| Literature DB >> 25191584 |
Olawunmi A Ajibola1, David Smith1, Patrik Spaněl2, Gordon A A Ferns3.
Abstract
Breath analysis is becoming increasingly established as a means of assessing metabolic, biochemical and physiological function in health and disease. The methods available for these analyses exploit a variety of complex physicochemical principles, but are becoming more easily utilised in the clinical setting. Whilst some of the factors accounting for the biological variation in breath metabolite concentrations have been clarified, there has been relatively little work on the dietary factors that may influence them. In applying breath analysis to the clinical setting, it will be important to consider how these factors may affect the interpretation of endogenous breath composition. Diet may have complex effects on the generation of breath compounds. These effects may either be due to a direct impact on metabolism, or because they alter the gastrointestinal flora. Bacteria are a major source of compounds in breath, and their generation of H2, hydrogen cyanide, aldehydes and alkanes may be an indicator of the health of their host.Entities:
Keywords: Breath analysis; Gut flora; Macronutrients; Micronutrients; PTR, proton transfer reaction; SIFT, selected ion flow tube; Selected ion flow tube-MS; VOC, volatile organic compounds; ppbv, parts per billion by volume
Year: 2013 PMID: 25191584 PMCID: PMC4153095 DOI: 10.1017/jns.2013.26
Source DB: PubMed Journal: J Nutr Sci ISSN: 2048-6790
Established and emerging clinical applications of breath analysis
| Breath analysis | References |
|---|---|
| Breath H2 test for carbohydrate metabolism | Rumessen |
| Breath NO test to monitor therapy for asthma | Eisenmann |
| Breath CO test for neonatal jaundice | Stevenson |
| Breath test for diagnosis of | Romagnuolo |
| Breath test for heart transplant rejection | Phillips |
| Breath NH3 has been identified as an indicator of the efficacy of renal dialysis | Endre |
| Breath H2 and the 13CO2:12CO2 ratio (following the ingestion of 13C-labelled compounds) as related to gastric emptying and bowel transit times | Bond & Levitt |
| Hydrogen cyanide is released by the pathogen,
| Shestivska |
Fig. 1.The complex interactions between diet and expired breath metabolites.
Fig. 2.Dietary and metabolic sources of the major metabolites in human breath. GI, gastrointestinal; F1P, fructose 1-phosphate; G6P, glucose 6-phosphate; G1P, glucose 1-phosphate; LCFA, long-chain fatty acids; BCFA, branched-chain fatty acids; HMG, hydroxy methyl glutaryl; carbomyl P, carbomyl phosphate. The grey boxes represent compounds that have been identified in breath.
Summary of methods used for breath analysis
| Method | Principle | References |
|---|---|---|
| GC | Sample injected onto a chromatographic column using an inert gaseous mobile phase. The column may be polar or non-polar. The separated compounds are then detected by MS, flame ionisation or ion mobility spectrometry | Phillips( |
| PTR-MS and PTR-TOF | VOC are ionised by their reaction with H3O+. Can be performed without pre-concentration, and can be undertaken online. Compounds identified by mass:charge ratio of characteristic ions, isomers cannot be differentiated by PTR-MS and therefore may require further characterisation. Recent PTR-TOF methods have substantially improved mass resolution and are sensitive down to parts per trillion | Moser |
| SIFT-MS | VOC are introduced at a controlled rate and reacted with a precursor ion (H3O+, NO+ or O2+) in a reaction (flow) tube. The product ions are analysed by quadrupole MS. This technique is sensitive down to parts per billion by volume in real time and online, and has good intra-individual repeatability | Smith & Španěl( |
| Chemical sensors (noses) | This method relies on the use of chemical sensors, often arranged as arrays, and linked to a data analysis system for chemical fingerprint analysis with reference to a database | Thaler |
| Chemiluminescence | This can be used if the trace gas reacts with ozone to generate a chemiluminescence signal which can be measured using a photomultiplier tube | Toda & Dasgupta( |
| Optical and laser spectroscopy | Absorbance spectroscopy of the trace gas is assessed using a laser light source in the mid-IR range. Recent improvements in the technique have allowed it to be applied to the real-time measurement of a number of potentially important trace gases including NH3, ethane and NO | Wang & Sahay( |
PTR, proton transfer reaction; TOF, time of flight; VOC, volatile organic compounds; SIFT, selected ion flow tube.
Summary of breath analytes with reported ranges and sources
| Compound | Study | Number of subjects | Method | Concentration (ppbv) | Source | Comments |
|---|---|---|---|---|---|---|
| Acetaldehyde | Diskin | 5 | SIFT-MS | Range 2–5 | Ethanol metabolism( | Oral microbes Liver |
| Fuchs | 12 lung cancer patients | GC-MS | Lung cancer: mean >200 | Carbohydrate metabolism, ambient air | No significant difference in exhaled acetaldehyde concentrations in all subject groups | |
| Turner | 30 | SIFT-MS | Mean 24 (range 0–104) | Ethanol metabolism | Increased levels detected with consumption of sweet drinks/food 2 h before | |
| Acetone | Turner | 30 | SIFT-MS | Mean 477 (range 148–2744) | Decarboxylation of acetoacetate, dehydrogenation of isopropanol | Levels strongly influenced by physiological factors other than diet |
| Smith | 6 | SIFT-MS | Pre-meal: range 200–600 | Related to blood glucose in some
studies( | ||
| NH3 | Diskin | 5 | SIFT-MS | Range 422–2389 | Protein metabolism( | Not much day-to-day variation in individuals |
| Turner | 30 | SIFT-MS | Mean 833 (range 248–2935) | Protein metabolism | Breath concentrations influenced by age and background air | |
| Španěl | 6 | SIFT-MS | Range 200–1750 | Protein diet | ||
| Smith | 6 | SIFT-MS | Pre-meal: range 300–600 | |||
| Allyl sulfides | Rosen | Simulated | Thermal desorption GC-MS | Garlic( | Allicin decomposes in gastric acid leading to the
formation of allyl sulfides( | |
| Carbon disulfide | Phillips( | 42 | GC-MS | Range 0·005–0·008 | Atmospheric( | |
| Ciaffoni | Laser absorption spectroscopy | Gut bacteria( | ||||
| Carbonyl sulfide | Wysocki | Simulated | Pulsed quantum cascade-based sensor | Gut bacteria( | ||
| Halmer | Simulated | Mid-cavity leak out spectroscopy | ||||
| CO | Middleton & Morice( | 65 | Electronic nose device | Non-smokers: mean 1830 | Haem catabolism( | Formation catalysed by hame oxygenase( |
| Paredi | 37 | Electrochemical | Non-diabetics: mean 2900 | |||
| Costello | 10 | Electrochemical detector | Non-smokers: mean 2100 (range
600–4900) | |||
| Dimethyl sulfide | Tangerman | GC | Normal: mean 7·6 ( | Methionine metabolism | Production is dependent on intestinal
bacteria( | |
| Azad | Simulated | Chemiluminescence | ||||
| Ethane | Paredi | 22 | GC | Mean 0·88 | Lipid peroxidation( | The production of ethane and pentane is dependent on
antioxidant status( |
| Ethanol | Diskin | 5 | SIFT-MS | Mean 196 (range 0–474) | Gut flora | |
| Smith | 6 | SIFT-MS | Range 55–121 | |||
| Ethylene | Dumitras | 1 non-smoker | Photo-acoustic spectroscopy | Mean 20 | Lipid peroxidation(46,48–50) | |
| H2 | Perman | 221 children | GC with electrochemical detection | Mean 7100 | Carbohydrate metabolism( | Formed by hydrolytic and saccharolytic
bacteria( |
| Costello | 10 | Electrochemical detector | Mean 9100 (range 300–34000) | Large within-individual variation | ||
| Hydrogen cyanide | Schmidt | 19 | Cavity ring down spectroscopy | Range 1·3–6·5 | Oxidation of thiocyanate by
peroxidase( | |
| Španěl | 26 | SIFT-MS | Mean 8 | |||
| Enderby | 16 children with cystic fibrosis | SIFT-MS | Mean 13·5 | Derived from | ||
| Hydrogen sulfide | Costello | 10 | Electrochemical detector | Mean 330 (range 0–1300) | Oral flora | |
| Isoprene | Turner | 20 | SIFT-MS | Mean 118 (range 0–474) | Cholesterol metabolism( | Exercise( |
| Jones | 16 | Thermal desorption GC and diode array UV detection | Range 36–231 | Cholesterol metabolism( | No statistical difference in isoprene concentrations between men and women | |
| Smith | 200 | SIFT-MS | Range 28–54 | Age dependent | ||
| Davies | 19 | SIFT-MS | Mean 89 ( | |||
| Taucher | PTR-MS | Range 100–1000 | Children <6 years had values less than adults | |||
| Methane | Dryahina | 75 | SIFT-MS | Range 6–30 | Carbohydrate metabolism(196–198) | Most produced by |
| Methanol | Turner | 30 | SIFT-MS | Median 461 (range 32–1684) | Fruit metabolism( | Inversely related to BMI |
| Taucher | PTR-MS | Mean 400 | ||||
| Methylamine | Marinov | Simulated | Near-IR laser spectrometer based on the cavity ring down detection | Protein metabolism | ||
| Methyl nitrate | Novak | 10 T1DM children | GC offline using electron capture( | Range 0·005–0·010 | Oxidative processes | Correlation with blood glucose |
| Methyl mercaptan | Chen | Methionine metabolism( | ||||
| NO | Paredi | Chemiluminescence, following its reaction with
O3( | Mean 6·7 | NO synthase and arginine(205–208) | Concentration is dependent on exhalation flow
rate( | |
| Cobos Barroso | Chemiluminescence | Mean 9·7 | Concentration in children >17 years predictive of asthma | |||
| Pentane | Olopade | 12 patients (acute asthma) | GC | Mean 188 ( | Lipid peroxidation( | Ethane and pentane production is dependent on
antioxidant status( |
| Propanol | 30 | SIFT-MS | Mean 18 (range 0–135) | Acetone metabolism( | Acetone | |
| 46 | PTR-MS | Mean 120 (range 50–250) | Natural levels in the body similar to methanol levels |
ppbv, Parts per billion by volume; SIFT, selected ion flow tube; PTR, proton transfer reaction; T1DM, type 1 diabetes mellitus.