Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies

Rangel-Huerta, Óscar Daniel; Pastor-Villaescusa, Belén; Gil, Ángel

doi:10.1007/s11306-019-1553-y

Cited by 180 publications

(207 citation statements)

References 203 publications

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“…73 Recently, studies have shown that the metabolomic signatures of blood from cattle 74 and pigs 75 can be used to accurately separate animals by their feed efficiency which is one of the most economically important traits in livestock production. Studies of the human metabolome and its relevance to human health has also clearly increased the last five years, 76,77 and is very likely to be one of the cornerstones in an implementation of personalised medicine. With the emergence of large human cohort projects, like the UK Biobank 78 , FinnGenn 79 and BioBank Japan 80 etc., the data samples are reaching a sample level that could provide ground-breaking research if full metabolomic profiles were obtained.…”

Section: Discussionmentioning

confidence: 99%

Prediction of complex phenotypes using theDrosophilametabolome

Rohde

Kristensen

Sarup

et al. 2020

Preprint

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Understanding the genotype -phenotype map and how variation at different levels of biological organization are associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins, metabolites. This facilitates our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We do this by performing NMR metabolomics on four replicate pools of male flies from each of 170 isogenic lines. We show first that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, we show that for four of five traits related to behaviour and stress resistance the metabolome predicts phenotypes more accurately than genomic data, and that the metabolites driving the increased accuracy was trait specific. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding has strong applied importance e.g. in human medicine and animal and plant breeding.1 is a design matrix linking the genomic ( 1 ) and metabolomic ( 1 ) effects to the phenotypes. The random genomic effects are 1~( , [1,1] ! " ), and the metabolomic effects are 1~( , [1,1] 5 " ), where is the additive genomic relationship matrix as specified previously, and is the metabolomic relationship matrix.The metabolomic relationship matrix was computed as = ′ NMR ⁄ , where is a × NMR matrix of *77 18* T 6 , N 6 ), and was compared (using paired t-test corrected for multiple testing by a false discovery rate (FDR) of <0.05) within and across 9/ clusters to identify the NMR features resulting in the largest prediction accuracy. We only considered Drosophila melanogaster Genetic Reference Panel lines.

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Section: Discussionmentioning

confidence: 99%

Prediction of complex phenotypes using theDrosophilametabolome

Rohde

Kristensen

Sarup

et al. 2020

Preprint

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“…RA patients present several comorbidities including obesity, metabolic syndrome, and sarcopenia, probably triggered by a disbalance of proinflammatory cytokines including TNF and IL-6 among other causes [73][74][75][76][77], that will modify the circulating metabolites [78]. Several studies have investigated circulating metabolic changes related to the metabolic syndrome and obesity [79,80]. Of interest, a lot of circulating metabolites that are different in RA patients compared to controls could be related to associated metabolic syndrome, since choline metabolism (especially TMAO and carnitine), aminoacids (alanine, glutamine, glutamate, arginine, aspartate, asparagine, histidine, methionine, cysteine, lysine, branched-chain amino acids (BCAA), phenylaniline, tyrosine, and tryptophan) and phospholipids (phosphatydilcholines) also change in those with metabolic syndrome [80].…”

Section: Comorbiditiesmentioning

confidence: 99%

Circulating Pro- and Anti-Inflammatory Metabolites and Its Potential Role in Rheumatoid Arthritis Pathogenesis

Coras

Murillo-Saich

Gumá

2020

Cells

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Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that affects synovial joints, leading to inflammation, joint destruction, loss of function, and disability. Although recent pharmaceutical advances have improved the treatment of RA, patients often inquire about dietary interventions to improve RA symptoms, as they perceive pain and/or swelling after the consumption or avoidance of certain foods. There is evidence that some foods have pro- or anti-inflammatory effects mediated by diet-related metabolites. In addition, recent literature has shown a link between diet-related metabolites and microbiome changes, since the gut microbiome is involved in the metabolism of some dietary ingredients. But diet and the gut microbiome are not the only factors linked to circulating pro- and anti-inflammatory metabolites. Other factors including smoking, associated comorbidities, and therapeutic drugs might also modify the circulating metabolomic profile and play a role in RA pathogenesis. This article summarizes what is known about circulating pro- and anti-inflammatory metabolites in RA. It also emphasizes factors that might be involved in their circulating concentrations and diet-related metabolites with a beneficial effect in RA.

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“…It carries information about gene expression as well as lifestyle-and environmental factors (Tzoulaki et al 2014;van Roekel et al 2019). Metabolomics has already been successfully applied in large-scale epidemiological studies, mainly in adult populations, to identify new biomarkers of cardio-metabolic disease status, development and progression, as well as the underlying pathophysiological mechanisms (Newgard 2017;Rangel-Huerta et al 2019;Ussher et al 2016).…”

Section: Introductionmentioning

confidence: 99%

A population-based resource for intergenerational metabolomics analyses in pregnant women and their children: the Generation R Study

et al. 2020

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Introduction Adverse exposures in early life may predispose children to cardio-metabolic disease in later life. Metabolomics may serve as a valuable tool to disentangle the metabolic adaptations and mechanisms that potentially underlie these associations. Objectives To describe the acquisition, processing and structure of the metabolomics data available in a population-based prospective cohort from early pregnancy onwards and to examine the relationships between metabolite profiles of pregnant women and their children at birth and in childhood. Methods In a subset of 994 mothers-child pairs from a prospective population-based cohort study among pregnant women and their children from Rotterdam, the Netherlands, we used LC-MS/MS to determine concentrations of amino acids, nonesterified fatty acids, phospholipids and carnitines in blood serum collected in early pregnancy, at birth (cord blood), and at child's age 10 years. Results Concentrations of diacyl-phosphatidylcholines, acyl-alkyl-phosphatidylcholines, alkyl-lysophosphatidylcholines and sphingomyelines were the highest in early pregnancy, concentrations of amino acids and non-esterified fatty acids were the highest at birth and concentrations of alkyl-lysophosphatidylcholines, free carnitine and acyl-carnitines were the highest at age 10 years. Correlations of individual metabolites between pregnant women and their children at birth and at the age of 10 years were low (range between r = − 0.10 and r = 0.35). Conclusion Our results suggest that unique metabolic profiles are present among pregnant women, newborns and school aged children, with limited intergenerational correlations between metabolite profiles. These data will form a valuable resource to address the early metabolic origins of cardio-metabolic disease. Keywords Metabolomics • Amino acids • Fatty acids • Phospholipids • Carnitines • Birth cohort Abbreviations AA Amino acids AAA Aromatic amino acids APCI Atmospheric pressure chemical ionization BCAA Branched-chain amino acids Carn Carnitines Carn.a Acyl-carnitines Berthold Koletzko and Romy Gaillard have contributed equally.

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Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies

Cited by 180 publications

References 203 publications

Prediction of complex phenotypes using theDrosophilametabolome

Prediction of complex phenotypes using theDrosophilametabolome

Circulating Pro- and Anti-Inflammatory Metabolites and Its Potential Role in Rheumatoid Arthritis Pathogenesis

A population-based resource for intergenerational metabolomics analyses in pregnant women and their children: the Generation R Study

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