Untargeted Metabolic Profiling Identifies Altered Serum Metabolites of Type 2 Diabetes Mellitus in a Prospective, Nested Case Control Study

Drogan, Dagmar; Dunn, Warwick B.; Lin, Wen; Buijsse, Brian; Schulze, Matthias B.; Langenberg, Claudia; Brown, Marie; Floegel, Anna; Dietrich, Suzanne W.; Rolandsson, Olov; Wedge, David C.; Goodacre, Royston; Forouhi, Nita G.; Sharp, Stephen J.; Spranger, Joachim; Wareham, Nick; Boeing, Heiner

doi:10.1373/clinchem.2014.228965

Cited by 118 publications

(88 citation statements)

References 45 publications

(47 reference statements)

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“…Similar results have also been demonstrated in other studies in both Western and Asian populations [3-5, 11, 12]. Other studies have noted that alterations in NEFA and long-chain fatty acids [2,3,5,8,9,14], carbohydrate derivatives [2,13] and tricarboxylic acid (TCA) cycle intermediate metabolites [9,10] may also be associated with an increased risk of diabetes. These observations provide novel insights into the underlying pathophysiology of diabetes and highlight the possibility that alterations in metabolites may help to identify at-risk individuals prior to the onset of diabetes, in addition to standard clinical biomarkers.…”

Section: Introductionsupporting

confidence: 87%

“…Recent metabolomics studies have suggested that certain metabolites and metabolite classes may be associated with the risk of obesity, insulin resistance and type 2 diabetes [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. The Framingham Offspring Study, a 12-year prospective cohort study, has shown that increases in certain branched-chain amino acids (BCAAs) (such as leucine, isoleucine and valine) and aromatic amino acids (AAAs) (such as tyrosine and phenylalanine) could predict the incidence of type 2 diabetes [6].…”

Section: Introductionmentioning

confidence: 99%

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Metabolic signatures and risk of type 2 diabetes in a Chinese population: an untargeted metabolomics study using both LC-MS and GC-MS

et al. 2016

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Aims/hypothesis Metabolomics has provided new insight into diabetes risk assessment. In this study we characterised the human serum metabolic profiles of participants in the Singapore Chinese Health Study cohort to identify metabolic signatures associated with an increased risk of type 2 diabetes. Methods In this nested case-control study, baseline serum metabolite profiles were measured using LC-MS and GC-MS during a 6-year follow-up of 197 individuals with type 2 diabetes but without a history of cardiovascular disease or cancer before diabetes diagnosis, and 197 healthy controls matched by age, sex and date of blood collection. Results A total of 51 differential metabolites were identified between cases and controls. Of these, 35 were significantly associated with diabetes risk in the multivariate analysis after false discovery rate adjustment, such as increased branchedchain amino acids (leucine, isoleucine and valine), nonesterified fatty acids (palmitic acid, stearic acid, oleic acid and linoleic acid) and lysophosphatidylinositol (LPI) species Conclusions/interpretation Our findings show that branched-chain amino acids and NEFA are potent predictors of diabetes development in Chinese adults. Our results also indicate the potential of lysophospholipids for predicting diabetes.Electronic supplementary material The online version of this article

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Section: Introductionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Metabolic signatures and risk of type 2 diabetes in a Chinese population: an untargeted metabolomics study using both LC-MS and GC-MS

et al. 2016

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“…In an article in the current issue of Clinical Chemistry, Drogan et al (7 ) applied an untargeted metabolomic approach with a coverage of Ͼ4500 metabolite features by ultraperformance LC-MS with a protocol specifically designed for large-scale metabolomic studies regarding robustness and repeatability. The study was nested in the wellcharacterized European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort, which has collected blood samples, detailed measurements of anthropometric parameters and blood pressure, dietary and lifestyle questionnaires, and verified clinical outcomes during a follow-up period of approximately 20 years.…”

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confidence: 99%

Comprehensive Metabolomic Profiling of Type 2 Diabetes

Zheng¹,

2015

Clinical Chemistry

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It is estimated that at least 29.1 million Americans, or 9.3% of the US population, currently have diabetes (1 ), a disease characterized by impaired insulin action and/or production. Although type 2 diabetes (T2D), 4 which accounts for Ͼ90% of diagnosed diabetes, is largely predictable through anthropometric, lifestyle, and clinical factors, and is preventable through diet and lifestyle modifications, the metabolic pathways underlying its development and progression are incompletely understood. The rapidly developing area of metabolomics, which is designed to quantitatively profile a large number of small molecules in cells or biofluids, has emerged as a promising approach to elucidate altered metabolic pathways and discover novel biomarkers in T2D.The past several years have seen the initial success of metabolomics in identifying novel biomarkers for insulin resistance and T2D. The above studies have revealed several promising novel biomarkers of T2D; however, they have been limited in the number of metabolites detected and analyzed (approximately 100 -200 targeted metabolites). In an article in the current issue of Clinical Chemistry, Drogan et al. (7 ) applied an untargeted metabolomic approach with a coverage of Ͼ4500 metabolite features by ultraperformance LC-MS with a protocol specifically designed for large-scale metabolomic studies regarding robustness and repeatability. The study was nested in the wellcharacterized European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort, which has collected blood samples, detailed measurements of anthropometric parameters and blood pressure, dietary and lifestyle questionnaires, and verified clinical outcomes during a follow-up period of approximately 20 years. The study included 300 incident T2D cases and 300 randomly selected controls matched on age, sex, fasting time, time of day of blood sampling, and season at blood sampling. The study population was then randomly split into 2 internal substudies, each containing 150 matched case-control pairs to verify the internal consistency of findings. The authors observed that diverse altered classes of metabolites, including 6 lipids and 7 nonlipids, preceded the onset of overt T2D by a median of 6 years. More specifically, higher serum concentrations of lipids in the phosphatidylcholine (PC) class, PC(22:4/ dm18:0) and PC(O-18:0/22:5), and lower concentrations of PC(O-20:0/O-20:0) were related to a higher risk of T2D. Higher serum concentrations of purine nucleotide isopentenyladenosine-5Ј-monophosphate showed an association with a higher risk of T2D, and for the large group of hexose sugars and derivatives, both positive and negative associations were detected. These findings were internally consistent and significant in the split samples. Several of the metabolites, including PC(O-20:0/ O-20:0), were linked to T2D incidence for the first time. Although further validation in independent external cohorts is warranted, these findings underscore the po-

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“…Metabolic profiles have been shown to be altered as early as 3-12 years before the diagnosis of T2DM [15][16][17]. Metabolites associated with T2DM include carbohydrates, lipids like lyso-phosphatidylcholines [17,18], fatty acids [19], acylcarnitines [20], and amino acids, particularly branched-chain amino acids (BCAAs, leucine, isoleucine, and valine) and aromatic amino acids (phenylalanine and tyrosine) [16]. Leucine, isoleucine, and valine are first converted to branched-chain keto acids (BCKAs) 4-methyl-2-oxopentanoate, 3-methyl-2-oxovalerate, and 3-methyl-2-oxobutyrate, respectively, and eventually lead to the production of C3 and C5 acylcarnitines.…”

Section: Introductionmentioning

confidence: 99%

First-Trimester Maternal Serum Amino Acids and Acylcarnitines Are Significant Predictors of Gestational Diabetes

et al. 2016

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■ AbstractBACKGROUND: Current screening methods for gestational diabetes mellitus (GDM) are insufficient in detecting the risk of GDM in the first trimester of the pregnancy. Recent metabolomic studies have detected altered amino acid and acylcarnitine concentrations in type 2 diabetes (T2D). Because of the similarities between T2D and GDM, the determination of these metabolites may be useful in early screening for GDM. AIM: To evaluate the association between GDM and first-trimester maternal serum concentrations of ten amino acids and 31 acylcarnitines. METHODS: This retrospective case-control study included data from pregnant women screened at Oulu University Hospital between 1. 1.2008 and 31.12.2011. A total of 31,146 women participated voluntarily in a first-trimester combined screening (for chromosomal abnormalities). The study population included 69 women who developed GDM during pregnancy and 295 women without diabetes before or after pregnancy. The serum concentrations of ten amino acids and 31 acylcarnitines were analyzed from frozen serum samples taken in the first-trimester screening. Multiple of median (MoM) values were compared between the two groups. RESULTS: In the GDM group, serum levels of arginine were significantly higher (1.13 MoM vs. 0.97 MoM), and those of glycine (0.93 MoM vs. 1.03 MoM) and 3-hydroxyisovalerylcarnitine (0.86 MoM vs. 1.03 MoM) significantly lower compared to the control group (all p < 0.01). In each case, arginine, glycine, and 3-hydroxy-isovaleryl-carnitine would have detected 46%, 32%, and 39% of GDM cases, with a false-positive rate of 20%. Combining these three metabolites with the first-trimester serum marker pregnancy-associated plasma protein A (PAPP-A) and prior risk (age, BMI, and smoking) achieved a detection rate of 72%. CONCLUSION: There are significant differences in the serum levels of arginine, glycine, and 3-hydroxy-isovalerylcarnitine between controls and women who subsequently develop GDM. These differences were already existent in the first trimester of the pregnancy. The use of metabolites in combination with prior risk and first-trimester PAPP-A represents a reliable method to identify women at risk of GDM.

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Untargeted Metabolic Profiling Identifies Altered Serum Metabolites of Type 2 Diabetes Mellitus in a Prospective, Nested Case Control Study

Abstract: BACKGROUND: Application of metabolite profiling could expand the etiological knowledge of type 2 diabetes mellitus (T2D). However, few prospective studies apply broad untargeted metabolite profiling to reveal the comprehensive metabolic alterations preceding the onset of T2D.

Cited by 118 publications

References 45 publications

Metabolic signatures and risk of type 2 diabetes in a Chinese population: an untargeted metabolomics study using both LC-MS and GC-MS

Metabolic signatures and risk of type 2 diabetes in a Chinese population: an untargeted metabolomics study using both LC-MS and GC-MS

Comprehensive Metabolomic Profiling of Type 2 Diabetes

First-Trimester Maternal Serum Amino Acids and Acylcarnitines Are Significant Predictors of Gestational Diabetes

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