European journal of preventive cardiology最新文献

Aim: There is no consensus on the optimal time horizon for predicting cardiovascular disease (CVD) risk to inform treatment decisions. New Zealand and Australia recommend 5 years, whereas most countries recommend 10 years. We compared predicted risk and treatment-eligible groups using 5-year and 10-year equations.

Methods: Individual-level linked administrative datasets identified 1,746,665 New Zealanders without CVD, aged 30-74 years in 2006, with follow-up to 2018. Participants were randomly allocated to derivation and validation cohorts. Sex-specific 5-year and 10-year risk prediction models were developed in the derivation cohort and applied in the validation cohort.

Results: 28,116 (3.2%) and 62,027 (7.1%) first CVD events occurred during 5-years and 10-years follow-up respectively (cumulative risk, derivation cohort). Median predicted 10-year CVD risk (3.8%) was approximately 2.5 times 5-year risk (1.6%) and 95% of individuals in the top quintile of 5-year risk were also in the top quintile of 10-year risk, across age/gender groups (validation cohort). Using common guideline-recommended treatment thresholds (5% 5-year, 10% 10-year risk), approximately 14% and 28% of women and men respectively were identified as treatment-eligible applying 5-year equations compared to 17% and 32% of women and men applying 10-year equations. Older age was the major contributor to treatment eligibility in both sexes.

Conclusions: Predicted 10-year CVD risk was approximately 2.5 times 5-year risk. Both equations identified mostly the same individuals in the highest risk quintile. Conversely, commonly used treatment thresholds identified more treatment-eligible individuals using 10-year equations and both equations identified approximately twice as many treatment-eligible men as women. The treatment threshold, rather than the risk horizon, is the main determinant of treatment eligibility.

Aims: Low cholesterol efflux capacity and elevated levels of Interleukin-1ß (IL-1ß) are both associated with residual cardiovascular risk in patients with acute myocardial infarction (MI) and may be used as new biomarkers to identify patients at higher cardiovascular risk.

Methods: We evaluated potential synergetic effect of cholesterol efflux capacity and IL-1ß on recurrent major adverse cardiovascular events (MACE) at one-year in 2012 patients with acute ST- segment elevation MI who underwent primary percutaneous coronary intervention. In addition, we evaluated the contribution to residual risk of HDL biological functions from 20 patients of the two extreme subgroups, focusing on cholesterol efflux capacity and anti-inflammatory properties.

Results: Patients with MACE during the first year after the MI had significantly lower serum cholesterol efflux capacity as compared to those without recurrent events and higher level of IL-1ß, both associations were confirmed after multivariate analysis. We found an inverse relationship between CEC and circulating levels of the inflammatory markers IL-1ß, defining a very high risk (Low CEC/High IL-1ß) and a low risk (High CEC/Low IL-1ß) group of patients. Patients combining Low CEC/High IL-1ß exhibited the highest risk of recurrent MACE at one year showing an additive prognostic value of these biomarkers, regardless of all the other clinical or biological factors. In this very high-risk subgroup, patients exhibited reduced HDL-efflux capacity and defective ABCA1 and SR-BI with enhanced pro-inflammatory activity as a potential explanation for our clinical findings.

Conclusion: Impaired cholesterol efflux capacity and elevated IL-1β synergistically increase the residual cardiovascular risk in MI patients, which could be explained by reduced HDL-efflux capacity and enhanced HDL pro-inflammatory activity.

Aims: To investigate the genetic correlations and potential causal relationships between obstructive sleep apnea (OSA) and various cardiovascular diseases (CVDs), aiming to enhance understanding of shared genetic mechanisms and improve recognition and treatment of OSA in patients with CVDs.

Methods: Utilizing genome-wide association study (GWAS) data, we analyzed shared genetics between OSA and CVDs using linkage disequilibrium score regression (LDSC), multi-trait analysis of GWAS (MTAG), and genotype-tissue expression analysis (GTEx TSEA). We further investigated causal relationships using Bayesian colocalization tests, bidirectional Mendelian randomization (MR), and latent causal variable (LCV) analysis.

Results: We found strong associations between OSA and multiple CVDs: coronary artery disease (CAD), heart failure (HF), myocardial infarction (MI), stroke, and atrial fibrillation (AF). Novel SNPs related to CVDs were identified during single-trait MTAG analysis. By applying cross-trait MTAG, we identified 15 shared loci between OSA and CAD, 25 shared loci between OSA and MI, and 7 shared loci between OSA and HF. Shared genes are primarily expressed in the blood, heart, kidney, liver, muscle, and pancreas. MR analysis indicated a significant causal effect of OSA on HF and AF as a causal factor for OSA. LCV analysis suggested that AF was causally associated with OSA, while HF showed partial causality.

Conclusions: Our study suggests strong genetic correlations between OSA and several CVDs. Further research is needed on the associations between OSA and CVDs, as well as the mechanisms of the identified loci.

Aims: Cardiovascular disease (CVD) remains a significant public health concern, influenced by both genetic susceptibility and lifestyle factors. Integrating genetic risk information into clinical practice shows promise but has yielded mixed results regarding its impact on CVD prevention and management. This systematic review aimed to assess the impact of providing genetic CVD risk information on health behaviours, psychological outcomes, and risk factors.

Methods: Following JBI methodology and PRISMA 2020 guidelines, four electronic databases and two trial registries were searched for randomised controlled trials evaluating the impact of genetic risk information on the CVD risk profile. Data was synthesised using a narrative synthesis approach.

Results: Of the 3,596 articles retrieved, 11 studies were eligible. Genetic risk information showed modest improvements in dietary behaviour but had inconclusive effects on physical activity and medication adherence. Minimal changes in psychological outcomes were noted, including a slight decrease in depression. The impact on traditional risk factors, such as systolic blood pressure and total cholesterol was also limited. Bias across all studies was noted.

Conclusions: Genetic CVD risk information has limited effects on clinical outcomes and psychological factors, despite its potential to encourage some health behaviour changes. These findings suggest that genetic risk information alone may not be sufficient to significantly reduce cardiovascular risk, highlighting the need for further research to better understand its long-term effects.