Breath Acetone Concentration Research Articles

Effects of a Carbohydrate Meal on Lipolysis

Background: Due to the increasing prevalence of obesity and type 2 diabetes, effective dietary recommendations are needed. Previously, we developed the low-insulin method: by avoiding insulinogenic, i.e., insulin-release-triggering foods, insulin secretion becomes reduced, lipolysis is stimulated, and energy production is shifted to ketosis with excess ketone bodies exhaled in the form of acetone. Now, we investigate how quickly stable ketosis (defined as fasting breath acetone concentration ≥ 7.0 ppm) is achieved, whether and for how long a carbohydrate meal inhibits ketosis, and whether the responses differ in healthy adults with different insulin levels. Methods: An oral glucose tolerance test was conducted, and body composition and fasting insulin were determined at the beginning and end of the 14-day study. Participants (n = 10) followed a ketogenic diet and performed continuous glucose monitoring. Ketosis levels were determined by measuring breath acetone concentrations. On day 8, two white bread rolls with jam (72 g carbohydrates) were consumed for breakfast. Results: After seven days, all participants achieved stable ketosis (defined as fasting breath acetone concentration ≥ 7.0 ppm), which dropped from 8.2 to 5.7 ppm (p = 0.0014) after the carbohydrate meal. It took five days to achieve stable ketosis again. The stratification of participants into tertiles according to their fasting insulin levels demonstrated that individuals with low fasting insulin levels achieved stable ketosis again after two days and those with medium insulin levels after five days, while those with high baseline values did not reach stable ketosis by the end of the study. Conclusions: By carbohydrate restriction, stable ketosis can be achieved within one week. However, a single carbohydrate meal inhibits ketosis for several days. This effect is pronounced in individuals with elevated fasting insulin levels.

Hollow CuO/Cu2O octahedrons for selective and stable detection of acetone gas

Acetone (C3H6O) is key member of the volatile organic compound family, which can cause health as well as environmental issues. The identification and sensing of acetone gas are important in many applications such as air quality control and biomarker-based diagnosis. Here, we synthesized hollow CuO/Cu2O octahedrons heterostructure composite through coprecipitation and subsequent heat treatment. The sensor manifested a response of 3.52 to 50 ppm acetone gas at 350 °C. Besides, it showed excellent stability in a 150-day test and excellent repeatability, with a response of 4.15. Also, exhaled gas sensing was carried out using standard method to estimate the acetone concentration in breath. The high sensing capability of the sensor was ascribed to the formation of p-p heterojunctions, the hollow morphology of octahedrons, and the presence of structural defects. The hollow CuO/Cu2O octahedron sensor with good performance may be employed for the detection and monitoring of acetone gas in real situations.

YSZ-based mixed-potential acetone sensor with LaBaCo2O5+δ sensitive electrode for diabetic diagnosis

The acetone concentration in exhaled breath can be utilized for the noninvasive diagnosis of diabetes. In this study, to fulfil the requirements of breath analysis for diabetes diagnosis, high-performance mixed-potential acetone sensors based on yttria-stabilized zirconia were successfully developed by employing a novel sensitive electrode (SE) material, namely, LaBaCo2O5+δ. Electrochemical measurement results revealed that the LaBaCo2O5+δ SE, sintered at 1000 ℃, exhibited outstanding electrochemical catalytic activity toward acetone. The device incorporating this SE demonstrated a peak response of −46 mV to 100 ppm acetone at 655 ℃. Sensitive performance assessments indicated the capability of the sensor to detect acetone concentrations ranging from 200 ppb to 100 ppm. Based on the mixed-potential model, the sensitivities of the current device were quantified as −1.3 and −53.7 mV/decade within the acetone concentration ranges of 0.2−5 and 10−100 ppm, respectively. Furthermore, the fabricated sensor exhibited exceptional selectivity, repeatability, humidity resistance, and long-term stability. In addition, simulated expiratory experiments showed that the developed sensor has substantial potential for application in breath analysis for diabetes diagnosis and noninvasive monitoring of a patient’s physical status.

Femtosecond Laser Ionization Mass Spectrometry for Online Analysis of Human Exhaled Breath.

A variety of organic compounds in human exhaled breath were measured online by mass spectrometry using the fifth (206 nm) and fourth (257 nm) harmonic emissions of a femtosecond ytterbium (Yb) laser as the ionization source. Molecular ions were enhanced significantly by means of resonance-enhanced, two-color, two-photon ionization, which was useful for discrimination of analytes against the background. The limit of detection was 0.15 ppm for acetone in air. The concentration of acetone in exhaled breath was determined for three subjects to average 0.31 ppm, which lies within the range of normal healthy subjects and is appreciably lower than the range for patients with diabetes mellitus. Many other constituents, which could be assigned to acetaldehyde, ethanol, isoprene, phenol, octane, ethyl butanoate, indole, octanol, etc., were observed in the exhaled air. Therefore, the present approach shows potential for use in the online analysis of diabetes mellitus and also for the diagnosis of various diseases, such as COVID-19 and cancers.

Construction of mesoporous silica-implanted tungsten oxides for selective acetone gas sensing

As a key biomarker for noninvasive diagnosis of diabetes, the selective detection of trace acetone in exhaled gas using a portable and low-cost device remains a great challenge. Semiconductor metal oxide (SMO) based gas sensors have drawn signification attention due to their potential in miniaturization, user-friendliness, high cost-effectiveness and selective real-time detection for noninvasive clinical diagnosis. Herein, we propose a one-pot solvent evaporation induced tricomponent co-assembly strategy to design a novel ordered mesoporous SMO of silica-implanted WO3 (SiO2/WO3) as sensing materials for trace acetone detection. The controlled co-assembly of silicon and tungsten precursors and amphiphilic diblock copolymer poly(ethylene oxide)-block-polystyrene (PEO-b-PS), and the subsequent thermal treatment enable the local lattice disorder of WO3 induced by the amorphous silica and the formation of ordered mesoporous SiO2/WO3 hybrid walls with a unique metastable ε-phase WO3 framework. The obtained mesoporous SiO2/WO3 composites possess highly crystalline framework with large uniform pore size (12.0–13.3 nm), high surface area (99–113 m2/g) and pore volume (0.17–0.23 cm3/g). Typically, the as-fabricated gas sensor based on mesoporous 2.5 %SiO2/WO3 exhibits rapid response/recovery rate (5/17 s), superior sensitivity (Rair/Rgas = 105 for 50 ppm acetone), as well as high selectivity towards acetone. The limit of detection is as low as 0.25 ppm, which is considerably lower than the thresh value of acetone concentration (>1.1 ppm) in the exhaled breath of diabetic patients, demonstrating its great prospect in real-time monitoring in diabetes diagnosis. Moreover, the mesoporous 2.5 %SiO2/WO3 sensor is integrated into a wireless sensing module connected to a smart phone, providing a convenient real-time detection of acetone.

An efficient convolutional neural network-extreme gradient boosting hybrid deep learning model for disease detection applications

In this paper, we present an efficient deep-learning hybrid model comprising an extreme gradient boosting (XGBoost) supervised learning algorithm and convolutional neural networks (CNN) for the automated detection of diseases. The proposed model is implemented and tested to detect type-2 diabetes by measuring the acetone concentration in the exhaled breath. Acetone will be present in much higher concentrations in type-2 diabetic patients compared to non-diabetic people. A novel sensing module is designed and implemented in our study to measure the acetone concentration in exhaled breath. The proposed approach delivered good results, with a classification accuracy of 97.14%. The findings of this study show how effectively the proposed detection module functions in disease diagnosis applications. As the detection process is simple and non-invasive, people can undergo routine checks for diabetes with the proposed detection module.

Boosting acetone response of p-type Co3O4 sensor via Sn and Ni co-doping for diabetes diagnosis

The hole accumulation layer (HAL) configuration of p-type oxides (including Co3O4) in ambient air causes intrinsically low gas response and hinders their promising applications in exhaled gas analysis. Herein, Sn and Ni co-doping has been proposed to trigger the response of chemiresistive Co3O4 sensor toward acetone (biomarker of diabetes). Via incorporating 1 at% Sn and 0.5 at% Ni doping (Co2.95Sn0.03Ni0.02O4), the response to 100 ppm acetone has been boosted ∼2 orders (from 1.24 to 125.6) at 70 °C, the limit of detection (LoD) has been reduced ∼4 times (from 47.9 to 12.4 ppb), the optimal operation temperature has been decreased from 130 °C to ∼70 °C. Various characterizations suggest that co-doping induced abundant surface asymmetric oxygen vacancy defects (Co-□-Ni, Sn-□-Ni), which facilitate the catalytic oxidation of acetone molecules at relatively low operation temperature. In addition to excellent reproducibility and long-term stability, Co2.95Sn0.03Ni0.02O4 sensor could also operate under highly humid air background and reliably detect acetone concentration in exhaled breath at 150 °C, opening the opportunity for the practical application of p-type oxide sensors for diabetes diagnosis.

The effect of high ethanol concentration on E-nose response for diabetes detection in exhaled breath: Laboratory studies

The growing number of people with diabetes has paved the way for the creation of non-invasive devices to measure blood glucose levels and enable the detection of diabetes non-invasively, for example, through exhaled air. This paper presents the development of a breath detection system using multiple sensors (e-nose system) for the non-invasive estimation of blood glucose levels. The system employs commercially available ethanol, CO2, and acetone sensors, with synthetic breath mixtures used for testing, including four scenarios with high ethanol concentrations (0–570 ppm) as an influence factor. Results showed a mean absolute error of 0.245 ppm when analysing commonly observed acetone concentrations in diabetic breath using four sensors and the XGBoost Regressor. In mixtures with high ethanol concentrations and varying acetone concentrations (0–8.62 ppm), the CatBoost Regressor outperformed other machine learning algorithms with a mean absolute error of 0.568 ppm. This study emphasises the significant impact of ethanol on acetone detection and the need to consider ethanol levels in the developing of non-invasive devices for blood glucose prediction based on the exhaled acetone measurements. The research shows that a set of three gas sensors is optimal for estimating acetone concentrations in gas mixtures. The presented results constitute a preliminary step towards developing a non-invasive device for estimating blood glucose levels based on breath analysis, with the novelty of considering alcohol intake as a potential influencing factor.

Reduced graphene oxide (rGO) decorated NiO-SnO2 nanocomposite based sensor towards room temperature diabetic biomarker detection

In the present work, reduced graphene oxide (rGO) decorated NiO-SnO2 nanocomposites were synthesized by using a facile sol-gel process and their structural (XRD, FTIR, Raman), morphological (BET, FESEM, TEM), and optical (UV-Vis, PL) characterizations were carried out. The as-synthesized NiO-SnO2 nanocomposite exhibited an enhancement of surface area after the incorporation of rGO and this substantial increment was expected to be beneficial for gas sensing at room temperature. Therefore, Taguchi-type sensors were fabricated and deployed for the detection of VOCs. The optimized sensor containing ∼ 3 wt% rGO in NiO-SnO2 nanocomposite demonstrated an excellent sensing response (Rg/Ra ∼ 7.8) towards ∼ 10 ppm acetone at room temperature (30 ± 5 °C) with response and recovery time of ∼ 10 s and ∼ 30 s respectively. It also exhibited good reproducibility and stability over ∼ 3 months. Additionally, this sensor selectively detected trace concentration (< 1 ppm) of acetone in exhaled human breath (ethanol present in a healthy person’s exhaled breath is insignificant), which is a non-invasive biomarker of diabetes. The sensing performance was analyzed from an electronic transportation point-of-view and consequent band structure modulation. The electrical property of the sensing material was illustrated using impedance spectroscopy to support the sensing mechanism.

Early Chronic Pulmonary Diseases Prediction System

Smart breath analyzers are developed as sensing terminals of a telemedicine architecture devoted to remote monitoring of patients suffering from Chronic Obstructive Pulmonary Disease (COPD). The devices based on different sensors CO2, smoke sensor and Volatile Organic Compounds (VOCs), relative humidity and temperature (R.H. &amp; T) and heart rate, respiration sensors) monitor the breath air exhaled into the expiratory line of the bi-tube patient breathing circuit. The sensor raw signals are transmitted to national health service units / Doctor by TCP/IP communication through a cloud remote platform using IoT module space.The work is a proof-of-concept of a sensors-based IoT system with the perspective to predict continuously the effectiveness of monitoring and/or any state of exacerbation of the disease requiring healthcare. By a gas-mixing bench towards gas, smoke in environment and acetone concentrations in exhaled breath collected in a sampling bag were carried out to test the realized prototypes. The paper reports on the longitudinal use of health tags among high- risk COPD patients in the indoor &amp; outdoor environment using PIC16F877A microcontroller. Key Word: Chronic Obstructive Pulmonary Disease (COPD); exacerbation ; Buprenorphine; Nalbuphine; Postoperative.

Electrospun Fibrous Nanocomposite Sensing Materials for Monitoring Biomarkers in Exhaled Breath.

Human-exhaled breath mainly contains water, oxygen, carbon dioxide, and endogenous gases closely related to human metabolism. The linear relationship between breath acetone and blood glucose concentration has been revealed when monitoring diabetes patients. Considerable attention has been directed toward developing a highly sensitive volatile organic compounds (VOCs) sensing material that can detect breath acetone. In this study, we propose a tungsten oxide/tin oxide/silver/poly (methyl methacrylate) (WO3/SnO2/Ag/PMMA) sensing material fabricated using the electrospinning technique. By monitoring the evolution of sensing materials' extinction spectra, low concentrations of acetone vapor can be detected. Moreover, the interfaces between SnO2 and WO3 nanocrystals construct n-n junctions, which generate more electron-hole pairs than those without such structure when the light strikes. This helps to improve the sensitivity of sensing materials when they are subjected to acetone surroundings. The established sensing materials (WO3/SnO2/Ag/PMMA) exhibit a sensing limit of 20 ppm for acetone vapor and show specificity for acetone even in ambient humidity.

Inter-Day Variation in the Fasting Plasma Lipopolysaccharide Concentration in the Morning Is Associated with Inter-Day Variation in Appetite in Japanese Males: A Short-Term Cohort Study

Injection of lipopolysaccharide (LPS), a product of gut bacteria, into the blood increases blood triglycerides and cortisol, an appetite-stimulating hormone. Meanwhile, small amounts of LPS derived from gut bacteria are thought to enter the bloodstream from the gut in daily basis. This study aimed to investigate the effect of LPS influx on appetite or lipid metabolism in humans in everyday life. We measured the fasting plasma LPS concentration before breakfast and the corresponding days' appetite and fat-burning markers for 10 days in four Japanese males (28-31 years) and analyzed the correlation of their inter-day variation. The LPS concentration was negatively correlated with fullness, and positively correlated with the carbohydrate intake. Against our hypothesis, the LPS concentration was positively correlated with the fasting breath acetone concentration, a fat-burning marker. There was a positive correlation between the LPS concentration and fasting body mass index (BMI), but the inter-day variation in BMI was slight. The results suggest that the LPS influx in everyday life is at least associated with appetite in the day.

A Strategy to Enhance Humidity Robustness of p‐Type CuO Sensors for Breath Acetone Quantification

Low‐cost metal oxide sensors are highly attractive for emerging applications such as breath analysis. Particularly promising are p‐type sensors that can operate at low temperatures, a key requirement for compact and low‐power devices. To date, however, these sensors lack sufficient sensitivity, selectivity, and humidity robustness to fulfil stringent requirements faced in real applications. Herein, a flame‐made and low‐power sensor (operated at 150 °C) that consists of CeO2‐decorated CuO nanoparticles is introduced, as determined by X‐ray diffraction and X‐ray photoelectron spectroscopy analysis. Most remarkably, this sensor features excellent robustness to 10–90% relative humidity. This is attributed to the presence of CeO2 nanoclusters, which may act by scavenging OH− and allow the readsorption of oxygen onto the CuO surface. To demonstrate its immediate impact, this sensor is investigated for the detection of acetone, a biomarker for fat burning. It detects acetone with high sensitivity (i.e., 50 ppb) and features excellent acetone selectivity (&gt;9.8) toward key inorganic interferants (i.e., NH3, H2, and CO). Most importantly, the CeO2–CuO sensor accurately quantifies acetone concentrations in the exhaled breath of 16 volunteers (bias and precision of 90 and 457 ppb). As a result, it is attractive for low‐power and humidity robust detection of volatiles in breath analysis.

Handheld device quantifies breath acetone for real-life metabolic health monitoring.

Non-invasive breath analysis with mobile health devices bears tremendous potential to guide therapeutic treatment and personalize lifestyle changes. Of particular interest is the breath volatile acetone, a biomarker for fat burning, that could help in understanding and treating metabolic diseases. Here, we report a hand-held (6 × 10 × 19.5 cm3), light-weight (490 g), and simple device for rapid acetone detection in breath. It comprises a tailor-made end-tidal breath sampling unit, connected to a sensor and a pump for on-demand breath sampling, all operated using a Raspberry Pi microcontroller connected with a HDMI touchscreen. Accurate acetone detection is enabled by introducing a catalytic filter and a separation column, which remove and separate undesired interferents from acetone upstream of the sensor. This way, acetone is detected selectively even in complex gas mixtures containing highly concentrated interferents. This device accurately tracks breath acetone concentrations in the exhaled breath of five volunteers during a ketogenic diet, being as high as 26.3 ppm. Most importantly, it can differentiate small acetone changes during a baseline visit as well as before and after an exercise stimulus, being as low as 0.5 ppm. It is stable for at least four months (122 days), and features excellent bias and precision of 0.03 and 0.6 ppm at concentrations below 5 ppm, as validated by proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS). Hence, this detector is highly promising for simple-in-use, non-invasive, and routine monitoring of acetone to guide therapeutic treatment and track lifestyle changes.

Ultrasensitive sensing performances of amphiphilic block copolymer induced gyrus-like In2O3 thick films to low-concentration acetone.

In the present work, an inducible assembly of di-block polymer compounds approach was employed for the synthesis of mesoscopic gyrus-like In2O3 by using lab-made high-molecular-weight amphiphilic di-block copolymer poly(ethylene oxide)-b-polystyrene (PEO-b-PS) as a revulsive, with indium chloride as an indium source and THF/ethanol as the solvent. The obtained mesoscopic gyrus-like In2O3 indium oxide materials exhibit a large surface area and a highly crystalline In2O3 nanostructure framework, and the gyrus distance is about 40 nm, which can facilitate the diffusion and transport of acetone vapor molecules. By using this material as a chemoresistance sensor, the obtained gyrus-like indium oxides were used as sensing materials, showing an excellent performance to acetone at a low operating temperature (150 °C) due to their high porosity and unique crystalline framework. The limit of detection of the thick-film sensor based on indium oxides is appropriate for diabetes exhaled breath acetone concentration detection. Moreover, the thick-film sensor shows a very fast response-recovery dynamics upon contacting acetone vapor due to its abundant open folds mesoscopic structure, and also to the large surface area of the nanocrystalline gyrus-like In2O3.

A Bilayer SnO2/MoS2-Coated Evanescent Wave Fiber Optic Sensor for Acetone Detection-An Experimental Study.

The need for sensors that measure the acetone content of exhaled breath for diabetes severity has recently increased. Clinical researchers have reported less than 0.8 ppm acetone concentration in the exhaled breath of an average individual, while that for a diabetic patient is higher than 1.8 ppm. This work reports the development of two sets of evanescent wave-based fiber optic sensor coated with SnO2 thin film and bilayer of SnO2/MoS2 to detect different acetone concentrations (0–250 ppm). In each set, we have studied the effect of clad thickness (chemical etch time 5min, 10 min, 15 min, 25 min, 40 min, and complete clad removal) to optimize the clad thickness for a better response. In Set 1, SnO2 thin film was used as the sensing layer, while in Set 2 a bilayer of SnO2 thin film/ MoS2 was used. Enhanced sensor response of ~23.5% is observed in the Set 2 probe with a response and recovery time of ~14 s/~17 s. A SnO2/MoS2-coated sensor prototype is developed using LEDs of different wavelength and intensity detector; its potential to detect different concentrations of acetone is tested. X-ray Diffraction (XRD), Scanning Electron Microscope (SEM), Ultraviolet (UV) Spectroscopy, and Ellipsometry were used to study the structural, morphological and optical properties of the sensing layers. The present study indicates that the SnO2/MoS2-coated sensor has the potential to create a handheld sensor system for monitoring diabetes.

A portable acetone detector based on SmFeO3 can pre-diagnose diabetes through breath analysis

Breath analysis using gas sensors is becoming one of the important supplementing ways for an early diagnosis of diseases. The feasibility of detecting acetone concentration in exhaled breath using a SmFeO3-based sensor has been studied herein. The detection limits of acetone using a SmFeO3-based sensor are extremely low (1.56–0.01 ppm; 1.87–0.02 ppm; 2.39–0.05 ppm; 3.22–0.1 ppm; 4.1–0.2 ppm; 4.79–0.5 ppm; 5.92–1 ppm). To remove the effects of carbon dioxide and relative humidity (RH), a mathematical model between the response and acetone concentration, carbon dioxide concentration and RH in a person’s exhaled breath is established via machine learning. Volunteer studies have proved the accuracy of this mathematical model. Compare with the acetone concentration obtained by GC-MS method, the accuracy of the proposed method exceeded 85 %. Finally, a portable acetone detector based on the SmFeO3 sensor is designed and realized to monitor the dynamic change in acetone concentration in the exhaled breath of volunteers before and after meals, and the experimental results have proved that the method based on the gas sensor and Machine learning (ML) is feasible and effective in accurately detecting acetone concentration in persons’ exhaled breath.

ANN based approach for selective detection of breath acetone by using hybrid GO-FET sensor array

This research used hybrid graphene oxide (GO) field effect transistors (FETs) based sensor array to design an electronic nose (e-nose) for identifying exhaled breath acetone to diagnose diabetes mellitus through noninvasive route. Six back gated FET sensors were fabricated with hybrid channel of GO, WO3 and noble metals (Au, Pd and Pt) nanoparticles. The experiment was carried out by using four distinct forms of synthetic breath, each with a different level of interference. Linear discriminant analysis (LDA) and artificial neural networks (ANN) were utilized to classify and analyze the sensor response vector. In contrast, partial least square (PLS) and multiple linear regression (MLR) were used to evaluate the exact acetone concentration in synthetic breath. First, LDA was used to lower the dimensionality of the response vector, which was then provided as an input to the ANN model. ANN was performed with ten perceptrons model in the hidden layer and highest accuracy of 99.1% was achieved. Additionally, by using the loading plot of PLS, three sensors (Pt/WO3/GO, Pd/WO3/GO, and WO3/GO) had the ample use to predict the concentration of breath acetone. Moreover, the MLR approach with correlation coefficient (R2) of 0.9572 and root mean square error (RMSE) of 5.63% were used for obtaining the exact concentration of acetone. Consequently, e-nose with matrix of hybrid GO-FET sensors and pattern recognition algorithms (LDA, ANN, PLS and MLR) exhibited considerable ability in selective detection of acetone in synthetic breath.

Detection of transdermal biomarkers using gradient-based colorimetric array sensor

Accurate assessment of dietary macronutrients intake is critical for the effective management of multiple diseases, such as obesity, diabetes, cardiovascular disease, metabolic disease, and cancer. Conventional self-reporting method is burdensome, inaccurate, and often biased. Though blood analysis and breath analysis can provide evidence-based information, they are either invasive or subject to human errors. Here we reported a wearable transdermal volatile biomarkers detection system based on novel colorimetric sensing technology for dietary macronutrients intake assessment. This technique quantifies the emission rates of transdermal volatile biomarkers via a gradient-based colorimetric array sensor (GCAS). The optical system of the GCAS device tracks the localized color development associated with the chemical reaction between the volatile biomarkers and the porous sensing probes, and determines the biomarkers emission rates through image processing algorithms. The localized chemical reaction and the image-based signal processing also make the GCAS capable for multiplexed detection of multiple analytes simultaneously. The GCAS sensor has been applied for transdermal acetone detection on 5 subjects in a keto diet intervention. The study indicates that the transdermal acetone increases after the subjects consuming keto diets and it decreases to basal level after intaking carb-rich diets. The transdermal acetone response from the GCAS sensor correlates well with breath acetone concentration in the range between 0 and 40 ppm and the correlation factor (R2) is as high as 0.8877. This method provides a noninvasive, low-cost, and wearable tool for assessing dietary macronutrients intake outside of lab or hospital settings. It could be widely applied in disease management, weight control, and nutrition management.

YSZ-based acetone sensor using a Cd2SnO4 sensing electrode for exhaled breath detection in medical diagnosis

Breath analysis using simple gas sensors is becoming one of the important methods for non-invasive, painless and early diagnosis of certain diseases. Among them, the acetone sensor plays a crucial role in screening and diagnosis of diabetes by detecting acetone concentration in exhaled breath. Here, a stabilized zirconia (YSZ)-based mixed potential type acetone sensor combining with Cd2SnO4 sensing electrode (SE) is introduced, which is expected to be used for pre-diagnosis of diabetes. The developed sensor can effectively detect acetone in the concentration range of 0.05−200 ppm at an optimal operating temperature of 600 °C, with sentivities of −17.3 mV/ppm to 0.05−2 ppm acetone and −88.6 mV/decade to 5−200 ppm acetone. Both the sensing behavior and electrochemical test confirm that the sensing principle of the sensor follows to the mixed potential mechanism. The excellent repeatability, selectivity and long-term stability of the sensor make it reliable in the actual application. In addition, clinical tests for the sensor toward exhaled breath of healthy people and diabetic patients are proposed and strong correlations have been established among the response values, acetone concentrations and blood ketone concentrations, which fully demonstrates the great potential of the sensor for pre-diagnosis of diabetes.