Next Article in Journal
Placental Metabolomics of Fetal Growth Restriction
Next Article in Special Issue
Metabolic Profile of Whole Unstimulated Saliva in Patients with Sjögren’s Syndrome
Previous Article in Journal
Muscle Pathology in Dystrophic Rats and Zebrafish Is Unresponsive to Taurine Treatment, Compared to the mdx Mouse Model for Duchenne Muscular Dystrophy
Previous Article in Special Issue
Updates and Original Case Studies Focused on the NMR-Linked Metabolomics Analysis of Human Oral Fluids Part III: Implementations for the Diagnosis of Non-Cancerous Disorders, Both Oral and Systemic
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Metabolites
Volume 13
Issue 2
10.3390/metabo13020233
metabolites-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Salivary Metabolomics in the Diagnosis and Monitoring of Neurodegenerative Dementia

by
Eelis Hyvärinen
1,
Eino Solje
2,3,
Jouko Vepsäläinen
4,
Arja Kullaa
1,* and
Tuulia Tynkkynen
4
1
Institute of Dentistry, University of Eastern Finland, 70210 Kuopio, Finland
2
Institute of Clinical Medicine, Neurology, University of Eastern Finland, 70210 Kuopio, Finland
3
Neuro Center, Neurology, Kuopio University Hospital, 70210 Kuopio, Finland
4
NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, 70210 Kuopio, Finland
*
Author to whom correspondence should be addressed.
Metabolites 2023, 13(2), 233; https://doi.org/10.3390/metabo13020233
Submission received: 19 December 2022 / Revised: 17 January 2023 / Accepted: 2 February 2023 / Published: 4 February 2023
(This article belongs to the Special Issue Salivary Metabolomics for Oral and Systemic Diseases Volume 2)
Browse Figures
Versions Notes

Abstract

:
Millions of people suffer with dementia worldwide. However, early diagnosis of neurodegenerative diseases/dementia (NDD) is difficult, and no specific biomarkers have been found. This study aims to review the applications of salivary metabolomics in diagnostics and the treatment monitoring of NDD A literature search of suitable studies was executed so that a total of 29 original research articles were included in the present review. Spectroscopic methods, mainly nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry, give us a broad view of changes in salivary metabolites in neurodegenerative diseases. The role of different salivary metabolites in brain function is discussed. Further studies with larger patient cohorts should be carried out to investigate the association between salivary metabolites and brain function and thus learn more about the complicated pathways in the human body.

1. Introduction

Approximately 55 million people suffer with dementia worldwide. Dementia is a syndrome affecting memory, thinking, orientation, comprehension, calculation, learning capacity, language and judgement [1]. Most commonly, dementia is caused by progressive diseases inducing neurodegeneration including Alzheimer’s disease (AD), frontotemporal dementia (FTD), vascular dementia (VaD) and alpha synucleinopathies: dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD). AD accounts for about 70% of all dementia cases, and the number of patients suffering from dementia is increasing due to increasing average lifetime [2]. Many research results suggest that pathophysiological changes initiate at least 10 to 25 years before the onset of dementia symptoms [3].
Diagnosis of neurodegenerative diseases is difficult, especially in the pre-clinical stages [4,5]. Many biomarkers based on imaging and cerebrospinal fluid (CSF) have been suggested to be positively associated with early diagnosis, but disease specificity is lacking [6]. In cognitively asymptomatic individuals with positive biomarkers for AD, the lifetime dementia risk is estimated to be from 5% to 42% [7]. Blood neurofilament light chain (NfL) is suggested to be a biomarker for neurodegenerative disorders, but it is not disease-specific and rather reflects neuronal damage in general [8]. Hence, there is an urgent need for new diagnostic, prognostic and monitoring biomarker innovations.
Saliva, a complex biofluid with a high variety of molecules, mainly consists of water (99%) and inorganic and organic substances [9]. Saliva is secreted from three pairs of major salivary glands (i.e., parotid, submandibular, sublingual) and numerous minor salivary glands throughout the oral cavity and pharynx. The functions of salivary glands are controlled by the sympathetic/parasympathetic nervous system. Primary saliva is produced from blood components by the acinar cells via transcellular diffusion and via the tight cell junctions of these cells [10]. Before entering the mouth, saliva is modified by the ductal cells, including the intercalated, striated and excretory cells, via reabsorption to the bloodstream. Furthermore, saliva flow rate, oral microbiota, oral mucosal transudate, immune cells and other environmental factors have an impact on the final composition of whole mouth saliva [10,11,12]. Saliva contains several compounds that are involved in oral health maintenance. In addition to oral diseases, the origin of saliva enables salivary diagnostics of systemic diseases [13].
Salivary glands work as an exocrine (external secretions as saliva) and endocrine organ. Some of the salivary products are transferred into the bloodstream via endocrine mechanisms and communicate with other organs, including the brain (Figure 1) [14]. Hence, saliva is an accessible source of information as a ‘mirror of the body’ and a promising biofluid for the diagnosis and monitoring of human diseases because of its bidirectional mechanisms. Furthermore, in contrast to blood or CSF, the collection of saliva is non-invasive and safe.
Salivary analysis requires precise methods due to the low concentration of salivary components. Metabolites provide comprehensive information about the cellular functions of oral tissues and changes in the phenotype of cells or tissues in response to genetic or environmental changes. The most common methods are enzyme-linked immunosorbent assays (ELISA) and different spectroscopic methods. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are frequently used methods in saliva research [13]. Mass spectrometry is commonly used in conjunction with either two-dimensional gas chromatography (2DGC-MS) or high-performance liquid chromatography (HPLC-MS) [13]. NMR spectroscopy is based on the behaviour of magnetically active atomic nuclei, e.g., 1H or 13C, in an external magnetic field. Identification of small molecules is possible because most compounds have highly characteristic resonance frequencies [15]. Additionally, Raman spectroscopy, Fourier-transform infrared (FTIR) spectroscopy and photoacoustic spectroscopy (PAS) have been used in salivary research [16,17].
Because of the precise molecular identification, spectroscopic methods are potential diagnostic tools in the field of salivary metabolomics. This study aimed to conduct this literature review on the applications of salivary metabolites in diagnostics and treatment monitoring of neurodegenerative diseases in order to form a basis for further studies.

2. Materials and Methods

We divided different neurodegenerative diseases into four groups: AD, FTD, VaD and alpha synucleinopathies, i.e., DLB and PDD.

Search Strategy and Study Selection

A literature search of suitable studies was conducted using the PubMed and Web of Science databases, utilizing the following keywords: ”Alzheimer’s disease” AND “saliva*” AND (“biomarker*” OR “metabolite*”); “dementia” AND “Lewy bod*” AND “saliva*” AND (“biomarker*” OR “metabolite*”); (“frontotemporal dementia” OR “frontotemporal lobe degeneration”) AND “saliva*” AND (“biomarker*” OR “metabolite*”); “vascular dementia” AND “saliva*” AND (“biomarker*” OR “metabolite*”); “Parkinson’s” AND “dementia” AND “saliva*” AND (“biomarker*” OR “metabolite*”). The search was performed without the limitations of publication year. The searches were conducted in December 2021. Additional searches (n = 5) were conducted until October 2022.
The literature search was executed in two phases. First, the following validity criteria were used for screening the titles of the articles: only English; publication year 2000 or later; saliva must be examined in the wanted disease. In the second phase, screening abstracts of the articles, literature reviews, conference abstracts and articles about non-human material were excluded.
Two authors (E.H. and A.K.) independently appraised full-text versions of the selected articles and then together excluded studies that did not handle the content of the present review. We excluded articles that handled only methodological issues or did not contain any metabolomic results. The reference lists of selected articles were manually reviewed to find suitable studies outside the literature review (Figure 2).

3. Results

A total of 29 original research articles were included in the present review, of which 12 addressed spectroscopic methods (Figure 2).
Different methods, mostly ELISA, have been used to study salivary biomarkers (Table 1). These methods often concentrate on single biomarkers that have earlier been associated with the diseases, such as amyloid-β42 (Aβ), t-tau and lactoferrin in AD. In plasma and CSF, NfL has been shown to be a promising biomarker for neurodegeneration. However, a similar trend has not been found using saliva samples [18].
Studies with MS and NMR spectroscopy involve many salivary metabolites associated with different stages of neurodegenerative diseases. In Table 2, we present all metabolites that have been shown to be related to neurodegenerative diseases.
Most spectroscopic studies investigate MCI and AD. We found just one article that addressed vascular dementia [36] and another that addressed FTD and dementia with Lewy bodies [37]. Two articles investigated Parkinson’s disease using spectroscopic methods [38,39]. These two articles are excluded because they handled only Parkinson’s disease and did not differentiate patients with cognitive symptoms (PDD). To our knowledge, there are no studies of Parkinson’s disease dementia using saliva samples and spectroscopic methods.
Table
Table 2. Changes in salivary metabolites when comparing mild cognitive impairment (MCI) with healthy controls (HC), Alzheimer’s disease (AD) with HC, and AD with MCI according to previous studies using spectroscopic methods (NMR and MS). Two studies compared multiple neurodegenerative dementia (NDD) with HC (36,37).
Table 2. Changes in salivary metabolites when comparing mild cognitive impairment (MCI) with healthy controls (HC), Alzheimer’s disease (AD) with HC, and AD with MCI according to previous studies using spectroscopic methods (NMR and MS). Two studies compared multiple neurodegenerative dementia (NDD) with HC (36,37).
Disease (N)MethodMetabolites (Elevated/Lowered)Reference
MCI (8) vs. HC (12)NMRacetone, imidazole
galactose		[40]
MCI (20) vs. HC (20)LC-FTICR-MStaurine[41]
MCI (25) vs. HC (25)FIA-MS/MSacyl-alkyl phosphatidylcholines[42]
MCI (20) vs. HC (40)GC-MS hydroxyphenyl lactate, tyramine, tyrosol
cholesterol		[43]
MCI (21) vs. HC (19)LC-MS/MStransthyretin[44]
MCI (59) vs. HC (131)MALDI-TOF/TOF MSlactoferrin[45]
MCI (20)/AD (20) vs. HC (40)GC-MSrhamnose, L-tyrosine,
L-fucose, L-ornithine, L-aspartate, serotonin		[43]
AD (9) vs. HC (12)NMRacetone, propionate[40]
AD (116) vs. HC (131)MALDI-TOF/TOF MSlactoferrin[45]
AD (21) vs. HC (38)MALDI-TOF- MS/MSp-tau/t-tau ratio[46]
AD (29) vs. HC (45)LC-MSphenylalanyl-proline, phenylalanyl-phenylalanine,
tryptophyl-tyrosine, urocanic acid		[47]
AD (256) vs. HC (218)FUPLC-MSornithine, phenyllactic acid,
sphinganine-1-phosphate
3-dehydrocarnitine, hypoxanthine, inosine		[48]
AD (20) vs. HC (40)GC-MSaspartate, ornithine, phenylalanine, pyruvate, tyrosine, putrescine, cholesterol
citrate, fumarate, succinate		[43]
AD (17) vs. HC (19)LC-MS/MStransthyretin[44]
AD (25) vs. HC (25)FIA-MS/MSacyl-alkyl phosphatidylcholines[42]
AD (9) vs. MCI (8)NMR5-aminopentanoate, creatine[40]
AD (29) vs. MCI (35)LC-MSalanyl-phenylalanine, phenylalanyl-glycine,
phenylalanyl-proline		[47]
AD (660) vs. MCI (583)FUPLC-MScytidine, L-glutamate, ornithine, phenyllactic acid,
pyroglutamate, L-tryptophan, sphinganine-1-phosphate
3-dehydrocarnitine, hypoxanthine, inosine		[49]
Dementia (17) (13 AD + 4 VaD) vs. HC (34)NMRacetic acid, histamine, propionate
dimethyl sulfone, glycerol, succinate, taurine		[36]
Dementia (10) (3 AD + 4 FTD + 3 DLB) vs. HC (9)CE-TOF-MSarginine, tyrosine[37]
N = number of subjects; AD = Alzheimer’s disease; MCI = mild cognitive impairment; VaD = vascular dementia; FTD = frontotemporal dementia; DLB = dementia with Lewy bodies; HC = healthy controls; LC = liquid chromatography; FTICR = Fourier transform ion cyclotron resonance; MS = mass spectrometry; FIA = flow injection analysis; MS/MS = tandem mass spectrometry; GC = gas chromatography; MALDI = matrix-assisted laser desorption/ionization; TOF = time-of-flight; FUPLC = faster ultra-performance liquid chromatography; CE = capillary electrophoresis; TOF/TOF = tandem time-of-flight; NMR = nuclear magnetic resonance spectroscopy; t-tau = total tau; p-tau = phosphorylated tau.

4. Discussion

In this review, we analysed the literature on the association between neurodegenerative dementia and salivary metabolites. We divided neurodegenerative diseases leading to dementia into different types: AD, FTD, VaD and alpha synucleinopathies: DLB and PDD. Most of the articles discussed AD and MCI. Only one study analysed AD and VaD [36] and only one analysed AD and FTD [37]. Only two articles [38,39] handled PD, but did not differentiate patients according to cognitive symptoms (PDD). In future studies, the underlying neuropathology or pathophysiologial process in the research subjects should be established using neuropathological analysis or modern beyond-state-of-the-art methods. In particular, CSF RT-quIC [8] in the identification of the underlying proteinopathy and transcranial magnetic stimulation [50] in the recognition of the disease-specific neurotransmitter system deficit could increase the validity of saliva biomarker studies.
Some single salivary metabolites, including Aβ, t-tau and lactoferrin, are associated with AD (Table 1). Increased salivary Aβ is shown in AD patients, but is not evident in studies with MS and NMR spectroscopy. Decreased salivary lactoferrin and increased t-tau are shown also with MS in some studies [45,46]. Lactoferrin, one component of the innate defence mechanism of saliva, is produced via salivary glands and also from gingival cervicular fluid, and it is active against oral microbes [10]. Hence, it can be a biomarker of gingivitis and periodontitis.
With spectroscopic methods, we can obtain a wide scale of different salivary metabolites and thus identify disease-associated changes in oral metabolism as a mirror of whole human body physiology. François et al. [43] discovered that serotonin is increased in patients with AD versus MCI and healthy controls. Tryptophan is a precursor for serotonin [51], and L-tryptophan has been discovered to be elevated in AD versus MCI [49]. Serotonin affects nearly all human behavioural processes, but a major amount of serotonin is found outside the central nervous system. Approximately 95% of total body serotonin is produced by the intestinal enterochromaffin cells [52] and therefore it may not be a promising salivary biomarker for AD. In addition, high levels of tryptophan-tyrosine dipeptide in the saliva of AD patients might indicate memory impairments due to altered dopaminergic activity [53]. In the future, studies of serotonin, tryptophan and dipeptides in the saliva might indicate pathway changes and episodic memory impairment in patients with AD.
Studied with NMR spectroscopy, salivary propionate has been found to be upregulated in patients with AD when compared to controls [36,40]. However, propionate is also increased in inflammatory oral diseases, including periodontal diseases and dental caries, therefore its effectiveness as a specific salivary biomarker for neurodegenerative diseases is questionable. On the other hand, periodontitis and tooth loss have been shown to increase the risk of dementia [54,55,56]. Gut microbiota and their metabolites, like propionate, have been mentioned in mediating brain function [57]. Salivary propionate is produced by oral bacteria [12], but the link between salivary propionate and the brain has not been studied. In addition to inflammatory diseases, oral dysbiosis together with salivary metabolomics could be one target to study further in patients with neurodegenerative dementia.
Salivary metabolites mainly reflect the oral microbiome. Concentrations of some metabolites, including short chain fatty acids (SCFAs: acetate, butyrate, propionate, formate), correlate with salivary bacterial load [12]. On the other hand, SCFAs, as immune-regulatory metabolites, can stimulate the autonomic nervous system [58,59]. These metabolites, produced by proteolytic bacteria, are associated with periodontitis [60,61] and some of these metabolites have also been found in patients with MCI and VaD vs. controls [36,40]. In this regard, we hypothesize that salivary SCFAs circulate in the blood and can cause low-level systemic inflammation and associate with brain function. The biological mechanisms and systemic communication between the brain and oral health are yet unknown. Hence, the association between inflammatory oral diseases and brain function presents a target for further study on salivary metabolites. The role of salivary SCFAs in the mouth–brain axis needs more investigation.
The level of salivary taurine was lower in patients with MCI [41] and AD/VaD [36] when compared to controls. Taurine has numerous functions in the nervous system, including neurotransmission, neuromodulation and osmoregulation, and it prevents the neurotoxicity of Aβ [62].
Salivary histamine was increased in patients with AD and VaD versus controls [36]. The central histaminergic system in the brain plays a major role in basic body functions, such as the sleep-waking cycle and learning, and has been reported to be involved in AD [63]. In addition to histaminergic neurons, histamine is primarily produced by mast cells, basophils, and enterochromaffin-like cells in the stomach [64].
Figueira et al. [36] also conducted a follow-up study with 28 dementia patients (14 AD, 11 VaD, 3 DLB or FTD) and 60 controls. They managed to differentiate controls and healthy, pre-dementia patients using seven metabolites: acetic acid, histamine and propionate increased, whereas dimethyl sulfone, glycerol, succinate and taurine decreased (Table 2). Future studies should increasingly concentrate on these kinds of follow-up studies to determine the specific and sensitive biomarkers of early stages of the diseases.
Recent metabolomic studies have often been conducted with relatively small study populations. To verify these results, multi-centre investigations with larger cross-sectional populations are needed. Such projects would also enable longitudinal, long-term follow-up studies and include more background information on patient health. Furthermore, an important object of biomarker research in neurodegenerative dementia is to compare the validated metabolic biomarkers from multiple biofluids including blood, CSF and saliva. Standardized collection and storage methods and increasing interest in saliva research could make high-quality saliva research possible in the future.
Salivary metabolites have recently been investigated with spectroscopic methods in different diseases [13]. However, the collection methods vary considerably. Stimulation of salivary secretion is necessary with some patients with hyposalivation, e.g., elderly people. Figueira et al. [65] highlighted in their study that comparable results are obtained only by using the same sample collection methods. We recommend collecting (masticatory or gustatory) stimulated saliva samples. Thus, the sample volume is higher on average and patients with lowered salivary secretion can be involved in the study.
The studies included in this review demonstrated the multifunctional character of salivary metabolites and their association with neurodegenerative dementia. MS and NMR spectroscopy provide more information about salivary metabolomic profiles and pathways in the oral cavity than analysis of simple metabolites. The biological mechanisms and systemic communication between the brain and oral health are yet unknown and need more studies in larger patient and multicentre cohorts.

5. Conclusions

Spectroscopic methods (NMR, MS) give us a broad view of changes in salivary metabolites in neurodegenerative diseases and deepen our knowledge of the systemic communication between the oral cavity and the brain. Further studies with larger patient cohorts should be carried out to investigate the association between salivary metabolites and brain function and thus learn more about the complicated pathways in the human body.

Author Contributions

Conceptualization, E.H. and A.K.; validation, E.H. and A.K; formal analysis, E.H.; investigation, E.H.; data curation, E.H.; writing—original draft preparation, E.H.; writing—review and editing, E.H.; J.V., A.K. and T.T.; visualization, E.H. and A.K.; supervision, E.S.; A.K. and T.T.; project administration, A.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received external funding from the Finnish Dental Society Apollonia (E.H.). Funding from Sigrid Jusélius Foundation, Finnish Medical Foundation, State Research Funding (VTR) (E.S.).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. World Health Organization. Dementia. Available online: https://www.who.int/news-room/fact-sheets/detail/dementia (accessed on 9 December 2022).
  2. Reitz, C.; Brayne, C.; Mayeux, R. Epidemiology of Alzheimer disease. Nat. Rev. Neurol. 2011, 7, 137–152. [Google Scholar] [CrossRef] [PubMed]
  3. Bateman, R.J.; Xiong, C.; Benzinger, T.L.; Fagan, A.M.; Goate, A.; Fox, N.C.; Marcus, D.S.; Cairns, N.J.; Xie, X.; Blazey, T.M.; et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N. Engl. J. Med. 2012, 367, 795–804. [Google Scholar] [CrossRef] [PubMed]
  4. Bloudek, L.M.; Spackman, D.E.; Blankenburg, M.; Sullivan, S.D. Review and meta-analysis of biomarkers and diagnostic imaging in Alzheimer’s disease. J. Alzheimers Dis. 2011, 26, 627–645. [Google Scholar] [CrossRef]
  5. Palmqvist, S.; Tideman, P.; Cullen, N.; Zetterberg, H.; Blennow, K.; Dage, J.L.; Stomrud, E.; Janelidze, S.; Mattsson-Carlgren, N.; Hansson, O. Prediction of future Alzheimer’s disease dementia using plasma phospho-tau combined with other accessible measures. Nat. Med. 2021, 27, 1034–1042. [Google Scholar] [CrossRef]
  6. Brookmeyer, R.; Abdalla, N. Estimation of lifetime risks of Alzheimer’s disease dementia using biomarkers for preclinical disease. Alzheimers Dement. 2018, 14, 981–988. [Google Scholar] [CrossRef]
  7. Dubois, B.; Villain, N.; Frisoni, G.B.; Rabinovici, G.D.; Sabbagh, M.; Cappa, S.; Bejanin, A.; Bombois, S.; Epelbaum, S.; Teichmann, M.; et al. Clinical diagnosis of Alzheimer’s disease: Recommendations of the international working group. Lancet Neurol. 2021, 20, 484–496. [Google Scholar] [CrossRef]
  8. Solje, E.; Benussi, A.; Buratti, E.; Remes, A.M.; Haapasalo, A.; Borroni, B. State-of-the-art methods and emerging fluid biomarkers in the diagnostics of dementia-a short review and diagnostic algorithm. Diagnostics 2021, 11, 788. [Google Scholar] [CrossRef]
  9. Navazesh, M. Methods for collecting saliva. Ann. N. Y. Acad. Sci. 1993, 694, 72–77. [Google Scholar] [CrossRef]
  10. Fábián, T.K.; Hermann, P.; Beck, A.; Fejérdy, P.; Fábián, G. Salivary defense proteins: Their network and role in innate and acquired oral immunity. Int. J. Mol. Sci. 2012, 13, 4295–4320. [Google Scholar] [CrossRef] [PubMed]
  11. Bardow, A.; Lynge Pedersen, A.M.; Nauntofte, B. Saliva. In Clinical Oral Physiology, 1st ed.; Miles, T.S., Nauntofte, B., Svensson, P., Eds.; Quintessence Publishing Co. Ltd.: Copenhagen, Denmark, 2004; pp. 17–51. [Google Scholar]
  12. Gardner, A.; Parkes, H.G.; So, P.W.; Carpenter, G.H. Determining bacterial and host contributions to the human salivary metabolome. J. Oral Microbiol. 2019, 11, 1617014. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Hyvärinen, E.; Savolainen, M.; Mikkonen, J.J.W.; Kullaa, A.M. Salivary metabolomics for diagnosis and monitoring diseases: Challenges and possibilities. Metabolites 2021, 11, 587. [Google Scholar] [CrossRef]
  14. Isenman, L.; Liebow, C.; Rothman, S. The endocrine secretion of mammalian digestive enzymes by exocrine glands. Am. J. Physiol. 1999, 276, E223–E232. [Google Scholar] [CrossRef] [PubMed]
  15. Mikkonen, J.J.W.; Singh, S.P.; Akhi, R.; Salo, T.; Lappalainen, R.; González-Arriagada, W.A.; Ajudarte Lopes, M.; Kullaa, A.M.; Myllymaa, S. Potential role of nuclear magnetic resonance spectroscopy to identify salivary metabolite alterations in patients with head and neck cancer. Oncol. Lett. 2018, 16, 6795–6800. [Google Scholar] [CrossRef] [PubMed]
  16. Carlomagno, C.; Bertazioli, D.; Gualerzi, A.; Picciolini, S.; Andrico, M.; Rodà, F.; Meloni, M.; Banfi, P.I.; Verde, F.; Ticozzi, N.; et al. Identification of the raman salivary fingerprint of Parkinson’s disease through the spectroscopic-computational combinatory approach. Front. Neurosci. 2021, 15, 704963. [Google Scholar] [CrossRef]
  17. Mikkonen, J.J.W.; Raittila, J.; Rieppo, L.; Lappalainen, R.; Kullaa, A.M.; Myllymaa, S. Fourier transform infrared spectroscopy and photoacoustic spectroscopy for saliva analysis. Appl. Spectrosc. 2016, 70, 1502–1510. [Google Scholar] [CrossRef]
  18. Gleerup, H.S.; Sanna, F.; Høgh, P.; Simrén, J.; Blennow, K.; Zetterberg, H.; Hasselbalch, S.G.; Ashton, N.J.; Simonsen, A.H. Saliva neurofilament light chain is not a diagnostic biomarker for neurodegeneration in a mixed memory clinic population. Front. Aging Neurosci. 2021, 13, 659898. [Google Scholar] [CrossRef]
  19. 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]
  20. 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]
  21. Lee, M.; Guo, J.P.; Kennedy, K.; McGeer, E.G.; McGeer, P.L. A method for diagnosing Alzheimer’s disease based on salivary amyloid-β protein 42 levels. J. Alzheimers Dis. 2017, 55, 1175–1182. [Google Scholar] [CrossRef]
  22. Cui, Y.; Zhang, H.; Zhu, J.; Liao, Z.; Wang, S.; Liu, W. Investigation of whole and glandular saliva as a biomarker for Alzheimer’s disease diagnosis. Brain Sci. 2022, 12, 595. [Google Scholar] [CrossRef]
  23. Santos, G.A.A.; Olave, E.; Pardi, P.C. Salivary biomarkers in Alzheimer’s disease. Int. J. Morphol. 2020, 38, 230–234. [Google Scholar] [CrossRef]
  24. 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] [PubMed]
  25. Marksteiner, J.; Defrancesco, M.; Humpel, C. Saliva tau and phospho-tau-181 measured by Lumipulse in patients with Alzheimer’s disease. Front. Aging Neurosci. 2022, 14, 1014305. [Google Scholar] [CrossRef] [PubMed]
  26. 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. (Amst). 2018, 11, 53–60. [Google Scholar] [CrossRef] [PubMed]
  27. Pukhalskaia, A.E.; Dyatlova, A.S.; Linkova, N.S.; Kozlov, K.L.; Kvetnaia, T.V.; Koroleva, M.V.; Kvetnoy, I.M. Sirtuins as possible predictors of aging and Alzheimer’s disease development: Verification in the hippocampus and saliva. Bull. Exp. Biol. Med. 2020, 169, 821–824. [Google Scholar] [CrossRef] [PubMed]
  28. Zalewska, A.; Klimiuk, A.; Zięba, S.; Wnorowska, O.; Rusak, M.; Waszkiewicz, N.; Szarmach, I.; Dzierżanowski, K.; Maciejczyk, M. Salivary gland dysfunction and salivary redox imbalance in patients with Alzheimer’s disease. Sci. Rep. 2021, 11, 23904. [Google Scholar] [CrossRef]
  29. de la Rubia Ortí, J.E.; Sancho Castillo, S.; Benlloch, M.; Julián Rochina, M.; Corchón Arreche, S.; García-Pardo, M.P. Impact of the relationship of stress and the immune system in the appearance of Alzheimer’s disease. J. Alzheimers Dis. 2017, 55, 899–903. [Google Scholar] [CrossRef]
  30. Gómez-Gallego, M.; Gómez-García, J. Effects of stress on emotional memory in patients with Alzheimer’s disease and in healthy elderly. Int. Psychogeriatr. 2018, 30, 1199–1209. [Google Scholar] [CrossRef]
  31. Ashton, N.J.; Ide, M.; Schöll, 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]
  32. Katsipis, G.; Tzekaki, E.E.; Tsolaki, M.; Pantazaki, A.A. Salivary GFAP as a potential biomarker for diagnosis of mild cognitive impairment and Alzheimer’s disease and its correlation with neuroinflammation and apoptosis. J. Neuroimmunol. 2021, 361, 577744. [Google Scholar] [CrossRef]
  33. 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] [PubMed]
  34. González-Sánchez, M.; Bartolome, F.; Antequera, D.; Puertas-Martín, V.; González, P.; Gómez-Grande, A.; Llamas-Velasco, S.; Herrero-San Martín, A.; Pérez-Martínez, D.; Villarejo-Galende, A.; et al. Decreased salivary lactoferrin levels are specific to Alzheimer’s disease. EBioMedicine 2020, 57, 102834. [Google Scholar] [CrossRef]
  35. Gleerup, H.S.; Jensen, C.S.; Høgh, P.; Hasselbalch, S.G.; Simonsen, A.H. Lactoferrin in cerebrospinal fluid and saliva is not a diagnostic biomarker for Alzheimer’s disease in a mixed memory clinic population. EBioMedicine 2021, 67, 103361. [Google Scholar] [CrossRef]
  36. Figueira, J.; Jonsson, P.; Nordin Adolfsson, A.; Adolfsson, R.; Nyberg, L.; Öhman, A. NMR analysis of the human saliva metabolome distinguishes dementia patients from matched controls. Mol. Biosyst. 2016, 12, 2562–2571. [Google Scholar] [CrossRef] [PubMed]
  37. Tsuruoka, M.; Hara, J.; Hirayama, A.; Sugimoto, M.; Soga, T.; Shankle, W.R.; Tomita, M. Capillary electrophoresis-mass spectrometry-based metabolome analysis of serum and saliva from neurodegenerative dementia patients. Electrophoresis 2013, 34, 2865–2872. [Google Scholar] [CrossRef]
  38. Kumari, S.; Goyal, V.; Kumaran, S.S.; Dwivedi, S.N.; Srivastava, A.; Jagannathan, N.R. Quantitative metabolomics of saliva using proton NMR spectroscopy in patients with Parkinson’s disease and healthy controls. Neurol. Sci. 2020, 41, 1201–1210. [Google Scholar] [CrossRef]
  39. Figura, M.; Sitkiewicz, E.; Świderska, B.; Milanowski, Ł.; Szlufik, S.; Koziorowski, D.; Friedman, A. Proteomic profile of saliva in Parkinson’s disease patients: A proof of concept study. Brain Sci. 2021, 11, 661. [Google Scholar] [CrossRef] [PubMed]
  40. 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]
  41. Zheng, J.; Dixon, R.A.; Li, L. Development of isotope labeling LC-MS for human salivary metabolomics and application to profiling metabolome changes associated with mild cognitive impairment. Anal. Chem. 2012, 84, 10802–10811. [Google Scholar] [CrossRef]
  42. Marksteiner, J.; Oberacher, H.; Humpel, C. Acyl-alkyl-phosphatidlycholines are decreased in saliva of patients with Alzheimer’s disease as identified by targeted metabolomics. J. Alzheimers Dis. 2019, 68, 583–589. [Google Scholar] [CrossRef]
  43. François, M.; Karpe, A.; Liu, J.W.; Beale, D.; Hor, M.; Hecker, J.; Faunt, J.; Maddison, J.; Johns, S.; Doecke, J.; et al. Salivaomics as a potential tool for predicting Alzheimer’s disease during the early stages of neurodegeneration. J. Alzheimers Dis. 2021, 82, 1301–1313. [Google Scholar] [CrossRef] [PubMed]
  44. Eldem, E.; Barve, A.; Sallin, O.; Foucras, S.; Annoni, J.M.; Schmid, A.W.; Alberi Auber, L. Salivary proteomics identifies transthyretin as a biomarker of early dementia conversion. J. Alzheimers Dis. Rep. 2022, 6, 31–41. [Google Scholar] [CrossRef]
  45. 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 (Amst). 2017, 8, 131–138. [Google Scholar] [CrossRef] [PubMed]
  46. 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]
  47. Huan, T.; Tran, T.; Zheng, J.; Sapkota, S.; MacDonald, S.W.; Camicioli, R.; Dixon, R.A.; Li, L. Metabolomics analyses of saliva detect novel biomarkers of Alzheimer’s disease. J. Alzheimers Dis. 2018, 65, 1401–1416. [Google Scholar] [CrossRef]
  48. Liang, Q.; Liu, H.; Zhang, T.Y.; Jiang, Y.; Xing, H.T.; Zhang, A.H. Metabolomics-based screening of salivary biomarkers for early diagnosis of Alzheimer’s disease. RSC Adv. 2015, 5, 96074–96079. [Google Scholar] [CrossRef]
  49. Liang, Q.; Liu, H.; Li, X.; Zhang, A.H. High-throughput metabolomics analysis discovers salivary biomarkers for predicting mild cognitive impairment and Alzheimer’s disease. RSC Adv. 2016, 6, 75499–75504. [Google Scholar] [CrossRef]
  50. Padovani, A.; Benussi, A.; Cantoni, V.; Dell’Era, V.; Cotelli, M.S.; Caratozzolo, S.; Turrone, R.; Rozzini, L.; Alberici, A.; Altomare, D.; et al. Diagnosis of mild cognitive impairment due to Alzheimer’s disease with transcranial magnetic stimulation. J. Alzheimers Dis. 2018, 65, 221–230. [Google Scholar] [CrossRef]
  51. Bear, M.F.; Connors, B.W.; Paradiso, M.A. (Eds.) Neurotransmitter systems. In Neuroscience: Exploring the Brain, 4th ed.; Wolters Kluwer Ltd.: Philadelphia, PA, USA, 2016; pp. 143–178. [Google Scholar]
  52. Berger, M.; Gray, J.A.; Roth, B.L. The expanded biology of serotonin. Annu. Rev. Med. 2009, 60, 355–366. [Google Scholar] [CrossRef]
  53. Cueno, M.E.; Ochiai, K. Gingival periodontal disease (PD) level-butyric acid affects the systemic blood and brain organ: Insights into the systemic inflammation of periodontal disease. Front Immunol. 2018, 9, 1158. [Google Scholar] [CrossRef]
  54. Leira, Y.; Domínguez, C.; Seoane, J.; Seoane-Romero, J.; Pías-Peleteiro, J.M.; Takkouche, B.; Blanco, J.; Aldrey, J.M. Is periodontal disease associated with Alzheimer’s disease? A systematic review with meta-analysis. Neuroepidemiology 2017, 48, 21–31. [Google Scholar] [CrossRef]
  55. Holmer, J.; Eriksdotter, M.; Schultzberg, M.; Pussinen, P.J.; Buhlin, K. Association between periodontitis and risk of Alzheimer’s disease, mild cognitive impairment and subjective cognitive decline: A case-control study. J. Clin. Periodontol. 2018, 45, 1287–1298. [Google Scholar] [CrossRef]
  56. Asher, S.; Stephen, R.; Mäntylä, P.; Suominen, A.L.; Solomon, A. Periodontal health, cognitive decline, and dementia: A systematic review and meta-analysis of longitudinal studies. J. Am. Geriatr. Soc. 2022, 70, 2695–2709. [Google Scholar] [CrossRef] [PubMed]
  57. Rogers, G.B.; Keating, D.J.; Young, R.L.; Wong, M.L.; Licinio, J.; Wesselingh, S. From gut dysbiosis to altered brain function and mental illness: Mechanisms and pathways. Mol. Psychiatry 2016, 21, 738–748. [Google Scholar] [CrossRef]
  58. Macfarlane, S.; Macfarlane, G.T. Regulation of short-chain fatty acid production. Proc. Nutr. Soc. 2003, 62, 67–72. [Google Scholar] [CrossRef] [PubMed]
  59. Kimura, I.; Ichimura, A.; Ohue-Kitano, R.; Igarashi, M. Free fatty acid receptors in health and disease. Physiol. Rev. 2020, 100, 171–210. [Google Scholar] [CrossRef] [PubMed]
  60. Aimetti, M.; Romano, F.; Guzzi, N.; Carnevale, G. Full-mouth disinfection and systemic antimicrobial therapy in generalized aggressive periodontitis: A randomized, placebo-controlled trial. J. Clin. Periodontol. 2012, 39, 284–294. [Google Scholar] [CrossRef]
  61. Rzeznik, M.; Triba, M.N.; Levy, P.; Jungo, S.; Botosoa, E.; Duchemann, B.; Le Moyec, L.; Bernaudin, J.F.; Savarin, P.; Guez, D. Identification of a discriminative metabolomic fingerprint of potential clinical relevance in saliva of patients with periodontitis using 1H nuclear magnetic resonance (NMR) spectroscopy. PLoS ONE 2017, 12, e0182767. [Google Scholar] [CrossRef]
  62. Louzada, P.R.; Paula Lima, A.C.; Mendonca-Silva, D.L.; Noël, F.; De Mello, F.G.; Ferreira, S.T. Taurine prevents the neurotoxicity of beta-amyloid and glutamate receptor agonists: Activation of GABA receptors and possible implications for Alzheimer’s disease and other neurological disorders. FASEB J. 2004, 18, 511–518. [Google Scholar] [CrossRef]
  63. Eissa, N.; Sadeq, A.; Sasse, A.; Sadek, B. Role of neuroinflammation in autism spectrum disorder and the emergence of brain histaminergic system. Lessons also for BPSD? Front. Pharmacol. 2020, 11, 886. [Google Scholar] [CrossRef]
  64. Huang, H.; Li, Y.; Liang, J.; Finkelman, F.D. Molecular regulation of histamine synthesis. Front. Immunol. 2018, 9, 1392. [Google Scholar] [CrossRef] [PubMed]
  65. Figueira, J.; Gouveia-Figueira, S.; Öhman, C.; Lif Holgerson, P.; Nording, M.L.; Öhman, A. Metabolite quantification by NMR and LC-MS/MS reveals differences between unstimulated, stimulated, and pure parotid saliva. J. Pharm. Biomed. Anal. 2017, 140, 295–300. [Google Scholar] [CrossRef] [PubMed]
Metabolites 13 00233 g001 550
Figure 1. Systemic and neural pathways linking the salivary gland with brain function. Metabolites play a central role in systemic communication.
Figure 1. Systemic and neural pathways linking the salivary gland with brain function. Metabolites play a central role in systemic communication.
Metabolites 13 00233 g001
Metabolites 13 00233 g002 550
Figure 2. Flow chart of the English literature review process in the time range January 2000–December 2021. Additional records were identified until October 2022. (AD = Alzheimer disease; DLB = dementia with Lewy bodies; FTD = frontotemporal dementia; VaD = vascular dementia; PDD = Parkinson’s disease dementia). * mark means a cut-off mark commonly used in a literary search that allows complete search of the subject in question.
Figure 2. Flow chart of the English literature review process in the time range January 2000–December 2021. Additional records were identified until October 2022. (AD = Alzheimer disease; DLB = dementia with Lewy bodies; FTD = frontotemporal dementia; VaD = vascular dementia; PDD = Parkinson’s disease dementia). * mark means a cut-off mark commonly used in a literary search that allows complete search of the subject in question.
Metabolites 13 00233 g002
Table
Table 1. Some salivary metabolites that have been studied with various neurodegenerative diseases using different methods.
Table 1. Some salivary metabolites that have been studied with various neurodegenerative diseases using different methods.
DiseaseMetabolites
(Elevated/No Association/Lowered)		Method
ADamyloid-β42ELISA [19,20,21,22]
ADamyloid-β42ELISA [23]
ADamyloid-β42Luminex assay [24]
ADcomplement C4Luminex assay [24]
ADt-tauELISA [23]
Lumipulse technology [25]
ADp-tau/t-tau ratioAntibodies + Western Blot analysis [26]
ELISA [22]
ADSIRT1, SIRT3, SIRT6ELISA [27]
AD glutathioneColorimetric method [28]
ADIgAELISA [29]
ADcortisolELISA [29]
ADcortisolRIA [30]
AD, MCI t-tauSingle molecule array [31]
AD, MCI GFAPELISA [32]
quantitative Dot Blot analysis [32]
SDS-PAGE + Western Blot analysis [32]
AD, MCIamyloid-β42Magnetoimmunoassay [33]
AD, FTDlactoferrinELISA [34]
AD, MCI, FTD, DLB, VaD, PDD lactoferrinELISA [35]
AD = Alzheimer’s disease; MCI = mild cognitive impairment; FTD = frontotemporal dementia; DLB = dementia with Lewy bodies; VaD = vascular dementia; PDD = Parkinson’s disease dementia; HC = healthy controls; t-tau = total tau; p-tau = phosphorylated tau; SIRT = sirtuin; IgA = immunoglobulin A; GFAP = glial fibrillary acidic protein; ELISA = enzyme-linked immunosorbent assay; SDS-PAGE = SDS polyacrylamide gel electrophoresis; RIA = radioimmunoassay kit.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Hyvärinen, E.; Solje, E.; Vepsäläinen, J.; Kullaa, A.; Tynkkynen, T. Salivary Metabolomics in the Diagnosis and Monitoring of Neurodegenerative Dementia. Metabolites 2023, 13, 233. https://doi.org/10.3390/metabo13020233

AMA Style

Hyvärinen E, Solje E, Vepsäläinen J, Kullaa A, Tynkkynen T. Salivary Metabolomics in the Diagnosis and Monitoring of Neurodegenerative Dementia. Metabolites. 2023; 13(2):233. https://doi.org/10.3390/metabo13020233

Chicago/Turabian Style

Hyvärinen, Eelis, Eino Solje, Jouko Vepsäläinen, Arja Kullaa, and Tuulia Tynkkynen. 2023. "Salivary Metabolomics in the Diagnosis and Monitoring of Neurodegenerative Dementia" Metabolites 13, no. 2: 233. https://doi.org/10.3390/metabo13020233

APA Style

Hyvärinen, E., Solje, E., Vepsäläinen, J., Kullaa, A., & Tynkkynen, T. (2023). Salivary Metabolomics in the Diagnosis and Monitoring of Neurodegenerative Dementia. Metabolites, 13(2), 233. https://doi.org/10.3390/metabo13020233

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