Exhaled volatile organic compounds (VOCs) represent ideal biomarkers of endogenous metabolism and could be used to noninvasively measure circulating variables, including plasma glucose. We previously demonstrated that hyperglycemia in different metabolic settings (glucose ingestion in pediatric Type 1 diabetes) is paralleled by changes in exhaled ethanol, acetone, and methyl nitrate. In this study we integrated these gas changes along with three additional VOCs (2 forms of xylene and ethylbenzene) into multi-linear regression models to predict plasma glucose profiles in 10 healthy young adults, during the 2 h following an intravenous glucose bolus (matched samples of blood, exhaled and room air were collected at 12 separate time points). The four-gas model with highest predictive accuracy estimated plasma glucose in each subject with a mean R value of 0.91 (range 0.70-0.98); increasing the number of VOCs in the model only marginally improved predictions (average R with best 5-gas model = 0.93; with 6-gas model = 0.95). While practical development of this methodology into clinically usable devices will require optimization of predictive algorithms on large-scale populations, our data prove the feasibility and potential accuracy of breath-based glucose testing.
Summary Full-waveform inversion (FWI) is a well-established technology for building high-resolution velocity models over complex areas. Conventional industry implementations of FWI typically use the acoustic approximation, which can break down in the presence of strong velocity contrasts where elastic effects in the recorded data cannot be ignored. These issues are well-known for salt and sub-salt areas, where elastic FWI becomes the recommended solution. While chalk deposits can produce a similarly-large velocity contrast, this geological setting has received much less scrutiny compared to salt environments in elastic FWI studies. In this paper, we demonstrate the effectiveness of elastic FWI over acoustic FWI on an ocean-bottom data set from the UK North Sea, with sparse node densities, in an area containing significant chalk layers in the shallow part of the section (< 1 km depth). The comparison highlights how acoustic FWI generates a persistent velocity ‘halo’ around the chalk that does not follow the sonic well-logs and gets worse with depth, whereas elastic FWI is stable. The final 30 Hz elastic FWI shows good agreement with the well data, as well as a cleaner and more accurate reflectivity over and below the chalk, highlighting the benefits of elastic FWI in these areas.