Diagnostic Biomarkers of Alzheimer's Disease as Identified in Saliva using 1H NMR-Based Metabolomics

Salivary metabolites are promising noninvasive biomarkers of drug-induced liver injury

BACKGROUND

Drug-induced liver injury (DILI) is one of the most common adverse events of medication use, and its incidence is increasing. However, early detection of DILI is a crucial challenge due to a lack of biomarkers and noninvasive tests.

AIM

To identify salivary metabolic biomarkers of DILI for the future development of noninvasive diagnostic tools.

METHODS

Saliva samples from 31 DILI patients and 35 healthy controls (HCs) were subjected to untargeted metabolomics using ultrahigh-pressure liquid chromatography coupled with tandem mass spectrometry. Subsequent analyses, including partial least squares-discriminant analysis modeling, t tests and weighted metabolite coexpression network analysis (WMCNA), were conducted to identify key differentially expressed metabolites (DEMs) and metabolite sets. Furthermore, we utilized least absolute shrinkage and selection operato and random fores analyses for biomarker prediction. The use of each metabolite and metabolite set to detect DILI was evaluated with area under the receiver operating characteristic curves.

RESULTS

We found 247 differentially expressed salivary metabolites between the DILI group and the HC group. Using WMCNA, we identified a set of 8 DEMs closely related to liver injury for further prediction testing. Interestingly, the distinct separation of DILI patients and HCs was achieved with five metabolites, namely, 12-hydroxydodecanoic acid, 3-hydroxydecanoic acid, tetradecanedioic acid, hypoxanthine, and inosine (area under the curve: 0.733-1).

CONCLUSION

Salivary metabolomics revealed previously unreported metabolic alterations and diagnostic biomarkers in the saliva of DILI patients. Our study may provide a potentially feasible and noninvasive diagnostic method for DILI, but further validation is needed.

The potential roles of salivary biomarkers in neurodegenerative diseases

Current research efforts on neurodegenerative diseases are focused on identifying novel and reliable biomarkers for early diagnosis and insight into disease progression. Salivary analysis is gaining increasing interest as a promising source of biomarkers and matrices for measuring neurodegenerative diseases. Saliva collection offers multiple advantages over the currently detected biofluids as it is easily accessible, non-invasive, and repeatable, allowing early diagnosis and timely treatment of the diseases. Here, we review the existing findings on salivary biomarkers and address the potential value in diagnosing neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease and Amyotrophic lateral sclerosis. Based on the available research, β-amyloid, tau protein, α-synuclein, DJ-1, Huntington protein in saliva profiles display reliability and validity as the biomarkers of neurodegenerative diseases.

Non-invasive Biomarkers for Early Detection of Alzheimer's Disease: a New-Age Perspective

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects the elderly population. It gradually leads to memory loss, loss of thinking ability, and an overall cognitive decline. However, exhaustive literature is available to suggest that pathological changes in the brain occur decades before the first clinical symptoms appear. This review provides insight into the non-invasive biomarkers for early detection of AD that have been successfully studied in populations across the globe. These biomarkers have been detected in the blood, saliva, breath, and urine samples. Retinal imaging techniques are also reported. In this study, PubMed and Google scholar were the databases employed using keywords "Alzheimer's disease," "neurodegeneration," "non-invasive biomarkers," "early diagnosis," "blood-based biomarkers," and "preclinical AD," among others. The evaluation of these biomarkers will provide early diagnosis of AD in the preclinical stages due to their positive correlation with brain pathology in AD. Early diagnosis with reliable and timely intervention can effectively manage this disease.

Saliva: a challenging human fluid to diagnose brain disorders with a focus on Alzheimer's disease

Biomarkers are molecules of biological processes that help in both the diagnosis of human diseases and in follow-up assessments of therapeutic responses. Biomarkers can be measured in many human fluids, such as blood, cerebrospinal fluid, urine, and saliva. The -omics methods (genomics, RNomics, proteomics, and metabolomics) are useful at measuring thousands of markers in a small volume. Saliva is a human fluid that is easily accessible, without any ethical concerns. Yet, saliva remains unexplored in regard to many human disease biomarkers. In this review, we will give an overview on saliva and how it can be influenced by exogenous factors. As we focus on the potential use of saliva as a diagnostic tool in brain disorders (especially Alzheimer's disease), we will cover how saliva is linked to the brain. We will discuss that saliva is a heterogeneous human fluid, yet useful for the discovery of biomarkers in human disorders. However, a procedure and consensus that is controlled, validated, and standardized for the collection and processing of saliva is required, followed by a highly sensitive diagnostic approach.

NMR-based metabolomics in Alzheimer's disease research: a review

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and represents the most common cause of dementia in the elderly population worldwide. Currently, there is no cure for AD, and the continuous increase in the number of susceptible individuals poses one of the most significant emerging threats to public health. However, the molecular pathways involved in the onset and progression of AD are not fully understood. This information is crucial for developing less invasive diagnostic instruments and discovering novel potential therapeutic targets. Metabolomics studies the complete ensemble of endogenous and exogenous metabolites present in biological specimens and may provide an interesting approach to identify alterations in multiple biochemical processes associated with AD onset and evolution. In this mini review, we summarize the results from metabolomic studies conducted using nuclear magnetic resonance (NMR) spectroscopy on human biological samples (blood derivatives, cerebrospinal fluid, urine, saliva, and tissues) from AD patients. We describe the metabolic alterations identified in AD patients compared to controls and to patients diagnosed with mild cognitive impairment (MCI). Moreover, we discuss the challenges and issues associated with the application of NMR-based metabolomics in the context of AD research.

Salivary metabolomic profile associated with cariogenic risk in children

OBJECTIVES

To identify the metabolomic differences in the saliva of healthy children versus children with active carious lesions and to estimate the predictive capacity of a model based on the salivary metabolomic profile.

METHODS

A study of cases (n = 31) and controls (n = 37) was designed for children aged between 6 and 12 (mean age of the cases: 8.9; controls: 8.7). The said children attended public health centers in Valencia, Spain. Intraoral examinations were performed by a single examiner using ICDAS II diagnostic criteria. Unstimulated total saliva samples were analyzed by nuclear magnetic resonance (NMR) spectroscopy.

RESULTS

The dft index for cases was 2.84 while it was 0.19 for the control group, the DMFT index was 1.13 and 0.11, respectively. The predictive model generated by the multivariate PLS-DA analysis projects a separation between the cases and the controls on the score chart with a predictive capacity and generating an area under the curve of 0.71. The metabolites: 3-methyl-2-oxovalerate, 3-hydroxybutyrate, lactate, acetone, citrate, ornithine, ethanolamine, taurine, proline, glycine, mannose, glucose, 1-6-Anhydro-β-d-glucose and citraconate, are those that show greater significance in the model. In the controls, glycine (Cohen's d = 0.430) and glucose (Cohen's d = 0.560) present higher means compared to the cases. On the contrary, taurine (Cohen's d= -0.474) and mannose (Cohen's d= -0.456) show higher means in cases compared to controls.

CONCLUSIONS

Our findings show a difference in the salivary metabolomic profiles, specifically in the groups of saccharides and amino acids, suggesting an association of these with the level of caries risk.

CLINICAL SIGNIFICANCE

The results reported in the present study reinforce the use of salivary metabolomics as a research method for the search for salivary biomarkers that allow the evaluation of caries risk in patients. Furthermore, it brings us closer to a personalized medicine that will help in dental caries prevention strategies.

An Investigation into the Relationship of Circulating Gut Microbiome Molecules and Inflammatory Markers with the Risk of Incident Dementia in Later Life

The gut microbiome may be involved in the occurrence of dementia primarily through the molecular mechanisms of producing bioactive molecules and promoting inflammation. Epidemiological evidence linking gut microbiome molecules and inflammatory markers to dementia risk has been mixed, and the intricate interplay between these groups of biomarkers suggests that their joint investigation in the context of dementia is warranted. We aimed to simultaneously investigate the association of circulating levels of selected gut microbiome molecules and inflammatory markers with dementia risk. This case-cohort epidemiological study included 805 individuals (83 years, 66% women) free of dementia at baseline. Plasma levels of 19 selected gut microbiome molecules comprising lipopolysaccharide, short-chain fatty acids, and indole-containing tryptophan metabolites as well as four inflammatory markers measured at baseline were linked to incident all-cause (ACD) and Alzheimer's disease dementia (AD) in binary outcomes and time-to-dementia analyses. Independent of several covariates, seven gut microbiome molecules, 5-hydroxyindole-3-acetic acid, indole-3-butyric acid, indole-3-acryloylglycine, indole-3-lactic acid, indole-3-acetic acid methyl ester, isobutyric acid, and 2-methylbutyric acid, but no inflammatory markers discriminated incident dementia cases from non-cases. Furthermore, 5-hydroxyindole-3-acetic acid (hazard ratio: 0.58; 0.36-0.94, P = 0.025) was associated with time-to-ACD. These molecules underpin gut microbiome-host interactions in the development of dementia and they may be crucial in its prevention and intervention strategies. Future larger epidemiological studies are needed to confirm our findings, specifically in exploring the repeatedly measured circulating levels of these molecules and investigating their causal relationship with dementia risk.

Identification of Salivary Metabolic Signatures Associated with Primary Sjögren's Disease

Sjögren's disease (SjD) is the second most prevalent autoimmune disorder that involves chronic inflammation of exocrine glands. Correct diagnosis of primary SjD (pSjD) can span over many years since disease symptoms manifest only in advanced stages of salivary and lachrymal glandular destruction, and consensus diagnostic methods have critical sensitivity and selectivity limitations. Using nuclear magnetic resonance (NMR) spectroscopy, we determined the composition of metabolites in unstimulated saliva samples from 30 pSjD subjects and 30 participants who do not have Sjögren's disease (non-Sjögren's control group, NS-C). Thirty-four metabolites were quantified in each sample, and analysis was conducted on both non-normalized (concentration) and normalized metabolomics data from all study participants (ages 23-78) and on an age-restricted subset of the data (ages 30-70) while applying false discovery rate correction in determining data significance. The normalized data of saliva samples from all study participants, and of the age-restricted subset, indicated significant increases in the levels of glucose, glycerol, taurine, and lactate, as well as significant decreases in the levels of 5-aminopentanoate, acetate, butyrate and propionate, in subjects with pSjD compared to subjects in the NS-C group. Additionally, a significant increase in choline was found only in the age-restricted subset, and a significant decrease in fucose was found only in the whole study population in normalized data of saliva samples from the pSjD group compared to the NS-C group. Metabolite concentration data of saliva samples from all study participants, but not from the age-restricted subset, indicated significant increases in the levels of glucose, glycerol, taurine, and lactate in subjects with pSjD compared to controls. The study showed that NMR metabolomics can be implemented in defining salivary metabolic signatures that are associated with disease status, and can contribute to differential analysis between subjects with pSjD and those who are not affected with this disease, in the clinic.

Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing?

Alzheimer's disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.

Identification of a Biomarker Panel for Diagnosis of Early Childhood Caries Using Salivary Metabolic Profile

Several studies have demonstrated that nuclear magnetic resonance (NMR) metabolic profiles can differentiate patients with caries from healthy individuals; however, these studies only identified individual metabolites. The present study aimed to identify a salivary metabolite biomarker panel for the diagnosis of early childhood caries (ECC). Saliva samples from children with and without caries were analyzed using NMR spectroscopy. Multivariate and univariate analyses were performed to identify the discriminating metabolites. Selected metabolites were further evaluated and used to detect ECC. The saliva samples of children with ECC were characterized based on the increased levels of formate, glycerophosphocholine, and lactate and reduced levels of alanine, glycine, isoleucine, lysine, proline, and tyrosine. The levels of these metabolites were significantly different from those in the control in the ECC subgroup according to caries severity and correlated with the number of decayed and filled teeth or surfaces. Subsequently, an optimal salivary metabolite biomarker panel comprising formate, lactate, proline, and glycine was developed. This panel exhibited a better diagnostic performance for ECC than a single metabolite. These results demonstrate that salivary metabolic signatures can reflect oral conditions associated with dental caries, thereby emphasizing the importance of distinct salivary metabolic profiles as potential biomarkers of ECC.

Salivary Metabolomics in the Diagnosis and Monitoring of Neurodegenerative Dementia

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.

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

This communication represents Part III of our series of reports based on the applications of human saliva as a useful and conveniently collectable medium for the discovery, identification and monitoring of biomarkers, which are of some merit for the diagnosis of human diseases. Such biomarkers, or others reflecting the dysfunction of specific disease-associated metabolic pathways, may also be employed for the prognostic pathological tracking of these diseases. Part I of this series set the experimental and logistical groundwork for this report, and the preceding paper, Part II, featured the applications of newly developed metabolomics technologies to the diagnosis and severity grading of human cancer conditions, both oral and systemic. Clearly, there are many benefits, both scientific and economic, associated with the donation of human saliva samples (usually as whole mouth saliva) from humans consenting to and participating in investigations focused on the discovery of biomolecular markers of diseases. These include usually non-invasive collection protocols, relatively low cost when compared against blood sample collection, and no requirement for clinical supervision during collection episodes. This paper is centred on the employment and value of 'state-of-the-art' metabolomics technologies to the diagnosis and prognosis of a wide range of non-cancerous human diseases. Firstly, these include common oral diseases such as periodontal diseases (from type 1 (gingivitis) to type 4 (advanced periodontitis)), and dental caries. Secondly, a wide range of extra-oral (systemic) conditions are covered, most notably diabetes types 1 and 2, cardiovascular and neurological diseases, and Sjögren's syndrome, along with a series of viral infections, e.g., pharyngitis, influenza, HIV and COVID-19. Since the authors' major research interests lie in the area of the principles and applications of NMR-linked metabolomics techniques, many, but not all, of the studies reviewed were conducted using these technologies, with special attention being given to recommended protocols for their operation and management, for example, satisfactory experimental model designs; sample collection and laboratory processing techniques; the selection of sample-specific NMR pulse sequences for saliva analysis; and strategies available for the confirmation of resonance assignments for both endogenous and exogenous molecules in this biofluid. This article also features an original case study, which is focussed on the use of NMR-based salivary metabolomics techniques to provide some key biomarkers for the diagnosis of pharyngitis, and an example of how to 'police' such studies and to recognise participants who perceive that they actually have this disorder but do not from their metabolic profiles and multivariate analysis pattern-based clusterings. The biochemical and clinical significance of these multidimensional metabolomics investigations are discussed in detail.

The Microbiome-Gut-Brain Axis and Dementia: A Bibliometric Analysis

BACKGROUND

Associations between the microbiome-gut-brain axis and dementia have attracted considerable attention in research literature. This study examined the microbiome-gut-brain axis and dementia-related research from a bibliometric perspective.

METHODS

A search for original research and review articles on the microbiome-gut-brain axis and dementia was conducted in the Web of Science Core Collection (WOSCC) database. The R package "bibliometrix" was used to collect information on countries, institutions, authors, journals, and keywords. VOSviewer software was used to visualize the co-occurrence network of keywords.

RESULTS

Overall, 494 articles met the study inclusion criteria, with an average of 29.64 citations per article. Corresponding authors of published articles were mainly from China, the United States and Italy. Zhejiang University in China and Kyung Hee University in Korea were the most active institutions, while the Journal of Alzheimer's Disease and Nutrients published the most articles in this field. Expected main search terms, "Parkinson disease" and "chain fatty-acids" were high-frequency keywords that indicate current and future research directions in this field.

CONCLUSIONS

This bibliometric study helped researchers to identify the key topics and trends in the microbiome-gut-brain axis and dementia-related research. High-frequency keywords identified in this study reflect current trends and possible future directions in this field related to methodologies, mechanisms and populations of interest.

Exploration of the Gut-Brain Axis through Metabolomics Identifies Serum Propionic Acid Associated with Higher Cognitive Decline in Older Persons

The gut microbiome is involved in nutrient metabolism and produces metabolites that, via the gut−brain axis, signal to the brain and influence cognition. Human studies have so far had limited success in identifying early metabolic alterations linked to cognitive aging, likely due to limitations in metabolite coverage or follow-ups. Older persons from the Three-City population-based cohort who had not been diagnosed with dementia at the time of blood sampling were included, and repeated measures of cognition over 12 subsequent years were collected. Using a targeted metabolomics platform, we identified 72 circulating gut-derived metabolites in a case−control study on cognitive decline, nested within the cohort (discovery n = 418; validation n = 420). Higher serum levels of propionic acid, a short-chain fatty acid, were associated with increased odds of cognitive decline (OR for 1 SD = 1.40 (95% CI 1.11, 1.75) for discovery and 1.26 (1.02, 1.55) for validation). Additional analyses suggested mediation by hypercholesterolemia and diabetes. Propionic acid strongly correlated with blood glucose (r = 0.79) and with intakes of meat and cheese (r > 0.15), but not fiber (r = 0.04), suggesting a minor role of prebiotic foods per se, but a possible link to processed foods, in which propionic acid is a common preservative. The adverse impact of propionic acid on metabolism and cognition deserves further investigation.

Saliva tau and phospho-tau-181 measured by Lumipulse in patients with Alzheimer's disease

Alzheimer's disease (AD) is a severe neurodegenerative brain disorder. The determination of beta-amyloid (Aβ)-40, -42, total tau, and phospho-tau-181 (pTau181) in cerebrospinal fluid (CSF) using Lumipulse technology has been established as biomarkers for AD in recent years. As CSF collection is an invasive procedure, one aims to find biomarkers in blood or other human fluids, such as saliva. In the present study, we aim to measure these markers in human saliva. Using Salivettes, we collected saliva samples from healthy controls (n = 27), patients with AD dementia (n = 44), mild cognitive impairment (MCI) (n = 45), depression (n = 31), and 21 blinded samples, all older than 60 years. Lumipulse technology with a G600II was used to detect all four biomarkers. Our data show that the levels of total protein were highly variable and thus biomarker levels were corrected to 1 mg/ml of total protein. Saliva Aβ-40 and -42 were not detectable, because it was not recovered from the Salivettes. However, saliva total tau (577 ± 134 pg/mg, n = 22) and phospho-tau-181 (9.7 ± 1.3 pg/mg, n = 21) could be analyzed by Lumipulse technology. Saliva total tau levels were significantly decreased in patients with AD (≤ 300 pg/mg protein), while pTau181 levels (≥ 18 pg/mg protein) were significantly enhanced in patients with MCI compared to controls. Laboratory diagnosis with a cut-off of ≥ 18 pg/mg protein pTau181 (for MCI) and ≤ 300 pg/mg protein tau (for AD) for blinded samples could diagnose MCI and AD with an accuracy of 71.4%. Despite these initial promising results, the findings must be replicated in larger cohorts, and several technical problems due to saliva processing must be solved and Salivettes should not be used.

Dietary Fiber Modulates the Release of Gut Bacterial Products Preventing Cognitive Decline in an Alzheimer's Mouse Model

Fiber intake is associated with a lower risk for Alzheimer´s disease (AD) in older adults. Intake of plant-based diets rich in soluble fiber promotes the production of short-chain fatty acids (SCFAs: butyrate, acetate, propionate) by gut bacteria. Butyrate administration has antiinflammatory actions, but propionate promotes neuroinflammation. In AD patients, gut microbiota dysbiosis is a common feature even in the prodromal stages of the disease. It is unclear whether the neuroprotective effects of fiber intake rely on gut microbiota modifications and specific actions of SCFAs in brain cells. Here, we show that restoration of the gut microbiota dysbiosis through the intake of soluble fiber resulted in lower propionate and higher butyrate production, reduced astrocyte activation and improved cognitive function in 6-month-old male APP/PS1 mice. The neuroprotective effects were lost in antibiotic-treated mice. Moreover, propionate promoted higher glycolysis and mitochondrial respiration in astrocytes, while butyrate induced a more quiescent metabolism. Therefore, fiber intake neuroprotective action depends on the modulation of butyrate/propionate production by gut bacteria. Our data further support and provide a mechanism to explain the beneficial effects of dietary interventions rich in soluble fiber to prevent dementia and AD. Fiber intake restored the concentration of propionate and butyrate by modulating the composition of gut microbiota in male transgenic (Tg) mice with Alzheimer´s disease. Gut dysbiosis was associated with intestinal damage and high propionate levels in control diet fed-Tg mice. Fiber-rich diet restored intestinal integrity and promoted the abundance of butyrate-producing bacteria. Butyrate concentration was associated with better cognitive performance in fiber-fed Tg mice. A fiber-rich diet may prevent the development of a dysbiotic microbiome and the related cognitive dysfunction in people at risk of developing Alzheimer´s disease.

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

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.

Mechanisms of Short-Chain Fatty Acids Derived from Gut Microbiota in Alzheimer's Disease

Short-chain fatty acids (SCFAs) are important metabolites derived from the gut microbiota through fermentation of dietary fiber. SCFAs participate a number of physiological and pathological processes in the human body, such as host metabolism, immune regulation, appetite regulation. Recent studies on gut-brain interaction have shown that SCFAs are important mediators of gut-brain interactions and are involved in the occurrence and development of many neurodegenerative diseases, including Alzheimer's disease. This review summarizes the current research on the potential roles and mechanisms of SCFAs in AD. First, we introduce the metabolic distribution, specific receptors and signaling pathways of SCFAs in human body. The concentration levels of SCFAs in AD patient/animal models are then summarized. In addition, we illustrate the effects and mechanisms of SCFAs on the cognitive level, pathological features (Aβ and tau) and neuroinflammation in AD. Finally, we analyze the translational value of SCFAs as potential therapeutic targets for the treatment of AD.

Salivary Metabolomics for Oral Cancer Detection: A Narrative Review

The development of low- or non-invasive screening tests for cancer is crucial for early detection. Saliva is an ideal biofluid containing informative components for monitoring oral and systemic diseases. Metabolomics has frequently been used to identify and quantify numerous metabolites in saliva samples, serving as novel biomarkers associated with various conditions, including cancers. This review summarizes the recent applications of salivary metabolomics in biomarker discovery in oral cancers. We discussed the prevalence, epidemiologic characteristics, and risk factors of oral cancers, as well as the currently available screening programs, in India and Japan. These data imply that the development of biomarkers by itself is inadequate in cancer detection. The use of current diagnostic methods and new technologies is necessary for efficient salivary metabolomics analysis. We also discuss the gap between biomarker discovery and nationwide screening for the early detection of oral cancer and its prevention.

Uncommon Noninvasive Biomarkers for the Evaluation and Monitoring of the Etiopathogenesis of Alzheimer's Disease

Background:

Alzheimer´s disease (AD) is the most widespread dementia in the world, followed by vascular dementia. Since AD is a heterogeneous disease that shows several varied phenotypes, it is not easy to make an accurate diagnosis, so it arises when the symptoms are clear and the disease is already very advanced. Therefore, it is important to find out biomarkers for AD early diagnosis that facilitate treatment or slow down the disease. Classic biomarkers are obtained from cerebrospinal fluid and plasma, along with brain imaging by positron emission tomography. Attempts have been made to discover uncommon biomarkers from other body fluids, which are addressed in this update.

Objective:

This update aims to describe recent biomarkers from minimally invasive body fluids for the patients, such as saliva, urine, eye fluid or tears.

Methods:

Biomarkers were determined in patients versus controls by single tandem mass spectrometry, and immunoassays. Metabolites were identified by nuclear magnetic resonance, and microRNAs with genome-wide high-throughput real-time polymerase chain reaction-based platforms.

Results:

Biomarkers from urine, saliva, and eye fluid were described, including peptides/proteins, metabolites, and some microRNAs. The association with AD neuroinflammation and neurodegeneration was analyzed, highlighting the contribution of matrix metalloproteinases, the immune system and microglia, as well as the vascular system.

Conclusion:

Unusual biomarkers have been developed, which distinguish each stage and progression of the disease, and are suitable for the early AD diagnosis. An outstanding relationship of biomarkers with neuroinflammation and neurodegeneration was assessed, clearing up concerns of the etiopathogenesis of AD.

Nucleic acid liquid biopsies in Alzheimer's disease: current state, challenges, and opportunities

Alzheimer's disease (AD) is the most common neurodegenerative disease and affects persons of all races, ethnic groups, and sexes. The disease is characterized by neuronal loss leading to cognitive decline and memory loss. There is no cure and the effectiveness of existing treatments is limited and depends on the time of diagnosis. The long prodromal period, during which patients' ability to live a normal life is not affected despite neuronal loss, often leads to a delayed diagnosis because it can be mistaken for normal aging of the brain. In order to make a substantial impact on AD patient survival, early diagnosis may provide a greater therapeutic window for future therapies to slow AD-associated neurodegeneration. Current gold standards for disease detection include magnetic resonance imaging and positron emission tomography scans, which visualize amyloid β and phosphorylated tau depositions and aggregates. Liquid biopsies, already an active field of research in precision oncology, are hypothesized to provide early disease detection through minimally or non-invasive sample collection techniques. Liquid biopsies in AD have been studied in cerebrospinal fluid, blood, ocular, oral, and olfactory fluids. However, most of the focus has been on blood and cerebrospinal fluid due to biomarker specificity and sensitivity attributed to the effects of the blood-brain barrier and inter-laboratory variation during sample collection. Many studies have identified amyloid β and phosphorylated tau levels as putative biomarkers, however, advances in next-generation sequencing-based liquid biopsy methods have led to significant interest in identifying nucleic acid species associated with AD from liquid tissues. Differences in cell-free RNAs and DNAs have been described as potential biomarkers for AD and hold the potential to affect disease diagnosis, treatment, and future research avenues.

Salivary Metabolomics for Oral Squamous Cell Carcinoma Diagnosis: A Systematic Review

Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer in which the consumption of tobacco and alcohol is considered to be the main aetiological factor. Salivary metabolome profiling could identify novel biochemical pathways involved in the pathogenesis of various diseases. This systematic review was designed to answer the question “Are salivary metabolites reliable for the diagnosis of oral squamous cell carcinoma?”. Following the inclusion and exclusion criteria, nineteen studies were included (according to PRISMA statement guidelines). In all included studies, the diagnostic material was unstimulated whole saliva, whose metabolome changes were determined by different spectroscopic methods. At the metabolic level, OSCC patients differed significantly not only from healthy subjects but also from patients with oral leukoplakia, lichen planus or other oral potentially malignant disorders. Among the detected salivary metabolites, there were the indicators of the impaired metabolic pathways, such as choline metabolism, amino acid pathways, polyamine metabolism, urea cycle, creatine metabolism, glycolysis or glycerolipid metabolism. In conclusion, saliva contains many potential metabolites, which can be used reliably to early diagnose and monitor staging in patients with OSCC. However, further investigations are necessary to confirm these findings and to identify new salivary metabolic biomarkers.

High Resolution Magic Angle Spinning Proton NMR Study of Alzheimer's Disease with Mouse Models

Alzheimer's disease (AD) is a crippling condition that affects millions of elderly adults each year, yet there remains a serious need for improved methods of diagnosis. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease; however, studies of human brain tissue metabolomics are challenging, due to sample limitations and ethical considerations. Comprehensive comparisons of imaging measurements in animal models to identify similarities and differences between aging- and AD-associated metabolic changes should thus be tested and validated for future human non-invasive studies. In this paper, we present the results of our highresolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) studies of AD and wild-type (WT) mouse models, based on animal age, brain regions, including cortex vs. hippocampus, and disease status. Our findings suggest the ability of HRMAS NMR to differentiate between AD and WT mice using brain metabolomics, which potentially can be implemented in in vivo evaluations.

A Nuclear Magnetic Resonance Spectroscopy Method in Characterization of Blood Metabolomics for Alzheimer's Disease

There is currently a crucial need for improved diagnostic techniques and targeted treatment methods for Alzheimer's disease (AD), a disease which impacts millions of elderly individuals each year. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease. In this report, we present our AD metabolomics results measured with high resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) on human blood plasma samples obtained from AD and non-AD subjects. Our study centers on developments of AD and non-AD metabolomics differentiating models with procedures of quality assurance (QA) and quality control (QC) through pooled samples. Our findings suggest that analysis of blood plasma samples using HRMAS NMR has the potential to differentiate between diseased and healthy subjects, which has important clinical implications for future improvements in AD diagnosis methodologies.

Biomarkers used in Alzheimer's disease diagnosis, treatment, and prevention

Alzheimer's disease (AD), being the number one in terms of dementia burden, is an insidious age-related neurodegenerative disease and is presently considered a global public health threat. Its main histological hallmarks are the Aβ senile plaques and the P-tau neurofibrillary tangles, while clinically it is marked by a progressive cognitive decline that reflects the underlying synaptic loss and neurodegeneration. Many of the drug therapies targeting the two pathological hallmarks namely Aβ and P-tau have been proven futile. This is probably attributed to the initiation of therapy at a stage where cognitive alterations are already obvious. In other words, the underlying neuropathological changes are at a stage where these drugs lack any therapeutic value in reversing the damage. Therefore, there is an urgent need to start treatment in the very early stage where these changes can be reversed, and hence, early diagnosis is of primordial importance. To this aim, the use of robust and informative biomarkers that could provide accurate diagnosis preferably at an earlier phase of the disease is of the essence. To date, several biomarkers have been established that, to a different extent, allow researchers and clinicians to evaluate, diagnose, and more specially exclude other related pathologies. In this study, we extensively reviewed data on the currently explored biomarkers in terms of AD pathology-specific and non-specific biomarkers and highlighted the recent developments in the diagnostic and theragnostic domains. In the end, we have presented a separate elaboration on aspects of future perspectives and concluding remarks.

Updates and Original Case Studies Focused on the NMR-Linked Metabolomics Analysis of Human Oral Fluids Part I: Emerging Platforms and Perspectives

1H NMR-based metabolomics analysis of human saliva, other oral fluids, and/or tissue biopsies serves as a valuable technique for the exploration of metabolic processes, and when associated with ’state-of-the-art’ multivariate (MV) statistical analysis strategies, provides a powerful means of examining the identification of characteristic metabolite patterns, which may serve to differentiate between patients with oral health conditions (e.g., periodontitis, dental caries, and oral cancers) and age-matched heathy controls. This approach may also be employed to explore such discriminatory signatures in the salivary 1H NMR profiles of patients with systemic diseases, and to date, these have included diabetes, Sjörgen’s syndrome, cancers, neurological conditions such as Alzheimer’s disease, and viral infections. However, such investigations are complicated in view of quite a large number of serious inconsistencies between the different studies performed by independent research groups globally; these include differing protocols and routes for saliva sample collection (e.g., stimulated versus unstimulated samples), their timings (particularly the oral activity abstention period involved, which may range from one to 12 h or more), and methods for sample transport, storage, and preparation for NMR analysis, not to mention a very wide variety of demographic variables that may influence salivary metabolite concentrations, notably the age, gender, ethnic origin, salivary flow-rate, lifestyles, diets, and smoking status of participant donors, together with their exposure to any other possible convoluting environmental factors. In view of the explosive increase in reported salivary metabolomics investigations, in this update, we critically review a wide range of critical considerations for the successful performance of such experiments. These include the nature, composite sources, and biomolecular status of human saliva samples; the merits of these samples as media for the screening of disease biomarkers, notably their facile, unsupervised collection; and the different classes of such metabolomics investigations possible. Also encompassed is an account of the history of NMR-based salivary metabolomics; our recommended regimens for the collection, transport, and storage of saliva samples, along with their preparation for NMR analysis; frequently employed pulse sequences for the NMR analysis of these samples; the supreme resonance assignment benefits offered by homo- and heteronuclear two-dimensional NMR techniques; deliberations regarding salivary biomolecule quantification approaches employed for such studies, including the preprocessing and bucketing of multianalyte salivary NMR spectra, and the normalization, transformation, and scaling of datasets therefrom; salivary phenotype analysis, featuring the segregation of a range of different metabolites into ‘pools’ grouped according to their potential physiological sources; and lastly, future prospects afforded by the applications of LF benchtop NMR spectrometers for direct evaluations of the oral or systemic health status of patients at clinical ‘point-of-contact’ sites, e.g., dental surgeries. This commentary is then concluded with appropriate recommendations for the conduct of future salivary metabolomics studies. Also included are two original case studies featuring investigations of (1) the 1H NMR resonance line-widths of selected biomolecules and their possible dependence on biomacromolecular binding equilibria, and (2) the combined univariate (UV) and MV analysis of saliva specimens collected from a large group of healthy control participants in order to potentially delineate the possible origins of biomolecules therein, particularly host- versus oral microbiome-derived sources. In a follow-up publication, Part II of this series, we conduct censorious reviews of reported observations acquired from a diversity of salivary metabolomics investigations performed to evaluate both localized oral and non-oral diseases. Perplexing problems encountered with these again include those arising from sample collection and preparation protocols, along with 1H NMR spectral misassignments.

Salivary Proteomics Identifies Transthyretin as a Biomarker of Early Dementia Conversion

Background:

Alzheimer’s disease (AD) remains to date an incurable disease with a long asymptomatic phase. Early diagnosis in peripheral biofluids has emerged as key for identifying subjects at risk and developing therapeutics and preventative approaches.

Objective:

We apply proteomics discovery to identify salivary diagnostic biomarkers for AD, which are suitable for self-sampling and longitudinal biomonitoring during aging.

Methods:

57 participants were recruited for the study and were categorized into Cognitively normal (CNh) (n = 19), mild cognitive impaired (MCI) (n = 21), and Alzheimer’s disease (AD) (n = 17). On a subset of subjects, 3 CNh and 3 mild AD, shot-gun filter aided sample preparation (FASP) proteomics and liquid chromatography mass spectroscopy (LC-MS/MS) was employed in saliva and cerebrospinal fluid (CSF) to identify neural-derived proteins. The protein level of salivary Transthyretin (TTR) was validated using western blot analysis across groups.

Results:

We found that 19.8% of the proteins in saliva are shared with CSF. When we compared the saliva and CSF proteome, 24 hits were decreased with only one protein expressed more. Among the differentially expressed proteins, TTR with reported function in amyloid misfolding, shows a significant drop in AD samples, confirmed by western blot showing a 0.5-fold reduction in MCI and AD compared to CNh.

Conclusion:

A reduction in salivary TTR appears with the onset of cognitive symptoms. More in general, the proteomic profiling of saliva shows a plethora of biomarkers worth pursuing as non-invasive hallmarks of dementia in the preclinical stage.

Salivary Metabolomics Reveals that Metabolic Alterations Precede the Onset of Schizophrenia

Schizophrenia is a complex and highly heterogeneous mental illness with a prodromal period called clinical high risk (CHR) for psychosis before onset. Metabolomics is greatly promising in analyzing the pathology of complex diseases and exploring diagnostic biomarkers. Therefore, we conducted salivary metabolomics analysis in 83 first-episode schizophrenia (FES) patients, 42 CHR individuals, and 78 healthy controls with ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. The mass spectrometry raw data have been deposited on the MetaboLights (ID: MTBLS3463). We found downregulated aromatic amino acid metabolism, disturbed glutamine and nucleotide metabolism, and upregulated tricarboxylic acid cycle in FES patients, which existed even in the CHR stage and became more intense with the onset of the schizophrenia. Moreover, differential metabolites can be considered as potential diagnostic biomarkers and indicate the severity of the different clinical stages of disease. Furthermore, three disordered pathways were closely related to peripheral indicators of inflammatory response, oxidative stress, blood-brain barrier damage, and salivary microbiota. These results indicate that the disorder of oral metabolism occurs earlier than the onset of schizophrenia and is concentrated and intensified with the onset of disease, which may originate from the dysbiotic salivary microbiota and cause the onset of schizophrenia through the peripheral inflammatory response and redox system, suggesting the importance of oral-brain connection in the pathogenesis of schizophrenia.

Multiple roles of short-chain fatty acids in Alzheimer disease

Alzheimer disease (AD) is the most common form of neurodegenerative disease in older adults and has a complicated etiology. Recently, the roles of short-chain fatty acids (SCFAs), the main metabolites generated by fermentation of dietary fiber by gut microbiota, in the pathogenesis of AD have attracted considerable interest. This study analyzed the multiple roles of SCFAs in AD pathogenesis from five aspects, including: 1) epigenetic regulation; 2) modulation of neuroinflammation; 3) maintenance of the blood-brain barrier (BBB); 4) regulation of brain metabolism; and 5) interference in amyloid protein formation. According to the currently available evidence, SCFAs, particularly butyrate, cause important biological effects that interfere with the development of AD. However, the effect of other SCFAs, such as propionate, on AD might be either beneficial or harmful to different pathways, indicating that the role of SCFAs in the pathogenesis of AD is rather complicated and warrants further investigations.

The Endocannabinoid System: A Bridge between Alzheimer's Disease and Gut Microbiota

Alzheimer's disease (AD) is a neurodegenerative disease that progresses from mild cognitive impairment to severe dementia over time. The main clinical hallmarks of the disease (e.g., beta-amyloid plaques and neurofibrillary tangles) begin during preclinical AD when cognitive deficits are not yet apparent. Hence, a more profound understanding of AD pathogenesis is needed to develop new therapeutic strategies. In this context, the endocannabinoid (eCB) system and the gut microbiome are increasingly emerging as important players in maintaining the general homeostasis and the health status of the host. However, their interaction has come to light just recently with gut microbiota regulating the eCB tone at both receptor and enzyme levels in intestinal and adipose tissues. Importantly, eCB system and gut microbiome, have been suggested to play a role in AD in both animal and human studies. Therefore, the microbiome gut-brain axis and the eCB system are potential common denominators in the AD physiopathology. Hence, the aim of this review is to provide a general overview on the role of both the eCB system and the microbiome gut-brain axis in AD and to suggest possible mechanisms that underlie the potential interplay of these two systems.

Salivary Metabolomics for Diagnosis and Monitoring Diseases: Challenges and Possibilities

Saliva is a useful biological fluid and a valuable source of biological information. Saliva contains many of the same components that can be found in blood or serum, but the components of interest tend to be at a lower concentration in saliva, and their analysis demands more sensitive techniques. Metabolomics is starting to emerge as a viable method for assessing the salivary metabolites which are generated by the biochemical processes in elucidating the pathways underlying different oral and systemic diseases. In oral diseases, salivary metabolomics has concentrated on periodontitis and oral cancer. Salivary metabolites of systemic diseases have been investigated mostly in the early diagnosis of different cancer, but also neurodegenerative diseases. This mini-review article aims to highlight the challenges and possibilities of salivary metabolomics from a clinical viewpoint. Furthermore, applications of the salivary metabolic profile in diagnosis and prognosis, monitoring the treatment success, and planning of personalized treatment of oral and systemic diseases are discussed.

Plasmonic Interferometers as TREM2 Sensors for Alzheimer's Disease

We report an effective surface immobilization protocol for capture of Triggering Receptor Expressed on Myeloid Cells 2 (TREM2), a receptor whose elevated concentration in cerebrospinal fluid has recently been associated with Alzheimer's disease (AD). We employ the proposed surface functionalization scheme to design, fabricate, and assess a biochemical sensing platform based on plasmonic interferometry that is able to detect physiological concentrations of TREM2 in solution. These findings open up opportunities for label-free biosensing of TREM2 in its soluble form in various bodily fluids as an early indicator of the onset of clinical dementia in AD. We also show that plasmonic interferometry can be a powerful tool to monitor and optimize surface immobilization schemes, which could be applied to develop other relevant antibody tests.

Metabolomics: A Scoping Review of Its Role as a Tool for Disease Biomarker Discovery in Selected Non-Communicable Diseases

Metabolomics is a branch of ‘omics’ sciences that utilises a couple of analytical tools for the identification of small molecules (metabolites) in a given sample. The overarching goal of metabolomics is to assess these metabolites quantitatively and qualitatively for their diagnostic, therapeutic, and prognostic potentials. Its use in various aspects of life has been documented. We have also published, howbeit in animal models, a few papers where metabolomic approaches were used in the study of metabolic disorders, such as metabolic syndrome, diabetes, and obesity. As the goal of every research is to benefit humankind, the purpose of this review is to provide insights into the applicability of metabolomics in medicine vis-à-vis its role in biomarker discovery for disease diagnosis and management. Here, important biomarkers with proven diagnostic and therapeutic relevance in the management of disease conditions, such as Alzheimer’s disease, dementia, Parkinson’s disease, inborn errors of metabolism (IEM), diabetic retinopathy, and cardiovascular disease, are noted. The paper also discusses a few reasons why most metabolomics-based laboratory discoveries are not readily translated to the clinic and how these could be addressed going forward.

Gut Microbiota Alterations and Cognitive Impairment Are Sexually Dissociated in a Transgenic Mice Model of Alzheimer's Disease

BACKGROUND

Normal aging is accompanied by cognitive deficiencies, affecting women and men equally. Aging is the main risk factor for Alzheimer's disease (AD), with women having a higher risk. The higher prevalence of AD in women is associated with the abrupt hormonal decline seen after menopause. However, other factors may be involved in this sex-related cognitive decline. Alterations in gut microbiota (GM) and its bioproducts have been reported in AD subjects and transgenic (Tg) mice, having a direct impact on brain amyloid-β pathology in male (M), but not in female (F) mice.

OBJECTIVE

The aim of this work was to determine GM composition and cognitive dysfunction in M and F wildtype (WT) and Tg mice, in a sex/genotype segregation design.

METHODS

Anxiety, short term working-memory, spatial learning, and long-term spatial memory were evaluated in 6-month-old WT and Tg male mice. Fecal short chain fatty acids were determined by chromatography, and DNA sequencing and bioinformatic analyses were used to determine GM differences.

RESULTS

We observed sex-dependent differences in cognitive skills in WT mice, favoring F mice. However, the cognitive advantage of females was lost in Tg mice. GM composition showed few sex-related differences in WT mice. Contrary, Tg-M mice presented a more severe dysbiosis than Tg-F mice. A decreased abundance of Ruminococcaceae was associated with cognitive deficits in Tg-F mice, while butyrate levels were positively associated with better working- and object recognition-memory in WT-F mice.

CONCLUSION

This report describes a sex-dependent association between GM alterations and cognitive impairment in a mice model of AD.

Salivary Aβ Secretion and Altered Oral Microbiome in Mouse Models of AD

BACKGROUND

Beta amyloid (Aβ) peptide containing plaque aggregations in the brain are a hallmark of Alzheimer's Disease (AD). However, Aβ is produced by cell types outside of the brain suggesting that the peptide may serve a broad physiologic purpose.

OBJECTIVE

Based upon our prior work documenting expression of amyloid β precursor protein (APP) in intestinal epithelium we hypothesized that salivary epithelium might also express APP and be a source of Aβ.

METHODS

To begin testing this idea, we compared human age-matched control and AD salivary glands to C57BL/6 wild type, App^NL-G-F , and APP/PS1 mice.

RESULTS

Both male and female AD, App^NL-G-F , and APP/PS1 glands demonstrated robust APP and Aβ immunoreactivity. Female App^NL-G-F mice had significantly higher levels of pilocarpine stimulated Aβ 1-42 compared to both wild type and APP/PS1 mice. No differences in male salivary Aβ levels were detected. No significant differences in total pilocarpine stimulated saliva volumes were observed in any group. Both male and female App^NL-G-F but not APP/PS1 mice demonstrated significant differences in oral microbiome phylum and genus abundance compared to wild type mice. Male, but not female, APP/PS1 and App^NL-G-F mice had significantly thinner molar enamel compared to their wild type counterparts.

CONCLUSION

These data support the idea that oral microbiome changes exist during AD in addition to changes in salivary Aβ and oral health.

Diagnose it yourself: will there be a home test kit for Alzheimer's disease?

Alzheimer's disease is the most common neurodegenerative process leading to dementia. To date, there is no curative approach; thus, establishing a diagnosis as early as possible is necessary to implement preventive measures. However, today's gold standard for diagnosing Alzheimer's disease is high in both cost and effort and is not readily available. This defines the need for low-effort and economic alternatives that give patients low-threshold access to testing systems at their general practitioners or even at home for an independent retrieval of a biologic specimen. This perspective gives an overview of established and novel approaches in the field and speculates on the future of test strategies eventually technically implementable at home.

Propionate and Alzheimer's Disease

Propionate, a short-chain fatty acid, serves important roles in the human body. However, our review of the current literature suggests that under certain conditions, excess levels of propionate may play a role in Alzheimer's disease (AD). The cause of the excessive levels of propionate may be related to the Bacteroidetes phylum, which are the primary producers of propionate in the human gut. Studies have shown that the relative abundance of the Bacteroidetes phylum is significantly increased in older adults. Other studies have shown that levels of the Bacteroidetes phylum are increased in persons with AD. Studies on the diet, medication use, and propionate metabolism offer additional potential causes. There are many different mechanisms by which excess levels of propionate may lead to AD, such as hyperammonemia. These mechanisms offer potential points for intervention.

Platelet Amyloid-β Protein Precursor (AβPP) Ratio and Phosphorylated Tau as Promising Indicators for Early Alzheimer's Disease

To identify whether platelet amyloid-β protein precursor (AβPP) ratio, phosphorylated-tau (P-tau) 231, P-tau181, and serine 396 and 404 (Ser396/404) phosphorylated tau are potential peripheral indicators for early Alzheimer's disease (AD). Forty-three amnesic mild cognitive impairment (aMCI) patients and 45 normal controls were recruited. Peripheral venous blood was drawn and platelets were collected and evaluated for potential indicators by Western blot analysis. Subsequent meta-analysis was completed on these selected indicators. In platelets of aMCI patients, the AβPP ratio level was significantly lower and levels of P-tau231 and Ser396/404 phosphorylated tau were significantly higher. Moreover, in aMCI patients, a negative correlation was observed between platelet P-tau231 level and the Trail Making Tests A score, and it was found that higher platelet P-tau231 levels significantly associated with a worse performance of information processing speed. Furthermore, values of the area under the curve of platelet P-tau231 and Ser396/404 phosphorylated tau were 0.624 and 0.657, respectively. Finally, a meta-analysis indicated platelet AβPP ratio level was significantly lower in MCI cohorts. In conclusion, platelets of aMCI subjects showed a lower AβPP ratio and higher levels of P-tau231 and Ser396/404 phosphorylated tau when compared to normal controls, which may be critical in identifying early AD.

The Evolving Landscape of Exosomes in Neurodegenerative Diseases: Exosomes Characteristics and a Promising Role in Early Diagnosis

Neurodegenerative diseases (ND) remains to be one of the biggest burdens on healthcare systems and serves as a leading cause of disability and death. Alzheimer's disease (AD) is among the most common of such disorders, followed by Parkinson's disease (PD). The basic molecular details of disease initiation and pathology are still under research. Only recently, the role of exosomes has been linked to the initiation and progression of these neurodegenerative diseases. Exosomes are small bilipid layer enclosed extracellular vesicles, which were once considered as a cellular waste and functionless. These nano-vesicles of 30-150 nm in diameter carry specific proteins, lipids, functional mRNAs, and high amounts of non-coding RNAs (miRNAs, lncRNAs, and circRNAs). As the exosomes content is known to vary as per their originating and recipient cells, these vesicles can be utilized as a diagnostic biomarker for early disease detection. Here we review exosomes, their biogenesis, composition, and role in neurodegenerative diseases. We have also provided details for their characterization through an array of available techniques. Their updated role in neurodegenerative disease pathology is also discussed. Finally, we have shed light on a novel field of salivary exosomes as a potential candidate for early diagnosis in neurodegenerative diseases and compared the biomarkers of salivary exosomes with other blood/cerebrospinal fluid (CSF) based exosomes within these neurological ailments.

Salivaomics as a Potential Tool for Predicting Alzheimer's Disease During the Early Stages of Neurodegeneration

BACKGROUND

The metabolomic and proteomic basis of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is poorly understood and the relationships between systemic abnormalities in metabolism and AD/AMCI pathogenesis are unclear.

OBJECTIVE

The aim of the study was to compare the metabolomic and proteomic signature of saliva from cognitively normal and patients diagnosed with MCI or AD, to identify specific cellular pathways altered with the progression of the disease.

METHODS

We analyzed 80 saliva samples from individuals with MCI or AD as well as age- and gender-matched healthy controls. Saliva proteomic and metabolomic analyses were conducted utilizing mass spectrometry methods and data combined using pathway analysis.

RESULTS

We found significant alterations in multiple cellular pathways, demonstrating that at the omics level, disease progression impacts numerous cellular processes. Multivariate statistics using SIMCA showed that partial least squares-data analysis could be used to provide separation of the three groups.

CONCLUSION

This study found significant changes in metabolites and proteins from multiple cellular pathways in saliva. These changes were associated with AD, demonstrating that this approach might prove useful to identify new biomarkers based upon integration of multi-omics parameters.

Salivary metabolomics for the diagnosis of periodontal diseases: a systematic review with methodological quality assessment

INTRODUCTION

Early diagnosis of periodontitis by means of a rapid, accurate and non-invasive method is highly desirable to reduce the individual and epidemiological burden of this largely prevalent disease.

OBJECTIVES

The aims of the present systematic review were to examine potential salivary metabolic biomarkers and pathways associated to periodontitis, and to assess the accuracy of salivary untargeted metabolomics for the diagnosis of periodontal diseases.

METHODS

Relevant studies identified from MEDLINE (PubMed), Embase and Scopus databases were systematically examined for analytical protocols, metabolic biomarkers and results from the multivariate analysis (MVA). Pathway analysis was performed using the MetaboAnalyst online software and quality assessment by means of a modified version of the QUADOMICS tool.

RESULTS

Twelve studies met the inclusion criteria, with sample sizes ranging from 19 to 130 subjects. Compared to periodontally healthy individuals, valine, phenylalanine, isoleucine, tyrosine and butyrate were found upregulated in periodontitis patients in most studies; while lactate, pyruvate and N-acetyl groups were the most significantly expressed in healthy individuals. Metabolic pathways that resulted dysregulated are mainly implicated in inflammation, oxidative stress, immune activation and bacterial energetic metabolism. The findings from MVA revealed that periodontitis is characterized by a specific metabolic signature in saliva, with coefficients of determination ranging from 0.52 to 0.99.

CONCLUSIONS

This systematic review summarizes candidate metabolic biomarkers and pathways related to periodontitis, which may provide opportunities for the validation of diagnostic or predictive models and the discovery of novel targets for monitoring and treating such a disease (PROSPERO CRD42020188482).

Evaluation of Saliva Stability for NMR Metabolomics: Collection and Handling Protocols

Maintaining a salivary metabolic profile upon sample collection and preparation is determinant in metabolomics. Nuclear magnetic resonance (NMR) spectroscopy was used to identify metabolite changes during short-term storage, at room temperature (RT)/4 °C/-20 °C, and after sample preparation, at RT/4 °C (mimicking typical clinical/laboratory settings). Interestingly, significant metabolic inter-individual and inter-day variability were noted, probably determining sample stability to some extent. After collection, no changes were noted at -20 °C (at least for 4 weeks). RT storage induced decreases in methylated macromolecules (6 h); lactate (8 h); alanine (12 h); galactose, hypoxanthine, pyruvate (24 h); sarcosine, betaine, choline, N-acetyl-glycoproteins (48 h), while acetate increased (48 h). Less, but different, changes were observed at 4 °C, suggesting different oral and microbial status at different temperatures (with a possible contribution from inter-individual and inter-day variability), and identifying galactose, hypoxanthine, and possibly, choline esters, as potential general stability indicators. After preparation, addition of NaN3 did not impact significantly on saliva stabilization, neither at RT nor at 4 °C, although its absence was accompanied by slight increases in fucose (6.5 h) and proline (8 h) at RT, and in xylose (24 h) at 4 °C. The putative metabolic origins of the above variations are discussed, with basis on the salivary microbiome. In summary, after collection, saliva can be stored at RT/4 °C for up to 6 h and at -20 °C for at least 4 weeks. Upon preparation for NMR analysis, samples are highly stable at 25 °C up to 8 h and at 4 °C up to 48 h, with NaN3 addition preventing possible early changes in fucose, proline (6-8 h), and xylose (24 h) levels.

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.

A Community-Based Study Identifying Metabolic Biomarkers of Mild Cognitive Impairment and Alzheimer's Disease Using Artificial Intelligence and Machine Learning

BACKGROUND

Currently, there is no objective, clinically available tool for the accurate diagnosis of Alzheimer's disease (AD). There is a pressing need for a novel, minimally invasive, cost friendly, and easily accessible tool to diagnose AD, assess disease severity, and prognosticate course. Metabolomics is a promising tool for discovery of new, biologically, and clinically relevant biomarkers for AD detection and classification.

OBJECTIVE

Utilizing artificial intelligence and machine learning, we aim to assess whether a panel of metabolites as detected in plasma can be used as an objective and clinically feasible tool for the diagnosis of mild cognitive impairment (MCI) and AD.

METHODS

Using a community-based sample cohort acquired from different sites across the US, we adopted an approach combining Proton Nuclear Magnetic Resonance Spectroscopy (1H NMR), Liquid Chromatography coupled with Mass Spectrometry (LC-MS) and various machine learning statistical approaches to identify a biomarker panel capable of identifying those patients with AD and MCI from healthy controls.

RESULTS

Of the 212 measured metabolites, 5 were identified as optimal to discriminate between controls, and individuals with MCI or AD. Our models performed with AUC values in the range of 0.72-0.76, with the sensitivity and specificity values ranging from 0.75-0.85 and 0.69-0.81, respectively. Univariate and pathway analysis identified lipid metabolism as the most perturbed biochemical pathway in MCI and AD.

CONCLUSION

A comprehensive method of acquiring metabolomics data, coupled with machine learning techniques, has identified a strong panel of diagnostic biomarkers capable of identifying individuals with MCI and AD. Further, our data confirm what other groups have reported, that lipid metabolism is significantly perturbed in those individuals suffering with dementia. This work may provide additional insight into AD pathogenesis and encourage more in-depth analysis of the AD lipidome.

Screening for Alzheimer's Disease Using Saliva: A New Approach Based on Machine Learning and Raman Hyperspectroscopy

BACKGROUND

Alzheimer's disease and related dementias (ADRDs) are being diagnosed at epidemic rates, with incidence to triple from 35 to 115 million cases worldwide. Most ADRDs are characterized by progressive neurodegeneration, and Alzheimer's disease (AD) is the sixth leading cause of death in the United States. The ideal moment for diagnosing ADRDs is during the earliest stages of its progression; however, current diagnostic methods are inefficient, expensive, and unsuccessful at making diagnoses during the earliest stages of the disease.

OBJECTIVE

The aim of this project was to utilize Raman hyperspectroscopy in combination with machine learning to develop a novel method for the diagnosis of AD based on the analysis of saliva.

METHODS

Raman hyperspectroscopy was used to analyze saliva samples collected from normative, AD, and mild cognitive impairment (MCI) individuals. Genetic Algorithm and Artificial Neural Networks machine learning techniques were applied to the spectral dataset to build a diagnostic algorithm.

RESULTS

Internal cross-validation showed 99% accuracy for differentiating the three classes; blind external validation was conducted using an independent dataset to further verify the results, achieving 100% accuracy.

CONCLUSION

Raman hyperspectroscopic analysis of saliva has a remarkable potential for use as a non-invasive, efficient, and accurate method for diagnosing AD.

Evidence that the Kennedy and polyamine pathways are dysregulated in human brain in cases of dementia with Lewy bodies

Disruptions of brain metabolism are considered integral to the pathogenesis of dementia, but thus far little is known of how dementia with Lewy bodies (DLB) impacts the brain metabolome. DLB is less well known than other neurodegenerative diseases such as Alzheimer's and Parkinson's disease which is perhaps why it is under-investigated. This exploratory study aimed to address current knowledge gaps in DLB research and search for potentially targetable biochemical pathways for therapeutics. It also aimed to better understand metabolic similarities and differences with other dementias. Combined metabolomic analyses of ¹H NMR and tandem mass spectrometry of neocortical post-mortem brain tissue (Brodmann region 7) from autopsy confirmed cases of DLB (n = 15) were compared with age/gender-matched, non-cognitively impaired healthy controls (n = 30). Following correction for multiple comparisons, only 2 metabolites from a total of 219 measured compounds significantly differed. Putrescine was suppressed (55.4%) in DLB and O-phosphocholine was elevated (52.5%). We identified a panel of 5 metabolites (PC aa C38:4, O-Phosphocholine, putrescine, 4-Aminobutyrate, and SM C16:0) capable of accurately discriminating between DLB and control subjects. Deep Learning (DL) provided the best predictive model following 10-fold cross validation (AUROC (95% CI) = 0.80 (0.60-1.0)) with sensitivity and specificity equal to 0.92 and 0.88, respectively. Altered brain levels of putrescine and O-phosphocholine indicate that the Kennedy pathway and polyamine metabolism are perturbed in DLB. These are accompanied by a consistent underlying trend of lipid dysregulation. As yet it is unclear whether these are a cause or consequence of DLB onset.

Targeted Metabolic Profiling of Urine Highlights a Potential Biomarker Panel for the Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment: A Pilot Study

The lack of sensitive and specific biomarkers for the early detection of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) is a major hurdle to improving patient management. A targeted, quantitative metabolomics approach using both ¹H NMR and mass spectrometry was employed to investigate the performance of urine metabolites as potential biomarkers for MCI and AD. Correlation-based feature selection (CFS) and least absolute shrinkage and selection operator (LASSO) methods were used to develop biomarker panels tested using support vector machine (SVM) and logistic regression models for diagnosis of each disease state. Metabolic changes were investigated to identify which biochemical pathways were perturbed as a direct result of MCI and AD in urine. Using SVM, we developed a model with 94% sensitivity, 78% specificity, and 78% AUC to distinguish healthy controls from AD sufferers. Using logistic regression, we developed a model with 85% sensitivity, 86% specificity, and an AUC of 82% for AD diagnosis as compared to cognitively healthy controls. Further, we identified 11 urinary metabolites that were significantly altered to include glucose, guanidinoacetate, urocanate, hippuric acid, cytosine, 2- and 3-hydroxyisovalerate, 2-ketoisovalerate, tryptophan, trimethylamine N oxide, and malonate in AD patients, which are also capable of diagnosing MCI, with a sensitivity value of 76%, specificity of 75%, and accuracy of 81% as compared to healthy controls. This pilot study suggests that urine metabolomics may be useful for developing a test capable of diagnosing and distinguishing MCI and AD from cognitively healthy controls.