Causal relationship between Alzheimer’s disease and cardiovascular disease: a bidirectional Mendelian randomization analysis

Observational studies suggest that cardiovascular disease (CVD) increases the risk of developing Alzheimer’s disease (AD). However, the causal relationship between the two is not clear. This study applied a two-sample bidirectional Mendelian randomization method to explore the causal relationship between CVD and AD. Genome-wide association study (GWAS) data from 46 datasets of European populations (21,982 cases of AD and 41,944 controls) were utilized to obtain genetic instrumental variables for AD. In addition, genetic instrumental variables for atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), coronary heart disease (CHD), angina pectoris (AP), and ischemic stroke (IS) (including large-artery atherosclerotic stroke [LAS] and cardioembolic stroke [CES]) were selected from GWAS data of European populations (P < 5E-8). The inverse variance weighting method was employed as the major Mendelian randomization analysis method. Genetically predicted AD odds ratios (OR) (1.06) (95% CI: 1.02–1.10, P = 0.003) were linked to higher AP analysis. A higher genetically predicted OR for CES (0.9) (95% CI 0.82–0.99, P = 0.02) was linked to a decreased AD risk. This Mendelian randomized study identified AD as a risk factor for AP. In addition, CES was related to a reduced incidence of AD. Therefore, these modifiable risk factors are crucial targets for preventing and treating AD.


AGING
Cardiovascular disease (CVD) is a collection of heart and vascular diseases consisting of ischemic heart diseases (IHD), such as myocardial infarction (MI), atrial fibrillation (AF), and heart failure (HF), and ischemic stroke (IS) [4].Currently, CVDs are the major cause of disability and mortality around the globe, burdening individuals and society significantly.The etiology of CVD is complex and is jointly influenced by cardiometabolic, genetic, lifestyle, environmental and social risk factors.
In recent years, there has been a growing interest in research concerning the association between cardiovascular diseases and Alzheimer's disease, as well as the management of risk factors [5][6][7].Compelling evidence suggests that cardiovascular diseases contribute to the progression of Alzheimer's disease, with the condition being more frequently observed in patients suffering from cardiovascular ailments than in the general population [8][9][10].However, the common risk factors of both make the study of causality between the two complicated and controversial [11].Observational evidence indicates a link between the incidence of atrial fibrillation and the risk of dementia, with the use of oral anticoagulants associated with a decreased risk of dementia [12].However, a previous Mendelian randomization study found no causal relationship between genetically predicted atrial fibrillation and Alzheimer's disease [13].Compared to subjects without AD, those with the disease have reduced mitral valve flow efficiency during diastolic filling and impaired consolidated diastolic function and vortex formation time [14].In addition, many studies have not adequately adjusted for confounding factors, leading to spurious associations.Thus, current studies on the connection between dementia and CVD have led to inadequate and indefinite conclusions.However, it is not possible to determine the sequence of CVD and AD due to the limitations of follow-up time and the number of people in traditional observational studies.Moreover, both usually share common risk factors such as diabetes, obesity, hypertension, metabolic syndrome, atherosclerosis, smoking, oxidative stress, inflammation, and APOE polymorphisms [15,16].
In Mendelian randomization (MR), a new approach to epidemiological studies, genetic variants were used as instrumental variables (IVs) to infer causal associations among exposure factors and outcomes [17,18].Furthermore, the IVs used in MR analysis are random assignments of genes at some point in meiosis, leading to a random dispersal of genetic variants in the population [17].Thus, MR analyses can avoid the interference of traditional confounders [19] to a large extent and conform to the natural causal order [20,21].In addition, genome-wide association studies (GWAS) are being developed, and independent GWAS databases can provide reliable IVs for MR analysis.A bidirectional MR analysis was performed in this investigation to explore the causal connection between AD and CVD.

Research design
In this study, AD was used as "exposure" and atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), coronary heart disease (CHD), Angina pectoris (AP), and ischemic stroke (IS) (including largeartery atherosclerotic stroke (LAS), and cardioembolic stroke (CES)) as "outcome".Subsequently, IVs for bidirectional Mendelian randomization analysis were screened for Mendelian randomization analysis.Heterogeneity was assessed using Cochran's Q analysis.In the end, the reliability of the causal relationship was verified by performing sensitivity analyses (horizontal pleiotropy analysis and "leave-one-out" analysis).However, reverse MR was also performed to determine the sequence of CVD and AD.The following three key assumptions need to be met for MR studies: (1) association: genetic variants/IVs should be strongly linked with exposure factors; (2) independence assumption: genetic variants/IVs are independent of any confounding factors affecting exposure factors and outcome; and (3) exclusivity assumption: IVs can affect the results only through exposure and not other pathways.The present study used bidirectional Mendelian randomization to assess the causal correlation between AD and CVD (Figure 1).

Data sources
The GWAS of AD conducted by Kunkle et al. [22] was utilized to extract pooled data for AD, including 21,982 cases of AD and 41,944 controls.The case data for the study were obtained from four separate consortia: Alzheimer Disease Genetics Consortium (ADGC), European AD Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), and Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for AD Consortium (GERAD/PERADES).In the second phase, the study included 8,362 cases and 10,483 controls, whereas the third phase comprised 4,930 Alzheimer's disease cases and 6,736 controls.In both phases, clinical diagnosis of Alzheimer's disease was based on the DSM-III-R and NINCDS-ADRDA criteria.The control group was defined as participants who did not meet the DSM-III-R criteria for dementia and had intact cognitive function (with an MMSE score greater than 25).

Screening of instrumental variables (IVs)
The design protocol of this study followed the STROBE-MR guidelines [27].Using R as the analysis tool, the package "TwoSampleMR V.4.0" was employed for analysis [28,29].Setting the parameter r 2 with a threshold of 0.01 and kilobase pairs (kb) of 5,000 was used to exclude linkage disequilibrium.P < 5E-8 was set to screen for significant SNPs.Subsequently, missing SNPs in the resultant database were excluded, and the final IVs obtained were the valid SNPs substantially linked to the exposure factors.The IVs that were weakly associated with the exposure factors may lead to weak instrumental bias.Therefore, the strength of IVs was assessed by the introduction of variance (R 2 ) and F-statistics using the following formulas [30]: ) In this equation, R 2 is the cumulative explained variance of the selected SNPs during exposure, MAF is the minor allele frequency, β is the effect value, SE is the standard error, K is the number of SNPs used for the final analysis, and N is the sample size [31].F > 10 suggests a sufficiently strong relationship between IV and exposure such that the outcomes of MR analysis are protected from weak instrumental bias [30].Finally, data from the outcome database were extracted for collation and merging, followed by effect allele alignment such that the effect values for exposure and outcome correspond to the same effect allele.

Statistical analysis
Statistical analyses were carried out on the R program.Mendelian randomization analyses were conducted utilizing the TwoSampleMR package.In this study, there are five methods used to analyze Mendelian randomization results: IVW, Weighted median, MR-Egger, Simple mode, and Weighted mode.IVW metaanalysis, a primary method of analysis, used a random effects model to translate the Wald ratio for each SNP into the effect of each risk factor on the outcome, where the impact of each SNP was shown at a standardized log-transformed exposure level.The weighted median method calculates the causal estimate as a median estimate of the ratio of each genetic variation, weighted by the reciprocal of its variance.The weighted mode assigns causal estimates for each genetic variation by the reciprocal of its variance.Simple mode estimates the causal effect considering each genetic variant individually.Thus, the transformation was done as a weighted regression of SNP outcome effect values on SNP exposure effect values.Subsequently, causal effect estimates (equivalent to beta coefficients) were calculated and converted to odds ratios (ORs).The method provides the highest statistical efficacy provided that the three key assumptions of MR (described in the study design) are met.Because IVW may be subjected to bias or multiple-effect due to the impact of invalid IVs, the validity and robustness of the outcomes were tested by a series of sensitivity analyses.
For sensitivity analysis, MR-Egger regression was first used.This method assumes that the magnitude of the direct effect of the genetic mutation on the outcome (not acting through exposure) is not affected by the impact of the variant on exposure, allowing for an additional intercept (alpha) term that provides an estimate of directional horizontal pleiotropy.To ensure a more reliable analysis of the presence of horizontal pleiotropy, four meta-analyses, including weighted median, simple model, penalized weighted median, and weighted model, were used [32].In addition, the MR-Egger intercept term was analyzed, and the global Q statistic was calculated to monitor heterogeneity and horizontal pleiotropy further.Finally, a leave-one-out analysis was performed for each SNP to identify IVs that may disproportionately affect the findings of MR analysis.

Instrumental variables (IVs)
There were enough AD-associated genome-wide loci (≥2) for MR analysis (number of IVs = 32, R 2 = 0.057, range of variation in F-statistic 29.82-962.33,total number of cases studied = 63,926) (Supplementary Table 1).In addition, there were sufficient CVDassociated genome-wide loci for reverse MR analysis (IVs: AF = 121, R 2 = 3.359%, the magnitude of change in F-statistic 30.03-1081.01,total number of   2).The F-statistics were all >10 in the analyses of this study, indicating strong IVs, and no evidence of weak IV bias was found.Thus, these IVs are proven appropriate estimates of the normal impact of exposure and outcome.

AD affects AP outcomes, whereas AP does not affect AD outcomes
Genetically predicted AD occurrence was significantly associated with a higher incidence of AP (1.06 (1.02-1.10),P = 0.003).Likewise, results from both the weighted median (1.07 (1.02-1.12),P = 0.005) and weighted model (1.06 (1.01-1.11),P = 0.023) demonstrated a significant relationship between the two (Figures 2 and 3).The Cochran Q test indicated a lack of heterogeneity among the SNPs for both the IVW method (P = 0.15) and MR-Egger method (P = 0.12).Similarly, the MR-Egger intercept showed a low likelihood of horizontal pleiotropy (P = 0.91) (Table 2).The leave-one-out sensitivity analysis revealed no apparent outliers among the SNPs (Supplementary Figures 1, 2).Conversely, in the reverse MR analysis, there was no genetic correlation between AP and AD (0.99 (0.95-1.04),P = 0.78), suggesting that AP does not lead to an increased incidence of AD (Figures 4  and 5, Supplementary Figure 3).

CES is a protective factor for AD, whereas AD does not affect CES
We utilized MR analysis to investigate the causal relationship between CES and AD.The results demonstrated a significant causal relationship between CES and AD (0.9 (0.82-0.99),P = 0.02) (Figures 4  and 5).The weighted median analysis also indicated that the incidence of CES led to a decrease in the occurrence of AD (0.9 (0.851-0.99)) (Figure 4).The P-values for the IVW-Q test and the MR Egger-Q test were 0.7 and 0.8, respectively, suggesting a lack of horizontal pleiotropy among the single nucleotide polymorphisms (SNPs), thus providing robust results (Table 2).However, no causal relationship was found between AD and CES (0.95 (0.9-1.0), P = 0.1), indicating non-significant results (Figure 2).

Comparison with other studies
Our findings are consistent with a previous Mendelian randomization study, which found no causal relationship between atrial fibrillation and the risk of AD [13].
However, in stark contrast to our conclusion, a largescale multi-ancestry stroke GWAS meta-analysis of stroke data and International Genomics of Alzheimer's Project data found no causal relationship between genetically correlated CES and AD [33].The discrepancy might be attributed to potential confounders, the selection of study populations, and disease data selection bias.

Possible mechanisms
AD may increase the risk of CVD, particularly AP, possibly due to the action of beta-amyloid (Aβ) peptides.
As one of the pathological hallmarks of AD, amyloid deposition in the heart triggers inflammatory reactions and organ dysfunction [34].The APOE4 gene, reportedly associated with AD [35], exerts significant impacts on lipid metabolism, thereby serving as a risk factor for CVD.The APOE locus is a common influence on the genetic structure of coronary artery disease and AD, and the association between the two vanishes when the impact of the APOE gene is excluded [36].
The mechanism by which genetically predicted cardioembolic stroke reduces the risk of AD may involve risk factor control, pharmacological interventions, and lifestyle changes.The treatment and management of cardioembolic stroke often involve improving cardiovascular health, which may enhance cerebral blood flow and reduce microvascular occlusions, thereby preventing the development of AD [37,38].Therapeutic drugs, such as aspirin and other antiplatelet drugs [39,40], and rivaroxaban and other anticoagulants [41], might ameliorate cerebral microcirculation, alleviate neuroinflammation, and counteract Aβ deposition, thus decelerating the progression of AD.A diagnosis of cardioembolic stroke may prompt patients to make lifestyle changes, such as increasing physical activity, improving dietary habits, and reducing smoking, which might enhance brain health and reduce the incidence of AD [42].

Strengths and limitations
This study utilized large GWAS datasets from AD and CVD, allowing for a systematic investigation of the causal relationship between genetically predisposed AD and CVD, while avoiding the confounding effects of reverse causality and potential covariates.The variety of analytical methods used in this study enhances the accuracy and reproducibility of the results.Furthermore, a series of additional sensitivity analyses ensured the robustness of the findings.
Despite these strengths, certain limitations should be acknowledged.First, the selected samples were from European cohorts, excluding other populations such as Asian and American populations.This limitation may reduce the generalizability of the findings to other populations.Second, the limitations associated with database statistics are evident.Limited statistical data, coupled with the lack of comprehensive raw data information (e.g., inclusion/exclusion criteria, interventions), could adversely impact the accuracy of the results.Third, as Mendelian randomization studies continue to evolve, the use of additional statistical methods such as multivariable and mediation analyses can assist in mitigating the interference from factors such as medication, smoking, and lifestyle, thereby enhancing the accuracy of the research results on the causal relationship between AD and CVD.Fourth, the effect estimates in this study are relatively low, which could be related to the sample size of the disease group.As research into these diseases continues to advance, incorporating more GWAS data from diverse populations could help address this issue.

CONCLUSION
Conclusively, this study suggests that AD increases the risk of AP and that CES is a protective factor for AD.However, no conclusive evidence suggested a causal association between other CVDs and AD.Therefore, more rigorous clinical and laboratory studies are needed for further investigation.

Figure 1 .
Figure 1.The flow chart of this study.

Figure 2 .
Figure 2. MR estimates the impact of AD on CVD.IVW was used as the main method to analyze the two-way causal relationshipbetween AD and CVD.Forest map: Visualize the causal effect of exposure on outcome risk by MR method (when the outcome is cardiovascular disease, i.e., the dichotomy variable, the standard line is the "X = 1" line (orange dashed line)), and the blue markers represent positive results with P < 0.05.Abbreviations: AD: Alzheimer's disease; CVD: cardiovascular disease; IVW: Inverse variance weighting; Beta: risk index; Se: standard error; OR (95% CI): odds ratio (95% confidence interval).

Figure 4 .
Figure 4. MR estimates the impact of CVD on AD.IVW was used as the main method to analyze the two-way causal relationshipbetween CVD and AD.Forest map: Visualize the causal effect of exposure on outcome risk by IVW method (when the outcome is AD, i.e., the dichotomy variable, the standard line is the "X = 1" line (orange dashed line)), and the blue markers represent positive results with P < 0.05.Abbreviations: CVD: cardiovascular disease; AD: Alzheimer's disease; IVW: Inverse variance weighting; Beta: risk index; Se: standard error; OR (95% CI): odds ratio (95% confidence interval).

Figure 5 .
Figure 5. Scatter plots of CVD and AD.The horizontal coordinate represents the effect of SNP on exposure when CVD is an exposure; the ordinate represents the effect of SNPS on outcomes when AD is the outcome.Abbreviations: AD: Alzheimer's disease; CVD: cardiovascular disease; AP: Angina pectoris; CES: Cardioembolic stroke; AF: Atrial fibrillation; HF: Heart failure; CHD: Coronary heart disease; MI: Myocardial infarction; IS: Ischemic stroke; LAS: Large-artery atherosclerotic stroke.