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Diabetes Technology and Therapeutics 2012-Oct

Human breath gas analysis in the screening of gestational diabetes mellitus.

Watumiaji waliosajiliwa tu ndio wanaweza kutafsiri nakala
Ingia / Ingia
Kiungo kimehifadhiwa kwenye clipboard
Susanne Halbritter
Mattia Fedrigo
Vera Höllriegl
Wilfried Szymczak
Joerg M Maier
Anette-Gabriele Ziegler
Michael Hummel

Maneno muhimu

Kikemikali

BACKGROUND

We present a pilot study on the feasibility of the application and advantages of online, noninvasive breath gas analysis (BGA) by proton transfer reaction quadrupole mass spectrometry for the screening of gestational diabetes mellitus (GDM) in 52 pregnant women by means of an oral glucose tolerance test (OGTT).

METHODS

We collected and identified samples of end-tidal breath gas from patients during OGTT. Time evolution parameters of challenge-responsive volatile organic compounds (VOCs) in human breath gas were estimated. Multivariate analysis of variance and permutation analysis were used to assess feasibility of BGA as a diagnostic tool for GDM.

RESULTS

Standard OGTT diagnosis identified pregnant women as having GDM (n = 8), impaired glucose tolerance (n = 12), and normal glucose tolerance (n = 32); a part of this latter group was further subdivided into a "marginal" group (n = 9) because of a marginal high 1-h or 2-h OGTT value. We observed that OGTT diagnosis (four metabolic groups) could be mapped into breath gas data. The time evolution of oxidation products of glucose and lipids, acetone metabolites, and thiols in breath gas after a glucose challenge was correlated with GDM diagnosis (P = 0.035). Furthermore, basal (fasting) values of dimethyl sulfide and values of methanol in breath gas were inversely correlated with phenotype characteristics such as homeostasis model assessment of insulin resistance index (R = -0.538; P = 0.0002, P(corrected) = 0.0034) and pregestational body mass index (R = -0.433; P = 0.0013, P(corrected) = 0.022).

CONCLUSIONS

Noninvasive BGA in challenge response studies was successfully applied to GDM diagnosis and offered an insight into metabolic pathways involved. We propose a new approach to the identification of diagnosis thresholds for GDM screening.

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