Blood-Based Biomarkers Promising for Predicting Alzheimer Disease


Previous research demonstrated that plasma neuronal-enriched extracellular vesicles exhibited elevated levels of p-tau, amyloid-beta 42, and phosphorylated insulin receptor substrate.

Dimitrios Kapogiannis, MD

Dimitrios Kapogiannis, MD

Longitudinal blood samples collected from cognitively normal patients are capable of predicting Alzheimer disease diagnosis, new findings demonstrate.

The study, presented at the 2019 Alzheimer’s Association International Conference, July 14-18, in Los Angeles, California, was simultaneously published in JAMA Neurology.

Investigators led by Dimitrios Kapogiannis, MD, of the National Institutes of Health, sought to validate the utility of plasma neuronal-enriched extracellular vesicle (nEVs) biomarkers as predictors of Alzheimer disease. Previous research demonstrated that nEVs exhibited elevated levels of phosphorylated tau (p-tau), amyloid-beta 42, and phosphorylated insulin receptor substrate (IRS-1).

The current case-control study included 887 longitudinal plasma samples from 128 cognitively normal participants in the Baltimore Longitudinal Study of Aging who eventually developed Alzheimer disease, as well as 222 age and sex-matched controls. Participants were followed for a mean of 3.5 years, with samples blind analyzed over 1 year.

Overall, 161 participants were included in the training set and 80 were included in the test set. Those in the aging study cohort with future Alzheimer disease showed longitudinally higher nEV average diameter (166 nm vs 152 nm; difference = 14 nm; 95% CI, 7-17; P <.001); higher p-tau231 (6.3 AU vs 5.2 AU; difference = 1.1 AU; 95% CI, 0.3-1.5; P =.004); higher p-tau181 (10.4 AU vs 8.4 AU; difference = 2 AU; 95% CI, 1.5-3.3; P <.001); higher pY-IRS-1 (3.2 AU vs 2.3 AU; difference = 0.9 AU; 95% CI, 0.3-1.5; P =.003); and higher pSer312-IRS-1 (8.0 AU vs 6.1 AU; difference = 1.9 AU; 95% CI, 0.2-4; P =.02) compared to controls, though the group had similar amyloid-beta 42, total tau, TSG101, and nEV concentration.

The investigators noted that “higher p-tau181 was associated with worse verbal memory, attention, executive function, and visuospatial function cross-sectionally. Higher pSer312-IRS-1 was associated with worse verbal memory and executive function cross-sectionally. Higher Aβ42 at the first preclinical visit was associated with better verbal memory and language longitudinally.”

Among those in the training dataset, a model that combined preclinical longitudinal data achieved 89.6% AUC for predicting Alzheimer disease. Results were similar in the test cohort, achieving 80% AUC for predicting Alzheimer disease.

Additional analyses of the biomarkers in a training cohort demonstrated 89.8% AUC for discriminating patients with Alzheimer disease from controls. The threshold risk score for the training set (>0.412) achieved 100% sensitivity (95% CI, 82.2%-100%) and 94.7% specificity (95% CI, 71.9%-99.7%), which in the testing cohort, the same model achieved 76.6% AUC with 91.7sensitivity (95% CI, 59.8%-99.6%) and 60% specificity (95% CI, 27.4%- 86.3%).

Overall, the odds of having a risk score over the threshold was 999 times higher for patients with Alzheimer disease versus controls in the training set, and 16.5 times higher in the testing set.

“We … demonstrated that, by leveraging repeated measures as long-term averages and rates of changes and optimally combining them in a prediction model, nEV biomarkers predict AD with high specificity,” the investigators wrote. “The discriminant ability of the final model outperformed both classic and recently proposed blood biomarkers … rivaling CSF biomarkers.”

They noted that nEV biomarkers outperformed other blood and cerebrospinal fluid-based biomarkers, as well as amyloid imaging, for predicting preclinical Alzheimer disease.

“These findings reaffirm the validity of nEV biomarkers and motivate their further development toward a clinical blood test for AD,” the investigators concluded. “The ultimate motivation for nEV biomarker studies is the hope that they may enable researchers to identify older individuals at the preclinical stage of AD and select them for clinical trials, augmenting our ability to test hypotheses and spearheading therapeutic discovery for the disease.”

For more coverage of AAIC 2019, click here.


Kapogiannis D, Mustapic M, Shardell MD, et al. Association of extracellular vesicle biomarkers with Alzheimer disease in the Baltimore Longitudinal Study of Aging. JAMA Neurol. Published online July 15, 2019. doi:10.1001/jamaneurol.2019.2462.

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