Background: Advances in ultrasensitive detection of phosphorylated tau (p-tau) in plasma has enabled the use of blood tests to measure Alzheimer’s disease (AD) biomarker changes. Examination of postmortem brains of participants with antemortem plasma p-tau levels remains critical to understanding comorbid and AD-specific contribution to these biomarker changes. Methods: We analyzed 35 population-based Mayo Clinic Study of Aging participants with plasma p-tau at threonine 181 and threonine 217 (p-tau181, p-tau217) available within 3 years of death. Autopsied participants included cognitively unimpaired, mild cognitive impairment, AD dementia, and non-AD neurodegenerative disorders. Global neuropathologic scales of tau, amyloid-β, TDP-43, and cerebrovascular disease were examined. Regional digital pathology measures of tau (phosphorylated threonine 181 and 217 [pT181, pT217]) and amyloid-β (6F/3D) were quantified in hippocampus and parietal cortex. Neurotransmitter hubs reported to influence development of tangles (nucleus basalis of Meynert) and amyloid-β plaques (locus coeruleus) were evaluated. Results: The strongest regional associations were with parietal cortex for tau burden (p-tau181 R = 0.55, p = 0.003; p-tau217 R = 0.66, p < 0.001) and amyloid-β burden (p-tau181 R = 0.59, p < 0.001; p-tau217 R = 0.71, p < 0.001). Linear regression analysis of global neuropathologic scales explained 31% of variability in plasma p-tau181 (Adj. R² = 0.31) and 59% in plasma p-tau217 (Adj. R² = 0.59). Neither TDP-43 nor cerebrovascular disease global scales independently contributed to variability. Global scales of tau pathology (β-coefficient = 0.060, p = 0.016) and amyloid-β pathology (β-coefficient = 0.080, p < 0.001) independently predicted plasma p-tau217 when modeled together with co-pathologies, but only amyloid-β (β-coefficient = 0.33, p = 0.021) significantly predicted plasma p-tau181. While nucleus basalis of Meynert neuron count/mm² was not associated with plasma p-tau levels, a lower locus coeruleus neuron count/mm² was associated with higher plasma p-tau181 (R = -0.50, p = 0.007) and higher plasma p-tau217 (R = -0.55, p = 0.002). Cognitive scores (Adj. R² = 0.25–0.32) were predicted by the global tau scale, but not by the global amyloid-β scale or plasma p-tau when modeled simultaneously. Conclusions: Higher soluble plasma p-tau levels may be the result of an intersection between insoluble deposits of amyloid-β and tau accumulation in brain, and may be associated with locus coeruleus degeneration.

The clinical presentations of early-onset Alzheimer’s disease (EOAD) and late-onset Alzheimer’s disease are distinct, with EOAD having a more aggressive disease course with greater heterogeneity. Recent publications from the Longitudinal Early-Onset Alzheimer’s Disease Study (LEADS) described EOAD as predominantly amnestic, though this phenotypic description was based solely on clinical judgment. To better understand the phenotypic range of EOAD presentation, we applied a neuropsychological data-driven method to subtype the LEADS cohort. Neuropsychological test performance from 169 amyloid-positive EOAD participants were analyzed. Education-corrected normative comparisons were made using a sample of 98 cognitively normal participants. Comparing the relative levels of impairment between each cognitive domain, we applied a cut-off of 1 SD below all other domain scores to indicate a phenotype of “predominant” impairment in a given cognitive domain. Individuals were otherwise considered to have multidomain impairment. Whole-cortex general linear modeling of cortical atrophy was applied as an MRI-based validation of these distinct clinical phenotypes. We identified 6 phenotypic subtypes of EOAD: Dysexecutive Predominant (22% of sample), Amnestic Predominant (11%), Language Predominant (11%), Visuospatial Predominant (15%), Mixed Amnestic/Dysexecutive Predominant (11%), and Multidomain (30%). These phenotypes did not differ by age, sex, or years of education. The APOE ɛ4 genotype was enriched in the Amnestic Predominant group, who were also rated as least impaired. Cortical thickness analysis validated these clinical phenotypes with dissociations in atrophy patterns observed between the Dysexecutive and Amnestic Predominant groups. In contrast to the heterogeneity observed from our neuropsychological data-driven approach, diagnostic classifications for this same sample based solely on clinical judgment indicated that 82% of individuals were amnestic-predominant, 9% were non-amnestic, 4% met criteria for Posterior Cortical Atrophy, and 5% met criteria for Primary Progressive Aphasia. A neuropsychological data-driven method to phenotype EOAD individuals uncovered a more detailed understanding of the presenting heterogeneity in this atypical AD sample compared to clinical judgment alone. Clinicians and patients may over-report memory dysfunction at the expense of non-memory symptoms. These findings have important implications for diagnostic accuracy and treatment considerations.

Anti-amyloid therapies (AATs) are increasingly being recognized as promising treatment options for Alzheimer’s disease (AD). Amyloid-related imaging abnormalities (ARIAs), small areas of edema and microbleeds in the brain presenting as abnormal signals in MRIs of the brain for patients with AD, are the most common side effects of AATs. While most ARIAs are asymptomatic, they can be associated with symptoms like nausea, headache, confusion, and gait instability and, less commonly, with more serious complications such as seizures and death. Cerebral amyloid angiopathy (CAA) has been found to be a major risk for ARIA development. The identification of sensitive and reliable non-invasive biomarkers for CAA has been an area of AD research over the years, but with the approval of AATs, this area has taken on a new urgency. This comprehensive review highlights several potential biomarkers, such as Aβ40, Aβ40/42, phosphorylated-tau217, neurofilament light chain, glial fibrillary acidic protein, secreted phosphoprotein 1, placental growth factor, triggering receptor expressed on myeloid cells 2, cluster of differentiation 163, proteomics, and microRNA. Identifying and staging CAA even before its consequences can be detected via neuroimaging are critical to allow clinicians to judiciously select appropriate candidates for AATs, stratify monitoring, properly manage therapeutic regimens for those experiencing symptomatic ARIAs, and optimize the treatment to achieve the best outcomes. Future studies can test potential plasma biomarkers in human beings and evaluate predictive values of individual markers for CAA severity.