Amlendu Prabhakar1#, Ashley Quach1#, Di Wang1, Haojiong Zhang1, Mirna Terrera1, David Jackemeyer1, Xiaojun Xian1, Francis Tsow1, Nongjian Tao1,3 and Erica S Forzani2*#
1Center for Bioelectronics and Biosensors, the Biodesign Institute, Arizona State University, USA
2School for Engineering of Matter, Transport, and Energy, Arizona State University, USA
3School of Electrical, Computer, and Energy Engineering, Arizona State University, USA
#Authors contributed equally
Received: 17 September, 2014; Accepted: 13 October, 2014; Published: 15 October, 2014
Erica Forzani, Center for Bioelectronics and Biosensors, The Biodesign Institute at Arizona State University, 1001 S McAllister Ave, Tempe, AZ 85287, USA, Email:
Prabhakar A, Quach A, Wang D, Zhang H, Terrera M, et al. (2014) Breath Acetone as Biomarker for Lipid Oxidation and Early Ketone Detection. Glob J Obes Diabetes Metab Syndr 1(1): 012-019. DOI: 10.17352/2455-8583.000003
© 2014 Prabhakar A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Ketonic diet; Acetone; Ketone; Fasting ketosis; Nutritional ketosis; Starvation
Former ketone studies, including ketoacidosis (KAD), fasting ketosis (FK), nutritional ketosis (NK), and exercis-eaffected ketosis have brought great advances to the field of ketones. In the present work, blood, urine and breath ketone detections were evaluated systematically. We found that breath ketone (acetone) is the ketone of choice for detecting early stages of ketosis. In addition, acetone was correlated with respiratory quotient, and found to be a highly sensitive non-invasive biomarker of lipid oxidation. Furthermore, acetone was used for fast screening of ketosis or ketoacidosis in populations, and demonstrated value upon screening a population of 48 individuals, among which a type I diabetes case with early symptoms of KAD and FK case were identified.
Ketosis or ketoacidosis is a physiological state in which fat metabolism rate is increased due to the lack of glucose as energy source, and ketone bodies’ levels are above normal levels . As fat is oxidized, ketones are produced, and monitoring ketones has a profound impact on the diagnosis of health status of an individual either under ketoacidosis or ketosis.
Ketoacidocis (KAD) is a state of diagnosis of metabolic unbalance in type I diabetic population, and indicative of diabetic coma risk . On the contrary, ketosis is a metabolic state where ketone is produced in healthy individuals by fasting or nutritional intervention [3,4]. Fasting ketosis (FK) is produced by oxidation of stored fat induced by negative energy balance (caloric restriction). Nutritional ketosis (NK) is found in individuals undergoing fat-rich diets with null energy balance (caloric intake equals energy expenditure).
Recently, NK has become popular  for weight loss therapy , and for treating certain type of epilepsy where ketones can be used as energy source by the brain to reduce epileptic seizures [7,8]. Ketosis can also be induced in a healthy individual via exercise as the body uses ketones as energy source in the muscles [9,10]. In general, the ketone level in a body is affected by several factors, such as diabetes, energy balance, diet composition, and physical activities, which underscores the significance of identifying best practices of detection of ketones in real time.
Under ketosis or ketoacidosis, fat is broken down by the liver to produce two water-soluble types of ketones: acetoacetic acid and beta-hydroxybutyric acid (Figure 1). In addition, a third type of ketone, acetone, is also formed with additional enzymatic decarboxylation of acetoacetic acid. Acetone crosses the membrane barrier, into the alveoli of the lung and the airway due to its high vapor pressure, and it is usually found in breath. Currently, there are different ketone detecting methods, which are aimed to detect each of the three types of ketones. Each of the method has advantages and disadvantages , but a comparison of the methods under rigorous clinical conditions is necessary to define which method has the highest sensitivity to detect increasing ketone levels or define ketosis/ketoacidosis states. Blood and urinary ketone detections have been widely used for diagnosis of KAD. However, blood ketone detection is considered invasive and painful while urinary ketone detection can be impaired by subject’s level of hydration, adaptation to ketosis states or kidney dysfunction . Most recently, breath acetone has been considered as a new ketone biomarker because it is non-invasive, convenient, and accurate reflection of the body’s ketone level . Several acetone detection products are commercially available such as KetoSense and Ketonix [13,14] or emerging to the market such as NTT Docomo acetone monitor , Medamonitor  and Invoy Technologies . These emerging technologies are still under evaluation for analytical and clinical accuracy. Currently the proven technology for accurate detection of ketone levels in breath are mass spectrometer-based methods [18,19].
In this study, we used Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) as a primary method for detection of breath ketone, acetone, and we compare the results with those obtained with the widely used blood and urine ketone detection methods. Blood ketone was quantified using ketone strips and Precision Xtra meter, which have proven to be highly accurate [20,21], and urinary ketone was measured using Ketostix strips, which has been recommended by the American Diabetes Association for monitoring ketones in urine .
We hypothesize that detecting ketone from breath (acetone) is the most sensitive method for early detection of ketosis or ketoacidosis; and levels of produced breath acetone reflect rates of lipid oxidation accurately. To validate our hypothesis, we evaluated different ketone assessment methods, and determined the most sensitive and selective way method to quantify ketosis. We also compared the ketone levels to the measured respiratory quotient (RQ), a reference biomarker of lipid oxidation. To determine whether breath ketone detection is valuable for diagnosis of ketosis or ketoacidosis, we collected real-time breath acetone from 48 subjects and analyzed the outcomes.
Material and Methods
Ketone assessment methods
Three common methods were utilized:
A-Blood ketone measurements: Blood ketones were measured using Precision Xtra, an lectrochemical capillary blood monitor from Abbott. This monitor determines the blood ketone: beta-hydroxybutyrate (β-OHB). Standard operation procedures as prescribed by the monitor were used for the analysis. The test meter was turned on while a ketone strip was inserted to prepare for the test. The subjects’ fingertip was cleaned with an alcohol swab and dried before being pricked with the provided lancing device. A drop of blood was applied to the assigned spot of the ketone strip. Ketone levels were read from the display 10 seconds after blood was delivered to the meter.
B-Urine ketone measurements: Urine ketone measurements were performed using over-the-counter reagent strips for urinalysis (Ketostix from Bayers). The strip monitors acetoacetic acid (AcAcA), which reacts with nitroprusside salt. The reagent end of strip was passed through urine stream, which resulted in color development on the strip. The color was compared to the color chart provided with the product 15-30 seconds after the reaction.
C-Breath ketone measurements: Concentration of breath ketone, acetone, was assessed from exhaled breath using Selected Ion Flow Tube - Mass Spectrometer (SIFT-MS) (Instrument Science, Profile Series, Crewe, UK ) in multiple ion monitoring (MIM) modes. H3O+ (m/z 19) was chosen as the precursor ion for reaction with breath samples in ultra-high purity 99.999% He as the carrier gas. Precursor ion peaks at m/z 19, 37, 55, and 73 corresponding to hydrated H3O+.nH2O (n = 0, 1, 2, 3) and product ion peaks after reaction with acetone at m/z 59 and 77, corresponding to C3H7O+ and its hydrate C3H7O+.H2O, were monitored. Quantification of the concentration was performed in the MIM mode by taking into account the known reaction rate coefficients for H3O+ and acetone reaction, and the measured ion flow velocity . Subjects’ breath was collected in a 4L-air bag and followed by immediate analysis by SIFT-MS. A small pump was used to ensure constant flow. Each measurement took about 30 seconds.
Respiratory Quotient measurements
In order to determine the correlation of breath ketone (acetone) with lipid oxidation, non-protein respiratory quotient (RQ), which indicates the percentage of lipid oxidation vs. carbohydrate oxidation  was assessed. The measurement was performed on subjects of the study (see conditions below), using both, Oxycon Mobile metabolic portable instrument (Carefusion, Yorba Linda, CA ), and a mobile Breezing metabolism tracker (prototype of professional version) (Breezing Co., Tempe, AZ ). The RQ values were obtained consecutively to acetone measurements (using SIFT-MS).
In addition to ketone analysis, blood glucose was measured for comparison using Precision Xtra, electrochemical capillary blood monitor from Abbott, and glucose strips, according to the standard procedure as prescribed by the vendor. All ketones and blood glucose measurements were carried out simultaneously for direct comparison.
Two types of experiments were performed with subjects involved in the donation of samples.
Subjects in diet-fasting group
Eleven healthy volunteers (7 males and 4 females) with an average age of 27 ± 7 years and average BMI of 23.1 ± 5 participated in the diet-fasting study (see conditions below). Physical parameters of weight, height, and BMI (a ratio of weight-to-height squared (Kg/(meters)2)) were assessed for each subject. Table 1 summarizes the features of the study group.
None of the subjects was on regular medication, nor had any history of respiratory diseases nor diabetes. The diet-fasting experiment consisted of two days. On day 1, isocaloric meals with different fat contents were given to each subject, who had the last meal between 9:00 pm and 10:00 pm at the previous night. On day 2, the subjects fasted until 7:00 pm with breath samples collected and measured from 10:00 am with a time interval of 90 to 120 minutes for 9 hours. Meanwhile, blood and urine ketones from each subject were measured. In most of the subjects, blood and urine measurements were performed 3 times a day (start, mid, and end of the day) in conjunction with a breath ketone measurement. Urine and blood measurements were performed 3 times only since these ketone detection methods showed lower sensitivity to rising ketone levels when compared to breath ketone detection method. Several subjects from the group had blood, urine, and breath ketones measured in parallel, 6-8 times a day, during the fasting day (Day 2). In addition, a group of the study subjects was also measured on Day 1, while having a fat-rich diet (see more details below).
It is important to mention that all subjects remained sedentary during the fasting day (Day 2). This condition was essential to minimized perturbations of ketone fasting patterns due to exercise. All subjects complied the study’s IRB protocol approved at Arizona State University.
Breath acetone measurements for ketone screening
Breath acetone samples of 48 random subjects visiting an exhibition event at Biodesign Institute on March 1st, 2014  were collected. The age of the subjects ranged from 4 to 55. Subject’s breath was collected in a 4L-air bag and followed by immediate analysis with SIFT-MS. A small pump was used to ensure a constant flow during sample collection. Each measurement took approximately 30 seconds.
Evaluation of ketone assessment methods
Different ketone detection methods were compared to determine the best strategy to capture accurate and high-resolution ketone buildup data. First, the relationship between breath ketone (acetone) and blood ketone (β-OHB) was studied in the group of fasting subjects (Day 2 of diet-fasting group). Figure 2A shows the correlation that emerged from the data. The correlation can be fitted with an exponential growth function with a squared-regression coefficient (R2)=0.69, which is in agreement with the literature for adults and children’ ketogenic dieters [19,28]. Second, the relationship between breath ketone (acetone), and urinary ketone (AcAcA) was studied in the same fasting subject group. Figure 2B shows the correlation with an exponential fitting with R2=0.81. In addition, Figure 3 shows the ketone profiles assessed in breath, urine, and blood in an individual over the period corresponding to the fasting day (Day 2, more details in discussion section).
Evaluation of breath ketone as biomarker of lipid oxidation
The correlation between breath ketone levels and lipid oxidation was studied to ensure that the acetone level buildup is associated with increased oxidation of lipids. For this purpose, RQ [29,30] was measured in parallel to acetone. RQ and acetone measurements were performed for 9 subjects of the diet-fasting group on the fasting day (Day 2). RQ was calculated by measuring the ratio of VCO2 to VO2, where VCO2 is the carbon dioxide production rate, and VO2 is the oxygen consumption rate, assessed by indirect calorimetry measures as described in Experimental Session. Figure 4A shows an example of dynamic changes of acetone levels and RQ for an individual during the fasting day. Similar results were obtained for the remaining subjects. The results are summarized in Figure 4B, which shows a plot of RQ vs. acetone levels with exponential correlation and R2=0.41. The results indicated the value of acetone as a biomarker of fat oxidation. However, in order to further assure this fact, RQ vs. acetone level was also investigated in a separate set of experiments where the subjects changed their diet composition throughout the day (Day 1 of diet-fasting group).
As an example, Figure 5 shows one of the study subjects, who changed the diet macronutrient content from 4.5:1 of fat:(carbohydrate + protein) with [82% fat, 9% protein, and 9% carbohydrate] to 3.0:1 of fat:(carbohydrate + protein) with [75% fat, 16% protein, and 9% carbohydrate]. The decreasing ingestion of fat (by changing diet), which is expected to lead to lesser oxidation of fat, was correlated to an increase in RQ values (in agreement with lesser fat oxidation) [29,30], and a decrease in the rate of acetone level increase (see more details in discussion section).
Since ketones have been studied as a marker for blood glucose [31,32], breath acetone and blood glucose on the fasting day (Day 2) have been simultaneously measured for the diet-fasting group. Figure 6 shows the results, which can be fitted with an exponentially decaying curve with R2=0.52 (more details in discussion section).
Breath acetone measurements for ketone screening
Breath samples from 48 subjects were collected from an exhibition event as described in the experimental section. Two thirds of the subjects were younger than 15 years old with a nearly equal distribution of male and female (Figure 7B). The acetone levels of the samples were found to be between 300 to 1000 ppbV (Figure 7A). However, several subjects had higher acetone levels (more details in discussion section).
Evaluation of ketone assessment methods
Figure 2B, illustrated urinary ketone as a “step-function” behavior due to the qualitative nature of the test. However, as shown in Figure 2, a strong correlation was evident between acetone (breath) and β-OHB (blood) as well as between acetone (breath) and AcAcA (urine) for the subjects under fasting conditions. The behavior has been observed before for acetone and β-OHB in subjects under ketogenic diets [19,28]. There are many hypotheses to explain the relationship. First, acetone is a metabolite produced after enzymatic decarboxylation of AcAcA, which is in equilibrium with β-OHB via an enzymatic controlled process by β-OHB dehydrogenase . The enzymatic controlled metabolic pathways may produce a non-linear relationship between acetone and β-OHB (blood), and AcAcA (urine) (Figure 1). Another hypothesis for the observed non-linear relationship between acetone and β-OHB or AcAcA is that acetone is a highly volatile organic compound, and therefore its blood/breath partition behavior is favored towards the breath phase. In fact, it is well known that acetone presents positive deviations from well-known gas/liquid partition laws, such as Henry’s law or Raoul’s law . Although exponential relationship between acetone and β-OHB, and acetone and AcAcA was observed, acetone reflected overall ketone metabolite concentrations in the subjects under fasting conditions.