Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic

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Biomarker discovery and development for clinical research, diagnostics and therapy monitoring in clinical trials have advanced rapidly in key areas of medicine — most notably, oncology and cardiovascular diseases — allowing rapid early detection and supporting the evolution of biomarker-guided, precision-medicine-based targeted therapies. In Alzheimer disease (AD), breakthroughs in biomarker identification and validation include cerebrospinal fluid and PET markers of amyloid-β and tau proteins, which are highly accurate in detecting the presence of AD-associated pathophysiological and neuropathological changes. However, the high cost, insufficient accessibility and/or invasiveness of these assays limit their use as viable first-line tools for detecting patterns of pathophysiology. Therefore, a multistage, tiered approach is needed, prioritizing development of an initial screen to exclude from these tests the high numbers of people with cognitive deficits who do not demonstrate evidence of underlying AD pathophysiology. This Review summarizes the efforts of an international working group that aimed to survey the current landscape of blood-based AD biomarkers and outlines operational steps for an effective academic–industry co-development pathway from identification and assay development to validation for clinical use.

Key points

  • Cerebrospinal fluid (CSF) and PET markers of amyloid-β and tau proteins are accurate in detecting the neuropathological changes of Alzheimer disease (AD).

  • The use of CSF and PET biomarkers is limited by invasiveness or high costs; to address these issues, blood-based AD biomarkers are eagerly awaited.

  • An international, interdisciplinary expert working group was convened by the Alzheimer’s Precision Medicine Initiative to discuss the ideal development process for blood-based biomarkers.

  • Nineteen blood-based biomarker assays were selected by the working group for further consideration.

  • The working group outlined the pathway from biomarker identification and assay development to validation for clinical use and proposed clear steps for effective academic–industry co-development of blood-based AD biomarkers.

  • The development, standardization and validation of blood-based biomarkers will be paramount to the implementation of precision medicine in AD.

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Fig. 1: Challenges in developing a blood-based biomarker for CNS disease.
Fig. 2: Candidate blood-based biomarkers identified in the landscape analysis.
Fig. 3: Promising blood-based biomarker candidates.
Fig. 4: Idealized validation process for blood-based biomarkers.
Fig. 5: Validation of blood-based biomarkers.
Fig. 6: Potential collaboration points between academia and industry.


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The authors acknowledge the following employees of F. Hoffmann-La Roche Ltd (Basel, Switzerland) who attended the working group meeting, the discussions from which formed the basis of this Review: Barbara Schaeuble, Bruce Jordan, Chiaki Yoda, Christian Czech, Estelle Vester-Blokland and Hai Zhang. Maryline Simon (Roche Diagnostics International, Rotkreuz, Switzerland) and Tobias Bittner (Genentech, Basel, Switzerland) also contributed to the meeting. Editorial support, in the form of minute-taking during the group meetings and formatting of the manuscript before submission, was provided by Jennifer Smith of MediTech Media Ltd (funded by Roche Diagnostics International). H.H. is supported by the AXA Research Fund, the Fondation Partenariale Sorbonne Université and the Fondation pour la Recherche sur Alzheimer, Paris, France. The research leading to these results has received funding from the programme Investissements d’Avenir ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA and Agence Institut Hospitalo-Universitaire-6). This research benefited from the support of the Program PHOENIX led by the Sorbonne University Foundation and sponsored by the Fondation pour la Recherche sur Alzheimer.

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Nature Reviews Neurology thanks T. Leyhe, P. Lewczuk and the other anonymous reviewer for their contribution to the peer review of this work.

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H.H. and K.B. provided the initial idea and outline of content for the manuscript. All authors contributed content and critically reviewed and edited the manuscript.

Correspondence to Harald Hampel or Kaj Blennow.

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Competing interests

H.H. received personal fees from Roche Diagnostics International during the conduct of the study. He has also received lecture fees from Biogen and Roche; research grants from Pfizer, Avid and MSD Avenir (paid to his institution); travel funding from Functional Neuromodulation, Axovant, Eli Lilly and Company, Takeda and Zinfandel, GE Healthcare and Oryzon Genomics; and consultancy fees from Jung Diagnostics, Cytox, Axovant, Anavex, Takeda and Zinfandel, GE Healthcare, Oryzon Genomics and Functional Neuromodulation. H.H. participated in scientific advisory boards of Functional Neuromodulation, Axovant, Eli Lilly and Company, Cytox, GE Healthcare, Takeda and Zinfandel, Oryzon Genomics and Roche Diagnostics International. H.H. is a co-inventor on numerous patents relating to biomarker measurement but has received no royalties from these patents. S.E.O. declares personal fees from Roche Diagnostics International and grants from the US National Institute on Aging during the conduct of the study as well as personal fees from Cx Precision Medicine outside the submitted work. In addition, S.E.O. has three patents licensed to Cx Precision Medicine. J.L.M. declares personal fees from Roche Diagnostics International during the conduct of the study and personal fees from IBL International, Raman Health and Fujirebio outside the submitted work. H.Z. is a co-founder of Brain Biomarker Solutions in Gothenburg AB, a GU Ventures-based platform company at the University of Gothenburg, Sweden, and reports personal fees from Roche Diagnostics International during the conduct of the study. C.L.M. declares personal fees from Roche Diagnostics International during the conduct of the study. S.L. received lecture honoraria from Roche. S.J.K. reports personal fees from Roche Diagnostics International, grants from the Medical Research Council (UK) and National Institute for Health Research Biomedical Research Centre for Mental Health during the conduct of the study and grants from Eli Lilly and Company outside the submitted work. R.B. is a current employee and stockholder of Roche Diagnostics International. K.B. declares personal fees from Roche Diagnostics International during the conduct of the study and personal fees from Fujirebio Europe, IBL International, Roche Diagnostics, Eli Lilly and Company and Alzheon outside the submitted work. In addition, K.B. has a European Patent application (EP 16002379.2) pending and is co-founder of Brain Biomarker Solutions in Gothenburg AB.

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Supplementary information



Diagnostic sensitivity is the probability that a test result is positive when the disease is present.

Subjective cognitive decline

A self-reported decline in cognition, undetected by standard neuropsychological tests.

Receiver operating characteristic

(ROC). The ROC curve is a plot of sensitivity versus 1 − specificity for the different possible cut-off points of a diagnostic test. Accuracy of the diagnostic test is based on the area under the ROC curve; the closer the area under the ROC curve is to 1, the better the test.

Positive predictive value

(PPV). The probability that a patient has the disease when the test result is positive.

Negative predictive value

(NPV). The probability that a patient does not have the disease when the test result is negative.

Context of use

(COU). A statement that describes the manner and purpose of use for the biomarker in drug development. The supporting data and analyses submitted with the biomarker qualification determine the acceptability of the qualified COU.

A/T/N classification system

A classification system that uses three binary biomarker categories reflecting Alzheimer disease pathophysiology. ‘A’ refers to biomarkers of amyloid-β (Aβ) pathology (cerebrospinal fluid (CSF) Aβ1–42 or amyloid PET), ‘T’ refers to biomarkers of tau pathology (CSF hyperphosphorylated tau or tau PET) and ‘N’ refers to biomarkers of neurodegeneration or neuronal injury (CSF total tau 18F-FDG–PET or structural MRI).


Diagnostic specificity is the probability that a test result is negative when the disease is absent.

Big data

A repository of many data sets generated by data-mining tools, including information obtained through systems-theory-based and knowledge-based approaches, and clinical records.

Integrative disease modelling

A multidisciplinary approach to standardize, manage, integrate and interpret multiple sources of structured and unstructured quantitative and qualitative data across biological scales using computational models that assist decision-making for translation of patient-specific molecular mechanisms into tailored clinical applications.

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