Breath Analysis for the In Vivo Detection of Diabetic Ketoacidosis
Human breath analysis of volatile organic compounds has gained significant attention recently because of its rapid and noninvasive potential to detect various metabolic diseases. The detection of ketones in the breath and blood is key to diagnosing and managing diabetic ketoacidosis (DKA) in patients with type 1 diabetes. It may also be of increasing importance to detect euglycemic ketoacidosis in patients with type 1 or type 2 diabetes or heart failure, treated with sodium-glucose transporter-2 inhibitors (SGLT2-i). The present research evaluates the efficiency of colorimetry for detecting acetone and ethanol in exhaled human breath with the response time, pH effect, temperature effect, concentration effect, and selectivity of dyes. Using the proposed multidye system, we obtained a detection limit of 0.0217 ppm for acetone and 0.029 ppm for ethanol in the detection range of 0.05–50 ppm. A smartphone-assisted unit consisting of a portable colorimetric device was used to detect relative red/green/blue values within 60 s of the interface for practical and real-time application. The developed method could be used for rapid, low-cost detection of ketones in patients with type 1 diabetes and DKA and patients with type 1 or type 2 diabetes or heart failure treated with SGLT2-I and euglycemic ketoacidosis.
Other Information
Published in: ACS Omega
License: https://creativecommons.org/licenses/by-nc-nd/4.0/
See article on publisher's website: https://dx.doi.org/10.1021/acsomega.1c05948
Funding
Qatar National Research Fund (NPRP11S-0110-180247), A noninvasive monitor to predict hypoglycemia in diabetes patients.
History
Language
- English
Publisher
American Chemical SocietyPublication Year
- 2022
License statement
This Item is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Institution affiliated with
- Qatar University
- Center for Advanced Materials - QU
- College of Engineering - QU
- KINDI Center for Computing Research - CENG
- Weill Cornell Medicine - Qatar