WO2014180974A1 - Procédé de diagnostic de la narcolepsie à base de composés organiques volatils - Google Patents

Procédé de diagnostic de la narcolepsie à base de composés organiques volatils Download PDF

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WO2014180974A1
WO2014180974A1 PCT/EP2014/059506 EP2014059506W WO2014180974A1 WO 2014180974 A1 WO2014180974 A1 WO 2014180974A1 EP 2014059506 W EP2014059506 W EP 2014059506W WO 2014180974 A1 WO2014180974 A1 WO 2014180974A1
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sample
narcolepsy
voc
voc profile
detection
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PCT/EP2014/059506
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English (en)
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Luis DOMÍNGUEZ ORTEGA
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Ramem, S.A.
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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
    • G01N33/497—Physical analysis of biological material of gaseous biological material, e.g. breath
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
    • G01N27/622—Ion mobility spectrometry
    • 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
    • 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

Definitions

  • the present invention relates to a process for the diagnosis of narcolepsia by the detection of volatile organic compounds (VOC) in a patient's sample.
  • VOC volatile organic compounds
  • Narcolepsy is a primary sleep disorder characterized by excessive daytime sleepiness, sleep attacks, cataplexy and sleep paralysis with hypnagogic hallucinations. REM sleep and sleep continuity variables are also disturbed in narcoleptic patients.
  • the etiology of narcolepsy in humans is unknown, although it gives great importance to the role of the orexin/hypo cretin system, both in animals and humans.
  • the prevalence of narcolepsy is 1/2000 in the general population.
  • There are also some reports of narcolepsy symptoms associated to organic lesions such as mid-brain tumors, pontine gliomas, hypothalamic glyosis, and Down syndrome.
  • narcolepsy is based on clinical data and polysomnographic studies. These studies are expensive and require the subject to go personally to specialized facilities where he is monitored during sleep with costly equipment. Thus, in addition to the costs, this method takes full hours to perform and interpret the results and is inconvenient for the patient. Overall, diagnosis comes with years of delay due to the inspecificity of the symptoms, high economic costs and low reliability of the diagnostic methods.
  • Peak detection results Each dot represents a peak detected at a particular sample in a particular retention time.
  • Figure 2 Cluster range distribution.
  • X axis shows the range of retention times;
  • y axis the number of clusters.
  • Figure 3 LDA (linear discriminant analysis) classification for "S".
  • the "x" axis represents the predicted probability of a sample being of type "S”.
  • Figure 4 Supervised Multivariate Analysis Orthogonal Projection to Latent Structures analysis on a population of 17 diseased (black) and 27 healthy (red) subjects (example 3).
  • the inventors have overcome the above problems by providing a method based on the detection of at least one VOC.
  • the method is useful since it provides a new noninvasive, and surprisingly easy, safe, cost effective and fast detection tool for the diagnosis of narcolepsy.
  • the invention provides a method for determining whether a patient suffers narcolepsy comprising the detection of at least one VOC in a sample obtained from a patient.
  • the method comprises the steps of:
  • the method comprises detecting the odor components (VOCs) of a sample obtained from a subject; and comparing said odor components of the sample with a reference odor.
  • VOCs odor components
  • the method comprises the steps of:
  • the present invention also may provide a method for the detection of orexin/hypo cretin system malfunction.
  • a further aspect of the invention is a kit for obtaining a narcolepsy diagnosis sample that comprises sterile and odorless material.
  • Another aspect of the invention is an apparatus comprising a sensor capable of detecting VOCs and comparative means by which a sample's VOC profile is compared with at least one reference VOC profile.
  • the diagnostic use of the test is important in the initial differential diagnosis in the study of hypersomnias, since their negativity would avoid more costly studies. It is important as a means of screening for studying populations or groups requiring treatment of narcolepsy. Its application is specially interesting in cases where hypersomnia could be hazardous, as in the case of drivers, pilots and in professions involving risk. This diagnostic technique will reduce costs and will help to make a better selection of patients that should be studied with more complex and costly tests. Furthermore, it will be a useful means of screening for the study of narcolepsy in risk populations.
  • the particular VOC profile caused by narcolepsy can be reflected on different tissues and body fluids.
  • the sample is preferably selected from the group consisting of blood, breath, swab (sample obtained by rubbing the subject with a, cloth, gauze or other means of absorbing odor on the skin), sweat, urine, feces, semen, vaginal discharge, hair, nails, soft body tissue and mucus.
  • the sample may be thus collected and then analyzed ex vivo.
  • the invention can thus be performed in vitro.
  • a kit comprising suitable means for collecting, manipulating, storing and transporting the sample.
  • kit is preferably sterile in order to prevent cross-contamination.
  • all components of the kit are isolated from the exterior by, for example, a sealed receptacle which is opened immediately before collecting the sample.
  • This may include containers for the sample that can be sealed, preferably opaque.
  • the kit further includes instructions on how to manipulate materials and how to take and manipulate the sample.
  • the kit may contain a sterile deodorized container for urine.
  • the sample may be used immediately for detection, or it can be sealed and transported to another location for analysis. The time the sample is useful since the moment is taken depends on the nature of the sample, and is preferably preserved cold (e.g. between -10°C and 10°C).
  • the kit may contain an odorless sterilized sample container, gauze, soap and/or tweezers to manipulate the gauze.
  • a part of the patient can be washed with said odorless soap, rinsed with water and then air dried before taking the sample by allowing the patient to rub with the gauze the washed area.
  • the kit may additionally comprise specially deodorized water for rinsing the soap in order to further improve reliability of the method and prevent cross-contamination.
  • the sample is blood, a convenient sample form that is not excessively invasive and readily available in health centers.
  • Typical means include electronic noses (also known as olfactory systems) such as quartz crystal microbalance, resistive or capacitive sensors or surface acoustic waves, mass spectrometry (MS), liquid chromatography, gas chromatography (GC), capillary electrophoresis, differential mobility analyzer (DMA) and ion mobility spectroscopy (IMS) in any of its variants (drift time IMS, Field Asymmetric IMS), infrared techniques,.
  • electronic noses also known as olfactory systems
  • MS mass spectrometry
  • GC gas chromatography
  • DMA differential mobility analyzer
  • IMS ion mobility spectroscopy
  • GC-MS Miekisch et al, Clinica Chi mica Acta (2004) 347 25-39
  • proton transfer reaction-mass spectroscopy for a review on this technique see Lindinger et al. Int J Mass Spectrom Ion Process (1998) 173 191-241 or Lindinger et al. Adv Gas Phase Ion Chem (2001) 4 191-241)
  • the means used is IMS-MS (e.g. Ells et al. J. Environ. Monit, 2000, 2, 393-397).
  • Detection can also be done by a properly trained dog.
  • the dog so trained develops an odor, VOC profile or odor "fingerprint" that he can remember, of a subject or group of subjects suffering narcolepsy (reference odor or reference VOC profile).
  • the dog detects the odor or VOC profile of the sample, and performs the comparison.
  • Di Natale et al. (Biosensors and Bioelectronics (2003) 18 1209- 1218) used an array of non-selective gas sensors for detecting various alkanes and benzene derivatives as possible candidate markers of lung cancer.
  • Gordon et al. (Clin Chem (1985) 31(8) 1278-1282) used breath collection technique and computer-assisted gas chromatography/mass spectrometry to identify several volatile organic compounds in the exhaled breath of lung cancer patients which appear to be associated with the disease.
  • Wehinger et al. (Inter J Mass Spectrometry (2007) 265 49-59) used proton transfer reaction mass-spectrometric analysis to detect lung cancer in human breath.
  • Peng et al. (Nature Nanotech (2009) 4 669-673) identified 42 VOCs that represent lung cancer biomarkers using gas chromatography/mass spectrometry.
  • IMS or DMA systems useful in the method of the invention are described in WO 2010/133714 Al or WO 2008/003797 Al, respectively.
  • the method uses two or more orthogonal detection means, providing a more accurate diagnosis means.
  • Orthogonal detection means are understood as two or more detection means that are mutually independent, i.e. they detect independent characteristics of the sample. Once the sample's VOC profile has been obtained, it is correlated with the diagnosis of narcolepsy. The determination of said levels can involve detecting whether said at least one VOC is present or absent, or alternatively the levels in which it is present.
  • the sample VOC profile is compared with at least one reference VOC negative profile acting as negative control sample, i.e. a VOC profile obtained from a subject or group of subjects known not have narcolepsy.
  • the comparison is done with a positive reference VOC profile acting as positive control sample, i.e. a VOC profile obtained from a subject or group of subjects known to have narcolepsy.
  • the reference VOC profile can be positive or negative.
  • the reference VOC can be obtained from a single subject or it can be obtained from a plurality of subjects.
  • the levels or abundance of one VOC are detected.
  • the levels or abundance of two or more VOC are detected, and the determination of the VOC profile of the sample can involve the detection of the levels or abundance and proportions in which different VOCs are found.
  • a further aspect of the invention is a method for comparing a sample from a patient suspected of suffering narcolepsia with a reference VOC profile obtained as explained below.
  • the method can be conveniently implemented in a computer or similar, and a further aspect of the invention is a data processing system having means for carrying out said method for comparing.
  • a computer implemented method comprising comparing the VOC profile obtained from a sample of a subject suspected of suffering narcolepsy with a reference VOC profile, and a computer readable medium comprising computer executed instructions for performing said method for comparing.
  • VOC's are organic substances susceptible of being detected by an animal nose or by instrumental methods such as gas chromatography or liquid chromatography, or others disclosed herein. They are typically substances with low molecular weight, e.g. below 1000 Dalton, typically below 800 or below 600 Dalton.
  • VOC examples include C1-C20 linear or branched hydrocarbons.
  • linear or branched hydrocarbons having between 10 and 16 carbon atoms.
  • VOCs can be saturated or unsaturated, cyclic or acyclic.
  • Non-limiting examples include decane or hexadecane.
  • said VOC detected is an aromatic hydrocarbon or comprises an aromatic moiety.
  • Typical examples include benzenes substituted with at least one moiety selected from the group consisting of Ci-C 6 alkyl, Ci-C 6 alkenyl, Ci-C 6 alkinyl, hydroxyl, Ci-C 6 alcoxy, ketones and sulfones.
  • Non- limitative examples include 2,4-bis(l , l-dimethylethyl)-phenol, 2-methyl-6-(2- propenyl)-phenol, 2-ethyl-l ,4-dimethylbenzene, 4'-hydroxy-acetophenone, l-ethyl-3- methyl-benzene or benzyl-2-chloroethyl sulfone.
  • Other exemplary VOCs can be acids, esters, ketones or aldhydes.
  • Non-limiting examples are 4-methyl-l-heptanol, acetic acid octyl ester, decane, 2-decanone, 3-methyl-decane, octanal, pentadecanenitrile, and tetradecene, 6-methyl-l- heptanol, 2-ethyl-l-hexanol, benzaldehyde, tetrahydrofuran, isopropyl myristate, 2,2,4- trimethyl-pentanenitrile, 2,2,4-trimethyl-3-carboxyisopropyl- isobutyl ester pentanoic acid, phenol, styrene, 4-methyl-tetradecane, toluene, tridecane, 6-methyl- tridecane, undecance, triethylamine, 2-hydroxy benzaldehyde, and decanal.
  • VOC's which can be detected according to the present invention are prednisolone or prednisolone acetate, or polycyclic alkanes optionally oxidated (e.g. 4,4,8,8-tetramethyloctahydro-4a,7-methano-4aH-napth[l ,8a-b]oxirene.
  • each can be used in isolation or in combination with two or more VOCs.
  • the variation can be measured in one or more VOC's.
  • the present invention can also involve the detection of the relative levels between two or more VOCs.
  • the VOC profile obtained from the patient can be compared to a reference VOC profile according to various methods known in the art. For example, a multi-linear regression and fuzzy logic can be used to analyze the sample (Phillips et al, Cancer Biomarkers (2007) 3 95-109).
  • the VOC profile of the sample can be analyzed with an algorithm selected from the group consisting of artificial neural networks, multi-layer perception (MLP), generalized regression neural network (GRNN), fuzzy inference systems (FIS), self-organizing map (SOM), radial bias function (RBF), genetic algorithms (GAS), neuro-fuzzy systems (NFS), adaptive resonance theory (ART) and statistical methods including, but not limited to, principal component analysis (PCA), partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), discriminant function analysis (DFA) including linear discriminant analysis (LDA), and cluster analysis including nearest neighbor.
  • the algorithm used to analyze the pattern is principal component analysis (PCA).
  • the algorithm used to analyze the pattern is discriminant function analysis (DFA).
  • the pattern can be analyzed using support vector machine (SVM) analysis.
  • the analysis is performed through a principal component analysis (PCA) and/or a linear discriminant analysis (LDA).
  • PCA principal component analysis
  • LDA linear discriminant analysis
  • the reference VOC profile is obtained through a method comprising the following steps:
  • the method of the invention is useful to predict with reasonable accuracy the presence or absence of narcolepsy in a patient by the analysis of its VOC profile through chromatographic or other instrumental techniques.
  • the creation of a suitable reference VOC profile may involve the analysis of a large sample of healthy and diseased subjects.
  • the reference VOC profile is obtained through a method comprising the following steps:
  • the method of the invention can be further refined by processing the compound:abundance matrix.
  • refining may include as way of example, elimination of signals or abundances derived from column bleed, normalization of signals with respect to the total signal of the chromatogram, normalization test, or keeping signals present in at least a predetermined amount of the VOC profiles (e.g. present in at least 75% of the chromatograms of a group).
  • the reference VOC profile is obtained through a method comprising the following steps:
  • a multivariate e.g. supervised: Orthogonal Projection to Latent Structures [Trygg, J.; Wold, S. J. Chemometrics 2002; 16: 119-128]
  • unsupervised Principal Component Analysis (PCA) or univariate analysis of the VOC profiles in order to obtain a model
  • PCA Principal Component Analysis
  • a computer implemented method for the construction of a reference VOC profile comprising acquiring the VOC profile data from one or more subjects, including one or more subjects known to suffer narcolepsy, and identifying one or more signals which distinguish subjects having narcolepsy from those not suffering narcolepsy.
  • a further aspect of the invention is a computer readable medium comprising computer executed instructions for performing said method for the construction of a reference VOC profile.
  • the data processing systems described in the present invention will be any capable of computing the methods of the invention, including, but not limited to computers, laptops, tablets, smartphones, whether or not connected to the detection means.
  • Such data processing systems will preferably comprise a display screen, data acquisition means, memory, one or more processors and one or more programs stored in the memory configured to be executed by one or more processors, the program including instructions for performing the methods of the invention.
  • the samples from both patients and controls were collected according the following protocol: wash the hands and forearms for 30 seconds with non-perfumed soap (water, vegetable glycerin, coconut, corn syrup soap, alcohol, citric acid, vegetable vitamin E, nothing else); rinse hands and forearms in water for 2 minutes; air dries the hands for 2 minutes; rub the palms of the hands on the forearms for 5 minutes; while walking for 10 minutes, rub gauze in the palms of the hands, forearms and arms; introduce the gauze into the vial and close with the septum; identify the sample (sex, age, date of collection) and keep the sample refrigerated at 5° C until remission for the test provided in the course of 24 hours.
  • non-perfumed soap water, vegetable glycerin, coconut, corn syrup soap, alcohol, citric acid, vegetable vitamin E, nothing else
  • rinse hands and forearms in water for 2 minutes air dries the hands for 2 minutes; rub the palms of the hands on the forearms
  • the training method for the detection of the suspected specific odor in narcoleptic patients was as follows: breeds of medium size retrievers with strong olfactory senses and socialized to adapt to the intervention locations. The dogs were exposed to the odor of narcoleptics using gauze soaked in their sweat until the dogs associated the rewards with the odor and demonstrated they could discriminate between other samples of odorants and sweat soaked gauze from the healthy control group. The dogs were trained to search and identify odors, utilizing a guide with a leash or away from the guide without a leash.
  • the dogs worked in a 6 x 9 m room.
  • the cabinet was made of formic lined agglomerate panel that measures 366 x 91 cm and 2 cm thick hanging perpendicular to the floor on metal legs. It has 4 holes, 7.5 cm in diameter, which are separated by 88 cm and are 57.5 cm from the floor. The division of the last hole at the end of the panel is 44 cm, which can be linked to the other panel to maintain the distance (88 cm) with the other holes. In the back of the panel there are 4 boxes with lids. Their placements coincide with the holes on the other side.
  • the panels are attached by wooden screws and measure 30 cm wide x 20 cm deep x 20 cm tall. As already mentioned, active and passive detection was used, each dog used a specific method. The dog training was completed during March and April 2011.
  • the likelihood ratios are calculated an alternative way to describe the performance of a diagnostic test, and can be used to calculate the probability of disease after a positive or negative test.
  • the initial clinical probability (pre-test probability, Ppre) heuristically established by the doctor (extent of clinical suspicion) is modified (post-test probability, Ppost) depending on the efficacy of the diagnostic test in accordance with the function:
  • Oddspost LR x Oddspre.
  • Table 2 shows the distribution matrix of the subjects studied.
  • the basic operational diagnostic characteristics obtained in the experiment are: sensitivity 0.92 (0.72-1.00), specificity 0.86 (0.70-1.00), PPV 0.79 (0.054-1.00) and NPV 0.95 (0.83-1.00).
  • the area under the ROC curve (Receiving Operator Characteristic Curve) is 0.89 (0.78-0.99 CI 95 %).
  • the likelihood ratio of a positive test is 6.72 (2.32-19.51) and that of a negative test is 0.10 (0.01-0.63).
  • Table 3 shows an analysis of sensitivity of the post-test probabilities obtained for positive and negative results in the test, in different situations of clinical suspicion (pre-test probability).
  • the first line shows the estimated population prevalence of narcolepsy in the European population. If the test result is positive, the initial probability estimates that the patient observed may in fact have narcolepsy increase up to 0.3, 0.7, 6.4, 24.6, 26.1, 42.7 and 62.7 %, respectively. If the test proves negative, they decrease to 0, 0, 0.1, 0.5, 1.1 and 2.4 %, respectively.
  • the results obtained in the study show that the patients with narcolepsy give off a specific VOC profile.
  • the momentary values of diagnostic indices are high, and the study results prove a high predictive capacity of the test, particularly when the result is negative. Further, when detection by VOC is negative, the post test probability that the patient has narcolepsy decreases to clinically irrelevant values.
  • a classifier was trained to develop factor values Met.A, based on 50 gas chromatograms of samples obtained following the procedure described in the previous example (see section "Sweat Collection” in example 1). Said factor values were thus used as reference VOC profiles.
  • Table 4 shows how samples were distributed for each factor.
  • Met.A factors behave like binary labels with “Yes/No” values with an extra “Unknown” value. Therefore "unknown” samples cannot be used for training the classifier and they can only be used to see which label would the classifier assign them.
  • samples are interpolated using splines in order to homogenize the retention time axis among different samples.
  • noise is removed using a Savitzky-Golay (Abraham, Savitzky and M. J. E. Golay, "Smoothing and Differentiation of Data by Simplified Least Squares Procedures.," Analytical Chemistry 36, no. 8 (July 1, 1964): 1627-1639, doi: 10.1021/ac60214a047.) filter with a window size of 7 samples and a second order polynomial.
  • a set of features are extracted from the chromatograms building a "feature matrix”.
  • features peaks from all the samples are detected, then peaks with similar retention times are clustered together and a set of clusters is defined, where each cluster has its own retention time range.
  • a matrix with samples as rows and clusters as columns is built having as element (i,j) of the matrix the integral of sample i along the retention time range of cluster j.
  • Peak detection is performed using a matched gaussian filter based on the xcms R package (Colin A. Smith et al, "XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification," Analytical Chemistry 78, no. 3 (February 1, 2006): 779-787, doi: 10.1021/ac051437y).
  • the peak detection algorithm is controlled by three parameters:
  • Peaks are detected until the maximum number of peaks is reached or the signal to noise ratio threshold of the peaks is achieved. Peak boundaries are detected for each detected peak.
  • a sigma of 1.2 seconds is suitable given the average peak widths, detecting a maximum of 325 peaks on each sample and requesting the peaks to be above 1 % S/N ratio.
  • the most stringent criterion is the signal to noise ratio as the number of peaks limitation is never reached.
  • Peak clustering Peak clustering was performed using a one dimensional k-means algorithm (Haizhou Wang and Mingzhou Song, "Ckmeans. Id. Dp: Optimal K-means Clustering in One Dimension by Dynamic Programming," A Peer-reviewed; Open-access Publication of the R Foundation for Statistical Computing (n.d.): 29, accessed March 19, 2013). Peaks were clustered in 325 groups based on how close were the peaks to each other. Each cluster was considered a "feature" and was given a unique identification. Each cluster was assigned a retention time range, where all of its peaks were contained.
  • the feature matrix was built by integrating each of the samples at the clusters' retention time. The integral was performed along the cluster time range and everything inside the time range was be integrated.
  • the first feature selection criteria was based on the idea that a cluster is not significant if appears in "very few" samples. In other words, a cluster was selected as valid if at least verified one of these criteria:
  • the cluster has peaks detected in more than 30% of the samples.
  • the second feature selection criteria was based on an analysis of variance (ANOVA). Each cluster was tested for significance against "Met.A” factor, and was selected if there was a statistical significance with a p-value less than 0.05 of the cluster against said factor. Therefore a feature matrice was built with the clusters statistically significant to "Met.A".
  • LDA Linear Discriminant Analysis
  • PCA Principal Component Analysis
  • Peaks detected by the matched filter are shown on figure 1. Between 4 and 6 peaks were detected every 30 seconds on average.
  • Peak clustering The number of clusters is a parameter that must be estimated for the K-means algorithm. Knowing that a chromatogram lasts for 30 minutes approximately, the following estimation was given:
  • FIG 3. A visual representation of the ability to predict the classes from our PCA/LDA classifier is shown on figure 3. LDA classification for "S".
  • the "x" axis represents the predicted probability of a sample being of type "S ".
  • Met.A classifier is very specific ("N" samples are predicted accurately).
  • This methodology described in example 2 can be applied to other detection means such as electronic noses, quartz crystal microbalance, surface acoustic waves, resistive or capacitive sensors, liquid chromatography, differential mobility analysis and ion mobility spectroscopy, capillary electrophoresis and infrared detection.
  • detection means such as electronic noses, quartz crystal microbalance, surface acoustic waves, resistive or capacitive sensors, liquid chromatography, differential mobility analysis and ion mobility spectroscopy, capillary electrophoresis and infrared detection.
  • Example 2 A sweat collection was obtained as described in Example 1. 17 diseased and 27 healthy patients participated in the experiment. Sweat samples were processed with an Agilent 7890 gas chromatograph coupled to q uadrupole mass spectrometer, all in full scan mode. A library of target compounds was built including possible compounds found in the whole chromatograms by comparison of any spectrum peaks with the reference spectral library NIST 2008. The signal/time/mass spectra matrix was deconvoluted with AMDIS using this home-built library. The obtained data was further aligned in order to obtain a compound/abundance matrix with Mass Profiler Professional B.02.01. (Agilent Technologies).
  • variations in at least one of the above compounds was capable of distinguishing healthy from diseased subjects with 95% of statistically significance, which is a great improvement with respect to the methods currently used.
  • the proposed method could be based not only in the relative abundance of a single variable in patients compared to controls but also in some selective combination of more than one variable.

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Abstract

L'invention concerne un procédé, un appareil et un kit permettant de déterminer si un patient souffre de narcolepsie, le procédé comprenant la détection d'au moins un composé organique volatil (VOC) dans un échantillon provenant du patient.
PCT/EP2014/059506 2013-05-09 2014-05-08 Procédé de diagnostic de la narcolepsie à base de composés organiques volatils WO2014180974A1 (fr)

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Cited By (4)

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EP3143930A1 (fr) * 2015-09-21 2017-03-22 Université de Liège Procédé pour le diagnostic du sous-type inflammatoire d'une maladie des voies respiratoires
WO2017178032A1 (fr) * 2015-04-22 2017-10-19 Lachlak Nassira Procedure de detection des infections ou maladies dans le domaine de la gynecologie a partir des composes organiques volatiles des exsudats vaginaux
WO2018134214A1 (fr) * 2017-01-23 2018-07-26 Koninklijke Philips N.V. Alignement de données d'échantillons d'haleine à des fins de comparaisons de bases de données
US10517879B2 (en) 2015-12-17 2019-12-31 Performance Labs PTE. LTD. Device and method of using volatile organic compounds that affect mood, emotion or a physiologic state

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EP3143930A1 (fr) * 2015-09-21 2017-03-22 Université de Liège Procédé pour le diagnostic du sous-type inflammatoire d'une maladie des voies respiratoires
WO2017050527A1 (fr) * 2015-09-21 2017-03-30 Universite De Liege Méthode pour le diagnostic de sous-type inflammatoire de maladie des voies respiratoires
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