Genetic and vascular risk factors for ischemic stroke and cortical morphometry in individuals without a history of stroke: A UK Biobank observational cohort study.

BACKGROUND

Stroke risk factors may contribute to cognitive decline and dementia by altering brain tissue integrity. If their effects on brain are nonnegligible, the target regions for stroke rehabilitation with brain stimulation identified by cross-sectional case-control studies may be biased due to the pre-existing brain differences caused by these risk factors. Here, we investigated the effects of stroke risk factors on cortical thickness (CT) and surface area (SA) in individuals without a history of stroke.

METHODS

In this observational study, we used data from the UK Biobank cohort to explore the effects of polygenic risk score for ischemic stroke (PRS), systolic blood pressure (SBP), diastolic blood pressure (DBP), glycated hemoglobin (HbA1c), triglycerides (TG), and low-density lipoprotein (LDL) on CT and SA of 62 cerebral regions. We excluded non-Caucasian participants and participants with missing data, unqualified brain images, or a history of stroke or any other brain diseases. We constructed a multivariate linear regression model for each phenotype to simultaneously test the effect of each factor and interaction between factors. The results were verified by sensitivity analyses of SDP or DBP input and adjusting for body-mass index, high-density lipoprotein cholesterol, or smoking and alcohol intake. By excluding participants with abnormal blood pressure, glucose, or lipid, we tested whether vascular risk factor within normal range also affected cortical phenotypes. To determine clinical relevance of our findings, we also investigated the effects of stroke risk factors and cortical phenotypes on cognitive decline assessed by fluid intelligence score (FIQ) and the mediation of cortical phenotype for the association between stroke risk factor and FIQ.

RESULTS

The study consisted of 27 120 eligible participants. Stroke risk factors were associated with 16 CT and two SA phenotypes in both main and sensitivity analyses (all p < 0.0004, Bonferroni corrected), which could explain portions of variances (partial R, median 0.62 % [IQR 0.44-0.75 %] in main analyses) in these phenotypes. Among the 18 cortical phenotypes associated with stroke risk factors, we identified 26 specific predictor-phenotype associations (all p < 0.0026), including the positive associations between PRS and SA and between HbA1c and CT, negative associations of SBP and TG with CT, and mixed associations of PRS and DBP with CT. Neither LDL nor interactions between risk factors affected cortical phenotypes. Of the 16 associations between vascular risk factors and cortical phenotypes, ten were still significant after excluding participants with abnormal vascular risk assessments and diagnoses. Stroke risk factors were associated with FIQ in all analyses (p < 0.0004; partial R, range 0.22-0.3 %), of which the associations of PRS and SBP with cognitive decline were mediated by CT phenotypes.

CONCLUSIONS

Stroke risk factors have substantial effects on cortical morphometry and cognitive decline in middle-aged and older people, which should be considered in the prevention of dementia and in the identification of target regions for stroke rehabilitation with brain stimulation.