Prediction of breast cancer risk with volatile biomarkers in breath.

BACKGROUND: Human breath contains volatile organic compounds (VOCs) that are biomarkers of breast cancer. We investigated the positive and negative predictive values (PPV and NPV) of breath VOC biomarkers as indicators of breast cancer risk. METHODS: We employed ultra-clean breath collection balloons to collect breath samples from 54 women with biopsy-proven breast cancer and 124 cancer-free controls. Breath VOCs were analyzed with gas chromatography (GC) combined with either mass spectrometry (GC MS) or surface acoustic wave detection (GC SAW). Chromatograms were randomly assigned to a training set or a validation set. Monte Carlo analysis identified significant breath VOC biomarkers of breast cancer in the training set, and these biomarkers were incorporated into a multivariate algorithm to predict disease in the validation set. In the unsplit dataset, the predictive algorithms generated discriminant function (DF) values that varied with sensitivity, specificity, PPV and NPV. RESULTS: Using GC MS, test accuracy = 90% (area under curve of receiver operating characteristic in unsplit dataset) and cross-validated accuracy = 77%. Using GC SAW, test accuracy = 86% and cross-validated accuracy = 74%. With both assays, a low DF value was associated with a low risk of breast cancer (NPV > 99.9%). A high DF value was associated with a high risk of breast cancer and PPV rising to 100%. CONCLUSION: Analysis of breath VOC samples collected with ultra-clean balloons detected biomarkers that accurately predicted risk of breast cancer.

Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening.

BACKGROUND: Breath volatile organic compounds (VOCs) have been reported as biomarkers of lung cancer, but it is not known if biomarkers identified in one group can identify disease in a separate independent cohort. Also, it is not known if combining breath biomarkers with chest CT has the potential to improve the sensitivity and specificity of lung cancer screening. METHODS: Model-building phase (unblinded): Breath VOCs were analyzed with gas chromatography mass spectrometry in 82 asymptomatic smokers having screening chest CT, 84 symptomatic high-risk subjects with a tissue diagnosis, 100 without a tissue diagnosis, and 35 healthy subjects. Multiple Monte Carlo simulations identified breath VOC mass ions with greater than random diagnostic accuracy for lung cancer, and these were combined in a multivariate predictive algorithm. Model-testing phase (blinded validation): We analyzed breath VOCs in an independent cohort of similar subjects (n = 70, 51, 75 and 19 respectively). The algorithm predicted discriminant function (DF) values in blinded replicate breath VOC samples analyzed independently at two laboratories (A and B). Outcome modeling: We modeled the expected effects of combining breath biomarkers with chest CT on the sensitivity and specificity of lung cancer screening. RESULTS: Unblinded model-building phase. The algorithm identified lung cancer with sensitivity 74.0%, specificity 70.7% and C-statistic 0.78. Blinded model-testing phase: The algorithm identified lung cancer at Laboratory A with sensitivity 68.0%, specificity 68.4%, C-statistic 0.71; and at Laboratory B with sensitivity 70.1%, specificity 68.0%, C-statistic 0.70, with linear correlation between replicates (r = 0.88). In a projected outcome model, breath biomarkers increased the sensitivity, specificity, and positive and negative predictive values of chest CT for lung cancer when the tests were combined in series or parallel. CONCLUSIONS: Breath VOC mass ion biomarkers identified lung cancer in a separate independent cohort, in a blinded replicated study. Combining breath biomarkers with chest CT could potentially improve the sensitivity and specificity of lung cancer screening. TRIAL REGISTRATION: ClinicalTrials.gov NCT00639067.

Breath biomarkers of whole-body gamma irradiation in the Göttingen minipig.

There is widespread interest in the development of tools to estimate radiation exposures. Exhaled breath provides a novel matrix for assessing biomarkers that could be correlated with exposures. The use of exhaled breath for estimating radiation exposure is warranted, as studies have shown that external exposure to ionizing radiation causes oxidative stress that accelerates lipid peroxidation of polyunsaturated fatty acids, liberating alkanes and alkane metabolites that are excreted in the breath as volatile organic compounds (VOCs). As a proof of principle study, small groups (n = 4) of Göttingen minipigs were whole-body irradiated with gamma rays delivered by a 60Co source at absorbed doses of 0, 0.25, 0.5, 0.75, 1, 1.25, 2, and 4 Gy. Additional groups (n = 4) were treated with lipopolysaccharide (LPS) or granulocyte colony stimulating factor (G-CSF), with and without concurrent 60Co exposure, at an absorbed dose of 1 Gy. Breath and background air VOC samples were collected on days -3, -2, -1, 0 pre-irradiation, then at 0.25, 24, 48, 72, and 168 h post-irradiation. VOCs were analyzed by automated thermal desorption with two-dimensional gas chromatography and time-of-flight mass spectrometry (ATD GCxGC TOF MS). The results show significant changes in 58 breath VOCs post-irradiation, mainly consisting of methylated and other derivatives of alkanes, alkenes, and benzene. Using a multivariate combination of these VOCs, a radiation response function was constructed, which was significantly elevated at 15 min post irradiation and remained elevated throughout the study (to 168 h post irradiation). As a binary test of radiation absorbed doses ≥ 0.25 Gy, the radiation response function distinguished irradiated animals from shams (0 Gy) with 83-84% accuracy. A randomly derived radiation response function was robust: When half of the biomarkers were removed, accuracy was 75%. An optimally derived function with two biomarkers was 82% accurate. As a binary test of radiation absorbed doses ≥ 0.5 Gy, the radiation response function identified irradiated animals with an accuracy of 87% at 15 min post irradiation and 75.5% at 168 h post irradiation. Treatment with LPS and G-CSF did not affect the radiation response function. This proof-of-principle study supports the hypothesis that breath VOCs may be used for estimating radiation exposures. Further studies will be required to validate the sensitivity and specificity of these potential biomarkers.

Rapid point-of-care breath test for biomarkers of breast cancer and abnormal mammograms.

BACKGROUND: Previous studies have reported volatile organic compounds (VOCs) in breath as biomarkers of breast cancer and abnormal mammograms, apparently resulting from increased oxidative stress and cytochrome p450 induction. We evaluated a six-minute point-of-care breath test for VOC biomarkers in women screened for breast cancer at centers in the USA and the Netherlands. METHODS: 244 women had a screening mammogram (93/37 normal/abnormal) or a breast biopsy (cancer/no cancer 35/79). A mobile point-of-care system collected and concentrated breath and air VOCs for analysis with gas chromatography and surface acoustic wave detection. Chromatograms were segmented into a time series of alveolar gradients (breath minus room air). Segmental alveolar gradients were ranked as candidate biomarkers by C-statistic value (area under curve [AUC] of receiver operating characteristic [ROC] curve). Multivariate predictive algorithms were constructed employing significant biomarkers identified with multiple Monte Carlo simulations and cross validated with a leave-one-out (LOO) procedure. RESULTS: Performance of breath biomarker algorithms was determined in three groups: breast cancer on biopsy versus normal screening mammograms (81.8% sensitivity, 70.0% specificity, accuracy 79% (73% on LOO) [C-statistic value], negative predictive value 99.9%); normal versus abnormal screening mammograms (86.5% sensitivity, 66.7% specificity, accuracy 83%, 62% on LOO); and cancer versus no cancer on breast biopsy (75.8% sensitivity, 74.0% specificity, accuracy 78%, 67% on LOO). CONCLUSIONS: A pilot study of a six-minute point-of-care breath test for volatile biomarkers accurately identified women with breast cancer and with abnormal mammograms. Breath testing could potentially reduce the number of needless mammograms without loss of diagnostic sensitivity.

Detection of an extended human volatome with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry.

BACKGROUND: Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC-TOF MS) has been proposed as a powerful new tool for multidimensional analysis of complex chemical mixtures. We investigated GCxGC-TOF MS as a new method for identifying volatile organic compounds (VOCs) in normal human breath. METHODS: Samples of alveolar breath VOCs and ambient room air VOC were collected with a breath collection apparatus (BCA) onto separate sorbent traps from 34 normal healthy volunteers (mean age = 40 yr, SD = 17 yr, male/female = 19/15). VOCs were separated on two serial capillary columns separated by a cryogenic modulator, and detected with TOF MS. The first and second dimension columns were non-polar and polar respectively. RESULTS: BCA collection combined with GC×GC-TOF MS analysis identified approximately 2000 different VOCs in samples of human breath, many of which have not been previously reported. The 50 VOCs with the highest alveolar gradients (abundance in breath minus abundance in ambient room air) mostly comprised benzene derivatives, acetone, methylated derivatives of alkanes, and isoprene. CONCLUSIONS: Collection and analysis of breath VOCs with the BCA-GC×GC-TOF MS system extended the size of the detectable human volatile metabolome, the volatome, by an order of magnitude compared to previous reports employing one-dimensional GC-MS. The size of the human volatome has been under-estimated in the past due to coelution of VOCs in one-dimensional GC analytical systems.

Detection of volatile biomarkers of therapeutic radiation in breath.

Breath testing could provide a rational tool for radiation biodosimetry because radiation causes distinct stress-producing molecular damage, notably an increased production of reactive oxygen species. The resulting oxidative stress accelerates lipid peroxidation of polyunsaturated fatty acids, liberating alkanes and alkane metabolites that are excreted in the breath as volatile organic compounds (VOCs). Breath tests were performed before and after radiation therapy over five days in 31 subjects receiving daily fractionated doses: 180-400 cGy d(-1) standard radiotherapy (n = 26), or 700-1200 cGy d(-1) high-dose stereotactic body radiotherapy (n = 5). Breath VOCs were assayed using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry. Multiple Monte Carlo simulations identified approximately 50 VOCs as greater-than-chance biomarkers of radiation on all five days of the study. A consistent subset of 15 VOCs was observed at all time points. A radiation response function was built by combining these biomarkers and the resulting dose-effect curve was significantly elevated at all exposures ⩾1.8 Gy. Cross-validated binary algorithms identified radiation exposures ⩾1.8 Gy with 99% accuracy, and ⩾5 Gy with 78% accuracy. In this proof of principal study of breath VOCs, we built a preliminary radiation response function based on 15 VOCs that appears to identify exposure to localized doses of 1.8 Gy and higher. VOC breath testing could provide a new tool for rapid and non-invasive radiation biodosimetry.

A Dose-Dependent Hematological Evaluation of Whole-Body Gamma-Irradiation in the Göttingen Minipig.

There is a great deal of interest in the establishment of a standardized animal model for the acute radiation syndrome to allow development of diagnostic approaches and countermeasure treatments following radiological terrorist events. Due to physiological, anatomical, and biochemical similarities to humans, the minipig is an attractive large animal model for evaluating countermeasure efficacy. This study was conducted in order to aid in the establishment of the minipig, and the Göttingen minipig in particular, as an animal model for the hematopoietic acute radiation syndrome. Animals were exposed whole-body to Co at doses of 0 (sham control), 0.25, 0.5, 0.75, 1.0, and 2.0 Gy, and hematological parameters followed in time from pre-irradiation to post-irradiation Day 7. Following irradiation, a dose-dependent decrease in total white blood cells was observed, which was determined to be statistically different as compared to control animals at all dose levels above 0.25 Gy at 24 h post-irradiation. Similarly, a dose-dependent reduction in both absolute lymphocyte count and absolute neutrophil count occurred by the earliest time point measured for all exposed animals. A significant decrease in platelets was observed at post-irradiation Day 7 in animals exposed only at the highest (2.0 Gy) level. The platelet-to-lymphocyte ratio generated for exposures ranging from 0.25-2.0 Gy was able to differentiate response between high and low exposure levels even at 7 d post exposure. In conclusion, the present study supports the development of the Göttingen minipig as a suitable large animal model to study radiation-induced hematopoietic syndrome.

Breath biomarkers of active pulmonary tuberculosis.

BACKGROUND: Volatile organic compounds (VOCs) in breath may contain biomarkers of active pulmonary tuberculosis derived from the infectious organism (metabolites of Mycobacterium tuberculosis) and from the infected host (products of oxidative stress). METHODS: We analyzed breath VOCs in 226 symptomatic high-risk patients in USA, Philippines, and UK, using gas chromatography/mass spectroscopy. Diagnosis of disease was based on sputum culture, smear microscopy, chest radiography and clinical suspicion of tuberculosis (CSTB). Chromatograms were converted to a series of 8s overlapping time slices. Biomarkers of active pulmonary tuberculosis were identified with a Monte Carlo analysis of time-slice alveolar gradients (abundance in breath minus abundance in room air). RESULTS: Breath VOCs contained apparent biomarkers of active pulmonary tuberculosis comprising oxidative stress products (alkanes and alkane derivatives) and volatile metabolites of M. tuberculosis (cyclohexane and benzene derivatives). Breath biomarkers identified active pulmonary tuberculosis with C-statistic (area under curve of receiver operating characteristic)=0.85 (i.e. 85% overall accuracy, sensitivity=84.0%, specificity=64.7%) when sputum culture, microscopy, and chest radiography were either all positive or all negative. Employing a single criterion of disease, C-statistic=0.76 (smear microscopy), 0.68 (sputum culture), 0.66 (chest radiography) and 0.65 (CSTB). CONCLUSION: A breath test identified apparent biomarkers of active pulmonary tuberculosis with 85% accuracy in symptomatic high-risk subjects.

Effect of influenza vaccination on oxidative stress products in breath.

Viral infections cause increased oxidative stress, so a breath test for oxidative stress biomarkers (alkanes and alkane derivatives) might provide a new tool for early diagnosis. We studied 33 normal healthy human subjects receiving scheduled treatment with live attenuated influenza vaccine (LAIV). Each subject was his or her own control, since they were studied on day 0 prior to vaccination, and then on days 2, 7 and 14 following vaccination. Breath volatile organic compounds (VOCs) were collected with a breath collection apparatus, then analyzed by automated thermal desorption with gas chromatography and mass spectroscopy. A Monte Carlo simulation technique identified non-random VOC biomarkers of infection based on their C-statistic values (area under curve of receiver operating characteristic). Treatment with LAIV was followed by non-random changes in the abundance of breath VOCs. 2, 8-Dimethyl-undecane and other alkane derivatives were observed on all days. Conservative multivariate models identified vaccinated subjects on day 2 (C-statistic = 0.82, sensitivity = 63.6% and specificity = 88.5%); day 7 (C-statistic = 0.94, sensitivity = 88.5% and specificity = 92.3%); and day 14 (C-statistic = 0.95, sensitivity = 92.3% and specificity = 92.3%). The altered breath VOCs were not detected in live attenuated influenza vaccine, excluding artifactual contamination. LAIV vaccination in healthy humans elicited a prompt and sustained increase in breath biomarkers of oxidative stress. A breath test for these VOCs could potentially identify humans who are acutely infected with influenza, but who have not yet developed clinical symptoms or signs of disease.

Volatile biomarkers in the breath of women with breast cancer.

We sought biomarkers of breast cancer in the breath because the disease is accompanied by increased oxidative stress and induction of cytochrome P450 enzymes, both of which generate volatile organic compounds (VOCs) that are excreted in breath. We analyzed breath VOCs in 54 women with biopsy-proven breast cancer and 204 cancer-free controls, using gas chromatography/mass spectroscopy. Chromatograms were converted into a series of data points by segmenting them into 900 time slices (8 s duration, 4 s overlap) and determining their alveolar gradients (abundance in breath minus abundance in ambient room air). Monte Carlo simulations identified time slices with better than random accuracy as biomarkers of breast cancer by excluding random identifiers. Patients were randomly allocated to training sets or test sets in 2:1 data splits. In the training sets, time slices were ranked according their C-statistic values (area under curve of receiver operating characteristic), and the top ten time slices were combined in multivariate algorithms that were cross-validated in the test sets. Monte Carlo simulations identified an excess of correct over random time slices, consistent with non-random biomarkers of breast cancer in the breath. The outcomes of ten random data splits (mean (standard deviation)) in the training sets were sensitivity = 78.5% (6.14), specificity = 88.3% (5.47), C-statistic = 0.89 (0.03) and in the test sets, sensitivity = 75.3% (7.22), specificity = 84.8 (9.97), C-statistic = 0.83 (0.06). A breath test identified women with breast cancer, employing a combination of volatile biomarkers in a multivariate algorithm.

Detection of lung cancer using weighted digital analysis of breath biomarkers.

BACKGROUND: A combination of biomarkers in a multivariate model may predict disease with greater accuracy than a single biomarker employed alone. We developed a non-linear method of multivariate analysis, weighted digital analysis (WDA), and evaluated its ability to predict lung cancer employing volatile biomarkers in the breath. METHODS: WDA generates a discriminant function to predict membership in disease vs no disease groups by determining weight, a cutoff value, and a sign for each predictor variable employed in the model. The weight of each predictor variable was the area under the curve (AUC) of the receiver operating characteristic (ROC) curve minus a fixed offset of 0.55, where the AUC was obtained by employing that predictor variable alone, as the sole marker of disease. The sign (+/-) was used to invert the predictor variable if a lower value indicated a higher probability of disease. When employed to predict the presence of a disease in a particular patient, the discriminant function was determined as the sum of the weights of all predictor variables that exceeded their cutoff values. The algorithm that generates the discriminant function is deterministic because parameters are calculated from each individual predictor variable without any optimization or adjustment. We employed WDA to re-evaluate data from a recent study of breath biomarkers of lung cancer, comprising the volatile organic compounds (VOCs) in the alveolar breath of 193 subjects with primary lung cancer and 211 controls with a negative chest CT. RESULTS: The WDA discriminant function accurately identified patients with lung cancer in a model employing 30 breath VOCs (ROC curve AUC=0.90; sensitivity=84.5%, specificity=81.0%). These results were superior to multilinear regression analysis of the same data set (AUC=0.74, sensitivity=68.4, specificity=73.5%). WDA test accuracy did not vary appreciably with TNM (tumor, node, metastasis) stage of disease, and results were not affected by tobacco smoking (ROC curve AUC=0.92 in current smokers, 0.90 in former smokers). WDA was a robust predictor of lung cancer: random removal of 1/3 of the VOCs did not reduce the AUC of the ROC curve by >10% (99.7% CI). CONCLUSIONS: A test employing WDA of breath VOCs predicted lung cancer with accuracy similar to chest computed tomography. The algorithm identified dependencies that were not apparent with traditional linear methods. WDA appears to provide a useful new technique for non-linear multivariate analysis of data.

Prediction of lung cancer using volatile biomarkers in breath.

BACKGROUND: Normal metabolism generates several volatile organic compounds (VOCs) that are excreted in the breath (e.g. alkanes). In patients with lung cancer, induction of high-risk cytochrome p450 genotypes may accelerate catabolism of these VOCs, so that their altered abundance in breath may provide biomarkers of lung cancer. METHODS: VOCs in 1.0 L alveolar breath were analyzed in 193 subjects with primary lung cancer and 211 controls with a negative chest CT. Subjects were randomly assigned to a training set or to a prediction set in a 2:1 split. A fuzzy logic model of breath biomarkers of lung cancer was constructed in the training set and then tested in subjects in the prediction set by generating their typicality scores for lung cancer. RESULTS: Mean typicality scores employing a 16 VOC model were significantly higher in lung cancer patients than in the control group (p<0.0001 in all TNM stages). The model predicted primary lung cancer with 84.6% sensitivity, 80.0% specificity, and 0.88 area under curve (AUC) of the receiver operating characteristic (ROC) curve. Predictive accuracy was similar in TNM stages 1 through 4, and was not affected by current or former tobacco smoking. The predictive model achieved near-maximal performance with six breath VOCs, and was progressively degraded by random classifiers. Predictions with fuzzy logic were consistently superior to multilinear analysis. If applied to a population with 2% prevalence of lung cancer, a screening breath test would have a negative predictive value of 0.985 and a positive predictive value of 0.163 (true positive rate =0.277, false positive rate =0.029). CONCLUSIONS: A two-minute breath test predicted lung cancer with accuracy comparable to screening CT of chest. The accuracy of the test was not affected by TNM stage of disease or tobacco smoking. Alterations in breath VOCs in lung cancer were consistent with a non-linear pathophysiologic process, such as an off-on switch controlling high-risk cytochrome p450 activity. Further research is needed to determine if detection of lung cancer with this test will reduce mortality.

Volatile biomarkers of pulmonary tuberculosis in the breath.

Pulmonary tuberculosis may alter volatile organic compounds (VOCs) in breath because Mycobacteria and oxidative stress resulting from Mycobacterial infection both generate distinctive VOCs. The objective of this study was to determine if breath VOCs contain biomarkers of active pulmonary tuberculosis. Head space VOCs from cultured Mycobacterium tuberculosis were captured on sorbent traps and assayed by gas chromatography/mass spectroscopy (GC/MS). One hundred and thirty different VOCs were consistently detected. The most abundant were naphthalene, 1-methyl-, 3-heptanone, methylcyclododecane, heptane, 2,2,4,6,6-pentamethyl-, benzene, 1-methyl-4-(1-methylethyl)-, and cyclohexane, 1,4-dimethyl-. Breath VOCs were assayed by GC/MS in 42 patients hospitalized for suspicion of pulmonary tuberculosis and in 59 healthy controls. Sputum cultures were positive for Mycobacteria in 23/42 and negative in19/42 patients. Breath markers of oxidative stress were increased in all hospitalized patients (p<0.04). Pattern recognition analysis and fuzzy logic analysis of breath VOCs independently distinguished healthy controls from hospitalized patients with 100% sensitivity and 100% specificity. Fuzzy logic analysis identified patients with positive sputum cultures with 100% sensitivity and 100% specificity (95.7% sensitivity and 78.9% specificity on leave-one-out cross-validation); breath VOC markers were similar to those observed in vitro, including naphthalene, 1-methyl- and cyclohexane, 1,4-dimethyl-. Pattern recognition analysis identified patients with positive sputum cultures with 82.6% sensitivity (19/23) and 100% specificity (18/18), employing 12 principal components from 134 breath VOCs. We conclude that volatile biomarkers in breath were sensitive and specific for pulmonary tuberculosis: the breath test distinguished between "sick versus well" i.e. between normal controls and patients hospitalized for suspicion of pulmonary tuberculosis, and between infected versus non-infected patients i.e. between those whose sputum cultures were positive or negative for Mycobacteria.

Prediction of breast cancer using volatile biomarkers in the breath.

We evaluated a breath test for volatile organic compounds (VOCs) as a predictor of breast cancer. Breath VOCs were assayed in 51 asymptomatic women with abnormal mammograms and biopsy-proven breast cancer, and 42 age-matched healthy women. A fuzzy logic model predicted breast cancer with accuracy superior to previously reported findings. Following random assignment to a training set (64) or a prediction set (29), a model was constructed in the training set employing five breath VOCs that predicted breast cancer in the prediction set with 93.8% sensitivity and 84.6% specificity. The same model predicted no breast cancer in 16/50 (32.0%) women with abnormal mammograms and no cancer on biopsy. A two-minute breath test could potentially provide a safe, accurate and painless screening test for breast cancer, but prospective validation studies are required.

Prediction of heart transplant rejection with a breath test for markers of oxidative stress.

The Heart Allograft Rejection: Detection with Breath Alkanes in Low Levels study evaluated a breath test for oxidative stress in heart transplant recipients, and we report here a mathematical model predicting the probability of grade 3 rejection. The breath test divided the heart transplant recipients into 3 groups: positive for grade 3 rejection, negative for grade 3 rejection, and intermediate. The test was 100% sensitive for grade 3 heart transplant rejection when the p value was >/=0.98, and 100% specific when the p value was

Heart allograft rejection: detection with breath alkanes in low levels (the HARDBALL study).

BACKGROUND: We evaluated a new marker of heart transplant rejection, the breath methylated alkane contour (BMAC). Rejection is accompanied by oxidative stress that degrades membrane polyunsaturated fatty acids, evolving alkanes and methylalkanes, which are excreted in the breath as volatile organic compounds (VOCs). METHODS: Breath VOC samples (n = 1,061) were collected from 539 heart transplant recipients before scheduled endomyocardial biopsy. Breath VOCs were analyzed by gas chromatography and mass spectroscopy, and BMAC was derived from the abundance of C4-C20 alkanes and monomethylalkanes. The "gold standard" of rejection was the concordant set of International Society for Heart and Lung Transplantation (ISHLT) grades in biopsies read by 2 reviewers. RESULTS: Concordant biopsies were: Grade 0, 645 of 1,061 (60.8%); 1A, 197 (18.6%); 1B, 84 (7.9%); 2, 93 (8.8%); and 3A, 42 (4.0%). A combination of 9 VOCs in the BMAC identified Grade 3 rejection (sensitivity 78.6%, specificity 62.4%, cross-validated sensitivity 59.5%, cross-validated specificity 58.8%, positive predictive value 5.6%, negative predictive value 97.2%). Site pathologists identified the same cases with sensitivity of 42.4%, specificity 97.0%, positive predictive value 45.2% and negative predictive value 96.7%. CONCLUSIONS: A breath test for markers of oxidative stress was more sensitive and less specific for Grade 3 heart transplant rejection than a biopsy reading by a site pathologist, but the negative predictive values of the 2 tests were similar. A screening breath test could potentially identify transplant recipients at low risk of Grade 3 rejection and reduce the number of endomyocardial biopsies.

Increased breath biomarkers of oxidative stress in diabetes mellitus.

BACKGROUND: Oxidative stress has been implicated in the major complications of diabetes mellitus, including retinopathy, nephropathy, neuropathy and accelerated coronary artery disease. There is a clinical need for a marker of oxidative stress which could potentially identify diabetic patients at increased risk for these complications. We measured oxidative age, a new breath marker of oxidative stress, in diabetic patients. METHODS: Three groups were studied: type 1 diabetes mellitus (n=9), type 2 diabetes mellitus (n=53) and non-diabetic normals (n=39). Volatile organic compounds (VOCs) in breath were assayed by gas chromatography and mass spectroscopy to construct the breath methylated alkane contour (BMAC), a three-dimensional display of oxidative stress markers, C4-C20 alkanes and monomethylated alkanes. The collective abundance of these VOCs was reduced to a single value, the oxidative age, comprising the volume under the curve of the BMAC corrected for chronological age. RESULTS: Oxidative age was significantly increased in type 1 diabetes (mean=0.103, S.E.M.=0.119, p<0.01) and type 2 diabetes (mean=0.103, S.E.M.=0.047, p<0.05) compared to age-matched normals (mean=-0.248, S.E.M.=0.079). No significant correlation between oxidative age and blood glucose or hemoglobin A1C was observed in either group. CONCLUSIONS: Oxidative age, a marker of oxidative stress, was significantly increased in both type 1 and type 2 diabetes mellitus. Oxidative age merits further study as a candidate marker of risk for the complications of diabetes mellitus.

Increased breath markers of oxidative stress in normal pregnancy and in preeclampsia.

OBJECTIVES: The purpose of this study was to compare the intensity of oxidative stress in normal pregnancy, preeclampsia, and nonpregnant women using a breath test. STUDY DESIGN: We studied primiparous women in third trimester pregnancy (38 uncomplicated, 26 with preeclampsia) and 60 nonpregnant control subjects. Volatile organic compounds (VOCs) in alveolar breath were analyzed by gas chromatography/mass spectroscopy to construct the breath methylated alkane contour (BMAC), a 3-dimensional display of abundance of C4-C20 alkanes and monomethylated alkanes. RESULTS: The mean volume under curve (VUC) of the BMAC was significantly higher in preeclampsia patients than in normal pregnant women (P < .003) and nonpregnant control subjects (P < .005). A predictive model employing 5 VOCs distinguished preeclampsia from uncomplicated pregnancy (sensitivity = 92.3%, specificity = 89.7%; cross-validated sensitivity = 88.5%, specificity = 79.3%). CONCLUSION: A breath test significantly demonstrated greater oxidative stress in women with preeclampsia than in uncomplicated pregnancy and nonpregnant control subjects. The breath test accurately identified women with established preeclampsia, but further studies are required to determine if this test can predict the onset of disease.

Detection of lung cancer with volatile markers in the breath.

STUDY OBJECTIVES: To evaluate volatile organic compounds (VOCs) in the breath as tumor markers in lung cancer. Alkanes and monomethylated alkanes are oxidative stress products that are excreted in the breath, the catabolism of which may be accelerated by polymorphic cytochrome p450-mixed oxidase enzymes that are induced in patients with lung cancer. DESIGN: Combined case-control and cross-sectional study. SETTING: Five academic pulmonary medicine services in the United States and the United Kingdom. PATIENTS AND PARTICIPANTS: One hundred seventy-eight bronchoscopy patients and 41 healthy volunteers. INTERVENTION: Breath samples were analyzed by gas chromatography and mass spectroscopy to determine alveolar gradients (ie, the abundance in breath minus the abundance in room air) of C4-C20 alkanes and monomethylated alkanes. MEASUREMENTS: Patients with primary lung cancer (PLC) were compared to healthy volunteers, and a predictive model was constructed using forward stepwise discriminant analysis of the alveolar gradients. This model was cross-validated with a leave-one-out jackknife technique and was tested in two additional groups of patients who had not been used to develop the model (ie, bronchoscopy patients in whom cancer was not detected, and patients with metastatic lung cancer [MLC]). RESULTS: Eighty-seven of 178 patients had lung cancer (PLC, 67 patients; MLC, 15 patients; undetermined, 5 patients). A predictive model employing nine VOCs identified PLC with a sensitivity of 89.6% (60 of 67 patients) and a specificity of 82.9% (34 of 41 patients). On cross-validation, the sensitivity was 85.1% (57 of 67 patients) and the specificity was 80.5% (33 of 41 patients). The stratification of patients by tobacco smoking status, histologic type of cancer, and TNM stage of cancer revealed no marked effects. In the two additional tests, the model predicted MLC with a sensitivity of 66.7% (10 of 15 patients), and it classified the cancer-negative bronchoscopy patients with a specificity of 37.4% (34 of 91 patients). CONCLUSIONS: Compared to healthy volunteers, patients with PLC had abnormal breath test findings that were consistent with the accelerated catabolism of alkanes and monomethylated alkanes. A predictive model employing nine of these VOCs exhibited sufficient sensitivity and specificity to be considered as a screen for lung cancer in a high-risk population such as adult smokers.

Volatile markers of breast cancer in the breath.

Breast cancer is accompanied by increased oxidative stress and induction of polymorphic cytochrome P-450 mixed oxidase enzymes (CYP). Both processes affect the abundance of volatile organic compounds (VOCs) in the breath because oxidative stress causes lipid peroxidation of polyunsaturated fatty acids in membranes, producing alkanes and methylalkanes which are catabolized by CYP. We performed a pilot study of breath VOCs, a potential new marker of disease in women with breast cancer. This was a combined case-control and cross-sectional study of women with abnormal mammograms scheduled for a breast biopsy. Breath samples were analyzed by gas chromatography and mass spectroscopy in order to determine the breath methylated alkane contour (BMAC), a three-dimensional display of the alveolar gradients (abundance in breath minus abundance in room air) of C4-C20 alkanes and monomethylated alkanes. BMACs in women with and without breast cancer were compared using forward stepwise discriminant analysis. Two hundred one breath samples were obtained from women with abnormal mammograms and biopsies read by two pathologists. There were 51 cases of breast cancer in 198 concordant biopsies. The breath test distinguished between women with breast cancer and healthy volunteers with a sensitivity of 94.1% (48/51) and a specificity of 73.8% (31/42) (cross-validated sensitivity 88.2% (45/51), specificity 73.8% (31/42)). Compared to women with abnormal mammograms and no cancer on biopsy, the breath test identified breast cancer with a sensitivity of 62.7% (32/51) and a specificity of 84.0% (42/50) (cross-validated sensitivity of 60.8% (31/51), specificity of 82.0% (41/50)). The negative predictive value (NPV) of a screening breath test for breast cancer was superior to a screening mammogram (99.93% versus 99.89%); the positive predictive value (PPV) of a screening mammogram was superior to a screening breath test (4.63% versus 1.29%). A breath test for markers of oxidative stress accurately identified women with breast cancer, with an NPV superior to a screening mammogram. This breath test could potentially be employed as a primary screen for breast cancer. Confirmatory studies in larger groups are required.