triglycerides), HbA1c and C-reactive protein were measured in venous and DBS samples and equivalence was calculated. The resultant values were used to confirm equivalent prevalence of risk of cardiovascular disease in each type of blood sample by mode of participation. Of participants interviewed by a nurse 69% consented to venous blood sample and 74% to a DBS sample, while in the self-collection modes, 35% consented to DBS collection. Demographic characteristics of participants in self-collection mode was not different to those in nurse collection mode. The percentage of participants with clinically raised biomarkers did not significantly differ between type of blood collection (for example, 62% had high cholesterol (> 5 mmol/l) measured by venepuncture and 67% had high cholesterol within the self-collected DBS sample (p = 0.13)). While self-collected DBS sampling had a lower response rate to DBS collected by a nurse, participation did not vary by key demographic characteristics. This study demonstrates that DBS collection is a feasible method of sample collection that can provide acceptable measures of clinically relevant biomarkers, enabling the calculation of population levels of cardiovascular disease risk., (© 2023. Springer Nature Limited.)

This study aimed at modelling the underlying predictor of ASCVD through the Bayesian network (BN). Data for the AZAR Cohort Study, which evaluated 500 healthcare providers in Iran, was collected through examinations, and blood samples. Two BNs were used to explore a suitable causal model for analysing the underlying predictor of ASCVD; Bayesian search through an algorithmic approach and knowledge-based BNs. Results showed significant differences in ASCVD risk factors across background variables' levels. The diagnostic indices showed better performance for the knowledge-based BN (Area under ROC curve (AUC) = 0.78, Accuracy = 76.6, Sensitivity = 62.5, Negative predictive value (NPV) = 96.0, Negative Likelihood Ratio (LR-) = 0.48) compared to Bayesian search (AUC = 0.76, Accuracy = 72.4, Sensitivity = 17.5, NPV = 93.2, LR- = 0.83). In addition, we decided on knowledge-based BN because of the interpretability of the relationships. Based on this BN, being male (conditional probability = 63.7), age over 45 (36.3), overweight (51.5), Mets (23.8), diabetes (8.3), smoking (10.6), hypertension (12.1), high T-C (28.5), high LDL-C (23.9), FBS (12.1), and TG (25.9) levels were associated with higher ASCVD risk. Low and normal HDL-C levels also had higher ASCVD risk (35.3 and 37.4), while high HDL-C levels had lower risk (27.3). In conclusion, BN demonstrated that ASCVD was significantly associated with certain risk factors including being older and overweight male, having a history of Mets, diabetes, hypertension, having high levels of T-C, LDL-C, FBS, and TG, but Low and normal HDL-C and being a smoker. The study may provide valuable insights for developing effective prevention strategies for ASCVD in Iran., (© 2024. The Author(s).)

Dyslipidemia is common in patients with chronic kidney disease (CKD), however the relationship between dyslipidemia and mortality in patients with moderate to severe CKD remains controversial. Non-high-density lipoprotein (HDL) cholesterol has been reported to be a more accurate predictor of clinical outcomes than conventional lipid measurements. Hence, the aim of this study was to investigate associations between non-HDL cholesterol and the risk of overall and cardiovascular mortality in patients with CKD stage 3-5. We enrolled 429 pre-dialysis patients with stage 3 to 5 CKD from May 2006 to January 2010. The patients were divided into four groups according to quartiles of non-HDL cholesterol. The patients were followed until death or until January 2020. During a median 11.6 years of follow-up, there were 78 (18.2%) deaths overall and 32 (7.5%) cardiovascular deaths. In adjusted models, the patients in quartile 1 (hazard ratio [HR] 3.368; 95% confidence interval [CI] 1.388-8.176; p = 0.007), quartile 3 (HR 3.666; 95% CI 1.486-9.044; p = 0.005), and quartile 4 (HR 2.868; 95% CI 1.136-7.240; p = 0.026) of non-HDL cholesterol had a higher risk of overall mortality (vs. quartile 2). In addition, the patients in quartile 1 (HR 19.503; 95% CI 2.185-174.0925 p = 0.008), quartile 3 (HR 28.702; 95% CI 2.990-275.559; p = 0.004), and quartile 4 (HR 11.136; 95% CI 1.126-110.108; p = 0.039) had a higher risk of cardiovascular mortality (vs. quartile 2). Our study showed a U-shaped relationship between non-HDL cholesterol and the risk of overall and cardiovascular mortality in patients with CKD stage 3-5. Assessing non-HDL cholesterol may help to identify subjects at high-risk of adverse outcomes.

Serum-derived extracellular vesicles (sEV) from healthy donors display in-vivo pro-angiogenic properties. To identify patients that may benefit from autologous sEV administration for pro-angiogenic purposes, sEV angiogenic capability has been evaluated in type 2 diabetic (T2DM) subjects (D), in obese individuals with (OD) and without (O) T2DM, and in subjects with ischemic disease (IC) (9 patients/group). sEV display different angiogenic properties in such cluster of individuals. miRNomic profile and TGFβ content in sEV were evaluated. We found that miR-130a and TGFβ content correlates with sEV in-vitro and in-vivo angiogenic properties, particularly in T2DM patients. Ingenuity Pathway Analysis (IPA) identified a number of genes as among the most significant miR-130a interactors. Gain-of-function experiments recognized homeoboxA5 (HOXA5) as a miR-130a specific target. Finally, ROC curve analyses revealed that sEV ineffectiveness could be predicted (Likelihood Ratio+ (LH+) = 3.3 IC 95% from 2.6 to 3.9) by comparing miR-130a and TGFβ content 'in Series'. We demonstrate that sEV from high cardiovascular risk patients have different angiogenic properties and that miR-130a and TGFβ sEV content predicts 'true ineffective sEVs'. These results provide the rationale for the use of these assays to identify patients that may benefit from autologous sEV administration to boost the angiogenetic process.

The aim of this study is to investigate the effect of cardiometabolic diseases (CMDs) on the development of depressive symptoms and to determine whether socioeconomic status (SES) moderates this effect. A total of 6,455 individual free from depressive symptoms were selected from the China Health and Retirement Longitudinal Study (CHARLS). CMDs and SES were self-reported. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CESD-10). Cox proportional hazards regression models were employed to examine the association between baseline CMDs and subsequent depressive symptoms. Stratified analysis were conducted to explore the moderating effect of SES on this association.Participants with any CMDs exhibited a higher risk of developing depressive symptoms compared to those without CMDs, with a hazard ratio (HR) of 1.10 (95% CI: 1.01, 1.20). Individuals with one CMD (HR = 1.08, 95% CI: 1.00, 1.20) or cardiometabolic multimorbidity (HR = 1.13, 95% CI: 1.02, 1.26) also demonstrated an increased risk. The effect of CMDs on depressive symptoms was observed primarily in adults aged 60 years and older with lower education attainment (HR = 1.28, 95%CI: 1.12, 1.46), lower income (HR = 1.29, 95%CI: 1.11, 1.50) and rural residency (HR = 1.29, 95%CI: 1.12,1.51). These findings emphasize the importance of closely monitoring the mental status of patients with CMDs and considering routine screening and evaluation for depressive symptoms, especially in individuals with low SES., Competing Interests: Declarations. Ethics approval and consent to participate: Study protocol carried out in this study is followed in accordance with the ethical guidelines laid down as per the Declaration of Helsinki. The study was approved by the Ethics Committee of the Peking University biomedical ethics committee approval (Ethical Approval Number: IRB00001052-11015), and written informed consent was obtained from either the participants. Competing interests: The authors declare no competing interests. CRediT authorship contribution statement: Dan Guo, Yanshang Wang: drafting the manuscript, study design, data analysis and interpretation. Yanan Zhao, Ruoxi Ding, Yanan Luo and Wanwei Dai: drafting the manuscript and critical revision of article for important intellectual content. Ping He: study concept and design, critical revision of article, and supervised the whole process of this work. All authors critically revised successive drafts of the paper and approved the final version. The authors have no possible conflicts of interest to declare., (© 2025. The Author(s).)

Cardiovascular disease (CVD) is a major global cause of premature mortality. While multiple studies propose CVD mortality prediction models based on regression frameworks, incorporating causal understanding through causal inference approaches can enhance accuracy. This paper demonstrates a methodology combining evidence synthesis and expert knowledge to construct a causal model for premature CVD mortality using Directed Acyclic Graphs (DAGs). The process involves three phases: (1) initial DAG development based on the Evidence Synthesis for Constructing Directed Acyclic Graphs (ESC-DAGs) framework, (2) validation and consensus-building with 12 experts using the Fuzzy Delphi method (FDM), and (3) application to data analysis using population-based survey data linked with death records. Expert input refined the initial DAG model, achieving consensus on 45 causal paths. The revised model guided selection of confounding variables for adjustment. For example, to estimate the total effect of diabetes on premature CVD mortality, the suggested adjustment set included age, dietary pattern, genetic/family history, sex hormones, and physical activity. Testing different DAG models showed agreement between expert ratings and data accuracy from regression models. This systematic approach contributes to DAG methodology, offering a transparent process for constructing causal pathways for premature CVD mortality., Competing Interests: Declarations. Competing interests: The authors declare no competing interests/, (© 2024. The Author(s).)

The risk of cardiovascular disease (CVD) is a serious health threat to human society worldwide. The use of machine learning methods to predict the risk of CVD is of great relevance to identify high-risk patients and take timely interventions. In this study, we propose the XGBH machine learning model, which is a CVD risk prediction model based on key contributing features. In this paper, the generalisation of the model was enhanced by adding retrospective data of 14,832 Chinese Shanxi CVD patients to the kaggle dataset. The XGBH risk prediction model proposed in this paper was validated to be highly accurate (AUC = 0.81) compared to the baseline risk score (AUC = 0.65), and the accuracy of the model for CVD risk prediction was improved with the inclusion of the conventional biometric BMI variable. To increase the clinical application of the model, a simpler diagnostic model was designed in this paper, which requires only three characteristics from the patient (age, value of systolic blood pressure and whether cholesterol is normal or not) to enable early intervention in the treatment of high-risk patients with a slight reduction in accuracy (AUC = 0.79). Ultimately, a CVD risk score model with few features and high accuracy will be established based on the main contributing features. Of course, further prospective studies, as well as studies with other populations, are needed to assess the actual clinical effectiveness of the XGBH risk prediction model. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases, characterized by high morbidity, disability, and mortality rates, are a collective term for disorders affecting the heart's structure or function. In clinical practice, physicians often manually delineate the left ventricular border on echocardiograms to obtain critical physiological parameters such as left ventricular volume and ejection fraction, which are essential for accurate cardiac function assessment. However, most state-of-the-art models focus excessively on pushing the boundaries of segmentation accuracy at the expense of computational complexity, overlooking the substantial demand for high-performance computing resources required for model inference in clinical applications. This paper introduces a novel left ventricle echocardiographic segmentation model that efficiently combines the SwiftFormer Encoder and U-Lite Decoder to reduce network parameter count and computational complexity. Additionally, we incorporate the Spatial and Channel reconstruction Convolution (SCConv) module through spatial and channel reconstruction during downsampling and replace the Binary Cross Entropy Loss (BCELoss) with Polynomial Loss (PolyLoss) to achieve superior segmentation performance. On the EchoNet-Dynamic dataset, our network achieves a Dice similarity coefficient of 0.92714 for left ventricle segmentation, with FLoating-point Operations Per Second (FLOPs) and Parameters of just 4472.55 M and 28.96 M respectively. Extensive experimental results on the EchoNet-Dynamic dataset demonstrate that the proposed modifications deliver competitive performance at a lower computational cost. [ABSTRACT FROM AUTHOR]

Recent studies revealed the importance of tracking continuous blood pressure (BP) changes in monitoring and controlling hypertension and diagnosing cardiovascular diseases. However, current evaluation protocols utilize distance measures as primary metrics, which cannot properly evaluate the ability of the estimation model to track BP changes. This paper proposes a comprehensive evaluation framework which evaluates the distance and trend similarity metrics, and the composite metric of both between the reference and estimated BPs. The results of applying both widely used conventional metrics and the new proposed metrics for BP estimations are demonstrated in an example of comparing the reference with a set of different BP estimations. Then, the metrics are applied to BP estimations obtained using state-of-the-art (SOTA) algorithms. It is shown that even though SOTA algorithms have a low mean and standard deviation of absolute difference, they are not capable of tracking short-term blood pressure changes. Additionally, the proposed metrics are normalized metrics and range from -1 to 1, making them intuitively interpretable, similar to well-known correlation coefficients. Therefore, we suggest that the proposed evaluation framework should be used regularly in evaluating continuous BP monitoring systems. [ABSTRACT FROM AUTHOR]

RNA modifications play an important role in actively controlling recently created formation in cellular regulation mechanisms, which link them to gene expression and protein. The RNA modifications have numerous alterations, presenting broad glimpses of RNA's operations and character. The modification process by the TET enzyme oxidation is the crucial change associated with cytosine hydroxymethylation. The effect of CR is an alteration in specific biochemical ways of the organism, such as gene expression and epigenetic alterations. Traditional laboratory systems that identify 5-hydroxymethylcytosine (5hmC) samples are expensive and time-consuming compared to other methods. To address this challenge, the paper proposed XGB5hmC, a machine learning algorithm based on a robust gradient boosting algorithm (XGBoost), with different residue based formulation methods to identify 5hmC samples. Their results were amalgamated, and six different frequency residue based encoding features were fused to form a hybrid vector in order to enhance model discrimination capabilities. In addition, the proposed model incorporates SHAP (Shapley Additive Explanations) based feature selection to demonstrate model interpretability by highlighting the high contributory features. Among the applied machine learning algorithms, the XGBoost ensemble model using the tenfold cross-validation test achieved improved results than existing state-of-the-art models. Our model reported an accuracy of 89.97%, sensitivity of 87.78%, specificity of 94.45%, F1-score of 0.8934%, and MCC of 0.8764%. This study highlights the potential to provide valuable insights for enhancing medical assessment and treatment protocols, representing a significant advancement in RNA modification analysis. [ABSTRACT FROM AUTHOR]

Healthcare systems worldwide are grappling with the challenge of providing high-quality healthcare in the face of evolving disease patterns. India, like many other countries, faces a significant treatment gap for various curable impairments, non-communicable diseases (NCDs), and cardiovascular diseases (CVDs). To address their healthcare needs, individuals often relocate in search of better treatment options. However, no studies were conducted to understand the spatial mobility. This paper explores the determinants of spatial mobility for treatment in India using data from NSS 75th round (2017-2018). A total of 64,779 individual medical cases of different diseases were taken into consideration for our analysis. Fixed effect and multinomial regression models were used to understand diseases specific mobility for treatment. It was found that those with CVDs, NCDs, and disabilities are more prone to travel outside their district for medical care. Rural and economically disadvantaged individuals also tend to travel further for treatment. The key factors impacting treatment-seeking mobility include insurance coverage, hospital quality, cost of medicine, and cost of X-rays/surgeries. The study highlights the need for improved policies to address the gap between healthcare needs and infrastructure in India, with a focus on prioritizing the development of local healthcare facilities for disabilities, NCDs, and CVDs., (© 2024. The Author(s).)

Muscular strength represents a specific component of health-related fitness. Hand grip strength is used as a simple and dynamic marker of maximum voluntary force of the hand and to estimate overall strength. Today, little is known about the relationship between grip strength and health in forcibly displaced populations. In the present study, we examined whether grip strength is associated with various health outcomes in a sample of forcibly displaced people living in a Greek refugee camp. The present analyses are part of a larger pragmatic randomized controlled trial. In this paper, cross-sectional baseline data of 143 participants (71 men, 72 women) will be presented. In addition to grip strength, the following physical and mental health outcomes were assessed: body weight and body composition, blood pressure, total cholesterol, low- and high-density lipoprotein cholesterol, triglycerides, blood glucose levels (HbA1c), post-traumatic stress disorder (PTSD) symptoms, depressive and anxiety symptoms, pain, and quality of life. Linear regression analyses were carried out to examine how grip strength is associated with the health outcomes, separately for absolute and normalized grip strength scores. Grip strength was positively and strongly associated with percentage muscle mass (normalized grip strength: Stand. B = 0.58, p < .001), whereas a negative association existed for percentage body fat (normalized grip strength: Stand. B = - 0.58, p < .001). No statistically significant associations occurred between grip strength and the other cardiovascular risk markers. In contrast, we found that participants with higher normalized grip strength reported higher levels of PTSD (normalized grip strength: Stand. B = 0.36, p < .05) and depressive symptoms (normalized grip strength: Stand. B = 0.29, p < .05). No significant association occurred between grip strength, anxiety, pain and quality of life. Measuring grip strength in forcibly displaced people can be a useful way to assess their overall muscle strength. Grip strength tests are easy to implement, and results can be used to assess the effects of specific intervention measures. Nevertheless, our results question the usefulness of grip strength as a marker of cardiovascular health and mental wellbeing in a refugee camp setting., (© 2023. The Author(s).)

While the direct effects of the pandemic are well documented, less is known about the indirect ones, including changes in healthcare provision or human behavior. This paper aims to study the impact of indirect consequences on mortality, focusing on two leading causes (cardiovascular diseases, COVID-19) and places of death in Czechia, during the COVID-19 pandemic, one of the most severely affected European countries. The analysis was performed using data from the Czech Statistical Office and the Institute of Health Information and Statistics. The study compares annual mortality changes during three time periods: pre-pandemic (2018-2019), pandemic beginning and peaking (2020-2021), and pandemic fading (2022). Pandemic years were covered by the WHO public health emergency of international concern. Abridged life tables were computed, and Pollard's decomposition was used to calculate the contributions of causes and places of death on annual differences in life expectancy. Seasonal decomposition of monthly time series revealed an increase in cardiovascular mortality at home or in social care facilities corresponding to limitations in healthcare. While COVID-19 had a systemic negative effect on life expectancy during the pandemic, the impact of cardiovascular mortality according to place of death changed over time. This study contributes to the evidence base of systemic risks during health crises and emergency response., (© 2023. The Author(s).)

The paper reflects the results of molecular docking and mathematical DFT simulation for antiplatelet drugs and the target platelet receptor/ferment interaction in the limited area. The results of Raman spectra simulation are implemented and obtained from the interaction of the clopidogrel metabolite of the P2Y12 receptor. The interaction of aspirin with the COX-1 enzyme was also investigated. As a result, theoretical Raman spectra of the drug-receptor area were obtained. The theoretical data were compared with the experimental SERS results. The characteristic bands corresponding to metabolite/ferment and antiplatelet drug vibrations were clarified. The prospects of obtaining results for pathologies based on platelet conformations during cardiovascular diseases have been demonstrated. [ABSTRACT FROM AUTHOR]

C-reactive protein (CRP) is an acute-phase protein involved in inflammation. Furthermore, CRP is an important biomarker used in diagnostics to predict risk of cardiovascular disease (CVD) in addition to monitoring bacterial and viral infections. To measure plasma CRP, venipuncture is still necessitated and has to be performed by trained phlebotomists. As a solution, dried blood spots (DBS) are used for minimally invasive at-home sampling of blood and can be send to diagnostic laboratories by regular mail. In this study, we included 53 patients that presented to the outpatient clinic of the University Medical Center Utrecht. Capillary finger stick was used to spot blood on a filter paper card and allowed to dry. After extraction of DBS, CRP was analyzed on an automated high-throughput chemistry analyzer. Additional validation steps regarding stability, effect of hematocrit, precision, and limits of blank and quantitation were conducted according to corresponding Clinical and Laboratory Standards Institute standards. An excellent regression analysis of R 2 (95% confidence interval) = 0.986 (0.982-0.989) was found. This enabled correct classification for high CVD risk of all 25 cases with sensitivity (95% CI) of 1.00 (1.00-1.00) and specificity (95% CI) of 0.96 (0.89-1.03) and correct diagnosis of inflammation of 12/13 cases with sensitivity (95% CI) of 0.92 (0.77-1.07) and specificity (95% CI) of 1.00 (1.00-1.00). Furthermore, CRP was found to be stable for 31 days and observed hematocrit variation amongst patients was clinically acceptable. CRP from DBS can be accurately measured on an automated high-throughput chemistry analyzer and used to diagnose inflammation and classify high CVD risk. This method enables individuals to engage in at-home sampling of blood on DBS for (tele)diagnostics, screening programs, patient follow-up, and medication management., (© 2023. The Author(s).)

Chronic Venous Disorders (CVD) of the lower limbs are one of the most prevalent medical conditions, affecting 35% of adults in Europe and North America. Due to the exponential growth of the aging population and the worsening of CVD with age, it is expected that the healthcare costs and the resources needed for the treatment of CVD will increase in the coming years. The early diagnosis of CVD is fundamental in treatment planning, while the monitoring of its treatment is fundamental to assess a patient's condition and quantify the evolution of CVD. However, correct diagnosis relies on a qualitative approach through visual recognition of the various venous disorders, being time-consuming and highly dependent on the physician's expertise. In this paper, we propose a novel automatic strategy for the joint segmentation and classification of CVDs. The strategy relies on a multi-task deep learning network, denominated VENet, that simultaneously solves segmentation and classification tasks, exploiting the information of both tasks to increase learning efficiency, ultimately improving their performance. The proposed method was compared against state-of-the-art strategies in a dataset of 1376 CVD images. Experiments showed that the VENet achieved a classification performance of 96.4%, 96.4%, and 97.2% for accuracy, precision, and recall, respectively, and a segmentation performance of 75.4%, 76.7.0%, 76.7% for the Dice coefficient, precision, and recall, respectively. The joint formulation increased the robustness of both tasks when compared to the conventional classification or segmentation strategies, proving its added value, mainly for the segmentation of small lesions., (© 2023. The Author(s).)

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

This paper proposes a generalized Phase Space Reconstruction (PSR) based Cardiovascular Diseases (CVD) classification methodology by exploiting the localized features of the ECG. The proposed methodology first extracts the ECG localized features including PR interval, QRS complex, and QT interval from the continuous ECG waveform using features extraction logic, then the PSR technique is applied to get the phase portraits of all the localized features. Based on the cleanliness and contour of the phase portraits CVD classification will be done. This is first of its kind approach where the localized features of ECG are being taken into considerations unlike the state-of-art approaches, where the entire ECG beats have been considered. The proposed methodology is generic and can be extended to most of the CVD cases. It is verified on the PTBDB and IAFDB databases by taking the CVD including Atrial Fibrillation, Myocardial Infarction, Bundle Branch Block, Cardiomyopathy, Dysrhythmia, and Hypertrophy. The methodology has been tested on 65 patients' data for the classification of abnormalities in PR interval, QRS complex, and QT interval. Based on the obtained statistical results, to detect the abnormality in PR interval, QRS complex and QT interval the Coefficient Variation (CV) should be greater than or equal to 0.1012, 0.083, 0.082 respectively with individual accuracy levels of 95.3%, 96.9%, and 98.5% respectively. To justify the clinical significance of the proposed methodology, the Confidence Interval (CI), the p-value using ANOVA have been computed. The p-value obtained is less than 0.05, and greater F-statistic values reveal the robust classification of CVD using localized features.

Australian Indigenous young people have a 10-fold excess of deaths from ischaemic heart disease compared with non-Indigenous Australians, yet the reasons behind this remain understudied. This paper aims to describe cardio-metabolic profiles among Australian Indigenous (n = 459) and non-Indigenous (n = 117) young adults (21-27 years). The association between body size and an adverse cardio-metabolic profile (≥3 abnormal cardio-metabolic markers) is assessed by gender and urban/rural residence, employing regression analyses. The prevalence of obesity was highest among urban Indigenous participants, both males and females (22% and 23%, respectively). Overall, BMI showed a significant positive association with an adverse cardio-metabolic profile. Moreover, adverse cardio-metabolic profile was present in a substantial proportion of Indigenous participants even in overweight and normal BMI categories. Among females, this could reflect elevated waist circumference, which was present in half of those of normal weight. Remote Indigenous females had the highest predicted probability of having an adverse cardio-metabolic profile across all BMI categories (13% for underweight, 54% for normal BMI, 93% for overweight, and 99% for obese). Our findings highlight the associations between obesity and adverse cardio-metabolic profiles among Indigenous and non-Indigenous youth. Culturally-relevant strategies that address lifestyle risks, including access to healthy food, are urgently needed in this age group.

The eye and the heart are two closely interlinked organs, and many diseases affecting the cardiovascular system manifest in the eye. To contribute to the understanding of blood flow propagation towards the retina, we developed a method to acquire electrocardiogram (ECG) coupled time-resolved dynamic optical coherence tomography (OCT) images. This method allows for continuous synchronised monitoring of the cardiac cycle and retinal blood flow dynamics. The dynamic OCT measurements were used to calculate time-resolved blood flow profiles using fringe washout analysis. The relative fringe washout was computed to generate the flow velocity profiles within arterioles at the optic nerve head rim. We found that the blood column between the heart and the retina propagates within one cardiac cycle, denoting the arrival time as the heart-retina time (HRT). In a group of healthy subjects, the HRT was 144 ± 19 ms (mean ± SD). The HRT could provide a novel potential biomarker for cardiovascular health in direct relation to retinal perfusion. [ABSTRACT FROM AUTHOR]

This study investigated the incidence of new-onset cardiovascular disorders up to 3.5 years post SARS-CoV-2 infection for 56,400 individuals with COVID-19 and 1,093,904 contemporary controls without COVID-19 in the Montefiore Health System (03/11/2020 to 07/01/2023). Outcomes were new incidence of major adverse cardiovascular event (MACE), arrhythmias, inflammatory heart disease, thrombosis, cerebrovascular disorders, ischemic heart disease and other cardiac disorders between 30 days and (up to) 3.5 years post index date. Results were also compared with a pre-pandemic cohort over similar observation duration (N = 64,541). Cumulative incidence and hazard ratios adjusted for competitive risks were analyzed. Compared to contemporary controls, hospitalized COVID-19 patients had significantly higher risk of developing MACE (aHR = 2.29, 95% confidence interval [2.27, 2.31], p < 0.001), arrhythmias (aHR = 2.54[2.50, 2.58], p < 0.001), inflammatory heart disease (aHR = 5.34[4.79, 5.96], p < 0.001), cerebrovascular (aHR = 2.05[2.00, 2.11], p < 0.001), other cardiac disorders (aHR = 2.31[2.26, 2.35], p < 0.001), thrombosis (aHR = 4.25[4.15, 4.36], p < 0.001), and ischemic heart disease (aHR = 1.89[1.86, 1.92], p < 0.001). Non-hospitalized COVID-19 patients had slightly higher risk of developing MACE (aHR = 1.04[1.03, 1.06], p < 0.001), arrhythmias (aHR = 1.10[1.08, 1.12], p < 0.001), inflammatory heart disease (aHR = 2.29 [2.03, 2.59], p < 0.001), cerebrovascular (aHR = 1.11[1.07, 1.15], p < 0.001), and ischemic heart disease (aHR = 1.10[1.08, 1.13], p < 0.001). Race and ethnicity were mostly not associated with increased risks (p > 0.05). aHRs with contemporary controls as a reference were similar to those with pre-pandemic cohort as a reference. We concluded that new incident cardiovascular disorders in COVID-19 patients, especially those hospitalized for COVID-19, were higher than those in controls. Identifying risk factors for developing new-onset cardiovascular disorders may draw clinical attention for the need for careful follow-up in at-risk individuals. [ABSTRACT FROM AUTHOR]

Cardiovascular arrhythmia, characterized by irregular heart rhythms, poses significant health risks, including stroke and heart failure, making accurate and early detection critical for effective treatment. Traditional detection methods often struggle with challenges such as imbalanced datasets, limiting their ability to identify rare arrhythmia types. This study proposes a novel hybrid approach that integrates ConvNeXt-X deep learning models with advanced data balancing techniques to improve arrhythmia classification accuracy. Specifically, we evaluated three ConvNeXt variants—ConvNeXtTiny, ConvNeXtBase, and ConvNeXtSmall—combined with Random Oversampling (RO) and SMOTE-TomekLink (STL) on the MIT-BIH Arrhythmia Database. Experimental results demonstrate that the ConvNeXtTiny model paired with STL achieved the highest accuracy of 99.75%, followed by ConvNeXtTiny with RO at 99.72%. The STL technique consistently enhanced minority class detection and overall performance across models, with ConvNeXtBase and ConvNeXtSmall achieving accuracies of 99.69% and 99.72%, respectively. These findings highlight the efficacy of ConvNeXt-X models, when coupled with robust data balancing techniques, in achieving reliable and precise arrhythmia detection. This methodology holds significant potential for improving diagnostic accuracy and supporting clinical decision-making in healthcare. [ABSTRACT FROM AUTHOR]

First-degree atrioventricular block (AVB) is a common electrocardiogram finding in clinical practice. Vascular dysfunction is associated with cardiovascular disease and events. There is no information on the association of AVB with vascular function. The purpose of this study was to evaluate the associations of PR interval with vascular function assessed in well-characterized subjects. A total of 2220 subjects were enrolled in this study. We evaluated endothelium-dependent vasodilatation assessed by flow-mediated vasodilation (FMD) and endothelium-independent vasodilatation assessed by nitroglycerine-induced vasodilation (NID) in 106 subjects with first-degree AVB (70 men; mean age, 64.3 ± 12.9 years) and 1241 subjects with normal PR interval (531 men; mean age, 56.1 ± 13.9 years). Subjects with first-degree AVB had a lower FMD value than that in subjects with normal PR interval (3.5 ± 2.4% vs. 4.9 ± 3.3%, P < 0.001). FMD was significantly lower in subjects with first-degree AVB than in subjects with normal PR interval even after adjustment of confounding factors for assessment of endothelium-dependent vasodilatation (OR: 1.73, 95% CI: 1.11–2.70; P = 0.015). Although NID was significantly lower in subjects with first-degree AVB than in subjects with normal PR interval (11.1 ± 6.0% vs. 13.6 ± 6.1%, P < 0.001), there was no significant difference in NID between the two groups in multivariate analysis (OR: 1.48, 95% CI: 0.91–2.41; P = 0.117). Both endothelium-dependent and endothelium-independent vasodilation in the brachial artery are impaired in parallel with the prolongation of atrioventricular conduction. After adjustments of confounding factors for vascular function, first-degree AVB is associated with impaired endothelium-dependent vasodilatation but not with impaired endothelium-independent vasodilatation. From the aspect of vascular function, it is unlikely that first-degree AVB is an electrocardiography abnormality with a good prognosis. [ABSTRACT FROM AUTHOR]

Obstructive Sleep Apnoea (OSA) is associated with the development of cardiovascular disease (CVD); however, the risk is only weekly related to OSA severity traditionally determined by the apnoea-hypopnoea index. The Baveno classification was developed to improve patient selection who would benefit from OSA treatment in terms of symptoms improvement and CVD risk reduction. However, it is unclear how the classification relates to biomarkers of CVD risk. Composite lipid indices were evaluated in various populations and showed good predictive value for incident CVD. One thousand two hundred and forty-two patients with OSA were included in the study. Composite lipid indices, including the atherogenic index of plasma (AIP), visceral adiposity index (VAI), lipid accumulation product (LAP) and cardiometabolic index (CMI) were compared between different Baveno groups (A – minor symptoms, minor comorbidities, B – severe symptoms, minor comorbidities, C – minor symptoms, severe comorbidities, D – severe symptoms, severe comorbidities). All composite lipid indices were higher in group D versus groups A and C. Only LAP (86.93 /58.40–119.49/ vs. 75.73 /48.37–99.04/) and CMI (1.03 /0.69–1.42/ vs. 0.91 /0.63–1.12/) were significantly different between the minimally symptomatic groups (C vs. A), and solely LAP was significantly higher (110.69 /76.76–147.22/ vs. 82.32 /60.35–113.50/) when the highly symptomatic groups (D vs. B) were compared. The Baveno classification correlates with cardiovascular risk estimated with composite lipid indices. Composite lipid indices may help better stratifying cardiovascular risk in both minimally- and highly symptomatic patients. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders. [ABSTRACT FROM AUTHOR]

Myocarditis is a common disease of the cardiovascular and immune systems, but the relationship between relevant blood metabolites and the risk of myocarditis has not been well-established. To identify potential biometabolic markers associated with myocarditis, we conducted a two-sample Mendelian randomization (MR) study. We performed preliminary MR analysis using the inverse variance weighted (IVW) method, supplemented by MR-Egger, weighted median, and weighted mode methods to adjust for false discovery rate (FDR). Confounders were screened using the GWAS Catalog website. Sensitivity analyses included Cochrane Q-test, Egger regression, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO), scatterplots, funnel plots, and forest plots. For genetic and directional analysis, we employed co-localization analysis and the Steiger test. MR analysis was performed using the FinnGen database and meta-analysis was performed using the IEU database. MR analysis identified significant correlations for five metabolic biomarkers after FDR correction. These included four known metabolites: kynurenine, 1-stearoyl-GPE (18:0), deoxycarnitine, and 5-acetylamino-6-formylamino-3-methyluracil, as well as one unknown metabolite, X-25,422. Among these, kynurenine (OR = 1.441, 95%CI = 1.089–1.906, p-value = 0.018) and 1-stearoyl-GPE (18:0) (OR = 1.263, 95%CI = 1.029–1.550, p-value = 0.029) were identified as risk factors for myocarditis, while deoxycarnitine (OR = 0.813, 95%CI = 0.676–0.979, p-value = 0.029), 5-acetylamino-6-formylamino-3-methyluracil (OR = 0.864, 95% CI = 0.775–0.962, p-value = 0.018), and X-25,422 (OR = 0.721, 95%CI = 0.587–0.886, p-value = 0.009) were found to be protective factors. No evidence of heterogeneity, horizontal pleiotropy, or sensitivity issues was observed, and no shared genetic factors between exposure and outcome were detected. The causality was in the correct direction. Meta-analysis further confirmed the causal relationship between the five metabolites and myocarditis. This study identifies a causal relationship between five circulating metabolites and myocarditis. Kynurenine, 1-stearoyl-GPE (18:0), deoxycarnitine, X-25,422, and 5-acetylamino-6-formylamino-3-methyluracil may serve as potential drug targets for myocarditis, providing a theoretical basis for the prevention, diagnosis, and treatment of the condition. [ABSTRACT FROM AUTHOR]

A major challenge of climate change adaptation is to assess the effect of changing weather on human health. In spite of an increasing literature on the weather-related health subject, many aspect of the relationship are not known, limiting the predictive power of epidemiologic models. The present paper proposes new models to improve the performances of the currently used ones. The proposed models are based on functional data analysis (FDA), a statistical framework dealing with continuous curves instead of scalar time series. The models are applied to the temperature-related cardiovascular mortality issue in Montreal. By making use of the whole information available, the proposed models improve the prediction of cardiovascular mortality according to temperature. In addition, results shed new lights on the relationship by quantifying physiological adaptation effects. These results, not found with classical model, illustrate the potential of FDA approaches.

This paper presents a novel multiple measurement information fusion approach to the estimation of cardiovascular risk predictors from non-invasive pulse volume waveforms measured at the body's diametric (arm and ankle) locations. Leveraging the fact that diametric pulse volume waveforms originate from the common central pulse waveform, the approach estimates cardiovascular risk predictors in three steps by: (1) deriving lumped-parameter models of the central-diametric arterial lines from diametric pulse volume waveforms, (2) estimating central blood pressure waveform by analyzing the diametric pulse volume waveforms using the derived arterial line models, and (3) estimating cardiovascular risk predictors (including central systolic and pulse pressures, pulse pressure amplification, and pulse transit time) from the arterial line models and central blood pressure waveform in conjunction with the diametric pulse volume waveforms. Experimental results obtained from 164 human subjects with a wide blood pressure range (systolic 144 mmHg and diastolic 103 mmHg) showed that the approach could estimate cardiovascular risk predictors accurately (r ≥ 0.78). Further analysis showed that the approach outperformed a generalized transfer function regardless of the degree of pulse pressure amplification. The approach may be integrated with already available medical devices to enable convenient out-of-clinic cardiovascular risk prediction.

Investigating DNA methylation (DNAm) in cardiac tissues is vital for epigenetic research in cardiovascular diseases (CVDs). During cardiac surgery, biopsies may not be immediately stored due to a lack of human or technical resources at the collection site. Assessing DNAm stability in cardiac samples left in suboptimal conditions is crucial for applying DNAm analysis. We investigated the stability of DNAm in human cardiac tissues kept at 4 °C and 22 °C for periods of 1, 7, 14, and 28 days (exposed samples) using the Illumina Infinium MethylationEPIC v1.0 BeadChip Array. We observed high correlations between samples analysed immediately after tissue collection and exposed ones (R2 > 0.992). Methylation levels were measured as β-values and median absolute β-value differences (|∆β|) ranged from 0.0093 to 0.0119 in all exposed samples. Pairwise differentially methylated position (DMP) analysis revealed no DMPs under 4 °C (fridge temperature) exposure for up to 28 days and 22 °C (room temperature) exposure for one day, while 3,437, 6,918, and 3,824 DMPs were observed for 22 °C samples at 7, 14, and 28 days, respectively. This study provides insights into the stability of genome-wide DNAm, showing that cardiac tissue can be used for reliable DNAm analysis even when stored suboptimally after surgery. [ABSTRACT FROM AUTHOR]

The association between self-rated health (SRH) and the development of atrial fibrillation (AF) in the general population remains underexplored. We reviewed the data of 9,895 participants in the Ansung–Ansan cohort study, a community-based Korean study. SRH was categorised as 'poor', 'fair', or 'good'. A newly developed AF was identified through biennial electrocardiography examinations and/or a self-reported history of physician-determined diagnoses. Over a median follow-up of 11 years, 149 patients (1.5%) developed AF. Compared with the 'good' group, the 'poor' group exhibited a higher risk of incident AF (adjusted hazard ratio, 1.85; 95% confidence interval, 1.19–2.87). Old age, female sex, lower educational level, smoking, cardiovascular diseases (hypertension, diabetes mellitus, and coronary artery disease), and inflammation were associated with 'poor' SRH. Along with SRH, age, male sex, urban residence, hypertension, and myocardial infarction were also associated with a higher risk of incidental AF. The combined model, which included conventional risk factors and SRH, demonstrated a marginally improved performance in predicting incident AF (concordance index: 0.704 vs. 0.714, P = 0.058). Poor SRH is independently associated with the development of AF in Korean adults. However, it plays a limited role in AF surveillance when combined with conventional AF risk factors. [ABSTRACT FROM AUTHOR]

The importance of 3D reconstruction of coronary arteries using multiple coronary angiography (CAG) images has been increasingly recognized in the field of cardiovascular disease management. This process relies on the camera matrix's optimization, needing correspondence info for identical point positions across two images. Therefore, an automatic method for determining correspondence between two CAG images is highly desirable. Despite this need, there is a paucity of research focusing on image matching in the CAG images. Additionally, standard deep learning image matching techniques often degrade due to unique features and noise in CAG images. This study aims to fill this gap by applying a deep learning-based image matching method specifically tailored for the CAG images. We have improved the structure of our point detector and redesigned loss function to better handle sparse labeling and indistinct local features specific to CAG images. Our method include changes to training loss and introduction of a multi-head descriptor structure leading to an approximate 6% improvement. We anticipate that our work will provide valuable insights into adapting techniques from general domains to more specialized ones like medical imaging and serve as an improved benchmark for future endeavors in X-ray image-based correspondence matching. [ABSTRACT FROM AUTHOR]

Continuous and non-invasive monitoring of hemodynamic parameters through unobtrusive wearable sensors can potentially aid in early detection of cardiac abnormalities, and provides a viable solution for long-term follow-up of patients with chronic cardiovascular diseases without disrupting the daily life activities. Electrocardiogram (ECG) and siesmocardiogram (SCG) signals can be readily acquired from light-weight electrodes and accelerometers respectively, which can be employed to derive systolic time intervals (STI). For this purpose, automated and accurate annotation of the relevant peaks in these signals is required, which is challenging due to the inter-subject morphological variability and noise prone nature of SCG signal. In this paper, an approach is proposed to automatically annotate the desired peaks in SCG signal that are related to STI by utilizing the information of peak detected in the sliding template to narrow-down the search for the desired peak in actual SCG signal. Experimental validation of this approach performed in conventional/controlled supine and realistic/challenging seated conditions, containing over 5600 heart beat cycles shows good performance and robustness of the proposed approach in noisy conditions. Automated measurement of STI in wearable configuration can provide a quantified cardiac health index for long-term monitoring of patients, elderly people at risk and health-enthusiasts.