The Relationship Between Physiological Risk Factors and Stroke: A Mendelian Randomization Study

Introduction: Physiological risk factors play a crucial role in stroke development and prognosis. However, traditional epidemiological research methods cannot clearly establish a causal relationship between the physiological risk factors and stroke. Mendelian Randomization (MR) has recently emerged as a method of studying causal relationships. This study used a two-sample MR to explore the causal relationship between physiological risk factors and stroke.

Methods: The four most commonly used MR methods were used in this analysis. All statistical analyses were performed using the "TwoSampleMR 0.5.7" package in R. The outcome variable was stroke (ischemic and hemorrhagic stroke), and the exposure factors were the five common physiological risk factors analyzed, including body mass index, fasting blood glucose, systolic blood pressure, LDL cholesterol, and renal dysfunction. The instrumental variables were genetic loci associated with ischemic or hemorrhagic stroke as derived from GWAS studies.

Results: The results showed that there was a significant causal association between body mass index and ischemic stroke (OR = 1.21, 95% CI: 1.14~1.30, P = 3.55×10^-5), systolic blood pressure (OR = 1.61.04, 95% CI: 1.50~1.74, P = 8.26 Ö10^-36), and cholesterol (OR = 1.71, 95% CI: 1.12~2.62, P = 1.26×10^-6) with significant causal associations. There was also a significant causal association between body mass index and hemorrhagic stroke (OR = 1.45, 95% CI: 1.23~1.71, P = 1.27 × 10^-5), and systolic blood pressure (OR = 1.65, 95% CI: 1.38~1.97, P = 2.84 × 10^-8).

Conclusion: Genetic variation-predicted physiological risk factor indicators, such as body mass index, systolic blood pressure, and cholesterol, were significantly associated with an increased risk of ischemic and hemorrhagic stroke and significantly increased the risk of stroke. Additionally, cholesterol is significantly associated with an increased risk of ischemic stroke but not with an increased risk of hemorrhagic stroke. Early detection and active management of body mass index, systolic blood pressure, and cholesterol are crucial for stroke prevention and prognosis and are important measures for preventing and controlling stroke. These risk factors can be effectively controlled by adjusting lifestyle and medical treatment, thereby reducing the risk of stroke.

In 2019, an estimated 12.2 million new cases of stroke occurred globally, resulting in 143 million disability-adjusted life years (DALYs) and 6.55 million deaths. Stroke has become the second leading cause of death worldwide, accounting for 11.6% of all deaths, and the third major cause of death and disability (DALYs), constituting 5.7% of the total global DALYs. Of all new stroke cases, 62.4% are ischemic strokes [1]. It is projected that by 2050, nearly 10 million people will die from stroke annually, with the majority of these deaths occurring in low- and middle-income countries [2]. With the acceleration of population aging and the increasing prevalence of unhealthy lifestyles, the incidence and mortality rates of stroke continue to rise, and the onset of the disease is trending towards younger ages. Stroke, commonly divided into ischemic and hemorrhagic strokes, is caused by a variety of risk factors, including physiological, behavioral, dietary, and environmental risks [3-4]. 

Physiological risk factors such as high systolic blood pressure, high body mass index, and high fasting blood glucose levels are closely associated with the risk of stroke [5-7]. Previous observational studies have suggested an association between these physiological risk factors and stroke, but due to the interference of confounding factors and reverse causality, the causal relationship is not yet clear. The exposure factors in this study are five common physiological risk factors, namely body mass index (BMI), systolic blood pressure, fasting blood glucose, cholesterol level, and renal dysfunction. These physiological risk factors have been suspected to be related to the occurrence and development of stroke in previous studies, but traditional research methods are difficult to determine their causal relationship [5-7]. Therefore, further exploring the potential causal relationship between them is of great significance. Understanding and recognizing these risk factors is crucial for prevention and early intervention. 

 Mendelian Randomization (MR) has recently emerged as a method for studying causal relationships [8-10]. Traditional observational epidemiological studies have many limitations when exploring the causes of diseases, such as the influence of potential confounding factors and the possibility of interference from reverse-causal relationships. Although randomized controlled trials have rigorous study designs, they can be difficult to implement. In recent years, MR design has used genetic variants as instrumental variables (IV) to study exposure factors. The design principle is based on Mendel's Law of Inheritance, where parental alleles are randomly allocated to offspring during inheritance and different genotypes of the offspring determine different intermediate phenotypes [11]. The association between genes and disease outcomes is not disrupted by postnatal environmental confounders and there is no concern for reverse causality; therefore, genes can serve as effective instrumental variables. Currently, owing to the wealth of research findings from Genome-Wide Association Studies (GWAS) [12-13], the two-sample MR approach has been widely used in the study of various diseases [8,13-14], providing a powerful tool for exploring the causal relationships of diseases.

In this study, we used a two-sample Mendelian Randomization (MR) approach to investigate the causal relationships between five common physiological risk factors and stroke (ischemic and hemorrhagic stroke). The Mendelian randomization (MR) method uses genetic variation as an instrumental variable (IV) to infer the causal relationship between exposure factors and outcomes. In this study, by selecting genetic variations related to physiological risk factors (such as single nucleotide polymorphisms (SNPs) related to BMI, blood pressure, etc. determined through large-scale genome-wide association studies (GWAS)), the causal effects of these physiological risk factors on stroke (including ischemic and hemorrhagic stroke) can be evaluated without interference from confounding factors and reverse causality. This study is expected to provide strong evidence for further analysis and validation of the causal relationship between these five physiological risk factors and ischemic stroke. This will help formulate effective prevention strategies to reduce the risk of ischemic stroke and improve the prognosis of patients with ischemic stroke.

Study Design

The basic principles of the Mendelian Randomization (MR) study design are as follows: (1) the instrumental variable is not associated with confounding factors; (2) the instrumental variable is associated with the exposure factor; and (3) the instrumental variable is not associated with the outcome variable, and the instrumental variable can only be associated with the outcome variable through the exposure factor. In this study, the outcome variable was stroke (ischemic and hemorrhagic stroke), and exposure factors were the five common physiological risk factors analyzed, including body mass index, fasting blood glucose, systolic blood pressure, LDL cholesterol, and impaired kidney function. The instrumental variables were the genetic loci associated with ischemic stroke/haemorrhagic stroke, which were derived from their respective GWAS studies (Figure 1). A two-sample MR design was used to explore the causal association between the physiological risk factors and stroke.

Figure 1: Design principles and schemes for MR study.

Data Sources

Exposure factors, which are the genetic loci of physiological risk factors, are mainly derived from European populations. The outcome variable, which was the GWAS results for stroke, was obtained from large-scale GWAS studies of each phenotype in recent years [15] (see Table 1).

Table 1: Data sources for MR study.

Data Processing

First, within the GWAS data, Single Nucleotide Polymorphisms (SNPs) that affect exposure factors were identified. Significant SNPs at the GWAS level (P < 5>

MR Analysis

The four most commonly used MR methods were employed for the analysis, including the classical Inverse Variance-Weighted (IVW) method, auxiliary MR-Egger method, Weighted Median method, and Weighted Mode.

To measure the reliability and stability of the MR analysis, various sensitivity analyses have been conducted, such as

Heterogeneity Test: This test examines whether there is heterogeneity among SNPs derived from different GWAS datasets.

Horizontal Pleiotropy Test: If a SNP is directly related to both the exposure factor and the outcome, horizontal pleiotropy exists, and the results of the MR analysis may not be credible.

Leave-one-out Sensitivity Test: This test calculated the MR results for the remaining SNPs after excluding each SNP individually. If the MR results estimated by the remaining SNPs differ significantly from the overall results after excluding a particular SNP, this indicates that the MR results are sensitive to that SNP.

Statistical Analysis

All statistical analyses were performed using the "TwoSampleMR 0.5.7" package in R. MR results are presented as Odds Ratios (OR) with 95% Confidence Intervals (95% CI) and p-values. Scatter plots and forest plots were used to visualize the MR results. Owing to multiple testing between the two samples, all analyses were adjusted using the Bonferroni method. A P-value of less than 0.007 (P = 0.05/2 outcome variables) was considered statistically significant. P-values between 0.007 and 0.05 are considered to have suggestive evidence and require further confirmation.

Causal Relationship between Physiological Risk Factors and Ischemic Stroke

A total of 436, 22, 405, 86, and 332 SNPs were screened as instrumental variables to assess the causal relationship between body mass index, fasting blood glucose, systolic blood pressure, high cholesterol, renal dysfunction, and ischemic stroke, respectively. The MR analysis results for the causal relationship between the physiological risk factors and ischemic stroke are shown in Figure 2, and the effects of each SNP on the relationship between the physiological risk factors and ischemic stroke are shown in Figure 3. The results show that in the classical MR analysis method IVW, there is a significant causal association between body mass index and ischemic stroke (OR = 1.21, 95% CI: 1.14~1.30, P = 3.55×10^-5), systolic blood pressure (OR = 1.61.04, 95% CI: 1.50~1.74, P = 8.26 Ö10^-36), and cholesterol (OR = 1.71, 95% CI: 1.12~2.62, P = 1.26×10^-6). Auxiliary MREgger, weighted median, and weighted modes also confirmed this result. However, there was no causal relationship between fasting blood glucose and renal dysfunction in ischemic stroke (P = 0.733 and P = 0.579, respectively), and the results of the MR-Egger, weighted median, and weighted modes were consistent with the IVW results. The sensitivity analysis results also showed no heterogeneity or horizontal pleiotropy in the above results, ensuring the reliability of the MR analysis results.

Figure 2: Forest Plot of MR Analysis Results for Physiological Risk Factors and Ischemic Stroke.

Figure 3: Scatter Plot of MR Analysis for Physiological Risk Factors and Ischemic Stroke.

Causal Relationship between Physiological Risk Factors and Hemorrhagic Stroke

A total of 436, 22, 406, 86, and 335 SNPs were screened as instrumental variables to assess the causal relationship between body mass index, fasting blood glucose, systolic blood pressure, high cholesterol, renal dysfunction, the five main physiological risk factors, and hemorrhagic stroke. The MR analysis results of the causal relationship between physiological risk factors and hemorrhagic stroke are shown in Figure 4, and the effects of each SNP on the relationship between systolic blood pressure and cardiovascular diseases are shown in Figure 5. The results indicate that in the classical MR analysis method IVW, there is a significant causal association between body mass index and hemorrhagic stroke (OR = 1.45, 95% CI: 1.23~1.71, P = 1.27 × 10^-5), as well as between systolic blood pressure (OR = 1.65, 95% CI:1.38~1.97, P = 2.84 × 10^-8). This result was confirmed by the auxiliary MR-Egger method, weighted median method, and weighted mode. Additionally, there was no causal relationship between fasting blood glucose and cholesterol levels, renal dysfunction, and hemorrhagic stroke (P > 0.05). The results of the MR-Egger method, weighted median method, and weighted mode also supported this finding.

Figure 4: Forest Plot of MR Analysis Results for Physiological Risk Factors and Hemorrhagic Stroke.

Figure 5: Scatter Plot of MR Analysis for Physiological Risk Factors and Hemorrhagic Stroke.

Over the past three decades, the total number of stroke-related DALYs attributed to risk factors has increased significantly (by 33.5 million, from 91.5 million in 1990 to 125 million in 2019). The substantial increase in the global burden of stroke may not only be due to population growth and aging but also due to the substantial increase in exposure to several important risk factors, such as high body mass index, environmental particulate pollution, high fasting plasma glucose, high systolic blood pressure, alcohol consumption, low physical activity, and renal dysfunction [3,16]. However, traditional epidemiological research methods cannot clearly establish a causal relationship between the physiological risk factors and stroke. The MR method compensates for the shortcomings of traditional observational studies. MR studies are generally more convenient and rapid, because they can be designed using large-scale GWAS data. Currently, they are widely used to assess causal relationships and identify drug targets [17-18]. This study used two-sample MR analysis and found that body mass index and systolic blood pressure were significantly associated with both hemorrhagic and ischemic strokes, increasing the risk of developing these diseases. Cholesterol is significantly associated with ischemic stroke and increases the risk of its development.

Regarding the Relationship between Body Mass Index (BMI) and Stroke

Several studies have provided insights into this relationship, and a study of residents in Jiangxi Province, China found that different obesity indicators, including BMI, were positively correlated with stroke risk. This suggests that an increase in obesity indicators may increase the risk of stroke [19]. Another study explored the relationship between body mass index (BMI) and carotid plaque in a high-risk stroke population. This study indicated that for high-risk stroke populations, there is a correlation between BMI and carotid plaque [20]. As the core driving force of atherosclerosis, LDL-C is the main treatment target for primary and secondary prevention in all the guidelines. According to a study published in " neurology, obesity is an independent risk factor for multiple diseases including stroke. This study specifically pointed out that the incidence of hemorrhagic stroke was higher in underweight patients, indicating that BMI may have different effects on the risk of hemorrhagic and ischemic strokes. The "obesity paradox" mentioned in the study suggests that compared to normal weight and underweight patients, overweight and obese patients may benefit in terms of survival and overall prognosis [21]. Considering these studies, it is evident that there is an association between BMI and stroke risk; however, this relationship may vary depending on the type of stroke (ischemic or hemorrhagic). Additionally, other potential factors, such as age, race/ethnicity, and socioeconomic status, should also be considered as they may also influence stroke risk and prognosis.

Concerning the Relationship between Systolic Blood Pressure (SBP) and Stroke

Studies have indicated that hypertension is a significant risk factor for strokes. Approximately 50% of the stroke cases are associated with hypertension. For every 10 mmHg increase in systolic blood pressure, the relative risk of stroke increased by 49%. This suggests that the higher the blood pressure, the greater the risk of stroke [22]. The management of blood pressure in patients with acute cerebral infarction and hemorrhage is controversial. Some studies have suggested that elevated systolic blood pressure in patients with cerebral infarction is associated with improved collateral circulation, whereas it is necessary to lower blood pressure in patients with cerebral hemorrhage. For the management of blood pressure in patients with acute stroke, current research data do not provide a consistent optimal blood pressure target; therefore, it is necessary to determine based on the specific circumstances of the patient [23]. In terms of antihypertensive treatment for stroke patients, 70%-80% of stroke patients have hypertension or a history of hypertension. While antihypertensive treatment helps prevent most hemorrhagic strokes, its effectiveness in preventing ischemic strokes is limited. This may be related to the changes in cerebral perfusion pressure caused by a reduction in blood pressure. Therefore, the safe and effective control of hypertension has become the focus of stroke treatment. For antihypertensive treatment of acute ischemic stroke, it is generally believed that within 10 h of infarction, if the blood pressure is below 160-220/90-110 mmHg, there is no need to use antihypertensive drugs to avoid worsening the condition owing to impaired cerebral perfusion [24].

Concerning the Relationship between Cholesterol and Stroke

Several important findings have been reported in literature. A study of over 50,000 people showed that fluctuations in high-density lipoprotein cholesterol (HDL-C, often referred to as "good cholesterol") are associated with an increased risk of stroke. Specifically, individuals in the highest quartile of HDL-C fluctuation had an 18% higher risk of stroke and 21% higher risk of ischemic stroke than those in the lowest quartile. However, fluctuations in HDL-C levels did not increase the risk of hemorrhagic stroke [25]. This is consistent with the results of a Mendelian Randomization study conducted by the research team. Another study pointed out that variability in remnant cholesterol (Remnant-C, which includes cholesterol other than LDL-C and HDL-C, such as VLDL-C and IDL-C) is closely associated with the occurrence of ischemic stroke. In the general population, greater variability in remnant cholesterol levels is associated with a higher risk of ischemic stroke. This finding challenges the traditional view of LDL-C as a primary treatment target [26]. These results indicated that different types of cholesterol and their fluctuations may be associated with the risk of stroke, particularly ischemic stroke. Therefore, controlling cholesterol levels, particularly the stability of HDL-C and remnant cholesterol, is important for stroke prevention.

Limitations of the Study

This study provides strong evidence to analyze and validate the causal relationship between these five physiological risk factors and ischemic stroke. This will help formulate effective prevention strategies to reduce the risk of ischemic stroke and improve the prognosis of patients with ischemic stroke. However, there were certain methodological limitations.

• Limitations of Genetic Variation: Genetic variation may not fully represent exposure levels, which may affect the accuracy of MR methods.
• Heterogeneity: There may be differences in exposure and outcomes among different populations, which could affect the accuracy of the MR method.
• Confounding Factors: Other unmeasured factors could influence the association between the risk factors and stroke, which could affect the accuracy of the MR method.

Genetic variation-predicted physiological risk factor indicators, such as body mass index, systolic blood pressure, and cholesterol, were significantly associated with an increased risk of ischemic and hemorrhagic stroke and significantly increased the risk of stroke. Additionally, cholesterol is significantly associated with an increased risk of ischemic stroke but not with an increased risk of hemorrhagic stroke. Early detection and active management of body mass index, systolic blood pressure, and cholesterol are crucial for stroke prevention and prognosis and are important measures for preventing and controlling stroke. These risk factors can be effectively controlled by adjusting lifestyle and medical treatment, thereby reducing the risk of stroke.

Ethics Approval and Consent to Participate: This study used anonymized data from large cohort studies and adhered to ethical guidelines. All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Consent for Publication: All authors agree to publish a version of the paper in this journal.

Data Access Statement: Research data from large-scale GWAS studies supporting this publication are available on the Web of ieu open gwas website (https://gwas.mrcieu.ac.uk/).

Conflicts of Interest: The authors declare that they have no competing interests.

Funding Statement: This study was not supported by any funding source.

Author Contributions: LF collected and sorted the literature databases, carried out data analysis, and wrote the manuscript. NL, QXJ and ZXC contributed to the application of the methodology and ZSW and YFS revised the manuscript. All authors contributed to the manuscript and approved the submitted version.

Acknowledgements: Not applicable.