Biomarkers for Type 2 Diabetes and Impaired Fasting Glucose Using a Nontargeted Metabolomics Approach

Menni, Cristina; Fauman, Eric B.; Erte, Idil; Perry, John R. B.; Kastenmüller, Gabi; Shin, So–Youn; Petersen, Ann-Kristin; Hyde, Craig; Psatha, Maria; Ward, Kirsten; Yuan, Wei; Milburn, Mike; Palmer, Colin N.A.; Frayling, Timothy M.; Trimmer, Jeff K.; Bell, Jordana T.; Gieger, Christian; Mohney, Rob P.; Brosnan, M. Julia; Suhre, Karsten; Soranzo, Nicole; Spector, Tim D.

doi:10.2337/db13-0570

Cited by 362 publications

(356 citation statements)

References 25 publications

(24 reference statements)

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“…Furthermore, it was recently reported that pregnant women with high fasting plasma glucose concentrations also had high fasting concentrations of several amino acids, including leucine [37]. Higher fasting concentrations of BCAAs were also found in individuals with type 2 diabetes and insulin resistance [34,[38][39][40]. These findings indicate that alterations in BCAA metabolism are associated with gestational diabetes, insulin resistance and type 2 diabetes.…”

Section: Discussionsupporting

confidence: 50%

“…PC aa, diacylphosphatidylcholine; PC ae, acyl-alkyl-phosphatidylcholine circulating concentrations of BCAAs interfere with fatty acid oxidation, and thus decrease insulin sensitivity [40]. Recent studies propose that the intermediate metabolites of BCAA catabolism are associated with insulin sensitivity and type 2 diabetes and not the BCAA concentrations themselves [38,39]. Indeed, in our study BCAA concentrations were associated with ISI and HOMA-IR and lactation for ≤3 months was associated with increased BCAA concentrations early after cessation of lactation.…”

Section: Discussionmentioning

confidence: 50%

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Lactation is associated with altered metabolomic signatures in women with gestational diabetes

et al. 2016

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Aims/hypothesis Lactation for >3 months in women with gestational diabetes is associated with a reduced risk of type 2 diabetes that persists for up to 15 years postpartum. However, the underlying mechanisms are unknown. We examined whether in women with gestational diabetes lactation for >3 months is associated with altered metabolomic signatures postpartum. Methods We enrolled 197 women with gestational diabetes at a median of 3.6 years (interquartile range 0.7-6.5 years) after delivery. Targeted metabolomics profiles (including 156 metabolites) were obtained during a glucose challenge test. Comparisons of metabolite concentrations and ratios between women who lactated for >3 months and women who lactated for ≤3 months or not at all were performed using linear regression with adjustment for age and BMI at the postpartum visit, time since delivery, and maternal education level, and correction for multiple testing. Gaussian graphical modelling was used to generate metabolite networks.Results Lactation for >3 months was associated with a higher total lysophosphatidylcholine/total phosphatidylcholine ratio; in women with short-term follow-up, it was also associated with lower leucine concentrations and a lower total branchedchain amino acid concentration. Gaussian graphical modelling identified subgroups of closely linked metabolites within phosphatidylcholines and branched-chain amino acids that were affected by lactation for >3 months and have been linked to the pathophysiology of type 2 diabetes in previous studies. Conclusions/interpretation Lactation for >3 months in women with gestational diabetes is associated with changes in the metabolomics profile that have been linked to the early pathogenesis of type 2 diabetes.

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Section: Discussionsupporting

confidence: 50%

Section: Discussionmentioning

confidence: 50%

Lactation is associated with altered metabolomic signatures in women with gestational diabetes

et al. 2016

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“…Obesity, IR and type 2 diabetes in humans cause significant changes in an array of plasma metabolites of which some have the quality to predict diabetes development in early phases [1]. In mouse models mimicking the human conditions of type 2 diabetes development, we assessed which metabolite changes associate mainly with obesity and which are specific for the diabetic state.…”

Section: Discussionmentioning

confidence: 99%

“…with the development of insulin resistance (IR) and type 2 diabetes [1]. The most discriminating metabolites are the branched-chain (BCAA) and aromatic amino acids (AAA) as well as glycine and some other amino acid degradation products.…”

Section: Introductionmentioning

confidence: 99%

Metabolite profiling in plasma and tissues of ob/ob and db/db mice identifies novel markers of obesity and type 2 diabetes

et al. 2015

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Aims/hypothesis Metabolomics approaches in humans have identified around 40 plasma metabolites associated with insulin resistance (IR) and type 2 diabetes, which often coincide with those for obesity. We aimed to separate diabetesassociated from obesity-associated metabolite alterations in plasma and study the impact of metabolically important tissues on plasma metabolite concentrations. Methods Two obese mouse models were studied; one exclusively with obesity (ob/ob) and another with type 2 diabetes (db/db). Both models have impaired leptin signalling as a cause for obesity, but the different genetic backgrounds determine the susceptibility to diabetes. In these mice, we profiled plasma, liver, skeletal muscle and adipose tissue via semiquantitative GC-MS and quantitative liquid chromatography (LC)-MS/MS for a wide range of metabolites. Results Metabolite profiling identified 24 metabolites specifically associated with diabetes but not with obesity. Among these are known markers such as 1,5-anhydro-D-sorbitol, 3-hydroxybutyrate and the recently reported marker glyoxylate. New metabolites in the diabetic model were lysine, Ophosphotyrosine and branched-chain fatty acids. We also identified 33 metabolites that were similarly altered in both models, represented by branched-chain amino acids (BCAA) as well as glycine, serine, trans-4-hydroxyproline, and various lipid species and derivatives. Correlation analyses showed stronger associations for plasma amino acids with adipose tissue metabolites in db/db mice compared with ob/ob mice, suggesting a prominent contribution of adipose tissue to changes in plasma in a diabetic state. Conclusions/interpretation By studying mice with metabolite signatures that resemble obesity and diabetes in humans, we have found new metabolite entities for validation in appropriate human cohorts and revealed their possible tissue of origin.

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“…Both studies identified half of the analysed metabolites to be associated with chronological age. Many of the those metabolites were also found to significantly correlate with age‐related phenotypes such as lung function, bone mineral density and cholesterol levels (Menni et al ., 2013b), AD (N = 93) (Orešič et al ., 2011), cancer (reviewed by Teicher et al ., 2012) and type 2 diabetes (N = 100) (Suhre et al ., 2010; Menni et al ., 2013a). One of those metabolites is C‐glycosyltryptophan, a potential degradation product of glycosylated proteins.…”

Section: Omics and Agingmentioning

confidence: 99%

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

et al. 2015

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SummaryAge is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age‐related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high‐throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so‐called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age‐related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

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Biomarkers for Type 2 Diabetes and Impaired Fasting Glucose Using a Nontargeted Metabolomics Approach

Cited by 362 publications

References 25 publications

Lactation is associated with altered metabolomic signatures in women with gestational diabetes

Lactation is associated with altered metabolomic signatures in women with gestational diabetes

Metabolite profiling in plasma and tissues of ob/ob and db/db mice identifies novel markers of obesity and type 2 diabetes

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

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