Metabolomics Analyses of Saliva Detect Novel Biomarkers of Alzheimer's Disease

Analysis types and quantification methods applied in UHPLC-MS metabolomics research: a tutorial

BACKGROUND

Different types of analytical methods, with different characteristics, are applied in metabolomics and lipidomics research and include untargeted, targeted and semi-targeted methods. Ultra High Performance Liquid Chromatography-Mass Spectrometry is one of the most frequently applied measurement instruments in metabolomics because of its ability to detect a large number of water-soluble and lipid metabolites over a wide range of concentrations in short analysis times. Methods applied for the detection and quantification of metabolites differ and can either report a (normalised) peak area or an absolute concentration.

AIM OF REVIEW

In this tutorial we aim to (1) define similarities and differences between different analytical approaches applied in metabolomics and (2) define how amounts or absolute concentrations of endogenous metabolites can be determined together with the advantages and limitations of each approach in relation to the accuracy and precision when concentrations are reported.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The pre-analysis knowledge of metabolites to be targeted, the requirement for (normalised) peak responses or absolute concentrations to be reported and the number of metabolites to be reported define whether an untargeted, targeted or semi-targeted method is applied. Fully untargeted methods can only provide (normalised) peak responses and fold changes which can be reported even when the structural identity of the metabolite is not known. Targeted methods, where the analytes are known prior to the analysis, can also report fold changes. Semi-targeted methods apply a mix of characteristics of both untargeted and targeted assays. For the reporting of absolute concentrations of metabolites, the analytes are not only predefined but optimized analytical methods should be developed and validated for each analyte so that the accuracy and precision of concentration data collected for biological samples can be reported as fit for purpose and be reviewed by the scientific community.

Salivary biomarkers: The early diagnosis of Alzheimer's disease

The precise identification of Alzheimer's disease and other prevalent neurodegenerative diseases remains a difficult issue that requires the development of early detection of the disease and inexpensive biomarkers that can replace the present cerebrospinal fluid and imaging biomarkers. Blood biomarkers, such as amyloid and neurofilament light, have been emphasized as an important and practical tool in a testing or examination procedure thanks to advancements in ultra-sensitive detection techniques. Although saliva is not currently being researched for neurodegenerative diseases, it is an important source of biomarkers that can be used for the identification of diseases and has some advantages over other biofluids. While this may be true for most people, getting saliva from elderly people presents some significant challenges. In this overview, we will first discuss how saliva is created and how aging-related illnesses may affect the amount and kind of saliva produced. The findings support the use of salivary amyloid protein, tau species, and novel biomarkers in the diagnosis of Alzheimer's disease.

Analyses of plasma metabolites using a high performance four-channel CIL LC-MS method and identification of metabolites associated with enteric methane emissions in beef cattle

Reducing enteric methane (one greenhouse gas) emissions from beef cattle not only can be beneficial in reducing global warming, but also improve efficiency of nutrient utilization in the production system. However, direct measurement of enteric methane emissions on individual cattle is difficult and expensive. The objective of this study was to detect plasma metabolites that are associated with enteric methane emissions in beef cattle. Average enteric methane emissions (CH4) per day (AVG_DAILYCH4) for each individual cattle were measured using the GreenFeed emission monitoring (GEM) unit system, and beef cattle with divergent AVG_DAILYCH4 from Angus (n = 10 for the low CH4 group and 9 for the high CH4 group), Charolais (n = 10 for low and 10 for = high), and Kinsella Composite (n = 10 for low and 10 for high) populations were used for plasma metabolite quantification and metabolite-CH4 association analyses. Blood samples of these cattle were collected near the end of the GEM system tests and a high performance four-channel chemical isotope labeling (CIL) liquid chromatography (LC) mass spectrometer (MS) method was applied to identify and quantify concentrations of metabolites. The four-channel CIL LC-MS method detected 4235 metabolites, of which 1105 were found to be significantly associated with AVG_DAILYCH4 by a t-test, while 1305 were significantly associated with AVG_DAILYCH4 by a regression analysis at p<0.05. Both the results of the t-test and regression analysis revealed that metabolites that were associated with enteric methane emissions in beef cattle were largely breed-specific whereas 4.29% to 6.39% CH4 associated metabolites were common across the three breed populations and 11.07% to 19.08% were common between two breed populations. Pathway analyses of the CH4 associated metabolites identified top enriched molecular processes for each breed population, including arginine and proline metabolism, arginine biosynthesis, butanoate metabolism, and glutathione metabolism for Angus; beta-alanine metabolism, pyruvate metabolism, glycolysis / gluconeogenesis, and citrate cycle (TCA cycle) for Charolais; phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arginine biosynthesis, and arginine and proline metabolism for Kinsella Composite. The detected CH4 associated metabolites and enriched molecular processes will help understand biological mechanisms of enteric methane emissions in beef cattle. The detected CH4 associated plasma metabolites will also provide valuable resources to further characterize the metabolites and verify their utility as biomarkers for selection of cattle with reduced methane emissions.

Salivary Biomarkers for Alzheimer's Disease: A Systematic Review with Meta-Analysis

Alzheimer's Disease (AD) is the most common neurodegenerative disease which manifests with progressive cognitive impairment, leading to dementia. Considering the noninvasive collection of saliva, we designed the systematic review to answer the question "Are salivary biomarkers reliable for the diagnosis of Alzheimer's Disease?" Following the inclusion and exclusion criteria, 30 studies were included in this systematic review (according to the PRISMA statement guidelines). Potential biomarkers include mainly proteins, metabolites and even miRNAs. Based on meta-analysis, in AD patients, salivary levels of beta-amyloid42 and p-tau levels were significantly increased, and t-tau and lactoferrin were decreased at borderline statistical significance. However, according to pooled AUC, lactoferrin and beta-amyloid42 showed a significant predictive value for salivary-based AD diagnosis. In conclusion, potential markers such as beta-amyloid42, tau and lactoferrin can be detected in the saliva of AD patients, which could reliably support the early diagnosis of this neurodegenerative disease.

Salivary metabolomics in oral potentially malignant disorders and oral cancer patients-a systematic review with meta-analysis

OBJECTIVES

The aim of this systematic review and meta-analysis is to assess the diagnostic potential of salivary metabolomics in the detection of oral potentially malignant disorders (OPMDs) and oral cancer (OC).

MATERIALS AND METHODS

A systematic review was performed in accordance with the 3rd edition of the Centre for Reviews and Dissemination (CRD) and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. Electronic searches for articles were carried out in the PubMed, Web of Science, and Scopus databases. The quality assessment of the included studies was evaluated using the Newcastle-Ottawa Quality Assessment Scale (NOS) and the new version of the QUADOMICS tool. Meta-analysis was conducted whenever possible. The effect size was presented using the Forest plot, whereas the presence of publication bias was examined through Begg's funnel plot.

RESULTS

A total of nine studies were included in the systematic review. The metabolite profiling was heterogeneous across all the studies. The expression of several salivary metabolites was found to be significantly altered in OPMDs and OCs as compared to healthy controls. Meta-analysis was able to be conducted only for N-acetylglucosamine. There was no significant difference (SMD = 0.15; 95% CI - 0.25-0.56) in the level of N-acetylglucosamine between OPMDs, OC, and the control group.

CONCLUSION

Evidence for N-acetylglucosamine as a salivary biomarker for oral cancer is lacking. Although several salivary metabolites show changes between healthy, OPMDs, and OC, their diagnostic potential cannot be assessed in this review due to a lack of data. Therefore, further high-quality studies with detailed analysis and reporting are required to establish the diagnostic potential of the salivary metabolites in OPMDs and OC.

CLINICAL RELEVANCE

While some salivary metabolites exhibit significant changes in oral potentially malignant disorders (OPMDs) and oral cancer (OC) compared to healthy controls, the current evidence, especially for N-acetylglucosamine, is inadequate to confirm their reliability as diagnostic biomarkers. Additional high-quality studies are needed for a more conclusive assessment of salivary metabolites in oral disease diagnosis.

A Novel Peptidome Technology for the Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease by Selected Reaction Monitoring

Background

With the aging of populations worldwide, Alzheimer's disease (AD) has become a concern due to its high prevalence and the continued lack of established treatments. Early diagnosis is required as a preventive intervention to modify the disease's progression. In our previous study, we performed peptidomic analysis of serum samples obtained from AD patients and age-matched healthy subjects to seek peptide biomarker candidates for AD by using BLOTCHIP-MS analysis, and identified four peptides as AD biomarker candidates.

Objective

The objective was to validate the serum biomarker peptides to distinguish mild cognitive impairment (MCI) and AD in comparison to cognitively healthy controls using a new peptidome technology, the Dementia Risk Test.

Methods

We enrolled 195 subjects with normal cognitive function (NC; n = 70), MCI (n = 55), and AD (n = 70), The concentrations of cognitive impairment marker peptides (Fibrinogen α chain (FAC), Fibrinogen β chain (FBC), Plasma protease C1 inhibitor (PPC1I), α2-HS-glycoprotein (AHSG)) were quantified by using a selected reaction monitoring assay based on liquid chromatography-MS/MS.

Results

The present study confirmed that three peptides, FAC, FBC, and PPC1I, were significantly upregulated during the onset of AD. This three-peptide set was both highly sensitive in determining AD (sensitivity: 85.7%, specificity: 95.7%, AUC: 0.900) and useful in distinguishing MCI (sensitivity: 61.8%, specificity: 98.6%, AUC: 0.824) from NC.

Conclusions

In this validation study, we confirmed the high diagnostic potential of the three peptides identified in our previous study as candidate serum biomarkers for AD. The Dementia Risk Test may be a powerful tool for detecting AD-related pathological changes.

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.

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression

Importance

Predictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.

Objective

To characterize the algorithmic fairness of longitudinal prediction models for AD progression.

Design, Setting, and Participants

This prognostic study investigated the algorithmic fairness of logistic regression, support vector machines, and recurrent neural networks for predicting progression to mild cognitive impairment (MCI) and AD using data from participants in the Alzheimer Disease Neuroimaging Initiative evaluated at 57 sites in the US and Canada. Participants aged 54 to 91 years who contributed data on at least 2 visits between September 2005 and May 2017 were included. Data were analyzed in October 2022.

Exposures

Fairness was quantified across sex, ethnicity, and race groups. Neuropsychological test scores, anatomical features from T1 magnetic resonance imaging, measures extracted from positron emission tomography, and cerebrospinal fluid biomarkers were included as predictors.

Main Outcomes and Measures

Outcome measures quantified fairness of prediction models (logistic regression [LR], support vector machine [SVM], and recurrent neural network [RNN] models), including equal opportunity, equalized odds, and demographic parity. Specifically, if the model exhibited equal sensitivity for all groups, it aligned with the principle of equal opportunity, indicating fairness in predictive performance.

Results

A total of 1730 participants in the cohort (mean [SD] age, 73.81 [6.92] years; 776 females [44.9%]; 69 Hispanic [4.0%] and 1661 non-Hispanic [96.0%]; 29 Asian [1.7%], 77 Black [4.5%], 1599 White [92.4%], and 25 other race [1.4%]) were included. Sensitivity for predicting progression to MCI and AD was lower for Hispanic participants compared with non-Hispanic participants; the difference (SD) in true positive rate ranged from 20.9% (5.5%) for the RNN model to 27.8% (9.8%) for the SVM model in MCI and 24.1% (5.4%) for the RNN model to 48.2% (17.3%) for the LR model in AD. Sensitivity was similarly lower for Black and Asian participants compared with non-Hispanic White participants; for example, the difference (SD) in AD true positive rate was 14.5% (51.6%) in the LR model, 12.3% (35.1%) in the SVM model, and 28.4% (16.8%) in the RNN model for Black vs White participants, and the difference (SD) in MCI true positive rate was 25.6% (13.1%) in the LR model, 24.3% (13.1%) in the SVM model, and 6.8% (18.7%) in the RNN model for Asian vs White participants. Models generally satisfied metrics of fairness with respect to sex, with no significant differences by group, except for cognitively normal (CN)-MCI and MCI-AD transitions (eg, an absolute increase [SD] in the true positive rate of CN-MCI transitions of 10.3% [27.8%] for the LR model).

Conclusions and Relevance

In this study, models were accurate in aggregate but failed to satisfy fairness metrics. These findings suggest that fairness should be considered in the development and use of machine learning models for AD progression.

Emerging Alzheimer's disease therapeutics: promising insights from lipid metabolism and microglia-focused interventions

More than 55 million people suffer from dementia, with this number projected to double every 20 years. In the United States, 1 in 3 aged individuals dies from Alzheimer's disease (AD) or another type of dementia and AD kills more individuals than breast cancer and prostate cancer combined. AD is a complex and multifactorial disease involving amyloid plaque and neurofibrillary tangle formation, glial cell dysfunction, and lipid droplet accumulation (among other pathologies), ultimately leading to neurodegeneration and neuronal death. Unfortunately, the current FDA-approved therapeutics do not reverse nor halt AD. While recently approved amyloid-targeting antibodies can slow AD progression to improve outcomes for some patients, they are associated with adverse side effects, may have a narrow therapeutic window, and are expensive. In this review, we evaluate current and emerging AD therapeutics in preclinical and clinical development and provide insight into emerging strategies that target brain lipid metabolism and microglial function - an approach that may synergistically target multiple mechanisms that drive AD neuropathogenesis. Overall, we evaluate whether these disease-modifying emerging therapeutics hold promise as interventions that may be able to reverse or halt AD progression.

Deep Multi-Branch CNN Architecture for Early Alzheimer's Detection from Brain MRIs

Alzheimer's disease (AD) is a neurodegenerative disease that can cause dementia and result in a severe reduction in brain function, inhibiting simple tasks, especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD-induced dementia, and unpaid care for people with AD-related dementia is valued at USD 271.6 billion. Hence, various approaches have been developed for early AD diagnosis to prevent its further progression. In this paper, we first review other approaches that could be used for the early detection of AD. We then give an overview of our dataset and propose a deep convolutional neural network (CNN) architecture consisting of 7,866,819 parameters. This model comprises three different convolutional branches, each having a different length. Each branch is comprised of different kernel sizes. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three-class accuracy. In summary, the deep CNN model demonstrated exceptional accuracy in the early diagnosis of AD, offering a significant advancement in the field and the potential to improve patient care.

Identifying Hair Biomarker Candidates for Alzheimer's Disease Using Three High Resolution Mass Spectrometry-Based Untargeted Metabolomics Strategies

High-resolution mass spectrometry (HRMS)-based untargeted metabolomics strategies have emerged as an effective tool for discovering biomarkers of Alzheimer's disease (AD). There are various HRMS-based untargeted metabolomics strategies for biomarker discovery, including the data-dependent acquisition (DDA) method, the combination of full scan and target MS/MS, and the all ion fragmentation (AIF) method. Hair has emerged as a potential biospecimen for biomarker discovery in clinical research since it might reflect the circulating metabolic profiles over several months, while the analytical performances of the different data acquisition methods for hair biomarker discovery have been rarely investigated. Here, the analytical performances of three data acquisition methods in HRMS-based untargeted metabolomics for hair biomarker discovery were evaluated. The human hair samples from AD patients (N = 23) and cognitively normal individuals (N = 23) were used as an example. The most significant number of discriminatory features was acquired using the full scan (407), which is approximately 10-fold higher than that using the DDA strategy (41) and 11% higher than that using the AIF strategy (366). Only 66% of discriminatory chemicals discovered in the DDA strategy were discriminatory features in the full scan dataset. Moreover, compared to the deconvoluted MS/MS spectra with coeluted and background ions from the AIF method, the MS/MS spectrum obtained from the targeted MS/MS approach is cleaner and purer. Therefore, an untargeted metabolomics strategy combining the full scan with the targeted MS/MS method could obtain most discriminatory features along with a high quality MS/MS spectrum for discovering the AD biomarkers.

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.

Alterations in Peripheral Metabolites as Key Actors in Alzheimer's Disease

Growing evidence supports that Alzheimer's disease (AD) could be regarded as a metabolic disease, accompanying central and peripheral metabolic disturbance. Nowadays, exploring novel and potentially alternative hallmarks for AD is needed. Peripheral metabolites based on blood and gut may provide new biochemical insights about disease mechanisms. These metabolites can influence brain energy homeostasis, maintain gut mucosal integrity, and regulate the host immune system, which may further play a key role in modulating the cognitive function and behavior of AD. Recently, metabolomics has been used to identify key AD-related metabolic changes and define metabolic changes during AD disease trajectory. This review aims to summarize the key blood- and microbial-derived metabolites that are altered in AD and identify the potential metabolic biomarkers of AD, which will provide future targets for precision therapeutic modulation.

Omics-based biomarkers discovery for Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disorders presenting with the pathological hallmarks of amyloid plaques and tau tangles. Over the past few years, great efforts have been made to explore reliable biomarkers of AD. High-throughput omics are a technology driven by multiple levels of unbiased data to detect the complex etiology of AD, and it provides us with new opportunities to better understand the pathophysiology of AD and thereby identify potential biomarkers. Through revealing the interaction networks between different molecular levels, the ultimate goal of multi-omics is to improve the diagnosis and treatment of AD. In this review, based on the current AD pathology and the current status of AD diagnostic biomarkers, we summarize how genomics, transcriptomics, proteomics and metabolomics are all conducing to the discovery of reliable AD biomarkers that could be developed and used in clinical AD management.

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.

Principal components from untargeted cerebrospinal fluid metabolomics associated with Alzheimer's disease biomarkers

Studying the correlation between cerebrospinal fluid (CSF) metabolites and the Alzheimer's Disease (AD) biomarkers may offer a window to the alterations of the brain metabolome and unveil potential biological mechanisms underlying AD. In this analysis, 308 CSF metabolites from 338 individuals of Wisconsin Registry for Alzheimer's Prevention and Wisconsin Alzheimer's Disease Research Center were included in a principal component analysis (PCA). The resulted principal components (PCs) were tested for association with CSF total tau (t-tau), phosphorylated tau (p-tau), amyloid β 42 (Aβ42), and Aβ42/40 ratio using linear regression models. Significant PCs were further tested with other CSF NeuroToolKit (NTK) and imaging biomarkers. Using a Bonferroni corrected p < 0.05, 5 PCs were significantly associated with CSF p-tau and t-tau and 3 PCs were significantly associated with CSF Aβ42. Pathway analysis suggested that these PCS were enriched in 6 pathways, including metabolism of caffeine and nicotinate and nicotinamide. This study provides evidence that CSF metabolites are associated with AD pathology through core AD biomarkers and other NTK markers and suggests potential pathways to follow up in future studies.

Salivary Proteomics Reveals Significant Changes in Relation to Alzheimer's Disease and Aging

BACKGROUND

Aging is a risk factor for several pathologies as Alzheimer's disease (AD). Great interest exists, therefore, in discovering diagnostic biomarkers and indicators discriminating biological aging and health status. To this aim, omic investigations of biological matrices, as saliva, whose sampling is easy and non-invasive, offer great potential.

OBJECTIVE

Investigate the salivary proteome through a statistical comparison of the proteomic data by several approaches to highlight quali-/quantitative variations associated specifically either to aging or to AD occurrence, and, thus, able to classify the subjects.

METHODS

Salivary proteomic data of healthy controls under-70 (adults) and over-70 (elderly) years old, and over-70 AD patients, obtained by liquid chromatography/mass spectrometry, were analyzed by multiple Mann-Whitney test, Kendall correlation, and Random-Forest (RF) analysis.

RESULTS

Almost all the investigated proteins/peptides significantly decreased in relation to aging in elderly subjects, with or without AD, in comparison with adults. AD subjects exhibited the highest levels of α-defensins, thymosin β4, cystatin B, S100A8 and A9. Correlation tests also highlighted age/disease associated differences. RF analysis individuated quali-/quantitative variations in 20 components, as oxidized S100A8 and S100A9, α-defensin 3, P-B peptide, able to classify with great accuracy the subjects into the three groups.

CONCLUSION

The findings demonstrated a strong change of the salivary protein profile in relation to the aging. Potential biomarkers candidates of AD were individuated in peptides/proteins involved in antimicrobial defense, innate immune system, inflammation, and in oxidative stress. RF analysis revealed the feasibility of the salivary proteome to discriminate groups of subjects based on age and health status.

Biomarkers for Alzheimer's Disease in the Current State: A Narrative Review

Alzheimer’s disease (AD) has become a problem, owing to its high prevalence in an aging society with no treatment available after onset. However, early diagnosis is essential for preventive intervention to delay disease onset due to its slow progression. The current AD diagnostic methods are typically invasive and expensive, limiting their potential for widespread use. Thus, the development of biomarkers in available biofluids, such as blood, urine, and saliva, which enables low or non-invasive, reasonable, and objective evaluation of AD status, is an urgent task. Here, we reviewed studies that examined biomarker candidates for the early detection of AD. Some of the candidates showed potential biomarkers, but further validation studies are needed. We also reviewed studies for non-invasive biomarkers of AD. Given the complexity of the AD continuum, multiple biomarkers with machine-learning-classification methods have been recently used to enhance diagnostic accuracy and characterize individual AD phenotypes. Artificial intelligence and new body fluid-based biomarkers, in combination with other risk factors, will provide a novel solution that may revolutionize the early diagnosis of AD.

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.

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.

Urinary and faecal metabolic characteristics in APP/PS1 transgenic mouse model of Alzheimer's disease with and without cognitive decline

Alzheimer's disease (AD) has been considered to be a systematic metabolic disorder, but little information is available about metabolic changes in the urine and feces. In this study, we investigated urinary and faecal metabolic profiles in amyloid precursor protein/presenilin 1 (APP/PS1) mice at 3 and 9 months of age (3 M and 9 M) and age-matched wild-type (WT) mice by using ¹H NMR-based metabolomics, and aimed to explore changes in metabolic pathways during amyloid pathology progression and identify potential metabolite biomarkers at earlier stage of AD. The results show that learning and memory abilities were impaired in APP/PS1 mice relative to WT mice at 9 M, but not at 3 M. However, metabolomics analysis demonstrates that AD disrupted metabolic phenotypes in the urine and feces of APP/PS1 mice at both 3 M and 9 M, including amino acid metabolism, microbial metabolism and energy metabolism. In addition, several potential metabolite biomarkers were identified for discriminating AD and WT mice prior to cognitive decline with the AUC values from 0.755 to 0.971, such as taurine, hippurate, urea and methylamine in the urine as well as alanine, leucine and valine in the feces. Therefore, our results not only confirmed AD as a metabolic disorder, but also contributed to the identification of potential biomarkers at earlier stage of AD.

Recent Advances in Understanding of Alzheimer's Disease Progression through Mass Spectrometry-Based Metabolomics

Alzheimer's disease (AD) is the leading cause of dementia in the aging population, but despite extensive research, there is no consensus on the biological cause of AD. While AD research is dominated by protein/peptide-centric research based on the amyloid hypothesis, a theory that designates dysfunction in beta-amyloid production, accumulation, or disposal as the primary cause of AD, many studies focus on metabolomics as a means of understanding the biological processes behind AD progression. In this review, we discuss mass spectrometry (MS)-based AD metabolomics studies, including sample type and preparation, mass spectrometry specifications, and data analysis, as well as biological insights gleaned from these studies, with the hope of informing future AD metabolomic studies.

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.

Neurobiological Highlights of Cognitive Impairment in Psychiatric Disorders

This review is focused on several psychiatric disorders in which cognitive impairment is a major component of the disease, influencing life quality. There are plenty of data proving that cognitive impairment accompanies and even underlies some psychiatric disorders. In addition, sources provide information on the biological background of cognitive problems associated with mental illness. This scientific review aims to summarize the current knowledge about neurobiological mechanisms of cognitive impairment in people with schizophrenia, depression, mild cognitive impairment and dementia (including Alzheimer's disease).The review provides data about the prevalence of cognitive impairment in people with mental illness and associated biological markers.

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.

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.

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.

Challenges in LC-MS-based metabolomics for Alzheimer's disease early detection: targeted approaches versus untargeted approaches

BACKGROUND

Alzheimer's disease (AD) is one of the most common causes of dementia in old people. Neuronal deficits such as loss of memory, language and problem-solving are severely compromised in affected patients. The molecular features of AD are Aβ deposits in plaques or in oligomeric structures and neurofibrillary tau tangles in brain. However, the challenge is that Aβ is only one piece of the puzzle, and recent findings continue to support the hypothesis that their presence is not sufficient to predict decline along the AD outcome. In this regard, metabolomic-based techniques are acquiring a growing interest for either the early diagnosis of diseases or the therapy monitoring. Mass spectrometry is one the most common analytical platforms used for detection, quantification, and characterization of metabolic biomarkers. In the past years, both targeted and untargeted strategies have been applied to identify possible interesting compounds.

AIM OF REVIEW

The overall goal of this review is to guide the reader through the most recent studies in which LC-MS-based metabolomics has been proposed as a powerful tool for the identification of new diagnostic biomarkers in AD. To this aim, herein studies spanning the period 2009-2020 have been reported. Advantages and disadvantages of targeted vs untargeted metabolomic approaches have been outlined and critically discussed.

Supercritical fluid extraction-supercritical fluid chromatography of saliva: Single-quadrupole mass spectrometry monitoring of caffeine for gastric emptying studies†

Saliva is an attractive sampling matrix for measuring various endogenous and exogeneous substances but requires sample treatment prior to chromatographic analysis. Exploiting supercritical CO₂ for both extraction and chromatography simplifies sample preparation, reduces organic solvent consumption, and minimizes exposure to potentially infectious samples, but has not yet been applied to oral fluid. Here, we demonstrate the feasibility and benefits of online supercritical fluid extraction coupled to supercritical fluid chromatography and single-quadrupole mass spectrometry for monitoring the model salivary tracer caffeine. A comparison of ¹³ C- and ³² S-labeled internal standards with external standard calibration confirmed the superiority of stable isotope-labeled caffeine over nonanalogous internal standards. As proof of concept, the validated method was applied to saliva from a magnetic resonance imaging study of gastric emptying. After administration of 35 mg caffeine via ice capsule, salivary levels correlated with magnetic resonance imaging data, corroborating caffeine's usefulness as tracer of gastric emptying (R²  = 0.945). In contrast to off-line methods, online quantification required only minute amounts of organic solvents and a single manual operation prior to online bioanalysis of saliva, thus demonstrating the usefulness of CO₂ -based extraction and separation techniques for potentially infective biomatrices.

Cyclopentenone Prostaglandins: Biologically Active Lipid Mediators Targeting Inflammation

Cyclopentenone prostaglandins (cyPGs) are biologically active lipid mediators, including PGA2, PGA1, PGJ2, and its metabolites. cyPGs are essential regulators of inflammation, cell proliferation, apoptosis, angiogenesis, cell migration, and stem cell activity. cyPGs biologically act on multiple cellular targets, including transcription factors and signal transduction pathways. cyPGs regulate the inflammatory response by interfering with NF-κB, AP-1, MAPK, and JAK/STAT signaling pathways via both a group of nuclear receptor peroxisome proliferator-activated receptor-gamma (PPAR-γ) dependent and PPAR-γ independent mechanisms. cyPGs promote the resolution of chronic inflammation associated with cancers and pathogen (bacterial, viral, and parasitic) infection. cyPGs exhibit potent effects on viral infections by repressing viral protein synthesis, altering viral protein glycosylation, inhibiting virus transmission, and reducing virus-induced inflammation. We summarize their anti-proliferative, pro-apoptotic, cytoprotective, antioxidant, anti-angiogenic, anti-inflammatory, pro-resolution, and anti-metastatic potential. These properties render them unique therapeutic value, especially in resolving inflammation and could be used in adjunct with other existing therapies. We also discuss other α, β -unsaturated carbonyl lipids and cyPGs like isoprostanes (IsoPs) compounds.

Emerging role of metabolomics in protein conformational disorders

Introduction: Metabolomics focuses on interactions among different metabolites associated with various cellular functions in cells, tissues, and organs. In recent years, metabolomics has emerged as a powerful tool to identify perturbed metabolites, pathways influenced by the environment, for protein conformational diseases (PCDs) and also offers wide clinical application.Area Covered: This review provides a brief overview of recent advances in metabolomics as applied to identify metabolic variations in PCDs, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, prion disease, and cardiac amyloidosis. The 'PubMed' and 'Google Scholar' database search methods have been used to screen the published reports with key search terms: metabolomics, biomarkers, and protein conformational disorders.Expert opinion: Metabolomics is the large-scale study of metabolites and is deemed to overwhelm other omics. It plays a crucial role in finding variations in diseases due to protein conformational changes. However, many PCDs are yet to be identified. Metabolomics is still an emerging field; there is a need for new high-resolution analytical techniques and more studies need to be carried out to generate new information.

Computational Variation: An Underinvestigated Quantitative Variability Caused by Automated Data Processing in Untargeted Metabolomics

Computational tools are commonly used in untargeted metabolomics to automatically extract metabolic features from liquid chromatography-mass spectrometry (LC-MS) raw data. However, due to the incapability of software to accurately determine chromatographic peak heights/areas for features with poor chromatographic peak shape, automated data processing in untargeted metabolomics faces additional quantitative variation (i.e., computational variation) besides the well-recognized analytical and biological variations. In this work, using multiple biological samples, we investigated how experimental factors, including sample concentrations, LC separation columns, and data processing programs, contribute to computational variation. For example, we found that the peak height (PH)-based quantification is more precise when MS-DIAL was used for data processing. We further systematically compared the different patterns of computational variation between PH- and peak area (PA)-based quantitative measurements. Our results suggest that the magnitude of computational variation is highly consistent at a given concentration. Hence, we proposed a quality control (QC) sample-based correction workflow to minimize computational variation by automatically selecting PH or PA-based measurement for each intensity value. This bioinformatic solution was demonstrated in a metabolomic comparison of leukemia patients before and after chemotherapy. Our novel workflow can be effectively applied on 652 out of 915 metabolic features, and over 31% (206 out of 652) of corrected features showed distinctly changed statistical significance. Overall, this work highlights computational variation, a considerable but underinvestigated quantitative variability in omics-scale quantitative analyses. In addition, the proposed bioinformatic solution can minimize computational variation, thus providing a more confident statistical comparison among biological groups in quantitative metabolomics.

Clinical Insights into Mitochondrial Neurodevelopmental and Neurodegenerative Disorders: Their Biosignatures from Mass Spectrometry-Based Metabolomics

Mitochondria are dynamic multitask organelles that function as hubs for many metabolic pathways. They produce most ATP via the oxidative phosphorylation pathway, a critical pathway that the brain relies on its energy need associated with its numerous functions, such as synaptic homeostasis and plasticity. Therefore, mitochondrial dysfunction is a prevalent pathological hallmark of many neurodevelopmental and neurodegenerative disorders resulting in altered neurometabolic coupling. With the advent of mass spectrometry (MS) technology, MS-based metabolomics provides an emerging mechanistic understanding of their global and dynamic metabolic signatures. In this review, we discuss the pathogenetic causes of mitochondrial metabolic disorders and the recent MS-based metabolomic advances on their metabolomic remodeling. We conclude by exploring the MS-based metabolomic functional insights into their biosignatures to improve diagnostic platforms, stratify patients, and design novel targeted therapeutic strategies.

The Role of Salivary Biomarkers in the Early Diagnosis of Alzheimer's Disease and Parkinson's Disease

Many neurodegenerative diseases present with progressive neuronal degeneration, which can lead to cognitive and motor impairment. Early screening and diagnosis of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) are necessary to begin treatment before the onset of clinical symptoms and slow down the progression of the disease. Biomarkers have shown great potential as a diagnostic tool in the early diagnosis of many diseases, including AD and PD. However, screening for these biomarkers usually includes invasive, complex and expensive methods such as cerebrospinal fluid (CSF) sampling through a lumbar puncture. Researchers are continuously seeking to find a simpler and more reliable diagnostic tool that would be less invasive than CSF sampling. Saliva has been studied as a potential biological fluid that could be used in the diagnosis and early screening of neurodegenerative diseases. This review aims to provide an insight into the current literature concerning salivary biomarkers used in the diagnosis of AD and PD. The most commonly studied salivary biomarkers in AD are β-amyloid_1-42/1-40 and TAU protein, as well as α-synuclein and protein deglycase (DJ-1) in PD. Studies continue to be conducted on this subject and researchers are attempting to find correlations between specific biomarkers and early clinical symptoms, which could be key in creating new treatments for patients before the onset of symptoms.

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.

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.

Metabolomics Research Conducted by Nurse Scientists: A Systematic Scoping Review

Metabolomics, one of the newest omics, allows for investigation of holistic responses of living systems to myriad biological, behavioral, and environmental factors. Researcher use metabolomics to examine the underlying mechanisms of clinically observed phenotypes. However, these methods are complex, potentially impeding their uptake by scientists. In this scoping review, we summarize literature illustrating nurse scientists' use of metabolomics. Using electronic search methods, we identified metabolomics investigations conducted by nurse scientists and published in English-language journals between 1990 and November 2019. Of the studies included in the review (N = 30), 9 (30%) listed first and/or senior authors that were nurses. Studies were conducted predominantly in the United States and focused on a wide array of clinical conditions across the life span. The upward trend we note in the use of these methods by nurse scientists over the past 2 decades mirrors a similar trend across scientists of all backgrounds. A broad range of study designs were represented in the literature we reviewed, with the majority involving untargeted metabolomics (n = 16, 53.3%) used to generate hypotheses (n = 13, 76.7%) of potential metabolites and/or metabolic pathways as mechanisms of clinical conditions. Metabolomics methods match well with the unique perspective of nurse researchers, who seek to integrate the experiences of individuals to develop a scientific basis for clinical practice that emphasizes personalized approaches. Although small in number, metabolomics investigations by nurse scientists can serve as the foundation for robust programs of research to answer essential questions for nursing.

Biochemical analysis of oral fluids for disease detection

The field of diagnostics using invasive blood testing represents the majority of diagnostic tests used as part of routine health monitoring. The relatively recent introduction of salivary diagnostics has lead to a major paradigm shift in diagnostic analyses. Additionally, in this era of big data, oral fluid testing has shown promising outcomes in a number of fields, particularly the areas of genomics, microbiomics, proteomics, metabolomics, and transcriptomics. Despite the analytical challenges involved in the interpretation of large datasets generated from biochemical studies involving bodily fluids, including saliva, many studies have identified novel oral biomarkers for diagnosing oral and systemic diseases. In this regard, oral biofluids, including saliva, gingival crevicular fluid (GCF), peri-implant crevicular fluid (PICF), dentinal tubular fluid (DTF), are now attracting increasing attention due to their important attributes, such as noninvasive sampling, easy handling, low cost, and more accurate diagnosis of oral diseases. Recently, the utilization of salivary diagnostics to evaluate systemic diseases and monitor general health has increased in popularity among clinicians. Saliva contains a wide range of protein, DNA and RNA biomarkers, which assist in the diagnosis of multiple diseases and conditions, including cancer, cardiovascular diseases (CVD), auto-immune and degenerative diseases, respiratory infections, oral diseases, and microbial (viral, bacterial and fungal) diseases. Moreover, due to its noninvasive nature and ease-of-adoption by children, it is now being used in mass screening programs, oral health-related studies and clinical trials in support of the development of therapeutic agents. The recent advent of highly sensitive technologies, such as next-generation sequencing, mass spectrometry, highly sensitives ELISAs, and homogeneous immunoassays, suggests that even small quantities of salivary biomarkers are able to be assayed accurately, providing opportunities for the development of many future diagnostic applications (including emerging technologies, such as point-of-care and rapid molecular technologies). The present article explores the omics and biochemical compositions of various oral biofluids with important value in diagnostics and monitoring.

Diagnostic Biomarkers for Alzheimer's Disease Using Non-Invasive Specimens

Studies in the field of Alzheimer’s disease (AD) have shown the emergence of biomarkers in biologic fluids that hold great promise for the diagnosis of the disease. A diagnosis of AD at a presymptomatic or early stage may be the key for a successful treatment, with clinical trials currently investigating this. It is anticipated that preventative and therapeutic strategies may be stage-dependent, which means that they have a better chance of success at a very early stage—before critical neurons are lost. Several studies have been investigating the use of cerebrospinal fluid (CSF) and blood as clinical samples for the detection of AD with a number of established core markers, such as amyloid beta (Aβ), total tau (T-tau) and phosphorylated tau (P-tau), being at the center of clinical research interest. The use of oral samples—including saliva and buccal mucosal cells—falls under one of the least-investigated areas in AD diagnosis. Such samples have great potential to provide a completely non-invasive alternative to current CSF and blood sampling procedures. The present work is a thorough review of the results and analytical approaches, including proteomics, metabolomics, spectroscopy and microbiome analyses that have been used for the study and detection of AD using salivary samples and buccal cells. With a few exceptions, most of the studies utilizing oral samples were performed in small cohorts, which in combination with the existence of contradictory results render it difficult to come to a definitive conclusion on the value of oral markers. Proteins such as Aβ, T-tau and P-tau, as well as small metabolites, were detected in saliva and have shown some potential as future AD diagnostics. Future large-cohort studies and standardization of sample preparation and (pre-)analytical factors are necessary to determine the use of these non-invasive samples as a diagnostic tool for AD.

Fold-Change Compression: An Unexplored But Correctable Quantitative Bias Caused by Nonlinear Electrospray Ionization Responses in Untargeted Metabolomics

The nonlinear signal response of electrospray ionization (ESI) presents a critical limitation for mass spectrometry (MS)-based quantitative analysis. In the field of metabolomics research, this issue has largely remained unaddressed; MS signal intensities are usually directly used to calculate fold changes for quantitative comparison. In this work, we demonstrate that, due to the nonlinear ESI response, signal intensity ratios of a metabolic feature calculated between two samples may not reflect their real metabolic concentration ratios (i.e., fold-change compression), implying that conventional fold-change calculations directly using MS signal intensities can be misleading. In this regard, we developed a quality control (QC) sample-based signal calibration workflow to overcome the quantitative bias caused by the nonlinear ESI response. In this workflow, calibration curves for every metabolic feature are first established using a QC sample injected in serial injection volumes. The MS signals of each metabolic feature are then calibrated to their equivalent QC injection volumes for comparative analysis. We demonstrated this novel workflow in a targeted metabolite analysis, showing that the accuracy of fold-change calculations can be significantly improved. Furthermore, in a metabolomic comparison of the bone marrow interstitial fluid samples from leukemia patients before and after chemotherapy, an additional 59 significant metabolic features were found with fold changes larger than 1.5, and an additional 97 significant metabolic features had fold changes corrected by more than 0.1. This work enables high-quality quantitative analysis in untargeted metabolomics, thus providing more confident biological hypotheses generation.

A multiomics approach to heterogeneity in Alzheimer's disease: focused review and roadmap

Aetiological and clinical heterogeneity is increasingly recognized as a common characteristic of Alzheimer's disease and related dementias. This heterogeneity complicates diagnosis, treatment, and the design and testing of new drugs. An important line of research is discovery of multimodal biomarkers that will facilitate the targeting of subpopulations with homogeneous pathophysiological signatures. High-throughput 'omics' are unbiased data-driven techniques that probe the complex aetiology of Alzheimer's disease from multiple levels (e.g. network, cellular, and molecular) and thereby account for pathophysiological heterogeneity in clinical populations. This review focuses on data reduction analyses that identify complementary disease-relevant perturbations for three omics techniques: neuroimaging-based subtypes, metabolomics-derived metabolite panels, and genomics-related polygenic risk scores. Neuroimaging can track accrued neurodegeneration and other sources of network impairments, metabolomics provides a global small-molecule snapshot that is sensitive to ongoing pathological processes, and genomics characterizes relatively invariant genetic risk factors representing key pathways associated with Alzheimer's disease. Following this focused review, we present a roadmap for assembling these multiomics measurements into a diagnostic tool highly predictive of individual clinical trajectories, to further the goal of personalized medicine in Alzheimer's disease.

Salivary Metabolomics: From Diagnostic Biomarker Discovery to Investigating Biological Function

Metabolomic profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, metabolomic analysis of saliva, the most readily-available human biofluid, has lagged. This review explores the history of saliva-based metabolomics and summarizes current knowledge of salivary metabolomics. Current applications of salivary metabolomics have largely focused on diagnostic biomarker discovery and the diagnostic value of the current literature base is explored. There is also a small, albeit promising, literature base concerning the use of salivary metabolomics in monitoring athletic performance. Functional roles of salivary metabolites remain largely unexplored. Areas of emerging knowledge include the role of oral host-microbiome interactions in shaping the salivary metabolite profile and the potential roles of salivary metabolites in oral physiology, e.g., in taste perception. Discussion of future research directions describes the need to begin acquiring a greater knowledge of the function of salivary metabolites, a current research direction in the field of the gut metabolome. The role of saliva as an easily obtainable, information-rich fluid that could complement other gastrointestinal fluids in the exploration of the gut metabolome is emphasized.

Monitoring the health of transitioning professional footballers: protocol of an observational prospective cohort study

INTRODUCTION

Transitioning out of professional football is a challenging time in most players' lives. During these preretirement and postretirement years, professional footballers may struggle with their mental, musculoskeletal, neurocognitive and cardiovascular health. Currently, longitudinal data about these health conditions are lacking. This article presents the design of a prospective cohort study with the primary aim of gathering epidemiological evidence about the onset and course of mental, musculoskeletal, neurocognitive and cardiovascular health conditions in professional footballers during their preretirement and postretirement years and evaluating the associations between risk indicators and the health conditions under study in these players.

METHODS AND ANALYSIS

An observational prospective cohort study with repeated measurements over a follow-up period of 10 years will be conducted among at least 200 professional footballers (male; 27 (±1) years old). Mental health will be explored by assessing symptoms of distress, anxiety, depression, sleep disturbance, alcohol misuse, drug misuse and disordered eating. Musculoskeletal health will be explored by assessing severe joint injury and related surgery, clinical and radiological osteoarthritis, and joint function (hips, knees and ankles). Neurocognitive health will be explored by assessing the concussion, brain structure and functioning, and neurocognitive functioning. Cardiovascular health will be explored by assessing blood pressure, lipid profile and ECG abnormalities.

ETHICS AND DISSEMINATION

Ethical approval for the study was provided by the Medical Ethics Review Committee of the Amsterdam University Medical Centers. The results of the study will be submitted to peer-reviewed journals, will be presented at scientific conferences and will be released in the media (postpublication).

TRIAL REGISTRATION NUMBER

The Dutch Trial Registry (Drake Football Study NL7999).

Inflammation markers in the saliva of infants born from Zika-infected mothers: exploring potential mechanisms of microcephaly during fetal development

Zika virus (ZIKV) has emerged as one of the most medically relevant viral infections of the past decades; the devastating effects of this virus over the developing brain are a major matter of concern during pregnancy. Although the connection with congenital malformations are well documented, the mechanisms by which ZIKV reach the central nervous system (CNS) and the causes of impaired cortical growth in affected fetuses need to be better addressed. We performed a non-invasive, metabolomics-based screening of saliva from infants with congenital Zika syndrome (CZS), born from mothers that were infected with ZIKV during pregnancy. We were able to identify three biomarkers that suggest that this population suffered from an important inflammatory process; with the detection of mediators associated with glial activation, we propose that microcephaly is a product of immune response to the virus, as well as excitotoxicity mechanisms, which remain ongoing even after birth.