The Consortium of Metabolomics Studies (COMETS): Metabolomics in 47 Prospective Cohort Studies

Genetic Architecture and Analysis Practices of Circulating Metabolites in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program

Circulating metabolite levels partly reflect the state of human health and diseases and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single-study analyses. Leveraging the rich metabolomics resources generated by the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally diverse samples. We provided a set of reasonable strategies for outlier and imputation handling to process metabolite data. Following the practical analysis framework, we further performed a genome-wide association analysis on 1,135 selected metabolites using whole genome sequencing data from 16,359 individuals passing the quality control filters, and discovered 1,778 independent loci associated with 667 metabolites. Among 108 novel locus-metabolite pairs, we detected not only novel loci within previously implicated metabolite associated genes but also novel genes (such as GAB3 and VSIG4 located in the X chromosome) that have putative roles in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, including well-known metabolic genes such as FADS2 , D2HGDH , SUGP1 , UTG2B17 , strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

Global metabolomic alterations associated with endocrine-disrupting chemicals among pregnant individuals and newborns

BACKGROUND

Gestational exposure to non-persistent endocrine-disrupting chemicals (EDCs) may be associated with adverse pregnancy outcomes. While many EDCs affect the endocrine system, their effects on endocrine-related metabolic pathways remain unclear. This study aims to explore the global metabolome changes associated with EDC biomarkers at delivery.

METHODS

This study included 75 pregnant individuals who delivered at the University of Cincinnati Hospital from 2014 to 2017. We measured maternal urinary biomarkers of paraben/phenol (12), phthalate (13), and phthalate replacements (4) from the samples collected during the delivery visit. Global serum metabolome profiles were analyzed from maternal blood (n = 72) and newborn (n = 63) cord blood samples collected at delivery. Fifteen of the 29 urinary biomarkers were excluded due to low detection frequency or potential exposures during hospital stay. We assessed metabolome-wide associations between 14 maternal urinary biomarkers and maternal/newborn metabolome profiles. Additionally, performed enrichment analysis to identify potential alterations in metabolic pathways.

RESULTS

We observed metabolome-wide associations between maternal urinary concentrations of phthalate metabolites (mono-isobutyl phthalate), phthalate replacements (mono-2-ethyl-5-carboxypentyl terephthalate, mono-2-ethyl-5-hydroxyhexyl terephthalate) and phenols (bisphenol-A, bisphenol-S) and maternal serum metabolome, using q-value < 0.2 as a threshold. Additionally, associations of phthalate metabolites (mono-n-butyl phthalate, monobenzyl phthalate) and phenols (2,5-dichlorophenol, BPA) with the newborn metabolome were noted. Enrichment analyses revealed associations (p-gamma < 0.05) with amino acid, carbohydrate, lipid, glycan, vitamin, and other cofactor metabolism pathways.

CONCLUSION

Maternal paraben, phenol, phthalate, and phthalate replacement biomarker concentrations at delivery were associated with maternal and newborn serum global metabolome.

A novel multivariable Mendelian randomization framework to disentangle highly correlated exposures with application to metabolomics

Mendelian randomization (MR) utilizes genome-wide association study (GWAS) summary data to infer causal relationships between exposures and outcomes, offering a valuable tool for identifying disease risk factors. Multivariable MR (MVMR) estimates the direct effects of multiple exposures on an outcome. This study tackles the issue of highly correlated exposures commonly observed in metabolomic data, a situation where existing MVMR methods often face reduced statistical power due to multicollinearity. We propose a robust extension of the MVMR framework that leverages constrained maximum likelihood (cML) and employs a Bayesian approach for identifying independent clusters of exposure signals. Applying our method to the UK Biobank metabolomic data for the largest Alzheimer disease (AD) cohort through a two-sample MR approach, we identified two independent signal clusters for AD: glutamine and lipids, with posterior inclusion probabilities (PIPs) of 95.0% and 81.5%, respectively. Our findings corroborate the hypothesized roles of glutamate and lipids in AD, providing quantitative support for their potential involvement.

Application of Metabolomics across the Spectrum of Pulmonary and Critical Care Medicine

In recent years, metabolomics, the systematic study of small-molecule metabolites in biological samples, has yielded fresh insights into the molecular determinants of pulmonary diseases and critical illness. The purpose of this article is to orient the reader to this emerging field by discussing the fundamental tenets underlying metabolomics research, the tools and techniques that serve as foundational methodologies, and the various statistical approaches to analysis of metabolomics datasets. We present several examples of metabolomics applied to pulmonary and critical care medicine to illustrate the potential of this avenue of research to deepen our understanding of pathophysiology. We conclude by reviewing recent advances in the field and future research directions that stand to further the goal of personalizing medicine to improve patient care.

Differences in metabolomic profiles between Black and White women in the U.S.: Analyses from two prospective cohorts

There is growing interest in incorporating metabolomics into public health practice. However, Black women are under-represented in many metabolomics studies. If metabolomic profiles differ between Black and White women, this under-representation may exacerbate existing Black-White health disparities. We therefore aimed to estimate metabolomic differences between Black and White women in the U.S. We leveraged data from two prospective cohorts: the Nurses' Health Study (NHS; n = 2077) and Women's Health Initiative (WHI; n = 2128). The WHI served as the replication cohort. Plasma metabolites (n = 334) were measured via liquid chromatography-tandem mass spectrometry. Observed metabolomic differences were estimated using linear regression and metabolite set enrichment analyses. Residual metabolomic differences in a hypothetical population in which the distributions of 14 risk factors were equalized across racial groups were estimated using inverse odds ratio weighting. In the NHS, Black-White differences were observed for most metabolites (75 metabolites with observed differences ≥ |0.50| standard deviations). Black women had lower average levels than White women for most metabolites (e.g., for N6, N6-dimethlylysine, mean Black-White difference = - 0.98 standard deviations; 95% CI: - 1.11, - 0.84). In metabolite set enrichment analyses, Black women had lower levels of triglycerides, phosphatidylcholines, lysophosphatidylethanolamines, phosphatidylethanolamines, and organoheterocyclic compounds, but higher levels of phosphatidylethanolamine plasmalogens, phosphatidylcholine plasmalogens, cholesteryl esters, and carnitines. In a hypothetical population in which distributions of 14 risk factors were equalized, Black-White metabolomic differences persisted. Most results replicated in the WHI (88% of 272 metabolites available for replication). Substantial differences in metabolomic profiles exist between Black and White women. Future studies should prioritize racial representation.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Analysis of plasma metabolomes from 11 309 subjects in five population-based cohorts

Plasma metabolomics holds potential for precision medicine, but limited information is available to compare the performance of such methods across multiple cohorts. We compared plasma metabolite profiles after an overnight fast in 11,309 participants of five population-based Swedish cohorts (50-80 years, 52% women). Metabolite profiles were uniformly generated at a core laboratory (Metabolon Inc.) with untargeted liquid chromatography mass spectrometry and a comprehensive reference library. Analysis of a second sample obtained one year later was conducted in a subset. Of 1629 detected metabolites, 1074 (66%) were detected in all cohorts while only 10% were unique to one cohort, most of which were xenobiotics or uncharacterized. The major classes were lipids (28%), xenobiotics (22%), amino acids (14%), and uncharacterized (19%). The most abundant plasma metabolome components were the major dietary fatty acids and amino acids, glucose, lactate and creatinine. Most metabolites displayed a log-normal distribution. Temporal variability was generally similar to clinical chemistry analytes but more pronounced for xenobiotics. Extensive metabolite-metabolite correlations were observed but mainly restricted to within each class. Metabolites were broadly associated with clinical factors, particularly body mass index, sex and renal function. Collectively, our findings inform the conduct and interpretation of metabolite association and precision medicine studies.

Measuring diet by metabolomics: a 14-d controlled feeding study of weighed food intake

BACKGROUND

Metabolomics has the potential to enhance dietary assessment by revealing objective measures of many aspects of human food intake. Although metabolomics studies indicate that hundreds of metabolites are associated with dietary intake, correlations have been modest (e.g., r < 0.50), and few have been evaluated in controlled feeding studies.

OBJECTIVES

The aim of this study was to evaluate associations between metabolites and weighed food and beverage intake in a controlled feeding study of habitual diet.

METHODS

Healthy postmenopausal females from the Women's Health Initiative (N = 153) were provided with a customized 2-wk controlled diet designed to emulate their usual diet. Metabolites were measured by liquid chromatography tandem mass spectrometry in end-of-study 24-h urine and fasting serum samples (1293 urine metabolites; 1113 serum metabolites). We calculated partial Pearson correlations between these metabolites and intake of 65 food groups, beverages, and supplements during the feeding study. The threshold for significance was Bonferroni-adjusted to account for multiple testing (5.94 × 10-07 for urine metabolites; 6.91 × 10-07 for serum metabolites).

RESULTS

Significant diet-metabolite correlations were identified for 23 distinct foods, beverages, and supplements (171 distinct metabolites). Among foods, strong metabolite correlations (r ≥ 0.60) were evident for citrus (highest r = 0.80), dairy (r = 0.65), and broccoli (r = 0.63). Among beverages and supplements, strong correlations were evident for coffee (r = 0.86), alcohol (r = 0.69), multivitamins (r = 0.69), and vitamin E supplements (r = 0.65). Moderate correlations (r = 0.50-0.60) were also observed for avocado, fish, garlic, grains, onion, poultry, and black tea. Correlations were specific; each metabolite correlated with one food, beverage, or supplement, except for metabolites correlated with juice or multivitamins.

CONCLUSIONS

Metabolite levels had moderate to strong correlations with weighed intake of habitually consumed foods, beverages, and supplements. These findings exceed in magnitude those previously observed in population studies and exemplify the strong potential of metabolomics to contribute to nutrition research. The Women's Health Initiative is registered at clinicaltrials.gov as NCT00000611.

Validation of biomarkers of aging

The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.

Effects of Preanalytical Sample Collection and Handling on Comprehensive Metabolite Measurements in Human Urine Biospecimens

Epidemiology studies evaluate associations between the metabolome and disease risk. Urine is a common biospecimen used for such studies due to its wide availability and non-invasive collection. Evaluating the robustness of urinary metabolomic profiles under varying preanalytical conditions is thus of interest. Here we evaluate the impact of sample handling conditions on urine metabolome profiles relative to the gold standard condition (no preservative, no refrigeration storage, single freeze thaw). Conditions tested included the use of borate or chlorhexidine preservatives, various storage and freeze/thaw cycles. We demonstrate that sample handling conditions impact metabolite levels, with borate showing the largest impact with 125 of 1,048 altered metabolites (adjusted P < 0.05). When simulating a case-control study with expected inconsistencies in sample handling, we predicted the occurrence of false positive altered metabolites to be low (< 11). Predicted false positives increased substantially (³63) when cases were simulated to undergo alternate handling. Finally, we demonstrate that sample handling impacts on the urinary metabolome were markedly smaller than those in serum. While changes in urine metabolites incurred by sample handling are generally small, we recommend implementing consistent handling conditions and evaluating robustness of metabolite measurements for those showing significant associations with disease outcomes.

Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium

INTRODUCTION

Meta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility.

OBJECTIVE

The objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework.

METHODS

We used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother-child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini-Hochberg procedure.

RESULTS

Only 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coefmeta = 0.40 [95% CI 0.10,0.70], Pmeta = 9.7 × 10-3), guanidinoacetate (Coefmeta = - 0.28 [- 0.54, - 0.02], Pmeta = 0.033), 3-ureidopropionate (Coefmeta = 0.22 [0.017,0.41], Pmeta = 0.033), 1-methylhistidine (Coefmeta = - 0.18 [- 0.33, - 0.04], Pmeta = 0.011), serine (Coefmeta = - 0.18 [- 0.36, - 0.01], Pmeta = 0.034), and lysine (Coefmeta = - 0.16 [- 0.32, - 0.01], Pmeta = 0.044). No associations were robust to multiple testing correction.

CONCLUSIONS

Despite including three cohorts with large sample sizes (N > 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.

Predictive metabolites for incident myocardial infarction: a two-step meta-analysis of individual patient data from six cohorts comprising 7897 individuals from the COnsortium of METabolomics Studies

AIMS

Myocardial infarction (MI) is a major cause of death and disability worldwide. Most metabolomics studies investigating metabolites predicting MI are limited by the participant number and/or the demographic diversity. We sought to identify biomarkers of incident MI in the COnsortium of METabolomics Studies.

METHODS AND RESULTS

We included 7897 individuals aged on average 66 years from six intercontinental cohorts with blood metabolomic profiling (n = 1428 metabolites, of which 168 were present in at least three cohorts with over 80% prevalence) and MI information (1373 cases). We performed a two-stage individual patient data meta-analysis. We first assessed the associations between circulating metabolites and incident MI for each cohort adjusting for traditional risk factors and then performed a fixed effect inverse variance meta-analysis to pull the results together. Finally, we conducted a pathway enrichment analysis to identify potential pathways linked to MI. On meta-analysis, 56 metabolites including 21 lipids and 17 amino acids were associated with incident MI after adjusting for multiple testing (false discovery rate < 0.05), and 10 were novel. The largest increased risk was observed for the carbohydrate mannitol/sorbitol {hazard ratio [HR] [95% confidence interval (CI)] = 1.40 [1.26-1.56], P < 0.001}, whereas the largest decrease in risk was found for glutamine [HR (95% CI) = 0.74 (0.67-0.82), P < 0.001]. Moreover, the identified metabolites were significantly enriched (corrected P < 0.05) in pathways previously linked with cardiovascular diseases, including aminoacyl-tRNA biosynthesis.

CONCLUSIONS

In the most comprehensive metabolomic study of incident MI to date, 10 novel metabolites were associated with MI. Metabolite profiles might help to identify high-risk individuals before disease onset. Further research is needed to fully understand the mechanisms of action and elaborate pathway findings.

Technical Report: A Comprehensive Comparison between Different Quantification Versions of Nightingale Health's 1H-NMR Metabolomics Platform

1H-NMR metabolomics data is increasingly used to track health and disease. Nightingale Health, a major supplier of 1H-NMR metabolomics, has recently updated the quantification strategy to further align with clinical standards. Such updates, however, might influence backward replicability, particularly affecting studies with repeated measures. Using data from BBMRI-NL consortium (~28,000 samples from 28 cohorts), we compared Nightingale data, originally released in 2014 and 2016, with a re-quantified version released in 2020, of which both versions were based on the same NMR spectra. Apart from two discontinued and twenty-three new analytes, we generally observe a high concordance between quantification versions with 73 out of 222 (33%) analytes showing a mean ρ > 0.9 across all cohorts. Conversely, five analytes consistently showed lower Spearman's correlations (ρ < 0.7) between versions, namely acetoacetate, LDL-L, saturated fatty acids, S-HDL-C, and sphingomyelins. Furthermore, previously trained multi-analyte scores, such as MetaboAge or MetaboHealth, might be particularly sensitive to platform changes. Whereas MetaboHealth replicated well, the MetaboAge score had to be retrained due to use of discontinued analytes. Notably, both scores in the re-quantified data recapitulated mortality associations observed previously. Concluding, we urge caution in utilizing different platform versions to avoid mixing analytes, having different units, or simply being discontinued.

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review

An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.

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.

Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study

Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and untargeted metabolomics are increasingly used in exposome studies to study the interactions between nongenetic factors and the blood metabolome. To reliably and efficiently link detected compounds to exposures and health phenotypes in such studies, it is important to understand the variability in metabolome measures. We assessed the within- and between-subject variability of untargeted LC-HRMS measurements in 298 nonfasting human serum samples collected on two occasions from 157 subjects. Samples were collected ca. 107 (IQR: 34) days apart as part of the multicenter EXPOsOMICS Personal Exposure Monitoring study. In total, 4294 metabolic features were detected, and 184 unique compounds could be identified with high confidence. The median intraclass correlation coefficient (ICC) across all metabolic features was 0.51 (IQR: 0.29) and 0.64 (IQR: 0.25) for the 184 uniquely identified compounds. For this group, the median ICC marginally changed (0.63) when we included common confounders (age, sex, and body mass index) in the regression model. When grouping compounds by compound class, the ICC was largest among glycerophospholipids (median ICC 0.70) and steroids (0.67), and lowest for amino acids (0.61) and the O-acylcarnitine class (0.44). ICCs varied substantially within chemical classes. Our results suggest that the metabolome as measured with untargeted LC-HRMS is fairly stable (ICC > 0.5) over 100 days for more than half of the features monitored in our study, to reflect average levels across this time period. Variance across the metabolome will result in differential measurement error across the metabolome, which needs to be considered in the interpretation of metabolome results.

Metabolomics acts as a powerful tool for comprehensively evaluating vaccines approved under emergency: a CoronaVac retrospective study

INTRODUCTION

To control the COVID-19 pandemic, great efforts have been made to realize herd immunity by vaccination since 2020. Unfortunately, most of the vaccines against COVID-19 were approved in emergency without a full-cycle and comprehensive evaluation process as recommended to the previous vaccines. Metabolome has a close tie with the phenotype and can sensitively reflect the responses to stimuli, rendering metabolomic analysis have the potential to appraise and monitor vaccine effects authentically.

METHODS

In this study, a retrospective study was carried out for 330 Chinese volunteers receiving recommended two-dose CoronaVac, a vaccine approved in emergency in 2020. Venous blood was sampled before and after vaccination at 5 separate time points for all the recipients. Routine clinical laboratory analysis, metabolomic and lipidomic analysis data were collected.

RESULTS AND DISCUSSION

It was found that the serum antibody-positive rate of this population was around 81.82%. Most of the laboratory parameters were slightly perturbated within the relevant reference intervals after vaccination. The metabolomic and lipidomic analyses showed that the metabolic shift after inoculation was mainly in the glycolysis, tricarboxylic acid cycle, amino acid metabolism, urea cycle, as well as microbe-related metabolism (bile acid metabolism, tryptophan metabolism and phenylalanine metabolism). Time-course metabolome changes were found in parallel with the progress of immunity establishment and peripheral immune cell counting fluctuation, proving metabolomics analysis was an applicable solution to evaluate immune effects complementary to traditional antibody detection. Taurocholic acid, lysophosphatidylcholine 16:0 sn-1, glutamic acid, and phenylalanine were defined as valuable metabolite markers to indicate the establishment of immunity after vaccination. Integrated with the traditional laboratory analysis, this study provided a feasible metabolomics-based solution to relatively comprehensively evaluate vaccines approved under emergency.

ALAN is a computational approach that interprets genomic findings in the context of tumor ecosystems

Gene behavior is governed by activity of other genes in an ecosystem as well as context-specific cues including cell type, microenvironment, and prior exposure to therapy. Here, we developed the Algorithm for Linking Activity Networks (ALAN) to compare gene behavior purely based on patient -omic data. The types of gene behaviors identifiable by ALAN include co-regulators of a signaling pathway, protein-protein interactions, or any set of genes that function similarly. ALAN identified direct protein-protein interactions in prostate cancer (AR, HOXB13, and FOXA1). We found differential and complex ALAN networks associated with the proto-oncogene MYC as prostate tumors develop and become metastatic, between different cancer types, and within cancer subtypes. We discovered that resistant genes in prostate cancer shared an ALAN ecosystem and activated similar oncogenic signaling pathways. Altogether, ALAN represents an informatics approach for developing gene signatures, identifying gene targets, and interpreting mechanisms of progression or therapy resistance.

Quality Control of Targeted Plasma Lipids in a Large-Scale Cohort Study Using Liquid Chromatography-Tandem Mass Spectrometry

High-throughput metabolomics has enabled the development of large-scale cohort studies. Long-term studies require multiple batch-based measurements, which require sophisticated quality control (QC) to eliminate unexpected bias to obtain biologically meaningful quantified metabolomic profiles. Liquid chromatography-mass spectrometry was used to analyze 10,833 samples in 279 batch measurements. The quantified profile included 147 lipids including acylcarnitine, fatty acids, glucosylceramide, lactosylceramide, lysophosphatidic acid, and progesterone. Each batch included 40 samples, and 5 QC samples were measured for 10 samples of each. The quantified data from the QC samples were used to normalize the quantified profiles of the sample data. The intra- and inter-batch median coefficients of variation (CV) among the 147 lipids were 44.3% and 20.8%, respectively. After normalization, the CV values decreased by 42.0% and 14.7%, respectively. The effect of this normalization on the subsequent analyses was also evaluated. The demonstrated analyses will contribute to obtaining unbiased, quantified data for large-scale metabolomics.

Metabolomic profiling of glucose homeostasis in African Americans: the Insulin Resistance Atherosclerosis Family Study (IRAS-FS)

INTRODUCTION

African Americans are at increased risk for type 2 diabetes.

OBJECTIVES

This work aimed to examine metabolomic signature of glucose homeostasis in African Americans.

METHODS

We used an untargeted liquid chromatography-mass spectrometry metabolomic approach to comprehensively profile 727 plasma metabolites among 571 African Americans from the Insulin Resistance Atherosclerosis Family Study (IRAS-FS) and investigate the associations between these metabolites and both the dynamic (S_I, insulin sensitivity; AIR, acute insulin response; DI, disposition index; and S_G, glucose effectiveness) and basal (HOMA-IR and HOMA-B) measures of glucose homeostasis using univariate and regularized regression models. We also compared the results with our previous findings in the IRAS-FS Mexican Americans.

RESULTS

We confirmed increased plasma metabolite levels of branched-chain amino acids and their metabolic derivatives, 2-aminoadipate, 2-hydroxybutyrate, glutamate, arginine and its metabolic derivatives, carbohydrate metabolites, and medium- and long-chain fatty acids were associated with insulin resistance, while increased plasma metabolite levels in the glycine, serine and threonine metabolic pathway were associated with insulin sensitivity. We also observed a differential ancestral effect of glutamate on glucose homeostasis with significantly stronger effects observed in African Americans than those previously observed in Mexican Americans.

CONCLUSION

We extended the observations that metabolites are useful biomarkers in the identification of prediabetes in individuals at risk of type 2 diabetes in African Americans. We revealed, for the first time, differential ancestral effect of certain metabolites (i.e., glutamate) on glucose homeostasis traits. Our study highlights the need for additional comprehensive metabolomic studies in well-characterized multiethnic cohorts.

Miniaturized Two-Dimensional Heart Cutting for LC-MS-Based Metabolomics

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics usually combines hydrophilic interaction liquid chromatography (HILIC) and reversed-phase (RP) chromatography to cover a wide range of metabolomes, requiring both significant sample consumption and analysis time for separate workflows. We developed an integrated workflow enabling the coverage of both polar and nonpolar metabolites with only one injection of the sample for each ionization mode using heart-cutting trapping to combine HILIC and RP separations. This approach enables the trapping of some compounds eluted from the first chromatographic dimension for separation later in the second dimension. In our case, we applied heart-cutting to non-retained metabolites in the first dimension. For that purpose, two independent miniaturized one-dimensional HILIC and RP methods were developed by optimizing the chromatographic and ionization conditions using columns with an inner diameter of 1 mm. They were then merged into one two-dimensional micro LC-MS method by optimization of the trapping conditions. Equilibration of the HILIC column during elution on the RP column and vice versa reduced the overall analysis time, and the multidimensionality allows us to avoid signal measurements during the solvent front. To demonstrate the benefits of this approach to metabolomics, it was applied to the analysis of the human plasma standard reference material SRM 1950, enabling the detection of hundreds of metabolites without the significant loss of some of them while requiring an injection volume of only 0.5 μL.

Normalization Approach by a Reference Material to Improve LC-MS-Based Metabolomic Data Comparability of Multibatch Samples

Large cohorts of samples from multiple batches are usually required for global metabolomic studies to characterize the metabolic state of human disease. As such, it is critical to eliminate systematic variation and truly reveal the biologically associated alterations. In this study, we proposed a reference material-based approach (Ref-M) for data correction by liquid chromatography-mass spectrometry and represented by an analysis of multibatch human serum samples. The reference material was generated by mixing serum from healthy donors and distributed to each extraction batch of subject samples. Pooled quality control samples and isotopic internal standards were then applied in each acquisition batch for data quality control. Finally, each metabolite in subject samples was normalized by its counterpart in the reference serum. We demonstrated that Ref-M significantly enhanced the numbers of efficient features and effectively eliminated the batch variation of 522 serum samples of healthy individuals, benign pulmonary nodules, and lung cancer patients. Twenty differential metabolites were identified to distinguish lung cancer from healthy controls in the training set. The discriminant model was validated in an independent data set with an area under the receiver operating characteristics (ROC) curve (AUC) of 0.853. Another 40 serum samples further tested with Ref-M were achieved an AUC of 0.843 by the established model. Our results showed that the reference material-based approach presents the potential to improve the data comparability and precision for biomarker discovery in large-scale metabolomic studies.

iDMET: network-based approach for integrating differential analysis of cancer metabolomics

BACKGROUND

Comprehensive metabolomic analyses have been conducted in various institutes and a large amount of metabolomic data are now publicly available. To help fully exploit such data and facilitate their interpretation, metabolomic data obtained from different facilities and different samples should be integrated and compared. However, large-scale integration of such data for biological discovery is challenging given that they are obtained from various types of sample at different facilities and by different measurement techniques, and the target metabolites and sensitivities to detect them also differ from study to study.

RESULTS

We developed iDMET, a network-based approach to integrate metabolomic data from different studies based on the differential metabolomic profiles between two groups, instead of the metabolite profiles themselves. As an application, we collected cancer metabolomic data from 27 previously published studies and integrated them using iDMET. A pair of metabolomic changes observed in the same disease from two studies were successfully connected in the network, and a new association between two drugs that may have similar effects on the metabolic reactions was discovered.

CONCLUSIONS

We believe that iDMET is an efficient tool for integrating heterogeneous metabolomic data and discovering novel relationships between biological phenomena.

LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data

Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

The metabolomics of human aging: Advances, challenges, and opportunities

As the global population becomes older, understanding the impact of aging on health and disease becomes paramount. Recent advancements in multiomic technology have allowed for the high-throughput molecular characterization of aging at the population level. Metabolomics studies that analyze the small molecules in the body can provide biological information across a diversity of aging processes. Here, we review the growing body of population-scale metabolomics research on aging in humans, identifying the major trends in the field, implicated biological pathways, and how these pathways relate to health and aging. We conclude by assessing the main challenges in the research to date, opportunities for advancing the field, and the outlook for precision health applications.

Metabolome-Wide Associations of Gestational Weight Gain in Pregnant Women with Overweight and Obesity

Excessive gestational weight gain (GWG) is associated with adverse pregnancy outcomes. This metabolome-wide association study aimed to identify metabolomic markers for GWG. This longitudinal study included 39 Black and White pregnant women with a prepregnancy body mass index (BMI) of ≥ 25 kg/m2. Untargeted metabolomic profiling was performed using fasting plasma samples collected at baseline (mean: 12.1 weeks) and 32 weeks of gestation. The associations of metabolites at each time point and changes between the two time points with GWG were examined by linear and least absolute shrinkage and selection operator (LASSO) regression analyses. Pearson correlations between the identified metabolites and cardiometabolic biomarkers were examined. Of the 769 annotated metabolites, 88 metabolites at 32 weeks were individually associated with GWG, with four (phosphatidylcholine (PC) 34:4, triacylglycerol (TAG) 52:6, arachidonic acid, isoleucine) jointly associated with GWG (area under the receiver operating characteristic curve (AUC) for excessive GWG: 0.80, 95% CI: 0.67, 0.93). No correlations were observed between the 88 metabolites and insulin, C-peptide, and high-sensitivity C-reactive protein at 32 weeks. Twelve metabolites at baseline (AUC for excessive GWG: 0.80, 95% CI: 0.62, 0.99) and three metabolite changes (AUC for excessive GWG: 0.73, 95% CI: 0.44, 1.00) were jointly associated with GWG. We identified novel metabolites in the first and third trimesters associated with GWG, which may shed light on the pathophysiology of GWG.

Pre-illness data reveals differences in multiple metabolites and metabolic pathways in those who do and do not recover from infectious mononucleosis

Metabolic pathways related to energy production, amino acids, nucleotides, nitrogen, lipids, and neurotransmitters in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) may contribute to the pathophysiology of ME/CFS. 4501 Northwestern University college students were enrolled in a prospective, longitudinal study. We collected data before illness, during infectious mononucleosis (IM), and at a 6 month follow-up for those who recovered (N = 18) versus those who went on to develop ME/CFS 6 months later (N = 18). Examining pre-illness blood samples, we found significant detectable metabolite differences between participants fated to develop severe ME/CFS following IM versus recovered controls. We identified glutathione metabolism, nucleotide metabolism, and the TCA cycle (among others) as potentially dysregulated pathways. The pathways that differed between cases and controls are essential for proliferating cells, particularly during a pro-inflammatory immune response. Performing a series of binary logistic regressions using a leave-one-out cross-validation (LOOCV), our models correctly classified the severe ME/CFS group and recovered controls with an accuracy of 97.2%, sensitivity of 94.4%, and specificity of 100.0%. These changes are consistent with the elevations in pro-inflammatory cytokines that we have reported for patients fated to develop severe ME/CFS 6 months after IM.

Dynamic Changes in the Human Milk Metabolome Over 25 Weeks of Lactation

Human milk (HM) provides essential nutrition for ensuring optimal infant growth and development postpartum. Metabolomics offers insight into the dynamic composition of HM. Studies have reported the impact of lactation stage, maternal genotype, and gestational age on HM metabolome. However, the majority of the studies have considered changes within the first month of lactation or sampled with large intervals. This leaves a gap in the knowledge of progressing variation in HM composition beyond the first month of lactation. The objective of this study was to investigate whether the HM metabolome from mothers with term deliveries varies beyond 1 month of lactation, during the period in which HM is considered fully mature. Human milk samples (n = 101) from 59 mothers were collected at weeks 1-2, 3-5, 7-9, and 20-25 postpartum and analyzed using 1H nuclear magnetic resonance spectroscopy. Several metabolites varied over lactation and exhibited dynamic changes between multiple time points. Higher levels of HM oligosaccharides, cis-aconitate, O-phosphocholine, O-acetylcarnitine, gluconate, and citric acid were observed in early lactation, whereas later in lactation, levels of lactose, 3-fucosyllactose, glutamine, glutamate, and short- and medium-chain fatty acids were increased. Notably, we demonstrate that the HM metabolome is dynamic during the period of maturity.

Variational autoencoders learn transferrable representations of metabolomics data

Dimensionality reduction approaches are commonly used for the deconvolution of high-dimensional metabolomics datasets into underlying core metabolic processes. However, current state-of-the-art methods are widely incapable of detecting nonlinearities in metabolomics data. Variational Autoencoders (VAEs) are a deep learning method designed to learn nonlinear latent representations which generalize to unseen data. Here, we trained a VAE on a large-scale metabolomics population cohort of human blood samples consisting of over 4500 individuals. We analyzed the pathway composition of the latent space using a global feature importance score, which demonstrated that latent dimensions represent distinct cellular processes. To demonstrate model generalizability, we generated latent representations of unseen metabolomics datasets on type 2 diabetes, acute myeloid leukemia, and schizophrenia and found significant correlations with clinical patient groups. Notably, the VAE representations showed stronger effects than latent dimensions derived by linear and non-linear principal component analysis. Taken together, we demonstrate that the VAE is a powerful method that learns biologically meaningful, nonlinear, and transferrable latent representations of metabolomics data.

Cross-Sectional Blood Metabolite Markers of Hypertension: A Multicohort Analysis of 44,306 Individuals from the COnsortium of METabolomics Studies

Hypertension is the main modifiable risk factor for cardiovascular morbidity and mortality but discovering molecular mechanisms for targeted treatment has been challenging. Here we investigate associations of blood metabolite markers with hypertension by integrating data from nine intercontinental cohorts from the COnsortium of METabolomics Studies. We included 44,306 individuals with circulating metabolites (up to 813). Metabolites were aligned and inverse normalised to allow intra-platform comparison. Logistic models adjusting for covariates were performed in each cohort and results were combined using random-effect inverse-variance meta-analyses adjusting for multiple testing. We further conducted canonical pathway analysis to investigate the pathways underlying the hypertension-associated metabolites. In 12,479 hypertensive cases and 31,827 controls without renal impairment, we identified 38 metabolites, associated with hypertension after adjusting for age, sex, body mass index, ethnicity, and multiple testing. Of these, 32 metabolite associations, predominantly lipid (steroids and fatty acyls) and organic acids (amino-, hydroxy-, and keto-acids) remained after further adjusting for comorbidities and dietary intake. Among the identified metabolites, 5 were novel, including 2 bile acids, 2 glycerophospholipids, and ketoleucine. Pathway analysis further implicates the role of the amino-acids, serine/glycine, and bile acids in hypertension regulation. In the largest cross-sectional hypertension-metabolomics study to date, we identify 32 circulating metabolites (of which 5 novel and 27 confirmed) that are potentially actionable targets for intervention. Further in-vivo studies are needed to identify their specific role in the aetiology or progression of hypertension.

MiMIR: R-shiny application to infer risk factors and endpoints from Nightingale Health's 1H-NMR Metabolomics data

MOTIVATION

1H-NMR metabolomics is rapidly becoming a standard resource in large epidemiological studies to acquire metabolic profiles in large numbers of samples in a relatively low-priced and standardized manner. Concomitantly, metabolomics-based models are increasingly developed that capture disease risk or clinical risk factors. These developments raise the need for user-friendly toolbox to inspect new 1H-NMR metabolomics data and project a wide array of previously established risk models.

RESULTS

We present MiMIR (Metabolomics-based Models for Imputing Risk), a graphical user interface that provides an intuitive framework for ad-hoc statistical analysis of Nightingale Health's 1H-NMR metabolomics data and allows for the projection and calibration of 24 pre-trained metabolomics-based models, without any pre-required programming knowledge.

AVAILABILITY

The R-shiny package is available in CRAN or downloadable at https://github.com/DanieleBizzarri/MiMIR, together with an extensive user manual (also available as Supplementary Documents to the paper).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolomics in pulmonary medicine - extracting the most from your data

The metabolome enables unprecedented insight into biochemistry, providing an integrated signature of the genome, transcriptome, proteome and exposome. Measurement requires rigorous protocols combined with specialised data analysis to achieve its promise.https://bit.ly/3yPiYkQ

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies

BACKGROUND

Due to the rapidly increasing availability of metabolomics data in prospective studies, an update of the meta evidence on metabolomics and type 2 diabetes risk is warranted.

PURPOSE

To conduct an updated systematic review and meta-analysis of plasma, serum, and urine metabolite markers and incident type 2 diabetes.

DATA SOURCES

We searched PubMed and Embase until 6 March 2021.

STUDY SELECTION

We selected prospective observational studies where investigators used high-throughput techniques to investigate the relationship between plasma, serum, or urine metabolites and incident type 2 diabetes.

DATA EXTRACTION

Baseline metabolites per-SD risk estimates and 95% CIs for incident type 2 diabetes were extracted from all eligible studies.

DATA SYNTHESIS

A total of 61 reports with 71,196 participants and 11,771 type 2 diabetes cases/events were included in the updated review. Meta-analysis was performed for 412 metabolites, of which 123 were statistically significantly associated (false discovery rate-corrected P < 0.05) with type 2 diabetes risk. Higher plasma and serum levels of certain amino acids (branched-chain, aromatic, alanine, glutamate, lysine, and methionine), carbohydrates and energy-related metabolites (mannose, trehalose, and pyruvate), acylcarnitines (C4-DC, C4-OH, C5, C5-OH, and C8:1), the majority of glycerolipids (di- and triacylglycerols), (lyso)phosphatidylethanolamines, and ceramides included in meta-analysis were associated with higher risk of type 2 diabetes (hazard ratio 1.07-2.58). Higher levels of glycine, glutamine, betaine, indolepropionate, and (lyso)phosphatidylcholines were associated with lower type 2 diabetes risk (hazard ratio 0.69-0.90).

LIMITATIONS

Substantial heterogeneity (I2 > 50%, τ2 > 0.1) was observed for some of the metabolites.

CONCLUSIONS

Several plasma and serum metabolites, including amino acids, lipids, and carbohydrates, are associated with type 2 diabetes risk.

The GenoDiabMar Registry: A Collaborative Research Platform of Type 2 Diabetes Patients

The GenoDiabMar registry is a prospective study that aims to provide data on demographic, biochemical, and clinical changes in type 2 diabetic (T2D) patients attending real medical outpatient consultations. This registry is also used to find new biomarkers related to the micro- and macrovascular complications of T2D, with a particular focus on diabetic nephropathy. With this purpose, longitudinal serum and urine samples, DNA banking, and data on 227 metabolomics profiles, 77 immunoglobulin G glycomics traits, and other emerging biomarkers were recorded in this cohort. In this study, we show a detailed longitudinal description of the clinical and analytical parameters of this registry, with a special focus on the progress of renal function and cardiovascular events. The main objective is to analyze whether there are differential risk factors for renal function deterioration between sexes, as well as to analyze cardiovascular events and mortality in this population. In total, 650 patients with a median age of 69 (14) with different grades of chronic kidney disease—G1−G2 (eGFR > 90−60 mL/min/1.73 m2) 50.3%, G3 (eGFR; 59−30 mL/min/1.73 m2) 31.4%, G4 (eGFR; 29−15 mL/min/1.73 m2) 10.8%, and G5 (eGFR < 15 mL/min/1.73 m2) 7.5%—were followed up for 4.7 (0.65) years. Regardless of albuminuria, women lost 0.93 (0.40−1.46) fewer glomerular filtration units per year than men. A total of 17% of the participants experienced rapid deterioration of renal function, 75.2% of whom were men, with differential risk factors between sexes—severe macroalbuminuria > 300 mg/g for men OR [IQ] 2.40 [1.29:4.44] and concomitant peripheral vascular disease 3.32 [1.10:9.57] for women. Overall mortality of 23% was detected (38% of which was due to cardiovascular etiology). We showed that kidney function declined faster in men, with different risk factors compared to women. Patients with T2D and kidney involvement have very high mortality and an important cardiovascular burden. This cohort is proposed as a great tool for scientific collaboration for studies, whether they are focused on T2D, or whether they are interested in comparing differential markers between diabetic and non-diabetic populations.

COMETS Analytics: An Online Tool for Analyzing and Meta-Analyzing Metabolomics Data in Large Research Consortia

Consortium-based research is crucial for producing reliable, high-quality findings, but existing tools for consortium studies have important drawbacks with respect to data protection, ease of deployment, and analytical rigor. To address these concerns, we developed COnsortium of METabolomics Studies (COMETS) Analytics to support and streamline consortium-based analyses of metabolomics and other -omics data. The application requires no specialized expertise and can be run locally to guarantee data protection or through a Web-based server for convenience and speed. Unlike other Web-based tools, COMETS Analytics enables standardized analyses to be run across all cohorts, using an algorithmic, reproducible approach to diagnose, document, and fix model issues. This eliminates the time-consuming and potentially error-prone step of manually customizing models by cohort, helping to accelerate consortium-based projects and enhancing analytical reproducibility. We demonstrated that the application scales well by performing 2 data analyses in 45 cohort studies that together comprised measurements of 4,647 metabolites in up to 134,742 participants. COMETS Analytics performed well in this test, as judged by the minimal errors that analysts had in preparing data inputs and the successful execution of all models attempted. As metabolomics gathers momentum among biomedical and epidemiologic researchers, COMETS Analytics may be a useful tool for facilitating large-scale consortium-based research.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

Metabolic profiling of charged metabolites in association with menopausal status in Japanese community-dwelling midlife women: Tsuruoka Metabolomic Cohort Study

BACKGROUND

Emerging evidence has shown that charged metabolites, such as amino acids, may play an important role in the pathogenesis of various metabolic disorders, many of which women in the postmenopausal period are at high risk of developing. This study examined the metabolic profile of middle-aged Japanese women to investigate alterations in charged metabolites induced by menopausal transition.

METHODS

The participants were 1193 female residents aged 40-60 at the baseline survey of the Tsuruoka Metabolomics Cohort Study. We investigated the cross-sectional association of menopausal status with 94 metabolomic biomarkers assayed in fasting plasma samples via capillary electrophoresis time-of-flight mass spectrometry using linear regression analysis.

RESULTS

Among the participants, 529 were premenopausal, 132 were in menopausal transition (MT), and 532 were postmenopausal. Significant differences were found in age, blood pressure, glucose and lipid levels, and smoking and drinking habits among the three groups. The concentrations of 5 metabolites in the MT group and 15 metabolites in the postmenopausal group were significantly higher than those in the premenopausal group after adjusting for confounding factors. When classified into pathways, these metabolites were related to the tricarboxylic cycle, urea cycle, and homocysteine metabolism, some of which are linked to arteriosclerosis.

CONCLUSION

Multiple charged metabolites were associated with women's menopausal status, showing a gradual increase as women shifted from pre-, to peri-, to postmenopause. These findings might reflect the early changes behind the increased risk of dyslipidemia, diabetes, cardiovascular disease, and osteoporosis in later life.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

Comparison of Kit-Based Metabolomics with Other Methodologies in a Large Cohort, towards Establishing Reference Values

Metabolic profiling is an omics approach that can be used to observe phenotypic changes, making it particularly attractive for biomarker discovery. Although several candidate metabolites biomarkers for disease expression have been identified in recent clinical studies, the reference values of healthy subjects have not been established. In particular, the accuracy of concentrations measured by mass spectrometry (MS) is unclear. Therefore, comprehensive metabolic profiling in large-scale cohorts by MS to create a database with reference ranges is essential for evaluating the quality of the discovered biomarkers. In this study, we tested 8700 plasma samples by commercial kit-based metabolomics and separated them into two groups of 6159 and 2541 analyses based on the different ultra-high-performance tandem mass spectrometry (UHPLC-MS/MS) systems. We evaluated the quality of the quantified values of the detected metabolites from the reference materials in the group of 2541 compared with the quantified values from other platforms, such as nuclear magnetic resonance (NMR), supercritical fluid chromatography tandem mass spectrometry (SFC-MS/MS) and UHPLC-Fourier transform mass spectrometry (FTMS). The values of the amino acids were highly correlated with the NMR results, and lipid species such as phosphatidylcholines and ceramides showed good correlation, while the values of triglycerides and cholesterol esters correlated less to the lipidomics analyses performed using SFC-MS/MS and UHPLC-FTMS. The evaluation of the quantified values by MS-based techniques is essential for metabolic profiling in a large-scale cohort.

A New Pipeline for the Normalization and Pooling of Metabolomics Data

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples' originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

Metabolomics datasets in the Born in Bradford cohort

Metabolomics is the quantification of small molecules, commonly known as metabolites. Collectively, these metabolites and their interactions within a biological system are known as the metabolome. The metabolome is a unique area of study, capturing influences from both genotype and environment. The availability of high-throughput technologies for quantifying large numbers of metabolites, as well as lipids and lipoprotein particles, has enabled detailed investigation of human metabolism in large-scale epidemiological studies. The Born in Bradford (BiB) cohort includes 12,453 women who experienced 13,776 pregnancies recruited between 2007-2011, their partners and their offspring. In this data note, we describe the metabolomic data available in BiB, profiled during pregnancy, in cord blood and during early life in the offspring. These include two platforms of metabolomic profiling: nuclear magnetic resonance and mass spectrometry. The maternal measures, taken at 26-28 weeks’ gestation, can provide insight into the metabolome during pregnancy and how it relates to maternal and offspring health. The offspring cord blood measurements provide information on the fetal metabolome. These measures, alongside maternal pregnancy measures, can be used to explore how they may influence outcomes. The infant measures (taken around ages 12 and 24 months) provide a snapshot of the early life metabolome during a key phase of nutrition, environmental exposures, growth, and development. These metabolomic data can be examined alongside the BiB cohorts’ extensive phenotype data from questionnaires, medical, educational and social record linkage, and other ‘omics data.

Metabolomics datasets in the Born in Bradford cohort

Metabolomics is the quantification of small molecules, commonly known as metabolites. Collectively, these metabolites and their interactions within a biological system are known as the metabolome. The metabolome is a unique area of study, capturing influences from both genotype and environment. The availability of high-throughput technologies for quantifying large numbers of metabolites, as well as lipids and lipoprotein particles, has enabled detailed investigation of human metabolism in large-scale epidemiological studies. The Born in Bradford (BiB) cohort includes 12,453 women who experienced 13,776 pregnancies recruited between 2007-2011, their partners and their offspring. In this data note, we describe the metabolomic data available in BiB, profiled during pregnancy, in cord blood and during early life in the offspring. These include two platforms of metabolomic profiling: nuclear magnetic resonance and mass spectrometry. The maternal measures, taken at 26-28 weeks' gestation, can provide insight into the metabolome during pregnancy and how it relates to maternal and offspring health. The offspring cord blood measurements provide information on the fetal metabolome. These measures, alongside maternal pregnancy measures, can be used to explore how they may influence outcomes. The infant measures (taken around ages 12 and 24 months) provide a snapshot of the early life metabolome during a key phase of nutrition, environmental exposures, growth, and development. These metabolomic data can be examined alongside the BiB cohorts' extensive phenotype data from questionnaires, medical, educational and social record linkage, and other 'omics data.

Associations of circulating choline and its related metabolites with cardiometabolic biomarkers: an international pooled analysis

BACKGROUND

Choline is an essential nutrient; however, the associations of choline and its related metabolites with cardiometabolic risk remain unclear.

OBJECTIVE

We examined the associations of circulating choline, betaine, carnitine, and dimethylglycine (DMG) with cardiometabolic biomarkers and their potential dietary and nondietary determinants.

METHODS

The cross-sectional analyses included 32,853 participants from 17 studies, who were free of cancer, cardiovascular diseases, chronic kidney diseases, and inflammatory bowel disease. In each study, metabolites and biomarkers were log-transformed and standardized by means and SDs, and linear regression coefficients (β) and 95% CIs were estimated with adjustments for potential confounders. Study-specific results were combined by random-effects meta-analyses. A false discovery rate <0.05 was considered significant.

RESULTS

We observed moderate positive associations of circulating choline, carnitine, and DMG with creatinine [β (95% CI): 0.136 (0.084, 0.188), 0.106 (0.045, 0.168), and 0.128 (0.087, 0.169), respectively, for each SD increase in biomarkers on the log scale], carnitine with triglycerides (β = 0.076; 95% CI: 0.042, 0.109), homocysteine (β = 0.064; 95% CI: 0.033, 0.095), and LDL cholesterol (β = 0.055; 95% CI: 0.013, 0.096), DMG with homocysteine (β = 0.068; 95% CI: 0.023, 0.114), insulin (β = 0.068; 95% CI: 0.043, 0.093), and IL-6 (β = 0.060; 95% CI: 0.027, 0.094), but moderate inverse associations of betaine with triglycerides (β = -0.146; 95% CI: -0.188, -0.104), insulin (β = -0.106; 95% CI: -0.130, -0.082), homocysteine (β = -0.097; 95% CI: -0.149, -0.045), and total cholesterol (β = -0.074; 95% CI: -0.102, -0.047). In the whole pooled population, no dietary factor was associated with circulating choline; red meat intake was associated with circulating carnitine [β = 0.092 (0.042, 0.142) for a 1 serving/d increase], whereas plant protein was associated with circulating betaine [β = 0.249 (0.110, 0.388) for a 5% energy increase]. Demographics, lifestyle, and metabolic disease history showed differential associations with these metabolites.

CONCLUSIONS

Circulating choline, carnitine, and DMG were associated with unfavorable cardiometabolic risk profiles, whereas circulating betaine was associated with a favorable cardiometabolic risk profile. Future prospective studies are needed to examine the associations of these metabolites with incident cardiovascular events.

A hierarchical approach to removal of unwanted variation for large-scale metabolomics data

Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological sample replicates to quantify variance within and between batches and a workflow that uses these replicates to remove unwanted variation in a hierarchical manner (hRUV). We use this design to produce a dataset of more than 1000 human plasma samples run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our tools not only provide a strategy for large scale data normalisation, but also provides guidance on the design strategy for large omics studies.

Mass spectrometry-based metabolomics is a powerful method for profiling large clinical cohorts but batch variations can obscure biologically meaningful differences. Here, the authors develop a computational workflow that removes unwanted data variation while preserving biologically relevant information.

Molecular Aspects of Lifestyle and Environmental Effects in Patients With Diabetes: JACC Focus Seminar

Diabetes is characterized as an integrated condition of dysregulated metabolism across multiple tissues, with well-established consequences on the cardiovascular system. Recent advances in precision phenotyping in biofluids and tissues in large human observational and interventional studies have afforded a unique opportunity to translate seminal findings in models and cellular systems to patients at risk for diabetes and its complications. Specifically, techniques to assay metabolites, proteins, and transcripts, alongside more recent assessment of the gut microbiome, underscore the complexity of diabetes in patients, suggesting avenues for precision phenotyping of risk, response to intervention, and potentially novel therapies. In addition, the influence of external factors and inputs (eg, activity, diet, medical therapies) on each domain of molecular characterization has gained prominence toward better understanding their role in prevention. Here, the authors provide a broad overview of the role of several of these molecular domains in human translational investigation in diabetes.

Biomarker Discovery in Atherosclerotic Diseases Using Quantitative Nuclear Magnetic Resonance Metabolomics

Despite great progress in the management of atherosclerosis (AS), its subsequent cardiovascular disease (CVD) remains the leading cause of morbidity and mortality. This is probably due to insufficient risk detection using routine lipid testing; thus, there is a need for more effective approaches relying on new biomarkers. Quantitative nuclear magnetic resonance (qNMR) metabolomics is able to phenotype holistic metabolic changes, with a unique advantage in regard to quantifying lipid-protein complexes. The rapidly increasing literature has indicated that qNMR-based lipoprotein particle number, particle size, lipid components, and some molecular metabolites can provide deeper insight into atherogenic diseases and could serve as novel promising determinants. Therefore, this article aims to offer an updated review of the qNMR biomarkers of AS and CVD found in epidemiological studies, with a special emphasis on lipoprotein-related parameters. As more researches are performed, we can envision more qNMR metabolite biomarkers being successfully translated into daily clinical practice to enhance the prevention, detection and intervention of atherosclerotic diseases.

MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights

Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.

Metabolic phenotyping and cardiovascular disease: an overview of evidence from epidemiological settings

Metabolomics, the comprehensive measurement of low-molecular-weight molecules in biological fluids used for metabolic phenotyping, has emerged as a promising tool to better understand pathways underlying cardiovascular disease (CVD) and to improve cardiovascular risk stratification. Here, we present the main methodologies for metabolic phenotyping, the methodological steps to analyse these data in epidemiological settings and the associated challenges. We discuss evidence from epidemiological studies linking metabolites to coronary heart disease and stroke. These studies indicate the systemic nature of CVD and identify associated metabolic pathways such as gut microbial cometabolism, branched-chain amino acids, glycerophospholipid and cholesterol metabolism, as well as activation of inflammatory processes. Integration of metabolomic with genomic data can provide new evidence for involved biochemical pathways and potential for causality using Mendelian randomisation. The clinical utility of metabolic biomarkers for cardiovascular risk stratification in healthy individuals has not yet been established. As sample sizes with high-dimensional molecular data increase in epidemiological settings, integration of metabolomic data across studies and platforms with other molecular data will lead to new understanding of the metabolic processes underlying CVD and contribute to identification of potentially novel preventive and pharmacological targets. Metabolic phenotyping offers a powerful tool in the characterisation of the molecular signatures of CVD, paving the way to new mechanistic understanding and therapies, as well as improving risk prediction of CVD patients. However, there are still challenges to face in order to contribute to clinically important improvements in CVD.