Novel serum metabolites associate with cognition phenotypes among Bogalusa Heart Study participants

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.

Circulating Metabolomic Associations with Neurocognitive Outcomes in Pediatric CKD

BACKGROUND

Children with CKD are at risk for impaired neurocognitive functioning. We investigated metabolomic associations with neurocognition in children with CKD.

METHODS

We leveraged data from the Chronic Kidney Disease in Children (CKiD) study and the Neurocognitive Assessment and Magnetic Resonance Imaging Analysis of Children and Young Adults with Chronic Kidney Disease (NiCK) study. CKiD is a multi-institutional cohort that enrolled children aged 6 months to 16 years with eGFR 30-90 ml/min per 1.73 m 2 ( n =569). NiCK is a single-center cross-sectional study of participants aged 8-25 years with eGFR<90 ml/min per 1.73 m 2 ( n =60) and matched healthy controls ( n =67). Untargeted metabolomic quantification was performed on plasma (CKiD, 622 metabolites) and serum (NiCK, 825 metabolites) samples. Four neurocognitive domains were assessed: intelligence, attention regulation, working memory, and parent ratings of executive function. Repeat assessments were performed in CKiD at 2-year intervals. Linear regression and linear mixed-effects regression analyses adjusting for age, sex, delivery history, hypertension, proteinuria, CKD duration, and glomerular versus nonglomerular diagnosis were used to identify metabolites associated with neurocognitive z-scores. Analyses were performed with and without adjustment for eGFR.

RESULTS

There were multiple metabolite associations with neurocognition observed in at least two of the analytic samples (CKiD baseline, CKiD follow-up, and NiCK CKD). Most of these metabolites were significantly elevated in children with CKD compared with healthy controls in NiCK. Notable signals included associations with parental ratings of executive function: phenylacetylglutamine, indoleacetylglutamine, and trimethylamine N-oxide-and with intelligence: γ -glutamyl amino acids and aconitate.

CONCLUSIONS

Several metabolites were associated with neurocognitive dysfunction in pediatric CKD, implicating gut microbiome-derived substances, mitochondrial dysfunction, and altered energy metabolism, circulating toxins, and redox homeostasis.

Association of serum metabolites and gut microbiota at hospital admission with nosocomial infection development in patients with cirrhosis

Cirrhosis is complicated by a high rate of nosocomial infections (NIs), which result in poor outcomes and are challenging to predict using clinical variables alone. Our aim was to determine predictors of NI using admission serum metabolomics and gut microbiota in inpatients with cirrhosis. In this multicenter inpatient cirrhosis study, serum was collected on admission for liquid chromatography-mass spectrometry metabolomics, and a subset provided stool for 16SrRNA analysis. Hospital course, including NI development and death, were analyzed. Metabolomic analysis using analysis of covariance (ANCOVA) (demographics, Model for End-Stage Liver Disease [MELD] admission score, white blood count [WBC], rifaximin, and infection status adjusted) and random forest analyses for NI development were performed. Additional values of serum metabolites over clinical variables toward NI were evaluated using logistic regression. Stool microbiota and metabolomic correlations were compared in patients with and without NI development. A total of 602 patients (231 infection admissions) were included; 101 (17%) developed NIs, which resulted in worse inpatient outcomes, including intensive care unit transfer, organ failure, and death. A total of 127 patients also gave stool samples, and 20 of these patients developed NIs. The most common NIs were spontaneous bacterial peritonitis followed by urinary tract infection, Clostridioides difficile, and pneumonia. A total of 247 metabolites were significantly altered on ANCOVA. Higher MELD scores (odds ratio, 1.05; p < 0.0001), admission infection (odds ratio, 3.54; p < 0.0001), and admission WBC (odds ratio, 1.05; p = 0.04) predicted NI (area under the curve, 0.74), which increased to 0.77 (p = 0.05) with lower 1-linolenoyl-glycerolphosphocholine (GPC) and 1-stearoyl-GPC and higher N-acetyltryptophan and N-acetyl isoputreanine. Commensal microbiota were lower and pathobionts were higher in those who developed NIs. Microbial-metabolite correlation networks were complex and dense in patients with NIs, especially sub-networks centered on Ruminococcaceae and Pseudomonadaceae. NIs are common and associated with poor outcomes in cirrhosis. Admission gut microbiota in patients with NIs showed higher pathobionts and lower commensal microbiota. Microbial-metabolomic correlations were more complex, dense, and homogeneous among those who developed NIs, indicating greater linkage strength. Serum metabolites and gut microbiota on admission are associated with NI development in cirrhosis.

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.

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.

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

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

Purine metabolites and complex diseases: role of genes and nutrients

PURPOSE OF REVIEW

Purines have several important physiological functions as part of nucleic acids and as intracellular and extracellular signaling molecules. Purine metabolites, particularly uric acid, have been implicated in congenital and complex diseases. However, their role in complex diseases is not clear and they have both beneficial and detrimental effects on disease pathogenesis. In addition, the relationship between purines and complex diseases is affected by genetic and nutritional factors. This review presents latest findings about the relationship between purines and complex diseases and the effect of genes and nutrients on this relationship.

RECENT FINDINGS

Evidence from recent studies show strong role of purines in complex diseases. Although they are causal in only few diseases, our knowledge about their role in other diseases is still evolving. Of all the purines, uric acid is the most studied. Uric acid acts as an antioxidant as well as a prooxidant under different conditions, thus, its role in disease also varies. Other purines, adenosine and inosine have been less studied, but they have neuroprotective properties which are valuable in neurodegenerative diseases.

SUMMARY

Purines are molecules with great potential in disease pathogenesis as either metabolic markers or therapeutic targets. More studies need to be conducted to understand their relevance for complex diseases.

Shifts in gut microbiome and metabolome are associated with risk of recurrent atrial fibrillation

Alternations of gut microbiota (GM) in atrial fibrillation (AF) with elevated diversity, perturbed composition and function have been described previously. The current work aimed to assess the association of GM composition with AF recurrence (RAF) after ablation based on metagenomic sequencing and metabolomic analyses and to construct a GM-based predictive model for RAF. Compared with non-AF controls (50 individuals), GM composition and metabolomic profile were significantly altered between patients with recurrent AF (17 individuals) and non-RAF group (23 individuals). Notably, discriminative taxa between the non-RAF and RAF groups, including the families Nitrosomonadaceae and Lentisphaeraceae, the genera Marinitoga and Rufibacter and the species Faecalibacterium spCAG:82, Bacillus gobiensis and Desulfobacterales bacterium PC51MH44, were selected to construct a taxonomic scoring system based on LASSO analysis. After incorporating the clinical factors of RAF, taxonomic score retained a significant association with RAF incidence (HR = 2.647, P = .041). An elevated AUC (0.954) and positive NRI (1.5601) for predicting RAF compared with traditional clinical scoring (AUC = 0.6918) were obtained. The GM-based taxonomic scoring system theoretically improves the model performance, and the nomogram and decision curve analysis validated the clinical value of the predicting model. These data provide novel possibility that incorporating the GM factor into future recurrent risk stratification.

Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC-MS based untargeted metabolomics practitioners

INTRODUCTION

The metabolomics quality assurance and quality control consortium (mQACC) evolved from the recognized need for a community-wide consensus on improving and systematizing quality assurance (QA) and quality control (QC) practices for untargeted metabolomics.

OBJECTIVES

In this work, we sought to identify and share the common and divergent QA and QC practices amongst mQACC members and collaborators who use liquid chromatography-mass spectrometry (LC-MS) in untargeted metabolomics.

METHODS

All authors voluntarily participated in this collaborative research project by providing the details of and insights into the QA and QC practices used in their laboratories. This sharing was enabled via a six-page questionnaire composed of over 120 questions and comment fields which was developed as part of this work and has proved the basis for ongoing mQACC outreach.

RESULTS

For QA, many laboratories reported documenting maintenance, calibration and tuning (82%); having established data storage and archival processes (71%); depositing data in public repositories (55%); having standard operating procedures (SOPs) in place for all laboratory processes (68%) and training staff on laboratory processes (55%). For QC, universal practices included using system suitability procedures (100%) and using a robust system of identification (Metabolomics Standards Initiative level 1 identification standards) for at least some of the detected compounds. Most laboratories used QC samples (>86%); used internal standards (91%); used a designated analytical acquisition template with randomized experimental samples (91%); and manually reviewed peak integration following data acquisition (86%). A minority of laboratories included technical replicates of experimental samples in their workflows (36%).

CONCLUSIONS

Although the 23 contributors were researchers with diverse and international backgrounds from academia, industry and government, they are not necessarily representative of the worldwide pool of practitioners due to the recruitment method for participants and its voluntary nature. However, both questionnaire and the findings presented here have already informed and led other data gathering efforts by mQACC at conferences and other outreach activities and will continue to evolve in order to guide discussions for recommendations of best practices within the community and to establish internationally agreed upon reporting standards. We very much welcome further feedback from readers of this article.

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.