WO2000028891A1 - Procede non invasif d'identification des individus presentant des risques de diabete - Google Patents

Procede non invasif d'identification des individus presentant des risques de diabete Download PDF

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WO2000028891A1
WO2000028891A1 PCT/US1999/027360 US9927360W WO0028891A1 WO 2000028891 A1 WO2000028891 A1 WO 2000028891A1 US 9927360 W US9927360 W US 9927360W WO 0028891 A1 WO0028891 A1 WO 0028891A1
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light
eye
subject
raman
collected
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PCT/US1999/027360
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Anthony J. Durkin
Marwood N. Ediger
Vivian M. Chenault
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The Government Of The United States Of America As Represented By The Secretary, Department Of Health And Human Services
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Priority to US09/856,186 priority Critical patent/US6721583B1/en
Priority to AU17353/00A priority patent/AU1735300A/en
Publication of WO2000028891A1 publication Critical patent/WO2000028891A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • 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
    • A61B5/1455—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 using optical sensors, e.g. spectral photometrical oximeters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/65—Raman scattering

Definitions

  • the invention relates to methods for non-invasive detection of ocular pathologies. More particularly, the invention provides a non-invasive method for detecting molecular changes in the eye of a subject that are associated with an ocular pathology. The method can be used for the identification of diabetes-associated molecular changes and the identification of individuals at risk for developing diabetes.
  • Diabetes mellitus is a complex group of syndromes that have in common a disturbance in the oxidation and utilization of glucose, which is secondary to a malfunction of pancreatic beta cells.
  • One of the most threatening aspects of diabetes mellitus is the development of visual impairment or blindness consequent to cataract formation, retinopathy or glaucoma. Diabetes-related ocular pathologies often go undiagnosed until visual function is compromised.
  • Non-invasive methods for prospective identification of individuals at risk for developing diabetes and for identifying ocular pathology in its earliest stages.
  • Non-invasive methods will require techniques which avoid exposing the eye to unsafe light intensities while still producing data capable of detection and analysis.
  • References describing techniques relevant to non-invasive ocular measurements, including Raman spectroscopy include J. Sebag et al., 1994, Invest. Ophthamol. Vis. Sci., 35(7):2976-2980; Y. Ozaki et al. 1987, Applied Spectroscopy 41(4):597-605; A. Mizuno and Y. Ozaki, 1991, Lens and Eye Toxicity Research, 8:177-187; N.T. Yu and E.J.
  • the invention provides methods for the use of Raman spectroscopy to non- invasive ly detect molecular characteristics of the constituents of the aqueous humor, vitreous humor, lens or retina.
  • the method can be employed for the detection of molecular changes underlying ocular pathologies.
  • the non-invasive method provided by the invention makes use of techniques and equipment that enable detection of Raman spectra with light intensities that fall within acceptable safety standards.
  • the method involves the steps of introducing light into the eye of the subject using a laser; collecting Raman spectra emitted from the eye; dispersing the collected Raman spectra onto a detector; and analyzing detected
  • the method is employed to detect early markers of diabetes or diabetes-induced ocular pathologies.
  • the ocular pathology is a pre-cataract marker or a pre-retinopathy marker.
  • the method further involves correlating the Raman spectra with traditional markers associated with known ocular pathologies.
  • the markers associated with diabetes induced ocular pathologies include blood glucose and insulin levels.
  • detected Raman spectra obtained from a subject suffering from an ocular pathology is analyzed and compared to a normal spectral pattern in order to identify a molecular change that is associated an ocular pathology.
  • the subject is a sand rat and the ocular pathology is diabetes induced ocular damage.
  • Raman spectroscopic measures obtained from a patient can be compared to a data set range of measures established from subjects with normal to severely pathological ocular measures to determine the current status of the subject's condition.
  • Figure 1 is an illustration of the apparatus for the acquisition of in vivo Raman spectra.
  • Figure 2 is a block diagram of the flow of the nonlinear iterative Partial Least Squares algorithm.
  • the invention provides methods for the use of Raman spectroscopy to non- invasively detect molecular characteristics of the constituents of the aqueous humor, vitreous humor, lens or retina.
  • the method can be employed for the detection of molecular changes underlying ocular pathologies.
  • the non-invasive method provided by the invention makes use of techniques and equipment that enable detection of Raman spectra with light intensities that fall within acceptable safety standards.
  • Embodiments of the invention include methods of using Raman spectroscopy for detecting changes in the eye and identifying those changes associated with ocular pathologies such as diabetes.
  • the ocular pathology is a pre-cataract marker or a pre-retinopathy marker.
  • the method involves the steps of introducing light into the eye of the subject using a laser; collecting Raman spectra emitted from the eye; dispersing the collected Raman spectra onto a detector; and analyzing detected Raman spectra to quantify a molecular change related to an ocular pathology.
  • this method involves correlating the
  • Raman spectra with traditional markers associated with known ocular pathologies are blood glucose and insulin levels.
  • Raman spectroscopic measures obtained from a patient can be compared to a data set range of measures established from subjects with normal to severely pathological ocular measures in order to determine the status of the subject's condition.
  • Such illustrative embodiments can be used to identify early disease states, to provide information on the long term status of the subject and to facilitate the early treatment of various pathologies.
  • Embodiments of the invention include methods for the non-invasive identification of molecular changes associated with ocular pathologies.
  • One embodiment of this method consists of using a laser to introduce light into an eye of a subject having an ocular pathology, collecting Raman spectra emitted from the eye, dispersing the collected Raman spectra onto a detector, analyzing detected Raman spectra obtained from the subject and comparing the subject's spectral pattern to a normal spectral pattern, wherein differences between the subject's spectral pattern and the normal spectral pattern indicate an ocular pathology.
  • the subject is a sand rat and the ocular pathology is diabetes induced ocular damage.
  • Embodiments of the invention can use a variety of lasers to introduce light into the eye of a subject.
  • the laser that is utilized is selected from the group consisting of HeNe, diode, Ti:Sapphire, Cr:Forsterite and Nd:YAG lasers. Lasers emitting light of various wavelengths are well known in the art. In one embodiment of the invention, the laser emits light of a visible red wavelength. In an alternative embodiment, the laser emits light of an infrared wavelength. In preferred embodiments, the laser emits light of a wavelength selected from the group consisting of about 600-700 nm; about 700-1300 nm; about 1200-1300 nm; and about 1550-1850 nm.
  • different embodiments of the invention can focus light into various regions of the eye of a subject.
  • the light introduced into the eye is focused into the region of the eye selected from the group consisting of the lens, the vitreous humor, the aqueous humor and the retina.
  • Embodiments of the invention can evaluate a variety of molecular changes that may be associated with ocular pathologies.
  • the molecular changes are assessed by comparing the ratios of various moieties including the -OH stretching / -CH stretching ratio or the -SH / S-S ratio of lens proteins.
  • the molecular change comprises alterations in the concentrations of aqueous metabolites.
  • the molecular change is the increase of products from non-enzymatic gly cation of retinal proteins.
  • a variety of embodiments of the invention exist for the collection, dispersion and analysis of Raman spectra.
  • light is introduced and collected in an imaging mode.
  • the light is introduced and collected in a non-imaging mode.
  • the light is introduced and collected through a graded index lens.
  • the Raman spectra collected are selected from the group consisting of the region in about 4000-200 cm -1 and about 3000-500 cm " '.
  • the collected light is dispersed onto a detector using a ruled grating.
  • the collected light is dispersed onto a detector using a holographic grating.
  • the detector is a red/blue enhanced CCD camera.
  • the detected spectral data are analyzed using partial least squares.
  • the detected spectral data are analyzed using principle component analysis.
  • the invention provides a method for non-invasive detection of ocular pathology in a subject.
  • the method comprises the introduction of light into the eye of the subject using a laser.
  • the useful spectral range extends from the long wavelength visible (red) into the near infrared.
  • the wavelength of light which is introduced into the eye can be selected to minimize scattering and fluorescence, as well as minimizing water absorption to maximize the signal which will ultimately be collected. Water absorption limits the technique beyond 1850 nm and also in a band around 1400 nm.
  • the useful spectra wavelength ranges are from about 600-700 nm in the visible red range, and 700- 1300 nm and 1550-1850 nm in the infrared range.
  • the light is infrared.
  • the preferred wavelength is about 1200- 1300 nm.
  • Different sources for a particular wavelength of light may be selected. Examples of lasers that can be used as a light source in the method include, but are not limited to, helium: neon (HeNe), diode, titanium: sapphire (Ti: Sapphire), chromium: forsterite (Cr:Forsterite), and neodymium: yttrium aluminum garnet (Nd:YAG).
  • the laser used to introduce light into the eye comprise an infrared wavelength producing laser, including, but not limited to a 632.8 nm HeNe laser, 700-900 nm Ti:Sapphire laser, 1150-1300 nm C ⁇ Forsterite laser and 1064 nm, 1300 nm, 1500 nm or 1600 nm Nd:YAG laser.
  • an infrared wavelength producing laser including, but not limited to a 632.8 nm HeNe laser, 700-900 nm Ti:Sapphire laser, 1150-1300 nm C ⁇ Forsterite laser and 1064 nm, 1300 nm, 1500 nm or 1600 nm Nd:YAG laser.
  • the lens through which light is introduced into the eye can be selected to minimize the illumination and collection volumes, thereby avoiding light intensities too high for compliance with safety standards.
  • the light is introduced into the eye by a non-spatially selective fiber bundle. This configuration provides a non-imaging mode of detection and can detect relatively weak signals from the target, at the expense of spatial resolution of the signal.
  • the light introduced into the eye is coupled into a single mode optical fiber using a microscope objective. This configuration provides an imaging mode of detection and can maximize the spatial resolution of the signal from a particular target in the eye, at the expense of sensitivity to the signal.
  • the light can be passed through a line pass filter to prevent HeNe plasma lines from reaching the sample.
  • Graded index (GRIN) lenses can be incorporated at the distal tip of the optical fiber bundle to obtain spatially resolved Raman spectra at reduced light levels.
  • the use of GRIN lenses has been described by Ansari and Suh R. (R. Ansari et al., 1996, Ophthalmic Technologies VI, pp. 12-20; R.R. Ansari et al., 1996, Lasers in Ophthalmology III, pp. 62-72; R.R. Ansari and K.I. Suh, 1996, Optical Diagnostics of Living Cells and Biofluids, Vol. 2678).
  • Targets can include, but are not limited to, the aqueous humor, vitreous humor, lens or retina. Raman spectral measurements from any one of these targets will reflect the molecular status of proteins in the target region examined.
  • the method for non-invasive detection of ocular pathology in a subject further comprises collecting Raman spectra emitted from the eye.
  • Various means for collecting and analyzing Raman spectra are well known in the art including those disclosed in U.S. Patent Nos. 5,751,415 and 5,048,959, which are incorporated herein by reference.
  • a spectrograph that will spectrally disperse input light and provide at the output plane the resulting spectra is sufficient.
  • an imaging spectrograph which adds the feature that the tall axis of the slits is imaged onto the spectral plane may be utilized. With imaging spectrographs, there is a one to one relationship of slit and the 'height' of the image.
  • the light which was emitted from an imaging or non-imaging mode of introduction is collected through a graded index lens (GRIN).
  • the Raman spectra collected are preferably in the region of about 4000 to about 200 cm ""1 and more preferably in the region of about 3000 to about 500 cm -1 .
  • the method for non-invasive detection of ocular pathology in a subject further comprises dispersing the collected Raman spectra onto a detector.
  • the collected light is dispersed onto a detector using a conventional ruled grating.
  • the light is collected and dispersed onto a detector using a holographic grating.
  • detectors are, but are not limited to, photomultipliers, reticon arrays, diode arrays or charge coupled device (CCD) cameras.
  • the detector is a red/blue enhanced CCD camera. Thermoelectric cooling of the camera improves signal-to-noise ratio.
  • the camera may be cooled to, but is not limited to being cooled to -40 C.
  • Raman spectroscopy is a sensitive technique for monitoring biochemical changes, allowing for the detection of molecular changes in such ocular structures as aqueous, vitreous and the lens.
  • Raman spectra exhibit distinctive features that are molecule specific.
  • Raman scattering is inelastic- he molecular bonds of the species under study scatter the incoming optical radiation and slightly change its wavelength. The differential energy is converted into vibrational energy of the molecule.
  • the spectrographic signal (monitoring the scattered radiation at a specific shift with respect to the incident radiation) is measured by recording the accumulated detector reading (in arbitrary units) at that (shifted) wavelength. Often the background/stray light is subtracted.
  • a meaningful indicator is frequently the ratio between the two spectral peaks. In most cases there is no quantified threshold for these ratio or other spectroscopic values. That is, changes in molecular bonds of the species under study are taken as relative to baseline.
  • Raman spectroscopy can be used to identify a wide range of different molecular changes which can be correlated to ocular pathology. Specifically, Raman spectroscopy can be used to identify early markers of diabetes or diabetes-induced ocular pathology, or markers of advanced procession of a diabetes induced ocular pathology. Generally, a catalog of Raman spectra collected from subjects with normal ocular history and subjects with known pathological ocular history can be compiled for comparison against subjects with unknown ocular conditions. Raman peaks in each sample will be compared with the catalog database in order to determine if peaks in the unknown sample are more similar to the normal or pathological condition. Studies have shown that changes in Raman peaks at various points (on the spectrum) change with pathology of the ocular target studies. Examples of changes in the Raman spectra which occur with ocular pathology, but to which this invention is not limited, are discussed below.
  • Raman spectroscopy can be used to detect alterations in lens hydration as a result of hyperglycemic stress, a phenomenon that is thought to be directly related to the formation of lens opacities.
  • the Raman peak in lens spectra corresponding to the O-H stretching mode (denoting water) can be compared to that of the C-H stretch (denoting protein) to monitor hydration of lens proteins (A. Mizuno and Y. Ozaki, 1991, Lens and Eye Toxicity Research 8:177-187).
  • An increase in the ratio of-OH/-CH or a reduction in the -SH/-S-S ratio is indicative of an increase in lens hydration.
  • increases in lens hydration are indicative of early lens opacification (A. Mizuno and Y. Ozaki, 1991, Lens and Eye Toxicity Research 8:177-187).
  • advanced glycation products that correspond to increased nonenzymatic glycation in retinopathy patients can be resolved in Raman spectra of vitreous samples.
  • the Raman spectra can detect changes in advanced glycation products. An increase in advanced glycation products will generate a larger spectrographic signal and be indicative of increased nonenzymatic glycation and of retinopathy.
  • the Raman spectra can detect various aqueous metabolites (see e.g. J. Wicksted, et al., 1995, Appl. Spec, 49, 987-93).
  • Raman scattering can be used to observe the metabolic concentrations of molecules including glucose, lactate and urea.
  • Two positions of a grating as discussed above can be used for the different laser excitation wavelengths.
  • lower frequency ranges can be used to study the C-N stretching vibration of urea as well as the C-COOH peak of lactic acid, while higher ranges can be used to observe the CH and CH stretches of glucose and lactate, respectively.
  • the method for non-invasive detection of ocular pathology in a subject further comprises analyzing the detected Raman spectra to quantify a molecular change related to ocular pathology.
  • a variety of techniques for analyzing Raman spectral data are known in the art. For example, a multivariate statistical method can be used to obtain predictive information based on the detected spectra.
  • the detected spectral data are analyzed using partial least squares (PLS; A.J. Durkin, et al., 1998 Lasers in Medical Science).
  • PLS partial least squares
  • PCA principle component analysis
  • Laboratory Systems 2:37-52) may be utilized to analyze the data.
  • PLS partial least squares
  • the PLS method is based on the regression between two matrices, X and Y, using the notation of Malinowski (E.R. Malinowski (1991), New York John Wiley and Sons, Inc. 169-172). Specifically, for spectroscopic analysis of n mixtures (sample) with p unknowns (constituents of interest), X and Y represent spectral and concentration matrices respectively. Through a sequence of matrix rotations and regression steps, which for the simplest case can be described as a singular value decomposition, PLS seeks to relate the matrix of spectra, X, to the matrix of concentrations of the constituent of interest, Y, via a calibration or model matrix B such that
  • Y X B nxp nxm mxp
  • B is set of calibration constants for the system.
  • the rows of X and Y contain information about n sample mixtures.
  • the columns of X contain emission spectra at m spectral wavelengths.
  • samples may contain many constituents, the rows of Y are composed only of the concentrations of the p known constituents of interest for each sample.
  • NPALS Nonlinear Iterative Partial Least Squares
  • the accuracy of prediction for PLS is effected by the composition of the training and validation sets as well as the spectral information included in the data (A.J. Durkin et al. (1994), Proceedings from The Conference on Lasers and Electro-optics (CLEO) May).
  • One technique used to assess the accuracy of prediction and to select the optimum number of factors to retain in the model is known as the method of cross- validation (D.M. Haaland (1990) supra; E.R. Malinowski (1991) supra). This technique evaluates the ability of a PLS calibration model to predict the concentrations of unknown spectra as a function of the rank (number of factors or principal components) used in creating the calibration model.
  • calibration and data acquisition can be performed using Kestrel Spec software (Rhea Corp.).
  • a 632.8 mn neon line can be used with the single point calibration routine provided in this software. Reproducibility of the calibration can be verified using the plasma lines which are permitted to reach the detector upon removal of the line pass filter.
  • the integration time for each acquired spectrum can be 1 second with a total of 200 accumulated spectra per sample.
  • data collection parameters can be loosely based on parameters used by various other groups involved in Raman studies of tissues (C.J. Franck, et al. (1993), Applied Spectroscopy 47(4):387-390; A.
  • Data from the samples can be analyzed using a PLS algorithm from the MATLAB Chemometrics Toolbox.
  • a block diagram depicting the important steps in the analysis method used here is shown in Figure 2.
  • the most accurate prediction model for the training data can be chosen using a cross-validation algorithm which can iteratively construct a model for N-l spectra was then decomposed into principal components, or factors, using singular value decomposition (D.M. Haaland, et al. (1988), Analytical Chemistry 60:1193-1202). Models can be built using a successively increasing number of these factors. On the first iteration, the model can consists of only the factor that accounts for most of the variance in the data (indicated by the magnitude of the corresponding eigen value).
  • the model for each factor level can be applied to the "unknown" spectrum to predict the concentration of the component of interest and the prediction error can be recorded in a matrix for future reference.
  • the next most important factor (indicated by the magnitude of the corresponding eigen value) can subsequently be included in the model and the prediction process repeated until a model consisting of all factors resulting from the decomposition of the N-l training set can be employed.
  • the "unknown” can then be returned to the training data and a different spectrum can be selected as the "unknown”. This entire process can be repeated until all samples play the role of "unknown” once.
  • the prediction error for each factor level can then be summed across the sample set and plotted.
  • the model for the entire set can then be constructed using the factor level for which the residuals across the sample set are minimized. Models consisting of the "optimum" number of factors+1 and the “optimum” number of factors- 1 can also be constructed. These models can also be applied to the data and the predictions compared to the results obtained using the "optimum" number of factors.
  • the detected spectra can be analyzed using other techniques such as neural networks (see e.g. Durkin and M.N. Ediger, 1998 SPIE- Least Invasive Diagnostics, 3253-3254).
  • variables such as the substance concentration of a biological analyte or analytes can be determined by comparing the Raman spectral characteristics of the sample with a comparative model, in particular, an artificial neural network discriminator (ANND) that can be trained with a plurality of Raman spectral characteristics from biological fluids or tissue possessing known Raman scattered light intensities versus wavelength characteristics at known concentrations.
  • ANND artificial neural network discriminator
  • a preferred implementation of the ANND employs fuzzy adaptive resonance theory-mapping (ARTMAP), which has noise rejection capabilities and can readily handle nonlinear phenomena.
  • ARTMAP fuzzy adaptive resonance theory-mapping
  • Patent No. 5,553,616 which is incorporated herein by reference.
  • a subject's eyes can be evaluated using Raman spectroscopic measurements of lens, aqueous humor or vitreous humor as described above and can also be evaluated for traditional measures of diabetes and diabetes-induced ocular pathology. Collected spectral data can be subjected to a multivariate statistical analysis. A data set and algorithm can be developed for using Raman spectroscopy to diagnose diabetes or diabetes-induced ocular pathology in humans using an approach based on that established in the animal model described in Example 2 below.
  • Blood can be drawn from the subject to determine levels of glucose and insulin.
  • Glucose can be measured, for example, using a glucose oxidase immunoassay of a whole blood sample.
  • Insulin can be measured using, for example, enzyme immunoassay methods of a blood serum sample.
  • An ophthalmic exam can be administered to determine if the subject has any ocular damage.
  • the pupils of the subject can be dilated to facilitate the exam.
  • the ophthalmic exam can include examining the eye for the presence of cataracts. If the subject has a cataract, it can be graded as: grade 0 (lens completely clear), grade 1 (opaque areas at lens periphery, vascular features of the retina still visible), grade 2 (widespread distribution of opaque areas within the lens, vascular features of the retina obscured), grade 3 (lens totally opaque, cataract visible as dense white mass to the naked eye).
  • grade 0 laens completely clear
  • grade 1 opaque areas at lens periphery, vascular features of the retina still visible
  • grade 2 widespread distribution of opaque areas within the lens, vascular features of the retina obscured
  • grade 3 lasculature of the retina and intraocular pressure can also be examined.
  • the visual acuity of the subject can be tested using a standard vision
  • Example 1 Detection of Diabetes-Induced Molecular Changes of the Eye Using Raman Spectroscopy.
  • One method for the detection of diabetes-related ocular pathologies involves the use of Raman spectroscopy to non-invasive ly monitor changes in the molecular constituents of the lens, vitreous humor, aqueous humor or retina. Ideally this method is employed to screen for early markers of ocular molecular pathologies so that permanent damage to the eye can be circumvented by preventative drug therapy.
  • This example demonstrates a method by which Raman spectroscopy can be used to detect such changes.
  • An optical probe is used to introduce light into and collect light emitted from a subject's eye ( Figure 1). Light is focused into the lens, vitreous humor, aqueous humor or retina of the subject.
  • the source of light introduced into the eye is a 632.8 nm helium neon (HeNe) laser (Spectra-Physics 127). This light is coupled into a single mode optical fiber using a microscope objective and is passed through a line pass filter to prevent HeNe plasma lines from reaching the sample. Graded index
  • GRIN GRIN lenses are incorporated at the distal tip of the optical fiber bundle to obtain spatially resolved Raman spectra at reduced light levels.
  • a line filter Keriser Super Notch, 6 OD, 632.8 nm
  • Calibration and data acquisition is performed using Kestrel Spec software (Rhea Corp.) single point calibration routine for 632.8 nm neon light.
  • the integration time for each acquired spectrum is optimized to obtain the best signal to noise ratio while minimizing the exposure of the eye to laser light.
  • Data is subsequently exported to Microsoft Excel and combined to form matrices.
  • MATLAB the Math Works, Natick MA
  • the Raman spectra collected exhibit distinctive features that are molecule specific, depending upon the region of the eye into which the light is focused. Where the light is focused into the lens of the subject, the Raman peak in lens spectra corresponding to the O-H stretching mode (water) /C-H stretch (protein) ratio may be analyzed to determine the hydration state of lens proteins. Where the light is focused into the vitreous humor of the subject, the level of advanced glycation products may be analyzed as an indication of the level of nonenzymatic glycation.
  • Example 2 Development and Optimization of an Algorithm for the Prediction of
  • One method for the early diagnosis or indication of the progression of diabetes in a subject is the detection of ocular molecular changes by Raman spectroscopy.
  • the correlation of molecular changes of the eye associated with the progression from normal to diabetic conditions can be evaluated.
  • This example demonstrates a method by which an algorithm can be created based on this correlation such that ocular Raman spectra measurements alone may be used to predict if a subject has diabetes and, if so, the level of progression of the disease.
  • the subjects used in an animal model are female sand rats (Psammomys obesus). This animal model is chosen because this strain of rat develops nutritionally- induced diabetes. Manipulation of the sand rat's diet has been shown to result in a slow onset and progression of diabetes. This model is more similar to the progression of diabetes observed in humans than other animal models which use pharmaceuticals to induce an abrupt diabetic condition.
  • Rats are divided into a control group (fed non-diabetogenic, low carbohydrate / high fiber sand rat chow diet (BioServ/Noight diet)) and the experimental group (fed diabetogenic diet (Purina 5002 rodent chow diet)). Rats are sacrificed each month for 12 months in order to examine the progression of diabetes-induced changes in these animals.
  • Blood is drawn from each animal every two weeks to determine levels of glucose and insulin.
  • Glucose is measured using a glucose oxidase immunoassay of a whole blood sample.
  • Insulin is measured using enzyme immunoassay of a blood serum sample.
  • An ophthalmic exam for cataract formation is administered every two weeks.
  • the pupils are dilated with application of Mydriacyl 5g/l (Tropicamide, ophthalmic solution, USP, 0.5%) to facilitate the exam.
  • Mydriacyl 5g/l Tropicamide, ophthalmic solution, USP, 0.5%) to facilitate the exam.
  • the subject If the subject has a cataract, it is graded as: grade 0 (lens completely clear), grade 1 (opaque areas at lens periphery, vascular features of the retina still visible), grade 2 (widespread distribution of opaque areas within the lens, vascular features of the retina obscured), grade 3 (lens totally opaque, cataract visible as dense white mass to the naked eye). Histological samples of the eyes and pancreas are collected from each animal at necropsy and evaluated for pathological histology.
  • Raman spectroscopy of the eye is performed prior to sacrificing of each animal as described in Example 1.
  • Aqueous and vitreous humor samples are collected prior to euthanasia for additional in vitro spectroscopic analysis.
  • Data from each subject are compiled such that a data set profile of ocular measures of Raman spectral features may be correlated with measures obtained by blood chemistry and ophthalmic examination made at different timepoints in the progression of diabetes in this animal model.
  • Multivariate statistical methods e.g., Principal Component Analysis (PCA), the method of Partial Least Squares (PLS), etc.
  • PCA Principal Component Analysis
  • PLS Partial Least Squares
  • the algorithm is selected such that it has the maximum sensitivity to pathological Raman spectrographic measures in comparison to normal measures, while minimizing the false positive identification of normal Raman spectra measures as pathological. Further, the algorithm is selected to distinguish between different stages in the progression of diabetes-induced ocular pathology. In order to accomplish this, several parameters are considered in analyzing the data, including but not limited to: (1) intrasubject variability; (2) calibration of the instrumentation with standards for each Raman measure to be examined; (3) transformation of raw data to reflect the weight of each variable measured in the diagnostic prediction of pathology; and (4) probability that a given measure exists within a particular diagnostic category (Ramanujam et al., 1996, Lasers in Surgery and Medicine 19:46-62).

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  • Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)

Abstract

L'invention concerne des procédés visant à utiliser la spectroscopie Raman pour détecter de façon non invasive les caractéristiques moléculaires des parties constitutives d'une humeur aqueuse ou vitreuse, de la lentille ou de la rétine. Le procédé peut servir à la détection de changements moléculaires signalant des pathologies oculaires. Dans un mode de réalisation de l'invention, le procédé consiste à faire pénétrer une lumière dans l'oeil du sujet au moyen d'un laser, à collecter le spectre Raman émis depuis l'oeil, à disperser le spectre Raman collecté sur un détecteur et à analyser les données spectrales Raman détectées pour identifier un changement moléculaire lié à une pathologie de l'oeil. Le procédé non invasif faisant l'objet de l'invention utilise des techniques et des équipements qui permettent la détection du spectre Raman avec des intensités lumineuses qui ne dépassent pas les normes de sécurité acceptables.
PCT/US1999/027360 1998-11-19 1999-11-18 Procede non invasif d'identification des individus presentant des risques de diabete WO2000028891A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
US09/856,186 US6721583B1 (en) 1998-11-19 1999-11-18 Method for non-invasive identification of individuals at risk for diabetes
AU17353/00A AU1735300A (en) 1998-11-19 1999-11-18 Method for non-invasive identification of individuals at risk for diabetes

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US10925798P 1998-11-19 1998-11-19
US60/109,257 1998-11-19

Publications (1)

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WO2000028891A1 true WO2000028891A1 (fr) 2000-05-25

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1480559A2 (fr) * 2002-01-07 2004-12-01 The University of Utah Research Foundation Procede et appareil pour obtenir une image de pigments maculaires par imagerie raman
EP1494577A1 (fr) * 2002-04-04 2005-01-12 Inlight Solutions, Inc. Analyse spectroscopique d'un tissu permettant de deceler le diabete

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WO1992010131A1 (fr) * 1990-12-14 1992-06-25 Georgia Tech Research Corporation Systeme de mesure non invasive du niveau de glucose dans le sang
US5553616A (en) * 1993-11-30 1996-09-10 Florida Institute Of Technology Determination of concentrations of biological substances using raman spectroscopy and artificial neural network discriminator
EP0776628A2 (fr) * 1995-10-31 1997-06-04 Akitoshi Yoshida Appareil destiné à la mesure de substances intraoculaires
WO1997043612A1 (fr) * 1996-05-13 1997-11-20 Process Instruments, Inc. Appareil et procede utilisant la spectroscopie raman pour l'analyse chimique continue des courants de fluides

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WO1992010131A1 (fr) * 1990-12-14 1992-06-25 Georgia Tech Research Corporation Systeme de mesure non invasive du niveau de glucose dans le sang
US5553616A (en) * 1993-11-30 1996-09-10 Florida Institute Of Technology Determination of concentrations of biological substances using raman spectroscopy and artificial neural network discriminator
EP0776628A2 (fr) * 1995-10-31 1997-06-04 Akitoshi Yoshida Appareil destiné à la mesure de substances intraoculaires
WO1997043612A1 (fr) * 1996-05-13 1997-11-20 Process Instruments, Inc. Appareil et procede utilisant la spectroscopie raman pour l'analyse chimique continue des courants de fluides

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J.SEBAG ET AL: "raman spectroscopy of human vitreous in proliferative diabetic retinopathy", INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, vol. 35, no. 7, June 1994 (1994-06-01), pages 2976 - 2980, XP000889929 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1480559A2 (fr) * 2002-01-07 2004-12-01 The University of Utah Research Foundation Procede et appareil pour obtenir une image de pigments maculaires par imagerie raman
EP1494577A1 (fr) * 2002-04-04 2005-01-12 Inlight Solutions, Inc. Analyse spectroscopique d'un tissu permettant de deceler le diabete
EP1494577B1 (fr) * 2002-04-04 2012-03-28 Veralight, Inc. Analyse spectroscopique d'un tissu permettant de deceler le diabete
EP1480559A4 (fr) * 2002-12-19 2006-04-05 Univ Utah Res Found Procede et appareil pour obtenir une image de pigments maculaires par imagerie raman

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