Next Article in Journal
Improved Therapeutic Efficacy of CBD with Good Tolerance in the Treatment of Breast Cancer through Nanoencapsulation and in Combination with 20(S)-Protopanaxadiol (PPD)
Previous Article in Journal
Thermosensitive PLGA–PEG–PLGA Hydrogel as Depot Matrix for Allergen-Specific Immunotherapy
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Non-Invasive Nasal Discharge Fluid and Other Body Fluid Biomarkers in Alzheimer’s Disease

1
Department of Brain Sciences, Graduate School, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea
2
Convergence Research Advanced Centre for Olfaction, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea
3
Neuroscience Research Institute, Gachon University, Incheon 21565, Korea
4
Department of Pharmacology, College of Medicine, Gachon University, Incheon 21936, Korea
5
Neuroscience of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21936, Korea
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Current address: Weill Institute for Neurosciences and Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA 94158, USA.
Pharmaceutics 2022, 14(8), 1532; https://doi.org/10.3390/pharmaceutics14081532
Submission received: 21 June 2022 / Revised: 12 July 2022 / Accepted: 19 July 2022 / Published: 22 July 2022
(This article belongs to the Section Biologics and Biosimilars)

Abstract

:
The key to current Alzheimer’s disease (AD) therapy is the early diagnosis for prompt intervention, since available treatments only slow the disease progression. Therefore, this lack of promising therapies has called for diagnostic screening tests to identify those likely to develop full-blown AD. Recent AD diagnosis guidelines incorporated core biomarker analyses into criteria, including amyloid-β (Aβ), total-tau (T-tau), and phosphorylated tau (P-tau). Though effective, the accessibility of screening tests involving conventional cerebrospinal fluid (CSF)- and blood-based analyses is often hindered by the invasiveness and high cost. In an attempt to overcome these shortcomings, biomarker profiling research using non-invasive body fluid has shown the potential to capture the pathological changes in the patients’ bodies. These novel non-invasive body fluid biomarkers for AD have emerged as diagnostic and pathological targets. Here, we review the potential peripheral biomarkers, including non-invasive peripheral body fluids of nasal discharge, tear, saliva, and urine for AD.

1. Introduction

Alzheimer’s disease (AD) is an irreversibly progressive neurodegenerative disease afflicting the elderly, accompanied by devastating cognitive and memory impairment caused by characteristic neuronal and synaptic loss and cortical and hippocampal atrophy. It is hallmarked by the accumulation of extracellular amyloid plaques and intracellular neurofibrillary tangles. The underlying mechanisms contributing to the development of the disease remain elusive and controversial. Despite the advancement in understanding the mechanism of pathogenesis, clinical trials have been unsuccessful and provided no relief from disease progression, only slowing the progression [1]. Recent FDA-approved anti-amyloid therapy aducanumab highlights that it is effective for patients with very mild, biomarker-proven AD [2,3]. Therefore, there is an urgent need to develop a more accessible biomarker screening test using less invasive and cost-effective body fluid biomarkers. These diagnostics will serve as the first line of effective AD therapies before extensive pathophysiological brain devastation occurs.
Currently, AD diagnosis involves a combination of neuroimaging techniques, detailed clinical review of family history, neuropsychological test results, and laboratory assay results [4,5,6,7]. In the field of early AD diagnosis, the biomarkers using cerebrospinal fluid (CSF), blood or neuroimaging, using magnetic resonance imaging (MRI) or positron emission tomography (PET), are being rapidly developed. However, the high cost of test procedures, potential complications, need for a specialist, requirement for high-performance equipment, lack of standardized cross-institution protocols, and inconsistencies in test result interpretation hinder the accessibility of these screening tests to individuals in economically disadvantaged areas or remote geographical regions [8]. These factors hinder easy accessibility to routinely approaching these diagnostic tests, and therefore, patients at risk may miss the opportunity for a timely and accurate diagnosis and pharmacological intervention.
In an attempt to overcome these shortcomings, a rising number of biomarker profiling research using non-invasive peripheral body fluids such as nasal discharge, saliva, urine, and tear has shown the potential to capture the pathological changes in the patients’ bodies. Of 15,445 AD biomarker-related articles published in the last 10 years, 355 articles related to peripheral body fluid biomarker were retrieved when searched in PubMed, and 71% of them were published in the last five years, showing that there is a growing awareness about the importance of peripheral body fluid biomarkers in AD research. In view of this, this review aims to provide an overview of recent contributions in the field of non-invasive body fluid biomarkers and to shed new light on the potential of non-invasive bodily fluid biomarkers. Few previous studies have provided standardized guidelines for interpreting diagnosis criteria or biomarker source origins regarding AD biomarker research papers. Taken together, we will address this issue and formulate guidelines for reading AD biomarker research papers.

2. Clinical Diagnosis of AD

The standard diagnostic criteria for AD were first established in 1984. First, the diagnosis of Alzheimer’s disease was initiated by the National Institute of Neurological Disorders and Stroke (NINDS) and the Alzheimer’s Disease and Related Disorders Association (ADRDA) [9]. The criteria were compatible with the Diagnostic and Statistical Manual of Mental Disorders (DSM-III) [10]. The NINCDS-ADRDA and DSM-III evaluated the cognitive impairment by AD and dementia syndrome, which are summarized in Table 1. Clinical diagnostic criteria of possible and probable AD were made based on neuropsychological tests. The criteria for diagnosing definite AD involved clinicopathologic investigations, meaning histopathological evidence from microscopic examination obtained from autopsy or biopsy.
Next, the National Institute on Aging-Alzheimer’s Association (NIA-AA) workgroups updated the diagnostic guideline for AD [11,12]. Revised diagnostic criteria provide earlier identification of AD progression, including preclinical and mild cognitive impairment (MCI) stages. Including the NIA-AA guideline, revised DSM-V is also incorporated into the classification of disease progression [13]. The criteria involve neuropsychological tests such as Mini-Mental State Examination (MMSE), Clinical Dementia Rate (CDR), and Global Deterioration Scale (GDS), and the postmortem examination confirms AD pathology, amyloid plaques, and neurofibrillary tangles. These provide an initiative research framework underlying pathologic processes that can be reported by postmortem examination. However, AD diagnosis’s significance shifted to observing the disease progression in living people rather than defining the consequences because the current treatment is ineffective after severe neurodegeneration.
Recent AD research has led to the biomarker analysis in vivo for timely and appropriate intervention before a severe brain injury. As a result, the research framework was updated to examine the AD pathologic processes [14]. In 2016, Jack et al. proposed the A/T/N classification as a means of evidencing the biological state of AD [15]. This classification is divided into binary categories, either positive or negative, and this system provides an improved understanding of the sequence of events in the AD continuum [14]. This framework includes A, the status of amyloid-β (Aβ) PET or CSF Aβ42; T, tau PET or CSF phosphorylated-tau (P-tau); N, neurodegeneration or neuronal injury measured by neuroimaging or CSF total-tau (T-tau). The presence of each A/T/N biomarker profiles the pathologic change with or without AD. Table 2 summarizes clinical AD stages based on the NIA-AA guideline and the results of neuropsychological batteries.

3. Conventional AD Body Fluid Biomarkers

A biomarker indicates the change of cells and tissues, which illustrates altered body conditions [16]. In the clinics, biomarkers represent the pathological status and monitor the disease progression. Appropriate methods to detect disease biomarkers in living patients are important for establishing early intervention times and evaluating clinical therapeutic efficacy. The characteristics of an ideal biomarker are outlined in Table 3.
Given that AD is progressive and incurable, an ideal method to detect AD biomarkers is required for AD-specific early detection, economic accessibility, and non-invasive sample collection [17]. The neuropsychological test is a worldwide and classic method to identify cognitive impairment, but multiple profiling is demanded to confirm AD from other neurodegenerative diseases. Although brain imaging (e.g., fMRI, FDG-, amyloid-, and tau-PET) observes disease-specific pathophysiology, the patients may find it difficult to perform neuroimaging tests due to their repeatedly high costs. Recently, observing amyloid-β and tau as biomarkers in CSF and plasma are approved by NIA-AA [14]. In addition to CSF and blood, body fluids that can closely reflect the patient’s pathological condition have been studied. Figure 1 illustrates body fluids that have the potential to be matrices of biomarker detection.

3.1. CSF

CSF is a clear and colorless body fluid in the subarachnoid space and circulates within the ventricular system of the brain and spinal cord to supply nutrients and chemicals, remove waste products, and provide the brain immunological protection and mechanical support [18,19]. CSF is produced in the choroid plexus of the brain’s ventricles and reabsorbed into venous sinus blood via the arachnoid granulations. The total volume of CSF is approximately 125–160 mL. CSF is replaced four to five times and regenerates about 500 mL every day [20]. Since CSF reflects biochemical and environmental changes within the central nervous system, CSF is an ideal and useful candidate for detecting potential neuropathology biomarkers [21,22]. CSF is usually obtained by a procedure called a lumbar puncture. The standardized collection protocol of lumbar puncture is carried out under sterile conditions by inserting a needle into the subarachnoid space between the third and fifth lumbar vertebrae [23]. The collection protocol is summarized in Table 4.
NIA-AA and International Working Group (IWG) 2 have recognized the significance of CSF biomarkers, including Aβ42, T-tau, and P-tau, and incorporated them into diagnostic criteria for AD and MCI [24,25,26]. In AD, Aβ42 concentration and Aβ42/Aβ40 ratio are reduced, and T-tau and P-tau concentrations increase in CSF [18,27,28,29]. CSF-related core AD biomarker changes are summarized in Table 5.
Although lumbar puncture is the most common and recommended method for CSF collection, there are some issues: lumbar puncture results in discomfort and pain due to the larger and longer needle and the possibility of CSF contamination by anesthesia. In addition, it is very difficult and expensive to perform the procedure on the subjects repeatedly [23].

3.2. Blood (Plasma and Serum)

Blood plasma is the liquid component in which blood cells are suspended [30]. It delivers nutrients and oxygen to the cells and transports cellular metabolic products. It amounts to about 55% of the total blood volume and is mostly water. Blood plasma is circulated through the body via blood vessels by the pumping of the heart. Functions of blood plasma are maintenance of the blood pressure, pH, immunity, and transportation of electrolytes, nutrients, clotting factors, carbon dioxide, oxygen, other waste products, and excretory proteins.
Blood serum is blood plasma without clotting factors such as fibrinogens [30]. Blood serum includes all electrolytes, antibodies, antigens, hormones, and other substances, except white blood cells, red blood cells, platelets, and clotting factors [31,32]. Blood serum is obtained by coagulation, which allows for clotting of the blood. Both plasma and serum are commonly used for proteomic analysis.
Table 4. Summary of CSF and blood acquisition procedures.
Table 4. Summary of CSF and blood acquisition procedures.
Body FluidAcquisitionProcedureReference
CSFLumbar puncture1. The subject lies on their side and bends knees toward the chest and chin.
2. An atraumatic spinal needle is injected into the vertebral body L3–L5.
3. CSF is collected in polypropylene tubes about 1–2 mL.
[23]
BloodPlasmaVenipuncture1. Clean the venipuncture site and insert the needle.
2. The blood is collected in blood collection tubes, including anticoagulant (EDTA or heparin).
3. Within 1–2 h, collecting tubes are centrifuged, and then supernatant is transferred into new tubes, including protease inhibitor cocktail.
[33,34]
SerumVenipuncture1. Clean the venipuncture site and insert the needle.
2. The blood is collected in blood collection tubes, including a silica clot activator.
3. After clotting for 30 min, samples are centrifuged, and then supernatant is transferred into new tubes, including a protease inhibitor cocktail.
Blood samples are collected by venipuncture, and the protocol commonly used is based on the Human Plasma Proteome Project (HPPP) by the Human Proteome Organization (HUPO) and the National Institute of Health [33,34]. Table 4 summarizes acquisition procedures for blood plasma and serum.
The conventional biomarkers of AD, such as Aβ42, Aβ40, T-tau, and P-tau, are commonly utilized as potential candidate screening molecules in blood samples because they can pass the blood–brain barrier (BBB) [35,36,37]. However, the BBB breaks down in AD patients, which leads to an accumulation of blood-derived neurotoxic proteins in the brain [38]. Blood-related core AD biomarker changes are summarized in Table 5.
Biomarkers for neuropathology from blood samples have been controversial because blood communicates with the brain through the BBB, lymphatic vessel, and lymphatic system, which indirectly interchange the materials and substances from the brain into blood, resulting in lower sensitivity and specificity than CSF [35,39,40,41].

3.3. Limitations of Current CSF and Blood AD Biomarkers

Although the core CSF and blood AD biomarkers reflect central pathological changes of the disease, current analyses have drawbacks: invasive procedure, high cost of test procedures, potential complications, between-institution differences in cut-off values, and inconsistencies in test result interpretation [42,43,44]. Furthermore, a plethora of studies have characterized the multifaceted nature of AD, highlighting the complexity of understanding the biochemical changes in the disease progression [45]. Due to these limitations, the accessibility of diagnosis assays is hindered, making the diagnosis belated, which adds cost to health care systems. Therefore, the development of novel biomarker detection in non-invasive body fluid is essential.
Table 5. Summary of core AD fluid biomarkers in CSF and blood.
Table 5. Summary of core AD fluid biomarkers in CSF and blood.
AD Pathology MechanismCore BiomarkersCSFBlood
ChangeReferenceChangeReference
Aβ pathology42Reduced[29,46,47]Reduced[48,49,50]
42/Aβ40Reduced[51,52,53]Reduced[48,50,54]
Tau pathologyP-tauIncreased[55,56,57]Increased[58,59,60]
T-tauIncreased[61,62,63]Increased[60,64,65]

4. Novel Peripheral Body Fluid Biomarkers

Recent research has shown that other peripheral body fluids, such as nasal discharge, tear, saliva, and urine, may represent a potential source of biomarkers for neurodegenerative diseases. These peripheral body fluids have advantages over CSF or blood since the collection methods are less invasive and enable low-cost biomonitoring. Table 6 summarizes acquisition methods for peripheral body fluids.

4.1. Nasal Discharge

The occurrence of olfactory deficits, named anosmia or hyposmia, in AD has been characterized for decades, and often these deficits precede the cognitive decline [82,83,84,85]. Olfactory neuropathology is the cause of olfactory dysfunction, and structural and functional evidence supports this view, including abnormal APP processing and neuroinflammation [86,87,88,89,90]. The central olfactory processing regions, such as entorhinal and transentorhinal areas, olfactory bulb, and other medial temporal lobes, anatomically overlap with the regions involved in early AD pathology [82,91,92]. AD postmortem and antemortem studies revealed that the olfactory system shows classic AD hallmarks such as intracellular neurofibrillary tau tangles and amyloid plaques [93,94,95,96,97]. In particular, nasal discharge surrounds the olfactory system and captures the neuropathology occurring in the system, emerging as a potential matrix of fluid biomarkers.
Nasal discharge is a slippery and gelatinous fluid produced by mucous membranes in the olfactory mucosa. Nasal discharge is 95% water, glycoproteins, proteoglycans, lipids, proteins, and DNA. The purpose of nasal discharge is to protect the olfactory epithelium (OE) and the respiratory system by blocking the infections of pathogenic antigens. Nasal discharge fluids serve to humidify and clean inhale air and provide proteins of the innate immune system. Additionally, nasal discharge fluids trap and dissolve odorants for the olfactory receptor neurons.
Since the olfactory system is exposed to the external environment, the collection of nasal discharge fluid is easily accessible and non-invasive. In Table 6, we described several protocols for collecting samples [66,69,71]. Saline buffers that have a similar composition to human body fluids are used for nasal irrigation. Various techniques and devices have been developed to deliver saline to the nasal cavity, such as douche, spray, and nebulizer. Nasal irrigation can effectively relieve sinusitis caused by respiratory tract infections [98] and symptoms involved in allergic responses [99]. Additionally, to analyze proteomic studies of nasal discharge fluids for biomarkers, nasal discharge fluid obtained through nasal irrigation can provide valuable information. The second method of nasal discharge fluid collection is to use sinus packs. The method is non-invasive and reproducible. Several techniques, such as nasal lavage, brush, and scraping, have been known as collection methods that may influence the results [100]. Watelet et al. proposed a new technique to obtain nasal discharge fluids using sinus packs [69]. The authors confirmed the fluid quantity and protein concentration from the sinus packs and evaluated the feasibility and reproductivity of this technique. Thirdly, a nasal swab (or nasopharyngeal swab) is a method for collecting a sample of nasal discharge fluid from the back of the nose or throat. This method is commonly used to analyze the presence of markers of disease, organisms, and viral infection. Recently, a nasal swab has been used to diagnose COVID-19 [101].
Early studies identified the presence of amyloid-β peptide and amyloid precursor proteins (APP) in postmortem AD patients’ olfactory mucosa samples [94,102]. Aggregation of amyloid-β expression was detected in 71% of AD cases, 22% in normal cases, and 14% in other neurodegenerative disease cases [103]. Biopsy examination identified Aβ expression from the normal, MCI, and AD subjects [104]. Sampling human olfactory environment for AD-related research advanced from taking autopsy or biopsy samples to collecting human nasal discharge. A study collected nasal smears by swabbing from multiple nasal areas, such as the common nasal meatus, inferior concha, middle nasal meatus, and olfactory cleft [71]. Subsequent studies analyzed Aβ expression in nasal discharge fluid by immunoassay and proved that the level of oligomeric Aβ in nasal discharge was higher in AD than normal [67,105]. A 3-year longitudinal study by Yoo et al. confirmed that the presence of oligomeric Aβ could predict the cognitive decline progression [67].
Similarly, early studies used the histopathological method to detect T-tau in postmortem and antemortem samples of AD patients’ olfactory systems [95,106]. Later, immuno-histochemical studies demonstrated that tau pathology in the olfactory system correlated with AD pathology progression [93,107]. ELISA-analysis of nasal smear swabs indicated that P-tau/T-tau ratios were more significant in AD than control [71,108].
Non-core AD hallmark biomarkers have also been identified in the olfactory system. Expression of α-synuclein was identified in postmortem OE of AD sample [103]. A study showed increased microRNA-206 in AD patients’ OE through qRT-PCR [109]. Proteome analysis was done on nasal discharge samples from young, healthy groups and elderly groups, and identified a list of associated proteins with age variability [110]. However, little is known about the molecular machinery responsible for mucus proteome and its changes in neurodegenerative diseases. Table 7 summarizes the results of core AD biomarker studies from the olfactory system.

4.2. Tears

Tears have a high protein content and have been widely investigated for biomarker studies for ocular diseases and diabetes [113,114]. Major tear proteins, lipocalin-1 and lactotransferrin, are involved in the inflammatory and immune processes [113,115]. Researchers studying neurodegenerative diseases have also hypothesized that neuroinflammation could be reflected in tear proteins due to the extension of the central nervous system. Recent AD studies conducted tear analyses and discovered the potential of tear biomarkers in AD.
The techniques of collecting tears were established in 1981 [72] and 1984 [73], and the protocols described in Table 6 are the most commonly used in tear proteomic investigations [74]. For proteomic analysis, tears can be collected using Schirmer’s tear strips and capillaries. The Schirmer strip is placed in the lower eyelid and allowed to absorb the tear for several minutes. The capillary tear is collected using sterile capillary tubes under the same conditions. However, to obtain aqueous humor samples, surgical treatment is required to use a fine needle [116], and vitreous humor samples are obtained by surgery vitrectomy [117].
Gijs et al. measured Aβ42 in tears using multiplex immunoassays and found the Aβ42 levels changed with increasing AD stage with an area under the curve (AUC) of 0.725 [118]. Other Aβ peptides, Aβ38 and Aβ40, were also detected in their subsequent study [119]. Recently, Wang et al. developed a biosensor and detected variable Aβ42 levels in different age groups of healthy participants [120]. Gijs et al. also analyzed T-tau, and its levels were also able to discriminate between AD patients and healthy controls with an AUC of 0.81 [118,119]. Quantitative proteomic results profiled that lipocalin-1, dermcidin, lysozyme-C, and lacritin can serve AD biomarkers [121]. One LC/MS evaluation identified elongation initiation factor 4E (elF4E) uniquely in AD tear samples, and a PCR-based analysis showed elevated total microRNA abundance in AD patients’ tears and especially higher microRNA-200b-5p levels in tears of AD patients compared to healthy controls [122]. Table 8 summarizes the results of core AD biomarker studies using tears.

4.3. Saliva

Saliva is an easily accessible, non-invasive body fluid containing 98% water containing electrolytes, proteins, peptides, hormones, sugar, epithelial cells, white blood cells, enzymes, and lysozymes [123]. The functions of saliva are the protection and maintenance of oral mucosa, digestion, the perception of taste, and the control of microorganisms [124,125]. Saliva is secreted from three major salivary glands, named the sublingual, submandibular, and parotid, and they are innervated by the cranial and facial nerves [126]. The direct innervation of the glossopharyngeal nerve through the otic ganglion suggests that saliva can be a promising candidate of biomarker source for assessing pathological physiologies of the nervous system [127].
Several various methods for collecting saliva have been described in the past years. In 2007, the World Health Organization and International Agency for Research on Cancer described the protocol for saliva proteomics [77]. The protocols for saliva collection depend on the specific categories of the saliva of interest, and these different methods are summarized in Table 6.
In the last few years, various studies have detected increased Aβ42 in AD patients’ saliva using sandwich and nanobead ELISAs. Bermejo-Pareja et al. analyzed saliva samples by immunoassays and identified a statistically significant increase in saliva Aβ42 levels in mild AD patients than normal control [128]. Subsequent studies similarly showed elevated Aβ42 levels in AD saliva samples [129,130,131]. In contrast to these findings, other results showed no detection of Aβ42 or Aβ40 in saliva with immunoassays [127,128,132]. On the other hand, a recent study demonstrated decreased Aβ42 level in AD patients’ saliva [133]. Some preliminary tau investigation was carried out in the 2010s, and Shi et al. reported an increased P-tau/T-tau ratio in patients with AD compared to healthy controls [127]. A subsequent study also confirmed this increased P-tau/T-tau ratio in AD versus healthy controls [134]. In contradiction with these findings, results demonstrated no significant difference in salivary T-tau between AD and mild cognitive impairment or healthy controls [135].
A growing number of studies have examined possible biomarker candidates other than Aβ and tau peptides. Lactoferrin, for instance, is a pleiotropic protein with several immunological properties, including antibacterial, antiviral, antioxidant, and anti-inflammatory functions [136,137]. The first investigations on salivary lactoferrin found a decreased lactoferrin level in AD patients’ saliva compared to healthy controls [138]. A more recent study supported this finding by comparing salivary lactoferrin levels with amyloid-PET neuroimaging data [139]. Acetylcholinesterase degrades acetylcholine neurotransmitters released into the synaptic cleft and terminates acetylcholine neurotransmission, and PET study results demonstrated decreased acetylcholinesterase catalytic activity in AD patients’ brain regions [140,141,142]. An initial report on the salivary acetylcholinesterase activity was carried out by Sayer et al. and showed a significant decrease in AD patients [143]. Further studies also suggested a reduced salivary acetylcholinesterase activity in AD patients [144,145]. Protein carbonyl levels result from protein oxidation, and multiple studies examined elevated protein carbonyls in brain regions of AD patients [146,147]. One study evaluated and identified protein carbonyl levels in saliva of AD patients and healthy controls [148]. Metabolomics is an emerging research technique used in various research fields to identify metabolites within a target sample. Yilmaz et al. analyzed saliva samples from healthy control, mild cognitive impairment sufferers, and AD patients. They profiled multiple metabolites that changed significantly in the saliva of MCI and AD patients compared to healthy controls [149]. Table 9 summarizes the results of core AD biomarker studies using saliva.

4.4. Urine

Urine contains thousands of proteins, mostly metabolic wastes, and is currently actively utilized to study pregnancy, aging, and kidney diseases [150,151]. Nevertheless, for many years urine has been neglected as a promising source of biomarkers for studying AD since there is little agreement on urine reflecting the changes occurring in the brain due to the BBB and glomerular filtration. However, several studies have indicated the potential of urinary biomarkers in neurodegenerative diseases, such as Parkinson’s disease and Alzheimer’s disease [152,153,154].
More importantly, urine collection does not require special equipment and can be repeated without discomfort to subjects. The Human Kidney and Urine Proteome Project (HKUPP) in 2005 and European Kidney and Urine Proteomics (EuroKUP) in 2008 were initiated to promote proteomics research, and they together have achieved the establishment of a standard protocol for urine collection and storage [78,79,80,81]. The collection method is summarized in Table 6.
Initial report on detecting Aβ peptide in the urine of AD patients was carried out by Takata et al. by Western blot analysis and suggested that monomeric Aβ level may reflect the severity of AD [155]. A key question raised by Takata et al. was that they could not pinpoint the origin of Aβ in urine. A recent study developed an indirect competitive ELISA to measure Aβ42 in human urine samples [156].
Several studies have profiled potential urine protein biomarkers for AD. One study identified 15 proteins using LC/MS-MS and validated three proteins, SPP1, GSN, and IFGBP7, by ELISA [157]. Higher urinary AD7c-NTP, Alzheimer-associated neural thread protein, was demonstrated in another study [158]. Watanabe et al. analyzed AD patients’ urine samples and profiled 109 proteomes differentially expressed in AD and healthy controls [159]. Their subsequent study showed that apolipoprotein C3 levels in AD patients’ urine samples were higher in the AD and MCI groups than healthy controls using ELISA [160]. Urine also contains many metabolites, reflecting the gut microbiome theory in neurodegeneration studies [161,162,163]. One study recently examined urinary metabolome using NMR spectroscopy and UHPLC-MS and built a model that could discriminate between AD and healthy age-matched controls [164]. Another study developed a new screening approach using LC/MS and proposed that lipid peroxidation compounds may be potential predictors of early AD [165]. In addition, many studies reported microRNA in human urine samples and demonstrated that urinary microRNAs are relatively stable under various storage conditions, supporting their utility as urinary biomarkers [166,167,168]. Table 10 summarizes the results of core AD biomarker studies using urine.

4.5. Limitations and Future Perspectives of Novel Peripheral Body Fluid Biomarkers

Currently, a multitude of studies are discovering potential body fluid biomarkers to assist in assessing disease progression, developing treatments, and monitoring treatment efficacy. Nevertheless, there are substantial challenges in the validation and application of peripheral body fluid biomarkers in AD to clinical practice, and there is no single ideal peripheral body fluid biomarker that exists. The main challenge arises from the fact that core AD biomarkers are proteins, which can be affected by preanalytical factors such as sample collection conditions, the timing of sample processing, and sample storage conditions [169]. Another limitation is low concentrations of biomarkers in peripheral body fluids. Commonly used detection methods utilize technologies, including immunoblot and electrochemiluminescent immunoassays, and quite often the low concentrations require highly sensitive novel technologies with a lower limit of detection and lower limit of quantification [170]. Besides, the peripheral body fluid biomarker research field has not yet established a consensus on experimental techniques and methods, resulting in low assay standardization [169].
Despite these limitations, the advantages that the novel peripheral body fluid biomarkers possess should be taken into account for easier, faster, and more accessible diagnosis for a wider spectrum of patients. Studies suggested that a combination of multiple biomarkers will improve the diagnostic accuracy when compared with the use of a single biomarker [171,172]. The use of multi-biomarker panels will provide a platform to monitor disease progression longitudinally. Validation of perspective biomarkers in large cohorts of patients would be crucial to be implemented in practical use.

5. Conclusions

In combination with clinical examination of cognition and neuropathology, biomarker studies have evolved quickly to understand the pathogenesis and implement early diagnosis for timely therapeutic interventions. Nevertheless, the accessibility to the conventional CSF- and blood-based biomarker tests is hindered due to their invasive and high-cost sampling measures.
We have also outlined the guidelines for AD diagnosis; however, each study offered its own classification criteria. Therefore, it was difficult to provide an encompassing A/T/N diagnosis status for all the reviewed studies. We hope that the use of A/T/N diagnosis will work in unison with the body fluid biomarkers and provide an overall spectrum of core biomarker modalities in AD. Such elaborated biomarker indices will help better understand the pathology and bolster overall diagnostic accuracy.
This review has explored considerable progress in identifying non-invasive peripheral body fluid biomarkers from nasal discharge, tears, saliva, and urine. A great deal of work on the potential of these non-invasive peripheral body fluid biomarkers has suggested that these biomarkers can be used for early detection and diagnosis of AD and monitoring the disease progression from preclinical to full-blown AD stages. Ideal biomarker characteristics include easy accessibility, high accuracy, minimal invasiveness, cost-efficiency, and reproducibility. A lot of evidence proposed that core AD biomarkers in the non-invasive peripheral body fluid discussed in this review have the possibilities to meet these criteria and be utilized in clinical practice with further research.
The discovery and application of the non-invasive peripheral body fluid biomarkers may enable early diagnosis, help patient monitoring in clinical trials, or identify disease-relevant molecular pathways to develop novel therapeutic targets. Further refinement in using these biomarkers may lead to the invention of AD screening tests, biosensors, or chip devices with high accuracy and reproducibility. We believe that future studies in this field will undoubtedly have a profound and positive impact on the patients and their families.

Author Contributions

Conceptualization, D.H.J., G.S. and O.-H.K.; Data curation, D.H.J., G.S. and O.-H.K.; Formal analysis, D.H.J., G.S. and O.-H.K.; Funding acquisition, K.-A.C. and C.M.; Methodology, D.H.J., G.S. and O.-H.K.; Project administration, C.M.; Supervision: C.M.; Visualization, D.H.J., G.S. and O.-H.K.; Writing—Original draft, D.H.J., G.S. and O.-H.K.; Writing—Review & editing, K.-A.C. and C.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2015M3A9E2028884 and 2020M3A9E41043844), and by the Ministry of Education (2020R1A6A1A03040516).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Long, J.M.; Holtzman, D.M. Alzheimer Disease: An Update on Pathobiology and Treatment Strategies. Cell 2019, 179, 312–339. [Google Scholar] [CrossRef] [PubMed]
  2. Sevigny, J.; Chiao, P.; Bussiere, T.; Weinreb, P.H.; Williams, L.; Maier, M.; Dunstan, R.; Salloway, S.; Chen, T.; Ling, Y.; et al. The antibody aducanumab reduces Abeta plaques in Alzheimer’s disease. Nature 2016, 537, 50–56. [Google Scholar] [CrossRef] [PubMed]
  3. Musiek, E.S.; Bennett, D.A. Aducanumab and the “post-amyloid” era of Alzheimer research? Neuron 2021, 109, 3045–3047. [Google Scholar] [CrossRef] [PubMed]
  4. Davda, N.; Corkill, R. Biomarkers in the diagnosis and prognosis of Alzheimer’s disease. J. Neurol. 2020, 267, 2475–2477. [Google Scholar] [CrossRef]
  5. Paraskevaidi, M.; Martin-Hirsch, P.L.; Martin, F.L. Progress and Challenges in the Diagnosis of Dementia: A Critical Review. ACS Chem. Neurosci. 2018, 9, 446–461. [Google Scholar] [CrossRef]
  6. Guest, F.L.; Rahmoune, H.; Guest, P.C. Early Diagnosis and Targeted Treatment Strategy for Improved Therapeutic Outcomes in Alzheimer’s Disease. In Reviews on New Drug Targets in Age-Related Disorders; Guest, P.C., Ed.; Springer International Publishing: Cham, Switzerland, 2020; pp. 175–191. [Google Scholar]
  7. Weller, J.; Budson, A. Current understanding of Alzheimer’s disease diagnosis and treatment. F1000Research 2018, 7, 1161. [Google Scholar] [CrossRef] [Green Version]
  8. Thal, L.J.; Kantarci, K.; Reiman, E.M.; Klunk, W.E.; Weiner, M.W.; Zetterberg, H.; Galasko, D.; Pratico, D.; Griffin, S.; Schenk, D.; et al. The role of biomarkers in clinical trials for Alzheimer disease. Alzheimer Dis. Assoc. Disord. 2006, 20, 6–15. [Google Scholar] [CrossRef] [Green Version]
  9. McKhann, G.; Drachman, D.; Folstein, M.; Katzman, R.; Price, D.; Stadlan, E.M. Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 1984, 34, 939–944. [Google Scholar] [CrossRef] [Green Version]
  10. Juva, K.; Sulkava, R.; Erkinjuntti, T.; Ylikoski, R.; Valvanne, J.; Tilvis, R. Staging the severity of dementia: Comparison of clinical (CDR, DSM-III-R), functional (ADL, IADL) and cognitive (MMSE) scales. Acta Neurol. Scand. 1994, 90, 293–298. [Google Scholar] [CrossRef]
  11. McKhann, G.M.; Knopman, D.S.; Chertkow, H.; Hyman, B.T.; Jack, C.R., Jr.; Kawas, C.H.; Klunk, W.E.; Koroshetz, W.J.; Manly, J.J.; Mayeux, R.; et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011, 7, 263–269. [Google Scholar] [CrossRef] [Green Version]
  12. Albert, M.S.; DeKosky, S.T.; Dickson, D.; Dubois, B.; Feldman, H.H.; Fox, N.C.; Gamst, A.; Holtzman, D.M.; Jagust, W.J.; Petersen, R.C.; et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011, 7, 270–279. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. American Psychiatric Association. DSM-5 Task Force. In Diagnostic and Statistical Manual of Mental Disorders: DSM-5, 5th ed.; American Psychiatric Association: Washington, DC, USA, 2013; p. xliv. 947p. [Google Scholar]
  14. Jack, C.R., Jr.; Bennett, D.A.; Blennow, K.; Carrillo, M.C.; Dunn, B.; Haeberlein, S.B.; Holtzman, D.M.; Jagust, W.; Jessen, F.; Karlawish, J.; et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018, 14, 535–562. [Google Scholar] [CrossRef] [PubMed]
  15. Jack, C.R., Jr.; Bennett, D.A.; Blennow, K.; Carrillo, M.C.; Feldman, H.H.; Frisoni, G.B.; Hampel, H.; Jagust, W.J.; Johnson, K.A.; Knopman, D.S.; et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 2016, 87, 539–547. [Google Scholar] [CrossRef]
  16. Baker, M. In biomarkers we trust? Nat. Biotechnol. 2005, 23, 297–304. [Google Scholar] [CrossRef] [PubMed]
  17. Ray, P.; Le Manach, Y.; Riou, B.; Houle, T.T. Statistical evaluation of a biomarker. Anesthesiology 2010, 112, 1023–1040. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Blennow, K.; Hampel, H.; Weiner, M.; Zetterberg, H. Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat. Rev. Neurol. 2010, 6, 131–144. [Google Scholar] [CrossRef]
  19. Sakka, L.; Coll, G.; Chazal, J. Anatomy and physiology of cerebrospinal fluid. Eur. Ann. Otorhinolaryngol. Head Neck Dis. 2011, 128, 309–316. [Google Scholar] [CrossRef] [Green Version]
  20. van Gool, A.J.; Hendrickson, R.C. The proteomic toolbox for studying cerebrospinal fluid. Expert Rev. Proteom. 2012, 9, 165–179. [Google Scholar] [CrossRef]
  21. Lee, J.C.; Kim, S.J.; Hong, S.; Kim, Y. Diagnosis of Alzheimer’s disease utilizing amyloid and tau as fluid biomarkers. Exp. Mol. Med. 2019, 51, 1–10. [Google Scholar] [CrossRef] [Green Version]
  22. Rosenberg, G.A. Chapter 4—Cerebrospinal Fluid: Formation, Absorption, Markers, and Relationship to Blood–Brain Barrier. In Primer on Cerebrovascular Diseases, 2nd ed.; Caplan, L.R., Biller, J., Leary, M.C., Lo, E.H., Thomas, A.J., Yenari, M., Zhang, J.H., Eds.; Academic Press: San Diego, CA, USA, 2017; pp. 25–31. [Google Scholar]
  23. Teunissen, C.E.; Petzold, A.; Bennett, J.L.; Berven, F.S.; Brundin, L.; Comabella, M.; Franciotta, D.; Frederiksen, J.L.; Fleming, J.O.; Furlan, R.; et al. A consensus protocol for the standardization of cerebrospinal fluid collection and biobanking. Neurology 2009, 73, 1914–1922. [Google Scholar] [CrossRef] [Green Version]
  24. Cummings, J.L.; Dubois, B.; Molinuevo, J.L.; Scheltens, P. International Work Group criteria for the diagnosis of Alzheimer disease. Med. Clin. N. Am. 2013, 97, 363–368. [Google Scholar] [CrossRef] [PubMed]
  25. Dubois, B.; Feldman, H.H.; Jacova, C.; Dekosky, S.T.; Barberger-Gateau, P.; Cummings, J.; Delacourte, A.; Galasko, D.; Gauthier, S.; Jicha, G.; et al. Research criteria for the diagnosis of Alzheimer’s disease: Revising the NINCDS-ADRDA criteria. Lancet Neurol. 2007, 6, 734–746. [Google Scholar] [CrossRef]
  26. Dubois, B.; Feldman, H.H.; Jacova, C.; Hampel, H.; Molinuevo, J.L.; Blennow, K.; DeKosky, S.T.; Gauthier, S.; Selkoe, D.; Bateman, R.; et al. Advancing research diagnostic criteria for Alzheimer’s disease: The IWG-2 criteria. Lancet Neurol. 2014, 13, 614–629. [Google Scholar] [CrossRef]
  27. Oddo, S.; Caccamo, A.; Kitazawa, M.; Tseng, B.P.; LaFerla, F.M. Amyloid deposition precedes tangle formation in a triple transgenic model of Alzheimer’s disease. Neurobiol. Aging 2003, 24, 1063–1070. [Google Scholar] [CrossRef] [PubMed]
  28. Braak, H.; Zetterberg, H.; Del Tredici, K.; Blennow, K. Intraneuronal tau aggregation precedes diffuse plaque deposition, but amyloid-β changes occur before increases of tau in cerebrospinal fluid. Acta Neuropathol. 2013, 126, 631–641. [Google Scholar] [CrossRef] [PubMed]
  29. Zetterberg, H.; Wahlund, L.O.; Blennow, K. Cerebrospinal fluid markers for prediction of Alzheimer’s disease. Neurosci. Lett. 2003, 352, 67–69. [Google Scholar] [CrossRef]
  30. Krebs, H.A. Chemical composition of blood plasma and serum. Annu. Rev. Biochem. 1950, 19, 409–430. [Google Scholar] [CrossRef]
  31. Jacobs, J.M.; Adkins, J.N.; Qian, W.-J.; Liu, T.; Shen, Y.; Camp, D.G.; Smith, R.D. Utilizing Human Blood Plasma for Proteomic Biomarker Discovery. J. Proteome Res. 2005, 4, 1073–1085. [Google Scholar] [CrossRef]
  32. Shen, Y.; Kim, J.; Strittmatter, E.F.; Jacobs, J.M.; Camp Ii, D.G.; Fang, R.; Tolié, N.; Moore, R.J.; Smith, R.D. Characterization of the human blood plasma proteome. Proteomics 2005, 5, 4034–4045. [Google Scholar] [CrossRef]
  33. Rai, A.J.; Gelfand, C.A.; Haywood, B.C.; Warunek, D.J.; Yi, J.; Schuchard, M.D.; Mehigh, R.J.; Cockrill, S.L.; Scott, G.B.; Tammen, H.; et al. HUPO Plasma Proteome Project specimen collection and handling: Towards the standardization of parameters for plasma proteome samples. Proteomics 2005, 5, 3262–3277. [Google Scholar] [CrossRef]
  34. Tuck, M.K.; Chan, D.W.; Chia, D.; Godwin, A.K.; Grizzle, W.E.; Krueger, K.E.; Rom, W.; Sanda, M.; Sorbara, L.; Stass, S.; et al. Standard Operating Procedures for Serum and Plasma Collection: Early Detection Research Network Consensus Statement Standard Operating Procedure Integration Working Group. J. Proteome Res. 2009, 8, 113–117. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Hampel, H.; O’Bryant, S.E.; Molinuevo, J.L.; Zetterberg, H.; Masters, C.L.; Lista, S.; Kiddle, S.J.; Batrla, R.; Blennow, K. Blood-based biomarkers for Alzheimer disease: Mapping the road to the clinic. Nat. Rev. Neurol. 2018, 14, 639–652. [Google Scholar] [CrossRef] [PubMed]
  36. Zetterberg, H. Blood-based biomarkers for Alzheimer’s disease-An update. J. Neurosci. Methods 2019, 319, 2–6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Milà-Alomà, M.; Suárez-Calvet, M.; Molinuevo, J.L. Latest advances in cerebrospinal fluid and blood biomarkers of Alzheimer’s disease. Ther. Adv. Neurol. Disord. 2019, 12, 1756286419888819. [Google Scholar] [CrossRef] [Green Version]
  38. Montagne, A.; Barnes, S.R.; Sweeney, M.D.; Halliday, M.R.; Sagare, A.P.; Zhao, Z.; Toga, A.W.; Jacobs, R.E.; Liu, C.Y.; Amezcua, L.; et al. Blood-brain barrier breakdown in the aging human hippocampus. Neuron 2015, 85, 296–302. [Google Scholar] [CrossRef] [Green Version]
  39. Zetterberg, H.; Blennow, K. From Cerebrospinal Fluid to Blood: The Third Wave of Fluid Biomarkers for Alzheimer’s Disease. J. Alzheimers Dis. 2018, 64, S271–S279. [Google Scholar] [CrossRef] [Green Version]
  40. Altuna-Azkargorta, M.; Mendioroz-Iriarte, M. Blood biomarkers in Alzheimer’s disease. Neurologia 2021, 36, 704–710. [Google Scholar] [CrossRef]
  41. Balasa, R.; Barcutean, L.; Balasa, A.; Motataianu, A.; Roman-Filip, C.; Manu, D. The action of TH17 cells on blood brain barrier in multiple sclerosis and experimental autoimmune encephalomyelitis. Hum. Immunol. 2020, 81, 237–243. [Google Scholar] [CrossRef]
  42. Hart, I.K.; Bone, I.; Hadley, D.M. Development of neurological problems after lumbar puncture. Br. Med. J. 1988, 296, 51–52. [Google Scholar] [CrossRef] [Green Version]
  43. Mattsson, N.; Zetterberg, H.; Hansson, O.; Andreasen, N.; Parnetti, L.; Jonsson, M.; Herukka, S.K.; van der Flier, W.M.; Blankenstein, M.A.; Ewers, M.; et al. CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 2009, 302, 385–393. [Google Scholar] [CrossRef]
  44. Mattsson, N.; Zetterberg, H. What is a certified reference material? Biomark. Med. 2012, 6, 369–370. [Google Scholar] [CrossRef] [PubMed]
  45. Kaur, D.; Behl, T.A.-O.; Sehgal, A.; Singh, S.; Sharma, N.; Bungau, S. Multifaceted Alzheimer’s Disease: Building a Roadmap for Advancement of Novel Therapies. Neurochem. Res. 2021, 46, 2832–2851. [Google Scholar] [CrossRef]
  46. Motter, R.; Vigo-Pelfrey, C.; Kholodenko, D.; Barbour, R.; Johnson-Wood, K.; Galasko, D.; Chang, L.; Miller, B.; Clark, C.; Green, R.; et al. Reduction of beta-amyloid peptide42 in the cerebrospinal fluid of patients with Alzheimer’s disease. Ann. Neurol. 1995, 38, 643–648. [Google Scholar] [CrossRef]
  47. Andreasen, N.; Hesse, C.; Davidsson, P.; Minthon, L.; Wallin, A.; Winblad, B.; Vanderstichele, H.; Vanmechelen, E.; Blennow, K. Cerebrospinal Fluid β-Amyloid(1-42) in Alzheimer Disease: Differences Between Early- and Late-Onset Alzheimer Disease and Stability During the Course of Disease. Arch. Neurol. 1999, 56, 673–680. [Google Scholar] [CrossRef] [Green Version]
  48. Lui, J.K.; Laws, S.M.; Li, Q.X.; Villemagne, V.L.; Ames, D.; Brown, B.; Bush, A.I.; De Ruyck, K.; Dromey, J.; Ellis, K.A.; et al. Plasma amyloid-beta as a biomarker in Alzheimer’s disease: The AIBL study of aging. J. Alzheimers Dis. 2010, 20, 1233–1242. [Google Scholar] [CrossRef] [PubMed]
  49. Lewczuk, P.; Kornhuber, J.; Vanmechelen, E.; Peters, O.; Heuser, I.; Maier, W.; Jessen, F.; Bürger, K.; Hampel, H.; Frölich, L.; et al. Amyloid beta peptides in plasma in early diagnosis of Alzheimer’s disease: A multicenter study with multiplexing. Exp. Neurol. 2010, 223, 366–370. [Google Scholar] [CrossRef] [PubMed]
  50. Janelidze, S.; Stomrud, E.; Palmqvist, S.; Zetterberg, H.; van Westen, D.; Jeromin, A.; Song, L.; Hanlon, D.; Tan Hehir, C.A.; Baker, D.; et al. Plasma β-amyloid in Alzheimer’s disease and vascular disease. Sci. Rep. 2016, 6, 26801. [Google Scholar] [CrossRef] [PubMed]
  51. Dumurgier, J.; Schraen, S.; Gabelle, A.; Vercruysse, O.; Bombois, S.; Laplanche, J.-L.; Peoc’h, K.; Sablonnière, B.; Kastanenka, K.V.; Delaby, C.; et al. Cerebrospinal fluid amyloid-β 42/40 ratio in clinical setting of memory centers: A multicentric study. Alzheimer’s Res. Ther. 2015, 7, 30. [Google Scholar] [CrossRef] [Green Version]
  52. Lewczuk, P.; Matzen, A.; Blennow, K.; Parnetti, L.; Molinuevo, J.L.; Eusebi, P.; Kornhuber, J.; Morris, J.C.; Fagan, A.M. Cerebrospinal Fluid Aβ 42/40 Corresponds Better than Aβ 42 to Amyloid PET in Alzheimer’s Disease. J. Alzheimers Dis. 2017, 55, 813–822. [Google Scholar] [CrossRef] [Green Version]
  53. Dorey, A.; Perret-Liaudet, A.; Tholance, Y.; Fourier, A.; Quadrio, I. Cerebrospinal Fluid Aβ40 Improves the Interpretation of Aβ42 Concentration for Diagnosing Alzheimer’s Disease. Front. Neurol. 2015, 6, 247. [Google Scholar] [CrossRef] [Green Version]
  54. Thijssen, E.H.; Verberk, I.M.W.; Vanbrabant, J.; Koelewijn, A.; Heijst, H.; Scheltens, P.; van der Flier, W.; Vanderstichele, H.; Stoops, E.; Teunissen, C.E. Highly specific and ultrasensitive plasma test detects Abeta(1–42) and Abeta(1–40) in Alzheimer’s disease. Sci. Rep. 2021, 11, 9736. [Google Scholar] [CrossRef] [PubMed]
  55. Janelidze, S.; Stomrud, E.; Smith, R.; Palmqvist, S.; Mattsson, N.; Airey, D.C.; Proctor, N.K.; Chai, X.; Shcherbinin, S.; Sims, J.R.; et al. Cerebrospinal fluid p-tau217 performs better than p-tau181 as a biomarker of Alzheimer’s disease. Nat. Commun. 2020, 11, 1683. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Ashton, N.J.; Benedet, A.L.; Pascoal, T.A.; Karikari, T.K.; Lantero-Rodriguez, J.; Brum, W.S.; Mathotaarachchi, S.; Therriault, J.; Savard, M.; Chamoun, M.; et al. Cerebrospinal fluid p-tau231 as an early indicator of emerging pathology in Alzheimer’s disease. EBioMedicine 2022, 76, 103836. [Google Scholar] [CrossRef] [PubMed]
  57. Barthélemy, N.R.; Bateman, R.J.; Hirtz, C.; Marin, P.; Becher, F.; Sato, C.; Gabelle, A.; Lehmann, S. Cerebrospinal fluid phospho-tau T217 outperforms T181 as a biomarker for the differential diagnosis of Alzheimer’s disease and PET amyloid-positive patient identification. Alzheimer’s Res. Ther. 2020, 12, 26. [Google Scholar] [CrossRef] [Green Version]
  58. Fossati, S.; Ramos Cejudo, J.; Debure, L.; Pirraglia, E.; Sone, J.Y.; Li, Y.; Chen, J.; Butler, T.; Zetterberg, H.; Blennow, K.; et al. Plasma tau complements CSF tau and P-tau in the diagnosis of Alzheimer’s disease. Alzheimers Dement. 2019, 11, 483–492. [Google Scholar] [CrossRef]
  59. Shen, X.-N.; Huang, Y.-Y.; Chen, S.-D.; Guo, Y.; Tan, L.; Dong, Q.; Yu, J.-T.; Weiner, M.W.; Aisen, P.; Petersen, R.; et al. Plasma phosphorylated-tau181 as a predictive biomarker for Alzheimer’s amyloid, tau and FDG PET status. Transl. Psychiatry 2021, 11, 585. [Google Scholar] [CrossRef]
  60. Barthélemy, N.R.; Horie, K.; Sato, C.; Bateman, R.J. Blood plasma phosphorylated-tau isoforms track CNS change in Alzheimer’s disease. J. Exp. Med. 2020, 217, e20200861. [Google Scholar] [CrossRef]
  61. Mattsson, N.; Insel, P.S.; Palmqvist, S.; Portelius, E.; Zetterberg, H.; Weiner, M.; Blennow, K.; Hansson, O.; Alzheimer’s Disease Neuroimaging Initiative. Cerebrospinal fluid tau, neurogranin, and neurofilament light in Alzheimer’s disease. EMBO Mol. Med. 2016, 8, 1184–1196. [Google Scholar] [CrossRef]
  62. Andreasen, N.; Vanmechelen, E.; Van de Voorde, A.; Davidsson, P.; Hesse, C.; Tarvonen, S.; Räihä, I.; Sourander, L.; Winblad, B.; Blennow, K. Cerebrospinal fluid tau protein as a biochemical marker for Alzheimer’s disease: A community based follow up study. J. Neurol. Neurosurg. Psychiatry 1998, 64, 298. [Google Scholar] [CrossRef]
  63. Sjögren, M.; Davidsson, P.; Tullberg, M.; Minthon, L.; Wallin, A.; Wikkelso, C.; Granérus, A.K.; Vanderstichele, H.; Vanmechelen, E.; Blennow, K. Both total and phosphorylated tau are increased in Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 2001, 70, 624. [Google Scholar] [CrossRef] [Green Version]
  64. Mattsson, N.; Zetterberg, H.; Janelidze, S.; Insel, P.S.; Andreasson, U.; Stomrud, E.; Palmqvist, S.; Baker, D.; Tan Hehir, C.A.; Jeromin, A.; et al. Plasma tau in Alzheimer disease. Neurology 2016, 87, 1827–1835. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Nam, E.; Lee, Y.B.; Moon, C.; Chang, K.A. Serum Tau Proteins as Potential Biomarkers for the Assessment of Alzheimer’s Disease Progression. Int. J. Mol. Sci. 2020, 21, 5007. [Google Scholar] [CrossRef] [PubMed]
  66. Casado, B.; Iadarola, P.; Pannell, L.K. Preparation of nasal secretions for proteome analysis. Methods Mol. Biol. 2008, 425, 77–87. [Google Scholar] [CrossRef] [PubMed]
  67. Yoo, S.J.; Son, G.; Bae, J.; Kim, S.Y.; Yoo, Y.K.; Park, D.; Baek, S.Y.; Chang, K.A.; Suh, Y.H.; Lee, Y.B.; et al. Longitudinal profiling of oligomeric Aβ in human nasal discharge reflecting cognitive decline in probable Alzheimer’s disease. Sci. Rep. 2020, 10, 11234. [Google Scholar] [CrossRef]
  68. Wingrave, W. XXXI. The Nature of Discharges and Douches. Ann. Otol. Rhinol. Laryngol. 1902, 11, 407–417. [Google Scholar] [CrossRef]
  69. Watelet, J.B.; Gevaert, P.; Holtappels, G.; Van Cauwenberge, P.; Bachert, C. Collection of nasal secretions for immunological analysis. Eur. Arch. Otorhinolaryngol. 2004, 261, 242–246. [Google Scholar] [CrossRef]
  70. Pipkorn, U.; Enerbäck, L. Nasal mucosal mast cells and histamine in hay fever. Effect of topical glucocorticoid treatment. Int. Arch. Allergy Appl. Immunol. 1987, 84, 123–128. [Google Scholar] [CrossRef] [PubMed]
  71. Liu, Z.; Kameshima, N.; Nanjo, T.; Shiino, A.; Kato, T.; Shimizu, S.; Shimizu, T.; Tanaka, S.; Miura, K.; Tooyama, I. Development of a High-Sensitivity Method for the Measurement of Human Nasal Aβ42, Tau, and Phosphorylated Tau. J. Alzheimers Dis. 2018, 62, 737–744. [Google Scholar] [CrossRef] [Green Version]
  72. Van Haeringen, N.J. Clinical biochemistry of tears. Surv. Ophthalmol. 1981, 26, 84–96. [Google Scholar] [CrossRef]
  73. Stuchell, R.N.; Feldman, J.J.; Farris, R.L.; Mandel, I.D. The effect of collection technique on tear composition. Investig. Ophthalmol. Vis. Sci. 1984, 25, 374–377. [Google Scholar]
  74. Saijyothi, A.V.; Angayarkanni, N.; Syama, C.; Utpal, T.; Shweta, A.; Bhaskar, S.; Geetha, I.K.; Vinay, P.S.; Thennarasu, M.; Sivakumar, R.M.; et al. Two dimensional electrophoretic analysis of human tears: Collection method in dry eye syndrome. Electrophoresis 2010, 31, 3420–3427. [Google Scholar] [CrossRef] [PubMed]
  75. Navazesh, M. Methods for collecting saliva. Ann. N. Y. Acad. Sci. 1993, 694, 72–77. [Google Scholar] [CrossRef] [PubMed]
  76. White, K.D. Salivation: A review and experimental investigation of major techniques. Psychophysiology 1977, 14, 203–212. [Google Scholar] [CrossRef] [PubMed]
  77. Rohleder, N.; Nater, U.M. Determinants of salivary alpha-amylase in humans and methodological considerations. Psychoneuroendocrinology 2009, 34, 469–485. [Google Scholar] [CrossRef]
  78. Mischak, H.; Kolch, W.; Aivaliotis, M.; Bouyssié, D.; Court, M.; Dihazi, H.; Dihazi, G.H.; Franke, J.; Garin, J.; Gonzalez de Peredo, A.; et al. Comprehensive human urine standards for comparability and standardization in clinical proteome analysis. Proteom.-Clin. Appl. 2010, 4, 464–478. [Google Scholar] [CrossRef] [Green Version]
  79. Yamamoto, T. The 4th Human Kidney and Urine Proteome Project (HKUPP) workshop. 26 September 2009, Toronto, Canada. Proteomics 2010, 10, 2069–2070. [Google Scholar] [CrossRef]
  80. Yamamoto, T.; Langham, R.G.; Ronco, P.; Knepper, M.A.; Thongboonkerd, V. Towards standard protocols and guidelines for urine proteomics: A report on the Human Kidney and Urine Proteome Project (HKUPP) symposium and workshop, 6 October 2007, Seoul, Korea and 1 November 2007, San Francisco, CA, USA. Proteomics 2008, 8, 2156–2159. [Google Scholar] [CrossRef]
  81. Zürbig, P.; Dihazi, H.; Metzger, J.; Thongboonkerd, V.; Vlahou, A. Urine proteomics in kidney and urogenital diseases: Moving towards clinical applications. Proteom.-Clin. Appl. 2011, 5, 256–268. [Google Scholar] [CrossRef]
  82. Murphy, C. Olfactory and other sensory impairments in Alzheimer disease. Nat. Rev. Neurol. 2019, 15, 11–24. [Google Scholar] [CrossRef]
  83. Waldton, S. Clinical observations of impaired cranial nerve function in senile dementia. Acta Psychiatr. Scand. 1974, 50, 539–547. [Google Scholar] [CrossRef]
  84. Doty, R.L.; Reyes, P.F.; Gregor, T. Presence of both odor identification and detection deficits in Alzheimer’s disease. Brain Res. Bull. 1987, 18, 597–600. [Google Scholar] [CrossRef]
  85. Doty, R.L.; Hawkes, C.H.; Good, K.P.; Duda, J.E. Odor Perception and Neuropathology in Neurodegenerative Diseases and Schizophrenia. In Handbook of Olfaction and Gustation, 3rd ed.; Wiley Online Books, Doty, R.L., Eds.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2015; pp. 403–452. [Google Scholar]
  86. Son, G.; Jahanshahi, A.; Yoo, S.J.; Boonstra, J.T.; Hopkins, D.A.; Steinbusch, H.W.M.; Moon, C. Olfactory neuropathology in Alzheimer’s disease: A sign of ongoing neurodegeneration. BMB Rep. 2021, 54, 295–304. [Google Scholar] [CrossRef] [PubMed]
  87. Kim, J.Y.; Rasheed, A.; Yoo, S.J.; Kim, S.Y.; Cho, B.; Son, G.; Yu, S.W.; Chang, K.A.; Suh, Y.H.; Moon, C. Distinct amyloid precursor protein processing machineries of the olfactory system. Biochem. Biophys. Res. Commun. 2018, 495, 533–538. [Google Scholar] [CrossRef]
  88. Wesson, D.W.; Levy, E.; Nixon, R.A.; Wilson, D.A. Olfactory dysfunction correlates with amyloid-beta burden in an Alzheimer’s disease mouse model. J. Neurosci. 2010, 30, 505–514. [Google Scholar] [CrossRef]
  89. Yoo, S.J.; Lee, J.H.; Kim, S.Y.; Son, G.; Kim, J.Y.; Cho, B.; Yu, S.W.; Chang, K.A.; Suh, Y.H.; Moon, C. Differential spatial expression of peripheral olfactory neuron-derived BACE1 induces olfactory impairment by region-specific accumulation of beta-amyloid oligomer. Cell Death Dis. 2017, 8, e2977. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  90. Son, G.; Steinbusch, H.W.; López-Iglesias, C.; Moon, C.; Jahanshahi, A. Severe histomorphological alterations in post-mortem olfactory glomeruli in Alzheimer’s disease. Brain Pathol. 2022, 32, e13033. [Google Scholar] [CrossRef] [PubMed]
  91. Braak, H.; Braak, E. Demonstration of amyloid deposits and neurofibrillary changes in whole brain sections. Brain Pathol. 1991, 1, 213–216. [Google Scholar] [CrossRef]
  92. Masurkar, A.V.; Devanand, D.P. Olfactory Dysfunction in the Elderly: Basic Circuitry and Alterations with Normal Aging and Alzheimer’s Disease. Curr. Geriatr. Rep. 2014, 3, 91–100. [Google Scholar] [CrossRef]
  93. Attems, J.; Lintner, F.; Jellinger, K.A. Olfactory involvement in aging and Alzheimer’s disease: An autopsy study. J. Alzheimers Dis. 2005, 7, 149–157. [Google Scholar] [CrossRef]
  94. Crino, P.B.; Martin, J.A.; Hill, W.D.; Greenberg, B.; Lee, V.M.; Trojanowski, J.Q. Beta-Amyloid peptide and amyloid precursor proteins in olfactory mucosa of patients with Alzheimer’s disease, Parkinson’s disease, and Down syndrome. Ann. Otol. Rhinol. Laryngol. 1995, 104, 655–661. [Google Scholar] [CrossRef]
  95. Hock, C.; Golombowski, S.; Muller-Spahn, F.; Peschel, O.; Riederer, A.; Probst, A.; Mandelkow, E.; Unger, J. Histological markers in nasal mucosa of patients with Alzheimer’s disease. Eur. Neurol. 1998, 40, 31–36. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  96. Attems, J.; Walker, L.; Jellinger, K.A. Olfactory bulb involvement in neurodegenerative diseases. Acta Neuropathol. 2014, 127, 459–475. [Google Scholar] [CrossRef] [PubMed]
  97. Franks, K.H.; Chuah, M.I.; King, A.E.; Vickers, J.C. Connectivity of Pathology: The Olfactory System as a Model for Network-Driven Mechanisms of Alzheimer’s Disease Pathogenesis. Front. Aging Neurosci. 2015, 7, 234. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  98. Rosenfeld, R.M.; Piccirillo, J.F.; Chandrasekhar, S.S.; Brook, I.; Kumar, K.A.; Kramper, M.; Orlandi, R.R.; Palmer, J.N.; Patel, Z.M.; Peters, A.; et al. Clinical practice guideline (update): Adult Sinusitis Executive Summary. Otolaryngol. Head Neck Surg. 2015, 152, 598–609. [Google Scholar] [CrossRef] [PubMed]
  99. Hermelingmeier, K.E.; Weber, R.K.; Hellmich, M.; Heubach, C.P.; Mösges, R. Nasal irrigation as an adjunctive treatment in allergic rhinitis: A systematic review and meta-analysis. Am. J. Rhinol. Allergy 2012, 26, e119–e125. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  100. Klimek, L.; Rasp, G. Norm values for eosinophil cationic protein in nasal secretions: Influence of specimen collection. Clin. Exp. Allergy 1999, 29, 367–374. [Google Scholar] [CrossRef]
  101. World Health, O. Laboratory Testing for Coronavirus Disease (COVID-19) in Suspected Human Cases: Interim Guidance, 19 March 2020; World Health Organization: Geneva, Switzerland, 2020. [Google Scholar]
  102. Struble, R.G.; Clark, H.B. Olfactory bulb lesions in Alzheimer’s disease. Neurobiol. Aging 1992, 13, 469–473. [Google Scholar] [CrossRef]
  103. Arnold, S.E.; Lee, E.B.; Moberg, P.J.; Stutzbach, L.; Kazi, H.; Han, L.Y.; Lee, V.M.; Trojanowski, J.Q. Olfactory epithelium amyloid-beta and paired helical filament-tau pathology in Alzheimer disease. Ann. Neurol. 2010, 67, 462–469. [Google Scholar] [CrossRef]
  104. Ayala-Grosso, C.A.; Pieruzzini, R.; Diaz-Solano, D.; Wittig, O.; Abrante, L.; Vargas, L.; Cardier, J. Amyloid-aβ Peptide in olfactory mucosa and mesenchymal stromal cells of mild cognitive impairment and Alzheimer’s disease patients. Brain Pathol. 2015, 25, 136–145. [Google Scholar] [CrossRef]
  105. Kim, Y.H.; Lee, S.M.; Cho, S.; Kang, J.H.; Minn, Y.K.; Park, H.; Choi, S.H. Amyloid beta in nasal secretions may be a potential biomarker of Alzheimer’s disease. Sci. Rep. 2019, 9, 4966. [Google Scholar] [CrossRef]
  106. Lee, J.H.; Goedert, M.; Hill, W.D.; Lee, V.M.; Trojanowski, J.Q. Tau proteins are abnormally expressed in olfactory epithelium of Alzheimer patients and developmentally regulated in human fetal spinal cord. Exp. Neurol. 1993, 121, 93–105. [Google Scholar] [CrossRef] [PubMed]
  107. Attems, J.; Jellinger, K.A. Olfactory tau pathology in Alzheimer disease and mild cognitive impairment. Clin. Neuropathol. 2006, 25, 265–271. [Google Scholar] [PubMed]
  108. Passali, G.C.; Politi, L.; Crisanti, A.; Loglisci, M.; Anzivino, R.; Passali, D. Tau Protein Detection in Anosmic Alzheimer’s Disease Patient’s Nasal Secretions. Chemosens. Percept. 2015, 8, 201–206. [Google Scholar] [CrossRef]
  109. Moon, J.; Lee, S.T.; Kong, I.G.; Byun, J.I.; Sunwoo, J.S.; Shin, J.W.; Shim, J.Y.; Park, J.H.; Jeon, D.; Jung, K.H.; et al. Early diagnosis of Alzheimer’s disease from elevated olfactory mucosal miR-206 level. Sci. Rep. 2016, 6, 20364. [Google Scholar] [CrossRef] [PubMed]
  110. Yoshikawa, K.; Wang, H.; Jaen, C.; Haneoka, M.; Saito, N.; Nakamura, J.; Adappa, N.D.; Cohen, N.A.; Dalton, P. The human olfactory cleft mucus proteome and its age-related changes. Sci. Rep. 2018, 8, 17170. [Google Scholar] [CrossRef] [PubMed]
  111. Murray, H.C.; Dieriks, B.V.; Swanson, M.E.V.; Anekal, P.V.; Turner, C.; Faull, R.L.M.; Belluscio, L.; Koretsky, A.; Curtis, M.A. The unfolded protein response is activated in the olfactory system in Alzheimer’s disease. Acta Neuropathol. Commun. 2020, 8, 109. [Google Scholar] [CrossRef]
  112. Carmona-Abellan, M.; Martinez-Valbuena, I.; Marcilla, I.; DiCaudo, C.; Gil, I.; Nunez, J.; Luquin, M.R. Microglia is associated with p-Tau aggregates in the olfactory bulb of patients with neurodegenerative diseases. Neurol. Sci. 2021, 42, 1473–1482. [Google Scholar] [CrossRef]
  113. Zhou, L.; Zhao, S.Z.; Koh, S.K.; Chen, L.; Vaz, C.; Tanavde, V.; Li, X.R.; Beuerman, R.W. In-depth analysis of the human tear proteome. J. Proteom. 2012, 75, 3877–3885. [Google Scholar] [CrossRef]
  114. Herber, S.; Grus, F.H.; Sabuncuo, P.; Augustin, A.J. Two-dimensional analysis of tear protein patterns of diabetic patients. Electrophoresis 2001, 22, 1838–1844. [Google Scholar] [CrossRef]
  115. Comoglu, S.S.; Guven, H.; Acar, M.; Ozturk, G.; Kocer, B. Tear levels of tumor necrosis factor-alpha in patients with Parkinson’s disease. Neurosci. Lett. 2013, 553, 63–67. [Google Scholar] [CrossRef]
  116. Kapnisis, K.; Doormaal, M.V.; Ross Ethier, C. Modeling aqueous humor collection from the human eye. J. Biomech. 2009, 42, 2454–2457. [Google Scholar] [CrossRef]
  117. Yamane, K.; Minamoto, A.; Yamashita, H.; Takamura, H.; Miyamoto-Myoken, Y.; Yoshizato, K.; Nabetani, T.; Tsugita, A.; Mishima, H.K. Proteome analysis of human vitreous proteins. Mol. Cell. Proteom. 2003, 2, 1177–1187. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  118. Gijs, M.; Nuijts, R.M.; Ramakers, I.; Verhey, F.; Webers, C.A.B. Differences in tear protein biomarkers between patients with Alzheimer’s disease and controls. Investig. Ophth. Vis. Sci. 2019, 60, 1744. [Google Scholar]
  119. Gijs, M.; Ramakers, I.H.G.B.; Visser, P.J.; Verhey, F.R.J.; van de Waarenburg, M.P.H.; Schalkwijk, C.G.; Nuijts, R.M.M.A.; Webers, C.A.B. Association of tear fluid amyloid and tau levels with disease severity and neurodegeneration. Sci. Rep. 2021, 11, 22675. [Google Scholar] [CrossRef] [PubMed]
  120. Wang, Y.R.; Chuang, H.C.; Tripathi, A.; Wang, Y.L.; Ko, M.L.; Chuang, C.C.; Chen, J.C. High-Sensitivity and Trace-Amount Specimen Electrochemical Sensors for Exploring the Levels of beta-Amyloid in Human Blood and Tears. Anal. Chem. 2021, 93, 8099–8106. [Google Scholar] [CrossRef]
  121. Kallo, G.; Emri, M.; Varga, Z.; Ujhelyi, B.; Tozser, J.; Csutak, A.; Csosz, E. Changes in the Chemical Barrier Composition of Tears in Alzheimer’s Disease Reveal Potential Tear Diagnostic Biomarkers. PLoS ONE 2016, 11, e0158000. [Google Scholar] [CrossRef] [Green Version]
  122. Kenny, A.; Jimenez-Mateos, E.M.; Zea-Sevilla, M.A.; Rabano, A.; Gili-Manzanaro, P.; Prehn, J.H.M.; Henshall, D.C.; Avila, J.; Engel, T.; Hernandez, F. Proteins and microRNAs are differentially expressed in tear fluid from patients with Alzheimer’s disease. Sci. Rep. 2019, 9, 15437. [Google Scholar] [CrossRef] [Green Version]
  123. Schenkels, L.C.; Veerman, E.C.; Nieuw Amerongen, A.V. Biochemical composition of human saliva in relation to other mucosal fluids. Crit. Rev. Oral Biol. Med. 1995, 6, 161–175. [Google Scholar] [CrossRef] [Green Version]
  124. Lamy, E.; Mau, M. Saliva proteomics as an emerging, non-invasive tool to study livestock physiology, nutrition and diseases. J. Proteom. 2012, 75, 4251–4258. [Google Scholar] [CrossRef]
  125. Vitorino, R.; Guedes, S.; Manadas, B.; Ferreira, R.; Amado, F. Toward a standardized saliva proteome analysis methodology. J. Proteom. 2012, 75, 5140–5165. [Google Scholar] [CrossRef]
  126. Teresi, L.M.; Kolin, E.; Lufkin, R.B.; Hanafee, W.N. MR imaging of the intraparotid facial nerve: Normal anatomy and pathology. AJR Am. J. Roentgenol. 1987, 148, 995–1000. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  127. Shi, M.; Sui, Y.T.; Peskind, E.R.; Li, G.; Hwang, H.; Devic, I.; Ginghina, C.; Edgar, J.S.; Pan, C.; Goodlett, D.R.; et al. Salivary Tau Species are Potential Biomarkers of Alzheimer’s Disease. J. Alzheimers Dis. 2011, 27, 299–305. [Google Scholar] [CrossRef] [PubMed]
  128. Bermejo-Pareja, F.; Antequera, D.; Vargas, T.; Molina, J.A.; Carro, E. Saliva levels of Abeta1-42 as potential biomarker of Alzheimer’s disease: A pilot study. BMC Neurol. 2010, 10, 108. [Google Scholar] [CrossRef] [Green Version]
  129. Lee, M.; Guo, J.P.; Kennedy, K.; McGeer, E.G.; McGeer, P.L. A Method for Diagnosing Alzheimer’s Disease Based on Salivary Amyloid-beta Protein 42 Levels. J. Alzheimers Dis. 2017, 55, 1175–1182. [Google Scholar] [CrossRef] [PubMed]
  130. Sabbagh, M.N.; Shi, J.; Lee, M.; Arnold, L.; Al-Hasan, Y.; Heim, J.; McGeer, P. Salivary beta amyloid protein levels are detectable and differentiate patients with Alzheimer’s disease dementia from normal controls: Preliminary findings. BMC Neurol. 2018, 18, 155. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  131. McGeer, P.L.; Guo, J.P.; Lee, M.; Kennedy, K.; McGeer, E.G. Alzheimer’s Disease Can Be Spared by Nonsteroidal Anti-Inflammatory Drugs. J. Alzheimers Dis. 2018, 62, 1219–1222. [Google Scholar] [CrossRef] [Green Version]
  132. Kim, C.B.; Choi, Y.Y.; Song, W.K.; Song, K.B. Antibody-based magnetic nanoparticle immunoassay for quantification of Alzheimer’s disease pathogenic factor. J. Biomed. Opt. 2014, 19, 051205. [Google Scholar] [CrossRef]
  133. Tvarijonaviciute, A.; Zamora, C.; Ceron, J.J.; Bravo-Cantero, A.F.; Pardo-Marin, L.; Valverde, S.; Lopez-Jornet, P. Salivary biomarkers in Alzheimer’s disease. Clin. Oral Investig. 2020, 24, 3437–3444. [Google Scholar] [CrossRef]
  134. Pekeles, H.; Qureshi, H.Y.; Paudel, H.K.; Schipper, H.M.; Gornistky, M.; Chertkow, H. Development and validation of a salivary tau biomarker in Alzheimer’s disease. Alzheimers Dement. 2019, 11, 53–60. [Google Scholar] [CrossRef]
  135. Ashton, N.J.; Ide, M.; Scholl, M.; Blennow, K.; Lovestone, S.; Hye, A.; Zetterberg, H. No association of salivary total tau concentration with Alzheimer’s disease. Neurobiol. Aging 2018, 70, 125–127. [Google Scholar] [CrossRef] [Green Version]
  136. Kruzel, M.L.; Zimecki, M.; Actor, J.K. Lactoferrin in a Context of Inflammation-Induced Pathology. Front. Immunol. 2017, 8, 1438. [Google Scholar] [CrossRef] [PubMed]
  137. Mayeur, S.; Spahis, S.; Pouliot, Y.; Levy, E. Lactoferrin, a Pleiotropic Protein in Health and Disease. Antioxid. Redox Signal 2016, 24, 813–836. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  138. Carro, E.; Bartolomé, F.; Bermejo-Pareja, F.; Villarejo-Galende, A.; Molina, J.A.; Ortiz, P.; Calero, M.; Rabano, A.; Cantero, J.L.; Orive, G. Early diagnosis of mild cognitive impairment and Alzheimer’s disease based on salivary lactoferrin. Alzheimers Dement. 2017, 8, 131–138. [Google Scholar] [CrossRef] [PubMed]
  139. Gonzalez-Sanchez, M.; Bartolome, F.; Antequera, D.; Puertas-Martin, V.; Gonzalez, P.; Gomez-Grande, A.; Llamas-Velasco, S.; Herrero-San Martin, A.; Perez-Martinez, D.; Villarejo-Galende, A.; et al. Decreased salivary lactoferrin levels are specific to Alzheimer’s disease. EBioMedicine 2020, 57, 102834. [Google Scholar] [CrossRef]
  140. Whitehouse, P.J.; Price, D.L.; Clark, A.W.; Coyle, J.T.; DeLong, M.R. Alzheimer disease: Evidence for selective loss of cholinergic neurons in the nucleus basalis. Ann. Neurol. 1981, 10, 122–126. [Google Scholar] [CrossRef]
  141. Jann, M.W. Rivastigmine, a new-generation cholinesterase inhibitor for the treatment of Alzheimer’s disease. Pharmacotherapy 2000, 20, 1–12. [Google Scholar] [CrossRef] [PubMed]
  142. Rinne, J.O.; Kaasinen, V.; Jarvenpaa, T.; Nagren, K.; Roivainen, A.; Yu, M.; Oikonen, V.; Kurki, T. Brain acetylcholinesterase activity in mild cognitive impairment and early Alzheimer’s disease. J. Neurol. Neurosurg. Psychiatry 2003, 74, 113–115. [Google Scholar] [CrossRef]
  143. Sayer, R.; Law, E.; Connelly, P.J.; Breen, K.C. Association of a salivary acetylcholinesterase with Alzheimer’s disease and response to cholinesterase inhibitors. Clin. Biochem. 2004, 37, 98–104. [Google Scholar] [CrossRef]
  144. Bakhtiari, S.; Moghadam, N.B.; Ehsani, M.; Mortazavi, H.; Sabour, S.; Bakhshi, M. Can Salivary Acetylcholinesterase be a Diagnostic Biomarker for Alzheimer? J. Clin. Diagn. Res. 2017, 11, ZC58–ZC60. [Google Scholar] [CrossRef]
  145. Boston, P.F.; Gopalkaje, K.; Manning, L.; Middleton, L.; Loxley, M. Developing a simple laboratory test for Alzheimer’s disease: Measuring acetylcholinesterase in saliva—A pilot study. Int. J. Geriatr. Psychiatry 2008, 23, 439–440. [Google Scholar] [CrossRef]
  146. Suzuki, Y.J.; Carini, M.; Butterfield, D.A. Protein Carbonylation. Antioxid Redox. Signal 2009, 12, 323–325. [Google Scholar] [CrossRef] [PubMed]
  147. Aksenov, M.Y.; Aksenova, M.V.; Butterfield, D.A.; Geddes, J.W.; Markesbery, W.R. Protein oxidation in the brain in Alzheimer’s disease. Neuroscience 2001, 103, 373–383. [Google Scholar] [CrossRef]
  148. Su, H.; Gornitsky, M.; Geng, G.; Velly, A.M.; Chertkow, H.; Schipper, H.M. Diurnal variations in salivary protein carbonyl levels in normal and cognitively impaired human subjects. Age 2008, 30, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  149. Yilmaz, A.; Geddes, T.; Han, B.; Bahado-Singh, R.O.; Wilson, G.D.; Imam, K.; Maddens, M.; Graham, S.F. Diagnostic Biomarkers of Alzheimer’s Disease as Identified in Saliva using 1H NMR-Based Metabolomics. J. Alzheimers Dis. 2017, 58, 355–359. [Google Scholar] [CrossRef] [PubMed]
  150. Wu, J.Q.; Gao, Y.H. Physiological conditions can be reflected in human urine proteome and metabolome. Expert Rev. Proteomic 2015, 12, 623–636. [Google Scholar] [CrossRef]
  151. Haubitz, M.; Wittke, S.; Weissinger, E.M.; Walden, M.; Rupprecht, H.D.; Floege, J.; Haller, H.; Mischak, H. Urine protein patterns can serve as diagnostic tools in patients with IgA nephropathy. Kidney Int. 2005, 67, 2313–2320. [Google Scholar] [CrossRef] [Green Version]
  152. Nam, D.; Lee, J.Y.; Lee, M.; Kim, J.; Seol, W.; Son, I.; Ho, D.H. Detection and Assessment of alpha-Synuclein Oligomers in the Urine of Parkinson’s Disease Patients. J. Parkinson’s Dis. 2020, 10, 981–991. [Google Scholar] [CrossRef]
  153. Wang, C.; Cui, Y.J.; Yang, J.C.; Zhang, J.R.; Yuan, D.C.; Wei, Y.; Li, Y.M.; Duo, Y.L.; Li, S.J.; Zhu, W.Y.; et al. Combining serum and urine biomarkers in the early diagnosis of mild cognitive impairment that evolves into Alzheimer’s disease in patients with the apolipoprotein E epsilon 4 genotype. Biomarkers 2015, 20, 84–88. [Google Scholar] [CrossRef]
  154. Luan, H.; Liu, L.F.; Meng, N.; Tang, Z.; Chua, K.K.; Chen, L.L.; Song, J.X.; Mok, V.C.; Xie, L.X.; Li, M.; et al. LC-MS-based urinary metabolite signatures in idiopathic Parkinson’s disease. J. Proteome Res. 2015, 14, 467–478. [Google Scholar] [CrossRef]
  155. Takata, M.; Nakashima, M.; Takehara, T.; Baba, H.; Machida, K.; Akitake, Y.; Ono, K.; Hosokawa, M.; Takahashi, M. Detection of amyloid beta protein in the urine of Alzheimer’s disease patients and healthy individuals. Neurosci. Lett. 2008, 435, 126–130. [Google Scholar] [CrossRef]
  156. Wongta, A.; Hongsibsong, S.; Chantara, S.; Pattarawarapan, M.; Sapbamrer, R.; Sringarm, K.; Xu, Z.L.; Wang, H. Development of an Immunoassay for the Detection of Amyloid Beta 1-42 and Its Application in Urine Samples. J. Immunol. Res. 2020, 2020, 8821181. [Google Scholar] [CrossRef] [PubMed]
  157. Yao, F.; Hong, X.; Li, S.; Zhang, Y.; Zhao, Q.; Du, W.; Wang, Y.; Ni, J. Urine-Based Biomarkers for Alzheimer’s Disease Identified Through Coupling Computational and Experimental Methods. J. Alzheimers Dis. 2018, 65, 421–431. [Google Scholar] [CrossRef] [PubMed]
  158. Ku, B.D.; Kim, H.; Kim, Y.K.; Ryu, H.U. Comparison of Urinary Alzheimer-Associated Neural Thread Protein (AD7c-NTP) Levels Between Patients With Amnestic and Nonamnestic Mild Cognitive Impairment. Am. J. Alzheimers Dis. 2020, 35, 1533317519880369. [Google Scholar] [CrossRef] [PubMed]
  159. Watanabe, Y.; Hirao, Y.; Kasuga, K.; Tokutake, T.; Semizu, Y.; Kitamura, K.; Ikeuchi, T.; Nakamura, K.; Yamamoto, T. Molecular Network Analysis of the Urinary Proteome of Alzheimer’s Disease Patients. Dement. Geriatr. Cogn. Dis. Extra 2019, 9, 53–65. [Google Scholar] [CrossRef]
  160. Watanabe, Y.; Hirao, Y.; Kasuga, K.; Tokutake, T.; Kitamura, K.; Niida, S.; Ikeuchi, T.; Nakamura, K.; Yamamoto, T. Urinary Apolipoprotein C3 Is a Potential Biomarker for Alzheimer’s Disease. Dement. Geriatr. Cogn. Dis. Extra 2020, 10, 94–104. [Google Scholar] [CrossRef]
  161. Nicholson, J.K.; Holmes, E.; Kinross, J.; Burcelin, R.; Gibson, G.; Jia, W.; Pettersson, S. Host-gut microbiota metabolic interactions. Science 2012, 336, 1262–1267. [Google Scholar] [CrossRef] [Green Version]
  162. Vogt, N.M.; Kerby, R.L.; Dill-McFarland, K.A.; Harding, S.J.; Merluzzi, A.P.; Johnson, S.C.; Carlsson, C.M.; Asthana, S.; Zetterberg, H.; Blennow, K.; et al. Gut microbiome alterations in Alzheimer’s disease. Sci. Rep. 2017, 7, 13537. [Google Scholar] [CrossRef]
  163. Giau, V.V.; Wu, S.Y.; Jamerlan, A.; An, S.S.A.; Kim, S.Y.; Hulme, J. Gut Microbiota and Their Neuroinflammatory Implications in Alzheimer’s Disease. Nutrients 2018, 10, 1765. [Google Scholar] [CrossRef] [Green Version]
  164. Kurbatova, N.; Garg, M.; Whiley, L.; Chekmeneva, E.; Jimenez, B.; Gomez-Romero, M.; Pearce, J.; Kimhofer, T.; D’Hondt, E.; Soininen, H.; et al. Urinary metabolic phenotyping for Alzheimer’s disease. Sci. Rep. 2020, 10, 21745. [Google Scholar] [CrossRef]
  165. Pena-Bautista, C.; Vigor, C.; Galano, J.M.; Oger, C.; Durand, T.; Ferrer, I.; Cuevas, A.; Lopez-Cuevas, R.; Baquero, M.; Lopez-Nogueroles, M.; et al. New screening approach for Alzheimer’s disease risk assessment from urine lipid peroxidation compounds. Sci. Rep. 2019, 9, 14244. [Google Scholar] [CrossRef]
  166. Mall, C.; Rocke, D.M.; Durbin-Johnson, B.; Weiss, R.H. Stability of miRNA in human urine supports its biomarker potential. Biomark. Med. 2013, 7, 623–631. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  167. Seol, W.; Kim, H.; Son, I. Urinary Biomarkers for Neurodegenerative Diseases. Exp. Neurobiol. 2020, 29, 325–333. [Google Scholar] [CrossRef] [PubMed]
  168. Cheng, L.; Sun, X.; Scicluna, B.J.; Coleman, B.M.; Hill, A.F. Characterization and deep sequencing analysis of exosomal and non-exosomal miRNA in human urine. Kidney Int. 2014, 86, 433–444. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  169. Lu, H.; Zhu, X.C.; Jiang, T.; Yu, J.T.; Tan, L. Body fluid biomarkers in Alzheimer’s disease. Ann. Transl. Med. 2015, 3, 70. [Google Scholar] [CrossRef]
  170. Janigro, D.; Bailey, D.M.; Lehmann, S.; Badaut, J.; O’Flynn, R.; Hirtz, C.; Marchi, N. Peripheral Blood and Salivary Biomarkers of Blood–Brain Barrier Permeability and Neuronal Damage: Clinical and Applied Concepts. Front. Neurol. 2021, 11, 577312. [Google Scholar] [CrossRef] [PubMed]
  171. Frölich, L.; Peters, O.; Lewczuk, P.; Gruber, O.; Teipel, S.J.; Gertz, H.J.; Jahn, H.; Jessen, F.; Kurz, A.; Luckhaus, C.; et al. Incremental value of biomarker combinations to predict progression of mild cognitive impairment to Alzheimer’s dementia. Alz Res. Ther. 2017, 9, 84. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  172. Walhovd, K.B.; Fjell, A.M.; Brewer, J.; McEvoy, L.K.; Fennema-Notestine, C.; Hagler, D.J.; Jennings, R.G.; Karow, D.; Dale, A.M.; Alzheimer’s Disease Neuroimaging Initiative. Combining MR Imaging, Positron-Emission Tomography, and CSF Biomarkers in the Diagnosis and Prognosis of Alzheimer Disease. Am. J. Neuroradiol. 2010, 31, 347. [Google Scholar] [CrossRef] [Green Version]
Figure 1. Body fluids for the identification of potential biomarkers for AD. Sample type, organ, sampling method (from above).
Figure 1. Body fluids for the identification of potential biomarkers for AD. Sample type, organ, sampling method (from above).
Pharmaceutics 14 01532 g001
Table 1. Clinical Alzheimer’s disease stages-I.
Table 1. Clinical Alzheimer’s disease stages-I.
NINDS-ADRDA+DSM-III a
Disease Stage
Dementia
Diagnostic SubgroupsNoneUnlikelyPossible AD bProbable ADDefinite AD
Other commentsAbsence of other diseases capable of producing a dementia syndrome
Onset ageSuddenAtypical40~90 years40~90 years
Neuro-psychologicaltest MMSE c, Blessed dementia scale, etc. ?+/−++
Neuroimage CT d?+/−++
Histology Microscopic examination of brain tissue -Confirmed by autopsy or biopsy
Others Other signs Focal neurologic signs, seizures, or gait disturbanceCognitive impairments have to be present in two or more areas of cognitionCognitive impairments have to be present in two or more areas of cognition
a NINDS-ADRDA+DSM: Alzheimer’s Criteria; the National Institute of Neurological Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association + Diagnostic and Statistical Manual of Mental Disorders; b AD: Alzheimer’s Disease; c MMSE: Mini-Mental State Examination; d CT: Computed Tomography.
Table 2. Clinical Alzheimer’s disease stages-II.
Table 2. Clinical Alzheimer’s disease stages-II.
NIA-AA+DSM-V a
Disease Stage
PreclinicalMCI bAD c DementiaNon-AD Dementia
Diagnostic SubgroupsNonePreclinical ADPossible ADMild
AD
Moderate ADSevere ADOND d
Neuro-psychological testMMSE e (30~0)30~2524~2019~1312~+/−
CDR f v1-1993
(0~3)
00.5
questionable
123
CDR v2-1997
(0~3)
00.5
questionable
1
mild CI h
2
moderate CI
3
severe CI
GDS g
(1~7)
12
very mildCI
3
mildCI
4
moderateCI
5, 6
moderately severe CI
6, 7
very severe CI
Neuro-imagingamyloid-PET i+++++
tau-PET++++/−
FDG j-PET+/−+/−+/-+/−+/−
Structural MRI+/−+/−+/-+/−+/−
CSF k-biomarkerCSF Aβ42+++++n/a
CSF P-tau+++n/a
CSF T-tau+/−+/−+/−+/−n/a
a NIA-AA+DSM: Alzheimer’s Criteria; b MCI: Alzheimer’s Disease; c AD: Alzheimer’s Disease; d OND: Other Neurodegenerative Disease; e MMSE: Mini-Mental State Examination; f CDR: Clinical Dementia Rate; g GDS: Global Deterioration Scale; h CI: Cognitive Impairment; i PET: Positron emission tomography; j FDG: Fluorodeoxyglucose; k CSF: Cerebrospinal Fluid.
Table 3. Characteristics of ideal body fluid biomarker.
Table 3. Characteristics of ideal body fluid biomarker.
TypeCharacteristicsGoals
DetectabilityDisease specificityHigh
Biomarker sensitivityHigh
AccuracyHigh
AccessibilityRepeatabilityHigh
InvasivenessLow
ExpenseLow
StabilityReproducibilityHigh
ReliabilityEarly detection
Containing pathological correlation
Table 6. Summary of acquisition procedures for nasal discharge, tear, saliva, and urine.
Table 6. Summary of acquisition procedures for nasal discharge, tear, saliva, and urine.
Body FluidAcquisitionProcedureReference
Nasal dischargeNasal irrigation1. Subjects are comfortably seated, and sterile normal saline (0.9% NaCl) is administered into each nostril.
2. Subjects must close one nostril and then spray or insert sterile normal saline several times into the other nostril.
3. After raising their head slightly back, let the solution stay as if washing the nasal cavities.4. After inserting sterile normal saline, subjects gently blow the nasal discharge fluids into a cup or tube.5. After a few minutes of rest, do the same for the other nostril.6. Samples are stored at −20 °C until use.
[66,67,68]
Sinus packs1. Sinus packs or sponges are placed in nasal cavities between the septum and inferior turbinate along the floor.
2. After 1–10 min, the sinus packs or sponges are removed and placed in tubes. In order to retrieve the secretions from sinus packs or sponges, sterile normal saline (0.9% NaCl) solution is added to the tube and stored at 4 °C for about 2 h.
3. The sinus packs or sponges are then placed into a syringe. Mechanical pressure is applied to them by moving the piston action to squeeze the nasal discharge fluid.
4. After the first pressure, the syringe is replaced with a tube and centrifugation is performed to recover all nasal discharge fluids from the sinus packs or sponges.
5. The nasal discharge fluids are then stored at −80 °C for further analysis.
[69]
Nasal swab1. Subjects are seated in a comfortable bed, placed in a high fowler’s position in bed, supporting the back of the head.
2. Enter a flexible cotton swab several centimeters with a slow and steady motion along the nose floor. Nasal smears are taken from the inferior concha, middle nasal meatus, olfactory cleft, and common nasal meatus.
3. After resistance is met, rotate the cotton swab several times and withdraw the swab.
4. All cotton swabs are placed in a microtube containing sterile normal saline (0.9% NaCl) for a few minutes, and swabs are removed from the microtube.
5. The solutions are then filtered by centrifugation, and then the filtered solutions are stored at −80 °C until further analysis.
[70,71]
TearCapillary tube1. Subjects are seated with their head raised and stimulated by a direct light source or airflow.
2. The reflex tears of the subject are collected with tubes.
[72,73,74]
Schirmer strip1. A local anesthetic is needed to collect basal tears, not reflex tears.
2. The bent end of the test strip is placed in the lower eyelid and allowed to absorb the tears for several minutes.
SalivaWhole salivaSpitting1. Subjects rinse their mouth and then spit the whole saliva into a sterile tube.[75,76,77]
Submandibular salivaDraining1. To block the opening of parotid ducts and sublingual glands, use cotton gauzes, and to dry up, the floor of the mouse is left.
2. Subjects raise the tongue to open the submandibular gland, and saliva is collected using a disposable pipette.
Sublingual salivaDraining1. To block the opening of parotid ducts and submandibular glands, use cotton gauzes, and to dry up, the floor of the mouse is left.
2. Subjects raise the tongue to open the sublingual gland, and saliva is collected using a disposable pipette.
Parotid salivaDraining1. To collect parotid saliva, parotid cups or collectors are placed, actively stimulating salivary collection.
UrineCollecting1. First morning and random collection are not preferred because of increasing variabilities.
2. The mid-stream and second-morning urine is collected in a urine container.
[78,79,80,81]
Table 7. Summary of core AD biomarkers in the olfactory system.
Table 7. Summary of core AD biomarkers in the olfactory system.
AD Pathology MechanismSpecimenBiomarkersAnalytical MethodResultsReference
Aβ pathologyNasal discharge fluid1–16Interdigitated microelectrode biosensorIncreased in AD than in OND and CU a[105]
Nasal discharge fluidAβ oligomerImmunoblotIncreased in AD than CU[67]
Nasal mucosa by nasal swab42, Aβ40ImmunoassayNo differences in median values between AD and CU[71]
Postmortem
olfactory
epithelium
HistopathologyIncreased Aβ aggregates in AD patient[103]
Postmortem
olfactory bulb
HistopathologyIncreased Aβ load in AD patients[90,111]
Tau pathologyNasal discharge fluidT-tau, P-tauImmunoassayPositive T- and P-tau in anosmic AD patients[108]
Nasal mucosa by nasal swabT-tau, P-tauImmunoassayPositive T- and P-tau in AD patients[71]
Postmortem
olfactory
epithelium
P-tauHistopathologyEvident PHF-tau b in AD[103]
Postmortem
olfactory bulb
P-tauHistopathologyP-tau deposits in the olfactory bulb of AD patients[112]
a CU: cognitively Unimpaired; b PHF-tau: Paired Helical Filament-tau.
Table 8. Summary of core AD biomarkers in tears.
Table 8. Summary of core AD biomarkers in tears.
AD Pathology MechanismBiomarkerAnalytical MethodResultsReference
Aβ pathology42ImmunoassayIncreased Aβ42 levels in AD patients[118]
Tau pathologyT-tauImmunoassayIncreased T-tau levels in AD patients[118]
Table 9. Summary of core AD biomarkers in saliva.
Table 9. Summary of core AD biomarkers in saliva.
AD Pathology MechanismBiomarkersAnalytical MethodResultsReference
Aβ pathology42ImmunoassayIncreased saliva Aβ42 levels in mild AD patients[128,129,130,131]
42Magneto-immunoassaySalivary Aβ42 levels increase as the AD severity increases[132]
42ImmunoassaySalivary Aβ42 levels were not detectable[127]
42Immunoassay
(MILLIPLEX)
Lower salivary Aβ42 levels in AD patients[133]
40Magneto-immunoassayNo statistically significant change[132]
Tau pathologyT-tau, P-tauImmunoassayIncreased P-tau/T-tau ratio in AD patients[127,134]
T-tauImmunoassayNo significant difference in salivary T-tau between AD and healthy control[135]
Table 10. Summary of core AD biomarkers in urine.
Table 10. Summary of core AD biomarkers in urine.
AD Pathology MechanismBiomarkerAnalytical MethodResultsReference
Aβ pathology42ImmunoblotMonomeric Aβ42 levels differed according to cognitive impairment[155]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Jung, D.H.; Son, G.; Kwon, O.-H.; Chang, K.-A.; Moon, C. Non-Invasive Nasal Discharge Fluid and Other Body Fluid Biomarkers in Alzheimer’s Disease. Pharmaceutics 2022, 14, 1532. https://doi.org/10.3390/pharmaceutics14081532

AMA Style

Jung DH, Son G, Kwon O-H, Chang K-A, Moon C. Non-Invasive Nasal Discharge Fluid and Other Body Fluid Biomarkers in Alzheimer’s Disease. Pharmaceutics. 2022; 14(8):1532. https://doi.org/10.3390/pharmaceutics14081532

Chicago/Turabian Style

Jung, Da Hae, Gowoon Son, Oh-Hoon Kwon, Keun-A Chang, and Cheil Moon. 2022. "Non-Invasive Nasal Discharge Fluid and Other Body Fluid Biomarkers in Alzheimer’s Disease" Pharmaceutics 14, no. 8: 1532. https://doi.org/10.3390/pharmaceutics14081532

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop