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.

Designed Directional Growth of Ti-Metal-Organic Frameworks for Decoding Alzheimer's Disease-Specific Exosome Metabolites

Exosomes, a growing focus for liquid biopsies, contain diverse molecular cargos. In particular, exosome metabolites with valuable information have exhibited great potential for improving the efficiency of liquid biopsies for addressing complex medical conditions. In this work, we design the directional growth of Ti-metal-organic frameworks on polar-functionalized magnetic particles. This design facilitates the rapid synergistic capture of exosomes with the assistance of an external magnetic field and additionally synergistically enhances the ionization of their metabolites during mass spectrometry detection. Benefiting from this dual synergistic effect, we identified three high-performance exosome metabolites through the differential comparison of a large number of serum samples from individuals with Alzheimer's disease (AD) and normal cognition. Notably, the accuracy of AD identification ranges from 93.18 to 100% using a single exosome metabolite and reaches a flawless 100% with three metabolites. These findings emphasize the transformative potential of this work to enhance the precision and reliability of AD diagnosis, ushering in a new era of improved diagnostic accuracy.

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.

Identification of potential salivary biomarkers for Sjögren's syndrome with an untargeted metabolomic approach

INTRODUCTION

Despite the rise of metabolomics over the past years, and particularly salivary metabolomics, little research on Sjögren's syndrome (SS) biomarkers has focused on the salivary metabolome.

OBJECTIVES

This study aims to identify metabolites that could be used as biomarkers for SS.

METHODS

Using the software called XCMS online, the salivary metabolic profiles obtained with liquid chromatography coupled to high-resolution mass spectrometry for 18 female SS patients were compared to those obtained for 22 age-matched female healthy controls.

RESULTS AND CONCLUSION

A total of 91 metabolites showed differential expression in SS patients. A putative identification was proposed with the use of a database for 37 of these metabolites and, of these, 16 identifications were confirmed. Given the identified metabolites, some important metabolic pathways, such as amino acid metabolism, purine metabolism, or even the citric acid cycle seem to be affected. Through the analyses of the ROC (receiver operating characteristic) curves, three metabolites, namely alanine, isovaleric acid, and succinic acid, showed both good sensitivity (respectively 1.000, 1.000, and 0.750) and specificity (respectively 0.692, 0.615, and 0.692) for identifying SS and could then be interesting biomarkers for a potential salivary diagnosis test.

Emerging trends in wearable glove-based sensors: A review

Glove-based wearable chemical sensors are universal analytical tools that provide surface analysis for various samples in dry or liquid form by swiping glove sensors on the sample surface. They are useful in crime scene investigation, airport security, and disease control for detecting illicit drugs, hazardous chemicals, flammables, and pathogens on various surfaces, such as foods and furniture. It overcomes the inability of most portable sensors to monitor solid samples. It outperforms most wearable sensors (e.g., contact lenses and mouthguard sensors) for healthcare monitoring by providing comfort that does not interfere with daily activities and reducing the risk of infection or other adverse health effects caused by prolonged usage. Detailed information is provided regarding the challenges and selection criteria for the desired glove materials and conducting nanomaterials for developing glove-based wearable sensors. Focusing on nanomaterials, various transducer modification techniques for various real-world applications are discussed. The steps taken by each study platform to address the existing issues are revealed, as are their benefits and drawbacks. The Sustainable Development Goals (SDGs) and strategies for properly disposing of used glove-based wearable sensors are critically evaluated. A glance at all the provided tables provides insight into the features of each glove-based wearable sensor and enables a quick comparison of their functionalities.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

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.

Long-term supplementation with anthocyanin-rich or -poor Rubus idaeus berries does not influence microvascular architecture nor cognitive outcome in the APP/PS-1 mouse model of Alzheimer's disease

Disruption of microvascular architecture is a common pathogenic mechanism in the progression of Alzheimer's disease (AD). Given the anti-angiogenic activity of berry (poly)phenols, we investigated whether long-term feeding of Rubus idaeus (raspberries) could ameliorate cerebral microvascular pathology and improve cognition in the APP/PS-1 mouse model of AD. Male C57Bl/6J mice (50 wild type, 50 APP/PS-1) aged 4-months were fed for 24-weeks, with a normal diet enriched with either 100 mg/day glucose (control diet) or supplemented with glucose and freeze-dried anthocyanin-rich (red) or -poor (yellow) raspberries (100 mg/day) and assessed/sampled post intervention. Cerebral microvascular architecture of wild-type mice was characterised by regularly spaced capillaries with uniform diameters, unlike APP/PS-1 transgenic mice which showed dysregulated microvascular architecture. Long-term feeding of raspberries demonstrated limited modulation of microbiota and no substantive effect on microvascular architecture or cognition in either mice model although changes were evident in endogenous cerebral and plasmatic metabolites.

Periodontal Status and Saliva Metabolic Signature in Patients with Alzheimer's Disease

BACKGROUND

Characterizing the periodontal status of patients with Alzheimer's disease (AD), investigating differences in salivary metabolism between patients with and without AD under the same periodontal conditions, and understanding how it is related to oral flora are critical.

OBJECTIVE

We aimed to examine the periodontal condition of patients with AD and to screen salivary metabolic biomarkers from the saliva of individuals with and without AD with matched periodontal conditions. Furthermore, we aimed to explore the possible relationship between salivary metabolic changes and oral flora.

METHODS

In total, 79 individuals were recruited into the experiment for periodontal analysis. Especially, 30 saliva samples from the AD group and 30 from healthy controls (HCs) with matched periodontal conditions were selected for metabolomic analysis. The random-forest algorithm was used to detect candidate biomarkers. Among these, 19 AD saliva and 19 HC samples were selected to investigate the microbiological factors influencing the alterations in saliva metabolism in patients with AD.

RESULTS

The plaque index and bleeding on probing were considerably higher in the AD group. Further, Cis-3-(1-carboxy-ethyl)-3,5-cyclohexadiene-1,2-diol, dodecanoic acid, genipic acid, and N, N-dimethylthanolamine N-oxide were determined as candidate biomarkers, based on the area under the curve (AUC) value (AUC = 0.95). The results of oral-flora sequencing showed that dysbacteriosis may be a reason for the differences in AD saliva metabolism.

CONCLUSION

Dysregulation of the proportion of specific bacterial flora in saliva plays a vital role in metabolic changes in AD. These results will contribute to further improving the AD saliva biomarker system.

Saliva metabolome alterations after acute stress

Major stress has systemic effects on the body that can have adverse consequences for physical and mental health. However, the molecular basis of these damaging effects remains incompletely understood. Here we use a longitudinal approach to characterise the acute systemic impact of major psychological stress in a pig model. We perform untargeted metabolomics on non-invasively obtained saliva samples from pigs before and 24 h after transfer to the novel physical and social environment of a slaughterhouse. The main molecular changes occurring include decreases in amino acids, B-vitamins, and amino acid-derived metabolites synthesized in B-vitamin-dependent reactions, as well as yet-unidentified metabolite features. Decreased levels of several of the identified metabolites are implicated in the pathology of human psychological disorders and neurodegenerative disease, suggesting a possible neuroprotective function. Our results provide a fingerprint of the acute effect of psychological stress on the metabolome and suggest candidate biomarkers with potential roles in stress-related disorders.

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.

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.

Chinmedomics Strategy for Elucidating the Pharmacological Effects and Discovering Bioactive Compounds From Keluoxin Against Diabetic Retinopathy

Keluoxin (KLX) is an active agent in the treatment of diabetic retinopathy (DR). However, its mechanism, targets, and effective constituents against DR are still unclear, which seriously restricts its clinical application. Chinmedomics has the promise of explaining the pharmacological effects of herbal medicines and investigating the effective mechanisms. The research results from electroretinography and electron microscope showed that KLX could reduce retinal dysfunction and pathological changes by the DR mouse model. Based on effectiveness, we discovered 64 blood biomarkers of DR by nontargeted metabolomics analysis, 51 of which returned to average levels after KLX treatment including leukotriene D4 and A4, l-tryptophan, 6-hydroxymelatonin, l-phenylalanine, l-tyrosine, and gamma-linolenic acid (GLA). The metabolic pathways involved were phenylalanine metabolism, steroid hormone biosynthesis, sphingolipid metabolism, etc. Adenosine monophosphate-activated protein kinase (AMPK), extracellular signal-regulated protein kinase1/2 (ERK1/2), phosphatidylinositol-3-kinase (PI3K), and protein 70 S6 kinase (p70 S6K) might be potential targets of KLX against DR. This was related to the mammalian target of rapamycin (mTOR) signaling and AMPK signaling pathways. We applied the chinmedomics strategy, integrating serum pharm-chemistry of traditional Chinese medicine (TCM) with metabolomics, to discover astragaloside IV (AS-IV), emodin, rhein, chrysophanol, and other compounds, which were the core effective constituents of KLX when against DR. Our study was the first to apply the chinmedomics strategy to discover the effective constituents of KLX in the treatment of DR, which fills the gap of unclear effective constituents of KLX. In the next step, the research of effective constituents can be used to optimize prescription preparation, improve the quality standard, and develop an innovative drug.

Therapeutic Effect and Mechanism of Si-Miao-Yong-An-Tang on Thromboangiitis Obliterans Based on the Urine Metabolomics Approach

Si-Miao-Yong-An-Tang (SMYAT) is a classic prescription for the treatment of thromboangiitis obliterans (TAO). However, the effect and mechanism are still unclear. This experiment aims to evaluate the therapeutic effect and mechanism of SMYAT on sodium laurate solution induced thromboangiitis obliterans model rats using urine metabolomics. The therapeutic effect of SMYAT was evaluated by histopathology, hemorheology and other indexes. The urine metabolomic method, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used for clustering group and discriminant analysis to screen urine differential metabolic biomarkers, and explore new insight into pathophysiological mechanisms of SMYAT in the treatment of TAO. SMYAT has significant antithrombotic and anti-inflammatory effects, according to the results of urine metabolomic analysis, and regulate the metabolic profile of TAO rats, and its return profile is close to the state of control group. Through metabolomics technology, a total of 35 urine biomarkers of TAO model were characterized. Among them, SMYAT treatment can regulate 22 core biomarkers, such as normetanephrine and 4-pyridoxic acid. It is found that the therapeutic effect of SMYAT is closely related to the tyrosine metabolism, vitamin B6 metabolism and cysteine and methionine metabolism. It preliminarily explored the therapeutic mechanism of SMYAT, and provided a scientific basis for the application of SMYAT.

High-Throughput and Untargeted Metabolic Profiling Revealed the Potential Effect and Mechanisms of Paeoniflorin in Young Asthmatic Rats

Paeoniflorin (PF) is a multi-target monoterpenoid glycoside and possesses broad pharmacological functions, e.g., anti-inflammation, anti-depression, antitumor, abirritation, neuroprotection, antioxidant, and enhancing cognitive and learning ability. PF has gained a large amount of attention for its effect on asthma disease as the growth rate of asthma has increased in recent years. However, its mechanism of action on asthma is still unclear. In this study, we have explored the action mechanism of PF on asthma disease. Furthermore, high-throughput untargeted metabolic profiling was performed through ultraperformance liquid chromatography/electrospray ionization quadruple time-of-flight high-definition mass spectrometry (QA) UPLC-Q/TOF-MS combined with pattern recognition approaches and pathway analysis. A total of 20 potential biomarkers were discovered by UPLC/MS and urine metabolic profiling. The key pathways including the citrate cycle (the TCA cycle), pyrimidine metabolism, pentose phosphate pathway, tyrosine metabolism, and tryptophan metabolism were affected by PF. In conclusion, we have discovered metabolite biomarkers and revealed the therapeutic mechanism of PF based on liquid chromatography coupled with mass spectrometry untargeted metabolomics. The untargeted metabolomics combined with UPLC-MS is a useful tool for exploring the therapeutic mechanism and targets of PF in the treatment of asthma. Metabolomics combined with UPLC-MS is an integrated method to explore the metabolic mechanism of PF in the treatment of asthma rats and to reveal the potential targets, providing theoretical support for the study of the treatment of PF.

Epitranscriptomic Modifications Modulate Normal and Pathological Functions in CNS

RNA is more than just a combination of four genetically encoded nucleobases as it carries extra information in the form of epitranscriptomic modifications. Diverse chemical groups attach covalently to RNA to enhance the plasticity of cellular transcriptome. The reversible and dynamic nature of epitranscriptomic modifications allows RNAs to achieve rapid and context-specific gene regulation. Dedicated cellular machinery comprising of writers, erasers, and readers drives the epitranscriptomic signaling. Epitranscriptomic modifications control crucial steps of mRNA metabolism such as splicing, export, localization, stability, degradation, and translation. The majority of the epitranscriptomic modifications are highly abundant in the brain and contribute to activity-dependent gene expression. Thus, they regulate the vital physiological processes of the brain, such as synaptic plasticity, neurogenesis, and stress response. Furthermore, epitranscriptomic alterations influence the progression of several neurologic disorders. This review discussed the molecular mechanisms of epitranscriptomic regulation in neurodevelopmental and neuropathological conditions with the goal to identify novel therapeutic targets.

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.

Liquid Chromatograph-Mass Spectrometry-Based Non-targeted Metabolomics Discovery of Potential Endogenous Biomarkers Associated With Prostatitis Rats to Reveal the Effects of Magnoflorine

Magnoflorine (Mag) has multiple pharmacological activities for the prevention and treatment of prostatitis. However, its molecular mechanisms andpharmacological targets are not clear. In this study, the ultra-performance liquid tandem mass spectrometry-based metabolomics method was used to clarify the intervention of Mag against prostatitis and the biological mechanism. A total of 25 biomarkers associated with the prostatitis model were identified by metabolomics, and a number of metabolic pathways closely related to the model were obtained by MetPA analysis. After given Mag treatment, the results of each indicator were shown that Mag alkaloid could inhibit the development of prostatitis effectively. We found that Mag had regulative effects on potential biomarkers of prostatitis model, which can regulate them to the control group. Our results indicated that alkaloids have an effective intervention therapy for prostatitis, and five types of metabolic pathways closely related to prostatitis model were obtained, including phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, tyrosine metabolism, arginine and proline metabolism, glycine, serine and threonine metabolism, alanine, aspartate and glutamate metabolism. This study has provided the basic experimental data for the development of Mag in the prevention and treatment of prostatitis.

Identification of the Raman Salivary Fingerprint of Parkinson's Disease Through the Spectroscopic- Computational Combinatory Approach

Despite the wide range of proposed biomarkers for Parkinson's disease (PD), there are no specific molecules or signals able to early and uniquely identify the pathology onset, progression and stratification. Saliva is a complex biofluid, containing a wide range of biological molecules shared with blood and cerebrospinal fluid. By means of an optimized Raman spectroscopy procedure, the salivary Raman signature of PD can be characterized and used to create a classification model. Raman analysis was applied to collect the global signal from the saliva of 23 PD patients and related pathological and healthy controls. The acquired spectra were computed using machine and deep learning approaches. The Raman database was used to create a classification model able to discriminate each spectrum to the correct belonging group, with accuracy, specificity, and sensitivity of more than 97% for the single spectra attribution. Similarly, each patient was correctly assigned with discriminatory power of more than 90%. Moreover, the extracted data were significantly correlated with clinical data used nowadays for the PD diagnosis and monitoring. The preliminary data reported highlight the potentialities of the proposed methodology that, once validated in larger cohorts and with multi-centered studies, could represent an innovative minimally invasive and accurate procedure to determine the PD onset, progression and to monitor therapies and rehabilitation efficacy.

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.

Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of AD's mechanism from machine learning (ML) is so far unrealized, majorly due to the lack of needed data density. To harness the AD mechanism's knowledge from the expression profiles of postmortem prefrontal cortex samples of 310 AD and 157 controls, we used seven predictive operators or combinations of RapidMiner Studio operators to establish predictive models from the input matrix and to assign a weight to each attribute. Besides, conventional fold-change methods were also applied as controls. The identified genes were further submitted to enrichment analysis for KEGG pathways. The average accuracy of ML models ranges from 86.30% to 91.22%. The overlap ratio of the identified genes between ML and conventional methods ranges from 19.7% to 21.3%. ML exclusively identified oxidative phosphorylation genes in the AD pathway. Our results highlighted the deficiency of oxidative phosphorylation in AD and suggest that ML should be considered as complementary to the conventional fold-change methods in transcriptome studies.

Effects of Aerobic Exercise Training on Systemic Biomarkers and Cognition in Late Middle-Aged Adults at Risk for Alzheimer's Disease

Increasing evidence indicates that physical activity and exercise training may delay or prevent the onset of Alzheimer's disease (AD). However, systemic biomarkers that can measure exercise effects on brain function and that link to relevant metabolic responses are lacking. To begin to address this issue, we utilized blood samples of 23 asymptomatic late middle-aged adults, with familial and genetic risk for AD (mean age 65 years old, 50% female) who underwent 26 weeks of supervised treadmill training. Systemic biomarkers implicated in learning and memory, including the myokine Cathepsin B (CTSB), brain-derived neurotrophic factor (BDNF), and klotho, as well as metabolomics were evaluated. Here we show that aerobic exercise training increases plasma CTSB and that changes in CTSB, but not BDNF or klotho, correlate with cognitive performance. BDNF levels decreased with exercise training. Klotho levels were unchanged by training, but closely associated with change in VO2peak. Metabolomic analysis revealed increased levels of polyunsaturated free fatty acids (PUFAs), reductions in ceramides, sphingo- and phospholipids, as well as changes in gut microbiome metabolites and redox homeostasis, with exercise. Multiple metabolites (~30%) correlated with changes in BDNF, but not CSTB or klotho. The positive association between CTSB and cognition, and the modulation of lipid metabolites implicated in dementia, support the beneficial effects of exercise training on brain function. Overall, our analyses indicate metabolic regulation of exercise-induced plasma BDNF changes and provide evidence that CTSB is a marker of cognitive changes in late middle-aged adults at risk for dementia.

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.

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.

Targeted metabolomics study of early pathological features in hippocampus of triple transgenic Alzheimer's disease male mice

Alzheimer's disease (AD) is a serious neurodegenerative disease in people of age 65 or above. The detailed etiology and pathogenesis of AD have not been elucidated yet. In this study, the hippocampi of 2- and 6-month-old triple transgenic Alzheimer's disease male mice and age-sex-matched wild-type (WT) mice were analyzed by using targeted metabolomics approach. Compared with WT mice, 24 and 60 metabolites were found with significant differences in 2- and 6-month-old AD mice. Among these, 14 metabolites were found common while 10 metabolites showed consistent variable trends in both groups. These differential metabolites are found associated with amino acid, lipid, vitamin, nucleotide-related base, neurotransmitter and energy metabolisms, and oxidative stress. The results suggest that these differential metabolites might play a critical role in AD pathophysiology, and may serve as potential biomarkers for AD. Moreover, the results highlight the involvement of abnormal purine, pyrimidine, arginine, and proline metabolism, along with glycerophospholipid metabolism in early pathology of AD. For the first time, several differential metabolites are found to be associated with AD in this study. Targeted metabolomics can be used for rapid and accurate quantitative analysis of specific target metabolites associated with AD.

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.

Functional Metabolomics Analysis Elucidating the Metabolic Biomarker and Key Pathway Change Associated With the Chronic Glomerulonephritis and Revealing Action Mechanism of Rhein

Chronic glomerulonephritis (CGN) as the culprit of kidney failure can increase the mortality of critically ill patients and seriously threatens people’s health all over the world. This study using metabolomics strategy is to reveal the potential therapeutic mechanism-related targets to evaluate the effects of rhein (RH) on CGN rats. Changes of serum metabolites and pathways were analyzed by non-targeted metabolomic method based on liquid chromatography-mass spectrometry (LC-MS) combined with ingenuity pathway analysis. In addition, the levels of biochemical indicators were also detected. A total of 25 potential biomarkers were identified to express serum metabolic turbulence in CGN animal model, and then 16 biomarkers were regulated by RH trending to the normal states. From metabolite enrichment and pathway analysis, pharmacological activity of RH on CGN were mainly involved in six vital metabolic pathways including phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arachidonic acid metabolism, tricarboxylic acid cycle (TCA cycle), alanine, aspartate, and glutamate metabolism, arginine and proline metabolism. It suggested CGN treatment with RH, which may be mediated via interference with metabolic pathway such as amino acid metabolism, arachidonic acid metabolism, and TCA cycle to regulating inflammation, oxidation response and immune regulation against CGN. It showed that metabolomics method offer deeply insight into the therapeutic mechanisms of natural product.

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.

Acyl-Alkyl-Phosphatidlycholines are Decreased in Saliva of Patients with Alzheimer's Disease as Identified by Targeted Metabolomics

Diagnosis of Alzheimer's disease (AD) is still a challenge. Salivary analysis could produce an easily accessible and inexpensive possibility to study metabolic changes in AD. In the present pilot study, we show for the first time using targeted metabolomics that acyl-alkyl phosphatidylcholines (PCae C34:1-2; PCae C36:1-2-3; PCaeC38:1c3; PCae C40:2-3) are significantly reduced in saliva of AD patients (n = 25) compared to healthy controls (n = 25). Saliva levels of PCae C36Λ1-2-3) were also decreased in patients with mild cognitive impairment (n = 25). No changes were seen for saliva diacyl-phosphatidylcholines, lyso-acyl-phosphatidylcholines, and sphinogomyelins. These data suggest specific lipid changes in the saliva of AD patients, thus salivary measures could establish new biomarkers. However, these preliminary results have to be established in larger scale studies.

UPLC-MS metabolomics method provides valuable insights into the effect and underlying mechanisms of Rhizoma Drynariae protecting osteoporosis

Osteoporosis (OP) is a metabolic bone disease in which that volume of bone tissue per unit volume decrease, which is a common disease disturbing the elderly or postmenopausal women. Rhizoma Drynariae (RD) is a kind of herb widely used in thousands of years of clinical practice in China to tonify kidney and prevent osteoporosis, with reliable curative effect. However, the mechanism of its anti-osteoporosis action is still unclear. This study is dedicated to exploration the therapeutic effect of RD on retinoic acid solution-induced OP model rats based on high-throughput metabolomics technology platform, and reveal its influence on metabolomics level, so as to find effective potential biomarkers and therapeutic targets for diagnosing OP. OP model was established by intragastric administration of retinoic acid solution for 21 days, and then the treatment group was treated by intragastric administration of RD solution for 60 days. Blood samples of all groups were collected and analyzed based on UPLC-MS metabolomics and combined with EZinfo 3.0 data analysis, 32 potential biomarkers were identified, including 22 in ESI+ and 10 in ESI-, these biomarkers are related to 9 metabolic pathways. After treatment with RD solution, 21 biomarkers were obviously regulated, these mainly affected linoleic acid metabolic, glycerophospholipid metabolism and arachidonic acid metabolism pathway. The results show that RD can reduce the risk of OP disease, which may be related to the metabolic pathway mentioned above, and provides the foundation for the administer prophylaxis and treatment of OP with natural products.

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.

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 Redox Biomarkers in Selected Neurodegenerative Diseases

Neurodegenerative diseases (NDDs), such as Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease, are disorders, which cause irreversible and progressive deterioration of the central nervous system. The pathophysiology of NDDs is still not fully explained; nevertheless, oxidative stress is considered as a critical mediator of cerebral degeneration, brain inflammation, as well as neuronal apoptosis. Therefore, it is not surprising that redox biomarkers are increasingly used in the diagnosis of neurodegenerative diseases. As saliva is a very easy to obtain bioliquid, it seems promising to use this biomaterial in the diagnosis of NDDs. Saliva collection is easy, cheap, stress-free, and non-infectious, and it does not require the help of a specialised medical personnel. Additionally, the concentrations of many salivary redox biomarkers correlate with their content in blood serum as well as the degree of disease progression, which makes them non-invasive indicators of NDDs. This paper reviews the latest knowledge concerning the use of salivary redox biomarkers in the diagnosis and prognosis of selected neurodegenerative diseases.

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.

Quantitative metabolomics of saliva using proton NMR spectroscopy in patients with Parkinson's disease and healthy controls

INTRODUCTION

Parkinson's disease (PD) is a multisystem disorder of unknown etiology, highlights a broad array of symptoms and pathological features influencing organs throughout the body. The metabolic profile of saliva in patients with PD may be influenced by malabsorption in the gastroenteric tract, neurodegeneration, and mitochondrial dysfunction. In the present study, we apply a powerful NMR metabolomics approach for biomarker identification in PD using saliva, a non-invasive bio-fluid.

METHODS

Metabolic profiling of saliva were studied in patients with PD (n = 76) and healthy controls (HC, n = 37) were analyzed and differentiated PD from HC. A total of 40 metabolites including aromatic amino acids, short-chain fatty acids, branched chain amino acids, taurine, and N-acetylglutamate were identified. Spectral binned data and concentration of metabolites were estimated for analysis.

RESULTS

Increased concentration of phenylalanine, tyrosine, histidine, glycine, acetoacetate, taurine, TMAO, GABA, N-acetylglutamate, acetoin, acetate, alanine, fucose, propionate, isoleucine, and valine were observed in PD as compared to HC. Further, subgroup analysis among early PD, advanced PD, and HC groups, revealed increased metabolite concentration in early PD group as compared to advanced PD and HC group.

DISCUSSION

Analysis revealed potential biomarkers and their involvement in amino acid metabolism, energy metabolism, neurotransmitters metabolism, and microflora system. Patients with early PD exhibited higher metabolite concentration as compared to advanced PD group which might be associated with dopaminergic treatment.

CONCLUSION

The results of our data indicate that patients with PD might be characterized by metabolic imbalances like gut microflora system, energy metabolites, and neurotransmitters which may contribute to the PD pathogenesis.

Exploring the metabolic biomarkers and pathway changes in crucian under carbonate alkalinity exposure using high-throughput metabolomics analysis based on UPLC-ESI-QTOF-MS

The aims of this study is to explore the metabolomic biomarker and pathway changes in crucian under carbonate alkalinity exposures using high-throughput metabolomics analysis based on ultra-performance liquid chromatography-electrospray ionization-quadrupole time of flight-tandem mass spectrometry (UPLC-ESI-QTOF-MS) for carrying out adaptive evolution of fish in environmental exposures and understanding molecular physiological mechanisms of saline–alkali tolerance in fishes. Under 60 day exposure management, the UPLC-ESI-QTOF-MS technology, coupled with a pattern recognition approach and metabolic pathway analysis, was utilized to give insight into the metabolic biomarker and pathway changes. In addition, biochemical parameters in response to carbonate alkalinity in fish were detected for chronic impairment evaluation. A total of twenty-seven endogenous metabolites were identified to distinguish the biochemical changes in fish in clean water under exposure to different concentrations of carbonate alkalinity (CA); these mainly involved amino acid synthesis and metabolism, arachidonic acid metabolism, glyoxylate and dicarboxylate metabolism, pyruvate metabolism and the citrate cycle (TCA cycle). Compared with the control group, CA exposure increased the level of blood ammonia; TP; ALB; Gln in the liver and gills; GS; urea in blood, the liver and gills; CREA; CPS; Glu and LDH; and decreased the level of weight gain rate, oxygen consumption, discharge rate of ammonia, SOD, CAT, ALT, AST and Na⁺/K⁺-ATPase. At low concentrations, CA can change the normal metabolism of fish in terms of changing the osmotic pressure regulation capacity, antioxidant capacity, ammonia metabolism and liver and kidney function to adapt to the CA exposure environment. As the concentration of CA increases, various metabolic processes in crucian are inhibited, causing chronic damage to the body. The results show that the metabolomic strategy is a potentially powerful tool for identifying the mechanisms in response to different environmental exposomes and offers precious information about the chronic response of fish to CA.

We explore the metabolic biomarker and pathway changes accompanying the adaptive evolution of crucian subjected to carbonate alkalinity exposure, using UPLC-ESI-QTOF-MS, in order to understand the molecular physiological mechanisms of saline–alkali tolerance.

Saliva, an easily accessible fluid as diagnostic tool and potent stem cell source for Alzheimer's Disease: Present and future applications

Alzheimer's disease (AD) is a progressive and multifactorial disease. Many scientific advances have advanced our understanding of the pathogenesis of AD. However, the clinical diagnosis of AD remains difficult, with only post-mortem assays confirming its definitive diagnosis. There is a crucial need for an early and accurate detection of AD related symptoms. To date, current diagnosis techniques are costly or invasive. Finding a peripheral biomarker that could provide a sensitive, reproducible, and accurate detection prior to the onset of the AD clinical symptoms will allow identification of "at risk" individuals, thereby facilitating early initiation of treatments that may prove more effective. Salivary glands contain stem cells, which are affected by aging, suggesting that tissue samples from these glands may reveal a stem cell biomarker of AD, but also stem cells may be harvested from these glands, with proper timing and isolation technique, for cell-based regenerative medicine. Alternatively, instead of the salivary glands, saliva may represent an attractive source for biomarkers due to minimal discomfort to the patient, non-invasive collection, and the possibility of cost-effective screening large populations, encouraging greater compliance in clinical trials and frequent testing. In addition, salivary glands contain stem cells, which are likely also present in the saliva, making these cells as potentially sensitive cellular biomarker of and a therapeutic agent for AD. The aim of this review is to critically analyze the use of saliva for the identification of circulating biological markers to help the diagnosis of early cognitive impairment associated with AD and to generate insights into the potential application of stem cells derived from salivary glands or saliva as therapeutics (i.e., stem cell transplantation) for the disease.

HS-SPME-GC-MS approach for the analysis of volatile salivary metabolites and application in a case study for the indirect assessment of gut microbiota

In this work, a straightforward analytical approach based on headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry was developed for the analysis of salivary volatile organic compounds without any prior derivatization step. With a sample volume of 500 μL, optimal conditions were achieved by allowing the sample to equilibrate for 10 min at 50 °C and then extracting the samples for 10 min at the same temperature, using a carboxen/polydimethylsiloxane fibre. The method allowed the simultaneous identification and quantification of 20 compounds in sample headspace, including short-chain fatty acids and their derivatives which are commonly analysed after analyte derivatization. The proof of applicability of the methodology was performed with a case study regarding the analysis of the dynamics of volatile metabolites in saliva of a single subject undergoing 5-day treatment with rifaximin antibiotic. Non-stimulated saliva samples were collected over 3 weeks from a nominally healthy volunteer before, during, and after antibiotic treatment. The variations of some metabolites, known to be produced by the microbiota and by bacteria that are susceptible to antibiotics, suggest that the study of the dynamics of salivary metabolites can be an excellent indirect method for analysing the gut microbiota. This approach is novel from an analytical standpoint, and it encourages further studies combining saliva metabolite profiles and gut microbiota dynamics. Graphical abstract.

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

Using a non-invasive biofluid (saliva), we apply a powerful metabolomics workflow for unbiased biomarker discovery in Alzheimer's disease (AD). We profile and differentiate Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD groups. The workflow involves differential chemical isotope labeling liquid chromatography mass spectrometry using dansylation derivatization for in-depth profiling of the amine/phenol submetabolome. The total sample (N = 109) was divided in to the Discovery Phase (DP) (n = 82; 35 CN, 25 MCI, 22 AD) and a provisional Validation Phase (VP) (n = 27; 10 CN, 10 MCI, 7 AD). In DP we detected 6,230 metabolites. Pairwise analyses confirmed biomarkers for AD versus CN (63), AD versus MCI (47), and MCI versus CN (2). We then determined the top discriminating biomarkers and diagnostic panels. A 3-metabolite panel distinguished AD from CN and MCI (DP and VP: Area Under the Curve [AUC] = 1.000). The MCI and CN groups were best discriminated with a 2-metabolite panel (DP: AUC = 0.779; VP: AUC = 0.889). In addition, using positively confirmed metabolites, we were able to distinguish AD from CN and MCI with good diagnostic performance (AUC > 0.8). Saliva is a promising biofluid for both unbiased and targeted AD biomarker discovery and mechanism detection. Given its wide availability and convenient accessibility, saliva is a biofluid that can promote diversification of global AD biomarker research.

Salivary biological biomarkers for Alzheimer's disease

Alzheimer's disease (AD) is becoming a threat to aging population all over the world. The pathogenic process of AD is likely initiated many years before clinical onset, thus biomarkers for AD diagnosis are critical for the prevention and therapy for the disease at the early stage in order to reduce the global burden brought by the disease. Saliva is treated as a potential alternative and universal diagnostic fluid that can be collected noninvasively by participants with moderate training and without side effects. Several potential salivary biomarkers, which might prove to be significant diagnostic tools in AD, have been researched. We address here the present and the future of these salivary biological biomarkers for AD.

Biomarkers for Alzheimer's Disease in Saliva: A Systematic Review

Background

The histopathological changes of Alzheimer's disease (AD) are detectable decades prior to its clinical expression. However, there is a need for an early, inexpensive, noninvasive diagnostic biomarker to detect specific Alzheimer pathology. Recently developed neuroimaging biomarkers show promising results, but these methods are expensive and cause radiation. Furthermore, the analysis of cerebrospinal fluid (CSF) biomarkers requires an invasive lumbar puncture. Saliva is an easily obtained body fluid, and a stable saliva biomarker would therefore be a promising candidate for a future method for diagnosing AD. The purpose of this systematic review was to investigate studies of biomarkers in saliva samples for the diagnosis of AD.

Methods

The included articles were identified through a literature search in PubMed and Google Scholar for all articles until November 1^st, 2018, and furthermore, all reference lists of included articles were reviewed by hand. We included articles written in English investigating saliva from patients with AD and a control group.

Results

A total of 65 studies were identified, whereof 16 studies met the inclusion criteria and were included in the systematic review. A plethora of different biomarkers were investigated, and ten out of the sixteen studies showed a statistical significance in biomarkers between patients with AD and healthy, elderly controls, among these biomarkers for specific AD pathology (amyloid beta 1-42 (Aβ42) and tau).

Conclusion

Aβ42 and tau seem to be worthy candidates for future salivary biomarkers for AD, but other biomarkers such as lactoferrin and selected metabolites also have potential. More studies must be carried out with larger sample sizes and a standardization of the sampling and processing method. Factors such as diurnal variation, AD patients' decreased ability of oral self-care, and salivary flowrates must be taken into consideration.

Untargeted Metabolomics Study of the In Vitro Anti-Hepatoma Effect of Saikosaponin d in Combination with NRP-1 Knockdown

Saikosaponin d (SSd) is one of the main active ingredients in Radix Bupleuri. In our study, network pharmacology databases and metabolomics were used in combination to explore the new targets and reveal the in-depth mechanism of SSd. A total of 35 potential targets were chosen through database searching (HIT and TCMID), literature mining, or chemical similarity predicting (Pubchem). Out of these obtained targets, Neuropilin-1 (NRP-1) was selected for further research based on the degree of molecular docking scores and novelty. Cell viability and wound healing assays demonstrated that SSd combined with NRP-1 knockdown could significantly enhance the damage of HepG2. Metabolomics analysis was then performed to explore the underlying mechanism. The overall difference between groups was quantitatively evaluated by the metabolite deregulation score (MDS). Results showed that NRP-1 knockdown exhibited the lowest MDS, which demonstrated that the metabolic profile experienced the slightest interference. However, SSd alone, or NRP-1 knockdown in combination with SSd, were both significantly influenced. Differential metabolites mainly involved short- or long-chain carnitines and phospholipids. Further metabolic pathway analysis revealed that disturbed lipid transportation and phospholipid metabolism probably contributed to the enhanced anti-hepatoma effect by NRP-1 knockdown in combination with SSd. Taken together, in this study, we provided possible interaction mechanisms between SSd and its predicted target NRP-1.

Mass spectrometry is a multifaceted weapon to be used in the battle against Alzheimer's disease: Amyloid beta peptides and beyond

Amyloid-β peptide (Aβ) accumulation and aggregation have been considered for many years the main cause of Alzheimer's disease (AD), and therefore have been the principal target of investigation as well as of the proposed therapeutic approaches (Grasso [2011] Mass Spectrom Rev. 30: 347-365). However, the amyloid cascade hypothesis, which considers Aβ accumulation the only causative agent of the disease, has proven to be incomplete if not wrong. In recent years, actors such as metal ions, oxidative stress, and other cofactors have been proposed as possible co-agents or, in some cases, main causative factors of AD. In this scenario, MS investigation has proven to be fundamental to design possible diagnostic strategies of this elusive disease, as well as to understand the biomolecular mechanisms involved, in the attempt to find a possible therapeutic solution. We review the current applications of MS in the search for possible Aβ biomarkers of AD to help the diagnosis of the disease. Recent examples of the important contributions that MS has given to prove or build theories on the molecular pathways involved with such terrible disease are also reviewed.

Alzheimer's Biomarkers From Multiple Modalities Selectively Discriminate Clinical Status: Relative Importance of Salivary Metabolomics Panels, Genetic, Lifestyle, Cognitive, Functional Health and Demographic Risk Markers

Background: Among the neurodegenerative diseases of aging, sporadic Alzheimer’s disease (AD) is the most prevalent and perhaps the most feared. With virtually no success at finding pharmaceutical therapeutics for altering progressive AD after diagnosis, research attention is increasingly directed at discovering biological and other markers that detect AD risk in the long asymptomatic phase. Both early detection and precision preclinical intervention require systematic investigation of multiple modalities and combinations of AD-related biomarkers and risk factors. We extend recent unbiased metabolomics research that produced a set of metabolite biomarker panels tailored to the discrimination of cognitively normal (CN), cognitively impaired and AD patients. Specifically, we compare the prediction importance of these panels with five other sets of modifiable and non-modifiable AD risk factors (genetic, lifestyle, cognitive, functional health and bio-demographic) in three clinical groups.

Method: The three groups were: CN (n = 35), mild cognitive impairment (MCI; n = 25), and AD (n = 22). In a series of three pairwise comparisons, we used machine learning technology random forest analysis (RFA) to test relative predictive importance of up to 19 risk biomarkers from the six AD risk domains.

Results: The three RFA multimodal prediction analyses produced significant discriminating risk factors. First, discriminating AD from CN was the AD metabolite panel and two cognitive markers. Second, discriminating AD from MCI was the AD/MCI metabolite panel and two cognitive markers. Third, discriminating MCI from CN was the MCI metabolite panel and seven markers from four other risk modalities: genetic, lifestyle, cognition and functional health.

Conclusions: Salivary metabolomics biomarker panels, supplemented by other risk markers, were robust predictors of: (1) clinical differences in impairment and dementia and even; (2) subtle differences between CN and MCI. For the latter, the metabolite panel was supplemented by biomarkers that were both modifiable (e.g., functional) and non-modifiable (e.g., genetic). Comparing, integrating and identifying important multi-modal predictors may lead to novel combinations of complex risk profiles potentially indicative of neuropathological changes in asymptomatic or preclinical AD.

A review of biomarkers of Alzheimer's disease in noninvasive samples

The discovery of biomarkers that confer high confidence of presymptomatic Alzheimer's disease (AD) diagnosis would be a valuable tool to study the etiology of the disease, to find risk factors, to discover more treatments and medicines. The present work reviews the potential biomarkers of AD based on the concentration changes of small molecules and chemical elements in noninvasive samples (urine, saliva, hair and others). An updated table with 74 target compounds is produced and ranked. Until the present date, there are a few biomarkers, present in urine, with the most promising potential: isoprostane 8,12-iso-iPF2a-VI, total free amino acids, 8-hydroxy-2'-deoxyguanosine, glycine and enzymatic activity of NaCl-stimulated PON1. All show increased levels in AD carriers, with the exception of NaCl-stimulated PON1.