- Open Access
Perspectives in fluid biomarkers in neurodegeneration from the 2019 biomarkers in neurodegenerative diseases course—a joint PhD student course at University College London and University of Gothenburg
Alzheimer's Research & Therapy volume 12, Article number: 20 (2020)
Until relatively recently, a diagnosis of probable Alzheimer’s disease (AD) and other neurodegenerative disorders was principally based on clinical presentation, with post-mortem examination remaining a gold standard for disease confirmation. This is in sharp contrast to other areas of medicine, where fluid biomarkers, such as troponin levels in myocardial infarction, form an integral part of the diagnostic and treatment criteria. There is a pressing need for such quantifiable and easily accessible tools in neurodegenerative diseases.
In this paper, based on lectures given at the 2019 Biomarkers in Neurodegenerative Diseases Course, we provide an overview of a range of cerebrospinal fluid (CSF) and blood biomarkers in neurodegenerative disorders, including the ‘core’ AD biomarkers amyloid β (Aβ) and tau, as well as other disease-specific and general markers of neuroaxonal injury. We then highlight the main challenges in the field, and how those could be overcome with the aid of new methodological advances, such as assay automation, mass spectrometry and ultrasensitive immunoassays.
As we hopefully move towards an era of disease-modifying treatments, reliable biomarkers will be essential to increase diagnostic accuracy, allow for earlier diagnosis, better participant selection and disease activity and treatment effect monitoring.
Considerable progress has been made in the field of fluid and imaging biomarker research in neurodegeneration in the last two decades. As a result, the most recent research and clinical guidelines (NIA-AA, IWG-2, NICE) incorporate cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers in the diagnostic criteria of Alzheimer’s disease (AD) and mild cognitive impairment (MCI) [1,2,3]. With more clinical trials of potential disease-modifying treatments shifting the focus towards pre-clinical stages of neurodegenerative disorders, there is an urgent need for more easily accessible, sensitive and specific biomarkers, which could aid earlier diagnosis, patient selection and tracking of disease activity.
The 2019 Biomarkers in Neurodegenerative Diseases Course provided attendants with an opportunity to gain basic and practical knowledge the rapidly developing field. At the 4-day conference aimed at PhD students, an international panel of experts discussed the current state of CSF and blood-derived biomarkers, including emerging technological advances and areas requiring further research. In this paper, based on the course proceedings, we will present a brief overview of the most important fluid biomarkers, focusing on AD, as well as other neurodegenerative disorders. We then outline the current limitations for use and the most recent technological advances in the field.
Biomarkers in neurodegeneration
Amyloid and tau
In AD, identification of amyloid β (Aβ) and phosphorylated tau (p-tau) as major components of extracellular plaques and neurofibrillary tangles led to establishment of the core biomarkers for the disease, with a CSF profile characterised by decreased Aβ42 levels, and elevated levels of total tau (t-tau) and p-tau (for example at threonine 181) . The reduction in CSF Aβ42 levels likely results from selective retention of Aβ42 in Aβ plaques, while the increase in t-tau and p-tau levels in CSF reflect increased tau secretion and phosphorylation from neurons affected by AD [4, 5]. A 2016 meta-analysis by Olsson and colleagues comprising over 230 studies helped establish that these biomarkers could help differentiate AD patients from controls, and individuals with MCI with subsequent progression to AD from those with stable MCI . Their high sensitivity and specificity—between 85 and 95% if combined—has led to their incorporation into research guidelines and clinical trials and increasingly into clinical practice in cases when there is a degree of uncertainty about the AD diagnosis [1,2,3].
CSF Aβ42 is one of the most well-validated biomarkers in neurodegeneration. CSF Aβ42 is reduced in MCI patients years before conversion to AD dementia and remains low throughout the disease course . Low CSF levels strongly correlate with cortical amyloid plaque load in the neocortex and hippocampus in post-mortem studies, as well as cortical Aβ deposition measured by PET [8, 9]. More recently, the CSF Aβ42/Aβ40 peptide ratio has shown to improve prediction of cortical amyloid deposition and differentiation between AD and other dementias in comparison to Aβ42 alone, likely by normalising the inter-individual differences in Aβ and release into CSF [10, 11].
In addition to Aβ42, numerous studies explored the role of alternatively cleaved Aβ peptides. Aβ43, for example, shows comparable diagnostic performance to CSF Aβ42 . Yet another Aβ peptide is the shorter Aβ38, with research suggesting an association between CSF Aβ38 levels and amyloid PET [6, 13].
Studies implementing novel techniques, such as mass spectrometry and ultrasensitive immunoassays, have shown promise in developing sensitive blood-based Aβ assays . Plasma Aβ42 measured using single molecule array (Simoa) technology was shown to be decreased in AD compared with controls and a ratio of plasma Aβ42/Aβ40 was reduced in amyloid PET positive cases in a manner similar to CSF, but according to most studies, with greater overlap between Aβ-positive and Aβ-negative patients [15, 16]. In contrast to Simoa, two recent papers utilising immunomagnetic reduction (IMR) developed by MagQu have demonstrated increase in plasma Aβ42 in AD patients in comparison to controls, which correlated negatively with CSF Aβ42 [17, 18]. Significant variability between studies remains an issue, with several potential confounders, including inter-assay differences and potential peripheral Aβ expression contributing to poor concordance and necessitating further validation studies to establish the role of plasma Aβ in AD diagnosis .
CSF concentrations of t-tau and p-tau are consistently increased in AD . Cognitive decline is more strongly associated with tau pathology than with amyloid pathology, with very high CSF t-tau and p-tau levels associated with worse clinical outcomes [20, 21]. While t-tau and p-tau concentration broadly reflect disease intensity, they correlate poorly with the burden of tau pathology measured by PET or in a post-mortem study [22, 23].
Latest research has focused on the fact that tau proteins can exist in multiple fragments and exhibit different phosphorylation patterns, with hope that some of them might be disease-specific and reflecting the underlying pathophysiological processes. In one study, N-terminal tau fragment truncated at 224 amino acids (N-224) colocalised to neurofibrillary tangles in brain extracts and showed significantly higher levels in CSF from patients with AD in comparison to controls, with higher baseline levels predictive of steeper cognitive decline . More recently, tau N-368 has also been found to be significantly elevated in CSF of AD patients, with a ratio of tau N-368 to total tau exhibiting a strong negative correlation with tau PET . AD pathology also significantly affects phosphorylation patterns, with hyperphosphorylation seen of a number CSF tau sites in comparison to healthy controls. In addition, a distinct phosphorylation site (T153) has been identified in AD CSF, which is absent in non-AD CSF .
Interestingly, elevated tau levels, including specific phosphorylated epitopes (P-tau181, P-tau231, and P-tau199) and N-terminal tau fragments truncated at 224, are not seen in many neurodegenerative diseases including primary tauopathies, such as frontotemporal dementia (FTD) or progressive supranuclear palsy (PSP) [24, 27,28,29]. A recent study by Sato et al. using stable isotope labelling method (SILK) to investigate tau metabolism suggests that the raised t-tau and p-tau levels seen in AD could be due to active production and secretion from neurons in response to Aβ pathology rather than a direct reflection of a neurodegenerative process .
Plasma t-tau was also found to be increased in AD, though this is not correlated with CSF [31, 32]. Promising results now exist for plasma p-tau, measured using a sensitive immunoassay with electrochemiluminescence detection and showing strong association with tau PET, as well as high concordance with CSF p-tau in a recent study by Palmqvist et al. [33, 34]. Several large replication studies, showing robust correlations with CSF p-tau and amyloid PET results, were presented during Alzheimer’s Association International Conference 2019 (AAIC) but have not yet been published.
In conclusion, while raised CSF tau levels are a well-validated feature of AD, studies examining the biology of tau, including its processing, secretion and aggregation are needed to fully understand its role as an AD biomarker. There is also a need for further research on tau pathology biomarkers in other tauopathies, such as PSP.
Neurofilament light (NfL) is a type of intermediate filament seen in the cytoplasm of axons, where it plays an important role in axonal homeostasis and synaptic transmission . NfL concentrations dynamically increase in response to concussion, as demonstrated in amateur boxers and ice hockey players [36, 37]. NfL has also been used as a biomarker of disease intensity, since it correlates with neuroaxonal damage in a wide range of neurological disorders . Importantly, CSF and serum NfL concentrations are highly correlated, hence they will be discussed together [39, 40].
CSF and blood NfL
Serum NfL concentration is increased in familial AD a decade prior to symptom onset and correlates with degree of whole-brain atrophy seen on magnetic resonance imaging (MRI) and cognition [41,42,43]. In sporadic AD, high plasma NfL levels distinguish between AD, MCI and healthy controls, with higher values among MCI subjects associated with more rapid brain atrophy . Plasma NfL also associates with the degree neurofilament staining and Braak staging at post-mortem . Longitudinal increase in plasma NfL positively correlates with longitudinal changes in other measures of neurodegeneration, including brain atrophy and cognition .
NfL is an useful biomarker in other forms of neurodegeneration. CSF NfL level has been shown to differ between AD and other forms of dementia—for example, FTD patients exhibit significantly higher values of CSF NfL in comparison to AD patients, as reported in recent post-mortem study . Serum NfL can also discriminate between idiopathic Parkinson’s disease (PD) and atypical parkinsonism that is clinically indistinguishable at the stage of testing [48, 49]. In Huntington’s disease (HD), plasma NfL levels are closely associated with MRI brain volume and clinical severity and may be a useful outcome measure in tracking clinical response to disease-modifying therapies . High levels of NfL are also seen in other neurodegenerative disorders, such as amyotrophic lateral sclerosis (ALS), HIV-associated dementia (HAD) and Creutzfeldt-Jakob disease (CJD) . In addition to very high NfL levels seen in CJD, the rapidly progressive disease exhibits unique, multi-fold increase in concentration of multiple other CSF biomarkers, including total tau, alpha-synuclein and neurogranin [52,53,54].
The role of NfL as a biomarker extends beyond the scope of neurodegeneration, with multiple sclerosis (MS), a common neuroinflammatory central nervous system (CNS) disorder being a prominent example. The levels of NfL are significantly increased in patients with MS versus healthy controls, and positively correlate with the burden of disease activity seen on MRI [55, 56]. Conversely, reduction of NfL concentration is seen in MS patients who commence disease-modifying treatment, or switch from first-line to a more high-potency treatment .
Taken together, the data suggests that CSF, serum and plasma NfL is a sensitive, but non-specific marker of disease activity in the CNS and peripheral nervous system (PNS), with additional benefit of being able to measure disease activity and severity, as demonstrated in MS and HD, as well as treatment response, as shown in MS or spinal muscular atrophy (SMA) [58, 59].
It has been widely shown that synaptic dysfunction occurs at early stages of AD, predating the onset of overt neuronal loss . Neurogranin (Ng), a calmodulin-binding postsynaptic protein, is highly expressed in brain regions important in memory processing, such as amygdala and hippocampus, where it plays a crucial role in long-term potentiation .
Since its discovery in the CSF, multiple studies have shown that Ng is increased in AD and MCI patients compared to controls and that higher levels are predictive of a steeper degree of cognitive decline, a reduction in cortical glucose metabolism and hippocampal volume loss .
The elevation of CSF Ng seems to be specific for AD and is not seen in other neurodegenerative disorders beside CJD [52, 63, 64]. A recent study examining post-mortem parietal and temporal cortex tissues found that the ratio of peptide-to-total full-length Ng was higher in patients with AD compared to controls, suggesting increased processing of Ng into peptides . Thus, the mechanisms underlying CSF Ng increase in AD could be similar to those of increased CSF tau processing and release in the disease .
Few studies have investigated plasma Ng levels and failed to show a significant difference between AD patients and healthy controls; however, pilot studies showed that the concentration of Ng from neuron-derived exosomes is lower in AD in comparison to controls and was associated with progression from MCI to AD [66, 67].
Altogether, the current evidence shows that Ng is a promising biomarker reflecting early synaptic dysfunction in AD, which can have a predictive value in healthy controls as well as MCI patients, in a surprisingly AD-specific manner.
α-Synuclein is a short cytoplasmic protein implicated in synaptic transmission and intracellular trafficking . Misfolding and aggregation of α-synuclein into oligomers and fibrils, with prion-like seeding throughout the CNS is believed to be central to the pathogenesis of a range of neurodegenerative disorders, including PD, LBD and multiple system atrophy (MSA) [68, 69]. It has been shown that α-synuclein is detectable in a range of biofluids, such as CSF, serum, saliva or tears .
Total α-synuclein is the most well studied in CSF, with a meta-analysis showing that the concentrations in patients with synucleinopathies are lower than those of healthy controls . However, results are neither sensitive nor specific enough to allow for use of the biomarker for diagnostic purposes, with evidence of significant inter-subject and inter-laboratory variation, complicated by the fact that blood contamination of the CSF could significantly raise total α-synuclein concentration . In addition, one study suggested that PD patients with an aggressive clinical course tend to have higher baseline α-synuclein concentration, complicating the interpretation . In contrast to PD, CSF α-synuclein levels were found to be raised in AD, with extremely high levels reported in CJD .
More recently, studies utilising prion-like properties of α-synuclein by measuring a degree of protein aggregation using real-time quaking-induced conversion assay (RT-QuiC) accurately distinguished between neuropathologically confirmed cases of PD or LBD and controls, with 92–95% sensitivity and 100% specificity [75, 76]. Interestingly, one study demonstrated significant α-synuclein aggregation in two control subjects who then went on to develop PD years after the sample was obtained .
In addition to total CSF α-synuclein, levels of the CSF oligomeric and phosphorylated α-synuclein have both been reported to be elevated in PD compared to controls, which requires further validation .
The protein is widely expressed in multiple fluids outside of the CNS, with red blood cells being a major source of α-synuclein in the blood and a source of potential contamination [70, 79]. Trials measuring α-synuclein in whole blood, plasma and serum of PD patients yielded conflicting results, limiting its utility as a diagnostic biomarker . However, similarly to CSF, studies measuring oligomeric or phosphorylated forms of the protein in the serum and in red blood cells have shown to be consistently elevated in PD patients in comparison to controls [78, 80].
Currently, α-synuclein remains one of the most complex biomarkers to interpret due to several potential confounding factors. Further research into aggregation assays, as well as oligomeric and Lewy body-enriched forms of the protein, is needed to establish it as a clinically useful biomarker.
Other biomarker candidates
TAR DNA-binding protein 43 (TDP-43) cytoplasmic accumulation is characteristic feature of ALS and FTD . TDP-43 pathology is also present in 20–50% of AD cases, but the protein is difficult to detect in body fluids and CSF TDP-43 seems to be primarily blood-derived . In one study, CSF TDP-43 was raised in ALS and FTD versus healthy controls, but considerable overlap between the groups was seen . Another paper showed raised plasma TDP-43 levels in a proportion of FTD and AD patients (46% and 22%, respectively) in comparison to controls . Currently, there are no fluid-based assays specific for pathological forms of the protein.
Inflammation contributes to AD pathogenesis and proteins involved in the inflammatory response, such as triggering receptor expressed on myeloid cells 2 (TREM2) and YKL-40 (also known as chitinase-3-like protein 1), could be used as potential AD biomarkers. TREM2 is expressed in microglia, and its soluble form is upregulated in the CSF of MCI and AD patients [85, 86]. YKL-40 is expressed in astrocytes, with CSF showing increased concentration in AD and predictive of progression of MCI to AD . Higher levels have also been shown to correlate with burden of tau pathology .
β-Site APP-cleaving enzyme 1 (BACE1) is an endoprotease closely involved in amyloid precursor protein (APP) processing. CSF BACE1 levels have been demonstrated to be higher in MCI and AD in comparison to healthy controls, especially in the presence of APOE ε4 allele [89, 90]. In another study, plasma BACE1 levels were able to indicate future MCI to AD progressors .
In addition to Ng, other synaptic proteins, such as synaptotagmin-1 (SYT-1), synaptosomal-associated protein-25 (SNAP-25) and growth-associated protein-43 (GAP-43), have been detected in CSF of AD patients and are a promising group of biomarkers, highlighting the importance of synaptic dysregulation in the disease [92,93,94].
Current limitations and future perspectives
In the field of neurodegeneration, most progress has been made with CSF biomarkers. Lumbar puncture is considered to be a safe and is generally well tolerated procedure, but its use can be limited by certain contraindications (e.g. taking anticoagulants), patient non-compliance or lack of resources . More accessible biofluids, such as blood or urine, would undoubtedly improve access to sample material and facilitate access to repeated longitudinal samples that could be valuable for tracking disease progression. However, concentration of CNS biomarkers outside of CSF is often extremely low, making it difficult to detect using standard assays. Other important factors complicating the analysis include peripheral expression of the protein of interest, endogenous antibodies interfering with assay results and presence of proteases which shorten the lifespan of the protein in peripheral tissues .
Sources of variation
The gold standard method for measuring CSF Aβ42 and tau is with enzyme-linked immunosorbent assays (ELISA). The methods of handling and storing CSF samples can differ between centres, and certain factors can be of critical importance. For example, storage tube material, aliquot volume and the number of consecutive tube transfers the sample is subjected to can significantly impact the measured biomarker concentration . Variation in CSF measures is also observed within assays and between centres. Interlaboratory coefficients of variation (CVs) are observed at 20–30% whereas intra-laboratory studies report CVs of < 10% [98,99,100]. Initiatives taken to improve analytical standardisation between centres were discussed, including the introduction of a certified reference materials for assay standardisation, an external quality control programme and the use of fully automated ELISA platforms, which has reduced intra- and inter-laboratory variation considerably (from 10 to 20% to 1–5%) [98, 101,102,103,104].
As a result of a selectivity of the blood-brain barrier, as well as high blood to CSF volume ratio, the concentration of CNS-derived proteins in blood is much less than in the CSF. The sensitivity of ELISA is therefore not high enough to identify and reliably quantify the concentration of CNS biomarkers in the plasma or serum. However, a number of ultrasensitive immunoassays with superior analytical sensitivity now exist, including Simoa (Quanterix), single molecule counting (SMC by Merck), proximity extension assay (OLINK) and immunomagnetic reduction (IMR by MagQu) .
With increased availability of ultrasensitive immunoassays, blood biomarkers hold promise for the future as less-invasive, cost-effective screening tests for neurodegenerative disorders .
Another approach to study biomarkers is mass spectrometry (MSp)-based tests, which allow for quantification and characterisation of peptides in a wide range of biofluids, including CSF and serum. MSp combines good sensitivity and specificity, high multiplexing capacity and the ability to detect proteins which have been post-translationally modified or truncated .
Two MSp-based reference methods for CSF Aβ42 have been certified by the Joint Committee for Traceability in Laboratory Medicine [107, 108].
Recent research utilising mass spectrometry (MSp) in the field of AD blood biomarker discovery have also shown promising results. A study by Kaneko et al. reported an almost 90% diagnostic accuracy in classifying amyloid PET positive and non-positive individuals using the ratio of a specific APP fragment (APP669-711) in plasma to plasma Aβ42 level . More recently, studies using a more sensitive IP-MSp method detected a decreased Aβ42/Aβ40 ratio in plasma and reported a diagnostic accuracy of almost 90% of plasma Aβ42 /Aβ40 ratio in predicting Aβ PET positivity in AD, MCI and cognitively normal states . Similar results have been presented by other groups [111, 112].
This approach represents a potentially cost-effective and accessible way of measuring Aβ burden in an individual; however, further validation and longitudinal studies, as well as standardisation across institution, are needed before potential clinical application of the promising MSp approach.
Proteomics, metabolomics and lipidomics
With the advent of novel proteomic techniques, proteomics-based approaches have become an important tool in biomarker discovery that can complement genomic analysis and provide important clues to the pathophysiology of many neurodegenerative disorders. An example of a new proteomics tool used in the field includes proximity extension assay developed by Olink Proteomics AB, which offers high sensitivity and multiplexing ability . A recent large study, utilising Olink measuring 270 CSF and plasma proteins in AD patients, identified significant differences in the concentrations of 10 CSF and 6 plasma proteins that take part in a variety of biological processes, including inflammation and apoptosis . In addition, plasma biomarkers were able to distinguish between AD, prodromal AD and healthy controls with high accuracy. In another study focusing on atypical parkinsonian syndromes (APS), 11 novel CSF proteins involved were identified that significantly differed between APS patients and healthy controls, with 4 protein levels also distinguishing between APS and PD patients . The identified proteins are involved in a variety of cellular processes, including cell proliferation and immune cell migration.
Metabolomics and lipidomics have emerged as promising approaches for the comprehensive study of complex biological samples and for biomarker discovery [116, 117]. There have been significant efforts to characterise metabolites and lipids in neurodegeneration, with hope that the observed lipid and metabolite profiles reflect metabolic changes and lipid-mediated mechanisms associated with disease pathology. These may serve as characteristic fingerprints of disease state and could potentially reveal therapeutic targets .
Over the past decade, targeted and non-targeted approaches for metabolomics/lipidomics have been significantly improved, largely due to improvements of MSp instrumentation [116, 117]. Several metabolomic studies have been reported in the context of AD biomarker discovery, most prominently using a commercial assay for targeted metabolite and lipid quantification in blood [119,120,121,122,123,124,125]. However, follow-up studies failed to replicate the findings, which fuelled the Alzheimer Disease Metabolomics Consortium (ADMC) initiative, where a network approach is used to establish a common metabolomic database of AD [126, 127].
A recent study reported that levels of primary fatty amides in plasma associated with CSF Aβ and hippocampal volume on MRI . Another metabolomic study on serum bile acid (BA) profiles in AD showed that serum-based BA metabolites are associated with CSF Aβ and p-tau . These examples highlight that well designed both targeted and untargeted metabolomic and lipidomic studies can reveal new biomarkers for AD pathology and improve our mechanistic understanding of AD pathophysiology.
The Biomarkers in Neurodegenerative Diseases Course provided delegates with an overview of the fluid biomarker field. There are now core biomarkers of neurodegenerative pathology (amyloid, tau and α-synuclein), a biomarker of disease intensity (NfL), synaptic function (neurogranin) and a range of novel analytical platforms such as Simoa and MSp. Future challenges include refining pre-analytical and analytical standardisation, measuring other aspects of neurodegenerative pathophysiology and developing less-invasive fluid biomarkers that can also be used for screening and tracking purposes.
Alzheimer’s Association International Conference
Alzheimer Disease Metabolomics Consortium
Amyotrophic lateral sclerosis
Amyloid precursor protein
β-site APP-cleaving enzyme 1
Central nervous system
Coefficient of variation
Enzyme-linked immunosorbent assay
International Working Group 2
Mild cognitive impairment
Magnetic resonance imaging
National Institute of Aging and Alzheimer’s Association
The National Institute for Health and Care Excellence
Positron emission tomography
Peripheral nervous system
Progressive supranuclear palsy
Real-time quaking-induced conversion assay
Single molecule array
Spinal muscular atrophy
TAR DNA-binding protein 43
Triggering receptor expressed on myeloid cells 2
Chitinase-3-like protein 1
Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018;14(4):535–62. Available from: https://www.ncbi.nlm.nih.gov/pubmed/29653606.
Excellence NI for H and C. Dementia: assessment, management and support for people living with dementia and their carers (NICE guideline [NG97]). 2018.
Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol. 2014;13(6):614–29.
Zetterberg H, Rohrer JD, Schott JM. Cerebrospinal fluid in the dementias. Handb Clin Neurol. 2017;146:85–97.
Zetterberg H. Blood-based biomarkers for Alzheimer’s disease-An update. J Neurosci Methods. 2019;319:2–6.
Olsson B, Lautner R, Andreasson U, Ohrfelt A, Portelius E, Bjerke M, 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.
Buchhave P, Minthon L, Zetterberg H, Wallin AK, Blennow K, Hansson O. Cerebrospinal fluid levels of beta-amyloid 1-42, but not of tau, are fully changed already 5 to 10 years before the onset of Alzheimer dementia. Arch Gen Psychiatry. 2012;69(1):98–106.
Strozyk D, Blennow K, White LR, Launer LJ. CSF Abeta 42 levels correlate with amyloid-neuropathology in a population-based autopsy study. Neurology. 2003;60(4):652–6.
Palmqvist S, Zetterberg H, Blennow K, Vestberg S, Andreasson U, Brooks DJ, et al. Accuracy of brain amyloid detection in clinical practice using cerebrospinal fluid beta-amyloid 42: a cross-validation study against amyloid positron emission tomography. JAMA Neurol. 2014;71(10):1282–9.
Janelidze S, Zetterberg H, Mattsson N, Palmqvist S, Vanderstichele H, Lindberg O, et al. CSF Abeta42/Abeta40 and Abeta42/Abeta38 ratios: better diagnostic markers of Alzheimer disease. Ann Clin Transl Neurol. 2016;3(3):154–65.
Hansson O, Lehmann S, Otto M, Zetterberg H, Lewczuk P. Advantages and disadvantages of the use of the CSF amyloid β (Aβ) 42/40 ratio in the diagnosis of Alzheimer’s disease. Alzheimers Res Ther. 2019;11(1):34.
Lauridsen C, Sando SB, Moller I, Berge G, Pomary PK, Grontvedt GR, et al. Cerebrospinal fluid Abeta43 is reduced in early-onset compared to late-onset Alzheimer’s disease, but has similar diagnostic accuracy to Abeta42. Front Aging Neurosci. 2017;9:210.
Mattsson N, Insel PS, Palmqvist S, Stomrud E, van Westen D, Minthon L, et al. Increased amyloidogenic APP processing in APOE varepsilon4-negative individuals with cerebral beta-amyloidosis. Nat Commun. 2016;7:10918.
Ashton NJ, Scholl M, Heurling K, Gkanatsiou E, Portelius E, Hoglund K, et al. Update on biomarkers for amyloid pathology in Alzheimer’s disease. Biomark Med. 2018;12(7):799–812.
Verberk IMW, Slot RE, Verfaillie SCJ, Heijst H, Prins ND, van Berckel BNM, et al. Plasma amyloid as Prescreener for the earliest Alzheimer pathological changes. Ann Neurol. 2018;84(5):648–58.
Janelidze S, Stomrud E, Palmqvist S, Zetterberg H, van Westen D, Jeromin A, et al. Plasma beta-amyloid in Alzheimer’s disease and vascular disease. Sci Rep. 2016;6:26801.
Teunissen CE, Chiu M-J, Yang C-C, Yang S-Y, Scheltens P, Zetterberg H, et al. Plasma amyloid-beta (Abeta42) correlates with cerebrospinal fluid Abeta42 in Alzheimer’s disease. J Alzheimers Dis. 2018;62(4):1857–63.
Fan L-Y, Tzen K-Y, Chen Y-F, Chen T-F, Lai Y-M, Yen R-F, et al. The relation between Brain amyloid deposition, cortical atrophy, and plasma biomarkers in amnesic mild cognitive impairment and Alzheimer’s disease. Front Aging Neurosci. 2018;10:175.
Citron M, Vigo-Pelfrey C, Teplow DB, Miller C, Schenk D, Johnston J, et al. Excessive production of amyloid beta-protein by peripheral cells of symptomatic and presymptomatic patients carrying the Swedish familial Alzheimer disease mutation. Proc Natl Acad Sci U S A. 1994;91(25):11993–7.
Wallin AK, Blennow K, Zetterberg H, Londos E, Minthon L, Hansson O. CSF biomarkers predict a more malignant outcome in Alzheimer disease. Neurology. 2010;74(19):1531–7.
Johnson KA, Schultz A, Betensky RA, Becker JA, Sepulcre J, Rentz D, et al. Tau positron emission tomographic imaging in aging and early Alzheimer disease. Ann Neurol. 2016;79(1):110–9.
Buerger K, Ewers M, Pirttila T, Zinkowski R, Alafuzoff I, Teipel SJ, et al. CSF phosphorylated tau protein correlates with neocortical neurofibrillary pathology in Alzheimer’s disease. Brain. 2006;129(Pt 11):3035–41.
Mattsson N, Scholl M, Strandberg O, Smith R, Palmqvist S, Insel PS, et al. (18)F-AV-1451 and CSF T-tau and P-tau as biomarkers in Alzheimer’s disease. EMBO Mol Med. 2017;9(9):1212–23.
Cicognola C, Brinkmalm G, Wahlgren J, Portelius E, Gobom J, Cullen NC, et al. Novel tau fragments in cerebrospinal fluid: relation to tangle pathology and cognitive decline in Alzheimer’s disease. Acta Neuropathol. 2019;137(2):279–96.
Blennow K, Chen C, Cicognola C, Wildsmith KR, Manser PT, Bohorquez SMS, et al. Cerebrospinal fluid tau fragment correlates with tau PET: a candidate biomarker for tangle pathology. Brain. 2020;143(2):650–60.
Barthelemy NR, Mallipeddi N, Moiseyev P, Sato C, Bateman RJ. Tau phosphorylation rates measured by mass spectrometry differ in the intracellular Brain vs. Extracellular Cerebrospinal Fluid Compartments and Are Differentially Affected by Alzheimer’s Disease. Front Aging Neurosci. 2019;11:121.
Zetterberg H. Review: tau in biofluids - relation to pathology, imaging and clinical features. Neuropathol Appl Neurobiol. 2017;43(3):194–9.
Buerger K, Teipel SJ, Zinkowski R, Blennow K, Arai H, Engel R, et al. CSF tau protein phosphorylated at threonine 231 correlates with cognitive decline in MCI subjects. Neurology. 2002;59(4):627–9.
Hampel H, Buerger K, Zinkowski R, Teipel SJ, Goernitz A, Andreasen N, et al. Measurement of phosphorylated tau epitopes in the differential diagnosis of Alzheimer disease: a comparative cerebrospinal fluid study. Arch Gen Psychiatry. 2004;61(1):95–102.
Sato C, Barthelemy NR, Mawuenyega KG, Patterson BW, Gordon BA, Jockel-Balsarotti J, et al. Tau Kinetics in Neurons and the Human Central Nervous System. Neuron. 2018;97(6):1284–98. e7.
Molinuevo JL, Ayton S, Batrla R, Bednar MM, Bittner T, Cummings J, et al. Current state of Alzheimer’s fluid biomarkers. Acta Neuropathol. 2018;136(6):821–53.
Zetterberg H, Wilson D, Andreasson U, Minthon L, Blennow K, Randall J, et al. Plasma tau levels in Alzheimer’s disease. Alzheimers Res Ther. 2013;5(2):9.
Mielke MM, Hagen CE, Xu J, Chai X, Vemuri P, Lowe VJ, 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.
Palmqvist S, Insel PS, Stomrud E, Janelidze S, Zetterberg H, Brix B, et al. Cerebrospinal fluid and plasma biomarker trajectories with increasing amyloid deposition in Alzheimer’s disease. EMBO Mol Med. 2019;11(12):e11170.
Yuan A, Rao M V, Veeranna, Nixon RA. Neurofilaments and Neurofilament Proteins in Health and Disease. Cold Spring Harb Perspect Biol. 2017;9(4):a018309.
Zetterberg H, Hietala MA, Jonsson M, Andreasen N, Styrud E, Karlsson I, et al. Neurochemical aftermath of amateur boxing. Arch Neurol. 2006;63(9):1277–80.
Shahim P, Tegner Y, Marklund N, Blennow K, Zetterberg H. Neurofilament light and tau as blood biomarkers for sports-related concussion. Neurology. 2018;90(20):e1780–8.
Bridel C, van Wieringen WN, Zetterberg H, Tijms BM, Teunissen CE, Alvarez-Cermeno JC, et al. Diagnostic value of cerebrospinal fluid Neurofilament light protein in neurology: a systematic review and meta-analysis. JAMA Neurol. 2019;76(9):1035–48.
Disanto G, Barro C, Benkert P, Naegelin Y, Schadelin S, Giardiello A, et al. Serum Neurofilament light: a biomarker of neuronal damage in multiple sclerosis. Ann Neurol. 2017;81(6):857–70.
Gaiottino J, Norgren N, Dobson R, Topping J, Nissim A, Malaspina A, et al. Increased neurofilament light chain blood levels in neurodegenerative neurological diseases. PLoS One. 2013;8(9):e75091.
Weston PSJ, Poole T, O’Connor A, Heslegrave A, Ryan NS, Liang Y, et al. Longitudinal measurement of serum neurofilament light in presymptomatic familial Alzheimer’s disease. Alzheimers Res Ther. 2019;11(1):19.
Weston PSJ, Poole T, Ryan NS, Nair A, Liang Y, Macpherson K, et al. Serum neurofilament light in familial Alzheimer disease: a marker of early neurodegeneration. Neurology. 2017;89(21):2167–75.
Preische O, Schultz SA, Apel A, Kuhle J, Kaeser SA, Barro C, et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat Med. 2019;25(2):277–83.
Mattsson N, Andreasson U, Zetterberg H, Blennow K. Association of Plasma Neurofilament Light with Neurodegeneration in patients with Alzheimer disease. JAMA Neurol. 2017;74(5):557–66.
Ashton NJ, Leuzy A, Lim YM, Troakes C, Hortobagyi T, Hoglund K, et al. Increased plasma neurofilament light chain concentration correlates with severity of post-mortem neurofibrillary tangle pathology and neurodegeneration. Acta Neuropathol Commun. 2019;7(1):5.
Mattsson N, Cullen NC, Andreasson U, Zetterberg H, Blennow K. Association Between Longitudinal Plasma Neurofilament Light and Neurodegeneration in Patients With Alzheimer Disease. JAMA Neurol. 2019;76(7):791–9.
Olsson B, Portelius E, Cullen NC, Sandelius A, Zetterberg H, Andreasson U, et al. Association of Cerebrospinal Fluid Neurofilament Light Protein Levels with Cognition in patients with dementia, motor neuron disease, and movement disorders. JAMA Neurol. 2019;76(3):318–25.
Marques TM, van Rumund A, Oeckl P, Kuiperij HB, Esselink RAJ, Bloem BR, et al. Serum NFL discriminates Parkinson disease from atypical parkinsonisms. Neurology. 2019;92(13):e1479–86.
Hansson O, Janelidze S, Hall S, Magdalinou N, Lees AJ, Andreasson U, et al. Blood-based NfL: a biomarker for differential diagnosis of parkinsonian disorder. Neurology. 2017;88(10):930–7.
Byrne LM, Rodrigues FB, Johnson EB, Wijeratne PA, De Vita E, Alexander DC, et al. Evaluation of mutant huntingtin and neurofilament proteins as potential markers in Huntington’s disease. Sci Transl Med. 2018;10(458):eaat7108.
Gaetani L, Blennow K, Calabresi P, Di Filippo M, Parnetti L, Zetterberg H. Neurofilament light chain as a biomarker in neurological disorders. J Neurol Neurosurg Psychiatry. 2019;90(8):870–81.
Blennow K, Diaz-Lucena D, Zetterberg H, Villar-Pique A, Karch A, Vidal E, et al. CSF neurogranin as a neuronal damage marker in CJD: a comparative study with AD. J Neurol Neurosurg Psychiatry. 2019;90(8):846–53.
Riemenschneider M, Wagenpfeil S, Vanderstichele H, Otto M, Wiltfang J, Kretzschmar H, et al. Phospho-tau/total tau ratio in cerebrospinal fluid discriminates Creutzfeldt–Jakob disease from other dementias. Mol Psychiatry. 2003;8(3):343–7. Available from: https://doi.org/10.1038/sj.mp.4001220.
Mollenhauer B, Cullen V, Kahn I, Krastins B, Outeiro TF, Pepivani I, et al. Direct quantification of CSF alpha-synuclein by ELISA and first cross-sectional study in patients with neurodegeneration. Exp Neurol. 2008;213(2):315–25.
Kuhle J, Plattner K, Bestwick JP, Lindberg RL, Ramagopalan SV, Norgren N, et al. A comparative study of CSF neurofilament light and heavy chain protein in MS. Mult Scler. 2013;19(12):1597–603.
Novakova L, Zetterberg H, Sundstrom P, Axelsson M, Khademi M, Gunnarsson M, et al. Monitoring disease activity in multiple sclerosis using serum neurofilament light protein. Neurology. 2017;89(22):2230–7.
de Flon P, Gunnarsson M, Laurell K, Soderstrom L, Birgander R, Lindqvist T, et al. Reduced inflammation in relapsing-remitting multiple sclerosis after therapy switch to rituximab. Neurology. 2016;87(2):141–7.
Khalil M, Teunissen CE, Otto M, Piehl F, Sormani MP, Gattringer T, et al. Neurofilaments as biomarkers in neurological disorders. Nat Rev Neurol. 2018;14(10):577–89.
Olsson B, Alberg L, Cullen NC, Michael E, Wahlgren L, Kroksmark A-K, et al. NFL is a marker of treatment response in children with SMA treated with nusinersen. J Neurol. 2019;266(9):2129–36.
DeKosky ST, Scheff SW. Synapse loss in frontal cortex biopsies in Alzheimer’s disease: correlation with cognitive severity. Ann Neurol. 1990;27(5):457–64.
Diez-Guerra FJ. Neurogranin, a link between calcium/calmodulin and protein kinase C signaling in synaptic plasticity. IUBMB Life. 2010;62(8):597–606.
Portelius E, Zetterberg H, Skillback T, Tornqvist U, Andreasson U, Trojanowski JQ, et al. Cerebrospinal fluid neurogranin: relation to cognition and neurodegeneration in Alzheimer’s disease. Brain. 2015;138(Pt 11):3373–85.
Wellington H, Paterson RW, Portelius E, Tornqvist U, Magdalinou N, Fox NC, et al. Increased CSF neurogranin concentration is specific to Alzheimer disease. Neurology. 2016;86(9):829–35.
Portelius E, Olsson B, Hoglund K, Cullen NC, Kvartsberg H, Andreasson U, et al. Cerebrospinal fluid neurogranin concentration in neurodegeneration: relation to clinical phenotypes and neuropathology. Acta Neuropathol. 2018;136(3):363–76.
Kvartsberg H, Lashley T, Murray CE, Brinkmalm G, Cullen NC, Höglund K, et al. The intact postsynaptic protein neurogranin is reduced in brain tissue from patients with familial and sporadic Alzheimer’s disease. Acta Neuropathol. 2019;137(1):89–102.
Kvartsberg H, Portelius E, Andreasson U, Brinkmalm G, Hellwig K, Lelental N, et al. Characterization of the postsynaptic protein neurogranin in paired cerebrospinal fluid and plasma samples from Alzheimer’s disease patients and healthy controls. Alzheimers Res Ther. 2015;7(1):40.
Winston CN, Goetzl EJ, Akers JC, Carter BS, Rockenstein EM, Galasko D, et al. Prediction of conversion from mild cognitive impairment to dementia with neuronally derived blood exosome protein profile. Alzheimer’s Dement (Amsterdam, Netherlands). 2016;3:63–72.
Poewe W, Seppi K, Tanner CM, Halliday GM, Brundin P, Volkmann J, et al. Parkinson disease. Nat Rev Dis Prim. 2017;3:17013.
McCann H, Stevens CH, Cartwright H, Halliday GM. alpha-Synucleinopathy phenotypes. Parkinsonism Relat Disord. 2014;20(Suppl 1):S62–7.
Malek N, Swallow D, Grosset KA, Anichtchik O, Spillantini M, Grosset DG. Alpha-synuclein in peripheral tissues and body fluids as a biomarker for Parkinson’s disease - a systematic review. Acta Neurol Scand. 2014;130(2):59–72.
Eusebi P, Giannandrea D, Biscetti L, Abraha I, Chiasserini D, Orso M, et al. Diagnostic utility of cerebrospinal fluid alpha-synuclein in Parkinson’s disease: a systematic review and meta-analysis. Mov Disord. 2017;32(10):1389–400.
Hong Z, Shi M, Chung KA, Quinn JF, Peskind ER, Galasko D, et al. DJ-1 and alpha-synuclein in human cerebrospinal fluid as biomarkers of Parkinson’s disease. Brain. 2010;133(Pt 3):713–26.
Hall S, Surova Y, Ohrfelt A, Zetterberg H, Lindqvist D, Hansson O. CSF biomarkers and clinical progression of Parkinson disease. Neurology. 2015;84(1):57–63.
Oeckl P, Metzger F, Nagl M, von Arnim CAF, Halbgebauer S, Steinacker P, et al. Alpha-, Beta-, and gamma-synuclein quantification in cerebrospinal fluid by multiple reaction monitoring reveals increased concentrations in Alzheimer’s and Creutzfeldt-Jakob disease but no alteration in Synucleinopathies. Mol Cell Proteomics. 2016;15(10):3126–38.
Groveman BR, Orrù CD, Hughson AG, Raymond LD, Zanusso G, Ghetti B, et al. Rapid and ultra-sensitive quantitation of disease-associated α-synuclein seeds in brain and cerebrospinal fluid by αSyn RT-QuIC. Acta Neuropathol Commun. 2018;6(1):7.
Fairfoul G, McGuire LI, Pal S, Ironside JW, Neumann J, Christie S, et al. Alpha-synuclein RT-QuIC in the CSF of patients with alpha-synucleinopathies. Ann Clin Transl Neurol. 2016;3(10):812–8.
Shahnawaz M, Tokuda T, Waragai M, Mendez N, Ishii R, Trenkwalder C, et al. Development of a biochemical diagnosis of Parkinson disease by detection of alpha-Synuclein Misfolded aggregates in cerebrospinal fluid. JAMA Neurol. 2017;74(2):163–72.
Parnetti L, Gaetani L, Eusebi P, Paciotti S, Hansson O, El-Agnaf O, et al. CSF and blood biomarkers for Parkinson’s disease. Lancet Neurol. 2019;18(6):573–86.
Barbour R, Kling K, Anderson JP, Banducci K, Cole T, Diep L, et al. Red blood cells are the major source of alpha-synuclein in blood. Neurodegener Dis. 2008;5(2):55–9.
Williams SM, Schulz P, Sierks MR. Oligomeric alpha-synuclein and beta-amyloid variants as potential biomarkers for Parkinson’s and Alzheimer’s diseases. Eur J Neurosci. 2016;43(1):3–16.
Steinacker P, Barschke P, Otto M. Biomarkers for diseases with TDP-43 pathology. Mol Cell Neurosci. 2019;97:43–59.
Feneberg E, Steinacker P, Lehnert S, Schneider A, Walther P, Thal DR, et al. Limited role of free TDP-43 as a diagnostic tool in neurodegenerative diseases. Amyotroph Lateral Scler Frontotemporal Degener. 2014;15(5–6):351–6.
Steinacker P, Hendrich C, Sperfeld AD, Jesse S, von Arnim CAF, Lehnert S, et al. TDP-43 in cerebrospinal fluid of patients with frontotemporal lobar degeneration and amyotrophic lateral sclerosis. Arch Neurol. 2008;65(11):1481–7.
Foulds P, McAuley E, Gibbons L, Davidson Y, Pickering-Brown SM, Neary D, et al. TDP-43 protein in plasma may index TDP-43 brain pathology in Alzheimer’s disease and frontotemporal lobar degeneration. Acta Neuropathol. 2008;116(2):141–6.
Heslegrave A, Heywood W, Paterson R, Magdalinou N, Svensson J, Johansson P, et al. Increased cerebrospinal fluid soluble TREM2 concentration in Alzheimer’s disease. Mol Neurodegener. 2016;11:3.
Brosseron F, Traschutz A, Widmann CN, Kummer MP, Tacik P, Santarelli F, et al. Characterization and clinical use of inflammatory cerebrospinal fluid protein markers in Alzheimer’s disease. Alzheimers Res Ther. 2018;10(1):25.
Craig-Schapiro R, Perrin RJ, Roe CM, Xiong C, Carter D, Cairns NJ, et al. YKL-40: a novel prognostic fluid biomarker for preclinical Alzheimer’s disease. Biol Psychiatry. 2010;68(10):903–12.
Querol-Vilaseca M, Colom-Cadena M, Pegueroles J, San Martin-Paniello C, Clarimon J, Belbin O, et al. YKL-40 (Chitinase 3-like I) is expressed in a subset of astrocytes in Alzheimer’s disease and other tauopathies. J Neuroinflammation. 2017;14(1):118.
Ewers M, Zhong Z, Burger K, Wallin A, Blennow K, Teipel SJ, et al. Increased CSF-BACE 1 activity is associated with ApoE-epsilon 4 genotype in subjects with mild cognitive impairment and Alzheimer’s disease. Brain. 2008;131(Pt 5):1252–8.
Zetterberg H, Andreasson U, Hansson O, Wu G, Sankaranarayanan S, Andersson ME, et al. Elevated cerebrospinal fluid BACE1 activity in incipient Alzheimer disease. Arch Neurol. 2008;65(8):1102–7.
Shen Y, Wang H, Sun Q, Yao H, Keegan AP, Mullan M, et al. Increased plasma Beta-Secretase 1 may predict conversion to Alzheimer’s disease dementia in individuals with mild cognitive impairment. Biol Psychiatry. 2018;83(5):447–55.
Öhrfelt A, Brinkmalm A, Dumurgier J, Brinkmalm G, Hansson O, Zetterberg H, et al. The pre-synaptic vesicle protein synaptotagmin is a novel biomarker for Alzheimer’s disease. Alzheimers Res Ther. 2016;8(1):41. Available from: https://doi.org/10.1186/s13195-016-0208-8.
Sandelius A, Portelius E, Kallen A, Zetterberg H, Rot U, Olsson B, et al. Elevated CSF GAP-43 is Alzheimer’s disease specific and associated with tau and amyloid pathology. Alzheimers Dement. 2019;15(1):55–64.
Brinkmalm A, Brinkmalm G, Honer WG, Frolich L, Hausner L, Minthon L, et al. SNAP-25 is a promising novel cerebrospinal fluid biomarker for synapse degeneration in Alzheimer’s disease. Mol Neurodegener. 2014;9:53.
Duits FH, Martinez-Lage P, Paquet C, Engelborghs S, Lleo A, Hausner L, et al. Performance and complications of lumbar puncture in memory clinics: results of the multicenter lumbar puncture feasibility study. Alzheimers Dement. 2016;12(2):154–63.
Zetterberg H, Burnham SC. Blood-based molecular biomarkers for Alzheimer’s disease. Mol Brain. 2019;12(1):26. Available from: https://www.ncbi.nlm.nih.gov/pubmed/30922367.
Perret-Liaudet A, Pelpel M, Tholance Y, Dumont B, Vanderstichele H, Zorzi W, et al. Risk of Alzheimer’s disease biological misdiagnosis linked to cerebrospinal collection tubes. J Alzheimers Dis. 2012;31(1):13–20.
Mattsson N, Andreasson U, Persson S, Carrillo MC, Collins S, Chalbot S, et al. CSF biomarker variability in the Alzheimer’s association quality control program. Alzheimers Dement. 2013;9(3):251–61.
Fourier A, Portelius E, Zetterberg H, Blennow K, Quadrio I, Perret-Liaudet A. Pre-analytical and analytical factors influencing Alzheimer’s disease cerebrospinal fluid biomarker variability. Clin Chim Acta. 2015;449:9–15.
Bittner T, Zetterberg H, Teunissen CE, Ostlund REJ, Militello M, Andreasson U, et al. Technical performance of a novel, fully automated electrochemiluminescence immunoassay for the quantitation of beta-amyloid (1-42) in human cerebrospinal fluid. Alzheimers Dement. 2016;12(5):517–26.
Blennow K, Zetterberg H. Biomarkers for Alzheimer’s disease: current status and prospects for the future. J Intern Med. 2018;284(6):643–63.
Bjerke M, Andreasson U, Kuhlmann J, Portelius E, Pannee J, Lewczuk P, et al. Assessing the commutability of reference material formats for the harmonization of amyloid-beta measurements. Clin Chem Lab Med. 2016;54(7):1177–91.
Hansson O, Seibyl J, Stomrud E, Zetterberg H, Trojanowski JQ, Bittner T, et al. CSF biomarkers of Alzheimer’s disease concord with amyloid-beta PET and predict clinical progression: a study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimers Dement. 2018;14(11):1470–81.
Kuhlmann J, Andreasson U, Pannee J, Bjerke M, Portelius E, Leinenbach A, et al. CSF Abeta1-42 - an excellent but complicated Alzheimer’s biomarker - a route to standardisation. Clin Chim Acta. 2017;467:27–33.
Andreasson U, Blennow K, Zetterberg H. Update on ultrasensitive technologies to facilitate research on blood biomarkers for central nervous system disorders. Alzheimer’s Dement (Amsterdam, Netherlands). 2016;3:98–102.
Cilento EM, Jin L, Stewart T, Shi M, Sheng L, Zhang J. Mass spectrometry: a platform for biomarker discovery and validation for Alzheimer’s and Parkinson’s diseases. J Neurochem. 2019;151(4):397–416.
Korecka M, Waligorska T, Figurski M, Toledo JB, Arnold SE, Grossman M, et al. Qualification of a surrogate matrix-based absolute quantification method for amyloid-beta(4)(2) in human cerebrospinal fluid using 2D UPLC-tandem mass spectrometry. J Alzheimers Dis. 2014;41(2):441–51.
Leinenbach A, Pannee J, Dulffer T, Huber A, Bittner T, Andreasson U, et al. Mass spectrometry-based candidate reference measurement procedure for quantification of amyloid-beta in cerebrospinal fluid. Clin Chem. 2014;60(7):987–94.
Kaneko N, Nakamura A, Washimi Y, Kato T, Sakurai T, Arahata Y, et al. Novel plasma biomarker surrogating cerebral amyloid deposition. Proc Jpn Acad Ser B Phys Biol Sci. 2014;90(9):353–64.
Nakamura A, Kaneko N, Villemagne VL, Kato T, Doecke J, Dore V, et al. High performance plasma amyloid-beta biomarkers for Alzheimer’s disease. Nature. 2018;554(7691):249–54.
Ovod V, Ramsey KN, Mawuenyega KG, Bollinger JG, Hicks T, Schneider T, et al. Amyloid beta concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement. 2017;13(8):841–9.
Schindler SE, Bollinger JG, Ovod V, Mawuenyega KG, Li Y, Gordon BA, et al. High-precision plasma beta-amyloid 42/40 predicts current and future brain amyloidosis. Neurology. 2019;93(17):e1647–59.
Assarsson E, Lundberg M, Holmquist G, Bjorkesten J, Thorsen SB, Ekman D, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS One. 2014;9(4):e95192.
Whelan CD, Mattsson N, Nagle MW, Vijayaraghavan S, Hyde C, Janelidze S, et al. Multiplex proteomics identifies novel CSF and plasma biomarkers of early Alzheimer’s disease. Acta Neuropathol Commun. 2019;7(1):169.
Jabbari E, Woodside J, Guo T, Magdalinou NK, Chelban V, Athauda D, et al. Proximity extension assay testing reveals novel diagnostic biomarkers of atypical parkinsonian syndromes. J Neurol Neurosurg Psychiatry. 2019;90(7):768–73.
Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol. 2016;17(7):451–9.
Patti GJ, Yanes O, Siuzdak G. Innovation: metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 2012;13(4):263–9.
Xia J, Broadhurst DI, Wilson M, Wishart DS. Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics. 2013;9(2):280–99.
Fiandaca MS, Zhong X, Cheema AK, Orquiza MH, Chidambaram S, Tan MT, et al. Plasma 24-metabolite panel predicts preclinical transition to clinical stages of Alzheimer’s disease. Front Neurol. 2015;6:237.
Han X, Rozen S, Boyle SH, Hellegers C, Cheng H, Burke JR, et al. Metabolomics in early Alzheimer’s disease: identification of altered plasma sphingolipidome using shotgun lipidomics. PLoS One. 2011;6(7):e21643.
Kaddurah-Daouk R, Rozen S, Matson W, Han X, Hulette CM, Burke JR, et al. Metabolomic changes in autopsy-confirmed Alzheimer’s disease. Alzheimers Dement. 2011;7(3):309–17.
Klavins K, Koal T, Dallmann G, Marksteiner J, Kemmler G, Humpel C. The ratio of phosphatidylcholines to lysophosphatidylcholines in plasma differentiates healthy controls from patients with Alzheimer’s disease and mild cognitive impairment. Alzheimer’s Dement (Amsterdam, Netherlands). 2015;1(3):295–302.
Koal T, Klavins K, Seppi D, Kemmler G, Humpel C. Sphingomyelin SM(d18:1/18:0) is significantly enhanced in cerebrospinal fluid samples dichotomized by pathological amyloid-beta42, tau, and phospho-tau-181 levels. J Alzheimers Dis. 2015;44(4):1193–201.
Motsinger-Reif AA, Zhu H, Kling MA, Matson W, Sharma S, Fiehn O, et al. Comparing metabolomic and pathologic biomarkers alone and in combination for discriminating Alzheimer’s disease from normal cognitive aging. Acta Neuropathol Commun. 2013;1:28.
Mapstone M, Cheema AK, Fiandaca MS, Zhong X, Mhyre TR, MacArthur LH, et al. Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med. 2014;20(4):415–8.
Casanova R, Varma S, Simpson B, Kim M, An Y, Saldana S, 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.
Toledo JB, Arnold M, Kastenmuller G, Chang R, Baillie RA, Han X, et al. Metabolic network failures in Alzheimer’s disease: a biochemical road map. Alzheimers Dement. 2017;13(9):965–84.
Kim M, Snowden S, Suvitaival T, Ali A, Merkler DJ, Ahmad T, et al. Primary fatty amides in plasma associated with brain amyloid burden, hippocampal volume, and memory in the European medical information framework for Alzheimer’s disease biomarker discovery cohort. Alzheimers Dement. 2019;15(6):817–27.
Nho K, Kueider-Paisley A, MahmoudianDehkordi S, Arnold M, Risacher SL, Louie G, et al. Altered bile acid profile in mild cognitive impairment and Alzheimer’s disease: relationship to neuroimaging and CSF biomarkers. Alzheimers Dement. 2019;15(2):232–44.
The Biomarkers in Neurodegenerative Diseases course was funded by researchers at the National Institute for Health Research University College London Hospitals Biomedical Research Centre.
RP is supported by an NIHR clinical lectureship.
DN is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.
HZ is a Wallenberg Academy Fellow supported by grants from the Swedish Research Council (#2018-02532), the European Research Council (#681712) and Swedish State Support for Clinical Research (#ALFGBG-720931) and the UK Dementia Research Institute at UCL.
Availability of data and materials
Data presented in the article is publicly available. Studies demonstrating correlation between CSF p-tau and amyloid PET that have been presented at Alzheimer’s Association International Conference 2019 are yet to be published.
Ethics approval and consent to participate
Consent for publication
HZ has served at scientific advisory boards for Roche Diagnostics, Wave, Samumed and CogRx, has given lectures in symposia sponsored by Biogen and Alzecure and is a co-founder of Brain Biomarker Solutions in Gothenburg AB, a GU Ventures-based platform company at the University of Gothenburg (all outside submitted work). The other authors declare that they have no competing interests.
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 (http://creativecommons.org/licenses/by/4.0/), 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 (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Cite this article
Obrocki, P., Khatun, A., Ness, D. et al. Perspectives in fluid biomarkers in neurodegeneration from the 2019 biomarkers in neurodegenerative diseases course—a joint PhD student course at University College London and University of Gothenburg. Alz Res Therapy 12, 20 (2020). https://doi.org/10.1186/s13195-020-00586-6
- Neurofilament light
- Mass spectrometry