MX2007002100A - Method and apparatus for analyzing amniotic fluid. - Google Patents

Abstract

Methods and spectra for monitoring fetal growth and predicting birth weight of an infant prior to birth are provided wherein one or more selected biological markers are measured in a sample of amniotic fluid obtained from a pregnant woman. Levels of the selected biochemical markers and/or spectra correlate with one or more medical conditions, such as fetal growth and birth weight of the infant, and gestational diabetes. A measurement probe for in situ measurement can be used safely and repeatedly. Monitoring and/or treatment of maternal and fetal health is also provided.

Description

METHOD AND APPARATUS FOR ANALYZING AMN-OTICAL FLUID CROSS REFERENCE TO RELATED REQUESTS This application claims priority of the provisional patent application of E.U.A. series number 60 / 496,884 filed on August 21, 2003, the content of which is incorporated herein by reference.

SEARCH SPONSORED BY THE FEDERAL GOVERNMENT OF E.U.A.

The inventions described and claimed in this application are not based on the search with funds from the federal government of E.U.A. or its agencies, and the government of E.U.A. You do not have rights to this patent application.

FIELD OF THE INVENTION The present invention relates to methods and apparatus for determining fetal health or a risk of developing a fetal health condition. The invention also relates to methods and apparatus for verifying maternal health. The invention further relates to methods and apparatuses for analyzing amniotic fluid to determine one or more biological markers.

BACKGROUND OF THE INVENTION The assessment of fetal birth weight forms an important part of prenatal care. Therefore, specifying the previous determination of fetal weight before birth could markedly improve perinatal outcomes. Thus, there is a need for a quick and easy method to estimate fetal weight in the uterus, particularly infants at risk of either extreme: (1) macrocosm (also referred to in large as gestational age or LGA) or ( 2) small for gestational age (SGA) or retarded intrauterine growth (IUGR). Currently the most reliable predictor of infant birth weight is ultrasonography where according to a recent review article is able to predict birth weight within 300 to 400 grams (Table 12, Nahum eMedicine Journal), but the authors mentioned that this is as other techniques even have significant degrees of inaccuracy and suggested that a reasonable strategy to arrive at the estimated fetal weight is to even use multiple estimates based on different sources of clinical and sonographic information. In addition, they noted that even with ultrasound, macrosomia is not easily predicted. Both ultrasonography and clinical palpitation of fetal size have sensations of less than 60% for the prediction of macrosomia with false positives greater than 40%. Similarly, for small fetuses, frequently the estimates of ultrasonic fetal weight less than 1800 grams are in error by as much as 25%. The disadvantages of ultrasonography include the complicated and labor-intensive origin of the methodology that is often limited by the suboptimal visualization of fetal organs. It also requires expensive equipment and highly trained personnel. The latest requirements often prevent the use of any of the current techniques in developing countries. The use of ultrasound measurements of the fetus and information on the mother are combined to determine the birth weight in WO2004 / 036359 published on April 29, 2004. The current North American and Canadian guidelines recommended that all pregnant women be monitored for diabetes mellitus gestational (GDM) between 24-28 weeks. Before monitoring occurs only if multiple risk factors such as more mature maternal age, higher pre-pregnancy weight, membership in a high-risk ethnic group or strong family history of diabetes or if the previous diagnosis of GDM or birth occurred of macrosomic infant. However, several studies acknowledge that these criteria can still result under the diagnosis of GDM. Monitoring and current diagnostic criteria for GDM were predicted in the observation that an abnormal oral glucose tolerance test (OGTT), with its accompanying gestational hyperglycemia, increases both morbidity and perinatal and adult mortality. It is argued that the increased flow of glucose through the placenta is the stimulus for in utero production of insulin from Pancreatic islet cells in development and is the precondition for fetal hyperinsilinism, which in turn leads to increased fetal abdominal circumference, macrosomia, obesity and neonatal hypoglycemia and the diagnosis of GDM.

BRIEF DESCRIPTION OF THE INVENTION According to a broad aspect of the invention, the amniotic fluid is analyzed in situ without affecting the amniotic sac. According to another broad aspect of the invention, the amniotic fluid is analyzed without altering the composition of the amniotic fluid, to assess information on concentration and / or other components of the matrix that form the fluid. In accordance with even another broad aspect of the invention, amniotic fluid analysis correlates with a risk of developing a medical condition in at least one of a mother and her offspring. According to another broad aspect of the invention, the natal weight prediction of amniotic fluid analysis is improved either by providing prediction earlier during pregnancy or by providing better prediction accuracy. The invention provides a method of analyzing amniotic fluid in which a device is provided for measuring one or more selected biological markers in amniotic fluid, and is ordered with respect to an amniotic sac to measure amniotic fluid in situ without insertion of any instrument in the amniotic sac. The device is used to acquire measurement data that is processed to obtain a value for one or more selected biological markers in the amniotic fluid. The invention provides an apparatus for analyzing amniotic fluid in situ in a pregnant patient who has an amniotic sac containing amniotic fluid without inserting any instrument into the amniotic sac. The apparatus comprises a device for measuring one or more biological markers selected in amniotic fluid, a coupler adapted to order the device with respect to the amniotic sac to measure the amniotic fluid in situ without inserting any instrument into the amniotic sac, and a unit of processing to process device measurement data to obtain a value for one or more selected biological markers in the amniotic fluid. The invention provides a method for treating at least one of the pregnant mother and her fetus by providing a device for measuring one or more selected biological markers in amniotic fluid, ordering the device with respect to an amniotic sac to measure amniotic fluid in situ without insertion of any instrument in the amniotic sac, use the device to acquire measurement data, process the measurement data to obtain a value for one or more of the selected biological markers in the amniotic fluid, and determine at least one of a change in diet and a pharmaceutical intervention in response to value.

The invention provides a method of predicting a risk of developing a medical condition in at least one of a mother and her offspring by providing a device for analyzing amniotic fluid from the mother, using the device to acquire analytical data from the amniotic fluid, and processing the Analytical data to obtain a prediction value for the risk. The invention provides an apparatus for predicting a risk of developing a medical condition in at least one of a mother and her offspring. The apparatus comprises a device for analyzing amniotic fluid, and a processing unit for processing analytical data of the device to obtain a prediction value for the risk. The present invention can be applied to any animal that has a bag of amniotic fluid that allows the fluid to be accessed for analysis, and in particular, the invention can be applied to humans. The terms "patient", "mother", "offspring", "fetus" and other similar terms that relate to subjects or their body parts that are intended here to relate to humans and non-humans, unless explicitly stated in Another way. In this specification, the term "medical condition" means a condition that is, or has a probability to be, related to the health of the mother, fetus or offspring. An example is the weight of offspring at birth, mainly birth weight, fetus weight such as small for gestational age (SGA) or retarded intrauterine growth (IUGR), appropriate gestational age (AGA) or health, and large for gestational age (LGA) or macrosomic. It is recognized that abnormal weight is the cause of a variety of health complications. "Medical condition" also includes an indication of an absence of a problem such as AGA, whose information for a pregnant woman is a reassessment that has a benefit to the well-being of the mother. Another example is diabetes in the mother, otherwise known as maternal diabetes and more specifically, gestational diabetes mellitus (GDM). Another example is the prematurity of birth. In this specification, the term "biological marker" includes one or more biochemical indicia such as glucose, lactate or other metabolic acid, one or more proteins that include but are not limited to insulin, insulin as growth actors (IGFs) and their proteins of binding and / or one or more fatty acids. A biological marker can also comprise cells that can be identified and counted within the amniotic fluid, as well as other physical properties of amniotic fluid, such as viscosity, that can be measured, either in vitro or in situ.

BRIEF DESCRIPTION OF THE DRAWINGS The invention will be better understood in the manner of the following detailed description of various embodiments with reference to the accompanying drawings, in which: Figure 1a illustrates the typical spectrum and wavelength regions selected for natal weight estimation using Raman NIR measurement of amniotic fluid for the wide range of natal weight ranging from 1 kg to 5.3 kg; Figure 1b illustrates the correlation adjustment between the estimated natal weight and the actual natal weight for the sample population for the wide range of natal weight that varies from 1kg to 5.3kg; Figure 1c illustrates the typical spectrum and wavelength regions selected from the natal weight estimate using Raman NIR measurement of amniotic fluid for the lowest range of birth weight ranging from 1 kg to 3.5 kg; Figure 1d illustrates a correlation adjustment between estimated birth weight and actual birth weight for the sample population for the lower range of birth weight ranging from 1 kg to 3.5 kg; Figure 1e illustrates the typical spectrum and wavelength regions selected for natal weight estimation using the NIR Raman measurement of amniotic fluid for the upper range of natal weight ranging from 3.5kg to 5.3kg; Figure 1f illustrates the correlation adjustment between estimated natal weight and natal weight for the sample population for the upper range of natal weight ranging from 3.5kg to 5.3kg! Figure 2 illustrates an endo-vaginal optical Raman spectrometer that operates in the NIR or IR range; Figure 3 is a block diagram of the apparatus according to the embodiment of Figure 2; Figure 4 schematically illustrates an abdominal probe utilizing an absorption spectrometer; Figure 5 is a diagram of the apparatus according to the embodiment of Figure 4; Figure 6 is a contour plot of probability of developing gestational diabetes mellitus (GDM) as a function of glucose level in amniotic fluid (X axis) and insulin (Y axis); Figure 7 is a contour plot of probability of developing gestational diabetes mellitus (GDM) as a function of glucose level in amniotic fluid (X axis) and intrauterine growth factor binding protein 1 or IGF-BP1 (Y axis); and Figure 8 is a flow chart illustrating the method for treating GDM according to the fifth embodiment.

DETAILED DESCRIPTION OF MODALITIES First modality, Raman NIR spectral analysis of amniotic fluid in vitro and correlation with human birth weight One embodiment of the invention focuses on the use of Raman spectral analysis to identify a panel of 8-12 biochemical markers in amniotic fluid that are predictable from infant birth weight. The advantages of this aspect are (1) that it requires an individual sample of a small volume of amniotic fluid (μL) to measure all the important biochemical components simultaneously and thus conserve in an important way its chemical properties within the fluid matrix of amniotic fluid, which inside and outside itself is an important barometer of fetal health either as too little (oligohydramniosis) or too much (polyhydramniosis) is a fetal health risk. This overcomes the limitations of other chemical techniques that require separate analyzes of individual components, which are not only susceptible to concentration differences if volume is disturbed, but lack of techniques for measuring components in this new compartment for which small volume analysis is not have developed yet. However, more importantly, our Raman spectral analysis is accurate to be within 100 to 40 grams of final birth weight when done much earlier than at 15 weeks of gestation. This in this way provides the first medical possibility of diagnosis in the early uterus of SGA and LGA. In addition, the methodology can be performed at the time of routine amniocentesis and does not require chemical processing of additional intensive work of samples. The method of the present modality can be easily conducted in the hospital, clinical configuration and field with the development of two machines: one that requires the use of a small portable Raman spectrometer to measure small drops of amniotic fluid at the time of taking " Fresh samples "for immediate bedside processing and (2) the development of an endo-vaginal or abdominal fiber optic probe to be used non-invasively through of course the pregnancy that provides in the first moment a means to gather serial measurements and to verify fetal growth in the uterus and sequentially develop the reliability of the method of the present modality makes the possibility of more expanded use of amniotic fluid possible for routine and feasible fetal verification and with an accuracy that exceeds current techniques. The present applicants identified several components of amniotic fluid suitable for measurement. These include but are not limited to glucose, a family of proteins that include but are not limited to insulin and two IGF binding proteins, mainly IGF BP 1 and 3, several amino acids, and two metabolic acids (lactic acid and uric acid). Other components for measurement include nitric oxide and various fatty acids that include trans fatty acids found only in highly hydrogenated food products and which could in fact limit the use of the essential fatty acids required for fetal growth.

NIR-RAMAN Spectroscopy of Amniotic Fluid Amniotic fluid was measured from 68 women in gestation of 14-16 weeks. All patients signed an Approved Human Subjects consent form at McGill University. After the generic test, all remaining amniotic samples were stored frozen. After the Near Infrared Raman spectral was obtained by using a Near Infrared Raman Spectrometer of Fourier Broker Transformation. Each sample of amniotic fluid was taken from the freezer and heated to 20 ° C. The samples were then transferred into a 2 mm diameter glass tube and placed in the Raman system. The Raman system was maintained at 20 +/- 1C during the course of the experiment. A Nd: YAG laser that is emitted in 1064 mm focused on the amniotic fluid samples. The changed spreading of the samples was collected by the FT spectrometer and detected by using a cooled NIR detector. The spectra were scanned at 1 / sec resulting in a resolution of 8 cm-1 change from 0-3750cm-1. A total of 180 scans are averaged for each sample. After a Fourier Transform of the incomplete interferogram, the data were stored as 1919 data points that rotate the spectral range of 0-3750 cm-1.

Data preprocessing The RAMAN spectra of amniotic fluid were preprocessed to reduce the effects of laser intensity variations. In particular, each spectrum was normalized to the Raman emission of Si-OH at 250cm-1. Similarly, the spectra were smoothed with an average 15-point moving closed wagon smoothing function to reduce noxious noise in the measurement.

Haar Transformation Haar Transformation (HT) is the oldest form of small wave analysis. Projects a given signal in an orthogonal group of base functions. The data contained in a time window of 0 < t < 1 is decomposed according to a small wave father f (t), a small wave mother? (T) and a series of small waves daughter? N, k (t), where n and k determine classification and translation respectively: 1 ifO = t = l (t) .- 0 otherwise (1) 'n, k (t) =? (2nt-k)) 0 < k < 2n-l (3) Similarly, each small daughter wave can be decomposed into the sum of two small son waves, fnk (t), with a positive and negative weight corresponding to the associated classification and translation. It is interesting to note that all daughter small waves can be decomposed into a sum of small son waves, ie compressed and changed versions of the small parent wave. For example, ? = f? .o-fi .1 - In this way, the HT can be carried out with a base group composed only of zeros and ones, which can be implemented experimentally by spectral filters. To determine the small wave coefficients, it is useful to represent small waves by a matrix. For example, the father, mother and first generation of small daughter waves can be written as A2: f (t)? (t)? l0 (x)? u (t) 1 1 1 0 1 1 -1 0 A2 = (4) 1 -1 0 1 1 -l 0 -1 where each column corresponds to a small wave, and each row represents the small wave values of Haar when the time window is broken into 4 equal segments. Decomposing a Raman signal of 4 equal wave number vessels in the small wave coefficients in that way is reduced to the next matrix coupling problem: a coefficient vector must be calculated so that its multiplication for A2 generates the Raman spectral profile. The small wave coefficients in the resulting vector will be ordered from the beginning of the small wave of lower resolution (small parent wave) and progressing to the higher spectral resolution. Matrix A2 can be expanded to include other generations of small daughter waves or small son waves, thereby extending the analysis to higher frequency levels. Further information on the transformation of Haar can be found in A. Graps, "An Introduction to Small Waves," IEEE. Comput. SCi. Eng. 2, 50-61 (1995), E. Aboufadel and S. Schlicker, Discovering Small Waves (John Wiley &Sons Inc., NY, 1999), and in J.S. Walter, A compendium of Small Waves and their Scientific Applications (Chapman &Hall, Boca Ratón, 1999).

Calculating the Haar coefficients of experimentally assembled distributions using the Raman instrument FTNIR was the first step in the data analysis. The calculation of Haar's transformation of small wave son was carried out by a custom program written in Matlab (The MathWorks Inc., Natick, MA) that iteratively calculated sums and differences. The calculation required that the length of the input data be 2 long power. The coefficients for a maximum of 1024 small waves of Haar son were obtained, ordered from low resolution to high spectral resolution.

Stepped Multilinear Regression Inverse least-squares regressions can be used to estimate the extrinsic parameters of a given sample of the pre-processed Raman spectrum. However, it is likely that all 1024 small waves are not needed, since HT gives a scattered representation of the signal. The stepped multilinear algorithm is an established method to choose a subset of variables that are highly correlated to an amount of interest. The goal of the generic algorithm was to identify the combination of small waves that best describes a given data set according to equation 5: Y = o + a? X? + 2X2 + ... + anXn (5) where Y is the dependent variable (natal weight), X ,, X2, ..., Xn are independent variables (ie, small wave coefficients), and 0, or &?, - • -, an are the coefficients determined from a group of X by regression of minus inverse squares. The combination of small waves that I estimate better and was determined according to the following scheme: 1.- Establish the range of HT coefficients that the Stepped aspect uses. 2.- Choose the number of small waves to be included in the model. 3.- Evaluate the fitness of each model. 4.- Repeat steps 2-4 with an increasing number of small waves included in the model. 5.- Choose the optimal number of variables. 6.- Evaluate the model that uses an independent data group. 1.- Repeat steps 1-6 changing the range of HT coefficients used by the stepped method. 1. - Establish the range of HT coefficients using MLR Stepped: The goal of STEPMLR was to determine a small subgroup of small waves correlated to the natal weight. Similarly, the low resolution components (long wavelength range) were preferably in view of developing spectral identification components associated with fetal development and for simplified instrumentation in the future. That way, in addition to allowing the Staggered method to choose among all the small waves of Haar son optimize the estimate, the algorithm also ran only with small waves of spectral resolution Less than 512, 256, 128, 64 and 32 small waves. 2.- Choose the number of small waves to be included in the model: Start with a small wave, that is, an X in equation 5, and increase progressively. The maximum number of small waves was established according to the number of small wave coefficients available for Staging selection. In all cases the maximum number of small waves to use was set to 10. 3. - Evaluate the aptitude of each individual: For each model in the population, the coefficients ai a an of equation 5 were calculated by regression of minus inverse squares when using the established calibration group with known values of absorption or determined relaxation of the same preparation. The natal weight estimates were obtained by applying equation 5 with the determined an parameters and the Haar coefficients of the test group, and a calibration standard error (SEC) was calculated. That way a smaller SEC was associated with a better model. 4. - Repeat steps 2-3 with an increasing number of small waves included in the model: The maximum number of small waves was chosen in step 2. 5. - Choose the optimal number of variables: A prediction error sum of squares graph (PRESSURE) was generated when plotting SEC against the number of small waves in the model. Allow h to designate the number of small waves in the model with the minimum PRESSURE value. The model selected was the smallest number of small waves so that the PRESSURE for that model is not significantly greater than the PRESSURE for the model with h small waves, based on a test f at the 95% confidence level.34 6. - Evaluate the model that uses an independent data group: The "optimal" model was evaluated when estimating the birth weight values of an independent data group, the validation group, with the calibration coefficients of the calibration group. R2 and the coefficient of variation (C.V.) were used as indicators of the validity of the model.

Results Three separate calibrations were made to estimate the natal weight of the RAMAN Spectrum of amniotic fluid. First, the spectra associated with all the samples were used to estimate natal weight. The results were shown in Figure 1b. The birth weight estimate was achieved within 500 grams for all samples. Significantly better results were obtained when the samples were subdivided into groups of < 3500 grams and > 3500 grams. The results of these two calibrations were shown in Figures 1d and 1f. As you can see, the estimates were found with approximately 200 grams error only with one absent for each group. This is significantly better than any current method. Second and Third Modalities, In situ Probe That Includes Optical Spectrometer In the second modality, endo-vaginal spectral measurements were taken at the time of routine ultrasound that varies 2-5 times during the course of pregnancy. Previous measurements present the opportunity for therapeutic or nutritional intervention. However, the sensitivity of previous gestational measurements can be reduced by the thickness of cervical tissue (~ 4mm). In contrast, endo-vaginal measurements made later in pregnancy provide less interference from cervical tissue (<; 1mm), while thinning through pregnancy, but there is less opportunity for medical intervention. It will be appreciated that the ultrasound images of the amniotic sac can be used to help order the probe to direct or confirm that the probe will measure the amniotic fluid without interference from the fetus. It is desired that the spectrometer can be incorporated into an endo-vaginal ultrasound probe. Spectral regions that are critical in predicting birth weight for in situ measurements, in particular for IUGR estimates and macrosomia can be expected to be slightly different from those obtained in the frozen, ex vivo samples described above due to the intervention tissue, temperature and pH of AMF. The specific regression model developed at different points in gestational time will be compared with standard "gold" measurements obtained by ultrasound. The regression relationship can be found between Raman spectra and natal weight using NIR endo-vaginal measurements. Similarly, the optical attenuation spectrum in the range of NIR or IR can also be measured and correlated to predict natal weight, and in accordance with the present invention other medical conditions. Raman scattering measurements provide good analytical information about amniotic fluid, however, optical attenuation or absorption measurements are expected to be more efficient in cases where measurement is made in situ and the measurement depth of the amniotic fluid can present measurements of Raman more difficult. As shown in Figure 2, the probe has a tip with an optical source and an optical detector. In the currently preferred embodiment, the optical fibers present light between a remote source and detector for the tip, although the integration of a suitable source and detector at the probe tip is alternatively desirable. As shown in Figure 3, the optical probe is operatively coupled to a spectrum analyzer that controls the source optics or sources and the detector or detectors to obtain the desired spectral information. As mentioned, such an analyzer is a Raman scattering analyzer. The resulting spectrum data is received by a correlator that calculates a medical condition risk value based on calibration data that specifies how the spectral information will be correlated with the risk value. It will be appreciated that either additionally or alternatively, the correlation can be made to determine a value for concentration of a biochemical marker or other constituent of the amniotic fluid. In the third embodiment, shown in Figures 4 and 5, the probe to be adapted would have a non-invasive tip with an optical source and two optical detectors. As illustrated, the first detector "sees" a significantly different path length through the tissue between nearby tissue and deeper tissue, while the second detector "sees" less path difference. The subtraction of intensity data measured by the two detectors in this way can generate information about deeper tissue, mainly the amniotic fluid. The probe is adapted to be in optical contact with the abdomen of the patient. In the case of an endo-vaginal probe, the device of Figure 4 may also be desirable, however, the depth of the tissue or fluid to be measured is not greater, and thus the separation between the first and second detectors does not need be older More specifically, in the case of a Raman NIR system suitable for endo-vaginal measurements, a Raman NIR system commercially available from Ocean Optics can be adapted for amniotic fluid measurements. The laser for the system can be a low energy laser (50 mW) at 785 nm. The choice of the lower wavelength when compared to the 1064 nm used in the first mode is desirable since the spreading is proportional to? -4 and will allow low optical energy to be used for non-invasive in situ measurements. Similarly, this near infrared region will be easily transmitted through the cervical tissue expected in the measurements. The detector for the Raman spectrometer is a high sensitivity cooled CCD detector that provides a voluminous system for portable use. Previous measurements suggest that relatively low resolution Raman spectra are sufficient for regression molding of natal weight. The adjustment of the input slot for the spectrograph provides a convenient means to optimize resolution and signal strength for on-site measurements. From our preliminary measurement, it is expected that the spectral acquisitions take approximately 3 minutes. Both laser excitation and scattering of Raman the patient by means of custom fiber optic groups. The group consists of separate lighting and collection fibers that focus on the same location in the fabric. It has been shown that this confocal optical arrangement can be used to isolate precise tissue locations for quantitative measurement three-dimensional At the distal end of the illumination fiber, a short, short wavelength step, optical filter is placed to remove unwanted spreading of the illumination fiber. A second long wavelength pitch filter is placed in front of the collection fiber to isolate the changed Raman signal transmitted to the spectrograph. The diameter of the fiber group is less than 2 mm. The confocal optical probe is only 2mm by 5mm in the head of ultrasound scanning. The fiber group is linked to a low cost ultrasound imaging system equipped with an endo-vaginal ultrasound scan head (Medison A-600) so that in vivo sampling locations can be determined. By using mechanical notches in the endo-vaginal probe, the optical fibers are clamped with Teflon retaining rings to maintain a known sampling location. In patients, condoms slip into the ultrasound / optics probe to provide a sterile environment. The location of the spectral acquisition is determined by using a tissue ghost. In addition to directing the location of spectral measurements, the endo-vaginal ultrasound images will provide information on the geometry of the uterus and the membrane that will be useful for comparisons of the spectra. In addition to the construction and calibration of the system with known composition samples, the spectra of the purified constituents present in significant amounts in amniotic fluid can be used to provide reference spectra for the constituents so that they can make comparisons between the spectral regions used in the regression. It will be appreciated that while the vaginal probe method described above is intended for use in women, however, it will be appreciated that the probes can be adapted for use in other mammals. It will also be appreciated that non-optical analytical tools in a similar manner can be used to gather in situ information about amniotic fluid composition that can be correlated with medical condition risk. For example, the MRS can give detailed analytical information on chemical composition. Physical parameters of amniotic fluid, such as viscosity can be measured in situ by ultrasound, can also be used either alone or in combination with other optical or non-optical analytical tools to determine the risk or measure one or more biological markers. The constituents of amniotic fluid that vary as a function of predicted birth weight are believed to affect viscosity, and thus the correlation between viscosity and natal weight is expected. In the case of optical spectrometry, the appropriate wavelength region is approximately between 200 nm to 400 μm. Dry samples of amniotic fluid can be analyzed through this range, while complete samples ex vivo, or in situ amniotic fluid is analyzed by using wavelengths that are not unduly absorbed by any intervention tissue or the same fluid. For example, water absorbs heavily in the range of 2μm to 50μm, and the presence of water in the amniotic fluid essentially prevents this range from being used for in situ measurements.

Fourth and Fifth Modalities, Verification of Gestational Diabetes Mellitus (GDM) Using Non-Invasive Amniotic Fluid Analysis Repeated The study goals are fourfold: 1) to describe the frequency of GDM in a population of older women who undergo routine amniocentesis for testing genetic and at higher risk due to age; 2) show whether elevations in glucose of amniotic fluid (AF), insulin or insulin-like growth factor binding protein (IGF BP) 1 pre-existed at the time of routine amniocentesis (range of 12-22 weeks) in those women diagnosed in 24-28 weeks with GDM; 3) to establish, when using multiple regressions, whether an association with these indications of amniotic fluid and subsequent GDM diagnosis existed; and 4) demonstrate, by using probability maps, amniotic fluid concentrations specific for glucose, insulin and IGF BP 1 that were predictable of increased risk for GDM. Design, Recruitment and Consent. From 1998-2002, pregnant women who undergo routine amniocentesis at St. Mary's Central Hospital in Montreal Canada approached to participate in this prospective study. Consents The researchers allowed the researchers to obtain amniotic fluid from the Children's Hospital of Montreal, which follows the genetic tests and to access maternal medical charts. Apply inclusion (individual pregnancy) and exclusion criteria (multiple births, genetic abnormalities) that resulted in 1008 participants. The medical chart review provided information on GDM status, maternal age, pre-pregnancy weight and height, race, parity, and smoking habit (n = 888-928), fetal weights estimated by ultrasound in 25 weeks (n = 70) and in 35 weeks (n = 149) and infant birth weight, gender and gestational age (n = 928). Gestational age was based on estimates from physicians using LMP and uniform hospital protocols. The integrity of each subgroup of data depended on the availability of information in medical tables and questionnaires. Ethnic approval was obtained from the McGill Institutional Review Panel, Montreal Children's Hospital and St. Mary's Hospital Center. Biochemical Analysis: Amniotic fluid samples, stored at -80 ° C, were analyzed for glucose, insulin and IGF BP 1. Insulin (n = 718) was analyzed using the Beckman Access ultrasensitive assay system, an assay One-step immunoenzymatic that added a conjugate of monoclonal anti-insulin, paramagnetic particles coated with antibody, and a chemiluminescent substrate for the reaction vessel. Insulin was measured at 0.03-300ul pmol / L. Glucose (n = 662) was analyzed after adapting the Abbott Laboratories test kit (Chicago North, Illinois) (No, 6082) for use with a microplate reader and IGFBP1 (n = 876) by ELISA utilizing Diagnostic Systems Laboratories Inc. (DSL equipment 10-7800, Webster, Texas). Statistical Analysis: All data analyzes using SAS (Version 8.02, SAS Inc., Cary, NC) with P < 0.05 established as the minimum for statistical significance. All data not normally distributed were transformed using square root: pre-pregnancy weight, BMI, ethnicity, parity, week of amniocentesis, smoking habit, infant's birth weight, 35-week fetal weight, and amniotic fluid glucose, insulin and IGF-BP 1. The biochemical comparisons between GDM and non-GDM mothers included as BMI of pre-pregnancy maternal covariates, ethnicity, parity and week in which amniocentesis was performed. Multiple regressions for GDM and natal weight as dependent variables and with previously established predoctors included in the models were also verified when using forward and backward regressions. Due to the co-linearity between IGF BP1, insulin and glucose, each was included in separate regression models. Data for both GDM and non-GDM mothers were modeled separately using a mixture of Gaussian distributions that employed a program written in Matlab V6.1 (Mathworks, Inc) called Bayesnet by Ian McNabbey of Cambridge University. A likelihood of post-development priority of GDM biomarkers of amniotic fluid for insulin and glucose was calculated by using a Bayesian weight of Gaussian profiles determined from the measured data. A contour map of the likelihood of development of GDM was then determined for variations in IGF-BP1 and insulin as related to glucose in AF, as shown in Figures 6 and 7, respectively. Population Characteristics: Comparisons between subpopulations with GDM and without GDM showed that mothers with GDM were shorter, had higher pre-pregnancy weights and BMIs; 54% of mothers with GDM were overweight or obese while only 26% of mothers without GDM. The average birth weight was 3396 ± 19 g in the mothers without healthy GDM against 3515 ± 52 g in the mothers with GDM. However, only 16% of offspring with GDM and 12% of offspring of non-GDM were > 4000g; that uses natal birth percentile that correct gender and gestational age, 23% of infants born to mothers with GDM were >90% (LGA) while only 10% was LGA in the population without GDM. Both classifications resulted in 3-4% of our mothers with GDM who gave birth to IUGR or SGA infants. Fetal weights did not differ at 25 weeks but were 134 grams greater than 35 weeks in mothers with GDM. In addition, at 35 weeks, gestational age (coefficient (B) = 215g) and GDM (ß = 54 g, p = 0.0450), but not BMI, smoking habit and infant gender, admitted as independent predoctors of fetal weight . This difference decreased to 119 grams per term, at which time, GDM admitted (coefficient ß = 165g) together with smoking habit (ß = -111g), infant gender (ß = 124g) and gestational age (ß = 135g), Y height and weight of pre-pregnancy (ß = 750 and 7.50 respectively) as independent predoctors of infant birth weight. The occurrence of GDM was 12% in our study population (n = 928) of older mothers (37.8 + 0.1 years, 26-45 years). Biochemical measurements: Glucose concentrations of amniotic fluid were higher while the amniotic fluid IGF BP1 was lower in GDM against mothers without GDM despite inclusion of BMI, ethnicity, parity, and week of amniocentesis; amniotic fluid insulin in an interesting way does not differ more in the inclusion of these covariates. However, we found that all three amniotic fluid biochemicals entered as independent predoctors for GDM, but only amniotic fluid IGF-BP1 entered for natal weight and as a negative predictor. By using probability maps to visualize the risk of GDM, we are able to show that if amniotic fluid glucose or insulin was higher and the other concentration decreased in amniotic fluid, the risk for GDM exceeded 90%. In addition, the low amniotic fluid IGF BP 1 in the presence of a high glucose is also associated with > 90% risk for GDM. The study explored the possibility that glucose concentrations of amniotic fluid, insulin and IGF BP1 may already be high in women subsequently diagnosed with GDM, which raise the possibility that these constituents of amniotic fluid may act as early predictors for GDM. Our findings are revolutionary because 1) we show that the High AF glucose and low IGF BP1 are associated with subsequent GDM diagnosis in women of variant BMI categories and offspring with variant natal weights and 2) we are able to predict by gestation for 15 weeks, using probability arguments, the risk of subsequent diagnosis of GDM for each glucose AF, insulin and concentration IGF BP 1. Our assessment of risk profile GDM using samples of amniotic fluid obtained at the time of routine amniocentesis for diagnostic protocols and current verification preceded by genetic testing for 10 weeks, It was based on the presence of insulin from high amniotic fluid and glucose concentrations measured earlier in pregnancy and showed that the developing fetus was exposed to a glucose-enriched environment much earlier in mothers with GDM. The screen of current protocols for maternal BMl superior, and effectively tried to minimize natal weights superior to i decrease fetal abdominal circumference, macrosomia, obesity, in GDM descendants, but do nothing to diminish the fetopathy associated with glycation in early uterus and protein glycosylation, reported to exist for the third trimester in mothers with GDM. With glucose elevations of amniotic fluid much earlier in pregnancy, fetal damage may be greater than previously expected since amniotic fluid glucose can diffuse through fetal skin without keratin until 20-24 weeks, and that would lead to to the exposure of the developing fetal pancreas for early elevations in fluid glucose amniotic predisposing to an increased risk of beta cell depletion later in life and increases the risk of adult disease, with higher BMl and higher risk of developing diabetes and later GDM in life. The population had a GDM frequency of 12%. This incidence is higher than that reported by CDA for a multiple ethnic population that includes aboriginals (ie 8-18%) and higher than that reported by the ADA (7%). This is not surprising given that the average age of our mothers who undergo routine amniocentesis for genetic testing was greater than indicated as a risk factor by both the ADC and CDA (ie> 25 and> 35 years) , but it provides the first report of the incidence in this high risk population of older women. Something important to note were the higher percentages of Asians in the GDM population when compared to the population without GDM (37% vs. 18%); however, this observation supports other reports of a higher frequency of GDM in Asians. Interestingly, GDM occurred as frequently in women with BMl less than 25 as in those with BMl greater than 25. Since traditional monitoring approaches emphasize high pre-pregnancy weight as a risk factor, our data could offer some view of why the GDM was under-diagnosed from so many normal and low-weight individuals are so susceptible. Interestingly, most of the women gave birth to non-macrosomal offspring where the presence of GDM was associated with a increase of 165 grams in natal weight. Traditionally Pedersen's hypothesis associated increased birth weight with fetal hyperglycemia and large infants for gestational age, but we also observed that mothers of GDM were just as likely to give birth to SGA and AGA. Otherwise, our multi-ethnic population did not smoke, with an incidence of IGUR lower than that of the normal population but with an incidence of AGA and macrosomia similar to the Canadian and U.S. populations. in big. The GDM is currently diagnosed between 24-28 weeks. As mentioned, our study revealed that glucose AF was already elevated in our GDM sub-population per gestation of 15 weeks. Some studies previously suggested that maternal rapidity and plasma glucose levels of 2 hours were positively associated with birth weight and that glucose passes freely through the placental barrier through facilitated diffusion, while another study reported that insulin AF was a better predictor than glucose AF of impaired maternal glucose intolerance; One study actually showed that glucose AF was not associated with fetal hyperinsulinism before 23 weeks of gestation. The amniotic fluid, which used a much larger and controlled sample size for established entanglements, shown in a series of multiple regressions amniotic fluid and insulin glucose are associated with GDM; no birth weight was predicted by anyone more likely because the amniotic fluid insulin measured earlier in pregnancy is not the primary growth factor during early pregnancy but may accumulate during this time and present later in pregnancy. As for glucose, he predicted GDM but failed to predict very likely birth weight due to GDM mothers, who were treated, gave birth to infants with a wide range of birth weights. Importantly, however, with the construction of probability maps, the composition of glucose AF and insulin to predict GDM was evident. The probability contour maps showed that the relationship was clearly not linear and if the amniotic fluid glucose or insulin were high and the other concentration was low, the risk for GDM exceeded 90%. In addition, the contour line for each risk profile was not linear. Therefore what appears to be more important is for a dissociation to exist between glucose and insulin values. The wide discrepancies between the two or more indications of future GDM emergence demonstrate that both fetal hyperinsulinism and elevations in amniotic fluid glucose are predictive of the subsequent development of GDM. Another interesting predictor of GDM was IGF BP 1 of low amniotic fluid in the presence of a high glucose, which was associated with > 90% risk for GDM. Previously the IGF BP2 was inversely associated with the natal weight, but we showed by using a much larger sample size than the lower IGF BP 1 was associated with an increase of 54 g in fetal weight for 35 weeks and an increase of 164 g in weight natal in term. Previous studies showed that this is done either by higher levels of growth hormone and / or increased levels of circulating IGF 1 or increased secondary insulin for increased food intake, both of which inhibit placental IGF BP 1 production. The increased active IGF 1 would simulate greater fetal growth during later gestation, and may be responsible for greater fetal weight already established for 35 weeks in offspring with GDM versus no GDM. In conclusion it was shown that the high glucose of AF, insulin and low IGF BP 1 predicted GDM and where the GDM positively predicted the infant's birth weight. Because our results convincingly demonstrated that the developing fetus of GDM mothers is already exposed to a "diabetogenic risk profile" before current GDM diagnosis, early monitoring and intervention is guaranteed in order to minimize fetal damage in the uterus. Additionally, an over-emphasis on BMl as a monitoring criterion may be responsible for much of the GDM sub-diagnosis, since we observe almost 50% of our GDM mothers with BMl <; 25 and many of the infants were not born large for gestational age. In the fifth embodiment, the medical condition of GDM is followed by the use of the invention when repeating measurements of amniotic fluid during pregnancy to verify health and the impact of diet and / or therapeutic intervention. The method was illustrated in Figure 8. It will be appreciated that the combination of in situ measurements with data obtained during amniocentesis is optional, although confirmation can reassure unused physicians to interpret or rely on in situ analysis of amniotic fluid. Similarly, while the invention allows in situ measurement that can be performed before 12 weeks of pregnancy in women, as illustrated at 10 weeks, and in that way before amniocentesis can be safely performed, a physician could choose initiate the maternal health check according to the invention at a later time during pregnancy. While this modality is illustrated with the example of GDM, it will be appreciated that it applies equally to verify fetal health, as in the case of natal weight. Changing the diet in the mother is recognized as being able to influence the final birth weight, and the symptoms of GDM risk. It is believed that early detection of risk of developing GDM, and consequently, early diet change will be efficient by reducing the actual result of developing GDM. Exercise and pharmaceutical intervention can also be applied according to medical guidelines.

Claims (41)

1. - A method for analyzing amniotic fluid, the method comprising: providing a device for measuring one or more selected biological markers in amniotic fluid; ordering the device with respect to an amniotic sac to measure amniotic fluid in situ without inserting any instrument into said amniotic sac; use said device to acquire measurement data; and processing said measurement data to obtain a value for said one or more biological markers selected in said amniotic fluid.

2. The method according to claim 1, wherein said device is a Raman spectrometer.

3. The method according to claim 2, wherein said arrangement comprises directing said spectrometer to analyze said fluid through an abdominal wall.

4. The method according to claim 2, wherein said arrangement comprises directing said spectrometer to analyze said fluid through a cervix.

5. The method according to any of claims 1 to 4, further comprising acquiring ultrasound images of the amniotic sac during said arrangement to direct or confirm that said device will measure said fluid without interference of a fetus.

6. A method to treat at least one pregnant mother and her offspring, the method comprising: a) providing a device for measuring one or more selected biological markers in amniotic fluid; b) order the device with respect to an amniotic sac to measure amniotic fluid in situ without the insertion of any instrument in said amniotic sac; c) use said device to acquire measurement data; d) processing said measurement data to obtain a value for said one or more selected markers in said amniotic fluid; and e) determine at least one of diet intervention and therapeutic intervention in response to said value.

7. The method according to claim 6, wherein the steps a and e are repeated during pregnancy, and step e comprises considering a response displayed on said value for at least one past intervention.

8. The method according to claim 6 or 7, wherein said mother is a human being, and the steps a to e first are performed before the 12 weeks of pregnancy.

9. The method according to claim 8, wherein an amniocentesis is performed after steps a to e are performed first, and step e is repeated using a value of said one or more selected markers obtained from said amniocentesis.

10. The method according to any of claims 7 to 9, wherein steps a to e are repeated at least three times during pregnancy.

11. The method according to any of claims 6 to 10, wherein said at least one marker comprises glucose, and said treatment is for controlling gestational diabetes.

12. The method according to claim 11, wherein said at least one marker further comprises at least one of insulin and IGF-BPI.

13. The method according to any of claims 6 to 12, wherein said device is an optical spectrometer.

14. The method according to claim 13, wherein said order comprises directing said spectrometer to analyze said fluid through an abdominal wall. 15.- The method of. according to claim 13, wherein said order comprises directing said spectrometer to analyze said fluid through a cervix. 16. A method for predicting a risk of developing a medical condition in at least one of a mother and her offspring, the method comprising: a) providing a device for analyzing amniotic fluid from said mother; b) using said device to acquire analytical data of said amniotic fluid, wherein the amniotic fluid is analyzed without processing said fluid to separate or concentrate its components; and c) processing said analytical data to obtain a prediction value for said risk. 17. The method according to claim 16, wherein said medical condition is natal weight, and said prediction value is indicative of a risk of said offspring that is born with one of high or low birth weight. 18. The method according to claim 16 or 17, wherein said device is a spectrometer. 19. The method according to claim 18, wherein said spectrometer is an optical spectrometer. 20. The method according to claim 19, wherein said spectrometer is a Raman near infrared spectrometer. 21. The method according to claim 18, wherein said spectrometer is a magnetic resonance spectrometer (MRS). 22. The method according to any of claims 18 to 21, wherein said analytical data are spectral data that correlate directly with said condition, thereby a value of specific biomechanical markers is not used to obtain said prediction value. 23. The method according to claim 22, which further comprises the steps of: d) storing said analytical data; e) subsequently obtain data concerning the development of said condition; and f) improving the correlation data using said stored analytical data and said development data for subsequent use in step c. 24. The method according to any of claims 16 to 23, wherein said use of said device comprises ordering the device with respect to an amniotic sac to measure said amniotic fluid in situ without insertion of any instrument in said amniotic sac. 25. The method according to claim 24, wherein said device is an optical spectrometer, and said order comprises directing said spectrometer to analyze said fluid through an abdominal wall. 26. The method according to claim 24, wherein said device is an optical spectrometer, and said order comprises directing said spectrometer to analyze said fluid through a cervix. 27. The method according to any of claims 16 to 26, wherein said mother is a human being, and the steps a to c first are performed at approximately 12 weeks of pregnancy. 28. The method according to claim 27, wherein An amniocentesis is performed after steps a through c are performed, and said medical condition is predicted by using data obtained from said amniocentesis. 29. The method according to any of claims 16 to 28, wherein steps a to c are repeated at least three times during pregnancy. 30.- An apparatus for predicting a risk of developing a medical condition in at least one of a mother and her offspring, the apparatus comprising: a device for analyzing amniotic fluid; and a processing unit for processing analytical data of said device to obtain a prediction value for said risk. 31. The apparatus according to claim 30, wherein said device is a spectrometer, said analytical data comprising a spectrum of said fluid. 32. The apparatus according to claim 31, wherein said spectrometer is a Raman spectrometer. 33. The apparatus according to claim 32, wherein said Raman spectrometer operates in the near infrared range. 34. The apparatus according to claim 31, wherein said spectrometer is a magnetic resonance spectrometer (MRS). 35.- The device according to any of the claims 31 to 34, wherein said spectrometer correlates directly with said condition, wherein a value of specific biochemical markers is not used to obtain said prediction value. 36. The apparatus according to claim 32 or 33, further comprising: an optical coupler adapted to order said device with respect to said amniotic sac to measure said amniotic fluid in situ without insertion of any instrument in said amniotic sac. 37. The apparatus according to claim 36, wherein said spectrum correlates directly with said condition, wherein a value of specific biochemical markers is not used to obtain said prediction value. 38.- The apparatus according to claim 36 or 37, wherein said coupler is adapted to order said spectrometer to analyze said fluid through an abdominal wall. 39.- The apparatus according to claim 36 or 37, wherein said coupler is adapted to order said spectrometer to analyze said fluid through a cervix. 40. The apparatus according to claim 36, wherein said coupler is adapted to operate in contact with said woman in a position near said amniotic sac. 41.- A system for analyzing amniotic fluid in situ in a pregnant patient who has an amniotic sac containing said fluid without insertion of any instrument in said bag, the system comprises: a device for measuring one or more selected biochemical markers in amniotic fluid; a coupler adapted to command the device with respect to said amniotic sac to measure said amniotic fluid in situ without insertion of any instrument into said amniotic sac; and a processing unit for processing measurement data of said device to obtain a value of said one or more selected biochemical markers in said amniotic fluid.