Review ArticleOpen Access

Cerebrospinal fluid and blood biomarkers in the diagnostic assays of Alzheimer’s disease

Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China

MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China

Search for more papers by this author

Xiaohan Liang

Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China

MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China

Search for more papers by this author

Zhihong Zhang

Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China

MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China

Search for more papers by this author

, and

Haiming Luo

Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China

MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China

E-mail Address: hemluo@hust.edu.cn

Corresponding author.

Search for more papers by this author

https://doi.org/10.1142/S1793545822300014Cited by:15 (Source: Crossref)

This article is part of the issue:

Contains the Special Issue on Advances in Biophotonics and Biomedical Optics: Part II
Guest Editors: Polina Dyachenko, Tingting Yu, Dan Zhu and Valery V. Tuchin

Abstract

The anti-amyloid-β (anti-Aβ) fibrils and soluble oligomers antibody aducanumab were approved to effectively slow down the progression of Alzheimer’s disease (AD) at higher doses in 2019, reaffirming the therapeutic effects of targeting the core pathology of AD. A timely and accurate diagnosis in the prodromal or pre-dementia stage of AD is essential for patient recruitment, stratification, and monitoring of treatment effects. AD core biomarkers amyloid-β (Aβ₁₋₄₂), total tau (t-tau), and phosphorylated tau (p-tau) have been clinically validated to reflect AD-type pathological changes through cerebrospinal fluid (CSF) measurement or positron-emission tomography (PET) and found to have high diagnostic performance for AD identification in the stage of mild cognitive impairment. The development of ultrasensitive immunoassay technology enables AD pathological proteins such as tau and neurofilament light (NFL) to be measured in blood samples. However, combined biomarker detection or targeting multiple biomarkers in immunoassays will increase detection sensitivity and specificity and improve diagnostic accuracy. This review summarizes and analyzes the performance of current detection methods for early diagnosis of AD, and provides a concept of detection method based on multiple biomarkers instead of a single target, which may become a potential tool for early diagnosis of AD in the future.

Keywords:

Abbreviations

AD	:	Alzheimer’s disease
Aβ	:	Amyloid-β
T-tau	:	Total tau
P-tau	:	Phosphorylated tau
CSF	:	Cerebrospinal fluid
NFL	:	Neurofilament light
APP	:	Amyloid precursor protein
ELISA	:	Enzyme-linked immunosorbent assay
BBB	:	Blood–brain barrier
PET	:	Positron-emission tomography
MRI	:	Magnetic resonance imaging
SPECT	:	Single-photon-emission computed tomography
SiMoA	:	Single-molecule array
CNT	:	Carbon nanotube
CJD	:	Creutzfeldt–Jakob disease
BACE1	:	The β-site APP-cleaving enzyme 1
VLP-1	:	Visinin-like protein 1
VAD	:	Vascular dementia
miRNA	:	Micro-RNA
exRNAs	:	Extracellular RNAs
BsAbs	:	Bispecific antibodies
MtAs	:	Multi-targeted antibodies
EAD	:	Early-onset AD
LAD	:	Late-onset AD
FTD	:	Frontotemporal dementia
HC	:	Healthy controls
MCI	:	Mild cognitive impairment
NAD	:	Non-AD dementia
NA	:	Not available
OD	:	Other dementias
OND	:	Other neurologic disorders
PAD	:	Probable AD
pMCI	:	Progressive MCI
SMC	:	Subjective memory complaints
sMCI	:	Stable MCI
pMCl	:	progressive MCI
CVD	:	Cardiovascular disease
Aβ₄₂xMAP	:	Aβ₄₂ quantified with xMAP technology
Aβ₄₂MSD	:	Aβ₄₂ quantified with MSD technology
ALS	:	Amyotrophic lateral sclerosis
SVD	:	Subcortical vascular dementia

1. Introduction

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that may start 20–30 years before the clinical onset of the disease, which is accompanied by progressive neuropathology, brain atrophy, and ultimately cognitive decline.^1,2,3 Unfortunately, with the increasing global population aging, the number of AD patients worldwide has a sharp increasing, of which the high-income Asia Pacific are more serious (Fig. 1(a)). The number of people with AD will have a steep increase from the currently estimated 7.5 million to over 10 million by 2029 in China (Fig. 1(b)).⁴ The cost of long-term care for those with AD is enormous, which not only brings a heavy financial burden on individuals and families but also has an inestimable impact on the Chinese national health system. There is currently a lack of treatment strategies for AD, and all treatments are limited to moderate relief of symptoms.^5,6 Thus, it is critical to find new effective therapies for AD to slow its progression and prevent it from developing.

Fig. 1. (a) The global incidence of AD is most severe in the high-income Asia-Pacific region. (b) The total number of AD patients in China from 2018 to 2029 has a 10-year compounded growth rate of 4%. This growth is particularly significant among the Chinese urban population, with a 10-year compounded growth rate of 6%. (c) The trajectories of different fluids and imaging biomarkers in the AD continuum are depicted. Adapted from Ref. 228. (d) Representative AD core biomarkers in the brain, plasma, and CSF for brain imaging and fluid biomarkers-based detection.

In 1906, Alois Alzheimer first described the neuropathological features of AD as “miliary bodies” (amyloid plaques) and “dense fiber bundles” (neurofibrillary tangles).⁷ In the 1980s, Masters et al. successfully purified amyloid plaque cores and identified 4-kDa amyloid-β (Aβ) peptides as the main component of extracellular deposits.⁸ Grundke-Iqbal et al. found that neurofibrillary tangles consist of abnormally hyperphosphorylated forms of tau protein.⁹ Thus, amyloid plaques and tau protein are the primary biomarkers of AD, providing opportunities for the early diagnosis of AD. In the past few decades, the total-tau (t-tau), phosphorylated tau (p-tau) at different epitopes (e.g., threonine 231, serine 396, serine 404, and threonine 181), the 42 amino acid forms of β-amyloid (Aβ₁₋₄₂),^10,11 Aβ₁₋₄₂/Aβ₁₋₄₀ ratio,^12,13,14 and Aβ protofibril^15,16,17 had been validated to be related with neuropathologic changes in the brain (Fig. 1(c)). Biomarkers that reflect the pathological changes of AD in the brain are essential for clinical diagnosis, predicting progression, and monitoring drug efficacy in clinical trials. Prompt diagnosis in the prodromal AD or mild AD stage may improve patients’ access to healthcare and promote early and personalized interventions.

The ideal biomarker for AD before death should diagnose AD with high sensitivity and specificity, as confirmed by the gold standard of autopsy.¹⁸ Biomarkers for AD diagnosis include Aβ and tau positron-emission tomography (PET), cerebrospinal fluid (CSF) or blood measures to confirm the presence of AD pathological changes, including synaptic dysfunction and neurodegeneration, tau phosphorylation with neurofibrillary tangles (NFT) formation, and Aβ plaques deposition (Fig. 1(d)). Imaging biomarkers can be used for magnetic resonance imaging (MRI) to measure brain volume and neuronal connections, and Aβ and tau PET can be used to detect the number of pathological protein deposits in the brain (Figs. 2(b) and 2(c)).¹⁹ Although MRI and PET imaging biomarkers with the reliability of accurately diagnosing brain diseases are approved for clinical use, the economic burden of imaging methods hinders their widespread application in identifying AD.²⁰ Compared with MRI and PET imaging, CSF and blood tests are more affordable and easier to obtain.

Fig. 2. (a) Schematic illustration of the principal usage of AD core biomarkers for diagnosis in CSF or blood measurement, including ELISA, electrochemical detection, and spectrometric detection. (b) The schematic diagram shows the stages of Aβ (up) and tau (down) deposition in the human brain. Adapted from Ref. 124. (c) Representative clinical PET imaging of tau and Aβ accumulation showing the regional brain distribution of [¹¹C]PBB3 and ¹¹C-PiB in the normal elderly controls and patients with different disease states from severe to mild. Adapted from Ref. 229.

CSF biomarkers and plasma neurofilament light (NFL) also have excellent diagnostic performance for the detection of AD.²¹ The diagnosis of early-onset AD relies on the detection of AD-related biomarkers, but routine screening tools for AD are elusive. The most commonly used method to measure AD biomarkers is the enzyme-linked immunosorbent assay (ELISA), including assays for t-tau, p-tau, and Aβ₁₋₄₂ (Fig. 2(a)).^10,11 Clinical studies have always found a significant increase in t-tau and p-tau in AD, as well as a decrease in CSF Aβ₁₋₄₂. These core AD CSF biomarkers have fully validated high AD diagnostic performance, consistent with autopsy diagnosis. The amyloid oligomer hypothesis is further supported by the use of a high-dose monoclonal antibody (mAb), aducanumab, to effectively slow down the progression of AD. Aducanumab selectively targets soluble Aβ aggregates (oligomers or protofibrils).²² Currently, the available AD laboratory biomarkers are mainly based on CSF analysis, whereas the highly invasive lumbar punctures limit their potential for serial measurement. Blood-based laboratory biomarker assays allow early identification of potential AD cases.

The measurement of brain-derived proteins that reflect neuropathological changes in the blood needs to overcome the measurement limitation of their low concentration, which is usually lower than the detection limit of standard ELISA and other detection methods. Recently, the use of novel ultrasensitive assay techniques, such as improved ELISA, electrochemical detection, or spectroscopy method, allows us to measure such proteins in the blood (Fig. 3). However, the heterogeneity of delayed AD pathology requires multiple biomarkers to reflect various aspects of AD pathophysiology. The current diagnostic analysis mainly depends on single biomarker immunoassay-based tools in CSF or blood measurement, resulting in variation in data detected by different laboratories. This review summarizes and analyzes the performance of current detection methods for early diagnosis of AD, and provides a novel concept of detection method based on multiple biomarkers instead of a single target, which may become a potential tool for early diagnosis of AD in the future.

Fig. 3. Schematic diagrams of the representative diagnosis tools. (a) Double-Antibody Sandwich ELISA (DAS-ELISA) for detecting AD biomarkers based on a pair of antibodies recognizing different antigenic determinants on the surface of biomarkers. (b) A sensitive interdigitated chain-shaped electrode sensor being developed for Aβ₁₋₄₂ in human serum. (c) The immuno-infrared sensor that is used for the detection of β-sheet Aβ or tau secondary structure distribution in CSF and plasma. Adapted from Ref. 66.

Fig. 4. Timeline for the evolution of the core biomarkers of AD in cerebrospinal fluid and blood. Green boxes represent pathophysiological findings; yellow boxes represent technical detection developments; and purple boxes represent clinical findings in exploring the AD biomarkers.

2. Current Fluid Biomarkers of Alzheimer’s Disease

2.1. Basic biomarkers

The blood–brain barrier (BBB) is formed by the limited permeability of the capillaries in the brain to keep the neuron microenvironment in a controllable state.¹¹ The integrity of BBB reflects the health of the brain. The primary biomarkers are mainly composed of biochemical molecules in the BBB status and the inflammatory processes in the brain. The standard biomarker for BBB function depends on the ratio of CSF to serum albumin.²³ Infections from neuroborreliosis, Guillain–Barré syndrome, brain tumors, and cerebrovascular disease (details are shown in Table 1) may cause an increase in the ratio of CSF to serum albumin, indicating that BBB is damaged. Generally, it is more common for patients with cerebrovascular disease to increase the ratio, but for patients with simple AD, the ratio is normal. A significant difference in the ratio of CSF to serum albumin could effectively distinguish other brain injuries from AD patients.²³ Besides, the intrathecal immunoglobulin (Ig) produced in chronic inflammatory or infectious diseases of the central nervous system has no or very slight signs in most AD patients (Table 1). Thus, the detection of intrathecal Ig allows excluding chronic inflammatory and infectious disorders in the clinical diagnosis of AD.

**Table 1. Biomarkers for AD.**
Biomarkers	Pathogenic process	Measure(s)	Changes in biomarker level in AD	Source
Basic biomarkers
CSF cell count	Inflammation	CSF	Unchanged	Ref. 172
CSF:serum albumin ratio	BBB function	Blood, CSF	Unchanged in the cases of pure AD, increase in mild-to-moderate AD	Ref. 173
IgG or IgM index; IgG or IgM	Intrathecal immunoglobulin production	Blood	Unchanged	Ref. 174
Core biomarkers
t-tau	Neurofibrillary tangles	CSF	Marked increase in AD and prodromal AD	Refs. 10,11,134, and 175
Aβ₁₋₄₂	Major component of senile plaques	Blood, CSF	Marked reduction in AD and prodromal AD	Refs. 25,132,133,176, and 177
p-tau	Precedes formation of neurofibrillary tangles	Blood, CSF	Marked increase in AD and prodromal AD	Refs. 133–135
Novel biomarkers
BACE1	Aβ metabolism	Blood, CSF	Marked increase in AD	Refs. 33,107,178, and 179
Aβ₁₋₃₈	Amyloidogenic pathway of APP metabolism	CSF	Increase in AD	Ref. 180
Aβ₁₋₁₆	Amyloidogenic pathway of APP metabolism	CSF	Marked increase in AD	Ref. 37
Endogenous Aβ antibodies	Produced by of truncated amyloid-β isoforms	Blood, CSF	Increases, decreases, or remain unchanged	Refs. 38–40,109, and 110
VLP-1	Highly expressed neuronal calcium sensor	CSF	Marked increase in AD	Ref. 112
F₂-IsoPs	Radical-catalyzed	CSF	Increase in AD	Ref. 181
miRNAs	Post-transcriptional regulation	Blood, CSF	Upregulated or downregulated in AD	Ref. 45
exRNAs	Reflecting pathological states	Blood, CSF	Upregulated in AD	Ref. 46
Exosomes	Neuroinflammatory mediators	Blood, CSF	Reduction and clearance of Aβ	Ref. 47

2.2. Core biomarkers

AD core biomarkers are the objective measurements of biological or pathogenic processes, combined with the underlying molecular pathology of the disease for the evaluation of disease risk or prognosis, guidance of clinical diagnosis, or monitoring of therapeutic interventions.²⁴ Currently, the main representatives of core biomarkers include Aβ₁₋₄₂,^8,25,26 Aβ₁₋₄₂/Aβ₁₋₄₀,^25,27 soluble oligomeric Aβ and Aβ protofibrils,^15,16,17 p-tau protein,^10,11 and t-tau^28,29,30 (Table 1). Based on these biomarkers, drugs have been developed to reflect the pathology of amyloid and neurofibrillary tangle pathology and axonal degeneration in AD.

2.3. Novel biomarkers

Except for Aβ, t-tau, and p-tau, many other candidate biomarkers include β-site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1),^31,32 APP isoforms,^33,34,35 truncated Aβ isoforms,^14,36,37 endogenous Aβ autoantibodies,^38,39,40 neuron and synaptic markers,⁴¹ and F₂-isoprostanes (F₂-IsoPs).^42,43,44 These biomarkers show high sensitivity and specificity for AD, which are confirmed by at least two independent studies. Besides, micro-RNA (miRNA),⁴⁵ extracellular RNAs (exRNAs),⁴⁶ and exosomes,⁴⁷ as promising biomarkers of AD, have shown potential in effectively identifying patients with increased pathological risk of AD.

3. Development of Immunosensors for AD Detection

Recently, various immunosensors have been developed and applied for AD detection, mainly including ELISA,^{48,49,50,51,52,53,54,55,56,57} modified sandwich ELISA (Fig. 3(a)),⁵⁸ aptamer sensor assay,^59,60,61 electrochemical biosensor (Fig. 3(b)),^62,63,64 spectral assay (Fig. 3(c)),^65,66 and single-molecule array (SiMoA).^67,68 ELISA is the first developed immunosensor to detect AD biomarkers, which is based on a pair of antibodies that recognize different epitopes on the surface of antigens. Presently, the developed diagnostic tools mainly rely on traditional antibodies. Of note, another new type of “molecular antibody” named aptamers is single-stranded DNA or RNA (ssDNA or ssRNA), which has a unique tertiary structure that enables it to specifically bind to homologous molecular targets.^69,70 Due to several obvious advantages such as smaller size and nucleic acid characteristics, aptamer-based diagnosis provides a promising opportunity for AD diagnosis. Electrochemical biosensors have been proposed as an important strategy for biomarkers sensing, with pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$ level of detection capabilities. Presently, various electrochemical biosensors have been used in AD diagnosis and have provided promising results. SiMoA is an ultrasensitive technology that can detect proteins in blood at sub-femtomolar concentrations (e.g., 10⁻¹⁶ M). Several SiMoA assays have been made in the diagnosis of AD that combined core biomarkers, including Aβ₁₋₄₀,⁷¹ Aβ₁₋₄₂,⁷² t-tau,⁷³ and p-tau.⁷⁴

Recently, a susceptible optical analysis platform based on immuno-infrared sensor and surface plasmon resonance has been reported, which verifies clinical relevance by measuring Aβ₁₋₄₂ in plasma.^75,76 Kim et al. employed a densely aligned single-walled carbon nanotube (CNT) sensor array to realize clinically accurate detection of multiple AD core biomarkers (Aβ₁₋₄₂, t-tau, and p-tau181) and composite biomarkers (t-tau/Aβ₄₂, p-tau/Aβ₄₂, and Aβ₄₂/Aβ₄₀) in human plasma, and the effective distinction between AD patients and healthy controls (HC) with a mean sensitivity of 90% and a specificity of 90%.⁷⁷ Thus, nanoparticle-based electrochemical biosensors have the potential for early AD diagnosis in the future.

4. Application of Biomarkers in the Diagnosis of AD

4.1. Aβ-based detection

Aβ is the typical product produced in cell metabolism and secreted into CSF and plasma and can be used to diagnose AD patients.^78,79 Unfortunately, the results of the CSF test report on Aβ were disappointing, and the results ranged from a slight decrease in AD to no change.^80,81,82 Subsequent research results showed that Aβ₁₋₄₂, as the primary component of plaques, are derived from APP and is the most abundant species of Aβ in amyloid plaques.^83,84,85 Various ELISA-related assays and several novel sensors were developed and used for AD tests. Compared with age-matched healthy elderly, the results of the CSF test of AD patients always show a level drop of about 50%.¹⁰ Later, soluble oligomeric Aβ, mainly including Aβ protofibrils, exhibited more neurotoxicity and induced more electrophysiological effects on neurons.^86,87,88 Several groups demonstrated that soluble Aβ protofibrils could be excreted into CSF and plasma, and their concentration was correlated with plaque burden, which provides the earliest clue for predicting the pathological progress of AD.¹

Recently, a sensitive electrochemical detection using interdigital chain electrodes was developed to detect Aβ₁₋₄₂ in human serum with the detection limit of 100pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$ , which was much lower than the detection of CSF Aβ₁₋₄₂ (Fig. 3(b)).⁶⁴ The immuno-infrared sensor is a novel detection method for detecting β-sheet Aβ in CSF and plasma, with excellent clinical diagnosis accuracy (90% for CSF and 84% for blood) (Fig. 3(c)).⁶⁶ Later, Nabers et al.⁸⁹ further used the immuno-infrared sensor and the tau secondary structure distribution in plasma and CSF as a structure-based biomarker for AD diagnosis, achieving an overall specificity of 97%. Recently, Zhang et al.⁹⁰ developed a sandwich ELISA that could simultaneously detect Aβ₁₋₄₂ monomers and oligomers as two core biomarkers of AD, improving the sensitivity and accuracy of detection. To compare the detection sensitivity and specificity from different studies, Table 2 summarizes 39 studies on the detection of Aβ in CSF and plasma, including about 3183 AD patients and 2202 controls with the mean specificity of 84.1% and the mean sensitivity of 82.3% to AD.

**Table 2. Summary of the studies based on Aβ assay.**
Epitope	Cut-off value	Sensitivity (%)	Specificity (%)	Comparator group(s)	Source
Aβ₁₋₄₂	376pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	51	67	AD (n = 54), HC (n = 15)	Ref. 182
Aβ₁₋₄₂	256fmol $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	55	47	AD (n = 93), HC (n = 41)	Ref. 183
Aβ₁₋₄₂	475pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	61	80	AD (n = 81), HC (n = 51)	Ref. 184
Aβ₁₋₄₂	738ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	63	95	FTD (n = 34), AD (n = 74), and HC (n = 40)	Ref. 185
Aβ₁₋₄₂	0.49ng $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	73	90	AD (n = 51), HC (n = 31)	Ref. 186
Aβ₁₋₄₂	500pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	76	78	AD (n = 41), HC (n = 21)	Ref. 187
Aβ₁₋₄₂	192pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	78	76.9	AD (n = 102), MCI (n = 200), and HC (n = 114)	Ref. 188
Aβ₁₋₄₂	550ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	78	83	AD (n = 248), SMC (n = 131)	Ref. 189
Aβ₁₋₄₂ monomer	150pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	78	90	AD (n = 36)	Ref. 190
Aβ₁₋₄₂	500pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	78	82	AD (n = 93), NAD (n = 33), and HC (n = 54)	Ref. 191
Aβ₁₋₄₂	NA	80	85	AD (n = 18), HC (n = 13)	Ref. 192
Aβ₁₋₄₂	NA	81	82	NA	Ref. 193
Aβ₁₋₄₂	505ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	81	76	AD (n = 57), MCI (n = 56), and OD (n = 21)	Ref. 194
Aβ₁₋₄₂	NA	81	74	MCI (n = 21), sMCI (n = 21), and HC (n = 26)	Ref. 195
Aβ₁₋₄₂ xMAP	209ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	82	71	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 196
Aβ₁₋₄₂ MSD	523ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	85	89	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. ¹⁹⁶
Aβ₁₋₄₂	157ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	87	76	AD (n = 118), sMCI (n = 165), and HC (n = 169)	Ref. ¹⁹⁷
Aβ₁₋₄₂	192ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	92.4	NA	HC (n = 169), AD (n = 118), sMCI (n = 165), and pMCI (n = 59)	Ref. ¹⁹⁸
Aβ₁₋₄₂	NA	89	79	AD (n = 158), NAD (n = 233)	Ref. ¹⁹⁹
Aβ₁₋₄₂	1130pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	89	95	AD (n = 53), HC (n = 21)	Ref. ²⁰⁰
Aβ₁₋₄₂	361pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	89	95	AD (n = 14), HC (n = 20)	Ref. ²⁰¹
Aβ₁₋₄₂	537pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	90	85	AD (n = 60), HC (n = 32)	Ref. 48
Aβ₁₋₄₂	505pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	90	94	AD (n = 9), HC (n = 17)	Ref. 51
Aβ₁₋₄₂	NA	92	95	AD (n = 74), HC (n = 40)	Ref. 202
Aβ₁₋₄₂	0.49ng $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	93	90	AD (n = 51), HC (n = 31)	Ref. 203
Aβ₁₋₄₂	537pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	93	85	AD (n = 560), FTD (n = 517), and HC (n = 532)	Ref. 51
Aβ₁₋₄₂	638pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	94	94	AD (n = 19), HC (n = 17)	Ref. 49
Aβ₁₋₄₂	643pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	95	81	AD (n = 150), HC (n = 100)	Ref. 50
Aβ₁₋₄₂	284.1pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	96	95	AD (n = 24), HC (n = 19)	Ref. 52
Aβ₁₋₄₂	490pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	96	80	AD (n = 49), HC (n = 49)	Ref. 53
Aβ₁₋₄₂	375pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	96.4	100	AD (n = 27), HC (n = 49)	Ref. 54
Aβ₁₋₄₂	505pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	100	63	AD (n = 37), OND (n = 32), and HC (n = 20)	Ref. 132
Aβ₁₋₄₂	445pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	100	85	AD (n = 38), HC (n = 47)	Ref. 55
Aβ₁₋₄₂	716ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	100	95	AD (n = 20), HC (n = 20)	Ref. 56

4.2. Tau-based detection

As another core biomarker, tau protein consists of six different isoforms formed by alternative splicing of exons 2, 3, and 10. It is an axonal protein that is located in the axons of human brain neurons and binds to microtubules to help keep the microtubules stable and assemble them in bundles.^4,11 Tau protein is highly expressed in nonmyelinated cortical axons, especially in memory consolidation areas. In 1993, Vandermeeren et al.²⁸ first reported that the concentration of tau protein in CSF was likely involved in AD pathology and could be used as a polyclonal antibody (pAb)-based ELISA biomarker. Later, an mAb-based ELISA method was developed to detect all isoforms of tau proteins regardless of their degree of phosphorylation.^29,30 Compared with approximately 50% reduction in CSF Aβ₁₋₄₂, the CSF t-tau levels of AD patients increased significantly, which was about three times higher than that of age-matched healthy elderly adults using t-tau-based assays.¹⁰ Notably, the concentrations of t-tau in CSF have been found in alcoholic dementia and chronic neurological disorders (for example, Parkinson’s disease and progressive supranuclear palsy) through several differential diagnoses.^{29,51,91,92,93,94} “Innogenetics ELISA” (Fig. 3(a)), as the most commonly used t-tau-based method, revealed an average specificity of 85% and an average sensitivity to AD of 79.4% from 46 different studies, including approximately 4549 AD patients and 2682 controls (Table 3).

**Table 3. Summary of the studies based on t-tau assay.**
Epitope	Cut-off value	Sensitivity (%)	Specificity (%)	Comparator group(s)	Source
t-tau	496.1pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	31	94	AD (n = 55), NAD (n = 23), and HC (n = 34)	Ref. 91
t-tau	474pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	40	86	AD (n = 93), HC (n = 41)	Ref. 183
t-tau	400pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	58	88	AD (n = 81), OD (n = 43), and HC (n = 33)	Ref. 204
t-tau	375pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	59.1	89.5	AD (n = 366), HC (n = 316)	Ref. 205
t-tau	312pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	59.4	92	AD (n = 37), HC (n = 20)	Ref. 132
t-tau	0.53–0.55ng $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	63	89	AD (n = 5), PAD (n = 25), and HC (n = 16)	Ref. 92
t-tau	NA	63	100	Manifest AD (n = 43), AD (n = 16), and HC (n = 16)	Ref. 201
t-tau	559pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	67	85	EAD (n = 21), LAD (n = 21), and HC (n = 18)	Ref. 206
t-tau	Sensitivity plus specificity	67	96	MCI (n = 21), sMCI (n = 21), and HC (n = 26)	Ref. 195
t-tau	93pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	69.6	92.3	AD (n = 102), MCI (n = 200), and HC (n = 114)	Ref. 188
t-tau	NA	72	93	AD (n = 39), SVD (n = 17), FTD (n = 14), and HC (n = 12)	Ref. 207
t-tau	380pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	77	92	AD (n = 47), FTD (n = 14), VAD (n = 16), and HC (n = 12)	Ref. 183
t-tau	51pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	78	83	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 196
t-tau	252pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	79	70	AD (n = 150), HC (n = 100)	Ref. 50
t-tau	370pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	79	100	AD (n = 52), VAD (n = 46), and HC (n = 56)	Ref. 100
t-tau	424pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	79	82	AD (n = 560), FTD (n = 517), and HC (n = 532)	Ref. 51
t-tau	55ng $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	80	85	AD (n = 25), HC (n = 19)	Ref. 204
t-tau	380pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	81.3	91	AD (n = 80), PAD (n = 82), and HC (n = 21)	Ref. 208
t-tau	NA	82	90	NA	Ref. 193
t-tau	NA	83.3	84	NA	Ref. 52
t-tau	22.6pmol $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	84	41	AD (n = 19), FTD (n = 14), ALS (n = 11), and HC (n = 17)	Ref. 49
t-tau	258pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	84	97	AD (n = 44), HC (n = 31)	Ref. 29
t-tau	260pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	84	62	AD (n = 38), HC (n = 28)	Ref. 201
t-tau	7.5pM	84	88	AD (n = 80), HC (n = 40)	Ref. 209
t-tau	85% Sensitivity for AD	85	62.7	AD (n = 529), MCI (n = 750), and HC (n = 304)	Ref. 210
t-tau	375ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	85	78	AD (n = 248), SMC (n = 131)	Ref. 189
t-tau	260pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	85.4	95	AD (n = 41), HC (n = 17)	Ref. 206
t-tau	320ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	86	56	AD (n = 529), MCI (n = 750), and HC (n = 304)	Ref. 184
t-tau	334.2pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	87	93	AD (n = 54), HC (n = 15)	Ref. 182
t-tau	225pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	87.5	76	PAD (n = 16), OD (n = 20), and HC (n = 16)	Ref. 195
t-tau	317pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	88	96	AD (n = 49), HC (n = 49)	Ref. 53
t-tau	440pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	89	74	AD (n = 27), HC (n = 49)	Ref. 54
t-tau	46.3pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	89	100	AD (n = 69), CVD (n = 21), and HC (n = 17)	Ref. 210
t-tau	295pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	89.5	91.5	AD (n = 38), HC (n = 47)	Ref. 55
t-tau	305pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	90	67	AD (n = 81), HC (n = 51)	Ref. 184
t-tau	360ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	90	90	AD (n = 20), HC (n = 20)	Ref. 56
t-tau	NA	90	67	AD (n = 81), HC (n = 15)	Ref. 193
t-tau	302pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	93	86	AD (n = 407), HC (n = 93)	Ref. 94
t-tau	254.5ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	95	98	AD (n = 74), FTD (n = 34), and HC (n = 40)	Ref. 185
t-tau	306pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	95	94	AD (n = 43), HC (n = 18)	Ref. 182
t-tau	4fmol $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	97.2	56	AD (n = 36), HC (n = 20)	Ref. 135
t-tau	22.6pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	98.6	100	AD (n = 70), NAD (n = 76), and HC (n = 17)	Ref. 211

Many studies have confirmed that the phosphorylation status of tau in the brain is more related to the progression of AD, which may be reflected in the concentration of p-tau protein in the CSF.¹¹ Soluble hyper-p-tau causes neuronal dysfunction before its aggregation and subsequently forms tangles,^95,96 because the accumulation of hyper-p-tau in the somatodendritic compartment of neurons interferes with neuronal functions.^97,98 Ittner and Götz⁴ reported that approximately 84 putative phosphorylation sites are shown in tau, including 45 serines (S), 35 threonines (T), and four tyrosines (Y), and p-tau (181),⁹⁹ p-tau (231),²⁹ p-tau (396), and p-tau (404)¹⁰⁰ are the recently discovered AD biomarkers and have been used in a variety of ELISA tests. Results from several p-tau-based methods consistently report a significant increase in p-tau in the CSF of AD.¹⁰ Unlike t-tau protein, no changes in p-tau protein concentration have been observed in acute stroke and Creutzfeldt–Jakob disease (CJD).¹⁰¹ In 2012, Nakamura et al.¹⁰² demonstrated that p-tau (231) is a potential new approach for the early diagnosis and treatment of AD because p-tau (231) is the first detectable phosphorylation event in human AD, and cis p-tau (231) and its ratio to trans might be better and easier than standardized biomarker, especially for the early diagnosis and patient comparison.¹⁰³ Janelidze et al.¹⁰⁴ and Thijssen et al.¹⁰⁵ also reported that the concentration of p-tau (181) in the plasma of AD patients increased by 3.5 folds compared with the control group. In conclusion, plasma p-tau (181) is a prognostic biomarker of AD and will benefit for AD diagnosis in clinical practice and trials. Taken together, the concentration of p-tau protein in CSF or plasma can more specifically reflect the state of AD than Aβ. The specificity and sensitivity values of the p-tau-based test are summarized in Table 4 from 40 different studies, including about 3686 AD patients and 1878 controls, with the mean specificity of 84.5% and the mean sensitivity of 78.3% to AD. It is worth noting that compared with other p-tau, p-tau (181) and p-tau (231) show better detection performance in terms of mean specificity and sensitivity in AD diagnosis.

**Table 4. Summary of the studies based on p-tau assay.**
Epitope	Cut-off value	Sensitivity (%)	Specificity (%)	Comparator group(s)	Source
p-tau (181)	21.2pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	40	81.4	AD (n = 23), PAD (n = 50), VAD (n = 39), and HC (n = 27)	Ref. 212
p-tau (181)	12.5pmol $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	44	95	AD (n = 41), FTD (n = 18), and HC (n = 17)	Ref. 206
p-tau	51pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	50	94	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 213
p-tau (231)	3.6fmol $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	52.8	100	AD (n = 36), HC (n = 20)	Ref. 135
p-tau (181)	51ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	57.4	82.9	AD (n = 215), HC (n = 38)	Ref. 214
p-tau (181)	22.6pmol $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	58	95.3	AD (n = 19), HC (n = 17)	Ref. 49
p-tau	59pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	64	98	AD (n = 57), HC (n = 45)	Ref. 215
p-tau (231)	52ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	68	73	AD (n = 529), MCI (n = 750), and HC (n = 304)	Ref. 199
p-tau (181)	73pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	71	94	Manifest AD (n = 43), incipient AD (n = 8), and HC (n = 16)	Ref. 201
p-tau (181)	61ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	73.1	93.1	AD (n = 67), HC (n = 72)	Ref. 216
p-tau (181)	51ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	73.5	75.7	NA	Ref. 217
p-tau (181)	15pmol $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	75.3	81.8	AD (n = 18), FTD (n = 26), and HC (n = 13)	Ref. 218
p-tau (181)	NA	77.6	87.9	NA	Ref. 219
p-tau	NA	80	83	NA	Ref. 193
p-tau (181)	50pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	83.3	84.6	AD (n = 18), HC (n = 13)	Ref. 192
p-tau (181)	10pmol $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	83.8	87.5	AD (n = 80), HC (n = 40)	Ref. 209
p-tau (181)	33pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	84	84	AD (n = 51), HC (n = 31)	Ref. 186
p-tau	52ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	84	47	AD (n = 529), MCI (n = 750), and HC (n = 304)	Ref. 184
p-tau	33pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	84	84	AD (n = 51), HC (n = 31)	Ref. 203
p-tau (181)	33pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	84.3	83.9	AD (n = 100), HC (n = 31)	Ref. 220
p-tau (231)	10.1ng $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	85	97	AD (n = 27), HC (n = 31)	Ref. 201
p-tau	52ng $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	85	68	AD (n = 248), SMC (n = 131)	Ref. 189
p-tau (199)	1.1fmol $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	85	82	AD (n = 108), HC (n = 23)	Ref. 221
p-tau (181)	14.3pmol $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	85	91	AD (n = 108), HC (n = 23)	Ref. 221
p-tau (181)	54pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	85.1	76.2	AD (n = 28), HC (n = 13)	Ref. 222
p-tau (231)	10.1ng $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	85.2	96.8	AD (n = 27), NAD (n = 4)	Ref. 223
p-tau (181)	14.3pmol $\cdot$ $\cdot$ L $^{- 1}$ $^{- 1}$	87	88.9	AD (n = 93), NAD (n = 33), OND (n = 56), and HC (n = 54)	Ref. 191
p-tau (231)	250pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	88	92	AD (n = 108), HC (n = 23)	Ref. 221
p-tau	1140pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	88	89	AD (n = 40), HC (n = 31)	Ref. 29
p-tau (181)	65pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	88.2	79.5	AD (n = 76), NAD (n = 48), and HC (n = 39)	Ref. 224
p-tau (231)	19pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	90.2	80	AD (n = 80), PAD (n = 82), VAD (n = 20), and HC (n = 21)	Ref. 48
p-tau (231)	18.1pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	90.3	80	AD (n = 82), HC (n = 21)	Ref. 205
p-tau (181)	8.1pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	91.7	83.8	AD (n = 56), MCI (n = 47), and HC (n = 69)	Ref. 105
p-tau (181)	1.81pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	92	87	A $β^{+}$ $β^{+}$ MCI (n = 109), A $β^{+}$ $β^{+}$ AD (n = 38), and NAD (n = 52)	Ref. 104
p-tau (231)	18.1pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	92.2	85.3	Possible AD (n = 17), PAD (n = 64), and HC (n = 21)	Ref. 225
p-tau (199)	3.2fmol $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	94.4	80	AD (n = 36), HC (n = 20)	Ref. 135

4.3. Assays based on novel biomarkers

Several promising detection methods for novel blood biomarkers have been documented. Here, we review these promising diagnosis analyses. The amount of Aβ directly relies on the activities of β-secretase and $γ$ $γ$ -secretase on APP, among which BACE1 is the main enzyme responsible for β-secretase activity.¹⁰ BACE1 can be measured in the CSF of AD patients and prodromal AD cases with a marked increase in the concentration and activity of BACE1, indicating that the upregulation of BACE1 may be an early event of AD.^33,106,107 Much attention has been focused on the Aβ₁₋₄₂/Aβ₁₋₄₀ ratio^12,13,14 and the application of other truncated amyloid-β isoforms in the diagnosis of AD. Individuals with elevated Aβ₁₋₃₈ levels in CSF are accompanied by a decrease in Aβ₁₋₄₂,^14,36 suggesting that the ratio of Aβ₁₋₄₂/Aβ₁₋₃₈ may improve the diagnostic accuracy of AD. Besides, it has been reported that there is a significant increase in Aβ₁₋₁₆ in the CSF of AD patients and an expected decrease in Aβ₁₋₄₂.³⁷ Portelius et al.¹⁰⁸ used immunoprecipitation mass spectrometry to determine other truncated Aβ subtypes at the carboxyl terminus, which may be useful for the diagnostic testing of AD. Different kinds of truncated Aβ isoforms are secreted into CSF or blood to produce various endogenous amyloid-β autoantibodies, of which oligomeric Aβ shows the highest antibody reactivity. The study aimed to detect the titers of such antibodies, but the results were inconsistent, with increases,^38,109 decreases,^39,110 or remaining unchanged.⁴⁰ Disappointingly, the level of endogenous oligomeric Aβ autoantibodies did not differ between AD cases and controls.¹¹¹

Visinin-like protein 1 (VLP-1) and neurofilament are representative biomarkers of neurons and synapses. VLP-1 is a neuronal calcium-sensing protein, which is abundantly expressed in the central nervous system and retinal neurons, and its expression level is significantly increased in AD, especially when carrying the apolipoprotein E (APOE) $ε$ $ε$ 4 allele.⁴¹ The sensitivity and specificity of VlP-1-based assays are all close to 80%, which is sufficient to distinguish AD from age-matched individuals and comparable to CSF t-tau, p-tau, and Aβ₁₋₄₂.¹¹² Considering its good diagnostic performance, VlP-1 is regarded as a promising candidate CSF biomarker for AD. Besides, neurofilament is a structural component of axons, especially highly expressed in vascular dementia (VAD), normal pressure hydrocephalus (NPH), and frontotemporal dementia (FTD), but it is expressed at normal levels in most AD patients.^113,114,115 Thus, neurofilaments help differentiate AD from VAD, NPH, and FTD.

In AD, neurons are mainly injured by Aβ, neurofibrillary tangles, and free radicals. F₂-IsoPs are the products formed by free radical-mediated oxidation of arachidonic acid. Compared with healthy elderly or non-AD dementia patients, AD patients have higher F₂-IsoP CSF levels, including the prodromal AD in individuals with cognitive impairment and asymptomatic carriers of familial AD mutations.^42,44,116 However, the measurement of F₂-IsoPs in plasma had contradicted CSF because brain-derived F₂-IsoPs are much smaller than peripheral F₂-IsoPs.⁴²

Besides, Cheng et al. reported that 12 miRNAs can discriminate AD from healthy controls with an average accuracy of 93%.⁴⁵ A recent review of miRNAs as AD biomarkers reported that 136 individual miRNAs changed significantly between AD and healthy states.¹¹⁷ Compared with age-matched control plasma, plasma phosphoglycerate dehydrogenase (PHGDH) exRNA levels have also been confirmed to exhibit a statistically significant increase in AD.⁴⁶ These miRNAs and exRNAs are thought to be transported within extracellular vesicles such as exosomes, high-density lipoprotein (HDL), neuronally derived extracellular vesicles, and other degrading proteins. Exosomes play an essential role in triggering Aβ pathogenesis and tau hyperphosphorylation, and in mediating the clearance and accumulation of Aβ and tau.⁴⁷ Lim et al. developed an amplified plasma exosome system to define the exosome-bounded Aβ population from AD blood samples, and confirmed that the exosome-bounded Aβ level was correlated with the corresponding PET imaging of brain amyloid plaque deposition.⁷⁵

5. Performance of Assays Developed Based on Core Biomarkers

5.1. Analysis of PET-based detection methods

Although pathological tau and Aβ represent distinct pathological processes, they are closely related in the context of AD.¹¹⁸ The use of tau and Aβ imaging allows AD-related cognitive dysfunction to be attributed to specific pathological processes and locations in the brain.¹¹⁹ Structural imaging techniques (e.g., MRI) reveal brain atrophy due to neuronal loss. In most cases, the molecular-level functional changes due to degeneration precede brain atrophy and may occur before any cognitive symptom appears.¹²⁰ Therefore, the use of functional techniques such as single-photon-emission computed tomography (SPECT) and PET for molecular neuroimaging may be a more promising tool for the diagnosis of AD. Presently, PET-based imaging methods can be divided into the following categories: glucose metabolism imaging,^121,122,123 Aβ imaging, and tau imaging, of which the latter two are more widely used in AD diagnosis.

Aβ deposition is one of the pathological hallmarks of AD in the cerebral cortex. It starts many years before the onset of symptoms and reaches a plateau when the first cognitive impairment becomes clear.^118,124 Currently, many radiotracers for Aβ molecular imaging of PET have been developed. Aβ PET imaging is highly sensitive to Aβ detection, but the specificity of Aβ-positive PET is relatively low because an increased ¹¹C-PiB uptake has also been found in 30% of ordinary older adults without cognitive impairment and other causes of neurodegenerative dementia.¹²⁵ In addition, compared with ¹¹C-PiB, fluorinated tracers show more nonspecific white matter absorption, resulting in higher background noise, which may mask the absorption of tracers in the cortex, especially when using visual analysis.¹²⁵ Therefore, there is an urgent need for an objective and widely applicable method to reveal a small number of Aβ deposits and determine the optimal cut-off level, which makes the results of different studies comparable.

On the other hand, pathological tau begins to spread from the temporal lobe to other parts of the brain in the later stage, which is also accompanied by the death of neurons.^118,126 Although the phosphorylation concentration of tau is much lower than that of Aβ (4–20 times lower), it provides multiple binding sites due to its larger size compared to Aβ.¹²⁷ The newly developed tau-PET method has high sensitivity and specificity (about 90–95%) and very few false-positive results for patients with other diseases.^128,129 Compared with MRI, the tau-PET method has significantly higher diagnostic accuracy and fewer false-positive results than Aβ-PET, which may be due to the high p-tau level that has only been found in AD rather than Aβ which is also shown in patients with dementia such as cerebral amyloid angiopathy¹³⁰ and Lewy bodies.⁵² In addition, Aβ is related to AD, but not directly related to symptoms, while tau protein, especially the tau protein in the temporal lobe region, is more closely related to cognitive decline in cognitive tests.¹¹⁹

5.2. Analysis of CSF-based detection methods

AD pathology is mainly restricted to the brain; therefore, an accurate and timely diagnosis of AD is essential for scientific understanding of brain function, activities, and the status of health, disease, and repair. Since the CSF is in direct contact with the extracellular space of the brain, the biochemical changes in the brain are directly reflected in the cerebrospinal fluid.¹¹ At present, Aβ in CSF has been widely used for the early diagnosis of AD in preclinical and clinical studies. However, subsequent studies have shown that CSF Aβ₁₋₄₂ are also significantly reduced in CJD,¹¹⁶ amyotrophic lateral sclerosis (ALS),⁴⁹ FTD and VAD, and multiple system atrophy diseases (MSADs)¹³¹ without Aβ plaque. These results from the Aβ₁₋₄₂ assays at least confirm that Aβ₁₋₄₂ are not sufficient to diagnose AD, with a specificity of 82.3% and an average sensitivity of 84.1% for CSF Aβ₁₋₄₂ to discriminate AD from normal aging in the “Innogenetics ELISA”.^{48,49,50,51,52,53,54,55,56,57} The specificity and sensitivity results based on Aβ₁₋₄₀/Aβ₁₋₄₂ assays are compromised. Many t-tau-based ELISA methods have been developed to detect all isoforms of tau protein. In the detection of CJD and VAD, the average specificity is lower because the concentration of tau protein in CSF may not only reflect the intensity of neuronal degeneration in AD, but also reflect the intensity of neuronal degeneration in chronic neurodegenerative diseases.^29,50,132 For this reason, the specificity for t-tau-based assays is not optimal to date. The specificity of p-tau (84.5%) seems to be better than Aβ (82.3%) because p-tau is only shown in AD and Myocardial Infarction (MI).^133,134,135 Unfortunately, CSF measurement is invasive, with high inter-laboratory variability, and having more side effects, resulting in limited availability, especially in primary care.

5.3. Analysis of plasma-based detection methods

The analysis of blood biomarkers is cost-effective and can be performed by minimally invasive surgery, which is of great significance for the early diagnosis of AD.¹³⁶ Reliable serum or plasma biomarkers are the key to timely treatment and help preventing the patient’s condition from getting worse, but only a few studies have been reported. Some studies have tested plasma Aβ as a biomarker of AD, but the results are inconsistent, ranging from no change in plasma Aβ to slightly higher Aβ₁₋₄₂ or Aβ₁₋₄₀ plasma levels in AD than those in healthy age-matched controls.^137,138 The conflicting results have originated from the fact that there is a wide overlap in the levels of Aβ₁₋₄₂ and Aβ₁₋₄₀ in plasma Aβ values between preclinical AD and non-AD. Also, high levels of plasma Aβ₁₋₄₂ or large Aβ₁₋₄₂: Aβ₁₋₄₀ ratios are risk factors for AD, while other studies have reported the opposite data.^{138,139,140,141} The phenomena could be explained because plasma Aβ is derived from peripheral tissues and does not reflect brain Aβ turnover or metabolism.¹⁴ Besides, the majority of Aβ in plasma is hydrophobic and needs to bind to plasma proteins, leading to epitope masking and other analytical interferences.¹⁴² Based on β-sheet Aβ, which mainly includes oligomers, many detection methods have been developed, such as ELISA,¹⁴³ immuno-infrared sensor,⁶⁶ and sensitive electrochemical detection,⁶⁴ but the specificity directly depends on the antibodies. For Aβ oligomers, as intermediates in the Aβ aggregation process, they are mainly synthesized by Aβ₁₋₄₂ or Arctic-mutant Aβ₁₋₄₂(E22G). However, the antibody detection methods based on Aβ oligomers need to overcome the challenge of distinguishing Aβ₁₋₄₂ or Aβ₁₋₄₂(E22G) surface epitope antibody recognition from Aβ fibrils. In addition, antibodies that cross-react with Aβ₁₋₄₂ or Aβ₁₋₄₂(E22G) may cause false positives.

Many mAbs, such as mAb 158,¹⁴³ 3D6, MOAB-2,⁶⁴ 211F12, 6E10,⁶⁶ BAN2401 (humanized IgG1 monoclonal),^144,145 and 1F12 and 2C6,⁹⁰ have been developed to target Aβ. Of note, 6E10 and mAb 158 or its mutant BAN2401 (humanized IgG1 mAb) have superior performance and are widely used in the diagnosis and treatment of AD. However, 6E10 reacts with almost all forms at the $N$ $N$ -terminus, making it difficult to distinguish different Aβ forms, largely limiting its application in diagnosis. On the contrary, it has been confirmed that mAb 158 and its mutant BAN2401 are at least 1000-fold higher in selectivity for protofibrils than monomers, and the binding to protofibrils is 10–15 times higher than the binding to fibrils. Besides, 1F12 and 2C6 (with four discrete epitopes) are the Aβ₁₋₄₂ sequence- and conformation-specific antibodies that can bind to Aβ₁₋₄₂ species with different conformations, but not the Aβ₁₋₄₀ or Aβ₁₋₃₈ species.⁹⁰ These antibodies provide great prospects in the treatment and diagnosis of AD. Recently, plasma p-tau (181) has been confirmed by two groups as a novel promising biomarker for AD.^104,105 Encouraging results indicate that the concentration of plasma p-tau (181) increases in preclinical AD and further increases during the MCI and dementia stages (compared to the control group, the concentration in AD is approximately 3.5 times). Interestingly, the plasma p-tau (181) concentration is correlated with the CSF p-tau (181) concentration and has been successfully used to predict the positive tau PET scans.^104,133 Two independent results consistently indicate that p-tau (181) is a noninvasive diagnostic and prognostic biomarker for AD, and may be useful in clinical practice and trials.

5.4. Analysis of combined biomarkers-based detection methods

Both CSF biomarkers and plasma biomarkers (e.g., Aβ₄₂, t-tau, and p-tau) have high diagnostic value in AD. In other medical fields, CSF or plasma biomarkers should not be used as isolated tests. In in-vitro AD diagnosis, the best choice is to combine CSF or plasma biomarkers detection with a variety of sensitive biosensors, such as electrochemical biosensors, spectrometry, and SiMoA, which can further improve the performance of current single-target assays. However, only a few studies have realized this. A combination of biomarkers in plasma or CSF has been established for the early diagnosis of AD, including tau + Aβ₄₂, low tau+elevated Aβ₄₂, Aβ₄₂/tau, Aβ₄₂/p-tau, tau + Aβ₄₂/p-tau (181), Aβ₄₂ + p-tau, Aβ₄₂ + tau + p-tau, Aβ₄₂/Aβ₄₀, and Aβ₄₂/p-tau + tau. From 73 different studies, the average specificity of 86% and the average sensitivity of 83.5% to AD are summarized in Table 5. The average specificity and sensitivity after combining two or more biomarkers are higher than those of a single biomarker. While two or more combination tests from 73 different studies are performed by using two or more diagnostic test kits, multiple target detection methods based on bispecific antibodies (BsAbs) or multi-targeted antibodies (MtAs) have not been applied to the diagnosis of AD.

**Table 5. Summary of the studies based on multiple-targeted AD biomarker assays.**
Epitope	Cut-off value(s)	Sensitivity (%)	Specificity (%)	Comparator group(s)	Source
t-tau + Aβ₄₂/Aβ₄₀	474pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$ + 13.3	49	100	AD (n = 93), HC (n = 41)	Ref. 183
t-tau + Aβ₄₂	474pg $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$ + 256fmol $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	50	90	AD (n = 93), HC (n = 41)	Ref. 183
p-tau (231) + p-tau (235)	0.18fmol $\cdot$ $\cdot$ mL $^{- 1}$ $^{- 1}$	53	100	AD (n = 36), HC (n = 20)	Ref. 135
Aβ₄₂/Aβ₄₀	13.3	56	73	AD (n = 93), HC (n = 41)	Ref. 183
t-tau + elevated Aβ₄₂	NA	61.5	100	AD (n = 37), OND (n = 32), and HC (n = 20)	Ref. 132
Aβ₄₂ × t-tau	3392.9	69	88	AD (n = 55), NAD (n = 23), and HC (n = 34)	Ref. 91
t-tau or Aβ₄₂/Aβ₄₀	474pg $\cdot$ mL $^{- 1}$ or 13.3	70	72	AD (n = 93), HC (n = 41)	Ref. 183
t-tau × Aβ₄₀/Aβ₄₂	3483	71	83	AD (n = 93), HC (n = 41)	Ref. 183
Aβ₄₂/t-tau	NA	71	83	AD (n = 93), NAD (n = 33), and HC (n = 54)	Ref. 191
p-tau (396)/t-tau	1	73	80	AD (n = 41), HC (n = 120)	Ref. 192
t-tau + Aβ₁₋₄₂	Disease state index 0.50	73	71	MCI (n = 391)	Ref. 199
t-tau + Aβ₄₂	NA	74	93	AD (n = 81), HC (n = 51)	Ref. 184
CSF t-tau	6fmol $\cdot$ mL $^{- 1}$	77.1	77.6	AD (n = 236), HC (n = 95)	Ref. 200
Aβ₄₂ + t-tau	505ng $\cdot$ L $^{- 1}$ + 350ng $\cdot$ L $^{- 1}$	82	90	MCI (n = 56), AD (n = 57), and OD (n = 21)	Ref. 194
Aβ₄₂/p-tau + t-tau	6.16 + 350ng $\cdot$ L $^{- 1}$	82	94	MCI (n = 56), AD (n = 57), and OD (n = 21)	Ref. 194
Aβ₄₂xMAP/p-tau + t-tau	6.6 + 62pg $\cdot$ mL $^{- 1}$	82	85	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 211
p-tau (396,404)	120ng $\cdot$ L $^{- 1}$	83	98	AD (n = 52), VAD (n = 46), and NAD (n = 37)	Ref. 100
t-tau + Aβ₄₂/p-tau (181)	Sensitivity of 85%	83	72	AD (n = 529), MCI (n = 750), and HC (n = 304)	Ref. 184
Aβ₄₂/p-tau + t-tau	Sensitivity of 85%	83	72	AD (n = 529), MCI (n = 750), and HC (n = 304)	Ref. 184
p-tau (404)/t-tau	1	83	80	AD (n = 41), FTD (n = 16), and HC (n = 120)	Ref. 192
Aβ₄₂xMAP/p-tau	6.6	83	83	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 196
Aβ₄₂/p-tau	Sensitivity of 85%	85	88	MCI (n = 750), AD (n = 529), and HC (n = 304)	Ref. 199
Aβ₄₂ + t-tau + p-tau	NA	85	86	NA	Ref. 226
Aβ₄₂ + t-tau	252pg $\cdot$ mL $^{- 1}$ + 643pg $\cdot$ mL $^{- 1}$	85	85	AD (n = 150), NAD (n = 79), and HC (n = 100)	Ref. 50
Aβ₄₂ + p-tau	NA	85	86	PAD (n = 27), NAD (n = 24)	Ref. 54
Aβ₄₂ + t-tau	101pg $\cdot$ mL $^{- 1}$ + 150pg $\cdot$ mL $^{- 1}$	85	100	PAD (n = 27), NAD (n = 24)	Ref. 54
CSF p-tau (199)	1.05fmol $\cdot$ mL $^{- 1}$	85	88.5	AD (n = 236), HC (n = 95)	Ref. 200
t-tau/Aβ₄₂	0.39	85	84.6	AD (n = 102), MCI (n = 200), and HC (n = 114)	Ref. 188
p-tau/Aβ₄₂	58	85.2	97	AD (n = 51), HC (n = 31)	Ref. 186
Aβ₄₂xMAP/Aβ₄₀	0.024	85.7	70	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 211
p-tau/Aβ₄₂	58	86	97	AD (n = 51), HC (n = 31)	Ref. 203
Aβ₄₂MSD/Aβ38	0.37	86	72	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 211
Aβ₄₂MSD/p-tau	21	86	82	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 211
Aβ₄₂/p-tau	6.16	87	90	AD (n = 57), MCI (n = 56), and OD (n = 21)	Ref. 194
Aβ₄₂xMAP + tau/Aβ₄₀	209pg $\cdot$ mL $^{- 1}$ + 0.088	88	92	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 211
Aβ₄₂/t-tau	85% Sensitivity	88	82	AD (n = 529), MCI (n = 750), and HC (n = 304)	Ref. 210
Aβ₄₂ + t-tau	240pg $\cdot$ mL $^{- 1}$ + 1.18pg $\cdot$ mL $^{- 1}$	88	92	AD (n = 49), VAD (n = 6), and HC (n = 49)	Ref. 53
Aβ₄₂ + t-tau	NA	88	87	NA	Ref. 193
t-tau/Aβ₄₀	0.01	88	86	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 211
Aβ₄₂xMAP + t-tau	209pg $\cdot$ mL $^{- 1}$ + 62pg $\cdot$ mL $^{- 1}$	88	88	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 211
Aβ₄₂/tau	Sensitivity plus specificity	89	72	MCI (n = 21), sMCI (n = 21), and HC (n = 26)	Ref. 218
Aβ₄₂MSD/Aβ₄₀ + t-tau	0.069 + 62pg $\cdot$ mL $^{- 1}$	89	86	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 211
p-tau (181)/Aβ₁₋₄₂	0.1	89	71.2	AD (n = 102), MCI (n = 200), and HC (n = 114)	Ref. 188
t-tau + Aβ₄₂	644.3 pg $\cdot$ mL $^{- 1}$ + 1.25 ng $\cdot$ mL $^{- 1}$	90	95	AD (n = 74), FTD (n = 34), and HC (n = 40)	Ref. 185
Aβ₄₂MSD/Aβ₄₀	0.069	91	86	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 211
Aβ₄₂ + t-tau + p-tau	NA	91.1	82.7	AD (n = 248), SMC (n = 131)	Ref. 189
p-tau (396,404)	100pg $\cdot$ mL $^{- 1}$	92	89	AD (n = 52), VAD (n = 46), and HC (n = 56)	Ref. 100
t-tau + Aβ₄₂ + p-tau (181)	350ng $\cdot$ L $^{- 1}$ + 530ng $\cdot$ L $^{- 1}$ +60ng $\cdot$ L $^{- 1}$	93	87	MCI (n = 180), HC (n = 39)	Ref. 187
t-tau + Aβ₄₂	350ng $\cdot$ L $^{- 1}$ + 530ng $\cdot$ L $^{- 1}$	93	83	MCI (n = 180), HC (n = 39)	Ref. 187
t-tau + Aβ₄₂/p-tau (181)	350ng $\cdot$ L $^{- 1}$ + 6.5	93.5	87	MCI (n = 180), HC (n = 39)	Ref. 187
Aβ₄₂ + t-tau	Sensitivity plus specificity	94	79	MCI (n = 21), sMCI (n = 21), and HC (n = 26)	Ref. 195
p-tau (396,404)/t-tau	0.33	95	95	AD (n = 52), VAD (n = 46), and HC (n = 56)	Ref. 100
p-tau (396) + p-tau (404)	NA	95	95	AD (n = 47), VAD (n = 16), and HC (n = 12)	Ref. 183
p-tau + Aβ₄₂	NA	95	100	AD (n = 57), HC (n = 57)	Ref. 191
Aβ₄₂xMAP/t-tau	2.5	96	82	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 211
t-tau/Aβ₄₂	0.44	96	86	AD (n = 49), VAD (n = 6), and HC (n = 49)	Ref. 53
Aβ₄₂MSD/t-tau	7.3	96	83	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 211
tau + Aβ₄₂ + p-tau (181, 396)	NA	96	100	AD (n = 57), HC (n = 57)	Ref. 191
Aβ₄₂ + t-tau + APOE	NA	96	80	AD (n = 102), MCI (n = 200), and HC (n = 114)	Ref. 188
Aβ₄₂MSD/Aβ₄₀	0.069	96	86	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 211
Aβ₄₂MSD/Aβ38	0.37	97	82	AD (n = 94), MCI (n = 166), and HC (n = 38)	Ref. 211
Aβ₄₀/Aβ₄₂	12.76	98	82	AD (n = 55), NAD (n = 23), and HC (n = 34)	Ref. 91
p-tau (181, 231)	1140pg $\cdot$ mL $^{- 1}$	98.2	76	AD (n = 44), VAD (n = 17), and HC (n = 31)	Ref. 227

6. Bispecific or Multi-Targeted Detection

Monoclonal antibodies have been widely used in diagnostics and molecular imaging to monitor biological processes and responses because of their excellent specificity for a single epitope.^146,147,148 Due to multiple disease pathways, a single mAb has limited effects on disease detection and treatment.^147,148 The development of immunosensors simultaneously targeting two or more biomarkers would be extremely valuable for improving the sensitivity and specificity of detection. Therefore, BsAbs or MtAs will be expected to recognize different antigenic determinants with high binding affinity and specificity.¹⁴⁹ More than 85 BsAbs are in clinical development, and some are used in the clinical treatment of diseases but less for clinical diagnosis.¹⁵⁰ Compared with mAbs, BsAbs have several advantages, including increased affinity, avidity, and efficacy, making them applicable to molecular imaging and molecular diagnostics. Bispecific antibody fragments are generated by chemical conjugation of the transferrin receptor antibody 8D3 Fab with single-chain variable fragments (scFvs) of the Aβ protofibril selective antibody mAb 158 to visualize Aβ pathology with PET in AD diagnostic imaging.^144,151 In contrast, transferrin receptor antibodies facilitated the entry of large molecules such as mAb 158 or scFv-158 into the brain. In our previous study, we developed a series of BsAbs for molecular cancer imaging by combining immuno-PET, near-infrared fluorescence, and nanographene.^{152,153,154,155} Unfortunately, several advantages of BsAbs or MtAs have not been realized in AD diagnosis or imaging. In the future, we hope that BsAb or MtA strategies can be applied to the diagnosis or imaging of AD or other degenerative neurological diseases.

7. Conclusions

A seismic shift is occurring in the ability of AD diagnosis in the past few decades with more valuable biomarkers are discovered (Fig. 4).¹⁵⁶ We have transformed from clinical–pathological diagnosis to several advanced instrument tests that combine AD core biomarkers. However, AD pathology is more complicated, and its early detection is essential for AD intervention, which brings great hope to early diagnosis. So far, the challenge of early diagnosis and identification of AD urgently requires multi-targeted diagnostic tools that can accurately reflect the core elements of the disease process and improve the diagnosis efficiency of clinicians for further understanding the pathogenesis of AD. We expect that BsAbs or MtAs strategy will provide exciting opportunities for diagnosis and have a lasting impact on clinical diagnosis.

8. Future Perspectives

Combined biomarkers improve the performance of these individual biomarkers. Therefore, developing methods by combining biomarkers from PET or MR imaging modalities with CSF/blood and genotype biomarkers is valuable for the early detection of AD. Many patients in preclinical AD testing are reluctant to analyze CSF biomarkers because the procedure is invasive with a lumbar puncture and back pain. In contrast, blood testing is less invasive, but the sensitivity and specificity of detecting blood biomarkers are low. Besides, the methods developed based on blood biomarkers such as ELISA,^157,158,159 nanoparticle-based immunoassays,¹⁶⁰ electrochemistry,¹⁶¹ surface-enhanced Raman spectroscopy,^162,163,164 fluorescence,¹⁶⁵ and electrochemical biosensors^166,167 require devices or/and expertise, which largely restrict their applications. Therefore, there is an urgent need for a simple, user-friendly, and point-of-care diagnosis tool to detect blood biomarkers. As an alternative, the lateral flow immunochromatographic strip (LFIS) is an ideal tool for detecting blood/CSF biomarkers due to its simplicity, portability, cost-effectiveness, and rapid detection, which has been widely used for the rapid diagnosis of blood biomarkers.^{168,169,170,171}

BsAbs or MtAs can specifically recognize two or more core plasma biomarkers such as Aβ₁₋₄₂, Aβ protofibril, and p-tau, which is of utmost importance for the early diagnosis of AD. Therefore, the use of blood organisms with multi-biomarker measurements such as multi-target lateral flow immunoassay strips for the diagnostic screening step followed by PET or MR imaging will quickly achieve rapid, noninvasive, high-quantitative, and low-cost clinical application. In clinical AD diagnosis, molecular imaging allows in-vivo visualization and quantification of Aβ or tau pathology. Multi-target probes combined with advanced equipment such as PET and MRI can improve the diagnostic accuracy of AD or other diseases.

Author Contributions

Haiming Luo coordinated the writing of the review, provided writing guidance, and did manuscript revision. Liding Zhang contributed to writing the first draft. Xiaohan Liang contributed to table preparation. Zhihong Zhang provided valuable revised suggestions. All authors reviewed and approved the final manuscript.

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. All authors declare no conflict of interest.

Acknowledgments

The authors would like to acknowledge support by grants from the National Natural Science Foundation of China (Grant No. 81971025) and the Startup Fund of Huazhong University of Science and Technology (Grant No. 0214187096).

References

1. F. Gao et al., “Multi-site dynamic recording for Aβ oligomers-induced Alzheimer’s disease in vitro based on neuronal network chip,” Biosens. Bioelectron. 133, 183–191 (2019). Crossref, Web of Science, Google Scholar
2. E. McDade, R. J. Bateman, “Stop Alzheimer’s before it starts,” Nature 547(7662), 153–155 (2017). Crossref, Web of Science, Google Scholar
3. C. A. Lane, J. Hardy, J. M. Schott, “Alzheimer’s disease,” Eur. J. Neurol. 25(1), 59–70 (2018). Crossref, Web of Science, Google Scholar
4. L. M. Ittner, J. Götz, “Amyloid-β and tau — a toxic pas de deux in Alzheimer’s disease,” Nat. Rev. Neurosci. 12(2), 65–72 (2011). Crossref, Web of Science, Google Scholar
5. M. Citron, “Strategies for disease modification in Alzheimer’s disease,” Nat. Rev. Neurosci. 5(9), 677–685 (2004). Crossref, Web of Science, Google Scholar
6. K. R. Brunden, J. Q. Trojanowski, V. M. Lee, “Advances in tau-focused drug discovery for Alzheimer’s disease and related tauopathies,” Nat. Rev. Drug Discov. 8(10), 783–793 (2009). Crossref, Web of Science, Google Scholar
7. R. A. Stelzmann et al., “An English translation of Alzheimer’s 1907 paper, “über eine eigenartige erkankung der hirnrinde”,” Clin Anat. 8(6), 429–431 (1995). Crossref, Google Scholar
8. C. L. Masters et al., “Amyloid plaque core protein in Alzheimer disease and Down syndrome,” Proc. Natl. Acad. Sci. USA 82(12), 4245–4249 (1985). Crossref, Web of Science, Google Scholar
9. I. Grundke-Iqbal et al., “Abnormal phosphorylation of the microtubule-associated protein tau (tau) in Alzheimer cytoskeletal pathology,” Proc. Natl. Acad. Sci. USA 83(13), 4913–4917 (1986). Crossref, Web of Science, Google Scholar
10. K. Blennow, “Cerebrospinal fluid protein biomarkers for Alzheimer’s disease,” NeuroRx 1(2), 213–225 (2004). Crossref, Google Scholar
11. K. Blennow, H. Hampel, “CSF markers for incipient Alzheimer’s disease,” Lancet Neurol. 2(10), 605–613 (2003). Crossref, Web of Science, Google Scholar
12. P. D. Mehta et al., “Plasma and cerebrospinal fluid levels of amyloid β proteins 1-40 and 1-42 in Alzheimer disease,” Arch. Neurol. 57(1), 100–105 (2000). Crossref, Google Scholar
13. O. Hansson et al., “Prediction of Alzheimer’s disease using the CSF Aβ42/Aβ40 ratio in patients with mild cognitive impairment,” Dement. Geriatr. Cogn. Disord. 23(5), 316–320 (2007). Crossref, Web of Science, Google Scholar
14. P. Lewczuk et al., “The amyloid-β (Aβ) peptide pattern in cerebrospinal fluid in Alzheimer’s disease: Evidence of a novel carboxyterminally elongated Aβ peptide,” Rapid Commun. Mass Spectrom. 17(12), 1291–1296 (2003). Crossref, Web of Science, Google Scholar
15. C. Haass, H. Steiner, “Protofibrils, the unifying toxic molecule of neurodegenerative disorders?,” Nat. Neurosci. 4(9), 859–860 (2001). Crossref, Web of Science, Google Scholar
16. W. L. Klein, “ADDLs & protofibrils — the missing links?,” Neurobiol. Aging 23(2), 231–235 (2002). Crossref, Web of Science, Google Scholar
17. B. Caughey, P. T. Lansbury, “Protofibrils, pores, fibrils, and neurodegeneration: Separating the responsible protein aggregates from the innocent bystanders,” Annu. Rev. Neurosci. 26, 267–298 (2003). Crossref, Web of Science, Google Scholar
18. T. K. Khan, D. L. Alkon, “Peripheral biomarkers of Alzheimer’s disease,” J. Alzheimer’s Dis. 44(3), 729–744 (2015). Crossref, Web of Science, Google Scholar
19. G. D. Rabinovici et al., “Association of amyloid positron emission tomography with subsequent change in clinical management among medicare beneficiaries with mild cognitive impairment or dementia,” JAMA 321(13), 1286–1294 (2019). Crossref, Web of Science, Google Scholar
20. J. C. Lee et al., “Diagnosis of Alzheimer’s disease utilizing amyloid and tau as fluid biomarkers,” Exp. Mol. Med. 51(5), 1–10 (2019). Crossref, Web of Science, Google Scholar
21. J. Fortea et al., “Plasma and CSF biomarkers for the diagnosis of Alzheimer’s disease in adults with Down syndrome: A cross-sectional study,” Lancet Neurol. 17(10), 860–869 (2018). Crossref, Web of Science, Google Scholar
22. L. Schneider, “A resurrection of aducanumab for Alzheimer’s disease,” Lancet Neurol. 19(2), 111–112 (2020). Crossref, Web of Science, Google Scholar
23. G. Tibbling, H. Link, S. Ohman, “Principles of albumin and IgG analyses in neurological disorders: I: Establishment of reference values,” Scand. J. Clin. Lab. Invest. 37(5), 385–390 (1977). Crossref, Web of Science, Google Scholar
24. “Consensus report of the Working Group on: “Molecular and Biochemical Markers of Alzheimer’s Disease”: The Ronald and Nancy Reagan Research Institute of the Alzheimer’s Association and the National Institute on Aging Working Group,” Neurobiol. Aging 19(2), 109–116 (1998). Google Scholar
25. D. Strozyk et al., “CSF Aβ 42 levels correlate with amyloid-neuropathology in a population-based autopsy study,” Neurology 60(4), 652–656 (2003). Crossref, Web of Science, Google Scholar
26. T. Tapiola et al., “Cerebrospinal fluid β-amyloid 42 and tau proteins as biomarkers of Alzheimer-type pathologic changes in the brain,” Arch. Neurol. 66(3), 382–389 (2009). Crossref, Google Scholar
27. E. Niemantsverdriet et al., “The cerebrospinal fluid Aβ₁₋₄₂/Aβ₁₋₄₀ ratio improves concordance with amyloid-PET for diagnosing Alzheimer’s disease in a clinical setting,” J. Alzheimer’s Dis. 60(2), 561–576 (2017). Crossref, Web of Science, Google Scholar
28. V. Vandermeeren et al., “Detection of tau proteins in normal and Alzheimer’s disease cerebrospinal fluid with a sensitive sandwich enzyme-linked immunosorbent assay,” J. Neurochem. 61(5), 1828–1834 (1993). Crossref, Web of Science, Google Scholar
29. K. Blennow et al., “Tau protein in cerebrospinal fluid: a biochemical marker for axonal degeneration in Alzheimer disease?,” Mol. Chem. Neuropathol. 26(3), 231–245 (1995). Crossref, Google Scholar
30. C. Vigo-Pelfrey et al., “Elevation of microtubule-associated protein tau in the cerebrospinal fluid of patients with Alzheimer’s disease,” Neurology 45(4), 788–793 (1995). Crossref, Web of Science, Google Scholar
31. H. Fukumoto et al., “β-Secretase protein and activity are increased in the neocortex in Alzheimer disease,” Arch. Neurol. 59(9), 1381–1389 (2002). Crossref, Google Scholar
32. L. B. Yang et al., “Elevated β-secretase expression and enzymatic activity detected in sporadic Alzheimer disease,” Nat. Med. 9(1), 3–4 (2003). Crossref, Web of Science, Google Scholar
33. H. Zetterberg et al., “Elevated cerebrospinal fluid BACE1 activity in incipient Alzheimer disease,” Arch. Neurol. 65(8), 1102–1107 (2008). Crossref, Google Scholar
34. A. Olsson et al., “Measurement of $α$ - and β-secretase cleaved amyloid precursor protein in cerebrospinal fluid from Alzheimer patients,” Exp. Neurol. 183(1), 74–80 (2003). Crossref, Web of Science, Google Scholar
35. P. Lewczuk et al., “Soluble amyloid precursor proteins in the cerebrospinal fluid as novel potential biomarkers of Alzheimer’s disease: a multicenter study,” Mol. Psychiatry 15(2), 138–145 (2010). Crossref, Web of Science, Google Scholar
36. N. S. Schoonenboom et al., “Amyloidβ 38, 40, and 42 species in cerebrospinal fluid: More of the same?,” Ann. Neurol. 58(1), 139–142 (2005). Crossref, Web of Science, Google Scholar
37. E. Portelius et al., “An Alzheimer’s disease-specific β-amyloid fragment signature in cerebrospinal fluid,” Neurosci. Lett. 409(3), 215–219 (2006). Crossref, Web of Science, Google Scholar
38. S. Mruthinti et al., “Autoimmunity in Alzheimer’s disease: Increased levels of circulating IgGs binding Aβ and RAGE peptides,” Neurobiol. Aging 25(8), 1023–1032 (2004). Crossref, Web of Science, Google Scholar
39. S. Brettschneider et al., “Decreased serum amyloid β₁₋₄₂ autoantibody levels in Alzheimer’s disease, determined by a newly developed immuno-precipitation assay with radiolabeled amyloid β₁₋₄₂ peptide,” Biol. Psychiatry 57(7), 813–816 (2005). Crossref, Web of Science, Google Scholar
40. B. T. Hyman et al., “Autoantibodies to amyloid-β and Alzheimer’s disease,” Ann. Neurol. 49(6), 808–810 (2001). Crossref, Web of Science, Google Scholar
41. O. F. Laterza et al., “Identification of novel brain biomarkers,” Clin. Chem. 52(9), 1713–1721 (2006). Crossref, Web of Science, Google Scholar
42. T. J. Montine et al., “F₂-isoprostanes as biomarkers of late-onset Alzheimer’s disease,” J. Mol. Neurosci. 33(1), 114–119 (2007). Crossref, Web of Science, Google Scholar
43. T. J. Montine et al., “Cerebrospinal fluid aβ₄₂, tau, and F₂-isoprostane concentrations in patients with Alzheimer disease, other dementias, and in age-matched controls,” Arch. Pathol. Lab. Med. 125(4), 510–512 (2001). Crossref, Web of Science, Google Scholar
44. M. Brys et al., “Prediction and longitudinal study of CSF biomarkers in mild cognitive impairment,” Neurobiol. Aging 30(5), 682–690 (2009). Crossref, Web of Science, Google Scholar
45. L. Cheng et al., “Prognostic serum miRNA biomarkers associated with Alzheimer’s disease shows concordance with neuropsychological and neuroimaging assessment,” Mol. Psychiatry 20(10), 1188–1196 (2015). Crossref, Web of Science, Google Scholar
46. Z. Yan et al., “Presymptomatic increase of an extracellular RNA in blood plasma associates with the development of Alzheimer’s disease,” Curr. Biol. 30(10), 1771–1782. e1–e3 (2020). Crossref, Web of Science, Google Scholar
47. Z. Y. Cai et al., “Exosomes: A novel therapeutic target for Alzheimer’s disease?,” Neural Regen. Res. 13(5), 930–935 (2018). Crossref, Web of Science, Google Scholar
48. M. Otto et al., “Decreased β-amyloid₁₋₄₂ in cerebrospinal fluid of patients with Creutzfeldt-Jakob disease,” Neurology 54(5), 1099–1102 (2000). Crossref, Web of Science, Google Scholar
49. M. Sjögren et al., “Decreased CSF-β-amyloid 42 in Alzheimer’s disease and amyotrophic lateral sclerosis may reflect mismetabolism of β-amyloid induced by disparate mechanisms,” Dement. Geriatr. Cogn. Disord. 13(2), 112–118 (2002). Crossref, Web of Science, Google Scholar
50. F. Hulstaert et al., “Improved discrimination of AD patients using β-amyloid $_{(1 - 42)}$ and tau levels in CSF,” Neurology 52(8), 1555–1562 (1999). Crossref, Web of Science, Google Scholar
51. M. Sjögren et al., “CSF levels of tau, β-amyloid₁₋₄₂ and GAP-43 in frontotemporal dementia, other types of dementia and normal aging,” J. Neural Transm. (Vienna) 107(5), 563–579 (2000). Crossref, Web of Science, Google Scholar
52. K. Kanemaru, N. Kameda, H. Yamanouchi, “Decreased CSF amyloid β42 and normal tau levels in dementia with Lewy bodies,” Neurology 54(9), 1875–1876 (2000). Crossref, Web of Science, Google Scholar
53. E. Kapaki et al., “CSF tau protein and β-amyloid (1-42) in Alzheimer’s disease diagnosis: discrimination from normal ageing and other dementias in the Greek population,” Eur. J. Neurol. 10(2), 119–128 (2003). Crossref, Web of Science, Google Scholar
54. N. Rösler, I. Wichart, K. A. Jellinger, “Clinical significance of neurobiochemical profiles in the lumbar cerebrospinal fluid of Alzheimer’s disease patients,” J. Neural Transm. (Vienna) 108(2), 231–246 (2001). Crossref, Web of Science, Google Scholar
55. E. Kapaki et al., “Highly increased CSF tau protein and decreased β-amyloid (1-42) in sporadic CJD: a discrimination from Alzheimer’s disease?,” J. Neurol. Neurosurg. Psychiatry 71(3), 401–403 (2001). Crossref, Web of Science, Google Scholar
56. C. Mulder et al., “CSF markers related to pathogenetic mechanisms in Alzheimer’s disease,” J. Neural Transm. (Vienna) 109(12), 1491–1498 (2002). Crossref, Web of Science, Google Scholar
57. N. Andreasen et al., “Cerebrospinal fluid tau and Aβ42 as predictors of development of Alzheimer’s disease in patients with mild cognitive impairment,” Neurosci. Lett. 273(1), 5–8 (1999). Crossref, Web of Science, Google Scholar
58. H. Lee et al., “Alzheimer’s disease diagnosis using misfolding proteins in blood,” Dement. Neurocogn. Disord. 19(1), 1–18 (2020). Crossref, Google Scholar
59. B. Shui et al., “A novel electrochemical aptamer-antibody sandwich assay for the detection of tau-381 in human serum,” Analyst 143(15), 3549–3554 (2018). Crossref, Web of Science, Google Scholar
60. C. Deng et al., “An electrochemical aptasensor for amyloid-β oligomer based on double-stranded DNA as “conductive spring”,” Microchim. Acta 187(4), 239 (2020). Crossref, Web of Science, Google Scholar
61. Y. Zhao et al., “A simple aptasensor for Aβ₄₀ oligomers based on tunable mismatched base pairs of dsDNA and graphene oxide,” Biosens. Bioelectron. 149, 111840 (2020). Crossref, Web of Science, Google Scholar
62. N. Xia et al., “Electrochemical detection of amyloid-β oligomers based on the signal amplification of a network of silver nanoparticles,” ACS Appl. Mater. Interfaces 8(30), 19303–19311 (2016). Crossref, Web of Science, Google Scholar
63. C. Li et al., “A simple approach to quantitative determination of soluble amyloid-β peptides using a ratiometric fluorescence probe,” Biosens. Bioelectron. 142, 111518 (2019). Crossref, Web of Science, Google Scholar
64. H. T. Ngoc Le et al., “Sensitive electrochemical detection of amyloid β peptide in human serum using an interdigitated chain-shaped electrode,” Biosens. Bioelectron. 144, 111694 (2019). Crossref, Web of Science, Google Scholar
65. X. Zhang et al., “Robust and universal SERS sensing platform for multiplexed detection of Alzheimer’s disease core biomarkers using PAapt-AuNPs conjugates,” ACS Sens. 4(8), 2140–2149 (2019). Crossref, Web of Science, Google Scholar
66. A. Nabers et al., “Amyloid-β-secondary structure distribution in cerebrospinal fluid and blood measured by an immuno-infrared-sensor: A biomarker candidate for Alzheimer’s disease,” Anal. Chem. 88(5), 2755–2762 (2016). Crossref, Web of Science, Google Scholar
67. D. Li, M. M. Mielke, “An update on blood-based markers of Alzheimer’s disease using the SiMoA platform,” Neurol. Ther. 8(Suppl 2), 73–82 (2019). Crossref, Web of Science, Google Scholar
68. F. Xing et al., “Structural and functional studies of erythrocyte membrane-skeleton by single-cell and single-molecule techniques,” J. Innov. Opt. Health Sci. 12(1), 1830004 (2019). Link, Web of Science, Google Scholar
69. K. S. Park, “Nucleic acid aptamer-based methods for diagnosis of infections,” Biosens. Bioelectron. 102, 179–188 (2018). Crossref, Web of Science, Google Scholar
70. G. Zhu, X. Chen, “Aptamer-based targeted therapy,” Adv. Drug Deliv. Rev. 134, 65–78 (2018). Crossref, Web of Science, Google Scholar
71. Y. Kim et al., “Comparative analyses of plasma amyloid-β levels in heterogeneous and monomerized states by interdigitated microelectrode sensor system,” Sci. Adv. 5(4), eaav1388 (2019). Crossref, Web of Science, Google Scholar
72. D. H. Wilson et al., “The Simoa HD-1 analyzer: A novel fully automated digital immunoassay analyzer with single-molecule sensitivity and multiplexing,” J. Lab. Autom. 21(4), 533–547 (2016). Crossref, Google Scholar
73. J.-C. Park et al., “Plasma tau/amyloid-β₁₋₄₂ ratio predicts brain tau deposition and neurodegeneration in Alzheimer’s disease,” Brain 142(5), 771–786 (2019). Crossref, Web of Science, Google Scholar
74. M. M. Mielke et al., “Plasma phospho-tau181 increases with Alzheimer’s disease clinical severity and is associated with tau- and amyloid-positron emission tomography,” Alzheimer’s Dement. 14(8), 989–997 (2018). Crossref, Web of Science, Google Scholar
75. C. Z. J. Lim et al., “Subtyping of circulating exosome-bound amyloid β reflects brain plaque deposition,” Nat. Commun. 10(1), 1144 (2019). Crossref, Web of Science, Google Scholar
76. K. S. Hong, M. A. Yaqub, “Application of functional near-infrared spectroscopy in the healthcare industry,” J. Innov. Opt. Health Sci. 12(6), 1930012 (2019). Link, Web of Science, Google Scholar
77. K. Kim et al., “Clinically accurate diagnosis of Alzheimer’s disease via multiplexed sensing of core biomarkers in human plasma,” Nat. Commun. 11(1), 119 (2020). Crossref, Web of Science, Google Scholar
78. P. Seubert et al., “Isolation and quantification of soluble Alzheimer’s β-peptide from biological fluids,” Nature 359(6393), 325–327 (1992). Crossref, Web of Science, Google Scholar
79. L. Mucke, D. J. Selkoe, “Neurotoxicity of amyloid β-protein: synaptic and network dysfunction,” Cold Spring Harb. Perspect. Med. 2(7), a006338 (2012). Crossref, Web of Science, Google Scholar
80. W. E. Van Nostrand et al., “Decreased levels of soluble amyloid β-protein precursor in cerebrospinal fluid of live Alzheimer disease patients,” Proc. Natl. Acad. Sci. USA 89(7), 2551–2555 (1992). Crossref, Web of Science, Google Scholar
81. M. Farlow et al., “Low cerebrospinal-fluid concentrations of soluble amyloid β-protein precursor in hereditary Alzheimer’s disease,” Lancet 340(8817), 453–454 (1992). Crossref, Web of Science, Google Scholar
82. W. A. van Gool et al., “Concentrations of amyloid β protein in cerebrospinal fluid of patients with Alzheimer’s disease,” Ann. Neurol. 37(2), 277–279 (1995). Crossref, Web of Science, Google Scholar
83. J. T. Jarrett, E. P. Berger, P. T. Lansbury, Jr., “The carboxy terminus of the β amyloid protein is critical for the seeding of amyloid formation: Implications for the pathogenesis of Alzheimer’s disease,” Biochemistry 32(18), 4693–4697 (1993). Crossref, Web of Science, Google Scholar
84. E. Portelius et al., “A novel pathway for amyloid precursor protein processing,” Neurobiol. Aging 32(6), 1090–1098 (2011). Crossref, Web of Science, Google Scholar
85. D. Beher et al., “Generation of C-terminally truncated amyloid-β peptides is dependent on gamma-secretase activity,” J. Neurochem. 82(3), 563–575 (2002). Crossref, Web of Science, Google Scholar
86. D. M. Hartley et al., “Protofibrillar intermediates of amyloid β-protein induce acute electrophysiological changes and progressive neurotoxicity in cortical neurons,” J. Neurosci. 19(20), 8876–8884 (1999). Crossref, Web of Science, Google Scholar
87. D. M. Walsh et al., “Amyloid β-protein fibrillogenesis: Structure and biological activity of protofibrillar intermediates,” J. Biol. Chem. 274(36), 25945–29552 (1999). Crossref, Web of Science, Google Scholar
88. R. V. Ward et al., “Fractionation and characterization of oligomeric, protofibrillar and fibrillar forms of β-amyloid peptide,” Biochem. J. 348, 137–144 (2000). Crossref, Web of Science, Google Scholar
89. A. Nabers et al., “Aβ and tau structure-based biomarkers for a blood- and CSF-based two-step recruitment strategy to identify patients with dementia due to Alzheimer’s disease,” Alzheimer’s Dement. (Amst.) 11, 257–263 (2019). Crossref, Web of Science, Google Scholar
90. L. Zhang et al., “Dynamic changes in the levels of amyloid-β 42 species in the brain and periphery of APP/PS1 mice and their significance for Alzheimer’s disease,” Front. Mol. Neurosci. 14, 723317 (2021). https://doi.org/10.3389/fnmol.2021.723317 Crossref, Web of Science, Google Scholar
91. M. Shoji et al., “Combination assay of CSF tau, Aβ1-40 and Aβ1-42(43) as a biochemical marker of Alzheimer’s disease,” J. Neurol. Sci. 158(2), 134–140 (1998). Crossref, Web of Science, Google Scholar
92. P. J. Kahle et al., “Combined assessment of tau and neuronal thread protein in Alzheimer’s disease CSF,” Neurology 54(7), 1498–1504 (2000). Crossref, Web of Science, Google Scholar
93. Y. Morikawa et al., “Cerebrospinal fluid tau protein levels in demented and nondemented alcoholics,” Alcohol Clin. Exp. Res. 23(4), 575–577 (1999). Crossref, Web of Science, Google Scholar
94. N. Andreasen et al., “Sensitivity, specificity, and stability of CSF-tau in AD in a community-based patient sample,” Neurology 53(7), 1488–1494 (1999). Crossref, Web of Science, Google Scholar
95. J. Götz et al., “Somatodendritic localization and hyperphosphorylation of tau protein in transgenic mice expressing the longest human brain tau isoform,” EMBO J. 14(7), 1304–1313 (1995). Crossref, Web of Science, Google Scholar
96. K. Santacruz et al., “Tau suppression in a neurodegenerative mouse model improves memory function,” Science 309(5733), 476–481 (2005). Crossref, Web of Science, Google Scholar
97. J. Götz, L. M. Ittner, S. Kins, “Do axonal defects in tau and amyloid precursor protein transgenic animals model axonopathy in Alzheimer’s disease?,” J. Neurochem. 98(4), 993–1006 (2006). Crossref, Web of Science, Google Scholar
98. L. M. Ittner, Y. D. Ke, J. Götz, “Phosphorylated tau interacts with c-Jun N-terminal kinase-interacting protein 1 (JIP1) in Alzheimer disease,” J. Biol. Chem. 284(31), 20909–20916 (2009). Crossref, Web of Science, Google Scholar
99. E. Vanmechelen et al., “Quantification of tau phosphorylated at threonine 181 in human cerebrospinal fluid: a sandwich ELISA with a synthetic phosphopeptide for standardization,” Neurosci. Lett. 285(1), 49–52 (2000). Crossref, Web of Science, Google Scholar
100. Y. Y. Hu et al., “Levels of nonphosphorylated and phosphorylated tau in cerebrospinal fluid of Alzheimer’s disease patients: an ultrasensitive bienzyme-substrate-recycle enzyme-linked immunosorbent assay,” Am. J. Pathol. 160(4), 1269–1278 (2002). Crossref, Web of Science, Google Scholar
101. M. Riemenschneider et al., “Phospho-tau/total tau ratio in cerebrospinal fluid discriminates Creutzfeldt-Jakob disease from other dementias,” Mol. Psychiatry 8(3), 343–347 (2003). Crossref, Web of Science, Google Scholar
102. K. Nakamura et al., “Proline isomer-specific antibodies reveal the early pathogenic tau conformation in Alzheimer’s disease,” Cell 149(1), 232–244 (2012). Crossref, Web of Science, Google Scholar
103. J. Luna-Muñoz et al., “Earliest stages of tau conformational changes are related to the appearance of a sequence of specific phospho-dependent tau epitopes in Alzheimer’s disease,” J. Alzheimer’s Dis. 12(4), 365–375 (2007). Crossref, Web of Science, Google Scholar
104. S. Janelidze et al., “Plasma P-tau181 in Alzheimer’s disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s dementia,” Nat. Med. 26(3), 379–386 (2020). Crossref, Web of Science, Google Scholar
105. E. H. Thijssen et al., “Diagnostic value of plasma phosphorylated tau181 in Alzheimer’s disease and frontotemporal lobar degeneration,” Nat. Med. 26(3), 387–397 (2020). Crossref, Web of Science, Google Scholar
106. R. M. Holsinger et al., “Increased β-secretase activity in cerebrospinal fluid of Alzheimer’s disease subjects,” Ann. Neurol. 55(6), 898–899 (2004). Crossref, Web of Science, Google Scholar
107. Z. Zhong et al., “Levels of β-secretase (BACE1) in cerebrospinal fluid as a predictor of risk in mild cognitive impairment,” Arch. Gen. Psychiatry 64(6), 718–726 (2007). Crossref, Google Scholar
108. E. Portelius et al., “Determination of β-amyloid peptide signatures in cerebrospinal fluid using immunoprecipitation-mass spectrometry,” J. Proteome Res. 5(4), 1010–1016 (2006). Crossref, Web of Science, Google Scholar
109. A. Nath et al., “Autoantibodies to amyloid β-peptide (Aβ) are increased in Alzheimer’s disease patients and Aβ antibodies can enhance Aβ neurotoxicity: Implications for disease pathogenesis and vaccine development,” Neuromolecular Med. 3(1), 29–39 (2003). Crossref, Web of Science, Google Scholar
110. Y. Du et al., “Reduced levels of amyloid β-peptide antibody in Alzheimer disease,” Neurology 57(5), 801–805 (2001). Crossref, Web of Science, Google Scholar
111. M. Britschgi et al., “Neuroprotective natural antibodies to assemblies of amyloidogenic peptides decrease with normal aging and advancing Alzheimer’s disease,” Proc. Natl. Acad. Sci. USA 106(29), 12145–12150 (2009). Crossref, Web of Science, Google Scholar
112. J. M. Lee et al., “The brain injury biomarker VLP-1 is increased in the cerebrospinal fluid of Alzheimer disease patients,” Clin. Chem. 54(10), 1617–1623 (2008). Crossref, Web of Science, Google Scholar
113. R. L. Friede, T. Samorajski, “Axon caliber related to neurofilaments and microtubules in sciatic nerve fibers of rats and mice,” Anat. Rec. 167(4), 379–387 (1970). Crossref, Google Scholar
114. M. Sjögren et al., “Neurofilament protein in cerebrospinal fluid: a marker of white matter changes,” J. Neurosci. Res. 66(3), 510–516 (2001). Crossref, Web of Science, Google Scholar
115. A. Agren-Wilsson et al., “CSF biomarkers in the evaluation of idiopathic normal pressure hydrocephalus,” Acta Neurol. Scand. 116(5), 333–339 (2007). Crossref, Web of Science, Google Scholar
116. J. M. Ringman et al., “Biochemical markers in persons with preclinical familial Alzheimer disease,” Neurology 71(2), 85–92 (2008). Crossref, Web of Science, Google Scholar
117. S. Nagaraj et al., “microRNA diagnostic panel for Alzheimer’s disease and epigenetic trade-off between neurodegeneration and cancer,” Ageing Res. Rev. 49, 125–143 (2019). Crossref, Web of Science, Google Scholar
118. K. Blennow, M. J. de Leon, H. Zetterberg, “Alzheimer’s disease,” Lancet 368(9533), 387–403 (2006). Crossref, Web of Science, Google Scholar
119. M. R. Brier et al., “Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease,” Sci. Transl. Med. 8(338), 338ra66 (2016). Crossref, Web of Science, Google Scholar
120. C. R. Jack, Jr., “Alzheimer disease: new concepts on its neurobiology and the clinical role imaging will play,” Radiology 263(2), 344–361 (2012). Crossref, Web of Science, Google Scholar
121. V. Valotassiou et al., “Clinical evaluation of brain perfusion SPECT with Brodmann areas mapping in early diagnosis of Alzheimer’s disease,” J. Alzheimer’s Dis. 47(3), 773–785 (2015). Crossref, Web of Science, Google Scholar
122. V. Valotassiou et al., “Perfusion SPECT studies with mapping of Brodmann areas in differentiating Alzheimer’s disease from frontotemporal degeneration syndromes,” Nucl. Med. Commun. 33(12), 1267–1276 (2012). Crossref, Web of Science, Google Scholar
123. N. L. Foster et al., “FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer’s disease,” Brain 130, 2616–2635 (2007). Crossref, Web of Science, Google Scholar
124. H. Braak, E. Braak, “Frequency of stages of Alzheimer-related lesions in different age categories,” Neurobiol. Aging 18(4), 351–357 (1997). Crossref, Web of Science, Google Scholar
125. C. C. Rowe et al., “Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging,” Neurobiol. Aging 31(8), 1275–1283 (2010). Crossref, Web of Science, Google Scholar
126. E. Giacobini, G. Gold, “Alzheimer disease therapy — moving from amyloid-β to tau,” Nat. Rev. Neurol. 9(12), 677–686 (2013). Crossref, Web of Science, Google Scholar
127. V. Valotassiou et al., “SPECT and PET imaging in Alzheimer’s disease,” Ann. Nucl. Med. 32(9), 583–593 (2018). Crossref, Web of Science, Google Scholar
128. C. R. Jack, Jr., et al., “Longitudinal tau PET in ageing and Alzheimer’s disease,” Brain 141(5), 1517–1528 (2018). Crossref, Web of Science, Google Scholar
129. A. Mueller et al., “Tau PET imaging with $^{18}$ F-PI-2620 in patients with Alzheimer disease and healthy controls: A first-in-humans study,” J. Nucl. Med. 61(6), 911–919 (2020). Crossref, Web of Science, Google Scholar
130. W. D. Brenowitz et al., “Cerebral amyloid angiopathy and its co-occurrence with Alzheimer’s disease and other cerebrovascular neuropathologic changes,” Neurobiol. Aging 36(10), 2702–2708 (2015). Crossref, Web of Science, Google Scholar
131. B. Holmberg et al., “Cerebrospinal fluid Aβ42 is reduced in multiple system atrophy but normal in Parkinson’s disease and progressive supranuclear palsy,” Mov. Disord. 18(2), 186–190 (2003). Crossref, Web of Science, Google Scholar
132. R. Motter et al., “Reduction of β-amyloid peptide $_{42}$ in the cerebrospinal fluid of patients with Alzheimer’s disease,” Ann. Neurol. 38(4), 643–648 (1995). Crossref, Web of Science, Google Scholar
133. K. Blennow, E. Vanmechelen, H. Hampel, “CSF total tau, Aβ42 and phosphorylated tau protein as biomarkers for Alzheimer’s disease,” Mol. Neurobiol. 24(1–3), 87–97 (2001). Crossref, Web of Science, Google Scholar
134. C. Bancher et al., “Accumulation of abnormally phosphorylated tau precedes the formation of neurofibrillary tangles in Alzheimer’s disease,” Brain Res. 477(1–2), 90–99 (1989). Crossref, Web of Science, Google Scholar
135. K. Ishiguro et al., “Phosphorylated tau in human cerebrospinal fluid is a diagnostic marker for Alzheimer’s disease,” Neurosci. Lett. 270(2), 91–94 (1999). Crossref, Web of Science, Google Scholar
136. A. Nakamura et al., “High performance plasma amyloid-β biomarkers for Alzheimer’s disease,” Nature 554(7691), 249–254 (2018). Crossref, Web of Science, Google Scholar
137. M. C. Irizarry, “Biomarkers of Alzheimer disease in plasma,” NeuroRx 1(2), 226–234 (2004). Crossref, Google Scholar
138. N. R. Graff-Radford et al., “Association of low plasma Aβ42/Aβ40 ratios with increased imminent risk for mild cognitive impairment and Alzheimer disease,” Arch. Neurol. 64(3), 354–362 (2007). Crossref, Google Scholar
139. R. Mayeux et al., “Plasma Aβ40 and Aβ42 and Alzheimer’s disease: Relation to age, mortality, and risk,” Neurology 61(9), 1185–1190 (2003). Crossref, Web of Science, Google Scholar
140. N. Pomara et al., “Selective reductions in plasma Aβ 1-42 in healthy elderly subjects during longitudinal follow-up: A preliminary report,” Am. J. Geriatr. Psychiatry 13(10), 914–917 (2005). Web of Science, Google Scholar
141. M. van Oijen et al., “Plasma Aβ₁₋₄₀ and Aβ₁₋₄₂ and the risk of dementia: A prospective case-cohort study,” Lancet Neurol. 5(8), 655–660 (2006). Crossref, Web of Science, Google Scholar
142. Y. M. Kuo et al., “High levels of circulating Aβ42 are sequestered by plasma proteins in Alzheimer’s disease,” Biochem. Biophys. Res. Commun. 257(3), 787–791 (1999). Crossref, Web of Science, Google Scholar
143. H. Englund et al., “Sensitive ELISA detection of amyloid-β protofibrils in biological samples,” J. Neurochem. 103(1), 334–345 (2007). Crossref, Web of Science, Google Scholar
144. D. Sehlin et al., “Antibody-based PET imaging of amyloid β in mouse models of Alzheimer’s disease,” Nat. Commun. 7, 10759 (2016). Crossref, Web of Science, Google Scholar
145. S. Tucker et al., “The murine version of BAN2401 (mAb158) selectively reduces amyloid-β protofibrils in brain and cerebrospinal fluid of tg-ArcSwe mice,” J. Alzheimer’s Dis. 43(2), 575–588 (2015). Crossref, Web of Science, Google Scholar
146. H. Luo et al., “Design and applications of bispecific heterodimers: molecular imaging and beyond,” Mol. Pharm. 11(6), 1750–1761 (2014). Crossref, Web of Science, Google Scholar
147. L. Zhang et al., “Development and characterization of double-antibody sandwich ELISA for detection of Zika virus infection,” Viruses 10(11), 634 (2018). Crossref, Web of Science, Google Scholar
148. L. Zhang et al., “Instrument-free and visual detection of Salmonella based on magnetic nanoparticles and an antibody probe immunosensor,” Int. J. Mol. Sci. 20(18), 4645 (2019). Crossref, Web of Science, Google Scholar
149. A. Nisonoff, F. C. Wissler, L. N. Lipman, “Properties of the major component of a peptic digest of rabbit antibody,” Science 132(3441), 1770–1771 (1960). Crossref, Web of Science, Google Scholar
150. A. F. Labrijn et al., “Bispecific antibodies: a mechanistic review of the pipeline,” Nat. Rev. Drug Discov. 18(8), 585–608 (2019). Crossref, Web of Science, Google Scholar
151. S. Syvanen et al., “A bispecific tribody PET radioligand for visualization of amyloid-β protofibrils — a new concept for neuroimaging,” Neuroimage 148, 55–63 (2017). Crossref, Web of Science, Google Scholar
152. H. Luo et al., “ImmunoPET and near-infrared fluorescence imaging of pancreatic cancer with a dual-labeled bispecific antibody fragment,” Mol. Pharm. 14(5), 1646–1655 (2017). Crossref, Web of Science, Google Scholar
153. H. Luo et al., “Dual targeting of tissue factor and CD105 for preclinical PET imaging of pancreatic cancer,” Clin. Cancer Res. 22(15), 3821–3830 (2016). Crossref, Web of Science, Google Scholar
154. H. Luo et al., “Noninvasive brain cancer imaging with a bispecific antibody fragment, generated via click chemistry,” Proc. Natl. Acad. Sci. USA 112(41), 12806–12811 (2015). Crossref, Web of Science, Google Scholar
155. S. Shi et al., “Chelator-free labeling of layered double hydroxide nanoparticles for in vivo PET imaging,” Sci. Rep. 5, 16930 (2015). Crossref, Web of Science, Google Scholar
156. R. C. Petersen, “How early can we diagnose Alzheimer disease (and is it sufficient)? The 2017 Wartenberg lecture,” Neurology 91(9), 395–402 (2018). Crossref, Web of Science, Google Scholar
157. B. C. Dickerson et al., “The cortical signature of Alzheimer’s disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals,” Cereb. Cortex 19(3), 497–510 (2009). Crossref, Web of Science, Google Scholar
158. C. Stenh et al., “Amyloid-β oligomers are inefficiently measured by enzyme-linked immunosorbent assay,” Ann. Neurol. 58(1), 147–150 (2005). Crossref, Web of Science, Google Scholar
159. T. Yang et al., “New ELISAs with high specificity for soluble oligomers of amyloid β-protein detect natural Aβ oligomers in human brain but not CSF,” Alzheimer’s Dement. 9(2), 99–112 (2013). Crossref, Web of Science, Google Scholar
160. H. Li et al., “A general way to assay protein by coupling peptide with signal reporter via supermolecule formation,” Anal. Chem. 85(2), 1047–1052 (2013). Crossref, Web of Science, Google Scholar
161. L. Liu et al., “Electrochemical detection of amyloid-β oligomer with the signal amplification of alkaline phosphatase plus electrochemical–chemical–chemical redox cycling,” J. Electroanal. Chem. 754, 40–45 (2015). Crossref, Web of Science, Google Scholar
162. M. K. Kang et al., “Label-free detection of ApoE4-mediated β-amyloid aggregation on single nanoparticle uncovering Alzheimer’s disease,” Biosens. Bioelectron. 72, 197–204 (2015). Crossref, Web of Science, Google Scholar
163. L. Guerrini et al., “SERS detection of amyloid oligomers on metallorganic-decorated plasmonic beads,” ACS Appl. Mater. Interfaces 7(18), 9420–9428 (2015). Crossref, Web of Science, Google Scholar
164. Y. Zhang et al., “Optical penetration of surface-enhanced micro-scale spatial offset Raman spectroscopy in turbid gel and biological tissue,” J. Innov. Opt. Health Sci. 14(4), 2141001 (2021). Link, Web of Science, Google Scholar
165. L. Zhu et al., “Selective amyloid β oligomer assay based on abasic site-containing molecular beacon and enzyme-free amplification,” Biosens. Bioelectron. 78, 206–212 (2016). Crossref, Web of Science, Google Scholar
166. J. V. Rushworth et al., “A label-free electrical impedimetric biosensor for the specific detection of Alzheimer’s amyloid-β oligomers,” Biosens. Bioelectron. 56, 83–90 (2014). Crossref, Web of Science, Google Scholar
167. A. J. Veloso et al., “Electrochemical immunosensors for effective evaluation of amyloid-β modulators on oligomeric and fibrillar aggregation processes,” Anal. Chem. 86(10), 4901–4909 (2014). Crossref, Web of Science, Google Scholar
168. S. Ling et al., “Development of ELISA and colloidal gold immunoassay for tetrodotoxin detection based on monoclonal antibody,” Biosens. Bioelectron. 71, 256–260 (2015). Crossref, Web of Science, Google Scholar
169. Y. Wang et al., “Rapid and sensitive detection of the food allergen glycinin in powdered milk using a lateral flow colloidal gold immunoassay strip test,” J. Agric. Food Chem. 63(8), 2172–2178 (2015). Crossref, Web of Science, Google Scholar
170. X. Huang et al., “Membrane-based lateral flow immunochromatographic strip with nanoparticles as reporters for detection: A review,” Biosens. Bioelectron. 75, 166–180 (2016). Crossref, Web of Science, Google Scholar
171. L. Hong et al., “High performance immunochromatographic assay for simultaneous quantitative detection of multiplex cardiac markers based on magnetic nanobeads,” Theranostics 8(22), 6121–6131 (2018). Crossref, Web of Science, Google Scholar
172. M. Andersson et al., “Cerebrospinal fluid in the diagnosis of multiple sclerosis: a consensus report,” J. Neurol. Neurosurg. Psychiatry 57(8), 897–902 (1994). Crossref, Web of Science, Google Scholar
173. K. Blennow et al., “Blood-brain barrier disturbance in patients with Alzheimer’s disease is related to vascular factors,” Acta Neurol. Scand. 81(4), 323–326 (1990). Crossref, Web of Science, Google Scholar
174. K. Blennow et al., “Intrathecal synthesis of immunoglobulins in patients with Alzheimer’s disease,” Eur. Neuropsychopharmacol. 1(1), 79–81 (1990). Crossref, Google Scholar
175. D. Galasko et al., “High cerebrospinal fluid tau and low amyloid β42 levels in the clinical diagnosis of Alzheimer disease and relation to apolipoprotein E genotype,” Arch. Neurol. 55(7), 937–945 (1998). Crossref, Google Scholar
176. A. M. Fagan et al., “Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Aβ42 in humans,” Ann. Neurol. 59(3), 512–519 (2006). Crossref, Web of Science, Google Scholar
177. A. Forsberg et al., “PET imaging of amyloid deposition in patients with mild cognitive impairment,” Neurobiol. Aging 29(10), 1456–1465 (2008). Crossref, Web of Science, Google Scholar
178. S. Barao et al., “BACE1 levels correlate with phospho-tau levels in human cerebrospinal fluid,” Curr. Alzheimer Res. 10(7), 671–678 (2013). Crossref, Web of Science, Google Scholar
179. S. D. Mulder et al., “BACE1 activity in cerebrospinal fluid and its relation to markers of AD pathology,” J. Alzheimer’s Dis. 20(1), 253–260 (2010). Crossref, Web of Science, Google Scholar
180. M. Ikeda et al., “CSF levels of Aβ1-38/Aβ1-40/Aβ1-42 and $^{11}$ C PiB-PET studies in three clinical variants of primary progressive aphasia and Alzheimer’s disease,” Amyloid 21(4), 238–245 (2014). Crossref, Web of Science, Google Scholar
181. K. M. Kim et al., “Increased urinary F₂-isoprostanes levels in the patients with Alzheimer’s disease,” Brain Res. Bull. 64(1), 47–51 (2004). Crossref, Web of Science, Google Scholar
182. M. Maruyama et al., “Cerebrospinal fluid amyloid β₁₋₄₂ levels in the mild cognitive impairment stage of Alzheimer’s disease,” Exp. Neurol. 172(2), 433–436 (2001). Crossref, Web of Science, Google Scholar
183. M. Kanai et al., “Longitudinal study of cerebrospinal fluid levels of tau, Aβ1-40, and Aβ1-42(43) in Alzheimer’s disease: a study in Japan,” Ann. Neurol. 44(1), 17–26 (1998). Crossref, Web of Science, Google Scholar
184. H. Arai et al., “No increase in cerebrospinal fluid tau protein levels in patients with vascular dementia,” Neurosci. Lett. 256(3), 174–176 (1998). Crossref, Web of Science, Google Scholar
185. M. Sjögren et al., “The cerebrospinal fluid levels of tau, growth-associated protein-43 and soluble amyloid precursor protein correlate in Alzheimer’s disease, reflecting a common pathophysiological process,” Dement. Geriatr. Cogn. Disord. 12(4), 257–264 (2001). Crossref, Web of Science, Google Scholar
186. H. Hampel et al., “Discriminant power of combined cerebrospinal fluid tau protein and of the soluble interleukin-6 receptor complex in the diagnosis of Alzheimer’s disease,” Brain Res. 823(1–2), 104–112 (1999). Crossref, Web of Science, Google Scholar
187. T. Nishimura et al., “Basic and clinical studies on the measurement of tau protein in cerebrospinal fluid as a biological marker for Alzheimer’s disease and related disorders: multicenter study in Japan,” Methods Find. Exp. Clin. Pharmacol. 20(3), 227–235 (1998). Crossref, Google Scholar
188. L. M. Shaw et al., “Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects,” Ann. Neurol. 65(4), 403–413 (2009). Crossref, Web of Science, Google Scholar
189. H. Pekeles et al., “Development and validation of a salivary tau biomarker in Alzheimer’s disease,” Alzheimer’s Dement. (Amst.) 11, 53–60 (2019). Google Scholar
190. C. M. Gao et al., “Aβ40 oligomers identified as a potential biomarker for the diagnosis of Alzheimer’s disease,” PLoS ONE 5(12), e15725 (2010). Crossref, Web of Science, Google Scholar
191. R. Kohnken et al., “Detection of tau phosphorylated at threonine 231 in cerebrospinal fluid of Alzheimer’s disease patients,” Neurosci. Lett. 287(3), 187–190 (2000). Crossref, Web of Science, Google Scholar
192. K. Buerger et al., “Differentiation of geriatric major depression from Alzheimer’s disease with CSF tau protein phosphorylated at threonine 231,” Am. J. Psychiatry 160(2), 376–379 (2003). Crossref, Web of Science, Google Scholar
193. S. M. Rosso et al., “Total tau and phosphorylated tau 181 levels in the cerebrospinal fluid of patients with frontotemporal dementia due to P301L and G272V tau mutations,” Arch. Neurol. 60(9), 1209–1213 (2003). Crossref, Google Scholar
194. K. Nägga et al., “Cerebrospinal fluid phospho-tau, total tau and β-amyloid₁₋₄₂ in the differentiation between Alzheimer’s disease and vascular dementia,” Dement. Geriatr. Cogn. Disord. 14(4), 183–190 (2002). Crossref, Web of Science, Google Scholar
195. C. Eckerström et al., “Combination of hippocampal volume and cerebrospinal fluid biomarkers improves predictive value in mild cognitive impairment,” Dement. Geriatr. Cogn. Disord. 29(4), 294–300 (2010). Crossref, Web of Science, Google Scholar
196. J. Hertze et al., “Evaluation of CSF biomarkers as predictors of Alzheimer’s disease: a clinical follow-up study of 4.7 years,” J. Alzheimer’s Dis. 21(4), 1119–1128 (2010). Crossref, Web of Science, Google Scholar
197. P. Buchhave et al., “Cerebrospinal fluid levels of β-amyloid 1-42, but not of tau, are fully changed already 5 to 10 years before the onset of Alzheimer dementia,” Arch. Gen. Psychiatry 69(1), 98–106 (2012). Crossref, Google Scholar
198. N. Mattsson et al., “Diagnostic accuracy of CSF Ab42 and florbetapir PET for Alzheimer’s disease,” Ann. Clin. Transl. Neurol. 1(8), 534–543 (2014). Crossref, Web of Science, Google Scholar
199. L. Parnetti et al., “Performance of Aβ₁₋₄₀, Aβ₁₋₄₂, total tau, and phosphorylated tau as predictors of dementia in a cohort of patients with mild cognitive impairment,” J. Alzheimer’s Dis. 29(1), 229–238 (2012). Crossref, Web of Science, Google Scholar
200. J. A. Monge Argilés et al., “A comparison of early diagnostic utility of Alzheimer disease biomarkers in brain magnetic resonance and cerebrospinal fluid,” Neurologia 29(7), 397–401 (2014). Web of Science, Google Scholar
201. P. Schönknecht et al., “Levels of total tau and tau protein phosphorylated at threonine 181 in patients with incipient and manifest Alzheimer’s disease,” Neurosci. Lett. 339(2), 172–174 (2003). Crossref, Web of Science, Google Scholar
202. C. Briani et al., “Combined analysis of CSF βA42 peptide and tau protein and serum antibodies to glycosaminoglycans in Alzheimer’s disease: preliminary data,” J. Neural Transm. (Vienna) 109(3), 393–398 (2002). Crossref, Web of Science, Google Scholar
203. Y. A. L. Pijnenburg et al., “Tau and Aβ42 protein in CSF of patients with frontotemporal degeneration,” Neurology 60(2), 353–354 (2003). Crossref, Web of Science, Google Scholar
204. T. Tapiola et al., “CSF tau is related to apolipoprotein E genotype in early Alzheimer’s disease,” Neurology 50(1), 169–174 (1998). Crossref, Web of Science, Google Scholar
205. M. Shoji et al., “Cerebrospinal fluid tau in dementia disorders: a large scale multicenter study by a Japanese study group,” Neurobiol. Aging 23(3), 363–370 (2002). Crossref, Web of Science, Google Scholar
206. M. Sjögren et al., “Cytoskeleton proteins in CSF distinguish frontotemporal dementia from AD,” Neurology 54(10), 1960–1964 (2000). Crossref, Web of Science, Google Scholar
207. A. Wallin et al., “Decreased cerebrospinal fluid acetylcholinesterase in patients with subcortical ischemic vascular dementia,” Dement. Geriatr. Cogn. Disord. 16(4), 200–207 (2003). Crossref, Web of Science, Google Scholar
208. H. Hampel et al., “Value of CSF β-amyloid₁₋₄₂ and tau as predictors of Alzheimer’s disease in patients with mild cognitive impairment,” Mol. Psychiatry 9(7), 705–710 (2004). Crossref, Web of Science, Google Scholar
209. H. Hampel et al., “Core biological marker candidates of Alzheimer’s disease — perspectives for diagnosis, prediction of outcome and reflection of biological activity,” J. Neural Transm. (Vienna) 111(3), 247–272 (2004). Crossref, Web of Science, Google Scholar
210. L. M. Bloudek et al., “Review and meta-analysis of biomarkers and diagnostic imaging in Alzheimer’s disease,” J. Alzheimer’s Dis. 26(4), 627–645 (2011). Crossref, Web of Science, Google Scholar
211. K. Wada-Isoe et al., “Diagnostic markers for diagnosing dementia with Lewy bodies: CSF and MIBG cardiac scintigraphy study,” J. Neurol. Sci. 260(1–2), 33–37 (2007). Crossref, Web of Science, Google Scholar
212. O. Hansson et al., “Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: a follow-up study,” Lancet Neurol. 5(3), 228–234 (2006). Crossref, Web of Science, Google Scholar
213. A. J. Mitchell, “CSF phosphorylated tau in the diagnosis and prognosis of mild cognitive impairment and Alzheimer’s disease: a meta-analysis of 51 studies,” J. Neurol. Neurosurg. Psychiatry 80(9), 966–975 (2009). Crossref, Web of Science, Google Scholar
214. H. Arai et al., “Tau in cerebrospinal fluid: A potential diagnostic marker in Alzheimer’s disease,” Ann. Neurol. 38(4), 649–652 (1995). Crossref, Web of Science, Google Scholar
215. M. Ingelsson et al., “P3-076: CSF tau levels and APOE€4 carriership predict the progression of mild cognitive impairment to Alzheimer’s disease,” Alzhemier’s Dement. 4(4SPart 16), T540 (2008). Crossref, Google Scholar
216. M. Riemenschneider et al., “Tau and Aβ42 protein in CSF of patients with frontotemporal degeneration,” Neurology 58(11), 1622–1628 (2002). Crossref, Web of Science, Google Scholar
217. K. Buerger et al., “Differential diagnosis of Alzheimer disease with cerebrospinal fluid levels of tau protein phosphorylated at threonine 231,” Arch. Neurol. 59(8), 1267–1272 (2002). Crossref, Google Scholar
218. N. Mattsson et al., “CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment,” JAMA 302(4), 385–393 (2009). Crossref, Web of Science, Google Scholar
219. M. Sjögren et al., “Both total and phosphorylated tau are increased in Alzheimer’s disease,” J. Neurol. Neurosurg. Psychiatry 70(5), 624–630 (2001). Crossref, Web of Science, Google Scholar
220. A. Maddalena et al., “Biochemical diagnosis of Alzheimer disease by measuring the cerebrospinal fluid ratio of phosphorylated tau protein to β-amyloid peptide42,” Arch. Neurol. 60(9), 1202–1206 (2003). Crossref, Google Scholar
221. K. Bürger née Buch et al., “Cerebrospinal fluid tau protein shows a better discrimination in young old ( $< 70$ years) than in old patients with Alzheimer’s disease compared with controls,” Neurosci. Lett. 277(1), 21–24 (1999). Crossref, Web of Science, Google Scholar
222. N. Itoh et al., “Large-scale, multicenter study of cerebrospinal fluid tau protein phosphorylated at serine 199 for the antemortem diagnosis of Alzheimer’s disease,” Ann. Neurol. 50(2), 150–156 (2001). Crossref, Web of Science, Google Scholar
223. A. Kurz et al., “Tau protein in cerebrospinal fluid is significantly increased at the earliest clinical stage of Alzheimer disease,” Alzheimer Dis. Assoc. Disord. 12(4), 372–377 (1998). Crossref, Web of Science, Google Scholar
224. N. Andreasen et al., “Cerebrospinal fluid tau protein as a biochemical marker for Alzheimer’s disease: a community based follow up study,” J. Neurol. Neurosurg. Psychiatry 64(3), 298–305 (1998). Crossref, Web of Science, Google Scholar
225. N. Rösler, I. Wichart, K. A. Jellinger, “Total tau protein immunoreactivity in lumbar cerebrospinal fluid of patients with Alzheimer’s disease,” J. Neurol. Neurosurg. Psychiatry 60(2), 237–238 (1996). Crossref, Web of Science, Google Scholar
226. E. N. Kapaki et al., “Cerebrospinal fluid tau, phospho-tau181 and β-amyloid₁₋₄₂ in idiopathic normal pressure hydrocephalus: A discrimination from Alzheimer’s disease,” Eur. J. Neurol. 14(2), 168–173 (2007). Crossref, Web of Science, Google Scholar
227. K. Buerger et al., “Validation of Alzheimer’s disease CSF and plasma biological markers: The multicentre reliability study of the pilot European Alzheimer’s Disease Neuroimaging Initiative (E-ADNI),” Exp. Gerontol. 44(9), 579–585 (2009). Crossref, Web of Science, Google Scholar
228. O. Hansson, “Biomarkers for neurodegenerative diseases,” Nat. Med. 27(6), 954–963 (2021). Crossref, Web of Science, Google Scholar
229. M. Maruyama et al., “Imaging of tau pathology in a tauopathy mouse model and in Alzheimer patients compared to normal controls,” Neuron 79(6), 1094–1108 (2013). Crossref, Web of Science, Google Scholar

Vol. 15, No. 01

Metrics

Downloaded 14,910 times

History

Received 25 July 2021

Accepted 10 September 2021

Published: 30 October 2021

Information

This is an Open Access article. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited.

Keywords

PDF download