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

Causal association between plasma metabolites and neurodegenerative diseases

BACKGROUND

Establishing causal relationships between metabolic biomarkers and neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) is a challenge faced by observational studies. In this study, our aim was to investigate the causal associations between plasma metabolites and neurodegenerative diseases using Mendelian Randomization (MR) methods.

METHODS

We utilized genetic associations with 1400 plasma metabolic traits as exposures. We used large-scale genome-wide association study (GWAS) summary statistics for AD and PD as our discovery datasets. For validation, we performed repeated analyses using different GWAS datasets. The main statistical method employed was inverse variance-weighted (IVW). We also conducted enrichment pathway analysis for IVW-identified metabolites.

RESULTS

In the discovered dataset, there are a total of 69 metabolites (36 negatively, 33 positively) potentially associated with AD, and 47 metabolites (24 negatively， 23 positively) potentially associated with PD. Among these, 4 significant metabolites overlap with significant metabolites (PIVW < 0.05)in the validation dataset for AD, and 1 metabolite overlaps with significant metabolites in the validation dataset for PD. Three metabolites serve as common potential metabolic markers for both AD and PD, including Tryptophan betaine, Palmitoleoylcarnitine (C16:1), and X-23655 levels. Further pathway enrichment analysis suggests that the SLC-mediated transmembrane transport pathway, involving tryptophan betaine and carnitine metabolites, may represent potential intervention targets for treating AD and PD.

CONCLUSION

This study offers novel insights into the causal effects of plasma metabolites on degenerative diseases through the integration of genomics and metabolomics. The identification of metabolites and metabolic pathways linked to AD and PD enhances our comprehension of the underlying biological mechanisms and presents promising targets for future therapeutic interventions in AD and PD.

Shared plasma metabolomic profiles of cognitive and mobility decline predict future dementia

Experiencing decline in both cognition and mobility is associated with a substantially higher dementia risk than cognitive decline only. Metabolites associated with both cognitive and mobility declines may be early predictors of dementia and reveal specific pathways to dementia. We analyzed data from 2450 participants initially free of dementia who had 613 metabolites measured in plasma in 1998-1999 (mean age = 75.2 ± 2.9 years old, 37.8% Black, 50% women) from the Health, Aging and Body Composition study. Dementia diagnosis was determined by race-specific decline in 3MS scores, medication use, and hospital records through 2014. Cognition and mobility were repeatedly measured using 3MS and a 20-m walking test up to 10 years, respectively. We examined metabolite associations with changes in 3MS (n = 2046) and gait speed (n = 2019) using multivariable linear regression adjusted for age, sex, race, and baseline performance and examined metabolite associations with dementia risk using Cox regression. During a mean follow-up of 9.3 years, 534 (21.8%) participants developed dementia. On average, 3MS declined 0.47/year and gait declined 0.04 m/sec/year. After covariate adjustment, 75 metabolites were associated with cognitive decline, and 111 metabolites were associated with gait decline (FDR-adjusted p < 0.05). Twenty-six metabolites were associated with both cognitive and gait declines. Eighteen of 26 metabolites were associated with dementia risk (p < 0.05), notably amino acids, glycerophospholipids (lysoPCs, PCs, PEs), and sphingolipids. Results remained similar after adjusting for cardiovascular disease or apolipoprotein E ɛ4 carrier status. During aging, metabolomic profiles of cognitive decline and mobility decline show distinct and shared signatures. Shared metabolomic profiles suggest that inflammation and deficits in mitochondria and the urea cycle in addition to the central nervous system may play key roles in both cognitive and mobility declines and predict dementia. Future studies are warranted to investigate longitudinal metabolite changes and metabolomic markers with dementia pathologies.

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.

Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning

BACKGROUND

Alzheimer's disease (AD) is a chronic neurodegenerative disorder that poses a substantial economic burden. The Random forest algorithm is effective in predicting AD; however, the key factors influencing AD onset remain unclear. This study aimed to analyze the key lipoprotein and metabolite factors influencing AD onset using machine-learning methods. It provides new insights for researchers and medical personnel to understand AD and provides a reference for the early diagnosis, treatment, and early prevention of AD.

METHODS

A total of 603 participants, including controls and patients with AD with complete lipoprotein and metabolite data from the Alzheimer's disease Neuroimaging Initiative (ADNI) database between 2005 and 2016, were enrolled. Random forest, Lasso regression, and CatBoost algorithms were employed to rank and filter 213 lipoprotein and metabolite variables. Variables with consistently high importance rankings from any two methods were incorporated into the models. Finally, the variables selected from the three methods, with the participants' age, sex, and marital status, were used to construct a random forest predictive model.

RESULTS

Fourteen lipoprotein and metabolite variables were screened using the three methods, and 17 variables were included in the AD prediction model based on age, sex, and marital status of the participants. The optimal random forest modeling was constructed with "mtry" set to 3 and "ntree" set to 300. The model exhibited an accuracy of 71.01%, a sensitivity of 79.59%, a specificity of 65.28%, and an AUC (95%CI) of 0.724 (0.645-0.804). When Mean Decrease Accuracy and Gini were used to rank the proteins, age, phospholipids to total lipids ratio in intermediate-density lipoproteins (IDL_PL_PCT), and creatinine were among the top five variables.

CONCLUSIONS

Age, IDL_PL_PCT, and creatinine levels play crucial roles in AD onset. Regular monitoring of lipoproteins and their metabolites in older individuals is significant for early AD diagnosis and prevention.

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.

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Serum metabolism alteration behind different etiology, diagnosis, and prognosis of disorders of consciousness

BACKGROUND

Patients with disorders of consciousness (DoC) exhibit varied revival outcomes based on different etiologies and diagnoses, the mechanisms of which remain largely unknown. The fluctuating clinical presentations in DoC pose challenges in accurately assessing consciousness levels and prognoses, often leading to misdiagnoses. There is an urgent need for a deeper understanding of the physiological changes in DoC and the development of objective diagnostic and prognostic biomarkers to improve treatment guidance.

METHODS

To explore biomarkers and understand the biological processes, we conducted a comprehensive untargeted metabolomic analysis on serum samples from 48 patients with DoC. Patients were categorized based on etiology (TBI vs. non-TBI), CRS-R scores, and prognosis. Advanced analytical techniques, including PCA and OPLS-DA models, were employed to identify differential metabolites.

RESULTS

Our analysis revealed a distinct separation in metabolomic profiles among the different groups. The primary differential metabolites distinguishing patients with varying etiologies were predominantly phospholipids, with a notable decrease in glycerophospholipids observed in the TBI group. Patients with higher CRS-R scores exhibited a pattern of impaired carbohydrate metabolism coupled with enhanced lipid metabolism. Notably, serum concentrations of both LysoPE and PE were reduced in patients with improved outcomes, suggesting their potential as prognostic biomarkers.

CONCLUSIONS

Our study underscores the critical role of phospholipid metabolism in the brain's metabolic alterations in patients with DoC. It identifies key biomarkers for diagnosis and prognosis, offering insights that could lead to novel therapeutic targets. These findings highlight the value of metabolomic profiling in understanding and potentially treating DoC.

Effects of xylo-oligosaccharide on gut microbiota, brain protein expression, and lipid profile induced by high-fat diet

Midlife overweight and obesity are risk factors of cognitive decline and Alzheimer' s disease (AD) in late life. In addition to increasing risk of obesity and cognitive dysfunction, diets rich in fats also contributes to an imbalance of gut microbiota. Xylo-oligosaccharides (XOS) are a kind of prebiotic with several biological advantages, and can selectively promote the growth of beneficial microorganisms in the gut. To explore whether XOS can alleviate cognitive decline induced by high-fat diet (HFD) through improving gut microbiota composition, mice were fed with normal control or 60% HFD for 9 weeks to induce obesity. After that, mice were supplemented with XOS (30 g or 60 g/kg-diet) or without, respectively, for 12 weeks. The results showed that XOS inhibited weight gain, decreased epidydimal fat weight, and improved fasting blood sugar and blood lipids in mice. Additionally, XOS elevated spatial learning and memory function, decreased amyloid plaques accumulation, increased brain-derived neurotrophic factor levels, and improved neuroinflammation status in hippocampus. Changes in glycerolipids metabolism-associated lipid compounds caused by HFD in hippocampus were reversed after XOS intervention. On the other hand, after XOS intervention, increase in immune-mediated bacteria, Faecalibacterium was observed. In conclusion, XOS improved gut dysbiosis and ameliorated spatial learning and memory dysfunction caused by HFD by decreasing cognitive decline-associated biomarkers and changing lipid composition in hippocampus.

Identification of blood metabolites associated with risk of Alzheimer's disease by integrating genomics and metabolomics data

Specific metabolites have been reported to be potentially associated with Alzheimer's disease (AD) risk. However, the comprehensive understanding of roles of metabolite biomarkers in AD etiology remains elusive. We performed a large AD metabolome-wide association study (MWAS) by developing blood metabolite genetic prediction models. We evaluated associations between genetically predicted levels of metabolites and AD risk in 39,106 clinically diagnosed AD cases, 46,828 proxy AD and related dementia (proxy-ADD) cases, and 401,577 controls. We further conducted analyses to determine microbiome features associated with the detected metabolites and characterize associations between predicted microbiome feature levels and AD risk. We identified fourteen metabolites showing an association with AD risk. Five microbiome features were further identified to be potentially related to associations of five of the metabolites. Our study provides new insights into the etiology of AD that involves blood metabolites and gut microbiome, which warrants further investigation.

Large-Scale Population-Based Studies of Blood Metabolome and Brain Health

Metabolomics technologies enable the quantification of multiple metabolomic measures simultaneously, which provides novel insights into molecular aspects of human health and disease. In large-scale, population-based studies, blood is often the preferred biospecimen. Circulating metabolome may relate to brain health either by affecting or reflecting brain metabolism. Peripheral metabolites may act at or cross the blood-brain barrier and, subsequently, influence brain metabolism, or they may reflect brain metabolism if similar pathways are engaged. Peripheral metabolites may also include those penetrating the circulation from the brain, indicating, for example, brain damage. Most brain health-related metabolomics studies have been conducted in the context of neurodegenerative disorders and cognition, but some studies have also focused on neuroimaging markers of these disorders. Moreover, several metabolomics studies of neurodevelopmental disorders have been performed. Here, we provide a brief background on the types of blood metabolites commonly assessed, and we review the literature describing the relationships between human blood metabolome (n > 50 metabolites) and brain health reported in large-scale studies (n > 500 individuals).

Metabolomic analysis of plasma biomarkers in children with autism spectrum disorders

Autism spectrum disorder (ASD) presents a significant risk to human well-being and has emerged as a worldwide public health concern. Twenty-eight children with ASD and 33 healthy children (HC) were selected for the quantitative determination of their plasma metabolites using an ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) platform. A total of 1997 metabolites were detected in the study cohort, from which 116 metabolites were found to be differentially expressed between the ASD and HC groups. Through analytical algorithms such as least absolute shrinkage selection operator (LASSO), support vector machine (SVM), and random forest (RF), three potential metabolic markers were identified as FAHFA (18:1(9Z)/9-O-18:0), DL-2-hydroxystearic acid, and 7(S),17(S)-dihydroxy-8(E),10(Z),13(Z),15(E),19(Z)-docosapentaenoic acid. These metabolites demonstrated superior performance in distinguishing the ASD group from the HC group, as indicated by the area under curves (AUCs) of 0.935, 0.897, and 0.963 for the three candidate biomarkers, respectively. The samples were divided into training and validation sets according to 7:3. Diagnostic models were constructed using logistic regression (LR), SVM, and RF. The constructed three-biomarker diagnostic model also exhibited strong discriminatory efficacy. These findings contribute to advancing our understanding of the underlying mechanisms involved in the occurrence of ASD and provide a valuable reference for clinical diagnosis.

Metabolomics and Risk of Dementia: A Systematic Review of Prospective Studies

BACKGROUND

The projected increase in the prevalence of dementia has sparked interest in understanding the pathophysiology and underlying causal factors in its development and progression. Identifying novel biomarkers in the preclinical or prodromal phase of dementia may be important for predicting early disease risk. Applying metabolomic techniques to prediagnostic samples in prospective studies provides the opportunity to identify potential disease biomarkers.

OBJECTIVE

The objective of this systematic review was to summarize the evidence on the associations between metabolite markers and risk of dementia and related dementia subtypes in human studies with a prospective design.

DESIGN

We searched PubMed, PsycINFO, and Web of Science databases from inception through December 8, 2023. Thirteen studies (mean/median follow-up years: 2.1-21.0 y) were included in the review.

RESULTS

Several metabolites detected in biological samples, including amino acids, fatty acids, acylcarnitines, lipid and lipoprotein variations, hormones, and other related metabolites, were associated with risk of developing dementia. Our systematic review summarized the adjusted associations between metabolites and dementia risk; however, our findings should be interpreted with caution because of the heterogeneity across the included studies and potential sources of bias. Further studies are warranted with well-designed prospective cohort studies that have defined study populations, longer follow-up durations, the inclusion of additional diverse biological samples, standardization of techniques in metabolomics and ascertainment methods for diagnosing dementia, and inclusion of other related dementia subtypes.

CONCLUSIONS

This study contributes to the limited systematic reviews on metabolomics and dementia by summarizing the prospective associations between metabolites in prediagnostic biological samples with dementia risk. Our review discovered additional metabolite markers associated with the onset of developing dementia and may help aid in the understanding of dementia etiology. The protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (https://www.crd.york.ac.uk/prospero/; registration ID: CRD42022357521).

Life course plasma metabolomic signatures of genetic liability to Alzheimer's disease

Mechanisms through which most known Alzheimer's disease (AD) loci operate to increase AD risk remain unclear. Although Apolipoprotein E (APOE) is known to regulate lipid homeostasis, the effects of broader AD genetic liability on non-lipid metabolites remain unknown, and the earliest ages at which metabolic perturbations occur and how these change over time are yet to be elucidated. We examined the effects of AD genetic liability on the plasma metabolome across the life course. Using a reverse Mendelian randomization framework in two population-based cohorts [Avon Longitudinal Study of Parents and Children (ALSPAC, n = 5648) and UK Biobank (n ≤ 118,466)], we estimated the effects of genetic liability to AD on 229 plasma metabolites, at seven different life stages, spanning 8 to 73 years. We also compared the specific effects of APOE ε4 and APOE ε2 carriage on metabolites. In ALSPAC, AD genetic liability demonstrated the strongest positive associations with cholesterol-related traits, with similar magnitudes of association observed across all age groups including in childhood. In UK Biobank, the effect of AD liability on several lipid traits decreased with age. Fatty acid metabolites demonstrated positive associations with AD liability in both cohorts, though with smaller magnitudes than lipid traits. Sensitivity analyses indicated that observed effects are largely driven by the strongest AD instrument, APOE, with many contrasting effects observed on lipids and fatty acids for both ε4 and ε2 carriage. Our findings indicate pronounced effects of the ε4 and ε2 genetic variants on both pro- and anti-atherogenic lipid traits and sphingomyelins, which begin in childhood and either persist into later life or appear to change dynamically.

Investigating metabolic dysregulation in serum of triple transgenic Alzheimer's disease male mice: implications for pathogenesis and potential biomarkers

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease that lacks convenient and accessible peripheral blood diagnostic markers and effective drugs. Metabolic dysfunction is one of AD risk factors, which leaded to alterations of various metabolites in the body. Pathological changes of the brain can be reflected in blood metabolites that are expected to explain the disease mechanisms or be candidate biomarkers. The aim of this study was to investigate the changes of targeted metabolites within peripheral blood of AD mouse model, with the purpose of exploring the disease mechanism and potential biomarkers. Targeted metabolomics was used to quantify 256 metabolites in serum of triple transgenic AD (3 × Tg-AD) male mice. Compared with controls, 49 differential metabolites represented dysregulation in purine, pyrimidine, tryptophan, cysteine and methionine and glycerophospholipid metabolism. Among them, adenosine, serotonin, N-acetyl-5-hydroxytryptamine, and acetylcholine play a key role in regulating neural transmitter network. The alteration of S-adenosine-L-homocysteine, S-adenosine-L-methionine, and trimethylamine-N-oxide in AD mice serum can served as indicator of AD risk. The results revealed the changes of metabolites in serum, suggesting that metabolic dysregulation in periphery in AD mice may be related to the disturbances in neuroinhibition, the serotonergic system, sleep function, the cholinergic system, and the gut microbiota. This study provides novel insights into the dysregulation of several key metabolites and metabolic pathways in AD, presenting potential avenues for future research and the development of peripheral biomarkers.

Multi-Omic Blood Biomarkers as Dynamic Risk Predictors in Late-Onset Alzheimer's Disease

Late-onset Alzheimer's disease is the leading cause of dementia worldwide, accounting for a growing burden of morbidity and mortality. Diagnosing Alzheimer's disease before symptoms are established is clinically challenging, but would provide therapeutic windows for disease-modifying interventions. Blood biomarkers, including genetics, proteins and metabolites, are emerging as powerful predictors of Alzheimer's disease at various timepoints within the disease course, including at the preclinical stage. In this review, we discuss recent advances in such blood biomarkers for determining disease risk. We highlight how leveraging polygenic risk scores, based on genome-wide association studies, can help stratify individuals along their risk profile. We summarize studies analyzing protein biomarkers, as well as report on recent proteomic- and metabolomic-based prediction models. Finally, we discuss how a combination of multi-omic blood biomarkers can potentially be used in memory clinics for diagnosis and to assess the dynamic risk an individual has for developing Alzheimer's disease dementia.

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.

Lysophosphatidylcholines are associated with P-tau181 levels in early stages of Alzheimer's Disease

Background

We profiled circulating plasma metabolites to identify systemic biochemical changes in clinical and biomarker-assisted diagnosis of Alzheimer's disease (AD).

Methods

We used an untargeted approach with liquid chromatography coupled to high-resolution mass spectrometry to measure small molecule plasma metabolites from 150 clinically diagnosed AD patients and 567 age-matched healthy elderly of Caribbean Hispanic ancestry. Plasma biomarkers of AD were measured including P-tau181, Aβ40, Aβ42, total-tau, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP). Association of individual and co-abundant modules of metabolites were tested with clinical diagnosis of AD, as well as biologically-defined AD pathological process based on P-tau181 and other biomarker levels.

Results

Over 6000 metabolomic features were measured with high accuracy. First principal component (PC) of lysophosphatidylcholines (lysoPC) that bind to or interact with docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and arachidonic acid (AHA) was associated with decreased risk of AD (OR = 0.91 [0.89-0.96], p = 2e-04). Association was restricted to individuals without an APOE ε4 allele (OR = 0.89 [0.84-0.94], p = 8.7e-05). Among individuals carrying at least one APOE ε4 allele, PC4 of lysoPCs moderately increased risk of AD (OR = 1.37 [1.16-1.6], p = 1e-04). Essential amino acids including tyrosine metabolism pathways were enriched among metabolites associated with P-tau181 levels and heparan and keratan sulfate degradation pathways were associated with Aβ42/Aβ40 ratio.

Conclusions

Unbiased metabolic profiling can identify critical metabolites and pathways associated with β-amyloid and phosphotau pathology. We also observed an APOE-ε4 dependent association of lysoPCs with AD and biologically based diagnostic criteria may aid in the identification of unique pathogenic mechanisms.

Decanoylcarnitine Inhibits Triple-Negative Breast Cancer Progression via Mmp9 in an Intermittent Fasting Obesity Mouse

Purpose: Treatment of triple-negative breast cancer (TNBC) remains challenging. Intermittent fasting (IF) has emerged as a promising approach to improve metabolic health of various metabolic disorders. Clinical studies indicate IF is essential for TNBC progression. However, the molecular mechanisms underlying metabolic remodeling in regulating IF and TNBC progression are still unclear. Methods: In this study, we utilized a robust mouse model of TNBC and exposed subjects to a high-fat diet (HFD) with IF to explore its impact on the metabolic reprogramming linked to cancer progression. To identify crucial serum metabolites and signaling events, we utilized targeted metabolomics and RNA sequencing (RNA-seq). Furthermore, we conducted immunoblotting, real-time quantitative polymerase chain reaction (RT-qPCR), cell migration assays, lentivirus-mediated Mmp9 overexpression, and Mmp9 inhibitor experiments to elucidate the role of decanoylcarnitine/Mmp9 in TNBC cell migration. Results: Our observations indicate that IF exerts notable inhibitory effects on both the proliferation and cancer metastasis. Utilizing targeted metabolomics and RNA-seq, we initially identified pivotal serum metabolites and signaling events in the progression of TNBC. Among the 349 serum metabolites identified, decanoylcarnitine was picked out to inhibit TNBC cell proliferation and migration. RNA-seq analysis of TNBC cells treated with decanoylcarnitine revealed its suppressive effects on extracellular matrix-related protein components, with a notable reduction observed in Mmp9. Further investigations confirmed that decanoylcarnitine could inhibit Mmp9 expression in TNBC cells, primary tumors, lung, and liver metastasis tissues. Mmp9 overexpression abolished the inhibitory effect of decanoylcarnitine on cell migration. Conclusion: This study pioneers the exploration of IF intervention and the role of decanoylcarnitine/Mmp9 in the progression of TNBC in obese mice, enhancing our comprehension of the potential roles of various dietary patterns in the process of cancer treatment.

Learning from vertically distributed data across multiple sites: An efficient privacy-preserving algorithm for Cox proportional hazards model with variable selection

OBJECTIVE

To develop a lossless distributed algorithm for regularized Cox proportional hazards model with variable selection to support federated learning for vertically distributed data.

METHODS

We propose a novel distributed algorithm for fitting regularized Cox proportional hazards model when data sharing among different data providers is restricted. Based on cyclical coordinate descent, the proposed algorithm computes intermediary statistics by each site and then exchanges them to update the model parameters in other sites without accessing individual patient-level data. We evaluate the performance of the proposed algorithm with (1) a simulation study and (2) a real-world data analysis predicting the risk of Alzheimer's dementia from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). Moreover, we compared the performance of our method with existing privacy-preserving models.

RESULTS

Our algorithm achieves privacy-preserving variable selection for time-to-event data in the vertically distributed setting, without degradation of accuracy compared with a centralized approach. Simulation demonstrates that our algorithm is highly efficient in analyzing high-dimensional datasets. Real-world data analysis reveals that our distributed Cox model yields higher accuracy in predicting the risk of Alzheimer's dementia than the conventional Cox model built by each data provider without data sharing. Moreover, our algorithm is computationally more efficient compared with existing privacy-preserving Cox models with or without regularization term.

CONCLUSION

The proposed algorithm is lossless, privacy-preserving and highly efficient to fit regularized Cox model for vertically distributed data. It provides a suitable and convenient approach for modeling time-to-event data in a distributed manner.

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.

Gut microbiota-driven metabolic alterations reveal gut-brain communication in Alzheimer's disease model mice

The gut microbiota (GM) and its metabolites affect the host nervous system and are involved in the pathogeneses of various neurological diseases. However, the specific GM alterations under pathogenetic pressure and their contributions to the "microbiota - metabolite - brain axis" in Alzheimer's disease (AD) remain unclear. Here, we investigated the GM and the fecal, serum, cortical metabolomes in APP/PS1 and wild-type (WT) mice, revealing distinct hub bacteria in AD mice within scale-free GM networks shared by both groups. Moreover, we identified diverse peripheral - central metabolic landscapes between AD and WT mice that featured bile acids (e.g. deoxycholic and isodeoxycholic acid) and unsaturated fatty acids (e.g. 11Z-eicosenoic and palmitoleic acid). Machine-learning models revealed the relationships between the differential/hub bacteria and these metabolic signatures from the periphery to the brain. Notably, AD-enriched Dubosiella affected AD occurrence via cortical palmitoleic acid and vice versa. Considering the transgenic background of the AD mice, we propose that Dubosiella enrichment impedes AD progression via the synthesis of palmitoleic acid, which has protective properties against inflammation and metabolic disorders. We identified another association involving fecal deoxycholic acid-mediated interactions between the AD hub bacteria Erysipelatoclostridium and AD occurrence, which was corroborated by the correlation between deoxycholate levels and cognitive scores in humans. Overall, this study elucidated the GM network alterations, contributions of the GM to peripheral - central metabolic landscapes, and mediatory roles of metabolites between the GM and AD occurrence, thus revealing the critical roles of bacteria in AD pathogenesis and gut - brain communications under pathogenetic pressure.

Comparing Symmetric Dimethylarginine and Amyloid-β42 as Predictors of Alzheimer's Disease Development

Background

Physicians may soon be able to diagnose Alzheimer's disease (AD) in its early stages using fluid biomarkers like amyloid. However, it is acknowledged that additional biomarkers need to be characterized which would facilitate earlier monitoring of AD pathogenesis.

Objective

To determine if a potential novel inflammation biomarker for AD, symmetric dimethylarginine, has utility as a baseline serum biomarker for discriminating prodromal AD from cognitively unimpaired controls in comparison to cerebrospinal fluid amyloid-β42 (Aβ42).

Methods

Data including demographics, magnetic resonance imaging and fluorodeoxyglucose-positron emission tomography scans, Mini-Mental State Examination and Functional Activities Questionnaire scores, and biomarker concentrations were obtained from the Alzheimer's Disease Neuroimaging Initiative for a total of 146 prodromal AD participants and 108 cognitively unimpaired controls.

Results

Aβ42 (p = 0.65) and symmetric dimethylarginine (p = 0.45) were unable to predict age-matched cognitively unimpaired controls and prodromal AD participants. Aβ42 was negatively associated with regional brain atrophy and hypometabolism as well as cognitive and functional decline in cognitively unimpaired control participants (p < 0.05) that generally decreased in time. There were no significant associations between Aβ42 and symmetric dimethylarginine with imaging or neurocognitive biomarkers in prodromal AD patients.

Conclusions

Correlations were smaller between Aβ42 and neuropathological biomarkers over time and were absent in prodromal AD participants, suggesting a plateau effect dependent on age and disease stage. Evidence supporting symmetric dimethylarginine as a novel biomarker for AD as a single measurement was not found.

Metabolic profiling of Alzheimer's disease: Untargeted metabolomics analysis of plasma samples

Alzheimer's disease (AD) is often not recognized or is diagnosed very late, which significantly reduces the effectiveness of available pharmacological treatments. Metabolomic analyzes have great potential for improving existing knowledge about the pathogenesis and etiology of AD and represent a novel approach towards discovering biomarkers that could be used for diagnosis, prognosis, and therapy monitoring. In this study, we applied the untargeted metabolomic approach to investigate the changes in biochemical pathways related to AD pathology. We used gas chromatography and liquid chromatography coupled to mass spectrometry (GC-MS and LC-MS, respectively) to identify metabolites whose levels have changed in subjects with AD diagnosis (N = 40) compared to healthy controls (N = 40) and individuals with mild cognitive impairment (MCI, N = 40). The GC-MS identified significant differences between groups in levels of metabolites belonging to the classes of benzene and substituted derivatives, carboxylic acids and derivatives, fatty acyls, hydroxy acids and derivatives, keto acids and derivatives, and organooxygen compounds. Most of the compounds identified by the LC-MS were various fatty acyls, glycerolipids and glycerophospholipids. All of these compounds were decreased in AD patients and in subjects with MCI compared to healthy controls. The results of the study indicate disturbed metabolism of lipids and amino acids and an imbalance of metabolites involved in energy metabolism in individuals diagnosed with AD, compared to healthy controls and MCI subjects.

Circulating metabolites and risk of incident dementia: A prospective cohort study

Identifying circulating metabolites associated with dementia, cognition, and brain volume may improve the understanding of dementia pathogenesis and provide novel insights for preventive and therapeutic interventions. This cohort study included a total of 87 885 participants (median follow-up of 9.1 years, 54% female) without dementia at baseline from the UK Biobank. A total of 249 plasma metabolites were measured using nuclear magnetic resonance spectroscopy at baseline. Cox proportional regression was used to examine the associations of each metabolite with incident dementia (cases = 1134), Alzheimer's disease (AD; cases = 488), and vascular dementia (VD; cases = 257) during follow-up. Dementia-associated metabolites were further analyzed for association with cognitive deficits (N = 87 885) and brain volume (N = 7756) using logistic regression and linear regression. We identified 26 metabolites associated with incident dementia, of which 6 were associated with incident AD and 5 were associated with incident VD. These 26 dementia-related metabolites were subfractions of intermediate-density lipoprotein, large low-density lipoprotein (L-LDL), small high-density lipoprotein (S-HDL), very-low-density lipoprotein, fatty acids, ketone bodies, citrate, glucose, and valine. Among them, the cholesterol percentage in L-LDL (L-LDL-C%) was associated with lower risk of AD (HR [95% CI] = 0.92 [0.87-0.97], p = 0.002), higher brain cortical (β = 0.047, p = 3.91 × 10-6 ), and hippocampal (β = 0.043, p = 1.93 × 10-4 ) volume. Cholesteryl ester-to-total lipid ratio in L-LDL (L-LDL-CE%) was associated with lower risk of AD (HR [95% CI] = 0.93 [0.90-0.96], p = 1.48 × 10-4 ), cognitive deficits (odds ratio = 0.98, p = 0.009), and higher hippocampal volume (β = 0.027, p = 0.009). Cholesteryl esters in S-HDL (S-HDL-CE) were associated with lower risk of VD (HR [95% CI] = 0.81 [0.71-0.93], p = 0.002), but not AD. Taken together, circulating levels of L-LDL-CE% and L-LDL-C% were robustly associated with risk of AD and AD phenotypes, but not with VD. S-HDL-CE was associated with lower risk of VD, but not with AD or AD phenotypes. These metabolites may play a role in the advancement of future intervention trials. Additional research is necessary to gain a complete comprehension of the molecular mechanisms behind these associations.

An arrayed CRISPR knockout screen identifies genetic regulators of GLUT1 expression

Glucose, a primary fuel source under homeostatic conditions, is transported into cells by membrane transporters such as glucose transporter 1 (GLUT1). Due to its essential role in maintaining energy homeostasis, dysregulation of GLUT1 expression and function can adversely affect many physiological processes in the body. This has implications in a wide range of disorders such as Alzheimer's disease (AD) and several types of cancers. However, the regulatory pathways that govern GLUT1 expression, which may be altered in these diseases, are poorly characterized. To gain insight into GLUT1 regulation, we performed an arrayed CRISPR knockout screen using Caco-2 cells as a model cell line. Using an automated high content immunostaining approach to quantify GLUT1 expression, we identified more than 300 genes whose removal led to GLUT1 downregulation. Many of these genes were enriched along signaling pathways associated with G-protein coupled receptors, particularly the rhodopsin-like family. Secondary hit validation confirmed that removal of select genes, or modulation of the activity of a corresponding protein, yielded changes in GLUT1 expression. Overall, this work provides a resource and framework for understanding GLUT1 regulation in health and disease.

Cardiovascular Disease and Alzheimer's Disease: The Heart-Brain Axis

Cardiovascular disease (CVD) remains one of the leading causes of morbidity and mortality in aging adults across the United States. Prior studies indicate that the presence of atherosclerosis, the pathogenic basis of CVD, is linked with dementias. Alzheimer's disease (AD) and AD-related dementias are a major public health challenge in the United States. Recent studies indicate that ≈3.7 million Americans ≥65 years of age had clinical AD in 2017, with projected increases to 9.3 million by 2060. Treatment options for AD remain limited. Development of disease-modifying therapies are challenging due, in part, to the long preclinical window of AD. The preclinical incubation period of AD starts in midlife, providing a critical window for identification and optimization of AD risk factors. Studies link AD with CVD risk factors such as hypertension, inflammation, and dyslipidemia. Both AD and CVD are progressive diseases with decades-long development periods. CVD can clinically manifest several years earlier than AD, making CVD and its risk factors a potential predictor of future AD. The current review focuses on the state of literature on molecular and metabolic pathways modulating the heart-brain axis underlying the potential association of midlife CVD risk factors and their effect on AD and related dementias. Further, we explore potential CVD/dementia preventive strategies during the window of opportunity in midlife and the future of research in the field in the multiomics and novel biomarker use era.

An integrated neuroimaging-omics approach for the gut-brain communication pathways in Alzheimer's disease

A key role of the gut microbiota in the pathogenesis of neurodegenerative diseases, such as Alzheimer's disease (AD), has been identified over the past decades. Increasing clinical and preclinical evidence implicates that there is bidirectional communication between the gut microbiota and the central nervous system (CNS), which is also known as the microbiota-gut-brain axis. Nevertheless, current knowledge on the interplay between gut microbiota and the brain remains largely unclear. One of the primary mediating factors by which the gut microbiota interacts with the host is peripheral metabolites, including blood or gut-derived metabolites. However, mechanistic knowledge about the effect of the microbiome and metabolome signaling on the brain is limited. Neuroimaging techniques, such as multi-modal magnetic resonance imaging (MRI), and fluorodeoxyglucose-positron emission tomography (FDG-PET), have the potential to directly elucidate brain structural and functional changes corresponding with alterations of the gut microbiota and peripheral metabolites in vivo. Employing a combination of gut microbiota, metabolome, and advanced neuroimaging techniques provides a future perspective in illustrating the microbiota-gut-brain pathway and further unveiling potential therapeutic targets for AD treatments.

Metabolomic Signatures of Alzheimer's Disease Indicate Brain Region-Specific Neurodegenerative Progression

Pathological mechanisms contributing to Alzheimer's disease (AD) are still elusive. Here, we identified the metabolic signatures of AD in human post-mortem brains. Using 1H NMR spectroscopy and an untargeted metabolomics approach, we identified (1) metabolomic profiles of AD and age-matched healthy subjects in post-mortem brain tissue, and (2) region-common and region-unique metabolome alterations and biochemical pathways across eight brain regions revealed that BA9 was the most affected. Phenylalanine and phosphorylcholine were mainly downregulated, suggesting altered neurotransmitter synthesis. N-acetylaspartate and GABA were upregulated in most regions, suggesting higher inhibitory activity in neural circuits. Other region-common metabolic pathways indicated impaired mitochondrial function and energy metabolism, while region-unique pathways indicated oxidative stress and altered immune responses. Importantly, AD caused metabolic changes in brain regions with less well-documented pathological alterations that suggest degenerative progression. The findings provide a new understanding of the biochemical mechanisms of AD and guide biomarker discovery for personalized risk prediction and diagnosis.

Lysophosphatidylcholines are associated with P-tau181 levels in early stages of Alzheimer's Disease

Background

We investigated systemic biochemical changes in Alzheimer's disease (AD) by investigating the relationship between circulating plasma metabolites and both clinical and biomarker-assisted diagnosis of AD.

Methods

We used an untargeted approach with liquid chromatography coupled to high-resolution mass spectrometry to measure exogenous and endogenous small molecule metabolites in plasma from 150 individuals clinically diagnosed with AD and 567 age-matched elderly without dementia of Caribbean Hispanic ancestry. Plasma biomarkers of AD were also measured including P-tau181, Aβ40, Aβ42, total tau, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP). Association of individual and co-expressed modules of metabolites were tested with the clinical diagnosis of AD, as well as biologically-defined AD pathological process based on P-tau181 and other biomarker levels.

Results

Over 4000 metabolomic features were measured with high accuracy. First principal component (PC) of lysophosphatidylcholines (lysoPC) that bind to or interact with docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and arachidonic acid (AHA) was associated with decreased risk of AD (OR=0.91 [0.89-0.96], p=2e-04). Restricted to individuals without an APOE ε4 allele (OR=0.89 [0.84-0.94], p= 8.7e-05), the association remained. Among individuals carrying at least one APOE ε4 allele, PC4 of lysoPCs moderately increased risk of AD (OR=1.37 [1.16-1.6], p=1e-04). Essential amino acids including tyrosine metabolism pathways were enriched among metabolites associated with P-tau181 levels and heparan and keratan sulfate degradation pathways were associated with Aβ42/Aβ40 ratio reflecting different pathways enriched in early and middle stages of disease.

Conclusions

Our findings indicate that unbiased metabolic profiling can identify critical metabolites and pathways associated with β-amyloid and phosphotau pathology. We also observed an APOE ε4 dependent association of lysoPCs with AD and that biologically-based diagnostic criteria may aid in the identification of unique pathogenic mechanisms.

Cerebrospinal fluid metabolite alterations in patients with different etiologies, diagnoses, and prognoses of disorders of consciousness

INTRODUCTION

Medical management of disorders of consciousness (DoC) is a growing issue imposing a major burden on families and societies. Recovery rates vary widely among patients with DoC, and recovery predictions strongly influence decisions on medical care. However, the specific mechanisms underlying different etiologies, consciousness levels, and prognoses are still unclear.

METHODS

We analyzed the comprehensive cerebrospinal fluid (CSF) metabolome through liquid chromatography-mass spectrometry. Metabolomic analyses were used to identify the metabolic differences between patients with different etiologies, diagnoses, and prognoses.

RESULTS

We found that the CSF levels of multiple acylcarnitines were lower in patients with traumatic DoC, suggesting mitochondrial function preservation in the CNS, which might contribute to the better consciousness outcomes of these patients. Metabolites related to glutamate and GABA metabolism were altered and showed a good ability to distinguish the patients in the minimally conscious state and the vegetative state. Moreover, we identified 8 phospholipids as potential biomarkers to predict the recovery of consciousness.

CONCLUSIONS

Our findings shed light on the differences in physiological activities underlying DoC with different etiologies and identified some potential biomarkers used for DoC diagnosis and prognosis.

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

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Metabolic changes in the plasma of mild Alzheimer's disease patients treated with Hachimijiogan

Background: Alzheimer's disease (AD), the most prevalent form of dementia, is a debilitating, progressive neurodegeneration. Amino acids play a wide variety of physiological and pathophysiological roles in the nervous system, and their levels and disorders related to their synthesis have been related to cognitive impairment, the core feature of AD. Our previous multicenter trial showed that hachimijiogan (HJG), a traditional Japanese herbal medicine (Kampo), has an adjuvant effect for Acetylcholine estelase inhibitors (AChEIs) and that it delays the deterioration of the cognitive dysfunction of female patients with mild AD. However, there are aspects of the molecular mechanism(s) by which HJG improves cognitive dysfunction that remain unclear. Objectives: To elucidate through metabolomic analysis the mechanism(s) of HJG for mild AD based on changes in plasma metabolites. Methods: Sixty-seven patients with mild AD were randomly assigned to either an HJG group taking HJG extract 7.5 g/day in addition to AChEI or to a control group treated only with AChEI (HJG:33, Control:34). Blood samples were collected before, 3 months, and 6 months after the first drug administration. Comprehensive metabolomic analyses of plasma samples were done by optimized LC-MS/MS and GC-MS/MS methods. The web-based software MetaboAnalyst 5.0 was used for partial least square-discriminant analysis (PLS-DA) to visualize and compare the dynamics of changes in the concentrations of the identified metabolites. Results: The VIP (Variable Importance in Projection) score of the PLS-DA analysis of female participants revealed a significantly higher increase in plasma metabolite levels after HJG administration for 6 months than was seen in the control group. In univariate analysis, the aspartic acid level of female participants showed a significantly higher increase from baseline after HJG administration for 6 months when compared with the control group. Conclusion: Aspartic acid was a major contributor to the difference between the female HJG and control group participants of this study. Several metabolites were shown to be related to the mechanism of HJG effectiveness for mild AD.

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

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

Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing

Molecular subtyping of brain tissue provides insights into the heterogeneity of common neurodegenerative conditions, such as Alzheimer's disease. However, existing subtyping studies have mostly focused on single data modalities and only those individuals with severe cognitive impairment. To address these gaps, we applied similarity network fusion, a method capable of integrating multiple high-dimensional multi-omic data modalities simultaneously, to an elderly sample spanning the full spectrum of cognitive ageing trajectories. We analyzed human frontal cortex brain samples characterized by five omic modalities: bulk RNA sequencing (18 629 genes), DNA methylation (53 932 CpG sites), histone acetylation (26 384 peaks), proteomics (7737 proteins) and metabolomics (654 metabolites). Similarity network fusion followed by spectral clustering was used for subtype detection, and subtype numbers were determined by Eigen-gap and rotation cost statistics. Normalized mutual information determined the relative contribution of each modality to the fused network. Subtypes were characterized by associations with 13 age-related neuropathologies and cognitive decline. Fusion of all five data modalities (n = 111) yielded two subtypes (n S1 = 53, n S2 = 58), which were nominally associated with diffuse amyloid plaques; however, this effect was not significant after correction for multiple testing. Histone acetylation (normalized mutual information = 0.38), DNA methylation (normalized mutual information = 0.18) and RNA abundance (normalized mutual information = 0.15) contributed most strongly to this network. Secondary analysis integrating only these three modalities in a larger subsample (n = 513) indicated support for both three- and five-subtype solutions, which had significant overlap, but showed varying degrees of internal stability and external validity. One subtype showed marked cognitive decline, which remained significant even after correcting for tests across both three- and five-subtype solutions (p Bonf = 5.9 × 10-3). Comparison to single-modality subtypes demonstrated that the three-modal subtypes were able to uniquely capture cognitive variability. Comprehensive sensitivity analyses explored influences of sample size and cluster number parameters. We identified highly integrative molecular subtypes of ageing derived from multiple high dimensional, multi-omic data modalities simultaneously. Fusing RNA abundance, DNA methylation, and histone acetylation measures generated subtypes that were associated with cognitive decline. This work highlights the potential value and challenges of multi-omic integration in unsupervised subtyping of post-mortem brain.

Causal Relationship Between Basal Metabolic Rate and Alzheimer's Disease: A Bidirectional Two-sample Mendelian Randomization Study

INTRODUCTION

Objective observational studies have shown that basal metabolic rate (BMR) decreases in patients with Alzheimer's disease (AD), but the causal relationship between BMR and AD has not been established. We determined the causal relationship between BMR and AD by two-way Mendelian randomization (MR) and investigated the impact of factors associated with BMR on AD.

METHODS

We obtained BMR (n = 454,874) and AD from a large genome-wide association study (GWAS) database (21,982 patients with AD, 41,944 controls). The causal relationship between AD and BMR was investigated using two-way MR. Additionally, we identified the causal relationship between AD and factors related with BMR, hyperthyroidism (hy/thy) and type 2 diabetes (T2D), height and weight.

RESULTS

BMR had a causal relationship with AD [451 single nucleotide polymorphisms (SNPs), odds ratio (OR) 0.749, 95% confidence intervals (CIs) 0.663-0.858, P = 2.40E-03]. There was no causal relationship between hy/thy or T2D and AD (P > 0.05). The bidirectional MR showed that there was also a causal relationship between AD and BMR (OR 0.992, Cls 0.987-0.997, N_SNPs18, P = 1.50E-03). BMR, height and weight have a protective effect on AD. Based on MVMR analysis, we found that genetically determined height and weight may be adjusted by BMR to have a causal effect on AD, not height and weight themselves.

CONCLUSION

Our study showed that higher BMR reduced the risk of AD, and patients with AD had a lower BMR. Because of a positive correlation with BMR, height and weight may have a protective effect on AD. The two metabolism-related diseases, hy/thy and T2D, had no causal relationship with AD.

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

BACKGROUND AND AIMS

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Integrated Lipidomics and Network Pharmacology Reveal Mechanism of Memory Impairment Improvement by Yuanzhi San

Memory impairment (MI) is caused by a variety of causes, endangering human health. Yuanzhi San (YZS) is a common prescription used for the treatment of MI, but its mechanism of action needs further exploration. The purpose of this study was to investigate this mechanism through lipidomics and network pharmacology. Sprague Dawley (SD) rats were divided randomly into the normal, model, and YZS groups. The rats were gavaged with aluminum chloride (200 mg/kg) and intraperitoneally injected with D-galactose (400 mg/kg) every day for 60 days, except for the normal group. From the 30th day, YZS (13.34 g/kg) was gavaged once a day to the rats in the YZS group. Post-YZS treatment, ultra-high-performance liquid chromatography-mass spectrometry (UHPLC/MS) analysis was implemented to conduct a lipidomics study in the hippocampus of rats with memory impairment induced by aluminum chloride and D-galactose. Eight differential metabolites were identified between the normal group and the model group, whereas between the model group and the YZS group, 20 differential metabolites were established. Metabolic pathway analysis was performed on the aforementioned lipid metabolites, all of which were involved in sphingolipid and glycerophospholipid metabolism. Furthermore, serum pharmacochemistry analysis of YZS was carried out at the early stage of our research, which discovered 62 YZS prototype components. The results of the network pharmacology analysis showed that they were related to 1030 genes, and 451 disease genes were related to MI. There were 73 intersections between the YZS and MI targets. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that these targets were closely related to the sphingolipid metabolic, calcium signaling, and other pathways. The integrated approach of lipidomics and network pharmacology was then focused on four major targets, including PHK2, GBA, SPTLC1, and AChE, as well as their essential metabolites (glucosylceramide, N-acylsphingosine, phosphatidylserine, phosphatidylcholine, and phosphatidylcholine) and pathways (sphingolipid, glycerophospholipid, and arachidonic acid metabolism). The significant affinity of the primary target for YZS was confirmed by molecular docking. The obtained results revealed that the combination of lipidomics and network pharmacology could be used to determine the effect of YZS on the MI biological network and metabolic state, and evaluate the drug efficacy of YZS and its related mechanisms of action.

Predictive metabolic networks reveal sex- and APOE genotype-specific metabolic signatures and drivers for precision medicine in Alzheimer's disease

INTRODUCTION

Late-onset Alzheimer's disease (LOAD) is a complex neurodegenerative disease characterized by multiple progressive stages, glucose metabolic dysregulation, Alzheimer's disease (AD) pathology, and inexorable cognitive decline. Discovery of metabolic profiles unique to sex, apolipoprotein E (APOE) genotype, and stage of disease progression could provide critical insights for personalized LOAD medicine.

METHODS

Sex- and APOE-specific metabolic networks were constructed based on changes in 127 metabolites of 656 serum samples from the Alzheimer's Disease Neuroimaging Initiative cohort.

RESULTS

Application of an advanced analytical platform identified metabolic drivers and signatures clustered with sex and/or APOE ɛ4, establishing patient-specific biomarkers predictive of disease state that significantly associated with cognitive function. Presence of the APOE ɛ4 shifts metabolic signatures to a phosphatidylcholine-focused profile overriding sex-specific differences in serum metabolites of AD patients.

DISCUSSION

These findings provide an initial but critical step in developing a diagnostic platform for personalized medicine by integrating metabolomic profiling and cognitive assessments to identify targeted precision therapeutics for AD patient subgroups through computational network modeling.

Combined metabolic activators improve metabolic functions in the animal models of neurodegenerative diseases

BACKGROUND

Neurodegenerative diseases (NDDs), including Alzheimer's disease (AD) and Parkinson's disease (PD), are associated with metabolic abnormalities. Integrative analysis of human clinical data and animal studies have contributed to a better understanding of the molecular and cellular pathways involved in the progression of NDDs. Previously, we have reported that the combined metabolic activators (CMA), which include the precursors of nicotinamide adenine dinucleotide and glutathione can be utilized to alleviate metabolic disorders by activating mitochondrial metabolism.

METHODS

We first analysed the brain transcriptomics data from AD patients and controls using a brain-specific genome-scale metabolic model (GEM). Then, we investigated the effect of CMA administration in animal models of AD and PD. We evaluated pathological and immunohistochemical findings of brain and liver tissues. Moreover, PD rats were tested for locomotor activity and apomorphine-induced rotation.

FINDINGS

Analysis of transcriptomics data with GEM revealed that mitochondrial dysfunction is involved in the underlying molecular pathways of AD. In animal models of AD and PD, we showed significant damage in the high-fat diet groups' brain and liver tissues compared to the chow diet. The histological analyses revealed that hyperemia, degeneration and necrosis in neurons were improved by CMA administration in both AD and PD animal models. These findings were supported by immunohistochemical evidence of decreased immunoreactivity in neurons. In parallel to the improvement in the brain, we also observed dramatic metabolic improvement in the liver tissue. CMA administration also showed a beneficial effect on behavioural functions in PD rats.

INTERPRETATION

Overall, we showed that CMA administration significantly improved behavioural scores in parallel with the neurohistological outcomes in the AD and PD animal models and is a promising treatment for improving the metabolic parameters and brain functions in NDDs.

Age and APOE affect L-carnitine system metabolites in the brain in the APOE-TR model

With age the apolipoprotein E (APOE) E4 allele (involved in lipid homeostasis) is associated with perturbation of bioenergetics pathways in Alzheimer's disease (AD). We therefore hypothesized that in aging mice APOE genotype would affect the L-carnitine system (central to lipid bioenergetics), in the brain and in the periphery. Using liquid chromatography-mass spectrometry, levels of L-carnitine and associated metabolites: γ-butyrobetaine (GBB), crotonobetaine, as well as acylcarnitines, were evaluated at 10-, 25-, and 50-weeks, in the brain and the periphery, in a targeted replacement mouse model of human APOE (APOE-TR). Aged APOE-TR mice were also orally administered 125 mg/kg of L-carnitine daily for 7 days followed by evaluation of brain, liver, and plasma L-carnitine system metabolites. Compared to E4-TR, an age-dependent increase among E2- and E3-TR mice was detected for medium- and long-chain acylcarnitines (MCA and LCA, respectively) within the cerebrovasculature and brain parenchyma. While following L-carnitine oral challenge, E4-TR mice had higher increases in the L-carnitine metabolites, GBB and crotonobetaine in the brain and a reduction of plasma to brain total acylcarnitine ratios compared to other genotypes. These studies suggest that with aging, the presence of the E4 allele may contribute to alterations in the L-carnitine bioenergetic system and to the generation of L-carnitine metabolites that could have detrimental effects on the vascular system. Collectively the E4 allele and aging may therefore contribute to AD pathogenesis through aging-related lipid bioenergetics as well as cerebrovascular dysfunctions.

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

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

APOE ε4 and Alzheimer's disease diagnosis associated differences in L-carnitine, GBB, TMAO, and acylcarnitines in blood and brain

BACKGROUND

The apolipoprotein E (APOE) ε4 allele, involved in fatty acid (FA) metabolism, is a major genetic risk factor for Alzheimer's disease (AD). This study examined the influence of APOE genotypes on blood and brain markers of the L-carnitine system, necessary for fatty acid oxidation (FAO), and their collective influence on the clinical and pathological outcomes of AD.

METHODS

L-carnitine, its metabolites γ-butyrobetaine (GBB) and trimethylamine-n-oxide (TMAO), and its esters (acylcarnitines) were analyzed in blood from predominantly White community/clinic-based individuals (n = 372) and in plasma and brain from the Religious Order Study (ROS) (n = 79) using liquid chromatography tandem mass spectrometry (LC-MS/MS).

FINDINGS

Relative to total blood acylcarnitines, levels of short chain acylcarnitines (SCAs) were higher whereas long chain acylcarnitines (LCAs) were lower in AD, which was observed pre-clinically in APOE ε4s. Plasma medium chain acylcarnitines (MCAs) were higher amongst cognitively healthy APOE ε2 carriers relative to other genotypes. Compared to their respective controls, elevated TMAO and lower L-carnitine and GBB were associated with AD clinical diagnosis and these differences were detected preclinically among APOE ε4 carriers. Plasma and brain GBB, TMAO, and acylcarnitines were also associated with post-mortem brain amyloid, tau, and cerebrovascular pathologies.

INTERPRETATION

Alterations in blood L-carnitine, GBB, TMAO, and acylcarnitines occur early in clinical AD progression and are influenced by APOE genotype. These changes correlate with post-mortem brain AD and cerebrovascular pathologies. Additional studies are required to better understand the role of the FAO disturbances in AD.

Early Diagnosis of Alzheimer's Disease with Blood Test; Tempting but Challenging

The increasing prevalence of Alzheimer's disease (AD) has led to a health crisis. According to official statistics, more than 55 million people globally have AD or other types of dementia, making it the sixth leading cause of death. It is still difficult to diagnose AD and there is no definitive diagnosis yet; post-mortem autopsy is still the only definite method. Moreover, clinical manifestations occur very late in the course of disease progression; therefore, profound irreversible changes have already occurred when the disease manifests. Studies have shown that in the preclinical stage of AD, changes in some biomarkers are measurable prior to any neurological damage or other symptoms. Hence, creating a reliable, fast, and affordable method capable of detecting AD in early stage has attracted the most attention. Seeking clinically applicable, inexpensive, less invasive, and much more easily accessible biomarkers for early diagnosis of AD, blood-based biomarkers (BBBs) seem to be an ideal option. This review is an inclusive report of BBBs that have been shown to be altered in the course of AD progression. The aim of this report is to provide comprehensive insight into the research status of early detection of AD based on BBBs.

Prior metabolite extraction fully preserves RNAseq quality and enables integrative multi-'omics analysis of the liver metabolic response to viral infection

Here, we provide an in-depth analysis of the usefulness of single-sample metabolite/RNA extraction for multi-'omics readout. Using pulverized frozen livers of mice injected with lymphocytic choriomeningitis virus (LCMV) or vehicle (Veh), we isolated RNA prior (RNA) or following metabolite extraction (MetRNA). RNA sequencing (RNAseq) data were evaluated for differential expression analysis and dispersion, and differential metabolite abundance was determined. Both RNA and MetRNA clustered together by principal component analysis, indicating that inter-individual differences were the largest source of variance. Over 85% of LCMV versus Veh differentially expressed genes were shared between extraction methods, with the remaining 15% evenly and randomly divided between groups. Differentially expressed genes unique to the extraction method were attributed to randomness around the 0.05 FDR cut-off and stochastic changes in variance and mean expression. In addition, analysis using the mean absolute difference showed no difference in the dispersion of transcripts between extraction methods. Altogether, our data show that prior metabolite extraction preserves RNAseq data quality, which enables us to confidently perform integrated pathway enrichment analysis on metabolomics and RNAseq data from a single sample. This analysis revealed pyrimidine metabolism as the most LCMV-impacted pathway. Combined analysis of genes and metabolites in the pathway exposed a pattern in the degradation of pyrimidine nucleotides leading to uracil generation. In support of this, uracil was among the most differentially abundant metabolites in serum upon LCMV infection. Our data suggest that hepatic uracil export is a novel phenotypic feature of acute infection and highlight the usefulness of our integrated single-sample multi-'omics approach.

Comprehensive metabolomics and lipidomics profiling uncovering neuroprotective effects of Ginkgo biloba L. leaf extract on Alzheimer's disease

Introduction: Ginkgo biloba L. leaf extract (GBLE) has been reported to be effective for alleviating cognitive and memory impairment in Alzheimer's disease (AD). Nevertheless, the potential mechanism remains unclear. Herein, this study aimed to explore the neuroprotective effects of GBLE on AD and elaborate the underlying therapeutic mechanism. Methods: Donepezil, the most widely prescribed drug for AD, was used as a positive control. An integrated metabolomics and lipidomics approach was adopted to characterize plasma metabolic phenotype of APP/PS1 double transgenic mice and describe the metabolomic and lipidomic fingerprint changes after GBLE intervention. The Morris water maze test and immunohistochemistry were applied to evaluate the efficacy of GBLE. Results: As a result, administration of GBLE significantly improved the cognitive function and alleviated amyloid beta (Aβ) deposition in APP/PS1 mice, showing similar effects to donepezil. Significant alterations were observed in metabolic signatures of APP/PS1 mice compared with wild type (WT) mice by metabolomic analysis. A total of 60 markedly altered differential metabolites were identified, including 28 lipid and lipid-like molecules, 13 organic acids and derivatives, 11 organic nitrogen compounds, and 8 other compounds, indicative of significant changes in lipid metabolism of AD. Further lipidomic profiling showed that the differential expressed lipid metabolites between APP/PS1 and WT mice mainly consisted of phosphatidylcholines, lysophosphatidylcholines, triglycerides, and ceramides. Taking together all the data, the plasma metabolic signature of APP/PS1 mice was primarily characterized by disrupted sphingolipid metabolism, glycerophospholipid metabolism, glycerolipid metabolism, and amino acid metabolism. Most of the disordered metabolites were ameliorated after GBLE treatment, 19 metabolites and 24 lipids of which were significantly reversely regulated (adjusted-p<0.05), which were considered as potential therapeutic targets of GBLE on AD. The response of APP/PS1 mice to GBLE was similar to that of donepezil, which significantly reversed the levels of 23 disturbed metabolites and 30 lipids. Discussion: Our data suggested that lipid metabolism was dramatically perturbed in the plasma of APP/PS1 mice, and GBLE might exert its neuroprotective effects by restoring lipid metabolic balance. This work provided a basis for better understanding the potential pathogenesis of AD and shed new light on the therapeutic mechanism of GBLE in the treatment of AD.

Microbiome and Metabolome Insights into the Role of the Gastrointestinal-Brain Axis in Parkinson's and Alzheimer's Disease: Unveiling Potential Therapeutic Targets

Neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome and metabolome studies have already revealed important insights into disease mechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the interplay between microbiota, metabolites, and neurodegeneration along the gastrointestinal-brain axis. We further illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.

Comparative Metagenomics and Metabolomes Reveals Abnormal Metabolism Activity Is Associated with Gut Microbiota in Alzheimer's Disease Mice

A common symptom in Alzheimer's disease (AD) is cognitive decline, of which the potential pathogenesis remains unclear. In order to understand the mechanism of gut microbiota in AD, it is necessary to clarify the relationship between gut microbiota and metabolites. Behavioral tests, pathological examination, metagenomics, and metabolomics were applied to analyze the difference of gut microbiota and metabolome between APP^swe/PS1^ΔE9 (PAP) mice with cognitive decline and age-matched controls, and their possible correlations. Our results showed that PAP mice and health mice had different structures of the bacterial communities in the gut. The abundances and diversities of the bacterial communities in health mice were higher than in PAP mice by metagenomics analysis. The abundances of Libanicoccus massiliensis, Paraprevotella clara, and Lactobacillus amylovorus were significantly increased in PAP mice, while the abundances of Turicibacter sanguinis, Dubosiella newyorkensis, and Prevotella oris were greatly reduced. Furthermore, PAP mice possessed peculiar metabolic phenotypes in stool, serum, and hippocampus relative to WT mice, as is demonstrated by alterations in neurotransmitters metabolism, lipid metabolism, aromatic amino acids metabolism, energy metabolism, vitamin digestion and absorption, and bile metabolism. Microbiota-host metabolic correlation analysis suggests that abnormal metabolism in stool, serum, and hippocampus of PAP mice may be modulated by the gut microbiota, especially T. sanguinis, D. newyorkensis, and P. oris. Therefore, abnormal metabolism activity is associated with gut microbiota in Alzheimer's disease mice. Our results imply that modifying host metabolism through targeting gut microbiota may be a novel and viable strategy for the prevention and treatment of AD in the future.

Clinical relevance of biomarkers, new therapeutic approaches, and role of post-translational modifications in the pathogenesis of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder that causes progressive loss of cognitive functions like thinking, memory, reasoning, behavioral abilities, and social skills thus affecting the ability of a person to perform normal daily functions independently. There is no definitive cure for this disease, and treatment options available for the management of the disease are not very effective as well. Based on histopathology, AD is characterized by the accumulation of insoluble deposits of amyloid beta (Aβ) plaques and neurofibrillary tangles (NFTs). Although several molecular events contribute to the formation of these insoluble deposits, the aberrant post-translational modifications (PTMs) of AD-related proteins (like APP, Aβ, tau, and BACE1) are also known to be involved in the onset and progression of this disease. However, early diagnosis of the disease as well as the development of effective therapeutic approaches is impeded by lack of proper clinical biomarkers. In this review, we summarized the current status and clinical relevance of biomarkers from cerebrospinal fluid (CSF), blood and extracellular vesicles involved in onset and progression of AD. Moreover, we highlight the effects of several PTMs on the AD-related proteins, and provide an insight how these modifications impact the structure and function of proteins leading to AD pathology. Finally, for disease-modifying therapeutics, novel approaches, and targets are discussed for the successful treatment and management of AD.

Associations of the Lipidome with Ageing, Cognitive Decline and Exercise Behaviours

One of the most recognisable features of ageing is a decline in brain health and cognitive dysfunction, which is associated with perturbations to regular lipid homeostasis. Although ageing is the largest risk factor for several neurodegenerative diseases such as dementia, a loss in cognitive function is commonly observed in adults over the age of 65. Despite the prevalence of normal age-related cognitive decline, there is a lack of effective methods to improve the health of the ageing brain. In light of this, exercise has shown promise for positively influencing neurocognitive health and associated lipid profiles. This review summarises age-related changes in several lipid classes that are found in the brain, including fatty acyls, glycerolipids, phospholipids, sphingolipids and sterols, and explores the consequences of age-associated pathological cognitive decline on these lipid classes. Evidence of the positive effects of exercise on the affected lipid profiles are also discussed to highlight the potential for exercise to be used therapeutically to mitigate age-related changes to lipid metabolism and prevent cognitive decline in later life.

Neurospecific Molecules Measured in Periphery in Humans: How Do They Correlate with the Brain Levels? A Systematic Review

Human brain state is usually estimated by brain-specific substances in peripheral tissues, but, for most analytes, a concordance between their content in the brain and periphery is unclear. In this systematic review, we summarized the investigated correlations in humans. PubMed was searched up to June 2022. We included studies measuring the same endogenous neurospecific analytes in the central nervous system and periphery in the same subjects. Not eligible were studies of cerebrospinal fluid, with significant blood–brain barrier disruption, of molecules with well-established blood-periphery concordance or measured in brain tumors. Seventeen studies were eligible. Four studies did not report on correlation and four revealed no significant correlation. Four molecules were examined twice. For BDNF, there was no correlation in both studies. For phenylalanine, glutamine, and glutamate, results were contradictory. Strong correlations were found for free tryptophan (r = 0.97) and translocator protein (r = 0.90). Thus, only for three molecules was there some certainty. BDNF in plasma or serum does not reflect brain content, whereas free tryptophan (in plasma) and translocator protein (in blood cells) can serve as peripheral biomarkers. We expect a breakthrough in the field with advanced in vivo metabolomic analyses, neuroimaging techniques, and blood assays for exosomes of brain origin.