Cerebral small vessel disease score associated with brain hypoperfusion predicts cognitive decline: a longitudinal study.

BACKGROUND: This study aimed to investigate the association between cerebral small vessel disease (CSVD) score and cerebral blood flow (CBF) at baseline in cognitively intact older adults and to explore whether the total CSVD burden score serves as an imaging marker that can predict cognitive impairment in aging populations. METHODS: MR images acquired from 509 participants with normal cognition were analyzed to evaluate the association between the total CSVD burden score and CBF. Structural scans, pseudo-continuous arterial spin labelling (pCASL) for CBF quantification, and 3D T1-weighted sequences were obtained. CSVD burden scores were rated using a validated 5-point scale by assessing white matter hyperintensity, lacunes, perivascular spaces, and microbleeds. Participants underwent structured cognitive assessments via telephone at a mean follow-up of 7.6 ± 0.1 years post-baseline. Differences in CBF between the CSVD burden groups were compared using univariate linear models, and logistic regression analysis was conducted to estimate the risk of longitudinal cognitive impairment. The predictive model was evaluated using the receiver operating characteristic (ROC) curve analysis. RESULTS: Severe CSVD scores (> 2) were significantly associated with decreased CBF in many cortical regions (Padj< 0.05). Participants with higher CSVD scores were more susceptible to longitudinal cognitive decline (OR 2.995, 95% CI = [1.540, 5.825]; P = 0.001, adjusted for age and sex). The CSVD score model effectively predicted cognitive impairment (AUC = 0.808, P < 0.001), with an optimal cut-off value of grade > 2 (specificity = 88.9%). CONCLUSION: Severe CSVD score, which is associated with cortical hypoperfusion, was found to be an effective imaging marker for predicting longitudinal cognitive decline. Thus, CSVD scoring may be considered a clinically accessible tool for risk stratification and individualized health monitoring in aging populations.