Blood biomarkers for Alzheimer's disease in clinical practice and trials

Abstract

Blood-based biomarkers hold great promise to revolutionize the diagnostic and prognostic work-up of Alzheimer's disease (AD) in clinical practice. This is very timely, considering the recent development of anti-amyloid-β (Aβ) immunotherapies. Several assays for measuring phosphorylated tau (p-tau) in plasma exhibit high diagnostic accuracy in distinguishing AD from all other neurodegenerative diseases in patients with cognitive impairment. Prognostic models based on plasma p-tau levels can also predict future development of AD dementia in patients with mild cognitive complaints. The use of such high-performing plasma p-tau assays in the clinical practice of specialist memory clinics would reduce the need for more costly investigations involving cerebrospinal fluid samples or positron emission tomography. Indeed, blood-based biomarkers already facilitate identification of individuals with pre-symptomatic AD in the context of clinical trials. Longitudinal measurements of such biomarkers will also improve the detection of relevant disease-modifying effects of new drugs or lifestyle interventions.

Fig. 1 |. Suggested blood-based biomarker-based workflow for Alzheimer’s disease diagnostics.

Patients with cognitive complaints undergo blood sampling as part of the standard diagnostic work-up. High-performing blood Alzheimer’s disease (AD) biomarkers (for example, p-tau217) are used to determine the individual-level probability of having AD. For patients deemed to have a very low probability based on blood-based biomarkers (BBMs), another cause of the symptomatology should be sought. For patients deemed to have a very high probability based on BBMs, appropriate treatments might be initiated. Patients with an intermediate probability, whose BBM results lie in an uncertain ‘gray zone’, might be referred for confirmatory testing with either cerebrospinal fluid (CSF) or positron emission tomography (PET) AD biomarkers. The percentage of individuals in such a ‘gray zone’ will depend on the accuracy of the blood-based diagnostic algorithm (very-high-performing BBM assays will have few results ending up in the ‘gray zone’).

Fig. 2 |. An overview of key blood-based biomarkers used in the diagnostic or prognostic work-up of Alzheimer’s disease.

The top two rows depict the evolution of amyloid-β (Aβ) and tau pathological brain changes during the different disease stages of Alzheimer’s disease (AD). The third row shows that high-performing p-tau217 assays will probably be sufficient for detection of AD brain pathological changes in patients with cognitive impairment (mild cognitive impairment (MCI) or dementia). However, during the preclinical stages of the disease (when individuals are still cognitively unimpaired), p-tau231 and Aβ₄₂/Aβ₄₀ are especially important to detect AD brain changes. The bottom row shows that plasma p-tau217 is very important for high-performing prognostic algorithms predicting subsequent development of AD dementia in cognitively unimpaired individuals and patients with MCI^,. Furthermore, in such prognostic algorithms, brief cognitive tests also contribute to the predictive accuracy. Future studies are needed to determine whether new fluid tau markers such as microtubule-binding region (MTBR)-tau will add diagnostic and prognostic information when combined with the already established markers. NA, not applicable.

Fig. 3 |. Suggested workflow for inclusion of study participants into preclinical Alzheimer’s disease trials.

In the ‘prescreening’ step, a diagnostic algorithm based on blood-based biomarkers (BBMs) for Alzheimer’s disease (AD) identifies cognitively normal individuals as being at low risk or high risk of having pre-symptomatic (preclinical) AD. In the ‘screening’ step, individuals deemed high risk will undergo further tests, involving amyloid-β (Aβ) positron emission tomography (PET) or cerebrospinal fluid (CSF) AD biomarkers, to confirm or rule out the presence of AD pathology. The prescreening step with BBMs will result in substantial time and cost savings, as far fewer CSF or PET tests will be needed to identify a certain number of individuals with preclinical AD. In the ‘enrichment’ step, a prognostic algorithm can be used to identify individuals who are likely to subsequently exhibit more severe spread of tau pathology and cognitive decline, so that the population to be included in the trial is enriched for such individuals. This latter enrichment step enables preclinical AD trials with shorter durations and/or fewer study participants.