MENDELIAN RANDOMIZATION ANALYSIS TO ASSESS WHETHER MAGNETIC RESONANCE IMAGING SIGNS OF CEREBRAL SMALL VESSEL DISEASE CAN CAUSE COGNITIVE DECLINE AND DEMENTIA

 

L. Liu, Q. Shen, D. Zhang, Y. Bao, F. Xu, H. Huang,Y. Xu

 

Sichuan University West China Hospital, West China Hospital of Sichuan University, China

Corresponding Author: Yanming Xu, Sichuan University West China Hospital, West China Hospital of Sichuan University, China, neuroxym999@163.com

J Prev Alz Dis 2024;
Published online May 28, 2024, http://dx.doi.org/10.14283/jpad.2024.95

 

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Abstract

OBJECTIVE: Cognitive decline and dementia have been linked to cerebral small vessel disease, so we explored using Mendelian randomization whether cerebral small vessel disease visible as 10 neuroimaging signs may cause cognitive decline and dementia.
METHODS: We analyzed publicly available data from genome-wide association studies using two-sample Mendelian randomization involving inverse variance weighting, weighted median, MR-Egger, and MR-PRESSO approaches.
RESULTS: Mendelian randomization suggested that cognitive decline can be caused by lacunar stroke (inverse variance weighting, β = -0.012, 95% CI -0.024 to -0.001, P = 0.033). Furthermore, an elevated burden of white matter hyperintensities was associated with an increased risk of Dementia due to Parkinson’s disease (inverse variance weighting, OR 2.035, 95% CI 1.105 to 3.745, P = 0.023). Notably, no significant associations were observed between neuroimaging markers of Cerebral Small Vessel Disease and other types of dementia.
CONCLUSION: This Mendelian randomization study provides evidence that lacunar stroke and white matter lesions can cause cognitive decline, and that white matter hyperintensity may increase risk of dementia due to Parkinson’s disease. These results underscore the need for further investigations into the neurocognitive effects of cerebral small vessel disease.

Key words: Alzheimer’s disease, cognitive dysfunction, cerebral small vessel disease, mendelian randomization.

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Introduction

The prevalences of cognitive decline and dementia continue to rise as the global population ages (1), with annual incidence of dementia projected to surpass 78 million by 2030 and 152 million by 2050 (2). These disorders therefore place a growing burden on individuals, their families, healthcare systems and society as a whole (3, 4). Clarifying the causes of cognitive decline and dementia could improve its early detection and management in order to optimize prognosis.
While the causes remain poorly understood, vascular pathology appears to be a likely candidate (5), particularly the several types of pathology grouped under the umbrella term «cerebral small vessel disease» (6–10) and identified based on specific signs and patterns on magnetic resonance imaging (MRI): white matter hyperintensity (WMH), lacunar stroke (LS), cerebral microbleeds (CMBs), and perivascular space (PVS). Numerous studies have consistently linked WMH (11–14), lacunar stroke (15–18), CMBs (19–22), and PVSs (23–25) to worse cognitive performance and faster cognitive decline.
While these studies have shown simple associations between neuroimaging signs of cerebral small vessel disease and cognitive decline, they have been unable to demonstrate that the pathology underlying the signs actually causes cognitive decline. This is because most studies have applied cross-sectional designs; focused on patient populations; or failed to take into account variables that may have confounded the association.
Mendelian Randomization (MR) analysis, founded on Mendel’s law of segregation, employs genetic variants—usually single-nucleotide polymorphisms (SNPs)—to scrutinize the causal links between observed modifiable exposures or risk factors and clinically significant outcomes (26). Here we executed a two-sample MR investigation to assess the potential that cerebral small vessel disease might cause cognitive decline and dementia using publicly available data from previously published genome-wide association studies (GWASs).

 

Materials

Study design

In accordance with Mendelian randomization (27), our study assumed robust association between SNPs and specific imaging markers of cerebral small vessel disease, independence of the frequencies of SNPs from confounding factors, and SNP influence on cognitive decline or dementia exclusively through the MRI signs.

Data sources

Data linking SNPs to MRI signs for LS were obtained from a study of Europeans (6,030 cases and 248,929 controls) (28). Data linking SNPs to the following MRI signs for white matter lesions were obtained from the UK Biobank (29): WMH, 18,381 cases; fractional anisotropy (FA), 17,673 cases; and mean diffusivity (MD), 17,467 cases. Data linking SNPs to MRI signs for any, lobar or mixed cerebral microbleeds were obtained from trans-ancestral analysis of, respectively, 2,889 cases, 1,843 cases, and 949 cases (30). Data linking SNPs to MRI signs for PVS were obtained from a study of 17 primarily European cohorts, in whom perivascular space was quantified using visual rating scales; and a cohort of individuals in the UK Biobank in whom perivascular space was quantified using an automated algorithm (31). In that work, 9,607 of 39,822 individuals exhibited extensive perivascular spaces in the white matter; 9,189 of 40,000, in the basal ganglia; and 9,339 of 40,095, in the hippocampus. The Cerebrovascular Disease Knowledge Portal (www.cerebrovascularportal.org) provided summary-level data for MRI markers of white matter lesions, cerebral microbleeds and perivascular space.
In our investigation of Alzheimer’s Disease (AD), we employed the latest and most extensive GWAS, encompassing an impressive cohort of 111,326 cases and 677,663 controls (32). For the study of Frontotemporal Dementia (FTD), we extracted SNPs from a comprehensive GWAS dataset (33), comprising 515 cases and 2509 controls of European ancestry. The GWAS summary statistics for Lewy body dementia (LBD) were sourced from the latest study (34), encompassing 2591 cases and 4027 controls of European ancestry. Data linking SNPs to Vascular Dementia (VD) and Dementia due to Parkinson’s disease (PDD) came from the the FinnGen Consortium (Round 9), including 2,335 cases and 360,778 normal controls, 267 cases and 216,628 controls, respectively, of European ancestry. Data linking SNPs to cognitive function came from a study of 257,828 subjects, primarily Europeans, from the UK Biobank and another cohort (35).

Selection of Instrument variants (IVs)

We set a genome-wide association study (GWAS) significance threshold of p < 5×10^-6 to select SNPs strongly associated with the exposure. We clumped the SNPs using a r2 < 0.3 clumping threshold and a clumping window of 10,000 kb, aiming to minimize the inclusion of variants in strong linkage disequilibrium that might be related to confounders. During harmonization, we excluded palindromic SNPs and the SNPs associated with cognitive decline or dementia with p < 5 × 10−6. We evaluated the strength of each SNP in this study using the F statistic, for which F > 10 was taken to indicate the absence of weak SNP bias.

Statistical analysis

In our MR analysis, we employed three primary methods to assess the impact of exposures on outcomes: Inverse Variance Weighting (IVW), Weighted Median (WM), and MR-Egger regression. The IVW method served as the primary MR analysis approach, acting essentially as a meta-analysis technique. This method aggregates causal estimates from each instrumental variant through weighted integration to derive the final estimate of causal effects (36, 37).
The remaining methods were utilized as supplementary approaches to investigate biases arising from invalid IVs and horizontal pleiotropic effects (38). The MR-Egger method utilizes a weighted linear regression to obtain consistent causal estimates, assuming that instrumental strength is independent of the direct effect (InSIDE), even when genetic IVs are not valid (39). The potential imprecision of MR-Egger estimates arises because this method assumes that the pleiotropic effects are evenly distributed, which may not always be the case. This limitation could lead to over- or underestimation of the causal effect if the pleiotropic effects are directional or if the InSIDE assumption is violated. Complementarily, the weighted median method is a statistical technique that calculates the weighted median of the Wald ratio estimates and does not require the InSIDE assumption. This method is resistant to horizontal pleiotropic biases. Compared to the MR-Egger method, the weighted median process has a lower type I error and is more effective for causal estimation (40).
Furthermore, we conducted MR-Egger regression analysis to assess the presence of horizontal pleiotropy in the selected SNPs. The regression intercept from MR-Egger quantifies the extent of pleiotropy, with values closer to zero suggesting a lower likelihood of gene pleiotropy. Heterogeneity tests were performed, and if the Q-p value exceeded 0.05, instrumental variables included in the analysis were considered to exhibit no heterogeneity, allowing us to disregard its impact on causal effects.
To enhance the robustness of our findings, we utilized the MR-PRESSO test to detect potential outliers and obtain corrected estimates. Additionally, we applied the leave-one-out method, systematically excluding each SNP one at a time and assessing the magnitude of the remaining SNP effects. This stepwise procedure helped evaluate the individual contribution of each SNP to the overall results. To rule out reverse causality, we conducted an MR-Steiger directionality test (41).
All statistical analyses were conducted using the “two-sample MR” package (version 0.5.6) (42) and “MR-PRESSO” package (version 1.0) in R (version 4.2.2) (43). Unless noted otherwise, significance was defined as p < 0.05.

 

Results

Effects of Cerebral Small Vessel Disease on Cognitive Function

In our study, mendelian randomization based on IVW method suggested a causal relationship between lacunar stroke and cognitive performance (β = -0.012, 95% CI -0.024 to -0.001, P = 0.033; Figure 1). Mendelian randomization using WM and MR-Egger regression methods indicated an association in the same direction, although it did not achieve statistical significance. No significant heterogeneity was detected, and neither “leave-one-out” sensitivity analysis nor MR-PRESSO analysis identified individual SNPs that substantially altered the causal association.

CI, confidence interval; WMH, white matter hyperintensities; FA, fractional anisotropy; MD, mean diffusivity; LS, lacunar stroke; CMBs, cerebral microbleeds; BG-PVS, basal ganglia perivascular spaces; HP-PVS, hippocampal perivascular spaces; WM-PVS, white matter perivascular spaces

 

In our study, IVW method failed to detect causal relationships of MD with cognitive decline. Nevertheless, the weighted median method indicated a positive association between MD and cognitive function (β = 0.007, 95% CI 0.001 to 0.014, P = 0.038; Figure 1). Besides, although MR-PRESSO identified three outlier SNPs, the results of the MR remained unchanged after removing these three outlier SNPs.
Mendelian randomization based on IVW suggested a negative causal effect of basal ganglia PVS on cognitive function (β = -0.125, 95% CI -0.251 to 0.000, P < 0.05; Figure 1). However, MR-Egger regression did not detect a causal effect (β = 0.018, 95% CI -0.410 to 0.447, P = 0.935; Figure 1). No significant heterogeneity was detected. MR-PRESSO identified one outlier SNP, however, the results of the MR remained unchanged after removing the SNP.
The other exposures, namely white matter hyperintensities, fractional anisotropy, brain microbleeds, hippocampal perivascular spaces, and white matter perivascular spaces, similarly showed no significant causal relationships with cognitive performance across all Mendelian randomization methods. While there were some signs of heterogeneity in specific analyses, we found no evidence of pleiotropy.

Effects of Cerebral Small Vessel Disease on Dementia

As for AD, the estimated results using the IVW method indicate that there is no significant causal relationship between WMH and the risk of AD (OR 1.078, 95% CI 0.978 to 1.188, P = 0.130; Supplement Table S1). The MR Egger method (OR 1.282, 95% CI 0.925 to 1.779, P = 0.156; Supplement Table S1) also suggests no statistically significant causal effect. However, MR-Egger estimation results reveal a positive association between WMH and the risk of AD (OR 1.132, 95% CI 1.021 to 1.255, P = 0.018; Supplement Table S1). A random-effects model was used because heterogeneity was significant. Neither “leave-one-out” sensitivity analysis nor MR-PRESSO analysis identified individual SNPs that substantially altered the causal association.
Using the IVW method, the results provide evidence of a positive causal effect of WMH on the risk of PDD (OR 2.035, 95% CI 1.105 to 3.745, P = 0.023; Figure 2). The direction of the overall effect remains consistent based on WM and MR-Egger regression methods, but the results lack statistical significance. No significant heterogeneity was detected, and neither «leave-one-out» sensitivity analysis nor MR-PRESSO analysis identified individual SNPs that substantially altered the causal association. The estimated results using the IVW method suggest a negative association between hippocampal PVS and the risk of PDD (OR 0.077, 95% CI 0.009 to 0.664, P = 0.012). However, MR-Egger analysis does not support this conclusion (OR 1.407, 95% CI 0.005 to 411.451, P = 0.909; Figure 2). No significant heterogeneity was detected, and neither «leave-one-out» sensitivity analysis nor MR-PRESSO analysis identified individual SNPs that substantially altered the causal association.
Using the IVW method, no significant causal relationships were found between cerebral small vessel disease imaging markers and the risk of VD, FTD, and LBD (Supplement S1).

CI, confidence interval; WMH, white matter hyperintensities; FA, fractional anisotropy; MD, mean diffusivity; LS, lacunar stroke; CMBs, cerebral microbleeds; BG-PVS, basal ganglia perivascular spaces; HP-PVS, hippocampal perivascular spaces; WM-PVS, white matter perivascular spaces

 

Discussion

Numerous studies have investigated the correlation between imaging markers of cerebral small vessel disease and dementia as well as cognitive function. However, the causal relationships among these factors have remained largely unknown. In this study, we conducted a comprehensive analysis using Mendelian Randomization to explore the associations between imaging markers of cerebral small vessel disease and various types of dementia, as well as cognitive performance. Our analysis suggests that LS is associated with worse cognitive function, and that WMH is causally associated with PDD. In contrast, we did not detect any significant causal associations between MRI signs of cerebral small vessel disease and dementia in AD, VD, FTD, or LBD. These findings argue for deeper investigation of how cerebral small vessel disease contributes to cognitive decline and dementia.
Previous studies have consistently reported a notable association between WMH and cognitive impairment (14, 44, 45). Specifically, WMH has been linked to an increased risk of all-cause dementia, AD, and VD, with relative risk elevations of 14%, 25%, and 73%, respectively (45). It is crucial to note that certain individuals with extensive WMH may not exhibit significant cognitive impairment (46). Research by Melazzini et al. (47) proposes that the total volume of WMH may not directly correlate with cognitive ability, emphasizing the potential role of factors such as the location of WMH, individual resilience, and cognitive reserve in determining clinical impact (14, 44). Furthermore, preliminary evidence suggests potential racial differences in the burden of WMH (48). In our MR analysis, we observed that WMH is associated with a higher risk of PDD, aligning with the findings of a previous meta-analysis (49) that included 23 individual studies and 2,429 patients, revealing an association between WMH and cognitive dysfunction in PD. The severity of WMH appears to impact cognitive dysfunction in PD. We did not detect the relationship between WMH and LBD, although DLB and PDD are nearly identical disorders pathologically. Previous studies (50, 51) showed an increased severity of WMH changes on MRI, but this result was not found for neuropathological white matter lesions (52, 53). One possibility is that an alternative pathology to cerebrovascular disease, such as inflammation or demyelination, results in the appearance of the WMHs on MRI.
Moreover, weighted median analysis showed that WMH was associated with a higher risk of AD.
In addition, identified a correlation between mean diffusivity on MRI and cognitive performance. Diffusion tensor imaging investigations have linked loss of normal-appearing white matter to loss of processing speed and executive function, independently of age (54, 55, 56). In our study, IVW method showed no significant associations between MD with cognitive decline. Further studies are required to ascertain the impact of MD on cognitive function.
Our finding of a causal association between LS and cognitive decline is consistent with previous studies linking LS to cognitive decline after stroke, particularly loss of cognitive flexibility, attention, and processing speed (57, 58, 59). We did not, however, observe a causal relationship between LS and dementia. A meta-analysis involving nearly 11,000 individuals reported an association between presence of LS and risk of incident dementia, but it did not withstand correction for multiple testing (62). Longitudinal studies have reported 11% prevalence of dementia among patients by 2-3 years after lacunar stroke or 15% by 9 years after it (60, 61). Moreover, dementia in these cases often coincides with stroke recurrence.
A growing body of research suggests a connection between CMBs and cognitive decline, as evidenced by several studies (22, 63, 64, 65). Previous investigations have demonstrated that a higher burden of CMBs correlates with more pronounced cognitive impairment (66). Furthermore, CMBs seem to affect cognitive function across various cognitive domains (67), with the location of CMBs influencing the extent of cognitive impairment. However, two prospective studies (68, 69) did not identify a significant association between CMBs and Cognitive Impairment. It is noteworthy that some studies have proposed that, after adjusting for age, gender, and educational level, the presence of CMBs may double the risk of dementia (70). Nevertheless, a comprehensive meta-analysis (62) indicated no significant link between CMBs and the risk of incident dementia or AD. Our MR analysis suggested no causal relationship between CMBs and cognitive function or dementia.
In our study, IVW method detected causal relationships of basal ganglia PVS with cognitive decline, and hippocampal PVS with PDD. However, MR-Egger analysis showed the different results. While several studies have linked the presence of perivascular spaces to cognitive impairment (71, 72, 73) and dementia (23, 74), other work failed to detect independent associations of such spaces cognitive impairment after ischemic stroke or transient ischemic attack (75) or with dementia (76). Further investigation is needed to clarify the potential role of PVS in cognitive decline and dementia.
Our findings warrant cautious interpretation due to several limitations inherent to our study’s design and methodology. Firstly, despite the implementation of control measures, instrumental variables may still be susceptible to uncontrollable confounding factors, potentially influencing the results. Secondly, the data used in this MR study is derived from GWAS data of European populations, necessitating further research to validate whether the research conclusions can be extrapolated to other populations. Thirdly, to identify more SNPs, we set the genome-wide significance P-value at 5.0E-06. Fourthly, our study did not include subgroup analyses based on demographic factors such as gender and age, which may have provided additional insights. Besides, our investigation predominantly centered on the binary presence or absence of specific neuroimaging features, a method that does not fully capture the dynamic and complex nature of cerebral small vessel disease. This approach overlooks the variability in lesion severity and location, which can evolve over time and potentially impact cognitive trajectories in ways not accounted for in our analysis. Lastly, our study is subject to inherent limitations arising from the non-specific nature of certain neuroimaging measures in identifying cerebral small vessel disease. Specifically, DWI metrics such as fractional anisotropy and mean diffusivity may reflect alterations caused by a variety of neurodegenerative and vascular conditions, not solely cerebral small vessel disease. This lack of specificity necessitates a cautious interpretation of these measures when associating them with cerebral small vessel disease. To improve the diagnostic specificity of neuroimaging markers for cerebral small vessel disease, future research should incorporate advanced imaging techniques and biomarkers capable of more accurately differentiating cerebral small vessel disease from other pathologies. Moreover, integrating quantitative assessments of lesion severity, volumetric analyses, and regional evaluations will enrich our understanding of the intricate relationship between neuroimaging characteristics and cognitive outcomes. Where data permits, conducting detailed subgroup analyses could also provide valuable evidence for diverse populations, further enhancing the applicability and relevance of future research in this domain.

 

Conclusion

In summary, at the genetic level, LS and lesions affecting white matter microstructural integrity are associated with decreased cognitive function. Moreover, our findings provide genetic support for the hypothesis that WMH might exacerbate the risk of PDD. Future research should continue to conduct standardized, large-scale clinical trials and relevant MR studies to further explore the impact of cerebral small vessel disease on cognitive dysfunction and dementia.

 

Acknowledgements: The authors thank Dr Chapin for his advice on the English revision and valuable statistical suggestions.

Funding: This work is financed by the Key Research and Development Project of the Sichuan Science and Technology Department (2023YFS0268).

Conflicts of Interest: The authors declare no conflicts of interest.

Ethical standards: No additional ethics approval was needed because all data in the Restfuedyr ewnas cperesviously collected, analyzed, and published.

 

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