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

Efficient mixed-potential acetone sensor with yttria-stabilized zirconia and porous Co3O4 nanofoam sensing electrode for hazardous gas monitoring and breath analysis

For hazardous gas monitoring and non-invasive diagnosis of diabetes using breath analysis, porous foams assembled by Co3O4 nanoparticles were designed as sensing electrode materials to fabricate efficient yttria-stabilized zirconia (YSZ)-based acetone sensors. The sensitivity of the sensors was improved by varying the sintering temperature to regulate the morphology. Compared to other materials sintered at different temperatures, the porous Co3O4 nanofoams sintered at 800 °C exhibited the highest electrochemical catalytic activity during the electrochemical test. The response of the corresponding Co3O4-based sensor to 10 ppm acetone was -77.2 mV and it exhibited fast response and recovery times. Moreover, the fabricated sensor achieved a low detection limit of 0.05 ppm and a high sensitivity of -56 mV/decade in the acetone concentration range of 1-20 ppm. The sensor also exhibited excellent repeatability, acceptable selectivity, good O2/humidity resistance, and long-term stability during continuous measurements for over 30 days. Moreover, the fabricated sensor was used to determine the acetone concentration in the exhaled breaths of patients with diabetic ketosis. The results indicated that it could distinguish between healthy individuals and patients with diabetic ketosis, thereby proving its abilities to diagnose and monitor diabetic ketosis. Based on its excellent sensitivity and exhaled breath measurement results, the developed sensor has broad application prospects.

Image Sensing of Gaseous Acetone Using Secondary Alcohol Dehydrogenase-Immobilized Mesh for Exhaled Air

Human-borne acetone is a potent marker of lipid metabolism. Here, an enzyme immobilization method for secondary alcohol dehydrogenase (S-ADH), which is suitable for highly sensitive and selective biosensing of acetone, was developed, and then its applicability was demonstrated for spatiotemporal imaging of concentration distribution. After various investigations, S-ADH-immobilized meshes could be prepared with less than 5% variation by cross-linking S-ADH with glutaraldehyde on a cotton mesh at 40 °C for 15 min. Furthermore, high activity was obtained by adjusting the concentration of the coenzyme nicotinamide adenine dinucleotide (NADH) solution added to the S-ADH-immobilized mesh to 500 μM and the solvent to a potassium phosphate buffer solution at pH 6.5. The gas imaging system using the S-ADH-immobilized mesh was able to image the decrease in NADH fluorescence (ex 340 nm, fl 490 nm) caused by the catalytic reaction of S-ADH and the acetone distribution in the concentration range of 0.1-10 ppm-v, including the breath concentration of healthy people at rest. The exhaled breath of two healthy subjects at 6 h of fasting was quantified as 377 and 673 ppb-v, which were consistent with the values quantified by gas chromatography-mass spectrometry.

Metabolic trajectories of diabetic ketoacidosis onset described by breath analysis

Purpose

This feasibility study aimed to investigate the use of exhaled breath analysis to capture and quantify relative changes of metabolites during resolution of acute diabetic ketoacidosis under insulin and rehydration therapy.

Methods

Breath analysis was conducted on 30 patients of which 5 with DKA. They inflated Nalophan bags, and their metabolic content was subsequently interrogated by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS).

Results

SESI-HRMS analysis showed that acetone, pyruvate, and acetoacetate, which are well known to be altered in DKA, were readily detectable in breath of participants with DKA. In addition, a total of 665 mass spectral features were found to significantly correlate with base excess and prompt metabolic trajectories toward an in-control state as they progress toward homeostasis.

Conclusion

This study provides proof-of-principle for using exhaled breath analysis in a real ICU setting for DKA monitoring. This non-invasive new technology provides new insights and a more comprehensive overview of the effect of insulin and rehydration during DKA treatment.

Mixed Potential Type Acetone Sensor with Ultralow Detection Limit for Diabetic Ketosis Breath Analysis

Breath analysis using gas sensors is an emerging method for disease screening and diagnosis. Since it is closely related to the lipid metabolism and blood ketone concentration of the body, the detection of acetone content in exhaled breath is helpful for the screening and monitoring of diabetes and ketosis. The development of an acetone sensor with high selectivity, stability, and low detection limit has been the research focus for this purpose. Here, we developed a mixed potential type acetone sensor based on Gd2Zr2O7 solid electrolyte and CoSb2O6 sensing electrode. The developed sensor exhibits an extremely low detection limit of 10 ppb, enabling linear detection for acetone in an extremely wide range of 10 ppb-100 ppm. The good results of systematic evaluation on selectivity, repeatability, and stability prove the superior reliability of the sensor, which is a prerequisite for the application in actual breath detection. The ability of the sensor to distinguish healthy people from diabetic ketosis patients was confirmed by using the sensor to detect the breath of healthy people and diabetic patients, proving the feasibility of the sensor in the diagnosis and monitoring of diabetic ketosis.

Rapid and non-invasive diagnosis of type 2 diabetes through sniffing urinary acetone by a proton transfer reaction mass spectrometry

Urinary acetone in urine is produced from fat metabolism in human body, which can be accelerated in diabetic patients because of insufficient utilization and storage of glucose. In this study, we tried to develop a novel diagnosis method of type 2 diabetes (T2D) through sniffing urinary acetone by a proton transfer reaction mass spectrometry (PTR-MS). A total of 180 T2D patients and 180 healthy volunteers were recruited from three hospitals for multicenter study. Urine samples were collected in the morning when donators were fasting and stored in glass bottles. Acetone in the headspace of these bottles was qualitatively and quantitatively detected by the PTR-MS in 8 h. Using a threshold of 690.1 ppbv, a diagnostic model was established using urinary acetone with an accuracy of 81.3% (sensitivity: 73.3%, specificity: 89.3%) in hospital Ⅰ. In the verification studies, the accuracies were 92.5% (sensitivity: 88.7%, specificity: 96.2%) in hospital Ⅱ and 83.7% (sensitivity: 76.9%, specificity: 90.4%) in hospital Ⅲ, respectively. The accuracy is comparable to that of clinically used diagnosis methods, fasting plasma glucose (FPG), oral glucose tolerance test (OGTT), and glycosylated hemoglobin A1c (HbA1c) test. The sensitivity for 35 newly diagnosed patients was 85.7%. The newly developed technology is completely non-invasive and much more rapid than clinical FPG, OGTT, and HbA1c tests. It has a promising prospect in clinical use. But the applicability in different human races still need more validations.

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

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.

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

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.

The need for multicomponent gas standards for breath biomarker analysis

Exhaled breath is a non-invasive, information-rich matrix with the potential to diagnose or monitor disease, including infectious disease. Despite significant effort dedicated to biomarker identification in case control studies, very few breath tests are established in practice. In this topical review, we identify how gas standards support breath analysis today and what is needed to support further expansion and translation to practice. We examine forensic and clinical breath tests and discuss how confidence has been built through unambiguous biomarker identification and quantitation supported by gas calibration standards. Based on this discussion, we identify a need for multicomponent gas standards with part-per-trillion to part-per-million concentrations. We highlight National Institute of Standards and Technology (NIST) gas standards developed for atmospheric measurements that are also relevant to breath analysis and describe investigations of long-term stability, chemical reactions, and interactions with gas cylinder wall treatments. An overview of emerging online instruments and their need for gas standards is also presented. This review concludes with a discussion of our ongoing research to examine the feasibility of producing multicomponent gas standards at breath-relevant concentrations. Such standards could be used to investigate interference from ubiquitous endogenous compounds and as a starting point for standards tailored to specific breath tests.

Accuracy of a breath ketone analyzer to detect ketosis in adults and children with type 1 diabetes

OBJECTIVE

To assess the accuracy of a breath ketone analyzer to detect ketosis in adults and children with type 1 diabetes.

RESEARCH DESIGN AND METHODS

This is a proof-of-concept, prospective study comparing breath ketone analyzer and blood ketone meter to detect ketosis.

RESULTS

A total of 500 measurements from 19 adults and children with type 1 diabetes were analyzed. There was a significant association between the breath ketone analyzer and blood ketone meter results in non-fasting adults (p = 0.0066), but not in children (p = 0.4579). In adults, a cut-off of 3.9 PPM on the breath ketone analyzer maximized the Youden Index with an AUC of 0.73. This cut-off for the breath ketone analyzer had 94.7% sensitivity and 54.2% specificity to detect ketosis (≥0.6 mmol/L in blood ketone meter).

CONCLUSIONS

The breath ketone analyzer may be considered as a non-invasive screening tool to rule out ketosis in adults with type 1 diabetes.

Effects of Reduced Carbohydrate Intake after Sprint Exercise on Breath Acetone Level

Assessment of breath acetone level may be an alternative procedure to evaluate change in fat metabolism. The purpose of the present study was to investigate the effect of insufficient carbohydrate (CHO) intake after sprint exercise on breath acetone level during post-exercise. Nine subjects conducted two trials, consisting of either reduced CHO trial (LOW trial) or normal CHO trial (NOR trial). In each trial, subjects visited to laboratory at 7:30 following an overnight fast to assess baseline breath acetone level. They commenced repeated sprint exercise from 17:00. After exercise, isoenergetic meals with different doses of CHO (LOW trial; 18% for CHO, 27% for protein, 55% for fat, NOR trial; 58% for CHO, 14% for protein, 28% for fat) were served. Breath acetone level was also monitored immediately before and after exercise, 1 h, 3 h, 4 h, and 15 h (on the following morning) after completing exercise. A significant higher breath acetone level was observed in LOW trial than in NOR trial 4 h after completion of exercise (NOR trial; 0.66 ppm, LOW trial; 0.9 ppm). However, breath acetone level did not differ on the following morning between two trials. Therefore, CHO intake following an exhaustive exercise affects breath acetone level during early phase of post-exercise.