WO2019058021A1 - A system and a method for producing information indicative of diabetes - Google Patents
A system and a method for producing information indicative of diabetes Download PDFInfo
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- WO2019058021A1 WO2019058021A1 PCT/FI2018/050638 FI2018050638W WO2019058021A1 WO 2019058021 A1 WO2019058021 A1 WO 2019058021A1 FI 2018050638 W FI2018050638 W FI 2018050638W WO 2019058021 A1 WO2019058021 A1 WO 2019058021A1
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- gas
- diabetes
- intestinal
- gas sensor
- gas sample
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Classifications
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/42—Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems
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- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/082—Evaluation by breath analysis, e.g. determination of the chemical composition of exhaled breath
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- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/097—Devices for facilitating collection of breath or for directing breath into or through measuring devices
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- A61B5/4222—Evaluating particular parts, e.g. particular organs
- A61B5/4255—Intestines, colon or appendix
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- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
- A61B5/6891—Furniture
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- E—FIXED CONSTRUCTIONS
- E03—WATER SUPPLY; SEWERAGE
- E03D—WATER-CLOSETS OR URINALS WITH FLUSHING DEVICES; FLUSHING VALVES THEREFOR
- E03D11/00—Other component parts of water-closets, e.g. noise-reducing means in the flushing system, flushing pipes mounted in the bowl, seals for the bowl outlet, devices preventing overflow of the bowl contents; devices forming a water seal in the bowl after flushing, devices eliminating obstructions in the bowl outlet or preventing backflow of water and excrements from the waterpipe
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/483—Physical analysis of biological material
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/483—Physical analysis of biological material
- G01N33/497—Physical analysis of biological material of gaseous biological material, e.g. breath
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/483—Physical analysis of biological material
- G01N33/497—Physical analysis of biological material of gaseous biological material, e.g. breath
- G01N33/4975—Physical analysis of biological material of gaseous biological material, e.g. breath other than oxygen, carbon dioxide or alcohol, e.g. organic vapours
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- A61B10/00—Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements
- A61B10/0038—Devices for taking faeces samples; Faecal examination devices
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- A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
- A61B2560/02—Operational features
- A61B2560/0204—Operational features of power management
- A61B2560/0214—Operational features of power management of power generation or supply
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- A—HUMAN NECESSITIES
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- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
- A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
- A61B5/002—Monitoring the patient using a local or closed circuit, e.g. in a room or building
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- A—HUMAN NECESSITIES
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
Definitions
- the disclosure relates to analysis of intestinal gases released by mammals, particularly to a method and a system for producing information indicative of diabetes.
- Type 1 and Type 2 diabetes have been rapidly increasing in recent years 1 .
- TIDM prevalence is estimated to double by 2020 in some populations 2 ; for T2DM, recent estimates indicate that in 2050 between 20% - 33% of all adults in the US may be diabetic.
- T2DM recent estimates indicate that in 2050 between 20% - 33% of all adults in the US may be diabetic.
- 3 Since a number of complications due to diabetes can be prevented by tight glycemic control, standard medical guidelines now call for patients to self-monitor their blood glucose multiple times a day. 4
- Present diabetes management typically relies on painful finger lancing for glucose testing, a daily practice that many patients have come to hate, often resulting in fewer measurements and worsened glycemic control.
- VOCs volatile organic compounds
- T1DM and T2DM are expected to affect nearly 450 million people globally within the next 20 years 7 . Diagnosis and management of this epidemic currently depends on blood tests, which are expensive, unpractical, and often considered to be painful. Frequent blood testing is needed for patients undergoing insulin treatment, for whom the American Diabetes Association "ADA" recommends to self-monitor blood glucose concentrations more than three times daily by using finger sticks 8 .
- a measurement of plasma insulin is clinically useful in assessing pre-diabetic states. As an indicator of impaired glucose metabolism, it increases prior to onset of glycemia itself 9 . The progression of T2DM from initial insulin resistance to eventual pancreatic failure, and to differentiate the increasingly common states in which components of both T1DM and T2DM are simultaneously present. Monitoring insulin also gives insight to other aspects of metabolism; insulin not only regulates glucose disposal but also exerts a strong anti- lipolytic effect, which is markedly reduced in patients
- intestinal gas-based devices have multiple advantages. Notably, gas analysis is readily acceptable, or even remain unnoticed, by patients, promising a potentially marked increase in testing compliance, which is currently one of the major obstacles to good glycemic control. Further, sample collection is easy and can even be obtained from unconscious patients. Such monitoring could thus facilitate tighter glucose management during surgery, which is currently difficult to achieve but believed to result in better clinical or private household outcomes 12 . There is also virtually no limit in intestinal gas collection volume, which is a critical issue in neonates with extremely small circulating volumes. Intestinal gas collection can also be useful for wide screening when phlebotomy is problematic, i.e. in obese subjects with difficult vein access or in apprehensive primary school children who may refuse to participate in screening procedures involving phlebotomy.
- the present innovation concentrates on the usage of intestinal gases as an alternative non-invasive tool for the diagnosis and analysis of diseases.
- intestinal gases By combining the analysis of intestinal and exhaled breath gases as tools of an early detection of diseases, important breakthroughs could be achieved.
- breath odors are traditionally associated with specific pathological states. For instance, renal failure is associated with a 'fishy' smell and diabetes with a 'fruity' smell.
- 19 th century the 19 th century
- VOCs Volatile Organic Compounds
- FDA US Food and Drug Administration
- the US Food and Drug Administration “FDA” has approved the breath-based diagnosis of alcohol intoxication, asthma, heart transplant rejection, Helicobacter pylori infection, carbon monoxide CO poisoning, and lactose intolerance 15 .
- Diabetes and its related dysmetabolic states now greatly benefit from these non-invasive tests in diagnostic, prevention and monitoring using intestinal gases and correlations between the gas based and other analysis methods.
- the interest on non- invasive, realtime analysis methods is rapidly growing and the intestinal gas test for plasma glucose, and foreseen parallel tests for plasma insulin and lipids, are of key interest today.
- VOCs Volatile organic compounds
- aerosolized particles in intestinal gases arise from many sources including inhaled room air, airways surfaces, blood, and peripheral tissues throughout the body. Some gases appear to be by-products of biochemical reactions, while others may be produced for specific physiological roles, such as cell-to-cell signaling. Production of other gases may only be present, or greatly amplified, during infections or other pathological conditions. An increasing number of previously undetected gases is being identified in gases released by humans, and there is growing interest in the direct identification of the origins of these compounds through the study of cellular and tissue gas emissions under a variety of physiological and experimental conditions.
- VOCs An obvious source of breath VOCs is inhalation from the immediate surroundings, e.g. pollutants like ethane and n-pentane. High atmospheric concentrations of VOCs will directly result in high out-gas concentrations 16 . Such exposure may also lead to increased uptake of the compounds beyond the
- VOCs e.g. trichloroethene, toluene, tetrachloroethylene, for instance, are absorbed into the systemic circulation and have been reported in blood samples of non-occupationally-exposed adult US populations 17 .
- the inhaled VOCs once absorbed into the bloodstream, may undergo partial or total endogenous enzymatic metabolism, altering the ratio between inhaled and out-gassed concentrations.
- the internal surfaces of the lungs may also directly contribute gases to the out-gassed mixture, i.e. by either generating lung-specific gases or increasing production of gases also produced elsewhere in the body.
- gases for example, ten VOCs, 4 hydrocarbons, 2 esters, 2 ketones, 2-methyl-2-propanol, and ethyl tert-butyl ether, were produced by human bronchial epithelial primary cells 18 .
- Ethane, isoprene, pentane, and other light hydrocarbons have been proposed as biomarkers of metabolic processes occurring on a systemic scale, such as fat oxidation 19 , cholesterol/LDL concentrations 20 , and oxidative stress levels 21 .
- Patients with insulin resistance may have increased lipid production and therefore increased out-gassed ketones: acetone, 2-pentanone and 2-butanone, from subsequent lipolysis 8 .
- VOCs are not detected in blood because they are bound to carrier proteins en route to delivery to the alveolar space.
- hemoglobin has been seriously considered, in addition, of course, to its known ability to transport oxygen, C0 2 , CO, and NO 22 .
- PMNs polymorphonuclear leukocytes
- Bacteria Bacteria and other micro-organisms can also produce unique gas profiles which, if identified within out-gassed mixtures, may be used for diagnostic or monitoring purposes.
- Atypical example involves the ingestion of radiolabeled urea to detect Helicobacter pylori, a urease-positive bacterium which causes gastroduodenal ulcers; only if the microorganism is present, urea will be hydro lyzed into radiolabeled CO2 and ammonia 28 .
- Hydrogen cyanide has been shown to be released by cells infected by Pseudomonas aeruginosa and investigated as target for noninvasive diagnosis 29 .
- Lactose ingestion by patients with hypolactasia is also known to increase their breath hydrogen concentrations, 48-168 ppmv vs 0- 3 ppmv in controls, due to the increased fermentation of carbohydrates by gut flora 30 .
- a group of VOCs were found produced in vitro by Mycobacterium tuberculosis and also associated with active infection (including derivatives of cyclohexane, benzene, heptane and hexane) 31 . More importantly to the field of diabetes, ethanol and other alcohols can be produced by gut bacteria in response to glucose ingestion or changes in systemic glycemia.
- VOCs present in human systems from established signaling pathways in other organisms. While these considerations are certainly just speculative, it is indeed possible that neutrophil-derived ethylene and/or other related gases contribute to a complex signaling network involving multiple aspects of glucose regulation. Alterations in this interactive pattern may have obvious implications in patients with diabetes and impaired glucose metabolism.
- Ethyl nitrate possesses vasodilatory activity and suppresses methemoglobin formation 32 .
- Methyl iodide can dose-dependently increase serum cholesterol, HDL, LDL, and decrease triglycerides in both rat and rabbit toxicity studies 33 .
- Inhalation of dichloromethane, ethylbenzene, and trichloroethylene were shown to make 1,217 identifiable changes in rat gene expression 34 .
- Intestinal functions and pulmonary vasculature are both thought to be impaired in diabetes, potentially affecting gas exchange kinetics of multiple VOC, concepts that need to be addressed if utilizing these compounds in the development of out-gas based tests.
- As the disease worsens, for instance, increasingly severe pathological changes may render it necessary to perform frequent recalibrations or exclude specific VOCs from use in out-gas testing.
- VOCs can now be routinely identified and quantified with a high degree of accuracy 40 . Additionally, direct measurements of compounds in EBC is now possible 41 ; typically, exhaled breath is trapped in a collection tube, cooled to an aqueous form, and analyzed by various chromatographic techniques 3 . New technologies with more frequent gas sampling or real-time analysis have been developed, which allow repeated testing during and following metabolic perturbations. These approaches include atmospheric pressure ionization mass spectrometry coupled with electrospray charging, which has been used for on-line measurements of volatilized fatty acids 42 as well as proton transfer reaction mass spectrometry (PTR-MS) 43 for measurement of breath VOCs.
- PTR-MS proton transfer reaction mass spectrometry
- Miekisch et al and Di Francesco et al introduced general instrumental techniques (chromatography and electronic sensors) and some clinical applications for breath testing 44 .
- Buszewski et al later elaborated on several considerations for breath sampling, preconcentration, and analysis 45 . More recently, Smith et al reviewed selected ion flow tube mass spectrometry and PTR-MS technology for VOC breath analysis in diabetes mellitus 46 . Other non- invasive glucose monitoring methods are still being actively pursued.
- Tura et al. 47 including several spectroscopic techniques, and newer breath and skin technologies 48 .
- Greiter et al distinguished patients with T2DM from healthy controls with a 90% sensitivity and 92% specificity 49 .
- Kulikov et al found light hydrocarbons (C2-C3 including ethanol and acetaldehyde) to be elevated in the exhaled breath of women who had risk factors for T2DM (i.e. relatives with diabetes and smoking) as compared to those without 50 . None of these tests have yet been translated into commercial devices, these studies constitute an important foundation for future research (especially on their underlying biochemical pathways) and product development.
- Aromatic compounds e.g. ethylbenzene, o/m/p-xylene, toluene
- Alkyl nitrates e.g. 2-, 3-pentyl nitrate, methyl nitrate
- Ketones e.g. acetone, 2-pentanone
- This improved 4-gas model allowed breath-based glucose prediction with a mean correlation coefficient of 0.91, range r 0.70- 0.98, when compared to standard glucose measurements 15 .
- a breath-based lipid test could contribute to the overall management of diabetic patients, whose systemic lipid levels represent a critical risk factor for cardiovascular events; of course, this test could be extended to many non-diabetic populations too.
- a new system for producing information indicative of diabetes comprising:
- - means, e.g. a bowl of a toilet seat, for collection of excrement
- the gas sensor is configured to determine, from the intestinal, gas sample, presence and concentrations of pre-determined volatile organic compounds, and the system comprises processing means for producing the information indicative of diabetes as a weighted linear combination of the determined concentrations of the pre-determined volatile organic compounds.
- the present invention is based on identifying, analyzing and matching signatures of specific gaseous compounds found in intestinal/exhaled gases, released by an individual, with the known candidate biomarkers of Diabetes Mellitus "DM".
- Different gases such as, but not limited to, acetone (CH3) 2 CO, ethanol C 2 H 6 0, methyl nitrate CH3NO3, ethyl benzene CeHsC bC b, plasma triglycerides TG e.g.
- C55H98O6, 2-pentyl nitrate C5H11NO3, propane C3H8, methanol CH3OH are identified and measured in the flow of intestinal/exhaled gas and are matched with a set of predicted biomarkers connected to plasma insulin, lipid and fatty acid related gaseous signatures of T1DM & T2DM by using a machine learning algorithm and individual calibrations.
- measurement of the intestinal and/or exhaled gas and ambient air in the vicinity of the intestinal gas measurement is used.
- the ambient gas measurement may be simultaneous or it can be performed by the same sensor before or after the actual measurement.
- a battery chargeable by using the existing auxiliary toilet mechanics e.g.
- a button for flushing the WC and the toilet seat may be utilized.
- the power may be harvested from mechanical energy, like generation of electricity from toilet seat cover opening and closing, or generating electricity from the pressurized incoming water same way as LED-lighted shower heads.
- the power need for analysis is small, and also the data amount that may be sent by for example Bluetooth or by Wi-Fi is moderately small and doesn't consume much energy.
- a sampling system may comprise at least one fan or other gas pump for moving gas samples and valves for taking samples.
- the samples may be drawn to one or more gas collection chambers, and from the chamber to sensors, and the used samples may be flushed away by fresh air.
- the odorless toilet like in Finnish utility model U20100452 may be used, and the house ventilation can be used for means of suction of gasses.
- a gas pump or fan for moving the sample to the sensor array and mixing the sample, and flushing the chamber and sensor with fresh air.
- the sampling may be continuous and/or discrete.
- the gas is carried past the sensors without storing the sample first.
- the continuous sample may be used for only some sensors, and the output of those sensors can be used for timing the sampling, that is opening the gas collection chamber and storing the gas sample for analysis.
- the exhale air sampling may be done by measuring continuously only CO2 -concentration for recognizing when the sample is proper exhaust air and taking the sample for analysis only after the CO2 -concentration is high enough.
- the tubes and chambers may be ventilated for example by a fan or by opening the air passage to house ventilation system and ambient air.
- the present invention concerns a system for intestinal gas characterization including: an intestinal gas sampling device, a pipe for the intestinal gas flow out from the toilet bowl, a gas collection chamber integrated with the pipe outputting the intestinal gas, a gas sensor within the gas collection chamber, equipped with integrated signal processing means for intestinal gas flows, a gas sensor outside the gas collection chamber, equipped with integrated signal processing means for measuring the surrounding gas, means for data acquisition and data transmission, real-time data display, an electricity supply for charging the battery of the gas sensor.
- the present invention concerns a system for intestinal gas characterization including: an intestinal gas sampling device, a pipe for the intestinal gas flow out from the toilet bowl, a gas mask for exhaled air with an output pipe connected to the pipe outputting the intestinal gas, a gas collection chamber integrated with the pipe outputting the intestinal gas from the toilet bowl, a gas sensor within the gas collection chamber, equipped with integrated signal processing means for intestinal gas flows, a switch operating the gas sensor alternatively for the input during the exhaling of breath gas into the gas mask, or for the input during the intestinal gas sampling , a gas sensor outside the gas collection chamber, equipped with integrated signal processing means for measuring the surrounding gas, means for data acquisition and data transmission, real-time data display, an electricity supply for charging a battery of the gas sensor.
- the present invention concerns a method of detecting the presence of T1DM or T2DM, comprising: identifying and analyzing the collected intestinal and breath gas for a presence of volatile organic compounds "VOC" as markers of T1DM or T2DM as inputs to an adaptable machine learning algorithm.
- the volatile organic compounds include, for example but not limited to, the following: acetone (CH3) 2 CO, ethanol C2H6O, methyl nitrate CH3NO3, ethyl benzene C6H5CH2CH3, plasma triglycerides TG e.g. C55H98O6, 2-pentyl nitrate C5H11NO3, propane C3H8, methanol CH3OH.
- the adaptable machine learning algorithm optimizes, for each individual case, an initial equation
- [TnDM: (t - 3t)] X Q + 1 [VOC 1 (t)] + 3 ⁇ 4 [VOC 2 (t)] + X 3 [VOC 3 (t)] + ... + 3 ⁇ 4 [VOC 8 (t)], where t denotes time, At is a time off-set, and 3 ⁇ 4 Xi, X2, X3, Xs are coefficients that represent the expected difference in glucose when the concentration of each corresponding gas is increased by one unit, whereas all the other gases are kept at constant concentrations.
- the algorithm is adapted for each individual patient and calibrated for each individual indicator VOC and, for an 'empty' case, the surrounding air.
- the measured and predicted TnDM values are displayed by the real-time data acquisition system, for example, as a Parkes glucose consensus error grid 56 and in terms of Bland- Altman plots 57 .
- the real-time data acquisition system also produces statistical analysis measures, such as Pearson's product-moment correlation coefficients used for accessing the clinical relevance of the findings.
- Figure 1 shows a system according to an exemplifying embodiment of the invention for producing information indicative of diabetes
- Figure 2 shows a system according to another exemplifying embodiment of the invention for producing information indicative of diabetes
- Figure 3 illustrates means for charging a battery of a system according to an exemplifying embodiment of the invention for producing information indicative of diabetes
- Figure 4 shows a device for flushing a toilet and for charging a battery of a system according to an exemplifying embodiment of the invention for producing information indicative of diabetes.
- Figure 5 shows a flowchart of a method according to an exemplifying embodiment of the invention for producing information indicative of diabetes.
- Figure 1 shows a system according to an embodiment of the invention for producing information indicative of diabetes.
- the system comprises: means 1 for collection of excrement, a gas sensor 3 for analyzing an intestinal gas sample, and means 2 for drawing the intestinal gas sample to the gas sensor.
- the gas sensor 3 is configured to determine, from the intestinal gas sample, presence and concentrations of pre-determined volatile organic compounds.
- the system comprises processing ⁇ means for producing the information indicative of diabetes as a weighted linear combination of the determined concentrations of the pre-determined volatile organic compounds.
- the gas sensor 1 is configured to determine, from the intestinal gas sample, the presence and the concentrations of the following predetermined volatile organic compounds: acetone, ethanol, methyl nitrate, ethyl benzene, plasma triglycerides, 2-pentyl nitrate, propane, and methanol.
- the means 1 for collection of excrement comprises a toilet seat bowl and the means 2 for drawing the intestinal gas sample comprises an odor exhaust ventilation adapted to conduct the intestinal gas sample from the toilet seat bowl to the gas sensor 3.
- the gas sensor 3 further comprises gas collection chamber for gas sampling.
- a system according to an exemplifying embodiment of the invention is adapted take samples from air for calibration when the means 1 for collection of excrement is not in use.
- a system according to an exemplifying embodiment of the invention comprises at least one pump or valve for controlling the intestinal gas sample flow to one or more gas sample collection chambers and for ventilation the gas sample collection chambers and the gas sensor with fresh air after analyzing the intestinal gas sample.
- Figure 2 shows a system according to an embodiment of the invention for producing information indicative of diabetes.
- the system comprises: a toilet seat bowl for collection of excrement, a gas sensor 3 for analyzing an intestinal gas sample, and means 2 for drawing the intestinal gas sample to the gas sensor.
- the system further comprises a gas mask 11 for exhaled air with an output pipe connected to the gas sensor 3.
- the gas sensor 3 is configured to determine, from the intestinal gas sample or from the exhaled air, presence and concentrations of pre-determined volatile organic compounds.
- the system comprises processing means for producing the information indicative of diabetes as a weighted linear combination of the determined concentrations of the pre-determined volatile organic compounds.
- the system comprises a switch operated valve 4 for choosing the operational mode of gas sampling: intestinal gas sampling or exhaled breath gas sampling. 2Q
- FIG 3 shows an exemplary toilet seat for generating electricity to charge a battery which powers the gas sensor, see Figures 1 and 2.
- the toilet seat comprises a piezoelectric module with a piezoelectric element underneath, a circuit to transmit the electricity generated by the piezoelectric effect, due to the weight of a person sitting on the seat, gravitational force F, to the battery of the gas sensor.
- Figure 4 shows an exemplifying device for flushing the toilet and for charging the battery of the gas sensor.
- the device comprises a wall mounted housing and a piezoelectric module.
- Figure 5 shows a flowchart of a method according to an exemplifying embodiment of the invention for producing information indicative of diabetes.
- the method comprises the following actions:
- action 1 receiving excrement with means for collection of the excrement
- action 3 using the gas sensor for determining, from the intestinal gas sample, presence and concentrations of pre-determined volatile organic compounds, and
- action 4 producing the information indicative of diabetes as a weighted linear combination of the determined concentrations of the pre-determined volatile organic compounds.
- a method according to an exemplifying embodiment of the invention further comprises actions 8 for system calibration, environmental controls, and/or mode selection.
- action 6 analyzing the information with machine learning algorithms for disease recognition
- action 7 sending the analysis results wirelessly to e.g. a portable phone or some other communication device.
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Abstract
A system for producing information indicative of diabetes comprises means (1) for collection of excrement, a gas sensor (3) for analyzing an intestinal gas sample, and means (2) for drawing the intestinal gas sample to the gas sensor. The gas sensor is configured to determine, from the intestinal gas sample, presence and concentrations of pre-determined volatile organic compounds. The system comprises processing means for producing the information indicative of diabetes as a weighted linear combination of the determined concentrations of the pre-determined volatile organic compounds.
Description
A system and a method for producing information indicative of diabetes Field of the disclosure
The disclosure relates to analysis of intestinal gases released by mammals, particularly to a method and a system for producing information indicative of diabetes.
Background General
The incidence of both Type 1 and Type 2 diabetes, TIDM and T2DM, respectively, has been rapidly increasing in recent years 1 . For TIDM, prevalence is estimated to double by 2020 in some populations2; for T2DM, recent estimates indicate that in 2050 between 20% - 33% of all adults in the US may be diabetic.3 Since a number of complications due to diabetes can be prevented by tight glycemic control, standard medical guidelines now call for patients to self-monitor their blood glucose multiple times a day.4 Present diabetes management typically relies on painful finger lancing for glucose testing, a daily practice that many patients have come to hate, often resulting in fewer measurements and worsened glycemic control.
Although alternative, noninvasive techniques such as near-infrared or ultrasound sensors, dielectric impedance, and ionophoresis5 are being actively pursued by several research laboratories, none are
'D'Angeli MA, Merzon E, Valbuena LF, Tirschwell D, Paris CA, Mueller BA., Environmental factors associated with childhood-onset type 1 diabetes mellitus: an exploration of the hygiene and overload hypotheses. Arch Pediatr Adolesc Med 164: 732-738, 2010; Gale EA. The rise of childhood type 1 diabetes in the 20th century, Diabetes 51 : 3353-3361, 2002; Pitkaniemi J, Onkamo P, Tuomilehto J, Arjas E. Increasing incidence of Type I diabetes— role for genes?, BMC Genetics 5: 5, 2004; Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030, Diabetes Res Clin Pract 87: 4-14, 2010.
2Patterson CC, Dahlquist GG, Gyurus E, Green A, Soltesz G, EURODIAB Study Grouplncidence trends for childhood type I diabetes in Europe during 1989-2003 and predicted new cases 2005-20: a multicentre prospective registration study, Lancet 373: 2027-2033, 2009.
3Boyle J, Thompson T, Gregg E, Barker L, Williamson D. Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence, Popul Health Metr 8: 29, 2010.
4 American Diabetes Association Standards of medical care in diabetes— 2010, Diabetes Care 33, Suppl 1 : S11-S61, 2010.
5 McGarraugh G. The chemistry of commercial continuous glucose monitors, Diabetes Technol Ther 11, Suppl 1 : S17-S24, 2009; Tura A, Maran A, Pacini G., Non-invasive glucose monitoring: assessment of technologies and devices according to quantitative criteria. Diabetes Res Clin Pract 77: 16-40, 2007.
currently proven to be ready for clinical practice. The most promising techniques also appear to be prohibitively costly for wider use.
Quantification of volatile organic compounds "VOCs" has been proposed in the exhaled breath as a novel, noninvasive methodology for plasma glucose monitoring. Breath analysis is foreseen to offer many potential advantages; it is completely painless, it is easily acceptable even by children, it does not require patient interaction, important during sleep, and it can become much cheaper than current glucose meters because it does not require an interface between sample and machine, i.e., for most glucose meters, hundreds of test strips are needed every month, which can add up to thousands of dollars annually. Moreover, additional information can conceivably be captured simultaneously from the same breath sample to provide a snapshot of an individual's metabolic status, including insulin and lipid levels6 .
Diagnostic technologies
T1DM and T2DM are expected to affect nearly 450 million people globally within the next 20 years7. Diagnosis and management of this epidemic currently depends on blood tests, which are expensive, unpractical, and often considered to be painful. Frequent blood testing is needed for patients undergoing insulin treatment, for whom the American Diabetes Association "ADA" recommends to self-monitor blood glucose concentrations more than three times daily by using finger sticks8. A measurement of plasma insulin is clinically useful in assessing pre-diabetic states. As an indicator of impaired glucose metabolism, it increases prior to onset of glycemia itself9. The progression of T2DM from initial insulin resistance to eventual pancreatic failure, and to differentiate the increasingly common states in which components of both T1DM and T2DM are simultaneously present. Monitoring insulin also gives insight to other aspects of metabolism; insulin not only regulates glucose disposal but also exerts a strong anti- lipolytic effect, which is markedly reduced in patients
6Galassetti PR, Ngo J, Oliver SR, Pontello A, Meindari S, Newcomb R, Blake DR. Non-invasive measurement of plasma insulin via exhaled breath analysis (Abstract). American Diabetes Association 69th Scientific Sessions, New Orleans, LA, p. 1460-P, 2009; Minh TD, Oliver SR, Blake DR, Carlson MK, Meinardi S, Galassetti PR. Non- invasive measurement of plasma triglycerides from exhaled breath (Abstract). American Diabetes Association 70th Scientific Session, Orlando, FL, p. 1086-P, 2010.
7Shaw JE, Sicree RA, Zimmet PZ., Global estimates of the prevalence of diabetes for 2010 and 2030, Diabetes Research and Clinical Practice. 2010;87:4-14.
s American Diabetes A. Standards of Medical Care in Diabetes— 2011, Diabetes Care. 2011;34:S11- S61.
9Tabak AG, Jokela M, Akbaraly TN, Brunner EJ, Kivimaki M, Witte DR., Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study, Lancet. 2009;373:2215-2221.
with insulin resistance10. Tests for insulin concentrations and sensitivity are complicated and time consuming. Similarly, testing circulating lipids is important for diabetic patients because hyperlipidemia is an independent risk factor for heart disease. As lipids increase ketone body formation11, their alterations may also be associated with changes in insulin or glucose metabolism. Knowing the interplay of these metabolic variables will allow clinicians to have a more comprehensive insight into patient health, and reliable non- invasive monitoring improves diagnosis, and treatment of diabetes.
Substantial resources have been invested globally in developing non-invasive devices for diabetes management, progress has been slow. Two early patents for non-invasive glucose testing, based on the changes of light through the eye, were filed in 1974, U.S. Patents 3,958,560 and 3,963,019.
Of many potential modalities of non-invasive testing for diabetes management, intestinal gas-based devices have multiple advantages. Notably, gas analysis is readily acceptable, or even remain unnoticed, by patients, promising a potentially marked increase in testing compliance, which is currently one of the major obstacles to good glycemic control. Further, sample collection is easy and can even be obtained from unconscious patients. Such monitoring could thus facilitate tighter glucose management during surgery, which is currently difficult to achieve but believed to result in better clinical or private household outcomes12. There is also virtually no limit in intestinal gas collection volume, which is a critical issue in neonates with extremely small circulating volumes. Intestinal gas collection can also be useful for wide screening when phlebotomy is problematic, i.e. in obese subjects with difficult vein access or in apprehensive primary school children who may refuse to participate in screening procedures involving phlebotomy.
The present innovation concentrates on the usage of intestinal gases as an alternative non-invasive tool for the diagnosis and analysis of diseases. By combining the analysis of intestinal and exhaled breath gases as tools of an early detection of diseases, important breakthroughs could be achieved. A number of breath odors are traditionally associated with specific pathological states. For instance, renal failure is associated with a 'fishy' smell and diabetes with a 'fruity' smell. In the 19th century,
10Blaak EE. Metabolic fluxes in skeletal muscle in relation to obesity and insulin resistance. Best Pract Res Clin Endocrinol Metab. 2005;19:391-403. nLaffel L. Ketone bodies: a review of physiology, pathophysiology and application of monitoring to diabetes. Diabetes/Metabolism Research and Reviews. 1999;15:412-426.
12Lecomte P, Foubert L, Nobels F, Coddens J, Nollet G, Casselman F, et al. Dynamic Tight Glycemic Control During and After Cardiac Surgery Is Effective, Feasible, and Safe. Anesthesia & Analgesia. 2008; 107:51-58.
acetone was found in the breath of diabetics by Nebelthau, and that exhaled ethanol was a byproduct of alcohol metabolism was discovered by Anstie. The apparent complexity of out-gassed substance composition was largely undefined until 1971, when Linus Pauling exploited gas chromatography to list some 250 gas components in human breath13. By now, more than 3000 different Volatile Organic Compounds "VOCs" and other aerosolized particles have been identified in exhaled breath gases14. The US Food and Drug Administration "FDA" has approved the breath-based diagnosis of alcohol intoxication, asthma, heart transplant rejection, Helicobacter pylori infection, carbon monoxide CO poisoning, and lactose intolerance15. Diabetes and its related dysmetabolic states now greatly benefit from these non-invasive tests in diagnostic, prevention and monitoring using intestinal gases and correlations between the gas based and other analysis methods. The interest on non- invasive, realtime analysis methods is rapidly growing and the intestinal gas test for plasma glucose, and foreseen parallel tests for plasma insulin and lipids, are of key interest today.
Sources VOCs
Volatile organic compounds "VOCs" and aerosolized particles in intestinal gases arise from many sources including inhaled room air, airways surfaces, blood, and peripheral tissues throughout the body. Some gases appear to be by-products of biochemical reactions, while others may be produced for specific physiological roles, such as cell-to-cell signaling. Production of other gases may only be present, or greatly amplified, during infections or other pathological conditions. An increasing number of previously undetected gases is being identified in gases released by humans, and there is growing interest in the direct identification of the origins of these compounds through the study of cellular and tissue gas emissions under a variety of physiological and experimental conditions.
Environment
An obvious source of breath VOCs is inhalation from the immediate surroundings, e.g. pollutants like ethane and n-pentane. High atmospheric concentrations of VOCs will directly result in high out-gas concentrations16. Such exposure may also lead to increased uptake of the compounds beyond the
13Pauling L, Robinson AB, Teranishi R, Cary P., Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography. Proc Natl Acad Sci U S A. 1971; 68:2374-2376.
142. Phillips M, Herrera J, Krishnan S, Zain M, Greenberg J, Cataneo RN., Variation in volatile organic compounds in the breath of normal humans. J Chromatogr B Biomed Sci Appl. 1999; 729:75-88; Horvath I, Hunt J, Barnes PJ., Exhaled breath condensate: methodological recommendations and unresolved questions, European Respiratory Journal. 2005; 26:523-548. 15Paschke KM, Mashir A, Dweik RA., Clinical applications of breath testing. F1000 medicine reports. 2010; 2:56.
16Lecomte P, Foubert L, Nobels F, Coddens J, Nollet G, Casselman F, et al. Dynamic Tight Glycemic Control During and After Cardiac Surgery Is Effective, Feasible, and Safe. Anesthesia & Analgesia. 2008; 107:51-58.
airways. Some VOCs, e.g. trichloroethene, toluene, tetrachloroethylene, for instance, are absorbed into the systemic circulation and have been reported in blood samples of non-occupationally-exposed adult US populations17.
The inhaled VOCs, once absorbed into the bloodstream, may undergo partial or total endogenous enzymatic metabolism, altering the ratio between inhaled and out-gassed concentrations.
Similar changes in the kinetics of VOC metabolism that may be associated with chronic dysmetabolic conditions may therefore conceivably be used as indirect biomarkers of tissue function and/or specific aspects of organ metabolism.
Lungs The internal surfaces of the lungs may also directly contribute gases to the out-gassed mixture, i.e. by either generating lung-specific gases or increasing production of gases also produced elsewhere in the body. For example, ten VOCs, 4 hydrocarbons, 2 esters, 2 ketones, 2-methyl-2-propanol, and ethyl tert-butyl ether, were produced by human bronchial epithelial primary cells18.
A variety of peripheral human cells and tissues, either in healthy or pathological states, are thought to contribute to out-gassed VOC composition via release of gases into the bloodstream and their subsequent transfer into the airways. Ethane, isoprene, pentane, and other light hydrocarbons have been proposed as biomarkers of metabolic processes occurring on a systemic scale, such as fat oxidation19, cholesterol/LDL concentrations20, and oxidative stress levels21. Patients with insulin resistance may have increased lipid production and therefore increased out-gassed ketones: acetone, 2-pentanone and 2-butanone, from subsequent lipolysis8 .
The exact mechanisms of transferring systemically-produced VOCs from tissues into the lungs are still unclear. The simplest possibility is that the VOCs are merely dissolved in the blood and released when out-gassed via pulmonary gas exchange, in which case the liquid/gas partition coefficients could
17Gorham KA, Sulbaek Andersen MP, Meinardi S, Delfmo RJ, Staimer N, Tjoa T, et al. Ethane and n-pentane in exhaled breath are biomarkers of exposure not effect. Biomarkers. 2009; 14: 17-25.
18Filipiak W, Sponring A, Filipiak A, Ager C, Schubert J, Miekisch W, et al. TD-GC-MS Analysis of Volatile Metabolites of Human Lung Cancer and Normal Cells In vitro. Cancer Epidemiology Biomarkers & Prevention. 2010; 19: 182-195.
19Frank Kneepkens CM, Lepage G, Roy CC. The potential of the hydrocarbon breath test as a measure of lipid peroxidation. Free Radical Biology and Medicine. 1994; 17: 127-160.
20Rieder J, Lirk P, Ebenbichler C, Gruber G, Prazeller P, Lindinger W, et al. Analysis of volatile organic compounds: possible applications in metabolic disorders and cancer screening. Wiener klinische Wochenschrift. 2001; 113:181-185.
2 Phillips M, Cataneo RN, Cheema T, Greenberg J. Increased breath biomarkers of oxidative stress in diabetes mellitus. Clin Chim Acta. 2004; 344: 189-194.
,
6
be estimated by classic chemical equations, taking into account ventilation and specific solubility characteristics of each gas. Often, however, measured gas concentrations, both out-gassed and in blood, seem to diverge significantly from these estimates.
Blood Another possible explanation for this discrepancy is that these VOCs are not detected in blood because they are bound to carrier proteins en route to delivery to the alveolar space. Among candidates for this transport role of isoprene, hemoglobin has been seriously considered, in addition, of course, to its known ability to transport oxygen, C02, CO, and NO22.
While blood can act as a gas conduit from peripheral tissue to the lungs, there is evidence that blood components themselves can produce VOCs. Miekisch et al reported acetone, dimethyl sulfide, isoflurane, isoprene, and pentane in the headspace of whole blood samples using gas chromatography23 . Deng et al also identified twenty-three compounds, including hexanal and heptanal, produced by whole blood that were present in out-gassed substances24. This has been reconfirmed by using alternative liquid-chromatography-based methodologies25. Of the various blood cell types, the importance of polymorphonuclear leukocytes "PMNs" in diabetes has been rapidly emerging; PMNs potentially emit gases of their own and have been found to be interrelated with multiple aspects of glucose regulation, see Figure 1. Shin et al recently discovered that both promyelocytic cells and isolated neutrophils, but not peripheral blood mononuclear cells, emit acetaldehyde 26 , a gas speculated to mediate blood vessel relaxation via calcium channel modulation27. As PMNs circulate throughout the body and respond to chemotactic factors, they may
Poulin P, Krishnan K. A Mechanistic Algorithm for Predicting Blood: Air Partition Coefficients of Organic Chemicals with the Consideration of Reversible Binding in Hemoglobin. Toxicology and Applied Pharmacology. 1996; 136: 131-137.
23 Miekisch W, Schubert JK, Vagts DA, Geiger K. Analysis of Volatile Disease Markers in Blood. Clinical Chemistry. 2001; 47: 1053-1060.
24Deng C, Zhang X, Li N. Investigation of volatile biomarkers in lung cancer blood using solid-phase micro extraction and capillary gas chromatography-mass spectrometry. Journal of Chromatography B. 2004;808:269-277.
25Xu H, Song D, Cui Y, Hu S, Yu Q-W, Feng Y-Q. Analysis of Hexanal and Heptanal in Human Blood by Simultaneous Derivatization and Dispersive Liquid-Liquid Microextraction then LC-APCI-MS- MS. Chromatographia. 2009;70:775-781. 781.
26Shin H, Umber BJ, Meinardi S, Leu S, Zaldivar F, Blake DR, et al. Gas Signatures from Cultured Neutrophils and Peripheral Blood Mononuclear Cells Obtained from Healthy Humans. J Mol Biomark Diagn. 2011;2:2; Shin HW, Umber BJ, Meinardi S, Leu SY, Zaldivar F, Blake DR, et al. Acetaldehyde and hexanaldehyde from cultured white cells. Journal of translational medicine. 2009;7:31.
27Li L, Moore PK. An overview of the biological significance of endogenous gases: new roles for old molecules. Biochemical Society Transactions. 2007;35: 1138-1141.
produce high local concentrations of gases upon aggregation at specific locations. It is possible that these VOCs may be also signaling through yet unknown pathways including supplementary PMN recruitment.
Bacteria Bacteria and other micro-organisms can also produce unique gas profiles which, if identified within out-gassed mixtures, may be used for diagnostic or monitoring purposes. Atypical example involves the ingestion of radiolabeled urea to detect Helicobacter pylori, a urease-positive bacterium which causes gastroduodenal ulcers; only if the microorganism is present, urea will be hydro lyzed into radiolabeled CO2 and ammonia28. Hydrogen cyanide has been shown to be released by cells infected by Pseudomonas aeruginosa and investigated as target for noninvasive diagnosis29 . Lactose ingestion by patients with hypolactasia is also known to increase their breath hydrogen concentrations, 48-168 ppmv vs 0- 3 ppmv in controls, due to the increased fermentation of carbohydrates by gut flora30. A group of VOCs were found produced in vitro by Mycobacterium tuberculosis and also associated with active infection (including derivatives of cyclohexane, benzene, heptane and hexane)31. More importantly to the field of diabetes, ethanol and other alcohols can be produced by gut bacteria in response to glucose ingestion or changes in systemic glycemia.
The concept that some gases may be released with specific signal transmission roles has gained increased prominence in recent years. Known signaling pathways of NO, for instance, whose discovery led to the 1998 Nobel Prize in Physiology or Medicine, are involved in the vasodilation of endothelial cells, neuronal synaptic plasticity, and antibacterial defense by directly targeting soluble guanylate cyclase41.
It is also likely that the biological activity of several additional VOC has simply not yet been identified; indirect inferences, however, may be derived for VOCs present in human systems from established signaling pathways in other organisms. While these considerations are certainly just speculative, it is indeed possible that neutrophil-derived ethylene and/or other related gases contribute to a complex signaling network involving multiple aspects of glucose regulation. Alterations in this interactive pattern may have obvious implications in patients with diabetes and impaired glucose metabolism.
Kearney DJ, Hubbard T, Putnam D. Breath ammonia measurement in Helicobacter pylori infection. Digestive diseases and sciences. 2002;47:2523-2530.
29Enderby B, Smith D, Carroll W, Lenney W. Hydrogen cyanide as a biomarker for Pseudomonas aeruginosa in the breath of children with cystic fibrosis. Pediatric pulmonology. 2009;44: 142-147. 30Metz G, Peters T, Jenkins D, Newman A, Blendis L. Breath hydrogen as a diagnostic method for hypolactasia. The Lancet. 1975;305: 1155-1157.
31 Phillips M, Basa-Dalay V, Bothamley G, Cataneo RN, Lam PK, Natividad MPR, et al. Breath biomarkers of active pulmonary tuberculosis. Tuberculosis. 2010;90: 145-151.
Interaction with the environment
Several VOCs have become widespread throughout the earth's atmosphere, and are present at stable and elevated enough concentrations to induce predictable and repeatable changes in human metabolism when inhaled. Ethyl nitrate possesses vasodilatory activity and suppresses methemoglobin formation32. Methyl iodide can dose-dependently increase serum cholesterol, HDL, LDL, and decrease triglycerides in both rat and rabbit toxicity studies 33 . Inhalation of dichloromethane, ethylbenzene, and trichloroethylene were shown to make 1,217 identifiable changes in rat gene expression34. Inhalation of a mixture of 22 VOCs, hydrocarbons, perturbed the immune system of healthy subjects, causing a migration of neutrophils to the upper airways35. While not obviously related to carbohydrate metabolism, these and other yet unidentified metabolically active exogenous VOCs may display changing exhaled profiles in the presence of fluctuating levels of energy substrates.
Premises of testing for diabetes mellitus
Understanding key physical differences induced by diabetes is a necessary prerequisite to accurate out-gas based measurement of diabetes related metabolic variables. A universal test, applicable to both healthy and diabetic populations, is ideal, and the changes in the intestins and metabolism occurring in diabetic patients have to be accounted for when assessing the out-gas composition as an indicator. Specific adjustments may be needed to the technique for every-day monitoring purposes.
Intestinal functions and pulmonary vasculature are both thought to be impaired in diabetes, potentially affecting gas exchange kinetics of multiple VOC, concepts that need to be addressed if utilizing these compounds in the development of out-gas based tests. As the disease worsens, for instance, increasingly severe pathological changes may render it necessary to perform frequent recalibrations or exclude specific VOCs from use in out-gas testing.
32Brandler MD, Powell SC, Craig DM, Quick G, McMahon TJ, Goldberg R , et al. A Novel Inhaled Organic Nitrate That Affects Pulmonary Vascular Tone in a Piglet Model of Hypoxia-Induced Pulmonary Hypertension. Pediatric Research. 2005:58.
33Himmelstein MW, Kegelman TA, DeLorme MP, Everds NE, O'Connor JC, Kemper RA, et al. Two- day inhalation toxicity study of methyl iodide in the rat. Inhalation Toxicology. 2009; 21 :480-487; Sloter E, Nemec M, Stump D, Holson J, Kirkpatrick D, Gargas M, et al. Methyl iodide-induced fetal hypothyroidism implicated in late-stage fetal death in rabbits. Inhalation Toxicology. 2009; 21 :462- 479.
34Kim JK, Jung KH, Noh JH, Eun JW, Bae HJ, Xie HJ, et al. Identification of characteristic molecular signature for volatile organic compounds in peripheral blood of rat. Toxicology and Applied Pharmacology. 2011; 250:162-169.
35Koren HS, Graham DE, Devlin RB. Exposure of Humans to a Volatile Organic Mixture. III. Inflammatory Response. Archives of Environmental Health. 1992:47.
Direct metabolic changes alter out-gassed substance composition in subjects with diabetes. Blood glucose may be degraded directly into VOCs measurable in the out-gas components (e.g. through fermentation to ethanol or methanol)15'36. Glucose may also indirectly affect other VOC levels (e.g. hyperglycemia changes the rate of acetone formation through the suppressive effect of concomitant, physiological compensatory hyperinsulinemia). Even greater hyperinsulinemia is a defining characteristic of early-stage T2DM, also resulting in suppression of lipo lysis7; changes in cholesterol synthesis may be reflected in exhaled isoprene37. Very small alterations in ketone concentrations and other VOCs may therefore reflect the fluctuations of insulin and glucose metabolism72. A complicating factor, not directly caused by the disease itself, is that T1DM patients tend to limit carbohydrate ingestion in favor of proteins and lipids while T2DM patients often ingest a large percentage of high-fat nutrients; this excessive fat ingestion leads not only to acute increases in insulin resistance but also to changes in out-gassed methyl nitrate (range: 6-15 pptv) and other ketones38 .
Exhaled breath gas
The potential of out-gassed substance analysis was recognized for decades in case of exhaled breath gases. Complicated and time consuming techniques in measuring very low concentrations of breath compounds have severely limited clinical applicability of out-gas analyses. The choices of analytical techniques with sufficient detection capabilities have recently increased. Breath analysis techniques available today include classic tracer studies that involve the infusion of molecules tagged with either stable or radioactive isotopes followed by isotope quantification in exhaled breath (which can be used to study β-oxidation and other metabolic pathways39). Gas chromatography and mass spectroscopy capabilities have also improved to such an extent that previously undetectable concentrations of
Galassetti PR, Novak B, Nemet D, Rose-Gottron C, Cooper DM, Meinardi S, et al. Breath ethanol and acetone as indicators of serum glucose levels: an initial report. Diabetes Technology and Therapeutics. 2005; 7: 115-123; Minh TDC, Oliver SR, Ngo J, Flores RL, Midyett J, Meinardi S, et al. Non-invasive Measurement of Plasma Glucose from Exhaled Breath in Healthy and Type 1 Diabetic Mellitus Subjects. American Journal of Physiology - Endocrinology and Metabolism. 2011;300: E1166-E1175.
37Salerno-Kennedy R, Cashman KD. Potential applications of breath isoprene as a biomarker in modern medicine: a concise overview. Wiener klinische Wochenschrift. 2005; 117:180-186.
38Musa-Veloso K, Likhodii SS, Cunnane SC. Breath acetone is a reliable indicator ofketosis in adults consuming ketogenic meals. American Journal of Clinical Nutrition. 2002; 76:65-70; Blake D, Iwanaga K, Novak B, Meinardi S, Pescatello A, Cooper DM, et al. Effect of a High-Fat Meal on Resting and Post-Exercise Exhaled Methyl Nitrate (CH3ONO2) Profile in Children. Diabetes. 2004;53: A372.
39McCloy U, Ryan MA, Pencharz PB, Ross RJ, Cunnane SC. A comparison of the metabolism of eighteen-carbon 13C-unsaturated fatty acids in healthy women. Journal of Lipid Research. 2004; 45:474-485.
1 Q
VOCs can now be routinely identified and quantified with a high degree of accuracy40. Additionally, direct measurements of compounds in EBC is now possible41; typically, exhaled breath is trapped in a collection tube, cooled to an aqueous form, and analyzed by various chromatographic techniques3. New technologies with more frequent gas sampling or real-time analysis have been developed, which allow repeated testing during and following metabolic perturbations. These approaches include atmospheric pressure ionization mass spectrometry coupled with electrospray charging, which has been used for on-line measurements of volatilized fatty acids42 as well as proton transfer reaction mass spectrometry (PTR-MS)43 for measurement of breath VOCs.
Miekisch et al and Di Francesco et al, introduced general instrumental techniques (chromatography and electronic sensors) and some clinical applications for breath testing44. Buszewski et al later elaborated on several considerations for breath sampling, preconcentration, and analysis45. More recently, Smith et al reviewed selected ion flow tube mass spectrometry and PTR-MS technology for VOC breath analysis in diabetes mellitus46. Other non- invasive glucose monitoring methods are still being actively pursued. A detailed summary of 14 such technologies is written by Tura et al.47, including several spectroscopic techniques, and newer breath and skin technologies48.
Colman JJ, Swanson AL, Meinardi S, Sive BC, Blake DR, Rowland FS. Description of the analysis of a wide range of volatile organic compounds in whole air samples collected during PEM-tropics A and B. Analytical Chemistry. 2001; 73:3723-3731.
86. Simpson I, Colman J, Swanson A, Bandy A, Thornton D, Blake D, et al. Aircraft measurements of dimethyl sulfide (DMS) using a whole air sampling technique. Journal of Atmospheric Chemistry. 2001; 39: 191-213.
41Janicka M, Kot-Wasik A, Kot J, Namiesnik J. Isoprostanes-Biomarkers of Lipid Peroxidation: Their Utility in Evaluating Oxidative Stress and Analysis. International Journal of Molecular Sciences. 2010; 11 :4631.
42Martinez-Lozano P, Zingaro L, Finiguerra A, Cristoni S. Secondary electrospray ionization-mass spectrometry: breath study on a control group. Journal of Breath Research. 2011; 5:016002; Fouty B. Diabetes and the pulmonary circulation. American Journal of Physiology - Lung Cellular and Molecular Physiology. 2008;295: L725-L726.
43Lindinger W, Taucher J, Jordan A, Hansel A, Vogel W. Endogenous Production of Methanol after the Consumption of Fruit. Alcoholism: Clinical and Experimental Research. 1997; 21 :939-943. 44Di Francesco F, Fuoco R, Trivella MG, Ceccarini A. Breath analysis: trends in techniques and clinical applications. Microchemical Journal. 2005 ;79: 405-410; Miekisch W, Schubert JK, Noeldge-Schomburg GFE. Diagnostic potential of breath analysis— focus on volatile organic compounds. Clin Chim Acta. 2004; 347:25-39.
45 Buszewski B, Kesy M, Ligor T, Amann A. Human exhaled air analytics: biomarkers of diseases. Biomedical chromatography. 2007; 21 :553-566.
46Smith D, Spanel P, Fryer AA, Hanna F, Ferns GAA. Can volatile compounds in exhaled breath be used to monitor control in diabetes mellitus? Journal of Breath Research. 2011; 5:022001.
47Tura A, Maran A, Pacini G. Non-invasive glucose monitoring: assessment of technologies and devices according to quantitative criteria. Diabetes research and clinical practice. 2007; 77: 16-40. 48Turner C. Potential of breath and skin analysis for monitoring blood glucose concentration in diabetes. Expert Review of Molecular Diagnostics. 2011; 11 :497-503.
Out-gassed VOCs have been studied for the identification of specific aspects of diabetes and energy substrate metabolism. For example, breath acetone has been linked with diabetic ketoacidosis and isoprene with cholesterol synthesis. Exemplifying Out-gassed VOCs are listed in Table 1. In addition to associating specific compounds with physiological processes experiments have also focused on diabetic screening. Using proton transfer reaction-mass spectrometry on mass ranges of out-gas substances, each representing an unknown group of VOCs, Greiter et al distinguished patients with T2DM from healthy controls with a 90% sensitivity and 92% specificity49. Similarly, Kulikov et al found light hydrocarbons (C2-C3 including ethanol and acetaldehyde) to be elevated in the exhaled breath of women who had risk factors for T2DM (i.e. relatives with diabetes and smoking) as compared to those without50. None of these tests have yet been translated into commercial devices, these studies constitute an important foundation for future research (especially on their underlying biochemical pathways) and product development.
Table 1: Gas Compounds Relevant for Diabetes Mellitus
Compound
Aerosolized glucose
Aromatic compounds e.g. ethylbenzene, o/m/p-xylene, toluene
Alkyl nitrates, e.g. 2-, 3-pentyl nitrate, methyl nitrate
Carbon dioxide
Carbon monoxide
Ethane and pentane
Ethanol and methanol
Ketones, e.g. acetone, 2-pentanone
Isoprene
Propane
Propionic and butanoic acids
Greiter MB, Keck L, Siegmund T, Hoeschen C, Oeh U, Paretzke HG. Differences in Exhaled Gas Profiles Between Patients with Type 2 Diabetes and Healthy Controls. Diabetes Technology & Therapeutics. 2010;12:455-463.
50Kulikov V, Ruyatkina L, Sorokin M, Shabanova E, Baldin M, Gruznov V, et al. Concentration of light hydrocarbons in exhaled air depending on metabolic syndrome risk factors. Human Physiology. 2011; 37:329-333.
^
Other experiments have focused on using VOCs for the quantification of altered metabolic conditions associated with diabetes. A common feature of the methods is the realization that a one-to-one correspondence between a single out-gassed compound and a given plasma metabolite, or the severity of a given metabolic condition, does not exist. As some individual compounds are traditionally considered more important, they have received particularly focussed attention; a typical example is breath acetone. Efforts have, in fact, been made to correlate breath levels of this gas with blood concentrations of ketone bodies, e.g. acetone, β-(η=9) and T2DM (n=53) subjects displayed significantly increased oxidative stress as compared to healthy controls (n=39)25.
Several studies have found associations between higher acetone levels and the presence of diabetes in various patient populations without attempting to actually derive plasma glucose values51. Few laboratories have been reporting their ability to estimate glycemic levels through integrated analysis of the kinetic profiles of multiple exhaled gases when usually oral and intravenous or glucose tolerance tests are used. A strong correlation between exhaled methyl nitrate profiles in the low part- per-trillion range and glycemic levels in the 100 to 400 mg/dl range was observed in a cohort of T 1 DM children52. These studies clearly indicated that any effective predictive model must incorporate the exhaled profiles of at least several VOCs. Each of these profiles is also likely influenced not only by glucose concentrations but possibly by a number of concomitant metabolic changes, including insulin, lipids, and oxidative status, which complicates data interpretation and potentially limits predictive ability across metabolic conditions and subject groups. On the basis of these results and considerations, it is hypothesized that stronger breath-derived predictive models for plasma glucose, applicable to both healthy and diabetic subjects, could be obtained through more prolonged and complex in vivo metabolic studies, encompassing multiple combinations of glycemic and insulinemic values. Therefore, experiments were designed in which hyperglycemia and hyperinsulinemia were induced both simultaneously and separately, and plasma glucose values were estimated accurately through analysis of the profiles of clusters of four exhaled gases collected at 12 time points during each study.
51Deng C, Zhang J, Yu X, Zhang W, Zhang X., Determination of acetone in human breath by gas chromatography-mass spectrometry and solid-phase micro extraction with on-fiber derivatization, J Chromatogr B Analyt Technol Biomed Life Sci 810: 269-275, 2004; Righettoni M, Tricoli A, Pratsinis SE, Si: WO(3) Sensors for highly selective detection of acetone for easy diagnosis of diabetes by breath analysis. Anal Chem 82: 3581-3587, 2010; Sulway MJ, Malins JM., Acetone in diabetic ketoacidosis, Lancet 296: 736-740, 1970; Tassopoulos CN, Barnett D, Fraser TR., Breath-acetone and blood-sugar measurements in diabetes, Lancet 1 : 1282-1286, 1969
52Novak BJ, Blake DR, Meinardi S, Rowland FS, Pontello A, Cooper DM, Galassetti PR. Exhaled methyl nitrate as a noninvasive marker of hyperglycemia in type 1 diabetes. Proc Natl Acad Sci USA 104: 15613-15618, 2007
^
Other studies, using multi-linear regression "MLR" on clusters of exhaled VOCs, focused on developing the conceptual background for a future breath-based, hand-held glucometer. Early experiments by Galassetti et al integrated exhaled ethanol, range: 9.6-45.0 ppbv, and acetone concentrations, range: 280-364 ppbv, obtained during a standard 75g oral glucose tolerance tests, into an model for estimating plasma glucose; the average individual correlation coefficient for the 10 healthy young participants, 5M/5F, was 0.7072. The group then modified their methodology to use intravenous glucose infusion in 10 healthy subjects, 5M/5F, and included 2 additional VOCs, methyl nitrate: range 5-216 pptv; ethylbenzene: range 46-434 pptv, into their MLR model, resulting in higher correlations with glucose. Methyl nitrate is believed to be a by-product of the interaction of oxidative radicals and NO while ethylbenzene reflects hepatic enzymatic activity. This improved 4-gas model allowed breath-based glucose prediction with a mean correlation coefficient of 0.91, range r=0.70- 0.98, when compared to standard glucose measurements15. In a subsequent study, the same 4-VOC MLR model was applied to both T1DM and healthy subjects, during more complex glycemic fluctuations, 4-h glucose clamp with a 60-min baseline, 90-min hyperglycemia at -220 mg/dl, 90- min hyperinsulinemia-euglycemia. VOC-derived estimates and direct measurements of glycemia had a very high degree of agreement with an average correlation coefficient of >0.86 over 30 study visits53. While these results appear extremely promising, their applicability to clinical practice is still limited by the enormous cost and analytical complexity; it is hoped that miniaturization of the procedures will lead within a reasonable timeframe to the development of clinically testable prototypes of portable devices.
While not tested as frequently as plasma glucose, circulating insulin and lipids also constitute diabetes-related variables whose non-invasive measurement may advance disease prevention and monitoring. Initial reports of breath-based estimates of plasma insulin and triglycerides have, in fact, been published. In a cohort of 13 young healthy subjects, undergoing 4-h clamp experiments in which insulin values ranges from basal levels to ~15-fold basal, breath-based plasma insulin predictions correlated with ELISA measurements with a mean correlation coefficients of 0.94. Importantly, in this study, which utilized MLR analysis of 5 exhaled VOCs54, a common predictive equation was used for all subjects - unlike the glucose prediction studies mentioned above, in which, while the same gases were used in all subjects, individual predictive equations were necessary, implying an individual calibration test. Out-gas based insulin testing is an important screening tool early screening
53Minh TDC, Oliver SR, Ngo J, Flores RL, Midyett J, Meinardi S, et al. Non-invasive Measurement of Plasma Glucose from Exhaled Breath in Healthy and Type I Diabetic Mellitus Subjects. American Journal of Physiology - Endocrinology and Metabolism. 2011;300: El 166— El 175.
54 Galassetti PR, Ngo J, Oliver SR, Pontello A, Meindari S, Newcomb R, et al. Non-Invasive Measurement of Plasma Insulin Via Exhaled Breath Analysis. American Diabetes Association 69th Scientific Session; New Orleans, Louisiana. 2009. p. 1460-P.
^ of diabetes and metabolic syndrome, conditions in which hyperinsulinemia by far precedes elevations of glycemia6. Exhaled VOC-based predictive models for plasma triglycerides "TG" and free fatty acids "FFA" were also obtained during similar 4-hour in vivo experiments, insulin-induced lipid suppression or i.v. infusion of a lipid emulsion, in 23 healthy volunteers 12m/l lf, 28.0±0.3 years55. Strong correlations between measured and breath-based predictions were observed, r=0.86 for TG, r=0.81 for FFA. If developed, a breath-based lipid test could contribute to the overall management of diabetic patients, whose systemic lipid levels represent a critical risk factor for cardiovascular events; of course, this test could be extended to many non-diabetic populations too.
Summary
The following presents a simplified summary in order to provide a basic understanding of some aspects of various invention embodiments. The summary is not an extensive overview of the invention. It is neither intended to identify key or critical elements of the invention nor to delineate the scope of the invention. The following summary merely presents some concepts of the invention in a simplified form as a prelude to a more detailed description of exemplifying embodiments of the invention.
In accordance with the present invention, there is provided a new system for producing information indicative of diabetes, the system comprising:
- means, e.g. a bowl of a toilet seat, for collection of excrement,
a gas sensor for analyzing an intestinal gas sample, and
means for drawing the intestinal gas sample to the gas sensor.
The gas sensor is configured to determine, from the intestinal, gas sample, presence and concentrations of pre-determined volatile organic compounds, and the system comprises processing means for producing the information indicative of diabetes as a weighted linear combination of the determined concentrations of the pre-determined volatile organic compounds.
In accordance with the present invention, there is provided also a new method for producing information indicative of diabetes, the method comprising:
- receiving excrement with means for collection of the excrement,
drawing an intestinal gas sample to a gas sensor,
55 Minh TDC, Oliver SR, Blake DR, Carlson MK, Meinardi S, Galassetti PR. Non-Invasive Measurement of Plasma Triglycerides from Exhaled Breath. American Diabetes Association 70th Scientific Session; Orlando, FL. 2010. p. 1086-P
j
- using the gas sensor for determining, from the intestinal gas sample, presence and concentrations of pre-determined volatile organic compounds, and
producing the information indicative of diabetes as a weighted linear combination of the determined concentrations of the pre-determined volatile organic compounds.
The present invention is based on identifying, analyzing and matching signatures of specific gaseous compounds found in intestinal/exhaled gases, released by an individual, with the known candidate biomarkers of Diabetes Mellitus "DM". Different gases, such as, but not limited to, acetone (CH3)2CO, ethanol C2H60, methyl nitrate CH3NO3, ethyl benzene CeHsC bC b, plasma triglycerides TG e.g. C55H98O6, 2-pentyl nitrate C5H11NO3, propane C3H8, methanol CH3OH, are identified and measured in the flow of intestinal/exhaled gas and are matched with a set of predicted biomarkers connected to plasma insulin, lipid and fatty acid related gaseous signatures of T1DM & T2DM by using a machine learning algorithm and individual calibrations. For calibration, measurement of the intestinal and/or exhaled gas and ambient air in the vicinity of the intestinal gas measurement is used. The ambient gas measurement may be simultaneous or it can be performed by the same sensor before or after the actual measurement. For powering the sensor and data acquisition system, a battery chargeable by using the existing auxiliary toilet mechanics, e.g. a button for flushing the WC and the toilet seat, may be utilized. The power may be harvested from mechanical energy, like generation of electricity from toilet seat cover opening and closing, or generating electricity from the pressurized incoming water same way as LED-lighted shower heads. The power need for analysis is small, and also the data amount that may be sent by for example Bluetooth or by Wi-Fi is moderately small and doesn't consume much energy.
A sampling system may comprise at least one fan or other gas pump for moving gas samples and valves for taking samples. The samples may be drawn to one or more gas collection chambers, and from the chamber to sensors, and the used samples may be flushed away by fresh air. The odorless toilet like in Finnish utility model U20100452 may be used, and the house ventilation can be used for means of suction of gasses. However, depending on the type of sensors and gas collection chambers, there may be needed a gas pump or fan for moving the sample to the sensor array and mixing the sample, and flushing the chamber and sensor with fresh air.
The sampling may be continuous and/or discrete. The gas is carried past the sensors without storing the sample first. The continuous sample may be used for only some sensors, and the output of those sensors can be used for timing the sampling, that is opening the gas collection chamber and storing the gas sample for analysis. There may be multiple chambers for example for exhale air and intestinal
Λ ,
16
gases and for calibration. For example, the exhale air sampling may be done by measuring continuously only CO2 -concentration for recognizing when the sample is proper exhaust air and taking the sample for analysis only after the CO2 -concentration is high enough. After the sample is taken, the tubes and chambers may be ventilated for example by a fan or by opening the air passage to house ventilation system and ambient air.
According to one aspect, the present invention concerns a system for intestinal gas characterization including: an intestinal gas sampling device, a pipe for the intestinal gas flow out from the toilet bowl, a gas collection chamber integrated with the pipe outputting the intestinal gas, a gas sensor within the gas collection chamber, equipped with integrated signal processing means for intestinal gas flows, a gas sensor outside the gas collection chamber, equipped with integrated signal processing means for measuring the surrounding gas, means for data acquisition and data transmission, real-time data display, an electricity supply for charging the battery of the gas sensor. According to another aspect, the present invention concerns a system for intestinal gas characterization including: an intestinal gas sampling device, a pipe for the intestinal gas flow out from the toilet bowl, a gas mask for exhaled air with an output pipe connected to the pipe outputting the intestinal gas, a gas collection chamber integrated with the pipe outputting the intestinal gas from the toilet bowl, a gas sensor within the gas collection chamber, equipped with integrated signal processing means for intestinal gas flows, a switch operating the gas sensor alternatively for the input during the exhaling of breath gas into the gas mask, or for the input during the intestinal gas sampling , a gas sensor outside the gas collection chamber, equipped with integrated signal processing means for measuring the surrounding gas, means for data acquisition and data transmission, real-time data display, an electricity supply for charging a battery of the gas sensor.
According to another aspect the present invention concerns a method of detecting the presence of T1DM or T2DM, comprising: identifying and analyzing the collected intestinal and breath gas for a presence of volatile organic compounds "VOC" as markers of T1DM or T2DM as inputs to an adaptable machine learning algorithm. The volatile organic compounds include, for example but not limited to, the following: acetone (CH3)2CO, ethanol C2H6O, methyl nitrate CH3NO3, ethyl benzene C6H5CH2CH3, plasma triglycerides TG e.g. C55H98O6, 2-pentyl nitrate C5H11NO3, propane C3H8, methanol CH3OH.
j
The adaptable machine learning algorithm uses the -dimensional vectors Rd in mapping the number d of identified volatile compounds into the predicted type T1DM, T2DM or TODM = negative diabetes incidence, or their combinations, as /: Rd→ ft(TnDM). As an example, for the eight volatile organic compounds listed above, the input vector is 8- dimensional, i.e. Rd = R8. The adaptable machine learning algorithm optimizes, for each individual case, an initial equation
[TnDM: (t - 3t)] = XQ + 1 [VOC1 (t)] + ¾ [VOC2 (t)] + X3 [VOC3 (t)] + ... + ¾ [VOC8 (t)], where t denotes time, At is a time off-set, and ¾ Xi, X2, X3, Xs are coefficients that represent the expected difference in glucose when the concentration of each corresponding gas is increased by one unit, whereas all the other gases are kept at constant concentrations. The algorithm is adapted for each individual patient and calibrated for each individual indicator VOC and, for an 'empty' case, the surrounding air.
For evaluating the statistical accuracy of the analysis results, the measured and predicted TnDM values are displayed by the real-time data acquisition system, for example, as a Parkes glucose consensus error grid56 and in terms of Bland- Altman plots57. The real-time data acquisition system also produces statistical analysis measures, such as Pearson's product-moment correlation coefficients used for accessing the clinical relevance of the findings.
Various exemplifying and non-limiting embodiments of the invention are described in accompanied dependent claims. The verbs "to comprise" and "to include" are used in this document as open limitations that neither exclude nor require the existence of also un-recited features. The features recited in depending claims are mutually freely combinable unless otherwise explicitly stated. Furthermore, it is to be understood that the use of "a" or "an", i.e. a singular form, throughout this document does not exclude a plurality.
Parkes JL, Slatin SL, Pardo S, Ginsberg BH. A new consensus error grid to evaluate the clinical significance of inaccuracies in the measurement of blood glucose. Diabetes Care 23: 1143-1148, 2000
57Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet: 307-310, 1986
Brief description of drawings
Exemplifying and non-limiting embodiments of the invention and their advantages are explained in greater detail below in the sense of examples and with reference to the accompanying drawings, in which:
Figure 1 shows a system according to an exemplifying embodiment of the invention for producing information indicative of diabetes,
Figure 2 shows a system according to another exemplifying embodiment of the invention for producing information indicative of diabetes,
Figure 3 illustrates means for charging a battery of a system according to an exemplifying embodiment of the invention for producing information indicative of diabetes,
Figure 4 shows a device for flushing a toilet and for charging a battery of a system according to an exemplifying embodiment of the invention for producing information indicative of diabetes. Figure 5 shows a flowchart of a method according to an exemplifying embodiment of the invention for producing information indicative of diabetes.
Description of exemplifying embodiments The specific examples provided in the description given below should not be construed as limiting the scope and/orthe applicability of the appended claims. Furthermore, it is to be understood that lists and groups of examples provided in the description given below are not exhaustive unless otherwise explicitly stated. Figure 1 shows a system according to an embodiment of the invention for producing information indicative of diabetes. The system comprises: means 1 for collection of excrement, a gas sensor 3 for analyzing an intestinal gas sample, and means 2 for drawing the intestinal gas sample to the gas sensor. The gas sensor 3 is configured to determine, from the intestinal gas sample, presence and concentrations of pre-determined volatile organic compounds. The system comprises processing
^ means for producing the information indicative of diabetes as a weighted linear combination of the determined concentrations of the pre-determined volatile organic compounds.
In a system according to an exemplifying embodiment of the invention, the gas sensor 1 is configured to determine, from the intestinal gas sample, the presence and the concentrations of the following predetermined volatile organic compounds: acetone, ethanol, methyl nitrate, ethyl benzene, plasma triglycerides, 2-pentyl nitrate, propane, and methanol.
In a system according to an exemplifying embodiment of the invention, the means 1 for collection of excrement comprises a toilet seat bowl and the means 2 for drawing the intestinal gas sample comprises an odor exhaust ventilation adapted to conduct the intestinal gas sample from the toilet seat bowl to the gas sensor 3.
In a system according to an exemplifying embodiment of the invention, the gas sensor 3 further comprises gas collection chamber for gas sampling.
A system according to an exemplifying embodiment of the invention is adapted take samples from air for calibration when the means 1 for collection of excrement is not in use. A system according to an exemplifying embodiment of the invention comprises at least one pump or valve for controlling the intestinal gas sample flow to one or more gas sample collection chambers and for ventilation the gas sample collection chambers and the gas sensor with fresh air after analyzing the intestinal gas sample. Figure 2 shows a system according to an embodiment of the invention for producing information indicative of diabetes. The system comprises: a toilet seat bowl for collection of excrement, a gas sensor 3 for analyzing an intestinal gas sample, and means 2 for drawing the intestinal gas sample to the gas sensor. The system further comprises a gas mask 11 for exhaled air with an output pipe connected to the gas sensor 3. The gas sensor 3 is configured to determine, from the intestinal gas sample or from the exhaled air, presence and concentrations of pre-determined volatile organic compounds. The system comprises processing means for producing the information indicative of diabetes as a weighted linear combination of the determined concentrations of the pre-determined volatile organic compounds. The system comprises a switch operated valve 4 for choosing the operational mode of gas sampling: intestinal gas sampling or exhaled breath gas sampling.
2Q
Figure 3 shows an exemplary toilet seat for generating electricity to charge a battery which powers the gas sensor, see Figures 1 and 2. The toilet seat comprises a piezoelectric module with a piezoelectric element underneath, a circuit to transmit the electricity generated by the piezoelectric effect, due to the weight of a person sitting on the seat, gravitational force F, to the battery of the gas sensor.
Figure 4 shows an exemplifying device for flushing the toilet and for charging the battery of the gas sensor. The device comprises a wall mounted housing and a piezoelectric module. Figure 5 shows a flowchart of a method according to an exemplifying embodiment of the invention for producing information indicative of diabetes.
The method comprises the following actions:
action 1 : receiving excrement with means for collection of the excrement,
- action 2: drawing an intestinal gas sample to a gas sensor,
action 3 : using the gas sensor for determining, from the intestinal gas sample, presence and concentrations of pre-determined volatile organic compounds, and
action 4: producing the information indicative of diabetes as a weighted linear combination of the determined concentrations of the pre-determined volatile organic compounds.
A method according to an exemplifying embodiment of the invention further comprises actions 8 for system calibration, environmental controls, and/or mode selection.
A method according to an exemplifying embodiment of the invention further comprises:
- action 5 : receiving the produced information indicative of diabetes,
action 6: analyzing the information with machine learning algorithms for disease recognition, and
action 7: sending the analysis results wirelessly to e.g. a portable phone or some other communication device.
The specific examples provided in the description given above should not be construed as limiting the scope and/or the applicability of the appended claims. Lists and groups of examples provided in the description given above are not exhaustive unless otherwise explicitly stated.
Claims
1. A system for producing information indicative of diabetes, the system comprising:
- means (1) for collection of excrement,
a gas sensor (3) for analyzing an intestinal gas sample, and
means (2) for drawing the intestinal gas sample to the gas sensor,
characterized in that the gas sensor is configured to determine, from the intestinal gas sample, presence and concentrations of pre-determined volatile organic compounds, and the system comprises processing means for producing the information indicative of diabetes as a weighted linear combination of the determined concentrations of the pre-determined volatile organic compounds.
2. A system according to claim 1, wherein the gas sensor is configured to determine, from the intestinal gas sample, the presence and the concentrations of the following pre-determined volatile organic compounds: acetone, ethanol, methyl nitrate, ethyl benzene, plasma triglycerides, 2-pentyl nitrate, propane, and methanol.
3. A system according to claim 1 or 2, wherein the means for collection of excrement comprises a toilet seat bowl and the means for drawing the intestinal gas sample comprises an odor exhaust ventilation adapted to conduct the intestinal gas sample from the toilet seat bowl to the gas sensor.
4. A system according to claim 3, wherein the gas sensor further comprises gas collection chamber for gas sampling.
5 A system according to any preceding claim, wherein the system is adapted take samples from air for calibration when the means for collection of excrement is not in use.
6. A system according to any preceding claim, wherein the system comprises a gas mask (11) for exhaled air with an output pipe connected to the gas sensor,
7. A system according to any preceding claim, wherein the system comprises at least one pump or valve for controlling the intestinal gas sample flow to one or more gas sample collection chambers and for ventilation the gas sample collection chambers and the gas sensor with fresh air after analyzing the intestinal gas sample.
8. A method for producing information indicative of diabetes, the method comprising: receiving (1) excrement with means for collection of the excrement, and
drawing (2) an intestinal gas sample to a gas sensor,
characterized in that the method comprises:
- using (3) the gas sensor for determining, from the intestinal gas sample, presence and concentrations of pre-determined volatile organic compounds, and
producing (4) the information indicative of diabetes as a weighted linear combination of the determined concentrations of the pre-determined volatile organic compounds.
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| FI20175835 | 2017-09-20 | ||
| FI20175835A FI20175835A1 (en) | 2017-09-20 | 2017-09-20 | Intestinal gas analysis |
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