Low Cardiovascular Disease Risk Groups Research Articles

OA40 Predicting cardiovascular disease risk in children and adolescents with juvenile-onset systemic lupus erythematosus using the APPLE (Atherosclerosis Prevention in Paediatric Lupus Erythematosus) clinical trial cohort

Abstract Background/Aims The risk of developing cardiovascular disease (CVD) through atherosclerosis in juvenile-onset systemic lupus erythematosus (JSLE) patients is significantly increased. This study aimed to stratify and characterise JSLE patients at elevated CVD risk using patient/disease-related factors and metabolomic data from patients recruited to the APPLE (Atherosclerosis Prevention in Paediatric Lupus Erythematosus) clinical trial, designed to assess atherosclerosis development. Methods Unsupervised hierarchical clustering was performed to stratify patients by arterial intima-media thickness (IMT) measurements at baseline (N = 151) and carotid (c)IMT progression over 36 months (placebo arm only, N = 60). Baseline metabolomic profiles (∼250 serum metabolites) were compared between clusters using conventional statistics, univariate logistic regression, sparse Partial Least-Squares Discriminant Analysis (sPLS-DA) and random forest classifier. An independent cohort (UCL-JSLE cohort, N = 89) with matching metabolomics, immunophenotyping and proteomics, was used to validate the discovered CVD risk-related signatures from the APPLE cohort. Results Baseline IMT stratification identified 3 clusters with high, intermediate, and low baseline IMT measurements and progression trajectories over 36 months, each having distinct racial/BMI/household education/income characteristics. Analysis of cIMT progression over 36 months identified 2 patient groups with high and low IMT progression. Unique metabolomic profiles differentiated high and low cIMT progression groups, with a good discriminatory ability (0.81 AUC in ROC analysis) using the top 6 metabolites (total cholesterol esters, total cholesterol, phospholipids in small LDL particles, total cholesterol in small LDL particles, free cholesterol in medium LDL particles, and total lipids in small LDL particles) selected from the analysis. cIMT progression over 36 months in the placebo group correlated positively with baseline disease activity (SLEDAI), damage score (SLICC), white blood cell count, serum complement C3, blood pressure (both systolic and diastolic) and BMI. Metabolomics signatures discovered from the APPLE cohort were applied to stratify JSLE patients in the validation cohort (UCL-JSLE), where 3 groups were identified with distinct metabolomics profiles indicating JSLE patients with high risk (N = 20), intermediate risk (N = 43) and low risk (N = 26) CVD-risk. Significant differences were observed in the frequency of classical monocytes (p = 0.015) and nonclassical monocytes (p = 0.005) when comparing the high and low CVD risk groups in the UCL-JSLE cohort. Conclusion Complex analysis of IMT patterns and progression in the APPLE trial cohort identified novel key determinants that could guide further research for CVD-risk stratification in JSLE. Disclosure J. Peng: None. G.A. Robinson: None. S.P. Ardoin: None. L.E. Schanberg: None. O. Carra: None. E. Jury: None. C. Ciurtin: None.

Fatness Predicts Cardiovascular Disease Risk Factor Profile Better than Fitness in Healthy Men A Discriminant Analysis Approach

Fitness is healthy and can help to reduce, but not eliminate, the negative effects of obesity. As a result,identifying variables for categorizing individuals into high-risk or low-risk groups is critical.The study’s goalwasto determine the relationship between fitness, fatness, and cardiovascular disease risk factors as well as to construct a discriminant model for categorizing individuals as high-risk or low-risk. A total of 120 in-service healthy armed forces personnel aged 25 to 49 years were randomly selected as subjects for this study and were measured for the selected fitness and fatness variables, namely cardio respiratory endurance, muscular endurance, muscular leg and back strength, flexibility, weight, waist circumference, hip circumference, waist-hip ratio, body mass index, waist to height ratio, and fat percentage. The findings revealed that fitness variables contribute little to CVD risk factors than specific fatness characteristics, which appear to play a larger role.Based on selected fitness and fatness indicators, a discriminant model was developed to classify subjects into high and low cardiovascular disease risk groups. The group centroid was found to be 0.829. The model validity was determined to be 80.6 per cent based on the classification matrix. Finally, the findings of the study suggest that age and waist circumference play an important role in distinguishing individuals with high and low CVD risk

Effect Modification of Hyperuricemia, Cardiovascular Risk, and Age on Chronic Kidney Disease in China: A Cross-Sectional Study Based on the China Health and Nutrition Survey Cohort.

IntroductionThe question of whether the increased burden of chronic kidney disease (CKD) is caused by the interaction of hyperuricemia and cardiovascular disease (CVD) risk factors or is accelerated by aging remains unresolved. The purpose of this study is to better understand the effect modification of hyperuricemia, cardiovascular risk, and age on CKD among the Chinese population.MethodsThis cross-sectional study of 8243 participants was derived from the China Health and Nutrition Survey (CHNS) in 2009. Inclusion criteria included age ≥18 years, non-pregnancy, and no history of high-protein diet prior to blood test. Demographics, comorbidities, health-related behaviors, and serum biomarkers were collected. Interaction association of hyperuricemia, CVD risk and age with CKD were analyzed using Logistic regression.ResultsCKD was detected in 359 (27.2%, 95% CI 24.8∼29.7%) of 1321 participants with hyperuricemia and 680 (9.8%, 95% CI 9.1∼10.5%) of 6,922 participants without hyperuricemia, and these patterns remained significant after controlling for age, gender, and Framingham risk score (adjusted odds ratio [aOR] 3.82, 95% CI 3.20∼4.57). We found a negative multiplicative interaction between hyperuricemia and CVD risk on CKD. The aOR in low-CVD risk groups was 5.51 (95% CI 4.03∼7.52), followed by medium-CVD risk groups (aOR: 3.64, 95% CI 2.61∼5.09) and high-CVD risk groups (aOR: 2.89, 95% CI 2.12∼3.96). CVD risk was less associated with CKD in hyperuricemia group (aOR: 0.92, 95% CI 0.68∼1.22) than in non-hyperuricemia group (aOR: 1.43, 95% CI 1.21∼1.70). Furthermore, hyperuricemia and age had a significant additive effect on CKD, with a synergy index of 2.26 (95% CI 1.45∼3.52). Coexisting with older age and hyperuricemia, the likelihood of developing CKD was higher than the sum of the two alone.ConclusionThe link between hyperuricemia and CKD begins at a young age and becomes stronger in the low CVD risk group. For young adults, early detection of hyperuricemia, routine CVD risk assessment, and timely intervention of modifiable factors are warranted.

Determining propensity for sub-optimal low-density lipoprotein cholesterol response to statins and future risk of cardiovascular disease.

Variability in low-density lipoprotein cholesterol (LDL-C) response to statins is underappreciated. We characterised patients by their statin response (SR), baseline risk of cardiovascular disease (CVD) and 10-year CVD outcomes. A multivariable model was developed using 183,213 United Kingdom (UK) patients without CVD to predict probability of sub-optimal SR, defined by guidelines as <40% reduction in LDL-C. We externally validated the model in a Hong Kong (HK) cohort (n = 170,904). Patients were stratified into four groups by predicted SR and 10-year CVD risk score: [SR1] optimal SR & low risk; [SR2] sub-optimal SR & low risk; [SR3] optimal SR & high risk; [SR4] sub-optimal SR & high risk; and 10-year hazard ratios (HR) determined for first major adverse cardiovascular event (MACE). Our SR model included 12 characteristics, with an area under the curve of 0.70 (95% confidence interval [CI] 0.70-0.71; UK) and 0.68 (95% CI 0.67-0.68; HK). HRs for MACE in predicted sub-optimal SR with low CVD risk groups (SR2 to SR1) were 1.39 (95% CI 1.35-1.43, p<0.001; UK) and 1.14 (95% CI 1.11-1.17, p<0.001; HK). In both cohorts, patients with predicted sub-optimal SR with high CVD risk (SR4 to SR3) had elevated risk of MACE (UK HR 1.36, 95% CI 1.32-1.40, p<0.001: HK HR 1.25, 95% CI 1.21-1.28, p<0.001). Patients with sub-optimal response to statins experienced significantly more MACE, regardless of baseline CVD risk. To enhance cholesterol management for primary prevention, statin response should be considered alongside risk assessment.

The Association of Overt and Subclinical Hyperthyroidism with the Risk of Cardiovascular Events and Cardiovascular Mortality: Meta-Analysis and Systematic Review of Cohort Studies.

BackgroundWhether hyperthyroidism is an independent risk factor for cardiovascular events remains controversial. We aimed to evaluate the association of overt and subclinical hyperthyroidism with the risk of ischemic heart disease (IHD), stroke, heart failure, and cardiovascular mortality.MethodsStudies regarding the association between hyperthyroidism and cardiovascular events were searched on PubMed and Embase databases. The cardiovascular disease (CVD) risk was classified as high and low, based on pre-existing diseases, including history of coronary, cerebral, or peripheral artery disease; heart failure; atrial fibrillation; diabetes mellitus; or chronic kidney disease.ResultsThirty-seven cohort studies were included in this meta-analysis. The pooled hazard ratio for subjects with overt hyperthyroidism compared with the control group was 1.11 (95% confidence interval [CI], 1.03 to 1.19) for IHD, 1.35 (95% CI, 1.03 to 1.75) for stroke, and 1.20 (95% CI, 1.00 to 1.46) for cardiovascular mortality. For subjects with subclinical hyperthyroidism, the pooled hazard ratio was 1.24 (95% CI, 1.07 to 1.45) for IHD, when compared with the control group. Subgroup analysis by CVD risk showed that the risk of stroke in overt hyperthyroidism was increased in the low CVD risk group; however, these association was not observed in the high CVD risk group. Similarly, the risk of IHD in subjects with subclinical hyperthyroidism was significantly increased in the low CVD risk group.ConclusionOvert hyperthyroidism is associated with increased risk of IHD, stroke, and cardiovascular mortality, and subclinical hyperthyroidism is associated with increased risk of IHD. These associations were particularly observed in the low risk CVD group without underlying CVD.

The association between walking speed and risk of cardiovascular disease in middle-aged and elderly people in Taiwan, a community-based, cross-sectional study.

The aim of this study was to investigate the association between walking speed and cardiovascular disease (CVD) risk among community-dwelling middle-aged and elderly populations in Taiwan. This was a cross-sectional and community-based study with 400 participants aged 50 years and over recruited from a community health promotion project in 2014 in Guishan district, Taoyuan city. We excluded 91 people, and a total of 309 participants were eligible for analysis. The statistical methods used in this study were one-way ANOVA and the Chi-square test, Pearson's correlation test and logistic regression model. In total, 309 participants (98 males and 211 females) aged 50 to 74 (62.05 ± 6.21) years without a CVD history were enrolled in this study. The walking speed gradually decreased from the low CVD risk group to the high CVD risk group (p < 0.05). A significant inverse association between walking speed and CVD risk was confirmed with a Pearson's correlation coefficient of-0.143 (p < 0.05) in middle-aged people, but this significant inverse association was not shown in elderly people. The multivariate logistic regression model for predicting CVD risk and walking speed with an adjusted odds ratio (OR) was 0.127 (95% CI = 0.021-0.771) in middle-aged people with adjustment for sex, age, waist circumference (WC), hypertension (HTN), diabetes mellitus (DM), and hyperlipidemia (p < 0.05). Our study clearly shows that slow walking speed is associated with an increased risk of CVD in middle-aged people rather than in elderly people.

The risk of cardiovascular disease following breast cancer by Framingham risk score

ObjectivesThis study evaluates the risk of cardiovascular disease (CVD) following breast cancer, accounting for baseline CVD risk.MethodsWithin the EPIC-NL (Dutch part of the European Prospective Investigation into Nutrition and Cancer) cohort, 1103 women were diagnosed with breast cancer. For every breast cancer patient, 3–4 women without breast cancer (n = 4328) were selected matched for age, year, and time since cohort enrollment. Based on CVD risk factors at cohort enrollment, 10-year risk of CVD was calculated and categorized: low (< 10%), intermediate (10–20%), high (> 20%). Cox proportional hazard models assessed the risk of CVD events (hospitalization or mortality) and CVD mortality of women with versus without breast cancer, adjusted for baseline CVD risk.ResultsAfter median follow-up of 5 and 6 years, 92 (8.3%) and 325 (7.5%) CVD events occurred in women with and without breast cancer, respectively. In the low CVD risk group, women with breast cancer had 1.44 (95% CI 1.00–2.06) times higher risk of CVD events than women without breast cancer. In the intermediate and high CVD risk categories, risk of CVD events was similar in women with and without breast cancer. Overall, women with breast cancer had 1.77 (95% CI 1.10–2.86) times higher risk of CVD mortality than women without breast cancer.ConclusionsAmong women with low CVD risk, women with breast cancer have a higher risk of CVD event than women without breast cancer. Overall, women with breast cancer have a higher risk of CVD mortality than women without breast cancer.

Screening for cardiovascular risk using pulse wave velocity

Introduction: Pulse Wave Velocity (PWV) is commonly used as a marker of arterial stiffness. Aim: To evaluate the relationship between PWV and cardiovascular risk factors and assess the applicability of PWV measurement in screening for cardiovascular risk. Methods: Pulse wave, anthropometric and laboratory measurements were obtained from 341 subjects (171 females, 170 males, aged 58±13 years old). The Framingham general Cardiovascular Disease (CVD) score predictive of the 10-year risk of cardiovascular disease was used for stratification of subjects into high, moderate and low CVD risk groups. Results: Mean pulse wave velocity was higher in patients with high (10.1±2.2, ANOVA p<0.001) and moderate CVD risk (9.8±2.3, ANOVA p<0.01) versus the low risk group (8.6±1.9). Multivariate regression analysis showed positive association of pulse wave velocity with age (beta=0.04, p<0.001), mean systolic blood pressure (beta=0.02, p<0.01) and presence of hypertension (beta=0.7, p<0.05) (r squared=0.1913). Pulse wave velocity was not significantly associated with the Framingham general CVD risk score. Logistic regression analysis was performed and areas under the Receiver Operating Characteristic (ROC) curve of the association of PWV with high risk or moderate stratifications were estimated. The area under the curve was 0.613 (CI: 0.53, 0.69) in the high risk stratification and 0.5064 (CI: 0.42, 0.59) in the moderate stratification. Pulse wave velocity was not shown to influence high and moderate general CVD risk stratifications. Conclusions: PWV absolute levels are significantly higher in patients with predicted high and moderate CVD risk. However, PWV does not independently improve the accuracy of discriminating high from low risk patients.