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Introduction Global metabolomics analyses using body fluids provide valuable results for the understanding and prediction of diseases. However, the mechanism of a disease is often tissue-based and it is advantageous to analyze metabolomic changes directly in the tissue. Metabolomics from tissue samples faces many challenges like tissue collection, homogenization, and metabolite extraction. Objectives We aimed to establish a metabolite extraction protocol optimized for tissue metabolitedoi:10.1007/s11306-017-1312-x pmid:29354024 pmcid:PMC5748028 fatcat:dqmbfmdoize4zfrxkk5btale5e
more »... ation by the targeted metabolomics AbsoluteIDQ™ p180 Kit (Biocrates). The extraction method should be non-selective, applicable to different kinds and amounts of tissues, monophasic, reproducible, and amenable to high throughput. Methods We quantified metabolites in samples of eleven murine tissues after extraction with three solvents (methanol, phosphate buffer, ethanol/phosphate buffer mixture) in two tissue to solvent ratios and analyzed the extraction yield, ionization efficiency, and reproducibility. Results We found methanol and ethanol/phosphate buffer to be superior to phosphate buffer in regard to extraction yield, reproducibility, and ionization efficiency for all metabolites measured. Phosphate buffer, however, outperformed both organic solvents for amino acids and biogenic amines but yielded unsatisfactory results for lipids. The observed matrix effects of tissue extracts were smaller or in a similar range compared to those of human plasma. Conclusion We provide for each murine tissue type an optimized high-throughput metabolite extraction protocol, which yields the best results for extraction, reproducibility, and quantification of metabolites in the p180 kit. Although the performance of the extraction protocol was monitored by the p180 kit, the protocol can be applicable to other targeted metabolomics assays.
A large part of metabolomics research relies on experiments involving mouse models, which are usually 6 to 20 weeks of age. However, in this age range mice undergo dramatic developmental changes. Even small age differences may lead to different metabolomes, which in turn could increase inter-sample variability and impair the reproducibility and comparability of metabolomics results. In order to learn more about the variability of the murine plasma metabolome, we analyzed male and femaledoi:10.3390/metabo10110472 pmid:33228074 fatcat:fjnl3kah25arroh257meqqhtfq
more »... , C57BL/6NTac, 129S1/SvImJ, and C3HeB/FeJ mice at 6, 10, 14, and 20 weeks of age, using targeted metabolomics (BIOCRATES AbsoluteIDQ™ p150 Kit). Our analysis revealed high variability of the murine plasma metabolome during adolescence and early adulthood. A general age range with minimal variability, and thus a stable metabolome, could not be identified. Age-related metabolomic changes as well as the metabolite profiles at specific ages differed markedly between mouse strains. This observation illustrates the fact that the developmental timing in mice is strain specific. We therefore stress the importance of deliberate strain choice, as well as consistency and precise documentation of animal age, in metabolomics studies.
E ssential hypertension is among the most important preclinical conditions of metabolic syndrome and affects nearly 1 billion people worldwide. 1,2 The risk to develop essential hypertension seems to be a function of age, triggered by an unhealthy lifestyle with obesity, and physical inactivity as major risk factors. 1 Furthermore, dyslipidemia, 3 inflammatory processes, 4 and oxidative stress 5 have been closely linked to this preclinical condition. Although many pathophysiological mechanismsdoi:10.1161/hypertensionaha.116.07292 pmid:27245178 fatcat:letzq5gvjfba3bigbtrc35bkhm
more »... f hypertension have been elucidated, knowledge is scarce about individual metabolic alterations promoting the development of essential hypertension in healthy subjects or subjects in early stages of this condition. Application of metabolomics can contribute to fill this gap and generate further insights into the pathogenesis of hypertension development. Metabolites represent intermediates and end products of cellular processes and are substantial for signaling, structuring of membranes, and catalytic activity. Metabolic alterations associated with development of hypertension, therefore, may be present years before hypertension diagnosis. Hence, investigating metabolic profiles in prospective cohorts is a promising opportunity to improve our knowledge of incident hypertension and to discover novel biomarkers that elucidate early changes in potential pathways. In the US cohort, of 204 metabolites, the metabolite 4-hydroxyhippurate and a metabolic sex steroids pattern were associated with incident hypertension. 6 Another US study using metabolic profiling revealed an association of diacylglycerols, in general, and of the 2 diacylglycerols 16:0/22:5 and 16:0/22:6, in particular, with blood pressure (BP) and incident hypertension. 7 However, to our knowledge, only a few prospective studies have used metabolic profiling to investigate metabolic alterations associated with incident hypertension, 6,7 and thus further studies are necessary to elucidate this promising approach. This study aimed to identify metabolites associated with incident hypertension using data of 127 serum metabolites (Biocrates AbsoluteIDQ p150) determined within the European Prospective Abstract-Metabolomics is a promising tool to gain new insights into early metabolic alterations preceding the development of hypertension in humans. We therefore aimed to identify metabolites associated with incident hypertension using measured data of serum metabolites of the European Prospective Investigation Into Cancer and Nutrition (EPIC)-Potsdam study. Targeted metabolic profiling was conducted on serum blood samples of a randomly drawn EPIC-Potsdam subcohort consisting of 135 cases and 981 noncases of incident hypertension, all of them being free of hypertension and not on antihypertensive therapy at the time of blood sampling. Mean follow-up was 9.9 years. A validated set of 127 metabolites was statistically analyzed with a random survival forest backward selection algorithm to identify predictive metabolites of incident hypertension taking into account important epidemiological hypertension risk markers. Six metabolites were identified to be most predictive for the development of hypertension. Higher concentrations of serine, glycine, and acyl-alkyl-phosphatidylcholines C42:4 and C44:3 tended to be associated with higher and diacyl-phosphatidylcholines C38:4 and C38:3 with lower predicted 10-year hypertensionfree survival, although visualization by partial plots revealed some nonlinearity in the above associations. The identified metabolites improved prediction of incident hypertension when used together with known risk markers of hypertension. In conclusion, these findings indicate that metabolic alterations occur early in the development of hypertension. However, these alterations are confined to a few members of the amino acid or phosphatidylcholine metabolism, respectively. (Hypertension. http://hyper.ahajournals.org/ Downloaded from RSF Model RSF Error Rate, Mean (95% CI) 6 selected metabolites+covariates* 0.2789 (0.2788-0.2790) Only covariates* 0.3168 (0.3168-0.3169) 127 metabolites+covariates* 0.3747 (0.3746-0.3748) Only 127 metabolites 0.4444 (0.4443-0.4447) BMI indicates body mass index; CI, confidence interval; DASH, Dietary Approaches to Stop Hypertension; IPAI, improved physical activity index; and RSF, random survival forest. *The RSF error rate is conform to 1 C-index, lower values corresponding to RSF models with more precise prediction accuracy. The covariates included in the RSF models were age, BMI, sex, IPAI, DASH index, alcohol intake from beverages, smoking behavior, education at attainment, and prevalent type 2 diabetes mellitus. by guest on July 18, 2018 http://hyper.ahajournals.org/ Downloaded from What Is New? • This is one of the first studies using targeted metabolomics in a prospective cohort (European Prospective Investigation into Cancer and Nutrition [EPIC]-Potsdam) to identify metabolites associated with incident hypertension. What Is Relevant? • Higher concentrations of serine, glycine, and the acyl-alkyl-phosphatidylcholines C42:4 and C44:3 tended to be associated with higher and diacyl-phosphatidylcholine C38:4 with lower predicted 10-year hypertension-free survival. • Nonlinear associations between concentrations of identified metabolites and predicted 10-year hypertension-free survival time were observed. Summary This study indicates that metabolic alterations occur early in the development of hypertension. However, these alterations are confined to a few members of the amino acid or phosphatidylcholine metabolism, respectively.
The metabolome, although very dynamic, is stable enough to provide specific quantitative traits related to health and disease. Metabolomics requires balanced use of state-of-the-art study design, chemical analytics, biostatistics, and bioinformatics to deliver meaningful answers for contemporary questions in human disease research. The technology is nowadays frequently employed for biomarker discovery and elucidation of mechanisms connected to endocrine-related diseases. Metabolomics has alsodoi:10.1016/j.tem.2017.07.001 pmid:28780001 fatcat:rl76cmevprc3xoosewejp6cswa
more »... riched genome wide association studies (GWAS) in this area by functional data. The contribution of rare genetic variants to the metabolome variance and to the human phenotype has been underestimated until now. , which you invited us to write for the Journal Trends in Endocrinology and Metabolism. I attach a detailed rebuttal letter illustrating how we met the comments and requirements raised by the reviewers and the editorial comments. All authors have agreed to the publication and the article has not appeared elsewhere nor is under consideration for a publication. We look forward to a favorable response. Sincerely yours
Protein imbalance during pregnancy affects women in underdeveloped and developing countries and is associated with compromised offspring growth and an increased risk of metabolic diseases in later life. We studied in a porcine model the glucose and urea metabolism, and circulatory hormone and metabolite profile of offspring exposed during gestation, to maternal isoenergetic low–high (LP-HC), high–low (HP-LC) or adequate (AP) protein–carbohydrate ratio diets. At birth, LP-HC were lighter and thedoi:10.3390/nu13093286 pmid:34579160 pmcid:PMC8471113 fatcat:ldmyflpaajeqvf6lusc4ejtpye
more »... plasma acetylcarnitine to free carnitine ratios at 1 day of life was lower compared to AP offspring. Plasma urea concentrations were lower in 1 day old LP-HC offspring than HP-LC. In the juvenile period, increased insulin concentrations were observed in LP-HC and HP-LC offspring compared to AP, as was body weight from HP-LC compared to LP-HC. Plasma triglyceride concentrations were lower in 80 than 1 day old HP-LC offspring, and glucagon concentrations lower in 80 than 1 day old AP and HP-LC offspring. Plasma urea and the ratio of glucagon to insulin were lower in all 80 than 1 day old offspring. Aminoacyl-tRNA, arginine and phenylalanine, tyrosine and tryptophan metabolism, histidine and beta-alanine metabolism differed between 1 and 80 day old AP and HP-LC offspring. Maternal protein imbalance throughout pregnancy did not result in significant consequences in offspring metabolism compared to AP, indicating enormous plasticity by the placenta and developing offspring.
Visceral adipose tissue (VAT) area is a strong predictor of obesity-related cardiometabolic alterations, but its measurement is costly, time consuming and, in some cases, involves radiation exposure. Glutamate, a by-product of branched-chain-amino-acid (BCAA) catabolism, has been shown to be increased in visceral obese individuals. In this follow-up data analysis, we aimed to investigate the ability of plasma glutamate to identify individuals with visceral obesity and concomitant metabolicdoi:10.1186/s12986-018-0316-5 pmid:30450120 pmcid:PMC6219091 fatcat:mli3pvk25jdy5f3fvpvl4hj6iu
more »... ations. Measurements of adiposity, targeted blood metabolomics and cardiometabolic risk factors were performed in 59 healthy middle-aged women. Visceral and subcutaneous adipose tissue areas were measured by computed tomography (CT) whereas body fat and lean mass were assessed by dual-energy x-ray absorptiometry (DEXA). The univariate Pearson correlation coefficient between glutamate and VAT area was r = 0.46 (p < 0.001) and it was r = 0.36 (p = 0.006) when adjusted for total body fat mass. Glutamate allowed to identify individuals with VAT areas ≥100 cm2 (ROC_AUC: 0.78, 95% CI: 0.66-0.91) and VAT ≥130 cm2 (ROC_AUC: 0.71, 95% CI: 0.56-0.87). The optimal glutamate concentration threshold determined from the ROC curve (glutamate ≥34.6 μmol/L) had a greater sensitivity than the metabolic syndrome (MetS) and the hypertriglyceridemic waist (HTW) phenotype to identify individuals with VAT ≥100 cm2 (83% for glutamate vs 52% for the MetS and 35% for the HTW). Variance analysis showed that women with a high circulating glutamate level (≥34.6 μmol/L) had an altered metabolic profile, particularly regarding total triglyceride levels and the amount of triglycerides and cholesterol in very-low-density lipoproteins (all p < 0.01). Circulating glutamate is strongly associated with VAT area and may represent a potential screening tool for visceral obesity and alterations of the metabolic profile.
Genome-wide association studies (GWAS) have identified hundreds of loci influencing complex human traits, however, their biological mechanism of action remains mostly unknown. Recent accumulation of functional genomics ('omics') including metabolomics data opens up opportunities to provide a new insight into the functional role of specific changes in the genome. Functional genomic data are characterized by high dimensionality, presence of (strong) statistical dependencies between traits, and,doi:10.1101/096982 fatcat:pibytiounfeohltp25whrr24wu
more »... tentially, complex genetic control. Therefore, analysis of such data asks for development of specific statistical genetic methods. Results: We propose a network-based, conditional approach to evaluate the impact of genetic variants on omics phenotypes (conditional GWAS, cGWAS). For each trait of interest, based on biological network, we select a set of other traits to be used as covariates in GWAS. The network could be reconstructed either from biological pathway databases or directly from the data. We evaluated our approach using data from a population-based KORA study (n=1,784, 1.7 M SNPs) with measured metabolomics data (151 metabolites) and demonstrated that our approach allows for identification of up to five additional loci not detected by conventional GWAS. We show that this gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific 'contra-intuitive' pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics. Conclusions: We demonstrate that in context of metabolomics network-based, conditional genome-wide association analysis is able to dramatically increase power of identification of loci with specific 'contra-intuitive' pleiotropic architecture. Our method has modest computational costs, can utilize summary level GWAS data, and is applicable to other omics data types. We anticipate that application of our method to new and existing data sets will facilitate progress in understanding genetic bases of control of molecular and complex phenotypes.
Objective: Sex hormone-binding globulin (SBHG) and androgen have been associated with mortality in women and men, but controversy still exists. Our objective was to investigate associations of SHBG and androgen with all-cause and cause-specific mortality in men and women. Design: 1006 men and 709 peri- and postmenopausal women (age range: 45-82 years) from the German population-based KORA F4 cohort study were followed up for a median of 8.7 years. Methods: SHBG was measured with an immunoassay,doi:10.1530/ec-20-0080 pmid:32168474 pmcid:PMC7219137 fatcat:sedbpkpceregzbc5pre243hbte
more »... total testosterone (TT) and dihydrotestosterone (DHT) with mass-spectrometry in serum samples and we calculated free testosterone (cFT). To assess associations between SHBG and androgen levels and mortality, we calculated hazard ratios (HRs) with 95% confidence intervals (CIs) using Cox proportional-hazards models. Results: 128 men (12.7%) and 70 women (9.9%) died. In women, we observed positive associations of SHBG with all-cause (HR: 1.54, 95% CI: 1.16-2.04) and with other-cause mortality (HR: 1.86, 95% CI: 1.08-3.20) and for DHT with all-cause mortality (HR: 1.32, 95% CI: 1.00-1.73). In men, we found a positive association of SHBG (HR: 1.24 95% CI: 1.00-1.54) and inverse associations of TT (HR: 0.87, 95% CI: 0.77-0.97) and cFT (HR: 0.84, 95% CI: 0.73-0.97) with all-cause mortality. No other associations were found for cause-specific mortality. Conclusions: Higher SHBG levels were associated with increased risk of all-cause mortality in men and women. Lower TT and cFT levels in men and higher DHT levels in women were associated with increased risk of all-cause mortality. Future, well-powered population-based studies should further investigate cause-specific mortality risk.
Aims/Hypothesis: Polymorphisms in the transcription factor 7-like 2 (TCF7L2) gene have been shown to display a powerful association with type 2 diabetes. The aim of the present study was to evaluate metabolic alterations in carriers of a common TCF7L2 risk variant. Methods: Seventeen non-diabetic subjects carrying the T risk allele at the rs7903146 TCF7L2 locus and 24 subjects carrying no risk allele were submitted to intravenous glucose tolerance test and euglycemic-hyperinsulinemic clamp.doi:10.1371/journal.pone.0078430 pmid:24205231 pmcid:PMC3813438 fatcat:upska6ccyngblimffoj77m6g6i
more »... ma samples were analysed for concentrations of 163 metabolites through targeted mass spectrometry.
Aims: Metformin intolerance symptoms are gastrointestinal in nature, but the underlying mechanism is poorly understood. The aim of this study was to assess potential causes of metformin intolerance including: This article is Accepted Article altered metformin uptake from the intestine; increased anaerobic glucose utilisation and subsequent lactate production; altered serotonin uptake; and altered bile acid pool. Methods: This pharmacokinetic study recruited ten severely intolerant and tendoi:10.1111/dom.13264 pmid:29457876 pmcid:PMC6033038 fatcat:a2qwf5m53ncttlttrgndb4zccu
more »... nt individuals matched for age, sex and BMI. A single 500mg dose of metformin was administered, with blood sampling at eleven time points over 24 hours. Blood samples were analysed for metformin, lactate, serotonin, and bile acid concentrations and compared across the phenotypes. Results: The intolerant individuals were severely intolerant to 500mg metformin. No significant difference was identified between tolerant and intolerant cohorts in metformin pharmacokinetics: median C max 2.1 (IQR 1.7 -2.3) and 2.0 (IQR 1.8 -2.2) mg/L respectively (p = 0.76); t max 2.5 hours; median AUC 0-24 16.9 (IQR 13.9 -18.6) and 13.9 (IQR 12.9 -16.8) (mg/L)*h respectively (p = 0.72). Lactate concentration peaked at 3.5 hours, with mean peak concentration of 2.4 mmol/L in both cohorts (95% CIs 2.0 -2.8, and 1.8 -3.0 mmol/L respectively), and comparable iAUC 0-24 : tolerant 6.98 (3.03 -10.93) and intolerant 4.47 (-3.12 -12.06) mmol/L*h, (p=0.55). Neither serotonin nor bile acid concentrations were significantly different. Conclusions: Despite evidence of severe intolerance in our cohort, there was no significant difference in metformin pharmacokinetics or systemic measures of lactate, serotonin or bile acids. This suggests that metformin intolerance may be due to local factors within the lumen or enterocyte.
Nutrition plays an important role in human metabolism and health. Metabolomics is a promising tool for clinical, genetic and nutritional studies. A key question is to what extent metabolomic profiles reflect nutritional patterns in an epidemiological setting. We assessed the relationship between metabolomic profiles and nutritional intake in women from a large cross-sectional community study. Food frequency questionnaires (FFQs) were applied to 1,003 women from the TwinsUK cohort with targeteddoi:10.1007/s11306-012-0469-6 pmid:23543136 pmcid:PMC3608890 fatcat:65kv7iboknepjd3nymgornte3i
more »... etabolomic analyses of serum samples using the Biocrates Absolute-IDQ TM Kit p150 (163 metabolites). We analyzed seven nutritional parameters: coffee intake, garlic intake Electronic supplementary material The online version of this article (
MS Targeted Urine -80, -20, 4, *9°C, and RT (*20°C) 0, 2, 8, and 24 h X X (-80°C) Compliance with ethical standards Conflict of interest Markus Rotter, Stefan Brandmaier, Cornelia Prehn, Jonathan ...doi:10.1007/s11306-016-1137-z pmid:27980503 pmcid:PMC5126183 fatcat:lisxrrfbnrgk3jb6dezmk7b6li
Genome-wide association studies (GWAS) are widely applied to analyze the genetic effects on phenotypes. With the availability of high-throughput technologies for metabolite measurements, GWAS successfully identified loci that affect metabolite concentrations and underlying pathways. In most GWAS, the effect of each SNP on the phenotype is assumed to be additive. Other genetic models such as recessive, dominant, or overdominant were considered only by very few studies. In contrast to this, theredoi:10.1534/genetics.115.175760 pmid:25977471 pmcid:PMC4512538 fatcat:uxdjahwcvvgrpfat634w5oofla
more »... are theories that emphasize the relevance of nonadditive effects as a consequence of physiologic mechanisms. This might be especially important for metabolites because these intermediate phenotypes are closer to the underlying pathways than other traits or diseases. In this study we analyzed systematically nonadditive effects on a large panel of serum metabolites and all possible ratios (22,801 total) in a population-based study [Cooperative Health Research in the Region of Augsburg (KORA) F4, N = 1,785]. We applied four different 1-degree-of-freedom (1-df) tests corresponding to an additive, dominant, recessive, and overdominant trait model as well as a genotypic model with two degree-of-freedom (2-df) that allows a more general consideration of genetic effects. Twenty-three loci were found to be genome-wide significantly associated (Bonferroni corrected P # 2.19 3 10 212 ) with at least one metabolite or ratio. For five of them, we show the evidence of nonadditive effects. We replicated 17 loci, including 3 loci with nonadditive effects, in an independent study (TwinsUK, N = 846). In conclusion, we found that most genetic effects on metabolite concentrations and ratios were indeed additive, which verifies the practice of using the additive model for analyzing SNP effects on metabolites.
Copyright © 2021 März, Kurlbaum, Roche-Lancaster, Deutschbein, Peitzsch, Prehn, Weismann, Robledo, Adamski, Fassnacht, Kunz and Kroiss. ...doi:10.3389/fendo.2021.722656 pmid:34557163 pmcid:PMC8453166 fatcat:enyklwe7ebdsnfjlgr6h2zgpcq
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