Identifying Suitable Targets for Alzheimer’s Disease and Other Eight Common Neurological Disorders Using the Human Plasma Proteome: A Mendelian Randomization Study

INTRODUCTION

Neurological diseases are the leading cause of disability and rank as the second leading cause of death in the world [1, 2]. Given the progressive aging of the global population, the prevalence of neurological diseases, such as Alzheimer’s disease (AD) [3], Parkinson’s disease (PD) [4], stroke [5], and amyotrophic lateral sclerosis (ALS) [6], is gradually increasing. Despite massive investments aimed at finding new therapeutic strategies for neurological disorders [1], most remain essentially intractable, owing to a combination of complex underlying biology and long prodromal phases.

Proteomics has revolutionized neurological research and has boosted the discovery of suitable biomarkers for many neurological diseases [7]. Human blood contains a dynamic flux of proteins important for biological processes [8], including cell signaling, tissue repair, and host defense against external stimuli. Thus, circulating proteins are not merely used as indicators of health status, but deep characterization of blood proteins can inform on pathological mechanisms and help identify clinical intervention strategies for diseases. However, while proteomics can very efficiently identify candidate proteins related to disease, the translation of these findings into practice is complicated by the redundant functions of proteins, the plasticity of biological pathways, and unknown factors that may influence the course of disease or the response to therapeutic intervention. One approach to improve the translatability of proteomics data is to integrate proteomic and genetic datasets. It helps systematically evaluate the putative causal roles and genetic evidence in support of disease-causing pathways [9].

Mendelian randomization (MR) is an effective genetic approach to investigate the causal effect of certain exposures on diseases or clinical traits [10]. This method has been widely used to prioritize targets for neurological diseases [11, 12]. In this study, we applied a two-sample MR method to leverage summary statistics from large-scale genome-wide association study (GWAS) datasets to uncover causal relationships between circulating proteins and neurological diseases, including AD, PD, ALS, multiple sclerosis (MS), epilepsy, ischemic stroke, intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and migraine. We identified a protein-disease associative pair for AD, MS, epilepsy, ischemic stroke, and migraine that warrant further study to determine whether the identified proteins have a causal role in disease pathology.

MATERIALS AND METHODS

Study design

We conducted a two-sample MR analysis to explore the causal relationships between 2,994 plasma proteins and nine neurological diseases (AD, PD, ALS, MS, epilepsy, ischemic stroke, ICH, SAH, and migraine). It is based on 3 core assumptions: 1) genetic variants are strongly associated with the exposure (2,994 plasma proteins); 2) the variants are not associated with confounding factors; 3) the variants affect the outcome (nine neurological diseases) only through their influences on the exposure of interest (2,994 plasma proteins). A graphical overview of the general MR design was shown in Fig. 1. This study follows the guidelines for Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian randomization (Supplementary Table 14) [13].

OPEN IN VIEWER

Fig. 1

The overall design of Mendelian randomization analysis in the present study. Assumption 1, the genetic variants are supposed to be strongly associated with the risk of interest; Assumption 2, the genetic variants should not be associated with any confounding factors; and Assumption 3, the genetic variants should affect the risk of the outcome only mediated by the exposures. IVs, Instrument variants. MR, Mendelian randomization.

RESULTS

The MR-predicted causal effects of human plasma proteins on the AD and other eight neurological disorders were shown in Fig. 2. Two risk proteins and three protective proteins were identified in the framework.

OPEN IN VIEWER

Fig. 2

Volcano plot showing the causal effects of human plasma proteins on nine neurological diseases. Data are expressed as raw odds ratios estimated by the IVW method. The red solid line represents the Bonferroni-corrected significant threshold of p < 1.52×10^-5. The black dotted line represents the suggestive association threshold of p < 0.05. A) Alzheimer’s disease, B) ischemic stroke, C) migraine, D) epilepsy, E) multiple sclerosis, F) amyotrophic lateral sclerosis, G) Parkinson’s disease, H) intracranial hemorrhage, I) subarachnoid hemorrhage.

DISCUSSION

Identifying suitable targets for neurological diseases remains a challenging task. This study integrated large-scale GWAS datasets from multiple cohorts and proposes a two-sample MR framework to identify candidate proteins and their links with nine common neurological diseases.

We identified five protein-disease associative pairs, and the robust statistical analysis suggested that these relationships were unlikely to be affected by confounding factors or pleiotropic effects. Among them, increased level of CD33 was prioritized across the models of IVW, MR-Egger, MR-PRESSO, weighted median and weighted mode, as a genetic proxy for higher risk of AD. The role of CD33 in AD is increasingly recognized. The link has been identified through GWAS, wherein rs3826656, rs3865444, and rs12459419 were discovered as susceptibility factors for AD [37]. Further, it has been reported that CD33, which is primarily expressed in microglia, is higher in patients with AD [38]. CD33 could facilitate AD pathology by inhibiting microglia-mediated clearance of amyloid-β [39]. Our results reinforced the association between CD33 and AD by providing MR-validated causality. Moreover, the current findings were consistent with a recent MR study by Gu et al. [40], in which they found that elevated CD33 protein level in plasma was causal to the development of AD. Indeed, the GWAS datasets for exposure (CD33) and outcome (AD) selected in our analysis were also used by Gu and the colleagues [40]. However, our study set a relaxed threshold during the selection of IVs (p = 1×10^-5 versus p = 5×10^-8 in Gu et al. [40]). As a result, we identified a total of 20 SNPs (compared to 2 SNPs in Gu et al. [40]) (Supplementary Table 3) that were analyzed in our MR model to ensure the accuracy of sensitivity analysis. Although our study re-emphasized the role of circulating CD33 as a causal factor for AD, some studies pointed out that the higher CD33 level could be a response to AD pathology. For instance, factors in neuroinflammation such as platelet derived microparticles or cytokines are able to upregulate the expression of CD33 [41, 42].

Prolactin is a polypeptide hormone, mainly produced and secreted by the lactotroph cells of the anterior pituitary gland [43]. It is implicated in lactation, metabolic homeostasis, maternal behavior, and the adrenal response to stress [43]. The role of prolactin has been well established for epilepsy, with wide application in the diagnosis of epileptic seizures [44]. The epileptic discharge is likely to excite the hypothalamic pituitary axis, inducing secretion of prolactin releasing hormone in the hypothalamus, which can cause the pituitary to secrete prolactin into serum [45]. Elevation of serum prolactin following seizures is considered to be a surrogate marker of epilepsy, and it is informative regarding the progression of the disorder [46]. As expected, our study identified a positive association between circulating prolactin and risk of epilepsy, emphasizing its importance in the disorder. However, studies have found that prolactin can only serve a sensitive and effective biomarker when it is measured 10 to 20 minutes after an epileptic event [46]. Therefore, our finding of evidence that prolactin may have a causative relationship with epilepsy should be applied with caution owing to the complexity and dynamicity of prolactin signaling and secretion. One possible explanation is regarding the negative feedback loop of prolactin secretion. It has been shown that a brief period of restraint stress in male mice results in increased circulating prolactin [47], which then activates tuberoinfundibular dopaminergic neurons. These neurons increase their release of dopamine [48], which suppresses the production of prolactin; thus, a self-regulated framework is formed for prolactin secretion. Therefore, while it is clear that prolactin is a useful adjunct to the diagnosis of epilepsy, its participation in the etiology of epilepsy requires further investigation.

We also found that genetically proxied CDK8 was negatively associated with the risk of ischemic stroke. CDKs and their associations with ischemic stroke stem from the findings of their reactivation in dying neurons [49]. The deregulation of CDKs seems to have a major role in causing neuronal death during ischemic stroke. It has been reported that CDK4 and CDK6 are important factors in ischemic brain that regulate the progression from G1 to S phase in cell cycle [50]. CDK8, however, probably participates in ischemic stroke by regulating the mediator complex and phosphorylating the corresponding transcription factors [51]. Furthermore, CDK8 has been reported to be upregulated in a hyperglycemic state [52]; and Cdk8-knockdown mice exhibited dramatically increased lipid biosynthesis [53]. Therefore, based on the literature and our results, we posit that CDK8 may exert neuroprotective effects against ischemic stroke by regulating transcriptional and metabolic programs.

Interestingly, the association between increasing NEURL1 and lower risk of migraine fits well with a published report demonstrating the reversed trend between NEURL1 expression and genetic susceptibility to arterial fibrillation [54]. It is believed that reduced NEURL1 expression can increase the incidence of arterial fibrillation by regulating the expression of ion transporters that evoke changes in action potential duration [55]. The implication represents an important supplement to the long-running debate regarding the overlap between migraine and heart problems, in which iron-related pathways underlying the two disorders may explain why they can co-occur. Moreover, studies have indicated that NEURL1 is highly expressed in brain, and it modulates the integrity of neurological system through its participation in synaptic plasticity [56]. Variations of the NEURL1 could, therefore, contribute to differences in brain structure and connectivity associated with migraine.

Fc receptor like proteins are members of the immunoglobulin family, that are preferentially expressed in B lymphocytes [57]. Specially, FCRL4⁺ memory B cells are important mediators in immune responses. They express high levels of CD20 and CD95, and express low level of CD21 [58]. Further, FCRL4 defines atypical memory B cells, which are involved in endoplasmic reticulum stress and IFN-γ responses in autoimmune conditions [59]. Therefore, these findings suggest that FCRL4 should be an active driver in MS. Interestingly, our analysis provided considerable evidence of a negative causal association between FCRL4 and risk of MS, which runs counter to the current understanding of the role of FCRL4 in autoimmune conditions. The possible explanation for the inconsistency could be the influences of the disease stages; that is, it is likely that immune cell phenotypes can change during the course of MS [60]. This should be further investigated if GWAS summary data from different MS stages become available.

Our MR analysis identified six possible AD-associated proteins, but none of the candidate proteins performed satisfactorily in the detection of heterogeneity and horizontal pleiotropy. Although these findings are not statistically significant, they may provoke additional follow-up of the candidate proteins to determine whether they may be relevant to the pathogenesis of AD.

The superior performance of our study should be mentioned. First, we used a multiplex proteomics dataset with a relatively large sample size to explore novel causal targets for a wide range of neurological disorders documented in large GWAS datasets, ensuring the precise estimation of their associations. Second, the two-sample MR design using different models avoided interference by confounding factors and invalid instruments, and the bidirectional design provided evidence that the reverse causation was unlikely to influence our findings. Moreover, the great power across all analyses (>85%) supported biological credibility of our findings.

Nevertheless, our study also had several limitations. First, it is important to note that the phenotypes examined in our framework was susceptibility of the disorder, rather than disease severity or subtype. Therefore, the identified protein-disease causality should be interpreted as nominating biological targets or pathways that may be regulated to alter the risk of developing certain diseases. Though the data may provide clues regarding disease severity or subtype, more specific analyses must be conducted to draw further conclusions. Second, the activities of circulating proteins are diverse and complex, especially regarding their effects on varied cell types in different tissue contexts. Moreover, protein-protein interaction plays an important role in regulating neurological diseases [8], and these could be further studied to help identify proteins that could be repurposed to target the associations we have identified to prevent or treat disease. Third, although we have made efforts to ensure the quality of the included genetic variants in the analysis, pleotropic effects could not be entirely avoided in our analysis of several possible AD-associated proteins. Pleotropic effects could be appropriately addressed if data were analyzed on individual level. Fourth, we used a relatively relaxed threshold (p = 1×10^-5) in the selection of SNP to include more protein targets, but more studies should be performed to confirm these links using GWAS with larger sample sizes. Fifth, despite participants in the selected GWAS were all of European ancestry, the possibility of residual confounding from other variables cannot be completely discounted, such as genetic (APOE) and social determinants of health (area level deprivation, access to healthcare). In addition, null findings were observed in the analysis of some disorders; however, MR findings assume a lifetime effect to risk factors and may overestimate the effects on the outcome. Therefore, our findings cannot be assumed to suggest that no suitable targets exist for these diseases. Moreover, some effect values in our study are very small, which may limit their use in practice and require further research. Last, datasets in the study were mostly collected from cohorts of patients of European descent, and it must be noted that protein-disease associations may vary among different ethnic groups. Our findings should therefore be interpreted with caution in populations of non-European ancestry.

CONCLUSION

Our findings identify five candidate protein targets for AD, MS, epilepsy, ischemic stroke, and migraine. Future studies are warranted to clarify the potential mechanisms of these proteins in the associated diseases.