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Updated: May 20, 2026

Breath Collection from Children for Disease Biomarker Discovery
Published on: February 14, 2019
Human breath gas analysis in the screening of gestational diabetes mellitus
Susanne Halbritter1, Mattia Fedrigo, Vera Höllriegl
1Research Unit Medical Radiation Physics and Diagnostics, Helmholtz Center Munich, Neuherberg, Germany.
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).
Subjects And 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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