Myocardial infarction in the Wisconsin Longitudinal Study: the interaction among environmental, health, social, behavioural and genetic factors

Association of Metabolic Diseases and Moderate Fat Intake with Myocardial Infarction Risk

BACKGROUND

Myocardial infarction (MI) can range from mild to severe cardiovascular events and typically develops through complex interactions between genetic and lifestyle factors.

OBJECTIVES

We aimed to understand the genetic predisposition associated with MI through genetic correlation, colocalization analysis, and cells' gene expression values to develop more effective prevention and treatment strategies to reduce its burden.

METHODS

A polygenic risk score (PRS) was employed to estimate the genetic risk for MI and to analyze the dietary interactions with PRS that affect MI risk in adults over 45 years (n = 58,701). Genetic correlation (rg) between MI and metabolic syndrome-related traits was estimated with linkage disequilibrium score regression. Single-cell RNA sequencing (scRNA-seq) analysis was performed to investigate cellular heterogeneity in MI-associated genes.

RESULTS

Ten significant genetic variants associated with MI risk were related to cardiac, immune, and brain functions. A high PRS was associated with a threefold increase in MI risk (OR: 3.074, 95% CI: 2.354-4.014, p < 0.001). This increased the risk of MI plus obesity, hyperglycemia, dyslipidemia, and hypertension by about twofold after adjusting for MI-related covariates (p < 0.001). The PRS interacted with moderate fat intake (>15 energy percent), alcohol consumption (<30 g/day), and non-smoking, reducing MI risk in participants with a high PRS. MI was negatively correlated with the consumption of olive oil, sesame oil, and perilla oil used for cooking (rg = -0.364). MI risk was associated with storkhead box 1 (STOX1) and vacuolar protein sorting-associated protein 26A (VPS26A) in atrial and ventricular cardiomyocytes and fibroblasts.

CONCLUSIONS

This study identified novel genetic variants and gene expression patterns associated with MI risk, influenced by their interaction with fat and alcohol intake, and smoking status. Our findings provide insights for developing personalized prevention and treatment strategies targeting this complex clinical presentation of MI.

Modelling of longitudinal data to predict cardiovascular disease risk: a methodological review

OBJECTIVE

The identification of methodology for modelling cardiovascular disease (CVD) risk using longitudinal data and risk factor trajectories.

METHODS

We screened MEDLINE-Ovid from inception until 3 June 2020. MeSH and text search terms covered three areas: data type, modelling type and disease area including search terms such as "longitudinal", "trajector*" and "cardiovasc*" respectively. Studies were filtered to meet the following inclusion criteria: longitudinal individual patient data in adult patients with ≥3 time-points and a CVD or mortality outcome. Studies were screened and analyzed by one author. Any queries were discussed with the other authors. Comparisons were made between the methods identified looking at assumptions, flexibility and software availability.

RESULTS

From the initial 2601 studies returned by the searches 80 studies were included. Four statistical approaches were identified for modelling the longitudinal data: 3 (4%) studies compared time points with simple statistical tests, 40 (50%) used single-stage approaches, such as including single time points or summary measures in survival models, 29 (36%) used two-stage approaches including an estimated longitudinal parameter in survival models, and 8 (10%) used joint models which modelled the longitudinal and survival data together. The proportion of CVD risk prediction models created using longitudinal data using two-stage and joint models increased over time.

CONCLUSIONS

Single stage models are still heavily utilized by many CVD risk prediction studies for modelling longitudinal data. Future studies should fully utilize available longitudinal data when analyzing CVD risk by employing two-stage and joint approaches which can often better utilize the available data.

In-Depth Analysis of Genetic Variation Associated with Severe West Nile Viral Disease

West Nile virus (WNV) is a mosquito-borne virus which causes symptomatic disease in a minority of infected humans. To identify novel genetic variants associated with severe disease, we utilized data from an existing case-control study of WNV and included population controls for an expanded analysis. We conducted imputation and gene-gene interaction analysis in the largest and most comprehensive genetic study conducted to date for West Nile neuroinvasive disease (WNND). Within the imputed West Nile virus dataset (severe cases n = 381 and asymptomatic/mild controls = 441), we found novel loci within the MCF.2 Cell Line Derived Transforming Sequence Like (MCF2L) gene (rs9549655 and rs2297192) through the individual loci analyses, although none reached statistical significance. Incorporating population controls from the Wisconsin Longitudinal Study on Aging (n = 9012) did not identify additional novel variants, a possible reflection of the cohort's inclusion of individuals who could develop mild or severe WNV disease upon infection. Many of the top gene-gene interaction results were intergenic, with currently undefined biological roles, highlighting the need for further investigation into these regions and other identified gene targets in severe WNND. Further studies including larger sample sizes and more diverse populations reflective of those at risk are needed to fully understand the genetic architecture of severe WNDD and provide guidance on viable targets for therapeutic and vaccine development.

Relative risks and odds ratios: Simple rules on when and how to use them

AIM

Relative risks and odds ratios are widely reported in the medical literature, but can be very difficult to understand. We sought to further clarify these important indices.

METHODS

We illustrated both relative risks and odds ratios using bar charts, then looked at the types of study for which each statistic is suited. We demonstrated calculation of relative risks and odds ratios through analysis of tabled data from a recent published longitudinal study, using a 2 × 2 table and R, the open-source statistical programming language. Simple rules for when and how to use relative risks and odds ratios are presented.

CONCLUSION

Understanding the difference between relative risks and odds ratios and when and how to use them may aid clinical interpretation, dissemination and translation of research findings.

Prior exercise training and experimental myocardial infarction: A systematic review and meta-analysis

Exercising prior to experimental infarction may have beneficial effects on the heart. The objective of this study was to analyze studies on animals that had exercised prior to myocardial infarction and to examine any benefits through a systematic review and meta-analysis. The databases MEDLINE, Google Scholar, and Cochrane were consulted. We analyzed articles published between January 1978 and November 2018. From a total of 858 articles, 13 manuscripts were selected in this review. When animals exercised before experimental infarction, there was a reduction in mortality, a reduction in infarct size, improvements in cardiac function, and a better molecular balance between genes and proteins that exhibit cardiac protective effects. Analyzing heart weight/body weight, we observed the following results - Mean difference 95% CI - -0.02 [-0.61,0.57]. Meta-analysis of the infarct size (% of the left ventricle) revealed a statistically significant decrease in the size of the infarction in animals that exercised before myocardial infarction, in comparison with the sedentary animals -5.05 [-7.68, -2.40]. Analysis of the ejection fraction, measured by echo (%), revealed that animals that exercised before myocardial infarction exhibited higher and statistically significant measures, compared with sedentary animals 8.77 [3.87,13.66]. We conclude that exercise performed prior to experimental myocardial infarction confers cardiac benefits to animals.

Are Cardiovascular Risk Factors Stronger Predictors of Incident Cardiovascular Disease in U.S. Adults With Versus Without a History of Clinical Depression?

Background

Several mechanisms underlying the depression-to-cardiovascular disease (CVD) relationship have been proposed; however, few studies have examined whether depression promotes CVD through potentiating traditional cardiovascular risk factors.

Purpose

To test the combined influence of three cardiovascular risk factors and lifetime depressive disorder on incident CVD in a large, diverse, and nationally representative sample of U.S. adults.

Methods

Respondents were 26,840 adults without baseline CVD who participated in Waves 1 (2001-2002) and 2 (2004-2005) of the National Epidemiologic Survey on Alcohol and Related Conditions. Lifetime depressive disorder, tobacco use, hypertension, and incident CVD were determined from structured interviews, and body mass index (BMI) was computed from self-reported height and weight.

Results

Logistic regression models predicting incident CVD (1,046 cases) revealed evidence of moderation, as the interactions between lifetime depressive disorder and current tobacco use (p = .002), hypertension (p < .001), and BMI (p = .031) were significant. The Former Tobacco Use × Lifetime Depressive Disorder interaction was not significant (p = .85). In models stratified by lifetime depressive disorder, current tobacco use (OR = 1.78, 95% CI = 1.36-2.32, p < .001 vs. OR = 1.41, 95% CI = 1.24-1.60, p < .001), hypertension (OR = 2.46, 95% CI = 1.98-3.07, p < .001 vs. OR = 1.39, 95% CI = 1.28-1.51, p < .001), and BMI (OR = 1.10, 95% CI = 1.01-1.20, p = .031 vs. OR = 1.03, 95% CI = 0.99-1.07, p = .16) were stronger predictors of incident CVD in adults with versus without a lifetime depressive disorder.

Conclusions

Our findings suggest that amplifying the atherogenic effects of traditional cardiovascular risk factors may be yet another candidate mechanism that helps to explain the excess CVD risk of people with depression.