Cardiovascular Disease Risk Prediction Research Articles

The relationship of health activation with risk of future cardiovascular disease among rural family caregivers of patients with chronic illnesses.

Family caregivers are at higher risk for developing cardiovascular disease (CVD) than non-caregivers. This risk is worse for those who live in rural compared to urban areas. Health activation, an indicator of engagement in self-care, is predictive of health outcomes and CVD risk in several populations. However, it is not known whether health activation is associated with CVD risk in rural caregivers of patients with chronic illnesses nor is it clear whether sex moderates any association. Our aims were to determine (1) whether health activation independently predicts 10-year CVD risk; and (2) whether sex interacts with health activation in the prediction of 10-year CVD risk among rural family caregivers (N=247) of patients with chronic illnesses. Health activation was measured using the Patient Activation Measure. The predicted 10-year risk of CVD was assessed using the Framingham Risk Score. Data were analyzed using nonlinear regression analysis. Higher levels of health activation were significantly associated with decreased risk of developing CVD (p<0.028). There was no interaction of sex with health activation on future CVD risk. However, male caregivers had greater risk of developing CVD in the next 10 years than female caregivers (p<0.001). We demonstrated the importance of health activation to future CVD risk in rural family caregivers of patients with chronic illnesses. We also demonstrated that despite the higher risk of future CVD among male, the degree of association between health activation and CVD risk did not differ by sex.

#1372 Association of plasma beta-2-microglobulin with risk of cardiovascular disease in patients with chronic kidney disease

Abstract Background and Aims In patients with chronic kidney disease(CKD), beta 2 microglobulin (B2MG) can be used to assess renal function, and predialysis serum B2MG levels can predict mortality risk. In addition to the kidney, B2MG regulates the expression of interleukin 1 and tumor necrosis factor, participates in the pathogenesis of vascular damage, and is a marker that can detect the formation of amyloid, therefore it is important in the diagnosis of cardiovascular disease (CVD). To assess the correlation of serum B2MG levels with some metabolic and cardiovascular function factors in patients with chronic kidney disease. Method 119 people with chronic kidney disease and 52 healthy control groups were selected as a case-control study and blood samples were analyzed for creatinine, urea, cholesterol, triglycerides, LDL, calcium, phosphorus, albumin, total protein, and B2MG with Cobas 6000 fully automatic analyzer and echocardiography was performed. Renal function was evaluated by CKD EPI crea-cys combined calculation. In the group with CKD, a person with eGFR &amp;lt;90 ml/min/1.73 m² was not included in the study due to the study exclusion criteria. Results The average age of the group with CKD was 54.92, and the average age of the healthy group was 50.66. B2MG has a significantly higher (P &amp;lt; 0.000) statistically significant correlation in the group with CKD compared to the control group. B2MG was significantly associated with creatinine (g = 0.516, P &amp;lt; 0.000), phosphorus (g = 0.342, P &amp;lt; 0.000), cystatin C (g = 0.700, P &amp;lt; 0.000), urea (g = 0.326, P &amp;lt; 0.000), and echocardiography with directly indicators LVMI(g = 0.113, P &amp;lt; 0.000), LVESD (r = 0.265, P &amp;lt; 0.000), thus BMI (g = −0.274, P &amp;lt; 0.000), tTSH (r = −0.516, P &amp;lt; 0.000), calcium ( g = −0.137, P &amp;lt; 0.000), albumin (g = −0.180, P &amp;lt; 0.000), total protein (r = −0.080, P &amp;lt; 0.000), cholesterol (g = −0.215, P &amp;lt; 0.000), BNLP (r = −0.163, P &amp;lt; 0.000), triglycerides (g = −0.142, P &amp;lt; 0.000), and EV of echocardiography (g = −0.292, P &amp;lt; 0.000) have inverse statistical significance. Conclusion B2MG levels have been shown to predict CVD risk and CVD-related mortality in all stages of chronic kidney disease. According to our study, B2MG is directly related to kidney function biomarkers, phosphorus, LVMI, and LVESD indicators of cardiac echocardiography, and inversely correlated with parameters of eGFR, protein, triglycerides, calcium, and cardiac echocardiogram EV indicators, which is similar to studies conducted in other countries. Therefore, B2MG can be used to detect the risk of CVD in people with severe CKD. Figure: Will be uploaded on our poster if accepted.

Hypoxic indices for obstructive sleep apnoea severity and cardiovascular disease risk prediction: A comparison and application in a community population.

The apnoea-hypopnoea index (AHI) and oxygen desaturation index (ODI) encounter challenges in capturing the intricate relationship between obstructive sleep apnoea (OSA) and cardiovascular disease (CVD) risks. Although novel hypoxic indices have been proposed to tackle these limitations, there remains a gap in comprehensive validation and comparisons across a unified dataset. Samples were derived from the Sleep Heart Health Study (SHHS), involving 4485 participants aged over 40 years after data quality screening. The study compared several key indices, including AHI, ODI, the reconstructed hypoxic burden (rHB), the percentage of sleep time with the duration of respiratory events causing desaturation (pRED_3p) and the sleep breathing impairment index (SBII), in relation to CVD mortality and morbidity risks. Adjusted Cox proportional models were employed to calculate hazard ratios (HRs) for each index, and comparisons were performed. SBII and pRED_3p exhibited significant correlations with both CVD mortality and morbidity, with SBII showing the highest adjusted HR (95% confidence interval) for mortality (2.04 [1.25, 3.34]) and pRED_3p for morbidity (1.43 [1.09-1.88]). In contrast, rHB was only significant in predicting CVD mortality (1.63 [1.05-2.53]), while AHI and ODI did not show significant correlations with CVD outcomes. The adjusted models based on SBII and pRED_3p exhibited optimal performance in the CVD mortality and morbidity datasets, respectively. This study identified the optimal indices for OSA-related CVD risks prediction, SBII for mortality and pRED_3p for morbidity. The open-source online platform provides the computation of the indices.

Cardiovascular Risk Estimation Is Suboptimal in People With HIV.

Established cardiovascular disease (CVD) risk prediction functions may not accurately predict CVD risk in people with HIV. We assessed the performance of 3 CVD risk prediction functions in 2 HIV cohorts. CVD risk scores were calculated in the Mass General Brigham and Kaiser Permanente Northern California HIV cohorts, using the American College of Cardiology/American Heart Association atherosclerotic CVD function, the FHS (Framingham Heart Study) hard coronary heart disease function and the Framingham Heart Study hard CVD function. Outcomes were myocardial infarction or coronary death for FHS hard coronary heart disease function; and myocardial infarction, stroke, or coronary death for American College of Cardiology/American Heart Association and FHS hard CVD function. We calculated regression coefficients and assessed discrimination and calibration by sex; predicted to observed risk of outcome was also compared. In the combined cohort of 9412, 158 (1.7%) had a coronary heart disease event, and 309 (3.3%) had a CVD event. Among women, CVD risk was generally underestimated by all 3 risk functions. Among men, CVD risk was underestimated by the American College of Cardiology/American Heart Association and FHS hard CVD function, but overestimated by the FHS hard coronary heart disease function. Calibration was poor for women using the FHS hard CVD function and for men using all functions. Discrimination in all functions was good for women (c-statistics ranging from 0.78 to 0.90) and moderate for men (c-statistics ranging from 0.71 to 0.72). Established CVD risk prediction functions generally underestimate risk in people with HIV. Differences in model performance by sex underscore the need for both HIV-specific and sex-specific functions. Development of CVD risk prediction models tailored to HIV will enhance care for aging people with HIV.

Preliminary Study on Pulse Wave Changes in Patients with Inflammatory Arthropathies Treated with bDMARDs.

Background: Patients with inflammatory arthropathies exhibit an increased cardiovascular disease (CVD) risk as compared to the general population, which is not fully quantified by the conventional CVD risk scores. Biotechnological disease-modifying drugs (bDMARDs) have proved beneficial to reduce the overall CVD risk in these patients, although CVD remains a major cause of increased mortality. Since it has been shown that pulse wave parameters and in particular carotid-femoral pulse wave velocity (cfPWV) are predictors of CVD risk, the aim of this study was to evaluate their changes in patients with inflammatory arthropathies before and after bDMARD therapy. Methods: Pulse wave parameters were evaluated with applanation tonometry in patients with ankylosing spondylitis (AS), psoriatic arthritis (PsA), and rheumatoid arthritis (RA), before and after two years of bDMARD therapy. Results: At baseline, cfPWV was significantly associated with age (p < 0.001) and, among pulse wave parameters, the subendocardial viability ratio was negatively associated with C-reactive protein (CRP) (p = 0.04) and the HAQ-disability index (p = 0.03). At baseline, PsA patients showed a higher percentage of male subjects, higher CRP, and the highest cfPWV values (p = 0.048). After two years, pulse wave parameters improved in the AS and RA groups, but not in the PsA group. Conclusions: Our data confirm that pulse wave parameters are potentially reversible after bDMARD therapy, as they improved in AS and RA patients. In PsA patients, there were no changes, which may be due to the higher percentage of male subjects and higher baseline cfPWV values.

Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data

BackgroundMachine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios.MethodsWe compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning.ResultsDepending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively.ConclusionsBy illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions.

ASSESSMENT OF CARDIOVASCULAR RISK IN PATIENTS WITH RHEUMATOID ARTHRITIS ACCORDING TO CURRENT HYPERTENSION GUIDELINES

Objective: Patients with rheumatoid arthritis (RA) are at increased risk of cardiovascular disease (CVD) by as much as 50% compared to the general population. Considering that disease-specific calculators have failed to provide more accurate CVD risk stratification in RA, CVD risk calculation in RA patients is challenging, especially for those who are not by definition at high risk based on concomitant established CVD or its equivalents. The presence of an immune-mediated inflammatory disease (e.g., RA) has been considered as a CVD risk modifier by the previous 2018 European Society of Hypertension/European Society of Cardiology (ESH/ESC) guidelines. Nevertheless, current 2023 ESH/ESC guidelines recommend assessment in RA using general CVD risk calculators. Design and method: We aimed to calculate CVD risk in RA patients using the most frequently applied CVD risk calculators in the general population. We only stratified patients free from established CVD (heart disease, stroke) or its equivalents (diabetes mellitus, chronic kidney disease) aged 40-74 years. The following algorithms were applied: (i) SCORE2 (Systemic Coronary Risk Estimation-2), (ii) Framingham Risk Score-Adult Treatment Panel (FRS-ATP) CVD risk calculator, and (iii) the American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Equation. We further correlated the estimated CVD risk by each algorithm with surrogate CVD markers, i.e. arterial stiffness and carotid atherosclerosis. For this purpose, carotid-femoral pulse wave velocity (cfPWV) was measured using applanation tonometry (Sphygmocor ®), and carotid intima-media thickness (cIMT) was estimated using carotid ultrasound. Results: A total of 91 patients aged 60.3±8.1 years were included (78% females). Using the FRS-ATP algorithm, more patients were classified as high risk and fewer as low risk compared to the other calculators (Table 1). Moreover, CVD risk calculated with FRS-ATP correlated stronger with both cfPWV and cIMT, compared to CVD risk calculated with the other algorithms (Table 2). Conclusions: In RA patients without established CVD, diabetes, or chronic kidney disease, the FRS-ATP algorithm may be applied as a useful tool for CVD risk stratification. Prospective, large-scale studies are warranted to determine the most reliable CVD risk prediction model for patients with RA.

International Migration and Cardiovascular Health: Unraveling the Disease Burden Among Migrants to North America and Europe.

Europe and North America are the 2largest recipients of international migrants from low-resource regions in the world. Here, large differences in cardiovascular disease (CVD) morbidity and death exist between migrants and the host populations. This review discusses the CVD burden and its most important contributors among the largest migrant groups in Europe and North America as well as the consequences of migration to high-income countries on CVD diagnosis and therapy. The available evidence indicates that migrants in Europe and North America generally have a higher CVD risk compared with the host populations. Cardiometabolic, behavioral, and psychosocial factors are important contributors to their increased CVD risk. However, despite these common denominators, there are important ethnic differences in the propensity to develop CVD that relate to pre- and postmigration factors, such as socioeconomic status, cultural factors, lifestyle, psychosocial stress, access to health care and health care usage. Some of these pre- and postmigration environmental factors may interact with genetic (epigenetics) and microbial factors, which further influence their CVD risk. The limited number of prospective cohorts and clinical trials in migrant populations remains an important culprit for better understanding pathophysiological mechanism driving health differences and for developing ethnic-specific CVD risk prediction and care. Only by improved understanding of the complex interaction among human biology, migration-related factors, and sociocultural determinants of health influencing CVD risk will we be able to mitigate these differences and truly make inclusive personalized treatment possible.

A Multi-Stage Approach for Cardiovascular Risk Assessment from Retinal Images Using an Amalgamation of Deep Learning and Computer Vision Techniques.

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Early detection and effective risk assessment are crucial for implementing preventive measures and improving patient outcomes for CVDs. This work presents a novel approach to CVD risk assessment using fundus images, leveraging the inherent connection between retinal microvascular changes and systemic vascular health. This study aims to develop a predictive model for the early detection of CVDs by evaluating retinal vascular parameters. This methodology integrates both handcrafted features derived through mathematical computation and retinal vascular patterns extracted by artificial intelligence (AI) models. By combining these approaches, we seek to enhance the accuracy and reliability of CVD risk prediction in individuals. The methodology integrates state-of-the-art computer vision algorithms and AI techniques in a multi-stage architecture to extract relevant features from retinal fundus images. These features encompass a range of vascular parameters, including vessel caliber, tortuosity, and branching patterns. Additionally, a deep learning (DL)-based binary classification model is incorporated to enhance predictive accuracy. A dataset comprising fundus images and comprehensive metadata from the clinical trials conducted is utilized for training and validation. The proposed approach demonstrates promising results in the early prediction of CVD risk factors. The interpretability of the approach is enhanced through visualization techniques that highlight the regions of interest within the fundus images that are contributing to the risk predictions. Furthermore, the validation conducted in the clinical trials and the performance analysis of the proposed approach shows the potential to provide early and accurate predictions. The proposed system not only aids in risk stratification but also serves as a valuable tool for identifying vascular abnormalities that may precede overt cardiovascular events. The approach has achieved an accuracy of 85% and the findings of this study underscore the feasibility and efficacy of leveraging fundus images for cardiovascular risk assessment. As a non-invasive and cost-effective modality, fundus image analysis presents a scalable solution for population-wide screening programs. This research contributes to the evolving landscape of precision medicine by providing an innovative tool for proactive cardiovascular health management. Future work will focus on refining the solution's robustness, exploring additional risk factors, and validating its performance in additional and diverse clinical settings.

Strategic Machine Learning Optimization for Cardiovascular Disease Prediction and High-Risk Patient Identification

Despite medical advancements in recent years, cardiovascular diseases (CVDs) remain a major factor in rising mortality rates, challenging predictions despite extensive expertise. The healthcare sector is poised to benefit significantly from harnessing massive data and the insights we can derive from it, underscoring the importance of integrating machine learning (ML) to improve CVD prevention strategies. In this study, we addressed the major issue of class imbalance in the Behavioral Risk Factor Surveillance System (BRFSS) 2021 heart disease dataset, including personal lifestyle factors, by exploring several resampling techniques, such as the Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), SMOTE-Tomek, and SMOTE-Edited Nearest Neighbor (SMOTE-ENN). Subsequently, we trained, tested, and evaluated multiple classifiers, including logistic regression (LR), decision trees (DTs), random forest (RF), gradient boosting (GB), XGBoost (XGB), CatBoost, and artificial neural networks (ANNs), comparing their performance with a primary focus on maximizing sensitivity for CVD risk prediction. Based on our findings, the hybrid resampling techniques outperformed the alternative sampling techniques, and our proposed implementation includes SMOTE-ENN coupled with CatBoost optimized through Optuna, achieving a remarkable 88% rate for recall and 82% for the area under the receiver operating characteristic (ROC) curve (AUC) metric.

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.

Development and validation of a new algorithm for improved cardiovascular risk prediction

QRISK algorithms use data from millions of people to help clinicians identify individuals at high risk of cardiovascular disease (CVD). Here, we derive and externally validate a new algorithm, which we have named QR4, that incorporates novel risk factors to estimate 10-year CVD risk separately for men and women. Health data from 9.98 million and 6.79 million adults from the United Kingdom were used for derivation and validation of the algorithm, respectively. Cause-specific Cox models were used to develop models to predict CVD risk, and the performance of QR4 was compared with version 3 of QRISK, Systematic Coronary Risk Evaluation 2 (SCORE2) and atherosclerotic cardiovascular disease (ASCVD) risk scores. We identified seven novel risk factors in models for both men and women (brain cancer, lung cancer, Down syndrome, blood cancer, chronic obstructive pulmonary disease, oral cancer and learning disability) and two additional novel risk factors in women (pre-eclampsia and postnatal depression). On external validation, QR4 had a higher C statistic than QRISK3 in both women (0.835 (95% confidence interval (CI), 0.833–0.837) and 0.831 (95% CI, 0.829–0.832) for QR4 and QRISK3, respectively) and men (0.814 (95% CI, 0.812–0.816) and 0.812 (95% CI, 0.810–0.814) for QR4 and QRISK3, respectively). QR4 was also more accurate than the ASCVD and SCORE2 risk scores in both men and women. The QR4 risk score identifies new risk groups and provides superior CVD risk prediction in the United Kingdom compared with other international scoring systems for CVD risk.

Correlation among job-induced stress, overall well-being, and cardiovascular risk in Italian workers of logistics and distribution.

Work-related stress is an occupational risk that has been linked to the development of cardiovascular disease (CVD). While previous studies have explored this association in various work contexts, none have focused specifically on logistics and distribution personnel. These workers may be exposed to significant job stress, which potentially increases the risk of CVD. In this study, we aimed to examine the relationship between work-related stress and cardiovascular risk in a sample of 413 healthy workers of a logistics and distribution company. To assess work-related stress and cardiovascular risk, we used the organisational well-being questionnaire proposed by the Italian National Anti-Corruption Authority, the Framingham Heart Study General Cardiovascular Disease (CVD) Risk Prediction Score and the WHO General Wellbeing Index (WHO-5). Our results revealed that individuals with low job support had a significantly higher CVD risk score and lower well-being index than those reporting high job support. Furthermore, workers with high-stress tasks showed higher well-being index scores than those with passive tasks. Approximately 58% of the subjects were classified as low CVD risk (CVD risk <10%), approximately 31% were classified as moderate risk (CVD risk between 10 and 20%) and 11% were considered high risk (CVD risk >20%). The overall median CVD risk for the population was moderate (6.9%), with individual scores ranging from 1 to 58%. Further analyses confirmed the protective effect of work support, also identifying physical inactivity, regular alcohol consumption and low educational level as factors contributing to an increased risk of CVD. Interestingly, factors such as job control and work support demonstrated a positive impact on psychological well-being. These results emphasise the importance of intervention strategies aimed at promoting health in the workplace. By addressing these combined factors, organisations can effectively reduce the risk of CVD and improve the general well-being of their workforce.

Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: the Personalized CARdiovascular DIsease risk Assessment for Chinese model.

Cardiovascular disease (CVD) is a leading cause of mortality, especially in developing countries. This study aimed to develop and validate a CVD risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events using machine learning technique. Three cohorts of Chinese patients with established CVD were included if they had used any of the public healthcare services provided by the Hong Kong Hospital Authority (HA) since 2004 and categorized by their geographical locations. The 10-year CVD outcome was a composite of diagnostic or procedure codes with specific International Classification of Diseases, Ninth Revision, Clinical Modification. Multivariate imputation with chained equations and XGBoost were applied for the model development. The comparison with Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS-2°P) and Secondary Manifestations of ARTerial disease (SMART2) used the validation cohorts with 1000 bootstrap replicates. A total of 48 799, 119 672 and 140 533 patients were included in the derivation and validation cohorts, respectively. A list of 125 risk variables were used to make predictions on CVD risk, of which 8 classes of CVD-related drugs were considered interactive covariates. Model performance in the derivation cohort showed satisfying discrimination and calibration with a C statistic of 0.69. Internal validation showed good discrimination and calibration performance with C statistic over 0.6. The P-CARDIAC also showed better performance than TRS-2°P and SMART2. Compared with other risk scores, the P-CARDIAC enables to identify unique patterns of Chinese patients with established CVD. We anticipate that the P-CARDIAC can be applied in various settings to prevent recurrent CVD events, thus reducing the related healthcare burden.

Integration of a polygenic score into guideline-recommended prediction of cardiovascular disease.

It is not clear how a polygenic risk score (PRS) can be best combined with guideline-recommended tools for cardiovascular disease (CVD) risk prediction, e.g. SCORE2. A PRS for coronary artery disease (CAD) was calculated in participants of UK Biobank (n = 432 981). Within each tenth of the PRS distribution, the odds ratios (ORs)-referred to as PRS-factor-for CVD (i.e. CAD or stroke) were compared between the entire population and subgroups representing the spectrum of clinical risk. Replication was performed in the combined Framingham/Atherosclerosis Risk in Communities (ARIC) populations (n = 10 757). The clinical suitability of a multiplicative model 'SCORE2 × PRS-factor' was tested by risk reclassification. In subgroups with highly different clinical risks, CVD ORs were stable within each PRS tenth. SCORE2 and PRS showed no significant interactive effects on CVD risk, which qualified them as multiplicative factors: SCORE2 × PRS-factor = total risk. In UK Biobank, the multiplicative model moved 9.55% of the intermediate (n = 145 337) to high-risk group increasing the individuals in this category by 56.6%. Incident CVD occurred in 8.08% of individuals reclassified by the PRS-factor from intermediate to high risk, which was about two-fold of those remained at intermediate risk (4.08%). Likewise, the PRS-factor shifted 8.29% of individuals from moderate to high risk in Framingham/ARIC. This study demonstrates that absolute CVD risk, determined by a clinical risk score, and relative genetic risk, determined by a PRS, provide independent information. The two components may form a simple multiplicative model improving precision of guideline-recommended tools in predicting incident CVD.

A Klotho-Based Machine Learning Model for Prediction of both Kidney and Cardiovascular Outcomes in Chronic Kidney Disease

Introduction: This study aimed to develop and validate machine learning (ML) models based on serum Klotho for predicting end-stage kidney disease (ESKD) and cardiovascular disease (CVD) in patients with chronic kidney disease (CKD). Methods: Five different ML models were trained to predict the risk of ESKD and CVD at three different time points (3, 5, and 8 years) using a cohort of 400 non-dialysis CKD patients. The dataset was divided into a training set (70%) and an internal validation set (30%). These models were informed by data comprising 47 clinical features, including serum Klotho. The best-performing model was selected and used to identify risk factors for each outcome. Model performance was assessed using various metrics. Results: The findings showed that the least absolute shrinkage and selection operator regression model had the highest accuracy (C-index = 0.71) in predicting ESKD. The features mainly included in this model were estimated glomerular filtration rate, 24-h urinary microalbumin, serum albumin, phosphate, parathyroid hormone, and serum Klotho, which achieved the highest area under the curve (AUC) of 0.930 (95% CI: 0.897–0.962). In addition, for the CVD risk prediction, the random survival forest model with the highest accuracy (C-index = 0.66) was selected and achieved the highest AUC of 0.782 (95% CI: 0.633–0.930). The features mainly included in this model were age, history of primary hypertension, calcium, tumor necrosis factor-alpha, and serum Klotho. Conclusion: We successfully developed and validated Klotho-based ML risk prediction models for CVD and ESKD in CKD patients with good performance, indicating their high clinical utility.

An unlabeled electrochemical sensor for the simultaneous detection of homocysteine and uric acid based on molecularly imprinted recognition for prediction of cardiovascular disease risk

An unlabeled molecular imprinted electrochemical sensor was developed for simultaneous detection of homocysteine and uric acid, based on gold nanoparticles and molecularly imprinted polymer composites modified glass carbon electrode. Gold nanoparticles stably enhanced the electron transfer and improved the sensitivity by acting as a base for enhancing signal for molecularly imprinted polymers. Specific molecular imprinting cavities based on electropolymerization with dopamine were formed to specifically and simultaneously identify homocysteine and uric acid. Combining the good sensitivity of electrochemistry and the excellent selectivity of molecularly imprinted polymer, the difference in peak current position was used to achieve simultaneous detection of homocysteine and uric acid. For this reason, the developed sensor showed the wide detection range of 5.00 × 10−3-1.00 × 103 μmol/L for homocysteine and 5.00 × 10−1-5.00 × 103 μmol/L for uric acid, with the LOD of 5.64 × 10−5 μmol/L and 0.128 μmol/L (S/N = 3). The developed sensor with easy preparation, great selectivity and short detection time could be used in simultaneous detection of homocysteine and uric acid in serum.

Abstract MP22: Age-Varying Implications of Recalibration of Cardiovascular Disease Risk Prediction in a New England-Based Healthcare System

Introduction: Atherosclerotic cardiovascular disease (ASCVD) risk estimation via the 2013 Pooled Cohort Equations (PCE) overestimates risk in population-based cohorts. We evaluated its recalibration performance in a contemporary healthcare cohort. Methods: We identified 160,926 patients in the Mass General Brigham healthcare system aged 40-79 years and without prior ASCVD or lipid-lowering medication use between 2010 and 2012. Recalibration was achieved using sex- and race-specific baseline survival and risk factor distributions of the derivation (80%) set. Internal validation compared Kaplan-Meier-adjusted observed and predicted risk based on original versus recalibrated PCE. Changes in sensitivity and specificity were calculated across various treatment thresholds for each age decile. Results: Of the 160,926 patients (mean [SD] age: 54.6 [8.6] years; 61.4% female; 82.7% non-Hispanic White), 20,373 (12.7%) underwent ASCVD over 10 years. The original PCE underestimated ASCVD risk (observed vs. predicted incidence rate: 0.13 vs. 0.05) globally and across all sex, race, and risk categories. After recalibration (observed vs. predicted incidence rate: 0.13 vs. 0.18), the prediction closely mirrored the actual ASCVD incidence in low-to-borderline risk but overestimated risk in the high risk. At ≥7.5% treatment threshold, patients aged 40-49 years derived the greatest net relative benefit (sensitivity: +42.9%; specificity: -26.6%) whereas the extent of specificity loss surpassed that of sensitivity gain beyond age 50 years with minimal reclassification by seventh decade in life. Treatment threshold yielding the greatest net benefit was incrementally higher with older age from ≥7.4% in age 40-49 years to ≥24.1% in 60-69 years. Conclusion: In healthcare setting, PCE significantly underestimated risk. Recalibration recovered sensitivity at the expense of reduced specificity in age-differential manner, highlighting the need for individualized prediction and clinical decision-making.

The bidirectional longitudinal association between academic performance and cardiovascular disease risk factors in adolescents.

The limited prior research examining the association between academic performance and cardiovascular disease (CVD) risk factors in youth did not explore the reciprocal association between these constructs, and analyzed CVD risk factors individually. Thus, the aim of the present study was to explore the bidirectional longitudinal association between clustered CVD risk score and academic performance in adolescents over a 24-month interval. A total of 237 adolescents (45.6% girls), aged 13.9 ± 0.3 years old at baseline, from DADOS (Deporte, ADOlescencia y Salud) study were included in this study. A clustered CVD risk score was created by calculating the mean age- and gender-standardized z-scores of waist circumference, systolic blood pressure, total cholesterol to high-density lipoprotein cholesterol ratio, triglycerides, homeostatic model assessment of insulin resistance, and cardiorespiratory fitness (inversed). Academic performance was assessed through the final academic grades and the test of educational abilities. Our results showed that the clustered CVD risk score at baseline was not associated with academic performance 24 months later (all p > .05). Nevertheless, except for physical education, academic grades at baseline were inversely associated with clustered CVD risk score at follow-up in adolescents (β ranged from -0.140 to -0.102; all p < .05). No associations were found between academic abilities at baseline and clustered CVD risk score at follow-up (all p > .05). Academic grades could help predict CVD risk 24 months later during adolescence. Education professionals should foster adolescents' academic performance not only to improve academic results but also to maximize future cardiovascular health benefits.

Serum Inflammatory Markers Used in Cardiovascular Disease Risk Prediction Models: A Systematic Review.

The aim of this review was to perform a critical appraisal of serum inflammatory biomarkers used for the prediction of cardiovascular disease (CVD) risk. We conducted a systematic review of studies listed on MEDLINE and Scopus from January 2000 to December 2023, focused on the prognostic value of serum inflammatory biomarkers [i.e., C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α)] in individuals without known CVD. Eligible studies used a multivariate prediction model and included discrimination or risk reclassification analysis. The Quality in Prognostic Studies (QUIPS) tool was used to evaluate study quality and potential bias. Thirty-five studies (i.e., total 208,897 participants) that evaluated the added prognostic value of CRP, IL-6, TNF-α on CVD risk prediction were retrieved. Significant improvements in CVD risk model's predictive ability were observed in 7 out of 32 studies relating CRP and 1 out of 8 studies relating IL-6 with CVD risk. The single study found no added prognostic value of TNF-α use in CVD risk model. The integration of serum inflammatory biomarkers into CVD risk prediction models does not appear to improve risk discrimination models, suggesting that these biomarkers may act as surrogate markers, but not as predictors of atherosclerotic CVD.