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Background: High consumption of ultraprocessed foods (UPFs) continues to draw significant public health interest because of the associated negative health outcomes. Metabolomics can contribute to the understanding of the biological mechanisms through which UPFs may influence health., Objectives: To investigate urine and plasma metabolomic biomarkers of UPF intake in adolescents and young adults., Methods: We used data from the Dortmund Nutritional and Anthropometric Longitudinally Designed study to investigate cross-sectional associations of UPF intake with concentrations of urine metabolites in adolescents using 3d weighed dietary records (3d-WDR) and 24-h urine samples (n = 339), and associations of repeatedly assessed UPF intake with concentrations of circulating plasma metabolites in young adults with 3-6 3d-WDRs within 5 y preceding blood measurement (n = 195). Urine and plasma samples were analyzed using mass spectrometry-based metabolomics. Biosample-specific metabolite patterns (MPs) were determined using robust sparse principal components analysis. Multivariable linear regression models were applied to assess the associations of UPF consumption (as a percentage of total food intake in g/d) with concentrations of individual metabolites and MP scores., Results: The median proportion of UPF intake was 22.0% [interquartile range (IQR): 12.3, 32.9] in adolescents and 23.2% (IQR: 16.0, 31.6) in young adults. We identified 42 and 6 UPF intake-associated metabolites in urine and plasma samples, respectively. One urinary MP, "xenobiotics and amino acids" [β = 0.042, 95% confidence interval (CI): 0.014, 0.070] and 1 plasma MP, "lipids, xenobiotics, and amino acids" (β = 0.074, 95% CI: 0.031, 0.117) showed positive association with UPF intake. Both patterns shared 29 metabolites, mostly of xenobiotic metabolism., Conclusions: We identified urine and plasma metabolites associated with UPF intake in adolescents and young adults, which may represent some of the biological mechanisms through which UPFs may influence metabolism and health., Competing Interests: Conflict of interest The authors report no conflicts of interest. Where authors are identified as personnel of the International Agency for Research on Cancer/WHO, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/WHO., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: The associations of sweetened beverages (SBs) and added sugar (AS) intake with adiposity are still debated. Metabolomics could provide insights into the mechanisms linking their intake to adiposity., Objectives: We aimed to identify metabolomics biomarkers of intake of low- and no-calorie sweetened beverages (LNCSBs), sugar-sweetened beverages (SSBs), and ASs and to investigate their associations with body mass index, body fat percentage, and waist circumference., Methods: We analyzed 3 data sets from the Dortmund Nutritional and Anthropometric Longitudinally Designed (DONALD) cohort study, of children who provided 2 urine samples (n = 297), adolescents who provided a single urine sample (n = 339), and young adults who provided a single plasma sample (n = 195). Urine and plasma were analyzed using untargeted metabolomics. Dietary intakes were assessed using 3-d weighed dietary records. The random forest, partial least squares, and least absolute shrinkage and selection operator were jointly used for metabolite selection. We examined associations of intakes with metabolites and anthropometric measures using linear and mixed-effects regression., Results: In adolescents, LNCSB were positively associated with acesulfame (β: 0.0012; 95% confidence interval [CI]: 0.0006, 0.0019) and saccharin (β: 0.0009; 95% CI: 0.0002, 0.0015). In children, the association was observed with saccharin (β: 0.0016; 95% CI: 0.0005, 0.0027). In urine and plasma, SSBs were positively associated with 1-methylxanthine (β: 0.0005; 95% CI: 0.0003, 0.0008; and β: 0.0010, 95% CI 0.0004, 0.0015, respectively) and 5-acetylamino-6-amino-3-methyluracil (β: 0.0005; 95% CI: 0.0002, 0.0008; and β: 0.0009; 95% CI: 0.0003, 0.0014, respectively). AS was associated with urinary sucrose (β: 0.0095; 95% CI: 0.0069, 0.0121) in adolescents. Some of the food-related metabolomics profiles were also associated with adiposity measures., Conclusions: We identified SBs- and AS-related metabolites, which may be important for understanding the interplay between these intakes and adiposity in young individuals., Competing Interests: Conflict of interest The authors report no conflicts of interests. Where authors are identified as personnel of the International Agency for Research on Cancer/WHO, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/WHO., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types., Competing Interests: MB, GS, PK, PR, VV No competing interests declared, (© 2023, Breeur, Stepaniants et al.)

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.

Hepatocellular carcinoma (HCC) development entails changes in liver metabolism. Current knowledge on metabolic perturbations in HCC is derived mostly from case-control designs, with sparse information from prospective cohorts. Our objective was to apply comprehensive metabolite profiling to detect metabolites whose serum concentrations are associated with HCC development, using biological samples from within the prospective European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (>520 000 participants), where we identified 129 HCC cases matched 1:1 to controls. We conducted high-resolution untargeted liquid chromatography-mass spectrometry-based metabolomics on serum samples collected at recruitment prior to cancer diagnosis. Multivariable conditional logistic regression was applied controlling for dietary habits, alcohol consumption, smoking, body size, hepatitis infection and liver dysfunction. Corrections for multiple comparisons were applied. Of 9206 molecular features detected, 220 discriminated HCC cases from controls. Detailed feature annotation revealed 92 metabolites associated with HCC risk, of which 14 were unambiguously identified using pure reference standards. Positive HCC-risk associations were observed for N1-acetylspermidine, isatin, p-hydroxyphenyllactic acid, tyrosine, sphingosine, l,l-cyclo(leucylprolyl), glycochenodeoxycholic acid, glycocholic acid and 7-methylguanine. Inverse risk associations were observed for retinol, dehydroepiandrosterone sulfate, glycerophosphocholine, γ-carboxyethyl hydroxychroman and creatine. Discernible differences for these metabolites were observed between cases and controls up to 10 years prior to diagnosis. Our observations highlight the diversity of metabolic perturbations involved in HCC development and replicate previous observations (metabolism of bile acids, amino acids and phospholipids) made in Asian and Scandinavian populations. These findings emphasize the role of metabolic pathways associated with steroid metabolism and immunity and specific dietary and environmental exposures in HCC development., (© 2020 UICC.)

Scope: The goal of this work is to identify circulating biomarkers of habitual coffee intake using a metabolomic approach, and to investigate their associations with coffee intake in four European countries., Methods and Results: Untargeted mass spectrometry-based metabolic profiling is performed on serum samples from 451 participants of the European Prospective Investigation on Cancer and Nutrition (EPIC) originating from France, Germany, Greece, and Italy. Eleven coffee metabolites are found to be associated with self-reported habitual coffee intake, including eight more strongly correlated (r = 0.25-0.51, p < 10E -07 ). Trigonelline shows the highest correlation, followed by caffeine, two caffeine metabolites (paraxanthine and 5-Acetylamino-6-amino-3-methyluracil), quinic acid, and three compounds derived from coffee roasting (cyclo(prolyl-valyl), cyclo(isoleucyl-prolyl), cyclo(leucyl-prolyl), and pyrocatechol sulfate). Differences in the magnitude of correlations are observed between countries, with trigonelline most highly correlated with coffee intake in France and Germany, quinic acid in Greece, and cyclo(isoleucyl-prolyl) in Italy., Conclusion: Several biomarkers of habitual coffee intake are identified. No unique biomarker is found to be optimal for all tested populations. Instead, optimal biomarkers are shown to depend on the population and on the type of coffee consumed. These biomarkers should help to further explore the role of coffee in disease risk., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Summary: Background: Altered lipid metabolism is a hallmark of cancer development. However, the role of specific lipid metabolites in colorectal cancer development is uncertain. Methods: In a case–control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC), we examined associations between pre-diagnostic circulating concentrations of 97 lipid metabolites (acylcarnitines, glycerophospholipids and sphingolipids) and colorectal cancer risk. Circulating lipids were measured using targeted mass spectrometry in 1591 incident colorectal cancer cases (55% women) and 1591 matched controls. Multivariable conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between concentrations of individual lipid metabolites and metabolite patterns with colorectal cancer risk. Findings: Of the 97 assayed lipids, 24 were inversely associated (nominally p

Obesity is a major health concern for breast cancer survivors, being associated with high recurrence and reduced efficacy during cancer treatment. Metformin treatment is associated with reduced breast cancer incidence, recurrence and mortality. To better understand the underlying mechanisms through which metformin may reduce recurrence, we aimed to conduct metabolic profiling of overweight/obese breast cancer survivors before and after metformin treatment. Fasting plasma samples from 373 overweight or obese breast cancer survivors randomly assigned to metformin (n = 194) or placebo (n = 179) administration were collected at baseline, after 6 months (Reach For Health trial), and after 12 months (MetBreCS trial). Archival samples were concurrently analyzed using three complementary methods: untargeted LC-QTOF-MS metabolomics, targeted LC-MS metabolomics (AbsoluteIDQ p180, Biocrates), and gas chromatography phospholipid fatty acid assay. Multivariable linear regression models and family-wise error correction were used to identify metabolites that significantly changed after metformin treatment. Participants (n = 352) with both baseline and study end point samples available were included in the analysis. After adjusting for confounders such as study center, age, body mass index and false discovery rate, we found that metformin treatment was significantly associated with decreased levels of citrulline, arginine, tyrosine, caffeine, paraxanthine, and theophylline, and increased levels of leucine, isoleucine, proline, 3-methyl-2-oxovalerate, 4-methyl-2-oxovalerate, alanine and indoxyl-sulphate. Long-chain unsaturated phosphatidylcholines (PC ae C36:4, PC ae C38:5, PC ae C36:5 and PC ae C38:6) were significantly decreased with the metformin treatment, as were phospholipid-derived long-chain n-6 fatty acids. The metabolomic profiles of metformin treatment suggest change in specific biochemical pathways known to impair cancer cell growth including activation of CYP1A2, alterations in fatty acid desaturase activity, and altered metabolism of specific amino acids, including impaired branched chain amino acid catabolism. Our results in overweight breast cancer survivors identify new metabolic effects of metformin treatment that may mechanistically contribute to reduced risk of recurrence in this population and reduced obesity-related cancer risk reported in observational studies. ClinicalTrials.gov identifier: NCT01302379 and EudraCT Protocol #: 2015-001001-14.

Abstract Background Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations. Methods We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty. Results Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk. Conclusions These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.

ABSTRACT Numerous studies have described specific metabolites as biomarkers of severe liver diseases, but very few have measured gut microbiota (GM)-produced metabolites in fatty liver disease. We aimed at finding GM signatures and metabolite markers in plasma and feces related to high liver fat content. Based on imaging, we divided study participants into low (5%, HF, n = 39) liver fat groups. Fecal (LF n = 14, HF n = 25) and plasma (LF n = 11, HF n = 7) metabolomes of subsets of participants were studied using liquid chromatography/high resolution mass spectrometry. The GM were analyzed using 16S rRNA gene sequencing. Additionally, blood clinical variables and diet were studied. Dyslipidemia, higher liver enzymes and insulin resistance characterized the HF group. No major differences in diet were found between the groups. In the GM, the HF group had lower abundance of Bacteroides and Prevotellaceae NK3B31 group than the LF group after adjusting for metformin use or obesity. In feces, the HF group had higher levels of lysine and histidine degradation products, while 6-hydroxybetatestosterone (metabolized by CYP3A4) was low. Higher plasma levels of caffeine and its metabolites in the HF group indicate that the activity of hepatic CYP1A2 was lower than in the LF group. Our results suggest, that low fecal Prevotellaceae NK3B31 and Bacteroides abundance, and increased lysine and histidine degradation may serve as GM biomarkers of high liver fat. Altered plasma caffeine metabolites and lowered testosterone metabolism may specify decreased CYP activities, and their potential utility, as biomarkers of fatty liver disease. IMPORTANCE Because the high prevalence of nonalcoholic fatty liver disease sets diagnostic challenges to health care, identification of new biomarkers of the disease that in the future could have potential utility as diagnostic biomarkers of high liver fat content is important. Our results show that increased amino acid degradation products in the feces may be such biomarkers. In the blood, molecules that indicate defective hepatic metabolic enzyme activities were identified in individuals with high liver fat content.

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.

Demographic, lifestyle and biospecimen-related factors at the time of blood collection can influence metabolite levels in epidemiological studies. Identifying the major influences on metabolite concentrations is critical to designing appropriate sample collection protocols and considering covariate adjustment in metabolomics analyses. We examined the association of age, sex, and other short-term pre-blood collection factors (time of day, season, fasting duration, physical activity, NSAID use, smoking and alcohol consumption in the days prior to collection) with 133 targeted plasma metabolites (acylcarnitines, amino acids, biogenic amines, sphingolipids, glycerophospholipids, and hexoses) among 108 individuals that reported exposures within 48 h before collection. The differences in mean metabolite concentrations were assessed between groups based on pre-collection factors using two-sided t-tests and ANOVA with FDR correction. Percent differences in metabolite concentrations were negligible across season, time of day of collection, fasting status or lifestyle behaviors at the time of collection, including physical activity or the use of tobacco, alcohol or NSAIDs. The metabolites differed in concentration between the age and sex categories for 21.8% and 14.3% metabolites, respectively. In conclusion, extrinsic factors in the short period prior to collection were not meaningfully associated with concentrations of selected endogenous metabolites in a cross-sectional sample, though metabolite concentrations differed by age and sex. Larger studies with more coverage of the human metabolome are warranted.

Circulating levels of acylcarnitines (ACs) have been associated with the risk of various diseases such as cancer and type 2 diabetes. Diet and lifestyle factors have been shown to influence AC concentrations but a better understanding of their biological, lifestyle and metabolic determinants is needed. Circulating ACs were measured in blood by targeted (15 ACs) and untargeted metabolomics (50 ACs) in 7770 and 395 healthy participants of the European Prospective Investigation into Cancer and Nutrition (EPIC), respectively. Associations with biological and lifestyle characteristics, dietary patterns, self-reported intake of individual foods, estimated intake of carnitine and fatty acids, and fatty acids in plasma phospholipid fraction and amino acids in blood were assessed. Age, sex and fasting status were associated with the largest proportion of AC variability (partial-r up to 0.19, 0.18 and 0.16, respectively). Some AC species of medium or long-chain fatty acid moiety were associated with the corresponding fatty acids in plasma (partial-r = 0.24) or with intake of specific foods such as dairy foods containing the same fatty acid. ACs of short-chain fatty acid moiety (propionylcarnitine and valerylcarnitine) were moderately associated with concentrations of branched-chain amino acids (partial-r = 0.5). Intake of most other foods and of carnitine showed little association with AC levels. Our results show that determinants of ACs in blood vary according to their fatty acid moiety, and that their concentrations are related to age, sex, diet, and fasting status. Knowledge on their potential determinants may help interpret associations of ACs with disease risk and inform on potential dietary and lifestyle factors that might be modified for disease prevention. [ABSTRACT FROM AUTHOR]

Background Biological perturbations caused by air pollution might be reflected in the compounds present in blood originating from air pollutants and endogenous metabolites influenced by air pollution (defined here as part of the blood metabolome). We aimed to assess the perturbation of the blood metabolome in response to short term exposure to air pollution. Methods We exposed 31 healthy volunteers to ambient air pollution for 5 h. We measured exposure to particulate matter, particle number concentrations, absorbance, elemental/organic carbon, trace metals, secondary inorganic components, endotoxin content, gaseous pollutants, and particulate matter oxidative potential. We collected blood from the participants 2 h before and 2 and 18 h after exposure. We employed untargeted metabolite profiling to monitor 3873 metabolic features in 493 blood samples from these volunteers. We assessed lung function using spirometry and six acute phase proteins in peripheral blood. We assessed the association of the metabolic features with the measured air pollutants and with health markers that we previously observed to be associated with air pollution in this study. Results We observed 89 robust associations between air pollutants and metabolic features two hours after exposure and 118 robust associations 18 h after exposure. Some of the metabolic features that were associated with air pollutants were also associated with acute health effects, especially changes in forced expiratory volume in 1 s. We successfully identified tyrosine, guanosine, and hypoxanthine among the associated features. Bioinformatics approach Mummichog predicted enriched pathway activity in eight pathways, among which tyrosine metabolism. Conclusions This study demonstrates for the first time the application of untargeted metabolite profiling to assess the impact of air pollution on the blood metabolome. [ABSTRACT FROM AUTHOR]