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

Metabolic Profiling of Brain Tissue and Brain-Derived Extracellular Vesicles in Alzheimer's Disease

Alzheimer´s disease (AD) is the most frequent neurodegenerative disorder in the world and is characterised by the loss of memory and other cognitive functions. Metabolic changes associated with AD are important players in the development of the disease. However, the mechanism underlying these changes is still unknown. Extracellular vesicles (EVs) are nano-sized particles that play an important role in regulating pathophysiological processes and are a non-invasive manner to obtain information of the cell that is secreting them. The analysis of brain-derived EVs (bdEVs) will provide new insights in the metabolic processes associated with AD. To characterize bdEVs in AD, we optimised a method to isolate them from tissue of different brain regions, obtaining the highest enrichment in isolations from the temporal cortex. We performed unbiased untargeted metabolomics analysis on post-mortem human temporal cortex tissue and bdEVs from the same region of AD patients and healthy controls. Both, univariate and multivariate statistical analysis were used to determine the metabolites that influence the separation between AD patients and controls. Interestingly, a clear separation between control and AD groups was obtained with bdEVs, which allowed to select 12 relevant features by a validated PLS-DA model. Furthermore, comparison of tissue and bdEVs identified 68 common features. The pathway enrichment analysis of the common metabolites showed that the alanine, aspartate and glutamate pathway and the arginine, phenylalanine, tyrosine pathway were the most significant ones in the separation between the AD patients and controls. The phenylalanine, tyrosine and tryptophan pathway, still had a very high influence in the separation between groups, albeit not significant. Notably, some metabolites were identified for the first time in bdEVs. For example, the N-acetyl aspartic acid (NAA) metabolite present in bdEVs was suitable to differentiate AD patients from healthy controls. Furthermore, the analysis of the hippocampus, midbrain, temporal and entorhinal cortex and their respective bdEVs indicated that the metabolic profiles of different brain areas were distinct and showed some correlation between the metabolome of the tissue and its respective bdEVs. Thus, our study highlights the potential of bdEVs to understand the metabolic fingerprint associated with AD and their potential use as diagnostic and therapeutic targets.

The Incidence, Risk Factors, and Predictive Model of Obstructive Disease in Post-Tuberculosis Patients

Objective

To assess the incidence and risk factors of tuberculosis-associated obstructive pulmonary disease (TOPD) in individuals with treatment-naive pulmonary tuberculosis (PTB) and develop a predictive model to enhance its management.

Methods

The incidence of TOPD among patients with treatment-naive PTB in Xinjiang, China, was followed up for one year. Patient characteristics, such as demographics, medical histories, laboratory test results, lung radiological evidence, and pulmonary function, were collected upon hospital admission and throughout follow-up visits. Risk factors associated with TOPD were evaluated by multivariate logistic regression analysis, and then a predictive model was established using LASSO regression.

Results

Of the 159 included patients, 69 (43.4%) developed TOPD during the follow-up period. Multivariate regression analysis identified age, body mass index, ESR, and symptom duration as significant risk factors. Subsequently, a model formula was derived from these factors to predict TOPD. Utilizing a cut-off value of 0.435, the model demonstrated a sensitivity of 89% and a specificity of 83%.

Conclusion

In Xinjiang, the prevalence of TOPD appears notably high among treatment-naive PTB patients. Our findings, such as the risk factors and predictive model, may facilitate the early detection and improved interventions for TOPD among patients with PTB, potentially leading to better patient outcomes.

AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling

Alzheimer's disease (AD) presents a significant challenge in neurodegenerative research and clinical practice due to its complex etiology and progressive nature. The integration of artificial intelligence (AI) into the diagnosis, treatment, and prognostic modelling of AD holds promising potential to transform the landscape of dementia care. This review explores recent advancements in AI applications across various stages of AD management. In early diagnosis, AI-enhanced neuroimaging techniques, including MRI, PET, and CT scans, enable precise detection of AD biomarkers. Machine learning models analyze these images to identify patterns indicative of early cognitive decline. Additionally, AI algorithms are employed to detect genetic and proteomic biomarkers, facilitating early intervention. Cognitive and behavioral assessments have also benefited from AI, with tools that enhance the accuracy of neuropsychological tests and analyze speech and language patterns for early signs of dementia. Personalized treatment strategies have been revolutionized by AI-driven approaches. In drug discovery, virtual screening and drug repurposing, guided by predictive modelling, accelerate the identification of effective treatments. AI also aids in tailoring therapeutic interventions by predicting individual responses to treatments and monitoring patient progress, allowing for dynamic adjustment of care plans. Prognostic modelling, another critical area, utilizes AI to predict disease progression through longitudinal data analysis and risk prediction models. The integration of multi-modal data, combining clinical, genetic, and imaging information, enhances the accuracy of these predictions. Deep learning techniques are particularly effective in fusing diverse data types to uncover new insights into disease mechanisms and progression. Despite these advancements, challenges remain, including ethical considerations, data privacy, and the need for seamless integration of AI tools into clinical workflows. This review underscores the transformative potential of AI in AD management while highlighting areas for future research and development. By leveraging AI, the healthcare community can improve early diagnosis, personalize treatments, and predict disease outcomes more accurately, ultimately enhancing the quality of life for individuals with AD.

Risk of Alzheimer's disease and genetically predicted levels of 1400 plasma metabolites: a Mendelian randomization study

Alzheimer's disease (AD) is a metabolic disorder. Discovering the metabolic products involved in the development of AD may help not only in the early detection and prevention of AD but also in understanding its pathogenesis and treatment. This study investigated the causal association between the latest large-scale plasma metabolites (1091 metabolites and 309 metabolite ratios) and AD. Through the application of Mendelian randomization analysis methods such as inverse-variance weighted (IVW), MR-Egger, and weighted median models, 66 metabolites and metabolite ratios were identified as potentially having a causal association with AD, with 13 showing significant causal associations. During the replication validation phase, six metabolites and metabolite ratios were confirmed for their roles in AD: N-lactoyl tyrosine, argininate, and the adenosine 5'-monophosphate to flavin adenine dinucleotide ratio were found to exhibit protective effects against AD. In contrast, ergothioneine, piperine, and 1,7-dimethyluric acid were identified as contributing to an increased risk of AD. Among them, argininate showed a significant effect against AD. Replication and sensitivity analyses confirmed the robustness of these findings. Metabolic pathway analysis linked "Vitamin B6 metabolism" to AD risk. No genetic correlations were found, but colocalization analysis indicated potential AD risk elevation through top SNPs in APOE and PSEN2 genes. This provides novel insights into AD's etiology from a metabolomic viewpoint, suggesting both protective and risk metabolites.

Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed.

METHOD

Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis.

RESULTS

Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others.

CONCLUSIONS

This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.

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.

Metabolic phenotyping reveals an emerging role of ammonia abnormality in Alzheimer's disease

The metabolic implications in Alzheimer's disease (AD) remain poorly understood. Here, we conducted a metabolomics study on a moderately aging Chinese Han cohort (n = 1397; mean age 66 years). Conjugated bile acids, branch-chain amino acids (BCAAs), and glutamate-related features exhibited strong correlations with cognitive impairment, clinical stage, and brain amyloid-β deposition (n = 421). These features demonstrated synergistic performances across clinical stages and subpopulations and enhanced the differentiation of AD stages beyond demographics and Apolipoprotein E ε4 allele (APOE-ε4). We validated their performances in eight data sets (total n = 7685) obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) and Religious Orders Study and Memory and Aging Project (ROSMAP). Importantly, identified features are linked to blood ammonia homeostasis. We further confirmed the elevated ammonia level through AD development (n = 1060). Our findings highlight AD as a metabolic disease and emphasize the metabolite-mediated ammonia disturbance in AD and its potential as a signature and therapeutic target for AD.

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.

Serum Bile Acids Improve Prediction of Alzheimer's Progression in a Sex-Dependent Manner

Sex disparities in serum bile acid (BA) levels and Alzheimer's disease (AD) prevalence have been established. However, the precise link between changes in serum BAs and AD development remains elusive. Here, authors quantitatively determined 33 serum BAs and 58 BA features in 4 219 samples collected from 1 180 participants from the Alzheimer's Disease Neuroimaging Initiative. The findings revealed that these BA features exhibited significant correlations with clinical stages, encompassing cognitively normal (CN), early and late mild cognitive impairment, and AD, as well as cognitive performance. Importantly, these associations are more pronounced in men than women. Among participants with progressive disease stages (n = 660), BAs underwent early changes in men, occurring before AD. By incorporating BA features into diagnostic and predictive models, positive enhancements are achieved for all models. The area under the receiver operating characteristic curve improved from 0.78 to 0.91 for men and from 0.76 to 0.83 for women for the differentiation of CN and AD. Additionally, the key findings are validated in a subset of participants (n = 578) with cerebrospinal fluid amyloid-beta and tau levels. These findings underscore the role of BAs in AD progression, offering potential improvements in the accuracy of AD prediction.

Association of metabolic dysfunction with cognitive decline and Alzheimer's disease: A review of metabolomic evidence

The discovery of new biomarkers that can distinguish Alzheimer's disease (AD) from mild cognitive impairment (MCI) in the early stages will help to provide new diagnostic and therapeutic strategies and slow the transition from MCI to AD. Patients with AD may present with a concomitant metabolic disorder, such as diabetes, obesity, and dyslipidemia, as a risk factor for AD that may be involved in the onset of both AD pathology and cognitive impairment. Therefore, metabolite profiling, or metabolomics, can be very useful in diagnosing AD, developing new therapeutic targets, and evaluating both the course of treatment and the clinical course of the disease. In addition, studying the relationship between nutritional behavior and AD requires investigation of the role of conditions such as obesity, hypertension, dyslipidemia, and elevated glucose level. Based on this literature review, nutritional recommendations, including weight loss by reducing calorie and cholesterol intake and omega-3 fatty acid supplementation can prevent cognitive decline and dementia in the elderly. The underlying metabolic causes of the pathology and cognitive decline caused by AD and MCI are not well understood. In this review article, metabolomics biomarkers for diagnosis of AD and MCI and metabolic risk factors for cognitive decline in AD were evaluated.

Investigating the Underlying Mechanisms of Ardisia japonica Extract's Anti-Blood-Stasis Effect via Metabolomics and Network Pharmacology

OBJECTIVE

Our study aims to assess Ardisia japonica (AJ)'s anti-blood-stasis effect and its underlying action mechanisms.

METHODS

The primary components of AJ were determined using liquid chromatography-mass spectrometry (LC-MS). The blood stasis model was used to investigate the anti-blood-stasis effect of AJ extract. The underlying mechanisms of AJ against blood stasis were investigated via network pharmacology, molecular docking, and plasma non-targeted metabolomics.

RESULTS

In total, 94 compounds were identified from an aqueous extract of AJ, including terpenoids, phenylpropanoids, alkaloids, and fatty acyl compounds. In rats with blood stasis, AJ reduced the area of stasis, decreased the inflammatory reaction in the liver and lungs of rats, lowered the plasma viscosity, increased the index of erythrocyte deformability, and decreased the index of erythrocyte aggregation, suggesting that AJ has an anti-blood-stasis effect. Different metabolites were identified via plasma untargeted metabolomics, and it was found that AJ exerts its anti-blood-stasis effect by reducing inflammatory responses through the cysteine and methionine metabolism, linolenic acid metabolism, and sphingolipid metabolism. For the effect of AJ on blood stasis syndrome, the main active ingredients predicted via network pharmacology include sinensetin, galanin, isorhamnetin, kaempferol, wogonin, quercetin, and bergenin, and their targets were TP53, HSP90AA1, VEGFA, AKT1, EGFR, and PIK3CA that were mainly enriched in the PI3K/AKT and MAPK signaling pathways, which modulate the inflammatory response. Molecular docking was also performed, and the binding energies of these seven compounds to six proteins were less than -5, indicating that the chemical components bind to the target proteins.

CONCLUSIONS

This study suggests AJ effectively prevents blood stasis by reducing inflammation.

The association of circulating endocannabinoids with neuroimaging and blood biomarkers of neuro-injury

BACKGROUND

Preclinical studies highlight the importance of endogenous cannabinoids (endocannabinoids; eCBs) in neurodegeneration. Yet, prior observational studies focused on limited outcome measures and assessed only few eCB compounds while largely ignoring the complexity of the eCB system. We examined the associations of multiple circulating eCBs and eCB-like molecules with early markers of neurodegeneration and neuro-injury and tested for effect modification by sex.

METHODS

This exploratory cross-sectional study included a random sample of 237 dementia-free older participants from the Framingham Heart Study Offspring cohort who attended examination cycle 9 (2011-2014), were 65 years or older, and cognitively healthy. Forty-four eCB compounds were quantified in serum, via liquid chromatography high-resolution mass spectrometry. Linear regression models were used to examine the associations of eCB levels with brain MRI measures (i.e., total cerebral brain volume, gray matter volume, hippocampal volume, and white matter hyperintensities volume) and blood biomarkers of Alzheimer's disease and neuro-injury (i.e., total tau, neurofilament light, glial fibrillary acidic protein and Ubiquitin C-terminal hydrolase L1). All models were adjusted for potential confounders and effect modification by sex was examined.

RESULTS

Participants mean age was 73.3 ± 6.2 years, and 40% were men. After adjustment for potential confounders and correction for multiple comparisons, no statistically significant associations were observed between eCB levels and the study outcomes. However, we identified multiple sex-specific associations between eCB levels and the various study outcomes. For example, high linoleoyl ethanolamide (LEA) levels were related to decreased hippocampal volume among men and to increased hippocampal volume among women (β ± SE =  - 0.12 ± 0.06, p = 0.034 and β ± SE = 0.08 ± 0.04, p = 0.026, respectively).

CONCLUSIONS

Circulating eCBs may play a role in neuro-injury and may explain sex differences in susceptibility to accelerated brain aging. Particularly, our results highlight the possible involvement of eCBs from the N-acyl amino acids and fatty acid ethanolamide classes and suggest specific novel fatty acid compounds that may be implicated in brain aging. Furthermore, investigation of the eCBs contribution to neurodegenerative disease such as Alzheimer's disease in humans is warranted, especially with prospective study designs and among diverse populations, including premenopausal women.

Multiomics profiling of human plasma and cerebrospinal fluid reveals ATN-derived networks and highlights causal links in Alzheimer's disease

INTRODUCTION

This study employed an integrative system and causal inference approach to explore molecular signatures in blood and CSF, the amyloid/tau/neurodegeneration [AT(N)] framework, mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD), and genetic risk for AD.

METHODS

Using the European Medical Information Framework (EMIF)-AD cohort, we measured 696 proteins in cerebrospinal fluid (n = 371), 4001 proteins in plasma (n = 972), 611 metabolites in plasma (n = 696), and genotyped whole-blood (7,778,465 autosomal single nucleotide epolymorphisms, n = 936). We investigated associations: molecular modules to AT(N), module hubs with AD Polygenic Risk scores and APOE4 genotypes, molecular hubs to MCI conversion and probed for causality with AD using Mendelian randomization (MR).

RESULTS

AT(N) framework associated with protein and lipid hubs. In plasma, Proprotein Convertase Subtilisin/Kexin Type 7 showed evidence for causal associations with AD. AD was causally associated with Reticulocalbin 2 and sphingomyelins, an association driven by the APOE isoform.

DISCUSSION

This study reveals multi-omics networks associated with AT(N) and causal AD molecular candidates.

Seasonal Monitoring of Volatiles and Antioxidant Activity of Brown Alga Cladostephus spongiosus

Cladostephus spongiosus was harvested once a month during its growing season (from May to August) from the Adriatic Sea. Algal volatile organic compounds (VOCs) were obtained by headspace solid-phase microextraction (HS-SPME) and hydrodistillation (HD) and analysed by gas chromatography and mass spectrometry (GC-MS). The effects of air drying and growing season on VOCs were determined. Two different extraction methods (ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE)) were used to obtain ethanolic extracts of C. spongiosus. In addition, the seasonal antioxidant potential of the extracts was determined, and non-volatile compounds were identified from the most potent antioxidant extract. Aliphatic compounds (e.g., pentadecane) were predominantly found by HS-SPME/GC-MS. Hydrocarbons were more than twice as abundant in the dry samples (except in May). Aliphatic alcohols (e.g., hexan-1-ol, octan-1-ol, and oct-1-en-3-ol) were present in high percentages and were more abundant in the fresh samples. Hexanal, heptanal, nonanal, and tridecanal were also found. Aliphatic ketones (octan-3-one, 6-methylhept-5-en-2-one, and (E,Z)-octa-3,5-dien-2-one) were more abundant in the fresh samples. Benzene derivatives (e.g., benzyl alcohol and benzaldehyde) were dominant in the fresh samples from May and August. (E)-Verbenol and p-cymen-8-ol were the most abundant in dry samples in May. HD revealed aliphatic compounds (e.g., heptadecane, pentadecanal, (E)-heptadec-8-ene, (Z)-heptadec-3-ene), sesquiterpenes (germacrene D, epi-bicyclosesquiphellandrene, gleenol), diterpenes (phytol, pachydictyol A, (E)-geranyl geraniol, cembra-4,7,11,15-tetraen-3-ol), and others. Among them, terpenes were the most abundant (except for July). Seasonal variations in the antioxidant activity of the ethanolic extracts were evaluated via different assays. MAE extracts showed higher peroxyl radical inhibition activity from 55.1 to 74.2 µM TE (Trolox equivalents). The highest reducing activity (293.8 µM TE) was observed for the May sample. Therefore, the May MAE extract was analysed via high-performance liquid chromatography with high-resolution mass spectrometry and electrospray ionisation (UHPLC-ESI-HRMS). In total, 17 fatty acid derivatives, 9 pigments and derivatives, and 2 steroid derivatives were found. The highest content of pheophorbide a and fucoxanthin, as well as the presence of other pigment derivatives, could be related to the observed antioxidant activity.

Gut microbiota-based metabolites of Xiaoyao Pills (a typical Traditional Chinese medicine) ameliorate depression by inhibiting fatty acid amide hydrolase levels in brain

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicines (TCMs) are often prepared in oral dosage forms, making TCMs interact with gut microbiota after oral administration, which could affect the therapeutic effect of TCM. Xiaoyao Pills (XYPs) are a commonly used TCM in China to treat depression. The biological underpinnings, however, are still in its infancy due to its complex chemical composition.

AIM OF THE STUDY

The study aims to explore XYPs' underlying antidepressant mechanism from both in vivo and in vitro.

MATERIALS AND METHODS

XYPs were composed of 8 herbs, including the root of Bupleurum chinense DC., the root of Angelica sinensis (Oliv.) Diels, the root of Paeonia lactiflora Pall., the sclerotia of Poria cocos (Schw.) Wolf, the rhizome of Glycyrrhiza uralensis Fisch., the leaves of Mentha haplocalyx Briq., the rhizome of Atractylis lancea var. chinensis (Bunge) Kitam., and the rhizome of Zingiber officinale Roscoe, in a ratio of 5:5:5:5:4:1:5:5. The chronic unpredictable mild stress (CUMS) rat models were established. After that, the sucrose preference test (SPT) was carried out to evaluate if the rats were depressed. After 28 days of treatment, the forced swimming test and SPT were carried out to evaluate the antidepressant efficacy of XYPs. The feces, brain and plasma were taken out for 16SrRNA gene sequencing analysis, untargeted metabolomics and gut microbiota transformation analysis.

RESULTS

The results revealed multiple pathways affected by XYPs. Among them, the hydrolysis of fatty acids amide in brain decreased most significant via XYPs treatment. Moreover, the XYPs' metabolites which mainly derived from gut microbiota (benzoic acid, liquiritigenin, glycyrrhetinic acid and saikogenin D) were found in plasma and brain of CUMS rats and could inhibit the levels of FAAH in brain, which contributed to XYPs' antidepressant effect.

CONCLUSIONS

The potential antidepressant mechanism of XYPs by untargeted metabolomics combined with gut microbiota-transformation analysis was revealed, which further support the theory of gut-brain axis and provide valuable evidence of the drug discovery.

Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer's disease

BACKGROUND

Identifying blood-based signatures of brain health and preclinical pathology may offer insights into early disease mechanisms and highlight avenues for intervention. Here, we systematically profiled associations between blood metabolites and whole-brain volume, hippocampal volume, and amyloid-β status among participants of Insight 46-the neuroscience sub-study of the National Survey of Health and Development (NSHD). We additionally explored whether key metabolites were associated with polygenic risk for Alzheimer's disease (AD).

METHODS

Following quality control, levels of 1019 metabolites-detected with liquid chromatography-mass spectrometry-were available for 1740 participants at age 60-64. Metabolite data were subsequently clustered into modules of co-expressed metabolites using weighted coexpression network analysis. Accompanying MRI and amyloid-PET imaging data were present for 437 participants (age 69-71). Regression analyses tested relationships between metabolite measures-modules and hub metabolites-and imaging outcomes. Hub metabolites were defined as metabolites that were highly connected within significant (p_FDR < 0.05) modules or were identified as a hub in a previous analysis on cognitive function in the same cohort. Regression models included adjustments for age, sex, APOE genotype, lipid medication use, childhood cognitive ability, and social factors. Finally, associations were tested between AD polygenic risk scores (PRS), including and excluding the APOE region, and metabolites and modules that significantly associated (p_FDR < 0.05) with an imaging outcome (N = 1638).

RESULTS

In the fully adjusted model, three lipid modules were associated with a brain volume measure (p_FDR < 0.05): one enriched in sphingolipids (hippocampal volume: ß = 0.14, 95% CI = [0.055,0.23]), one in several fatty acid pathways (whole-brain volume: ß =  - 0.072, 95%CI = [- 0.12, - 0.026]), and another in diacylglycerols and phosphatidylethanolamines (whole-brain volume: ß =  - 0.066, 95% CI = [- 0.11, - 0.020]). Twenty-two hub metabolites were associated (p_FDR < 0.05) with an imaging outcome (whole-brain volume: 22; hippocampal volume: 4). Some nominal associations were reported for amyloid-β, and with an AD PRS in our genetic analysis, but none survived multiple testing correction.

CONCLUSIONS

Our findings highlight key metabolites, with functions in membrane integrity and cell signalling, that associated with structural brain measures in later life. Future research should focus on replicating this work and interrogating causality.

Effect of leisure activities on cognitive aging in older adults: A systematic review and meta-analysis

Abnormal cognitive aging in older adults is a growing public health problem. Previous studies showed inconsistent results pertaining to the effects of leisure activities on cognitive function in older adults. We conducted a systematic review and meta-analysis of published observational longitudinal studies to examine and synthesize the effects of leisure activities on cognitive function in older adults. MEDLINE, PubMed, EMBASE, PsycINFO (Ovid), CINAHL (EBSCO), and Web of Science databases were searched from January 2012 to January 2022. Relative risks (RRs) with 95% confidence intervals (CIs) were pooled using random-effects meta-analysis. Most studies found that leisure activities had a positive effect on cognitive function in older adults. The pooled RR for the effect of leisure activity on cognitive function was 0.77 (95% CI: 0.72-0.81, p < 0.01). The effects of leisure activities on cognitive function varied by different cognitive statuses in older adults, with RRs ranging from 0.55 (95% CI: 0.37-0.83) to 1.07 (95% CI: 0.95-1.22). Meta-regression analysis showed that compared with studies with percentage of female ≥50%, studies with female participant percentage <50% had significantly increased RR (p = 0.01). Moreover, studies conducted in European and American countries had significantly lower RR (p = 0.019), compared with those conducted in Asian countries. Our study revealed different effects of various types of leisure activities on different cognitive statuses in older adults. To make innovative recommendations for promoting cognitive function in older adults, more detailed observational longitudinal studies investigating the effects of different types of leisure activities on different cognitive statuses in older adults are needed.

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.

Blood Metabolomics May Discriminate a Sub-Group of Patients with First Demyelinating Episode in the Context of RRMS with Increased Disability and MRI Characteristics Indicative of Poor Prognosis

Biomarker research across the health-to-disease continuum is being increasingly applied. We applied blood-based metabolomics in order to identify patient clusters with a first demyelinating episode, and explored the prognostic potential of the method by thoroughly characterizing each cluster in terms of clinical, laboratory and MRI markers of established prognostic potential for Multiple Sclerosis (MS). Recruitment consisted of 11 patients with Clinically Isolated Syndrome (CIS), 37 patients with a first demyelinating episode in the context of Relapsing-Remitting MS (RRMS) and 11 control participants. Blood-based metabolomics and hierarchical clustering analysis (HCL) were applied. Constructed OPLS-DA models illustrated a discrimination between patients with CIS and the controls (p = 0.0014), as well as between patients with RRMS and the controls (p = 1 × 10−5). Hierarchical clustering analysis (HCL) for patients with RRMS identified three clusters. RRMS-patients-cluster-3 exhibited higher mean cell numbers in the Cerebro-spinal Fluid (CSF) compared to patients with CIS (18.17 ± 6.3 vs. 1.09 ± 0.41, p = 0.004). Mean glucose CSF/serum ratio and infratentorial lesion burden significantly differed across CIS- and HCL-derived RRMS-patient clusters (F = 14.95, p < 0.001 and F = 6.087, p = 0.002, respectively), mainly due to increased mean values for patients with RRMS-cluster-3. HCL discriminated a cluster of patients with a first demyelinating episode in the context of RRMS with increased disability, laboratory findings linked with increased pathology burden and MRI markers of poor prognosis.

In vivo brain endocannabinoid metabolism is related to hippocampus glutamate and structure - a multimodal imaging study with PET, 1H-MRS, and MRI

Dysregulation of hippocampus glutamatergic neurotransmission and reductions in hippocampal volume have been associated with psychiatric disorders. The endocannabinoid system modulates glutamate neurotransmission and brain development, including hippocampal remodeling. In humans, elevated levels of anandamide and lower activity of its catabolic enzyme fatty acid amide hydrolase (FAAH) are associated with schizophrenia diagnosis and psychotic symptom severity, respectively (Neuropsychopharmacol, 29(11), 2108-2114; Biol. Psychiatry 88 (9), 727-735). Although preclinical studies provide strong evidence linking anandamide and FAAH to hippocampus neurotransmission and structure, these relationships remain poorly understood in humans. We recruited young adults with and without psychotic disorders and measured FAAH activity, hippocampal glutamate and glutamine (Glx), and hippocampal volume using [11C]CURB positron emission tomography (PET), proton magnetic resonance spectroscopy (1H-MRS) and T1-weighted structural MRI, respectively. We hypothesized that higher FAAH activity would be associated with greater hippocampus Glx and lower hippocampus volume, and that these effects would differ in patients with psychotic disorders relative to healthy control participants. After attrition and quality control, a total of 37 participants (62% male) completed [11C]CURB PET and 1H-MRS of the left hippocampus, and 45 (69% male) completed [11C]CURB PET and hippocampal volumetry. Higher FAAH activity was associated with greater concentration of hippocampal Glx (F1,36.36 = 9.17, p = 0.0045; Cohen's f = 0.30, medium effect size) and smaller hippocampal volume (F1,44.70 = 5.94, p = 0.019, Cohen's f = 0.26, medium effect size). These effects did not differ between psychosis and healthy control groups (no group interaction). This multimodal imaging study provides the first in vivo evidence linking hippocampal Glx and hippocampus volume with endocannabinoid metabolism in the human brain.

Associations among drinking water quality, dyslipidemia, and cognitive function for older adults in China: evidence from CHARLS

BACKGROUND

The current study aimed to examine the association between drinking water quality and cognitive function and to identify the direct and indirect effects of drinking water quality and dyslipidemia on cognitive function among older adults in China.

METHODS

Primary data for the study were selected from China Health and Retirement Longitudinal Study (CHARLS, 2015) and 4,951 respondents aged 60 and above were included. Data on drinking water quality were selected from the 2015 prefectural water quality data from the Institute of Public and Environment Affairs in China and measured by the Blue City Water Quality Index. Dyslipidemia was measured by self-reported dyslipidemia diagnosis and lipid panel. Three composite measures of cognitive function included mental status, episodic memory, and global cognition. Mixed effects models were conducted to assess the associations between drinking water quality or dyslipidemia and cognitive function. The mediation effects of dyslipidemia were examined by path analyses.

RESULTS

Exposure to high quality drinking water was significantly associated with higher scores in mental status, episodic memory, and global cognition (β = 0.34, p < 0.001 for mental status; β = 0.24, p < 0.05 for episodic memory; β = 0.58, p < 0.01 for global cognition). Respondents who reported dyslipidemia diagnosis had higher scores in the three composite measures of cognitive function (β = 0.39, p < 0.001 for mental status; β = 0.27 p < 0.05 for episodic memory; β = 0.66, p < 0.001 for global cognition). An elevated blood triglycerides was only associated with higher scores in mental status (β = 0.21, p < 0.05). Self-reported dyslipidemia diagnosis was a suppressor, which increased the magnitude of the direct effect of drinking water quality on mental status, episodic memory, and global cognition.

CONCLUSION

Drinking water quality was associated with cognitive function in older Chinese and the relationship was independent of natural or socioeconomic variations in neighborhood environments. Improving drinking water quality could be a potential public health effort to delay the onset of cognitive impairment and prevent the dementia pandemic in older people.

Alteration of plasma metabolic profile and physical performance combined with metabolites is more sensitive to early screening for mild cognitive impairment

Objective

Unbiased metabolic profiling has been initiated to identify novel metabolites. However, it remains a challenge to define reliable biomarkers for rapid and accurate diagnosis of mild cognitive impairment (MCI). Our study aimed to evaluate the association of serum metabolites with MCI, attempting to find new biomarkers and combination models that are distinct for MCI.

Methods

A total of 380 participants were recruited (mean age: 72.5 ± 5.19 years). We performed an untargeted metabolomics analysis on older adults who underwent the Mini-Mental State Examination (MMSE), the Instrumental Activities of Daily Living (IADL), and physical performance tests such as hand grip, Timed Up and Go Test (TUGT), and walking speed. Orthogonal partial least squares discriminant analysis (OPLS-DA) and heat map were utilized to distinguish the metabolites that differ between groups.

Results

Among all the subjects, 47 subjects were diagnosed with MCI, and methods based on the propensity score are used to match the MCI group with the normal control (NC) group (n = 47). The final analytic sample comprised 94 participants (mean age: 75.2 years). The data process from the metabolic profiles identified 1,008 metabolites. A cluster and pathway enrichment analysis showed that sphingolipid metabolism is involved in the development of MCI. Combination of metabolite panel and physical performance were significantly increased discriminating abilities on MCI than a single physical performance test [model 1: the area under the curve (AUC) = 0.863; model 2: AUC = 0.886; and model 3: AUC = 0.870, P < 0.001].

Conclusion

In our study, untargeted metabolomics was used to detect the disturbance of metabolism that occurs in MCI. Physical performance tests combined with phosphatidylcholines (PCs) showed good utility in discriminating between NC and MCI, which is meaningful for the early diagnosis of MCI.

Comparative Metabolomics Analysis Reveals Key Metabolic Mechanisms and Protein Biomarkers in Alzheimer’s Disease

Alzheimer’s disease (AD) is one of the most common progressive neurodegenerative diseases, accompanied by global alterations in metabolic profiles. In the past 10 years, over hundreds of metabolomics studies have been conducted to unravel metabolic changes in AD, which provides insight into the identification of potential biomarkers for diagnosis, treatment, and prognostic assessment. However, since different species may lead to systemic abnormalities in metabolomic profiles, it is urgently needed to perform a comparative metabolomics analysis between AD animal models and human patients. In this study, we integrated 78 metabolic profiles from public literatures, including 11 metabolomics studies in different AD mouse models and 67 metabolomics studies from AD patients. Metabolites and enrichment analysis were further conducted to reveal key metabolic pathways and metabolites in AD. We totally identified 14 key metabolites and 16 pathways that are both differentially significant in AD mouse models and patients. Moreover, we built a metabolite-target network to predict potential protein markers in AD. Finally, we validated HER2 and NDF2 as key protein markers in APP/PS1 mice. Overall, this study provides a comprehensive strategy for AD metabolomics research, contributing to understanding the pathological mechanism of AD.

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

Alzheimer's Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ1-42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.

Metabolites Associated with Memory and Gait: A Systematic Review

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

Blood metabolites predicting mild cognitive impairment in the study of Latinos-investigation of neurocognitive aging (HCHS/SOL)

INTRODUCTION

Blood metabolomics-based biomarkers may be useful to predict measures of neurocognitive aging.

METHODS

We tested the association between 707 blood metabolites measured in 1451 participants from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), with mild cognitive impairment (MCI) and global cognitive change assessed 7 years later. We further used Lasso penalized regression to construct a metabolomics risk score (MRS) that predicts MCI, potentially identifying a different set of metabolites than those discovered in individual-metabolite analysis.

RESULTS

We identified 20 metabolites predicting prevalent MCI and/or global cognitive change. Six of them were novel and 14 were previously reported as associated with neurocognitive aging outcomes. The MCI MRS comprised 61 metabolites and improved prediction accuracy from 84% (minimally adjusted model) to 89% in the entire dataset and from 75% to 87% among apolipoprotein E ε4 carriers.

DISCUSSION

Blood metabolites may serve as biomarkers identifying individuals at risk for MCI among US Hispanics/Latinos.

A Nuclear Magnetic Resonance Spectroscopy Method in Characterization of Blood Metabolomics for Alzheimer's Disease

There is currently a crucial need for improved diagnostic techniques and targeted treatment methods for Alzheimer's disease (AD), a disease which impacts millions of elderly individuals each year. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease. In this report, we present our AD metabolomics results measured with high resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) on human blood plasma samples obtained from AD and non-AD subjects. Our study centers on developments of AD and non-AD metabolomics differentiating models with procedures of quality assurance (QA) and quality control (QC) through pooled samples. Our findings suggest that analysis of blood plasma samples using HRMAS NMR has the potential to differentiate between diseased and healthy subjects, which has important clinical implications for future improvements in AD diagnosis methodologies.

Recent Advances in Understanding of Alzheimer's Disease Progression through Mass Spectrometry-Based Metabolomics

Alzheimer's disease (AD) is the leading cause of dementia in the aging population, but despite extensive research, there is no consensus on the biological cause of AD. While AD research is dominated by protein/peptide-centric research based on the amyloid hypothesis, a theory that designates dysfunction in beta-amyloid production, accumulation, or disposal as the primary cause of AD, many studies focus on metabolomics as a means of understanding the biological processes behind AD progression. In this review, we discuss mass spectrometry (MS)-based AD metabolomics studies, including sample type and preparation, mass spectrometry specifications, and data analysis, as well as biological insights gleaned from these studies, with the hope of informing future AD metabolomic studies.

Assessing Causal Relationship Between Human Blood Metabolites and Five Neurodegenerative Diseases With GWAS Summary Statistics

Background: Neurodegenerative diseases (NDDs) are the leading cause of disability worldwide while their metabolic pathogenesis is unclear. Genome-wide association studies (GWASs) offer an unprecedented opportunity to untangle the relationship between metabolites and NDDs.

Methods: By leveraging two-sample Mendelian randomization (MR) approaches and relying on GWASs summary statistics, we here explore the causal association between 486 metabolites and five NDDs including Alzheimer’s Disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Parkinson’s disease (PD), and multiple sclerosis (MS). We validated our MR results with extensive sensitive analyses including MR-PRESSO and MR-Egger regression. We also performed linkage disequilibrium score regression (LDSC) and colocalization analyses to distinguish causal metabolite-NDD associations from genetic correlation and LD confounding of shared causal genetic variants. Finally, a metabolic pathway analysis was further conducted to identify potential metabolite pathways.

Results: We detected 164 metabolites which were suggestively associated with the risk of NDDs. Particularly, 2-methoxyacetaminophen sulfate substantially affected ALS (OR = 0.971, 95%CIs: 0.961 ∼ 0.982, FDR = 1.04E-4) and FTD (OR = 0.924, 95%CIs: 0.885 ∼ 0.964, FDR = 0.048), and X-11529 (OR = 1.604, 95%CIs: 1.250 ∼ 2.059, FDR = 0.048) and X-13429 (OR = 2.284, 95%CIs: 1.457 ∼ 3.581, FDR = 0.048) significantly impacted FTD. These associations were further confirmed by the weighted median and maximum likelihood methods, with MR-PRESSO and the MR-Egger regression removing the possibility of pleiotropy. We also observed that ALS or FTD can alter the metabolite levels, including ALS and FTD on 2-methoxyacetaminophen sulfate. The LDSC and colocalization analyses showed that none of the identified associations could be driven by genetic correlation or confounding by LD with common causal loci. Multiple metabolic pathways were found to be involved in NDDs, such as “urea cycle” (P = 0.036), “arginine biosynthesis” (P = 0.004) on AD and “phenylalanine, tyrosine and tryptophan biosynthesis” (P = 0.046) on ALS.

Conclusion: our study reveals robust bidirectional causal associations between servaral metabolites and neurodegenerative diseases, and provides a novel insight into metabolic mechanism for pathogenesis and therapeutic strategies of these diseases.

The role of fatty acid amide hydrolase enzyme inhibitors in Alzheimer's disease

Fatty acid amide hydrolase (FAAH) is a prominent enzyme of the endocannabinoid system that degrades endogenous cannabinoid anandamide and oleamide. These lipid amides are involved in reducing neuroinflammation, pain and regulation of other neurological-related activities including feeding behaviours, sleep patterns, body temperature, memory processes and locomotory activity. Many of these activities are affected in most neurological disorders. Increased levels of brain FAAH expressions are speculated to correlate with decreased levels of lipid amides and increased AD-related symptoms. Thus, inhibition of FAAH shows promising potential in amelioration of symptoms associated with Alzheimer's disease (AD). The review aims at establishing the detrimental role of increased FAAH expression in AD and highlights the translational potential and therapeutic application of FAAH inhibitors in AD.

Integrated Metabolomics and Proteomics Dynamics of Serum Samples Reveals Dietary Zeolite Clinoptilolite Supplementation Restores Energy Balance in High Yielding Dairy Cows

Dairy cows can suffer from a negative energy balance (NEB) during their transition from the dry period to early lactation, which can increase the risk of postpartum diseases such as clinical ketosis, mastitis, and fatty liver. Zeolite clinoptilolite (CPL), due to its ion-exchange property, has often been used to treat NEB in animals. However, limited information is available on the dynamics of global metabolomics and proteomic profiles in serum that could provide a better understanding of the associated altered biological pathways in response to CPL. Thus, in the present study, a total 64 serum samples were collected from 8 control and 8 CPL-treated cows at different time points in the prepartum and postpartum stages. Labelled proteomics and untargeted metabolomics resulted in identification of 64 and 21 differentially expressed proteins and metabolites, respectively, which appear to play key roles in restoring energy balance (EB) after CPL supplementation. Joint pathway and interaction analysis revealed cross-talks among valproic acid, leucic acid, glycerol, fibronectin, and kinninogen-1, which could be responsible for restoring NEB. By using a global proteomics and metabolomics strategy, the present study concluded that CPL supplementation could lower NEB in just a few weeks, and explained the possible underlying pathways employed by CPL.

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

Introduction

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

Methods

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

Results

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

Discussion

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

Sex-Specific Metabolic Pathways Were Associated with Alzheimer's Disease (AD) Endophenotypes in the European Medical Information Framework for AD Multimodal Biomarker Discovery Cohort

BACKGROUND

physiological differences between males and females could contribute to the development of Alzheimer's Disease (AD). Here, we examined metabolic pathways that may lead to precision medicine initiatives.

METHODS

We explored whether sex modifies the association of 540 plasma metabolites with AD endophenotypes including diagnosis, cerebrospinal fluid (CSF) biomarkers, brain imaging, and cognition using regression analyses for 695 participants (377 females), followed by sex-specific pathway overrepresentation analyses, APOE ε4 stratification and assessment of metabolites' discriminatory performance in AD.

RESULTS

In females with AD, vanillylmandelate (tyrosine pathway) was increased and tryptophan betaine (tryptophan pathway) was decreased. The inclusion of these two metabolites (area under curve (AUC) = 0.83, standard error (SE) = 0.029) to a baseline model (covariates + CSF biomarkers, AUC = 0.92, SE = 0.019) resulted in a significantly higher AUC of 0.96 (SE = 0.012). Kynurenate was decreased in males with AD (AUC = 0.679, SE = 0.046).

CONCLUSIONS

metabolic sex-specific differences were reported, covering neurotransmission and inflammation pathways with AD endophenotypes. Two metabolites, in pathways related to dopamine and serotonin, were associated to females, paving the way to personalised treatment.

Differential lncRNA expression profiling of cognitive function in middle and old aged monozygotic twins using generalized association analysis

Cognitive impairment is the most prominent symptom in neurodegenerative disorders affecting quality of life and mortality. However, despite years of research, the molecular mechanism underlying the regulation of cognitive function and its impairment is poorly understood. This study aims to elucidate the role of long non-coding RNAs (lncRNAs) expression and lncRNA-mRNA interaction networks, by analyzing lncRNA expression in whole blood samples of 400 middle and old aged monozygotic twins in association with cognitive function using both linear models and a generalized correlation coefficient (GCC) to capture the diverse patterns of correlation. We detected 13 probes (p < 1e-03) displaying nonlinear and 7 probes (p < 1e-03) showing linear correlations. After combining the results, we identified 20 lncRNA probes with p < 1e-03. The top lncRNA probes were annotated to genes, along with the non-coding MALAT1, that play roles in neurodegenerative diseases. The top lncRNAs were linked to functional clusters including peptidyl-glycine modification, vascular smooth muscle cells, mitotic spindle organization and protein tyrosine phosphatase. In addition, mapping of the top significant lncRNAs to the lncRNA-mRNA interaction network detected significantly enriched biological pathways involving neuroactive ligand-receptor interaction, proteasome and chemokines. We show that GCC served as a complementary approach in detecting lncRNAs missed by the conventional linear models. A combination of GCC and linear models identified lncRNAs of diverse patterns of association enriched for GO biological and molecular functions meaningful in cognitive performance and cognitive decline. The novel lncRNA regulatory network further contributed to detect significant pathways implicated in cognition.

Pharmacokinetic and metabolomics approach based on UHPLC-MS to evaluate therapeutic effect of lignans from S. Chinensis in alzheimer's disease

Lignans from Schisandra chinensis (Turcz.) Baill (LFS) has been proved to improve impaired cognitive ability thereby show potential in treating Alzheimer's disease (AD). In this study, UHPLC-Q-TOF-MS and UHPLC-QQQ-MS were adopted cooperatively to establish a method synchronously detecting 10 kinds of LFS monomers in rat plasma samples. And this method was further applied for pharmacokinetic study to compare the metabolism of LFS in normal and AD rats. The results indicated that AD rats showed an observably better absorption of LFS compared to normal rats. Based on time-varying plasma concentration of LFS, metabolomics was used to establish a plasma concentration-time-endogenous metabolite connection. In total 54 time-varying endogenous metabolites were screened and most of which were closely associated with AD. And LFS exerted a concentration dependent regulating effect to most of these metabolites. Through biomarker related pathways and biological function analysis, LFS might treat AD through neuroprotection, antioxidant damage and regulating the metabolism of unsaturated fatty acids. This is the first study connecting LFS absorbtion and endogenous metabolite changes with the time lapse. The pharmacokinetics and metabolic profile differences between normal and AD rats were firstly investigated as well. This study provides a novel perspective in exploring the effect and mechanism of LFS in treating AD.

Alteration in the Cerebrospinal Fluid Lipidome in Parkinson's Disease: A Post-Mortem Pilot Study

Lipid metabolism is clearly associated to Parkinson's disease (PD). Although lipid homeostasis has been widely studied in multiple animal and cellular models, as well as in blood derived from PD individuals, the cerebrospinal fluid (CSF) lipidomic profile in PD remains largely unexplored. In this study, we characterized the post-mortem CSF lipidomic imbalance between neurologically intact controls (n = 10) and PD subjects (n = 20). The combination of dual extraction with ultra-performance liquid chromatography-electrospray ionization quadrupole-time-of-flight mass spectrometry (UPLC-ESI-qToF-MS/MS) allowed for the monitoring of 257 lipid species across all samples. Complementary multivariate and univariate data analysis identified that glycerolipids (mono-, di-, and triacylglycerides), saturated and mono/polyunsaturated fatty acids, primary fatty amides, glycerophospholipids (phosphatidylcholines, phosphatidylethanolamines), sphingolipids (ceramides, sphingomyelins), N-acylethanolamines and sterol lipids (cholesteryl esters, steroids) were significantly increased in the CSF of PD compared to the control group. Interestingly, CSF lipid dyshomeostasis differed depending on neuropathological staging and disease duration. These results, despite the limitation of being obtained in a small population, suggest extensive CSF lipid remodeling in PD, shedding new light on the deployment of CSF lipidomics as a promising tool to identify potential lipid markers as well as discriminatory lipid species between PD and other atypical parkinsonisms.

Mendelian randomization identifies blood metabolites previously linked to midlife cognition as causal candidates in Alzheimer's disease

There are currently no disease-modifying treatments for Alzheimer's disease (AD), and an understanding of preclinical causal biomarkers to help target disease pathogenesis in the earliest phases remains elusive. Here, we investigated whether 19 metabolites previously associated with midlife cognition-a preclinical predictor of AD-translate to later clinical risk, using Mendelian randomization (MR) to tease out AD-specific causal relationships. Summary statistics from the largest genome-wide association studies (GWASs) for AD and metabolites were used to perform bidirectional univariable MR. Bayesian model averaging (BMA) was additionally performed to address high correlation between metabolites and identify metabolite combinations that may be on the AD causal pathway. Univariable MR indicated four extra-large high-density lipoproteins (XL.HDL) on the causal pathway to AD: free cholesterol (XL.HDL.FC: 95% CI = 0.78 to 0.94), total lipids (XL.HDL.L: 95% CI = 0.80 to 0.97), phospholipids (XL.HDL.PL: 95% CI = 0.81 to 0.97), and concentration of XL.HDL particles (95% CI = 0.79 to 0.96), significant at an adjusted P < 0.009. MR-BMA corroborated XL.HDL.FC to be among the top three causal metabolites, in addition to total cholesterol in XL.HDL (XL.HDL.C) and glycoprotein acetyls (GP). Both XL.HDL.C and GP demonstrated suggestive univariable evidence of causality (P < 0.05), and GP successfully replicated within an independent dataset. This study offers insight into the causal relationship between metabolites demonstrating association with midlife cognition and AD. It highlights GP in addition to several XL.HDLs-particularly XL.HDL.FC-as causal candidates warranting further investigation. As AD pathology is thought to develop decades prior to symptom onset, expanding on these findings could inform risk reduction strategies.

Effects of sevoflurane anesthesia and abdominal surgery on the systemic metabolome: a prospective observational study

Background

Metabolic status can be impacted by general anesthesia and surgery. However, the exact effects of general anesthesia and surgery on systemic metabolome remain unclear, which might contribute to postoperative outcomes.

Methods

Five hundred patients who underwent abdominal surgery were included. General anesthesia was mainly maintained with sevoflurane. The end-tidal sevoflurane concentration (ET_sevo) was adjusted to maintain BIS (Bispectral index) value between 40 and 60. The mean ET_sevo from 20 min after endotracheal intubation to 2 h after the beginning of surgery was calculated for each patient. The patients were further divided into low ET_sevo group (mean − SD) and high ET_sevo group (mean + SD) to investigate the possible metabolic changes relevant to the amount of sevoflurane exposure.

Results

The mean ET_sevo of the 500 patients was 1.60% ± 0.34%. Patients with low ET_sevo (n = 55) and high ET_sevo (n = 59) were selected for metabolomic analysis (1.06% ± 0.13% vs. 2.17% ± 0.16%, P < 0.001). Sevoflurane and abdominal surgery disturbed the tricarboxylic acid cycle as identified by increased citrate and cis-aconitate levels and impacted glycometabolism as identified by increased sucrose and D-glucose levels in these 114 patients. Glutamate metabolism was also impacted by sevoflurane and abdominal surgery in all the patients. In the patients with high ET_sevo, levels of L-glutamine, pyroglutamic acid, sphinganine and L-selenocysteine after sevoflurane anesthesia and abdominal surgery were significantly higher than those of the patients with low ET_sevo, suggesting that these metabolic changes might be relevant to the amount of sevoflurane exposure.

Conclusions

Sevoflurane anesthesia and abdominal surgery can impact principal metabolic pathways in clinical patients including tricarboxylic acid cycle, glycometabolism and glutamate metabolism. This study may provide a resource data for future studies about metabolism relevant to general anaesthesia and surgeries.

Trial registration

www.chictr.org.cn. identifier: ChiCTR1800014327.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01301-0.

Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer's Disease

Background: Alzheimer’s disease (AD) is a complex and severe neurodegenerative disease that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on cognitive tests, imaging techniques and cerebrospinal fluid (CSF) levels of amyloid-β1-42 (Aβ42), total tau protein and hyperphosphorylated tau (p-tau). However, the available methods are expensive and relatively invasive. Artificial intelligence techniques like machine learning tools have being increasingly used in precision diagnosis. Methods: We conducted a meta-analysis to investigate the machine learning and novel biomarkers for the diagnosis of AD. Methods: We searched PubMed, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews for reviews and trials that investigated the machine learning and novel biomarkers in diagnosis of AD. Results: In additional to Aβ and tau-related biomarkers, biomarkers according to other mechanisms of AD pathology have been investigated. Neuronal injury biomarker includes neurofiliament light (NFL). Biomarkers about synaptic dysfunction and/or loss includes neurogranin, BACE1, synaptotagmin, SNAP-25, GAP-43, synaptophysin. Biomarkers about neuroinflammation includes sTREM2, and YKL-40. Besides, d-glutamate is one of coagonists at the NMDARs. Several machine learning algorithms including support vector machine, logistic regression, random forest, and naïve Bayes) to build an optimal predictive model to distinguish patients with AD from healthy controls. Conclusions: Our results revealed machine learning with novel biomarkers and multiple variables may increase the sensitivity and specificity in diagnosis of AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing AD in outpatient clinics.

Mass spectrometry-based metabolomics diagnostics - myth or reality?

ABSTACTIntroduction: Metabolomics, one of the most high-promising technologies, is the most recently developed post-genomics discipline for developing new diagnostic tests for future implementation in medicine. More than 2,000 scientific papers, using mass spectrometry-based (MS-based) metabolomics analysis for human disease diagnostics, have been published during the past two decades, and almost every metabolomics study shows high diagnostic accuracy. However, despite the great results and promising perspectives, there are currently no diagnostic tests based on metabolomics that have been approved and introduced into clinics.Areas covered: In this report, the advantages and challenges of MS-based metabolomics are discussed with a focus on its developing role in diagnostics, and the current trends in implementing metabolomics diagnostics in the clinic.Expert opinion: In the development of new clinical diagnostics tests, MS-based metabolomics has potential as both a preliminary discovery base for routine testing and a multi-test prototype, which is hoped to be introduced into clinical practice in the near future. A laboratory-developed test (LDT) is one possible way that multi-testing could be developed.

A Urinary Metabolomics Analysis Based on UPLC-MS and Effects of Moxibustion in APP/PS1 Mice

OBJECTIVE

Alzheimer's disease (AD) is a common neurodegenerative disorder with the symptoms of cognitive impairment and decreased learning and memory abilities. Metabolomics can reflect the related functional status and physiological and pathological changes in the process of AD. Moxibustion is a unique method in traditional Chinese medicine, which has been used in the treatment and prevention of diseases for thousands of years.

METHODS

A total of 32 APP/PS1 mice were randomly divided into the model group, moxibustion group, moxa smoke group and smoke-free moxibustion group (n=8/group), using the random number table method, while eight C57BL/6 mice were used as the control group. The five groups were measured for 20 min/day, 6 days/week, for 4 weeks. After 4 weeks' experiment, all the mice were placed in metabolic cages to collect urine continuously for 24 hours, for UPLC-MS analysis.

RESULTS

Principal component analysis (PCA) was used to identify the different metabolites among the five groups, and partial least squares discriminant analysis (PLS-DA) was performed to reveal the effects on the metabolic variance. Sixteen potential biomarkers were identified among the five groups, primarily related to amino acid metabolism, starch metabolism, sucrose metabolism, interconversion of pentose and glucuronate, and aminoacyl biosynthesis. There were 17 differences in the potential metabolites between the control and model groups, involving the metabolism of amino acid, purine, pyrimidine, nicotinic acid and nicotinamide, and biosynthesis of pantothenate and coenzyme A. Fifteen potential biomarkers were identified between the model and moxibustion groups, related to starch metabolism, sucrose metabolism, interconversion of pentose and glucuronate, glyoxylate, dicarboxylate anions and some amino acid metabolism.

CONCLUSION

Moxibustion can regulate the metabolism of substance and energy by improving the synthesis and decomposition of carbohydrates and amino acids in APP/PS1 transgenic AD model mice.

Deep learning meets metabolomics: a methodological perspective

Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of 'big data', including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.

A panel of blood lipids associated with cognitive performance, brain atrophy, and Alzheimer's diagnosis: A longitudinal study of elders without dementia

INTRODUCTION

We sought lipid-metabolic biomarkers involved in the processes underlying cognitive decline and detected them in association with Alzheimer's disease (AD) phenotypes.

METHODS

A least absolute shrinkage and selection operator logistic regression model was used to select lipids that best classified cognitive decline defined by a fast-annual rate of cognition. Lipid summary scores were constructed as predictors of cognitive decline by using this model. Multivariable-adjusted models tested the associations of risk score with AD phenotypes.

RESULTS

A model incorporating 17 selected lipids showed good discrimination and calibration. The lipid risk score was positively associated with the baseline Alzheimer Disease Assessment Scale-13-item cognitive subscale (ADAS-Cog13) score and cerebrospinal tau protein level, and predicted cognitive diagnoses. Additional results showing that individuals with increased lipid risk scores had rapid change rates of ADAS-Cog13 and brain atrophy further corroborated the predictive role of lipids.

DISCUSSION

A panel of blood lipids instead of individual lipid molecules could better diagnose and predict cognitive decline.

Metabolome of canine and human saliva: a non-targeted metabolomics study

INTRODUCTION

Saliva metabolites are suggested to reflect the health status of an individual in humans. The same could be true with the dog (Canis lupus familiaris), an important animal model of human disease, but its saliva metabolome is unknown. As a non-invasive sample, canine saliva could offer a new alternative material for research to reveal molecular mechanisms of different (patho)physiological stages, and for veterinary medicine to monitor dogs' health trajectories.

OBJECTIVES

To investigate and characterize the metabolite composition of dog and human saliva in a non-targeted manner.

METHODS

Stimulated saliva was collected from 13 privately-owned dogs and from 14 human individuals. We used a non-targeted ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-qTOF-MS) method to measure metabolite profiles from saliva samples.

RESULTS

We identified and classified a total of 211 endogenous and exogenous salivary metabolites. The compounds included amino acids, amino acid derivatives, biogenic amines, nucleic acid subunits, lipids, organic acids, small peptides as well as other metabolites, like metabolic waste molecules and other chemicals. Our results reveal a distinct metabolite profile of dog and human saliva as 25 lipid compounds were identified only in canine saliva and eight dipeptides only in human saliva. In addition, we observed large variation in ion abundance within and between the identified saliva metabolites in dog and human.

CONCLUSION

The results suggest that non-targeted metabolomics approach utilizing UHPLC-qTOF-MS can detect a wide range of small compounds in dog and human saliva with partially overlapping metabolite composition. The identified metabolites indicate that canine saliva is potentially a versatile material for the discovery of biomarkers for dog welfare. However, this profile is not complete, and dog saliva needs to be investigated in the future with other analytical platforms to characterize the whole canine saliva metabolome. Furthermore, the detailed comparison of human and dog saliva composition needs to be conducted with harmonized study design.

Integrated Metabolomic and Lipidomic Analysis Reveals the Neuroprotective Mechanisms of Bushen Tiansui Formula in an Aβ1-42-Induced Rat Model of Alzheimer's Disease

Bushen Tiansui Formula (BSTSF) is a traditional Chinese medicine prescription. It has been widely applied to treat Alzheimer's disease (AD) in the clinic; however, the mechanisms underlying its effects remain largely unknown. In this study, we used a rat AD model to study the effects of BSTSF on cognitive performance, and UPLC-MS/MS-based metabolomic and lipidomic analysis was further performed to identify significantly altered metabolites in the cerebral cortices of AD rats and determine the effects of BSTSF on the metabolomic and lipidomic profiles in the cerebral cortices of these animals. The results revealed that the levels of 47 metabolites and 30 lipids primarily associated with sphingolipid metabolism, glycerophospholipid metabolism, and linoleic acid metabolism were significantly changed in the cerebral cortices of AD rats. Among the altered lipids, ceramides, phosphatidylethanolamines, lysophosphatidylethanolamines, phosphatidylcholines, lysophosphatidylcholines, phosphatidylserines, sphingomyelins, and phosphatidylglycerols showed robust changes. Moreover, 34 differential endogenous metabolites and 21 lipids, of which the levels were mostly improved in the BSTSF treatment group, were identified as potential therapeutic targets of BSTSF against AD. Our results suggest that lipid metabolism is highly dysregulated in the cerebral cortices of AD rats, and BSTSF may exert its neuroprotective mechanisms by restoring metabolic balance, including that of sphingolipid metabolism, glycerophospholipid metabolism, alanine, aspartate, and glutamate metabolism, and D-glutamine and D-glutamate metabolism. Our data may lead to a deeper understanding of the AD-associated metabolic profile and shed new light on the mechanism underlying the therapeutic effects of BSTSF.

Isoprostanoids Levels in Cerebrospinal Fluid Do Not Reflect Alzheimer's Disease

Previous studies showed a relationship between lipid oxidation biomarkers from plasma samples and Alzheimer's Disease (AD), constituting a promising diagnostic tool. In this work we analyzed whether these plasma biomarkers could reflect specific brain oxidation in AD. In this work lipid peroxidation compounds were determined in plasma and cerebrospinal fluid (CSF) samples from AD and non-AD (including other neurological pathologies) participants, by means of an analytical method based on liquid chromatography coupled with mass spectrometry. Statistical analysis evaluated correlations between biological matrices. The results did not show satisfactory correlations between plasma and CSF samples for any of the studied lipid peroxidation biomarkers (isoprostanes, neuroprostanes, prostaglandines, dihomo-isoprostanes). However, some of the analytes showed correlations with specific CSF biomarkers for AD and with neuropsychological tests (Mini-Mental State Examination (MMSE), Repeatable Battery for the Assessment of Neuropsychological Status (RBANS)). In conclusion, lipid peroxidation biomarkers in CSF samples do not reflect their levels in plasma samples, and no significant differences were observed between participant groups. However, some of the analytes could be useful as cognitive decline biomarkers.

Utility of Animal Models to Understand Human Alzheimer's Disease, Using the Mastermind Research Approach to Avoid Unnecessary Further Sacrifices of Animals

To diagnose and treat early-stage (preclinical) Alzheimer’s disease (AD) patients, we need body-fluid-based biomarkers that reflect the processes that occur in this stage, but current knowledge on associated processes is lacking. As human studies on (possible) onset and early-stage AD would be extremely expensive and time-consuming, we investigate the potential value of animal AD models to help to fill this knowledge gap. We provide a comprehensive overview of processes associated with AD pathogenesis and biomarkers, current knowledge on AD-related biomarkers derived from on human and animal brains and body fluids, comparisons of biomarkers obtained in human AD and frequently used animal AD models, and emerging body-fluid-based biomarkers. In human studies, amyloid beta (Aβ), hyperphosphorylated tau (P-tau), total tau (T-tau), neurogranin, SNAP-25, glial fibrillary acidic protein (GFAP), YKL-40, and especially neurofilament light (NfL) are frequently measured. In animal studies, the emphasis has been mostly on Aβ. Although a direct comparison between human (familial and sporadic) AD and (mostly genetic) animal AD models cannot be made, still, in brain, cerebrospinal fluid (CSF), and blood, a majority of similar trends are observed for human AD stage and animal AD model life stage. This indicates the potential value of animal AD models in understanding of the onset and early stage of AD. Moreover, animal studies can be smartly designed to provide mechanistic information on the interrelationships between the different AD processes in a longitudinal fashion and may also include the combinations of different conditions that may reflect comorbidities in human AD, according to the Mastermind Research approach.

Perspectives in fluid biomarkers in neurodegeneration from the 2019 biomarkers in neurodegenerative diseases course-a joint PhD student course at University College London and University of Gothenburg

Until relatively recently, a diagnosis of probable Alzheimer's disease (AD) and other neurodegenerative disorders was principally based on clinical presentation, with post-mortem examination remaining a gold standard for disease confirmation. This is in sharp contrast to other areas of medicine, where fluid biomarkers, such as troponin levels in myocardial infarction, form an integral part of the diagnostic and treatment criteria. There is a pressing need for such quantifiable and easily accessible tools in neurodegenerative diseases.In this paper, based on lectures given at the 2019 Biomarkers in Neurodegenerative Diseases Course, we provide an overview of a range of cerebrospinal fluid (CSF) and blood biomarkers in neurodegenerative disorders, including the 'core' AD biomarkers amyloid β (Aβ) and tau, as well as other disease-specific and general markers of neuroaxonal injury. We then highlight the main challenges in the field, and how those could be overcome with the aid of new methodological advances, such as assay automation, mass spectrometry and ultrasensitive immunoassays.As we hopefully move towards an era of disease-modifying treatments, reliable biomarkers will be essential to increase diagnostic accuracy, allow for earlier diagnosis, better participant selection and disease activity and treatment effect monitoring.

Mass spectrometry-based metabolomics in health and medical science: a systematic review

Metabolomics is the study of the investigation of small molecules derived from cellular and organism metabolism, which reflects the outcomes of the complex network of biochemical reactions in living systems. As the most recent member of the omics family, there has been notable progress in metabolomics in the last decade, mainly driven by the improvement in mass spectrometry (MS). MS-based metabolomic strategies in modern health and medical science studies provide innovative tools for novel diagnostic and prognostic approaches, as well as an augmented role in drug development, nutrition science, toxicology, and forensic science. In the present review, we not only introduce the application of MS-based metabolomics in the above fields, but also discuss the MS analysis technologies commonly used in metabolomics and the application of metabolomics in precision medicine, and further explore the challenges and perspectives of metabolomics in the field of health and medical science, which are expected to make a little contribution to the better development of metabolomics.