Metabolites

Research

16 pages, 1409 KiB

Open AccessArticle

Whole Blood Metabolite Profiles Reflect Changes in Energy Metabolism in Heart Failure

by Carl Beuchel, Julia Dittrich, Janne Pott, Sylvia Henger, Frank Beutner, Berend Isermann, Markus Loeffler, Joachim Thiery, Uta Ceglarek and Markus Scholz

Metabolites 2022, 12(3), 216; https://doi.org/10.3390/metabo12030216 - 27 Feb 2022

Cited by 4 | Viewed by 3277

Abstract

A variety of atherosclerosis and cardiovascular disease (ASCVD) phenotypes are tightly linked to changes in the cardiac energy metabolism that can lead to a loss of metabolic flexibility and to unfavorable clinical outcomes. We conducted an association analysis of 31 ASCVD phenotypes and [...] Read more.

A variety of atherosclerosis and cardiovascular disease (ASCVD) phenotypes are tightly linked to changes in the cardiac energy metabolism that can lead to a loss of metabolic flexibility and to unfavorable clinical outcomes. We conducted an association analysis of 31 ASCVD phenotypes and 97 whole blood amino acids, acylcarnitines and derived ratios in the LIFE-Adult (n = 9646) and LIFE-Heart (n = 5860) studies, respectively. In addition to hundreds of significant associations, a total of 62 associations of six phenotypes were found in both studies. Positive associations of various amino acids and a range of acylcarnitines with decreasing cardiovascular health indicate disruptions in mitochondrial, as well as peroxisomal fatty acid oxidation. We complemented our metabolite association analyses with whole blood and peripheral blood mononuclear cell (PBMC) gene-expression analyses of fatty acid oxidation and ketone-body metabolism related genes. This revealed several differential expressions for the heart failure biomarker N-terminal prohormone of brain natriuretic peptide (NT-proBNP) in peripheral blood mononuclear cell (PBMC) gene expression. Finally, we constructed and compared three prediction models of significant stenosis in the LIFE-Heart study using (1) traditional risk factors only, (2) the metabolite panel only and (3) a combined model. Area under the receiver operating characteristic curve (AUC) comparison of these three models shows an improved prediction accuracy for the combined metabolite and classical risk factor model (AUC = 0.78, 95%-CI: 0.76–0.80). In conclusion, we improved our understanding of metabolic implications of ASCVD phenotypes by observing associations with metabolite concentrations and gene expression of the mitochondrial and peroxisomal fatty acid oxidation. Additionally, we demonstrated the predictive potential of the metabolite profile to improve classification of patients with significant stenosis. Full article

(This article belongs to the Special Issue Metabolomics Meets Epidemiology)

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18 pages, 2450 KiB

Open AccessArticle

Sex-Specific Causal Relations between Steroid Hormones and Obesity—A Mendelian Randomization Study

by Janne Pott, Katrin Horn, Robert Zeidler, Holger Kirsten, Peter Ahnert, Jürgen Kratzsch, Markus Loeffler, Berend Isermann, Uta Ceglarek and Markus Scholz

Metabolites 2021, 11(11), 738; https://doi.org/10.3390/metabo11110738 - 28 Oct 2021

Cited by 11 | Viewed by 4003

Abstract

Steroid hormones act as important regulators of physiological processes including gene expression. They provide possible mechanistic explanations of observed sex-dimorphisms in obesity and coronary artery disease (CAD). Here, we aim to unravel causal relationships between steroid hormones, obesity, and CAD in a sex-specific [...] Read more.

Steroid hormones act as important regulators of physiological processes including gene expression. They provide possible mechanistic explanations of observed sex-dimorphisms in obesity and coronary artery disease (CAD). Here, we aim to unravel causal relationships between steroid hormones, obesity, and CAD in a sex-specific manner. In genome-wide meta-analyses of four steroid hormone levels and one hormone ratio, we identified 17 genome-wide significant loci of which 11 were novel. Among loci, seven were female-specific, four male-specific, and one was sex-related (stronger effects in females). As one of the loci was the human leukocyte antigen (HLA) region, we analyzed HLA allele counts and found four HLA subtypes linked to 17-OH-progesterone (17-OHP), including HLA-B*14*02. Using Mendelian randomization approaches with four additional hormones as exposure, we detected causal effects of dehydroepiandrosterone sulfate (DHEA-S) and 17-OHP on body mass index (BMI) and waist-to-hip ratio (WHR). The DHEA-S effect was stronger in males. Additionally, we observed the causal effects of testosterone, estradiol, and their ratio on WHR. By mediation analysis, we found a direct sex-unspecific effect of 17-OHP on CAD while the other four hormone effects on CAD were mediated by BMI or WHR. In conclusion, we identified the sex-specific causal networks of steroid hormones, obesity-related traits, and CAD. Full article

(This article belongs to the Special Issue Metabolomics Meets Epidemiology)

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18 pages, 3133 KiB

Open AccessFeature PaperArticle

A New Pipeline for the Normalization and Pooling of Metabolomics Data

by Vivian Viallon, Mathilde His, Sabina Rinaldi, Marie Breeur, Audrey Gicquiau, Bertrand Hemon, Kim Overvad, Anne Tjønneland, Agnetha Linn Rostgaard-Hansen, Joseph A. Rothwell, Lucie Lecuyer, Gianluca Severi, Rudolf Kaaks, Theron Johnson, Matthias B. Schulze, Domenico Palli, Claudia Agnoli, Salvatore Panico, Rosario Tumino, Fulvio Ricceri, W. M. Monique Verschuren, Peter Engelfriet, Charlotte Onland-Moret, Roel Vermeulen, Therese Haugdahl Nøst, Ilona Urbarova, Raul Zamora-Ros, Miguel Rodriguez-Barranco, Pilar Amiano, José Maria Huerta, Eva Ardanaz, Olle Melander, Filip Ottoson, Linda Vidman, Matilda Rentoft, Julie A. Schmidt, Ruth C. Travis, Elisabete Weiderpass, Mattias Johansson, Laure Dossus, Mazda Jenab, Marc J. Gunter, Justo Lorenzo Bermejo, Dominique Scherer, Reza M. Salek, Pekka Keski-Rahkonen and Pietro Ferrari Show full author list Hide full author list

Metabolites 2021, 11(9), 631; https://doi.org/10.3390/metabo11090631 - 17 Sep 2021

Cited by 15 | Viewed by 7370

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Metabolomics Meets Epidemiology)

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13 pages, 2240 KiB

Open AccessArticle

A Mediation Approach to Discovering Causal Relationships between the Metabolome and DNA Methylation in Type 1 Diabetes

by Tim Vigers, Lauren A. Vanderlinden, Randi K. Johnson, Patrick M. Carry, Ivana Yang, Brian C. DeFelice, Alexander M. Kaizer, Laura Pyle, Marian Rewers, Oliver Fiehn, Jill M. Norris and Katerina Kechris

Metabolites 2021, 11(8), 542; https://doi.org/10.3390/metabo11080542 - 14 Aug 2021

Cited by 1 | Viewed by 3276

Abstract

Environmental factors including viruses, diet, and the metabolome have been linked with the appearance of islet autoimmunity (IA) that precedes development of type 1 diabetes (T1D). We measured global DNA methylation (DNAm) and untargeted metabolomics prior to IA and at the time of [...] Read more.

Environmental factors including viruses, diet, and the metabolome have been linked with the appearance of islet autoimmunity (IA) that precedes development of type 1 diabetes (T1D). We measured global DNA methylation (DNAm) and untargeted metabolomics prior to IA and at the time of seroconversion to IA in 92 IA cases and 91 controls from the Diabetes Autoimmunity Study in the Young (DAISY). Causal mediation models were used to identify seven DNAm probe-metabolite pairs in which the metabolite measured at IA mediated the protective effect of the DNAm probe measured prior to IA against IA risk. These pairs included five DNAm probes mediated by histidine (a metabolite known to affect T1D risk), one probe (cg01604946) mediated by phostidyl choline p-32:0 or o-32:1, and one probe (cg00390143) mediated by sphingomyelin d34:2. The top 100 DNAm probes were over-represented in six reactome pathways at the FDR <0.1 level (q = 0.071), including transport of small molecules and inositol phosphate metabolism. While the causal pathways in our mediation models require further investigation to better understand the biological mechanisms, we identified seven methylation sites that may improve our understanding of epigenetic protection against T1D as mediated by the metabolome. Full article

(This article belongs to the Special Issue Metabolomics Meets Epidemiology)

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12 pages, 10634 KiB

Open AccessArticle

Serum Retinal and Retinoic Acid Predict the Development of Type 2 Diabetes Mellitus in Korean Subjects with Impaired Fasting Glucose from the KCPS-II Cohort

by Youngmin Han, Yeunsoo Yang, Minjoo Kim, Sun Ha Jee, Hye Jin Yoo and Jong Ho Lee

Metabolites 2021, 11(8), 510; https://doi.org/10.3390/metabo11080510 - 3 Aug 2021

Cited by 3 | Viewed by 2599

Abstract

We aimed to investigate whether retinal and retinoic acid (RA), which are newly discovered biomarkers from our previous research, reliably predict type 2 diabetes mellitus (T2DM) development in subjects with impaired fasting glucose (IFG). Among the Korean Cancer Prevention Study (KCPS)-II cohort, subjects [...] Read more.

We aimed to investigate whether retinal and retinoic acid (RA), which are newly discovered biomarkers from our previous research, reliably predict type 2 diabetes mellitus (T2DM) development in subjects with impaired fasting glucose (IFG). Among the Korean Cancer Prevention Study (KCPS)-II cohort, subjects were selected and matched by age and sex (IFG-IFG group, n = 100 vs. IFG-DM group, n = 100) for study 1. For real-world validation of two biomarkers (study 2), other participants in the KCPS-II cohort who had IFG at baseline (n = 500) were selected. Targeted LC/MS was used to analyze the baseline serum samples; retinal and RA levels were quantified. In study 1, we revealed that both biomarkers were significantly decreased in the IFG-DM group (retinal, p = 0.017; RA, p < 0.001). The obese subjects in the IFG-DM group showed markedly lower retinal (p = 0.030) and RA (p = 0.003) levels than those in the IFG-IFG group. In study 2, the results for the two metabolites tended to be similar to those of study 1, but no significant difference was observed. Notably, the predictive ability for T2DM was enhanced when the metabolites were added to conventional risk factors for T2DM in both studies (study 1, AUC 0.682 → 0.775; study 2, AUC 0.734 → 0.786). The results suggest that retinal- and RA-related metabolic pathways are altered before the onset of T2DM. Full article

(This article belongs to the Special Issue Metabolomics Meets Epidemiology)

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11 pages, 910 KiB

Open AccessArticle

Effect of Korean Red Ginseng on Plasma Ceramide Levels in Postmenopausal Women with Hypercholesterolemia: A Pilot Randomized Controlled Trial

by Yu-Jin Kwon, Gyung-Min Lee, Kwang-Hyeon Liu and Dong-Hyuk Jung

Metabolites 2021, 11(7), 417; https://doi.org/10.3390/metabo11070417 - 24 Jun 2021

Cited by 4 | Viewed by 3029

Abstract

Cardiovascular disease (CVD) is a crucial cause of death in postmenopausal women. Plasma ceramide concentrations are correlated with the development of atherosclerosis and are significant predictors of CVD. Here, we conducted a 4-week, double-blinded, placebo-controlled clinical pilot study to investigate the effect of [...] Read more.

Cardiovascular disease (CVD) is a crucial cause of death in postmenopausal women. Plasma ceramide concentrations are correlated with the development of atherosclerosis and are significant predictors of CVD. Here, we conducted a 4-week, double-blinded, placebo-controlled clinical pilot study to investigate the effect of Korean red ginseng (KRG) on serum ceramide concentrations in 68 postmenopausal women with hypercholesterolemia. Patients were randomly assigned to two groups: the experimental group (n = 36) received KRG and the control (n = 32) group received placebo, 2 g each, once daily. Serum ceramides were measured using liquid chromatography–tandem mass spectrometry at baseline and study completion, with changes in serum ceramide levels as the primary end point. We detected significantly greater mean changes in C16 ceramide levels (d18:1/16:0: −6.4 ± 6.3 pmol/mL vs. 14.6 ± 6.8 pmol/mL, respectively, p = 0.040; d18:1/22:0: −20.8 ± 24.4 pmol/mL vs. 71.1 ± 26.2 pmol/mL, respectively, p = 0.020). Additionally, changes in the median C16 (d18:1/16:0) and C22 (d18:1/22:0) ceramide levels were significantly greater in KRG-group subjects with metabolic syndrome than those without. Therefore, we found that KRG decreases the serum levels of several ceramides in postmenopausal women with hypercholesterolemia, suggesting it may be beneficial for preventing CVD in these individuals. Full article

(This article belongs to the Special Issue Metabolomics Meets Epidemiology)

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20 pages, 6240 KiB

Open AccessArticle

Plasma Metabolomic Profiling in 1391 Subjects with Overweight and Obesity from the SPHERE Study

by Gianfranco Frigerio, Chiara Favero, Diego Savino, Rosa Mercadante, Benedetta Albetti, Laura Dioni, Luisella Vigna, Valentina Bollati, Angela Cecilia Pesatori and Silvia Fustinoni

Metabolites 2021, 11(4), 194; https://doi.org/10.3390/metabo11040194 - 24 Mar 2021

Cited by 23 | Viewed by 3992

Abstract

Overweight and obesity have high prevalence worldwide and assessing the metabolomic profile is a useful approach to study their related metabolic processes. In this study, we assessed the metabolomic profile of 1391 subjects affected by overweight and obesity, enrolled in the frame of [...] Read more.

Overweight and obesity have high prevalence worldwide and assessing the metabolomic profile is a useful approach to study their related metabolic processes. In this study, we assessed the metabolomic profile of 1391 subjects affected by overweight and obesity, enrolled in the frame of the SPHERE study, using a validated LC–MS/MS targeted metabolomic approach determining a total of 188 endogenous metabolites. Multivariable censored linear regression Tobit models, correcting for age, sex, and smoking habits, showed that 83 metabolites were significantly influenced by body mass index (BMI). Among compounds with the highest association, aromatic and branched chain amino acids (in particular tyrosine, valine, isoleucine, and phenylalanine) increased with the increment of BMI, while some glycerophospholipids decreased, in particular some lysophosphatidylcholines (as lysoPC a C18:2) and several acylalkylphosphatidylcholines (as PC ae C36:2, PC ae C34:3, PC ae C34:2, and PC ae C40:6). The results of this investigation show that several endogenous metabolites are influenced by BMI, confirming the evidence with the strength of a large number of subjects, highlighting differences among subjects with different classes of obesity and showing unreported associations between BMI and different phosphatidylcholines. Full article

(This article belongs to the Special Issue Metabolomics Meets Epidemiology)

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10 pages, 250 KiB

Open AccessEditor’s ChoiceArticle

Pre-Diagnostic Circulating Metabolites and Colorectal Cancer Risk in the Cancer Prevention Study-II Nutrition Cohort

by Marjorie L. McCullough, Rebecca A. Hodge, Peter T. Campbell, Victoria L. Stevens and Ying Wang

Metabolites 2021, 11(3), 156; https://doi.org/10.3390/metabo11030156 - 9 Mar 2021

Cited by 17 | Viewed by 3461

Abstract

Untargeted metabolomic studies have identified potential biomarkers of colorectal cancer risk, but evidence is still limited and broadly inconsistent. Among 39,239 Cancer Prevention Study II Nutrition cohort participants who provided a blood sample between 1998–2001, 517 newly diagnosed colorectal cancers were identified through [...] Read more.

Untargeted metabolomic studies have identified potential biomarkers of colorectal cancer risk, but evidence is still limited and broadly inconsistent. Among 39,239 Cancer Prevention Study II Nutrition cohort participants who provided a blood sample between 1998–2001, 517 newly diagnosed colorectal cancers were identified through 30 June 2015. In this nested case–control study, controls were matched 1:1 to cases on age, sex, race and date of blood draw. Mass spectroscopy-based metabolomic analyses of pre-diagnostic plasma identified 886 named metabolites, after quality control exclusions. Conditional logistic regression models estimated multivariable-adjusted odds ratios (OR) and 95% confidence intervals (CI) for 1 standard deviation (SD) increase in each metabolite with risk of colorectal cancer. Six metabolites were associated with colorectal cancer risk at a false discovery rate < 0.20. These metabolites were of several classes, including cofactors and vitamins, nucleotides, xenobiotics, lipids and amino acids. Five metabolites (guanidinoacetate, 2’-O-methylcytidine, vanillylmandelate, bilirubin (E,E) and N-palmitoylglycine) were positively associated (OR per 1 SD = 1.29 to 1.32), and one (3-methylxanthine) was inversely associated with CRC risk (OR = 0.79, 95% CI, 0.69–0.89). We did not replicate findings from two earlier prospective studies of 250 cases each after adjusting for multiple comparisons. Large pooled prospective analyses are warranted to confirm or refute these findings and to discover and replicate metabolites associated with colorectal cancer risk. Full article

(This article belongs to the Special Issue Metabolomics Meets Epidemiology)

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13 pages, 4376 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Metabolomics Analysis of Postmenopausal Breast Cancer Risk in the Cancer Prevention Study II

by Steven C. Moore, Kaitlyn M. Mazzilli, Joshua N. Sampson, Charles E. Matthews, Brian D. Carter, Mary C. Playdon, Ying Wang and Victoria L. Stevens

Metabolites 2021, 11(2), 95; https://doi.org/10.3390/metabo11020095 - 10 Feb 2021

Cited by 23 | Viewed by 5783

Abstract

Breast cancer is the most common cancer in women, but its incidence can only be partially explained through established risk factors. Our aim was to use metabolomics to identify novel risk factors for breast cancer and to validate recently reported metabolite-breast cancer findings. [...] Read more.

Breast cancer is the most common cancer in women, but its incidence can only be partially explained through established risk factors. Our aim was to use metabolomics to identify novel risk factors for breast cancer and to validate recently reported metabolite-breast cancer findings. We measured levels of 1275 metabolites in prediagnostic serum in a nested case-control study of 782 postmenopausal breast cancer cases and 782 matched controls. Metabolomics analysis was performed by Metabolon Inc using ultra-performance liquid chromatography and a Q-Exactive high resolution/accurate mass spectrometer. Controls were matched by birth date, date of blood draw, and race/ethnicity. Odds ratios (ORs) and 95% confidence intervals (CIs) of breast cancer at the 90th versus 10th percentile (modeled on a continuous basis) of metabolite levels were estimated using conditional logistic regression, with adjustment for age. Twenty-four metabolites were significantly associated with breast cancer risk at a false discovery rate <0.20. For the nine metabolites positively associated with risk, the ORs ranged from 1.75 (95% CI: 1.29–2.36) to 1.45 (95% CI: 1.13–1.85), and for the 15 metabolites inversely associated with risk, ORs ranged from 0.59 (95% CI: 0.43–0.79) to 0.69 (95% CI: 0.55–0.87). These metabolites largely comprised carnitines, glycerolipids, and sex steroid metabolites. Associations for three sex steroid metabolites validated findings from recent studies and the remainder were novel. These findings contribute to growing data on metabolite-breast cancer associations by confirming prior findings and identifying novel leads for future validation efforts. Full article

(This article belongs to the Special Issue Metabolomics Meets Epidemiology)

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Review

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8 pages, 553 KiB

Open AccessReview

Metabolomics Meets Nutritional Epidemiology: Harnessing the Potential in Metabolomics Data

by Lorraine Brennan, Frank B. Hu and Qi Sun

Metabolites 2021, 11(10), 709; https://doi.org/10.3390/metabo11100709 - 19 Oct 2021

Cited by 21 | Viewed by 5652

Abstract

Traditionally, nutritional epidemiology is the study of the relationship between diet and health and disease in humans at the population level. Commonly, the exposure of interest is food intake. In recent years, nutritional epidemiology has moved from a “black box” approach to a [...] Read more.

Traditionally, nutritional epidemiology is the study of the relationship between diet and health and disease in humans at the population level. Commonly, the exposure of interest is food intake. In recent years, nutritional epidemiology has moved from a “black box” approach to a systems approach where genomics, metabolomics and proteomics are providing novel insights into the interplay between diet and health. In this context, metabolomics is emerging as a key tool in nutritional epidemiology. The present review explores the use of metabolomics in nutritional epidemiology. In particular, it examines the role that food-intake biomarkers play in addressing the limitations of self-reported dietary intake data and the potential of using metabolite measurements in assessing the impact of diet on metabolic pathways and physiological processes. However, for full realisation of the potential of metabolomics in nutritional epidemiology, key challenges such as robust biomarker validation and novel methods for new metabolite identification need to be addressed. The synergy between traditional epidemiologic approaches and metabolomics will facilitate the translation of nutritional epidemiologic evidence to effective precision nutrition. Full article

(This article belongs to the Special Issue Metabolomics Meets Epidemiology)

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18 pages, 846 KiB

Open AccessReview

A Systematic Review of Metabolomic Biomarkers for the Intake of Sugar-Sweetened and Low-Calorie Sweetened Beverages

by Samuel Muli, Jantje Goerdten, Kolade Oluwagbemigun, Anna Floegel, Matthias Schmid and Ute Nöthlings

Metabolites 2021, 11(8), 546; https://doi.org/10.3390/metabo11080546 - 19 Aug 2021

Cited by 11 | Viewed by 4954

Abstract

Intake of added sugars (AS) is challenging to assess compared with total dietary sugar because of the lack of reliable assessment methods. The reliance on self-reported dietary data in observational studies is often cited as biased, with evidence of AS intake in relation [...] Read more.

Intake of added sugars (AS) is challenging to assess compared with total dietary sugar because of the lack of reliable assessment methods. The reliance on self-reported dietary data in observational studies is often cited as biased, with evidence of AS intake in relation to health outcomes rated as low to moderate quality. Sugar-sweetened beverages (SSBs) are a major source of AS. A regular and high intake of SSBs is associated with an overall poor diet, weight gain, and cardiometabolic risks. An elevated intake of low-calorie sweetened beverages (LCSBs), often regarded as healthier alternatives to SSBs, is also increasingly associated with increased risk for metabolic dysfunction. In this review, we systematically collate evidence and provide perspectives on the use of metabolomics for the discovery of candidate biomarkers associated with the intake of SSBs and LCSBs. We searched the Medline, Embase, Scopus, and Web of Science databases until the end of December 2020. Seventeen articles fulfilled our inclusion criteria. We evaluated specificity and validity of the identified biomarkers following Guidelines for Biomarker of Food Intake Reviews (BFIRev). We report that the ¹³C:¹²C carbon isotope ratio (δ¹³C), particularly, the δ¹³C of alanine is the most robust, sensitive, and specific biomarker of SSBs intake. Acesulfame-K, saccharin, sucralose, cyclamate, and steviol glucuronide showed moderate validity for predicting the short-term intake of LCSBs. More evidence is required to evaluate the validity of other panels of metabolites associated with the intake of SSBs. Full article

(This article belongs to the Special Issue Metabolomics Meets Epidemiology)

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Other

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17 pages, 787 KiB

Open AccessSystematic Review

Assessment of Fruit and Vegetables Intake with Biomarkers in Children and Adolescents and Their Level of Validation: A Systematic Review

by Li Yuan, Samuel Muli, Inge Huybrechts, Ute Nöthlings, Wolfgang Ahrens, Augustin Scalbert and Anna Floegel

Metabolites 2022, 12(2), 126; https://doi.org/10.3390/metabo12020126 - 28 Jan 2022

Cited by 19 | Viewed by 4475

Abstract

Fruit and vegetables (FV) are part of a healthy diet and should be frequently consumed already at a young age. However, intake of FV is difficult to assess in children and adolescents due to various misreporting aspects. Thus, measurement of dietary biomarkers may [...] Read more.

Fruit and vegetables (FV) are part of a healthy diet and should be frequently consumed already at a young age. However, intake of FV is difficult to assess in children and adolescents due to various misreporting aspects. Thus, measurement of dietary biomarkers may be a promising alternative to assess FV intake more objectively at young age. To date, dietary biomarkers have been primarily studied in adults, and research focused on their usefulness in children is scarce. However, clinical studies have revealed important differences between children and adults, most importantly in their gut microbiome composition, resulting in differences in postprandial metabolism, as well as in food choices and meal compositions that may influence individual biomarker levels. Therefore, the present review aimed to identify biomarkers of FV intake (BFVI) currently available in children and adolescents and to explore whether there are any differences in the BFVI profile above between children and adolescents and adults. In addition, the current level of validation of BFVI in children and adolescents was examined. In total, 28 studies were eligible for this review, and 18 compounds were identified as potential biomarkers for FV intake in children and adolescents. Carotenoid concentration in skin was a valuable biomarker for total FV intake for both children and adult populations. Common BFVI in blood in adults (e.g., carotenoids and vitamin C) showed inconsistent results in children and adolescents. Biomarkers particularly useful in children included urinary hippuric acid as a biomarker of polyphenolic compound intake originating from FV and the combination of N-methylnicotinic acid and acetylornithine as a biomarker of bean intake. Further studies are needed to assess their kinetics, dose–response, and other validation aspects. There is limited evidence so far regarding valid BFVI in children and adolescents. Thus, to put BFVI into practice in children and adolescents, further studies, particularly based on metabolomics, are needed to identify and validate BFVI that can be used in future epidemiological studies. Full article

(This article belongs to the Special Issue Metabolomics Meets Epidemiology)

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21 pages, 1492 KiB

Open AccessSystematic Review

Could Reducing Body Fatness Reduce the Risk of Aggressive Prostate Cancer via the Insulin Signalling Pathway? A Systematic Review of the Mechanistic Pathway

by Rachel James, Olympia Dimopoulou, Richard M. Martin, Claire M. Perks, Claire Kelly, Louise Mathias, Stefan Brugger, Julian P. T. Higgins and Sarah J. Lewis

Metabolites 2021, 11(11), 726; https://doi.org/10.3390/metabo11110726 - 23 Oct 2021

Cited by 1 | Viewed by 2667

Abstract

Excess body weight is thought to increase the risk of aggressive prostate cancer (PCa), although the biological mechanism is currently unclear. Body fatness is positively associated with a diminished cellular response to insulin and biomarkers of insulin signalling have been positively associated with [...] Read more.

Excess body weight is thought to increase the risk of aggressive prostate cancer (PCa), although the biological mechanism is currently unclear. Body fatness is positively associated with a diminished cellular response to insulin and biomarkers of insulin signalling have been positively associated with PCa risk. We carried out a two-pronged systematic review of (a) the effect of reducing body fatness on insulin biomarker levels and (b) the effect of insulin biomarkers on PCa risk, to determine whether a reduction in body fatness could reduce PCa risk via effects on the insulin signalling pathway. We identified seven eligible randomised controlled trials of interventions designed to reduce body fatness which measured insulin biomarkers as an outcome, and six eligible prospective observational studies of insulin biomarkers and PCa risk. We found some evidence that a reduction in body fatness improved insulin sensitivity although our confidence in this evidence was low based on GRADE (Grading of Recommendations, Assessment, Development and Evaluations). We were unable to reach any conclusions on the effect of insulin sensitivity on PCa risk from the few studies included in our systematic review. A reduction in body fatness may reduce PCa risk via insulin signalling, but more high-quality evidence is needed before any conclusions can be reached regarding PCa. Full article

(This article belongs to the Special Issue Metabolomics Meets Epidemiology)

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