Assessment of the effects of dentifrice on periodontal disease biomarkers in gingival crevicular fluid

Periodontal disease metabolomics signatures from different biofluids: a systematic review

BACKGROUND

Periodontitis is resulted from a complex interaction between genetics and epigenetics, microbial factors, and the host response. Metabolomics analyses reflect both the steady-state physiological equilibrium of cells or organisms as well as their dynamic metabolic responses to environmental stimuli.

AIM OF REVIEW

This systematic review of the literature aimed to assess which low molecular weight metabolites are more often found in biological fluids of individuals with periodontitis compared to individuals with gingivitis or periodontal health.

KEY SCIENTIFIC CONCEPTS OF REVIEW

All the included studies employed untargeted analysis. One or more biological fluids were analyzed, including saliva (n = 14), gingival crevicular fluid (n = 6), mouthwash (n = 1), serum (n = 3) and plasma (n = 1). Fifty-six main metabolites related to periodontitis have been identified in at least two independent studies by NMR spectroscopy or MS-based metabolomics. Saliva was the main biological fluid sampled. It is noteworthy that 14 metabolites of the 56 detected were identified as main metabolites in all studies that sampled the saliva. The majority of metabolites found consistently among studies were amino acids, organic acids and derivates: acetate, alanine, butyrate, formate, GABA, lactate, propionate, phenylalanine and valine. They were either up- or down-regulated in the studies or this information was not mentioned. The main metabolic pathway was related to phenylalanine, tyrosine and tryptophan biosynthesis. Metabolites more frequently found in individuals with periodontitis were related to both the host and to microorganism responses. Future studies are needed, and they should follow some methodological standards to facilitate their comparison.

Metabolic Disturbance and Th17/Treg Imbalance Are Associated With Progression of Gingivitis

Objective

This study sought to explore the role of metabolic disturbance in immunoregulation of gingivitis targeting T helper 17 cells (Th17)/regulatory T cell (Treg).

Materials and Methods

A total of 20 gingivitis patients and 19 healthy volunteers were recruited. Quantitative real time polymerase chain reaction (qRT-PCR) was used to evaluate expression patterns of Forkhead box protein P3 (Foxp3), transforming growth factor-β (TGF-β), retinoid-related orphan receptor-gammat (RORγt) and interleukin 17A (IL-17A) in the peripheral blood lymphocytes of subjects across the two groups. Moreover, the enzyme-linked immunosorbent assay (ELISA) technique was used to detect levels of TGF-β, IL-4, IL-6,TL-10 and L-17A secreted in the plasma as well as the SIgA secreted in saliva. Flow cytometry was used to detect the percentage of CD4⁺CD25⁺ Foxp3⁺Treg cells and the percentage of CD4⁺IL-17A⁺ Th17 cells in whole blood of subjects in both groups. Gas chromatography-mass spectrometry (GC-MS) was employed to analyze the plasma metabolites in the gingivitis patient group. Statistical analysis was applied to determine whether the plasma metabolites and related metabolic pathways significantly differed between gingivitis patients and healthy controls. Ingenuity pathway analysis (IPA) was employed to identify the potential relation between the metabolites and the Th17 and Treg related pathway.

Results

The percentages of CD4⁺IL17A⁺Th17 cells and IL-17 significantly increased in the peripheral blood in the gingivitis group. Moreover, the upregulation of IL-17A mRNA and RORγt mRNA were also found in the gingivitis group. However, the percentage of CD4⁺CD25⁺ Foxp3⁺Treg cells and Foxp3 mRNA in the whole blood did not significantly change. However, TGF-β mRNA as well as TGF-β, IL-4, IL-6, IL-10 in the periperial blood and SIgA in the saliva were higher in the gingivitis group. Notably, that the ratio of Th17/Treg cells was significantly increased during peripheral circulation. Furthermore, we identified 18 different metabolites which were differentially expressed in plasma between the gingivitis and healthy control groups. Notably, the levels of cholesterol, glycerol 1-octadecanoate, d-glucose, uric acid, cyclohexaneacetic acid, 3-pyridine, tryptophan, and undecane 2,4-dimethyl were significantly up-regulated. whereas the levels of lactic acid, glycine, linoleic acid, monopalmitic acid, glycerol, palmitic acid, pyruvate, 1-(3-methylbutyl)-2,3,4,6-tetramethylbenzene, 1 5-anhydro d-altrol, and boric acid were down-regulated in the gingivitis group, relative to healthy controls. IPA showed that these metabolites are connected to IL17 signaling, TGF-B signaling, and IL10 signaling, which are related closely to Th17 and Treg pathway.

Conclusion

Overall, these results showed that disturbance to glycolysis as well as amino and fatty acid metabolism are associated with Th17/Treg balance in gingivitis. Impaired immunometabolism may influence some periodontally involved systemic diseases, hence it is a promising strategy in targeted development of treatment therapies.

Changes in the Salivary Metabolic Profile of Generalized Periodontitis Patients after Non-surgical Periodontal Therapy: A Metabolomic Analysis Using Nuclear Magnetic Resonance Spectroscopy

Pattern analysis of the salivary metabolic profile has been proven accurate in discriminating between generalized periodontitis (GP) patients and healthy individuals (HI), as this disease modifies the salivary concentrations of specific metabolites. Due to the scarcity of data from previous studies, this study aimed to evaluate if non-surgical periodontal therapy (NST) could affect the metabolomic profile in GP patients’ saliva and if it compares to that of HI. Unstimulated salivary samples were collected from 11 HI and 12 GP patients before and 3 months after NST. Nuclear Magnetic Resonance (NMR) spectroscopy, followed by a supervised multivariate statistical approach on entire saliva spectra and partial least square (PLS) discriminant analysis, were performed to obtain metabolic profiles. In the GP group, periodontal treatment improved all clinical parameters, but not all the diseased sites were eradicated. PLS revealed an accuracy of 100% in distinguishing between metabolic profiles of GP patients before and after NST. Orthogonal projection to latent structure was able to discriminate between the three groups of subjects with an accuracy of 85.6%. However, the post-NST metabolic profile of GP patients could not be completely assimilated to that of HI. Although NST may produce significant changes in the metabolic profile, GP patients maintained a distinctive fingerprint compared to HI.

Assessment of mucin and alpha-amylase levels in gingival crevicular fluid of chronic periodontitis patients

BACKGROUND

Chronic periodontitis is the most common form of periodontitis. Several immune and inflammatory factors responsible for periodontal destruction have been found in gingival crevicular fluid (GCF). The current study was conducted to determine the correlation between mucin and alpha-amylase protein values in GCF with chronic periodontitis.

METHOD

Forty-five patients with moderate-to-severe chronic periodontitis were selected. Samples of GCF were taken from a specific part of a single root tooth and placed in a closed test tube containing phosphate-buffered saline (PBS) (pH = 7). Sampling was done again after one month. Pre- and post-treatment samples were analyzed for measuring the levels of mucin and alpha-amylase proteins.

RESULTS

Paired t test results for these two variables showed that the difference between mucin and alpha-amylase levels before and after treatment is significant.

CONCLUSION

The level of both mucin and alpha-amylase in GCF in patients with chronic periodontitis was higher than that of patients who have recovered successfully; and evaluating the values of these two markers could be used to determine the activity of the periodontal disease.

Gingival fibroblasts dynamically reprogram cellular metabolism during infection of Porphyromonas gingivalis

OBJECTIVE

The purpose of the present study was to explore the sequential changes in the cellular metabolism in gingival fibroblasts (GFs) in response toPorphyromonas gingvalis (P. gingivalis) ATCC33277 infection.

DESIGN

GFs were treated withP. gingivalis at the MOI of 50 for 4, 24 and 48 h to mimic the early, medium, and late phase in the bacterial infection. LDH assay and cell counting kit-8 were utilized to explore cell death and proliferation. Real-time PCR was utilized to explore the gene transcription of pro-inflammatory genes. The relative levels of biomolecules in GFs were measured by gas chromatography-mass spectrometry. Principal component analysis and orthogonal partial least-squares-discriminant analysis were performed to visualize the metabolic difference among experimental groups. In addition, pathway analysis was conducted regarding differential metabolites in GFs.

RESULTS

P. gingivalis infection triggered significant gene transcription of IL-1β, IL 6, MCP 1, and MMP 1 in GFs. In addition, P. gingivalis stimulated cell proliferation of GFs at MOI of 10, 50 and 250. Moreover, P. gingivalis triggered significant cell death at higher MOI. 69, 173 and 148 metabolites were qualitatively detected at 4, 24 and 48 h after P. gingivalis infection respectively in GFs, showing a sequential change of different phase. Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated that ATP-binding cassette transporters, glutathione, purine and pyrimidine metabolism was significantly altered in different phase.

CONCLUSIONS

Human GFs may sequentially rewire metabolomics to shape the inflammatory responses and support the proliferation of host cells during P. gingivalis infection.

Triclosan toothpaste as an adjunct therapy to plaque control in children from periodontitis families: a crossover clinical trial

OBJECTIVES

Studies have demonstrated that children from aggressive periodontitis (AgP) parents presented precocious alterations in their periodontal condition, and the use of chemical agents in association to plaque control could be useful to control these alterations. This study aimed to evaluate the effect of Triclosan toothpaste to modulate the clinical and subgingival condition in children from AgP parents.

METHODS

Fifteen children from AgP parents and 15 from periodontally healthy parents were included in this crossover placebo study. Children were randomly allocated into triclosan or placebo therapy, using selected toothpaste for 45 days. After 15 days of wash-out, groups were crossed, changing the used toothpaste. Clinical examination and saliva, crevicular gingival fluid (GCF), and subgingival biofilm collection were performed at baseline and 45 days of each phase. GCF cytokines' levels were analyzed by Luminex/MAGpix platform and subgingival and salivary periodontal pathogens' levels by qPCR.

RESULTS

At baseline, AgP group presented higher plaque index (PI), gingival index (GI), and bleeding on probing (BoP), higher Aggregatibacter actinomycetemcomitans (Aa) abundance in saliva and subgingival biofilm, and lower levels of INF-ɣ, IL-4, and IL-17 in GCF. Placebo therapy only reduced PI in both groups. Triclosan toothpaste reduced PI and GI in both groups. Triclosan promoted reduction of BoP and probing depth (PD), Aa salivary, and IL-1β levels in AgP group. In health group, triclosan reduced INF-ɣ and IL-4 concentration.

CONCLUSION

Triclosan toothpaste demonstrated to be more effective than placebo toothpaste to control the periodontal condition in children from AgP parents, by reducing the BoP, PD, salivary Aa, and IL-1β.

CLINICAL RELEVANCE

Triclosan toothpaste can improve oral conditions in higher-risk population for AgP.

TRIAL REGISTRATION

This study was registered at ClinicalTrials.gov with the identifier NCT03642353.

Metabolomics toward personalized medicine

Metabolomics, which is the metabolites profiling in biological matrices, is a key tool for biomarker discovery and personalized medicine and has great potential to elucidate the ultimate product of the genomic processes. Over the last decade, metabolomics studies have identified several relevant biomarkers involved in complex clinical phenotypes using diverse biological systems. Most diseases result in signature metabolic profiles that reflect the sums of external and internal cellular activities. Metabolomics has a major role in clinical practice as it represents >95% of the workload in clinical laboratories worldwide. Many of these metabolites require different analytical platforms, such as Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), and Ultra Performance Liquid Chromatography (UPLC), while many clinically relevant metabolites are still not routinely amenable to detection using currently available assays. Combining metabolomics with genomics, transcriptomics, and proteomics studies will result in a significantly improved understanding of the disease mechanisms and the pathophysiology of the target clinical phenotype. This comprehensive approach will represent a major step forward toward providing precision medical care, in which individual is accounted for variability in genes, environment, and personal lifestyle. In this review, we compare and evaluate the metabolomics strategies and studies that focus on the discovery of biomarkers that have "personalized" diagnostic, prognostic, and therapeutic value, validated for monitoring disease progression and responses to various management regimens.

The Saliva Metabolome in Association to Oral Health Status

Periodontitis is one of the most prevalent oral diseases worldwide and is caused by multifactorial interactions between host and oral bacteria. Altered cellular metabolism of host and microbes releases a number of intermediary end products known as metabolites. There is an increasing interest in identifying metabolites from oral fluids such as saliva to widen the understanding of the complex pathogenesis of periodontitis. It is believed that some metabolites might serve as indicators toward early detection and screening of periodontitis and perhaps even for monitoring its prognosis in the future. Because contemporary periodontal screening methods are deficient, there is an urgent need for novel approaches in periodontal screening procedures. To this end, we associated oral parameters (clinical attachment level, periodontal probing depth, supragingival plaque, supragingival calculus, number of missing teeth, and removable denture) with a large set of salivary metabolites ( n = 284) obtained by mass spectrometry among a subsample ( n = 909) of nondiabetic participants from the Study of Health in Pomerania (SHIP-Trend-0). Linear regression analyses were performed in age-stratified groups and adjusted for potential confounders. A multifaceted image of associated metabolites ( n = 107) was revealed with considerable differences according to age groups. In the young (20 to 39 y) and middle-aged (40 to 59 y) groups, metabolites were predominantly associated with periodontal variables, whereas among the older subjects (≥60 y), tooth loss was strongly associated with metabolite levels. Metabolites associated with periodontal variables were clearly linked to tissue destruction, host defense mechanisms, and bacterial metabolism. Across all age groups, the bacterial metabolite phenylacetate was significantly associated with periodontal variables. Our results revealed alterations of the salivary metabolome in association with age and oral health status. Among our comprehensive panel of metabolites, periodontitis was significantly associated with the bacterial metabolite phenylacetate, a promising substance for further biomarker research.

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.

Effect of non-surgical periodontal therapy on salivary metabolic fingerprint of generalized chronic periodontitis using nuclear magnetic resonance spectroscopy

OBJECTIVE

Metabolomic analysis of saliva proved its accuracy in discriminating patients with generalized chronic periodontitis (GCP) from healthy subjects by identifying specific molecular signatures of the disease. There is lack of investigations concerning the effect of periodontal treatment on individual metabolic fingerprints. Therefore, the aim of this study was to determine whether non-surgical periodontal therapy could change salivary metabolomic profile in GCP to one more similar to periodontal health.

DESIGN

Unstimulated whole saliva of 32 controls and 19 GCP patients were obtained prior to and 3 months after conventional staged non-surgical periodontal therapy. Metabolic profiling was performed using Nuclear Magnetic Resonance (NMR) spectroscopy, followed by univariate and multivariate paired approaches to assess the changes introduced by the therapy.

RESULTS

In GCP group, periodontal treatment led to an improvement in all clinical parameters (p < 0.001). The accuracy of the multivariate model in discriminating the metabolomic profile of each GCP patient at two time points was 92.5%. Despite the almost perfect separation of the spectra in the metabolic space, the univariate analysis failed to identify significant variations in single metabolite content. The post-treatment metabolic profile of GCP patients could not be assimilated to that of healthy controls who exhibited different levels of lactate, pyruvate, valine, proline, tyrosine, and formate.

CONCLUSIONS

Based on these data, NMR-spectroscopic analysis revealed that, despite significant changes in the overall metabolomic fingerprint after non-surgical therapy, GCP patients maintained a distinctive metabolic profile compared to healthy individuals.

Analysis of metabolic profiles of generalized aggressive periodontitis

BACKGROUND AND OBJECTIVE

The specific pathogenesis of generalized aggressive periodontitis (GAgP) has not yet been clarified, and few studies have focused on the association between GAgP and metabolomics. To elucidate the roles of metabolic profiles in the status of GAgP, this study aimed to identify the differential metabolic profiles between patients with GAgP and healthy controls using an untargeted metabolomic profiling method.

MATERIAL AND METHODS

Serum and gingival crevicular fluid samples were collected from healthy controls (n = 20) and patients with GAgP (n = 20) in this cross-sectional study. The relative levels of biomarkers in the samples were measured by gas chromatography-mass spectrometry. Principal components analysis and orthogonal partial least-squares discriminant analysis were used for statistical analysis. Metabolites were analysed qualitatively using the FiehnLib and NIST databases. Full-mouth probing depth and clinical attachment loss were recorded as indexes of periodontal disease.

RESULTS

A total of 349 metabolites were qualitatively detected in the gingival crevicular fluid samples, and 200 metabolites were detected in the serum samples. Compared with healthy controls, patients with GAgP showed significant increases in serum urea and allo-inositol levels. In contrast, glutathione, 2,5-dihydroxybenzaldehyde, adipic acid and 2-deoxyguanosine levels were decreased in patients with GAgP. In the gingival crevicular fluid samples, noradrenaline, uridine, α-tocopherol, dehydroascorbic acid, xanthine, galactose, glucose-1-phosphate and ribulose-5-phosphate levels were increased in patients with GAgP, while thymidine, glutathione and ribose-5-phosphate levels were decreased.

CONCLUSION

The metabolomics analysis by gas chromatography-mass spectrometry is an effective and minimally non-invasive way to differentiate the metabolites characteristic of patients with GAgP. Both serum and gingival crevicular fluid metabolomics are significantly different between patients with GAgP and healthy controls. These metabolic profiles have great potential in detecting GAgP and helping to understand its underlying mechanisms.

Metabolomics for the masses: The future of metabolomics in a personalized world

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics '('omic)' approaches have been used for therapeutic interventions previously. Metabolomics now a well-established'omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.

Metabolomic Analysis of Gingival Crevicular Fluid Using Gas Chromatography/Mass Spectrometry

Periodontitis is one of the most prevalent threats to oral health as the most common cause of tooth loss. In order to perform effective treatment, a clinical test that detect sites where disease activity is high and predicts periodontal tissue destruction is strongly desired, however, it is still difficult to prognose the periodontal tissue breakdown on the basis of conventional methods. The aim of this study is to examine the usefulness of gas chromatography/mass spectrometry (GC/MS), which could eventually be used for on-site analysis of metabolites in gingival crevicular fluid (GCF) in order to objectively diagnose periodontitis at a molecular level. GCF samples were collected from two diseased sites (one site with a moderate pocket and another site with a deep pocket) from each patient and from clinically healthy sites of volunteers. Nineteen metabolites were identified using GC/MS. Total ion current chromatograms showed broad differences in metabolite peak patterns between GCF samples obtained from healthy sites, moderate-pocket sites, and deep-pocket sites. The intensity difference of some metabolites was significant at sites with deep pockets compared to healthy sites. Additionally, metabolite intensities at moderate-pocket sites showed an intermediate profile between the severely diseased sites and healthy sites, which suggested that periodontitis progression could be observed with a changing metabolite profile. Principal component analysis confirmed these observations by clearly delineating healthy sites and sites with deep pockets. These results suggest that metabolomic analysis of GCF could be useful for prediction and diagnosis of periodontal disease in a single visit from a patient and provides the groundwork for establishing a new, on-site diagnostic method for periodontitis.

Salivary metabolomics in the diagnosis of oral cancer and periodontal diseases

Metabolomics is a systemic study of metabolites, which are small molecules generated by the process of metabolism. The metabolic profile of saliva can provide an early outlook of the changes associated with a wide range of diseases, including oral cancer and periodontal diseases. It is possible to measure levels of disease-specific metabolites using different methods as presented in this study. However, many challenges exist including incomplete understanding of the complicated metabolic pathways of different oral diseases. The review concludes with the discussion on future perspectives of salivary metabolomics from a clinician point of view. Salivary metabolomics may afford a new research avenue to identify local and systemic disorders but also to aid in the design and modification of therapies. A MEDLINE search using keywords "salivary metabolomics" returned 23 results in total, of which seven were omitted for being reviews or letters to the editor. The rest of the articles were used for preparation of the review, 13 of these were published in the last 5 years.

Global metabolomic analysis of human saliva and plasma from healthy and diabetic subjects, with and without periodontal disease

Recent studies suggest that periodontal disease and type 2 diabetes mellitus are bi-directionally associated. Identification of a molecular signature for periodontitis using unbiased metabolic profiling could allow identification of biomarkers to assist in the diagnosis and monitoring of both diabetes and periodontal disease. This cross-sectional study identified plasma and salivary metabolic products associated with periodontitis and/or diabetes in order to discover biomarkers that may differentiate or demonstrate an interaction of these diseases. Saliva and plasma samples were analyzed from 161 diabetic and non-diabetic human subjects with a healthy periodontium, gingivitis and periodontitis. Metabolite profiling was performed using Metabolon's platform technology. A total of 772 metabolites were found in plasma and 475 in saliva. Diabetics had significantly higher levels of glucose and α-hydroxybutyrate, the established markers of diabetes, for all periodontal groups of subjects. Comparison of healthy, gingivitis and periodontitis saliva samples within the non-diabetic group confirmed findings from previous studies that included increased levels of markers of cellular energetic stress, increased purine degradation and glutathione metabolism through increased levels of oxidized glutathione and cysteine-glutathione disulfide, markers of oxidative stress, including increased purine degradation metabolites (e.g. guanosine and inosine), increased amino acid levels suggesting protein degradation, and increased ω-3 (docosapentaenoate) and ω-6 fatty acid (linoleate and arachidonate) signatures. Differences in saliva between diabetic and non-diabetic cohorts showed altered signatures of carbohydrate, lipid and oxidative stress exist in the diabetic samples. Global untargeted metabolic profiling of human saliva in diabetics replicated the metabolite signature of periodontal disease progression in non-diabetic patients and revealed unique metabolic signatures associated with periodontal disease in diabetics. The metabolites identified in this study that discriminated the periodontal groups may be useful for developing diagnostics and therapeutics tailored to the diabetic population.

Mass spectrometry-based metabolomic profiling identifies alterations in salivary redox status and fatty acid metabolism in response to inflammation and oxidative stress in periodontal disease

Periodontal diseases represent the most common chronic inflammatory diseases in humans and a major cause of tooth loss. Combining mass spectrometry-based ionomics and targeted lipidomics on fatty acid metabolites, we identified significant alterations in redox status and fatty acid metabolism in saliva in response to chronic inflammation and oxidative stress in periodontal disease in a cohort of nonsmoker subjects with chronic periodontitis. For the first time, ionomic profiling of around 30 ions in saliva revealed significantly decreased levels of redox-active metal ions including Mn, Cu, and Zn in the periodontal group, which is consistent with decreased levels of superoxide dismutases in saliva and serum. A targeted lipidomic approach was employed to monitor the major metabolites of arachidonic acid and linoleic acid in saliva. We observed increased levels of cyclooxygenase products including PGE2, PGD2, and PGF2α and TXB2, but decreased level of PGI2 in the periodontal group. A unique pattern of the lipoxygenase products of arachidonic acid and linoleic acid was observed with increased level of 5-HETE but decreased levels of 13-HODE and 9-HODE. Levels of salivary F2-isoprostanes, free radical lipid peroxidation products, and a gold standard for oxidative stress in vivo were also significantly elevated. Taking these data together, our study using multiple powerful omics techniques demonstrates that local redox alteration contributes significantly to periodontitis through the modulation of fatty acid metabolism in response to inflammation and oxidative stress. This study highlights the importance of redox status in periodontitis and provides a rationale for preventing periodontal disease by dietary interventions aiming to restore redox balance.

Application of metabolomics to diagnosis of insulin resistance

Metabolomics, the global interrogation of the biochemical components in a biological sample, has become an important complement to genomics and proteomics to aid in the understanding of pathophysiology. Major advantages of metabolomics are the size of the metabolome relative to the genome or proteome and the fact that it provides a view of the existing biochemical phenotype. As such, metabolomics is fast becoming an important discovery tool for new diagnostic and prognostic biomarkers. Although many methods exist for performing metabolomics, relatively few have led to successful development of new diagnostic tests. This review will aid the reader in understanding various metabolomic methods and their applications, as well as some of their inherent advantages and disadvantages. In addition, we present one example of the application of metabolomics to the identification of new fasting blood biomarkers for the diagnosis and monitoring of insulin resistance.

Metabolomics reveals elevated macromolecular degradation in periodontal disease

Periodontitis is a chronic inflammatory disease characterized by tissue destruction. In the diseased oral environment, saliva has primarily been considered to act as a protectant by lubricating the tissue, mineralizing the bones, neutralizing the pH, and combating microbes. To understand the metabolic role that saliva plays in the diseased state, we performed untargeted metabolomic profiling of saliva from healthy and periodontitic individuals. Several classes of biochemicals, including dipeptide, amino acid, carbohydrate, lipids, and nucleotide metabolites, were altered, consistent with increased macromolecular degradation of proteins, triacylglycerol, glycerolphospholipids, polysaccharides, and polynucleotides in the individuals with periodontal disease. These changes partially reflected the enhanced host-bacterial interactions in the diseased state as supported by increased levels of bacterially modified amino acids and creatine metabolite. More importantly, the increased lipase, protease, and glycosidase activities associated with periodontitis generated a more favorable energy environment for oral bacteria, potentially exacerbating the disease state.