Cardiovascular Disease Risk Assessment Research Articles

Body mass index is similar to alternative anthropometric indices in evaluating plasma lipids as proxy for cardiovascular disease in women with previous hypertensive disorders of pregnancy: A cross-sectional study.

Women with previous hypertensive disorders of pregnancy (HDP) have increased risk of cardiovascular disease (CVD). Overweight is a modifiable risk factor for both conditions. Anthropometric indices such as waist circumference, hip circumference, waist-to-hip ratio, estimated total body fat, a body shape index, waist-to-hip-to-height ratio, and index of central obesity improve estimation of cardiovascular death risk in the general population as compared to body mass index (BMI). We aimed to assess whether alternative body mass composition indices associate more strongly with postpartum blood lipid levels, as a proxy for CVD risk, than BMI. We also aimed to investigate whether associations differ between women with previous normotensive or hypertensive index pregnancies. In this cross-sectional study, we examined 296 women 1 or 3 years after an index pregnancy that was normotensive (n = 116) or complicated by a hypertensive pregnancy disorder, including preeclampsia (n = 133) or gestational hypertension (n = 47). Uni- and multivariable regression analyses, adjusted for age and smoking, were conducted to evaluate associations between postpartum body mass composition indices and blood lipids. p < 0.05 was considered statistically significant. Median BMI and overweight rates were higher in women with previous HDP than in controls (23.9 kg/m2 versus 22.8 kg/m2 and 44.4% versus 30.2%, both p ⩽ 0.03). No body mass composition indices in any pregnancy complication group showed stronger associations with adverse lipid levels than BMI. However, women with previous HDP more often displayed significant associations between adverse body mass composition indices and adverse lipid levels, compared with controls. Alternative anthropometric measurements are not better suited to evaluate circulating lipids as proxy for CVD risk after HDP, compared to BMI. We hence recommend using BMI in CVD risk assessment after HDP due to its current widespread use and feasibility.

Correlation between atherogenic index of plasma and cardiovascular disease risk across Cardiovascular–kidney–metabolic syndrome stages 0–3: a nationwide prospective cohort study

BackgroundThe Cardiovascular–kidney–metabolic (CKM) syndrome, a concept recently proposed by the American Heart Association (AHA), highlights the intricate connection between metabolic, renal, and cardiovascular illnesses. Furthermore, the Atherogenic Index of Plasma (AIP), a useful biomarker for evaluating the risk of Cardiovascular Diseases (CVDs), has been associated with the risk of Adverse Cardiovascular Events (ACEs). Nonetheless, its precise function in populations in CKM syndrome Stages 0–3 remains unknown.MethodsThis prospective study analyzed the data of 7,708 eligible participants (aged ≥ 45 years) from the Chinese Longitudinal Research of Ageing (CHARLS), particularly the 2011–2012 baseline survey (Wave 1). The primary exposure variable was AIP—a natural logarithm of the ratio of Triglycerides (TGs) to High-Density Lipoprotein Cholesterol (HDL-C). On the other hand, the primary endpoint was CVD incidence, which was determined based on self-reported past diagnoses. The relationship between AIP and CVD risk in the population in CKM syndrome stages 0–3 was examined using a Cox proportional risk model. Subgroup and mediation analyses were performed to further elucidate the interactions among these factors.ResultsThis study involved 7,708 participants in the CKM syndrome stages 0–3 [Mean age = 58.00 years; Interquartile Range (IQR) = 52.00–65.00 years]. The risk of developing CVD increased significantly with higher AIP levels. Specifically, the risk ratio for each unit increase in AIP was 1.31 (95% CI 1.11–1.55), while the Hazard Ratio (HR) for the group with the highest AIP levels compared to the group with the lowest AIP levels was 1.22 (95% CI 1.08–1.39). Mediation analysis revealed that metabolic syndrome accounted for 12.3% of the association between AIP levels and CVD risk (p = 0.024), highlighting its significance in CVD risk assessment.ConclusionHerein, AIP levels correlated significantly positively with CVD risk in individuals in CKM stages 0–3, with metabolic syndrome as a key mediating factor. These findings suggest that AIP levels could be valuable not only for CVD risk assessment but also for clinical screening.

Exploring Emerging and Innovative Biomarkers for the Assessment and Monitoring of Cardiovascular Disease Risk and Treatment Efficacy in Patients

Exploring Emerging and Innovative Biomarkers for the Assessment and Monitoring of Cardiovascular Disease Risk and Treatment Efficacy in Patients

Opportunistic AI for enhanced cardiovascular disease risk stratification using abdominal CT scans.

This study introduces the Deep Learning-based Cardiovascular Disease Incident (DL-CVDi) score, a novel biomarker derived from routine abdominal CT scans, optimized to predict cardiovascular disease (CVD) risk using deep survival learning. CT imaging, frequently used for diagnosing various conditions, contains opportunistic biomarkers that can be leveraged beyond their initial diagnostic purpose. Using a Cox proportional hazards-based survival loss, the DL-CVDi score captures complex, non-linear relationships between anatomical features and CVD risk. Clinical validation demonstrated that participants with high DL-CVDi scores had a significantly elevated risk of CVD incidents (hazard ratio [HR]: 2.75, 95% CI: 1.27-5.95, p-trend <0.005) after adjusting for traditional risk factors. Additionally, the DL-CVDi score improved the concordance of baseline models, such as age and sex (from 0.662 to 0.700) and the Framingham Risk Score (from 0.697 to 0.742). Given its reliance on widely available abdominal CT data, the DL-CVDi score has substantial potential as an opportunistic screening tool for CVD risk in diverse clinical settings. Future research should validate these findings across multi-ethnic cohorts and explore its utility in patients with comorbid conditions, establishing the DL-CVDi score as a valuable addition to current CVD risk assessment strategies.

A 10-Year Risk Assessment and Primary Prevention Study of Atherosclerotic Cardiovascular Disease Among Adult Patients in Saudi Arabia: A Cross-Sectional Study.

Background and Objectives: Cardiovascular diseases (CVDs), primarily driven by atherosclerosis, are the leading cause of mortality worldwide. In Saudi Arabia, the prevalence of atherosclerotic cardiovascular disease (ASCVD) poses a significant public health challenge. To estimate the 10-year ASCVD risk among adults in Al-Ahsa, Saudi Arabia, and identify prevalent risk factors such as age, gender, diabetes, hypertension, smoking, cholesterol, and preventive medication use. Materials and Methods: This cross-sectional study included 58,743 adults aged 35-75 years from the Al-Ahsa Health Cluster. The ASCVD risk was calculated using the ACC/AHA Pooled Cohort Equations. Statistical analysis identified predictors of high ASCVD risk. Results: Most participants (76.3%) were at low ASCVD risk (≤10%), 13.3% at borderline to intermediate risk (10-19%), and 10.4% at high risk (≥20%). Smoking, diabetes (39.6%), hypertension (40.8%), and male gender were key predictors of high ASCVD risk. High-density lipoprotein (HDL) was protective, reducing ASCVD risk by 3%. Among high-risk individuals, 29.7% used aspirin, and 58.3% used statins. Conclusions: While most adults in Al-Ahsa are at low ASCVD risk, a significant portion remains at elevated risk. Modifiable risk factors, including smoking, diabetes, and hypertension, combined with statin and aspirin adherence, highlight critical areas for targeted interventions to reduce the ASCVD burden in this population.

Association Between Geriatric Oral Health Assessment Index and Cardio-cerebrovascular Disease in Korean Older Adults.

This study examined the association between oral health-related quality of life (OHRQoL), as assessed by the Geriatric Oral Health Assessment Index (GOHAI), and cardiovascular disease (CVD) outcomes among Korean older adults. Data from 5413 participants in the Korean Longitudinal Study of Aging were analyzed. GOHAI scores were categorized as either "poor" (<40) or "not poor" (≥40). Generalized estimating equation models were used to assess the relationship between GOHAI scores and CVD prevalence, with analyses stratified by sex. Poor GOHAI score was significantly associated with elevated odds of CVD (odds ratio [OR], 1.13; 95% confidence interval [CI], 1.07 to 1.19; p<0.001). This association was stronger in women (OR, 1.36) compared to men (OR, 1.12). Poor oral health is indicative of systemic inflammation and age-related vulnerabilities, underscoring the utility of the GOHAI as an instrument for early identification of CVD risk. Poor oral health, as measured by the GOHAI, is associated with an increased risk of CVD among older adults, especially women. These findings support the use of the GOHAI as a cost-effective screening tool for the early assessment of CVD risk. Further research is warranted to explore inflammatory biomarkers and sex-specific mechanisms that could inform targeted interventions.

Machine learning-based blood pressure estimation using impedance cardiography data.

Accurate blood pressure (BP) measurement is crucial for the diagnosis, risk assessment, treatment decision-making, and monitoring of cardiovascular diseases. Unfortunately, cuff-based BP measurements suffer from inaccuracies and discomfort. This study is the first to access the feasibility of machine learning-based BP estimation using impedance cardiography (ICG) data. We analyzed ICG data from 71 young and healthy adults. Nine different machine learning algorithms were evaluated for their BP estimation performance against quality controlled, oscillometric (cuff-based), arterial BP measurements during mental (Trier social stress test), and physical exercise (bike ergometer). Models were optimized to minimize the root mean squared error and their performance was evaluated against accuracy and regression metrics. The multi-linear regression model demonstrated the highest measurement accuracy for systolic BP with a mean difference of -0.01 mmHg, a standard deviation (SD) of 10.79 mmHg, a mean absolute error (MAE) of 8.20 mmHg, and a correlation coefficient of r = 0.82. In contrast, the support vector regression model achieved the highest accuracy for diastolic BP with a mean difference of 0.15 mmHg, SD = 7.79 mmHg, MEA = 6.05 mmHg, and a correlation coefficient of r = 0.51. The study demonstrates the feasibility of ICG-based machine learning algorithms for estimating cuff-based reference BP. However, further research into limiting biases, improving performance, and standardized validation is needed before clinical use.

An Innovative algorithm framework for cardiovascular risk assessment based on ECG data

Background:Cardiovascular disease (CVD) is a primary universal physical problem, with conventional prediction systems frequently being persistent and expensive. Modern advancements in machine learning (ML)offer a hopeful option for accurate CVD risk assessment by leveraging multifaceted relations among diverse risk factors.Aim:Their search proposes a novel deep learning (DL) system, Dynamic Owl Search algorithm-driven Adaptive Long Short-Term Memory (DOS-ALSTM), to enhance cardiovascular risk prediction utilizing electrocardiogram (ECG) data.Method:The study utilizes ECG data from a diverse population group to train and assess the proposed model. Data is cleaned and normalized employing standard techniques to handle lost values and ensure reliability. Relevant features are extracted using statistical and signal processing technique to detain crucial features from the ECG data. The DOS-ALSTM system integrates a DOS optimization algorithm for optimized parameter regulation and ALSTM networks to detain sequential dependencies in ECG data for accurate risk prediction. The recognized method is evaluated using Python software.Result:The DOS-ALSTM system demonstrates superior performance with superioraccuracy of 99%, recall of 98%, F1-Score of 97.9% and Precision of 98.8% in CVD risk assessment compared to traditional methods

Atherogenic Index of Plasma and High-Sensitivity C-Reactive Protein Levels Among People Living With HIV on Dolutegravir and Ritonavir-Boosted Atazanavir-Based Antiretroviral Therapy and Their Correlations to CD4 Cell Counts.

Background: Atherogenic index of plasma (AIP) and high-sensitivity C-reactive protein (hsCRP) levels which are strong predictors of the risk of cardiovascular disease (CVD) seen elevated in the serum of people living with HIV (PLWH) on HAART and in those with low cluster of differentiation-4 (CD4) cell counts. Thus, this study aimed to evaluate AIP and hsCRP levels among PLWH on dolutegravir (DTG) and ritonavir-boosted atazanavir-based (ATV/r) antiretroviral therapy (ART) and their correlations to CD4 cell counts. Methods: The study design was an institutional-based comparative cross-sectional study conducted from November 4, 2021, to January 4, 2022. The total sample size was 172 with equal number of DTG and ritonavir-boosted atazanavir-treated PLWH. Participants were recruited by a consecutive sampling method. The data were entered into EpiData Version 4.6, then exported to SPSS Version 25.0, and analyzed using Chi-square, Student's t-test, and Pearson correlation. Statistical significance was set at p < 0.05. Results: AIP was significantly higher among individuals on ATV/r, with levels in the range of 0.1-0.24 at 44.2% and > 0.24 at 31.4%, compared with those on DTG-based regimens of ART, which showed levels of 39.5% and 9.3%, respectively. Similarly, the higher hsCRP level of ≥ 2 mg/L was observed among patients on ATV/r (44.2%) than in DTG-based (24.4%) regimens of ART. AIP and hsCRP were negatively correlated with CD4 cell counts with Pearson correlation coefficients of -0.46 and -0.38, respectively. Conclusion: From the study conducted, it can be concluded that the higher levels of AIP and hsCRP were seen in patients treated by ATV/r than in DTG-based regimens of ART and in PLWH with low CD4 cell counts. Therefore, routine monitoring of both AIP and hsCRP levels was a good marker of HIV disease progression and cardiovascular disease risk assessment in PLWH, particularly in developing countries where CD4 cell count testing is expensive and not easily available.

Assessing cardiovascular disease risk and social determinants of health: A comparative analysis of five risk estimation instruments using data from the Eastern Caribbean Health Outcomes Research Network.

Accurate assessment of cardiovascular disease (CVD) risk is crucial for effective prevention and resource allocation. However, few CVD risk estimation tools consider social determinants of health (SDoH), despite their known impact on CVD risk. We aimed to estimate 10-year CVD risk in the Eastern Caribbean Health Outcomes Research Network Cohort Study (ECS) across multiple risk estimation instruments and assess the association between SDoH and CVD risk. Five widely used CVD risk estimation tools (Framingham and WHO laboratory, both laboratory and non-laboratory-based, and ASCVD) were applied using data from ECS participants aged 40-74 without a history of CVD. SDoH variables included educational attainment, occupational status, household food security, and perceived social status. Multivariable logistic regression models were used to compare differences in the association between selected SDoH and high CVD risk according to the five instruments. Among 1,777 adult participants, estimated 10-year CVD risk varied substantially across tools. Framingham non-lab and ASCVD demonstrated strong agreement in categorizing participants as high risk. Framingham non-lab categorized the greatest percentage as high risk, followed by Framingham lab, ASCVD, WHO lab, and WHO non-lab. Fifteen times more people were classified as high risk by Framingham non-lab compared with WHO non-lab (31% vs 2%). Mean estimated 10-year risk in the sample was over 2.5 times higher using Framingham non-lab vs WHO non-lab (17.3% vs 6.6%). We found associations between food insecurity, those with the lowest level compared to the highest level of education, and non-professional occupation and increased estimated CVD risk. Our findings highlight significant discrepancies in CVD risk estimation across tools and underscore the potential impact of incorporating SDoH into risk assessment. Further research is needed to validate and refine existing risk tools, particularly in ethnically diverse populations and resource-constrained settings, and to develop race- and ethnicity-free risk estimation models that consider SDoH.

Colorimetric aptasensors for sensitive low-density lipoprotein detection based on reduced oxide graphene@molybdenum disulfide-ferrocene nanosheets with peroxidase-like activity.

Low-density lipoprotein (LDL) is a key biomarker for cardiovascular disease (CVD) risk assessment. Monitoring LDL for the early diagnosis of CVD and its complications is an important clinical analysis tool. In this work, a novel colorimetric aptasensor for LDL detection was constructed via reduced graphene oxide@molybdenum disulfide-ferrocene-carboxylic nanosheets (rGO@MoS2-Fc) with excellent peroxidase-like activity. On this basis, the LDL aptamer (LDLapt) immobilized on the surface of rGO@MoS2-Fc served as a signal probe (rGO@MoS2-Fc/LDLapt), while the unmodified LDLapt served as a capture probe. When LDL was present, it was recognized by the LDLapt and rGO@MoS2-Fc/LDLapt to form an rGO@MoS2-Fc/LDLapt/LDL/LDLapt sandwich-type conjugate with excellent enzymatic catalytic properties that can catalyze the generation of hydroxyl radicals (·OH) from hydrogen peroxide (H2O2), which in turn oxidized the colorless substrate o-phenylenediamine (OPD) to the yellow compound 2,3-diamino phenothiazine (DAP). In addition, the catalytic mechanism of the reaction was confirmed to be induced by ·OH through free radical experiments. The aptasensor had a linear range of 15.0 to 200.0 μg mL-1, and a limit of detection (LOD) of 2.199 μg mL-1. Overall, the assay has high selectivity, sensitivity and operability, showing broad application prospects in the clinical diagnosis of CVD.

Cardiovascular Diseases: Advances in Diagnosis, Treatment, and Emerging Therapeutic Trends

Cardiovascular diseases (CVDs) continue to be the leading cause of death worldwide, demanding constant innovation in their diagnosis and treatment. Recent advances have revolutionized the approach to managing these conditions, offering new hope for improved patient outcomes. In diagnostics, the integration of advanced imaging technologies such as cardiac MRI, CT angiography, and high-sensitivity troponin assays has enhanced the early detection and risk assessment of CVDs. Artificial intelligence (AI) and machine learning algorithms are now being employed to analyze large datasets, offering more accurate predictive models for cardiovascular events. These tools enable more personalized risk stratification, facilitating timely and targeted interventions. The therapeutic landscape for CVDs has also evolved significantly. New pharmacological treatments, including PCSK9 inhibitors and SGLT2 inhibitors, have shown promise in reducing cardiovascular risk beyond traditional therapies like statins. Antithrombotic therapies have been refined to minimize bleeding risks while effectively preventing thromboembolic events. Furthermore, the advent of gene therapy and regenerative medicine offers the potential to address the underlying causes of certain cardiovascular conditions, providing options for treatment that were previously unimaginable. Minimally invasive procedures, such as transcatheter aortic valve replacement (TAVR) and percutaneous coronary interventions, have transformed the management of structural heart diseases and coronary artery disease, reducing recovery times and improving patient outcomes. Additionally, the rise of telemedicine and wearable technology has revolutionized patient monitoring, enabling continuous assessment of cardiovascular health and more timely interventions. Emerging trends in the field, such as precision medicine and the use of AI-driven decision-making tools, promise to further refine the treatment of CVDs, tailoring interventions to individual patient profiles. These advances, coupled with ongoing research, are critical in the fight against CVDs, offering new avenues for prevention, early diagnosis, and effective treatment.

Association between triglyceride-glucose index and cardiovascular disease in US adults with chronic kidney disease: a population-based study

ObjectivesTriglyceride-glucose (TyG) index, which is a valuable measure of insulin resistance, has been found to have predictive value for cardiovascular disease (CVD). However, its relationship with CVD among individuals with chronic kidney disease (CKD) has not been thoroughly investigated. This study focused on examining the relationship of the TyG index and CVD among CKD patients in United States.Methods3507 eligible participants from the National Health and Nutrition Examination Survey (2003–2018) were surveyed in this study. Methods such as multivariable logistic regression analysis and smooth curve fitting were employed to estimate how the TyG index is linked to CVD in CKD populations. Subgroup and interaction analyses were conducted in order to figure out potential moderating effects of various factors.ResultsIn this cross-sectional study which based on a population with CKD, 29.5% of patients also had CVD. An independent and positive link of TyG and CVD was revealed (OR 1.213, 95% CI 1.059, 1.389). This association was nonlinear, with a threshold effect observed at a TyG index of 8.98. The results of subgroup analysis suggested that the relationships of TyG and CVD differed according to CKD stage: (OR 1.048, 95% CI 0.857, 1.282) in CKD stages 1–2; (OR 1.267, 95% CI 1.030–1.560) in stage 3; and (OR 2.131, 95% CI 1.224–3.709) in stages 4–5. Interaction analyses further suggested the potential moderating effects of CKD stage. Additionally, among specific CVD types, only coronary heart disease (CHD) had a significant and positive relationship with TyG (OR 1.617, 95% CI 1.123, 2.327).ConclusionsTyG index was identified to be independently and positively related to the likelihood of CVD, specifically in populations with an eGFR < 60 mL/min/1.73 m². The association had a threshold effect. The results highlight the potential of the TyG index as a tool for screening and risk assessment of CVD in populations suffering from CKD, warranting further investigation.

Association Between Cardiovascular Risk and Nonalcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) can be considered either an independent disease or a component of metabolic syndrome, and it is associated with conditions such as diabetes, obesity, lipid metabolism disorders, and hypertension. NAFLD and atherosclerotic cardiovascular diseases share common risk factors, including obesity, diabetes, and lipid metabolism disorders. Currently, the hypothesis that NAFLD is an independent cardiovascular risk factor is widely accepted, with this association influencing the onset or exacerbation of atherosclerotic processes. This has significant implications for the diagnosis and risk assessment of cardiovascular diseases in patients with NAFLD. The aim of the study is to analyze the role of NAFLD as a risk factor for cardiovascular diseases associated with metabolic dysfunction. The focus will be on common cardiovascular manifestations in patients with NAFLD to identify the relationships between metabolic disorders and the development of cardiovascular diseases. To achieve this goal, an analysis of sources from foreign and domestic literature on this issue was conducted, primarily from 1990 to 2023. The databases used include PubMed and E-library. The impact of NAFLD on cardiovascular diseases is attributed to chronic inflammation, cholesterol metabolism disorders, and endothelial dysfunction. Additionally, liver cirrhosis leads to alterations in blood pressure regulation, cardiac dysfunction, and hemodynamic disturbances. Liver pathology, as a significant factor in the development of cardiovascular diseases, requires a comprehensive approach to treatment. Atherosclerosis and cardiovascular diseases are also prevalent among patients with NAFLD due to shared risk factors. The higher prevalence of coronary artery calcification, hypertension, aortic valve sclerosis, diastolic dysfunction, atherosclerotic plaques, and increased intima-media thickness of the carotid arteries in patients with NAFLD compared to those without further supports this observation.

Comparison of cardiovascular risk prediction in type 1 diabetes: an Australian viewpoint.

Cardiovascular disease (CVD) is a significant burden in individuals with type 1 diabetes mellitus (T1DM). Yet the optimal method of CVD risk stratification remains uncertain. We found that the new Australian CVD risk calculator could overestimate risk category compared with the Steno Type 1 Risk Engine and underestimate risk category compared with the new Swedish/Scottish prediction tool, both of which were validated for T1DM. More research is needed to derive a CVD risk assessment pathway for individuals with T1DM in Australia.

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery.

Quantifying the mechanical properties of coronary arterial walls could provide meaningful information for the diagnosis, management, and treatment of coronary artery diseases. Since patient-specific coronary samples are not available for patients requiring continuous monitoring, direct experimental testing of vessel material properties becomes impossible. Current coronary models typically use material parameters from available literature, leading to significant mechanical stress/strain calculation errors. Here, we would introduce a finite element model-based updating approach (FEMBUA) to quantify patient-specific in vivo material properties of coronary arteries based on medical images. In vivo cine intravascular ultrasound (IVUS) and virtual histology (VH)-IVUS images of coronary arteries were acquired from a patient with coronary artery disease. Cine IVUS images showing the vascular movement over one cardiac cycle were segmented, and two IVUS frames with maximum and minimum lumen circumferences were selected to represent the coronary geometry under systolic and diastolic pressure conditions, respectively. VH-IVUS image was also segmented to obtain the vessel contours, and a layer thickness of 0.05 cm was added to the VH-IVUS contours to reconstruct the coronary geometry. A computational finite element model was created with an anisotropic Mooney-Rivlin material model used to describe the vessel's mechanical properties and pulsatile blood pressure conditions prescribed to the coronary luminal surface to make it contract and expand. Then, an iterative updating approach was employed to determine the material parameters of the anisotropic Mooney-Rivlin model by matching minimum and maximum lumen circumferences from the computational finite element model with those from cine IVUS images. This image-based finite element model-based updating approach could be successfully extended to determine the material properties of arterial walls in various vascular beds and holds the potential for risk assessment of cardiovascular diseases.

Disease Risk Score Derivation and Validation in Abu Dhabi, United Arab Emirates: A Retrospective Cohort Study.

Cardiovascular disease risk assessment is a key tool in primary prevention. The ADRS (Abu Dhabi Risk Study) is a retrospective cohort study aiming to develop 10-year risk prediction equations for coronary artery disease (CAD), stroke, and atherosclerotic cardiovascular disease (ASCVD), and validate international risk equations. The 8699 participants were examined in the Abu Dhabi cardiovascular screening program from 2011 to 2013 with a subsequent average follow-up of 9.2 years. They were assessed in 2023 for new CAD, admissions for acute coronary syndrome, or stroke. The validation cohort, 2554 subjects, is from the 2016 to 2017 Abu Dhabi community screening program, with 6.67 years average follow-up. Of 8504 ASCVD-free subjects, 250 experienced new CAD events. ASCVD risk factors in this population were age, sex, smoking, high cholesterol/high-density lipoprotein ratio, and diabetes diagnosis, in addition to low vitamin D level and low glomerular filtration rate. Three ADRS prediction models were derived using Cox regression. The ADRS-CAD had a C statistic of 0.899 (0.882-0.916) compared with 0.828 (0.803-0.852) for the Framingham Risk Score in the same sample. ADRS-stroke had a C statistic of 0.904 (0.865-0.944). The ADRS-ASCVD had a C statistic of 0.898 (0.883-0.913) compared with 0.891 (0.875-0.907) of pooled cohort equations and 0.825 (0.802-0.847) for Framingham Risk Score-cardiovascular disease. Applying our formulas to the validation cohort yielded C statistics of 0.825 (0.803-0.846), 0.799 (0.774-0.824), and 0.761 (0.71-0.813) for ASCVD, CAD, and stroke, respectively. The pooled cohort equations in this cohort had a C statistic for ASCVD of 0.824 (0.802-0.846). This study demonstrates the value of tailoring risk assessments to local populations and health care contexts.

The association between plasma levels of Sestrin2 and risk factors of cardiovascular diseases in healthy and diabetic adults: A study of Qatar Biobank data.

This study examines the association between serum Sestrin2 (SESN2) levels and cardiovascular disease (CVD) risk factors in healthy and diabetic adults, using data from the Qatar Biobank (QBB). A total of 844 participants were included, with 518 in the diabetic cohort and 326 in the healthy cohort. Clinical characteristics, cardiometabolic markers, and SESN2 levels were measured, and binomial logistic regression analyses were conducted to assess the associations between SESN2 and various health indices. Diabetic patients had significantly lower SESN2 levels compared to healthy controls (5.49 ± 5.94 vs 8.25 ± 7.57 ng/mL, P < 0.001). A significant negative correlation was observed between SESN2 and HbA1c (-0.19, P = 0.0006), insulin (-0.19, P = 0.0006), HOMA-IR (-0.17, P = 0.0024), C-peptide (-0.18, P = 0.0012), triglycerides (TG)/HDL ratio (-0.12, P = 0.0283), and the pulsatility index (PI) (-0.15, P = 0.006). In healthy individuals, higher SESN2 levels were associated with lower odds of elevated HbA1c (adjusted odds ratio [AOR] = 0.33, P = 0.00), insulin (AOR = 0.23, P = 0.00), HOMA-IR (AOR = 0.58, P = 0.06), C-peptide (AOR = 0.56, P = 0.04), and TG (AOR = 0.37, P = 0.03). In contrast, diabetic patients showed a positive correlation between SESN2 and insulin (0.15, P = 0.0005), HOMA-IR (0.11, P = 0.0106), and C-peptide (0.12, P = 0.0048). Participants in the highest SESN2 tertile had increased risks for high BMI (AOR = 1.96, P = 0.05), high TG (AOR = 1.57, P = 0.04), high NT-proBNP (AOR = 7.27, P = 0.01), and high fibrinogen (AOR = 1.92, P = 0.03). These findings suggest that while high SESN2 levels are cardioprotective in healthy individuals, they may indicate higher cellular stress in diabetics. Determining optimal SESN2 levels could help assess CVD risk, particularly in diabetic patients.

The interaction between triglyceride-glucose index and visceral adiposity in cardiovascular disease risk: findings from a nationwide Chinese cohort

BackgroundGlobally, cardiovascular disease (CVD) constitutes the primary cause of death, with insulin resistance (IR), measured by the triglyceride-glucose (TyG) index, and visceral obesity, reflected by the Chinese Visceral Adiposity Index (CVAI), as key contributors. However, the relationship between the TyG index and CVAI regarding CVD risk remains insufficiently understood. This research investigates the interactive impact of the TyG index and CVAI on the risk of cardiovascular disease.MethodsWe analyzed data from 8,358 participants from the China Health and Retirement Longitudinal Study (CHARLS) over a 9-year follow-up period. Participants were classified into four groups based on median TyG index (8.59) and CVAI values (101.26), and baseline characteristics were summarized. Missing data were addressed using multiple imputation by chained equations (MICE). Cox proportional hazards models assessed associations between TyG index, CVAI, CVD, coronary heart disease (CHD), and stroke risks, with Kaplan-Meier analysis used for cumulative hazard. Interaction effects were evaluated using both multiplicative and additive measures. Subgroup analyses by age, gender, and clinical conditions were conducted to explore interaction effects across different populations. Sensitivity analyses re-tested models, excluding the covariates BMI and diabetes, using tertiles for classification, and re-evaluating imputed data.ResultsOver the 9-year follow-up, 1,240 participants (14.8%) developed CVD, including 896 cases of CHD and 475 strokes. Kaplan-Meier curves indicated that participants with low TyG index but high CVAI had the highest cumulative hazard of CVD. Cox regression showed that this group had the highest CVD risk (HR = 1.87, 95% CI: 1.57–2.24), followed by those with both high TyG index and high CVAI (HR = 1.75, 95% CI: 1.49–2.06). Interaction analysis revealed a negative interaction effect between high TyG and high CVAI on CVD and CHD risks, with no significant effect on stroke. Subgroup and sensitivity analyses further confirmed these findings, showing consistent results across demographic groups and under various analytical conditions.ConclusionThis study suggests that the interaction between IR (TyG index) and visceral fat accumulation (CVAI) plays a complex role in CVD risk, with a potential antagonistic effect observed between high TyG and high CVAI on CVD events. These findings highlight the importance of considering both IR and visceral adiposity in CVD risk assessments to improve the identification of high-risk individuals.

Interplay between lipid profile and anthropometric measures as indicators of cardiometabolic risk in women with polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder that often coexists with cardiometabolic risk factors. Women with PCOS have a two-fold increased risk of developing type 2 diabetes and substantially elevated risk for cardiovascular disease (CVD) events later in life. PCOS patients may require more comprehensive metabolic screening to identify populations at higher risk of developing CVD and dyslipidemia. It is recommended to evaluate lipid profile, glucose tolerance and of women with PCOS every 2-3 years. Simple, short, and easy methods for the assessment of CVD risk in women with PCOS may be useful tools for implementing CVD prevention strategies by doctors or nutritionists. The aim of this study was to investigate the usefulness of anthropometric indices in the assessment of cardiometabolic risk based on lipid profile in patients with PCOS. The study involved 49 of Caucasian women aged 18-39 who were diagnosed with PCOS based on the Rotterdam criteria and divided into two groups with normal lipid profile (N=14) and dyslipidemia (N=35). Biochemical parameters were tested in the morning while fasting. Anthropometric parameters such as Body Mass Index (BMI), Body Adiposity Index (BAI), Waist-to-Hip Ratio (WHR), and Waist-to-Height Ratio (WHtR) were calculated, while the Percent of Body Fat was measured using a body analyzer. The study demonstrated that women with dyslipidemia were older than the control group, 33 years (27-37) vs 24 years (21-26), p<0.01. Neither BMI nor BAI (%) correlated with total cholesterol (p=0.63 and p=0.27). Other lipid parameters, such as serum HDL cholesterol (R=-0.68, p<0.01; R=-0.58, p<0.01), LDL cholesterol (R=0.34, p=0.02; R=0.37, p=0.01), non-HDL cholesterol (R=0.40, p<0.01; R=0.42, p<0.01), and triglycerides (R=0.56, p<0.01; R=0.51, p<0.01) correlated with BMI and BAI (%). ROC analysis demonstrated a high predictive value for age in identifying dyslipidemia. ROC analysis demonstrated poor predictive value for BMI, BAI, WHR, WHtR in identifying dyslipidemia. Analysis of simple and rapid parameters used to assess body fat, such as BMI, BAI, WHR, and WHtR, has shown that they are poor predictors of dyslipidemia in women with PCOS. In young women with PCOS, age appears to be a more reliable predictor of dyslipidemia.