Novel biomarkers for pre‐diabetes identified by metabolomics

Wang-Sattler, Rui; Yu, Zhonghao; Herder, Christian; Messias, Ana C.; Floegel, Anna; He, Ying; Heim, Katharina; Campillos, Mónica; Holzapfel, Christina; Thorand, Barbara; Grallert, Harald; Xu, Tao; Bader, Erik; Huth, Cornelia; Mittelstraß, Kirstin; Döring, Angela; Meisinger, Christa; Gieger, Christian; Prehn, Cornelia; Roemisch-Margl, Werner; Carstensen, Maren; Xie, Lu; Yamanaka-Okumura, Hisami; Xing, Guolan; Ceglarek, Uta; Thiery, Joachim; Giani, Guido; Lickert, Heiko; Lin, Xu; Li, Yixue; Boeing, Heiner; Joost, Hans‐Georg; Angelis, Martin Hrabé de; Rathmann, Wolfgang; Suhre, Karsten; Prokisch, Holger; Peters, Annette; Meitinger, Thomas; Roden, Michael; Wichmann, H‐Erich; Pischon, Tobias; Adamski, Jerzy; Illig, Thomas

doi:10.1038/msb.2012.43

Cited by 615 publications

(592 citation statements)

References 52 publications

Supporting

Mentioning

526

Contrasting

Unclassified

Order By: Relevance

“…Very recently, data on lipids and small metabolites have become available from prospective studies, and these are summarised in Table 2 [84][85][86][87][88][89][90][91]. These studies showed that elevated levels of branched-chain and aromatic amino acids and lower levels of glycine are associated with incident type 2 diabetes or deteriorating glucose homeostasis [84,[86][87][88][89][90].…”

Section: Lipids and Small Metabolitesmentioning

confidence: 99%

The potential of novel biomarkers to improve risk prediction of type 2 diabetes

et al. 2013

Self Cite

View full text Add to dashboard Cite

The incidence of type 2 diabetes can be reduced substantially by implementing preventive measures in highrisk individuals, but this requires prior knowledge of disease risk in the individual. Various diabetes risk models have been designed, and these have all included a similar combination of factors, such as age, sex, obesity, hypertension, lifestyle factors, family history of diabetes and metabolic traits. The accuracy of prediction models is often assessed by the area under the receiver operating characteristic curve (AROC) as a measure of discrimination, but AROCs should be complemented by measures of calibration and reclassification to estimate the incremental value of novel biomarkers. This review discusses the potential of novel biomarkers to improve model accuracy. The range of molecules that serve as potential predictors of type 2 diabetes includes genetic variants, RNA transcripts, peptides and proteins, lipids and small metabolites. Some of these biomarkers lead to a statistically significant increase of model accuracy, but their incremental value currently seems too small for routine clinical use. However, only a fraction of potentially relevant biomarkers have been assessed with regard to their predictive value. Moreover, serial measurements of biomarkers may help determine individual risk. In conclusion, current risk models provide valuable tools of risk estimation, but perform suboptimally in the prediction of individual diabetes risk. Novel biomarkers still fail to have a clinically applicable impact. However, more efficient use of biomarker data and technological advances in their measurement in clinical settings may allow the development of more accurate predictive models in the future.

show abstract

Section: Lipids and Small Metabolitesmentioning

confidence: 99%

The potential of novel biomarkers to improve risk prediction of type 2 diabetes

et al. 2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, population-based studies of previously healthy adults revealed that low lyso-PC C18:2 concentrations were associated with an increased risk of pre-type 2 diabetes and type 2 diabetes [30,34], while high concentrations of several diacyl-PCs (C32:1, C36:1, C38:3 and C40:5) were associated with a risk of type 2 diabetes independent of dietary, lifestyle and anthropometric factors. In our study, the total lyso-PC/total PC ratio was significantly increased in women who had lactated for >3 months as a consequence of higher concentrations of several lyso-PCs and lower concentrations of several diacyl-PCs in these women.…”

Section: Discussionmentioning

confidence: 99%

“…Glycerophospholipid and BCAA levels are reported to be related to insulin resistance, type 2 diabetes and obesity [29][30][31][32][33]. Moreover, population-based studies of previously healthy adults revealed that low lyso-PC C18:2 concentrations were associated with an increased risk of pre-type 2 diabetes and type 2 diabetes [30,34], while high concentrations of several diacyl-PCs (C32:1, C36:1, C38:3 and C40:5) were associated with a risk of type 2 diabetes independent of dietary, lifestyle and anthropometric factors.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…It was envisaged from the point of discovery of pharmacometabonomics that the methodology would have this broader utility. [46] If the intervention is just passively the passage of time, the methodology can be used to predict disease onset in subsets of a population, and this has been demonstrated in several important cases, including the prediction of the onset of type-2 diabetes, [92][93][94] as well as in other diseases. [90] The power of metabolic profiling to provide prognosis of outcome in a wide variety of situations is one of the most exciting current aspects of this new science.…”

Section: Conclusion and The Future For Pharmacometabonomicsmentioning

confidence: 99%

NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine

Everett

2017

Progress in Nuclear Magnetic Resonance Spectroscopy

View full text Add to dashboard Cite

Metabolic profiling by NMR spectroscopy or hyphenated mass spectrometry, known as metabonomics or metabolomics, is an important tool for systemsbased approaches in biology and medicine. The experiments are typically done in a diagnostic fashion where changes in metabolite profiles are interpreted as a consequence of an intervention or event; be that a change in diet, the administration of a drug, physical exertion or the onset of a disease. By contrast, pharmacometabonomics takes a prognostic approach to metabolic profiling, in order to predict the effects of drug dosing before it occurs. Differences in predose metabolite profiles between groups of subjects are used to predict postdose differences in response to drug administration. Thus the paradigm is inverted and pharmacometabonomics is the metabolic equivalent of pharmacogenomics. Although the field is still in its infancy, it is expected that pharmacometabonomics, alongside pharmacogenomics, will assist with the delivery of personalised or precision medicine to patients, which is a critical goal of 21 st century healthcare. Highlights metabonomics or metabolomics involves metabolic profiling by NMR or MS methods  metabonomics is used in a diagnostic mode to study the effects of an intervention  pharmacometabonomics is a prognostic method to predict the effects of drugs  pharmacometabonomics is a special case of predictive metabonomics  pharmacometabonomics will help to deliver personalised medicine in the future Graphical AbstractNMR-Based Pharmacometabonomics PNMRS 2017

show abstract

Novel biomarkers for pre‐diabetes identified by metabolomics

Abstract: A targeted metabolomics approach was used to identify candidate biomarkers of pre-diabetes. The relevance of the identified metabolites is further corroborated with a protein-metabolite interaction network and gene expression data.

Cited by 615 publications

References 52 publications

The potential of novel biomarkers to improve risk prediction of type 2 diabetes

The potential of novel biomarkers to improve risk prediction of type 2 diabetes

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

NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine

Contact Info

Product

Resources

About