Methods of determining impaired glucose tolerance

Abstract

To provide methods, compositions and kits for determining whether a subject has impaired glucose tolerance.SOLUTION: A method comprises forming a biomarker panel having N biomarker proteins, where at least one of the N biomarker proteins is selected from ACY1, COL1A1, RTN4R, CRLF1:CLCF1 complex, CBX7, KIN, SERPINA11, PELI2, TFF3, FABP12, GAD1, SVEP1, SOCS7, F9, STC1, MYOC, WFDC11, CALB1, CCL16, SMCO2, CCL23, OSTM1, RNASE10, ITIH1, ZNF134, CFAP45, and SFTPD.SELECTED DRAWING: None

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Application No. 62 / 959,660, filed January 10, 2020, which is incorporated by reference in its entirety for all purposes.

[0002] FIELD OF THE INVENTION This application relates generally to the detection of biomarkers and characterization of glucose tolerance, e.g., to identifying subjects who have or are likely to have impaired glucose tolerance, which is indicative of prediabetes or diabetes. In various embodiments, the invention relates to one or more biomarkers, methods, devices, reagents, systems, and kits for characterizing impaired glucose tolerance. [Background technology]

[0003] background The current method for determining glucose tolerance involves measuring 2-hour plasma glucose levels in participants undergoing an oral glucose tolerance test (OGTT). A typical OGTT involves the ingestion of a 75-gram oral glucose solution after an overnight fast, with plasma glucose measurements taken at baseline ("fasting plasma glucose") and 2 hours after ingestion ("2-hour OGTT glucose value"). Currently, both fasting plasma glucose and 2-hour OGTT plasma glucose, including HbA1c levels, are used by the American Diabetes Association for the clinical diagnosis of type 2 diabetes. Prevention of type 2 diabetes is primarily based on the assessment of individuals with impaired glucose tolerance, defined as a 2-hour OGTT plasma glucose level of ≥ 7.8 mmol / L. See, e.g., Knowler et al. N. Engl. J. Med. 2002;346:393-403 and Tuomilehto et al. N. Engl. J. Med. 2001;344:1343-1350. It would be desirable to develop a proteomic model that indicates the OGTT plasma glucose threshold for impaired glucose tolerance without the need for fasting or glucose administration. Summary of the Invention

[0004] overview In some embodiments, methods are provided for determining whether a subject has or is likely to have impaired glucose tolerance. In some embodiments, methods are provided for identifying a subject who is pre-diabetic or is likely to develop pre-diabetes. In some embodiments, methods are provided for identifying a subject who is likely to develop diabetes.

[0005] In some embodiments, the methods described herein are methods of determining whether a subject has or is likely to have impaired glucose tolerance, which is a sign of prediabetes or diabetes, the methods comprising forming a biomarker panel having N biomarker proteins and detecting a level of each of the N biomarker proteins in a sample from the subject, wherein N is at least three, and at least one of the N biomarker proteins is selected from ACY1, COL1A1, RTN4R, CRLF1:CLCF1 complex, CBX7, KIN, SERPINA11, PELI2, TFF3, FABP12, GAD1, SVEP1, SOCS7, F9, STC1, MYOC, WFDC11, CALB1, CCL16, SMCO2, CCL23, OSTM1, RNASE10, ITIH1, ZNF134, CFAP45, and SFTPD. In some embodiments, N is 3 to 41, N is 4 to 41, N is 5 to 41, or N is 6 to 41, or or N is 7 to 41, or N is 8 to 41, or N is 9 to 41, or N is 10 to 41, or N is 11 to 41, or N is 12 to 41, or N is 13 to 41, or N is 14 to 41, or N is 15 to 41, or N is 16 to 41, or N is at least 4, or N is at least 5, or N is at least 6, or N is at least 7, or N is at least 8, or N is at least 9, or N is at least 10, or N is at least 11, or N is at least 12, or N is at least 13, or N is at least 14, or N is at least 15, or N is at least 16. In some embodiments, N is 3, or N is 4, or N is 5, or N is 6, or N is 7, or N is 8, or N is 9, or N is 10, or N is 11, or N is 12, or N is 13, or N is 14, or N is 15, or N is 16, or N is 17, or N is 18, or N is 19, or N is 20, or N is 21, or N is In some embodiments, the subject has impaired glucose tolerance. In some embodiments, the subject is likely to have impaired glucose tolerance. In some embodiments, the subject is likely to develop pre-diabetes. In some embodiments, the subject is pre-diabetic. In some embodiments, the subject is likely to develop diabetes. In some embodiments, the subject has impaired glucose tolerance and diabetes. In some embodiments, subjects at high risk of developing diabetes take preventative measures or receive prophylactic treatment to reduce the onset of diabetes.

[0006] In some embodiments, each of the N biomarkers is selected from Table 1. In some embodiments, at least one of the N biomarker proteins is selected from ACY1, COL1A1, RTN4R, CRLF1:CLCF1 complex, CBX7, and KIN. In some embodiments, one or two of the N biomarker proteins are INHBC and / or SHBG. In some embodiments, at least two, or at least three of the N protein biomarkers are selected from ACY1, COL1A1, RTN4R, CRLF1:CLCF1 complex, CBX7, KIN, SERPINA11, PELI2, TFF3, FABP12, GAD1, SVEP1, SOCS7, F9, STC1, MYOC, WFDC11, CALB1, CCL16, SMCO2, CCL23, OSTM1, RNASE10, ITIH1, ZNF134, CFAP45, and SFTPD. In some embodiments, at least one of the N biomarker proteins is selected from FAM20B, COL15A1, MARCKSL1, HTRA1, CHAD, CPM, DLK1, HERC1, IL20RB, MAP2K4, GPX2, and FGFR4. In some embodiments, two of the N biomarker proteins are INHBC and ACY1, or two of the N biomarker proteins are SHBG and ACY1, or three of the N biomarker proteins are INHBC, SHBG, and ACY1. In some embodiments, two of the N biomarker proteins are INHBC and COL1A1, or two of the N biomarker proteins are SHBG and COL1A1, or three of the N biomarker proteins are INHBC, SHBG, and COL1A1. In some embodiments, two of the N biomarker proteins are INHBC and RTN4R, or two of the N biomarker proteins are SHBG and RTN4R, or three of the N biomarker proteins are INHBC, SHBG, and RTN4R. In some embodiments, two of the N biomarker proteins are INHBC and CRLF1:CLCF1 complex, or two of the N biomarker proteins are SHBG and CRLF1:CLCF1 complex, or three of the N biomarker proteins are INHBC, SHBG, and CRLF1:CLCF1 complex. In some embodiments, two of the N biomarker proteins are INHBC and CBX7, or two of the N biomarker proteins are SHBG and CBX7, or three of the N biomarker proteins are INHBC, SHBG, and CBX7. In some embodiments, two of the N biomarker proteins are INHBC and KIN, or two of the N biomarker proteins are SHBG and KIN, or three of the N biomarker proteins are INHBC, SHBG, and KIN. In some embodiments, N is at least five, and five of the N biomarker proteins are INHBC, SHBG, ACY1, COL1A1, and RTN4R. In some embodiments, N is at least 16, and 16 of the N biomarker proteins are ACY1, COL1A1, RTN4R, CRLF1, CBX7, KIN, SERPINA11, PELI2, TFF3, FABP12, INHBC, SHBG, FAM20B, COL15A1, MARCKSL1, and HTRA1.

[0007] In any of the embodiments described herein, the subject may be at risk for developing impaired glucose tolerance. In any of the embodiments described herein, the subject may be at risk for developing prediabetes. In any of the embodiments described herein, the subject may be at risk for developing diabetes. In some embodiments, the method includes determining whether the subject has or is likely to have impaired glucose tolerance, which is a sign of prediabetes or diabetes. In some embodiments, the method includes determining whether the subject has prediabetes or is likely to develop prediabetes or diabetes. In some embodiments, the diabetes is type 2 diabetes. In some embodiments, the method includes treating the subject. In some embodiments, the treatment includes administering insulin and / or metformin to the subject. In some embodiments, the treatment includes implementing a weight loss program, a diet, a calorie restriction, and / or an exercise program for the subject.

[0008] In any of the embodiments described herein, each of the N biomarker proteins is different from one another. In some embodiments, the method includes contacting biomarkers from a sample obtained from a subject with a set of biomarker capture reagents, and each biomarker capture reagent in the set of biomarker capture reagents specifically binds to a different biomarker to be detected. In some embodiments, each biomarker capture reagent is an antibody or an aptamer. In some embodiments, each biomarker capture reagent is an aptamer. In some embodiments, at least one aptamer is an aptamer that exhibits a slow off-rate. In some embodiments, at least one slow off-rate aptamer has at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten modified nucleotides. In some embodiments, each aptamer exhibiting a slow off-rate has an off-rate (t) of ≥ 30 minutes, ≥ 60 minutes, ≥ 90 minutes, ≥ 120 minutes, ≥ 150 minutes, ≥ 180 minutes, ≥ 210 minutes, or ≥ 240 minutes. 1 / 2 ) and binds to the biomarker protein.

[0009] In any of the embodiments described herein, the sample may be a blood sample. In any of the embodiments described herein, the sample may be selected from a serum sample or a plasma sample. In any of the embodiments described herein, the sample is a plasma sample. In some embodiments, the subject eats a normal meal before providing the sample, and the normal meal does not result in a longer than normal period of fasting.

[0010] In some embodiments, a level of at least one biomarker selected from SHBG, COL1A1, CRLF1:CLCF1 complex, FAM20B, COL15A1, KIN, SERPINA11, PELI2, MARCKSL1, CHAD, IL20RB, MYOC, WFDC11, MAP2K4, CALB1, FGFR4, OSTM1, ITIH1, CFAP45, and SFTPD that is higher than the control level of the respective biomarker indicates that the subject has or is likely to have impaired glucose tolerance, is pre-diabetic, and / or is likely to develop pre-diabetes or diabetes.

[0011] In some embodiments, a level of at least one biomarker selected from INHBC, ACY1, RTN4R, CBX7, TFF3, HTRA1, FABP12, GAD1, CPM, SVEP1, SOCS7, F9, DLK1, HERC1, STC1, CCL16, SMCO2, GPX2, CCL23, RNASE10, and ZNF134 that is lower than the control level of the respective biomarker indicates that the subject has or is likely to have impaired glucose tolerance, is pre-diabetic, and / or is likely to develop pre-diabetes or diabetes.

[0012] In some embodiments, the methods described herein are for determining medical or life insurance premiums. In some embodiments, the methods further include determining medical or life insurance premiums. In some embodiments, the methods described herein further include using information obtained from the methods to predict and / or manage utilization of medical resources.

[0013] In some embodiments, a kit is provided, which includes N biomarker protein capture reagents, wherein N is at least three, and at least one of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from ACY1, COL1A1, RTN4R, CRLF1:CLCF1 complex, CBX7, KIN, SERPINA11, PELI2, TFF3, FABP12, GAD1, SVEP1, SOCS7, F9, STC1, MYOC, WFDC11, CALB1, CCL16, SMCO2, CCL23, OSTM1, RNASE10, ITIH1, ZNF134, CFAP45, and SFTPD. In some embodiments, N is 3 to 41, or N is 4 to 41, or N is 5 to 41, or N is 6 to 41, or N is 7 to 41, or N is 8 to 41, or N is 9 to 41, or N is 10 to 41, or N is 11 to 41, or N is 12 to 41, or N is 13 to 41, or N is 14 to 41, or N is 15 to 41, or N is 16 to 41, or N is at least 4, or N is at least 5, or N is at least 6, or N is at least 7, or N is at least 8, or N is at least 9, or N is at least 10, or N is at least 11, or N is at least 12, or N is at least 13, or N is at least 14, or N is at least 15, or N is at least 16.In some embodiments, N is 3, or N is 4, or N is 5, or N is 6, or N is 7, or N is 8, or N is 9, or N is 10, or N is 11, or N is 12, or N is 13, or N is 14, or N is 15, or N is 16, or N is 17, or N is 18, or N is 19, or N is 20, or N is 21, or N is 22, or N is 23, or N is 24, or N is 25, or N is 26, or N is 27, or N is 28, or N is 29, or N is 30, or N is 31, or N is 32. where N is 33, or N is 34, or N is 35, or N is 36, or N is 37, or N is 38, or N is 39, or N is 40, or N is 41. In some embodiments, the kit is used to detect the levels of N biomarker proteins in a sample, and the sample is from a subject. In some embodiments, the kit is used to determine whether a subject has or is likely to have impaired glucose tolerance, is pre-diabetic, and / or is likely to develop pre-diabetes or diabetes.

[0014] In some embodiments, each of the N biomarkers is selected from Table 1. In some embodiments, at least one of the N biomarker proteins is selected from ACY1, COL1A1, RTN4R, CRLF1:CLCF1 complex, CBX7, and KIN. In some embodiments, one or two of the N biomarker proteins are INHBC and / or SHBG. In some embodiments, at least two or at least three of the N protein biomarkers are selected from ACY1, COL1A1, RTN4R, CRLF1:CLCF1 complex, CBX7, KIN, SERPINA11, PELI2, TFF3, FABP12, GAD1, SVEP1, SOCS7, F9, STC1, MYOC, WFDC11, CALB1, CCL16, SMCO2, CCL23, OSTM1, RNASE10, ITIH1, ZNF134, CFAP45, and SFTPD. In some embodiments, at least one of the N biomarker proteins is selected from FAM20B, COL15A1, MARCKSL1, HTRA1, CHAD, CPM, DLK1, HERC1, IL20RB, MAP2K4, GPX2, and FGFR4. In some embodiments, two of the N biomarker proteins are INHBC and ACY1, or two of the N biomarker proteins are SHBG and ACY1, or three of the N biomarker proteins are INHBC, SHBG, and ACY1. In some embodiments, two of the N biomarker proteins are INHBC and COL1A1, or two of the N biomarker proteins are SHBG and COL1A1, or three of the N biomarker proteins are INHBC, SHBG, and COL1A1. In some embodiments, two of the N biomarker proteins are INHBC and RTN4R, or two of the N biomarker proteins are SHBG and RTN4R, or three of the N biomarker proteins are INHBC, SHBG, and RTN4R.In some embodiments, two of the N biomarker proteins are INHBC and CRLF1:CLCF1 complex, or two of the N biomarker proteins are SHBG and CRLF1:CLCF1 complex, or three of the N biomarker proteins are INHBC, SHBG, and CRLF1:CLCF1 complex. In some embodiments, two of the N biomarker proteins are INHBC and CBX7, or two of the N biomarker proteins are SHBG and CBX7, or three of the N biomarker proteins are INHBC, SHBG, and CBX7. In some embodiments, two of the N biomarker proteins are INHBC and KIN, or two of the N biomarker proteins are SHBG and KIN, or three of the N biomarker proteins are INHBC, SHBG, and KIN. In some embodiments, N is at least 5, and 5 of the N biomarker proteins are INHBC, SHBG, ACY1, COL1A1, and RTN4R. In some embodiments, N is at least 16, and 16 of the N biomarker proteins are ACY1, COL1A1, RTN4R, CRLF1, CBX7, KIN, SERPINA11, PELI2, TFF3, FABP12, INHBC, SHBG, FAM20B, COL15A1, MARCKSL1, and HTRA1.

[0015] In some embodiments, each biomarker capture reagent is an antibody or an aptamer. In some embodiments, each biomarker capture reagent is an aptamer. In some embodiments, at least one aptamer is an aptamer that exhibits a slow off-rate.

[0016] In some embodiments, each slow off-rate aptamer exhibits an off-rate (t) of ≥ 30 minutes, ≥ 60 minutes, ≥ 90 minutes, ≥ 120 minutes, ≥ 150 minutes, ≥ 180 minutes, ≥ 210 minutes, or ≥ 240 minutes. 1 / 2 ) and binds to the biomarker protein.

[0017] In any of the embodiments described herein, the sample may be a blood sample. In any of the embodiments described herein, the sample may be selected from a serum sample and a plasma sample. In some embodiments, the sample is a plasma sample.

[0018] In any embodiment described herein, each of the N biomarker proteins is different from the other N biomarker proteins. In any embodiment described herein, an aptamer exhibiting at least one slow off-rate has at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten nucleotide modifications. In some embodiments, the modification is a hydrophobic modification. In some embodiments, the modification is a hydrophobic base modification. In some embodiments, the one or more modifications may be selected from the modifications shown in Figure 2. [Brief explanation of the drawings]

[0019] [Figure 1] A shows the results of a model in which one biomarker protein at a time is added to the model, as described in Example 4. B shows the results of a model in which one biomarker protein at a time is removed from the model, as described in Example 4. C shows the results of a model including N biomarkers randomly selected from Table 1, as described in Example 4. [Figure 2] Specific nucleobase modifications that can be used in aptamers are shown. [Figure 3] An exemplary computer system is shown herein for use with the various computer-implemented methods, without limitation. [Figure 4] Glucose tolerance status determined as described in Example 2 (y-axis) is shown using levels determined in a conventional OGTT (x-axis). DETAILED DESCRIPTION OF THE INVENTION

[0020] Detailed Description While the invention will be described in conjunction with exemplary specific embodiments, it will be understood that the invention, as defined by the claims, is not limited to those embodiments.

[0021] One skilled in the art will recognize that many methods and materials similar or equivalent to those described herein could be used in the practice of the present invention, and the present invention is in no way limited to the methods and materials described herein.

[0022] Unless otherwise defined, technical and scientific terms used herein have the meaning commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice of the present invention, specific methods, devices, and materials are described herein.

[0023] All publications, patent applications, and patent applications cited in this specification are herein each and every one of which is specifically and individually indicated by reference to be so incorporated by reference. the contents of which are incorporated herein by reference in the same manner as if fully set forth herein.

[0024] As used in this application, including the claims appended hereto, the singular forms "a," "an," and "the" include the plural forms unless the context clearly dictates otherwise, and may be used interchangeably with "at least one" and "one or more." Thus, reference to an "aptamer" includes a mixture of aptamers, reference to a "probe" includes a mixture of probes, and so forth.

[0025] As used herein, the terms "comprises," "comprising," "includes," "including," "contains," "containing," and any variations thereof are not intended to be exclusive inclusions of processes, methods, product-by-processes, or compositions that comprise, include, or contain elements, and a list of elements may include other elements not explicitly included in the list.

[0026] The present application includes biomarkers, methods, devices, reagents, systems, and kits for determining whether a subject has or is likely to have impaired glucose tolerance, or whether a subject has or is likely to develop prediabetes and / or diabetes. In some embodiments, biomarkers, methods, devices, reagents, systems, and kits are provided for determining whether a subject with impaired glucose tolerance has or is likely to develop prediabetes or diabetes.

[0027] As used herein, the term "CRLF1:CLCF1 complex" is used to refer to CRLF1 and / or CLCF1, and / or a complex of CRLF1 and CLCF1. Thus, if a method includes detecting the biomarker "CRLF1:CLCF1 complex," the method may include detecting CRLF1, CLCF1, both CRLF1 and CLCF1, and / or a complex of CRLF1 and CLCF1. A biomarker capture reagent that specifically binds to the CRLF1:CLCF1 complex may bind to CRLF1, and / or CLCF1, and / or both CRLF1 and CLCF1, and / or a complex of CRLF1 and CLCF1.

[0028] In some embodiments, the biomarkers, alone or in various combinations, determine whether a subject has or is likely to have impaired glucose tolerance, or has developed or is likely to develop prediabetes and / or diabetes. As described below, exemplary embodiments include the biomarkers provided in Table 1.

[0029] Biomarkers were identified using multiplexed aptamer-based assays. Table 1 shows biomarkers that help distinguish between samples from individuals with glucose tolerance and those with impaired glucose tolerance.

[0030] The terms "sensitivity" and "specificity" are used herein in reference to the ability to correctly distinguish between individuals with a disease and individuals without a disease based on the level of one or more biomarkers detected in a biological sample. In some embodiments, the terms "sensitivity" and "specificity" may be used herein in reference to the ability to correctly distinguish between individuals with impaired glucose tolerance and individuals with normal glucose tolerance based on the level of one or more biomarkers detected in a biological sample. In such embodiments, "sensitivity" refers to the ability of a biomarker(s) to correctly distinguish between individuals with impaired glucose tolerance. "Specificity" refers to the ability of a biomarker(s) to correctly distinguish between individuals without impaired glucose tolerance. For example, a panel of biomarkers used to test 85% specificity and 90% sensitivity For a series of control samples (such as samples from healthy individuals or subjects known not to have impaired glucose tolerance) and test samples (such as samples from individuals with impaired glucose tolerance), 85% of the control samples are correctly classified as control samples by the panel, and 90% of the test samples are correctly classified as test samples by the panel.

[0031] In some embodiments, the terms "sensitivity" and "specificity" may be used herein to refer to the ability to correctly identify individuals with impaired glucose tolerance based on one or more biomarker levels detected in a sample, which may generally be indicative of prediabetes and, in some cases, may indicate diabetes. "Sensitivity" refers to the ability of a biomarker(s) to correctly identify individuals with impaired glucose tolerance based on one or more biomarker levels detected in a sample, which may generally be indicative of prediabetes and, in some cases, may indicate diabetes. "Specificity" refers to the ability of a biomarker(s) to correctly identify individuals without impaired glucose tolerance based on one or more biomarker levels detected in a sample, which may generally be indicative of prediabetes and, in some cases, may indicate diabetes. For example, a specificity of 85% and a sensitivity of 90% for a panel of biomarkers used to test a set of samples from individuals with normal glucose tolerance would correctly identify 85% of the individuals. Similarly, in a set of samples from individuals with impaired glucose tolerance, 90% of the individuals would be correctly classified.

[0032] In some embodiments, the overall performance of a panel of one or more biomarkers is expressed as an area under the curve (AUC) value. This AUC value is derived from a receiver operating characteristic (ROC) curve. This ROC curve is a plot of the true positive rate (sensitivity) of a test against the false positive rate (1-specificity) of the test. The terms "area under the curve" or "AUC" refer to the area under a receiver operating characteristic (ROC) curve, both of which are well known in the art. Measuring AUC is useful for comparing the accuracy of classifiers across the complete data range. A classifier with a larger AUC increases its ability to correctly classify unknowns between two groups of interest (e.g., normal individuals and diabetic individuals, or individuals with impaired glucose tolerance and individuals who may have diabetes). ROC curves are useful for plotting the performance of a particular feature (e.g., any of the biomarkers described herein and / or any feature of additional biomedical information) in distinguishing between two populations. Typically, the feature data across the populations is sorted in ascending order based on the value of a single feature. Next, for each value for that feature, the true positive rate and false positive rate for the data are calculated. The true positive rate is determined by counting the number of cases that exceed the value for that feature and dividing by the total number of cases. The false positive rate is determined by counting the number of controls that exceed the value for that feature and dividing by the total number of controls. While this definition refers to situations where the feature is improved compared to the control, it can also be used in situations where the feature is not improved compared to the control (in such situations, samples that fall below the value for that feature would be counted). ROC curves can be generated for single features and other single outputs; for example, combinations of two or more features can be combined mathematically (e.g., by addition, subtraction, multiplication, etc.) to provide a single total value that can be plotted on an ROC curve. Additionally, any combination of multiple features, i.e., combinations that result in a single output value, can be plotted on an ROC curve.

[0033] As used herein, "obese" with respect to a subject refers to a subject having a BMI of 30 or greater.

[0034] "Biological sample" and "sample" are used interchangeably herein and refer to any material, body fluid, tissue, or cell obtained or otherwise obtained from an individual, including blood (including whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, and serum), sputum, tears, mucus, nasal washes, nasal aspirates, urine, saliva, peritoneal washings, ascites, cyst fluid, glandular fluid, lymph, bronchial aspirate, synovial joint aspirate, organ secretions, cells, cell extracts, and and cerebrospinal fluid. This includes all experimentally separated fractions described above. For example, a blood sample can be fractionated into serum, plasma, or fractions containing specific types of blood cells, such as red blood cells and white blood cells (leukocytes). In some embodiments, a sample can be a combination of samples from an individual, such as a combination of tissue and liquid samples. The term "biological sample" also includes materials containing homogenized solid material, such as a fecal sample, tissue sample, or tissue biopsy. The term "biological sample" also includes materials derived from tissue or cell culture. Any suitable method for obtaining a biological sample can be used; exemplary methods include, for example, phlebotomy, swabbing (e.g., buccal swabs), and fine-needle aspiration biopsy. Exemplary tissues amenable to fine-needle aspiration include lymph nodes, lung, thyroid, breast, pancreas, and liver. Samples may also be collected, for example, by microdissection (e.g., laser capture microdissection (LCM) or laser microdissection (LMD)), bladder washing, smear (e.g., PAP smear), or ductal lavage. A "biological sample" obtained from or derived from an individual includes a sample that has been processed in any suitable manner after being obtained from the individual.

[0035] As used herein, "normal diet" refers to an individual's daily eating habits. An individual's normal diet can be the same as, similar to, or different from the normal diet of other individuals. A normal diet refrains from dietary changes, such as fasting for longer than usual, eating more or less than usual, or dietary changes made in preparation for a medical test.

[0036] Furthermore, in some embodiments, the biological sample may be derived by collecting and pooling biological samples from multiple individuals, or by pooling aliquots of each individual's biological sample. These pooled samples may be processed as described herein for samples from a single individual, and, for example, once a poor prognosis is established in the pooled samples, each biological sample may then be tested anew to determine which individual(s) have impaired glucose tolerance and / or are likely to have or develop pre-diabetes or diabetes.

[0037] "Target," "target molecule," and "analyte" are used interchangeably herein and refer to any molecule of interest that may be present in a sample. A "molecule of interest" includes any small variation of a particular molecule, such as, in the case of a protein, a slight change in amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling moiety, that does not substantially change the identity of the molecule. A "target molecule," "target," or "analyte" refers to a set of copies of one type or species of molecule or multimolecular structure. A "target molecule," "target," and "analyte" refer to two or more types or species of molecule or multimolecular structure. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affibodies, antibody mimetics, viruses, pathogens, toxicants, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragment or portion of any of the above. In some embodiments, the target molecule is a protein, in which case the target molecule may be referred to as a "target protein."

[0038] As used herein, "capture agent" or "capture reagent" refers to a molecule that can specifically bind to a biomarker protein. "Biomarker protein capture reagent" refers to a molecule that can specifically bind to a biomarker protein. Examples of capture reagents include aptamers, antibodies, adnectins, ankyrins, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, nucleic acids, lectins, ligand-binding receptors, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, synthetic receptors, and any of the capture reagents listed above. In some embodiments, the capture reagent is selected from an aptamer and an antibody.

[0039] The term "antibody" refers to full-length antibodies of all species, as well as fragments and derivatives of such antibodies, such as Fab fragments, F(ab')2 fragments, single-chain antibodies, Fv fragments, and single-chain Fv fragments. The term "antibody" also refers to synthetic antibodies, such as phage-display-derived antibodies and fragments, affibodies, and nanobodies.

[0040] As used herein, the terms "marker" and "biomarker" are used interchangeably and refer to a target molecule that is indicative of a normal or abnormal process in an individual, or of a disease or other pathological condition in an individual. More specifically, a "marker" or "biomarker" is an anatomical, physiological, biochemical, or molecular parameter associated with the presence of a particular physiological state or process, whether normal or abnormal, and if abnormal, whether chronic or acute. Biomarkers are detectable and measurable by a variety of methods, including laboratory assays and medical imaging. In some embodiments, a biomarker is a target protein.

[0041] As used herein, "biomarker level" and "level" refer to a measurement obtained using any analytical method to detect a biomarker in a biological sample, and may indicate presence, absence, absolute amount or concentration, relative amount or concentration, titer, level, expression level, ratio of measured levels, etc., depending on the biomarker in the biological sample. The exact meaning of "level" is related to the particular design and components of the particular analytical method employed to detect the biomarker.

[0042] A "control level" of a target molecule refers to the level of the target molecule in the same sample type obtained from an individual who is free of, or not suspected of having, a disease or condition. The "control level" of a target molecule need not be determined each time a method of the invention is performed, but can be a previously determined level used as a reference or threshold for determining whether the level in a particular sample is higher or lower than normal. In some embodiments, the control level for the methods described herein is a level observed in one or more subjects with normal glucose tolerance. In some embodiments, the control level for the methods described herein is a level observed in one or more subjects with impaired glucose tolerance but who do not have diabetes. In some embodiments, the control level for the methods described herein is the average or mean level observed in multiple normal subjects or subjects with impaired glucose tolerance but who do not have diabetes, optionally adjusting for statistical variation.

[0043] As used herein, "individual" and "subject" are used interchangeably to refer to a test subject or patient. An individual may be a mammal or a non-mammal. In various embodiments, an individual is a mammal. A mammalian individual may be a human or a non-human. In various embodiments, an individual is a human. A healthy or normal individual is one in which the disease or condition of interest (such as impaired glucose tolerance) is not detectable by conventional diagnostic methods.

[0044] "Diagnose," "diagnosing," "diagnosis," and variations thereof, refer to detecting, quantifying, or recognizing the health or pathological condition of an individual based on one or more signs, symptoms, data, or other information associated with that individual. An individual's health can be diagnosed as healthy / normal (i.e., a diagnosis of the absence of a disease or pathological condition) or diseased / abnormal (i.e., a diagnosis of the presence or characterization of a disease or pathological condition). Terms such as "Diagnose," "diagnosing," and "diagnosis" With respect to a particular disease or condition, includes the initial detection of disease; characterization or classification of disease; detection of disease progression, remission, or recurrence; and detection of disease response after administration of treatment or therapy to an individual. The terms "diagnose," "diagnosing," "diagnosis," and the like, with respect to a particular disease or condition, include the initial detection of disease; characterization or classification of disease; detection of disease progression, remission, or recurrence; and detection of disease response after administration of treatment or therapy to an individual. Diagnosis of impaired glucose tolerance includes distinguishing between individuals with impaired glucose tolerance and those with normal glucose tolerance. Diagnosis of prediabetes or diabetes includes distinguishing between individuals with impaired glucose tolerance, but who are likely not diabetic, and those with normal glucose tolerance.

[0045] "Prognose," "prognosing," "prognosis," and variations thereof, refer to the prediction of the future course of a disease or condition in an individual having the disease or condition (e.g., predicting patient survival), and such terms refer to the ability to predict disease response after administering a treatment or therapy to an individual.

[0046] "Evaluate," "evaluating," "evaluation," and variations thereof include both "diagnosis" and "prognosis," and include the judgment or prediction of the future course of a disease or condition in disease-free individuals, as well as the judgment and prediction of the likelihood of a disease or condition occurring in individuals who are apparently cured of the disease. The term "evaluate" also includes assessing an individual's response to a therapy, e.g., predicting whether an individual will respond favorably to a therapeutic agent or will not respond to a therapeutic agent (or, for example, suffer from toxicity or other undesirable side effects), selecting a therapeutic agent to administer to the individual, or monitoring or determining an individual's response to a therapy being administered to the individual. Thus, "evaluating" glucose tolerance can include, for example, any of: predicting the future course of glucose tolerance in an individual; predicting whether impaired glucose tolerance will progress to prediabetes or diabetes; predicting whether a particular stage of prediabetes or diabetes will progress to a higher stage of prediabetes or diabetes;

[0047] As used herein, "detecting" or "determining" with respect to biomarker levels includes both the use of equipment used to sense and record a signal corresponding to the biomarker level, as well as the materials necessary to generate that signal. In various embodiments, the level is detected using any suitable method, such as fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic wave, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection, nuclear magnetic resonance detection, quantum dots, etc.

[0048] As used herein, a "subject with impaired glucose tolerance" refers to a subject who has been diagnosed with impaired glucose tolerance. In some embodiments, impaired glucose tolerance is likely to emerge during routine testing, monitoring for metabolic syndrome and obesity, or monitoring for possible side effects of medications.

[0049] As used herein, a "subject with prediabetes" or a "subject with diabetes" refers to a subject who has been diagnosed with prediabetes or diabetes. In some embodiments, diagnosing prediabetes or diabetes includes the methods described above for impaired glucose tolerance.

[0050] As used herein, a "subject at risk of developing" a condition refers to a subject who has one or more risk factors or comorbidities for the condition. In some embodiments, the condition is diabetes Risk factors associated with the development of diabetes include being over 45 years old, being male, and having a body weight of approximately 25 kg / m 2 These include, but are not limited to, BMI above 18.5, family history of diabetes, less than 3 hours / week of exercise, race (e.g., African American, Hispanic / Latino, Native American, or Alaska Native), gestational diabetes, and / or a history of polycystic sclerosing syndrome.

[0051] As used herein, "likely" means a probability of greater than 0.50.

[0052] As used herein, a "solid support" refers to any substrate having a surface to which molecules can be attached, directly or indirectly, via either covalent or non-covalent bonds. A "solid support" can have a variety of physical forms, such as membranes; chips (e.g., protein chips); slides (e.g., glass slides or cover slips); columns; particles that are hollow, solid, semi-solid, pore- or cavity-containing, such as beads; gels; fibers, including fiber optic materials; matrices; and sample receptacles. Examples of sample receptacles include sample wells, tubes, capillaries, vials, and any other container, groove, or depression that can hold a sample. Sample receptacles can be contained in multi-sample platforms such as microtiter plates, slides, and microfluidics devices. Supports can be composed of natural or synthetic, organic, or inorganic materials. The composition of the solid support to which the capture reagent is attached generally depends on the method of attachment (e.g., covalent attachment). Other exemplary receptacles include microdroplets and microfluidic controls, or bulk oil / aqueous emulsions, within which assays and related manipulations can occur. Suitable solid supports include, for example, plastics, resins, polysaccharides, silica or silica-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glass, membranes, nylon, natural fibers (e.g., silk, wool, and cotton), and polymers. Materials comprising solid supports can contain reactive groups, such as carboxy, amino, or hydroxyl groups, for attachment of capture reagents. Polymeric solid supports include, for example, polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinylpyrrolidone, polyacrylonitrile, polymethyl methacrylate, polytetrafluoroethylene, butyl rubber, styrene butadiene rubber, natural rubber, polyethylene, polypropylene, (poly)tetrafluoroethylene, (poly)vinylidene fluoride, polycarbonate, and polymethylpentene. Suitable solid support particles that can be used include, for example, encoded particles such as Luminex® type encoded particles, magnetic particles, and glass particles.

[0053] Exemplary Uses of Biomarkers In various exemplary embodiments, methods are provided for determining whether a subject has or is likely to have impaired glucose tolerance, is pre-diabetic, and / or is likely to develop pre-diabetes or diabetes. In various embodiments, methods are provided for determining whether a subject has impaired glucose tolerance and / or is likely to develop prediabetes or diabetes, comprising obtaining a sample from the subject; forming a biomarker panel having N biomarker proteins; and detecting a level of each of the N biomarker proteins in the sample, wherein N is at least 3, and at least one of the N biomarker proteins is selected from ACY1, COL1A1, RTN4R, CRLF1:CLCF1 complex, CBX7, KIN, SERPINA11, PELI2, TFF3, FABP12, GAD1, SVEP1, SOCS7, F9, STC1, MYOC, WFDC11, CALB1, CCL16, SMCO2, CCL23, OSTM1, RNASE10, ITIH1, ZNF134, CFAP45, and SFTPD.

[0054] In various embodiments, each of the N biomarkers is selected from Table 1. [Table 1-1] [Table 1-2]

[0055] In some embodiments, the biomarker is present at different levels in individuals with impaired glucose tolerance compared to individuals with normal glucose tolerance.

[0056] Detection of differential levels of biomarkers in an individual can be used to determine, for example, whether an individual has or is likely to have impaired glucose tolerance, or whether an individual with impaired glucose tolerance is pre-diabetic or likely to develop pre-diabetes. In some embodiments, any of the biomarkers described herein can be used to monitor an individual for the development of impaired glucose tolerance, or to monitor an individual with impaired glucose tolerance for the development of pre-diabetes or diabetes.

[0057] As an example of how any of the biomarkers described herein can be used to determine whether a subject has or is likely to have impaired glucose tolerance, an individual who has not been diagnosed with impaired glucose tolerance may have levels of one or more biomarkers described herein that indicate tolerance, but if the individual has one or more risk factors or comorbidities for impaired glucose tolerance, another test may be used to indicate that the individual has developed impaired glucose tolerance at an earlier stage. Early detection of impaired glucose tolerance may allow for more effective medical intervention. Such medical interventions include, but are not limited to, weight loss and glycemic control. In some embodiments, therapeutic agents such as insulin or metformin may be used. do.

[0058] Similarly, as a further example of how the biomarkers described herein can be used to determine whether a subject with impaired glucose tolerance will develop prediabetes or diabetes, the level of one or more of the biomarkers described herein in an individual with impaired glucose tolerance may indicate that the individual will develop prediabetes or diabetes. Early detection of prediabetes or diabetes may allow for more effective medical intervention. Such medical interventions include, but are not limited to, weight loss and glycemic control. In some embodiments, therapeutic agents such as insulin or metformin may be used.

[0059] In addition, in some embodiments, differential expression levels of one or more biomarkers in an individual over time may indicate the individual's response to a particular treatment regimen. In some embodiments, changes in expression of one or more biomarkers during follow-up monitoring may indicate that a particular treatment is effective or may suggest that the treatment regimen should be modified in some way, for example, to control blood glucose more aggressively or to focus more aggressively on weight loss. In some embodiments, consistent expression levels of one or more biomarkers in an individual over time may indicate that the individual's impaired glucose tolerance has not worsened or that they have not developed prediabetes or diabetes.

[0060] In addition to testing biomarker levels as a stand-alone diagnostic test, biomarker levels can also be determined in combination with single nucleotide polymorphisms (SNPs) or other genetic regions or variability that indicate an increased risk of disease susceptibility.

[0061] In addition to testing biomarker levels as a stand-alone diagnostic test, biomarker levels can also be combined with other screening methods for impaired glucose tolerance. In some cases, methods using the biomarkers described herein may improve the medical and economic justification for implementing more aggressive treatment, more frequent follow-up screening, etc. for impaired glucose tolerance, or pre-diabetes, or diabetes. For individuals who are at risk for developing impaired glucose tolerance but have not been diagnosed with impaired glucose tolerance, biomarkers may also be used to initiate treatment even if diagnostic tests indicate a high likelihood of developing the disease.

[0062] In addition to testing biomarker levels in combination with other methods for diagnosing impaired glucose tolerance, information about the biomarkers can also be evaluated in combination with other types of data, particularly data indicative of an individual's risk for impaired glucose tolerance. These various data can be evaluated by automated methods, such as computer programs / software that can be embodied in a computer or other device / instrument.

[0063] Detection and Determination of Biomarkers and Biomarker Levels Biomarker levels of the biomarkers described herein can be detected using any of a variety of known analytical methods. In certain embodiments, biomarker levels are detected using a capture reagent. In various embodiments, the capture reagent can be contacted with the biomarker in solution or while the capture reagent is immobilized on a solid support. In other embodiments, the capture reagent includes a property that reacts with a secondary feature of the solid support. In these embodiments, the capture reagent can be contacted with the biomarker in solution, and then the capture reagent's characteristics can be used in combination with the secondary feature of the solid support to immobilize the biomarker on the solid support. The capture reagent is selected based on the type of analysis to be performed. Capture reagents include aptamers, antibodies, adnectins, ankyrins, other antibody mimetics, and other protein scaffolds, autoantibodies, chimeras, small molecules, F(ab')2 fragments, single-chain antibody fragments, Fv These include, but are not limited to, fragments, single chain Fv fragments, nucleic acids, lectins, ligand binding receptors, antibodies, nanobodies, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, synthetic receptors, and modifications and fragments thereof.

[0064] In some embodiments, biomarker levels are detected using a biomarker / capture reagent complex.

[0065] In some embodiments, the biomarker level is derived from the biomarker / capture reagent complex and is detected indirectly, e.g., as a result of the biomarker / capture reagent interaction, but is dependent on the formation of the biomarker / capture reagent complex.

[0066] In some embodiments, the biomarker level is detected directly from the biomarker in the biological sample.

[0067] In some embodiments, biomarkers are detected using a multiplexed format that allows for simultaneous detection of two or more biomarkers in a biological sample. In some embodiments of the multiplexed format, capture reagents are immobilized directly or indirectly, covalently or non-covalently, at distinct locations on a solid support. In some embodiments, the multiplexed format uses separate solid supports, each with a unique capture reagent associated with it, such as a quantum dot. In some embodiments, a separate device is used for detecting each of the multiple biomarkers detected in the biological sample. The separate device can be configured to process each biomarker in the biological sample simultaneously. For example, a microtiter plate can be used, with each well in the plate analyzing one or more of the multiple biomarkers detected in the biological sample.

[0068] In one or more of the above-described embodiments, a fluorescent tag can be used to label a component of the biomarker / capture reagent complex to detect biomarker levels. In various embodiments, a fluorescent label can be conjugated to a capture reagent specific for any of the biomarkers described herein using known techniques, and the fluorescent label can then be used to detect the corresponding biomarker level. Suitable fluorescent labels include rare earth chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, Lissamine, phycoerythrin, Texas Red, and other such compounds.

[0069] In some embodiments, the fluorescent label is a fluorescent dye molecule. In some embodiments, the fluorescent dye molecule comprises at least one substituted indolium ring system, where the substituents on three carbons of the indolium ring comprise a chemically reactive group or conjugate. In some embodiments, the dye molecule comprises an AlexFluor molecule, such as, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700. In some embodiments, the dye molecule comprises a first type and a second type of dye molecule, such as, for example, two different AlexaFluor molecules. In some embodiments, the dye molecule comprises a first type and a second type of dye molecule, and the two dye molecules have different emission spectra.

[0070] Fluorescence can be measured by a variety of instruments that are compatible with various assay formats. For example, spectrofluorometers are designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. Principles of Fluorescence Spectroscopy, by J.R. Lakowicz , Springer Science + Business Media, Inc., 2004. See Bioluminescence & Chemiluminescence: Progress & Current Applications; Philip E. Stanley and Larry J. Kricka editors, World Scientific Publishing Company, January 2002.

[0071] In one or more embodiments, a chemiluminescent tag can optionally be used to label a component of the biomarker / capture complex to detect biomarker levels. Suitable chemiluminescent materials include oxalyl chloride, rhodamine 6G, Ru(bipy)3, 2+, TMAE (tetrakis(dimethylamino)ethylene), Pyrogallol (1,2,3-trihydroxybenzene), Lucigenin, peroxyoxalates, aryl oxalates, acridinium esters, and dioxetanes.

[0072] In some embodiments, the detection method involves an enzyme / substrate combination that generates a detectable signal corresponding to the level of the biomarker. Typically, the enzyme catalyzes a chemical alteration of a chromogenic substrate that can be measured using a variety of techniques, including spectrophotometry, fluorescence, and chemiluminescence. Suitable enzymes include, for example, luciferase, luciferin, malate dehydrogenase, urea, horseradish peroxidase (HRPO), alkaline phosphatase, beta-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, and microperoxidase.

[0073] In some embodiments, the detection method can be fluorescent, chemiluminescent, a radionuclide combination, or an enzyme / substrate combination that generates a measurable signal. In some embodiments, multimodal signaling has unique and advantageous properties in biomarker assay formats.

[0074] In some embodiments, biomarker levels of the biomarkers described herein can be detected using any analytical method, such as singleplex aptamer assays, multiplex aptamer assays, singleplex or multiplex immunoassays, mRNA expression profiling, miRNA expression profiling, mass spectrometry, histological / cytological methods, etc., as described below.

[0075] Determining biomarker levels using aptamer-based assays Assays for the detection and quantification of physiologically important molecules in biological and other samples are important tools in scientific research and healthcare. One such assay involves the use of microarrays containing one or more aptamers immobilized on a solid support. Each aptamer can bind to a target molecule in a highly specific manner and with very high affinity. See, e.g., U.S. Pat. No. 5,475,096, entitled "Nucleic Acid Ligands"; also see, e.g., U.S. Pat. Nos. 6,242,246, 6,458,543, and 6,503,715, each entitled "Nucleic Acid Ligand Diagnostic Biochip." When the microarray is contacted with a sample, the aptamers bind to the respective target molecules present in the sample, thereby enabling the determination of the biomarker levels corresponding to the biomarkers.

[0076] As used herein, "aptamer" refers to a nucleic acid that has specific binding affinity for a target molecule. In this regard, the "specific binding affinity" of an aptamer for its target refers to the degree to which the aptamer binds to the target, although it is recognized that affinity interactions are a matter of degree. Generally, this means that an aptamer binds to its target with a much higher level of affinity than it binds to other components in the test sample. An "aptamer" is a type or set of copies of a nucleic acid molecule having a specific nucleotide sequence. An aptamer can contain any suitable number of nucleotides, including any number of chemically modified nucleotides. An "aptamer" refers to a set of multiple such molecules. Different aptamers can have the same or different numbers of nucleotides. Aptamers can be DNA, RNA, or chemically modified nucleic acids, and can contain single-stranded, double-stranded, or duplex regions, as well as higher-order structures. Furthermore, aptamers containing photoreactive or chemically reactive functional groups can be photoaptamers that covalently bind to their corresponding targets. Any of the aptamer methods disclosed herein can involve the use of two or more aptamers that specifically bind to the same target molecule. As described below, aptamers can contain tags. If an aptamer contains a tag, not all copies of the aptamer need to have the same tag. Furthermore, if different aptamers each contain a tag, these different aptamers can have either the same tag or different tags.

[0077] Aptamers can be identified using any known method, including the SELEX process. Once identified, aptamers can be prepared or synthesized according to any known method, including chemical and enzymatic synthesis.

[0078] The terms "SELEX" and "SELEX process" are used interchangeably herein and generally refer to the combination of (1) the selection of aptamers that interact with a target molecule in a desired manner, e.g., binding with high affinity to a protein, and (2) the amplification of those selected nucleic acids. The SELEX process can be used to identify aptamers with high affinity for a particular target or biomarker.

[0079] SELEX generally involves preparing a candidate mixture of nucleic acids, combining the candidate mixture with a desired target molecule to form an affinity complex, separating the affinity complex from unbound candidate nucleic acids, separating and isolating the nucleic acid from the affinity complex, purifying the nucleic acid, and identifying a specific aptamer sequence. This process may be performed multiple times to further improve the affinity of the selected aptamer. The process may include an amplification step at one or more points in the process. See, for example, U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands." The SELEX process can be used to generate aptamers that bind covalently to their targets, in addition to aptamers that bind noncovalently to their targets. See, for example, U.S. Patent No. 5,475,096, entitled "Systematic Evolution of Nucleic Acid Ligands." See U.S. Patent No. 5,705,337, "Acid Ligands by Exponential Enrichment: Chemi-SELEX."

[0080] The SELEX process can be used to identify high-affinity aptamers containing modified nucleotides that confer improved properties to the aptamer, such as improved in vivo stability or improved delivery properties. Examples of such modifications include chemical substitutions at the ribose, phosphate, and / or base positions. Aptamers containing modified nucleotides identified by the SELEX process are described in U.S. Patent No. 5,660,985, entitled "High Affinity Nucleic Acid Ligands Containing Modified Nucleotides," which describes oligonucleotides containing nucleotide derivatives chemically modified at the 5' and 2' positions of the pyrimidine. U.S. Patent No. 5,580,737, supra, describes oligonucleotides containing one or more 2'-amino (2'-NH2), 2'-fluoro (2'-F), and / or 2'-O-methyl (2'-OMe) modifications. See also U.S. Patent Application Publication No. 2009 / 0098549, entitled "SELEX and PHOTOSELEX," which describes nucleotide-containing aptamers with excellent specificity and describes nucleic acid libraries with a wide range of physical and chemical properties and their use in SELEX and photoSELEX.

[0081] SELEX can also be used to identify aptamers with desirable slow off-rate properties. See U.S. Patent Application Publication No. 2009 / 0004667, entitled "Method for Generating Aptamers with Improved Off-Rates," which describes an improved SELEX method for generating aptamers capable of binding to target molecules. This application describes a method for producing aptamers and photoaptamers derived from each target molecule with slower off-rates. The method involves contacting a candidate mixture with the target molecule, allowing nucleic acid-target complexes to form, and then performing a process that enriches for nucleic acid-target complexes with slow off-rates, where nucleic acid-target complexes with fast off-rates dissociate and do not reform, while complexes exhibiting slow off-rates remain intact. Additionally, the method involves using modified nucleotides in the production of the candidate nucleic acid mixture to generate aptamers with improved off-rate performance. Exemplary modified nucleotides include, but are not limited to, the modified pyrimidines shown in Figure 2. In some embodiments, an aptamer comprises at least one nucleotide with a modification, such as a base modification. In some embodiments, an aptamer comprises at least one nucleotide with a hydrophobic modification, such as a hydrophobic base modification, that allows for hydrophobic contact with a target protein. Such hydrophobic contacts, in some embodiments, contribute to binding that exhibits greater affinity and / or a slower off-rate than aptamers. Exemplary nucleotides with hydrophobic modifications are shown, but are not limited to, in FIG. 2. In some embodiments, an aptamer comprises at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten nucleotides with hydrophobic modifications, which may be the same as or different from the others.In some embodiments, at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten hydrophobic modifications in the aptamer are independently selected from the hydrophobic modifications shown in FIG.

[0082] In some embodiments, aptamers that exhibit slow off-rates (including aptamers that include at least one nucleotide with a hydrophobic modification) exhibit off-rates (t) of ≥ 30 minutes, ≥ 60 minutes, ≥ 90 minutes, ≥ 120 minutes, ≥ 150 minutes, ≥ 180 minutes, ≥ 210 minutes, or ≥ 240 minutes. 1 / 2 )

[0083] In some embodiments, the assay uses aptamers containing photoreactive functional groups that allow the aptamers to covalently bind or "photocrosslink" to their target molecules. See, e.g., U.S. Patent No. 6,544,776, entitled "Nucleic Acid Ligand Diagnostic Biochip." These photoreactive aptamers are also referred to as photoaptamers. See, e.g., U.S. Patent Nos. 5,763,177, 6,001,577, and 6,291,184, all entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX"; also see, e.g., U.S. Patent No. 6,458,539, entitled "Photoselection of Nucleic Acid Ligands." The microarray is then transferred to a sample. After the photoaptamers have had an opportunity to bind to their target molecules, the photoaptamers are photoactivated and the solid support is washed to remove any non-specifically bound molecules. Because target molecules that bind to the photoaptamers are typically not removed due to the covalent bond created by the photoactivated functional group(s) on the photoaptamer, extensive washing conditions can be used. The assay thus allows for the detection of biomarker levels corresponding to the biomarker in a test sample.

[0084] In some assay formats, aptamers are immobilized on solid supports before contacting with samples.However, under certain circumstances, immobilizing aptamers before contacting with samples may not provide optimal assays.For example, if aptamers are immobilized in advance, the aptamers and target molecules may be inefficiently mixed on the surface of the solid support, which may lead to a prolonged reaction time, and therefore a long incubation time for the aptamers to effectively bind with their target molecules.In addition, when photoaptamers are used in assays and depending on the material used as a solid support, the solid support may tend to scatter or absorb the light used to form covalent bonds between photoaptamers and their target molecules.In addition, depending on the method used, detecting target molecules by binding to their aptamers may lead to inaccuracies, since the surface of the solid support may be exposed to and affected by any labeling agent used. Finally, immobilization of aptamers on a solid support generally involves an aptamer preparation step (i.e., immobilization) prior to exposing the aptamer to a sample, and this preparation step may affect the activity or functionality of the aptamer.

[0085] Aptamer assays have also been described that use separation steps designed to allow aptamers to capture their targets in solution, followed by removal of specific components of the aptamer-target mixture prior to detection (see U.S. Patent Application Publication No. 2009 / 0042206, entitled "Multiplexed Analyses of Test Samples"). The aptamer assay methods described therein allow for the detection and quantification of non-nucleic acid targets (e.g., protein targets) in test samples by detecting and quantifying nucleic acids (i.e., aptamers). The methods described therein create nucleic acid surrogates (i.e., aptamers) that detect and quantify non-nucleic acid targets, thereby enabling the application of a wide variety of nucleic acid technologies, including amplification, to a wider range of desired targets, such as protein targets.

[0086] Aptamers can be constructed to facilitate separation of assay components from the aptamer-biomarker complex (or photoaptamer-biomarker covalent complex) and allow isolation of the aptamer for detection and / or quantitation. In certain embodiments, such constructs can include a cleavable or releasable element in the aptamer sequence. In other embodiments, additional functionality can be introduced into the aptamer, such as a label or detectable moiety, a spacer moiety, a specific binding tag, or an immobilization element. For example, an aptamer can include a tag connected to the aptamer via a cleavable moiety, a label, a spacer moiety that separates the label, and a cleavable moiety. In certain embodiments, the cleavable element is a photocleavable linker. The photocleavable linker can be attached to a biotin moiety and a spacer moiety and can include an NHS group for amine derivatization, and can be used to introduce a biotin group into the aptamer, thereby allowing for subsequent release of the aptamer in an assay.

[0087] Homogeneous assays are performed with all assay components contained in solution and do not require separation of sample and reagents prior to signal detection. These methods are fast and easy to use. These methods generate signals based on molecular capture or binding reagents that react with their specific targets. In some embodiments of the methods described herein, the molecular capture reagents include aptamers or antibodies, and the specific targets can be biomarkers listed in Table 1.

[0088] In some embodiments, the method for signal generation utilizes anisotropic signal changes resulting from the interaction of a fluorophore-labeled capture reagent with its specific biomarker target. When the labeled capture agent reacts with its target, the increased molecular weight causes rotational motion of the fluorophore bound to the complex, resulting in a very slow change in anisotropy value. By monitoring the anisotropy change, the binding event can be used to quantitatively measure the biomarker in solution. Other methods include fluorescence polarization assays, molecular beacons, time-resolved fluorescence quenching, chemiluminescence, and fluorescence resonance energy transfer.

[0089] An exemplary solution-based aptamer assay that can be used to detect biomarker levels in a biological sample involves: (a) contacting the biological sample with an aptamer that includes a first tag and has specific affinity for the biomarker, and if the biomarker is present in the sample, forming an aptamer affinity complex; (b) exposing the mixture to a first solid support that includes a first capture element, and allowing the first tag to associate with the first capture element; (c) removing components of the mixture that are not associated with the first solid support; and (d) separating the biomarker from the aptamer affinity complex. (e) releasing the aptamer affinity complex from the first solid support; (f) exposing the released aptamer affinity complex to a second solid support comprising a second capture element, and associating the second tag with the second capture element; (g) partitioning uncomplexed aptamers from the aptamer affinity complex to remove the uncomplexed aptamers from the mixture; (h) eluting the aptamers from the solid support; and (i) detecting the aptamer component of the aptamer affinity complex to detect the analyte.

[0090] An exemplary method for detecting biomarkers in biological samples using aptamers is described, but is not limited to, in Example 3. Kraemer et al., PLoS One 6(10):e26332.

[0091] Quantifying biomarker levels using immunoassays Immunoassays are based on the reaction of antibodies with corresponding targets or analytes, e.g., biomarker proteins, and can detect analytes contained in a sample depending on the specific assay format. Due to their specific epitope recognition, monoclonal antibodies and their fragments are often used to improve the specificity and sensitivity of immunoreactivity-based assays. Polyclonal antibodies have also been successfully used in various immunoassays due to their higher affinity for targets compared to monoclonal antibodies. Immunoassays are designed for use with a wide range of biological sample matrices. Immunoassay formats are designed to provide qualitative, semi-quantitative, and quantitative results.

[0092] Quantitative results are obtained using a standard curve generated with known concentrations of the particular analyte to be detected. The response or signal from an unknown sample is plotted against the standard curve, and the corresponding amount or level of the target in the unknown sample is established.

[0093] Numerous immunoassay formats have been designed. ELISA or EIA can be quantitative in detecting an analyte. The method relies on the binding of a label to either the analyte or an antibody, and the label component includes an enzyme, either directly or indirectly. ELISA tests can be used in a variety of formats for direct, indirect, competitive, or sandwich detection of an analyte. Other methods include the use of radioisotopes (I 125 ) or rely on labels such as fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidity, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assays, etc. (See ImmunoAssay: A Practical Guide (ed. Brian Law, published by Taylor & Francis, Ltd., 2005)).

[0094] Exemplary assay formats include enzyme-linked immunosorbent assays (ELISAs), radioimmunoassays, fluorescence, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time-resolved FRET (TR-FRET) immunoassays. Exemplary procedures for detecting biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow size and peptide level differentiation (e.g., gel electrophoresis, capillary electrophoresis, planar electrochromatography, etc.).

[0095] Methods for detecting and / or quantifying a detectable label or signal-generating material depend on the nature of the label. The product of a suitable enzyme-catalyzed reaction (when the detectable label is an enzyme; see above) can be, but is not limited to, fluorescent, luminescent, or radioactive, or such a product can absorb visible or ultraviolet light. Examples of detectors suitable for detecting such detectable labels include, but are not limited to, X-ray film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, luminometers, and densitometers.

[0096] Any of the detection methods can be performed in any format that allows for any suitable preparation, processing, and analysis of the reaction, including, for example, using multiwell assay plates (e.g., 96-well or 386-well) or any suitable array or microarray. Stock solutions of the various agents can be generated manually or robotically, and all subsequent pipetting, dilution, mixing, dispensing, washing, incubation, sample readout, data collection, and analysis can be performed robotically using commercially available analysis software, robotics, and detection instrumentation capable of detecting the detectable label.

[0097] Determining Biomarker Levels Using Gene Expression Profiling Measuring mRNA in a biological sample may, in some embodiments, be used as a surrogate for detecting the level of the corresponding protein in the biological sample. Thus, in some embodiments, a biomarker or panel of biomarkers described herein may be detected by detecting the appropriate RNA.

[0098] In some embodiments, mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed by qPCR). RT-PCR is used to generate cDNA from mRNA. This cDNA can be used in a qPCR assay to generate fluorescence as the DNA amplification process progresses. By comparing with a standard curve, qPCR can provide absolute measurements, such as the number of mRNA copies per cell. Northern blots, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis are all used to measure the expression levels of mRNA in a sample. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

[0099] Biomarker detection using in vivo molecular imaging techniques In some embodiments, the biomarkers described herein are detected in molecular imaging studies. For example, imaging agents coupled to capture reagents can be used to detect biomarkers in vivo.

[0100] In vivo imaging techniques provide a non-invasive method for determining the pathology of certain diseases within an individual's body. For example, entire body parts, or even the entire body, can be viewed as three-dimensional images, which can provide useful information about the morphology and structure of the body. Such techniques, in combination with the detection of biomarkers described herein, can provide information about biomarkers in vivo.

[0101] In vivo molecular imaging techniques are evolving due to various technological advances. These advances include the development of new imaging agents or labels, such as radiolabels and / or fluorescent labels, that can generate strong signals inside the body; and the development of powerful new imaging technologies that can detect and analyze these signals from outside the body with sufficient sensitivity and precision to provide useful information. The imaging agents can be visualized with an appropriate imaging system, thereby obtaining an image of the area(s) in the body where the imaging agent is located. The imaging agents can be bound or linked to, for example, capture reagents such as aptamers or antibodies, and / or peptides or proteins, or oligonucleotides (e.g., for detecting gene expression), or complexes comprising any of these with one or more macromolecules and / or other particle types.

[0102] Contrast agents may also be characterized by radioactive atoms useful in imaging. Suitable radioactive atoms include technetium-99m or iodine-123 for scintigraphic examination. Other easily detectable moieties include, for example, spin labels for magnetic resonance imaging (MRI), such as iodine-123, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, gadolinium, manganese, or iron. Such labels are well known in the art and can be easily selected by those skilled in the art.

[0103] Standard imaging techniques include, but are not limited to, magnetic resonance imaging, computed tomography scans, positron emission tomography (PET), and single-photon emission computed tomography (SPECT). For in vivo diagnostic imaging, the type of detection instrument available is a key factor in the selection of a given imaging agent, e.g., a given radionuclide, and the specific biomarker (protein, mRNA, etc.) to be used to target. Typically, the radionuclide selected exhibits a type of decay that can be detected by a given type of instrument. Additionally, when selecting a radionuclide for in vivo diagnosis, its half-life should be short enough to maximize uptake in the target tissue while minimizing harmful radiation to the host.

[0104] Exemplary imaging techniques include, but are not limited to, PET and SPECT, which are imaging techniques that involve synthetic or localized irradiation of an individual with radioactive nuclides. Subsequent measurement of the uptake of the radioactive tracer over time is used to obtain information about the target tissue and biomarkers. Due to the high-energy (gamma-ray) emissions from the specific isotopes used and the sensitivity and sophistication of the instruments used to detect them, the two-dimensional distribution of radioactivity can be estimated from outside the body.

[0105] Commonly used positron-emitting nuclides in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18. SPECT uses isotopes that decay by electron capture and / or gamma emission, such as iodine-123 and technetium-99m. An exemplary method for labeling amino acids with technetium-99m involves the reduction of pertechnetate ions in the presence of a chelating precursor, followed by the decomposition of labile ions. A suitable technetium-99m precursor complex is formed, which is then reacted with the metal binding group of the bifunctionally modified chemotactic peptide to form a technetium-99m-chemotactic peptide conjugate.

[0106] Antibodies are frequently used in such in vivo diagnostic imaging methods. The preparation and use of in vivo diagnostic antibodies is well known in the art. Similarly, aptamers can be used in such in vivo diagnostic imaging methods. For example, aptamers used to identify specific biomarkers described herein can be appropriately labeled, injected into an individual, and the biomarker can be detected in vivo. The label used is selected according to the imaging modality used, as described above. Aptamer-specific imaging agents have unique and advantageous properties in terms of tissue penetration, tissue distribution, kinetics, clearance, efficacy, and selectivity compared to other imaging agents.

[0107] In addition, such techniques can optionally use labeled oligonucleotides to detect gene expression, for example, by imaging using antisense oligonucleotides.These methods utilize, for example, in situ hybridization using fluorescent molecules or radionuclides as labels.Other methods for detecting gene expression include, for example, detecting reporter gene activity.

[0108] Another common type of imaging technique is optical imaging, in which fluorescent signals within the subject's body are detected by an optical device external to the subject. These signals can result from actual fluorescence and / or bioluminescence. Improving the sensitivity of optical detection devices can improve the usefulness of optical imaging for in vivo diagnostic assays.

[0109] For a review of other techniques, see N. Blow, Nature Methods, 6, 465-469, 2009.

[0110] Biomarker determination using histological / cytological methods In some embodiments, the biomarkers described herein can be detected in various tissue samples using histological or cytological methods. For example, endobronchial and transbronchial biopsies, fine needle aspiration biopsies, cutting needles, and core biopsies can be used for histology. Bronchial lavage and scraping, pleural aspirate, and sputum can be used for cytology. Any of the biomarkers identified herein can be used to stain specimens as a sign of disease.

[0111] In some embodiments, one or more capture reagents specific for the corresponding biomarker(s) are used for cytological evaluation of the sample, and may include one or more of: recovering the cell sample, fixing the cell sample, dehydrating, clearing, immobilizing the cell sample on a microscope slide, permeabilizing the cell sample, processing for analyte recovery, staining, destaining, washing, blocking, and reacting with one or more capture reagents in a buffer. In another embodiment, the cell sample is generated from a cell block.

[0112] In some embodiments, one or more capture reagents specific for the corresponding biomarkers are used for histological evaluation of the tissue sample, and may include one or more of: retrieving the tissue specimen, fixing the tissue sample, dehydrating, clearing, immobilizing the tissue sample on a microscope slide, permeabilizing the tissue sample, retrieving the analyte, staining, destaining, washing, blocking, rehydrating, and reacting with the capture reagent(s) in a buffer. In another embodiment, fixation and dehydration are replaced by freezing.

[0113] In another embodiment, one or more aptamers specific for the corresponding biomarker(s) can react with a histological or cytological sample to serve as nucleic acid targets in a nucleic acid amplification method. Suitable nucleic acid amplification methods include, for example, PCR, q-beta PCR, and the like. These include precursor, rolling circle amplification, strand displacement, helicase-dependent amplification, loop-mediated isothermal amplification, ligase chain reaction, and restriction and circularization-assisted rolling circle amplification.

[0114] In certain embodiments, one or more capture reagents specific for corresponding biomarkers for use in histological or cytological evaluation are mixed in a buffer that may contain any of: blocking agents, competitors, surfactants, stabilizers, carrier nucleic acids, polyanionic materials, etc.

[0115] A "cytology protocol" generally includes sample collection, sample fixation, sample immobilization, and staining. "Cell preparation" can include several processing steps after sample collection, including the use of one or more aptamers for staining the prepared cells.

[0116] Determining Biomarker Levels Using Mass Spectrometry Mass spectrometers of various configurations can be used to detect biomarker levels. Several types of mass spectrometers are available or can be manufactured in various configurations. Generally, a mass spectrometer has the following major components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and an instrument control and data system. The differences in the sample inlet, ion source, and mass analyzer generally reflect the type of instrument and its capabilities. For example, the inlet can be a capillary column liquid chromatography source, a direct probe, or a stage, such as used in matrix-assisted laser desorption. Common ion sources are, for example, electrospray, e.g., nanospray or microspray, or matrix-assisted laser desorption. Common mass analyzers include quadrupole mass filters, ion trap mass analyzers, and time-of-flight mass analyzers. Additional mass spectrometry methods are well known in the art (see Burlingame et al. Anal. Chem. 70:647 R-716R (1998); Kinter and Sherman, New York (2000)).

[0117] Protein biomarkers and biomarker levels were analyzed using: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS)n, matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF-MS), silicon desorption / ionization (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF / TOF) technology called ultraflex III TOF / TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, and APCI-(MS). N , atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS and APPI-(MS) N , quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry.

[0118] Sample preparation strategies are used to label and enrich samples prior to mass spectrometric characterization of protein biomarkers and quantification of biomarker levels. Labeling methods include, but are not limited to, isobaric tagging for relative and absolute quantification (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC). Capture reagents used to selectively enrich samples for candidate biomarker proteins prior to mass spectrometry analysis include, but are not limited to, aptamers, antibodies, nucleic acid probes, chimeras, small molecules, F(ab')2 fragments, single-chain antibody fragments, Fv fragments, single-chain Fv fragments, nucleic acids, lectins, ligand-binding receptors, affibodies, nanobodies, ankyrins, domain antibodies, alternative antibody scaffolds (e.g., diabodies), imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, as well as modifications and fragments thereof. Not limited to these.

[0119] The assays described above allow for the detection of biomarker levels useful in the methods described herein, which include detecting in a biological sample from an individual at least three, at least four, at least five, at least six, at least seven, at least eight, or at least nine of the biomarkers listed in Table 1. According to any of the methods described herein, biomarker levels can be detected and classified individually, or they can be detected and classified collectively, e.g., in a multiplex assay format.

[0120] Biomarker classification and disease score calculation In some embodiments, a biomarker "signature" for a given diagnostic test includes a set of biomarkers, each with a characteristic level in a population of interest. A characteristic level, in some embodiments, can refer to the average or mean value of a biomarker for individuals within a particular group. In some embodiments, the diagnostic methods described herein can be used to assign an unknown sample from an individual to one of two groups: impaired glucose tolerance or normal glucose tolerance. In some embodiments, the diagnostic methods described herein can be used to assign an unknown sample from an individual to one of three groups: normal glucose tolerance, impaired glucose tolerance without pre-diabetes or diabetes, and pre-diabetes or diabetes.

[0121] The assignment of a sample to one of two or more groups is known as classification, and the techniques for achieving this assignment are known as classifiers or classification methods. Classification methods may also be referred to as scoring methods. There are numerous classification methods that can be used to build diagnostic classifiers from a set of biomarker levels. In some cases, classification methods are performed using supervised learning techniques, where a dataset is collected using samples from individuals of two (or more, in the case of multivariate conditions) distinct groups to be distinguished. Because the class (group or population) to which each sample belongs is known in advance for each sample, the classification method can be trained to obtain a desired classification response. It is possible to create a diagnostic classifier without using supervised learning techniques.

[0122] Common techniques for developing diagnostic classifiers include decision trees, bagging, boosting, and forests, inference rule-based learning, Parzen windows, linear models, symbolic logic, neural network methods, unsupervised clustering, k-means, hierarchical ascent / descent, semi-supervised learning, prototype methods, nearest neighbor methods, kernel density estimation, supportive vector machines, hidden Markov models, and Boltzmann learning. Classifiers can be combined simply or in ways that minimize specific objective functions. For a general discussion, see, e.g., "Pattern Classification," by R. O. Duda, et al., John Wiley & Sons, 2nd edition, 2001. See also "The Elements of Statistical Learning—Data Mining, Inference, and Prediction," by T. Hastie, et al., Springer Science+Business Media, LLC, 2nd edition, 2009.

[0123] To create a classifier using supervised learning techniques, a set of samples, referred to as training data, is obtained. In the context of a diagnostic test, the training data will include samples from distinct groups (classes) to which unknown samples will later be assigned. For example, samples collected from individuals in a control population and samples collected from individuals in a population with a particular disease. This can constitute training data for developing a classifier that can classify unknown samples (or, more specifically, the individuals from which the samples were obtained) as either disease-positive or disease-negative. Developing a classifier from the training data is known as training the classifier. The specific details regarding training the classifier depend on the nature of the supervised learning technique. The naive Bayes classifier is an example of such a supervised learning technique (see, e.g., Pattern Classification, R.O. Duda, et al., editors, John Wiley & Sons, 2nd edition, 2001; also see, The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009). Training of the naive Bayes classifier is described, for example, in U.S. Patent Publication Nos. 2012 / 0101002 and 2012 / 0077695.

[0124] Because there are typically many more potential biomarker levels than samples in the training set, care must be taken to avoid overfitting. Overfitting occurs when a statistical model represents random error or noise instead of the underlying relationship. Overfitting can be avoided in various ways, such as by limiting the number of biomarkers used in classifier development, assuming biomarker responses are independent of each other, limiting the complexity of the underlying statistical model used, and ensuring that the underlying statistical model fits the data.

[0125] A specific example of the development of a diagnostic test using a set of biomarkers is the use of a naive Bayes classifier, i.e., a simple probabilistic classifier based on Bayes' theorem, which allows for strict independent processing of biomarkers. Each biomarker is described by a class-dependent probability density function (pdf) for the RFU measurements or log-RFU (relative fluorescence units) measurements in each class. The joint pdf for a set of biomarkers in a class is estimated to be the product of the individual class-dependent pdfs for each biomarker. In this regard, training a naive Bayes classifier is equivalent to assigning parameters ("parameterization") to characterize the class-dependent pdf. Any basic model can be used for the class-dependent pdf, but this model generally needs to match the data observed in the training set.

[0126] The performance of a naive Bayes classifier depends on the number and quality of biomarkers used to build and train the classifier. Single biomarkers follow the K-S distance (Kolmogorov-Smirnov). Adding subsequent biomarkers with good K-S distances (e.g., >0.3) generally improves classification performance, provided the subsequent biomarkers are independent of the first biomarker. Using specificity in addition to sensitivity as the classifier score, many highly scoring classifiers can be created using variations of a greedy algorithm. (A greedy algorithm is any algorithm that follows a metaheuristic approach to problem solving, making locally optimal choices at each stage with the goal of finding a globally optimal solution.)

[0127] Another way to describe classifier performance is through the receiver operating characteristic (ROC), or simply the ROC curve, or ROC plot. The ROC is a graphical plot of the sensitivity, or true positive rate, against the false positive rate (1-specificity, or 1-true positive rate) for a binary classifier system as the system's decision threshold is changed. The ROC can also be equivalently expressed as the proportion of true positives among positives (TPR=true positive rate) plotted against the proportion of false positives among negatives (FPR=false positive rate). This means that a change in the criterion will affect the performance of the two systems. Since it is a comparison of characteristics (TPR & FPR), it is also known as a relative operating characteristic curve. The area under the ROC curve (AUC) is commonly used as a summary measure of diagnostic accuracy. It can range from 0.0 to 1.0. AUC has an important statistical property: the AUC of a classifier is equivalent to the probability that the classifier will rank a randomly selected positive case higher than a randomly selected negative case (Fawcett T, 2006. An introduction to ROC analysis. Pattern Recognition Letters. 27:861-874). It is equivalent to the Wilcoxon test of ranks (Hanley, JA, McNeil, BJ, 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29-36).

[0128] Exemplary embodiments use various combinations of any number of biomarkers listed in Table 1 to create a diagnostic test that identifies individuals with impaired glucose tolerance. The biomarkers listed in Table 1 can be combined in various ways to create a classifier. In some embodiments, a panel of biomarkers is composed of different sets of biomarkers depending on the particular diagnostic performance criteria selected. For example, certain combinations of biomarkers may constitute a more sensitive (or more specific) test than other combinations.

[0129] In some embodiments, a panel is defined to include a specific set of biomarkers, and a classifier is constructed from a set of training data to complete the diagnostic test parameters. In some embodiments, one or more assays are performed on the biological sample to determine relevant quantitative biomarker levels for classification. The measured biomarker levels are used as input values ​​for the classification method to determine the classification, and as an optional score for the sample that reflects the reliability of the class assignment.

[0130] In some embodiments, samples are optionally diluted and subjected to multiplexed aptamer assays, and the data are evaluated as follows: First, the data from the assays are optionally normalized and standardized, and the resulting biomarker levels are used as inputs to a Bayesian classification scheme. Next, log-likelihood ratios are calculated for each individual biomarker measured and summed to generate a final classification score, also known as a diagnostic score. The resulting assignment and overall classification score can be reported. In some embodiments, the individual log-likelihood risk factors calculated for each biomarker level can also be reported.

[0131] kit For example, suitable kits can be utilized to detect any combination of biomarkers described herein for use in practicing the methods disclosed herein. Additionally, any kit can include one or more detectable labels, such as fluorescent moieties, as described herein.

[0132] In some embodiments, the kit comprises (a) one or more capture reagents (e.g., at least one aptamer or antibody) for detecting one or more biomarkers in a biological sample, and, optionally, (b) one or more software or computer program products for predicting whether an individual providing the biological sample has or is likely to have impaired glucose tolerance, or is pre-diabetic, or is likely to develop pre-diabetes or diabetes. Alternatively, rather than one or more computer program products, one or more instructions for a human to manually perform the above steps may be provided.

[0133] In some embodiments, the kit includes a solid support, a capture reagent, and at least one signal-generating material. The kit can also include instructions for use of the instrument and reagents, sample handling, and data analysis. Furthermore, the kit can be used with a computer system or software for analyzing and reporting the results of the analysis of biological samples.

[0134] Additionally, the kit can include one or more reagents for processing the biological sample (e.g., a solubilization buffer, a detergent, a wash solution, or a buffer). Any of the kits described herein can include software and information such as, for example, buffers, blocking agents, matrix materials for mass spectrometry, antibody capture agents, positive control samples, negative control samples, protocols, guidance, and reference data.

[0135] In some embodiments, kits are provided for analyzing impaired glucose tolerance, and the kits include PCR primers for one or more aptamers specific to the biomarkers described herein. In some embodiments, the kits may further include instructions for using the biomarkers and instructions for correlating the biomarkers with impaired glucose tolerance and / or prognosis for prediabetes or diabetes. In some embodiments, the kits may include a DNA array containing the complement of one or more biomarkers described herein, reagents, and / or enzymes for amplifying or isolating sample DNA. The kits may include reagents for real-time PCR, e.g., TaqMan probes and / or primers, and enzymes.

[0136] For example, a kit can include (a) reagents, including at least one capture reagent, for determining the level of one or more biomarkers in a sample, and, optionally, (b) one or more algorithms or computer programs for performing a step of comparing the amount of each biomarker quantified in the sample to one or more predetermined cutoffs. In some embodiments, the algorithm or computer program assigns a score for each quantified biomarker based on the comparison, and in some embodiments, adds up the scores assigned for each biomarker to obtain a total score. Further, in some embodiments, the algorithm or computer program compares the total score to a predetermined score and uses this comparison to determine whether or not an individual has impaired glucose tolerance. Alternatively, rather than one or more algorithms or computer programs, one or more instructions for a human to manually perform the steps described above can be provided.

[0137] Computer-based methods and software Once a biomarker or panel of biomarkers has been selected, a method for determining whether an individual has or is likely to have impaired glucose tolerance, or is pre-diabetic, or is likely to develop pre-diabetes or diabetes, may include: 1) collecting or obtaining a biological sample from the individual; 2) performing an analytical method to detect and measure the biomarkers in the panel in the biological sample; and 3) reporting the resulting biomarker levels. In some embodiments, the resulting biomarker levels are reported, for example, as a diagnosis made ("impaired glucose tolerance" or "pre-diabetes") or simply as positive / negative, defined as "positive" and "negative." In some embodiments, a method for determining whether an individual has impaired glucose tolerance may include: 1) collecting or obtaining a biological sample; 2) performing an analytical method to detect and measure the biomarkers in the panel in the biological sample; 3) normalizing or standardizing any data; 4) determining the levels of each biomarker; and 5) reporting the results. In some embodiments, the biomarker levels are combined in some way, and a single value for the combined biomarker levels is reported. In this approach, in some embodiments, the score is a single number determined from the sum of all biomarker calculations, which may be compared to a pre-set threshold as an indication of the presence or absence of a pathological condition. Alternatively, a diagnostic score may be calculated by comparing each of the biomarkers. The response pattern can be compared to a pre-defined pattern to determine the presence or absence of a pathology.

[0138] At least some implementations of the methods described herein can be implemented using a computer. Figure 3 illustrates an example computer system 100. Referring to Figure 3, system 100 is comprised of hardware elements electrically connected via bus 108, including processor 101, input device 102, output device 103, storage device 104, computer-readable storage medium reader 105a, communication system 106, accelerated processing device (e.g., DSP or special-purpose processor) 107, and storage device 109. Computer-readable storage medium reader 105a is further connected to computer-readable storage medium 105b, which collectively represents computer-readable information storage devices and media, memory, etc., for temporary and / or long-term storage, remote access, locally connected, fixed, and / or removable storage devices and media, including storage device 104, storage device 109, and / or any other such accessible system 100 resources. System 100 also includes software elements (shown as located in working memory 191) including an operating system 192 and other code 193, eg, programs, data, etc.

[0139] Referring to FIG. 3, system 100 is highly adaptable and configurable. Thus, for example, one or more servers may be implemented using a single architecture, and such servers may currently be reconfigured according to desired protocols, protocol modifications, extensions, and the like. However, those skilled in the art will readily appreciate that embodiments may be adapted to meet specific application requirements. For example, one or more system elements may be implemented as subcomponents of system 100 (e.g., communications system 106). Customized hardware may also be utilized, and / or particular elements may be implemented in hardware, software, or both. Furthermore, connections to other computing devices, such as network input / output devices (not shown), may be made, with the understanding that wired, wireless, modem, and / or other connection(s) to other computing devices may also be utilized.

[0140] In some embodiments, the system can include a database containing biomarker features characteristic of impaired glucose tolerance and / or prediabetes. This biomarker data (or biomarker information) can be input into a computer and used as part of a computer-implemented method. The biomarker data can include data as described herein.

[0141] In an embodiment, the system further includes one or more devices for providing input data to the one or more processors.

[0142] The system further includes a storage device for storing the dataset of ranked data elements.

[0143] In another embodiment, the device for providing input data includes a detector for detecting characteristics of the data elements, such as, for example, a mass spectrometer or a gene chip reader.

[0144] The system may further include a database management system. A user's request or question may be formatted in an appropriate language understood by the database management system, which extracts relevant information from a database of training sets to process the question.

[0145] The system may be connectable to a network connecting a network server and one or more clients. The network may be a local area network (LAN) or a wide area network (WAN), as known in the art. Preferably, the server includes the necessary hardware to run a computer program product (e.g., software) and access data in a database to process user requests.

[0146] The system may include an operating system (e.g., UNIX or Linux) that executes instructions from a database management system. In some embodiments, the operating system operates on a global communications network, such as the Internet, and may utilize a global communications network server to connect to such a network.

[0147] The system may include one or more devices that include a graphical display interface that includes interface elements such as buttons, pull-down menus, scroll bars, and text entry fields routinely found in graphical user interfaces known in the art. Requests entered at the user interface can be formatted for transmission to application programs within the system to search for relevant information in one or more system databases. User-entered requests or questions can be formulated in any suitable database language.

[0148] A graphical user interface may be created with graphical user interface code as part of the operating system and may be utilized to input data and / or display input data. The results of the processed data may be displayed by the interface, printed on a printer connected to the system, stored on a storage device, and / or transmitted over a network, or provided in the form of a computer-readable medium.

[0149] The system can be interfaced with an input device that provides data about the data elements (e.g., expression values) to the system. In some embodiments, the input device can include a gene expression profiling system, such as a mass spectrometer, gene chip, or array reader.

[0150] The methods and apparatus for analyzing biomarker information according to various embodiments can be implemented in any suitable manner, for example, using a computer program running on a computer system. Conventional computer systems including a processor and random access storage, such as a remotely accessible application server, network server, personal computer, or workstation, can be utilized. Additional computer system elements can include storage devices or information storage systems, such as a mass storage system, and a user interface, e.g., a conventional monitor, keyboard, and tracking device. The computer system can be a standalone system or part of a network of computers, including a server and one or more databases.

[0151] A biomarker analysis system can provide functions and operations to complete data analysis, such as data collection, processing, analysis, reporting, and / or diagnosis. For example, in some embodiments, a computer system can execute a computer program that can receive, store, retrieve, analyze, and report information about biomarkers. The computer program can include multiple modules that perform various functions or operations, such as processing raw data to generate supplemental data. modules, and analytical modules that analyze the raw data and supplemental data to arrive at a disease state and / or diagnosis. Identifying the likelihood of impaired glucose tolerance, pre-diabetes, and / or diabetes may include generating or retrieving any other information, including further biomedical information, regarding the individual's condition related to the disease, determining whether further testing is advisable, or otherwise assessing the individual's health status.

[0152] Some embodiments described herein may be embodied in the form of a computer program product, including a computer-readable medium having computer-readable program code therein producing an application program that can operate a computer with a database.

[0153] As used herein, a "computer program product" refers to a set of instructions, organized in the form of natural language or programming language statements, including any type of physical medium (e.g., paper, electronic, magnetic, optical, or other format), that can be used by a computer or other automated data processing system. Execution of such programming language statements by a computer or data processing system causes the computer or data processing system to operate according to the specific content of the statements. Computer program products include, but are not limited to, programs in source and object code and / or test or data libraries embedded in a computer-readable medium. Furthermore, computer program products that enable a computer system or data processing device to operate in a preselected manner can be provided in numerous forms, including, but not limited to, original source code, assembly code, object code, machine language, encrypted or condensed versions of the above code, and any and all equivalents.

[0154] In one aspect, a computer program product is provided for indicating whether an individual has impaired glucose tolerance and / or whether the individual has or is likely to develop prediabetes and / or is likely to develop diabetes, the computer program product including a computer-readable medium embodying program code executable by a processor of a computing device or system, the program code including: code for retrieving data resulting from a biological sample from the individual, the data including biomarker levels corresponding to one or more biomarkers described herein, and code for implementing a classification method indicative of impaired glucose tolerance in the individual responsive to the biomarker levels.

[0155] While various embodiments have been described in terms of methods or apparatus, it should be understood that these embodiments can be implemented via code in connection with a computer, e.g., code stored in or connectable to a computer. For example, software and databases can be used to implement many of the methods described above. Thus, in addition to hardware-implemented embodiments, it should also be noted that these embodiments can achieve the functionality disclosed in the description herein using an article of manufacture comprising a computer-usable medium having computer-readable program code embodied thereon. Therefore, it is desirable to consider these embodiments as equally protected by this patent in their program code means. Furthermore, these embodiments can be embodied as code stored on virtually any type of computer-readable storage device, including, but not limited to, RAM, ROM, magnetic media, optical media, or magneto-optical media. Even more generally, such embodiments can be implemented in software, hardware, or any combination thereof. The present invention may be implemented in any combination of, for example, but not limited to, software running on a general purpose processor, microcode, a programmable logic array (PLA), or an application specific integrated circuit (ASIC).

[0156] It is also contemplated that embodiments may be achieved with computer signals embodied in carrier waves and signals (e.g., electrical and optical) propagating through a transmission medium. Thus, the various types of information described above may be formatted into structures, such as data structures, and transmitted as electrical signals over a transmission medium or stored on a computer-readable medium.

[0157] Treatment methods In some embodiments, following a determination that a subject has, or is likely to have, impaired glucose tolerance, or is prediabetic or is likely to develop prediabetes or diabetes, the subject undergoes a treatment regimen to delay or prevent the progression of the disease. Exemplary treatment regimens for impaired glucose tolerance, prediabetes, and / or diabetes include, but are not limited to, weight loss and glycemic control. In some embodiments, the subject receives a therapeutic agent such as insulin or metformin.

[0158] In some embodiments, methods of monitoring impaired glucose tolerance are provided. In some embodiments, the methods of the invention for determining the presence or absence of impaired glucose tolerance in a subject are performed at time 0. In some embodiments, the methods are performed again at time 1, and optionally at time 2, and optionally at time 3, etc., to monitor the progression of impaired glucose tolerance in the subject. In some embodiments, different biomarkers are used at different time points depending on the individual's current state of the disease and / or the predicted rate at which the disease is suspected or thought to be progressing.

[0159] Other methods In some embodiments, the biomarkers and methods described herein are used to determine health insurance and / or life insurance premiums. In some embodiments, the results of the methods described herein are used to determine health insurance and / or life insurance premiums. In some such cases, an organization providing health insurance or life insurance will review a health insurance or life insurance application or otherwise obtain information regarding a subject's likelihood of developing impaired glucose tolerance or pre-diabetes, or a pre-diabetic or diabetic condition, and use that information to determine the appropriate health insurance or life insurance premium for the subject. In some embodiments, this review is requested by, and the costs of the review are borne by, the organization providing health insurance or life insurance.

[0160] In some embodiments, the biomarkers and methods described herein are used to predict and / or manage healthcare resource utilization. In some such embodiments, the methods are not performed for such predictive purposes, but information obtained from the methods is used to predict and / or manage such healthcare resource utilization. For example, a laboratory or hospital may use the methods to gather information about a large number of subjects in order to predict and / or manage healthcare resource utilization at a particular facility or in a particular geographic area. [Example]

[0161] The following examples are provided for illustrative purposes and are not intended to limit the scope of this application, which is defined by the appended claims. Certain molecular biology techniques described in the following examples may be performed using techniques described in standard laboratory manuals, e.g., Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd ed., Cold Spring Harbor Laboratory Press, Co. This is performed as described in [Id. Spring Harbor, NY, (2001)].

[0162] Example 1. Multiplexed aptamer assays and statistical methods for biomarker identification A multiplexed aptamer assay was used to analyze test and control samples to identify biomarkers predictive of impaired glucose tolerance. The multiplexed assay used in this study employed aptamers to detect approximately 5,000 proteins in blood from small sample volumes (approximately 65 μl of serum or plasma), with a low detection limit (median 1 pM), a dynamic range of approximately 7 logs, and a median coefficient of variation of 5%. Multiplexed aptamer assays are generally described, for example, in Gold et al. (2010) Aptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery. PLoS ONE 5(12): e15004; and U.S. Publications: 2012 / 0101002 and 2012 / 0077695.

[0163] Impaired glucose tolerance classifier A panel of 41 biomarkers selected by stability is shown in Table 1 and subjected to a random forest algorithm to generate a model. The model utilized for the biomarkers described herein is a classification model, specifically an elastic net logistic regression model.

[0164] The Beta_hat values ​​for each biomarker are shown below in Table 2. The Beta_hat values ​​indicate the relative change in biomarker level in the test sample, obtained compared to the control sample, which indicates that the test sample was obtained from a subject with impaired glucose tolerance. [Table 2-1] [Table 2-2]

[0165] Developmental Cohorts and Model Development The Developmental Cohort included over 12,000 male and female participants (aged 29–64 years) in the UK. [EGC1]This is a population-based study. The purpose of this study was to identify genetic and lifestyle risk factors for diabetes, obesity, and related health conditions in the general population. Participants with a clinical diagnosis of diabetes, a clinical diagnosis of psychiatric illness, terminal illness, pregnant women, or those unable to walk independently were excluded from the study. In this cohort, the multiplexed assay described herein was used to measure samples from participants in Phase 1 (baseline) and Phase 2 (follow-up visit approximately 6 years from baseline) from four study enrollment sites.

[0166] The sample processing protocol changed between the two measurements. This change resulted in a wider distribution of processing times per site in Phase 1 than in Phase 2. During the Phase 1 (baseline) visit, efficient execution of study participant tests (e.g., OGTT, DEXA, treadmill) was prioritized, resulting in a range of several hours for blood sample processing after sample collection. During the Phase 2 visit, additional staff were employed to rapidly process all samples upon collection. Thus, sample processing at the second time point (Phase 2) was more uniform across study sites than at the first time point (Phase 1). To account for the protocol change and increase model robustness across time series data, the dataset used for model development, verification, and validation included measurements from Phase 1 and Phase 2. These data included 7,116 Phase 1 samples (from participants who participated in Phase 1 only) and 5,003 Phase 2 samples (from participants who participated in both Phases 1 and 2). Using valid 2h-OGTT plasma glucose measurements, only one measurement from each participant was used to maintain the assumption of independence between samples.

[0167] In this dataset, the prevalence of individuals with impaired glucose tolerance as measured by a standard OGTT was 6.5%, as shown in Table 3. The estimated prevalence of impaired glucose tolerance or prediabetes across the UK for this study period was 10.5% (see https: / / www.diabetes.co.uk / pre-diabetes.html). In the United States, current estimates are higher, projecting a prevalence of 33.9% among adults aged 18 years and older (see https: / / www.niddk.nih.gov / health-information / health-statistics / diabetes-statistics). In high-prevalence populations such as the United States, the PPV (positive predictive value) of this test result is likely to improve, while the NPV (negative predictive value) may decline slightly compared to test performance in lower-prevalence populations. The high-prevalence population in the United States poses a small, acceptable risk of underprediction for self-pay, high-end specialty care, the current target market.

[0168] The cohort dataset was divided into training, validation, and verification datasets, 70% / 15% / 15%, respectively. The demographics of the training and validation datasets, measured by standard OGTT, are shown in Table 3. [Table 3]

[0169] When developing predictive models using machine learning techniques, multiple datasets should be used to identify the model with the best predictive capabilities. To this end, the following data splitting strategy was used: the data were divided into three sets: a training set (used to identify top models through cross-validation), a validation set (a second training set where the parameters of the top models could be adjusted), and a validation test set (a holdout set used only to evaluate the final model and not for model development). Splitting the data in three ways is ideal, but because it requires a large sample size, it is not commonly used or considered necessary for model development. This approach mitigates the problem of overfitting when performing feature selection and parameter estimation.

[0170] Because the cohort had sample measurements using the multiplexed assay described herein at two time points and changed sample processing protocols between the two time points, the dataset used for model development, verification, and validation included measurements at both time points. The cohort selected for this analysis included 7,116 samples from individuals with only Phase 1 samples and 5,003 individuals with data at both time points and Phase 2 data only.

[0171] To ensure data quality, four preprocessing steps were performed before the data were analyzed. 1. Normalization by ANML: Adaptive normalization was performed using maximum likelihood (ANML) to correct for dilution-specific sample and assay biases, such as pipetting errors, changes in reagent concentrations, assay timing, and other sources of system variation. A factor was calculated by maximizing the probability that a sample measurement was derived from a reference distribution (control sample set). Analytes with a Z-score exceeding |2| compared to the reference distribution were excluded to mitigate bias from sample processing artifacts or other large proteomic changes. 2. Data Quality Control (QC): This step involved sample processing and checking for normalization issues. First, sample data was normalized to account for hybridization variations during processing. This was followed by median normalization across calibrator samples to account for other assay biases during the analysis. Next, global scaling was performed for each plate to account for overall intensity differences between runs. Calibration was then performed to account for inter-assay variations. Finally, median normalization to a reference was performed for QC, buffer, and individual samples. 3. Pre-analysis: In this step, the relationship between clinical variables and normalized scaling factors was examined to ensure there was minimal correlation between the two.

[0172] 4. Missing Data: There were no missing data points requiring removal of subjects or samples prior to model development. Models were developed that were dichotomous: patients' glucose tolerance was normal (corresponding to an OGTT glucose measurement of <7.8 mmol / L) or impaired glucose tolerance (corresponding to an OGTT glucose measurement of ≥7.8 mmol / L). Response variables were calculated within the modeling BI utilized.

[0173] After data quality control and preliminary analysis, model development was completed in two steps: 1. Proof of Concept (POC): A univariate machine learning analysis designed to understand the presence or absence of evidence of a signal of interest at an endpoint. 2. Refinement: Modeler-led analysis to confirm and extend the model created in the POC, addressing any additional concerns about the data and the model.

[0174] Only the training data were used in the proof-of-concept step. Univariate analyses using t-tests, K-S tests, Mann-Whitney tests, and Wald tests of logistic regression coefficients were performed to determine whether there was a statistically significant association between the analytes and OGTT status. For each univariate test, multiple testing was corrected using the false discovery rate (FDR) calculated using the Benjamini-Hochberg procedure (Hochberg, et al.) and Bonferroni-corrected p-values. A preliminary elastic net logistic regression model was also constructed to assess whether the minimum index was met. This model was developed using 10-fold cross-validation, repeated five times. Due to class imbalance (only 6.5% of the training data were labeled "glucose disorder"), low-resolution processing within the cross-validation was employed. Satisfaction of the initial model performance criteria provided sufficient evidence to proceed to model refinement.

[0175] The refined models developed used the cohort's training and validation datasets. Initial models were calculated using five iterations of 10-fold cross-validation on the training data, using low-resolution processing within the cross-validation to address endpoint class imbalance. Model accuracy was used to select top models: accuracy = (true positives + true negatives) / n. This metric was used because it represents a balance between sensitivity and specificity (unlike AUC, which can be very high even in models with low sensitivity or specificity). While accuracy was used as an analytical tool during refinement, the model acceptance criteria remained a combination of AUC, sensitivity, and specificity. These top models were then further refined using the validation data and various model improvement tools.

[0176] The primary method used to build the model was an elastic net penalty model, which repeatedly filters features until the model performance no longer improves.

[0177] The algorithm of iterative elastic net feature reduction modeling is as follows: 1. Build an elastic net model to filter features based on rank. 2. From the top model created in step 1, retain all features whose estimates are not equal to zero. 3. Create a new elastic model that includes all the features retained in step 2. 4. Repeat steps 2 and 3 at least 10 times, and continue until accuracy no longer improves.

[0178] This process was run multiple times, filtering features in various ways: by univariate rank (including the top 100, 200, or 500 features); removing features that were statistically significantly associated with fasting status (FDR <= 0.01); removing features associated with failed interference trials; and removing features based on the variability of the external dataset used to power the model.

[0179] The final model was also evaluated for robustness by examining various synthetic datasets to determine the impact of data imputation on minimum and maximum values, and by assessing the effect of sample treatment on model predictions.

[0180] Next, we examined the predictive performance of the best models in both Phase 1 and Phase 2 to confirm that there were no significant differences between the two time points. Additionally, we examined the remaining models for correlation with age, sex, or visit.

[0181] Data QC showed that 219 samples failed the row check, meaning that at least one or three of the hybridizations had a median scale factor outside the 0.4-2.5 range, indicating a sample-specific technical issue (e.g., clogging) that did not lead to sample correction even after reruns. In addition, there were 18 samples with a MAD greater than 6 and two samples with large normalized scale factors in at least 5% of the measurements from the median signal. These 239 samples (1.4%) were excluded from further analysis. Finally, only analytes that passed the target confirmation specificity test were used in the analysis.

[0182] The PCA plot of PC1 versus PC2 also indicated the possibility of nonlinear relationships between the two principal components, but these were not of major concern as no significant differences were observed between the collection time points (Phase 1 versus Phase 2). Comparison of the two time points was important because the sample collection protocol was changed between Phase 1 and Phase 2, and therefore it was important to confirm that the change in protocol was not the largest source of variability in the assay signal.

[0183] Preliminary analyses showed no evidence of a strong relationship between endpoints and normalized scale factors at either the first or second time point.

[0184] The POC results showed a number of significant analytes at various FDR levels, the numbers and percentages of which are shown in Table 4 for the univariate t-tests. [Table 4]

[0185] The best-performing model was the elastic net logistic regression model with an AUC of 0.856 and a sensitivity / specificity of 0.78 / 0.77, both of which exceeded the feasibility criteria for AUC and sensitivity, and a specificity of ≥ 0.70.

[0186] This type of model has shown the most success in the POC stage, and we used an elastic net logistic regression model for refinements to reduce the burden of the model transfer process to the software. The performance of the model on the training and validation data is shown in Table 5. The 95% bootstrap intervals are shown in parentheses in Table 5. [Table 5]

[0187] The effect of data imputation for out-of-range values ​​was investigated in two stages. 1. Using the training data, the minimum and maximum acceptable RFU values ​​for each analyte were calculated as follows: a. Use the training data to create a bootstrap dataset (with the same sample size as the training data). b. Divide the data by k-fold, i.e., k=max{10, sample size / 10}. c. Within each fold and for each aptamer, calculate the minimum and maximum values ​​of the dataset and save them. d. Repeat steps A to C 100 times. ek * The standard deviation is calculated for each aptamer across the 100 replicates. f. Aptamer-specific min / max RFU values ​​are min / max training data RFU values ​​- / + 2SD (SD is from step E). 2. The next step was performed using the minimum and maximum values ​​calculated in step 1 and the validation data. a. n in the validation dataset * Of the p observations (n ​​is the validation sample size, and p is the number of aptamers in the model), we randomly collect 1.5% of the outlying RFU values ​​(1.5% is based on previous assay performance and empirical observations of RFU measurements). These row- and column-specific values ​​are tagged. b. Replace the tagged values ​​of the validation data with the maximum and minimum values ​​from 1(F). Use this dataset to calculate the predictive accuracy of the final model (as shown in the formula in Section 5.4). c. Replace tagged values ​​in the validation data with 0 (this is similar to removing aptamers from the model). Use this dataset to calculate the predictive accuracy of the final model (using the same method as in step B). d. Repeat steps A through E 100 times. Summarize the indications of forecast accuracy. The data imputation method that provides the best forecast accuracy metric is the recommended method for production.

[0188] The resulting prediction metrics are the same (see Table 6), and since this process is the most commonly used, outlying aptamers should be winsorized. [Table 6]

[0189] Example 2: Model validation The final model was evaluated on a 15% holdout validation (test) set. This data was stored in a separate folder in the test repository and was examined only for the purposes of generating demographic tables of the validation data (see Table 7).

[0190] The model prediction is a probability indicating the probability that an individual will have impaired glucose tolerance. The cutoff threshold is 0.5, and individuals with probabilities higher than the cutoff are classified as "impaired glucose tolerance." Values ​​closer to "1" indicate subjects most likely to have impaired glucose tolerance.

[0191] In the validation phase, the prediction probabilities and associated classifiers were calculated based on the validation data. The AUC, sensitivity, and specificity were required to be, and were, greater than 0.70 (see Table 8).

[0192] The validation dataset consisted of the final 15% of the cohort dataset, containing a total of 1,761 patients. The demographics of this dataset (Table 7) were qualitatively similar to the training and validation sets (Table 3). [Table 7]

[0193] AUC was calculated using the final 41 biomarker panel model to classify the final 15% holdout cohort validation dataset. This data was not used in POC or refinement. The final model passed validation with AUC, sensitivity, and specificity exceeding 0.70. The results of the prediction metrics for the validation dataset are shown in Table 8 below. The 95% bootstrap intervals are shown in parentheses in Table 8. [Table 8]

[0194] Figure 4 shows a boxplot of the probability of impaired glucose tolerance predicted by the 41-biomarker panel model, stratified by diagnosis based on standard oral glucose tolerance test scores (normal, impaired, and probable diabetes) in the validation dataset. The separation between the three groups is substantial, indicating a robust model.

[0195] The model met validation criteria of AUC / sensitivity / specificity ≥ 0.7 / 0.7 / 0.7 (based in part on the 10-year diabetes predictive value of 2-hour OGTT plasma glucose levels). The AUC of the final model was 0.764, and the sensitivity / specificity of the holdout validation set was 0.794 / 0.734. The conclusion of this report is that the test meets clinical acceptability criteria and can be moved into production.

[0196] Example 3: Exemplary Biomarker Detection Using Aptamers Exemplary methods for detecting one or more biomarkers in a sample are described, for example, in Kraemer et al., PLoSOne 6(10):e26332, and are described below. Three different quantification methods are described: microarray-based hybridization, Luminex bead-based methods, and qPCR.

[0197] reagent HEPES, NaCl, KCl, EDTA, EGTA, MgCl2, and Tween-20 can be purchased, for example, from Fisher Biosciences. Dextran sulfate sodium salt (DxSO4), nominal molecular weight 8000, can be purchased, for example, from AIC and dialyzed against deionized water for at least 20 hours with one change. KOD EX DNA polymerase can be purchased, for example, from VWR. Tetramethylammonium chloride and CAPSO can be purchased, for example, from Sigma-Aldrich, and streptavidin-phycoerythrin (SAPE) can be purchased, for example, from Moss Inc. 4-(2-aminoethyl)-benzenesulfonyl fluoride hydrochloride (AEBSF) can be purchased, for example, from Gold Biotechnology. Streptavidin-coated 96-well plates can be purchased, for example, from Thermo Scientific (Pierce Streptavidin Coated Plates HBC, clear, 96-well, product numbers 15500 or 15501). NHS-PEO4-biotin can be purchased, for example, from Thermo Scientific (EZ-Link NHS-PEO4-Biotin, product number 21329), dissolved in anhydrous DMSO, and stored frozen in single-use aliquots. IL-8, MIP-4, lipocalin-2, RANTES, MMP-7, and MMP-9 can be purchased, for example, from R&D Systems, resistin and MCP-1 can be purchased, for example, from PeproTech, and tPA can be purchased, for example, from VWR.

[0198] nucleic acid Conventional oligodeoxynucleotides (including those substituted with amines and biotin) can be purchased commercially, for example, from Integrated DNA Technologies (IDT). Z-Block is a single-stranded oligodeoxynucleotide of the sequence 5'-(AC-BnBn)7-AC-3', where Bn represents a benzyl-substituted deoxyuridine residue. Z-Blocks can be synthesized using conventional phosphoramidite chemistry. Aptamer capture reagents can also be synthesized using conventional phosphoramidite chemistry and purified on a 21.5 x 75 mm PRP-3 column operated at 80°C, for example, on a Waters Autopurification 2767 system (or a Waters 600 series semi-automated system) using a Timberline TL-600 or TL-150 heater and a triethylammonium bicarbonate (TEAB) / ACN gradient to elute the product. Detection is performed at 260 nm, and fractions are collected across the main peak before pooling the best fractions.

[0199] buffer Buffer SB18 consists of 40 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl, and 0.05% (v / v) Tween-20 adjusted to pH 7.5 with NaOH. Buffer SB17 consists of SB18 plus 1 mM trisodium EDTA. Buffer PB1 consists of 10 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl, 1 mM trisodium EDTA, and 0.05% (v / v) Tween-20 adjusted to pH 7.5 with NaOH. CAPSO elution buffer consists of 100 mM CAPSO pH 10.0 and 1 M NaCl. Neutralization buffer contains 500 mM HEPES, 500 mM HCl, and 0.05% (v / v) Tween-20. Agilent Hybridization Buffer is our formulation provided as part of the kit (Oligo aCGH / ChIP-on-chip Hybridization Kit). Agilent Wash Buffer 1 is our formulation (Oligo aCGH / ChIP-on-chip Wash Buffer 1, Agilent). Agilent Wash Buffer 2 is our formulation (Oligo aCGH / ChIP-on-chip Wash Buffer 2, Agilent). TMAC Hybridization Solution consists of 4.5 M tetramethylammonium chloride, 6 mM trisodium EDTA, 75 mM Tris-HCl (pH 8.0), and 0.15% (v / v) sarkosyl. KOD Buffer (10x concentrated) consists of 1200 mM Tris-HCl, 15 mM MgSO4, 100 mM KCl, 60 mM (NH4)2SO4, 1% v / v Triton-X100, and 1 mg / mL BSA.

[0200] Sample preparation Serum (100 μL aliquots stored at -80°C) is thawed in a 25°C water bath for 10 minutes and stored on ice before diluting the samples. Samples are mixed by gently vortexing for 8 seconds. A 6% serum sample solution is prepared by diluting into 0.94x SB17 supplemented with 0.6 mM MgCl2, 1 mM trisodium EGTA, 0.8 mM AEBSF, and 2 μM Z-Block. A portion of the 6% serum stock solution is diluted 10-fold with SB17 to prepare a 0.6% serum stock solution. In some embodiments, the 6% and 0.6% stock solutions are used to detect high- and low-abundance analytes, respectively.

[0201] Preparation of capture reagent (aptamer) and streptavidin plates The aptamers were split into two mixtures based on the relative abundance of the relevant analyte (or biomarker). The stock concentration was 4 nM for each aptamer, and the final concentration of each aptamer was 0.5 nM. The aptamer bulk mixture was diluted 4-fold with SB17 buffer, heated to 95°C for 5 minutes, and cooled to 37°C for 15 minutes before use. This denaturation-renaturation cycle was intended to normalize the conformer distribution of the aptamers and ensure reproducible aptamer activity despite their varying history. The streptavidin plate was washed twice with 150 μL of buffer PB1 before use.

[0202] Incubation and plate capture The 2x aptamer mix (55 μL) was combined with an equal volume of 6% or 0.6% serum dilution to create incubation mixes containing 3% and 0.3% serum. These plates were sealed with a Silicone Sealing Mat (Axymat Silicone Sealing Mat, VWR) and incubated at 37°C for 1.5 hours. The incubation mix was then transferred to the wells of a washed 96-well streptavidin plate and further incubated for 2 hours with shaking at 800 rpm in an Eppendorf Thermomixer set at 37°C.

[0203] Manual Assay Unless otherwise specified, the liquid was removed by pouring, followed by tapping twice with a layer of paper towels. The wash volume was 150 μL, and all shaking incubations were performed in an Eppendorf Thermomixer set at 25°C and 800 rpm. The incubation mix was removed by pipetting, and the plate was washed twice for 1 minute with Buffer PB1 containing 1 mM dextran sulfate and 500 μM biotin, followed by four washes for 15 seconds with Buffer PB1. A freshly prepared solution containing 1 mM NHS-PEO4-biotin in Buffer PB1 (150 μL / well) was added, and the plate was incubated for 5 minutes with shaking. The NHS-biotin solution was removed, and the plate was washed three times with Buffer PB1 containing 20 mM glycine, followed by three washes with Buffer PB1. Next, 85 μL of Buffer PB1 supplemented with 1 mM DxSO4 was added to each well, and the plate was irradiated under a BlackRay UV lamp (nominal wavelength 365 nm) at a distance of 5 cm for 20 minutes with shaking. The samples were transferred to unused wells of a freshly washed streptavidin-coated plate or a previously washed streptavidin plate, with the mixture of the more and less diluted samples combined in a single well. The samples were incubated at room temperature for 10 minutes with shaking. Unadsorbed material was removed, and the plate was washed eight times with Buffer PB1 supplemented with 30% glycerol for 15 seconds each. The plate was then washed once with Buffer PB1. The aptamer was eluted using 100 μL CAPSO elution buffer for 5 minutes at room temperature. 90 μL of the eluate was transferred to a 96-well HybAid plate, and 10 μL of neutralization buffer was added.

[0204] Semi-automated assay The streptavidin plate containing the adsorbed incubation mix is ​​placed on the deck of a BioTek EL406 plate washer. The BioTek EL406 plate washer is programmed to: aspirate any unadsorbed material and wash the wells four times with 300 μL of Buffer PB1 containing 1 mM dextran sulfate and 500 μM biotin. The wells are then washed three times with 300 μL of Buffer PB1. 150 μL of a freshly prepared solution (100 mM stock solution in DMSO) containing 1 mM NHS-PEO4-biotin is added. The plate is incubated for 5 minutes with shaking. The liquid is aspirated and the wells are washed eight times with 300 μL of Buffer PB1 containing 10 mM glycine. 100 μL of Buffer PB1 containing 1 mM dextran sulfate is added. After these automated steps, the plate is removed from the plate washer and placed 5 cm away from a thermoshaker under a UV light source (BlackRay, nominal wavelength 365 nm) for 20 minutes. The thermoshaker is set to 800 rpm and 25°C. After 20 minutes of irradiation, the samples are manually transferred to a freshly washed streptavidin plate (or to unused wells of a previously washed plate). At this point, a sufficient volume (3% serum + 3% aptamer mix) and a small volume of reaction mix (0.3% serum + 0.3% aptamer mix) are combined in one well. This "Catch-2" plate is placed on the deck of a BioTek EL406 plate washer. The plate washer is programmed to perform the following steps: incubate the plates for 10 minutes with shaking. The liquid is aspirated, and the wells are washed 21 times with 300 μL of PB1 buffer supplemented with 30% glycerol. The wells are washed five times with 300 μL of Buffer PB1, and the final wash is aspirated. 100 μL of CAPSO elution buffer is added, and the aptamers are eluted for 5 minutes with shaking. Following these automated steps, the plate is removed from the plate washer deck, and 90 μL aliquots of sample are manually transferred to wells of a HybAid 96-well plate containing 10 μL Neutralization Buffer.

[0205] Hybridization to custom Agilent 8x15k microarrays 24 μL of the neutralized eluate is transferred to a new 96-well plate, and 6 μL of 10× Agilent Block (Oligo aCGH / ChIP-on-chip Hybridization Kit, Large Capacity, Agilent 5188-5380), containing a set of hybridization controls consisting of 10 Cy3 aptamers, is added to each well. 30 μL of 2× Agilent Hybridization Buffer is added to each sample and mixed. 40 μL of the resulting hybridization solution is transferred to a hybridization gasket slide (Hybridization Manually pipette the aptamer into each "well" of a Gasket Slide (8 microarray / slide format, Agilent). A custom Agilent microarray slide containing 10 probes for each array complementary to the 40-nucleotide random region of each aptamer with 20x dT linkers is placed on the gasket slide according to the manufacturer's protocol. This assembly (Hybridization Chamber Kit - SureHyb compatible, Agilent) is clamped and incubated at 60°C for 19 hours with rotation at 20 rpm.

[0206] Post-hybridization washes Place approximately 400 mL of Agilent Wash Buffer 1 into each of two separate glass staining dishes. Slides (not more than two at a time) are disassembled and separated while submerged in Wash Buffer 1, then transferred to the slide rack in the second staining dish containing Wash Buffer 1. Slides are then washed in Wash Buffer 1. Incubate at 37°C for an additional 5 minutes with agitation. Transfer slides to pre-equilibrated Wash Buffer 2 at 37°C and incubate for 5 minutes with agitation. Transfer slides to a fourth staining dish containing acetonitrile and incubate for 5 minutes with agitation.

[0207] Microarray imaging Microarray slides are imaged using an Agilent G2565CA Microarray Scanner System at 100% PMT, 5 μm resolution, using the Cy3 channel, and with the XRD option enabled at 0.05. The resulting TIFF images are processed using Agilent Feature Extraction software version 10.5.1.1 using the GE1_105_Dec08 protocol.

[0208] Luminex Probe Design The probes immobilized on the beads have 40 nucleotides complementary to the 3' end of the 40 nucleotide random region of the target aptamer. This aptamer-complementary region is attached to the Luminex via a hexaethylene glycol (HEG) linker with a 5' amino terminus. Binding to Microspheres. Biotinylated detector deoxyoligonucleotides contain 17-21 deoxynucleotides complementary to the 5' primer region of the target aptamer. A biotin moiety is added to the 3' end of the detector oligo.

[0209] Binding of probes to Luminex Microspheres The probes were prepared using Luminex Microplex® probes, essentially according to the manufacturer's instructions. Coupling to microspheres was performed with the following modifications: the amount of amino-terminal oligonucleotide was 0.08 nmol / 2.5 x 10 microspheres, and a second EDC addition of 5 µL at 10 mg / mL was added. The coupling reaction was performed in an Eppendorf ThermoShaker set at 25 °C and 600 rpm.

[0210] Microsphere hybridization The microsphere stock solution (approximately 40,000 microspheres / μL) was vortexed and sonicated for 60 seconds in a Health Sonics ultrasonic bath (Model: T1.9C) to suspend the microspheres. The suspended microspheres were diluted with 1.5x TMAC hybridization solution to 2,000 microspheres per reaction and mixed by vortexing and sonication. For each reaction, 33 μL of the bead mixture was transferred to a 96-well HybAid plate. 7 μL of 1x TE buffer containing 15 nM biotinylated detection oligonucleotide stock was added to each reaction and mixed. 10 μL of neutralized assay sample was added, and the plate was sealed with a silicone cap mat seal. The plate was first incubated at 96°C for 5 minutes, and then incubated overnight at 50°C in a conventional hybridization oven without agitation. A filter plate (Durapore, Millipore part number MSBVN1250, 1.2 μm pore size) is pre-wetted with 75 μL of 1× TMAC hybridization solution containing 0.5% (w / v) BSA. The entire sample volume from the hybridization reaction is transferred to the filter plate. The hybridization plate is rinsed with 75 μL of 1× TMAC hybridization solution containing 0.5% BSA, and any residual material is transferred to the filter plate. The sample is slowly filtered under vacuum, and 150 μL of buffer is degassed for approximately 8 seconds. The filter plate is washed once with 75 μL of 1× TMAC hybridization solution containing 0.5% BSA, and the microspheres in the filter plate are resuspended in 75 μL of 1× TMAC hybridization solution containing 0.5% BSA. Protect the filter plate from light and incubate in an Eppendorf Thermalmixer R at 1000 rpm for 5 min. The filter plate is then washed once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. 75 μL of 1×TMAC hybridization solution containing 10 μg / mL streptavidin phycoerythrin (SAPE-100, MOSS, Inc.) is added to each reaction and incubated at 25°C and 1000 rpm for 60 minutes in an Eppendorf Thermalmixer®. The filter plate is washed twice with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA, and the microspheres in the filter plate are resuspended in 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. The filter plate is then incubated at 1000 rpm for 5 minutes in an Eppendorf Thermalmixer® while protected from light. The filter plate is then washed once with 75 μL of 1× TMAC hybridization solution containing 0.5% BSA. The microspheres are resuspended in 75 μL of 1× TMAC hybridization solution supplemented with 0.5% BSA and analyzed on a Luminex 100 instrument running XPonent 3.0 software. At least 100 microspheres per bead type are counted using a large PMT calibration and doublet rejection setting of 7500-18000.

[0211] QPCR readout A qPCR standard curve is generated in water, ranging from 10 to 10 copies, using 10-fold dilutions and a no-template control. Neutralized assay samples are diluted 40-fold with diH2O. A qPCR master mix is ​​prepared at 2x final concentrations (2x KOD buffer, 400 μM dNTP mix, 400 nM forward and reverse primer mix, 2x SYBR Green I, and 0.5 U KODEX). 10 μL of 2x qPCR master mix is ​​added to 10 μL of diluted assay sample. qPCR is performed on a BioRad MyIQ iCycler at 96°C for 2 minutes, followed by 40 cycles of 96°C for 5 seconds and 72°C for 30 seconds.

[0212] Example 4. Analysis of biomarker panel models Models containing various combinations of the 41 biomarkers listed in Tables 1 and 2 were analyzed to determine the AUC, sensitivity, and specificity of panels containing various biomarker combinations. Table 9 below shows the results for models where the biomarker proteins in the panel were added or subtracted one by one in the order listed in Table 9. Thus, the first row of Table 9 shows the results for models where the panel included only INHBC ("add one by one" results) or all 41 listed biomarkers except INHBC ("subtract one by one" results). The results in Table 9 show that once the panel included at least the first five biomarker proteins, specifically INHBC, SHBG, ACY1, COL1A1, and RTN4R, the addition of each subsequent biomarker protein did not significantly improve the AUC. Sensitivity and specificity values ​​continued to improve with the addition of the remaining biomarker proteins. The results in Table 9 also show that the AUC, sensitivity, and specificity are significantly reduced when the panel lacks at least the first 10 biomarker proteins, specifically, INHBC, SHBG, ACY1, COL1A1, RTN4R, CRLF1, CBX7, FAM20B, COL15A, and KIN. The results shown in Table 9 are also presented in graphical form in Figures 1A and 1B. [Table 9-1] [Table 9-2]

[0213] The AUC, sensitivity, and specificity of the model were also analyzed, with the panel consisting of the N above biomarkers from Tables 1 and 2, with the biomarkers added one by one in random order. The results are shown in Figure 1C. The results indicate that adding biomarkers to the panel improves model performance, with or without the addition of biomarkers selected from Tables 1 and 2. Furthermore, the 10 random biomarker proteins generally exhibit an AUC of 0.7.

[0214] The above-described embodiments and examples are intended to be illustrative only. A particular embodiment, example, or element of a particular embodiment or example should not be construed as a critical, required, or essential element or feature of any of the claims. Various changes, modifications, substitutions, and other variations can be made to the disclosed embodiments without departing from the scope of the present application, as defined by the appended claims. The specification, including the figures and examples, is not intended to be limiting, but should be considered in an exemplary manner, and all such modifications and substitutions are intended to be included within the scope of the application. The steps recited in any method or process claim may be performed in any order possible and are not limited to the order set forth in any of the embodiments, examples, or claims. Furthermore, in any of the above-described methods, one or more specifically recited biomarkers may be specifically excluded, either as individual biomarkers or as biomarkers from any panel. [Explanation of symbols]

[0215] 100...Computer Systems 101 Processor 102 Input device 103 Output device 104, 109...Storage device 105a, 105b Storage media reader 106 Communication Systems 107 Acceleration processing unit 108 Bus 191 Working Memory 192 Operating Systems 193···Code

Claims

1. A method for determining the presence or absence of impaired glucose tolerance in a subject, or the likelihood that a subject has impaired glucose tolerance, comprising: forming a biomarker panel having N biomarker proteins; and detecting the level of each of the N biomarker proteins in a sample obtained from the subject, wherein N is at least 3, the biomarker panel comprises ACY1 and RTN4R, and at least one of the N biomarker proteins is COL1A1, CRLF1:CLCF1 complex, CBX The method, selected from 7, KIN, SERPINA11, PELI2, TFF3, FABP12, GAD1, SVEP1, SOCS7, F9, STC1, MYOC, WFDC11, CALB1, CCL16, SMCO2, CCL23, OSTM1, RNASE10, ITIH1, ZNF134, CFAP45, SFTPD, INHBC, SHBG, FAM20B, COL15A1, MARCSL1, HTRA1, CHAD, CPM, DLK1, HERC1, IL20RB, MAP2K4, GPX2, and FGFR4.

2. A method for determining the presence or absence of prediabetes in a subject, or the likelihood that a subject will develop prediabetes or diabetes, comprising: forming a biomarker panel having N biomarker proteins; and detecting the level of each of the N biomarker proteins in a sample obtained from the subject, wherein N is at least 3, the biomarker panel comprises ACY1 and RTN4R, and at least one of the N biomarker proteins is COL1A1, CRLF1:CLCF1 complex, CBX The method, selected from 7, KIN, SERPINA11, PELI2, TFF3, FABP12, GAD1, SVEP1, SOCS7, F9, STC1, MYOC, WFDC11, CALB1, CCL16, SMCO2, CCL23, OSTM1, RNASE10, ITIH1, ZNF134, CFAP45, SFTPD, INHBC, SHBG, FAM20B, COL15A1, MARCSL1, HTRA1, CHAD, CPM, DLK1, HERC1, IL20RB, MAP2K4, GPX2, and FGFR4.

3. A method for detecting the levels of N biomarker proteins in a sample obtained from a subject, comprising forming a biomarker panel having N biomarker proteins, and detecting the level of each of the N biomarker proteins in the sample, wherein N is at least 3, the biomarker panel comprises ACY1 and RTN4R, and at least one of the N biomarker proteins is COL1A1, CRLF1:CLCF1 complex, CBX7, KIN, SE The method selected from RPINA11, PELI2, TFF3, FABP12, GAD1, SVEP1, SOCS7, F9, STC1, MYOC, WFDC11, CALB1, CCL16, SMCO2, CCL23, OSTM1, RNASE10, ITIH1, ZNF134, CFAP45, SFTPD, INHBC, SHBG, FAM20B, COL15A1, MARCSL1, HTRA1, CHAD, CPM, DLK1, HERC1, IL20RB, MAP2K4, GPX2, and FGFR4.

4. (i) N is 3 to 41, or N is 4 to 41, or N is 5 to 41, or N is 6 to 41, or N is 7 to 41, or N is 8 to 41, or N is 9 to 41, or N is 10 to 41, or N is 11 to 41, or N is 12 to 41, or N is 13 to 41, or N is 14 to 41, or N is 15 to 41, or N is 16 to 41, or (ii) N is 3, or N is 4, or N is 5, or N is 6, or N is 7, or N is 8, or N is 9, or N is 10, or N is 11, or N is 12, or N is 13, or N is 14, or N is 15, or N is 16, or N is 17, or N is 18, or N is 19, or N is 20, or N is 21, or N is 22, or N is 23, or N is 24, or N is 25, or N is 26, or N is 27, or N is 28, or N is 29, or N is 30, or N is 31, or N is 32, or N is 33, or N is 34, or N is 35, or N is 36, or N is 37, or N is 38, or N is 39, or N is 40, or N is 41. The method according to any one of claims 1 to 3.

5. The method according to any one of claims 1 to 4, wherein at least one of the N biomarker proteins is selected from COL1A1, CRLF1:CLCF1 complex, CBX7, and KIN.

6. (i) At least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten of the N biomarker proteins are selected from COL1A1, CRLF1:CLCF1 complex, CBX7, KIN, SERPINA11, PELI2, TFF3, FABP12, GAD1, SVEP1, SOCS7, F9, STC1, MYOC, WFDC11, CALB1, CCL16, SMCO2, CCL23, OSTM1, RNASE10, ITIH1, ZNF134, CFAP45, and SFTPD, or (ii) At least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten of the N biomarker proteins are selected from FAM20B, COL15A1, MARCSL1, HTRA1, CHAD, CPM, DLK1, HERC1, IL20RB, MAP2K4, GPX2, and FGFR4. The method according to any one of claims 1 to 5.

7. (i) one of the N biomarker proteins is INHBC, or one of the N biomarker proteins is SHBG, or two of the N biomarker proteins are INHBC and SHBG (ii) Two of the N biomarker proteins are INHBC and COL1A1, or two of the N biomarker proteins are SHBG and COL1A1, or three of the N biomarker proteins are INHBC, SHBG and COL1A1, (iii) Two of the N biomarker proteins are INHBC and CRLF1:CLCF1 complex, or two of the N biomarker proteins are SHBG and CRLF1:CLCF1 complex, or three of the N biomarker proteins are INHBC, SHBG and CRLF1:CLCF1 complex (iv) Two of the N biomarker proteins are INHBC and CBX7, or two of the N biomarker proteins are SHBG and CBX7, or three of the N biomarker proteins are INHBC, SHBG and CBX7, or (v) Two of the N biomarker proteins are INHBC and KIN, or two of the N biomarker proteins are SHBG and KIN, or three of the N biomarker proteins are INHBC, SHBG and KIN The method according to any one of claims 1 to 6.

8. (i) N is at least 5, and 5 of the N biomarker proteins are INHBC, SHBG, ACY1, COL1A1, and RTN4R, or (ii) N is at least 16, and the 16 of the N biomarker proteins are ACY1, COL1A1, RTN4R, CRLF1, CBX7, KIN, SERPINA11, PELI2, TFF3, FABP12, INHBC, SHBG, FAM20B, COL15A1, MARCKSL1, and HTRA1. The method according to any one of claims 1 to 7.

9. The method according to any one of claims 1 to 8, wherein the sample is a blood sample, a plasma sample, or a serum sample.

10. (i) A level of at least one biomarker selected from SHBG, COL1A1, CRLF1:CLCF1 complex, FAM20B, COL15A1, KIN, SERPINA11, PELI2, MARCSL1, CHAD, IL20RB, MYOC, WFDC11, MAP2K4, CALB1, FGFR4, OSTM1, ITIH1, CFAP45, and SFTPD that is higher than the control level of the biomarker, indicates that the subject has or is likely to have impaired glucose tolerance, has or is likely to develop prediabetes, and / or is likely to develop diabetes, or (ii) The level of at least one biomarker selected from INHBC, COL1A1, CBX7, TFF3, HTRA1, FABP12, GAD1, CPM, SVEP1, SOCS7, F9, DLK1, HERC1, STC1, CCL16, SMCO2, GPX2, CCL23, RNASE10, and ZNF134, which is lower than the control level of each biomarker, indicates that the subject has or is likely to have impaired glucose tolerance, has or is likely to develop prediabetes, and / or is likely to develop diabetes. The method according to any one of claims 1 to 9.

11. The method according to any one of claims 1 to 10, wherein the subject is at risk of developing diabetes.

12. The method according to any one of claims 1 to 11, comprising determining whether the subject has impaired glucose tolerance or is likely to have impaired glucose tolerance.

13. The object is, (i) having prediabetes or being at high risk of developing it, (ii) Is there a high probability of developing diabetes? Including determining, The method according to any one of claims 2 to 12, wherein the diabetes referred to here is type 2 diabetes.

14. The method according to any one of claims 1 to 13, comprising contacting one or more biomarker proteins of the sample with a set of biomarker capture reagents, wherein each biomarker capture reagent of the set of biomarker capture reagents specifically binds to a different biomarker protein to be detected.

15. The method according to claim 14, wherein each biomarker capture reagent is an antibody or an aptamer.

16. The method according to claim 15, wherein each biomarker capture reagent is an aptamer.

17. At least one aptamer is an aptamer exhibiting a low off-rate, In at least one aptamer exhibiting slow offrate, at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten nucleotides are modified. Each aptamer exhibiting a slow offrate binds to its target protein at an offrate of ≥30 minutes, ≥60 minutes, ≥90 minutes, ≥120 minutes, ≥150 minutes, ≥180 minutes, ≥210 minutes, or ≥240 minutes (t 1 / 2). The method according to claim 16.

18. The method according to any one of claims 1 to 17, wherein the determination comprises analyzing the levels of N biomarker proteins using a classification model or an elastic net logistic regression model.

19. A kit comprising N biomarker protein capture reagents, wherein N is at least 3, two of the N biomarker protein capture reagents specifically bind to ACY1 and RTN4R, and at least one of the N biomarker protein capture reagents is COL1A1, CRLF1:CLCF1 complex, CBX7, KIN, SERPINA11, PELI2, TFF3, FABP12, GAD1, SVEP1, SOCS7, F9, S The kit specifically binds to a biomarker protein selected from TC1, MYOC, WFDC11, CALB1, CCL16, SMCO2, CCL23, OSTM1, RNASE10, ITIH1, ZNF134, CFAP45, SFTPD, INHBC, SHBG, FAM20B, COL15A1, MARCSL1, HTRA1, CHAD, CPM, DLK1, HERC1, IL20RB, MAP2K4, GPX2, and FGFR4.

20. (i) N is 3 to 41, or N is 4 to 41, or N is 5 to 41, or N is 6 to 41, or N is 7 to 41, or N is 8 to 41, or N is 9 to 41, or N is 10 to 41, or N is 11 to 41, or N is 12 to 41, or N is 13 to 41, or N is 14 to 41, or N is 15 to 41, or N is 16 to 41, or (ii) N is 3, or N is 4, or N is 5, or N is 6, or N is 7, or N is 8, or N is 9, or N is 10, or N is 11, or N is 12, or N is 13, or N is 14, or N is 15, or N is 16, or N is 17, or N is 18, or N is 19, or N is 20, or N is 21, or N is 22, or N is 23, or N is 24, or N is 25, or N is 26, or N is 27, or N is 28, or N is 29, or N is 30, or N is 31, or N is 32, or N is 33, or N is 34, or N is 35, or N is 36, or N is 37, or N is 38, or N is 39, or N is 40, or N is 41. The kit according to claim 19.

21. The kit according to claim 19 or 20, wherein each of the N biomarker protein capture reagents specifically binds to a different biomarker protein.

22. The kit according to any one of claims 19 to 21, wherein at least one of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from COL1A1, CRLF1:CLCF1 complex, CBX7, and KIN.

23. (i) Each of the N biomarker protein capture reagents is such that at least two or at least three of them specifically bind to a protein selected from COL1A1, CRLF1:CLCF1 complex, CBX7, KIN, SERPINA11, PELI2, TFF3, FABP12, GAD1, SVEP1, SOCS7, F9, STC1, MYOC, WFDC11, CALB1, CCL16, SMCO2, CCL23, OSTM1, RNASE10, ITIH1, ZNF134, CFAP45, and SFTPD, or (ii) At least one of the N biomarker protein capture reagents specifically binds to a protein selected from FAM20B, COL15A1, MARCSL1, HTRA1, CHAD, CPM, DLK1, HERC1, IL20RB, MAP2K4, GPX2, and FGFR4. A kit according to any one of claims 19 to 22.

24. (i) one of the N biomarker protein capture reagents specifically binds to INHBC, one of the N biomarker protein capture reagents specifically binds to SHBG, or each of two of the N biomarker protein capture reagents specifically binds to INHBC or SHBG. (ii) Each of two of the N biomarker protein capture reagents specifically binds to INHBC or COL1A1, each of two of the N biomarker protein capture reagents specifically binds to SHBG or COL1A1, or each of three of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from INHBC, SHBG, and COL1A1. (iii) Each of two of the N biomarker protein capture reagents specifically binds to INHBC or the CRLF1:CLCF1 complex, each of two of the N biomarker protein capture reagents specifically binds to SHBG or the CRLF1:CLCF1 complex, or each of three of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from INHBC, SHBG, and the CRLF1:CLCF1 complex. (iv) Each of two of the N biomarker protein capture reagents specifically binds to INHBC or CBX7, each of two of the N biomarker protein capture reagents specifically binds to SHBG or CBX7, or each of three of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from INHBC, SHBG, and CBX7, or (v) The kit according to any one of claims 19 to 23, wherein each of two of the N biomarker protein capture reagents specifically binds to INHBC or KIN, each of two of the N biomarker protein capture reagents specifically binds to SHBG or KIN, or each of three of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from INHBC, SHBG, and KIN.

25. (i) N is at least 5, and each of the 5 of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from INHBC, SHBG, ACY1, COL1A1, and RTN4R, or (ii) The kit according to any one of claims 19 to 24, wherein N is at least 16, and each of the 16 of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from ACY1, COL1A1, RTN4R, CRLF1, CBX7, KIN, SERPINA11, PELI2, TFF3, FABP12, INHBC, SHBG, FAM20B, COL15A1, MARCKSL1, and HTRA1.

26. The kit according to any one of claims 19 to 25, wherein each of the N biomarker capture reagents is an antibody or an aptamer.

27. ​​The kit according to claim 26, wherein each biomarker capture reagent is an aptamer.

28. At least one aptamer is an aptamer exhibiting a low off-rate, In at least one aptamer exhibiting slow offrate, at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten nucleotides are modified. Each aptamer exhibiting a slow offrate binds to its target protein at an offrate of ≥30 minutes, ≥60 minutes, ≥90 minutes, ≥120 minutes, ≥150 minutes, ≥180 minutes, ≥210 minutes, or ≥240 minutes (t 1 / 2). The kit according to claim 27.

29. The kit according to any one of claims 19 to 28, used for detecting N biomarker proteins in a sample obtained from a subject, and for determining whether the subject has or is likely to have impaired glucose tolerance, or is prediabetic or is likely to develop prediabetes or diabetes.

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