Saliva as Biomarker for Oral and Chronic Degenerative Non-Communicable Diseases
Abstract
:1. Introduction
2. Search Methods
3. Potential Role of Salivary Biomarkers in the Diagnosis of Chronic Degenerative NCDs
4. The Role of Current Salivary Biomarkers in Use and of Oral Microbiota
- -
- Next generation sequencing (NGS): gene marker analysis (the most used are 16S ribosomal RNA gene sequencing and internal transcribed spacer (ITS)), shogun metagenomics (using untargeted sequencing method), and metatrascriptomics;
- -
- Liquid chromatography–mass spectrometry (LC/CG-MS): metabolomics and metaproteomics.
5. Sample Collection for Saliva Proteomics
- Flow rate: stimulation, such as chewing, talking, or smelling food leads to an increased flow of saliva compared to the resting or unstimulated state. Flow rate affects the concentrations of various salivary components.
- Water content: unstimulated saliva has a lower water content compared to stimulated saliva, and stimulation triggers the release of larger volumes of watery saliva.
- Protein composition: the protein composition of saliva can differ between stimulated and unstimulated states. Stimulation typically leads to increased secretion of proteins, such as amylase, mucins and immunoglobulins. These proteins play roles in enzymatic digestion, lubrication and immune defense.
- Electrolytes: stimulation can affect the concentration of electrolytes in saliva. Stimulated saliva often contains higher levels of electrolytes like sodium, potassium, calcium and bicarbonate compared to unstimulated saliva. These electrolytes are important for maintaining oral pH balance and overall oral health.
- pH level: stimulation can result in changes in salivary pH. Unstimulated saliva generally has a slightly acidic pH, while stimulated saliva tends to be more neutral or slightly alkaline. The buffering capacity of stimulated saliva helps in neutralizing acids and maintaining a healthier oral environment.
- Enzymes: stimulation triggers the release of various enzymes in saliva. For example, stimulated saliva contains higher levels of alpha-amylase, which initiates the digestion of carbohydrates. Other enzymes, such as lingual lipase and lysozyme, may be more abundant in stimulated saliva.
- Immunological factors: the immune-related components in saliva, including immunoglobulins (e.g., IgA) and antimicrobial peptides, may be influenced by stimulation. Increased saliva flow during stimulation can enhance the presence of these immune factors, contributing to oral defense mechanisms.
6. Impact of Oral Diseases on Oral Microbiota Composition
- (i)
- the haematogenic route, whereby oral bacteria systematically circulate until they colonize the gastrointestinal mucosa;
- (ii)
- the enteral route, in which bacteria from the oral cavity, via the stomach, reach the intestine.
A | Phase of life | Age-related changes in Oral Microbiota | Reference |
Newborn | Streptococcus (S. salivarius is the pioneer and then S. sanguinis, S. peroris, S. lactarius), Actinomyces | [82] | |
Child (after 1 year of life) | Streptococcus (S. mutans), Granulicatella, Actinomyces (A. odontolyticus), Fusobacterium, Abiotrophia | [82] | |
Adult | Streptococcus, Lactobacillus, Bifidobacterium, Neisseria, Haemophilus, Corynebacterium, Rothia, Actinomyces, Prevotella, Capnocytophaga, Porphyromonas | [83] | |
Elderly | Increase in Prevotella, Veillonella, Streptococcus, Candida | [82] | |
B | Presence of healthy oral cavity or oral diseases | Changes in Oral Microbiota related to oral cavity health | |
Healthy subjects | Streptococcus, Lactobacillus, Bifidobacterium, Neisseria, Haemophilus, Corynebacterium, Rothia, Actinomyces, Prevotella, Capnocytophaga, Porphyromonas | [83] | |
Periodontal Diseases | Increase in Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola | [84] [85] | |
Caries | Increase in Streptococcus mutans, Lactobacillus, Propionibacterium, Atopobium genera, Scardovia wiggsiae | [86] [87] | |
Root infections | Increase in Enterococcus faecalis, Filifactor alocis, Pseudoramibacter alactolyticus, Parvimonas micra, Propionibacterium propionicus, Streptococcus constellatus, Streptococcus anginosus | [90] | |
C | NCDs | Oral microbiota changes in NCDs | |
CKD | Increase in Lautropia, Pseudomonas, Neisseria and decrease in Actinomyces, Prevotella, Veillonella, Haemophilus, Trichococcus | [92] | |
Gastrointestinal diseases | Increase in Staphylococcus, Porphyromonas, Veillonella, Fusobacterium, Actinomyces, Parvimonas | [93] | |
Diabetes mellitus | Increase in Porphyromonas gingivalis, Tannerella forsythia, Filifactor alocis | [13] | |
Arterial hypertension | Increase in Actinobacillus actinomycetemcomitans | [13] |
7. Pharmacological Treatment of Oral Dysbiosis
8. The Possible Link between Oral Dysbiosis and Gut Dysbiosis and Its Influence on CKD Onset and Progression
- (i)
- HD can restore the intravascular volume because of an ultrafiltration mechanism. The latter causes a high gland perfusion able to stimulate saliva production [155].
- (ii)
- HD treatment corrects blood concentrations of electrolytes and bicarbonate and reduces serum creatinine and urea levels. This correction induces a higher production of saliva, and it may also happen after the first hemodialysis session [156].
- (iii)
- HD session reduces arterial blood pressure. This phenomenon could favor the sympathetic activity of salivary glands and, therefore, the production of saliva [142].
- (i)
- gut bacteria produce metabolites, such as TMAO, IS, PCS and phenylacetylglutamine (PAG), with a toxic action against the kidneys and the cardiovascular system [99].
- (ii)
- In CKD patients, there are alterations in the gut microflora characterized by an increase in pathogenic species. In these patients, alterations in gut permeability can be observed. These permeability alterations allow the translocation of endotoxins into the bloodstream. This phenomenon worsens the systemic low-grade inflammatory state, accelerating the CKD progression [183].
- (i)
- twelve of the nineteen bacteria families with urease activity, including Clostridiaceae, Dermabacteraceae, Halomonadaceae, Methylococcaceae, Alteromonadaceae, Cellulomonadaceae, Pseudomonadaceae, Xanthomonadaceae, Enterobacteriaceae, Moraxellaceae, Polyangiaceae, and Micrococcaceae;
- (ii)
- three families with tryptophanase activity, including Verrucomicrobiaceae, Clostridiaceae, and Enterobacteriaceae;
- (iii)
- five families with uricase activity, including Dermabacteraceae, Micrococcaceae, Cellulomonadaceae, Xanthomonadaceae, and Polyangiaceae.
9. Pharmacological and Nutritional Treatment of Gut Dysbiosis in CKD
10. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Salivary Biomarker | Method | Pathological Condition | Reference |
---|---|---|---|
Alfa-amylase | Enzymatic colorimetric Chromolytic assay | Diabetes mellitus Renal diseases Physical exercise CVD | [27,28,29,30] |
Glucose | Colorimetric assay Enzymatic colorimetric with guaiacol diazo derivative Luminescent method LC-MS GC-MS | Diabetes mellitus Arterial hypertension CVD Caries Periodontitis Obesity | [27,31,32,33,34,35] |
Calcium | LC-MS GC-MS | Caries Periodontitis Arterial hypertension Alzheimer’s disease Parkinson’s disease Diabetes mellitus | [14,36,37,38,39] |
Magnesium | LC-MS GC-MS | Caries Periodontitis Diabetes mellitus | [36,38,39] |
Testosterone | ELISA CLIA LC-MS/MS Radioimmunoassay Different HPLC-MS/MS | Periodontitis Diabetes mellitus Obesity | [40,41,42,43,44,45,46] |
C-reactive protein | ELISA Immuno-turbidimetric method EIA | Periodontitis Oral disorders CVD Pneumonia Metabolic disorders HIV CKD COVID-19 Rheumatic disease | [47,48,49,50,51] |
Cortisol | Enzyme immunoassay Enzyme immunoassay ELISA ECLIA RIA | CVD Oxidative stress Physiological stress Metabolic syndrome Obesity | [27,41,52,53] |
SARS-CoV-2 specific IgA | EIA ELISA designed for POC ELISA | COVID-19 | [54,55,56] |
SARS-CoV-2 antigen | Rapid Salivary Test (RST) based on the LFA Electrochemiluminescence (ECL)-based immunoassay | COVID-19 | [57,58] |
RNA SARS- CoV-2 | rRT-PCR Colorimetric RT-LAMP assay Saliva-based, loop-mediated, isothermal amplification (LAMP) technology | COVID-19 | [57,59,60,61] |
Interleukines (IL-1β, IL-2, IL-4, IL-5, IL-6, IL-10) | ELISA Bead-based Xmap Bead-based flow cytometry | Lung cancer Graft-versus-host disease Mucositits HIV OSCC CVD Rheumatic diseases | [27,51,62,63,64,65,66,67] |
TNFα | ELISA Bead-based xMAP Bead-based flow cytometry | Mucostitis Lung cancer HIV Tubercolosis OSCC CVD Rheumatic diseases | [27,51,62,66,67,68,69] |
INFγ | ELISA Bead-based xMAP Bead-based flow cytometry | Lung cancer HIV Tubercolosis CVD | [27,62,67,69] |
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Basilicata, M.; Pieri, M.; Marrone, G.; Nicolai, E.; Di Lauro, M.; Paolino, V.; Tomassetti, F.; Vivarini, I.; Bollero, P.; Bernardini, S.; et al. Saliva as Biomarker for Oral and Chronic Degenerative Non-Communicable Diseases. Metabolites 2023, 13, 889. https://doi.org/10.3390/metabo13080889
Basilicata M, Pieri M, Marrone G, Nicolai E, Di Lauro M, Paolino V, Tomassetti F, Vivarini I, Bollero P, Bernardini S, et al. Saliva as Biomarker for Oral and Chronic Degenerative Non-Communicable Diseases. Metabolites. 2023; 13(8):889. https://doi.org/10.3390/metabo13080889
Chicago/Turabian StyleBasilicata, Michele, Massimo Pieri, Giulia Marrone, Eleonora Nicolai, Manuela Di Lauro, Vincenza Paolino, Flaminia Tomassetti, Ilaria Vivarini, Patrizio Bollero, Sergio Bernardini, and et al. 2023. "Saliva as Biomarker for Oral and Chronic Degenerative Non-Communicable Diseases" Metabolites 13, no. 8: 889. https://doi.org/10.3390/metabo13080889
APA StyleBasilicata, M., Pieri, M., Marrone, G., Nicolai, E., Di Lauro, M., Paolino, V., Tomassetti, F., Vivarini, I., Bollero, P., Bernardini, S., & Noce, A. (2023). Saliva as Biomarker for Oral and Chronic Degenerative Non-Communicable Diseases. Metabolites, 13(8), 889. https://doi.org/10.3390/metabo13080889