Skip to main content

Blood-based molecular biomarkers for Alzheimer’s disease


A major barrier to the effective conduct of clinical trials of new drug candidates against Alzheimer’s disease (AD) and to identifying patients for receiving future disease-modifying treatments is the limited capacity of the current health system to find and diagnose patients with early AD pathology. This may be related in part to the limited capacity of the current health systems to select those people likely to have AD pathology in order to confirm the diagnosis with available cerebrospinal fluid and imaging biomarkers at memory clinics. In the current narrative review, we summarize the literature on candidate blood tests for AD that could be implemented in primary care settings and used for the effective identification of individuals at increased risk of AD pathology, who could be referred for potential inclusion in clinical trials or future approved treatments following additional testing. We give an updated account of blood-based candidate biomarkers and biomarker panels for AD-related brain changes. Our analysis centres on biomarker candidates that have been replicated in more than one study and discusses the need of further studies to achieve the goal of a primary care-based screening algorithm for AD.


Alzheimer’s disease (AD) is characterized by the accumulation of extracellular amyloid β (Aβ) plaques, intraneuronal inclusions (neurofibrillary tangles) composed of truncated and phosphorylated forms of the microtubule-stabilizing protein tau, dystrophic neurites, loss of synapses and neurons, and a prominent gliosis that involves changes in the morphology and function of microglia and astrocytes [1]. Currently, we have validated biomarkers for amyloid pathology (Aβ positron emission tomography [PET] and the ratio of 42 over 40 amino acid long Aβ cerebrospinal fluid [CSF Aβ42/Aβ40]) [2], as well as tau dysfunction and aggregation (cerebrospinal fluid [CSF] total or phosphorylated tau and tau PET) [3]. CSF and PET examinations are, however, far from standard tests in general practice and, given the high prevalence of the disease, alternatives such as blood-based biomarkers would represent a significant development, even as screening tools to determine who should be referred to a memory clinic for specific testing. Here, we review recent developments in the field of blood biomarkers for AD and discuss the results in the context of the quests to improve diagnostic algorithms and facilitate clinical trials of novel candidate drugs.

Blood biomarkers – general considerations

Measuring biomarkers for brain diseases in the blood poses a number of challenges that demand sensitive and specific assays and careful validation work. Brain-derived biomarkers are typically present at relatively low concentrations in the blood because of the blood-brain barrier preventing free passage of molecules between the CNS and blood compartments. In addition, some of the biomarkers related to AD pathology are expressed in non-cerebral tissues, which may confound their measurement in the blood. Further, in blood, there may be heterophilic antibodies (endogenous antibodies that react with the antibodies of the immunochemical test to measure the biomarker), which may give falsely high or low results. These types of antibodies are much less of a problem in CSF where antibody levels are much lower. Finally, the analyte of interest may undergo proteolytic degradation by various proteases in plasma. Below, we discuss recent developments of biomarkers for the pathological processes involved in AD. We discuss biomarker tests that are directed against a single pathological change and biomarker panels that may reflect tissue reactions to such changes.

Targeted blood-based biomarkers

Plasma Aβ

Early results on plasma Aβ, described in the AlzBiomarker database (, revealed no consistent change in either plasma Aβ42 or Aβ40 in AD [4]. This result was most likely due to assay-related difficulties; plasma Aβ measurements may be influenced by matrix effects (mainly other plasma proteins binding Aβ) and the analytical sensitivities of the first assays did not allow for diluting away such matrix effects. In 2011, an ultrasensitive Single molecule array (Simoa) assay for Aβ42 was published [5]. A correlation of plasma with CSF Aβ42 emerged and the improved analytical sensitivity clarified that the ratio of Aβ42 to Aβ40 in plasma was reduced in amyloid PET-positive individuals in a manner similar to CSF Aβ42/Aβ40, although with less marked separation [6, 7]. A few years ago, reliable immunoprecipitation mass spectrometry (IP-MS)-based assays for Aβ40 and Aβ42 were described showing a decrease in the plasma Aβ42/Aβ40 ratio (similar to the CSF test) with around 90% diagnostic accuracy [8, 9]. These are highly promising developments that have spurred several method comparison and standardization studies that are just about to start. These new assay formats do not solve the confounding problem with non-cerebral expression of Aβ, e.g., in blood platelets [10], which affects the specificity of the test for cerebral Aβ plaques, but still represent an important step forward.

Plasma tau

In the dementia stage of AD, plasma tau concentrations, measured using ultrasensitive assays, are increased compared with cognitively normal control individuals, but not as clearly as in CSF [11], which is a well-replicated finding [4]. There is little clarity on the application of these findings to intermediary participants in the mild cognitive impairment (MCI) stage of the disease with reports being less clear [12]. Nevertheless, in a recent paper, Mielke and colleagues examined the relationship of plasma T-tau concentration, determined by Simoa, with cognitive decline in 458 participants from the Mayo Clinic Study on Aging and found high plasma T-tau associated with faster clinical disease progression [13]. Another study prospective cohort study used data from the US community-based Framingham Heart Study with replication in the Memento study, and found that plasma T-tau was strongly associated with incident AD dementia [14]. Mielke et al. also developed an assay for tau phosphorylated at amino acid 181 [15]. Plasma P-tau concentration, measured using this assay, was associated with both Aβ and tau PET, as well as with other AD-associated phenotypes [15]; these associations were stronger than those obtained using the plasma T-tau test. The general findings of this study resonate well with two studies showing increased plasma P-tau concentration in AD, as determined using immunomagnetic reduction technology [16] and Simoa [17], respectively.

Plasma neurofilament light

A well-replicated biomarker for neurodegeneration in AD is plasma neurofilament light (NfL), which is an intra-axonal structural protein that leaks into body fluids upon axonal injury irrespective of cause [18]. Serum or plasma levels of NfL (either matrix has good efficacy) correlate strongly with CSF NfL, are increased in several non-AD neurodegenerative diseases and are increased in both familial and sporadic AD [18]. In familial AD, the levels increase in the time window around 10 years prior to expected clinical disease onset [19]; in sporadic disease the changes may appear slightly later and may also be influenced by age-related changes (NfL concentration increase around 3% per year in CSF in normal aging [20]), or non-AD pathologies [21]. Altogether, plasma or serum NfL may be a reliable blood biomarker for neurodegeneration in AD and other neurodegenerative diseases.

Panels of blood-based biomarkers

Protein biomarker panels

It has been suggested that panels of markers may outperform single candidate markers for diagnosing, prognosing and characterising AD [22]. Thus, a number of multivariate blood-based biomarker panels have been proposed for classifying the disease and its pathology. A large number of reports show efficacy of stratifying cases from controls using multivariate panels of protein markers. Ray et al. published one of the first of these studies, which identified 18 plasma proteins whose combined signature could distinguish AD patients from controls with 90% accuracy, as well as identify MCI patients who were at risk for progression to AD within five years [23]. The same group subsequently published results from independent samples detailing that many of the 18 proteins originally identified were also associated with CSF levels of Aβ and tau [24]. However, other attempts to replicate the original findings have not been successful. Marksteiner and colleagues found that only five out of 16 of the original 18 markers had significantly different concentrations in MCI and AD patients vs. controls [25]. They found the sensitivity and specificity of the panel for identifying AD and MCI to be 65–75% and 52–63%, respectively [25]. Similarly, Soares et al. found the diagnostic accuracy of the panel to be 60% [26], whilst Björkqvist et al. reported an area under the curve (AUC) of 63% [27]. Soares et al., after failing to replicate efficacy in the Ray panel, proposed an alternative 89-analyte panel with a diagnostic accuracy of 70% [26]. Subsequently, there have been numerous contributions of multivariate signatures for differentiating AD and/or MCI patients from controls [28,29,30,31,32,33,34,35,36,37,38,39], as well as a number of review articles [22, 40,41,42,43]. Two studies of most note include contributions by O’Bryant and colleagues [44] and Doecke and colleagues [45] who identified algorithmic signatures of multivariate analytes in large, well-characterised cohorts with AUC characteristics of at least 93% in serum and plasma, respectively. Doecke et al. also validated their signature in a second cohort with an AUC of 85% [45]. Many of the analytes identified across these studies are related to amyloidosis and/or inflammation, but still there has been a notable lack of harmonisation and replication of the findings. This is an area of research that has been much scrutinised, with a number of considerations and recommendations being made by O’Bryant and colleagues regarding study design, pre-analytical protocols and assay-related issues [46,47,48,49].

Panels of blood-based biomarker-associated disease phenotypes

Further to case-control studies, blood-based biomarker panels have been designed to estimate disease-related phenotypes, such as cognitive decline, brain atrophy and neocortical Aβ deposition. Inflammatory markers [50,51,52], as well as proteins associated with the complement cascade [53], have reported associations with cognitive performance, cognitive decline and clinical progression. Most of these contributions also examined the association of proteomic signatures with brain volumetry finding similar signatures [50, 54,55,56]. Reports suggest that such signatures are able to explain approximately one third of the between subject variation observed in brain volumes [54, 57].

Blood biomarkers able to identify AD in its earliest stages are predicted to have the most impact for use as a screening tool. The early timing of abnormal levels of neocortical Aβ deposition make it the ‘gold standard’ for early identification of disease, and therefore blood signatures associated with neocortical Aβ deposition have received considerable interest. Burnham et al. identified six plasma proteins that contributed to a signature that was able to estimate levels of neocortical Aβ deposition in the AIBL cohort with a sensitivity and specificity of 80 and 82%, respectively [58]. Validation of this signature in ADNI samples resulted in 79% sensitivity and 76% specificity [58]. The same group published follow-up results 54 months later, detailing the predictive ability of the same signature for identifying progression towards disease; cognitively normal individuals considered at risk by the signature progressed to MCI or AD with an odds ratio of 2.4 in comparison to those considered not at risk [59]. MCI participants considered at risk by the signature progressed to AD with an odds ratio of 12.3 in comparison to those considered not at risk [59]. Kiddle et al. similarly reported a signature that was associated with neocortical Aβ deposition, identifying 13 markers which were able to explain over 30 % of the between subject variation observed in neocortical Aβ deposition [60]. Other studies with the same aim of inferring neocortical Aβ deposition from a blood biomarker signature have reported efficacies of between 58 and 78% [61,62,63]. A replication study by Voyle and colleagues found pancreatic polypeptide and IgM to correlate with neocortical Aβ deposition, with IgM also correlating with neocortical Aβ deposition within cognitively normal participants [63]. Again, the signatures across these contributions aligned with both inflammatory response and the complement cascade.


Recent advances in nuclear magnetic resonance and mass spectroscopy, coupled with high performance liquid/gas chromatography have allowed the dynamic evaluation of thousands of metabolites, reflecting functional networks of downstream changes of the genome, transcriptome and proteome. Metabolic changes in plasma demonstrating a separation between controls and AD were reported by Greenberg et al. [64], with the two most influential metabolites being glycerophosphocholine and d-glucosaminide. Another contribution from Oresic et al. demonstrated metabolites that were differentially expressed in MCI participants who did and did not progress to AD within a 2-year time frame, with this finding being specifically driven by 2,4-dihydroxybutanoic acid [65]. Trushina and Mielke discuss results suggesting beta-alanine, aspartate and aspargine, alanine, l-cysteine, l-methionine, methionine–cysteine–glutamate, l-arginine, lysine and bile acid biosynthesis and metabolism were significantly different between controls and AD [66].

With links between cardiovascular disease and AD being drawn, as well as some promising results from lifestyle intervention on cognitive performance [67], lipid evaluation has become another area of interest in the search for blood-based biomarkers of AD. Two recent pilot studies have shown levels of lipids to differ significantly between mutation carriers and non-carriers in a study on familial AD [68], as well as AD and controls in a sporadic AD study [69]. However, neither study was able to use these profiles to accurately discriminate between the respective classes. Mapstone and colleagues were able to successfully discriminate AD from controls with 90% sensitivity and specificity in two separate studies. Firstly, using a panel of 10 lipids [70] and secondly with a panel of 22 lipids combined with an amino acid and a biogenic amine [71]. However, an independent group was unable to validate the findings from the 10-lipid panel in larger studies [72].

miRNA biomarker panels

Epigenetics are also of increasing interest to the field, with gene regulation by micro RNA (miRNA) representing one such focus for biomarker discovery. It is hypothesised that miRNAs are transported within liposomes, HDLs, exosomes and other proteins protecting the miRNA from degradation. Leidinger et al. first reported a panel of 12 miRNAs able to discriminate AD from controls with an accuracy of 93% [73]. The panel also had the ability to discriminate AD patients from patients with MCI, multiple sclerosis, parkinsonism, major depression, schizophrenia, and bipolar disorder with accuracies of 74–78% [73]. Other studies identifying panels of miRNAs have been reported to discriminate between AD patients and controls with accuracies between 75 and 95% [74,75,76]. A recent review of miRNAs as biomarkers for AD is provided by Nagaraj and colleagues [77] where they report 136 individual miRNAs to be significantly altered between AD and control states from the literature. Of these 136 miRNAs, 36 were independently validated and reported in two or more contributions, with hsa-miR-125b being reported the most, 6, times.


This is a hot and controversial topic in the field of blood-based biomarkers for AD. Initial reports, in which neuronally derived exosomes were isolated from serum using antibodies against CNS-enriched antigens, lysed and analysed for AD-related biomarkers, showed promising results [78, 79], but the protocols are hard to follow and it is at the moment difficult to draw any strong conclusions on where this field is going.

Future perspective

As instruments for quantifying blood biomarkers become more sensitive and the understanding and implementation of standardisation procedures for sample processing and analysis are increased, the field moves ever closer to the quest for blood biomarkers in AD. These improvements appear to have provided a shift in the most recent literature, away from multiplexed assays that were popular approximately 5 years ago, back full circle to single candidate markers that were also the focus of investigations 10–15 years ago. The candidate markers showing the most promise as markers for AD are plasma Aβ42/Aβ40 ratio, tau and NfL but there is also promising data on new panels that may represent tissue responses to AD-related pathologies. However, replication and more detailed analyses are required to understand their merits as potential screening, diagnostic, prognostic or monitoring markers of the disease. An important goal will be to develop a screening/triage algorithm based on blood tests for Aβ pathology and neurodegeneration that could be implemented in primary care settings and used for the effective identification of individuals who could be referred for further testing using CSF and PET markers and potential inclusion in clinical trials or future approved treatments. To validate such candidate algorithms, the AD biomarker research community needs to collaborate with primary healthcare specialists to establish new primary care-based cohorts that are evaluated using currently available gold standard measures of AD pathology (CSF and/or amyloid PET in specialised memory clinics) following application of the index test (the blood-based biomarker algorithm). When validated, such a diagnostic algorithm would facilitate AD drug development and prepare effective clinical pathways for the advent of disease-modifying therapies targeted against amyloid pathology.



Alzheimer’s disease


Area under the curve


Amyloid β


Cerebrospinal fluid


Immunoprecipitation mass spectrometry


Mild cognitive impairment


Micro RNA


Neurofilament light


Positron emission tomography


Single molecule array


  1. 1.

    Calderon-Garciduenas AL, Duyckaerts C. Alzheimer disease. Handb Clin Neurol. 2017;145:325–37.

    PubMed  Article  Google Scholar 

  2. 2.

    Cohen AD, Landau SM, Snitz BE, Klunk WE, Blennow K, Zetterberg H. Fluid and PET biomarkers for amyloid pathology in Alzheimer's disease. Mol Cell Neurosci. 2018.

  3. 3.

    Scholl M, Maass A, Mattsson N, Ashton NJ, Blennow K, Zetterberg H, Jagust W. Biomarkers for tau pathology. Mol Cell Neurosci. 2018.

  4. 4.

    Olsson B, Lautner R, Andreasson U, Ohrfelt A, Portelius E, Bjerke M, Holtta M, Rosen C, Olsson C, Strobel G, et al. CSF and blood biomarkers for the diagnosis of Alzheimer's disease: a systematic review and meta-analysis. Lancet Neurol. 2016;15(7):673–84.

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Zetterberg H, Mortberg E, Song L, Chang L, Provuncher GK, Patel PP, Ferrell E, Fournier DR, Kan CW, Campbell TG, et al. Hypoxia due to cardiac arrest induces a time-dependent increase in serum amyloid beta levels in humans. PLoS One. 2011;6(12):e28263.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Janelidze S, Stomrud E, Palmqvist S, Zetterberg H, van Westen D, Jeromin A, Song L, Hanlon D, Tan Hehir CA, Baker D, et al. Plasma beta-amyloid in Alzheimer's disease and vascular disease. Sci Rep. 2016;6:26801.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Verberk IMW, Slot RE, Verfaillie SCJ, Heijst H, Prins ND, van Berckel BNM, Scheltens P, Teunissen CE, van der Flier WM. Plasma amyloid as Prescreener for the earliest Alzheimer pathological changes. Ann Neurol. 2018;84(5):648–58.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Nakamura A, Kaneko N, Villemagne VL, Kato T, Doecke J, Dore V, Fowler C, Li QX, Martins R, Rowe C, et al. High performance plasma amyloid-beta biomarkers for Alzheimer's disease. Nature. 2018;554(7691):249–54.

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Ovod V, Ramsey KN, Mawuenyega KG, Bollinger JG, Hicks T, Schneider T, Sullivan M, Paumier K, Holtzman DM, Morris JC, Benzinger T, Fagan AM, Patterson BW, Bateman RJ. Amyloid β concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement. 2017;13(8):841–49.

  10. 10.

    Li QX, Berndt MC, Bush AI, Rumble B, Mackenzie I, Friedhuber A, Beyreuther K, Masters CL. Membrane-associated forms of the beta A4 amyloid protein precursor of Alzheimer's disease in human platelet and brain: surface expression on the activated human platelet. Blood. 1994;84(1):133–42.

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Zetterberg H, Wilson D, Andreasson U, Minthon L, Blennow K, Randall J, Hansson O. Plasma tau levels in Alzheimer's disease. Alzheimers Res Ther. 2013;5(2):9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Mattsson N, Zetterberg H, Janelidze S, Insel PS, Andreasson U, Stomrud E, Palmqvist S, Baker D, Tan Hehir CA, Jeromin A, et al. Plasma tau in Alzheimer disease. Neurology. 2016;87(17):1827–35.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Mielke MM, Hagen CE, Wennberg AMV, Airey DC, Savica R, Knopman DS, Machulda MM, Roberts RO, Jack CR Jr, Petersen RC, et al. Association of Plasma Total tau Level with Cognitive Decline and Risk of mild cognitive impairment or dementia in the Mayo Clinic study on aging. JAMA Neurol. 2017;74(9):1073–80.

    PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Pase MP, Beiser AS, Himali JJ, Satizabal CL, Aparicio HJ, DeCarli C, Chêne G, Dufouil C, Seshadri S. Assessment of plasma Total tau level as a predictive biomarker for dementia and related Endophenotypes. JAMA Neurol. 2019. [Epub ahead of print].

  15. 15.

    Mielke MM, Hagen CE, Xu J, Chai X, Vemuri P, Lowe VJ, Airey DC, Knopman DS, Roberts RO, Machulda MM, et al. Plasma phospho-tau181 increases with Alzheimer's disease clinical severity and is associated with tau- and amyloid-positron emission tomography. Alzheimers Dement. 2018;14(8):989–97.

    PubMed  Article  Google Scholar 

  16. 16.

    Yang CC, Chiu MJ, Chen TF, Chang HL, Liu BH, Yang SY. Assay of plasma phosphorylated tau protein (threonine 181) and Total tau protein in early-stage Alzheimer's disease. J Alzheimers Dis. 2018;61(4):1323–32.

    CAS  PubMed  Article  Google Scholar 

  17. 17.

    Tatebe H, Kasai T, Ohmichi T, Kishi Y, Kakeya T, Waragai M, Kondo M, Allsop D, Tokuda T. Quantification of plasma phosphorylated tau to use as a biomarker for brain Alzheimer pathology: pilot case-control studies including patients with Alzheimer's disease and Down syndrome. Mol Neurodegener. 2017;12(1):63.

    PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Khalil M, Teunissen CE, Otto M, Piehl F, Sormani MP, Gattringer T, Barro C, Kappos L, Comabella M, Fazekas F, et al. Neurofilaments as biomarkers in neurological disorders. Nat Rev Neurol. 2018;14(10):577–89.

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Weston PSJ, Poole T, Ryan NS, Nair A, Liang Y, Macpherson K, Druyeh R, Malone IB, Ahsan RL, Pemberton H, et al. Serum neurofilament light in familial Alzheimer disease: a marker of early neurodegeneration. Neurology. 2017;89(21):2167–75.

    PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Yilmaz A, Blennow K, Hagberg L, Nilsson S, Price RW, Schouten J, Spudich S, Underwood J, Zetterberg H, Gisslen M. Neurofilament light chain protein as a marker of neuronal injury: review of its use in HIV-1 infection and reference values for HIV-negative controls. Expert Rev Mol Diagn. 2017;17(8):761–70.

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Mattsson N, Andreasson U, Zetterberg H, Blennow K. Alzheimer's disease neuroimaging I: Association of Plasma Neurofilament Light with Neurodegeneration in patients with Alzheimer disease. JAMA Neurol. 2017;74(5):557–66.

    PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Baird AL, Westwood S, Lovestone S. Blood-based proteomic biomarkers of Alzheimer's disease pathology. Front Neurol. 2015;6:236.

    PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Ray S, Britschgi M, Herbert C, Takeda-Uchimura Y, Boxer A, Blennow K, Friedman LF, Galasko DR, Jutel M, Karydas A, et al. Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins. Nat Med. 2007;13(11):1359–62.

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Britschgi M, Rufibach K, Huang SL, Clark CM, Kaye JA, Li G, Peskind ER, Quinn JF, Galasko DR, Wyss-Coray T. Modeling of pathological traits in Alzheimer's disease based on systemic extracellular signaling proteome. Mol Cell Proteomics. 2011;10(10):M111 008862.

    PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Marksteiner J, Kemmler G, Weiss EM, Knaus G, Ullrich C, Mechtcheriakov S, Oberbauer H, Auffinger S, Hinterholzl J, Hinterhuber H, et al. Five out of 16 plasma signaling proteins are enhanced in plasma of patients with mild cognitive impairment and Alzheimer's disease. Neurobiol Aging. 2011;32(3):539–40.

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Soares HD, Chen Y, Sabbagh M, Roher A, Schrijvers E, Breteler M. Identifying early markers of Alzheimer's disease using quantitative multiplex proteomic immunoassay panels. Ann N Y Acad Sci. 2009;1180:56–67.

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    Bjorkqvist M, Ohlsson M, Minthon L, Hansson O. Evaluation of a previously suggested plasma biomarker panel to identify Alzheimer's disease. PLoS One. 2012;7(1):e29868.

    PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Hu WT, Holtzman DM, Fagan AM, Shaw LM, Perrin R, Arnold SE, Grossman M, Xiong C, Craig-Schapiro R, Clark CM, et al. Plasma multianalyte profiling in mild cognitive impairment and Alzheimer disease. Neurology. 2012;79(9):897–905.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Laske C, Leyhe T, Stransky E, Hoffmann N, Fallgatter AJ, Dietzsch J. Identification of a blood-based biomarker panel for classification of Alzheimer's disease. Int J Neuropsychopharmacol. 2011;14(9):1147–55.

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Guo LH, Alexopoulos P, Wagenpfeil S, Kurz A, Perneczky R. Alzheimer's disease neuroimaging I: plasma proteomics for the identification of Alzheimer disease. Alzheimer Dis Assoc Disord. 2013;27(4):337–42.

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Llano DA, Devanarayan V, Simon AJ. Alzheimer's disease neuroimaging I: evaluation of plasma proteomic data for Alzheimer disease state classification and for the prediction of progression from mild cognitive impairment to Alzheimer disease. Alzheimer Dis Assoc Disord. 2013;27(3):233–43.

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Zhang J, Jia J, Qin W, Wang S. Combination of plasma tumor necrosis factor receptors signaling proteins, beta-amyloid and apolipoprotein E for the detection of Alzheimer's disease. Neurosci Lett. 2013;541:99–104.

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Agarwal S, Ghanty P, Pal NR. Identification of a small set of plasma signalling proteins using neural network for prediction of Alzheimer's disease. Bioinformatics. 2015;31(15):2505–13.

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Peng M, Jia J, Qin W. Plasma gelsolin and matrix metalloproteinase 3 as potential biomarkers for Alzheimer disease. Neurosci Lett. 2015;595:116–21.

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Uchida K, Shan L, Suzuki H, Tabuse Y, Nishimura Y, Hirokawa Y, Mizukami K, Akatsu H, Meno K, Asada T. Amyloid-beta sequester proteins as blood-based biomarkers of cognitive decline. Alzheimers Dement (Amst). 2015;1(2):270–80.

    Google Scholar 

  36. 36.

    Zhao X, Lejnine S, Spond J, Zhang C, Ramaraj TC, Holder DJ, Dai H, Weiner R, Laterza OF. A candidate plasma protein classifier to identify Alzheimer's disease. J Alzheimers Dis. 2015;43(2):549–63.

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Johnstone D, Milward EA, Berretta R, Moscato P. Alzheimer's disease neuroimaging I: multivariate protein signatures of pre-clinical Alzheimer's disease in the Alzheimer's disease neuroimaging initiative (ADNI) plasma proteome dataset. PLoS One. 2012;7(4):e34341.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Olazaran J, Gil-de-Gomez L, Rodriguez-Martin A, Valenti-Soler M, Frades-Payo B, Marin-Munoz J, Antunez C, Frank-Garcia A, Acedo-Jimenez C, Morlan-Gracia L, et al. A blood-based, 7-metabolite signature for the early diagnosis of Alzheimer's disease. J Alzheimers Dis. 2015;45(4):1157–73.

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Wang G, Zhou Y, Huang FJ, Tang HD, Xu XH, Liu JJ, Wang Y, Deng YL, Ren RJ, Xu W, et al. Plasma metabolite profiles of Alzheimer's disease and mild cognitive impairment. J Proteome Res. 2014;13(5):2649–58.

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Lista S, Faltraco F, Prvulovic D, Hampel H. Blood and plasma-based proteomic biomarker research in Alzheimer's disease. Prog Neurobiol. 2013;101-102:1–17.

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Zurbig P, Jahn H. Use of proteomic methods in the analysis of human body fluids in Alzheimer research. Electrophoresis. 2012;33(24):3617–30.

    PubMed  Article  Google Scholar 

  42. 42.

    Shi L, Baird AL, Westwood S, Hye A, Dobson R, Thambisetty M, Lovestone S. A decade of blood biomarkers for Alzheimer's disease research: An evolving field, improving study designs, and the challenge of replication. J Alzheimers Dis. 2018;62(3):1181–98.

    PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Jammeh E, Zhao P, Carroll C, Pearson S, Ifeachor E. Identification of blood biomarkers for use in point of care diagnosis tool for Alzheimer's disease. Conf Proc IEEE Eng Med Biol Soc. 2016;2016:2415–8.

    CAS  PubMed  Google Scholar 

  44. 44.

    O'Bryant SE, Xiao G, Barber R, Reisch J, Doody R, Fairchild T, Adams P, Waring S, Diaz-Arrastia R. Texas Alzheimer's research C: a serum protein-based algorithm for the detection of Alzheimer disease. Arch Neurol. 2010;67(9):1077–81.

    PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Doecke JD, Laws SM, Faux NG, Wilson W, Burnham SC, Lam CP, Mondal A, Bedo J, Bush AI, Brown B, et al. Blood-based protein biomarkers for diagnosis of Alzheimer disease. Arch Neurol. 2012;69(10):1318–25.

    PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    O'Bryant SE, Xiao G, Barber R, Huebinger R, Wilhelmsen K, Edwards M, Graff-Radford N, Doody R, Diaz-Arrastia R, Texas Alzheimer's R, et al. A blood-based screening tool for Alzheimer's disease that spans serum and plasma: findings from TARC and ADNI. PLoS One. 2011;6(12):e28092.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    O'Bryant SE, Xiao G, Zhang F, Edwards M, German DC, Yin X, Como T, Reisch J, Huebinger RM, Graff-Radford N, et al. Validation of a serum screen for Alzheimer's disease across assay platforms, species, and tissues. J Alzheimers Dis. 2014;42(4):1325–35.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    O'Bryant SE, Gupta V, Henriksen K, Edwards M, Jeromin A, Lista S, Bazenet C, Soares H, Lovestone S, Hampel H, et al. Guidelines for the standardization of preanalytic variables for blood-based biomarker studies in Alzheimer's disease research. Alzheimers Dement. 2017;11(5):549-60.

  49. 49.

    O'Bryant SE, Mielke MM, Rissman RA, Lista S, Vanderstichele H, Zetterberg H, Lewczuk P, Posner H, Hall J, Johnson L, et al. Blood-based biomarkers in Alzheimer disease: current state of the science and a novel collaborative paradigm for advancing from discovery to clinic. Alzheimers Dement. 2017;13(1):45–58.

  50. 50.

    Leung R, Proitsi P, Simmons A, Lunnon K, Guntert A, Kronenberg D, Pritchard M, Tsolaki M, Mecocci P, Kloszewska I, et al. Inflammatory proteins in plasma are associated with severity of Alzheimer's disease. PLoS One. 2013;8(6):e64971.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Popp J, Oikonomidi A, Tautvydaite D, Dayon L, Bacher M, Migliavacca E, Henry H, Kirkland R, Severin I, Wojcik J, et al. Markers of neuroinflammation associated with Alzheimer's disease pathology in older adults. Brain Behav Immun. 2017;62:203–11.

    CAS  PubMed  Article  Google Scholar 

  52. 52.

    Hye A, Riddoch-Contreras J, Baird AL, Ashton NJ, Bazenet C, Leung R, Westman E, Simmons A, Dobson R, Sattlecker M, et al. Plasma proteins predict conversion to dementia from prodromal disease. Alzheimers Dement. 2014;10(6):799–807 e792.

    PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Sattlecker M, Khondoker M, Proitsi P, Williams S, Soininen H, Kloszewska I, Mecocci P, Tsolaki M, Vellas B, Lovestone S, et al. Longitudinal protein changes in blood plasma associated with the rate of cognitive decline in Alzheimer's disease. J Alzheimers Dis. 2016;49(4):1105–14.

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Thambisetty M, Simmons A, Hye A, Campbell J, Westman E, Zhang Y, Wahlund LO, Kinsey A, Causevic M, Killick R, et al. Plasma biomarkers of brain atrophy in Alzheimer's disease. PLoS One. 2011;6(12):e28527.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Toledo JB, Da X, Bhatt P, Wolk DA, Arnold SE, Shaw LM, Trojanowski JQ, Davatzikos C. Alzheimer's disease neuroimaging I: relationship between plasma analytes and SPARE-AD defined brain atrophy patterns in ADNI. PLoS One. 2013;8(2):e55531.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Jefferson AL, Massaro JM, Wolf PA, Seshadri S, Au R, Vasan RS, Larson MG, Meigs JB, Keaney JF Jr, Lipinska I, et al. Inflammatory biomarkers are associated with total brain volume: the Framingham heart study. Neurology. 2007;68(13):1032–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Thambisetty M, Simmons A, Velayudhan L, Hye A, Campbell J, Zhang Y, Wahlund LO, Westman E, Kinsey A, Guntert A, et al. Association of plasma clusterin concentration with severity, pathology, and progression in Alzheimer disease. Arch Gen Psychiatry. 2010;67(7):739–48.

    PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Burnham SC, Faux NG, Wilson W, Laws SM, Ames D, Bedo J, Bush AI, Doecke JD, Ellis KA, Head R, et al. A blood-based predictor for neocortical Abeta burden in Alzheimer's disease: results from the AIBL study. Mol Psychiatry. 2014;19(4):519–26.

    CAS  PubMed  Article  Google Scholar 

  59. 59.

    Burnham SC, Rowe CC, Baker D, Bush AI, Doecke JD, Faux NG, Laws SM, Martins RN, Maruff P, Macaulay SL, et al. Predicting Alzheimer disease from a blood-based biomarker profile: a 54-month follow-up. Neurology. 2016;87(11):1093–101.

    CAS  PubMed  Article  Google Scholar 

  60. 60.

    Kiddle SJ, Thambisetty M, Simmons A, Riddoch-Contreras J, Hye A, Westman E, Pike I, Ward M, Johnston C, Lupton MK, et al. Plasma based markers of [11C] PiB-PET brain amyloid burden. PLoS One. 2012;7(9):e44260.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Ashton NJ, Kiddle SJ, Graf J, Ward M, Baird AL, Hye A, Westwood S, Wong KV, Dobson RJ, Rabinovici GD, et al. Blood protein predictors of brain amyloid for enrichment in clinical trials? Alzheimers Dement (Amst). 2015;1(1):48–60.

    Google Scholar 

  62. 62.

    Apostolova LG, Hwang KS, Avila D, Elashoff D, Kohannim O, Teng E, Sokolow S, Jack CR, Jagust WJ, Shaw L, et al. Brain amyloidosis ascertainment from cognitive, imaging, and peripheral blood protein measures. Neurology. 2015;84(7):729–37.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Voyle N, Baker D, Burnham SC, Covin A, Zhang Z, Sangurdekar DP, Tan Hehir CA, Bazenet C, Lovestone S, Kiddle S, et al. Blood protein markers of neocortical amyloid-beta burden: a candidate study using SOMAscan technology. J Alzheimers Dis. 2015;46(4):947–61.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Greenberg N, Grassano A, Thambisetty M, Lovestone S, Legido-Quigley C. A proposed metabolic strategy for monitoring disease progression in Alzheimer's disease. Electrophoresis. 2009;30(7):1235–9.

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    Oresic M, Hyotylainen T, Herukka SK, Sysi-Aho M, Mattila I, Seppanan-Laakso T, Julkunen V, Gopalacharyulu PV, Hallikainen M, Koikkalainen J, et al. Metabolome in progression to Alzheimer's disease. Transl Psychiatry. 2011;1:e57.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Trushina E, Mielke MM. Recent advances in the application of metabolomics to Alzheimer's disease. Biochim Biophys Acta. 2014;1842(8):1232–9.

    CAS  PubMed  Article  Google Scholar 

  67. 67.

    Kivipelto M, Mangialasche F, Ngandu T. Lifestyle interventions to prevent cognitive impairment, dementia and Alzheimer disease. Nat Rev Neurol. 2018;14(11):653–66.

    PubMed  Article  Google Scholar 

  68. 68.

    Chatterjee P, Lim WL, Shui G, Gupta VB, James I, Fagan AM, Xiong C, Sohrabi HR, Taddei K, Brown BM, et al. Plasma phospholipid and sphingolipid alterations in Presenilin1 mutation carriers: a pilot study. J Alzheimers Dis. 2016;50(3):887–94.

    CAS  PubMed  Article  Google Scholar 

  69. 69.

    Costa AC, Joaquim HPG, Forlenza O, Talib LL, Gattaz WF. Plasma lipids metabolism in mild cognitive impairment and Alzheimer's disease. World J Biol Psychiatry. 2017;9:1–7.

    Article  Google Scholar 

  70. 70.

    Mapstone M, Cheema AK, Fiandaca MS, Zhong X, Mhyre TR, MacArthur LH, Hall WJ, Fisher SG, Peterson DR, Haley JM, et al. Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med. 2014;20(4):415–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Fiandaca MS, Zhong X, Cheema AK, Orquiza MH, Chidambaram S, Tan MT, Gresenz CR, FitzGerald KT, Nalls MA, Singleton AB, et al. Plasma 24-metabolite panel predicts preclinical transition to clinical stages of Alzheimer's disease. Front Neurol. 2015;6:237.

    PubMed  PubMed Central  Article  Google Scholar 

  72. 72.

    Casanova R, Varma S, Simpson B, Kim M, An Y, Saldana S, Riveros C, Moscato P, Griswold M, Sonntag D, et al. Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals. Alzheimers Dement. 2016;12(7):815–22.

    PubMed  PubMed Central  Article  Google Scholar 

  73. 73.

    Leidinger P, Backes C, Deutscher S, Schmitt K, Mueller SC, Frese K, Haas J, Ruprecht K, Paul F, Stahler C, et al. A blood based 12-miRNA signature of Alzheimer disease patients. Genome Biol. 2013;14(7):R78.

    PubMed  PubMed Central  Article  Google Scholar 

  74. 74.

    Zendjabil M. Circulating microRNAs as novel biomarkers of Alzheimer's disease. Clin Chim Acta. 2018;484:99–104.

    CAS  PubMed  Article  Google Scholar 

  75. 75.

    Cheng L, Doecke JD, Sharples RA, Villemagne VL, Fowler CJ, Rembach A, Martins RN, Rowe CC, Macaulay SL, Masters CL, et al. Prognostic serum miRNA biomarkers associated with Alzheimer's disease shows concordance with neuropsychological and neuroimaging assessment. Mol Psychiatry. 2015;20(10):1188–96.

    CAS  PubMed  Article  Google Scholar 

  76. 76.

    Nagaraj S, Laskowska-Kaszub K, Debski KJ, Wojsiat J, Dabrowski M, Gabryelewicz T, Kuznicki J, Wojda U. Profile of 6 microRNA in blood plasma distinguish early stage Alzheimer's disease patients from non-demented subjects. Oncotarget. 2017;8(10):16122–43.

    PubMed  PubMed Central  Article  Google Scholar 

  77. 77.

    Nagaraj S, Zoltowska KM, Laskowska-Kaszub K, Wojda U. microRNA diagnostic panel for Alzheimer's disease and epigenetic trade-off between neurodegeneration and cancer. Ageing Res Rev. 2019;49:125–43.

    CAS  PubMed  Article  Google Scholar 

  78. 78.

    Fiandaca MS, Kapogiannis D, Mapstone M, Boxer A, Eitan E, Schwartz JB, Abner EL, Petersen RC, Federoff HJ, Miller BL, et al. Identification of preclinical Alzheimer's disease by a profile of pathogenic proteins in neurally derived blood exosomes: a case-control study. Alzheimers Dement. 2015;11(6):600–7 e601.

    PubMed  Article  Google Scholar 

  79. 79.

    Goetzl EJ, Boxer A, Schwartz JB, Abner EL, Petersen RC, Miller BL, Kapogiannis D. Altered lysosomal proteins in neural-derived plasma exosomes in preclinical Alzheimer disease. Neurology. 2015;85(1):40–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references


Not applicable.


No specific funding was provided for writing this report but work in the authors’ laboratories is supported by grants from the Swedish Research Council (HZ), the European Research Council (HZ), the Knut and Alice Wallenberg Foundation (HZ), the Olav Thon Foundation (HZ), the UK Dementia Research Institute at UCL (HZ), the Australian National Health & Medical Research Council (SB), the Australian Science and Industry Endowment Fund (SB), and the US National Institute of Health (SB).

Availability of data and materials

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Author information




HZ and SB wrote the paper and approved the final manuscript.

Corresponding authors

Correspondence to Henrik Zetterberg or Samantha C. Burnham.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

HZ has served at scientific advisory boards for Roche Diagnostics, Wave, Samumed and CogRx and is one of the founders of Brain Biomarker Solutions in Gothenburg AB, a GU Ventures-based platform company at the University of Gothenburg. SB reports no disclosures.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zetterberg, H., Burnham, S.C. Blood-based molecular biomarkers for Alzheimer’s disease. Mol Brain 12, 26 (2019).

Download citation


  • Alzheimer’s disease
  • Serum
  • Plasma
  • Blood
  • Biomarkers
  • Amyloid
  • Tau