Brain and blood metabolome for Alzheimer's dementia: findings from a targeted metabolomics analysis

Plasma metabolomic profiles of dementia: a prospective study of 110,655 participants in the UK Biobank

BACKGROUND

Plasma metabolomic profile is disturbed in dementia patients, but previous studies have discordant conclusions.

METHODS

Circulating metabolomic data of 110,655 people in the UK Biobank study were measured with nuclear magnetic resonance technique, and incident dementia records were obtained from national health registers. The associations between plasma metabolites and dementia were estimated using Cox proportional hazard models. The 10-fold cross-validation elastic net regression models selected metabolites that predicted incident dementia, and a 10-year prediction model for dementia was constructed by multivariable logistic regression. The predictive values of the conventional risk model, the metabolites model, and the combined model were discriminated by comparison of area under the receiver operating characteristic curves (AUCs). Net reclassification improvement (NRI) was used to estimate the change of reclassification ability when adding metabolites into the conventional prediction model.

RESULTS

Amongst 110,655 participants, the mean (standard deviation) age was 56.5 (8.1) years, and 51 186 (46.3%) were male. A total of 1439 (13.0%) developed dementia during a median follow-up of 12.2 years (interquartile range: 11.5-12.9 years). A total of 38 metabolites, including lipids and lipoproteins, ketone bodies, glycolysis-related metabolites, and amino acids, were found to be significantly associated with incident dementia. Adding selected metabolites (n=24) to the conventional dementia risk prediction model significantly improved the prediction for incident dementia (AUC: 0.824 versus 0.817, p =0.042) and reclassification ability (NRI = 4.97%, P = 0.009) for identifying high risk groups.

CONCLUSIONS

Our analysis identified various metabolomic biomarkers which were significantly associated with incident dementia. Metabolomic profiles also provided opportunities for dementia risk reclassification. These findings may help explain the biological mechanisms underlying dementia and improve dementia prediction.

Acylcarnitines: Nomenclature, Biomarkers, Therapeutic Potential, Drug Targets, and Clinical Trials

Acylcarnitines are fatty acid metabolites that play important roles in many cellular energy metabolism pathways. They have historically been used as important diagnostic markers for inborn errors of fatty acid oxidation and are being intensively studied as markers of energy metabolism, deficits in mitochondrial and peroxisomal β -oxidation activity, insulin resistance, and physical activity. Acylcarnitines are increasingly being identified as important indicators in metabolic studies of many diseases, including metabolic disorders, cardiovascular diseases, diabetes, depression, neurologic disorders, and certain cancers. The US Food and Drug Administration-approved drug L-carnitine, along with short-chain acylcarnitines (acetylcarnitine and propionylcarnitine), is now widely used as a dietary supplement. In light of their growing importance, we have undertaken an extensive review of acylcarnitines and provided a detailed description of their identity, nomenclature, classification, biochemistry, pathophysiology, supplementary use, potential drug targets, and clinical trials. We also summarize these updates in the Human Metabolome Database, which now includes information on the structures, chemical formulae, chemical/spectral properties, descriptions, and pathways for 1240 acylcarnitines. This work lays a solid foundation for identifying, characterizing, and understanding acylcarnitines in human biosamples. We also discuss the emerging opportunities for using acylcarnitines as biomarkers and as dietary interventions or supplements for many wide-ranging indications. The opportunity to identify new drug targets involved in controlling acylcarnitine levels is also discussed. SIGNIFICANCE STATEMENT: This review provides a comprehensive overview of acylcarnitines, including their nomenclature, structure and biochemistry, and use as disease biomarkers and pharmaceutical agents. We present updated information contained in the Human Metabolome Database website as well as substantial mapping of the known biochemical pathways associated with acylcarnitines, thereby providing a strong foundation for further clarification of their physiological roles.

Simultaneous Multiplexed Imaging of Biomolecules in Transgenic Mouse Brain Tissues Using Mass Spectrometry Imaging: A Multi-omic Approach

The importance of multi-omic-based approaches to better understand diverse pathological mechanisms including neurodegenerative diseases has emerged. Spatial information can be of great help in understanding how biomolecules interact pathologically and in elucidating target biomarkers for developing therapeutics. While various analytical methods have been attempted for imaging-based biomolecule analysis, a multi-omic approach to imaging remains challenging due to the different characteristics of biomolecules. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful tool due to its sensitivity, chemical specificity, and high spatial resolution in visualizing chemical information in cells and tissues. In this paper, we suggest a new strategy to simultaneously obtain the spatial information of various kinds of biomolecules that includes both labeled and label-free approaches using ToF-SIMS. The enzyme-assisted labeling strategy for the targets of interest enables the sensitive and specific imaging of large molecules such as peptides, proteins, and mRNA, a task that has been, to date, difficult for any MS analysis. Together with the strength of the analytical performance of ToF-SIMS in the label-free tissue imaging of small biomolecules, the proposed strategy allows one to simultaneously obtain integrated information of spatial distribution of metabolites, lipids, peptides, proteins, and mRNA at a high resolution in a single measurement. As part of the suggested strategy, we present a sample preparation method suitable for MS imaging. Because a comprehensive method to examine the spatial distribution of multiple biomolecules in tissues has remained elusive, our strategy can be a useful tool to support the understanding of the interactions of biomolecules in tissues as well as pathological mechanisms.

Blood-Based Biomarkers for Alzheimer's Disease Diagnosis and Progression: An Overview

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ_1–42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.

Metabolites Associated with Memory and Gait: A Systematic Review

We recently found that dual decline in memory and gait speed was consistently associated with an increased risk of dementia compared to decline in memory or gait only or no decline across six aging cohorts. The mechanisms underlying this relationship are unknown. We hypothesize that individuals who experience dual decline may have specific pathophysiological pathways to dementia which can be indicated by specific metabolomic signatures. Here, we summarize blood-based metabolites that are associated with memory and gait from existing literature and discuss their relevant pathways. A total of 39 eligible studies were included in this systematic review. Metabolites that were associated with memory and gait belonged to five shared classes: sphingolipids, fatty acids, phosphatidylcholines, amino acids, and biogenic amines. The sphingolipid metabolism pathway was found to be enriched in both memory and gait impairments. Existing data may suggest that metabolites from sphingolipids and the sphingolipid metabolism pathway are important for both memory and gait impairments. Future studies using empirical data across multiple cohorts are warranted to identify metabolomic signatures of dual decline in memory and gait and to further understand its relationship with future dementia risk.

Glucose Metabolism, Neural Cell Senescence and Alzheimer’s Disease

Alzheimer’s disease (AD), an elderly neurodegenerative disorder with a high incidence and progressive memory decline, is one of the most expensive, lethal, and burdening diseases. To date, the pathogenesis of AD has not been fully illustrated. Emerging studies have revealed that cellular senescence and abnormal glucose metabolism in the brain are the early hallmarks of AD. Moreover, cellular senescence and glucose metabolism disturbance in the brain of AD patients may precede amyloid-β deposition or Tau protein phosphorylation. Thus, metabolic reprogramming targeting senescent microglia and astrocytes may be a novel strategy for AD intervention and treatment. Here, we recapitulate the relationships between neural cell senescence and abnormal glucose metabolism (e.g., insulin signaling, glucose and lactate metabolism) in AD. We then discuss the potential perspective of metabolic reprogramming towards an AD intervention, providing a theoretical basis for the further exploration of the pathogenesis of and therapeutic approach toward AD.

Dihuang-Yinzi Alleviates Cognition Deficits via Targeting Energy-Related Metabolism in an Alzheimer Mouse Model as Demonstrated by Integration of Metabolomics and Network Pharmacology

Energy metabolism disturbance and the consequent reactive oxygen species (ROS) overproduction play a key and pathogenic role in the onset and progression of Alzheimer’s disease (AD). Dihuang-Yinzi (DHYZ) is a traditional Chinese herbal prescription clinically applied to treat AD and other neurodegenerative diseases for a long time. However, the systematical metabolic mechanism of DHYZ against AD remains largely unclear. Here we aimed to explore the mechanism of DHYZ in the treatment of AD comprehensively in an in vivo metabolic context by performing metabolomics analysis coupled with network pharmacology study and experimental validation. The network pharmacology was applied to dig out the potential target of DHYZ against AD. The metabolomics analysis based on UPLC-HRMS was carried out to profile the urine of 2× Tg-AD mice treated with DHYZ. By integrating network pharmacology and metabolomics, we found DHYZ could ameliorate 4 key energy-related metabolic pathways, including glycerophospholipid metabolism, nicotinate/nicotinamide metabolism, glycolysis, and tricarboxylic acid cycle. Besides, we identified 5 potential anti-AD targets of DHYZ, including DAO, HIF1A, PARP1, ALDH3B2, and ACHE, and 14 key differential metabolites involved in the 4 key energy-related metabolic pathways. Furthermore, DHYZ depressed the mitochondrial dysfunction and the resultant ROS overproduction through ameliorating glycerophospholipid metabolism disturbance. Thereby DHYZ increased nicotinamide adenine dinucleotide (NAD⁺) content and promoted glycolysis and tricarboxylic acid (TCA) cycle, and consequently improved oxidative phosphorylation and energy metabolism. In the present study, we provided a novel, comprehensive and systematic insight into investigating the therapeutic efficacy of DHYZ against AD via ameliorating energy-related metabolism.

Identification of Serum Biomarkers Associated With Emergence Agitation After General Anesthesia in Adult Patients: A Metabolomics Analysis

Background

Emergence agitation (EA) is a conscious disturbance after general anesthesia in adult patients that can lead to severe respiratory or circulatory complications and serious physical injury to patients and caregivers. However, the pathophysiological mechanisms underlying EA remain unclear. The present study aimed to identify serum metabolites with significant alterations in EA patients after general anesthesia and enable inferences on their associations with EA.

Methods

EA patients were identified by Richmond Agitation-Sedation Scale (RASS) ≥ + 2 among a cohort of adult patients who received elective surgery under general anesthesia in Peking University Third Hospital between 01 June 2020 and 30 December 2020. We further selected sex-, age-, and surgery type-matched non-EA control patients at a 1:1.5 ratio. Postoperative serum samples were collected from both groups of patients. An untargeted metabolic method was used to identify differences in serum metabolomic profiles between the EA patients and the non-EA patients.

Results

A total of 19 EA patients and 32 matched non-EA patients were included in the study. After screening and mapping with a database, 12 metabolites showed significant postoperative alterations in EA patients compared with non-EA patients, and were mainly involved in lipid, fatty acid and amino acid metabolism pathways. Receiver operating characteristic curve analyses indicated that vanillic acid, candoxatril, tiglylglycine, 5-methoxysalicylic acid, decanoylcarnitine, and 24-epibrassinolide may be involved in EA pathogenesis after general anesthesia.

Conclusion

In this study, we found differences in the serum levels of vanillic acid, candoxatril, tiglylglycine, 5-methoxysalicylic acid, decanoylcarnitine, and 24-epibrassinolide involved in fatty acid metabolism, lipid metabolism, and amino acid metabolism pathways in EA patients compared with non-EA patients, which may demonstrate an EA pathogenesis-associated molecular pattern and contribute toward better understanding of EA occurrence.

Predictive Modeling of Alzheimer's and Parkinson's Disease Using Metabolomic and Lipidomic Profiles from Cerebrospinal Fluid

In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer's disease (AD), Parkinson's disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies.

Advances in Proteomic and Metabolomic Profiling of Neurodegenerative Diseases

Proteomics and metabolomics are two emerging fields that hold promise to shine light on the molecular mechanisms causing neurodegenerative diseases. Research in this area may reveal and quantify specific metabolites and proteins that can be targeted by therapeutic interventions intended at halting or reversing the neurodegenerative process. This review aims at providing a general overview on the current status of proteomic and metabolomic profiling in neurodegenerative diseases. We focus on the most common neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. We discuss the relevance of state-of-the-art metabolomics and proteomics approaches and their potential for biomarker discovery. We critically review advancements made so far, highlighting how metabolomics and proteomics may have a significant impact in future therapeutic and biomarker development. Finally, we further outline technologies used so far as well as challenges and limitations, placing the current information in a future-facing context.

Investigating Causal Relations Between Circulating Metabolites and Alzheimer's Disease: A Mendelian Randomization Study

BACKGROUND

Metabolomics is a promising approach that can be used to understand pathophysiological pathways of Alzheimer's disease (AD). However, the causal relationships between metabolism and AD are poorly understood.

OBJECTIVE

We aimed to investigate the causal association between circulating metabolites and risk of AD through two-sample Mendelian randomization (MR) approach.

METHODS

Genetic associations with 123 circulating metabolic traits were utilized as exposures. Summary statistics data from International Genomics of Alzheimer's Project was used in primary analysis, including 21,982 AD cases and 41,944 controls. Validation was performed using family history of AD data from UK Biobank (27,696 cases of maternal AD, 14,338 cases of paternal AD, and 272,244 controls). We utilized inverse-variance weighted method as primary method.

RESULTS

We found significantly increased risks of developing AD per standard deviation increase in the levels of circulating ApoB (odd ratio[OR] = 3.18; 95% confidence interval[CI]: 1.52-6.66, p = 0.0022), glycoprotein acetyls (OR = 1.21; 95% CI: 1.05-1.39, p = 0.0093), total cholesterol (OR = 2.73; 95% CI: 1.41-5.30, p = 0.0030), and low-density lipoprotein (LDL) cholesterol (OR = 2.34; 95% CI: 1.53-3.57, p = 0.0001). Whereas glutamine (OR = 0.81; 95% CI: 0.71-0.92, p = 0.0011) were significantly associated with lower risk of AD. We also detected causal effects of several different composition of LDL fractions on increased AD risk, which has been verified in validation. However, we found no association between circulating high-density lipoprotein cholesterol and AD.

CONCLUSION

Our findings suggest causal effects of circulating glycoprotein acetyls, ApoB, LDL cholesterol, and serum total cholesterol on higher risk of AD, whereas glutamine showed the protective effect.

Metabolomics comparison of serum and urine in dairy cattle using proton nuclear magnetic resonance spectroscopy

OBJECTIVE

The aim of the study was to conduct metabolic profiling of dairy cattle serum and urine using proton nuclear magnetic resonance (1H-NMR) spectroscopy and to compare the results obtained with those of other dairy cattle herds worldwide so as to provide a basic dataset to facilitate research on metabolites in serum and urine.

METHODS

Six dairy cattle were used in this study; all animals were fed the same diet, which was composed of total mixed ration; the fed amounts were based on voluntary intake. Blood from the jugular neck vein of each steer was collected at the same time using a separate serum tube. Urine samples were collected by hand sweeping the perineum. The metabolites were determined by 1H-NMR spectroscopy, and the obtained data were statistically analyzed by performing principal component analysis, partial least squares-discriminant analysis, variable importance in projection scores, and metabolic pathway data using Metaboanalyst 4.0.

RESULTS

The total number of metabolites in the serum and urine was measured to be 115 and 193, respectively, of which 47 and 81, respectively were quantified. Lactate (classified as an organic acid) and urea (classified as an aliphatic acylic compound) exhibited the highest concentrations in serum and urine, respectively. Some metabolites that have been associated with diseases such as ketosis, bovine respiratory disease, and metritis, and metabolites associated with heat stress were also found in the serum and urine samples.

CONCLUSION

The metabolites measured in the serum and urine could potentially be used to detect diseases and heat stress in dairy cattle. The results could also be useful for metabolomic research on the serum and urine of ruminants in Korea.

The Application of Metabolomics in Forensic Science with Focus on Forensic Toxicology and Time-of-Death Estimation

Recently, the diagnostic methods used by scientists in forensic examinations have enormously expanded. Metabolomics provides an important contribution to analytical method development. The main purpose of this review was to investigate and summarize the most recent applications of metabolomics in forensic science. The primary research method was an extensive review of available international literature in PubMed. The keywords “forensic” and “metabolomics” were used as search criteria for the PubMed database scan. Most authors emphasized the analysis of different biological sample types using chromatography methods. The presented review is a summary of recently published implementations of metabolomics in forensic science and types of biological material used and techniques applied. Possible opportunities for valuable metabolomics’ applications are discussed to emphasize the essential necessities resulting in numerous nontargeted metabolomics’ assays.

Alterations in Glycerolipid and Fatty Acid Metabolic Pathways in Alzheimer's Disease Identified by Urinary Metabolic Profiling: A Pilot Study

An easily accessible and non-invasive biomarker for the early detection of Alzheimer's disease (AD) is needed. Evidence suggests that metabolic dysfunction underlies the pathophysiology of AD. While urine is a non-invasively collectable biofluid and a good source for metabolomics analysis, it is not yet widely used for this purpose. This small-scale pilot study aimed to examine whether the metabolic profile of urine from AD patients reflects the metabolic dysfunction reported to underlie AD pathology, and to identify metabolites that could distinguish AD patients from cognitively healthy controls. Spot urine of 18 AD patients (AD group) and 18 age- and sex-matched, cognitively normal controls (control group) were analyzed by mass spectrometry (MS). Capillary electrophoresis time-of-flight MS and liquid chromatography–Fourier transform MS were used to cover a larger range of molecules with ionic as well as lipid characteristics. A total of 304 ionic molecules and 81 lipid compounds of 12 lipid classes were identified. Of these, 26 molecules showed significantly different relative concentrations between the AD and control groups (Wilcoxon's rank-sum test). Moreover, orthogonal partial least-squares discriminant analysis revealed significant discrimination between the two groups. Pathway searches using the KEGG database, and pathway enrichment and topology analysis using Metaboanalyst software, suggested alterations in molecules relevant to pathways of glycerolipid and glycerophospholipid metabolism, thermogenesis, and caffeine metabolism in AD patients. Further studies of urinary metabolites will contribute to the early detection of AD and understanding of its pathogenesis.

Predictive Value of Preoperative Profiling of Serum Metabolites for Emergence Agitation After General Anesthesia in Adult Patients

Background: Emergence agitation (EA) in adult patients under general anesthesia leads to increased postoperative complications and heavy medical burden. Unfortunately, its pathogenesis has not been clarified until now. The purpose of the present study was to explore the relationship between preoperative serum metabolites and EA. Methods: We used an untargeted metabolic analysis method to investigate the different metabolomes in the serum of EA patients and non-EA patients undergoing elective surgical procedures after the induction of general anesthesia. A Richmond Agitation-Sedation Scale score ≥ +2 was diagnosed as EA during postoperative emergence. Non-EA patients were matched with EA patients according to general characteristics. Preoperative serum samples of the two groups were collected to investigate the association between serum metabolites and EA development. Results: The serum samples of 16 EA patients with 34 matched non-EA patients were obtained for metabolic analysis. After screening and alignment with databases, 31 altered metabolites were detected between the two groups. These metabolites were mainly involved in the metabolism of lipids, purines, and amino acids. Analyses of receiver-operating characteristic curves showed that the preoperative alterations of choline, cytidine, glycerophosphocholine, L-phenylalanine, oleamide, and inosine may be associated with adult EA. Conclusion: Multiple metabolic abnormalities (including those for lipids, purines, and amino acids) and other pathological processes (e.g., neurotransmitter imbalance and oxidative stress) may contribute to EA. Several altered metabolites in serum before surgery may have predictive value for EA diagnosis. This study might afford new metabolic clues for the understanding of EA pathogenesis.

Absolute Quantification of Acylcarnitines Using Integrated Tmt-PP Derivatization-Based LC-MS/MS and Quantitative Analysis of Multi-Components by a Single Marker Strategy

Acylcarnitines (ACs) play important roles in the fatty acid β-oxidation and are considered as diagnostic markers for many diseases. Accurate determination of ACs remains challenging due to their low abundance, high structure diversity, and limited availability of standard compounds. In this study, microwave-assisted Tmt-PP (p-[3,5-(dimethylamino)-2,4,6-triazine] benzene-1-sulfonyl piperazine) derivatization was utilized to facilitate the liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) determination of ACs. The result indicated that Tmt-PP labeling enables the prediction of the retention time and MS response of ACs and enhances their MS response up to 4 times. The introduction of the microwave during the derivatization procedure greatly improved the reaction efficiency, demonstrated by the shortened reaction time from 90 to 1 min. Furthermore, we applied a strategy named quantitative analysis of multi-components by a single marker (QAMS) for the assay of 26 ACs with only 5 AC standards, solving the standard availability issue to a large extent. The established workflow was applied to discover dysregulated ACs in xenograft colon cancer mice, and the quantification results were highly comparable with traditional methods where there were the corresponding standards for each AC. Our study demonstrated that chemical derivatization-based LC-MS/MS integrated with the QAMS strategy is robust for the identification and quantification of ACs and has great potential in targeted metabolomics study.

Metabolomic Alterations in the Blood and Brain in Association with Alzheimer's Disease: Evidence from in vivo to Clinical Studies

Alzheimer's disease (AD) has become a major health problem among the elderly population. Some evidence suggests that metabolic disturbance possibly plays a role in the pathophysiology of AD. Currently, the study of metabolomics has been used to explore changes in multiple metabolites in several diseases, including AD. Thus, the metabolomics research in AD might provide some information regarding metabolic dysregulations, and their possible associated pathophysiology. This review summarizes the information discovered regarding the metabolites in the brain and the blood from the metabolomics research of AD from both animal and clinical studies. Additionally, the correlation between the changes in metabolites and outcomes, such as pathological findings in the brain and cognitive impairment are discussed. We also deliberate on the findings of cohort studies, demonstrating the alterations in metabolites before changes of cognitive function. All of these findings can be used to inform the potential identity of specific metabolites as possible biomarkers for AD.

Urinary metabolomic changes and microbiotic alterations in presenilin1/2 conditional double knockout mice

Background

Given the clinical low efficient treatment based on mono-brain-target design in Alzheimer’s disease (AD) and an increasing emphasis on microbiome-gut-brain axis which was considered as a crucial pathway to affect the progress of AD along with metabolic changes, integrative metabolomic signatures and microbiotic community profilings were applied on the early age (2-month) and mature age (6-month) of presenilin1/2 conditional double knockout (PS cDKO) mice which exhibit a series of AD-like phenotypes, comparing with gender and age-matched C57BL/6 wild-type (WT) mice to clarify the relationship between microbiota and metabolomic changes during the disease progression of AD.

Materials and methods

Urinary and fecal samples from PS cDKO mice and gender-matched C57BL/6 wild-type (WT) mice both at age of 2 and 6 months were collected. Urinary metabolomic signatures were measured by the gas chromatography-time-of-flight mass spectrometer, as well as 16S rRNA sequence analysis was performed to analyse the microbiota composition at both ages. Furthermore, combining microbiotic functional prediction and Spearman’s correlation coefficient analysis to explore the relationship between differential urinary metabolites and gut microbiota.

Results

In addition to memory impairment, PS cDKO mice displayed metabolic and microbiotic changes at both of early and mature ages. By longitudinal study, xylitol and glycine were reduced at both ages. The disturbed metabolic pathways were involved in glycine, serine and threonine metabolism, glyoxylate and dicarboxylate metabolism, pentose and glucuronate interconversions, starch and sucrose metabolism, and citrate cycle, which were consistent with functional metabolic pathway predicted by the gut microbiome, including energy metabolism, lipid metabolism, glycan biosynthesis and metabolism. Besides reduced richness and evenness in gut microbiome, PS cDKO mice displayed increases in Lactobacillus, while decreases in norank_f_Muribaculaceae, Lachnospiraceae_NK4A136_group, Mucispirillum, and Odoribacter. Those altered microbiota were exceedingly associated with the levels of differential metabolites.

Conclusions

The urinary metabolomics of AD may be partially mediated by the gut microbiota. The integrated analysis between gut microbes and host metabolism may provide a reference for the pathogenesis of AD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03032-9.

Age-Related Neurometabolomic Signature of Mouse Brain

Neurometabolites are the ultimate gene products in the brain and the most precise biomolecular indicators of brain endophenotypes. Metabolomics is the only "omics" that provides a moment-to-moment "snapshot" of brain circuits' biochemical activities in response to external stimuli within the context of specific genetic variations. Although the expression levels of neurometabolites are highly dynamic, the underlying metabolic processes are tightly regulated during brain development, maturation, and aging. Therefore, this study aimed to identify mouse brain metabolic profiles in neonatal and adult stages and reconstruct both the active metabolic network and the metabolic pathway functioning. Using high-throughput metabolomics and bioinformatics analyses, we show that the neonatal mouse brain has its distinct metabolomic signature, which differs from the adult brain. Furthermore, lipid metabolites showed the most profound changes between the neonatal and adult brain, with some lipid species reaching 1000-fold changes. There were trends of age-dependent increases and decreases among lipids and non-lipid metabolites, respectively. A few lipid metabolites such as HexCers and SHexCers were almost absent in neonatal brains, whereas other non-lipid metabolites such as homoarginine were absent in the adult brains. Several molecules that act as neurotransmitters/neuromodulators showed age-dependent levels, with adenosine and GABA exhibiting around 100- and 10-fold increases in the adult compared with the neonatal brain. Of particular interest is the observation that purine and pyrimidines nucleobases exhibited opposite age-dependent changes. Bioinformatics analysis revealed an enrichment of lipid biosynthesis pathways in metabolites, whose levels increased in adult brains. In contrast, pathways involved in the metabolism of amino acids, nucleobases, glucose (glycolysis), tricarboxylic acid cycle (TCA) were enriched in metabolites whose levels were higher in the neonatal brains. Many of these pathways are associated with pathological conditions, which can be predicted as early as the neonatal stage. Our study provides an initial age-related biochemical directory of the mouse brain and warrants further studies to identify temporal brain metabolome across the lifespan, particularly during adolescence and aging. Such neurometabolomic data may provide important insight about the onset and progression of neurological/psychiatric disorders and may ultimately lead to the development of precise diagnostic biomarkers and more effective preventive/therapeutic strategies.

Hypothesis and Theory: Characterizing Abnormalities of Energy Metabolism Using a Cellular Platform as a Personalized Medicine Approach for Alzheimer's Disease

Sporadic or late-onset Alzheimer’s disease (LOAD) is characterized by slowly progressive deterioration and death of CNS neurons. There are currently no substantially disease-modifying therapies. LOAD pathology is closely related to changes with age and include, among others, accumulation of toxic molecules and altered metabolic, microvascular, biochemical and inflammatory processes. In addition, there is growing evidence that cellular energy deficits play a critical role in aging and LOAD pathophysiology. However, the exact mechanisms and causal relationships are largely unknown. In our studies we tested the hypothesis that altered bioenergetic and metabolic cell functions are key elements in LOAD, using a cellular platform consisting of skin fibroblasts derived from LOAD patients and AD-unaffected control individuals and therefrom generated induced pluripotent stem cells that are differentiated to brain-like cells to study LOAD pathogenic processes in context of age, disease, genetic background, cell development, and cell type. This model has revealed that LOAD cells exhibit a multitude of bioenergetic and metabolic alterations, providing evidence for an innate inefficient cellular energy management in LOAD as a prerequisite for the development of neurodegenerative disease with age. We propose that this cellular platform could ultimately be used as a conceptual basis for a personalized medicine tool to predict altered aging and risk for development of dementia, and to test or implement customized therapeutic or disease-preventive intervention strategies.

Biomarkers for the Clinical Diagnosis of Alzheimer's Disease: Metabolomics Analysis of Brain Tissue and Blood

With an increase in aging populations worldwide, age-related diseases such as Alzheimer's disease (AD) have become a global concern. At present, a cure for neurodegenerative disease is lacking. There is an urgent need for a biomarker that can facilitate the diagnosis, classification, prognosis, and treatment response of AD. The recent emergence of highly sensitive mass-spectrometry platforms and high-throughput technology can be employed to discover and catalog vast datasets of small metabolites, which respond to changed status in the body. Metabolomics analysis provides hope for a better understanding of AD as well as the subsequent identification and analysis of metabolites. Here, we review the state-of-the-art emerging candidate biomarkers for AD.

Serum metabolites associated with brain amyloid beta deposition, cognition and dementia progression

Metabolomics in the Alzheimer's Disease Neuroimaging Initiative cohort provides a powerful tool for mapping biochemical changes in Alzheimer's disease, and a unique opportunity to learn about the association between circulating blood metabolites and brain amyloid-β deposition in Alzheimer's disease. We examined 140 serum metabolites and their associations with brain amyloid-β deposition, cognition and conversion from mild cognitive impairment to Alzheimer's disease in the Alzheimer's Disease Neuroimaging Initiative. Processed [18F] Florbetapir PET images were used to perform a voxel-wise statistical analysis of the effect of metabolite levels on amyloid-β accumulation across the whole brain. We performed a multivariable regression analysis using age, sex, body mass index, apolipoprotein E ε4 status and study phase as covariates. We identified nine metabolites as significantly associated with amyloid-β deposition after multiple comparison correction. Higher levels of one acylcarnitine (C3; propionylcarnitine) and one biogenic amine (kynurenine) were associated with decreased amyloid-β accumulation and higher memory scores. However, higher levels of seven phosphatidylcholines (lysoPC a C18:2, PC aa C42:0, PC ae C42:3, PC ae C44:3, PC ae C44:4, PC ae C44:5 and PC ae C44:6) were associated with increased brain amyloid-β deposition. In addition, higher levels of PC ae C44:4 were significantly associated with lower memory and executive function scores and conversion from mild cognitive impairment to Alzheimer's disease dementia. Our findings suggest that dysregulation of peripheral phosphatidylcholine metabolism is associated with earlier pathological changes noted in Alzheimer's disease as measured by brain amyloid-β deposition as well as later clinical features including changes in memory and executive functioning. Perturbations in phosphatidylcholine metabolism may point to issues with membrane restructuring leading to the accumulation of amyloid-β in the brain. Additional studies are needed to explore whether these metabolites play a causal role in the pathogenesis of Alzheimer's disease or if they are biomarkers for systemic changes during preclinical phases of the disease.

Metabolism-Based Gene Differences in Neurons Expressing Hyperphosphorylated AT8- Positive (AT8+) Tau in Alzheimer's Disease

Metabolic adaptations in the brain are critical to the establishment and maintenance of normal cellular functions and to the pathological responses to disease processes. Here, we have focused on specific metabolic pathways that are involved in immune-mediated neuronal processes in brain using isolated neurons derived from human autopsy brain sections of normal individuals and individuals diagnosed as Alzheimer's disease (AD). Laser capture microscopy was used to select specific cell types in immune-stained thin brain sections followed by NanoString technology to identify and quantify differences in mRNA levels between age-matched control and AD neuronal samples. Comparisons were also made between neurons isolated from AD brain sections expressing pathogenic hyperphosphorylated AT8- positive (AT8+) tau and non-AT8+ AD neurons using double labeling techniques. The mRNA expression data showed unique patterns of metabolic pathway expression between the subtypes of captured neurons that involved membrane based solute transporters, redox factors, and arginine and methionine metabolic pathways. We also identified the expression levels of a novel metabolic gene, Radical-S-Adenosyl Domain1 (RSAD1) and its corresponding protein, Rsad1, that impact methionine usage and radical based reactions. Immunohistochemistry was used to identify specific protein expression levels and their cellular location in NeuN+ and AT8+ neurons. APOE4 vs APOE3 genotype-specific and sex-specific gene expression differences in these metabolic pathways were also observed when comparing neurons from individuals with AD to age-matched individuals.

Towards resolving the enigma of the dichotomy of resveratrol: cis- and trans-resveratrol have opposite effects on TyrRS-regulated PARP1 activation

Unlike widely perceived, resveratrol (RSV) decreased the average lifespan and extended only the replicative lifespan in yeast. Similarly, although not widely discussed, RSV is also known to evoke neurite degeneration, kidney toxicity, atherosclerosis, premature senescence, and genotoxicity through yet unknown mechanisms. Nevertheless, in vivo animal models of diseases and human clinical trials demonstrate inconsistent protective and beneficial effects. Therefore, the mechanism of action of RSV that elicits beneficial effects remains an enigma. In a previously published work, we demonstrated structural similarities between RSV and tyrosine amino acid. RSV acts as a tyrosine antagonist and competes with it to bind to human tyrosyl-tRNA synthetase (TyrRS). Interestingly, although both isomers of RSV bind to TyrRS, only the cis-isomer evokes a unique structural change at the active site to promote its interaction with poly-ADP-ribose polymerase 1 (PARP1), a major determinant of cellular NAD+-dependent stress response. However, retention of trans-RSV in the active site of TyrRS mimics its tyrosine-bound conformation that inhibits the auto-poly-ADP-ribos(PAR)ylation of PARP1. Therefore, we proposed that cis-RSV-induced TyrRS-regulated auto-PARylation of PARP1 would contribute, at least in part, to the reported health benefits of RSV through the induction of protective stress response. This observation suggested that trans-RSV would inhibit TyrRS/PARP1-mediated protective stress response and would instead elicit an opposite effect compared to cis-RSV. Interestingly, most recent studies also confirmed the conversion of trans-RSV and its metabolites to cis-RSV in the physiological context. Therefore, the finding that cis-RSV and trans-RSV induce two distinct conformations of TyrRS with opposite effects on the auto-PARylation of PARP1 provides a potential molecular basis for the observed dichotomic effects of RSV under different experimental paradigms. However, the fact that natural RSV exists as a diastereomeric mixture of its cis and trans isomers and cis-RSV is also a physiologically relevant isoform has not yet gained much scientific attention.

Disentangling the Molecular Pathways of Parkinson's Disease using Multiscale Network Modeling

Parkinson's disease (PD) is a complex neurodegenerative disorder. The identification of genetic variants has shed light on the molecular pathways for inherited PD, while the disease mechanism for idiopathic PD remains elusive, partly due to a lack of robust tools. The complexity of PD arises from the heterogeneity of clinical symptoms, pathologies, environmental insults contributing to the disease, and disease comorbidities. Molecular networks have been increasingly used to identify molecular pathways and drug targets in complex human diseases. Here, we review recent advances in molecular network approaches and their application to PD. We discuss how network modeling can predict functions of PD genetic risk factors through network context and assist in the discovery of network-based therapeutics for neurodegenerative diseases.

Early signature in the blood lipidome associated with subsequent cognitive decline in the elderly: A case-control analysis nested within the Three-City cohort study

BACKGROUND

Brain lipid metabolism appears critical for cognitive aging, but whether alterations in the lipidome relate to cognitive decline remains unclear at the system level.

METHODS

We studied participants from the Three-City study, a multicentric cohort of older persons, free of dementia at time of blood sampling, and who provided repeated measures of cognition over 12 subsequent years. We measured 189 serum lipids from 13 lipid classes using shotgun lipidomics in a case-control sample on cognitive decline (matched on age, sex and level of education) nested within the Bordeaux study center (discovery, n = 418). Associations with cognitive decline were investigated using bootstrapped penalized regression, and tested for validation in the Dijon study center (validation, n = 314).

FINDINGS

Among 17 lipids identified in the discovery stage, lower levels of the triglyceride TAG50:5, and of four membrane lipids (sphingomyelin SM40:2,2, phosphatidylethanolamine PE38:5(18:1/20:4), ether-phosphatidylethanolamine PEO34:3(16:1/18:2), and ether-phosphatidylcholine PCO34:1(16:1/18:0)), and higher levels of PCO32:0(16:0/16:0), were associated with greater odds of cognitive decline, and replicated in our validation sample.

INTERPRETATION

These findings indicate that in the blood lipidome of non-demented older persons, a specific profile of lipids involved in membrane fluidity, myelination, and lipid rafts, is associated with subsequent cognitive decline.

FUNDING

The complete list of funders is available at the end of the manuscript, in the Acknowledgement section.

Cu, Fe, and Zn isotope ratios in murine Alzheimer's disease models suggest specific signatures of amyloidogenesis and tauopathy

Alzheimer’s disease (AD) is characterized by accumulation of tau and amyloid-beta in the brain, and recent evidence suggests a correlation between associated protein aggregates and trace elements, such as copper, iron, and zinc. In AD, a distorted brain redox homeostasis and complexation by amyloid-beta and hyperphosphorylated tau may alter the isotopic composition of essential mineral elements. Therefore, high-precision isotopic analysis may reveal changes in the homeostasis of these elements. We used inductively coupled plasma–mass spectrometry (ICP-MS)-based techniques to determine the total Cu, Fe, and Zn contents in the brain, as well as their isotopic compositions in both mouse brain and serum. Results for male transgenic tau (Line 66, L66) and amyloid/presenilin (5xFAD) mice were compared with those for the corresponding age- and sex-matched wild-type control mice (WT). Our data show that L66 brains showed significantly higher Fe levels than did those from the corresponding WT. Significantly less Cu, but more Zn was found in 5xFAD brains. We observed significantly lighter isotopic compositions of Fe (enrichment in the lighter isotopes) in the brain and serum of L66 mice compared with WT. For 5xFAD mice, Zn exhibited a trend toward a lighter isotopic composition in the brain and a heavier isotopic composition in serum compared with WT. Neither mouse model yielded differences in the isotopic composition of Cu. Our findings indicate significant pathology-specific alterations of Fe and Zn brain homeostasis in mouse models of AD. The associated changes in isotopic composition may serve as a marker for proteinopathies underlying AD and other types of dementia.

Brain cells derived from Alzheimer's disease patients have multiple specific innate abnormalities in energy metabolism

Altered energy metabolism has been implicated both in aging and the pathogenesis of late-onset Alzheimer's disease (LOAD). However, it is unclear which anomalies are acquired phenotypes and which are inherent and predispose to disease. We report that neural progenitor cells and astrocytes differentiated from LOAD patient-derived induced pluripotent stem cells exhibit multiple inter-related bioenergetic alterations including: changes in energy production by mitochondrial respiration versus glycolysis, as a consequence of alterations in bioenergetic substrate processing and transfer of reducing agents, reduced levels of NAD/NADH, diminished glucose uptake and response rates to insulin (INS)/IGF-1 signaling, decreased INS receptor and glucose transporter 1 densities, and changes in the metabolic transcriptome. Our data confirm that LOAD is a "multi-hit" disorder and provide evidence for innate inefficient cellular energy management in LOAD that likely predisposes to neurodegenerative disease with age. These processes may guide the development and testing of diagnostic procedures or therapeutic agents.

Metabolic Profiling of Cognitive Aging in Midlife

Alzheimer's dementia (AD) begins many years before its clinical symptoms. Metabolic dysfunction represents a core feature of AD and cognitive impairment, but few metabolomic studies have focused on cognitive aging in midlife. Using an untargeted metabolomics approach, we identified metabolic predictors of cognitive aging in midlife using fasting plasma sample from 30 middle-aged (mean age 57.2), male-male twin pairs enrolled in the Vietnam Era Twin Study of Aging (VETSA). For all twin pairs, one twin developed incident MCI, whereas his co-twin brother remained to be cognitively normal during an average 5.5-year follow-up. Linear mixed model was used to identify metabolites predictive of MCI conversion or cognitive change over time, adjusting for traditional risk factors. Results from twins were replicated in an independent cohort of middle-aged adults (mean age 59.1) in the Wisconsin Registry for Alzheimer's Prevention (WRAP). Results in twins showed that higher baseline levels of four plasma metabolites, including sphingomyelin (d18:1/20:1 and d18:2/20:0), sphingomyelin (d18:1/22:1, d18:2/22:0, and d16:1/24:1), DAG (18:2/20:4), and hydroxy-CMPF, were significantly associated with a slower decrease in one or more domains of cognitive function. The association of sphingomyelin (d18:1/20:1 and d18:2/20:0) was replicated in WRAP. Our results support that metabolic perturbation occurs many years before cognitive impairment and plasma metabolites may serve as early biomarkers for prediction or monitoring of cognitive aging and AD in midlife.

Metabolic correlates of prevalent mild cognitive impairment and Alzheimer's disease in adults with Down syndrome

INTRODUCTION

Disruption of metabolic function is a recognized feature of late onset Alzheimer's disease (LOAD). We sought to determine whether similar metabolic pathways are implicated in adults with Down syndrome (DS) who have increased risk for Alzheimer's disease (AD).

METHODS

We examined peripheral blood from 292 participants with DS who completed baseline assessments in the Alzheimer's Biomarkers Consortium-Down Syndrome (ABC-DS) using untargeted mass spectrometry (MS). Our sample included 38 individuals who met consensus criteria for AD (DS-AD), 43 who met criteria for mild cognitive impairment (DS-MCI), and 211 who were cognitively unaffected and stable (CS).

RESULTS

We measured relative abundance of 8,805 features using MS and 180 putative metabolites were differentially expressed (DE) among the groups at false discovery rate-corrected q< 0.05. From the DE features, a nine-feature classifier model classified the CS and DS-AD groups with receiver operating characteristic area under the curve (ROC AUC) of 0.86 and a two-feature model classified the DS-MCI and DS-AD groups with ROC AUC of 0.88. Metabolite set enrichment analysis across the three groups suggested alterations in fatty acid and carbohydrate metabolism.

DISCUSSION

Our results reveal metabolic alterations in DS-AD that are similar to those seen in LOAD. The pattern of results in this cross-sectional DS cohort suggests a dynamic time course of metabolic dysregulation which evolves with clinical progression from non-demented, to MCI, to AD. Metabolomic markers may be useful for staging progression of DS-AD.