Causal associations between diet and aneurysmal subarachnoid hemorrhage: A Mendelian randomization analysis

doi:10.21203/rs.3.rs-5025109/v1

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Research Article

Causal associations between diet and aneurysmal subarachnoid hemorrhage: A Mendelian randomization analysis

https://doi.org/10.21203/rs.3.rs-5025109/v1

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Background

Numerous observational studies have demonstrated that specific dietary factors influence aneurysmal subarachnoid hemorrhage (aSAH). However, whether a causal relationship exists between diet and aSAH remains unknown.

Methods

We used a two-sample Mendelian randomization (MR) method to characterise the causal associations between 26 different diets extracted from the UK Biobank dataset and aSAH risk. The aSAH data were obtained from a meta-analysis of genome-wide association studies from the International Stroke Genetics Union. The inverse variance-weighted method, weighted median, and MR-Egger methods were employed for the MR analyses. A sensitivity analysis was performed to elucidate the heterogeneity and horizontal pleiotropy.

Results

Our results showed that moderate consumption of red wine was associated with a lower risk of aSAH (odds ratio [OR] = 0.136; 95% confidence interval [CI] (0.052–0.353), p < 0.001), with no heterogeneity or horizontal pleiotropy detected. Suggestive correlations were detected between two dietary intakes and aSAH (beef, OR = 6.063, 95% CI (1.203–30.569), p = 0.029; mutton, OR = 4.375, 95% CI (1.273–15.032), p = 0.019). No significant associations were detected between other diets and aSAH.

Conclusions

These findings provide strong genetic evidence for a causal relationship between red wine consumption and aSAH risk. Moderate consumption of red wine was linked to a reduced risk of aSAH. Further larger genome-wide association studies or randomized controlled trials are warranted to confirm these findings.

aneurysmal subarachnoid hemorrhage

diet

causal association

Mendelian randomization

Aneurysmal subarachnoid hemorrhage (aSAH), a form of subarachnoid hemorrhage resulting from the rupture of an intracranial aneurysm, is a serious cerebrovascular ailment [1]. Despite existing treatment modalities, aSAH still exhibits high mortality and disability rates, imposing a major burden on society and families [2, 3]. Consequently, it is imperative to identify modifiable risk factors to prevent aSAH [4].

Diet, a crucial modifiable lifestyle element, potentially influences the occurrence of aSAH [5, 6]. Numerous observational studies have demonstrated an association between diet, including the consumption of tea, coffee, red meat, and red wine, and the likelihood of aSAH [7–10]. However, it is essential to note that these studies had limitations regarding the identification of potential confounders and reverse causation [11, 12]. Therefore, the precise causal relationship between diet and the occurrence of aSAH still lacks clarity [5, 6].

Mendelian randomization (MR), a research methodology based on Mendel's second law [13, 14], allows for the assessment of causality between exposure and outcome, thereby circumventing the issues of confounding and reverse causation commonly encountered in traditional observational studies [11, 15]. By employing genetic variation as an instrumental variable (IV), MR studies follow the principle of the random assignment of alleles to offspring, a process similar to that of a randomized controlled trial. The allocation of interventions in MR analysis exhibits a randomization pattern similar to that observed in randomized clinical trials [16]. However, conducting randomized controlled trials can be expensive, time-consuming, and ethically challenging, necessitating the use of MR [13].

In this study, our objective was to explore the potential causal association between dietary factors and the risk of aSAH using a two-sample MR analysis to provide new evidence and optimize the prevention of aSAH.

Study design and MR assumptions

This study utilized a publicly available Genome-Wide Association Study (GWAS) to make causal inferences between exposure to 26 types of diets, including the consumption of coffee, tea, milk, yoghurt, cheese, cereals, bread, oily fish, non-oily fish, beef, mutton, pork, bacon, processed meat, cooked vegetables, raw vegetables, fresh fruit, dried fruit, pickled nuts, unsalted nuts, pickled peanuts, unsalted peanuts, red wine, beer, saturated fatty acids, and polyunsaturated fatty acids, and the outcome of aSAH. The primary method utilized in this study was the inverse-variance weighted (IVW) method, with multiple sensitivity analyses conducted to ensure the reliability of the results [16]. The present MR study was required to adhere to three fundamental assumptions of relevance, independence, and exclusivity: 1) instrumental variables must be strongly correlated with exposure factors; 2) instrumental variables could not be associated with any confounding factors associated with "exposure-outcome"; and 3) instrumental variables can solely influence outcome variables through exposure factors [17] (Fig. 1).

Since the data utilized in this study were derived from published summary statistics of GWASs, ethical approval and informed consent were not deemed necessary. The current study adhered to the guidelines set forth by the Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization (STROBE-MR) for its design. A step-by-step flowchart illustrating the study design is shown in Fig. 2.

Data sources

GWASs data for different diets were obtained from the UK Biobank [18]. Summary-level data of aSAH were extracted from recent GWASs on intracranial aneurysms [19]. A pooled data set of SAH (5140 cases and 71,934 controls) was extracted from the meta-analysis of GWASs from the International Stroke Genetics Union [19]. The analysis was limited to 77,074 European individuals to reduce the population stratification bias. Detailed information on the summary-level data included in this study is provided in Table 1.

Table 1

The summary of information regarding data resources used in our study.
Exposure	Sample Size	p-value	Consortium	Access Link
Milk intake	64,949	5 × 10^− 6	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-2966/
Yogurt intake	64,949	5 × 10^− 6	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-7753/
Salted peanuts intake	64,949	5 × 10^− 6	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-1099/
Unsalted peanuts intake	64,949	5 × 10^− 6	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-15555/
Salted nuts intake	64,949	5 × 10^− 6	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-15960/
Unsalted nuts intake	64,949	5 × 10^− 6	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-12217/
Coffee intake	428,860	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-5237/
Tea intake	447,485	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-6066/
Cheese intake	451,486	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-1489/
Cereal intake	441,640	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-15926/
Bread intake	452,236	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-11348/
Oily fish intake	460,443	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-2209/
Non-oily fish intake	460,880	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-17627/
Beef intake	461,053	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-2862/
Lamb intake	460,006	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-14179/
Pork intake	460,162	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-5640/
Bacon intake	64,949	5 × 10^− 6	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-4414/
Processed meat intake	461,981	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-6324/
Cooked vegetable intake	448,651	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-8089/
Raw vegetable intake	435,435	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-1996/
Fresh fruit intake	446,462	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-3881/
Dried fruit intake	421,764	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-16576/
Red wine intake	327,026	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-5239/
Beer intake	327,634	5 × 10^− 8	MRC-IEU	https://gwas.mrcieu.ac.uk/datasets/ukb-b-5174/
Saturated fatty acids	114,999	5 × 10^− 8	NA	https://gwas.mrcieu.ac.uk/datasets/met-d-SFA/
Polyunsaturated fatty acids	114,999	5 × 10^− 8	NA	https://gwas.mrcieu.ac.uk/datasets/met-d-PUFA/
Outcome
aSAH	77074 (5140 cases, 71934controls)	NA	International Stroke Genetics Union	http://www.cerebrovascularportal.org/
Note: aSAH: aneurysmal subarachnoid hemorrhage; MRC-IEU: Medical Research Council-Integrative Epidemiology Unit; NA: not available.

Selection of IVs

To identify single-nucleotide polymorphism (SNP) loci that were significantly associated with different dietary habits, a threshold of P < 5e^− 8 was set. Because the SNPs contents of milk, yoghurt, salted nuts, salt-free nuts, salted peanuts, salt-free peanuts, and bacon were low, a loose threshold (P < 5e^− 6) was set for these diets to include more IVs [20]. To reduce the linkage disequilibrium of different SNP loci, screening conditions of a clumping window < 10,000 kb and linkage disequilibrium level R2 < 0.001 were set [21]. To test the second key hypothesis, the phenotype scanner database (P < 5e^− 8) was used to evaluate the subphenotypes of selected SNPs [22]. According to clinical guidelines [23], blood pressure is a risk factor for aSAH. Therefore, the SNPs associated with blood pressure were excluded by screening the Phenoscanner website (http://www.phenoscanner.medschl.cam.ac.uk/). Simultaneously, SNPs related to aSAH were excluded to prevent contravening the third key hypothesis that an IV was not directly related to the outcome. The F statistic was computed to assess the presence of a weak instrumental variable offset in the chosen instrumental variable. The following formula used was used: F = [R² × (N-2)]/(1-R²), where N represents the sample size of exposure factors and R² denotes the proportion of exposure factors explained by IVs. To further test the correlation assumption, an F value of SNPs higher than 10 indicated that our study avoided weak instrumental bias [24].

MR analysis

In the two-sample MR, the IVW method with multiplicative random effects was used to estimate the causal effect of different diets on aSAH, by performing a meta-analysis of the Wald ratio of a single SNP and assuming that genetic variation could only affect the results through exposure of interest rather than through other pathways [25]. The IVW method may be affected by instrumental bias or pleiotropic effects. Therefore, two complementary analysis methods were employed: the weighted median and MR-Egger methods. The weighted median method is capable of tolerating high levels of pleiotropy and yields robust estimates, particularly when more than half of the SNPs serve as valid IVs [26]. The MR-Egger method was used to obtain estimates after correcting for pleiotropic effects.

MR sensitivity analysis

MR-Egger regression and MR pleiotropic repetition and outliers (MR-PRESSO) were used to assess and correct the potential level pleiotropy among the selected IVs [27]. The MR-Egger intercept and homodyne can provide evidence of directional multi-directionality. To mitigate the bias stemming from horizontal pleiotropy, which affects outcomes via causal pathways other than exposure, MR-PRESSO was used to detect a wide range of horizontal pleiotropies for all outcomes. To evaluate the robustness of the results, further heterogeneity tests were performed on statistically significant results using the MR-Egger intercept test, sensitivity analysis, and Cochran’s Q statistics [28]. The leave-one-out method was used to determine whether causal inference was affected by a specific SNP locus. In addition, forest plots, scatter plots, funnel plots, and leave-one-out analysis plots were used to visualize the results with high reliability. In particular, the forest plots intuitively showed the influence of each SNP on the results, the leave-one-out method determined whether the results were visually robust, and the scatter plot showed the fitting outcomes of various MR analyses. The funnel plot intuitively judged the heterogeneity of IVs [29].

Statistical analysis

All statistical tests were two tailed. For dichotomous variables, effect estimates were converted into odds ratios (OR) to observe the relationship between diet and aSAH more intuitively. For the MR analysis, the P-value of the IVW method was key to demonstrating a causal association. The Bonferroni correction was used to mitigate the risk of false-positive results due to multiple tests, with a statistical significance of 0.0019 (0.05/26 [26 exposures and 1 outcome]). A P < 0.05, but higher than the statistical significance of the Bonferroni correction, was considered as implied evidence of potential causality. For sensitivity analysis, a P < 0.05 indicated significant heterogeneity and horizontal pleiotropy. All MR analyses performed in this study were carried out using the R software (version 4.2.1) and analyzed utilizinging the "Two Sample MR" package (version 0.5.6 ) [30].

We used suitable SNPs associated with the 26 different diets as IVs (Supplemental Table 1). The SNPs associated to blood pressure are shown in Supplemental Table 2. The F-statistics of the SNPs included in the study were all higher than 10, suggesting that our study avoided weak instrumental bias, thereby affirming the reliability of the results (Supplemental Table 1). We determined the effect of each diet on aSAH using two-sample Mendelian randomization, as shown in Fig. 3.

According to the random-effects IVW method, red wine consumption was significantly associated with aSAH risk (odds ratio (OR) = 0.136; 95% confidence interval (CI) (0.052–0.353), p < 0.001). In addition, consumption of beef (OR = 6.063, 95% CI (1.203–30.569), p = 0.029), mutton (OR = 4.375, 95% CI (1.273–15.032), p = 0.019), and unsaturated fatty acids (OR = 0.806, 95% CI (0.651–0.998), p = 0.048) was associated with aSAH (Fig. 3). Leave-one-out analysis showed that SNPs in beef (rs784251, rs7791463, rs1105388, and rs4676964) (Supplemental Fig. 1–4) and mutton (s26789900, rs673696, rs2926119, rs3105056, rs7447465, rs3964074, rs35797675, and rs2222760) (Supplemental Fig. 5–8) had a large effect on aSAH, indicating that the results for beef and mutton were not robust.

We did not detect any horizontal pleiotropy when using the MR-Egger intercept test (Table 2). In addition to the consumption of red wine, beef, and mutton, the MR-PRESSO global test revealed outliers in the dietary intake of protected unsaturated fatty acids (Fig. 3). We further observed that after excluding outliers, the implied association between unsaturated fatty acid intake and aSAH disappeared (OR = 0.827, 95% CI (0.682–1.002), p = 0.052).

Table 2

Using different methods to evaluation the heterogeneity and pleiotropy of 26 dietary intakes on aSAH.
Exposure	Outcome	No. of SNPs	Heterogeneity		Pleiotropy
Exposure	Outcome	No. of SNPs	Cochran’s Q statistic¹	p-value	MR-Egger intercept²	p-value	Global Test³	p-value
Milk intake	aSAH	9	5.794	0.670	-0.004	0.919	7.359	0.687
Yogurt intake	aSAH	4	6.853	0.077	0.153	0.060	11.967	0.154
Salted peanuts intake	aSAH	3	1.106	0.575	0.039	0.684	NA	NA
Unsalted peanuts intake	aSAH	7	5.119	0.529	0.071	0.125	7.319	0.520
Salted nuts intake	aSAH	6	3.247	0.662	0.029	0.513	4.570	0.675
Unsalted nuts intake	aSAH	5	9.901	0.042	-0.157	0.076	15.882	0.082
Coffee intake	aSAH	19	30.336	0.223	0.223	0.496	23.993	0.256
Tea intake	aSAH	23	31.637	0.084	-0.014	0.467	35.179	0.079
Cheese intake	aSAH	34	51.105	0.023	-0.053	0.058	54.208	0.024
Cereal intake	aSAH	20	29.226	0.063	0.035	0.493	0.059	0.059
Bread intake	aSAH	16	23.368	0.077	0.029	0.462	26.626	0.079
Oily fish intake	aSAH	37	45.207	0.140	-0.018	0.442	47.944	0.139
Non-oily fish intake	aSAH	8	10.086	0.184	-0.025	0.730	13.261	0.201
Beef intake	aSAH	6	3.472	0.628	0.033	0.711	5.012	0.655
Lamb intake	aSAH	16	10.817	0.765	0.016	0.728	12.221	0.789
Pork intake	aSAH	9	9.667	0.289	-0.070	0.337	12.789	0.297
Bacon intake	aSAH	6	11.753	0.038	-0.047	0.612	16.826	0.062
Processed meat intake	aSAH	13	17.266	17.266	-0.087	0.251	20.126	0.156
Cooked vegetable intake	aSAH	9	7.600	0.474	0.110	0.294	9.592	0.481
Raw vegetable intake	aSAH	7	7.924	0.244	0.046	0.652	10.544	0.291
Fresh fruit intake	aSAH	36	38.479	0.315	0.058	0.001	42.064	0.273
Dried fruit intake	aSAH	26	31.512	0.173	-0.021	0.539	34.094	0.174
Red wine intake	aSAH	10	5.330	0.805	0.008	0.841	6.524	0.816
Beer intake	aSAH	12	11.843	0.375	-0.069	0.057	13.855	0.411
Saturated fatty acids	aSAH	32	53.536	0.007	-0.011	0.396	60.751	0.005
Polyunsaturated fatty acids	aSAH	37	63.540	0.003	0.014	0.300	70.105	0.004
Note: ¹ The Cochran’s Q test is a statistical test for heterogeneity.
² The intercept term from the MR-Egger regression method is a statistical test of horizontal pleiotropy.
³ The MR-PRESSO method detected the existence of outlier IVs that may have horizontal pleiotropy through the global test.
aSAH: aneurysmal subarachnoid hemorrhage; MR-PRESSO: Mendelian randomisation pleiotropy residual sum and outlier; SNPs: single nucleotide polymorphisms; SE: standard error; NA, not available.

The results of the significant relationship between red wine consumption and aSAH are shown in Supplemental Fig. 9–10. To test the stability of the aforementioned results, we performed MR-Egger and MR-PRESSO analyses on the included SNPs but did not detect any potential level of pleiotropicity (p > 0.05), with the funnel plot showing the lack of offset in the study (Supplemental Fig. 11). In addition, Cochran’s Q statistics revealed no significant heterogeneity in the SNP effects (p = 0.805). Finally, leave-one-out analysis verified the robustness of the reason for the positive results (Supplemental Fig. 12).

In this study, the causal relationship between the genetic susceptibility to 26 diets and the risk of aSAH was systematically evaluated using the MR method. Gene prediction results showed that red wine consumption was significantly associated with a decreased risk of aSAH. In addition, an implied association was detected between beef and mutton consumption, and aSAH.

The findings of the present study align with those of a previous one showing that red meat consumption is linked to an increased risk of aSAH [7]. A meta-analysis of seven prospective observational studies showed that red meat consumption increased the risk of stroke [31]. This may be attributed to heme iron, primarily found in red meat, which serves as a redox-active substance, promoting the generation of oxygen free radicals. Consequently, this process can induce vascular inflammation and injury [32–34]. Our results supported a relationship between beef and mutton consumption and the occurrence of aSAH. It is worth noting that, in the present study, only suggestive effects of beef and mutton were detected on the incidence of aSAH, which are not stable for aSAH causal inference. Therefore, the relationship between the consumption of beef or mutton and aSAH should be interpreted with caution.

The mean ± standard error of weekly consumption of red wine in our study was in line with that of a moderate consumption of red wine of 1–2 glass/d [35]. This was consistent with the results of previous studies that moderate consumption of red wine is associated with a reduced risk of aSAH [36, 37]. Another meta-analysis of 27 clinical studies showed that moderate consumption of red wine could reduce the risk of stroke [38]. However, the exact mechanism by which red wine reduces the occurrence of aSAH remains unclear [37, 38]. This may be explained by the complex composition of red wine, including biologically active substances such as dauphin and resveratrol, which exert important anti-oxidation, anti-inflammatory, and immune regulation activities [39, 40]. The complex mechanism by which consumption of red wine reduces the risk of aSAH requires further investigation. Our results supported a significant association between moderate consumption of red wine and reduced risk of aSAH; that is, red wine may be a protective factor against aSAH.

Our study had the following advantages: First, the two sample populations in this study were both from Europe, reducing population heterogeneity. Secondly, we investigated the relationship between various dietary intakes and aSAH using MR analysis, which represents the most comprehensive approach for describing the causal correlation between diet and aSAH. Thus, confounding fac tors and reverse causality in observational studies can be effectively avoided. We also used various sensitivity and heterogeneity analyses to evaluate the robustness and effectiveness of the results. To the best of our knowledge, this is the first study to implement an MR analysis to reveal the causal relationship between different diets and aSAH, affecting healthy eating strategies for early prevention and timely intervention.

There were several limitations in our study. First, the study only included people of European ancestry, and caution should be exercised when applying it to other ethnic groups. Second, we only assessed the genetic predisposition to diet and aSAH, and confounding factors other than blood pressure cannot be completely excluded. Third, limited by the availability of data, a causal relationship between diet and particular aSAH characteristics, including aneurysm location, size, and shape, could not be detected in our study. However, further researches are required to address this issue.

In summary, strong genetic evidence for a causal relationship between the consumption of red wine and risk of aSAH were observed. Moderate consumption of red wine was significantly associated with a decreased risk of aSAH. Further larger GWASs or randomized controlled trials are warranted to confirm these findings, which may provide new insights for the prevention of aSAH.

Data availability statement

Publicly available datasets were analyzed in this study. These data are available at https://www.ebi.ac.uk/gwas/.

Acknowledgments

The authors extend their gratitude to the International Stroke Genetics Consortium (ISGC) Intracranial Aneurysm Working Group for providing the summary statistics of GWASs for intracranial aneurysms. They also acknowledge the invaluable contributions of all investigators and participants involved in this study. Additionally, the authors express their appreciation to the IEU Open GWAS project developed by The MRC Integrative Epidemiology Unit (IEU) at the University of Bristol.

Ethics approval and consent to participate

As the current study was based on published studies and public databases, no additional ethics approval or consent to participate was required.

Consent for publication

Not applicable.

Contribution statement

Xinyue Huang: Writing—original draft. Xutang Jiang: Writing—original draft. Yinfeng Xiao: Writing—original draft. Wen Gao: Conceptualization. Xiumei Guo: Conceptualization. Hanlin Zheng: Methodology. Zhigang Pan: Methodology. Shuni Zheng: Data curation. Chuhan Ke: Visualization. Weipeng Hu: Writing—review and editing. Lichao Ye: Writing—review and editing. Aihua Liu: Writing—review and editing. Feng Zheng: Writing—review and editing. All authors have approved the final version of this paper. All authors have approved the final version of this paper.

Funding

This research received support from the Quanzhou City Science and Technology Program of China (Grant Number 2022NS084) and the Doctoral Startup Fund of the Second Affiliated Hospital of Fujian Medical University (Grant Number BS202205).

Conflict of Interest

The authors declare no competing financial interests.

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Causal associations between diet and aneurysmal subarachnoid hemorrhage: A Mendelian randomization analysis

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