Metabolomics in the Development and Progression of Dementia: A Systematic Review

The communication mechanism of the gut-brain axis and its effect on central nervous system diseases: A systematic review

Gut microbiota is involved in intricate and active metabolic processes the host's brain function, especially its role in immune responses, secondary metabolism, and symbiotic connections with the host. Gut microbiota can promote the production of essential metabolites, neurotransmitters, and other neuroactive chemicals that affect the development and treatment of central nervous system diseases. This article introduces the relevant pathways and manners of the communication between the brain and gut, summarizes a comprehensive overview of the current research status of key gut microbiota metabolites that affect the functions of the nervous system, revealing those adverse factors that affect typical communication between the brain-gut axis, and outlining the efforts made by researchers to alleviate these neurological diseases through targeted microbial interventions. The relevant pathways and manners of communication between the brain and gut contribute to the experimental design of new treatment plans and drug development. The factors that may cause changes in gut microbiota and affect metabolites, as well as current intervention methods are summarized, which helps improve gut microbiota brain dialogue, prevent adverse triggering factors from interfering with the gut microbiota system, and minimize neuropathological changes.

Dysregulation of Lipid Metabolism Serves as A Link Between Alzheimer's and Cardiovascular Disease, As Witnessed in A Cross-Sectional Study

Cardiovascular risk factors and established cardiovascular disease (CVD) increase the risk of suffering dementia of the Alzheimer's type (DAT). Here, we set out to define specific molecular profiles of CVD in patients with DAT to better understand its relationship, to unravel the mechanisms underlying the high risk of developing DAT in CVD patients and to define new markers of early disease. Plasma samples from patients with DAT, with and without CVD, were analyzed through a multiomics approach, with integration of metabolomics and proteomics datasets using the OmicsNet web-based tool. Metabolomics results showed an enrichment in lipids and lipid-like molecules. Similarly, the most significant cluster identified through proteomics was formed by 5 proteins related to lipoprotein and cholesterol metabolism. After integration and functional enrichment, glycerolipid metabolism, fatty acid degradation and sphingolipid metabolism were among the most significant functions. Finally, differential expression of ABCA1 and APOH proteins was verified, in an independent cohort also including controls and patients with CVD alone. Both proteins positively correlated with phospho-Tau (181), a classical hallmark of DAT. Different molecular profiles exist in patients with DAT, with and without CVD, with exacerbated alterations in patients in which DAT and CVD co-exist. This information may help to define biomarkers like ABCA1 and APOH that identify patients with cardiovascular dysfunction that are at high risk of developing DAT. Such markers will allow more personalized interventions to be selected, a further step towards precision medicine for individuals whose molecular profiles indicate a distinct response to the same management strategies.

A principal component analysis of metabolome and cognitive decline among Japanese older adults: cross-sectional analysis using Tohoku Medical Megabank Cohort Study

BackgroundDementia is the leading cause of disability and imposes a significant burden on society. Previous studies have suggested an association between metabolites and cognitive decline. Although the metabolite composition differs between Western and Asian populations, studies targeting Asian populations remain scarce.MethodsThis cross-sectional study used data from a cohort survey of community-dwelling older adults aged ≥ 60 years living in Miyagi, Japan, conducted by Tohoku Medical Megabank Organization between 2013 and 2016. Forty-three metabolite variables quantified using nuclear magnetic resonance spectroscopy were used as explanatory variables. Dependent variable was the presence of cognitive decline (≤ 23 points), assessed by the Mini-Mental State Examination. Principal component (PC) analysis was performed to reduce the dimensionality of metabolite variables, followed by logistic regression analysis to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for cognitive decline.ResultsA total of 2,940 participants were included (men: 49.0%, mean age: 67.6 years). Among them, 1.9% showed cognitive decline. The first 12 PC components (PC1-PC12) accounted for 71.7% of the total variance. Multivariate analysis showed that PC1, which mainly represented essential amino acids, was associated with lower odds of cognitive decline (OR = 0.89; 95% CI, 0.80-0.98). PC2, which mainly included ketone bodies, was associated with cognitive decline (OR = 1.29; 95% CI, 1.11-1.51). PC3, which included amino acids, was associated with lower odds of cognitive decline (OR = 0.81; 95% CI, 0.66-0.99).ConclusionAmino acids are protectively associated with cognitive decline, whereas ketone metabolites are associated with higher odds of cognitive decline.

Integrative Multimodal Metabolomics to Early Predict Cognitive Decline Among Amyloid Positive Community-Dwelling Older Adults

Alzheimer's disease is strongly linked to metabolic abnormalities. We aimed to distinguish amyloid-positive people who progressed to cognitive decline from those who remained cognitively intact. We performed untargeted metabolomics of blood samples from amyloid-positive individuals, before any sign of cognitive decline, to distinguish individuals who progressed to cognitive decline from those who remained cognitively intact. A plasma-derived metabolite signature was developed from Supercritical Fluid chromatography coupled with high-resolution mass spectrometry (SFC-HRMS) and nuclear magnetic resonance (NMR) metabolomics. The 2 metabolomics data sets were analyzed by Data Integration Analysis for Biomarker discovery using Latent approaches for Omics studies (DIABLO), to identify a minimum set of metabolites that could describe cognitive decline status. NMR or SFC-HRMS data alone cannot predict cognitive decline. However, among the 320 metabolites identified, a statistical method that integrated the 2 data sets enabled the identification of a minimal signature of 9 metabolites (3-hydroxybutyrate, citrate, succinate, acetone, methionine, glucose, serine, sphingomyelin d18:1/C26:0 and triglyceride C48:3) with a statistically significant ability to predict cognitive decline more than 3 years before decline. This metabolic fingerprint obtained during this exploratory study may help to predict amyloid-positive individuals who will develop cognitive decline. Due to the high prevalence of brain amyloid-positivity in older adults, identifying adults who will have cognitive decline will enable the development of personalized and early interventions.

Therapeutic potential of Thai herbal formula for cognitive impairment: A metabolomics approach for Comprehensive Insights

Chronic cerebral ischemia hypoperfusion plays a role in the initiation and progression of vascular dementia, which causes changes in metabolites. Currently, there is no standard treatment to treat, prevent and reduce the severity of this condition. Thai herbal Yahom no.20 (YHF20) is indicated for fatigue and dizziness. The components of YHF20 have been found to have pharmacological effects related to the pathology of chronic cerebral ischemia hypoperfusion. This study aimed to investigate metabolomic changes after YHF20 administration in a rat model of permanent bilateral common carotid artery occlusion (2-VO) induced chronic cerebral ischemia hypoperfusion, and to explore its impact on spatial learning and memory. Albino Wistar rats were randomly allocated to 5 groups; sham, 2-VO, 2-VO+ 100 mg/kg YHF20, 2-VO+300 mg/kg YHF20, and 2-VO+1000 mg/kg YHF20. The rats were administered YHF20 daily by oral gavage for 56 days after 2-VO induction. Plasma was collected weekly for metabolome change analysis using LC-MS/QTof and toxicity study. The rats were evaluated for spatial learning and memory using the Morris water maze. The results showed that 78 known metabolites and 10 tentative pathways altered after chronic cerebral hypoperfusion, although it was not able to determine the effect on memory and learning behaviors of rats. Glutathione and glutathione metabolism might be metabolite-pathway that were the affect after YHF20 administration in cerebral ischemic condition. The 4 known metabolites may be the metabolites from the constituents of YHF20 could be considered and confirmed for quality control purpose. In conclusion, YHF20 administration might contribute to metabolic changes related to cerebral ischemia condition without the effect on spatial learning and memory, including hepatotoxicity and nephrotoxicity after 56 days of treatment. Alterations in the potential metabolites may provide data support for elucidating dementia pathogenesis and selecting pathways for intervention.

Sex modulates the diet-induced changes to the plasma lipidome in a rat model of cardiorespiratory fitness

OBJECTIVE

Individuals with higher intrinsic cardiorespiratory fitness (CRF) experience decreased rates of cardiometabolic disease and mortality, and high CRF is associated with increased utilization of fatty acids (FAs) for energy. Studies suggest a complex relationship between CRF, diet, and sex with health outcomes, but this interaction is understudied. We hypothesized that FA utilization differences by fitness and sex could be detected in the plasma metabolome when rats or humans were fed a high carbohydrate (HC) or high fat (HF) diet.

METHODS

Male and female rats selectively bred for low (LCR) and high (HCR) CRF were fed a chow diet or a sucrose-free HF (45 % fat) or HC (10 % fat) diet. Plasma samples were collected at days 0, 3, and 14. Human plasma data was collected from male and female participants who were randomized into a HC or HF diet for 21 days. Samples were analyzed using liquid chromatography-mass spectrometry and regression statistics were used to quantify the effect of diet, CRF, and sex on the lipidome.

RESULTS

In rats, the baseline lipidome is more significantly influenced by sex than by CRF, especially as elevated diglycerides, triglycerides, phosphatidylcholines, and lysophosphatidylcholines in males. A dynamic response to diet was observed 3 days after diet, but after 14 days of either diet, the lipidome was modulated by sex with a larger effect size than by diet. Data from the human study also suggests a sex-dependent response to diet with opposite directionality of affect compared to rats, highlighting species-dependent responses to dietary intervention.

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).

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.

Comprehensive Analysis of Metabolites in Postmortem Brains of Patients with Alzheimer's Disease

BACKGROUND

Disturbed metabolism has been proposed as being involved in the pathogenesis of Alzheimer's disease (AD), and more evidence from human AD brains is required.

OBJECTIVE

In this study, we attempted to identify or confirm modulations in the levels of metabolites associated with AD in postmortem AD brains.

METHODS

We performed metabolomics analyses using a gas chromatography mass spectrometry system in postmortem brains of patients with confirmed AD, patients with CERAD score B, and control subjects.

RESULTS

Impaired phosphorylation of glucose and elevation of several tricarboxylic acid (TCA) metabolites, except citrate, were observed and the degree of impaired phosphorylation and elevation in the levels of the TCA cycle metabolites were negatively and positively correlated, respectively, with the clinical phenotypes of AD. The levels of uronic acid pathway metabolites were modulated in AD and correlated positively with the amyloid-β content. The associations of nucleic acid synthesis and amino acid metabolites with AD depended on the kinds of metabolites; in particular, the contents of ribose 5-phosphate, serine and glycine were negatively correlated, while those of ureidosuccinic acid and indole-3-acetic acid were positively modulated in AD. Comprehensive statistical analyses suggested that alterations in the inositol pathway were most closely associated with AD.

CONCLUSIONS

The present study revealed many novel associations between metabolites and AD, suggesting that some of these might serve as novel potential therapeutic targets for AD.

Assessing the Biological Mechanisms Linking Smoking Behavior and Cognitive Function: A Mediation Analysis of Untargeted Metabolomics

(1) Smoking is the most significant preventable health hazard in the modern world. It increases the risk of vascular problems, which are also risk factors for dementia. In addition, toxins in cigarettes increase oxidative stress and inflammation, which have both been linked to the development of Alzheimer's disease and related dementias (ADRD). This study identified potential mechanisms of the smoking-cognitive function relationship using metabolomics data from the longitudinal Wisconsin Registry for Alzheimer's Prevention (WRAP). (2) 1266 WRAP participants were included to assess the association between smoking status and four cognitive composite scores. Next, untargeted metabolomic data were used to assess the relationships between smoking and metabolites. Metabolites significantly associated with smoking were then tested for association with cognitive composite scores. Total effect models and mediation models were used to explore the role of metabolites in smoking-cognitive function pathways. (3) Plasma N-acetylneuraminate was associated with smoking status Preclinical Alzheimer Cognitive Composite 3 (PACC3) and Immediate Learning (IMM). N-acetylneuraminate mediated 12% of the smoking-PACC3 relationship and 13% of the smoking-IMM relationship. (4) These findings provide links between previous studies that can enhance our understanding of potential biological pathways between smoking and cognitive function.

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 epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Association of Baseline Metabolomic Profiles With Incident Stroke and Dementia and With Imaging Markers of Cerebral Small Vessel Disease

BACKGROUND AND OBJECTIVES

Cerebral small vessel disease is a major cause of stroke and dementia. Metabolomics can help identify novel risk factors to better understand pathogenesis and predict disease progression and severity.

METHODS

We analyzed baseline metabolomic profiles from 118,021 UK Biobank participants. We examined cross-sectional associations of 325 metabolites with MRI markers of small vessel disease, evaluated longitudinal associations with incident stroke and dementia, and ascertained causal relationships using Mendelian randomization.

RESULTS

In cross-sectional analyses, lower levels of apolipoproteins, free cholesterol, cholesteryl esters, fatty acids, lipoprotein particle concentrations, phospholipids, and triglycerides were associated with increased white matter microstructural damage on diffusion tensor MRI. In longitudinal analyses, lipoprotein subclasses of very large high-density lipoprotein cholesterol (HDL) were associated with an increased risk of stroke, and acetate and 3-hydroxybutyrate were associated with an increased risk of dementia. Mendelian randomization analyses identified strong evidence supporting causal relationships for many findings. A few metabolites had consistent associations across multiple analysis types. Increased total lipids in very large HDL and increased HDL particle size were associated with increased white matter damage (lower fractional anisotropy: OR: 1.44, 95% CI 1.07-1.95, and OR: 1.19, 95% CI 1.06-1.34, respectively; mean diffusivity: OR: 1.49, 95% CI 1.11-2.01, and OR: 1.24, 95% CI 1.11-1.40, respectively) and an increased risk of incident all stroke (HR: 4.04, 95% CI 2.13-7.64, and HR: 1.54, 95% CI 1.20-1.98, respectively) and ischemic stroke (HR: 3.12, 95% CI 1.53-6.38; HR: 1.37, 95% CI 1.04-1.81). Valine was associated with decreased mean diffusivity (OR: 0.51, 95% CI 0.30-0.88) and had a protective association with all-cause dementia (HR: 0.008, 95% CI 0.002-0.035). Increased levels of cholesterol in small HDL were associated with a decreased risk of incident all stroke (HR: 0.17, 95% CI 0.08-0.39) and ischemic stroke (HR: 0.19, 95% CI 0.08-0.46) and were supported by evidence of a causal association with MRI-confirmed lacunar stroke (OR: 0.96, 95% CI 0.93-0.99).

DISCUSSION

In this large-scale metabolomics study, we found multiple metabolites associated with stroke, dementia, and MRI markers of small vessel disease. Further studies may help inform the development of personalized prediction models and provide insights into mechanistic pathways and future treatment approaches.

Genetic loci of beta-aminoisobutyric acid are associated with aging-related mild cognitive impairment

We studied the genetic associations of a previously developed Metabolomic Risk Score (MRS) for Mild Cognitive Impairment (MCI) and beta-aminoisobutyric acid metabolite (BAIBA)-the metabolite highlighted by results from a genome-wide association study (GWAS) of the MCI-MRS, and assessed their association with MCI in datasets of diverse race/ethnicities. We first performed a GWAS for the MCI-MRS and BAIBA, in Hispanic/Latino adults (n = 3890) from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We identified ten independent genome-wide significant (p value <5 × 10-8) variants associated with MCI-MRS or BAIBA. Variants associated with the MCI-MRS are located in the Alanine-Glyoxylate Aminotransferase 2 (AGXT2 gene), which is known to be associated with BAIBA metabolism. Variants associated with BAIBA are located in the AGXT2 gene and in the SLC6A13 gene. Next, we tested the variants' association with MCI in independent datasets of n = 3178 HCHS/SOL older individuals, n = 3775 European Americans, and n = 1032 African Americans from the Atherosclerosis Risk In Communities (ARIC) study. Variants were considered associated with MCI if their p value <0.05 in the meta-analysis of the three datasets and their direction of association was consistent with expectation. Rs16899972 and rs37369 from the AGXT2 region were associated with MCI. Mediation analysis supported the mediation effect of BAIBA between the two genetic variants and MCI (p value = 0.004 for causal mediated effect). In summary, genetic variants in the AGXT2 region are associated with MCI in Hispanic/Latino, African, and European American populations in the USA, and their effect is likely mediated by changes in BAIBA levels.

Plasma metabolites associated with cognitive function across race/ethnicities affirming the importance of healthy nutrition

INTRODUCTION

We studied the replication and generalization of previously identified metabolites potentially associated with global cognitive function in multiple race/ethnicities and assessed the contribution of diet to these associations.

METHODS

We tested metabolite-cognitive function associations in U.S.A. Hispanic/Latino adults (n = 2222) from the Community Health Study/ Study of Latinos (HCHS/SOL) and in European (n = 1365) and African (n = 478) Americans from the Atherosclerosis Risk In Communities (ARIC) Study. We applied Mendelian Randomization (MR) analyses to assess causal associations between the metabolites and cognitive function and between Mediterranean diet and cognitive function.

RESULTS

Six metabolites were consistently associated with lower global cognitive function across all studies. Of these, four were sugar-related (e.g., ribitol). MR analyses provided weak evidence for a potential causal effect of ribitol on cognitive function and bi-directional effects of cognitive performance on diet.

DISCUSSION

Several diet-related metabolites were associated with global cognitive function across studies with different race/ethnicities.

HIGHLIGHTS

Metabolites associated with cognitive function in Puerto Rican adults were recently identified. We demonstrate the generalizability of these associations across diverse race/ethnicities. Most identified metabolites are related to sugars. Mendelian Randomization (MR) provides weak evidence for a causal effect of ribitol on cognitive function. Beta-cryptoxanthin and other metabolites highlight the importance of a healthy diet.

The effect of prions on cellular metabolism: The metabolic impact of the [RNQ+] prion and potential role of native Rnq1p

Within the field of amyloid and prion disease there is a need for a more comprehensive understanding of the fundamentals of disease biology. In order to facilitate the progression treatment and underpin comprehension of toxicity, fundamental understanding of the disruption to normal cellular biochemistry and trafficking is needed. Here, by removing the complex biochemistry of the brain, we have utilised known prion forming strains of Saccharomyces cerevisiae carrying different conformational variants of the Rnq1p to obtain Liquid Chromatography-Mass Spectrometry (LC-MS) metabolic profiles and identify key perturbations of prion presence. These studies reveal that prion containing [RNQ+] cells display a significant reduction in amino acid biosynthesis and distinct perturbations in sphingolipid metabolism, with significant downregulation in metabolites within these pathways. Moreover, that native Rnq1p appears to downregulate ubiquinone biosynthesis pathways within cells, suggesting that Rnq1p may play a lipid/mevalonate-based cytoprotective role as a regulator of ubiquinone production. These findings contribute to the understanding of how prion proteins interact in vivo in both their prion and non-prion confirmations and indicate potential targets for the mitigation of these effects. We demonstrate specific sphingolipid centred metabolic disruptions due to prion presence and give insight into a potential cytoprotective role of the native Rnq1 protein. This provides evidence of metabolic similarities between yeast and mammalian cells as a consequence of prion presence and establishes the application of metabolomics as a tool to investigate prion/amyloid-based phenomena.

Comparative Investigation of Raw and Processed Radix Polygoni Multiflori on the Treatment of Vascular Dementia by Liquid Chromatograph-Mass Spectrometry Based Metabolomic Approach

Radix Polygoni Multiflori (PM) is a well-known nootropic used in traditional Chinese medicine (TCM). Considering the efficacy and application discrepancy between raw (RPM) and processed PM (PPM), the similarities and differences between them in the treatment of vascular dementia (VaD) is intriguing. In this study, a VaD rat model was constructed by 2-vessel occlusion (2-VO). During 28 days of treatment, plasma was collected on days 7, 14, 21, and 28 after the start of dosing and the metabolic profile was analyzed by HPLC-MS/MS-based metabolomics. The Morris Water Maze Test, hematoxylin-eosin and Nissl staining, and biochemical analysis were used to assess cognitive function, pathogenic alterations and oxidative stress, respectively. RPM and PPM effectivelyreducedthe 2VO-induced cognitive impairment and mitigated histological alterations in hippocampus tissue. The 2-VO model significantly elevated MDA level and decreased SOD activity and GSH level, indicating severe oxidative stress, which could also be attenuated by RPM and PPM treatment. RPM outperformed PPM in decreasing MDA levels while PPM outperformed RPM in increasing GSH levels. Differential metabolites were subjected to Metabolite Set Enrichment Analysis (MSEA) and genes corresponding to proteins having interactions with metabolites were further annotated with Gene Ontology (GO). Both RPM and PPM ameliorated VaD-relevant vitamin B6 metabolism, pentose phosphate pathways, and taurine and hypotaurine metabolism. In addition, the metabolism of cysteine and methionine was regulated only by RPM, and riboflavin metabolism was modulated only by PPM. The results suggested that raw and processed PM had comparable efficacy in the treatment of VaD but also with some mechanistic differenece.

Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study

BACKGROUND

Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predicting dementia. We examined the role of midlife serum metabolites using a machine learning approach and determined whether the selected metabolites improved prediction accuracy beyond the effect of age.

METHODS

Five thousand three hundred seventy-four participants from the Whitehall II study, mean age 55.8 (standard deviation (SD) 6.0) years in 1997-1999 when 233 metabolites were quantified using nuclear magnetic resonance metabolomics. Participants were followed for a median 21.0 (IQR 20.4, 21.7) years for clinically-diagnosed dementia (N=329). Elastic net penalized Cox regression with 100 repetitions of nested cross-validation was used to select models that improved prediction accuracy for incident dementia compared to an age-only model. Risk scores reflecting the frequency with which predictors appeared in the selected models were constructed, and their predictive accuracy was examined using Royston's R², Akaike's information criterion, sensitivity, specificity, C-statistic and calibration.

RESULTS

Sixteen of the 100 models had a better c-statistic compared to an age-only model and 15 metabolites were selected at least once in all 16 models with glucose present in all models. Five risk scores, reflecting the frequency of selection of metabolites, and a 1-SD increment in all five risk scores was associated with higher dementia risk (HR between 3.13 and 3.26). Three of these, constituted of 4, 5 and 15 metabolites, had better prediction accuracy (c-statistic from 0.788 to 0.796) compared to an age-only model (c-statistic 0.780), all p<0.05.

CONCLUSIONS

Although there was robust evidence for the role of glucose in dementia, metabolites measured in midlife made only a modest contribution to dementia prediction once age was taken into account.

The effect of chronic exposure to a low concentration of perfluorooctanoic acid on cognitive function and intestinal health of obese mice induced by a high-fat diet

Perfluorooctanoic acid (PFOA) is a persistent organic pollutant associated with many adverse health risks. Evidence suggests that obese individuals may be more susceptible to environmental substances. In the present work, we explored the effects of PFOA exposure on the cognitive function and intestinal health of obese mice. Obese mice induced by a high-fat diet were exposed to PFOA (0.5 mg/kg (bw)/day) via drinking water for 100 days. After exposure to PFOA, decreased body weight, enlarged liver, abnormal behavior, impaired synapse structure, neuroinflammation, activated glial cell, decreased nerve growth factor, altered gut microbiota, and disturbed serum metabolites were observed, while the gut inflammation and intestinal barrier were not significantly influenced. These results suggest that exposure to PFOA is associated with cognitive impairment in obese mice.

Metabolomic profiling in small vessel disease identifies multiple associations with disease severity

Cerebral small vessel disease is a major cause of vascular cognitive impairment and dementia. There are few treatments, largely reflecting limited understanding of the underlying pathophysiology. Metabolomics can be used to identify novel risk factors to better understand pathogenesis and to predict disease progression and severity. We analysed data from 624 patients with symptomatic cerebral small vessel disease from two prospective cohort studies. Serum samples were collected at baseline and patients underwent MRI scans and cognitive testing at regular intervals with up to 14 years of follow-up. Using ultra-performance liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy, we obtained metabolic and lipidomic profiles from 369 annotated metabolites and 54 764 unannotated features and examined their association with respect to disease severity, assessed using MRI small vessel disease markers, cognition and future risk of all-cause dementia. Our analysis identified 28 metabolites that were significantly associated with small vessel disease imaging markers and cognition. Decreased levels of multiple glycerophospholipids and sphingolipids were associated with increased small vessel disease load as evidenced by higher white matter hyperintensity volume, lower mean diffusivity normalized peak height, greater brain atrophy and impaired cognition. Higher levels of creatine, FA(18:2(OH)) and SM(d18:2/24:1) were associated with increased lacune count, higher white matter hyperintensity volume and impaired cognition. Lower baseline levels of carnitines and creatinine were associated with higher annualized change in peak width of skeletonized mean diffusivity, and 25 metabolites, including lipoprotein subclasses, amino acids and xenobiotics, were associated with future dementia incidence. Our results show multiple distinct metabolic signatures that are associated with imaging markers of small vessel disease, cognition and conversion to dementia. Further research should assess causality and the use of metabolomic screening to improve the ability to predict future disease severity and dementia risk in small vessel disease. The metabolomic profiles may also provide novel insights into disease pathogenesis and help identify novel treatment approaches.

Plasma nervonic acid levels were negatively associated with attention levels in community-living older adults in New Zealand

The global population is aging. Preserving function and independence of our aging population is paramount. A key component to maintaining independence is the preservation of cognitive function. Metabolomics can be used to identify biomarkers of cognition before noticeable deterioration. Our study investigated the plasma metabolome of 332 community-living New Zealanders between 65 and 74 years of age, using gas chromatography-mass spectrometry. Six cognitive domains were assessed. Of the 123 metabolites identified using an in-house mass spectral libraries of standards, nervonic acid had a significant, inverse association with the attention domain (P-value = 1.52E^- 4; FDR = 0.019), after adjusting for covariates (apolipoprotein E -ε4 genotype, sex, body fat percentage (standardised by sex), age, education, deprivation index, physical activity, metabolic syndrome, polypharmacy, smoking status, and alcohol intake) and multiple testing. Attention is defined as the ability to concentrate on selected aspects of the environment while ignoring other stimuli. This is the first study to identify nervonic acid as a potential biomarker of attention in older adults. Future research should confirm this association in a longitudinal study.

The Influence of Orthopedic Surgery on Circulating Metabolite Levels, and their Associations with the Incidence of Postoperative Delirium

The mechanisms underlying the occurrence of postoperative delirium development are unclear and measurement of plasma metabolites may improve understanding of its causes. Participants (n = 54) matched for age and gender were sampled from an observational cohort study investigating postoperative delirium. Participants were ≥65 years without a diagnosis of dementia and presented for primary elective hip or knee arthroplasty. Plasma samples collected pre- and postoperatively were grouped as either control (n = 26, aged: 75.8 ± 5.2) or delirium (n = 28, aged: 76.2 ± 5.7). Widespread changes in plasma metabolite levels occurred following surgery. The only metabolites significantly differing between corresponding control and delirium samples were ornithine and spermine. In delirium cases, ornithine was 17.6% higher preoperatively, and spermine was 12.0% higher postoperatively. Changes were not associated with various perioperative factors. In binary logistic regression modeling, these two metabolites did not confer a significantly increased risk of delirium. These findings support the hypothesis that disturbed polyamine metabolism is an underlying factor in delirium that warrants further investigation.

Structured Long-Chain Omega-3 Fatty Acids for Improvement of Cognitive Function during Aging

Although the human lifespan has increased in the past century owing to advances in medicine and lifestyle, the human healthspan has not kept up the same pace, especially in brain aging. Consequently, the role of preventive health interventions has become a crucial strategy, in particular, the identification of nutritional compounds that could alleviate the deleterious effects of aging. Among nutrients to cope with aging in special cognitive decline, the long-chain omega-3 polyunsaturated fatty acids (ω-3 LCPUFAs) docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), have emerged as very promising ones. Due to their neuroinflammatory resolving effects, an increased status of DHA and EPA in the elderly has been linked to better cognitive function and a lower risk of dementia. However, the results from clinical studies do not show consistent evidence and intake recommendations for old adults are lacking. Recently, supplementation with structured forms of EPA and DHA, which can be derived natural forms or targeted structures, have proven enhanced bioavailability and powerful benefits. This review summarizes present and future perspectives of new structures of ω-3 LCPUFAs and the role of "omic" technologies combined with the use of high-throughput in vivo models to shed light on the relationships and underlying mechanisms between ω-3 LCPUFAs and healthy aging.

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.

Random-Forest-Algorithm-Based Applications of the Basic Characteristics and Serum and Imaging Biomarkers to Diagnose Mild Cognitive Impairment

Background

Mild cognitive impairment (MCI) is considered the early stage of Alzheimer's Disease (AD). The purpose of our study was to analyze the basic characteristics and serum and imaging biomarkers for the diagnosis of MCI patients as a more objective and accurate approach.

Methods

The Montreal Cognitive Test was used to test 119 patients aged ≥65. Such serum biomarkers were detected as preprandial blood glucose, triglyceride, total cholesterol, Aβ1-40, Aβ1-42, and P-tau. All the subjects were scanned with 1.5T MRI (GE Healthcare, WI, USA) to obtain DWI, DTI, and ASL images. DTI was used to calculate the anisotropy fraction (FA), DWI was used to calculate the apparent diffusion coefficient (ADC), and ASL was used to calculate the cerebral blood flow (CBF). All the images were then registered to the SPACE of the Montreal Neurological Institute (MNI). In 116 brain regions, the medians of FA, ADC, and CBF were extracted by automatic anatomical labeling. The basic characteristics included gender, education level, and previous disease history of hypertension, diabetes, and coronary heart disease. The data were randomly divided into training sets and test ones. The recursive random forest algorithm was applied to the diagnosis of MCI patients, and the recursive feature elimination (RFE) method was used to screen the significant basic features and serum and imaging biomarkers. The overall accuracy, sensitivity, and specificity were calculated, respectively, and so were the ROC curve and the area under the curve (AUC) of the test set.

Results

When the variable of the MCI diagnostic model was an imaging biomarker, the training accuracy of the random forest was 100%, the correct rate of the test was 86.23%, the sensitivity was 78.26%, and the specificity was 100%. When combining the basic characteristics, the serum and imaging biomarkers as variables of the MCI diagnostic model, the training accuracy of the random forest was found to be 100%; the test accuracy was 97.23%, the sensitivity was 94.44%, and the specificity was 100%. RFE analysis showed that age, Aβ1-40, and cerebellum_4_6 were the most important basic feature, serum biomarker, imaging biomarker, respectively.

Conclusion

Imaging biomarkers can effectively diagnose MCI. The diagnostic capacity of the basic trait biomarkers or serum biomarkers for MCI is limited, but their combination with imaging biomarkers can improve the diagnostic capacity, as indicated by the sensitivity of 94.44% and the specificity of 100% in our model. As a machine learning method, a random forest can help diagnose MCI effectively while screening important influencing factors.

Metabolomics Provides Novel Insights into Epilepsy Diagnosis and Treatment: A Review

Epilepsy is one of the most common diseases of the central nervous system. The diagnosis of epilepsy mainly depends on electroencephalograms and symptomatology, while diagnostic biofluid markers are still lacking. In addition, approximately 30% of patients with epilepsy (PWE) show a poor response to the currently available anti-seizure medicines. An increasing number of studies have reported alterations in the blood, brain tissue, cerebrospinal fluid and urine metabolome in PWE and animal models of epilepsy. The aim of this review was to identify potential metabolic biomarkers and pathways that might facilitate diagnostic, therapeutic and prognostic determination in PWE and the understanding of the pathogenesis of the disease. The PubMed and Embase databases were searched for metabolomic studies of PWE and epileptic models published before December 2020. The study objectives, types of models and reported differentially altered metabolites were examined and compared. Pathway analyses were performed using MetaboAnalyst 5.0 online software. Thirty-five studies were included in this review. Metabolites such as glutamate, lactate and citrate were disturbed in both PWE and epileptic models, which might be potential biomarkers of epilepsy. Metabolic pathways including alanine, aspartate and glutamate metabolism; glycine, serine and threonine metabolism; glycerophospholipid metabolism; glyoxylate and dicarboxylate metabolism; and arginine and proline metabolism were involved in epilepsy. These pathways might play important roles in the pathogenesis of the disease. This review summarizes metabolites and metabolic pathways related to epilepsy and provides a novel perspective for the identification of potential biomarkers and therapeutic targets for epilepsy.

Apolipoprotein E and sex modulate fatty acid metabolism in a prospective observational study of cognitive decline

BACKGROUND

Fatty acids play prominent roles in brain function as they participate in structural, metabolic and signaling processes. The homeostasis of fatty acids and related pathways is known to be impaired in cognitive decline and dementia, but the relationship between these metabolic disturbances and common risk factors, namely the ɛ4 allele of the apolipoprotein E (ApoE-ɛ4) gene and sex, remains elusive.

METHODS

In order to investigate early alterations associated with cognitive decline in the fatty acid-related serum metabolome, we here applied targeted metabolomics analysis on a nested case-control study (N=368), part of a prospective population cohort on dementia.

RESULTS

When considering the entire study population, circulating levels of free fatty acids, acyl-carnitines and pantothenic acid were found to be increased among those participants who had greater odds of cognitive decline over a 12-year follow-up. Interestingly, stratified analyses indicated that these metabolomic alterations were specific for ApoE-ɛ4 non-carriers and women.

CONCLUSIONS

Altogether, our results highlight that the regulation of fatty acids and related metabolic pathways during ageing and cognitive decline depends on complex inter-relationships between the ApoE-ε4 genotype and sex. A better understanding of the ApoE-ɛ4 and sex dependent modulation of metabolism is essential to elucidate the individual variability in the onset of cognitive decline, which would help develop personalized therapeutic approaches.

Sex Differences in the Metabolome of Alzheimer's Disease Progression

Alzheimer's disease (AD) is the leading cause of dementia; however, men and women face differential AD prevalence, presentation, and progression risks. Characterizing metabolomic profiles during AD progression is fundamental to understand the metabolic disruptions and the biological pathways involved. However, outstanding questions remain of whether peripheral metabolic changes occur equally in men and women with AD. Here, we evaluated differential effects of metabolomic and brain volume associations between sexes. We used three cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI), evaluated 1,368 participants, two metabolomic platforms with 380 metabolites in total, and six brain segment volumes. Using dimension reduction techniques, we took advantage of the correlation structure of the brain volume phenotypes and the metabolite concentration values to reduce the number of tests while aggregating relevant biological structures. Using WGCNA, we aggregated modules of highly co-expressed metabolites. On the other hand, we used partial least squares regression-discriminant analysis (PLS-DA) to extract components of brain volumes that maximally co-vary with AD diagnosis as phenotypes. We tested for differences in effect sizes between sexes in the association between single metabolite and metabolite modules with the brain volume components. We found five metabolite modules and 125 single metabolites with significant differences between sexes. These results highlight a differential lipid disruption in AD progression between sexes. Men showed a greater negative association of phosphatidylcholines and sphingomyelins and a positive association of VLDL and large LDL with AD progression. In contrast, women showed a positive association of triglycerides in VLDL and small and medium LDL with AD progression. Explicitly identifying sex differences in metabolomics during AD progression can highlight particular metabolic disruptions in each sex. Our research study and strategy can lead to better-tailored studies and better-suited treatments that take sex differences into account.

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.

Metabolic correlates of late midlife cognitive outcomes: findings from the 1946 British Birth Cohort

Investigating associations between metabolites and late midlife cognitive function could reveal potential markers and mechanisms relevant to early dementia. Here, we systematically explored the metabolic correlates of cognitive outcomes measured across the seventh decade of life, while untangling influencing life course factors. Using levels of 1019 metabolites profiled by liquid chromatography-mass spectrometry (age 60-64), we evaluated relationships between metabolites and cognitive outcomes in the British 1946 Birth Cohort (N = 1740). We additionally conducted pathway and network analyses to allow for greater insight into potential mechanisms, and sequentially adjusted for life course factors across four models, including sex and blood collection (Model 1), Model 1 + body mass index and lipid medication (Model 2), Model 2 + social factors and childhood cognition (Model 3) and Model 3 + lifestyle influences (Model 4). After adjusting for multiple tests, 155 metabolites, 10 pathways and 5 network modules were associated with cognitive outcomes. Of the 155, 35 metabolites were highly connected in their network module (termed 'hub' metabolites), presenting as promising marker candidates. Notably, we report relationships between a module comprised of acylcarnitines and processing speed which remained robust to life course adjustment, revealing palmitoylcarnitine (C16) as a hub (Model 4: β = -0.10, 95% confidence interval = -0.15 to -0.052, P = 5.99 × 10-5). Most associations were sensitive to adjustment for social factors and childhood cognition; in the final model, four metabolites remained after multiple testing correction, and 80 at P < 0.05. Two modules demonstrated associations that were partly or largely attenuated by life course factors: one enriched in modified nucleosides and amino acids (overall attenuation = 39.2-55.5%), and another in vitamin A and C metabolites (overall attenuation = 68.6-92.6%). Our other findings, including a module enriched in sphingolipid pathways, were entirely explained by life course factors, particularly childhood cognition and education. Using a large birth cohort study with information across the life course, we highlighted potential metabolic mechanisms associated with cognitive function in late midlife, suggesting marker candidates and life course relationships for further study.

Association of Lipidomics Signatures in Blood with Clinical Progression in Preclinical and Prodromal Alzheimer's Disease

BACKGROUND

Lipidomics may provide insight into biochemical processes driving Alzheimer's disease (AD) pathogenesis and ensuing clinical trajectories.

OBJECTIVE

To identify a peripheral lipidomics signature associated with AD pathology and investigate its potential to predict clinical progression.

METHODS

We used Bayesian elastic net regression to select plasma lipid classes associated with the CSF pTau/Aβ42 ratio as a biomarker of AD pathology in preclinical and prodromal AD cases from the ADNI cohort. Consensus clustering of the selected lipid classes was used to identify lipidomic endophenotypes and study their association with clinical progression.

RESULTS

In the APOE4-adjusted model, ether-glycerophospholipids, lyso-glycerophospholipids, free-fatty acids, cholesterol esters, and complex sphingolipids were found to be associated with the CSF pTau/Aβ 42 ratio. We found an optimal number of five lipidomic endophenotypes in the prodromal and preclinical cases, respectively. In the prodromal cases, these clusters differed with respect to the risk of clinical progression as measured by clinical dementia rating score conversion.

CONCLUSION

Lipid alterations can be captured at the earliest phases of AD. A lipidomic signature in blood may provide a dynamic overview of an individual's metabolic status and may support identifying different risks of clinical progression.

Lipid Metabolism in the Development and Progression of Vascular Cognitive Impairment: A Systematic Review

Background: Vascular cognitive impairment (VCI) is a major public health problem. The current diagnosis of VCI is made based on the assessment of clinical symptoms and neuropsychological measurements, and is supported by neuroimaging. These methods are both time-consuming and expensive, which leads to needs for alternative biomarkers for VCI. Metabolomics is an emerging and powerful tool to discover of new biomarkers of disease, which can investigate variations in different metabolic processes such as lipid, since the brain is highly enriched in lipids and that lipid changes may lead to pathology in the brain. Vascular cognitive impairment is vulnerable to the disturbance of lipid metabolism. Furthermore, blood samples, which could be identified as reliable clinical biomarkers are relatively convenient to obtain and provide a non-invasive assessment. Therefore, our study aims to understand whether peripheral lipid biomarkers can be used as diagnostic biomarkers and monitor the progression of VCI.

Methods: We systematically searched the PubMed, Embase, CNKI, and VIP databases to find VCI and lipid metabolism in reports from inception through February 2021. Studies meeting the following criteria were eligible: (1) original studies in humans; (2) lipid metabolites in blood; (3) reports of VCI.

Results: Through our review, nine original articles were eligible. Blood-based metabolites that might be potential biomarkers were identified. Most of them including PC, PE, Cers, and ChEs were significantly lower, while elevation of FAs and DGs were associated with VCI. Most importantly, these blood-based metabolites might be proposed as potential biomarkers for VCI, which provides direction for further validation.

Discussion and Conclusion: To the best of our knowledge, this is the first systemic review concerning the relationship of lipid metabolism and VCI. It identifies potential biomarkers and provides insights into the disease pathobiology. However, more advanced studies and researches on a lipidomic platform must be done to understand the exact pathology behind and identify potential lipid biomarkers, which might help achieve the goal of discovering novel therapeutics.

Personalized nutrition for dementia prevention

The role of nutrition has been investigated for decades under the assumption of one-size-fits-all. Yet there is heterogeneity in metabolic and neurobiological responses to diet. Thus a more personalized approach may better fit biological reality and have increased efficacy to prevent dementia. Personalized nutrition builds on the food exposome, defined as the history of diet-related exposures over the lifetime, and on its interactions with the genome and other biological characteristics (eg, metabolism, the microbiome) to shape health. We review current advances of personalized nutrition in dementia research. We discuss key questions, success milestones, and future roadmap from observational epidemiology to clinical studies through basic science. A personalized nutrition approach based on the best prescription for the most appropriate target population in the most relevant time-window has the potential to strengthen dementia-prevention efforts.

Food and Microbiota Metabolites Associate with Cognitive Decline in Older Subjects: A 12-Year Prospective Study

SCOPE

Diet is considered an important modulator of cognitive decline and dementia, but the available evidence is, however, still fragmented and often inconsistent.

METHODS AND RESULTS

The article studies the long-term prospective Three-City Cohort, which consists of two separate nested case-control sample sets from different geographic regions (Bordeaux, n = 418; Dijon, n = 424). Cognitive decline is evaluated through five neuropsychological tests (Mini-Mental State Examination, Benton Visual Retention Test, Isaac's Set Test, Trail-Making Test part A, and Trail-Making Test part B). The food-related and microbiota-derived circulating metabolome is studied in participants free of dementia at baseline, by subjecting serum samples to large-scale quantitative metabolomics analysis. A protective association is found between metabolites derived from cocoa, coffee, mushrooms, red wine, the microbial metabolism of polyphenol-rich foods, and cognitive decline, as well as a negative association with metabolites related to unhealthy dietary components, such as artificial sweeteners and alcohol.

CONCLUSION

These results provide insight into the early metabolic events that are associated with the later risk to develop cognitive decline within the crosstalk between diet, gut microbiota and the endogenous metabolism, which can help identify potential targets for preventive and therapeutic strategies to preserve cognitive health.

A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations

BACKGROUND

Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links.

RESULTS

In this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites "robustly well-predicted". To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite's level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis.

CONCLUSIONS

Combined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies. Video abstract.

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

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

Emerging role of metabolomics in protein conformational disorders

Introduction: Metabolomics focuses on interactions among different metabolites associated with various cellular functions in cells, tissues, and organs. In recent years, metabolomics has emerged as a powerful tool to identify perturbed metabolites, pathways influenced by the environment, for protein conformational diseases (PCDs) and also offers wide clinical application.Area Covered: This review provides a brief overview of recent advances in metabolomics as applied to identify metabolic variations in PCDs, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, prion disease, and cardiac amyloidosis. The 'PubMed' and 'Google Scholar' database search methods have been used to screen the published reports with key search terms: metabolomics, biomarkers, and protein conformational disorders.Expert opinion: Metabolomics is the large-scale study of metabolites and is deemed to overwhelm other omics. It plays a crucial role in finding variations in diseases due to protein conformational changes. However, many PCDs are yet to be identified. Metabolomics is still an emerging field; there is a need for new high-resolution analytical techniques and more studies need to be carried out to generate new information.

Dose-Related Urinary Metabolic Alterations of a Combination of Quercetin and Resveratrol-Treated High-Fat Diet Fed Rats

Most herbal polyphenols and flavonoids reveals multiple ameliorative benefits for obesity caused by chronic metabolic disorders. Accumulated studies have revealed that preferable therapeutic effects can be obtained through clinical combination of these two kinds of natural compounds for obesity improvement. The typical representative research was the combination of quercetin and resveratrol (CQR), in which the ratio of quercetin and resveratrol is 2:1, demonstrating a synergistic effect in anti-obesity process. Although there exists reports clarifying the mechanism of the combination of two to improve obesity from the perspective of improving adipose tissue inflammation or modulating the composition of intestinal flora, there are few further studies on the mechanism of drug action from the perspective of metabolites transformation. In this research, we mainly focused on the alterations of endogenous metabolites in rats, and analyzed the urine metabolites of obese and intervention model. Therefore, a gas chromatography-mass spectrometry (GC-MS) based metabolomics approach was applied to assess the potential effects and mechanisms of CQR at different dosages (45, 90, and 180 mg/kg) in high fat diet (HFD)-induced obesity rats. Body weight gain and visceral fat weight were reduced by CQR, as well as blood lipid and inflammatory factor levels were increased by CQR in a dose-related manner. Urinary metabolomics revealed 22 differential metabolites related to the HFD-induced obesity, which were reversed in a dose-dependent manner by CQR, of which 8 were reversed in the 45 mg/kg CQR group, 15 were reversed in the 90 mg/kg CQR group, and 18 were reversed in the 180 mg/kg CQR group. Combined with bioinformatics and pattern recognition, the results demonstrated that the key differential metabolites were basically involved in amino acid metabolism, galactose metabolism, pantothenate and CoA biosynthesis, pyruvate metabolism and lysine degradation. In summary, our results showed significant therapeutic action by CQR administration and remarkable metabolomic changes after HFD feeding and CQR intervention. Urinary metabolomic analysis was highlighted on account of providing holistic and comprehensive insights into the pathophysiological mechanisms of the HFD-induced obesity, which also supplied clues for the future mechanism studies of CQR's anti-obesity effects.

Combining Field-Enabled Capillary Vibrating Sharp-Edge Spray Ionization with Microflow Liquid Chromatography and Mass Spectrometry to Enhance 'Omics Analyses

Field-enabled capillary vibrating sharp-edge spray ionization (cVSSI) has been combined with high-flow liquid chromatography (LC) and mass spectrometry (MS) to establish current ionization capabilities for metabolomics and proteomics investigations. Comparisons are made between experiments employing cVSSI and a heated electrospray ionization probe representing the state-of-the-art in microflow LC-MS methods for 'omics studies. For metabolomics standards, cVSSI is shown to provide an ionization enhancement by factors of 4 ± 2 for both negative and positive ion mode analyses. For chymotryptic peptides, cVSSI is shown to provide an ionization enhancement by factors of 5 ± 2 and 2 ± 1 for negative and positive ion mode analyses, respectively. Slightly broader high-performance liquid chromatography peaks are observed in the cVSSI datasets, and several studies suggest that this results from a slightly decreased post-split flow rate. This may result from partial obstruction of the pulled-tip emitter over time. Such a challenge can be remedied with the use of LC pumps that operate in the 10 to 100 μL·min-1 flow regime. At this early stage, the proof-of-principle studies already show ion signal advantages over state-of-the-art electrospray ionization (ESI) for a wide variety of analytes in both positive and negative ion mode. Overall, this represents a ∼20-50-fold improvement over the first demonstration of LC-MS analyses by voltage-free cVSSI. Separate comparisons of the ion abundances of compounds eluting under identical solvent conditions reveal ionization efficiency differences between cVSSI and ESI and may suggest varied contributions to ionization from different physicochemical properties of the compounds. Future investigations of parameters that could further increase ionization gains in negative and positive ion mode analyses with the use of cVSSI are briefly presented.

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

BACKGROUND

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

METHODS

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

FINDINGS

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

INTERPRETATION

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

FUNDING

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

Combining metabolomics with bioanalysis methods to investigate the potential toxicity of dihexyl phthalate

Dihexyl phthalate (DHP) is one of the most commonly used phthalate esters in various plastic and consumer products. Human are inevitably exposed to DHPs. Although several animal and human experiments have revealed that DHP can cause multiple toxicities, few studies have previously assessed the effects of DHP exposure by liquid chromatography mass spectrometry (LC-MS) analysis combine with molecular biology methods on human cells. Therefore, the purpose of our study was to investigate the effect of DHP on human cell metabolism by systems biology methods. In this study, U₂ OS cancer cells were treated with 10 μM DHP for metabolomics analysis and apoptosis analysis at indicate time. Metabolomic study of the metabolic changes caused by DHP in U₂ OS cells was performed for the first time using integrative liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). To investigate the possible reason of fatty acids level altered by DHP, we measured some key fatty acid synthesis and oxidation-related enzyme expression levels by quantitative real-time PCR (Q-PCR). Apoptotic cells were analyzed by flow cytometry and apoptosis-related gene expressions were measured by Q-PCR. 2',7'-Dichlorofluorescein diacetate (DCFH-DA) staining was used to evaluate ROS content. Partial least squares-discriminate analysis (PLS-DA) clearly showed that significant differences in metabolic profiles were observed in U₂ OS cells exposed to DHP compared with controls. A total of 58 putative metabolites in electrospray ionization source (ESI) + mode and 32 putative metabolites in ESI-mode were detected, the majority of the differential metabolites being lipids and lipid-like molecules. Among them, the altered fatty acids level corresponded to expression levels of genes encoding enzymes related to fatty acids synthesis and oxidation. Moreover, DHP induced reactive oxygen species (ROS) accumulation, promoted cell apoptosis and inflammation, and resulted in a significant increase in apoptosis and inflammation-related gene expression levels compared with controls. In summary, our results suggested that metabolomics combined with molecular bioanalysis methods could be an efficient tool to assess toxic effects, which contribute to explore the possible cytotoxicity mechanisms of DHP, and provide a basis for further research.

Plasma metabolomics of presymptomatic PSEN1-H163Y mutation carriers: a pilot study

BACKGROUND AND OBJECTIVE

PSEN1-H163Y carriers, at the presymptomatic stage, have reduced ¹⁸ FDG-PET binding in the cerebrum of the brain (Scholl et al., Neurobiol Aging 32:1388-1399, 2011). This could imply dysfunctional energy metabolism in the brain. In this study, plasma of presymptomatic PSEN1 mutation carriers was analyzed to understand associated metabolic changes.

METHODS

We analyzed plasma from noncarriers (NC, n = 8) and presymptomatic PSEN1-H163Y mutation carriers (MC, n = 6) via untargeted metabolomics using gas and liquid chromatography coupled with mass spectrometry, which identified 1199 metabolites. All the metabolites were compared between MC and NC using univariate analysis, as well as correlated with the ratio of Aβ_{1-42/A β 1-40} , using Spearman's correlation. Altered metabolites were subjected to Ingenuity Pathway Analysis (IPA).

RESULTS

Based on principal component analysis the plasma metabolite profiles were divided into dataset A and dataset B. In dataset A, when comparing between presymptomatic MC and NC, the levels of 79 different metabolites were altered. Out of 79, only 14 were annotated metabolites. In dataset B, 37 metabolites were significantly altered between presymptomatic MC and NC and nine metabolites were annotated. In both datasets, annotated metabolites represent amino acids, fatty acyls, bile acids, hexoses, purine nucleosides, carboxylic acids, and glycerophosphatidylcholine species. 1-docosapentaenoyl-GPC was positively correlated, uric acid and glucose were negatively correlated with the ratio of plasma Aβ_1-42 /Aβ_1-40 (P < 0.05).

INTERPRETATION

This study finds dysregulated metabolite classes, which are changed before the disease symptom onset. Also, it provides an opportunity to compare with sporadic Alzheimer's Disease. Observed findings in this study need to be validated in a larger and independent Familial Alzheimer's Disease (FAD) cohort.

Metabolites Associated with Early Cognitive Changes Implicated in Alzheimer's Disease

BACKGROUND

Understanding metabolic mechanisms associated with cognitive changes preceding an Alzheimer's disease (AD) diagnosis could advance our understanding of AD progression and inform preventive methods.

OBJECTIVE

We investigated the metabolomics of the early changes in executive function and delayed recall, the earliest aspects of cognitive function to change in the course of AD development, in order to better understand mechanisms that could contribute to early stages and progression of this disease.

METHODS

This investigation used longitudinal plasma samples from the Wisconsin Registry for Alzheimer's Prevention (WRAP), a cohort of participants who were dementia free at enrollment and enriched with a parental history of AD. Metabolomic profiles were quantified for 2,324 fasting plasma samples among 1,200 participants, each with up to three study visits, which occurred every two years. Metabolites were individually tested for association with executive function and delayed recall trajectories across age.

RESULTS

Of 1,097 metabolites tested, levels of seven were associated with executive function trajectories, including an amino acid cysteine S-sulfate and three fatty acids, including erucate (22 : 1n9), while none were associated with delayed recall trajectories. Replication was attempted for four of these metabolites that were present in the Vietnam Era Twin Study of Aging (VETSA). Although none reached statistical significance, three of these associations showed consistent effectdirections.

CONCLUSION

Our results suggest potential metabolomic mechanisms that could contribute to the earliest signs of cognitive decline. In particular, fatty acids may be associated with cognition in a manner that is more complex than previously suspected.

Plasma Sphingomyelins in Late-Onset Alzheimer's Disease

BACKGROUND

Altered plasma levels of sphingolipids, including sphingomyelins (SM), have been found in mouse models of Alzheimer's disease (AD) and in AD patient plasma samples.

OBJECTIVE

This study assesses fourteen plasma SM species in a late-onset AD (LOAD) patient cohort (n = 138).

METHODS

Specimens from control, preclinical, and symptomatic subjects were analyzed using targeted mass-spectrometry-based metabolomic methods.

RESULTS

Total plasma SM levels were not significantly affected by age or cognitive status. However, one metabolite that has been elevated in manifest AD in several recent studies, SM OHC14:1, was reduced significantly in pre-clinical AD and MCI relative to normal controls.

CONCLUSION

We recommend additional comprehensive plasma lipidomics in experimental and clinical biospecimens related to LOAD that might advance the utility of plasma sphingomyelin levels in molecular phenotyping and interpretations of pathobiological mechanisms.

Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.

Evidence that the Kennedy and polyamine pathways are dysregulated in human brain in cases of dementia with Lewy bodies

Disruptions of brain metabolism are considered integral to the pathogenesis of dementia, but thus far little is known of how dementia with Lewy bodies (DLB) impacts the brain metabolome. DLB is less well known than other neurodegenerative diseases such as Alzheimer's and Parkinson's disease which is perhaps why it is under-investigated. This exploratory study aimed to address current knowledge gaps in DLB research and search for potentially targetable biochemical pathways for therapeutics. It also aimed to better understand metabolic similarities and differences with other dementias. Combined metabolomic analyses of ¹H NMR and tandem mass spectrometry of neocortical post-mortem brain tissue (Brodmann region 7) from autopsy confirmed cases of DLB (n = 15) were compared with age/gender-matched, non-cognitively impaired healthy controls (n = 30). Following correction for multiple comparisons, only 2 metabolites from a total of 219 measured compounds significantly differed. Putrescine was suppressed (55.4%) in DLB and O-phosphocholine was elevated (52.5%). We identified a panel of 5 metabolites (PC aa C38:4, O-Phosphocholine, putrescine, 4-Aminobutyrate, and SM C16:0) capable of accurately discriminating between DLB and control subjects. Deep Learning (DL) provided the best predictive model following 10-fold cross validation (AUROC (95% CI) = 0.80 (0.60-1.0)) with sensitivity and specificity equal to 0.92 and 0.88, respectively. Altered brain levels of putrescine and O-phosphocholine indicate that the Kennedy pathway and polyamine metabolism are perturbed in DLB. These are accompanied by a consistent underlying trend of lipid dysregulation. As yet it is unclear whether these are a cause or consequence of DLB onset.

The Metabolomic Underpinnings of Symptom Burden in Patients With Multiple Chronic Conditions

Over 25% of the adult population in the United States suffers from multiple chronic conditions, with numbers continuing to rise. Those with multiple chronic conditions often experience symptoms or symptom clusters that undermine their quality of life and ability to self-manage. Importantly, symptom severity in those with even the same multiple chronic conditions varies, suggesting that the mechanisms driving symptoms in patients with multiple chronic conditions are not fixed but may differ in ways that could make them amenable to targeted interventions. In this manuscript we describe at a metabolic level, the symptom experience of persons with multiple chronic conditions, including how symptoms may synergize or cluster across multiple chronic conditions to augment one's symptom burden. To guide this discussion, we consider the metabolites and metabolic pathways known to span multiple adverse health conditions and associate with severe symptoms of fatigue, depression, and anxiety and their cluster. We also describe how severe versus mild symptoms, and their associated metabolites and metabolic pathways, may vary, depending on the presence of covariates; two of which, sex as a biological variable and the contribution of gut microbiota dysbiosis, are discussed in additional detail. Intertwining metabolomics and symptom science into nursing research, offers the unique opportunity to better understand how the metabolites and metabolic pathways affected in those with multiple chronic conditions may initiate or exacerbate symptom presence within a given individual, ultimately allowing clinicians to develop targeted interventions to improve the health quality of patients their families.

Lifestyle, multi-omics features, and preclinical dementia among Chinese: The Taizhou Imaging Study

China has the largest number of patients with dementia in the world. However, dementia in the Chinese population is still poorly understood and under-researched. Given the differences in genetic, demographic, sociocultural, lifestyle, and health profiles among Chinese and other ethnic/racial groups, it is crucial to build appropriate infrastructure for long-term longitudinal studies to advance Chinese cognitive aging and dementia research. We initiated a community-based prospective cohort-the Taizhou Imaging Study (TIS)-to accelerate the understanding of dementia and cerebrovascular diseases in Chinese. This article presents the rationale, aims, study design, and organization of TIS. In addition, we described some examples of the types of studies such a resource might support. The TIS provides a new framework for facilitating Chinese dementia research, encompassing invaluable resources including detailed epidemiological, sociocultural, neuroimaging, and omics data.

Current Status of Metabolomic Biomarker Discovery: Impact of Study Design and Demographic Characteristics

Widespread application of omic technologies is evolving our understanding of population health and holds promise in providing precise guidance for selection of therapeutic interventions based on patient biology. The opportunity to use hundreds of analytes for diagnostic assessment of human health compared to the current use of 10–20 analytes will provide greater accuracy in deconstructing the complexity of human biology in disease states. Conventional biochemical measurements like cholesterol, creatinine, and urea nitrogen are currently used to assess health status; however, metabolomics captures a comprehensive set of analytes characterizing the human phenotype and its complex metabolic processes in real-time. Unlike conventional clinical analytes, metabolomic profiles are dramatically influenced by demographic and environmental factors that affect the range of normal values and increase the risk of false biomarker discovery. This review addresses the challenges and opportunities created by the evolving field of clinical metabolomics and highlights features of study design and bioinformatics necessary to maximize the utility of metabolomics data across demographic groups.

Association of Taurine with In-Hospital Mortality in Patients after Out-of-Hospital Cardiac Arrest: Results from the Prospective, Observational COMMUNICATE Study

Background: Studies have suggested that taurine may have neuro- and cardio-protective functions, but there is little research looking at taurine levels in patients after out-of-hospital cardiac arrest (OHCA). Our aim was to evaluate the association of taurine with mortality and neurological deficits in a well-defined cohort of OHCA patients. Methods: We prospectively measured serum taurine concentration in OHCA patients upon admission to the intensive care unit (ICU) of the University Hospital Basel (Switzerland). We analyzed the association of taurine levels and in-hospital mortality (primary endpoint). We further evaluated neurological outcomes assessed by the cerebral performance category scale. We calculated logistic regression analyses and report odds ratios (OR) and 95% confidence intervals (CI). We calculated different predefined multivariable regression models including demographic variables, comorbidities, initial vital signs, initial blood markers and resuscitation measures. We assessed discrimination by means of area under the receiver operating curve (ROC). Results: Of 240 included patients, 130 (54.2%) survived until hospital discharge and 110 (45.8%) had a favorable neurological outcome. Taurine levels were significantly associated with higher in-hospital mortality (adjusted OR 4.12 (95%CI 1.22 to 13.91), p = 0.02). In addition, a significant association between taurine concentration and a poor neurological outcome was observed (adjusted OR of 3.71 (95%CI 1.13 to 12.25), p = 0.03). Area under the curve (AUC) suggested only low discrimination for both endpoints (0.57 and 0.57, respectively). Conclusion: Admission taurine levels are associated with mortality and neurological outcomes in OHCA patients and may help in the risk assessment of this vulnerable population. Further studies are needed to assess whether therapeutic modulation of taurine may improve clinical outcomes after cardiac arrest.