Causal association between modifiable risk factors and aortic aneurysm and aortic dissection: A comprehensive Mendelian randomization study

Abstract

Aortic aneurysms (AA) and aortic dissection (AD) are life-threatening cardiovascular conditions. While epidemiological studies have suggested associations with modifiable risk factors, their causal nature remains unclear. This study aimed to explore the causal links between modifiable risk factors and risks of AA and AD using Mendelian randomization. A 2-sample Mendelian randomization approach was applied, utilizing genetic instrumental variables from genome-wide association studies with a significance threshold of P < 5 × 10⁸. Summary data for AA and AD were obtained from 209,836 participants. Causal effect estimates were calculated for the 46 risk factors using inverse-variance-weighted models. Vigorous physical activity reduced the risk of AA, whereas alcohol consumption and smoking increased it. Obesity traits, including high body mass index, waist circumference, and body fat mass, were associated with a higher AA risk. High-density lipoprotein cholesterol (HDL-C) had a protective effect against AA, whereas low-density lipoprotein cholesterol (LDL-C) and triglycerides increased the risk of AA. Hypertension and high diastolic blood pressure significantly elevated the risk of both AA and AD, whereas type 2 diabetes appeared to be protective against AA. A diet rich in oily fish was found to protect against AD. Liver enzymes and sex hormones were not significantly associated with AA risk. This study identified causal relationships between modifiable risk factors and AA/AD risk, emphasizing the importance of physical activity, obesity management, and dietary adjustments in preventing aortic diseases. These findings provide insights into public health and clinical practice.

1. Introduction

Aortic aneurysm (AA) and aortic dissection (AD) are life-threatening cardiovascular diseases that significantly contribute to global morbidity and mortality rates.^[1] AA is the second most common aortic disease after atherosclerosis, and is characterized by progressive and irreversible dilation of the entire local aortic layer.^[2] Annually, approximately 150,000 to 200,000 deaths are attributed to ruptured AA.^[3] Abdominal aortic aneurysm (AAA) is particularly prevalent among men aged >55 years and women aged >70 years, with men being 4 to 5 times more likely to develop the condition than women.^[4,5] The incidence of thoracic aortic aneurysm (TAA) ranges from approximately 5 to 10 cases per 100,000 individuals per year and increases with age. Although TAA is more prevalent in men, women often experience worse outcomes than men do. The risk of death is extremely high if a TAA ruptures, with mortality rates reaching 60% to 70%. AD is characterized by a tear in the intima that allows blood to enter the media layer, and has a high mortality rate and poor prognosis, with an incidence of approximately 7.7 cases per 100,000 individuals per year. Although AA and AD are distinct pathological entities, they both involve destructive changes in the aortic structure and share overlapping pathophysiological mechanisms. Therefore, studying these 2 conditions together provides a more comprehensive understanding of their pathogenesis and the associated modifiable risk factors.

Epidemiological studies suggest that the occurrence of AA and AD is associated with modifiable risk factors.^[6–8] Understanding these causal relationships is vital for developing effective preventive and interventional strategies. However, the relationship between these factors and the development of AA and AD remains complex and controversial. For example, while smoking is widely recognized as a significant risk factor, some studies have reported a paradoxical increase in aneurysm risk during the early stages of smoking cessation.^[9] Similarly, the impact of diabetes on AA and AD is multifaceted.^[10–12] Some studies suggest that diabetes may confer a protective effect through mechanisms such as arterial wall thickening, whereas others indicate that diabetes could elevate the risk of aneurysm rupture or dissection due to enhanced atherosclerosis. The role of hyperlipidemia is equally nuanced, with concerns that overly aggressive reduction of LDL-C levels might be associated with an increased risk of aneurysm formation, particularly in the elderly population.^[13] These conflicting findings underscore the challenges of establishing clear causal relationships, a task further complicated by residual confounding and reverse causality, in traditional observational studies. Mendelian randomization (MR) provides a robust method to disentangle these complex associations and accurately determine the direct impact of these risk factors on AA and AD.

MR employs genetic variants as instrumental variables (IVs) to evaluate the causal effects of modifiable risk factors on disease outcome. Genetic variants associated with risk factors are randomly assigned at conception, rendering them less susceptible to confounding factors that typically affect observational studies.^[14] This method provides robust and reliable evidence of causality, as genetic variants are not influenced by external environmental factors or by reverse causality. In recent years, MR has been instrumental in elucidating the roles of various risk factors in cardiovascular diseases. Applying this method to research on AA and AD can significantly enhance our understanding of how modifiable risk factors contribute to AA and AD.

This study aimed to explore the causal associations between modifiable risk factors and risks associated with these 2 conditions through a comprehensive MR analysis. By integrating large-scale genetic data with advanced epidemiological techniques, we sought to elucidate the extent to which these risk factors contribute to the etiology of aortic diseases.

2. Methods

2.1. MR design

The study design for the MR analysis is shown in Figure 1. Single nucleotide polymorphisms (SNPs) associated with these risk factors were used as IVs. The MR approach is based on 3 key assumptions: genetic variants are closely linked to the risk factors; genetic variants are uncorrelated with a range of potential confounding factors; genetic variants influence the outcomes only through the risk factors.

Figure 1.

Overview of the MR study design and methods. MR analysis was used to explore the causal association of lifestyle factors, obesity traits, serum parameters and metabolic comorbidities with AA and AD. AA = aortic aneurysm, AD = aortic dissection, IV = instrumental variable, MR = Mendelian randomization.

2.2. Selection of instrumental variants

Through an extensive literature search of PubMed, we meticulously identified the potential risk factors associated with AA and AD. A total of 46 primary modified risk factors were selected and categorized into the following 4 categories: lifestyle factors, including diet, physical activity, sleep habits, and education level; obesity traits, including body mass and shape and blood lipids level; serum parameters, including liver enzymes and sex hormones; metabolic comorbidities, included cardiovascular diseases and diabetes mellitus.

We extracted IVs of traits from the following source (Table S1, Supplemental Digital Content, https://links.lww.com/MD/P470): Medical Research Council Integrative Epidemiology Unit (MRC-IEU); The Within Family Consortium (https://www.withinfamilyconsortium.com/); genome-wide association studies (GWAS) and Sequencing Consortium of Alcohol and Nicotine use consortium (https://conservancy.umn.edu/handle/11299/201564); Neale Lab (http://www.nealelab.is/uk-biobank); Genetic Investigation of ANthropometric Traits (http://portals.broadinstitute.org/collaboration/giant/); UK Biobank study. SNPs that were closely associated with IV exposure were selected. Initially, we extracted SNPs that were highly related to each exposure (P < 5 × 10⁸). Subsequently, to ensure the independence of each instrumental variable, SNPs that might have existing linkage disequilibrium were excluded. We established a threshold of r² < 0.001 and implemented a window size of 10,000 kb for exclusion. Third, the F-statistic was used to assess the genetic instrument strength. F-statistics (F = beta²/se²) were calculated for each SNP and F > 10 was considered sufficient. Finally, using the PhenoScanner tool (https://ldlink.nci.nih.gov/?tab=ldtrait), we excluded any SNPs associated with confounding factors of the outcome.^[15] All the study participants were of European descent.

2.3. GWAS summary statistics for AA and AD

Given that the majority of the exposure factors were derived from MRC-IEU, we procured GWAS summary statistics for AA and AD from the FinnGen consortium to minimize overlap. This dataset included 209,836 participants (2825 patients with AA, 470 patients with AD, and 206,541 controls). All selected GWASs from the FinnGen consortium received ethical approval from the FinnGen Committee.

2.4. Statistical analysis

Based on instrumental SNPs, a 2-sample MR analysis was performed to evaluate the causal impact of modifiable risk factors on AA and AD risks. The random-effects inverse-variance-weighted (IVW) method was used as the primary analysis to estimate the association between genetic liability and modifiable risk factors and the risk of AA and AD.^[16] To ensure the robustness of our findings and provide unbiased estimates of causal effects, we complemented our primary analysis with several additional methods: MR-Egger, weighted median, simple mode, and weighted mode.^[17,18] False discovery rate (FDR) correction was incorporated to mitigate the impact of multiple testing. Results with an FDR-adjusted P-value < .05 were considered statistically significant. A P < .05, with FDR-adjusted P > .05, was considered to be of suggestive significance. MR-PRESSO and Cochrane Q^[18,19] statistics were used to evaluate pleiotropy and heterogeneity, respectively. A P-value smaller than .05 was considered to suggest statistically significant heterogeneity. Furthermore, we performed a “leave-one-out”^[20] sensitivity analysis to identify the potentially influential SNPs. All statistical analyses were performed using R, version 4.2.0.

3. Results

3.1. Baseline characteristics

A total of 46 potentially modifiable risk factors were included in this study. Lifestyle factors included 12 diet-related traits, 4 physical activity traits, 1 measure of education level, and 3 sleep habits, providing a comprehensive view of daily habits that could influence health outcomes; obesity traits encompassed 8 body mass and shape-related traits and 6 blood lipid levels. Serum parameters consisted of 2 liver enzyme-related traits and 2 sex hormone-related traits. Metabolic comorbidities included 5 cardiovascular disease-related traits and 3 diabetes-related traits. The number of SNPs ranged from 4 to 475. The F-statistics across all analyses exceeded the threshold of 10, indicating robustness and freedom from weak instrument bias (Table S4-1, Supplemental Digital Content, https://links.lww.com/MD/P471 and Table S5-1, Supplemental Digital Content, https://links.lww.com/MD/P472).

3.2. Lifestyle factors for the risk of AA

The odds ratio (OR) derived from the IVW method was 0.344 (95% CI = 0.172–0.690; P = .003) for vigorous physical activity, indicating a significant protective effect. No significant causal association was observed between AA and moderate physical activity, heavy manual work, or overall health rating. Genetically instrumented alcohol intake frequency and cigarette consumption increased the risk of AA (IVW OR = 1.395; 95% CI = 1.039–1.874; P = .027 for alcohol; IVW OR = 1.333, 95% CI = 1.022–1.738; P = .034 for cigarettes), although these associations were not significant after FDR correction. Education level showed suggestive significance (IVW OR = 0.534; 95% CI = 0.290–0.983; P = .044). Sleep habit was not a significant risk factor for AA (Figs. 2 and 3).

Figure 2.

Heatmap of correlation coefficients between modifiable risk factors and AA and AD. Results with an FDR-adjusted P-value were described here, which can be found in Tables S2 and S3 (Supplemental Digital Content, https://links.lww.com/MD/P470), Table S4-2 (Supplemental Digital Content, https://links.lww.com/MD/P471), and Table S5-2 (Supplemental Digital Content, https://links.lww.com/MD/P472). AA = aortic aneurysm, AD = aortic dissection, HDL-C = high-density lipoprotein cholesterol, IVW = inverse-variance-weighted, LDL-C = low-density lipoprotein cholesterol, SNP = single nucleotide polymorphism, WM = weighted median.

Figure 3.

Forest plot to visualize the causal effect of modifiable risk factors on AA using the inverse-variance-weighted method. All results described here can be found in Table S2 (Supplemental Digital Content, https://links.lww.com/MD/P470) and Table S4-2 (Supplemental Digital Content, https://links.lww.com/MD/P471). AA = aortic aneurysm, AD = aortic dissection, CI = confidence interval, HDL-C = high-density lipoprotein cholesterol, LDL-C = low-density lipoprotein cholesterol, OR = odd ratio.

3.3. Obesity traits for the risk of AA

Regarding body mass and shape, our analysis indicates that genetically predicted higher body mass index (BMI), waist circumference, hip circumference, arm fat mass (left), leg fat mass (left), whole-body fat mass, and trunk fat mass were significantly associated with a heightened risk of AA (Figs. 2 and 3). Genetically predicted higher BMI (OR = 1.634; 95% CI = 1.353–1.972; P < .001), waist circumference (OR = 1.765; 95% CI = 1.369–2.274; P < .001), hip circumference (OR = 1.387; 95% CI = 1.127–1.708; P = .002), arm fat mass (left) (OR = 1.451; 95% CI = 1.185–1.776; P < .001), leg fat mass (left) (OR = 1.608; 95% CI = 1.246–2.075; P < .001), whole-body fat mass (OR = 1.414; 95% CI = 1.158–1.728; P = .001), and trunk fat mass (OR = 1.426; 95% CI = 1.166–1.745; P = .001) were all associated with an increased risk of AA. However, no significant causal association was identified between genetically predicted waist-to-hip ratio and AA.

Blood lipid levels have varying effects on AA risk. LDL-C (OR = 1.516; 95% CI = 1.212–1.898; P < .001), triglycerides (OR = 1.333; 95% CI = 1.147–1.548; P < .001), and total cholesterol were associated with an increased risk of AA. In contrast, high HDL-C levels were associated with a protective effect against AA (OR = 0.797; 95% CI = 0.666–0.953; P = .013). Notably, no significant causal association was observed between genetically predicted total cholesterol, nonalcoholic fatty liver disease, and AA.

3.4. Serum parameters for the risk of AA

The MR analysis conducted to estimate the causal effects of liver enzymes and sex hormones on the risk of AA is shown in Figures 2 and 3, Table S2 (Supplemental Digital Content, https://links.lww.com/MD/P470). In the primary IVW analyses, liver enzymes were not associated with AA. The ORs (95% CI) were 1.112 (0.982–1.259; P = .093) for gamma-glutamyl transferase and 1.180 (0.981–1.421; P = .080) for aspartate aminotransferase (AST) levels. Alternative MR methods yielded different results: weighted median analysis (OR = 1.352; 95% CI = 1.010–1.810; P = .043) and MR-Egger analysis for AST (OR = 1.377; 95% CI = 1.004–1.888; P = .049). Sex hormones showed no association with AA risk (IVW OR = 1.047; 95% CI = 0.709–1.546); (P = .816 for testosterone; IVW OR = 1.033; 95% CI = 0.883–1.209; P = .687 for estradiol) (Table S2, Supplemental Digital Content, https://links.lww.com/MD/P470). Consistent with the IVW findings, the results from other methods also indicated that sex hormones were not associated with AA risk (all P > .05).

3.5. Metabolic comorbidities for the risk of AA

Notably, genetically predicted hypertension showed a robust association with an increased risk of AA (OR = 4.464; 95% CI = 2.562–7.780; P < .001) according to the IVW method. Similarly, a higher diastolic blood pressure was significantly associated with an elevated risk of AA (IVW OR = 1.869; 95% CI = 1.528–2.285; P < .001). However, no significant causal association was found between systolic blood pressure and AA across all MR analysis methods. In the primary IVW analyses, coronary and peripheral atherosclerosis demonstrated ORs of 1.197 (95% CI = 1.028–1.394; P = .020) and 1.262 (95% CI = 1.024–1.555; P = .029), respectively, suggesting a potential association with AA. However, after applying FDR correction, these associations were not statistically significant, indicating that the initial findings may have been influenced by multiple tests. Interestingly, type 2 diabetes was identified as a protective factor against AA (IVW OR = 0.886; 95% CI = 0.791–0.991; P = .035); however, this effect did not withstand FDR correction. Type 1 diabetes and fasting glucose did not significantly influence AA risk (Figs. 2 and 3, Table S2, Supplemental Digital Content, https://links.lww.com/MD/P470).

3.6. Modifiable risk factors for the risk of AD

Our analysis demonstrated a strong causal link between hypertension and diastolic blood pressure with an increased risk of AD (IVW OR = 10.194; 95% CI = 2.609–39.833; P = .001 for hypertension; IVW OR = 2.590; 95% CI = 1.655–4.054; P < .001 for diastolic blood pressure). Larger hip circumference, whole-body fat mass, and trunk fat mass were suggestively associated with higher AD risk (IVW OR = 1.644; 95% CI = 1.055–2.561; P = .028) for hip circumference; IVW OR = 1.609; 95% CI = 1.048–2.470; P = .030 for whole-body fat mass; IVW OR = 1.749; 95% CI = 1.148–2.667; P = .009 for trunk fat mass). Oily fish intake showed a protective effect against AD (IVW OR = 0.208; 95% CI = 0.054–0.796; P = .022), suggesting that increased consumption of oily fish may confer protective benefits (Figs. 2 and 4).

Figure 4.

Forest plot to visualize the causal effect of modifiable risk factors on AD using the inverse-variance-weighted method. All results described here can be found in Table S3 (Supplemental Digital Content, https://links.lww.com/MD/P470) and Table S5-2 (Supplemental Digital Content, https://links.lww.com/MD/P472). AD = aortic dissection, CI = confidence interval, HDL-C = high-density lipoprotein cholesterol, LDL-C = low-density lipoprotein cholesterol, OR = odd ratio.

4. Discussion

This MR study explored the causal relationships between 46 potentially modifiable risk factors and the risk of AA and AD. We identified a greater number of modifiable risk factors for AA than AD. Key findings include the following: vigorous physical activity significantly reduces the risk of AA; various obesity traits and high blood lipid levels increase the risk of AA; liver enzymes and sex hormones show no significant associations with AA; and hypertension and DBP are robust risk factors for both AA and AD. Additionally, dietary intake of oily fish was found to be protective against AD. These findings have significant implications for public health and clinical practice by informing targeted preventive strategies.

4.1. Lifestyle factors

The protective effect of vigorous physical activity on AA is notable. This result aligns with those of recent studies that highlight the benefits of regular physical activity in reducing arterial stiffness, improving endothelial function, and reducing systemic inflammation. Oliveira et al conducted a systematic review and meta-analysis, demonstrating that structured exercise is safe for patients with asymptomatic AA and reduces preoperative and postoperative complications.^[21] Tanimura et al found that greater participation in sports was associated with reduced mortality from AA in a Japanese population.^[22] Aune et al conducted a meta-analysis, indicating that higher levels of physical activity reduce the risk of AAA.^[23] However, the MR-Egger method did not support this finding, indicating potential pleiotropy. Future research should aim to disentangle these complex interactions by using more sophisticated genetic instruments and larger datasets. Interestingly, other lifestyle factors such as diet, sleep habits, and education level did not exhibit significant causal relationships with AA. This may reflect the multifactorial nature of these traits, involving complex interactions between genetic predispositions and environmental factors. Recent evidence suggests that specific dietary patterns, rather than individual dietary components, may have a more pronounced impact on cardiovascular outcomes.^[24–26] Therefore, further studies should explore the cumulative effects of comprehensive lifestyle interventions on AA risk.

4.2. Obesity traits

Our findings indicate a clear link between various obesity-related traits and AA risk. Specifically, a higher genetically predicted BMI, waist circumference, hip circumference, whole-body fat mass, and trunk fat mass were associated with an elevated risk of AA. These results are consistent with the existing literature, such as the studies by Zhou et al and Van’t Hof, which also suggested a potential causal role of adiposity in AA. Furthermore, our study revealed novel associations between arm circumference, leg circumference, and increased AA risk, highlighting the significance of overall and localized fat deposition in the development of aortic pathology. The absence of a significant causal relationship between the genetically predicted waist-to-hip ratio and AA is particularly intriguing.^[24,27] This finding suggests that while overall and localized fat deposition contribute to AA risk, the distribution of fat between the waist and hips may not be a crucial determinant. This discrepancy could be due to differences in the metabolic profiles associated with overall fat versus fat distribution, warranting further investigation to elucidate the underlying mechanisms.

Additionally, our genetic analysis revealed that lipid levels play a crucial role in modulating AA risk. Elevated levels of LDL cholesterol,^[28,29] triglycerides, and total cholesterol are associated with an increased risk of AA. These lipids are well-known contributors to atherosclerosis, which may explain their impact on AA risk.^[30] In contrast, high levels of HDL-C appear to confer a protective effect against AA. HDL is often termed “good cholesterol” due to its role in reverse cholesterol transport and its anti-inflammatory properties, which may help mitigate the development of atherosclerotic lesions and thereby reduce AA risk.

Notably, we also found that hip circumference and trunk fat mass were associated with an elevated risk of AD, which is consistent with the results of previous retrospective cohort studies.^[31,32] This aligns with the understanding that obesity-related metabolic disturbances exacerbate vascular inflammation and atherosclerosis.^[33] Additionally, the protective effect of oily fish intake against AD suggests the potential benefits of omega-3 fatty acids, which have anti-inflammatory and plaque-stabilizing properties. Nakao and Morita discussed the inverse relationship between omega-3 polyunsaturated fatty acids and cardiovascular morbidity, highlighting their roles in reducing inflammation and stabilizing plaques.^[34] Another study by Gladine et al showed that docosahexaenoic acid, an omega-3 fatty acid, modulates inflammatory pathways and macrophage polarization in the aorta, indicating its protective effect against atherosclerosis.^[35]

4.3. Serum parameters

The MR analysis of liver enzymes, specifically gamma-glutamyl transferase and AST, did not show significant associations with AA using the primary IVW method. However, alternative MR methods suggest potential associations, indicating a need for cautious interpretation. Elevated liver enzyme levels may indicate underlying metabolic disturbances such as nonalcoholic fatty liver disease, which has been implicated in cardiovascular risk. Maman reported that fatty liver disease, as indicated by elevated liver enzyme levels, is associated with an increased risk of cardiovascular disease.^[36] Lioudaki et al examined the potential of liver enzymes such as ALT and gamma-glutamyl transferase as markers of cardiovascular risk, noting their association with metabolic syndrome and atherosclerotic plaques.^[37] These findings suggest that while liver enzymes alone may not be strong predictors of AA, they could be part of a broader metabolic risk profile. Similarly, the sex hormones (testosterone and estradiol) showed no significant causal relationship with AA. This result is consistent with recent studies that have reported mixed findings regarding the role of sex hormones in cardiovascular diseases.^[38] Hormonal regulation of vascular function is complex and may be influenced by various factors such as age, sex, and comorbid conditions. Further research is required to explore these interactions in more detail.

4.4. Metabolic comorbidities

Hypertension is a major risk factor for AA and AD. Although observational studies have found that both systolic and diastolic blood pressure elevations are associated with these conditions,^[39,40] recent research suggests that an increase in diastolic pressure may play a more critical role,^[41,42] which is consistent with the findings of this study. First, long-term high diastolic pressure can lead to a sustained increase in pressure on the arterial wall, resulting in chronic damage and structural changes such as rupture of elastic fibers and proliferation of smooth muscle cells.^[43] These changes weaken the strength and elasticity of the arterial wall, making it susceptible to aneurysm formation and dissection. Second, the nutrition of the arterial wall mainly depends on the microvascular network beneath the intima, and high diastolic pressure can damage these microvessels, leading to inadequate nutrition supply to the arterial wall and further exacerbating its vulnerability.^[44] Additionally, high diastolic pressure increases the mechanical stress and shear force on the arterial wall, which can promote rupture of the intima of the arterial wall, leading to the formation of AD. Finally, an increase in diastolic pressure is usually accompanied by diastolic dysfunction of the heart and an increase in systemic vascular resistance, both of which increase the pressure burden on the arterial wall, further promoting the formation of aneurysms and dissections.

Although type 2 diabetes initially appeared to be protective against AA, this association did not withstand correction in multiple testing. Type 1 diabetes mellitus and fasting blood glucose levels did not have a significant impact on AA risk. The relationship between diabetes and AA is complex, and may involve various mechanisms. Diabetes can cause vascular damage through mechanisms, such as chronic inflammation, endothelial dysfunction, and increased oxidative stress, which may predispose individuals to aneurysm formation. Patients with diabetes often exhibit dyslipidemia, characterized by elevated levels of triglycerides and LDL-C and reduced levels of HDL-C, which is consistent with the findings of Li et al Multiple studies have indicated a negative correlation between diabetes, particularly type 2 diabetes, and AA (particularly TAA). A MR study found a significant negative correlation between genetically predicted type 2 diabetes and TAA risk.^[45] Similarly, other studies have identified a negative correlation between type 2 diabetes and AAA, although the underlying mechanisms remain unclear.^[46] Studies have shown that fasting blood glucose and glycated hemoglobin levels are differentially associated with AAA characteristics. Some studies have suggested that higher levels of fasting blood glucose and glycated hemoglobin are associated with smaller aortic diameters, implying a potential protective effect of chronic hyperglycemia on aneurysm formation. However, other studies have not found a significant association between blood glucose levels and aortic expansion rates, suggesting that this relationship may be more complex.

5. Conclusion

This comprehensive MR study provides robust evidence for the causal roles of various modifiable risk factors in the development of AA and AD. Vigorous physical activity and higher HDL cholesterol levels are protective against AA, whereas obesity, hypertension, and dyslipidemia increase the risk of AA. Hypertension and adiposity measures are significant risk factors for AD, with dietary intake of oily fish showing a protective effect. These findings underscore the importance of addressing modifiable risk factors through targeted lifestyle and clinical interventions to reduce AA and AD burden. Future research should continue to explore these relationships in diverse populations using advanced genetic methodologies to validate and extend these insights.

Supplemental digital contents “Supplementary Figures 1 and 2” are available for this article (http://links.lww.com/MD/P473).

Acknowledgments

We acknowledge the contributions of the GWAS consortia and FinnGen consortium for providing the summary statistics used in this study. We also thank the Medical Research Council Integrative Epidemiology Unit (MRC-IEU), The Within Family Consortium, GWAS and Sequencing Consortium of Alcohol and Nicotine Use, Neale Lab, Genetic Investigation of Anthropometric Traits, and the UK Biobank Study for their valuable datasets. Special thanks go to the Key Laboratory of Emergency and Trauma of the Ministry of Education and Nature Science Foundation of Hainan for their financial support. Demeng Xia reports financial support and statistical analysis were provided by Nature Science Foundation of Hainan.

Author contributions

Conceptualization: Xuren Wang, Xiaoying Lu.

Data curation: Shaokang Wang.

Formal analysis: Xuren Wang.

Funding acquisition: Xiaoying Lu.

Investigation: Jie Sun, Junjun Gu.

Methodology: Xuren Wang.

Project administration: Jie Sun, Junjun Gu.

Resources: Jie Sun, Junjun Gu.

Software: Shaokang Wang.

Supervision: Xiaoying Lu.

Writing – original draft: Xuren Wang.

Writing – review & editing: Shaokang Wang, Xiaoying Lu.

Supplementary Material

medi-104-e43134-s001.pdf ^{(135.7KB, pdf)}

medi-104-e43134-s002.xlsx ^{(984.8KB, xlsx)}

medi-104-e43134-s003.xlsx ^{(986.5KB, xlsx)}

medi-104-e43134-s004.pdf ^{(3.9MB, pdf)}