To our knowledge, this is the first study to investigate the causal relationship between DNA methylation, metabolic traits, and AD using a network with bidirectional MR design integrating cis-mQTLs and summary GWAS data. Our study showed cis-mQTLs determined DNA methylation to higher TC was associated with higher AD risk, whereas the relation of the cis-mQTL determined AD and metabolic traits were unlikely to be causal.
The causal relationship between metabolic indicators and AD has been widely explored, and the findings showed that metabolic dysregulation related genetic risk factors did not predict AD risk [17, 20, 21, 26–28]. There are two possible explanations for these negative results. First, the findings might be faced with the bias of weak IVs due to the complexity of metabolic dysregulation and the small fraction of metabolic dysregulation variance accounting for the genetic variants [20, 21, 28, 29]. Completely ruling out an alternative direct causal pathway is a challenge for all MR analyses [30, 31], particularly for complex traits determined by both multiple genetic variants and complexly environmental exposures. Therefore, a promising approach was urgently needed to reduce the weak instrument bias and provide new clues to the common molecular mechanisms and biological processes of complex traits including AD and metabolic dysregulation. DNA methylation of CpGs is strongly associated with genetic loci, might explaining additional phenotypic variation in diseases besides genetic variants. Thus DNA methylation was considered as IVs (or intermediate phenotype) in a wide range of MR studies to infer the causal association between DNA methylation and complex diseases [11, 12, 14, 15], bridging the GWAS gap regarding SNPs to diseases. Therefore, we explored the casual DNA methylation for metabolic traits and AD with the cis-mQTLs determined DNA methylation as IVs. Incorporating the cis-mQTLs information into GWAS analyses, demonstrated a high potential to increase the power of GWAS in identifying loci associated with metabolic traits and AD, and improve the explanation of traits variance. The large number of SNPs was included as IVs for metabolic traits and AD; therefore, the sensitivity analysis was performed to rule out the issue of pleiotropy and linkage disequilibrium. The findings were robust in sensitivity analyses with different IVs. Secondly, these null findings suggest that the associations between metabolic factors and AD could attribute from the reverse causation bias . Our bidirectional MR might reduce the bias, and the reverse MR analyses showed no significant association of cis-mQTLs determined AD with metabolic traits. The consistent causal direction indicated that the association of AD with the metabolic dysregulation were unlikely to be causal.
The genetic linkage and association studies have identified some AD susceptibility genes, a number of which are related to cholesterol metabolism or transport [3, 17, 32]. Lipid metabolism play an important pathway involved in the development of AD [32, 33]. However, contradictory evidence comes from epidemiological studies showing no or controversial association between dyslipidemia and AD risk [34–36]. In our study, cis-mQTLs determined DNA methylation to higher TG is associated with a higher AD risk (i.e., SNP→DNA methylation→higher TC→AD). Several studies reported that lipid-lowering medications-statins were of a protective effect against the development of AD [36, 37]. In addition, high cholesterol in late life was associated with decreased AD risk [35, 36], which may be explained by the timing of the cholesterol measurements in relationship to age and the clinical onset of AD. Taken together, inherited lifetime exposure to higher TC was associated with higher AD risk, however, the patients with higher TC and took lipid-lowering medications might reduce the risk of AD. Further analysis of AD outcomes in total cholesterol intervention trials is warranted. In addition, our findings suggest the imperative need for further investigation of the possibility that lipid-lowering medications might be of an independent effect on the prognosis of AD risk without dyslipidemia.
In the present study, we found no evidence to support causal associations of cis-mQTLs determined T2D and obesity with AD, and vice versa. The result was in line with the previously reported MR result [20, 28, 29]. For metabolic dysregulation, T2D was the only risk factor with convincing evidence for an association with AD [5, 6, 38]. The increasing evidence showed that obesity in midlife was associated with the risk of AD [5, 39, 40]. A wide range of studies suggest the genetic and pathological links between AD and T2D/obesity [17, 40, 41]. DNA methylation plays an important regulatory role in the pathogenesis of T2D, obesity and AD [11, 14]. In addition, our study identified that AD and T2D/obesity share several common genetic and epigenetic architectures. These findings indicated that the casual DNA methylation overlapped between AD and T2D/obesity might attribute to pleiotropy, shedding light on molecular mechanisms of DNA methylation underlying these comorbidities.
Almost half of the overlapped epigenetic architectures changing in AD and metabolic dysregulation were in the reverse direction. It is important to recognize that the relationship between AD and metabolic dysregulation is far from simple. These findings open the path toward a more detailed investigation of etiologic processes linking metabolic dysregulation and AD. The understanding of the epigenetic mechanisms of these diseases are likely only the tip of the iceberg. The potential downstream effects of DNA methylation (known as cis- gene expression- quantitative trait methylation analysis) on disease etiology still needs to be further verified. To the best of our knowledge, this is the first study to explore possible biological mechanisms in the causal pathway from DNA methylation to AD. There is considerable merit for using cis-mQTLs along with metabolic traits related cis-mQTLs to reveal much broader and more complex networks underlying genetic variant-AD associations. However, most causal DNA methylation of CpG-sites directly affect AD and are independent of metabolic dysregulation (SNP→DNA methylation→AD), which also suggests that AD is a highly heritable disease and DNA methylation is the critical the heritable epigenetic marks of the genome linked to AD.
Given the multifactorial etiology of AD, multidomain interventions that target several risk factors and mechanisms simultaneously might be necessary for an optimal preventive effect on AD. The previous study indicated that a third of AD cases might be attributable to modifiable factors such as diabetes mellitus, mid-life obesity and hypertension, physical activity, depression, smoking and low educational attainment [5, 6, 42]. Aside from metabolic traits, other modifiable factors also play important role in AD. Future researches are supposed to explore the association with DNA methylation, other modifiable factors and AD.