Circulating Metabolite Predictors of Glycemia in Middle-Aged Men and Women

Würtz, Peter; Tiainen, Mika; Mäkinen, Ville‐Petteri; Kangas, Antti J.; Soininen, Pasi; Saltevo, Juha; Keinänen‐Kiukaanniemi, Sirkka; Mäntyselkä, Pekka; Lehtimäki, Terho; Laakso, Markku; Jula, Antti; Kähönen, Mika; Vanhala, Mauno; Ala‐Korpela, Mika

doi:10.2337/dc11-1838

Cited by 193 publications

(201 citation statements)

References 38 publications

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“…Alpha-1 acid glycoprotein has been prospectively associated with both fasting and postchallenge glucose [45] and with the risk of developing diabetes [46]. In a recent study among participants from the Estonian biobank, alpha-1 acid glycoprotein was the strongest circulating biomarker predicting all-cause mortality after adjusting for conventional risk factors [8].…”

Section: Discussionmentioning

confidence: 99%

Abdominal obesity and circulating metabolites: A twin study approach

et al. 2016

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Objective. To investigate how obesity, insulin resistance and low-grade inflammation link to circulating metabolites, and whether the connections are due to genetic or environmental factors.Subjects and methods. Circulating serum metabolites were determined by proton NMR spectroscopy. Data from 1368 (531 monozygotic (MZ) and 837 dizygotic (DZ)) twins were used for bivariate twin modeling to derive the genetic (r g ) and environmental (r e ) correlations between waist circumference (WC) and serum metabolites. Detailed examination of the associations between fat distribution (DEXA) and metabolic health (HOMA-IR, CRP) was performed among 286 twins including 33 BMI-discordant MZ pairs. Results. Fat, especially in the abdominal area (i.e. WC, android fat % and android to gynoid fat ratio), together with HOMA-IR and CRP correlated significantly with an atherogenic lipoprotein profile, higher levels of branched-chain (BCAA) and aromatic amino acids, higher levels of glycoprotein, and a more saturated fatty acid profile. In contrast, a higher proportion of gynoid to total fat associated with a favorable metabolite profile. There was a significant genetic overlap between WC and several metabolites, most strongly with phenylalanine (r g = 0.40), glycoprotein (r g = 0.37), serum triglycerides (r g = 0.36), BCAAs (r g = 0.30-0.40), HDL Keywords:Twin study Bivariate twin model 2 0 1 6 ) 1 1 Conclusions. A wide range of unfavorable alterations in the serum metabolome was associated with abdominal obesity, insulin resistance and low-grade inflammation. Twin modeling and obesity-discordant twin analysis suggest that these associations are partly explained by shared genes but also reflect mechanisms independent of genetic liability. Genetic correlation Serum metabolites Obesity measures M E T A B O L I S M C L I N I C A L A N D E X P E R I M E N T A L 6 5 (

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Section: Discussionmentioning

confidence: 99%

Abdominal obesity and circulating metabolites: A twin study approach

et al. 2016

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“…A high-throughput NMR metabolomic platform (serum) targeted to multiple pathways with relevance to insulin resistance was employed (http://computationalmedicine.fi/, accessed 10 January 2017) and analyses were carried out in two batches. This NMR metabolite profile has been used for many large-scale epidemiological studies [4,[13][14][15][16][17] and the methodology has been described previously. There are no discernible batch effects [18].…”

Section: Methodsmentioning

confidence: 99%

Metabolic profiling of gestational diabetes in obese women during pregnancy

et al. 2017

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Aims/hypothesis Antenatal obesity and associated gestational diabetes (GDM) are increasing worldwide. While pre-existing insulin resistance is implicated in GDM in obese women, the responsible metabolic pathways remain poorly described. Our aim was to compare metabolic profiles in blood of obese pregnant women with and without GDM 10 weeks prior to and at the time of diagnosis by OGTT. Methods We investigated 646 women, of whom 198 developed GDM, in this prospective cohort study, a secondary analysis of UK Pregnancies Better Eating and Activity Trial (UPBEAT), a multicentre randomised controlled trial of a complex lifestyle intervention in obese pregnant women. Multivariate regression analyses adjusted for multiple testing, and accounting for appropriate confounders including study intervention, were performed to compare obese women with GDM with obese non-GDM women. We measured 163 analytes in serum, plasma or whole blood, including 147 from a targeted NMR metabolome, at time point 1 (mean gestational age 17 weeks 0 days) and time point 2 (mean gestational age 27 weeks 5 days, at time of OGTT) and compared them between groups. Results Multiple significant differences were observed in women who developed GDM compared with women without GDM (false discovery rate corrected p values <0.05). Most were evident prior to diagnosis. Women with GDM demonstrated raised lipids and lipoprotein constituents in VLDL subclasses, greater triacylglycerol enrichment across lipoprotein particles, higher branched-chain and aromatic amino acids and different fatty acid, ketone body, adipokine, liver and inflammatory marker profiles compared with those without GDM. Conclusions/interpretation Among obese pregnant women, differences in metabolic profile, including exaggerated dyslipidaemia, are evident at least 10 weeks prior to a diagnosis of GDM in the late second trimester.

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“…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]. In addition, various lipid species and lipid fractions [85,[87][88][89][90], as well as other small metabolites [87,89,91], showed significant associations with the risk of type 2 diabetes or incident impaired glucose metabolism after adjustment for multiple confounders. Some of the aforementioned studies compared the accuracy of prediction models without and with metabolites (Table 2) and found fairly modest improvements in AROCs for models that included metabolomics in addition to established risk factors for type 2 diabetes [84,88,89,91].…”

Section: Lipids and Small Metabolitesmentioning

confidence: 98%

“…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

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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.

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Circulating Metabolite Predictors of Glycemia in Middle-Aged Men and Women

Cited by 193 publications

References 38 publications

Abdominal obesity and circulating metabolites: A twin study approach

Abdominal obesity and circulating metabolites: A twin study approach

Metabolic profiling of gestational diabetes in obese women during pregnancy

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

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