Cardiovascular Disease Risk Assessment Research Articles

Assessment of Lifetime Risk for Cardiovascular Disease: Time to Move Forward.

Over the past decades, there has been a notable increase in the risk of Cardiovascular Disease (CVD), even among younger individuals. Policymakers and the health community have revised CVD prevention programs to include younger people in order to take these new circumstances into account. A variety of CVD risk assessment tools have been developed in the past years with the aim of identifying potential CVD candidates at the population level; however, they can hardly discriminate against younger individuals at high risk of CVD.Therefore, in addition to the traditional 10-year CVD risk assessment, lifetime CVD risk assessment has recently been recommended by the American Heart Association/American College of Cardiology and the European Society of Cardiology prevention guidelines, particularly for young individuals. Methodologically, the benefits of these lifetime prediction models are the incorporation of left truncation observed in survival curves and the risk of competing events which are not considered equivalent in the common survival analysis. Thus, lifetime risk data are easily understandable and can be utilized as a risk communication tool for Public Health surveillance. However, given the peculiarities behind these estimates, structural harmonization should be conducted in order to create a sex-, race-specific tool that is sensitive to accurately identifying individuals who are at high risk of CVD. In this review manuscript, we present the most commonly used lifetime CVD risk tools, elucidate several methodological and critical points, their limitations, and the rationale behind their integration into everyday clinical practice.

Cardiovascular disease risk reduction for all-cause mortality benefit in low-risk individuals

Background: Current recommendations for the primary prevention of cardiovascular disease (CVD) are based solely on 10-year CVD risk assessment. However, CVD mortality is not compared with all-cause mortality, which could provide insights into the potential benefits of CVD risk interventions on overall mortality. Methods: CVD risk for average American white and black males and females is calculated using the 2013 American Heart Association equations. CVD death risk is determined by combining CVD risk with the death rate following a CVD diagnosis. This CVD death risk is then compared with all-cause mortality data from National Vital Statistics Reports. A larger ratio indicates that most deaths are attributed to CVD, suggesting that intervention may be more beneficial. Results: The CVD mortality ratio is highest for white men and lowest for black men, indicating that white male likely to benefit most from CVD risk modification, while black males will benefit the least. Additionally, the ratio, and the potential benefit, decreases with age for all groups, especially after the age of 70. Conclusion: For the average U.S. population without a CVD diagnosis, the potential mortality benefit from interventions targeting CVD risk factors varies significantly based on patient characteristics such as race, age, and gender. The potential benefit is greatest for white males and decreases with age for all groups.

Biomarker identification and risk assessment of cardiovascular disease based on untargeted metabolomics and machine learning

Cardiovascular disease (CVD) is the leading cause of mortality, disability, and healthcare costs, with a significant impact on the elderly and contributing to premature deaths across various age groups, including those below age 70. Despite decades of transformative discoveries and clinical efforts, the challenges of diagnosis, prevention, and treatment of CVD persist on a massive scale. This study aimed to unravel potential CVD-associated biomarkers and establish a machine learning model for the risk assessment of CVD. Untargeted metabolic assay with ultra-high performance liquid chromatography-tandem mass spectrometry and routine clinical biochemistry test were undertaken on the fasting venous blood specimens from 57 subjects. Four relevant clinical traits and 164 CVD-associated metabolites were identified, especially those related to glycerophospholipid metabolism and biosynthesis of unsaturated fatty acids. The machine learning model achieved from an integrated biomarker panel of palmitic amide, oleic acid, 138-pos (the 138th detected metabolomic feature in positive ion mode), phosphatidylcholine, linoleic acid, age, direct bilirubin, and inorganic phosphate, was able to improve the accuracy of CVD risk assessment up to a high satisfactory value of 0.91. The findings indicate that disorders in the metabolic processes of biological membranes and energy are significantly associated with increased risk of vascular damage in CVD patients. With machine learning methods, the pivotal metabolites and clinical biomarkers offer a promising potential for the efficient risk assessment and diagnosis of CVD.

Cardiovascular diseases risk assessment: the Italian Health Examination Survey 2023 - CUORE Project

Abstract Background The reduction of NCDs premature mortality is pursued by the WHO; risk conditions targets are recommended as well as population-level monitoring of cardiovascular disease (CVD) risk. The Italian Ministry of Health (MoH) has strengthened prevention/health promotion and supported periodic CVD risk assessment through national health examination surveys (HESs), funded by the MoH-CCM and conducted within the CUORE Project. Methods In 2023 a new HES started including the 10-year fatal and non-fatal CVD risk assessment based on knowledge of sex, age, smoking habit, measured systolic blood pressure and fasting total and HDL serum cholesterol, and presence of diabetes (on measured fasting serum glycemia and/or treatment) and anti-hypertensive therapy. Up to now, data from random samples of residents in 7 Regions (of 20 regions) distributed in North, Centre and South of Italy, aged 35-69 years, without previous CVD, are available (585 men, 617 women). Determinations were assayed by a central lab. CUORE Project functions developed on and for the Italian adults were used. Results Mean values of the CVD risk assessment resulted 6.9% (95% CI: 6.3-7.4) in men and 2.3% (2.1-2.5) in women. For men aged 35-44 years risk was 1.6% (1.5-1.8), for 45-54 years 3.8% (3.5-4.2), for 55-64 years 10.0% (9.0-10.9), for 65-69 years 17.6% (16.4-18.9); for women was 0.5% (0.4-0.6), 1.0% (0.9-1.1), 3.3% (2.9-3.7) and 6.0% (5.5-6.6) respectively. Prevalence of CVD risk less than 5% was 57% (49-64) in men and 88% (84-93) in women, of 10-15% risk were 22% (15-28) of men and 9% (5-14) of women, of 15-20% risk were 5% (1.6-8.0) of men and 0% of women, of over 20% risk were 2% (0-3.6) of men and 0% of women. Conclusions Compared to 2008, these data showed stable mean levels of CVD risk both in men and women at any age-classes and stable prevalence of CVD risk classes. CVD risk perception represents a challenge in primary prevention especially for young age groups and women. Key messages • In the Italian general population, CVD risk assessment remained stable compared to 15 years ago. • CVD risk perception represents a challenge in primary prevention.

Cardiovascular disease risk in people with diabetes in the era of universal diabetes screening

Abstract Background/Introduction Traditionally, most people with diabetes have been considered at high cardiovascular disease (CVD) risk and CVD preventive treatment has been recommended for almost all patients after diagnosis. However, guidelines are increasingly recommending a more stratified approach to preventive treatment informed by predicted CVD risk. In the early 2000’s, New Zealand initiated the world-first national diabetes screening programme, as a component of a national CVD risk assessment programme, and by 2016, approximately 90% of eligible New Zealand adults had been screened. We hypothesised that in the era of universal diabetes screening, many recently diagnosed patients will be at low CVD risk and that contemporary CVD risk prediction equations would be required to accurately assess their risk. Purpose We aimed to develop sex-specific 5-year CVD risk prediction models in people with diabetes in New Zealand using a contemporary natural-population based cohort, and to characterise the distribution of CVD risk. Methods De-identified individual-level linkage of multiple health administrative datasets was undertaken to establish a cohort of almost all adults with diabetes in the northern region of New Zealand (one-third of the national population), without CVD or heart failure, and aged 30–74 years on January 1, 2014, with follow-up linkage to hospitalisations and mortality until December 31, 2018. Sex-specific models were developed to estimate the 5-year risk of CVD hospitalisation or death, with 15 pre-defined predictors, including sociodemographic variables, diabetes-related and renal function measures. Results 64,645 participants (median age: 57 years [IQR: 49–65]) were included in the final cohort, of whom 4,992 had first CVD events during 301,272 person-years follow-up. Models calibrated well in both women and men (Figure 1). Overall, increasing age; high deprivation level; smoking-related hospitalisations or use of smoking cessation medications; history of atrial fibrillation; high levels of haemoglobin A1c, total cholesterol to HDL cholesterol ratio, and albumin-to-creatinine ratio; use of blood pressure-lowering medications and insulin were associated with elevated CVD risk. Median 5-year CVD risks were 4.9% (IQR: 2.9%–8.1%) in women and 7.7% (4.8%–12.0%) in men. There was a wide range of predicted 5-year CVD risk, with 51% of women and 27% of men having a predicted risk below 5%, and 6% of women and 16% of men having a risk of 15% or above (Figure 2). Conclusions This study demonstrated marked heterogeneity in predicted CVD risk in people with diabetes in an era of universal diabetes screening. As diabetes screening is increasingly undertaken as part of a routine CVD risk assessment, these findings will have important clinical implications for accurately targeting new but expensive CVD preventive medications to the highest risk patients.

Serum Malondialdehyde-Modified Low-Density Lipoprotein Level May Be a Biomarker Associated with Aortic Stiffness Among Patients Undergoing Peritoneal Dialysis

Background: Serum malondialdehyde-oxidized low-density lipoprotein (MDA-oxLDL) is associated with atherosclerosis and increased risk of cardiovascular disease (CVD). Vascular calcification frequently occurs with arterial stiffness in patients on peritoneal dialysis (PD). This cross-sectional study aimed to elucidate the correlation between aortic stiffness and MDA-oxLDL levels in patients on PD. Methods: Overall, 92 patients on PD were included. The carotid–femoral pulse wave velocity (cfPWV) was evaluated using cuff-based volumetric displacement, and blood samples were obtained from all patients. Aortic stiffness was classified based on cfPWV values (>10 m/s indicating aortic stiffness). Serum MDA-ox-LDL levels were quantified using commercial enzyme-linked immunosorbent assay kits. Results: In total, 33 (35.9%) patients were classified into the aortic stiffness group. Factors, including systolic blood pressure (SBP), serum triglyceride levels, C-reactive protein levels, age, weight, body mass index (BMI), waist circumference, MDA-oxLDL levels, and diabetes mellitus (DM) prevalence, were significantly higher in the aortic stiffness group. Multivariable logistic regression analysis revealed significant associations between aortic stiffness and MDA-oxLDL levels, BMI, and SBP. Furthermore, multivariable forward stepwise linear regression analysis revealed serum MDA-oxLDL levels as a significant independent predictor of cfPWV values. Conclusions: Serum MDA-oxLDL levels correlate positively with cfPWV values and may predict aortic stiffness among PD patients, highlighting its potential role in assessing CVD risk in this population.

Using deep learning to detect atherosclerotic plaques on carotid ultrasound images in the UK Biobank

Abstract Background Atherosclerosis is the main underlying cause of cardiovascular disease (CVD). Existing CVD risk assessment tools do not consider the burden of subclinical atherosclerosis. The presence of carotid plaques on carotid ultrasound is a well-known marker of subclinical atherosclerosis. The accumulation of population-scale data on the presence of atherosclerotic plaques, along with deep phenotyping, can allow not only to address the effectiveness of carotid ultrasound in routine clinical practice, but to shed light on the biology of atherosclerosis development. Purpose To develop an effective deep learning model for plaque detection in carotid ultrasound images in the UK Biobank. Methods We used 680 carotid ultrasound images with manually annotated plaques to train a deep learning model employing the YOLOv8 architecture. Different augmentation techniques were used to increase the generalizability of the model. The developed model was applied to automatically detect plaques in raw ultrasound images from 19,507 UK Biobank participants. Logistic and Cox regression were used to explore the associations of plaque presence and number as predicted by the model with conventional CVD risk factors and the risk of future CVD events over follow-up. To explore the genetic architecture of subclinical atherosclerosis, we conducted a genome-wide association study (GWAS) on plaque presence, followed by meta-analysis with data from the CHARGE Consortium. Results Our plaque detection model achieved high classification metrics of accuracy, sensitivity, and specificity (89.3%, 89.5%, and 89.2%, respectively) and detected atherosclerotic plaques in 44% of UK Biobank participants. As expected, plaques were more common among men than women and their prevalence increased linearly with age. Both plaque presence and number of plaques were correlated with conventional CVD risk factors including diabetes, hypertension, and hyperlipidemia, and showed strong associations with future risk of incident CVD events (Hazard Ratio for plaque presence: 1.48 [95%CI: 1.21-1.82], for 2 plaques or more: 1.65, [95% CI: 1.28-2.13]). Incorporating plaque-derived phenotypes minimally altered the C-index of the time-to-event model. GWAS meta-analysis of carotid plaque presence revealed 5 previously known loci, as well as a significant locus including the LPA gene that had not previously been associated with carotid plaque. Conclusion We have developed and implemented an efficient plaque detection model to data from the UK Biobank, which holds significant promise for studying atherosclerosis at a population-wide scale through integration with multiomics data and electronic health records.

Comparison of cardiovascular disease risk assessment tools in primary prevention patients with raised lipoprotein(a)

Abstract Background Genetically inherited lipoprotein (a) (lp(a)) is an independent cardiac risk factor. The approach to cardiovascular disease (CVD) risk modification is, however, uncertain. Current CVD risk calculators do not take lp(a) into account and guidance suggests binary treatment decisions based on presentation of lp(a) without considering other risk factors. A new CVD risk assessment tool has been developed which includes lp(a) concentration, Lp(a) Clinical Guidance (lpaclinicalguidance.com). Purpose The aim was to compare the CVD risk estimations and management impact, over both 10 years and lifetime, using the HEART UK consensus statement, QRISK3, Lp(a) Score, Score-2, Score-2 OP, and LIFE-CVD assessment tools in 20 primary prevention patients with raised lp(a). Methods This was a retrospective analysis from an existing database, recruited through lipid and cardiology outpatient services, with raised lp(a), and no history of CVD. A random selection with a broad range of lp(a) concentrations were chosen. The 10-year risk threshold to treat was based on current NICE guidance at 10% and, in the absence of consensus, a lifetime risk threshold of 25%. Lp(a) was measured by a reference clinical laboratory using a Roche platform (Denka Seiken reagent, isoform insensitive). Results The subset (n=20) included patients with lp(a) ranging from 64 nmol/L to >720 nmol/L, aged 20 to 78 (12 females). Risk assessment tool age restrictions prohibited risk estimation in all patients (explaining the variation in denominator below). Figure 1 shows a comparison of 10-year risk assessment tools, figure 2 lifetime risk. HEART UK guidance would indicate 90% of patients (18/20) were eligible to commence lipid lowering therapy, however, all patients would have treatment indicated based on Lp(a) Score (16/16). Using Score-2, no patients would have treatment indicated (0/6). Only 25% of patients (1/4) would have treatment indicated using Score-2 OP despite a minimum age criterion that alone introduces a major CVD risk factor. Of the remaining risk estimation tools: LIFE-CVD, QRISK3 lifetime risk, and QRISK3 10-year risk indicated 85% (11/13), 50% (8/16), and 38% (6/16) of patients, respectively, would have treatment indicated. Conclusions In primary prevention patients with raised lp(a), CVD risk estimation and decision-making is impaired by discrepancies in risk assessment tools. There may be underestimation in CVD risk, and subsequently under-treatment, with principal use of QRISK3 10-year risk to guide lipid lowering therapy. Lifetime CVD risk may be underestimated by QRISK and LIFE-CVD proportionally to the degree of elevation of lp(a). Risk assessment is imperfect; population data can only provide limited information when applied to an individual and the wide discrepancy found here calls into question the validity of risk estimation formulae in this population, or alternatively provides a range of tools for clinicians to select to suit the question asked.

Innovative Approaches for Personalized Cardiovascular Prevention: the INNOPREV multicentric trial

Abstract Background Cardiovascular diseases (CVD) represent a major global health concern, requiring innovative primary prevention strategies. Personalized prevention, through the use of digital technologies and genetic risk scores (PRS), holds promise but requires further investigation to assess clinical efficacy. Consequently, the INNOPREV trial aims to investigate the efficacy of PRS and/or digital technologies in the uptake of CV preventive behaviors uptakes. Methods The INNOPREV trial is a multicenter randomized controlled trial conducted in Italy across three hospital centers in Rome, Catania, and Palermo. It aims to recruit 1020 participants aged 40-69 with a high 10-year CVD risk. Participants are allocated to traditional CVD risk assessment, or genetic testing, or digital interventions, or a combination of both. The study involves comprehensive CV risk assessments at baseline and follow-up visits at one, five, and twelve months. Data analysis will utilize mixed-effects models to analyze longitudinally collected data and explore potential moderators and mediators of behavior change. Results Preliminary results indicate good progress in recruitment, which currently stands at 250 participants (25% of expected). Recruited females have a mean age of 59 ± 6 years, while males have a mean age of 58 ± 6 years. Currently, PRS analyses indicate that 77% of participants have intermediate scores, and 23% have low scores. Follow-up will be essential to evaluate the efficacy of the proposed interventions. Conclusions We expect that the INNOPREV trial will provide evidence towards integration of innovative approaches based on PRS and digital devices, in the traditional preventive approaches for CVDs. Expectations are high based on the limited effectiveness of the current approaches in CV risk reduction based on lifestyle advice. Key messages • INNOPREV trial examines PRS and digital technology for CVD prevention, aiming to enhance preventive efficacy. Initial findings suggest promising potential for innovative CVD prevention strategies. • The INNOPREV trial aims to investigate the efficacy of personalized prevention strategies, including PRS and digital technologies, in improving cardiovascular preventive behaviors.

Validation of digital cardiovascular risk score (DiCAVA) in the United States All of Us dataset

Abstract Introduction The cardiovascular disease (CVD) risk score DiCAVA was developed in 2021 using UK Biobank data with the aim to be used for population health screening by deriving its predictions only from variables readily available to every individual, omitting difficult-to-obtain measurements such as blood pressure and invasive blood tests for metrics like cholesterol or HbA1c, which are commonly used in clinical CVD risk assessment tools. The original set of variables was reduced to 30 in the DiCAVA Lite score to further improve usability in a digital solution accessible to the general population, while maintaining its discrimination ability. Purpose Like many countries, the US has a large burden of CVD and would benefit from easily accessible screening tools. However, to ensure generalisability of the UK-developed score, it is necessary to evaluate its performance in a population where the score is intended to be used. Methods Data from a large real-world modern observational study in the US, All of Us, was used to evaluate discrimination and calibration metrics of the risk score. Participants with pre-existing CVD and without available electronic health records were excluded from the study. Variables from the All of Us dataset were matched to the original variables and any missing values were imputed using multiple imputation. Results 216,985 participants with a median follow-up time of 3.44 years were included in the study. The median age was 51 years and 63% of the population were female. Discrimination ability in the All of Us dataset (C-index 0.76) slightly surpassed the discrimination achieved at training with UK Biobank (C-index 0.73), and that of Framingham risk score calculated for the All of Us population (C-index 0.72), demonstrating the score’s usability in the US. The score showed very good discrimination in both males (C-index 0.74) and females (C-index 0.77), all ethnic groups and age groups, with the exception of the population over 65 years of age, where the C-index dropped to 0.65 as expected (similar to that observed in the UK Biobank). Calibration of the original model was assessed at 3 years and showed under-estimation of the predicted risk (calibration-in-the-large of 2.37%). Following re-calibration, the algorithm demonstrated excellent results with the average predicted 3-year risk showing 0.59% difference from the observed CVD rate (calibration plot in Figure). Conclusions Overall, the DiCAVA Lite score has shown very good discrimination in the US, showing its generalisability in different demographic populations. Its re-calibrated form is now validated for use in the US population and because of its ability to be utilised outside clinical settings, it can be deployed widely in the general population as an initial screening tool and potentially boost awareness of cardiovascular health in an effort to drive preventative care of CVD.Re-calibration of the DiCAVA Lite score

Office-based risk equation of Globorisk for prediction of ten-years cardiovascular risk among Iranian population: findings from Fasa PERSIAN cohort study

BackgroundGloborisk is one of the prediction tools for 10-year risk assessment of cardiovascular disease, featuring an office-based (non-laboratory-based) version. This version does not require laboratory tests for determining the CVD risk. The present study aims to determine the 10-year CVD risk using the office-based Globorisk model and factors associated with the 10-year CVD risk.MethodsIn this study, baseline data from 6810 individuals participating in the Fasa cohort study, with no history of CVD or stroke, were utilized. The risk equation of the office-based Globorisk model incorporates age, sex, systolic blood pressure (SBP), body mass index (BMI), and smoking status. The Globorisk model categorizes the risk into three groups: low risk (< 10%), moderate risk (10% to < 20%), and high risk (≥ 20%). To identify factors associated with the 10-year CVD risk, the predicted risk was categorized into two groups: <10% and ≥ 10%. Multivariable logistic regression analysis was employed to determine factors associated with an increased CVD risk.ResultsAccording to the 10-year CVD risk categorization, 78.3%, 16.4%, and 5.3% of men were in the low, moderate, and high risk groups, respectively, while 85.8%, 10.0%, and 4.2%, of women were in the respective risk groups. Multivariable logistic regression results indicated that in men, the 10-year CVD risk decreases with being an opium user, and increases with being illiterate, having abdominal obesity, and low or moderate physical activity compared to high physical activity. In women, being married, and higher fiber consumption decrease the 10-year CVD risk, while being illiterate, low or moderate physical activity compared to high physical activity, having abdominal obesity, opium use, and being in wealth quintiles 1 to 4 compared to quintile 5 increase the risk.ConclusionsConsidering the factors associated with increased CVD risk, there is a need to enhance awareness and modify lifestyle to mitigate and reduce the risk of CVD. Additionally, early identification of individuals at moderate to high risk is essential for preventing disease progression. The use of the office-based Globorisk model can be beneficial in settings where resources are limited for determining the 10-year CVD risk.

Risk-enhancing factors and social determinants of health in risk assessment for atherosclerotic cardiovascular disease.

The Pooled Cohort Equations (PCEs) do not accurately estimate atherosclerotic cardiovascular disease (ASCVD) risk in certain populations. The 2018 AHA/ACC cholesterol guideline identified risk-enhancing factors as a supplement to PCEs-based risk assessment. However, the role of each risk-enhancing factor in ASCVD risk assessment has not been well quantified. Further, social determinants of health (SDOH) are not included in the PCEs nor considered as risk-enhancing factors in the US cholesterol guideline. We sought to evaluate ASCVD risk associated with each risk-enhancing factor and commonly collected SDOH including education, income, and employment status, and to assess if adding risk-enhancing factors and SDOH to the PCEs improve ASCVD risk prediction. We included individuals aged 40 to 75 years, without ASCVD or diabetes at baseline, and with low-density lipoprotein cholesterol 70-189 mg/dL from two contemporary prospective cohort studies (MESA and REGARDS) and from Kaiser Permanente Southern California (KPSC). The primary endpoint was incident ASCVD defined as nonfatal myocardial infarction, fatal coronary heart disease, or fatal or nonfatal stroke over a 10-year period (median follow-up 10 years). We used Cox proportional hazards models to estimate associations between risk-enhancing factors and SDOH with ASCVD. We also assessed changes in model performance after adding risk-enhancing factors and SDOH to the PCEs. We included 13,863 adults (mean age 60.7 years) from the prospective cohorts and 307,931 adults (mean age 54.8 years) from KPSC. Risk-enhancing factors including hypercholesterolemia, hypertriglyceridemia, metabolic syndrome, and chronic kidney disease were associated with a higher ASCVD risk, independent of 10-year risk estimated by the PCEs. Low education, low income, and unemployment were also associated with higher ASCVD risk. While adding individual risk-enhancing factors or SDOH to the PCEs had limited impact on model performance, adding multiple risk-enhancing factors and SDOH simultaneously led to modest improvements in discrimination (C-index increased by up to 0.07), calibration (integrated Brier score reduced by up to 2.3%), and net reclassification improvement up to 41.4%. These findings suggest including SDOH and risk-enhancing factors may improve ASCVD risk assessment.

Prevalence of Cardiovascular Disease and Comparison of Risk Category Predictions of Systemic Coronary Risk Evaluation Score-2 and 4 Other Cardiovascular Disease Risk Assessment Tools Among People Living with Human Immunodefficiency Virus in Türkiye.

Cardiovascular disease (CVD) is a major cause of mortality among people living with HIV (PLWH). We aimed to assess the prevalence of diagnosed CVD and the risk of CVD among PLWH using 5 different tools. This retrospective, cross-sectional study was conducted in 20 tertiary centers in Türkiye between October 2021 and March 2022, among 1425 PLWH aged 40-75 years. About 82.7% were male, with a median age of 51. Web-based tools for each score were used for CVD risk calculations. Of 1425 PLWH enrolled, 10.8% had confirmed CVD, and 1132 had their risk scores evaluated. Of those participants, 42.8% had a higher risk of CVD (10-year risk of atherosclerotic CVD risk score (ASCVD) above 7.5%), and according to the European Society of Cardiology systemic coronary risk evaluation 2 (SCORE2), 71.7% had a high- to very high-risk rate. The agreement between various CVD risk tools varied, with Framingham heart study risk score (FRS), modified FRS, data collection on adverse effects of anti-HIV drugs (DAD), and SCORE2 for high-risk countries showing overall agreement rates of 82%, 94%, 91%, and 36%, respectively, compared to ASCVD. According to the 2021 European and 2019 American Cardiology guidelines, 75.3% and 47.1% of PLWH would be eligible for lipid-lowering agents, respectively. The diagnosed CVD prevalence highlighted the importance of monitoring cardiovascular health and comorbidities in this population. SCORE2 identified a greater number of individuals at high/very high risk compared to other prediction tools. The implementation of CVD prevention through lipid-lowering therapy was far from desired levels in our cohort.

Calling for Action: The Need of Large-Scale Cohorts to Uncover the Cardiovascular Risk in Non-Sleepy Obstructive Sleep Apnea.

Since randomized clinical trials currently do not support continuous positive airway pressure treatment of asymptomatic obstructive sleep apnea (OSA) we proposed the Obesity, Symptoms, and CARdiovascular assessment (OSCAR) algorithm to aid clinicians in the management of asymptomatic low-risk moderate-severe OSA, focusing on weight loss, symptoms and cardiovascular disease (CVD) risk assessment. Exploiting the data of the Sleep Heart Health Study we selected subjects with a body mass index (BMI) < 30 Kg/m2, no history of CVD or sleepiness and compared 552 patients with moderate-severe OSA (OSCAR(-)) to 916 individuals without OSA (No-OSA). After adjusting for age, gender, and BMI, there was no significant difference in the risk of major adverse cardiovascular events (MACE) between OSCAR(-) and No-OSA (1.05; 95%CI 0.81-1.37). The study suggests that low-risk moderate-severe OSA patients may not have a greater risk of MACE compared to those without OSA and highlights the need for further research on this topic.

Neutrophil-to-High-Density Lipoprotein Ratio (NHR) and Neutrophil-to-Lymphocyte Ratio (NLR) as prognostic biomarkers for incident cardiovascular disease and all-cause mortality: A comparison study

Cardiovascular diseases (CVD) remain a leading cause of global mortality, with atherosclerosis and inflammation playing pivotal roles in their development. The neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-HDL cholesterol ratio (NHR) have emerged as potential biomarkers for assessing CVD risk. In this community-based cohort study conducted in Taiwan, involving 3278 participants, we investigated the associations between NHR, NLR, and the risks of CVD and all-cause mortality. Our findings revealed that both NHR and NLR were effective in identifying individuals at high risk for CVD. However, when assessing their joint effect, NHR alone demonstrated a stronger predictive value for CVD prognosis than NLR or the combination of both markers. Furthermore, NLR alone showed potential as a predictor of all-cause mortality when compared with NHR alone or in combination with NLR and NHR. These findings underscore the complex interplay between inflammation and lipid metabolism in the pathogenesis of CVD. While NHR shows promise as a cost-effective tool for CVD risk assessment, NLR emerges potential as a prognostic marker for mortality. Further research is warranted to explore the dynamic changes in these markers and their implications for clinical practice.

Aortic-Femoral Stiffness Gradient and Cardiovascular Risk in Older Adults.

The aortic-femoral arterial stiffness gradient, calculated as the ratio of lower-limb pulse-wave velocity (PWV) to central (aortic) PWV, is a promising tool for assessing cardiovascular disease (CVD) risk, but whether it predicts incident CVD is unknown. We examined the association of the aortic-femoral arterial stiffness gradient measures carotid-femoral stiffness gradient (femoral-ankle PWV divided by carotid-femoral PWV) and the heart-femoral stiffness gradient (femoral-ankle PWV divided by heart-femoral PWV), as well as PWV, with incident CVD (coronary disease, stroke, and heart failure) and all-cause mortality among 3109 participants of the Atherosclerosis Risk in Communities Study cohort (age, 75±5 years; carotid-femoral PWV, 11.5±3.0 m/s), free of CVD. Cox regression was used to estimate hazard ratios (HR) and 95% CIs. Over a median 7.4-year follow-up, there were 322 cases of incident CVD and 410 deaths. In fully adjusted models, only top quartiles of carotid-femoral stiffness gradient (quartile 4: HR, 1.43 [95% CI, 1.03-1.97]; and quartile 3: HR, 1.49 [95% CI, 1.08-2.05]) and heart-femoral stiffness gradient (quartile 4: HR, 1.77 [95% CI, 1.27-2.48]; and quartile 3: HR, 1.41 [95% CI, 1.00-2.00]) were significantly associated with a greater risk of incident CVD. Only high aortic stiffness in combination with low lower-limb stiffness was significantly associated with incident CVD (HR, 1.46 [95% CI, 1.06-2.02]) compared with the referent low aortic stiffness and high lower-limb stiffness. No PWVs were significantly associated with incident CVD. No exposures were associated with all-cause mortality. The aortic-femoral arterial stiffness gradient may enhance CVD risk assessment in older adults in whom the predictive capacity of traditional risk factors and PWV are attenuated.

A-001 The Postprandial Levels of the Metabolites in the Metaorganismal Trimethylamine N-Oxide (TMAO) Pathway are Altered in A Diet-, Microbe-, and Sex-Specific Manner

Abstract Background Cardiovascular disease (CVD) remains the leading cause of death in developed countries. Elevated levels of the gut-microbe-associated metabolite trimethylamine-N-oxide (TMAO) have been associated with increased risk for CVD mortality in many large independent studies. In fact, large laboratory corporations such as Labcorp and Quest Diagnostic now offer TMAO diagnostic tests for the assessment of CVD risk and as a marker for disease-associated dysbiosis, using samples obtained from fasting patients. Although the strong association between TMAO levels and CVD risk has been established in fasting blood samples, here we are investigating the potential for a single meal to dynamically alter TMAO-related metabolites based on sex, diverse dietary substrates, and microbial influences. Methods 35 healthy participants were randomized to one of four study groups with approximately 8-9 subjects in each arm. Half of the participants were randomly assigned to receive a three-day broad spectrum antibiotic regimen (ciprofloxacin, metronidazole, and vancomycin) while the others received no antibiotics. These two groups were further subdivided into a group consuming a highly processed meal (originating from local fast food restaurants) or alternatively a whole food meal (containing a variety of fruits, vegetables, and healthy fiber). Blood samples were taken at baseline (after an overnight fast), and then postprandially at 15 minutes, 30 minutes, 1 hour, 2 hours, 4 hours, and 6 hours after meal ingestion. Targeted plasma metabolites were quantified using a stable isotope dilution, liquid chromatography - tandem mass spectrometry (LC-MS/MS) method Results While plasma TMAO levels between food groups did not change significantly within the total cohort, there were clear individualized responses to either highly processed or whole food meals. Separation based on sex showed that TMAO levels were significantly reduced in females at 6 hours but remained steady in males for both food groups. Particularly, in the processed food group, TMAO levels were significantly lower in females than males at the 6-hour time point. Neither of these observations held true for TMAO precursors choline, carnitine, or betaine. However, plasma levels of a dietary precursor for TMAO, γ-butyrobetaine, showed clear diet-microbe-host interactions. For males in the processed food group, plasma γ-butyrobetaine levels were significantly increased in subjects on broad spectrum antibiotics. Conclusions Our results show the postprandial levels of TMAO, and its nutrient precursors dynamically change in a diet-, microbe-, and sex-dependent manner. These findings provide new insights into the postprandial levels of TMAO-related metabolites and may inform precision nutritional approaches in those who could benefit from TMAO-lowering strategies.

A-223 Direct LDL or not Direct-LDL, that is the question

Abstract Background Management of Cardiovascular Disease (CVD) risk is dependent on Low Density Lipoprotein Cholesterol (LDL-C) levels; therefore LDL-C serves as the marker for the assessment of CVD risk and the focus of lipid lowering treatment. The gold standard reference method for measurement of LDL-C is beta-quantification. We compared calculated LDL-C with Friedewald (F-LDL-C), Martin-Hopkins (M-LDL-C) and Sampson (S-LDL-C) equations versus LDL-C directly measured (D-LDL-C). In CVD management also cholesterol ratios are evaluated. TC/HDL is associated with morbidity and mortality in the general population. Furthermore, several studies have stated that high triglycerides (TG) levels and low HDL-C (TG/HDL ratio) could function as an atherogenic index. Methods 11399 samples were analyzed on Siemens Atellica Solution® for the lipid profile determination measuring Total Cholesterol (TC), D-LDL-C, HDL-C and TG. In the absence of additional clinical parameters, based on desirable levels for LDL-C recommended by guidelines, CVD risk was evaluated by assigning to LDL-C a cut-off of 100 mg/dL. In presence of misclassifications, we used two other parameters to assess LDL-C results: TG/HDL-C ratio and CT/HDL-C. Results Patients cohort had a mean age of 57.7 (±16.8) years of which 53% (age=58.6, ±15.9) were males and 47% (age=56.5, ±17.8 years) were females. Of these, the 57.7% were outpatients and 42.3% inpatients (42.1% of infectious diseases - 24.4% of the cardiovascular department). Data about LDL-C misclassification and ratio data parameters are shown in table 1. Discordant patients for D-LDL-C vs F-LDL-C are 8.5%, that of D-LDL-C vs M-LDL-C are 7.7% and D-LDL-C vs S-LDL-C are 7.2%. Conclusions Is known that LDL plays a central role in the pathogenesis of atherosclerosis. Accurate estimation of LDL-C is a fundamental point for the stratification of CVD risk and the achievement of the therapeutic goal; however, we are still uncertain about the best and accurate way to measure it.

A-224 Non HDL Cholesterol: could be e new cardiovascular disease risk stratification factor?

Abstract Background A worldwide study demonstrated that among all modifiable risk factors of Cardiovascular Disease (CVD) to the abnormal cholesterol levels is attributable the highest risk of events. LDL-C is considered the most important lipoprotein risk factor. Some recent epidemiologic studies have suggested that non-HDL cholesterol (N-HDL-C) may be superior to LDL-C for identifying CVD risk. N-HDL-C is easily calculated from a lipid profile and represents all apolipoprotein B containing lipoproteins. Recent guidelines recommend LDL-C as a primary indicator and non-HDL-C as secondary. Moreover, cholesterol ratios are useful to stratification; the TC/HDL-C is associated with morbidity and mortality in the general population; in addition, several studies have stated that high triglycerides (TG) levels and low HDL-C are associated with increased risk and could function as atherogenic index. Methods 11399 samples were analyzed on Atellica Solution for the determination of Total Cholesterol (TC), D-LDL-C, HDL-C and TG. In the absence of clinical parameters, based on the desirable levels recommended by the guidelines, CVD risk was evaluated by assigning a cut-off of 100 and 130 mg/dL for D-LDL-C and N-HDL-C respectively Results Patients cohort mean (±SD) for CT, D-LDL, HDL, TG and N-HDL-C are 180 mg/dL (±43.2), 113 mg/dL (± 38.5), 51.7 mg/dL (±15.6), 116 mg/dL (±73.8), 128 mg/dL (±39.5) respectively. Of the examined patients, the 57.7% were outpatients, 21.8% of infectious diseases and 12.6% of the cardiovascular department. Data about D-LDL-C and N-HDL-C comparison, evaluated on discordant patients, are shown in table 1. Conclusions The data obtained from evaluation would seem to confirm hypothesis according to which non-HDL cholesterol would have a greater power in the identification of CVD risk because of cholesterol ratios are suggestive of increased risk for patients with an LDL-C&amp;lt;100 and N-LDL-C&amp;gt;130 mg/dL. Further studies assessing CVD risk, not only through laboratory data, are necessary to confirm this supposition.

External Validation of the American Heart Association PREVENT Cardiovascular Disease Risk Equations

The American Heart Association's Predicting Risk of Cardiovascular Disease Events (PREVENT) equations were developed to extend and improve on previous cardiovascular disease (CVD) risk assessments for the purpose of treatment initiation and patient-clinician communication. To assess prognostic capabilities, calibration, and discrimination of the PREVENT equations in a study sample representative of the noninstitutionalized, US general population. This prognostic study used data from the National Health and Nutrition Examination Survey (NHANES) 1999 to 2010 data cycles. Participants included adults for whom 10-year follow-up data were available. Data curation and analyses took place from December 2023 through May 2024. Primary measures were risk estimated by the PREVENT equations, as well as risk estimates from the previous Pooled Cohort Equations (PCEs). The primary outcome was composite CVD-related mortality at 10 years of follow-up. Additional analyses compared the PREVENT equations against the PCEs. Model discrimination was assessed with receiver-operator characteristic curves and Harrell C statistic from proportional hazard regression; model calibration was determined as the slope of predicted versus observed risk. The study cohort, accounting for NHANES complex survey design, consisted of 172.9 million participants (mean age, 45.0 years [95% CI, 44.6-45.4 years]; 52.1% women [95% CI, 51.5%-52.6%]). In analyses adjusted for the NHANES survey design, a 1% increase in PREVENT risk estimates was statistically significantly associated with increased CVD mortality risk (hazard ratio, 1.090; 95% CI, 1.087-1.094). PREVENT risk scores demonstrated excellent discrimination (C statistic, 0.890; 95% CI, 0.881-0.898) but moderate underfitting of the model (calibration slope, 1.13; 95% CI, 1.06-1.21). PREVENT risk models performed statistically significantly better than the PCEs, as assessed by the net reclassification index (0.093; 95% CI, 0.073-0.115). In this prognostic study of the PREVENT equations, PREVENT risk estimates demonstrated excellent discrimination and only modest discrepancies in calibration. These findings provided evidence supporting utilization of the PREVENT equations for application in the intended population as suggested by the American Heart Association.