Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Serum metabolic profiling in diabetic kidney disease patients using ultra-high performance liquid chromatography-tandem mass spectrometry

Background

Diabetic kidney disease (DKD) remains one of the leading causes of end-stage renal failure. The currently available diagnostic and classification markers, such as the urinary albumin-to-creatinine ratio and estimated glomerular filtration rate, demonstrate inadequate precision in forecasting the onset and progression of DKD. This study aims to investigate the serum metabolic profile of patients with DKD, with the objective of identifying reliable biomarkers that can enhance the prediction of the transition from diabetes mellitus (DM) to DKD and distinguishing DKD from nondiabetic kidney disease (NDKD).

Methods

Untargeted metabolomic analysis was performed on serum samples obtained from 53 DKD patients, 54 NDKD patients, 59 individuals diagnosed with simple diabetes mellitus (SDM), and 56 healthy controls utilizing ultra-high performance liquid chromatography-tandem mass spectrometry. Differential metabolites among the groups were identified, metabolic pathways were investigated, and the diagnostic efficacy of selected metabolites was evaluated.

Results

The metabolic enrichment pathways shared between DKD and NDKD encompassed glycerophospholipid metabolism, glycerolipid metabolism, and tryptophan metabolism. In contrast, pyrimidine metabolism and arginine biosynthesis were uniquely enriched in DKD. Compared to the NDKD group, significantly elevated levels of phosphatidylglycerol (PG, 14:0) and D-Maltose were observed in DKD patients. Additionally, in comparison to the SDM group, the DKD group exhibited significant increases in lysophosphatidic acid (LPA, 16:3), LPA (18:5), LPA (22:5), phosphatidic acid (PA, 18:3), PG (26:4), L-Glutamine, Uridine, Cytidine, Formyl-N-acetyl-5-methoxykynurenamine, 2-Oxoadipate, Thymidine, L-Citrulline, and 5-Hydroxy-L-tryptophan, while PG (28:4) levels were markedly reduced. Among these, Uridine, Cytidine, Thymidine, and L-Citrulline were associated with pyrimidine metabolism, whereas L-Glutamine and L-Citrulline participated in the arginine biosynthesis pathway. Furthermore, the differential metabolites exhibited varying degrees of correlation with renal function indicators in DKD patients.

Conclusions

PG (14:0) and D-Maltose may help distinguish DKD from NDKD, while L-Glutamine, Uridine, Cytidine, Thymidine, and L-Citrulline are linked to the progression from DM to DKD. Larger studies are needed to validate these findings and assess their diagnostic and causal significance.

Exploring the pharmacological mechanism of Bu-Wang San on Alzheimer's disease through multiple GEO datasets of the human hippocampus, network pharmacology, and metabolomics based on GC-MS and UPLC-Q/TOF-MS

ETHNOPHARMACOLOGICAL RELEVANCE

Bu-Wang San (BWS) is a prominent traditional Chinese medicine known for calming the mind and promoting intelligence. It has been reported to improve learning and memory, enhance memory ability, and promote synaptic plasticity. However, the complexity of the material basis and the diversity of therapeutic targets of BWS on Alzheimer's disease (AD) have not been elucidated.

AIM OF THE STUDY

This study aimed to investigate the therapeutic material basis and the mechanism of BWS in AD treatment by comprehensively analyzing multiple GEO datasets of the human hippocampus, network pharmacology, and multi-platform metabolomics validation.

MATERIALS AND METHODS

Three GEO datasets of the human hippocampus were utilized to identify AD-associated targets using weighted gene co-expression network analysis (WGCNA) and differential analysis. Network pharmacology analyses were performed to investigate BWS's therapeutic material basis and predict the therapeutic targets of BWS on AD. A rat model was induced through the concurrent administration of AlCl3 and D-galactose to validate BWS's therapeutic potential and underlying mechanisms in AD. To validate the results of GEO data mining and network pharmacology, a comprehensive metabolomics approach integrating gas chromatography-mass spectrometry (GC-MS) and ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was conducted on rat serum samples to uncover potential metabolic alterations and their associated pathways.

RESULTS

A total of 6367 genes were selected as AD drug targets through WGCNA analysis and enrichment analysis of disease-associated gene expression profiles in the GEO database. Network pharmacology was performed in this study for the identification of potential interactions between the components of BWS and its targets, TP53, STAT3, EGFR, MAOA, NOS3, PPARG, PRKCA, MAPK8, AChE, ARG1, among others, which were among the top 25 highest probable targets of BWS acting on AD. The multi-platform metabolomics indicated that amino sugar and nucleotide sugar metabolism, glycine, serine and threonine metabolism pathways, and other pathways may be associated with the AD model based on AlCl3 and D-galactose. The comparison of differential metabolites between the AD model group and the BWS intervention group revealed that 66 of the 97 differential metabolites exhibited a pullback trend, indicating a potential therapeutic effect of BWS on these metabolites.

CONCLUSION

This study builds a systematic strategy combining GEO datasets, network pharmacology, and multi-platform metabolomics and provides valuable insights into the pharmacological mechanism of BWS on AD. The results suggest that BWS may exert its therapeutic effects on AD by modulating the amino sugar and nucleotide sugar metabolism, glycerophospholipid metabolism, glycine, serine and threonine metabolism pathway and acting on the drug targets of ARG1, MAOA, AChE, XDH, GAD2 et al. This strategy provides a deep understanding of the molecular mechanisms of herbal medicine in treating AD at a systematic level.

Antiepileptic Effects of Acorus tatarinowii Schott in a Rat Model of Epilepsy: Regulation of Metabolic Axes and Gut Microbiota

As a phytotherapeutic agent with historical applications in epilepsy management, Acorus tatarinowii Schott (ATS) remains pharmacologically enigmatic, particularly regarding its pathophysiological mechanisms. This knowledge gap significantly hinders the clinical application of ATS-based treatments. To explore the potential of ATS in combating epileptogenesis, we utilized a pentylenetetrazole (PTZ)-induced chronic epilepsy rat model. Brain metabolomic analysis was performed by ultra-performance liquid chromatography coupled with mass spectrometry (UPLC/MS). Principal component analysis (PCA) and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were performed for screening differential metabolites. Gut microbiota composition was analyzed through 16S rRNA gene sequencing and examined using Spearman correlation analysis. The results show that oral ATS (50 mg/kg) significantly improved the seizure latency and pathology of rats with epilepsy. Ascorbate and aldarate metabolism, glycerophospholipid metabolism, arachidonic acid metabolism, and intestinal flora were crucial for ATS's ability to counteract epilepsy. The therapeutic effects of ATS against epilepsy were investigated with brain metabolomics and gut microbiota analysis, providing the basis for further comprehensive research.

Identification of Key Fatty Acid Metabolism-Related Genes in Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder, and the role of fatty acid metabolism in its pathogenesis remains incompletely understood. Using AD transcriptome sequencing data from the GEO database, we initially screened for differentially expressed genes and applied Weighted Gene Correlation Network Analysis (WGCNA) to identify crucial gene modules. By intersecting these genes with fatty acid metabolism-related genes (FAMRGs), we obtained AD-related fatty acid metabolism genes (AD-FAMRGs). Subsequently, we conducted KEGG, GO, and Single-sample Gene Set Enrichment Analysis (ssGSEA). Furthermore, we employed three machine learning algorithms to determine the key AD-FAMRGs. Risk genes were thus identified, leading to the construction of a risk model which was subsequently validated through receiver operating characteristic (ROC) curve analysis. Additionally, protein docking studies were performed to assess interactions between key AD-FAMRGs and Tau as well as amyloid beta (Aβ) proteins. To explore potential therapeutic avenues, we searched the DrugBank database for agents targeting these AD-FAMRGs, followed by molecular docking and dynamics simulations. Our investigations highlighted three key AD-FAMRGs: DLD, ELOVL5, and HMGCS1. Functional enrichment analysis indicated their association with metabolism, oxidative stress, and AD pathogenesis. ZDOCK analysis further suggested their interactions with Tau and Aβ proteins, pointing to their possible involvement in AD's pathological processes. ROC analysis demonstrated the predictive accuracy of these AD-FAMRGs, with AUC values ranging from 0.764 to 0.876. Molecular docking and dynamic simulations confirmed the favorable binding of predicted therapeutic agents to these key AD-FAMRGs. Our findings suggest that fatty acid metabolism may be involved in AD pathogenesis, and DLD, ELOVL5, and HMGCS1 may serve as potential therapeutic targets for AD.

Studying Alzheimer's disease through an integrative serum metabolomic and lipoproteomic approach

BACKGROUND

Alzheimer's disease (AD) is the most frequent neurodegenerative disorder worldwide. The great variability in disease evolution and the incomplete understanding of the molecular mechanisms underlying AD make it difficult to predict when a patient will convert from prodromal stage to dementia. We hypothesize that metabolic alterations present at the level of the brain could be reflected at a systemic level in blood serum of patients, and that these alterations could be used as prognostic biomarkers.

METHODS

This pilot study proposes a serum investigation via nuclear magnetic resonance (NMR) spectroscopy in a consecutive series of AD patients including 57 patients affected by Alzheimer's disease at dementia stage (AD-dem) and 45 patients with mild cognitive impairment (MCI) due to AD (MCI-AD). As control group, we considered 31 subjects with mild cognitive impairment in whom AD and other neurodegenerative disorders were excluded (MCI). A panel of 26 metabolites and 112 lipoprotein-related parameters was quantified and the logistic LASSO regression algorithm was employed to identify the optimal combination of metabolites-lipoproteins and their ratios to discriminate the groups of interest.

RESULTS

In the training set, our model classified AD-dem and MCI with an accuracy of 81.7%. These results were reproduced in the validation set (accuracy 75.0%). Evolution of MCI-AD patients was evaluated over time. Patients who displayed a decrease in MMSE < 1.5 point per year were considered at lower progression rate: we obtained a division in 18 MCI-AD at lower progression rate (MCI-AD LR) and 27 at higher progression rate (MCI-AD HR). The model calculated using 4 metabolic features identified MCI-AD LR and MCI-AD HR with an accuracy of 73.3%.

CONCLUSIONS

The identification of potential novel peripheral biomarkers of Alzheimer's disease, as proposed in this study, opens a new prospect for an innovative and minimally invasive method to identify AD in its very early stages. We proposed a novel approach able to sub-stratify MCI-AD patients identifying those associated with a faster rate of clinical progression.

Untargeted metabolomics analysis of glycerophospholipid metabolism in very low birth weight infants administered multiple oil lipid emulsions

BACKGROUND

To compare the impact of two different lipid emulsions, specifically a soybean oil-based emulsion and a multiple oil emulsion (soybean oil, medium-chain triglycerides, olive oil, and fish oil, SMOF), on serum metabolites of very low birth weight (VLBW) infants using untargeted metabolomics analysis.

METHODS

A comparative study was conducted on 25 VLBW infants hospitalized in neonatal intensive care units (NICU) of Hangzhou Women's Hospital in 2023. The infants were divided into the SMOF group (13 cases) and the soybean oil group (12 cases) based on the type of lipid emulsion used during parenteral nutrition. Serum samples were collected at birth and 14 days later from both groups. An untargeted metabolomics analysis was conducted on serum samples using Ultra-high Performance Liquid Chromatography - Tandem Mass Spectrometry (UHPLC-MS/MS). Differential metabolites were identified using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) modeling, with criteria of variable importance in the projection (VIP) > 1 and false discovery rate (FDR) < 0.05. Functional annotation of the top 30 differential metabolites, ranked by VIP value, was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Subsequently, enrichment and topological analyses were conducted with MetaboAnalyst software to identify key metabolic pathways. Finally, receiver operating characteristic (ROC) analysis was performed on metabolites associated with the identified key metabolic pathways.

RESULTS

The metabolomic analysis revealed that, in the OPLS-DA model, no significant metabolite differences were found in umbilical cord blood between the two groups. However, 84 differential metabolites were identified in the 14-day samples from the SMOF group compared to the soybean oil group. According to MetaboAnalyst analysis, glycerophospholipid metabolism emerged as the most significantly altered metabolic pathway following the administration of SMOF lipid emulsion. Additionally, ROC curve analysis confirmed a significant increase in LysoPC(20:5), PE(16:0/20:5), and PE-NMe(22:5/16:0) metabolites in the SMOF group.

CONCLUSION

The administration of SMOF lipid emulsion in VLBW infants resulted in significant modifications of serum metabolites compared to traditional soybean oil-based lipid emulsions. Notably, glycerophospholipid metabolism was identified as the most prominently altered metabolic pathway. Metabolites, including LysoPC(20:5), PE(16:0/20:5), and PE-NMe(22:5/16:0), emerged as potential biomarkers.

CLINICAL TRIAL NUMBER

Not applicable.

An interpretable machine learning model for precise prediction of biomarkers for intermittent fasting pattern

Intermittent fasting is currently a highly sought-after dietary pattern. To explore the potential biomarkers of intermittent fasting, untargeted metabolomics analysis of fecal metabolites in two groups of mice, intermittent fasting and normal feeding, was conducted using UPLC-HRMS. The data was further analyzed through interpretable machine learning (ML) to data mine the biomarkers for two dietary patterns. We developed five machine learning models and results showed that under three-fold cross-validation, Random Forest model was the most suitable for distinguishing the two dietary patterns. Finally, Shapely Additive exPlanations (SHAP) were explored to perform a weighted explanatory analysis on the Random Forest model, and the contribution of each metabolite to the model was calculated. Results indicated that Ganoderenic Acid C is the potential biomarkers to distinguish the two dietary patterns. Our work provides new insights for metabolic biomarker analysis and lays a theoretical foundation for the selection of a healthieir dietary lifestyle.

Dried blood spots-based metabolomic analysis in preterm infants with necrotizing enterocolitis

OBJECTIVE

Necrotizing enterocolitis (NEC) is the leading cause of death among premature infants, and there is a lack of specific early diagnostic markers. Blood sampling is expected to better reflect pathophysiological and metabolic changes in systematic illness, but there is a risk of iatrogenic anemia, especially in premature infants. Dried blood spots technique seems to have important advantages compared to whole blood sampling as it requires only 12-15 μL as sample volume. This study aimed to investigate the special metabolomics of preterm neonates at high risk of NEC using dried blood spots.

METHODS

Cases and controls were strictly matched 1:1. Dried blood spots (n = 32, 16 cases-16 controls) from newborn screening were subjected to LC-MS/MS. Metabolomic data were analyzed by orthogonal partial least squares-discriminant analysis (OPLS-DA) and univariate/multivariate statistical analysis.

RESULTS

Compared to the control group, the NEC group had a significant reduction in seven amino acids (glycine, alanine, threonine, proline, ornithine, lysine, and asparagine).

CONCLUSIONS

The metabolic profile of neonates with NEC differs significantly from that of controls, making possible their separation with the use of targeted (LC-MS/MS) dried blood spots-based metabolomic analysis. Seven specific markers were identified for early detection and intervention.

The longevity factor spermidine is part of a highly heritable complex erythrocyte phenotype associated with longevity

Extreme longevity in humans is known to be a heritable trait. In a well-established twin erythrocyte metabolomics and proteomics database, we identified the longevity factor spermidine and a cluster of correlated molecules with high heritability estimates. Erythrocyte spermidine is 82% heritable and significantly correlated with 59 metabolites and 22 proteins. Thirty-eight metabolites and 19 proteins were >20% heritable, with a mean heritability of 61% for metabolites and 49% for proteins. Correlated metabolites are concentrated in energy metabolism, redox homeostasis, and autophagy pathways. Erythrocyte mean cell volume (MCV), an established heritable trait, was consistently negatively correlated with the top 25 biomolecules most strongly correlated with spermidine, indicating that smaller MCVs are associated with higher concentrations of spermidine and correlated molecules. Previous studies have linked larger MCVs with poorer memory, cognition, and all-cause mortality. Analysis of 432,682 unique patient records showed a linear increase in MCV with age but a significant deviation toward smaller than expected MCVs above age 86, suggesting that smaller MCVs are associated with extreme longevity. Consistent with previous reports, a subset of 78,158 unique patient records showed a significant skewing toward larger MCV values in a deceased cohort compared to an age-matched living cohort. Our study supports the existence of a complex, heritable phenotype in erythrocytes associated with health and longevity.

Investigating gene expression datasets of hippocampus tissue to discover Alzheimer's disease-associated molecular markers

BACKGROUND

Alzheimer's disease (AD) is an advancing neurodegenerative disorder distinguished by the formation of amyloid plaques and neurofibrillary tangles in the human brain. Nevertheless, the lack of peripheral biomarkers that can detect the development of AD remains a significant limitation.

OBJECTIVE

The main aim of this work was to discover the molecular markers associated with AD.

METHODS

We conducted a comprehensive microarray analysis of gene expression data from hippocampus tissue in AD patients and control samples using three microarray datasets (GSE1297, GSE28146, and GSE29378) collected from Gene Expression Omnibus (GEO). The datasets were pre-processed and normalized, revealing 346 significant genes, 103 of which were upregulated and 243 downregulated. The PPI network of significant genes was constructed to detect the top 50 hub genes, which were then further analyzed using Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes pathway (KEGG), and GSEA, revealing 47 key genes involved in AD-related pathways. These key genes were then subjected to feed forward loop (FFL) motif analysis for the prediction of transcriptional factors (TFs) and microRNAs (miRNAs) mediated gene regulatory networks.

RESULTS

The interaction of AD-associated TFs HNF4A, SPI1, EGR1, STAT3, and MYC and miRNAs hsa-miR-155-5p and hsa-miR-16-5p in the transcriptional and post-transcriptional events of 3 upregulated and 10 downregulated genes: H2AFZ, MCM3, MYO1C, AXIN1, CCND1, ETS2, MYH9, RELA, RHEB, SOCS3, TBL1X, TBP, TXNIP, and YWHAZ, respectively, has been identified. The miRNA/TF-mediated three types of the FFL motifs, i.e., miRNA-FFL, TF-FFL, and composite-FFL, were constructed, and seven common genes among these FFL were identified: CCND1, MYH9, SOCS3, RHEB, MYO1C, TXNIP, AXIN1, and TXNIP.

CONCLUSIONS

These findings may provide insights into the development of potential molecular markers for therapeutic management of AD.

Monitoring drug Efficacy through Multi-Omics Research initiative in Alzheimer's Disease (MEMORI-AD): A protocol for a multisite exploratory prospective cohort study on the drug response-related clinical, genetic, microbial and metabolomic signatures in Filipino patients with Alzheimer's disease

INTRODUCTION

Dementia is one of the leading causes of disability among older people aged 60 years and above, with majority eventually being diagnosed with Alzheimer's disease (AD). Pharmacological agents approved for dementia include acetylcholinesterase enzyme (AChE) inhibitors like rivastigmine, donepezil and galantamine and the N-methyl-D-aspartate (NMDA) receptor antagonist memantine, prescribed as monotherapy or in combination with each other, depending on the severity of disease. There is currently no available study demonstrating the clinical response to these drugs for AD in the Filipino population. Hence, this protocol aims to characterise the clinical, genetic, microbial and metabolic factors associated with drug responses to donepezil, rivastigmine and/or memantine for AD in a cohort of Filipinos with late-onset AD.

METHODS AND ANALYSIS

This protocol involves a multisite descriptive study that will use two study designs: (1) a descriptive, cross-sectional study to characterise the clinical profile of Filipino dementia patients with AD and (2) an exploratory prospective cohort study to investigate drug response-related genetic, gut microbiome and metabolome signatures of a subset of the recruited AD patients. At least 153 patients with mild or moderate AD aged 65 years old and above will be recruited regardless of their treatment status. A subset of these patients (n=60) who meet inclusion and exclusion criteria will be included further in the exploratory cohort study. These patients will be grouped according to their baseline medications and will be observed for treatment response in 6 months. The cognitive, functional and behavioural domains of patients and levels of functioning will be measured using different assessment tools. Drug responses of Filipino patients will then be investigated employing multi-omics technology to characterise genetic variations via whole exome sequencing, gut microbiome profile via shotgun metagenomic sequencing and metabolome profile via liquid chromatography with mass spectrometry.

ETHICS AND DISSEMINATION

The study has received ethical clearance from the Department of Health Single Joint Research Ethics Board (SJREB-2022-15). Results of psychometric scales will be made available to enrolled patients. The study results will be presented at national/international conferences and published in international peer-reviewed scientific journals, and summaries of the results will be provided to the study funders and institutional review boards of the three tertiary referral hospitals.

TRIAL REGISTRATION NUMBER

Philippine Health Research Registry ID PHRR230220-0054116; ClinicalTrials.gov ID NCT05801380.

Decreased plasma nicotinamide and altered NAD+ metabolism in glial cells surrounding Aβ plaques in a mouse model of Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease and a leading cause of senile dementia. Amyloid-β (Aβ) accumulation triggers chronic neuroinflammation, initiating AD pathogenesis. Recent clinical trials for anti-Aβ immunotherapy underscore that blood-based biomarkers have significant advantages and applicability over conventional diagnostics and are an unmet clinical need. To further advance ongoing clinical trials and identify novel therapeutic targets for AD, developing additional plasma biomarkers closely associated with pathogenic mechanisms downstream of Aβ accumulation is critically important. To identify plasma metabolites reflective of neuroinflammation caused by Aβ pathology, we performed untargeted metabolomic analyses of the plasma by capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS) and analyzed the potential roles of the identified metabolic changes in the brain neuroinflammatory response using the female App knock-in (AppNLGF) mouse model of Aβ amyloidosis. The CE-TOFMS analysis of plasma samples from female wild-type (WT) and AppNLGF mice revealed that plasma levels of nicotinamide, a nicotinamide adenine dinucleotide (NAD+) precursor, were decreased in AppNLGF mice, and altered metabolite profiles were enriched for nicotinate/nicotinamide metabolism. In AppNLGF mouse brains, NAD+ levels were unaltered, but mRNA levels of NAD+-synthesizing nicotinate phosphoribosyltransferase (Naprt) and NAD+-degrading Cd38 genes were increased. These enzymes were induced in reactive astrocytes and microglia surrounding Aβ plaques in the cortex and hippocampus of female AppNLGF mouse brains, suggesting neuroinflammation increases NAD+ metabolism. This study suggests plasma nicotinamide could be indicative of the neuroinflammatory response and that nicotinate and nicotinamide metabolism are potential therapeutic targets for AD, by targeting both neuroinflammation and neuroprotection.

Integrating amyloid and tau imaging with proteomics and genomics in Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disease and is characterized by the aggregation of β-amyloid (Aβ) and tau in the brain. Breakthroughs in disease-modifying treatments targeting Aβ bring new hope for the management of AD. But to effectively modify and someday even prevent AD, a better understanding is needed of the biological mechanisms that underlie and link Aβ and tau in AD. Developments of high-throughput omics, including genomics, proteomics, and transcriptomics, together with molecular imaging of Aβ and tau with positron emission tomography (PET), allow us to discover and understand the biological pathways that regulate the aggregation and spread of Aβ and tau in living humans. The field of integrated omics and PET studies of Aβ and tau in AD is growing rapidly. We here provide an update of this field, both in terms of biological insights and in terms of future clinical implications of integrated omics-molecular imaging studies.

Chuanxiong Renshen Decoction Inhibits Alzheimer's Disease Neuroinflammation by Regulating PPARγ/NF-κB Pathway

Background and Aim

Previous studies of our research group have shown that Chuanxiong Renshen Decoction (CRD) has the effect of treating AD, but the exact mechanism of its effect is still not clarified. The aim of this study was to investigate the effect and mechanism of CRD on AD neuroinflammation.

Materials and Methods

Morris Water Maze (MWM) tests were employed to assess the memory and learning capacity of AD mice. HE and Nissl staining were used to observe the neural cells of mice. The expression of Iba-1 and CD86 were detected by immunohistochemical staining. Utilize UHPLC-MS/MS metabolomics techniques and the KEGG to analyze the metabolic pathways of CRD against AD. Lipopolysaccharide (LPS) induced BV2 microglia cells to construct a neuroinflammatory model. The expression of Iba-1 and CD86 were detected by immunofluorescence and flow cytometry. The contents of TNF-α and IL-1β were detected by ELISA. Western blot assay was used to detect the expression of PPARγ, p-NF-κB p65, NF-κB p65 proteins and inflammatory cytokines iNOS and COX-2 in PPARγ/NF-κB pathway with and without PPARγ inhibitor GW9662.

Results

CRD ameliorated the learning and memory ability of 3×Tg-AD mice, repaired the damaged nerve cells in the hippocampus, reduced the area of Iba-1 and CD86 positive areas in both the hippocampus and cortex regions, as well as attenuated serum levels of IL-1β and TNF-α in mice. CRD-containing serum significantly decreased the expression level of Iba-1, significantly reduced the levels of TNF-α and IL-1β, significantly increased the protein expression of PPARγ, and significantly decreased the proteins expression of iNOS, COX-2 and p-NF-κB p65 in BV2 microglia cells. After addition of PPARγ inhibitor GW9662, the inhibitory effect of CRD-containing serum on NF-κB activation was significantly weakened.

Conclusion

CRD can activate PPARγ, regulating PPARγ/NF-κB signaling pathway, inhibiting microglia over-activation and reducing AD neuroinflammation.

Essential New Complexity-Based Themes for Patient-Centered Diagnosis and Treatment of Dementia and Predementia in Older People: Multimorbidity and Multilevel Phenomenology

Dementia is a highly prevalent condition with devastating clinical and socioeconomic sequela. It is expected to triple in prevalence by 2050. No treatment is currently known to be effective. Symptomatic late-onset dementia and predementia (SLODP) affects 95% of patients with the syndrome. In contrast to trials of pharmacological prevention, no treatment is suggested to remediate or cure these symptomatic patients. SLODP but not young onset dementia is intensely associated with multimorbidity (MUM), including brain-perturbating conditions (BPCs). Recent studies showed that MUM/BPCs have a major role in the pathogenesis of SLODP. Fortunately, most MUM/BPCs are medically treatable, and thus, their treatment may modify and improve SLODP, relieving suffering and reducing its clinical and socioeconomic threats. Regrettably, the complex system features of SLODP impede the diagnosis and treatment of the potentially remediable conditions (PRCs) associated with them, mainly due to failure of pattern recognition and a flawed diagnostic workup. We suggest incorporating two SLODP-specific conceptual themes into the diagnostic workup: MUM/BPC and multilevel phenomenological themes. By doing so, we were able to improve the diagnostic accuracy of SLODP components and optimize detecting and favorably treating PRCs. These revolutionary concepts and their implications for remediability and other parameters are discussed in the paper.

Exploring the multifaceted therapeutic mechanism of Schisanlactone E (XTS) in APP/PS1 mouse model of Alzheimer's disease through multi-omics analysis

Background

Schisanlactone E, also known as XueTongSu (XTS), is an active compound extracted from the traditional Tujia medicine Kadsura heteroclita ("XueTong"). Recent studies highlight its anti-inflammatory and antioxidant properties, yet the mechanisms of XTS's therapeutic effects on Alzheimer's disease (AD) are unclear. This study aims to elucidate the therapeutic efficacy and mechanisms of XTS in AD.

Methods

Ten C57BL/6 mice were assigned to the control group (NC), and twenty APP/PS1 transgenic mice were randomly divided into the model group (M) (10 mice) and the XTS treatment group (Tre) (10 mice). After an acclimatization period of 7 days, intraperitoneal injections were administered over a 60-day treatment period. The NC and M groups received saline, while the Tre group received XTS at 2 mg/kg. Learning and memory abilities were assessed using the Morris Water Maze (MWM) test. Histopathological changes were evaluated using hematoxylin and eosin (HE) and Nissl staining, and immunofluorescence was used to assess pathological products and glial cell activation. Cytokine levels (IL-1β, IL-6, TNF-α) in the hippocampus were quantified by qPCR. 16S rDNA sequencing analyzed gut microbiota metabolic alterations, and metabolomic analysis was performed on cortical samples. The KEGG database was used to analyze the regulatory mechanisms of XTS in AD treatment.

Results

XTS significantly improved learning and spatial memory in APP/PS1 mice and ameliorated histopathological changes, reducing Aβ plaque aggregation and glial cell activation. XTS decreased the expression of inflammatory cytokines IL-1β, IL-6, and TNF-α. It also enhanced gut microbiota diversity, notably increasing Akkermansia species, and modulated levels of metabolites such as isosakuranetin, 5-KETE, 4-methylcatechol, and sphinganine. Pathway analysis indicated that XTS regulated carbohydrate metabolism, neuroactive ligand-receptor interactions, and alanine, aspartate, and glutamate metabolism, mitigating gut microbiota dysbiosis and metabolic disturbances.

Conclusion

XTS ameliorates cognitive deficits, pathological changes, and inflammatory responses in APP/PS1 mice. It significantly modulates the gut microbiota, particularly increasing Akkermansia abundance, and influences levels of key metabolites in both the gut and brain. These findings suggest that XTS exerts anti-AD effects through the microbial-gut-brain axis (MGBA).

Therapeutic application of nicotinamide: As a potential target for inhibiting fibrotic scar formation following spinal cord injury

AIM

We aimed to confirm the inhibitory effect of nicotinamide on fibrotic scar formation following spinal cord injury in mice using functional metabolomics.

METHODS

We proposed a novel functional metabolomics strategy to establish correlations between gene expression changes and metabolic phenotypes using integrated multi-omics analysis. Through the integration of quantitative metabolites analysis and assessments of differential gene expression, we identified nicotinamide as a functional metabolite capable of inhibiting fibrotic scar formation and confirmed the effect in vivo using a mouse model of spinal cord injury. Furthermore, to mimic fibrosis models in vitro, primary mouse embryonic fibroblasts and spinal cord fibroblasts were stimulated by TGFβ, and the influence of nicotinamide on TGFβ-induced fibrosis-associated genes and its underlying mechanism were examined.

RESULTS

Administration of nicotinamide led to a reduction in fibrotic lesion area and promoted functional rehabilitation following spinal cord injury. Nicotinamide effectively downregulated the expression of fibrosis genes, including Col1α1, Vimentin, Col4α1, Col1α2, Fn1, and Acta2, by repressing the TGFβ/SMADs pathway.

CONCLUSION

Our functional metabolomics strategy identified nicotinamide as a metabolite with the potential to inhibit fibrotic scar formation following SCI by suppressing the TGFβ/SMADs signaling. This finding provides new therapeutic strategies and new ideas for clinical treatment.

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.

Bile acid metabolism in health and ageing-related diseases

Bile acids (BAs) have surpassed their traditional roles as lipid solubilizers and regulators of BA homeostasis to emerge as important signalling molecules. Recent research has revealed a connection between microbial dysbiosis and metabolism disruption of BAs, which in turn impacts ageing-related diseases. The human BAs pool is primarily composed of primary BAs and their conjugates, with a smaller proportion consisting of secondary BAs. These different BAs exert complex effects on health and ageing-related diseases through several key nuclear receptors, such as farnesoid X receptor and Takeda G protein-coupled receptor 5. However, the underlying molecular mechanisms of these effects are still debated. Therefore, the modulation of signalling pathways by regulating synthesis and composition of BAs represents an interesting and novel direction for potential therapies of ageing-related diseases. This review provides an overview of synthesis and transportion of BAs in the healthy body, emphasizing its dependence on microbial community metabolic capacity. Additionally, the review also explores how ageing and ageing-related diseases affect metabolism and composition of BAs. Understanding BA metabolism network and the impact of their nuclear receptors, such as farnesoid X receptor and G protein-coupled receptor 5 agonists, paves the way for developing therapeutic agents for targeting BA metabolism in various ageing-related diseases, such as metabolic disorder, hepatic injury, cardiovascular disease, renal damage and neurodegenerative disease.

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.

4,4'-methylenediphenol reduces Aβ-induced toxicity in a Caenorhabditis elegans model of Alzheimer's disease

Introduction

Gastrodia elata Blume is a widely used medicinal and edible herb with a rich chemical composition. Moreover, prescriptions containing Gastrodia elata are commonly used for the prevention and treatment of cardiovascular, cerebrovascular, and aging-related diseases. Recent pharmacological studies have confirmed the antioxidant and neuroprotective effects of Gastrodia elata, and, in recent years, this herb has also been used in the treatment of Alzheimer's disease (AD) and other neurodegenerative disorders. We have previously shown that 4,4'-methylenediphenol, a key active ingredient of Gastrodia elata, can mitigate amyloid-β (Aβ)-induced paralysis in AD model worms as well as prolong the lifespan of the animals, thus displaying potential as a treatment of AD.

Methods

We investigated the effects of 4,4'-methylenediphenol on AD and aging through paralysis, lifespan, and behavioral assays. In addition, we determined the anti-AD effects of 4,4'-methylenediphenol by reactive oxygen species (ROS) assay, lipofuscin analysis, thioflavin S staining, metabolomics analysis, GFP reporter gene worm assay, and RNA interference assay and conducted in-depth studies on its mechanism of action.

Results

4,4'-Methylenediphenol not only delayed paralysis onset and senescence in the AD model worms but also enhanced their motility and stress tolerance. Meanwhile, 4,4'-methylenediphenol treatment also reduced the contents of reactive oxygen species (ROS) and lipofuscin, and decreased Aβ protein deposition in the worms. Broad-spectrum targeted metabolomic analysis showed that 4,4'-methylenediphenol administration had a positive effect on the metabolite profile of the worms. In addition, 4,4'-methylenediphenol promoted the nuclear translocation of DAF-16 and upregulated the expression of SKN-1, SOD-3, and GST-4 in the respective GFP reporter lines, accompanied by an enhancement of antioxidant activity and a reduction in Aβ toxicity; importantly, our results suggested that these effects of 4,4'-methylenediphenol were mediated, at least partly, via the activation of DAF-16.

Conclusion

We have demonstrated that 4,4'-methylenediphenol can reduce Aβ-induced toxicity in AD model worms, suggesting that it has potential for development as an anti-AD drug. Our findings provide ideas and references for further research into the anti-AD effects of Gastrodia elata and its active ingredients.

Human Cerebrospinal Fluid Sample Preparation and Annotation for Integrated Lipidomics and Metabolomics Profiling Studies

Cerebrospinal fluid (CSF) is a metabolically diverse biofluid and a key specimen for exploring biochemical changes in neurodegenerative diseases. Detecting lipid species in CSF using mass spectrometry (MS)-based techniques remains challenging because lipids are highly complex in structure, and their concentrations span over a broad dynamic range. This work aimed to develop a robust lipidomics and metabolomics method based on commonly used two-phase extraction systems from human CSF samples. Prioritizing lipid detection, biphasic extraction methods, Folch, Bligh and Dyer (B&D), Matyash, and acidified Folch and B&D (aFolch and aB&D) were compared using 150 μL of human CSF samples for the simultaneous extraction of lipids and metabolites with a wide range of polarity. Multiple chromatographical separation approaches, including reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), and gas chromatography (GC), were utilized to characterize human CSF metabolome. The aB&D method was found as the most reproducible technique (RSD < 15%) for lipid extraction. The aB&D and B&D yielded the highest peak intensities for targeted lipid internal standards and displayed superior extracting power for major endogenous lipid classes. A total of 674 unique metabolites with a wide polarity range were annotated in CSF using, combining RPLC-MS/MS lipidomics (n = 219), HILIC-MS/MS (n = 304), and GC-quadrupole time of flight (QTOF) MS (n = 151). Overall, our findings show that the aB&D extraction method provided suitable lipid coverage, reproducibility, and extraction efficiency for global lipidomics profiling of human CSF samples. In combination with RPLC-MS/MS lipidomics, complementary screening approaches enabled a comprehensive metabolite signature that can be employed in an array of clinical studies.

Clinical metabolomics characteristics of diabetic kidney disease: A meta-analysis of 1875 cases with diabetic kidney disease and 4503 controls

AIMS

Diabetic Kidney Disease (DKD), one of the major complications of diabetes, is also a major cause of end-stage renal disease. Metabolomics can provide a unique metabolic profile of the disease and thus predict or diagnose the development of the disease. Therefore, this study summarises a more comprehensive set of clinical biomarkers related to DKD to identify functional metabolites significantly associated with the development of DKD and reveal their driving mechanisms for DKD.

MATERIALS AND METHODS

We searched PubMed, Embase, the Cochrane Library and Web of Science databases through October 2022. A meta-analysis was conducted on untargeted or targeted metabolomics research data based on the strategy of standardized mean differences and the process of ratio of means as the effect size, respectively. We compared the changes in metabolite levels between the DKD patients and the controls and explored the source of heterogeneity through subgroup analyses, sensitivity analysis and meta-regression analysis.

RESULTS

The 34 clinical-based metabolomics studies clarified the differential metabolites between DKD and controls, containing 4503 control subjects and 1875 patients with DKD. The results showed that a total of 60 common differential metabolites were found in both meta-analyses, of which 5 metabolites (p < 0.05) were identified as essential metabolites. Compared with the control group, metabolites glycine, aconitic acid, glycolic acid and uracil decreased significantly in DKD patients; cysteine was significantly higher. This indicates that amino acid metabolism, lipid metabolism and pyrimidine metabolism in DKD patients are disordered.

CONCLUSIONS

We have identified 5 metabolites and metabolic pathways related to DKD which can serve as biomarkers or targets for disease prevention and drug therapy.

Effect of woohwangchungsimwon and donepezil co-treatment on cognitive function and serum metabolic profiles in a scopolamine-induced model of Alzheimer's disease

ETHNOPHARMACOLOGICAL RELEVANCE

Woohwangchungsimwon (WCW) is a traditional medicine used in East Asian countries to treat central nervous system disorders. Reported pharmacological properties include antioxidant effects, enhanced learning and memory, and protection against ischemic neuronal cell death, supporting its use in treating neurodegenerative diseases like Alzheimer's disease (AD).

AIM OF THE STUDY

The study aims to assess the effects of co-treatment with WCW and donepezil on cognitive functions and serum metabolic profiles in a scopolamine-induced AD model.

MATERIALS AND METHODS

Cell viability and reactive oxygen species (ROS) levels were measured in amyloid β-peptide25-35 (Aβ25-35)-induced SH-SY5Y cells. An AD model was established in ICR mice by intraperitoneal scopolamine administration. Animals underwent the step-through passive avoidance test (PAT) and Morris water maze (MWM) test. Hippocampal tissues were collected to examine specific protein expression. Serum metabolic profiles were analyzed using nuclear magnetic resonance (NMR) spectroscopy.

RESULTS

Co-treatment with WCW and donepezil increased cell viability and reduced ROS production in Aβ25-35-induced SH-SY5Y cells compared to that with donepezil treatment alone. Co-treatment improved cognitive functions and was comparable to donepezil treatment alone in the PAT and MWM tests. Pathways related to tyrosine, phenylalanine, and tryptophan biosynthesis, phenylalanine metabolism, and cysteine and methionine metabolism were altered by co-treatment. Levels of tyrosine and methionine, major serum metabolites in these pathways, were significantly reduced after co-treatment.

CONCLUSIONS

Co-treatment with WCW and donepezil shows promise as a therapeutic strategy for AD and is comparable to donepezil alone in improving cognitive function. Reduced tyrosine and methionine levels after co-treatment may enhance cognitive function by mitigating hypertyrosinemia and hyperhomocysteinemia, known risk factors for AD. The serum metabolic profiles obtained in this study can serve as a foundation for developing other bioactive compounds using a scopolamine-induced mouse model.

NMR analysis seeking for cognitive decline and dementia metabolic markers in plasma from aged individuals

BACKGROUND

Blood biomarkers can improve the ability to diagnose dementia, providing new information to better understand the pathophysiology and causes of the disease. Some studies with patients have already shown changes in metabolic profiles among patients with pathological cognitive decline or Alzheimer's disease, when compared to individuals with normal cognition.

METHODS

To search for new metabolic biomarkers of dementia, we analyzed serum levels of several metabolites, measured by nuclear magnetic resonance spectroscopy, in elderly individuals, a group with normal cognitive decline (control), and three other groups with cognitive decline. pathological (low, moderate, and severe).

RESULTS

Decreased plasma levels of tyrosine, glutamate, valine, leucine, and isoleucine are associated with worsening of pathological cognitive decline. However, the area under analysis of receptor operating characteristics suggests that tyrosine and glutamate have low specificity and sensitivity. Valine, leucine, and isoleucine are influenced by blood glucose or diabetes, but these conditions do not seem to be of great influence in the differences observed. Isobutyrate, histidine, acetone and unknown-1 metabolite also decrease their plasma levels with increasing CD. Isobutyrate ad histidine could have neuroprotective and antioxidant actions, respectively. To elucidate the role of decreased unknown metabolite-1 as a CD biomarker, it will be necessary to previously investigate its identity. To define and elucidate the role of acetone in pathological CD, additional laboratory and clinical studies must be performed. All these metabolites together may constitute a set of biomarkers with capability to identify pathological CD or dementia.

SIGNIFICANCE AND NOVELTY

Decrease of glutamate, tyrosine, valine, leucine, isoleucine, histidine, isobutyrate, acetone and unknown-1 metabolite together are a set of biomarkers able to identify pathological CD or dementia. Histidine, isobutyrate, acetone and unknown-1 metabolite are more specific biomarkers of CD.

Metabolic Regulations of Smilax china L. against β-Amyloid Toxicity in Caenorhabditis elegans

Smilax china L. (Chinaroot) is a natural herb that has multiple uses, such as being used to make tea and food. Both its roots and leaves have different uses due to their unique components. In this study, we analyzed the extract of S. china. roots using LC-HRMS and evaluated the neuroprotective effects and metabolic regulation of S. china on Caenorhabditis elegans. Chinaroot extract prolonged the life span of healthy nematodes, delayed the paralysis time of transgenic CL4176, and reduced the level of β-amyloid deposition in transgenic CL2006. The comprehensive analysis of metabolomics and qRT-PCR revealed that Chinaroot extract exerted neuroprotective effects through the valine, leucine and isoleucine degradation and fatty acid degradation pathways. Moreover, we first discovered that the expressions of T09B4.8, ech-7, and agxt-1 were linked to the neuroprotective effects of Chinaroot. The material exerted neuroprotective effects by modulating metabolic abnormalities in AD model C. elegans. Our study provides a new foundation for the development of functional food properties and functions.

Danggui Shaoyao San: comprehensive modulation of the microbiota-gut-brain axis for attenuating Alzheimer's disease-related pathology

Background: Alzheimer's disease (AD), an age-associated neurodegenerative disorder, currently lacks effective clinical therapeutics. Traditional Chinese Medicine (TCM) holds promising potential in AD treatment, exemplified by Danggui Shaoyao San (DSS), a TCM formulation. The precise therapeutic mechanisms of DSS in AD remain to be fully elucidated. This study aims to uncover the therapeutic efficacy and underlying mechanisms of DSS in AD, employing an integrative approach encompassing gut microbiota and metabolomic analyses. Methods: Thirty Sprague-Dawley (SD) rats were allocated into three groups: Blank Control (Con), AD Model (M), and Danggui Shaoyao San (DSS). AD models were established via bilateral intracerebroventricular injections of streptozotocin (STZ). DSS was orally administered at 24 g·kg-1·d-1 (weight of raw herbal materials) for 14 days. Cognitive functions were evaluated using the Morris Water Maze (MWM) test. Pathological alterations were assessed through hematoxylin and eosin (HE) staining. Bloodstream metabolites were characterized, gut microbiota profiled through 16S rDNA sequencing, and cortical metabolomics analyzed. Hippocampal proinflammatory cytokines (IL-1β, IL-6, TNF-α) were quantified using RT-qPCR, and oxidative stress markers (SOD, CAT, GSH-PX, MDA) in brain tissues were measured with biochemical assays. Results: DSS identified a total of 1,625 bloodstream metabolites, predominantly Benzene derivatives, Carboxylic acids, and Fatty Acyls. DSS significantly improved learning and spatial memory in AD rats and ameliorated cerebral tissue pathology. The formulation enriched the probiotic Ligilactobacillus, modulating metabolites like Ophthalmic acid (OA), Phosphocreatine (PCr), Azacridone A, Inosine, and NAD. DSS regulated Purine and Nicotinate-nicotinamide metabolism, restoring balance in the Candidatus Saccharibacteria-OA interplay and stabilizing gut microbiota-metabolite homeostasis. Additionally, DSS reduced hippocampal IL-1β, IL-6, TNF-α expression, attenuating the inflammatory state. It elevated antioxidative enzymes (SOD, CAT, GSH-PX) while reducing MDA levels, indicating diminished oxidative stress in AD rat brains. Conclusion: DSS addresses AD pathology through multifaceted mechanisms, encompassing gut microbiome regulation, specific metabolite modulation, and the mitigation of inflammation and oxidative stress within the brain. This holistic intervention through the Microbial-Gut-Brain Axis (MGBA) underscores DSS's potential as an integrative therapeutic agent in combatting AD.

Associations Between Metabolomics Findings and Brain Hypometabolism in Mild Cognitive Impairment and Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disease with rising prevalence due to the aging global population. Existing methods for diagnosing AD are struggling to detect the condition in its earliest and most treatable stages. One early indicator of AD is a substantial decrease in the brain's glucose metabolism. Metabolomics can detect disturbances in biofluids, which may be advantageous for early detection of some AD-related changes. The study aims to predict brain hypometabolism in Alzheimer's disease using metabolomics findings and develop a predictive model based on metabolomic data.

METHODS

The data used in this study were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We conducted a longitudinal study with three assessment time points to investigate the predictive power of baseline metabolomics for modeling longitudinal fluorodeoxyglucose- positron emission tomography (FDG-PET) trajectory changes in AD patients. A total of 44 participants with AD were included. The Alzheimer's Disease Assessment Scale (ADAS), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) were used for cognitive assessments. A single global brain hypo-metabolism index was used as the outcome variable.

RESULTS

Across models, we observed consistent positive relationships between specific cholesterol esters - CE (20:3) (p = 0.005) and CE (18:3) (p = 0.0039) - and FDG-PET metrics, indicating these baseline metabolites may be valuable indicators of future PET score changes. Selected triglycerides like DG-O (16:0-20:4) also showed time-specific positive associations (p = 0.017).

CONCLUSION

This research provides new insights into the disruptions in the metabolic network linked to AD pathology. These findings could pave the way for identifying novel biomarkers and potential treatment targets for AD.

Quantitative Metabolomic Analysis of the Rat Hippocampus: Effects of Age and of the Development of Alzheimer's Disease-Like Pathology

Background

Alzheimer's disease (AD) is the most common type of dementia in the elderly. Incomplete knowledge about the pathogenesis of this disease determines the absence of medications for the treatment of AD today. Animal models can provide the necessary knowledge to understand the mechanisms of biochemical processes occurring in the body in health and disease.

Objective

To identify the most promising metabolomic predictors and biomarkers reflecting metabolic disorders in the development of AD signs.

Methods

High resolution 1H NMR spectroscopy was used for quantitative metabolomic profiling of the hippocampus of OXYS rats, an animal model of sporadic AD, which demonstrates key characteristics of this disease. Animals were examined during several key periods: 20 days group corresponds to the "preclinical" period preceding the development of AD signs, during their manifestation (3 months), and active progression (18 months). Wistar rats of the same age were used as control.

Results

Ranges of variation and mean concentrations were established for 59 brain metabolites. The main metabolic patterns during aging, which are involved in energy metabolism pathways and metabolic shifts of neurotransmitters, have been established. Of particular note is the significant increase of scyllo-inositol and decrease of hypotaurine in the hippocampus of OXYS rats as compared to Wistars for all studied age groups.

Conclusions

We suggest that the accumulation of scyllo-inositol and the reduction of hypotaurine in the brain, even at an early age, can be considered as predictors and potential biomarkers of the development of AD signs in OXYS rats and, probably, in humans.

How far are we from reliable metabolomics-based biomarkers? The often-overlooked importance of addressing inter-individual variability factors

Metabolomics has proven great potential to unravel the molecular basis of diseases. However, most attempts aimed at identifying reliable metabolomics-based biomarkers for diagnosis, prediction, and prognosis of diseases have repeatedly failed because of inconsistent results and unsatisfactory replication in independent cohorts. This review article explores the possible causes behind this reproducibility crisis, with special focus on the role that inter-individual variability factors play in modulating the susceptibility to disease development. Furthermore, we provide future perspectives on the applicability of metabolomics in biomedical research and its translatability into clinical practice.