Acetone, Ethanol and Isopropanol as a Set of Biomarkers in the Exhaled Breath of Patients with Type 1 Diabetes

Abstract

Type 1 diabetes mellitus (autoimmune diabetes) is a chronic disease characterized by the insulin deficiency due to the loss of the pancreatic beta cells. According to the International Diabetes Federation, 537 million people (~ 8 %) suffer from this disease. According to statistics, by 2030 the number of patients suffering from diabetes would increase to 643 million, and by 2045 it would amount to 783 million people in the world. Non-invasive diagnostics makes it possible to identify the disease at an early stage, which will make it possible to reduce the burden on the country's healthcare system and improve the quality of life of the population. The paper describes an experimental setup based on the infrared laser spectroscopy. The main setup elements include a quantum cascade laser emitting in the range of 5.3--12.8 μm with peak power of 150 mW and a tuning step of 1 cm^--1, as well as a multi-pass Herriot gas cell with the optical path length of 76 m. The obtained spectra of exhaled breath air were analyzed with healthy volunteers (60 people) and patients suffering from the type 1 diabetes (60 people). Concentration range diagrams of three main biomarker molecules (acetone, ethanol and isopropanol) were calculated. Median values of the obtained diagrams of the biomarker molecules concentration range make it possible to reliably differentiate volunteers from the different health groups. The results could be used to determine reference values for the biomarker molecules concentration in the exhaled air and to design and develop devices for rapid diagnostics of diseases using spectral analysis of the exhaled air

The work was carried out within the framework of implementing the Strategic Academic Leadership Program "Priority-2030", approved by the Resolution of the Government of the Russian Federation dated May 13, 2021 no. 729

Please cite this article in English as:

Nebritova O.A., Demkin P.P., Morozov A.N., et al. Acetone, ethanol and isopropanol as a set of biomarkers in the exhaled breath of patients with type 1 diabetes. Herald of the Bauman Moscow State Technical University, Series Natural Sciences, 2023, no. 6 (111), pp. 39--54 (in Russ.). DOI: https://doi.org/10.18698/1812-3368-2023-6-39-54

References

[1] Paschou S.A., Petsiou A., Chatzigianni K., et al. Type 1 diabetes as an autoimmune disease: the evidence. Diabetologia, 2014, vol. 57, no. 7, pp. 1500--1501. DOI: https://doi.org/10.1007/s00125-014-3229-5

[2] Magliano D.J., Boyko E.J. Federation I.D. IDF diabetes atlas. Brussels, International Diabetes Federation, 2021.

[3] Selvaraj R., Vasa N.J., Nagendra S.S., et al. Advances in mid-infrared spectroscopy-based sensing techniques for exhaled breath diagnostics. Molecules, 2020, vol. 25, iss. 9, art. 2227. DOI: https://doi.org/10.3390/molecules25092227

[4] Galassetti P.R., Novak B., Nemet D., et al. Breath ethanol and acetone as indicators of serum glucose levels: an initial report. Diabetes Technol. Ther., 2005, vol. 7, no. 1, pp. 115--123. DOI: https://doi.org/10.1089/dia.2005.7.115

[5] Salerno-Kennedy R., Cashman K.D. Isopren in der Atemluft als potenzieller Biomarker in der modernen Medizin. Wien. Klin. Wochenschr., 2005, vol. 117, pp. 180--186. DOI: https://doi.org/10.1007/s00508-005-0336-9

[6] Diskin A.M., Spanel P., Smith D. Time variation of ammonia, acetone, isoprene and ethanol in breath: a quantitative SIFT-MS study over 30 days. Physiol. Meas., 2003, vol. 24, no. 1, art. 107. DOI: https://doi.org/10.1088/0967-3334/24/1/308

[7] Deng C., Zhang J., Yu X., et al. Determination of acetone in human breath by gas chromatography--mass spectrometry and solid-phase microextraction with on-fiber derivatization. J. Chromatogr. B, 2004, vol. 810, iss. 2, pp. 269--275. DOI: https://doi.org/10.1016/j.jchromb.2004.08.013

[8] Smith D., Turner C., Spanel P. Volatile metabolites in the exhaled breath of healthy volunteers: their levels and distributions. J. Breath Res., 2007, vol. 1, no. 1, art. 014004. DOI: https://doi.org/10.1088/1752-7155/1/1/014004

[9] Park Y.L., Chen B.R., Wood R.J. Design and fabrication of soft artificial skin using embedded microchannels and liquid conductors. IEEE Sens. J., 2012, vol. 12, iss. 8, pp. 2711--2718. DOI: https://doi.org/10.1109/JSEN.2012.2200790

[10] Spanel P., Dryahina K., Smith D. Acetone, ammonia and hydrogen cyanide in exhaled breath of several volunteers aged 4--83 years. J. Breath Res., 2007, vol. 1, no. 1, art. 011001. DOI: https://doi.org/10.1088/1752-7155/1/1/011001

[11] Storer M., Dummer J., Lunt H., et al. Measurement of breath acetone concentrations by selected ion flow tube mass spectrometry in type 2 diabetes. J. Breath Res., 2011, vol. 5, no. 4, art. 046011. DOI: https://doi.org/10.1088/1752-7155/5/4/046011

[12] Goyal A.K., Kotidis P., Deutsch E.R., et al. Detection of chemical clouds using widely tunable quantum cascade lasers. Proc. SPIE, 2015, vol. 9455, pp. 144--152. DOI: https://doi.org/10.1117/12.2177527

[13] Ninham B.W., Bolotskova P.N., Gudkov S.V., et al. Nafion swelling in salt solutions in a finite sized cell: curious phenomena dependent on sample preparation protocol. Polymers, 2022, vol. 14, iss. 8, art. 1511. DOI: https://doi.org/10.3390/polym14081511

[14] Bunkin N.F., Kozlov V.A., Kir’yanova M.S., et al. Fourier IR spectroscopy study of the effects of unsteadiness on the process of swelling of polymeric membranes. Opt. Spectrosc., 2021, vol. 129, no. 4, pp. 460--470. DOI: https://doi.org/10.1134/S0030400X2104007X

[15] Richard L., Romanini D., Ventrillard I. Nitric oxide analysis down to ppt levels by optical-feedback cavity-enhanced absorption spectroscopy. Sensors, 2018, vol. 18, iss. 7, art. 1997. DOI: https://doi.org/10.3390/s18071997

[16] Ghorbani R., Schmidt F.M. ICL-based TDLAS sensor for real-time breath gas analysis of carbon monoxide isotopes. Opt. Express, 2017, vol. 25, iss. 1, pp. 12743--12752. DOI: https://doi.org/10.1364/OE.25.012743

[17] McManus J.B., Zahniser M.S., Nelson Jr D.D., et al. Application of quantum cascade lasers to high-precision atmospheric trace gas measurements. Opt. Eng., 2010, vol. 49, iss. 11. DOI: https://doi.org/10.1117/1.3498782

[18] Shcherbakova A.V., Anfimov D.R., Fufurin I.L., et al. Experimental setup based on a quantum cascade laser tunable in the wavelength range of 5.3--12.8 µm for spectral analysis of human exhaled air. Opt. Spectrosc., 2021, vol. 129, no. 7, pp. 830--837. DOI: https://doi.org/10.1134/S0030400X21060151

[19] van Mastrigt E., Reyes-Reyes A., Brand K., et al. Exhaled breath profiling using broadband quantum cascade laser-based spectroscopy in healthy children and children with asthma and cystic fibrosis. J. Breath Res., 2016, vol. 10, no. 2, art. 026003. DOI: https://doi.org/10.1088/1752-7155/10/2/026003

[20] Fufurin I., Berezhanskiy P., Golyak I., et al. Deep learning for type 1 diabetes mellitus diagnosis using infrared quantum cascade laser spectroscopy. Materials, 2022, vol. 15, iss. 9, art. 2984. DOI: https://doi.org/10.3390/ma15092984

[21] Golyak I.S., Kareva E.R., Fufurin I.L., et al. Numerical methods of spectral analysis of multicomponent gas mixtures and human exhaled breath. Comput. Opt., 2022, vol. 46, iss. 4, pp. 650--658. DOI: http://dx.doi.org/10.18287/2412-6179-CO-1058