Effects of APOE promoter polymorphism on the topological organization of brain structural connectome in nondemented elderly

Abstract

The polymorphism of the Apolipoprotein E (APOE) promoter rs405509 can regulate the transcriptional activity of the APOE gene and is related to Alzheimer's disease (AD). However, its effects on cognitive performance and the underlying brain mechanisms remain unknown. Here, we performed a battery of neuropsychological tests in a large sample (837 subjects) of nondemented elderly Chinese people, and explored the related brain mechanisms via the construction of diffusion MRI-based structural connectome and graph analysis from a subset (84 subjects) of the sample. Cognitively, the rs405509 risk allele (TT) carriers showed decreased attention and execution functions compared with noncarriers (GG/GT). Regarding the topological alterations of the brain connectome, the risk allele group exhibited reduced global and local efficiency of white matter structural networks, mainly in the left anterior and posterior cingulate cortices (PCC). Importantly, the efficiency of the left PCC is correlated with the impaired attention function and mediates the impacts of the rs405509 genotype on attention. These results demonstrated that the rs405509 polymorphism affects attention function in nondemented elderly people, possibly by modulating brain structural connectivity of the PCC. This polymorphism may help us to understand the neural mechanisms of cognitive aging and to serve as a potential marker assessing the risk of AD.

Keywords: APOE promoter; brain connectome; cognition; diffusion MRI; graph theory.

Figure 1

The flowchart of WM network construction by diffusion MRI. The reconstruction of whole‐brain WM fibers was performed using dMRI deterministic tractography with the Diffusion toolkit (http://trackvis.org). The network nodes were defined by parcellating the brain into 90 regions of AAL template through a T1‐weighted image. Two regions were considered structurally connected if at least three fiber streamlines with two endpoints was located in these two regions. Then, the weighted networks of each subject were created by computing the mean FA along the fiber pathways (FA‐weighted) that connect each pair of brain regions. The matrix and three‐dimensional representation (axial view) of the WM network of a normal subject are shown in the right panel. The nodes and connections were mapped onto the cortical surfaces using the in‐house BrainNet viewer software (http://www.nitrc.org/projects/bnv/). For further details, see Materials and Methods. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 2

Group differences in global metrics of WM structural connectome. The bar and error bar represent the mean values and standard deviations of the network properties in each group after removing the effects of age, gender and APOE ɛ4, respectively. Significantly reduced global efficiency, local efficiency, clustering coefficient and increased Lp and lambda of WM networks in risk allele carriers relative to the noncarriers were observed (all P < 0.05). [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 3

The distribution of brain regions with decreased nodal efficiency in the TT allele group. (A) The node sizes indicate the significance of between‐group differences in the regional efficiency. Nodes in yellow and red showed reduced efficiency in risk allele carriers versus noncarriers (P < 0.01); nodes in red represent regions with group differences remained after FDR corrections. The network shown here was constructed by averaging the structural connection matrices across all subjects and thresholded with a sparsity of 10%. The nodal regions are located according to their centroid stereotaxic coordinates. ACC: anterior cingulate cortex; PCC: posterior cingulate cortex; DCC: dorsal middle cingulate cortex; MTG: middle temporal gyrus; STG: superior temporal gyrus. (B) The bar and error bar represent the mean values and standard deviations of the nodal efficiency of the left PCC in each group after removing the effects of age, gender and APOE ɛ4, respectively. (C) Plots showing the decrease in nodal efficiency of the left PCC with SDMT scores. The blue and red dots represent the adjusted values of TT allele carriers and GG/GT carriers after controlling for age, gender, APOE ɛ4 and rs405509 genotype, respectively. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 4

Mediation analysis. Independent factor was rs405509 variants, dependent variables were SDMT scores and the mediator was the nodal efficiency of the left PCC. Path a shows coefficient for the effect of genotype on the nodal efficiency of left PCC. Path b shows the coefficient for the effect of the nodal efficiency of left PCC on SDMT. Paths c and c′show coefficients for the total (yellow) and direct (blue) effects of genotype on SDMT, respectively. The nodal efficiency of the left PCC in the WM structural network mediated the effect of rs405509 variants on SDMT. All coefficients standardized. Sobel test was performed for mediation analysis (Z = 2.01, P = 0.045). [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]