Neurofibrillary tangles predict dementia in patients with carotid stenosis

Objective: There is growing appreciation that extracranial carotid atherosclerotic disease (ECAD) is associated with increased dementia risk. Despite this, clinical management of ECAD does not involve evaluation for cognitive outcomes or risk stratification for dementia. One impediment to studying and improving clinical care for this cohort (roughly 10% of adults aged >60 years) is that factors to identify patients with ECAD at risk for dementia are not known. Methods: Our prospective clinicopathologic study, the Arizona Study of Aging and Neurodegenerative Disorders study, evaluated clinical and histopathologic factors for dementia in subjects with ECAD. The primary outcome (dementia) was defined as a composite of Alzheimer's disease and/or vascular dementia based on a clinical/neuropathologic diagnosis. Of 1234 subjects, those with dementia other than Alzheimer's disease and/or vascular dementia were excluded; there remained 111 subjects with ECAD to be evaluated. Logistic regression analysis was performed to examine the association of key risk factors for dementia including age, sex, cardiovascular risk factors, apolipoprotein E4 (APOE4) genetic status, and dementia biomarkers. A precision recall curve was also generated to evaluate the diagnostic accuracy of dementia prediction models. Results: Individuals with dementia compared with those without had significantly increased levels of stroke, APOE4 genotype, and dementia biomarkers, neurofibrillary tangles (NFTs), and amyloid plaques. Models of multiple combined risk factors were little or no better than NFTs alone, which showed a 96.9% positive predictive value at an NFT threshold of 10. Conclusions: Although we hypothesized that a combination of clinical and histopathologic biomarkers would result in the strongest predictive model for dementia, we found that NFTs alone had the highest association and positive predictive value for dementia risk in patients with ECAD. As blood-based assays for NFT quantification become more clinically reliable and available, these data support the possibility that NFT quantification may help identify patients with ECAD at increased risk for dementia.