The correlation between breath acetone and blood betahydroxybutyrate in individuals with type 1 diabetes

Hancock, Gus; Sharma, Shrinivas; Galpin, Martin R.; Lunn, Daniel; Megson, Clare; Peverall, R.; Richmond, Graham; Ritchie, Grant A. D.; Owen, Katharine R.

doi:10.1088/1752-7163/abbf37

Cited by 29 publications

(17 citation statements)

References 27 publications

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“…There is good agreement between both data sets, and the overall correlation coefficients between breath acetone and blood BOHB are r p = 0.75 (Pearson's, p < 0.05) and r s = 0.74 (Spearman's, p < 0.05). This is in line with previous studies that as well correlated these parameters, for instance, in type‐1 diabetics after overnight fasting ( r p = 0.57 [ 65 ] ), during the day (morning and afternoon, 0.93 [ 66 ] ) or ketoacidosis (0.82 [ 67 ] ) and in healthy subjects during ketogenic diets (0.78 [ 39 ] ) for different concentration ranges. Linear relationships between blood BOHB and breath acetone for the present data (red dashed line, Figure 5), Güntner et al, [ 43 ] (blue dashed line), and the combined data set (green solid line) are indicated together with the equations of those fits and the coefficient of determination ( R 2 = 0.56).…”

Section: Resultssupporting

confidence: 91%

Monitoring Lipolysis by Sensing Breath Acetone down to Parts‐per‐Billion

et al. 2021

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Mobile health technologies can provide information routinely and on demand to manage metabolic diseases (e.g., diabetes and obesity) and optimize their treatment (e.g., exercise or dieting). Most promising is breath acetone monitoring to track lipolysis and complement standard glucose monitoring. Yet, accurate quantification of acetone down to parts‐per‐billion (ppb) is difficult with compact and mobile devices in the presence of interferants at comparable or higher concentrations. Herein, a low‐cost detector that quantifies end‐tidal acetone during exercise and rest is presented with excellent bias (25 ppb) and unprecedented precision (169 ppb) in 146 breath samples. It combines a flame‐made Pt/Al2O3 catalyst with a chemoresistive Si/WO3 sensor. The detector is robust against orders of magnitude higher ethanol concentrations from disinfection and exercise‐driven endogenous breath isoprene ones, as validated by mass spectrometry. This detector accurately tracks the individual lipolysis dynamics in all volunteers, as confirmed by blood ketone measurements. It can be integrated readily into handheld devices for personalized metabolic assessment at home, in gyms, and clinics.

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Section: Resultssupporting

confidence: 91%

Monitoring Lipolysis by Sensing Breath Acetone down to Parts‐per‐Billion

et al. 2021

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“…Over 100,000 data were collected from 9 different concentrations of acetone balanced with simulated human breath. The data were labeled into several different classes based on the concentration of acetone: background (0 μmol/mol), safe (≤5 μmol/mol), low risk (≤40 μmol/mol), and high risk (>40 μmol/mol). − 80% of the data were used for training, 10% were used for testing, and 10% were used for cross-validation.…”

Section: Resultsmentioning

confidence: 99%

“…The concentration of acetone in exhaled breath is less than 2 μmol/mol (ppm), and the blood BHB is less than 0.6 mmol/L for nondiabetic ketoacidosis conditions. In diabetic ketoacidosis, the concentration of breath acetone is higher than 40 μmol/mol (ppm), and the blood BHB is higher than 3.0 mmol/L …”

Section: Introductionmentioning

confidence: 99%

Deep Learning Image Analysis of Nanoplasmonic Sensors: Toward Medical Breath Monitoring

Zhao

Dong²,

Benkstein

et al. 2022

ACS Appl. Mater. Interfaces

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Sensing biomarkers in exhaled breath offers a potentially portable, cost-effective, and noninvasive strategy for disease diagnosis screening and monitoring, while high sensitivity, wide sensing range, and target specificity are critical challenges. We demonstrate a deep learning-assisted plasmonic sensing platform that can detect and quantify gas-phase biomarkers in breath-related backgrounds of varying complexity. The sensing interface consisted of Au/SiO2 nanopillars covered with a 15 nm metal–organic framework. A small camera was utilized to capture the plasmonic sensing responses as images, which were subjected to deep learning signal processing. The approach has been demonstrated at a classification accuracy of 95 to 98% for the diabetic ketosis marker acetone within a concentration range of 0.5–80 μmol/mol. The reported work provides a thorough exploration of single-sensor capabilities and sets the basis for more advanced utilization of artificial intelligence in sensing applications.

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“…Patients with diabetes tend to have increased levels of ketone bodies, acetone, acetoacetic acid, and beta-hydroxybutyric acid in blood and urine. Breath acetone has been shown to correlate with the metabolic state in patients with diabetes [ 124 ]. Breath analysis may in the future offer an alternative to blood sampling for glucose monitoring for patients with diabetes mellitus.…”

Section: Factors Affecting Breath Analysismentioning

confidence: 99%

The Use of Breath Analysis in the Management of Lung Cancer: Is It Ready for Primetime?

Keogh

Riches

2022

Current Oncology

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Breath analysis is a promising non-invasive method for the detection and management of lung cancer. Exhaled breath contains a complex mixture of volatile and non-volatile organic compounds that are produced as end-products of metabolism. Several studies have explored the patterns of these compounds and have postulated that a unique breath signature is emitted in the setting of lung cancer. Most studies have evaluated the use of gas chromatography and mass spectrometry to identify these unique breath signatures. With recent advances in the field of analytical chemistry and machine learning gaseous chemical sensing and identification devices have also been created to detect patterns of odorant molecules such as volatile organic compounds. These devices offer hope for a point-of-care test in the future. Several prospective studies have also explored the presence of specific genomic aberrations in the exhaled breath of patients with lung cancer as an alternative method for molecular analysis. Despite its potential, the use of breath analysis has largely been limited to translational research due to methodological issues, the lack of standardization or validation and the paucity of large multi-center studies. It is clear however that it offers a potentially non-invasive alternative to investigations such as tumor biopsy and blood sampling.

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The correlation between breath acetone and blood betahydroxybutyrate in individuals with type 1 diabetes

Cited by 29 publications

References 27 publications

Monitoring Lipolysis by Sensing Breath Acetone down to Parts‐per‐Billion

Monitoring Lipolysis by Sensing Breath Acetone down to Parts‐per‐Billion

Deep Learning Image Analysis of Nanoplasmonic Sensors: Toward Medical Breath Monitoring

The Use of Breath Analysis in the Management of Lung Cancer: Is It Ready for Primetime?

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