Blood-based metabolic signatures in Alzheimer's disease

CSF amino acid profiles in ICV-streptozotocin-induced sporadic Alzheimer's disease in male Wistar rat: a metabolomics and systems biology perspective

Alzheimer's disease (AD) is an increasingly important public health concern due to the increasing proportion of older individuals within the general population. The impairment of processes responsible for adequate brain energy supply primarily determines the early features of the aging process. Restricting brain energy supply results in brain hypometabolism prior to clinical symptoms and is anatomically and functionally associated with cognitive impairment. The present study investigated changes in metabolic profiles induced by intracerebroventricular-streptozotocin (ICV-STZ) in an AD-like animal model. To this end, male Wistar rats received a single injection of STZ (3 mg·kg-1) by ICV (2.5 μL into each ventricle for 5 min on each side). In the second week after receiving ICV-STZ, rats were tested for cognitive performance using the Morris Water Maze test and subsequently prepared for positron emission tomography (PET) to confirm AD-like symptoms. Tandem Mass Spectrometry (MS/MS) analysis was used to detect amino acid changes in cerebrospinal fluid (CFS) samples. Our metabolomics study revealed a reduction in the concentrations of various amino acids (alanine, arginine, aspartic acid, glutamic acid, glycine, isoleucine, methionine, phenylalanine, proline, serine, threonine, tryptophane, tyrosine, and valine) in CSF of ICV-STZ-treated animals as compared to controls rats. The results of the current study indicate amino acid levels could potentially be considered targets of nutritional and/or pharmacological interventions to interfere with AD progression.

Mendelian Randomization of Blood Metabolites Suggests Circulating Glutamine Protects Against Late-Onset Alzheimer's Disease

Background

Late-onset Alzheimer's disease (LOAD) represents a growing health burden. Previous studies suggest that blood metabolite levels influence risk of LOAD.

Objective

We used a genetics-based study design which may overcome limitations of other epidemiological studies to assess the influence of metabolite levels on LOAD risk.

Methods

We applied Mendelian randomization (MR) to evaluate bi-directional causal effects using summary statistics from the largest genome-wide association studies (GWAS) of 249 blood metabolites (n = 115,082) and GWAS of LOAD (ncase = 21,982, ncontrol = 41,944).

Results

MR analysis of metabolites as exposures revealed a negative association of genetically-predicted glutamine levels with LOAD (Odds Ratio (OR) = 0.83, 95% CI = 0.73, 0.92) that was consistent in multiple sensitivity analyses. We also identified a positive association of genetically-predicted free cholesterol levels in small LDL (OR = 1.79, 95% CI = 1.36, 2.22) on LOAD. Using genetically-predicted LOAD as the exposure, we identified associations with phospholipids to total lipids ratio in large LDL (OR = 0.96, 95% CI = 0.94, 0.98), but not with glutamine, suggesting that the relationship between glutamine and LOAD is unidirectional.

Conclusions

Our findings support previous evidence that higher circulating levels of glutamine may be a target for protection against LOAD.

Targeted Metabolomic Analysis of the Eye Tissue of Triple Transgenic Alzheimer's Disease Mice at an Early Pathological Stage

Alzheimer's disease (AD) is a severe neurodegenerative disease in older people. Despite some consensus on pathogenesis of AD established by previous researches, further elucidation is still required for better understanding. This study analyzed the eye tissues of 2- and 6-month-old triple transgenic AD (3 × Tg-AD) male mice and age-sex-matched wild-type (WT) mice using a targeted metabolomics approach. Compared with WT mice, 20 and 44 differential metabolites were identified in 2- and 6-month-old AD mice, respectively. They were associated with purine metabolism, pantothenate and CoA biosynthesis, pyruvate metabolism, lysine degradation, glycolysis/gluconeogenesis, and pyrimidine metabolism pathways. Among them, 8 metabolites presented differences in both the two groups, and 5 of them showed constant trend of change. The results indicated that the eye tissues of 3 × Tg-AD mice underwent changes in the early stages of the disease, with changes in metabolites observed at 2 months of age and more pronounced at 6 months of age, which is consistent with our previous studies on hippocampal targeted metabolomics in 3 × Tg-AD mice. Therefore, a joint analysis of data from this study and previous hippocampal study was performed, and the differential metabolites and their associated mechanisms were similar in eye and hippocampal tissues, but with tissue specificity.

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.

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.

The identification of a potential plasma metabolite marker for Alzheimer’s disease by LC-MS untargeted metabolomics

BACKGROUND AND AIMS

Alzheimer's disease (AD), the most common type of dementia, is hard to recognize early, resulting in delayed treatment and poor outcome. At present, there is neither reliable, non-invasive methods to diagnose it accurately and nor effective drugs to recover it. Discovery and quantification of novel metabolite markers in plasma of AD patients and investigation of the correlation between the markers and AD assessment scores.

MATERIALS AND METHODS

Untargeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomics with LC-quadrupole- time-of-flight (Q-TOF) was performed in plasma samples of age-matched AD patients and healthy controls. The potential markers were further quantified with targeted multiple reaction monitoring (MRM) approach.

RESULTS

Among the candidates, progesterone, and 3-indoleacetic acid (3-IAA) were successfully identified and then validated in 50 plasma samples from 25 AD patients and 25 matched normal controls with MRM approach. As a result, 3-IAA was significantly altered in AD patients and correlated with some AD assessment scores.

CONCLUSION

By using untargeted LC-MS metabolomic and LC-MRM approaches to analyze plasma metabolites of AD patients and normal subjects, 3-IAA was discovered and quantified to be significantly altered in AD patients and correlated with several AD assessment scores.

Serum metabolomics analysis in patients with alcohol dependence

Objective

Alcohol dependence (AD) is a chronic recurrent mental disease caused by long-term drinking. It is one of the most prevalent public health problems. However, AD diagnosis lacks objective biomarkers. This study was aimed to shed some light on potential biomarkers of AD patients by investigating the serum metabolomics profiles of AD patients and the controls.

Methods

Liquid chromatography-mass spectrometry (LC-MS) was used to detect the serum metabolites of 29 AD patients (AD) and 28 controls. Six samples were set aside as the validation set (Control: n = 3; AD group: n = 3), and the remaining were used as the training set (Control: n = 26; AD group: n = 25). Principal component analysis (PCA) and partial least squares discriminant analysis (PCA-DA) were performed to analyze the training set samples. The metabolic pathways were analyzed using the MetPA database. The signal pathways with pathway impact >0.2, value of p <0.05, and FDR < 0.05 were selected. From the screened pathways, the metabolites whose levels changed by at least 3-fold were screened. The metabolites with no numerical overlap in their concentrations in the AD and the control groups were screened out and verified with the validation set.

Results

The serum metabolomic profiles of the control and the AD groups were significantly different. We identified six significantly altered metabolic signal pathways, including protein digestion and absorption; alanine, aspartate, and glutamate metabolism; arginine biosynthesis; linoleic acid metabolism; butanoate metabolism; and GABAergic synapse. In these six signal pathways, the levels of 28 metabolites were found to be significantly altered. Of these, the alterations of 11 metabolites changed by at least 3-fold compared to the control group. Of these 11 metabolites, those with no numerical overlap in their concentrations between the AD and the control groups were GABA, 4-hydroxybutanoic acid, L-glutamic acid, citric acid and L-glutamine.

Conclusion

The metabolite profile of the AD group was significantly different from that of the control group. GABA, 4-hydroxybutanoic acid, L-glutamic acid, citric acid, and L-glutamine could be used as potential diagnostic markers for AD.

Mapping Conscience: Network Analysis Into the Differences in Maturation of Offending and Non-Offending Adolescents

Conscience is a diagnostically relevant concept in forensic psychiatry, but often misinterpreted as an all-or-none phenomenon. We conceptualize the conscience as a psychic function in which elements like empathy, self-conscience emotions such as shame, guilt and pride, and moral orientation work together. The differences in conscience functioning can be described in terms of developmental levels of integration. We conducted network analyses on data collected via a questionnaire survey held among 52 offending and 243 non-offending juveniles. We displayed two networks: One representing the non-offenders' normative and one representing the offenders' defiantly maturing conscience. As was hypothesized, in the non-offenders network, almost all elements clustered into one clinically meaningful network, indicating integration of the different elements of the normative maturing conscience. In the offenders network, the correlations between the elements were sporadic, indicating a lack of integration of the defiantly maturing conscience. The difference between the two networks was more prominent for empathy and moral orientation than for self-conscious emotions. This research supports the theory of differences in maturation of conscience instead of being an all-or-none phenomenon and calls for further research, taking a deeper look at the significance of integration of the conscience and its implications for offending behaviour.

The promise of multi-omics approaches to discover biological alterations with clinical relevance in Alzheimer's disease

Beyond the core features of Alzheimer's disease (AD) pathology, i.e. amyloid pathology, tau-related neurodegeneration and microglia response, multiple other molecular alterations and pathway dysregulations have been observed in AD. Their inter-individual variations, complex interactions and relevance for clinical manifestation and disease progression remain poorly understood, however. Heterogeneity at both pathophysiological and clinical levels complicates diagnosis, prognosis, treatment and drug design and testing. High-throughput "omics" comprise unbiased and untargeted data-driven methods which allow the exploration of a wide spectrum of disease-related changes at different endophenotype levels without focussing a priori on specific molecular pathways or molecules. Crucially, new methodological and statistical advances now allow for the integrative analysis of data resulting from multiple and different omics methods. These multi-omics approaches offer the unique advantage of providing a more comprehensive characterisation of the AD endophenotype and to capture molecular signatures and interactions spanning various biological levels. These new insights can then help decipher disease mechanisms more deeply. In this review, we describe the different multi-omics tools and approaches currently available and how they have been applied in AD research so far. We discuss how multi-omics can be used to explore molecular alterations related to core features of the AD pathologies and how they interact with comorbid pathological alterations. We further discuss whether the identified pathophysiological changes are relevant for the clinical manifestation of AD, in terms of both cognitive impairment and neuropsychiatric symptoms, and for clinical disease progression over time. Finally, we address the opportunities for multi-omics approaches to help discover novel biomarkers for diagnosis and monitoring of relevant pathophysiological processes, along with personalised intervention strategies in AD.

Alzheimer's disease: a scoping review of biomarker research and development for effective disease diagnosis

INTRODUCTION

Alzheimer's disease (AD) is regarded as the foremost reason for neurodegeneration that prominently affects the geriatric population. Characterized by extracellular accumulation of amyloid-beta (Aβ), intracellular aggregation of hyperphosphorylated tau (p-tau), and neuronal degeneration that causes impairment of memory and cognition. Amyloid/tau/neurodegeneration (ATN) classification is utilized for research purposes and involves amyloid, tau, and neuronal injury staging through MRI, PET scanning, and CSF protein concentration estimations. CSF sampling is invasive, and MRI and PET scanning requires sophisticated radiological facilities which limit its widespread diagnostic use. ATN classification lacks effectiveness in preclinical AD.

AREAS COVERED

This publication intends to collate and review the existing biomarker profile and the current research and development of a new arsenal of biomarkers for AD pathology from different biological samples, microRNA (miRNA), proteomics, metabolomics, artificial intelligence, and machine learning for AD screening, diagnosis, prognosis, and monitoring of AD treatments.

EXPERT OPINION

It is an accepted observation that AD-related pathological changes occur over a long period of time before the first symptoms are observed providing ample opportunity for detection of biological alterations in various biological samples that can aid in early diagnosis and modify treatment outcomes.

Lipids as Early and Minimally Invasive Biomarkers for Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide. Specifically, typical late-onset AD is a sporadic form with a complex etiology that affects over 90% of patients. The current gold standard for AD diagnosis is based on the determination of amyloid status by analyzing cerebrospinal fluid samples or brain positron emission tomography. These procedures can be used widely as they have several disadvantages (expensive, invasive). As an alternative, blood metabolites have recently emerged as promising AD biomarkers. Small molecules that cross the compromised AD blood-brain barrier could be determined in plasma to improve clinical AD diagnosis at early stages through minimally invasive techniques. Specifically, lipids could play an important role in AD since the brain has a high lipid content, and they are present ubiquitously inside amyloid plaques. Therefore, a systematic review was performed with the aim of identifying blood lipid metabolites as potential early AD biomarkers. In conclusion, some lipid families (fatty acids, glycerolipids, glycerophospholipids, sphingolipids, lipid peroxidation compounds) have shown impaired levels at early AD stages. Ceramide levels were significantly higher in AD subjects, and polyunsaturated fatty acids levels were significantly lower in AD. Also, high arachidonic acid levels were found in AD patients in contrast to low sphingomyelin levels. Consequently, these lipid biomarkers could be used for minimally invasive and early AD clinical diagnosis.

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.

Serum Metabolomic Analysis of Male Patients with Cannabis or Amphetamine Use Disorder

Studies have demonstrated that chronic consumption of abused drugs induces alterations in several proteins that regulate metabolism. For instance, methamphetamine exposure reduces glucose levels. Fatty and amino acid levels were altered in groups exposed to abused drugs. Therefore, in our study, we investigated the serum metabolomic profile of patients diagnosed with cannabis and/or amphetamine use disorders. Blood was obtained from subjects (control, amphetamine, and cannabis). Detection of serum metabolites was performed using gas chromatography. The ratio peak areas for metabolites were analyzed across the three groups. Both cannabis and amphetamine groups showed higher d-erythrotetrafuranose, octadecanoic acid, hexadecenoic acid, trans-9-octadecanoic acid, lactic acid and methyl thio hydantoin metabolites compared with the control group. Moreover, cannabis patients were found to possess higher glycine, 9,12 octadecanoic acid malonic acid, phosphoric acid and prostaglandin F1a than controls. Our analysis showed that the identified metabolic profile of cannabis or amphetamine use disorder patients was different than control group. Our data indicated that chronic exposure to cannabis or amphetamine dysregulated metabolites in the serum. Future studies are warranted to explore the effects of these abused drugs on the metabolic proteins.

Alzheimer Disease: Recent Updates on Apolipoprotein E and Gut Microbiome Mediation of Oxidative Stress, and Prospective Interventional Agents

Alzheimer's disease (AD) is a current public health challenge and will remain until the development of an effective intervention. However, developing an effective treatment for the disease requires a thorough understanding of its etiology, which is currently lacking. Although several studies have shown the association between oxidative damage and AD, only a few have clarified the specific mechanisms involved. Herein, we reviewed recent preclinical and clinical studies that indicated the significance of oxidative damage in AD, as well as potential antioxidants. Although several factors regulate oxidative stress in AD, we centered our investigation on apolipoprotein E and the gut microbiome. Apolipoprotein E, particularly apolipoprotein E-ε4, can impair the structural facets of the mitochondria. This, in turn, can minimize the mitochondrial functionality and result in the progressive build-up of free radicals, eventually leading to oxidative stress. Similarly, the gut microbiome can influence oxidative stress to a significant degree via its metabolite, trimethylamine N-oxide. Given the various roles of these two factors in modulating oxidative stress, we also discuss the possible relationship between them and provide future research directions.

Erythrocyte sphingolipid species as biomarkers of Alzheimer's disease

Diagnosing Alzheimer's disease (AD) in the early stage is challenging. Informative biomarkers can be of great value for population-based screening. Metabolomics studies have been used to find potential biomarkers, but commonly used tissue sources can be difficult to obtain. The objective of this study was to determine the potential utility of erythrocyte metabolite profiles in screening for AD. Unlike some commonly-used sources such as cerebrospinal fluid and brain tissue, erythrocytes are plentiful and easily accessed. Moreover, erythrocytes are metabolically active, a feature that distinguishes this sample source from other bodily fluids like plasma and urine. In this preliminary pilot study, the erythrocyte metabolomes of 10 histopathologically confirmed AD patients and 10 patients without AD (control (CTRL)) were compared. Whole blood was collected post-mortem and erythrocytes were analyzed using ultra-performance liquid chromatography tandem mass spectrometry. Over 750 metabolites were identified in AD and CTRL erythrocytes. Seven were increased in AD while 24 were decreased (P<0.05). The majority of the metabolites increased in AD were associated with amino acid metabolism and all of the decreased metabolites were associated with lipid metabolism. Prominent among the potential biomarkers were 10 sphingolipid or sphingolipid-related species that were consistently decreased in AD patients. Sphingolipids have been previously implicated in AD and other neurological conditions. Furthermore, previous studies have shown that erythrocyte sphingolipid concentrations vary widely in normal, healthy adults. Together, these observations suggest that certain erythrocyte lipid phenotypes could be markers of risk for development 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.

Kynurenine Pathway Metabolites as Biomarkers in Alzheimer’s Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that deteriorates cognitive function. Patients with AD generally exhibit neuroinflammation, elevated beta-amyloid (Aβ), tau phosphorylation (p-tau), and other pathological changes in the brain. The kynurenine pathway (KP) and several of its metabolites, especially quinolinic acid (QA), are considered to be involved in the neuropathogenesis of AD. The important metabolites and key enzymes show significant importance in neuroinflammation and AD. Meanwhile, the discovery of changed levels of KP metabolites in patients with AD suggests that KP metabolites may have a prominent role in the pathogenesis of AD. Further, some KP metabolites exhibit other effects on the brain, such as oxidative stress regulation and neurotoxicity. Both analogs of the neuroprotective and antineuroinflammation metabolites and small molecule enzyme inhibitors preventing the formation of neurotoxic and neuroinflammation compounds may have potential therapeutic significance. This review focused on the KP metabolites through the relationship of neuroinflammation in AD, significant KP metabolites, and associated molecular mechanisms as well as the utility of these metabolites as biomarkers and therapeutic targets for AD. The objective is to provide references to find biomarkers and therapeutic targets for patients with AD.

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.

A metabolite panel that differentiates Alzheimer's disease from other dementia types

Introduction

Alzheimer's disease (AD) is associated with altered metabolites. This study aimed to determine the validity of using circulating metabolites to differentiate AD from other dementias.

Methods

Blood metabolites were measured in three data sets. Data set 1 (controls, 27; AD, 28) was used for analyzing differential metabolites. Data set 2 (controls, 93; AD, 92) was used to establish a diagnostic AD model with use of a metabolite panel. The model was applied to Data set 3 (controls, 76; AD, 76; other dementias, 205) to verify its capacity for differentiating AD from other dementias.

Results

Data set 1 revealed 7 upregulated and 77 downregulated metabolites. In Data set 2, a panel of 11 metabolites was included in a model that could distinguish AD from controls. In Data set 3, this panel was used to successfully differentiate AD from other dementias.

Discussion

This study revealed an AD‐specific panel of 11 metabolites that may be used for AD diagnosis.

Blood biomarkers in Alzheimer's disease

INTRODUCTION

Early diagnosis of Alzheimer disease (AD) through the use of biomarkers could assist in the implementation and monitoring of early therapeutic interventions, and has the potential to significantly modify the course of the disease.

DEVELOPMENT

The classic cerebrospinal fluid and approved structural and functional neuroimaging biomarkers are of limited clinical application given their invasive nature and/or high cost. The identification of more accessible and less costly biomarkers, such as blood biomarkers, would increase their use in clinical practice. We review the available published evidence on the main blood biochemical biomarkers potentially useful for diagnosing AD.

CONCLUSIONS

Blood biomarkers are more cost- and time-effective than CSF biomarkers. However, immediate applicability in clinical practice is relatively unlikely. The main limitations come from the difficulty of measuring and standardising thresholds between different laboratories and the failure to replicate results. Of all the molecules studied, apoptosis and neurodegeneration biomarkers and the biomarker panels obtained through "omics" approaches, such as isolated or combined metabolomics, offer the most promising results.

Discovery of a Metabolic Signature Predisposing High Risk Patients with Mild Cognitive Impairment to Converting to Alzheimer's Disease

Assessing dementia conversion in patients with mild cognitive impairment (MCI) remains challenging owing to pathological heterogeneity. While many MCI patients ultimately proceed to Alzheimer’s disease (AD), a subset of patients remain stable for various times. Our aim was to characterize the plasma metabolites of nineteen MCI patients proceeding to AD (P-MCI) and twenty-nine stable MCI (S-MCI) patients by untargeted metabolomics profiling. Alterations in the plasma metabolites between the P-MCI and S-MCI groups, as well as between the P-MCI and AD groups, were compared over the observation period. With the help of machine learning-based stratification, a 20-metabolite signature panel was identified that was associated with the presence and progression of AD. Furthermore, when the metabolic signature panel was used for classification of the three patient groups, this gave an accuracy of 73.5% using the panel. Moreover, when specifically classifying the P-MCI and S-MCI subjects, a fivefold cross-validation accuracy of 80.3% was obtained using the random forest model. Importantly, indole-3-propionic acid, a bacteria-generated metabolite from tryptophan, was identified as a predictor of AD progression, suggesting a role for gut microbiota in AD pathophysiology. Our study establishes a metabolite panel to assist in the stratification of MCI patients and to predict conversion to AD.

Role of Oxidative Damage in Alzheimer's Disease and Neurodegeneration: From Pathogenic Mechanisms to Biomarker Discovery

Alzheimer’s disease (AD) is a neurodegenerative disorder accounting for over 50% of all dementia patients and representing a leading cause of death worldwide for the global ageing population. The lack of effective treatments for overt AD urges the discovery of biomarkers for early diagnosis, i.e., in subjects with mild cognitive impairment (MCI) or prodromal AD. The brain is exposed to oxidative stress as levels of reactive oxygen species (ROS) are increased, whereas cellular antioxidant defenses are decreased. Increased ROS levels can damage cellular structures or molecules, leading to protein, lipid, DNA, or RNA oxidation. Oxidative damage is involved in the molecular mechanisms which link the accumulation of amyloid-β and neurofibrillary tangles, containing hyperphosphorylated tau, to microglia response. In this scenario, microglia are thought to play a crucial role not only in the early events of AD pathogenesis but also in the progression of the disease. This review will focus on oxidative damage products as possible peripheral biomarkers in AD and in the preclinical phases of the disease. Particular attention will be paid to biological fluids such as blood, CSF, urine, and saliva, and potential future use of molecules contained in such body fluids for early differential diagnosis and monitoring the disease course. We will also review the role of oxidative damage and microglia in the pathogenesis of AD and, more broadly, in neurodegeneration.

Metabolism navigates neural cell fate in development, aging and neurodegeneration

An uninterrupted energy supply is critical for the optimal functioning of all our organs, and in this regard the human brain is particularly energy dependent. The study of energy metabolic pathways is a major focus within neuroscience research, which is supported by genetic defects in the oxidative phosphorylation mechanism often contributing towards neurodevelopmental disorders and changes in glucose metabolism presenting as a hallmark feature in age-dependent neurodegenerative disorders. However, as recent studies have illuminated roles of cellular metabolism that span far beyond mere energetics, it would be valuable to first comprehend the physiological involvement of metabolic pathways in neural cell fate and function, and to subsequently reconstruct their impact on diseases of the brain. In this Review, we first discuss recent evidence that implies metabolism as a master regulator of cell identity during neural development. Additionally, we examine the cell type-dependent metabolic states present in the adult brain. As metabolic states have been studied extensively as crucial regulators of malignant transformation in cancer, we reveal how knowledge gained from the field of cancer has aided our understanding in how metabolism likewise controls neural fate determination and stability by directly wiring into the cellular epigenetic landscape. We further summarize research pertaining to the interplay between metabolic alterations and neurodevelopmental and psychiatric disorders, and expose how an improved understanding of metabolic cell fate control might assist in the development of new concepts to combat age-dependent neurodegenerative diseases, particularly Alzheimer's disease.

Genetic overlap between Alzheimer's disease and blood lipid levels

Late-onset Alzheimer's disease (AD) has a significant genetic component, but the molecular mechanisms through which genetic risk factors contribute to AD pathogenesis are unclear. We screened for genetic sharing between AD and the blood levels of 615 metabolites to elucidate how the polygenic architecture of AD affects metabolomic profiles. We retrieved summary statistics from genome-wide association studies of AD and the metabolite blood levels and assessed for shared genetic etiology, using a polygenic risk score-based approach. For the blood levels of 31 metabolites, all of which were lipids, we identified and replicated genetic sharing with AD. We also found a positive genetic concordance - implying that genetic risk factors for AD are associated with higher blood levels - for 16 of the 31 replicated metabolites. In the brain, lipids and their intermediate metabolites have essential structural and functional roles, such as forming and dynamically regulating synaptic membranes. Our results imply that genetic risk factors for AD affect lipid levels, which may be leveraged to develop novel treatment strategies for AD.

Plasma Metabolomics of Acute Coronary Syndrome Patients Based on Untargeted Liquid Chromatography-Mass Spectrometry

Background: Acute coronary syndrome (ACS) is the main cause of death and morbidity worldwide. The present study aims to investigate the altered metabolites in plasma from patients with ACS and sought to identify metabolic biomarkers for ACS. Methods: The plasma metabolomics profiles of 284 ACS patients and 130 controls were carried out based on an untargeted liquid chromatography coupled with tandem mass spectrometry (LC-MS) approach. Multivariate statistical methods, pathway enrichment analysis, and univariate receiver operating characteristic (ROC) curve analysis were performed. Results: A total of 328 and 194 features were determined in positive and negative electrospray ionization mode in the LC-MS analysis, respectively. Twenty-eight metabolites were found to be differentially expressed, in ACS patients relative to controls (p < 0.05). Pathway analysis revealed that these metabolites are mainly involved in synthesis and degradation of ketone bodies, phenylalanine metabolism, and arginine and proline metabolism. Furthermore, a diagnostic model was constructed based on the metabolites identified and the areas under the curve (AUC) for 5-oxo-D-proline, creatinine, phosphatidylethanolamine lyso 16:0, and LPC (20:4) range from 0.764 to 0.844. The higher AUC value of 0.905 was obtained for the combined detection of phosphatidylethanolamine lyso 16:0 and LPC (20:4). Conclusions: Differential metabolic profiles may be useful for the effective diagnosis of ACS and may provide additional insight into the molecular mechanisms underlying ACS.

Alzheimer's disease

In this Seminar, we highlight the main developments in the field of Alzheimer's disease. The most recent data indicate that, by 2050, the prevalence of dementia will double in Europe and triple worldwide, and that estimate is 3 times higher when based on a biological (rather than clinical) definition of Alzheimer's disease. The earliest phase of Alzheimer's disease (cellular phase) happens in parallel with accumulating amyloid β, inducing the spread of tau pathology. The risk of Alzheimer's disease is 60-80% dependent on heritable factors, with more than 40 Alzheimer's disease-associated genetic risk loci already identified, of which the APOE alleles have the strongest association with the disease. Novel biomarkers include PET scans and plasma assays for amyloid β and phosphorylated tau, which show great promise for clinical and research use. Multidomain lifestyle-based prevention trials suggest cognitive benefits in participants with increased risk of dementia. Lifestyle factors do not directly affect Alzheimer's disease pathology, but can still contribute to a positive outcome in individuals with Alzheimer's disease. Promising pharmacological treatments are poised at advanced stages of clinical trials and include anti-amyloid β, anti-tau, and anti-inflammatory strategies.

An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer's disease

BACKGROUND

Multiple pathophysiological processes have been described in Alzheimer's disease (AD). Their inter-individual variations, complex interrelations, and relevance for clinical manifestation and disease progression remain poorly understood. We hypothesize that specific molecular patterns indicating both known and yet unidentified pathway alterations are associated with distinct aspects of AD pathology.

METHODS

We performed multi-level cerebrospinal fluid (CSF) omics in a well-characterized cohort of older adults with normal cognition, mild cognitive impairment, and mild dementia. Proteomics, metabolomics, lipidomics, one-carbon metabolism, and neuroinflammation related molecules were analyzed at single-omic level with correlation and regression approaches. Multi-omics factor analysis was used to integrate all biological levels. Identified analytes were used to construct best predictive models of the presence of AD pathology and of cognitive decline with multifactorial regression analysis. Pathway enrichment analysis identified pathway alterations in AD.

RESULTS

Multi-omics integration identified five major dimensions of heterogeneity explaining the variance within the cohort and differentially associated with AD. Further analysis exposed multiple interactions between single 'omics modalities and distinct multi-omics molecular signatures differentially related to amyloid pathology, neuronal injury, and tau hyperphosphorylation. Enrichment pathway analysis revealed overrepresentation of the hemostasis, immune response, and extracellular matrix signaling pathways in association with AD. Finally, combinations of four molecules improved prediction of both AD (protein 14-3-3 zeta/delta, clusterin, interleukin-15, and transgelin-2) and cognitive decline (protein 14-3-3 zeta/delta, clusterin, cholesteryl ester 27:1 16:0 and monocyte chemoattractant protein-1).

CONCLUSIONS

Applying an integrative multi-omics approach we report novel molecular and pathways alterations associated with AD pathology. These findings are relevant for the development of personalized diagnosis and treatment approaches in 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.

Urinary metabolic phenotyping for Alzheimer's disease

Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer’s Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer’s Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer’s Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer’s Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer’s pathology in previous studies.

The Role of Mitochondrial Calcium Homeostasis in Alzheimer's and Related Diseases

Calcium signaling is essential for neuronal function, and its dysregulation has been implicated across neurodegenerative diseases, including Alzheimer’s disease (AD). A close reciprocal relationship exists between calcium signaling and mitochondrial function. Growing evidence in a variety of AD models indicates that calcium dyshomeostasis drastically alters mitochondrial activity which, in turn, drives neurodegeneration. This review discusses the potential pathogenic mechanisms by which calcium impairs mitochondrial function in AD, focusing on the impact of calcium in endoplasmic reticulum (ER)–mitochondrial communication, mitochondrial transport, oxidative stress, and protein homeostasis. This review also summarizes recent data that highlight the need for exploring the mechanisms underlying calcium-mediated mitochondrial dysfunction while suggesting potential targets for modulating mitochondrial calcium levels to treat neurodegenerative diseases such as AD.

Novel biomarkers in Alzheimer's disease using high resolution proteomics and metabolomics: miRNAS, proteins and metabolites

Alzheimer's disease (AD) is the most common form of dementia. It affects approximately 6% of people over the age of 65 years. It is a clinicopathological, degenerative, chronical and progressive disease that exhibits a deterioration of memory, orientation, speech and other functions. Factors contributing to the pathogenesis of the disease are the presence of extracellular amyloid deposits, called neuritic senile plaques, and fibrillary protein deposits inside neurons, known as neurofibrillary bundles, that appear mainly in the frontal and temporal lobes. AD has a long preclinical latency and is difficult to diagnose and prevent at early stages. Despite the advent of novel high-throughput technologies, it is a great challenge to identify precise biomarkers to understand the progression of the disease and the development of new treatments. In this sense, important knowledge is emerging regarding novel molecular and biological candidates with diagnostic potential, including microRNAs that have a key role in gene repression. On the other hand, proteomic approaches offer a platform for the comprehensive analysis of the whole proteome in a certain physiological time. Proteomic technology investigates protein expression directly and reveals post-translational modifications known to be determinant for many human diseases. Clinically, there is growing evidence for the role of proteomic and metabolomic technologies in AD biomarker discovery. This review discusses the role of several miRNAs identified using genomic technologies, and the importance of novel proteomic and metabolomic approaches to identify new proteins and metabolites that may be useful as biomarkers for monitoring the progression and treatment of AD.

Association of lysophosphatidic acids with cerebrospinal fluid biomarkers and progression to Alzheimer's disease

Background

Lysophosphatidic acids (LPAs) are bioactive signaling phospholipids that have been implicated in Alzheimer’s disease (AD). It is largely unknown whether LPAs are associated with AD pathology and progression from mild cognitive impairment (MCI) to AD.

Methods

The current study was performed on cerebrospinal fluid (CSF) and plasma samples of 182 MCI patients from two independent cohorts. We profiled LPA-derived metabolites using liquid chromatography-mass spectrometry. We evaluated the association of LPAs with CSF biomarkers of AD, Aβ-42, p-tau, and total tau levels overall and stratified by APOE genotype and with MCI to AD progression.

Results

Five LPAs (C16:0, C16:1, C22:4, C22:6, and isomer-LPA C22:5) showed significant positive association with CSF biomarkers of AD, Aβ-42, p-tau, and total tau, while LPA C14:0 and C20:1 associated only with Aβ-42 and alkyl-LPA C18:1, and LPA C20:1 associated with tau pathology biomarkers. Association of cyclic-LPA C16:0 and two LPAs (C20:4, C22:4) with Aβ-42 levels was found only in APOE ε4 carriers. Furthermore, LPA C16:0 and C16:1 also showed association with MCI to AD dementia progression, but results did not replicate in an independent cohort.

Conclusions

Our findings provide evidence that LPAs may contribute to early AD pathogenesis. Future studies are needed to determine whether LPAs play a role in upstream of AD pathology or are downstream markers of neurodegeneration.

Circulating Metabolites as Potential Biomarkers for Neurological Disorders-Metabolites in Neurological Disorders

There are, still, limitations to predicting the occurrence and prognosis of neurological disorders. Biomarkers are molecules that can change in different conditions, a feature that makes them potential tools to improve the diagnosis of disease, establish a prognosis, and monitor treatments. Metabolites can be used as biomarkers, and are small molecules derived from the metabolic process found in different biological media, such as tissue samples, cells, or biofluids. They can be identified using various strategies, targeted or untargeted experiments, and by different techniques, such as high-performance liquid chromatography, mass spectrometry, or nuclear magnetic resonance. In this review, we aim to discuss the current knowledge about metabolites as biomarkers for neurological disorders. We will present recent developments that show the need and the feasibility of identifying such biomarkers in different neurological disorders, as well as discuss relevant research findings in the field of metabolomics that are helping to unravel the mechanisms underlying neurological disorders. Although several relevant results have been reported in metabolomic studies in patients with neurological diseases, there is still a long way to go for the clinical use of metabolites as potential biomarkers in these disorders, and more research in the field is needed.

Blood biomarkers indicate that the preclinical stages of Alzheimer's disease present overlapping molecular features

It is still debated whether non-specific preclinical symptoms of Alzheimer's disease (AD) can have diagnostic relevance. We followed the evolution from cognitively normal to AD by NMR-based metabolomics of blood sera. Multivariate statistical analysis of the NMR profiles yielded models that discriminated subjective memory decline (SMD), mild cognitive impairment (MCI) and AD. We validated a panel of six statistically significant metabolites that predicted SMD, MCI and AD in a blind cohort with sensitivity values ranging from 88 to 95% and receiver operating characteristic values from 0.88 to 0.99. However, lower values of specificity, accuracy and precision were observed for the models involving SMD and MCI, which is in line with the pathological heterogeneity indicated by clinical data. This excludes a "linear" molecular evolution of the pathology, pointing to the presence of overlapping "gray-zones" due to the reciprocal interference of the intermediate stages. Yet, the clear difference observed in the metabolic pathways of each model suggests that pathway dysregulations could be investigated for diagnostic purposes.

Neuroinflammation, microglial activation, and glucose metabolism in neurodegenerative diseases

Alzheimer's disease is characterized by aggregated amyloid beta plaques and neurofibrillary tangles. Apart from the plaques and tangles, microglial activation plays a significant role in neurodegeneration and neuronal function. This review discusses the way in which microglial activation influences neurodegeneration and how systemic inflammation, type 2 diabetes mellitus, obesity and hypercholesterolemia influence neuroinflammation. Also reviewed is how systemic inflammation influences microglial activation along with the relationship between microglial activation and glucose metabolism.

Tyr-Trp administration facilitates brain norepinephrine metabolism and ameliorates a short-term memory deficit in a mouse model of Alzheimer's disease

The physiological actions of orally ingested peptides on the brain remain poorly understood. This study examined the effects of 39 orally administered synthetic Tyr-containing dipeptides on the enhancement of brain norepinephrine metabolism in mice by comparing the concentration of 3-methoxy-4-hydroxyphenylethyleneglycol (MHPG). Although Tyr-Tyr administration increased blood and cerebral cortex (Cx) Tyr concentrations the most, Tyr-Trp increased Cx MHPG concentration the most. The oral administration of Tyr-Trp ameliorated a short-term memory deficit of a mouse model of cognitive dysfunction induced by amyloid beta peptide 25–35. Gene expression profiling of mouse brain using a microarray indicated that Tyr-Trp administration led to a wide variety of changes in mRNA levels, including the upregulation of genes encoding molecules involved in catecholamine metabolism. A comparative metabolome analysis of the Cx of mice given Tyr-Trp or Tyr-Tyr demonstrated that Tyr-Trp administration yielded higher concentrations of Trp and kynurenine pathway metabolites than Tyr-Tyr administration, as well as higher L-dopa levels, which is the initial product of catecholamine metabolism. Catecholamines were not significantly increased in the Cx of the Tyr-Tyr group compared with the Tyr-Trp group, despite a marked increase in Tyr. Presumably, Tyr-Trp administration enhances catecholamine synthesis and metabolism via the upregulation of genes involved in Tyr and Trp metabolism as well as metabolites of Tyr and Trp. These findings strongly suggest that orally ingested Tyr-Trp modulates the brain metabolome involved in catecholamine metabolism and contributes to higher brain function.

Fingerprinting Alzheimer's Disease by 1H Nuclear Magnetic Resonance Spectroscopy of Cerebrospinal Fluid

In this study, we sought for a cerebrospinal fluid (CSF) metabolomic fingerprint in Alzheimer's disease (AD) patients characterized, according to the clinical picture and CSF AD core biomarkers (Aβ₄₂, p-tau, and t-tau), both at pre-dementia (mild cognitive impairment due to AD, MCI-AD) and dementia stages (ADdem) and in a group of patients with a normal CSF biomarker profile (non-AD) using untargeted ¹H nuclear magnetic resonance (NMR) spectroscopy-based metabolomics. This is a retrospective study based on two independent cohorts: a Dutch cohort, which comprises 20 ADdem, 20 MCI-AD, and 20 non-AD patients, and an Italian cohort, constituted by 14 ADdem and 12 non-AD patients. ¹H NMR CSF spectra were analyzed using OPLS-DA. Metabolomic fingerprinting in the Dutch cohort provides a significant discrimination (86.1% accuracy) between ADdem and non-AD. MCI-AD patients show a good discrimination with respect to ADdem (70.0% accuracy) but only slight differences when compared with non-AD (59.6% accuracy). Acetate, valine, and 3-hydroxyisovalerate result to be altered in ADdem patients. Valine correlates with cognitive decline at follow-up (R = 0.53, P = 0.0011). The discrimination between ADdem and non-AD was confirmed in the Italian cohort. The CSF metabolomic fingerprinting shows a signature characteristic of ADdem patients with respect to MCI-AD and non-AD patients.

A Review on MS-Based Blood Biomarkers for Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of neurodegenerative dementia and there is no cure to date. Biomarkers in cerebrospinal fluid (CSF) are already included in the diagnostic work-up of symptomatic patients but markers for preclinical diagnosis and disease progression are not available. Furthermore, blood biomarkers are highly appreciated because they are minimally invasive and more accessible in primary care and in clinical studies. Mass spectrometry (MS) is an established tool for the measurement of various analytes in biological fluids such as blood. Its major strength is the high selectivity which is why it is also preferred as a reference method for immunoassays. MS has been used in several studies in the past for blood biomarker discovery and validation in AD using targeted MS such as multiple/selected reaction monitoring (MRM/SRM) or unbiased approaches (proteomics, metabolomics). In this short review, we give an overview on the status of current MS-based biomarker candidates for AD in blood plasma and serum.Plain Language Summary: Plain language summary available for this article.

Systemic and central nervous system metabolic alterations in Alzheimer's disease

Background

Metabolic alterations, related to cerebral glucose metabolism, brain insulin resistance, and age-induced mitochondrial dysfunction, play an important role in Alzheimer’s disease (AD) on both the systemic and central nervous system level. To study the extent and significance of these alterations in AD, quantitative metabolomics was applied to plasma and cerebrospinal fluid (CSF) from clinically well-characterized AD patients and cognitively healthy control subjects. The observed metabolic alterations were associated with core pathological processes of AD to investigate their relation with amyloid pathology and tau-related neurodegeneration.

Methods

In a case-control study of clinical and biomarker-confirmed AD patients (n = 40) and cognitively healthy controls without cerebral AD pathology (n = 34) with paired plasma and CSF samples, we performed metabolic profiling, i.e., untargeted metabolomics and targeted quantification. Targeted quantification focused on identified deregulated pathways highlighted in the untargeted assay, i.e. the TCA cycle, and its anaplerotic pathways, as well as the neuroactive tryptophan and kynurenine pathway.

Results

Concentrations of several TCA cycle and beta-oxidation intermediates were higher in plasma of AD patients, whilst amino acid concentrations were significantly lower. Similar alterations in these energy metabolism intermediates were observed in CSF, together with higher concentrations of creatinine, which were strongly correlated with blood-brain barrier permeability. Alterations of several amino acids were associated with CSF Amyloidβ1–42. The tryptophan catabolites, kynurenic acid and quinolinic acid, showed significantly higher concentrations in CSF of AD patients, which, together with other tryptophan pathway intermediates, were correlated with either CSF Amyloidβ1–42, or tau and phosphorylated Tau-181.

Conclusions

This study revealed AD-associated systemic dysregulation of nutrient sensing and oxidation and CNS-specific alterations in the neuroactive tryptophan pathway and (phospho)creatine degradation. The specific association of amino acids and tryptophan catabolites with AD CSF biomarkers suggests a close relationship with core AD pathology.

Our findings warrant validation in independent, larger cohort studies as well as further investigation of factors such as gender and APOE genotype, as well as of other groups, such as preclinical AD, to identify metabolic alterations as potential intervention targets.

Serum Metabolite Markers of Dementia Through Quantitative NMR Analysis: The Importance of Threonine-Linked Metabolic Pathways

There is a great need for diagnostic biomarkers of impending dementia. Metabolite markers in blood have been investigated in several studies, but inconclusive findings encourage further investigation, particularly in the pre-diagnostic phase. In the present study, the serum metabolomes of 110 dementia or pre-diagnostic dementia individuals and 201 healthy individuals matched for age, gender, and education were analyzed by nuclear magnetic resonance spectroscopy in combination with multivariate data analysis. 58 metabolites were quantified in each of the 311 samples. Individuals with dementia were discriminated from controls using a panel of seven metabolites, while the pre-diagnostic dementia subjects were distinguished from controls using a separate set of seven metabolites, where threonine was a common significant metabolite in both panels. Metabolite and pathway alterations specific for dementia and pre-diagnostic dementia were identified, in particular a disturbed threonine catabolism at the pre-diagnostic stage that extends to several threonine-linked pathways at the dementia stage.

Study of gene expressions' correlation structures in subgroups of Chronic Lymphocytic Leukemia Patients

In chronic lymphocytic leukemia (CLL) the interaction of leukemic cells with the microenvironment ultimately affects patient outcome. CLL cases can be divided in two subgroups with different clinical course based on the mutational status of the immunoglobulin heavy variable (IGHV) genes: mutated CLL (M-CLL) and unmutated CLL (U-CLL). Since in CLL, the differentiated relation of genes between the two subgroups is of greater importance than the individual gene behavior, this paper investigates the differences between the groups' gene interactions, by comparing their correlation structures. Fisher's test and Zou's confidence intervals are employed to detect differences of correlation coefficients. Afterwards, networks created by the genes participating in most differences are estimated with the use of structural equation models (SEM). The analysis is enhanced with graph modeling in order to visualize the between group differences in the gene structures of the two subgroups. The applied methodology revealed stronger correlations between genes in U-CLL patients, a finding in line with related biomedical literature. Using SEM for multigroup analysis, different gene structures between the two groups of patients were confirmed. The study of correlation structures can facilitate the detection of differential gene expression profiles in CLL subgroups, with potential applications in other diseases. Comparison of correlations can be very useful in understanding the complex internal structural differences which signify the variations of a disease.

Primary fatty amides in plasma associated with brain amyloid burden, hippocampal volume, and memory in the European Medical Information Framework for Alzheimer's Disease biomarker discovery cohort

Introduction:

A critical and as-yet unmet need in Alzheimer’s disease (AD) is the discovery of peripheral small molecule biomarkers. Given that brain pathology precedes clinical symptom onset, we set out to test whether metabolites in blood associated with pathology as indexed by cerebrospinal fluid (CSF) AD biomarkers.

Methods:

This study analyzed 593 plasma samples selected from the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery study, of individuals who were cognitively healthy (n = 242), had mild cognitive impairment (n = 236), or had AD-type dementia (n = 115). Logistic regressions were carried out between plasma metabolites (n = 883) and CSF markers, magnetic resonance imaging, cognition, and clinical diagnosis.

Results:

Eight metabolites were associated with amyloid b and one with t-tau in CSF, these were primary fatty acid amides (PFAMs), lipokines, and amino acids. From these, PFAMs, glutamate, and aspartate also associated with hippocampal volume and memory.

Discussion:

PFAMs have been found increased and associated with amyloid b burden in CSF and clinical measures.

Peripheral transcriptomic biomarkers for early detection of sporadic Alzheimer disease?

Alzheimer disease (AD) is the major epidemic of the 21^st century, its prevalence rising along with improved human longevity. Early AD diagnosis is key to successful treatment, as currently available therapeutics only allow small benefits for diagnosed AD patients. By contrast, future therapeutics, including those already in preclinical or clinical trials, are expected to afford neuroprotection prior to widespread brain damage and dementia. Brain imaging technologies are developing as promising tools for early AD diagnostics, yet their high cost limits their utility for screening at-risk populations. Blood or plasma transcriptomics, proteomics, and/or metabolomics may pave the way for cost-effective AD risk screening in middle-aged individuals years ahead of cognitive decline. This notion is exemplified by data mining of blood transcriptomics from a published dataset. Consortia blood sample collection and analysis from large cohorts with mild cognitive impairment followed longitudinally for their cognitive state would allow the development of a reliable and inexpensive early AD screening tool.

Plasma metabolomics in early Alzheimer's disease patients diagnosed with amyloid biomarker

An untargeted metabolomics study has been carried out using plasma samples from patients with Mild Cognitive Impairment due to Alzheimer's disease patients (MCI-AD, n = 29) and healthy people (n = 29)). They have been classified following the National Institute on Aging and Alzheimer's Association (NIA-AA) recommendations and cerebrospinal fluid biomarkers. The analytical method was based on liquid chromatography coupled to high resolution mass spectrometry. The data process from the corresponding metabolic profiles retained 1158 molecular features in positive and 424 in negative ionization mode. Differences between metabolomic profiles from MCI-AD patients and healthy participants were investigated using a penalized logistic regression analysis (ElasticNet), and being able to select automatically the most informative variables (53 molecular features). From the molecular features selected for the elastic net models, 16 variables were preliminarily identified by The Human Metabolome Database (amino acids, lipids…). However, only 4 of these variables were tentatively identified by MS/MS and all ions fragmentation modes, being choline the only confirmed metabolite. Regarding their metabolic pathways, they could be involved in cholinergic system, energy metabolism, amino acids and lipids pathways. To conclude, this is a reliable approach to early AD mechanisms, and choline has been identified as a promising AD diagnosis metabolite. SIGNIFICANCE: The untargeted analysis carried out in human plasma samples from early Alzheimer's disease patients and healthy individuals, and the use of sophisticated statistical tools, identified some metabolic pathways and plasma biomarkers. Preliminarily, cholinergic system, energy metabolism, and aminoacids and lipids pathways may be involved in early Alzheimer's disease development.

Lag-time in Alzheimer's disease patients: a potential plasmatic oxidative stress marker associated with ApoE4 isoform

In the brain, Oxidative Stress (OS) contribute to structural and functional changes associated with vascular aging, such as endothelial dysfunction, extracellular matrix degradation, resulting in age-related reduced vasodilatation in response to agonists. For this reason, OS is considered a key factor in Alzheimer’s Disease (AD) development and recent evidence correlated oxidative stress with vascular lesion in the pathogenesis of AD, but the mechanism still need to be fully clarified.

The etiology of AD is still not completely understood and is influenced by several factors including Apolipoprotein E (ApoE) genotype. In particular, the Apo ε4 isoform is considered a risk factor for AD development.

This study was aimed to evaluate the possible relationship between three plasmatic OS marker and Apo ε4 carrier status. Plasmatic soluble receptor for advanced glycation end products (sRAGE) levels, plasma antioxidant total defenses (by lag-time method) and plasmatic Reactive Oxygen species (ROS) levels were evaluated in 25 AD patients and in 30 matched controls. ROS were significantly higher while plasma antioxidant total defenses and sRAGE levels were significantly lower in AD patients compared to controls. In AD patients lag-time values show a significant positive linear correlation with sRAGE levels and a (even not significant) negative correlation with ROS levels. Lag-time is significantly lower in ε4 carrier (N = 13) than in ε4 non-carrier (N = 12). Our result confirms the substantial OS in AD. Lag-time levels showed a significant positive correlation with sRAGE levels and a significant association with ε4 carrier status suggesting that plasmatic lag-time evaluation can be considered as a potential useful OS risk marker in AD.

Omics-based Biomarkers for the Early Alzheimer Disease Diagnosis and Reliable Therapeutic Targets Development

BACKGROUND

Alzheimer's disease (AD), the most common cause of dementia in adulthood, has great medical, social, and economic impact worldwide. Available treatments result in symptomatic relief, and most of them are indicated from the early stages of the disease. Therefore, there is an increasing body of research developing accurate and early diagnoses, as well as diseasemodifying therapies.

OBJECTIVE

Advancing the knowledge of AD physiopathological mechanisms, improving early diagnosis and developing effective treatments from omics-based biomarkers.

METHODS

Studies using omics technologies to detect early AD, were reviewed with a particular focus on the metabolites/lipids, micro-RNAs and proteins, which are identified as potential biomarkers in non-invasive samples.

RESULTS

This review summarizes recent research on metabolomics/lipidomics, epigenomics and proteomics, applied to early AD detection. Main research lines are the study of metabolites from pathways, such as lipid, amino acid and neurotransmitter metabolisms, cholesterol biosynthesis, and Krebs and urea cycles. In addition, some microRNAs and proteins (microglobulins, interleukins), related to a common network with amyloid precursor protein and tau, have been also identified as potential biomarkers. Nevertheless, the reproducibility of results among studies is not good enough and a standard methodological approach is needed in order to obtain accurate information.

CONCLUSION

The assessment of metabolomic/lipidomic, epigenomic and proteomic changes associated with AD to identify early biomarkers in non-invasive samples from well-defined participants groups will potentially allow the advancement in the early diagnosis and improvement of therapeutic interventions.

Peripheral transcriptomic biomarkers for early detection of sporadic Alzheimer disease?

Alzheimer disease (AD) is the major epidemic of the 21st century, its prevalence rising along with improved human longevity. Early AD diagnosis is key to successful treatment, as currently available therapeutics only allow small benefits for diagnosed AD patients. By contrast, future therapeutics, including those already in preclinical or clinical trials, are expected to afford neuroprotection prior to widespread brain damage and dementia. Brain imaging technologies are developing as promising tools for early AD diagnostics, yet their high cost limits their utility for screening at-risk populations. Blood or plasma transcriptomics, proteomics, and/or metabolomics may pave the way for cost-effective AD risk screening in middle-aged individuals years ahead of cognitive decline. This notion is exemplified by data mining of blood transcriptomics from a published dataset. Consortia blood sample collection and analysis from large cohorts with mild cognitive impairment followed longitudinally for their cognitive state would allow the development of a reliable and inexpensive early AD screening tool.

Distinct disruptions in Land's cycle remodeling of glycerophosphocholines in murine cortex mark symptomatic onset and progression in two Alzheimer's disease mouse models

Changes in glycerophosphocholine metabolism are observed in Alzheimer's disease; however, it is not known whether these metabolic disruptions are linked to cognitive decline. Here, using unbiased lipidomic approaches and direct biochemical assessments, we profiled Land's cycle lipid remodeling in the hippocampus, frontal cortex, and temporal-parietal-entorhinal cortices of human amyloid beta precursor protein (ΑβPP) over-expressing mice. We identified a cortex-specific hypo-metabolic signature at symptomatic onset and a cortex-specific hyper-metabolic signature of Land's cycle glycerophosphocholine remodeling over the course of progressive behavioral decline. When N5 TgCRND8 and ΑβPP^{S we} /PSI^dE9 mice first exhibited deficits in the Morris Water Maze, levels of lyso-phosphatidylcholines, LPC(18:0/0:0), LPC(16:0/0:0), LPC(24:6/0:0), LPC(25:6/0:0), the lyso-platelet-activating factor (PAF), LPC(O-18:0/0:0), and the PAF, PC(O-22:6/2:0), declined as a result of reduced calcium-dependent cytosolic phospholipase A₂ α (cPLA₂ α) activity in all cortices but not hippocampus. Chronic intermittent hypoxia, an environmental risk factor that triggers earlier learning memory impairment in ΑβPP^{S we} /PSI^dE9 mice, elicited these same metabolic changes in younger animals. Thus, this lipidomic signature of phenoconversion appears age-independent. By contrast, in symptomatic N5 TgCRND8 mice, cPLA₂ α activity progressively increased; overall Lyso-phosphatidylcholines (LPC) and LPC(O) and PC(O-18:1/2:0) levels progressively rose. Enhanced cPLA₂ α activity was only detected in transgenic mice; however, age-dependent increases in the PAF acetylhydrolase 1b α₁ to α₂ expression ratio, evident in both transgenic and non-transgenic mice, reduced PAF hydrolysis thereby contributing to PAF accumulation. Taken together, these data identify distinct age-independent and age-dependent disruptions in Land's cycle metabolism linked to symptomatic onset and progressive behavioral decline in animals with pre-existing Αβ pathology. OPEN SCIENCE BADGES: This article has received a badge for *Open Materials* because it provided all relevant information to reproduce the study in the manuscript. The complete Open Science Disclosure form for this article can be found at the end of the article. More information about the Open Practices badges can be found at https://cos.io/our-services/open-science-badges/.

Next-generation biomarker discovery in Alzheimer's disease using metabolomics - from animal to human studies

Alzheimer's disease (AD) is a complex disease driven mainly by neuronal loss due to accumulation of intracellular neurofibrillary tangles and amyloid β aggregates in the brain. The diagnosis of AD currently relies on clinical symptoms while the disease can only be confirmed at autopsy. The few available biomarkers allowing for diagnosis are typically detected many years after the onset of the disease. New diagnostic approaches, particularly in easily-accessible biofluids, are essential. By providing an exhaustive information of the phenotype, metabolomics is an ideal approach for identification of new biomarkers. This review investigates the current position of metabolomics in the field of AD research, focusing on animal and human studies, and discusses the improvements carried out over the past decade.