
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-24-01435
00091
10.1097/MD.0000000000039595
3
3400
Research Article
Observational Study
The causal relationship between triglycerides and myocardial infarction: A two-sample Mendelian randomization
Kong Lu RN 394796572@qq.com
ab
Yang Zhong-Bin BSMed yzb2591784066@gmail.com
c
Chen Xie-Hui MD xhchen66@126.com
ab
Quan Xiao-Qing MD quanxiaoqing@hotmail.com
ab
Liu Hong-Tao MD lht1376@163.com
d
https://orcid.org/0009-0005-3394-8070
Qiu Ai-Ping RN ab*
a Department of Geriatrics, Shenzhen Longhua District Central Hospital, Shenzhen, China
b Key Laboratory of Personalized Precision Treatment for Elderly Coronary Heart Disease, Longhua District, Shenzhen, China
c School of Stomatology, Hubei University of Medicine, Shiyan, China
d Department of Cardiology, Shenzhen Longhua District Central Hospital, Shenzhen, China.
* Correspondence: Ai-Ping Qiu, Department of Geriatrics, Shenzhen Longhua District Central Hospital, Shenzhen 518110, China (e-mail: 1213362410@qq.com).
13 9 2024
13 9 2024
103 37 e3959510 2 2024
27 6 2024
09 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

The causal relationship between triglycerides and myocardial infarction (MI) was investigated using Mendelian randomization (MR) studies. Triglycerides were the exposure factor, and MI served as the outcome variable. Inverse variance weighting was used as the main analysis method, MR-Egger, and weight median as other analysis methods for MR analysis. In addition, heterogeneity test, level multivariate analysis, and sensitivity analysis were carried out. Inverse variance weighting results showed that the increase in triglyceride level affected the incidence of MI (OR = 1.287; 95% CI = 1.185–1.398; P = 1.988 × 10−9). Consistently, the results from all 3 methods indicated a statistically significant increase in the risk of MI with higher triglyceride levels (P < .05). The results showed that patients with high triglyceride levels had a higher incidence of MI, suggesting that MI should be prevented in the high triglyceride population.

Mendelian randomization
myocardial infarction
triglycerides
This work is supported by Shenzhen Science and Technology ProgramJCYJ20230807151309019 Xiao-Qing QuanInstituto de Ciencia y TecnologÃa del Distrito Federal 10.13039/100007774 10162A20220810B2051AC Xie-Hui ChenNanjing Military Region Medical Scientific and Technical Innovation Foundation Projects of Peopleâ€™s Liberation Army of China 10.13039/501100014218 2022035 Xie-Hui ChenOPEN-ACCESSTRUE
SDCT
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pmc1. Introduction

Cardiovascular disease (CVD) stands as the foremost contributor to illness and death among the global population. As per the World Health Organization’s data, CVD claims the lives of approximately 17.8 million individuals each year, constituting around 30% of the total global mortality rate.[1,2] Myocardial infarction (MI), commonly known as a heart attack, is one of the most serious CVDs due to its high rates of incidence, mortality, and disability.[3] MI is caused by an atherosclerotic plaque rupture that leads to partial or complete thrombotic vessel occlusion.[4] Therefore, exploring MI risk factors for primary prevention should be a major public health priority.

Several observational studies have suggested that elevated triglyceride levels are associated with an increased risk of MI.[5,6] However, these studies are subject to potential confounding factors and reverse causality, which pose challenges in establishing a causal relationship. Mendelian randomization (MR) is a method that can support resolving these limitations. In MR, systematic biases are minimized by using genetic variants associated with the exposure as instrumental variables (IVs), similar to how alleles are randomly allocated at conception according to Mendel’s second law in randomized controlled trials.[7,8] This approach helps to distribute confounding factors across the population randomly. Given credit to the primarily increased genome-wide association studies (GWASs) in the past decade, other studies already used MR to establish causation between common CVDs and blood pressure, tumor necrosis factor levels, and other risk factors.[9,10]

To address the constraints of traditional observational studies and to determine if triglycerides play a causal role in MI, we employed a two-sample Mendelian randomization (TSMR) approach. We assessed the connections between single nucleotide polymorphisms (SNPs) linked to triglycerides and the risk of MI using data from 2 separate publicly accessible GWASs.

2. Materials and methods

2.1. Study overview

We chose triglycerides and MI as the research objects for this MR analysis and selected SNPs closely related to triglycerides and MI as IVs.

2.2. Data source

Triglycerides and MI data (sample sizes of 177,861 and 171,875 respectively) are from the Integrated Epidemiology Unit GWAS database (https://gwas.mrcieu.ac.uk).

2.3. Instrumental variable selection

We have set a unified filtering criterion for IVs. P < 5 × 10−8 is statistically significant. According to the hypothesis of the MR analysis, the selected instrument SNP should be closely related to the exposure. To test whether there is a weak IV bias, we subsequently calculated the F statistic using the following formula: F = [R2combined/(1 − R2combined)] × [(N − K − 1)/K], where N = GWAS sample size, K = number variants comprising the instrument. R2 was calculated using the following formula: R2 = [beta.exposure2]/[se.exposure2 × N + beta.exposure2]; R2 combined = SUM[R2], where beta.exposure = SNP exposure effect and se. exposure = standard error of SNP exposure effect. If the F statistic of the instrument exposure association is much >10, a weak instrument variable bias is unlikely.[11]

2.4. Mendelian randomization analyses

We employed the inverse variance weighted (IVW) method as the primary analysis to evaluate the causal effect between triglycerides and MI in our TSMR study.[12] IVW calculates the exposure–outcome effect corresponding to each SNP using the Wald ratio method, then performs a weighted linear regression with a forced intercept of zero. It achieved higher estimate accuracy and test power when IVs satisfied the 3 underlying assumptions.[13] To avoid the interference of unknown and unmeasurable confounders, we performed the MR-Egger and weighted median to test the robustness of our results.[14,15] A reliable MR-Egger assessment of the causal effect is possible if the strength of the genetic instrument does not correlate with the impact of the instrument on the outcome, known as the InSIDE (Instrument Strength Independent of Direct Effect) assumption.

2.5. Sensitivity analyses

We performed sensitivity analysis using 4 methods including leave-one-out analysis, MR-Egger intercept, and MR-PRESSO to estimate pleiotropy and heterogeneity. We used the leave-one-out test to check if any variables affect causal effect estimation.[12] We also evaluated horizontal pleiotropy by measuring the average multi-effect of IVs through the intercept term of MR-Egger.[14] The existence of multi-effect was also evaluated by MR multi-effect residual and outlier (MR-PRESSO) after excluding outliers and reevaluating effect estimation.[16] This study used R packages “TwoSampleMR” and “MR-PRESSO.” We conducted all statistical data analysis using R software version 4.2.3.[16,17]

3. Results

3.1. Selection of the tool variables

We included 278 SNPs explaining 0.1% (R2) of the triglycerides variation as IVs for triglycerides–MI causal estimations. The Table S1, Supplemental Digital Content, http://links.lww.com/MD/N514 contains the effect estimates of the associations between each SNP and both triglycerides and MI.

3.2. Causation and effect of exposure (triglycerides) for outcome (MI)

We assessed the causality of triglyceride exposure on MI outcome in this MR analysis (Fig. 1). The value of triglycerides is significantly and positively correlated with the incidence of MI.

Figure 1. Forest plot: results of Mendelian randomization of 3 causal analysis methods.

3.3. Mendelian randomization

Figure 1 shows the MR estimates of increased triglycerides with the risk of MI. Specifically, the results of the 3 methods consistently demonstrated that the risk of MI increased with the increment of triglycerides and achieved statistical significance. Using the IVW method, we discovered a causal relationship between triglycerides and MI risk. A 1-standard deviation genetically determined increase in triglycerides was causally associated with an additional 28.7% relative risk of MI (N = 278 SNPs; OR = 1.287; 95% CI = 1.185–1.398; P = 1.988 × 10−9). The estimates from MR-Egger and weighted median analyses were consistent with these results (Figs. 2 and 3).

Figure 2. Scatter plot of triglycerides and myocardial infarction.

Figure 3. Forest plot of triglycerides and myocardial infarction.

3.4. Sensitivity analyses

No single SNP was showing a significant impact on the MR estimation results based on leave-one-out analysis (Fig. 4). When a single SNP is used as IV, Figure 5 shows that the distribution of the effect of increased triglycerides on MI risk is nearly symmetrical.

Figure 4. Analysis of triglycerides and myocardial infarction by the leave-one-out method.

Figure 5. Funnel plot to assess the robustness. Scattering points represented the effect estimated using a single SNP as an instrumental variable.

4. Discussion

In this TSMR study, we found a positive causality between triglycerides and the risk of MI, showing an average of 29.7% increased risk of MI per 1-standard deviation increment of triglycerides. Our findings are in line with previous observational studies. The TSMR analysis had several notable advantages: (1) in contrast to conventional observational studies, the MR method allowed us to generate more dependable effect estimates by minimizing the influence of confounding factors and reverse causality; (2) the summary data included in the analysis were derived from individuals of European descent, which greatly diminished the impact of population stratification; (3) the identification and selection of IVs followed a rigorous procedure, thus minimizing bias stemming from inappropriate IVs. Considering the random distribution of genotypes related to triglycerides among the general population, along with the inherent stability of germline genotypes, the findings from these studies are expected to be less prone to confounding factors and reverse causation compared to observations made in observational studies.[18]

Genetic studies have identified specific gene variants associated with elevated triglyceride levels, which have been linked to an increased risk of CVD. For instance, mutations in the apolipoprotein A5 gene have been shown to cause hypertriglyceridemia, ultimately leading to a higher likelihood of experiencing a MI.[19] The precise pathophysiological mechanisms that explain the impact of elevated triglyceride levels on MI remain incompletely understood. Previous research findings indicate that high triglyceride levels may contribute to this adverse outcome through various pathways, including atherosclerosis, platelet aggregation, and thrombosis, as well as inflammatory response.[20,21]

Elevated triglyceride levels are frequently associated with disturbances in lipid metabolism, characterized by increased levels of low-density lipoprotein cholesterol and decreased levels of high-density lipoprotein cholesterol.[22] This dysregulation in lipid metabolism can result in excessive accumulation of cholesterol and lipids within the arterial walls, leading to the formation of atherosclerotic plaques and sclerosis, which represents a major underlying cause of MI. These plaques restrict blood flow through the arteries, ultimately resulting in myocardial ischemia and infarction.[23] Furthermore, high triglyceride levels may heighten the risk of platelet aggregation and thrombosis by influencing platelet activation, aggregation, coagulation dysfunction, and endothelial function, thereby augmenting the susceptibility to CVDs, including MI.[18,24] Moreover, elevated triglyceride levels may stimulate adipocytes to release inflammatory mediators such as tumor necrosis factor-alpha, interleukin-6, and C-reactive protein, triggering an inflammatory response that can destabilize coronary plaques and ultimately culminate in MI.[24]

Studying how triglycerides affect heart attacks could help develop targeted prevention methods. Healthcare providers may stress managing triglycerides with lifestyle changes, diet adjustments, and medications to lower heart attack risk. Public health efforts might raise awareness, offer screenings, and promote better lipid profiles in communities. By integrating these findings into heart attack prevention strategies, professionals and policymakers aim to lessen heart attack burden and enhance heart health outcomes more widely.

While MR has been successful in mitigating the impact of numerous confounding variables, its scope is constrained by the inability to address nongenetic confounders. Environmental and lifestyle elements linked to triglyceride levels and the risk of MI could potentially disrupt the outcomes. The influence of these nongenetic factors might introduce ambiguity in the results, necessitating their consideration in data analysis and interpretation. Subsequent research endeavors should holistically examine the interplay among genetic, environmental, and lifestyle factors to attain a more nuanced comprehension of the correlation between triglycerides and MI.

Our study also had a few limitations: (1) due to the absence of individual information in the secondary data, we were unable to conduct a stratified analysis based on gender and age. (2) The MR method assumes a linear relationship between exposure and outcome effects, which prevented us from assessing the potential nonlinear association between triglycerides and MI risk. (3) The samples being predominantly of European ancestry limited the generalizability of our findings to other populations. (4) This research has initially explored the causal link between triglycerides and MI, yet the investigation did not extend to examining the association between other blood lipids (like cholesterol, low-density lipoprotein cholesterol, etc) and MI. Consequently, we anticipate forthcoming studies that comprehensively investigate the potential connections among various lipid types and the onset of MI to enhance our insights into the role of lipid metabolism in CVD progression.

5. Conclusion

In conclusion, this study employed a two-sample MR analysis to examine and investigate the genetic data. The findings indicated that individuals with elevated triglyceride levels had a greater likelihood of experiencing MI, highlighting the importance of preventive measures for MI in populations with high triglyceride levels.

Author contributions

Conceptualization: Lu Kong, Zhong-Bin Yang.

Data curation: Lu Kong, Ai-Ping Qiu.

Formal analysis: Lu Kong, Zhong-Bin Yang.

Funding acquisition: Xie-Hui Chen, Xiao-Qing Quan.

Methodology: Zhong-Bin Yang, Ai-Ping Qiu.

Project administration: Xiao-Qing Quan.

Resources: Lu Kong, Xiao-Qing Quan.

Software: Lu Kong, Xie-Hui Chen.

Supervision: Hong-Tao Liu, Ai-Ping Qiu.

Validation: Xie-Hui Chen, Hong-Tao Liu.

Visualization: Xie-Hui Chen, Hong-Tao Liu.

Writing – original draft: Lu Kong, Zhong-Bin Yang.

Writing – review & editing: Zhong-Bin Yang, Ai-Ping Qiu.

Supplementary Material

Abbreviations:

CVD cardiovascular disease

GWAS genome-wide association study

IVs instrumental variables

IVW inverse variance weighting

MI myocardial infarction

MR Mendelian randomization

SNP single nucleotide polymorphism

TSMR two-sample Mendelian randomization

LK, Z-BY, and X-QQ contributed equally to this work.

This work is supported by the Shenzhen Science and Technology Program (JCYJ20230807151309019), Shenzhen Longhua District Foundation of Science and Technology (10162A20230325A5A1507), the Key Laboratory of Personalized Precision Treatment for Elderly Coronary Heart Disease, Longhua District, Shenzhen (Shen Long Hua Ke Chuang Ke Ji Zi (2024) No. 2).

Due to the nonexperimental nature of the research, the study protocol did not need to be submitted for consideration and approval to an ethical review committee.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

Supplemental Digital Content is available for this article.

How to cite this article: Kong L, Yang Z-B, Chen X-H, Quan X-Q, Liu H-T, Qiu A-P. The causal relationship between triglycerides and myocardial infarction: A two-sample Mendelian randomization. Medicine 2024;103:37(e39595).
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