Determination of glycemic monitoring marker 1,5-anhydroglucitol in plasma by liquid chromatography-electrospray tandem mass spectrometry

Li, Shuijun; Heng, Xiaoxiang; Wang, Yiping; Chen, Yu

doi:10.1016/j.jchromb.2008.09.033

Cited by 14 publications

(4 citation statements)

References 27 publications

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“…The strongest difference was detected for 1,5-anhydroglucitol (p = 6.72 × 10 −17 ), a well-known marker for short-term glycemic control (Dungan 2008 ). We detect higher levels in males, which is in accordance with a previous report of non-diabetic individuals (Li et al 2008 ). Moreover, the two hexoses mannose (p = 8.57 × 10 −16 ) and glucose (p = 1.25 × 10 −12 ) showed higher levels in males.…”

Section: Resultssupporting

confidence: 94%

Gender-specific pathway differences in the human serum metabolome

et al. 2015

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The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-015-0829-0) contains supplementary material, which is available to authorized users.

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Section: Resultssupporting

confidence: 94%

Gender-specific pathway differences in the human serum metabolome

et al. 2015

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“…The test is standardized to the IFCC reference method. [6] Fructosamine was determined by the testkit from Labor und Technik (Berlin, Germany). Fructosamine exists in the enaminolic form in an alkaline milieu.…”

Section: Methodsmentioning

confidence: 99%

Detection of diabetic metabolism disorders post‐mortem – forensic case reports on cause of death hyperglycaemia

Heß

Wöllner

Mußhoff

et al. 2013

Drug Testing and Analysis

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Diabetic coma is the most severe form of hyperglycaemic metabolic disorders. The post-mortem diagnosis of this disorder of glucose metabolism can be difficult and vague due to a lack of characteristic morphological findings. Six death cases caused by diabetic coma are described with special focus on biochemical (and histological) findings. The possible glycaemia markers glucose, lactate, HbA1c, fructosamine, anhydroglucitol, and ketone bodies were measured and the usefulness of these parameters is evaluated and discussed. Estimations of glucose concentrations in vitreous humour or cerebrospinal fluid and of ketone bodies in blood or other matrices are obligatory while measurements of HbA1c, fructosamine, or anhydroglucitol can only provide additional information on the long-term adjustment of diabetes in the deceased. Lactate concentrations (addition of glucose and lactate levels to form the sum formula of Traub) do not give more information than the glucose concentration itself and can be therefore omitted.

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“…In our present study, we evaluated a possible method for measuring 1,5-AG in saliva samples using a set-up compatible with clinical analytical laboratories. The 1,5-AG could be quantified in saliva samples using NMR as well as LC/MS based targeted metabolomics approach, however it implementation into the clinical setting is limited [ 27 ]. Thus, we deployed an automated biochemical assay for 1,5-AG concentrations (Glycomark kit) to analyse plasma and saliva samples for which metabolic profiles had previously been determined [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Measurement of 1,5-anhydroglucitol in blood and saliva: from non-targeted metabolomics to biochemical assay

et al. 2016

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BackgroundDiabetes testing using saliva, rather than blood and urine, could facilitate diabetes screening in public spaces. We previously identified 1,5-anhydro-d-glucitol (1,5-AG) in saliva as a diabetes biomarker. The Glycomark™ assay kit is FDA approved for 1,5-AG measurement in blood. Here we evaluated its applicability for 1,5-AG quantification in saliva.MethodsUsing pooled saliva samples, we validated Glycomark™ assay use with a RX Daytona+ clinical chemistry analyser. We then used this set-up to analyse 82 paired blood and saliva samples from a diabetes case–control study, for which broad mass spectrometry-based characterization of the blood and saliva metabolome was also available. Osmolality was measured to account for potential variability in saliva samples.ResultsThe technical variability of the read-outs for the pooled saliva samples (CV = 2.05 %) was comparable to that obtained with manufacturer-provided blood surrogate quality controls (CV = 1.38–1.8 %). We found a high correlation between Glycomark assay and mass spectrometry measurements of serum 1,5-AG (r2 = 0.902), showing reproducibility of the non-targeted metabolomics results. The significant correlation between the osmolality measurements performed at two independent platforms with the time interval of 2 years (r2 = 0.887), also indicates the sample integrity. The assay read-out for saliva was not correlated with the mass spectrometry-based 1,5-AG saliva measurements. Comparison with the full saliva metabolome revealed a high correlation of the saliva assay read-outs with galactose.ConclusionsGlycomark™ assay read-outs for saliva were stable and replicable. However, the signal was dominated by galactose, which is biochemically similar to 1,5-AG and absent in blood. Adapting the 1,5-AG kit for saliva analysis will require enzymatic depletion of galactose. This should be feasible, since the assay already includes a similar step for glucose depletion from blood samples.

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Determination of glycemic monitoring marker 1,5-anhydroglucitol in plasma by liquid chromatography-electrospray tandem mass spectrometry

Cited by 14 publications

References 27 publications

Gender-specific pathway differences in the human serum metabolome

Gender-specific pathway differences in the human serum metabolome

Detection of diabetic metabolism disorders post‐mortem – forensic case reports on cause of death hyperglycaemia

Measurement of 1,5-anhydroglucitol in blood and saliva: from non-targeted metabolomics to biochemical assay

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