Polygenic risk score for schizophrenia and structural brain connectivity in older age: A longitudinal connectome and tractography study

Abstract

Higher polygenic risk score for schizophrenia (szPGRS) has been associated with lower cognitive function and might be a predictor of decline in brain structure in apparently healthy populations. Age-related declines in structural brain connectivity-measured using white matter diffusion MRI -are evident from cross-sectional data. Yet, it remains unclear how graph theoretical metrics of the structural connectome change over time, and whether szPGRS is associated with differences in ageing-related changes in human brain connectivity. Here, we studied a large, relatively healthy, same-year-of-birth, older age cohort over a period of 3 years (age ∼ 73 years, N = 731; age ∼76 years, N = 488). From their brain scans we derived tract-averaged fractional anisotropy (FA) and mean diffusivity (MD), and network topology properties. We investigated the cross-sectional and longitudinal associations between these structural brain variables and szPGRS. Higher szPGRS showed significant associations with longitudinal increases in MD in the splenium (β = 0.132, p_FDR = 0.040), arcuate (β = 0.291, p_FDR = 0.040), anterior thalamic radiations (β = 0.215, p_FDR = 0.040) and cingulum (β = 0.165, p_FDR = 0.040). Significant declines over time were observed in graph theory metrics for FA-weighted networks, such as mean edge weight (β = -0.039, p_FDR = 0.048) and strength (β = -0.027, p_FDR = 0.048). No significant associations were found between szPGRS and graph theory metrics. These results are consistent with the hypothesis that szPGRS confers risk for ageing-related degradation of some aspects of structural connectivity.

Keywords: Ageing; Genetics; Longitudinal; Schizophrenia; Structural connectivity.

Fig. 1

Diagram of the structural equation model (SEM) for white matter connectivity. A separate model was applied to each white matter tract (FA and MD) and each graph theory measure. Water diffusion and graph theory metrics were measured at baseline (age 73) and follow-up (age 76). From each individual bilateral white matter tract, a latent score was calculated for FA and MD. For callosal tracts and graph theory metrics a latent score was derived after the manifest variable was corrected for scaled age at scanning and sex. From these models, a latent change score variable was calculated for each model (Δ Connectivity). Relation between baseline FA/MD/graph theory measures and polygenic risk score for schizophrenia (szPGRS) is indicated by path A; path B represents the association between change in white matter FA/MD/graph theory measures and szPGRS. For all bilateral tracts, we further constrained equality of the factor loading of the left hemisphere (c). szPGRS was corrected for sex and population stratification while water diffusion MRI and graph theory measures at the manifest level were corrected for scaled age at scanning and sex at each time point within the model (paths not shown). Note that graph theory metrics were corrected for density outside the SEM model.

Fig. 2

Diagram of the mediation model. The SEM model for white matter connectivity has been already described in Fig. 1. From each individual cognitive test, a latent score was calculated for general fluid intelligence (g_f). From this model, a latent change score variable was calculated (Δ g_f). Relation between polygenic risk score for schizophrenia (szPGRS) and change in white matter connectivity is indicated by path A; path B represents the association between change in white matter and change in g_f. Path C represents the association between szPGRS and change in g_f. C′ denotes the effect of szPGRS on change in g_f when change in white matter connectivity is taken into account in the model.

Fig. 3

Trajectories of water diffusion MRI parameters over time. Each colour represents a different fibre for FA (plot A) and MD (plot B). The x-axis represents age in days at MRI scanning. The black line denotes linear regression. ATR = Anterior thalamic radiations; ILF = Inferior longitudinal fasciculus. Beta: standardised estimates from the linear mixed models. Asterisks represent significance from the linear mixed models (p_FDR < 0.05).

Fig. 4

Trajectories of graph theory metrics between age 73 and 76 years. Plotted are residuals for each participant from the regression of the graph metric as the dependent variable and density and sex as the predictor variables. The x-axis represents age in days at MRI scanning. The black line represents linear regression. Beta: standardised estimates from the linear mixed models. Asterisks represent significance from the linear mixed models (p_FDR < 0.05).

Fig. 5

Heatmap illustrating Spearman's correlation coefficients for baseline level (age 73 years old, lower diagonal) and change (73–76 years old, upper diagonal) in white matter diffusion parameters and graph theory metrics. Diagonal coefficients represent the association between baseline and change for each metric derived from the SEM models described in Fig. 1. Individual slopes for change were derived from the SEM models. Blank cells denote those associations that did not survive multiple comparisons correction (p_FDR < 0.05). ATR = Anterior thalamic radiations; ILF = Inferior longitudinal fasciculus.