Early Metabolic Markers of the Development of Dysglycemia and Type 2 Diabetes and Their Physiological Significance

Ferrannini, Ele; Natali, Andrea; Camastra, Stefania; Nannipieri, Monica; Mari, Andrea; Adam, Klaus-Peter; Milburn, Michael V.; Kastenmüller, Gabi; Adamski, Jerzy; Tuomi, Tiinamaija; Lyssenko, Valeriya; Groop, Leif; Gall, Walter

doi:10.2337/db12-0707

Cited by 328 publications

(337 citation statements)

References 42 publications

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“…However, this finding is in agreement with Bentley-Lewis et al (2015) who found that there are differences in metabolic levels, despite several similarities between T2DM and GDM [38]. Ferrannini et al (2012) observed that patients who developed T2DM later had increased BCAA levels, including those of leucine and valine, and of three major glucogenic amino acids (including arginine), whereas glycine was significantly decreased [44]. Scholtens et al (2014) found higher arginine levels in women with high fasting plasma glucose (FPG) (n = 67, 96.7 ± 20.6 µmol/l) than in women with low FPG (n = 49, 90.2 ± 15.7 µmol/l, p < 0.01).…”

Section: Resultssupporting

confidence: 86%

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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Section: Resultssupporting

confidence: 86%

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

et al. 2016

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“…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: 96%

“…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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“…43 In the Botnia study, α-HB was found to be a positive predictor and L-GPC a negative predictor of progression to T2DM. 45 …”

Section: Metabolite Biomarkers Of Insulin Resistancementioning

confidence: 99%

A Novel Fasting Blood Test for Insulin Resistance and Prediabetes

Cobb

Gall

Adam

et al. 2013

J Diabetes Sci Technol

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Background: Insulin resistance (IR) can precede the dysglycemic states of prediabetes and type 2 diabetes mellitus (T2DM) by a number of years and is an early marker of risk for metabolic and cardiovascular disease. There is an unmet need for a simple method to measure IR that can be used for routine screening, prospective study, risk assessment, and therapeutic monitoring. We have reported several metabolites whose fasting plasma levels correlated with insulin sensitivity. These metabolites were used in the development of a novel test for IR and prediabetes. Methods: Data from the Relationship between Insulin Sensitivity and Cardiovascular Disease Study were used in an iterative process of algorithm development to define the best combination of metabolites for predicting the M value derived from the hyperinsulinemic euglycemic clamp, the gold standard measure of IR. Subjects were divided into a training set and a test set for algorithm development and validation. The resulting calculated M score, MQ, was utilized to predict IR and the risk of progressing from normal glucose tolerance to impaired glucose tolerance (IGT) over a 3 year period. Results: MQ correlated with actual M values, with an r value of 0.66. In addition, the test detects IR and predicts 3 year IGT progression with areas under the curve of 0.79 and 0.70, respectively, outperforming other simple measures such as fasting insulin, fasting glucose, homeostatic model assessment of IR, or body mass index. Conclusions: The result, Quantose™, is a simple test for IR based on a single fasting blood sample and may have value as an early indicator of risk for the development of prediabetes and T2DM.

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Early Metabolic Markers of the Development of Dysglycemia and Type 2 Diabetes and Their Physiological Significance

Cited by 328 publications

References 42 publications

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

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

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

A Novel Fasting Blood Test for Insulin Resistance and Prediabetes

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