HK1187960A - Methods for detecting low grade inflammation - Google Patents

Abstract

The present invention relates to methods for detecting the presence of low grade inflammation in a patient.

Description

Method for detecting low grade inflammation

Cross Reference to Related Applications

This application claims the benefit of european patent application No. 10192405.8 filed on 24/11/2010, the disclosure of which is incorporated herein by reference in its entirety.

Technical Field

The present invention relates to a method for detecting low grade tissue inflammation.

Background

Low grade tissue inflammation is increasingly recognized as one of the major factors associated with insulin resistance and diabetes in obese subjects (1, 2). Tissue inflammation is thought to be mediated primarily by factors secreted by adipose tissue (e.g., TNFa, IL6, MIF, CSF1, MCP1, fatty acids) and consists of a series of cellular responses, such as intracellular pathway activation and endoplasmic reticulum stress responses, responsible for the expansion of inflammatory conditions and alterations in tissue metabolism. For example, TNFa mediates inactivation of insulin receptor signaling (mediated by IKKb and JNK mediated phosphorylation of IRS serine). At the same time, IKKb and JNK also activate NFkB and AP-1 pathways, mediating the expansion of cytokine release. NFkB and AP-1 mediated cytokine release is also a final step in other pathways triggered by other circulating factors, such as the induction of free fatty acids through Toll-like receptors, or elevated glucose through the endoplasmic reticulum stress response. Low grade inflammation is also associated with endothelial dysfunction, which increases cardiovascular risk in obese and insulin resistant subjects.

The evaluation of low grade inflammation in tissues can be a useful approach to understand the mode of action of insulin sensitizers and also a potential approach to identify the phenotype of a subpopulation within the prediabetes and diabetic population for personalized medicine. However, obtaining a tissue biopsy in a clinical setting has several problems. Therefore, alternative systems must be developed for the purpose of evaluating the pharmacodynamics of inflammatory pathway modulation and patient stratification.

Soon in the past, Peripheral Blood Mononuclear Cells (PBMCs) have been investigated as potential alternatives to the study of the modulation of NFkB-associated pathways in the insulin resistant state. It was demonstrated that expression of genes associated with the NFkB pathway in blood cells would: (i) reflects systemic chronic inflammation, and generally represents tissue inflammation in T2D and/or comorbidities (hypertension, diabetic nephropathy) (3-12), and (ii) is regulated by lifestyle and therapeutic interventions (e.g. obesity, hypertension) (3-12). In addition, innate immune system activation (e.g., granulocytes) has been shown to be associated with obesity (13, 14).

It would be desirable to provide a simplified method for detecting the presence of low grade inflammation in a patient to aid in the identification of such patients and to provide effective therapeutic intervention.

Disclosure of Invention

The present invention provides methods for detecting the presence of low grade inflammation in a patient by analyzing a whole blood sample taken from the patient. In certain embodiments, the patient is an obese patient and/or a patient suffering from type II diabetes. In certain embodiments, the patient is insulin resistant.

One aspect of the invention provides a method for detecting the presence of low grade inflammation in a patient, the method comprising: determining the expression pattern of a set of genes selected from the group consisting of the genes of table 1 in a whole blood test sample taken from a patient. An alteration in the expression pattern of the set of genes relative to a healthy control sample is indicative of the presence of low grade inflammation in the patient.

Another aspect of the invention provides a method of identifying a patient who may benefit from treatment with an insulin sensitizer, the method comprising: determining an expression pattern of a set of genes selected from the group consisting of the genes of table 1 in a whole blood test sample taken from a patient, wherein a change in the expression pattern of the set of genes relative to a non-insulin resistant control sample indicates that the patient may benefit from insulin sensitizer treatment.

Another aspect of the invention provides a method of monitoring the effectiveness of an insulin sensitizer treatment administered to a patient, the method comprising: determining an expression pattern of a set of genes selected from the group consisting of the genes of table 1 in a whole blood test sample taken from the patient, wherein a change in the expression pattern of the set of genes relative to a non-insulin resistant control sample indicates that the insulin sensitizer treatment is ineffective.

In one embodiment of the above aspect, the set of genes comprises one or more genes that exhibit an association between whole blood and adipose tissue expression. In one embodiment, the gene is selected from the group consisting of: insulin resistance protein (resistance), leptin, FoxP3, CD79A, and CTLA 4. In one embodiment, the gene set comprises 2, 3, 4 or all 5 of insulin resistance protein, leptin, FoxP3, CD79A and CTLA 4.

In one embodiment of the above aspect, the set of genes comprises one or more genes that exhibit an association with insulin resistance. In one embodiment, the gene is selected from the group consisting of: IL1R1, CD36, TNFRSF10 and ICOS. In one embodiment, the set of genes comprises 2, 3 or all 4 of IL1R1, CD36, TNFRSF10 and ICOS. In one embodiment, insulin resistance is determined by hyperinsulinemic euglycemia clamp, or by homeostatic model assessment (HOMA-IR) as an index of insulin resistance.

In one embodiment of the above aspect, the set of genes comprises one or more genes associated with elevated BMI. In one embodiment, the gene is selected from the group consisting of: CEACAM8, insulin resistance protein, TNFa, IL6, ILR1, TLR4, TNFRSF1A, TNFRSF1B, MIF, CMKLR1, NFkB, CD36 and ICOS. In one embodiment, the collection of genes comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or all 13 of CEACAM8, insulin resistance protein, TNFa, IL6, ILR1, TLR4, TNFRSF1A, TNFRSF1B, MIF, CMKLR1, NFkB, CD36, and ICOS.

In one embodiment of the above aspect, the set of genes comprises one or more genes from table 1, wherein the genes are associated with inflammation and the NFkB pathway.

In one embodiment of the above aspects, the patient suffers from a metabolic disease, such as type2diabetes, obesity, insulin resistance status, nonalcoholic steatohepatitis (NASH), or nonalcoholic fatty liver disease (NAFLD).

In one embodiment, the expression pattern in the above aspects is determined by measuring mRNA expression levels. In one embodiment, the mRNA expression level is measured using qRT-PCR.

In one embodiment, the change in the expression level of the set of genes in the test sample is at least about a 1.5-fold difference relative to a control sample.

In one embodiment, the set of genes comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, or all 42 of the genes of table 1.

Another aspect of the invention provides a method of monitoring the effectiveness of an insulin sensitizer treatment administered to a patient, said method comprising the steps of: a) determining the expression pattern of a set of genes selected from the group consisting of the genes of table 1 in a whole blood test sample taken from a patient, b) comparing the expression pattern of the set of genes with the expression pattern of the set of genes in a whole blood reference sample taken from the patient prior to treatment with an insulin sensitizer, and c) determining that the treatment is effective when the expression pattern of the genes in the test sample is more similar to the expression pattern of the genes in the reference sample than to the expression pattern of a non-insulin resistant control sample. In one embodiment, the set of genes comprises one or more genes selected from the group described above. In one embodiment, the set of genes comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, or all 42 of the genes of table 1.

Drawings

FIG. 1 is a graphical representation of the differential gene expression pattern between obese patients and lean patients.

FIG. 2 shows a whole blood Spearman correlation of gene expression patterns from obese and lean patients.

FIG. 3 shows differential regulation of certain genes (Δ CT) in obese subjects and lean subjects.

FIG. 4 shows the gene expression levels of blood cell specific genes in obese subjects and lean subjects.

FIG. 5 compares gene expression measured in PBMC and whole blood samples.

FIG. 6: the gene sets in this table show the association between fat and whole blood.

FIG. 7: graph of genes associated with insulin resistance in type2diabetes mellitus (T2D) as measured by HOMAS-IR.

FIG. 8: a map of genes associated with insulin resistance in Normal Glucose Tolerance (NGT), impaired glucose tolerance/impaired fasting glucose (IGT/IFG) and type2diabetes (T2 diabetes, T2D), measured by the hyperinsulinemic normal glucose clamp.

Detailed Description

The present invention provides methods for profiling whole blood gene expression in a patient as an alternative assessment of tissue low grade inflammation. The data obtained from this method can be used as a complementary readout of insulin resistance/associated comorbidities for patient stratification and/or pharmacodynamic assessment. The method may be used to monitor the effectiveness of an insulin sensitizer treatment or an anti-diabetic treatment that exerts an insulin sensitizer effect administered to a patient in need thereof. Examples of the insulin sensitizer include, for example, metformin (metformin) and thiazolidinediones (thiazolidinediones) such as glitazones (glitazones) (troglitazone, rosiglitazone (rosiglitazone) and pioglitazone (pioglitazone)).

The methods can also be applied to animal models (e.g., mouse, rat, non-human primate) and provide a translational tool linking preclinical studies to clinical studies. As used herein, "patient" means any mammal, including, but not limited to: humans, non-human primates, bovines, equines, canines, ovines, felines, and rodents. In one embodiment, the patient is a human. In one embodiment, the patient is a non-human primate.

In certain embodiments, the patient is suffering from, or suspected of suffering from, or susceptible to suffering from obesity, type2diabetes, insulin resistance, or other metabolic-based disorders such as NASH or NAFLD.

Provided herein are genes or gene sets (also referred to herein as biomarkers) for detecting the presence of low grade inflammation, for assessing the sensitivity or resistance of a patient to insulin and insulin sensitizers, and for monitoring the effectiveness of a treatment or therapy for a metabolic disease. The gene or gene set may also be used to detect or analyze the presence or prevalence of metabolic diseases such as type2diabetes, obesity, insulin resistance states, nonalcoholic steatohepatitis (NASH), and nonalcoholic fatty liver disease (NAFLD).

Gene expression patterns can also be used in the methods described herein. An expression pattern or gene expression pattern represents a pattern resulting from the measured expression level of each gene of the set of genes. Gene expression patterns can be used to monitor and compare changes in a set of genes. The gene expression pattern may be used, for example, to detect the presence of low grade inflammation, to assess patient sensitivity or resistance to insulin and insulin sensitizers, to determine the presence or prevalence of a metabolic disorder, or to monitor the effectiveness of a treatment or therapy for a metabolic disorder.

In one embodiment, the gene expression profile can be used to monitor the effectiveness of a therapy for treating a metabolic disease by: the expression pattern of a set of genes in a patient receiving treatment is determined and compared to the expression pattern of the same set of genes in a reference sample taken from the patient prior to treatment. The change in expression pattern can then be compared to a control expression pattern generated using the same set of genes from one or more individuals not suffering from the metabolic disease. An effective treatment is indicated if the expression pattern of the gene after treatment is more similar to the expression pattern of a control sample than the expression pattern of a sample taken from the patient prior to treatment. An ineffective treatment is indicated if the gene expression pattern is the same as the gene expression pattern of the patient prior to treatment, or is less similar to the expression pattern of the control sample than the expression pattern of the sample taken from the patient prior to treatment.

As used herein, a control sample refers to any sample, standard, or level used for comparative purposes. In one embodiment, the control sample is obtained from a healthy individual that is not the patient. In certain embodiments, the control sample is a single sample or a combined plurality of samples obtained from one or more healthy individuals that are not patients. In certain embodiments, the control sample is a single sample or a combined plurality of samples obtained from one or more individuals suffering from a metabolic disease. In one embodiment, the control sample is a whole blood sample taken from one or more healthy individuals. In certain embodiments, one or more healthy individuals are not suffering from a metabolic disease that is present in the patient or suspected to be present in the patient. In one embodiment, the one or more healthy individuals do not suffer from obesity, type2diabetes, insulin resistance, or other metabolic-based disorders such as NASH or NAFLD. For example, a non-insulin resistant control sample is a sample that has a gene expression pattern of a non-insulin resistant patient and can be prepared, for example, as follows: the gene expression levels of the gene set used in the method are determined from whole blood taken from a patient who does not suffer from obesity, type2diabetes, insulin resistance or other metabolic-based disorders, such as NASH or NAFLD.

Genes that may be used in the practice of the present invention include the genes of table 1. In one embodiment, the set of genes used in the methods described herein comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 34, 35, 36, 37, 38, 39, 40, 41, or all 42 of the genes of table 1.

One aspect of the invention provides a method for detecting the presence of low grade inflammation in a patient, the method comprising: determining the expression pattern of a set of genes selected from the group consisting of the genes of table 1 in a whole blood test sample taken from a patient. In one embodiment, an alteration in the expression pattern of the set of genes relative to a control sample of whole blood taken from a healthy individual is indicative of the presence of low grade inflammation in the patient. In one embodiment, the set of genes comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, or all 42 of the genes of table 1.

In one embodiment, the method for detecting the presence of low grade inflammation in a patient comprises: the expression pattern of a gene set comprising one or more genes exhibiting a correlation between whole blood and adipose tissue expression, such as insulin resistance protein, leptin, FoxP3, CD79A, and CTLA4, was determined. In one embodiment, the method for detecting the presence of low grade inflammation in a patient comprises: the expression pattern of 1, 2, 3, 4 or all 5 of insulin resistance protein, leptin, FoxP3, CD79A and CTLA4 was determined.

In one embodiment, the method for detecting the presence of low grade inflammation in a patient comprises: the expression pattern of a set of genes comprising one or more genes exhibiting an association with insulin resistance, such as IL1R1, CD36, TNFRSF10, and ICOS, is determined. In one embodiment, the method for detecting the presence of low grade inflammation in a patient comprises: the expression pattern of 1, 2, 3 or all 4 of IL1R1, CD36, TNFRSF10 and ICOS was determined. In one embodiment, insulin resistance is determined by hyperinsulinemic normal glucose clamping, or by evaluation of the homeostatic model (HOMA-IR) as an index of insulin resistance.

In one embodiment, the method for detecting the presence of low grade inflammation in a patient comprises: determining an expression pattern of a set of genes comprising one or more genes exhibiting an association with elevated BMI, such as CEACAM8, insulin resistance protein, TNFa, IL6, ILR1, TLR4, TNFRSF1A, TNFRSF1B, MIF, CMKLR1, NFkB, CD36, and ICOS. In one embodiment, the method for detecting the presence of low grade inflammation in a patient comprises: the expression pattern of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or all 13 of CEACAM8, insulin resistance protein, TNFa, IL6, ILR1, TLR4, TNFRSF1A, TNFRSF1B, MIF, CMKLR1, NFkB, CD36 and ICOS was determined.

In one embodiment, the method for detecting the presence of low grade inflammation in a patient comprises: determining an expression pattern for a set of genes comprising one or more genes in table 1 associated with inflammation and NFkB pathways.

In the above methods, an alteration in the expression pattern of the set of genes in the patient's sample relative to the control sample is indicative of the presence of low grade inflammation in the patient.

The patient is suffering from, or suspected of suffering from, or susceptible to suffering from obesity, type2diabetes, insulin resistance or other metabolic-based disorders such as NASH or NAFLD.

Another aspect of the invention provides a method of identifying a patient who may benefit from treatment with an insulin sensitizer, the method comprising: determining an expression pattern of a set of genes selected from the group consisting of the genes of table 1 in a whole blood test sample taken from a patient, wherein an alteration in the expression pattern of the set of genes relative to a control sample indicates that the patient may benefit from insulin sensitizer treatment. In one embodiment, the control sample is a whole blood sample taken from a non-insulin resistant patient. In one embodiment, the set of genes comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, or all 42 of the genes of table 1.

In one embodiment, the method of identifying a patient who may benefit from treatment with an insulin sensitizer comprises: determining expression levels of a gene set comprising one or more genes that exhibit a correlation between whole blood and adipose tissue expression, such as insulin resistance protein, leptin, FoxP3, CD79A, and CTLA 4. In one embodiment, the method of identifying a patient who may benefit from treatment with an insulin sensitizer comprises: the expression levels of 1, 2, 3, 4 or all 5 of insulin resistance protein, leptin, FoxP3, CD79A and CTLA4 were determined.

In one embodiment, the method of identifying a patient who may benefit from treatment with an insulin sensitizer comprises: the expression pattern of a set of genes comprising one or more genes exhibiting an association with insulin resistance, such as IL1R1, CD36, TNFRSF10, and ICOS, is determined. In one embodiment, the method of identifying a patient who may benefit from treatment with an insulin sensitizer comprises: the expression pattern of 1, 2, 3 or all 4 of IL1R1, CD36, TNFRSF10 and ICOS was determined. In one embodiment, insulin resistance is determined by hyperinsulinemic normal glucose clamping, or by evaluation of the homeostatic model (HOMA-IR) as an index of insulin resistance.

In one embodiment, the method of identifying a patient who may benefit from treatment with an insulin sensitizer comprises: determining the expression pattern of a set of genes comprising one or more genes that exhibit an association with elevated BMI, such as CEACAM8, insulin resistance protein, TNFa, IL6, ILR1, TLR4, TNFRSF1A, TNFRSF1B, MIF, CMKLR1, NFkB, CD36, and ICOS in one embodiment, the method of identifying a patient who may benefit from treatment with an insulin sensitizer comprises: the expression pattern of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or all 13 of CEACAM8, insulin resistance protein, TNFa, IL6, ILR1, TLR4, TNFRSF1A, TNFRSF1B, MIF, CMKLR1, NFkB, CD36 and ICOS was determined.

In one embodiment, the method of identifying a patient who may benefit from treatment with an insulin sensitizer comprises: determining expression levels of a set of genes comprising one or more genes in table 1 associated with inflammation and NFkB pathways.

In the above method, a change in the expression pattern of the set of genes in the patient's sample relative to the control sample indicates that the patient may benefit from treatment with an insulin sensitizer.

In one embodiment, following the analysis, an insulin sensitizer is administered to the patient identified as benefiting from treatment with the insulin sensitizer. In one embodiment, the methods described herein are combined with other clinical parameters typically used to select patients for treatment with insulin sensitizers. These clinical parameters include, for example, HbA1c and plasma glucose (see reference PositionsTetement: Standards of Medical Care in Diabetes-2010, American Diabetes Association).

Another aspect of the invention provides a method of monitoring the effectiveness of an insulin sensitizer treatment administered to a patient, the method comprising: determining an expression pattern of one or more genes selected from the genes of Table 1 in a test sample of whole blood taken from the patient, wherein a change in the expression pattern of the one or more genes relative to a control sample indicates that the insulin sensitizer treatment is ineffective. In one embodiment, the control sample is a whole blood sample taken from a non-insulin resistant patient. In one embodiment, the set of genes comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, or all 42 of the genes of table 1.

In one embodiment, the method of monitoring the effectiveness of an insulin sensitizer therapy administered to a patient comprises: the expression pattern of a gene set comprising one or more genes exhibiting a correlation between whole blood and adipose tissue expression, such as insulin resistance protein, leptin, FoxP3, CD79A, and CTLA4, was determined. In one embodiment, the method of identifying a patient who may benefit from treatment with an insulin sensitizer comprises: the expression pattern of 1, 2, 3, 4 or all 5 of insulin resistance protein, leptin, FoxP3, CD79A and CTLA4 was determined.

In one embodiment, the method of monitoring the effectiveness of an insulin sensitizer therapy administered to a patient comprises: the expression pattern of a set of genes comprising one or more genes exhibiting an association with insulin resistance, such as IL1R1, CD36, TNFRSF10, and ICOS, is determined. In one embodiment, the method of monitoring the effectiveness of an insulin sensitizer therapy administered to a patient comprises: the expression pattern of 1, 2, 3 or all 4 of IL1R1, CD36, TNFRSF10 and ICOS was determined. In one embodiment, insulin resistance is determined by hyperinsulinemic normal glucose clamping. In one embodiment, insulin resistance is determined by evaluation of an in vivo homeostasis model (HOMA-IR) as an index of insulin resistance.

In one embodiment, the method of monitoring the effectiveness of an insulin sensitizer therapy administered to a patient comprises: determining an expression pattern of a set of genes comprising one or more genes exhibiting an association with elevated BMI, such as CEACAM8, insulin resistance protein, TNFa, IL6, ILR1, TLR4, TNFRSF1A, TNFRSF1B, MIF, CMKLR1, NFkB, CD36, and ICOS. In one embodiment, the method of monitoring the effectiveness of an insulin sensitizer therapy administered to a patient comprises: the expression pattern of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or all 13 of CEACAM8, insulin resistance protein, TNFa, IL6, ILR1, TLR4, TNFRSF1A, TNFRSF1B, MIF, CMKLR1, NFkB, CD36 and ICOS was determined.

In one embodiment, the method of monitoring the effectiveness of an insulin sensitizer therapy administered to a patient comprises: determining expression levels of a set of genes comprising one or more genes in table 1 associated with inflammation and NFkB pathways.

In the above method, a change in the expression pattern of the set of genes in the patient's sample relative to the control sample is indicative of the effectiveness of the insulin sensitizer treatment.

In one embodiment, the insulin sensitizer treatment is altered or discontinued based on the analysis.

Another aspect of the invention provides a method of monitoring the effectiveness of an insulin sensitizer treatment administered to a patient, said method comprising the steps of: a) determining the expression pattern of a set of genes selected from the group consisting of the genes of table 1 in a whole blood test sample taken from a patient, b) comparing the expression pattern of the set of genes with the expression pattern of the set of genes in a whole blood reference sample taken from the patient prior to treatment with an insulin sensitizer, and c) determining that the treatment is effective when the expression pattern of the genes in the test sample is more similar to the expression pattern of the genes in the reference sample than to the expression pattern of a non-insulin resistant control sample. Conversely, when the expression pattern of the genes in the test sample is the same as, or less similar than, the expression pattern of the genes in the reference sample to that of the non-insulin resistant control sample, then the treatment is determined to be ineffective. If the treatment is ineffective, the treatment can be altered, for example, different doses or dosing regimens of the same insulin sensitizer can be administered and effectiveness similarly monitored, or different insulin sensitizers can be used in the treatment. In one embodiment, the set of genes comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, or all 42 of the genes of table 1.

Another aspect of the invention provides a method of monitoring the effectiveness of an insulin sensitizer treatment administered to a patient, said method comprising the steps of: a) determining an expression pattern of a gene set of a whole blood test sample taken from a patient, the gene set selected from the group consisting of: insulin resistance protein, leptin, FoxP3, CD79A and CTLA4, b) comparing the expression pattern of the set of genes to the expression pattern of the set of genes in a whole blood reference sample taken from the patient prior to treatment with an insulin sensitizer and c) determining that the treatment is effective when the expression pattern of the genes in the test sample is more similar to the expression pattern of the genes in the reference sample than to the expression pattern of a non-insulin resistant control sample. Conversely, when the expression pattern of the genes in the test sample is the same as, or less similar than, the expression pattern of the genes in the reference sample to that of the non-insulin resistant control sample, then the treatment is determined to be ineffective. If the treatment is ineffective, the treatment can be altered, for example, different doses or dosing regimens of the same insulin sensitizer can be administered and effectiveness similarly monitored, or different insulin sensitizers can be used in the treatment. In one embodiment, the gene set comprises 1, 2, 3, 4 or all 5 genes selected from insulin resistance protein, leptin, FoxP3, CD79A and CTLA 4.

Another aspect of the invention provides a method of monitoring the effectiveness of an insulin sensitizer treatment administered to a patient, said method comprising the steps of: a) determining an expression pattern of a gene set of a whole blood test sample taken from a patient, the gene set selected from the group consisting of: IL1R1, CD36, TNFRSF10 and ICOS, b) comparing the expression pattern of said set of genes to the expression pattern of said set of genes in a whole blood reference sample taken from said patient prior to treatment with an insulin sensitizer and c) determining that said treatment is effective when the expression pattern of the genes in said test sample is more similar to the expression pattern of the genes in a non-insulin resistant control sample than the expression pattern of the genes in said reference sample. Conversely, when the expression pattern of the genes in the test sample is the same as, or less similar than, the expression pattern of the genes in the reference sample to that of the non-insulin resistant control sample, then the treatment is determined to be ineffective. If the treatment is ineffective, the treatment can be altered, for example, different doses or dosing regimens of the same insulin sensitizer can be administered and effectiveness similarly monitored, or different insulin sensitizers can be used in the treatment. In one embodiment, the set of genes comprises 1, 2, 3 or all 4 genes selected from IL1R1, CD36, TNFRSF10 and ICOS.

Another aspect of the invention provides a method of monitoring the effectiveness of an insulin sensitizer treatment administered to a patient, said method comprising the steps of: a) determining an expression pattern of a set of genes in a whole blood test sample taken from a patient, the set of genes selected from the group consisting of: CEACAM8, insulin resistance protein, TNFa, IL6, ILR1, TLR4, TNFRSF1A, TNFRSF1B, MIF, CMKLR1, NFkB, CD36 and ICOS, b) comparing the expression pattern of said set of genes to the expression pattern of said set of genes in a whole blood reference sample taken from said patient prior to treatment with an insulin sensitizer, and c) determining that said treatment is effective when the expression pattern of genes in said test sample is more similar to the expression pattern of a non-insulin resistant control sample than the expression pattern of genes in said reference sample. Conversely, when the expression pattern of the genes in the test sample is the same as, or less similar than, the expression pattern of the genes in the reference sample to that of the non-insulin resistant control sample, then the treatment is determined to be ineffective. If the treatment is ineffective, the treatment can be altered, for example, different doses or dosing regimens of the same insulin sensitizer can be administered and effectiveness similarly monitored, or different insulin sensitizers can be used in the treatment. In one embodiment, the collection of genes comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or all 13 of CEACAM8, insulin resistance protein, TNFa, IL6, ILR1, TLR4, TNFRSF1A, TNFRSF1B, MIF, CMKLR1, NFkB, CD36, and ICOS.

Another aspect of the invention provides the following use of a gene or a set of genes selected from the genes of table 1: for determining the presence of low grade inflammation in a patient, for identifying a patient who may benefit from treatment with an insulin sensitizer, or for monitoring the effectiveness of an insulin sensitizer treatment administered to a patient. In one embodiment, the expression level of the gene or the expression pattern of the set of genes is determined.

The sequence can be detected (if expressed) and measured using database entries of known sequence or sequence information provided by the chip manufacturer using techniques well known to those of ordinary skill in the art. The level/amount of expression of a gene or biomarker can be determined based on any suitable criteria known in the art, including, but not limited to, mRNA, cDNA, protein fragment, and/or gene copy number.

The expression of various genes or biomarkers in a sample can be analyzed by a variety of methods, many of which are known in the art and are understood by the skilled artisan, including, but not limited to: immunohistochemical analysis and/or western blot analysis, immunoprecipitation, molecular binding assays, ELISA, ELIFA, Fluorescence Activated Cell Sorting (FACS), etc., quantitative blood-based assays (e.g., serum ELISA) for, e.g., examining the level of protein expression, biochemical enzymatic activity assays, in situ hybridization, northern blot analysis, and/or PCR analysis of mRNA, as well as any of a variety of assays that can be performed by gene and/or tissue array analysis. Typical protocols for assessing the status of genes and gene products are found, for example, In Ausubel et al, eds, 1995, Current methods In Molecular Biology (Current protocols In Molecular Biology), Unit 2 (Northern Blotting), 4 (Southern Blotting), 15 (Immunoblotting) and 18(PCR analysis), multiplex immunoassays such as those available from Rules Based Medicine or MesoScale Discovery (MSD) may also be used.

In certain embodiments, if the expression level/amount of a gene or biomarker in a sample is greater than the expression level/amount of a gene or biomarker in a reference or control sample, the expression/amount of the gene or biomarker in the sample is increased as compared to the expression/amount in the reference or control sample. Similarly, if the expression level/amount of a gene or biomarker in a sample is less than the expression level/amount of a gene or biomarker in a reference or control sample, the expression/amount of the gene or biomarker in the sample is reduced compared to the expression/amount in the reference or control sample. In one embodiment, the expression level is mRNA expression level. In one embodiment, the alteration in the level of mRNA expression is an increase. In another embodiment, the change in the level of mRNA expression is a decrease. In certain embodiments, the samples are normalized for differences in the amount of RNA or protein assayed and for differences in the mass of RNA or protein sample used, as well as differences between assay runs. Such normalization can be accomplished by measuring and introducing the expression of certain normalization genes, including well-known housekeeping genes such as GAPDH or beta actin. Alternatively, normalization can be based on the mean or median signal of all assayed genes or a large subset thereof (global normalization approach). The measured normalized amount of patient tumor mRNA or protein is compared to the amount found in a reference or control group based on each gene. The normalized expression level of each mRNA or protein for each test tumor for each patient can be expressed as a percentage of the expression level measured in the reference or control group. The expression level measured in a particular patient sample to be analyzed will be some percentage point within this range, which can be determined by methods well known in the art.

In certain embodiments, a gene or biomarker in a test sample is considered to have an altered expression level if its expression level is altered (increased or decreased) by about 1.5-fold, 2-fold, 3-fold, 5-fold, 10-fold, or more relative to the expression level of the corresponding gene or biomarker in a reference or control sample. In certain embodiments, a gene or biomarker in a test sample is considered to have an altered expression level if its expression level is altered (increased or decreased) by about 50%, 75%, 100% 150%, 200%, 500% or more relative to the expression level of the corresponding gene or biomarker in a reference or control sample. In one embodiment, the expression level is mRNA expression level. In one embodiment, the expression level is determined based on the protein expression level.

The methods of the invention also include protocols for examining the presence and/or expression levels of mRNA of one or more target genes in a tissue or cell sample.

Methods for evaluating mRNA in a cell are well known and include, for example, hybridization assays (e.g., in situ hybridization using labeled ribonucleic acid probes) using complementary DNA probes specific for one or more genes (including, but not limited to, the genes of table 1), northern blotting and related techniques, and various nucleic acid amplification assays (e.g., RT-PCR using complementary primers specific for one or more genes and other amplification type detection methods, e.g., branched DNA, SISBA, TMA, etc.).

In one embodiment, the sample is a whole blood sample. In another embodiment, the sample is Peripheral Blood Mononuclear Cells (PBMCs).

The mRNA of a patient sample can be conveniently determined using northern blot, dot blot or PCR analysis. For example, RT-PCR assays, such as quantitative PCR assays, are well known in the art. In an illustrative embodiment of the invention, a method for detecting a target mRNA in a biological sample comprises generating cDNA from the sample by reverse transcription using at least one primer; amplifying the thus-produced cDNA using the target polynucleotide as sense and antisense primers to amplify the target cDNA therein; and detecting the presence of the amplified target cDNA. In addition, the method can include one or more steps that allow the skilled artisan to determine the level of the target mRNA in the biological sample (e.g., by simultaneously examining the level of a comparative control mRNA sequence for a "housekeeping" gene, such as an actin family member). Optionally, the sequence of the amplified target cDNA can be determined.

Optional methods of the invention include methods of examining or detecting mRNA (e.g., target mRNA) in a sample by microarray technology. Test and control mRNA samples from the test and control tissue samples are reverse transcribed and labeled using a nucleic acid microarray to generate cDNA probes. The probes are then hybridized to an array of nucleic acids immobilized on a solid support. The array is constructed such that the sequence and location of each member of the array is known. For example, gene selectors, the expression of which is correlated with the detection of inflammation, can be arrayed on a solid support. Hybridization of a labeled probe to a particular array member indicates that the sample from which the probe is derived expresses the gene. Differential gene expression analysis of diseased tissues can provide valuable information. Microarray technology uses nucleic acid hybridization technology and computer technology to evaluate the mRNA expression patterns of thousands of genes in a single experiment (see, e.g., WO01/75166, published on 10/11/2001); (see, e.g., U.S. Pat. No. 5,700,637, U.S. Pat. No. 5,445,934, and U.S. Pat. No. 5,807,522, Lockart, Nature Biotechnology, 14: 1675-. DNA microarrays are microarrays that contain gene segments synthesized directly on or spotted onto glass or other substrates. Thousands of genes are typically displayed in a single array. A typical microarray experiment comprises the following steps: 1) preparing fluorescently labeled targets for RNA isolated from a sample, 2) hybridizing the labeled targets to a microarray, 3) washing, staining, and scanning the array, 4) analyzing the scanned images, and 5) generating gene expression patterns. Two main types of DNA microarrays are currently used: oligonucleotide (usually 25-70mers) arrays and gene expression arrays comprising PCR products prepared from cDNAs. In forming the array, the oligonucleotides can be preformed and spotted onto the surface or synthesized directly onto the surface (in situ).

AffymetrixThe system is a commercially available microarray system comprising an array prepared by synthesizing oligonucleotides directly on a glass surface.

Probe/gene array: oligonucleotides, typically 25mers, were synthesized directly onto glass plates (glass wafers) by a combination of semiconductor-based photolithography and solid phase chemical synthesis techniques. Each array contains up to 400,000 different oligomers, and each oligomer is present in millions of copies. Since oligonucleotide probes are synthesized at known locations on the array, the hybridization pattern and signal intensity can be interpreted by Affymetrix Microarray Suite software based on gene identity and relative expression levels. Each gene is displayed on the array by a series of different oligonucleotide probes. Each probe pair consists of a perfect match oligonucleotide and a mismatch oligonucleotide. A perfectly matched probe has a sequence that is perfectly complementary to a particular gene and thus measures the expression of that gene.

Mismatched probes differ from perfectly matched probes by a single base substitution at the central base position that interferes with binding of the target gene transcript. This helps to determine background and non-specific hybridization, which contributes to the signal measured for perfectly matched oligomers. Microarray Suite software subtracts the hybridization intensity of the mismatch probes from the hybridization intensity of the perfect match probes to determine absolute or specific intensity values for each probe set. Probes were selected based on current information from Genbank and other nucleotide repertoires (repositories). The sequence is believed to recognize a unique region at the 3' end of the gene. A genechip hybridization oven ("rotisserie" oven) was used to simultaneously perform hybridization of up to 64 arrays.

The fluidics station performs washing and staining of the probe array. The fluidics station is fully automated and contains four modules, each of which controls one probe array. Each component is independently controlled by Microarray Suite software using preprogrammed flow control routines. The scanner is a confocal laser fluorescence scanner that measures the intensity of fluorescence emitted by labeled crnas bound to the probe array. A computer workstation with Microarray Suite software controls the fluidics station and the scanner. The microarraysoite software can control up to 8 fluidics stations using preprogrammed hybridization, wash, and stain protocols for probe arrays. The software also obtains hybridization intensity data and converts the hybridization intensity data into a presence/absence readout for each gene using a suitable algorithm. Finally, the software detects changes in gene expression between experiments by comparative analysis and formats the output as a txt file, which can be used by other software programs for further data analysis.

Expression of a selected gene or biomarker in a tissue or cell sample may also be examined by functional assays or activity-based assays. For example, if the biomarker is an enzyme, assays known in the art may be performed to determine or detect the presence of a given enzymatic activity in a tissue or cell sample.

Also provided are kits comprising a compound capable of specifically detecting the expression level of a gene of table 1, wherein the kit additionally comprises instructions for using the kit to determine the presence of low grade inflammation in a patient or to predict or monitor responsiveness of a patient to treatment with an insulin sensitizer.

One embodiment provides a kit comprising: a container, a label on the container, and a composition contained within the container; wherein the composition comprises one or more polynucleotides that specifically hybridize to a table I gene, the label on the container indicating that the composition can be used to evaluate the presence of the table I gene in a sample, and instructions for using the polynucleotides to evaluate the presence of the table I gene in the sample. In one embodiment, the sample is a whole blood sample or is derived from a whole blood sample.

Other optional components in the kit include one or more buffers (e.g., blocking buffer, wash buffer, substrate buffer, etc.), other reagents such as a substrate chemically altered by an enzyme label (e.g., a chromophore), an epitope retrieval solution, a control sample (positive and/or negative control), a control slide, and the like.

Examples

Example 1-List of genes that can be used to practice the invention

Table 1 provides a list of genes that can be used to practice the invention, including for detecting the presence of low grade inflammation, for assessing sensitivity or resistance to insulin and insulin sensitizers, and for monitoring the effectiveness of a treatment or therapy for metabolic disorders. Also included in table 1 are gene IDs associated with the listed genes and sequence listing identifiers representing exemplary sequences of the genes in the sequence listing provided herein.

TABLE 1

TNFRSF1B	7133	34	Hs00153550_ml
				ADIPOQ (adiponectin)	9370	35	Hs00605917_ml*
FABP4	2167	36	Hs01086177_ml*
				IFNG	3458	37	Hs00989291_ml*
RARRES2	5919	38	Hs01123775_ml*
				CD36	948	39	Hs00169627_ml
FoxP3	50943	40	Hs01085834_ml
				PPIB	5479	41	Hs01018503_ml
GUSB	2990	42	Hs99999908_ml

Example 2 analysis of Whole blood and PBMC

In a representative sample collection library of lean and obese and Insulin Resistant (IR) subjects, a set of genes associated with key nodes of the inflammatory pathway and a set of genes specific for each blood cell type present in the whole blood product were measured. Table 1. The total sample size was 40, including 20 obese subjects and 20 normal weight subjects. Measuring blood cell specific genes as a means to perform indirect assessment of the enrichment and/or activation state of different populations in samples from obese subjects and lean subjects.

Quantitative real-time PCR

Samples from PBMC and whole blood were analyzed using quantitative real-time PCR. RNA was extracted from PAXgene Blood on a BioRobot MDx and from PBMC samples using QIAamp RNA Blood kit (Qiagen, germany) using Qiagen qiaclub according to the manufacturer's protocol (Qiagen). RNA was quantified at 50-fold dilutions relative to a standard curve of E.coli ribosomal RNA (Roche diagnostics) using the Quant-IT RiboGreen kit (Molecular Probes, Invitrogen). In addition, samples were analyzed for number of RNA Integrity (RIN) according to the manufacturer's recommended protocol (Agilent) on an Agilent bioanalyzer.

cDNA synthesis for quantitative real-time PCR (qRT-PCR; Invitrogen) was performed on 400ng total RNA using SuperScript II first strand synthesis Supermix according to the manufacturer's protocol, but omitting RNase H digestion. For each reverse transcription reaction, the universal human reference total rna (stratagene) was run on the same plate as positive and negative controls (non-enzymatic controls). Controls were determined by qRT-PCR. A pre-designed gene expression assay was obtained from Applied Biosystems. The TaqMan assay ID is shown in Table 1.

Where possible, a trans-exon assay was chosen to ensure cDNA specificity. All gene expression assays were performed on the ABIPRISM7900HT sequence detection System (Applied Biosystems) with the recommended standard settings. All assays were prepared using TaqMan Universal PCR Master mix (Applied Biosystems) according to the manufacturer's recommendations.

All runs included standard curve dilutions of cDNA, non-enzyme controls, non-template controls, and calibration samples. Standard curve cDNA was synthesized from calibration samples of universal reference total RNA (stratagene) and a pool of total blood RNA obtained from healthy donors. The cDNA samples were diluted 10-fold in molecular-scale water and 2uL was added to 18uL of the pre-dispensed assay master mix. This corresponds to cDNA from 4ng total RNA. All samples on the plate were assayed with one assay for the gene of interest and an endogenous control gene assay (GUSB and PPIB TaqMan gene expression assay; Applied Biosystems). Each measurement was performed in triplicate.

Example 3 statistical analysis of qRT-PCR data

For each target, the median Ct was calculated. Calculated by the following equationThe value:

the expression value (Δ CT) was multiplied by-1, so that higher values represent greater gene expression. Forward selection was used to enter other clinical covariates into the model one at a time, and significant (p < 0.05) covariates were retained. A full example analysis was performed. PBMC and whole blood samples were analyzed separately.

Thus, the data is modeled as

yi ═ beta 0+ beta 1BMIi + beta 2 age i + beta 3 sex + epsilon i

Wherein, yi is gene expression of individual I; β 0 ═ intercept (intercept); β 1 ═ BMI effect; β 2 — effect of age; the effect of β 3 ═ gender (0 ═ female, 1 ═ male); and ε i-N (0, σ)²)。

Example 4 Whole blood Gene expression analysis to detect Low grade inflammation in obese patients

As shown in figure 1, obese (body mass index (BMI) > 30) and lean (BMI < 25) subjects were clustered separately from each other based on gene expression patterns (descriptive statistical analysis). In addition, as shown in fig. 2, some of the genes analyzed showed a positive correlation with BMI (e.g., IL6, TNFa, insulin resistance protein, MIF), while some of the other genes showed a negative correlation (e.g., IL1R1, TNFRSF1A, TLR 4). In detail, as shown in fig. 3 (showing Δ CTs of genes of lean and obese patients), of the 33 genes analyzed, some were up-regulated in whole blood of obese subjects than in lean subjects (IL6, TNFa, CEACAM8, insulin resistance protein), while some others were down-regulated in whole blood of obese subjects than in lean subjects (e.g., TNFRSF1A, TNFRSF1B, IL1R1, TLR 4). Since some of these genes are located on the NFkB pathway (upstream NFkB: TNFRSs, IL1R1, TLR4, downstream NFkB: IL6 and TNFa), these results support the differential regulation of the NFkB pathway in whole blood from obese subjects compared to lean subjects. Although the upregulation of IL6 and TNFa is consistent with previous findings and clearly indicates upregulation of NFkB activity (as known in pro-inflammatory states), it is unclear why genes encoding cell membrane receptors directly facing the pro-inflammatory environment (e.g. TNFRSF1A, B, TLR4, IL1R1) are downregulated. Gene expression may reflect a regulatory feedback mechanism by which genes are down-regulated in response to higher expression/activity of the encoded protein on the cell membrane. In addition, in obese subjects, some genes expressed in granulocytes and activated monocytes (such as CEACAM8 and insulin resistance protein) are up-regulated, which supports the activation of the innate immune system in these subjects. Interestingly, there is no evidence for a general change in cell abundance based on the gene expression levels of blood cell specific genes (fig. 4), and thus alterations of genes upstream and downstream of NFkB nodes appear to be truly associated with differential regulation of the corresponding pathways of pro-inflammatory states.

This study shows that gene expression in whole blood reflects activation of a key pathway (such as NfKB) that is shown to be also up-regulated in adipose tissue, supporting the use of whole blood gene expression as a surrogate for tissue gene expression.

These findings are consistent with previous reports on gene expression of some of the indicated genes in PBMC and whole blood flow cytometry analysis (1, 2). In addition, the present study provides additional genes involved in inflammation detection, such as CEACAM 8.

As shown in the examples, gene expression analysis of the same target was also performed in matched PBMC samples for comparison. As expected, PBMCs show overall different expression patterns compared to whole blood. As shown in fig. 5, whole blood showed an enrichment of granulocyte specific genes (e.g. FCGR3B, TNFRSF10C, VNN2, CEACAM8, CD16) relative to PBMC (left part of the figure, values below 0), whereas PBMC showed an enrichment of monocyte specific genes (e.g. CSF1R and MARCO). Genes found to be differentially regulated in whole blood of lean and obese subjects showed only a modest trend in matched PBMC samples (data not shown). This can be explained by the greater contribution of granulocytes to the overall inflammatory state.

Taken together, these results indicate that (i) whole blood gene expression can be used in clinical settings as a tool to assess low grade inflammatory status for patient stratification and/or for pharmacodynamic assessment, (ii) the method can also be applied to animal models (e.g., non-human primates) and provide a translation tool linking preclinical studies with clinical studies.

Example 5 Whole blood Gene expressionFor the replacement of adipose tissue inflammation

In adipose tissue and in whole blood, a set of genes associated with insulin resistance (insulin resistant protein), leptin resistance (leptin), and T-cells (CD79A), T regulatory cells (FoxP3), and B-cell mediated inflammation (CTLA4) are associated. Involvement of B cells, T cells and T regulatory cells in adiposities is supported by previous evidence (15, 16, 17). The finding that the labeling of those cells is regulated in the same way in whole blood supports the following concept: whole blood may be used as a surrogate matrix for assessing tissue inflammation. Fig. 6.

Example 6 expression of genes associated with type2diabetes

Steady state model evaluation (HOMA-IR) was used to determine gene expression associated with type2 diabetes. The HOMA-IR index (18) was calculated as follows: fasting insulin x fasting glucose was 22.5. The scatter plot shown in fig. 7 represents the correlation between the expression (Δ Ct) of the 3 genes (IL1R1, TNFRSF10C, ICOS) of this panel and the insulin sensitivity index (HOMAS or HOMA-IR) in the type2diabetes population (n 87, mean HbAlc: 7.8%).

Example 7 expression of genes involved in IR in NGT, IGT/IFG and T2D

Gene expression associated with insulin resistance in Normal Glucose Tolerance (NGT), impaired glucose tolerance/impaired fasting glucose (IGT/IFG), and type2diabetes (T2D) was determined using a hyperinsulinemic normal glucose clamp assay.

Hyperinsulinemic normal glucose clamping (HEC) was performed based on the principles established by DeFronzo1979(19) and ferrannii 1998 (20). Briefly, an intravenous catheter was inserted into the antecubital vein of one arm for infusion of insulin and 20% glucose solution (insulin Yoghelin R, 100m.j./ml, Lilly; glucose 20% intravenous infusion B.P Bieffe, composition: Glucosummonohydratum 220g ═ 200g Glucosum/1000 ml). A second cannula was inserted into the dorsal vein of the hand, warmed at about 70 ℃ for collection at 5min intervalsArterial blood samples. The glucose infusion rate is adjusted according to changes in blood glucose concentration. Insulin continuous infusion rate was 1mIU/kg/min (for BMI < 30 kg/m)²Patients of (2) and 40mIU/m²Min (for BMI ≥ 30kg/m²The patient of (a). During the first 10min, the infusion rate was doubled for faster insulin loading. Insulin infusion was maintained for 180min, with a steady state period lasting for 120-. 2 insulin sensitivity indices M and ISI were obtained as follows:

calculation of M

(according to De Fronzo1979(19))

The glucose infusion rate INF (sometimes referred to as GIR) is calculated as follows:

the glucose infusion rate INF (sometimes referred to as GIR) is calculated as follows:

the spatial correction SC is calculated as: the difference between the plasma glucose concentration at the beginning of the steady-state period, G1, minus the plasma glucose concentration at the end of the steady-state period, G2, is multiplied by the unit correction number of 18 (from mmol/L to mg/dL), multiplied by the body glucose space fraction, divided by the duration of the steady-state period (for more explanation see De fronto 1979):

calculation of metabolic glucose M:

M＝INF-SC＝mg/(kg*min)

calculation of insulin sensitivity index

(according to coats 1995(21))

ISI-M/(G X Δ I), where M is glucose metabolized at steady stateG is the steady state blood glucose concentration, and Δ I is the difference between fasting and steady state plasma insulin concentrations. For steady state blood glucose concentrations, the mean values of blood glucose values (mmol/L) at 120min and at 180min were taken and multiplied by 18 to convert to mg/dL. For Δ I, basal insulin concentrations (-5min, 0min and mean of pre-clamp values) were subtracted from steady state plasma insulin concentrations (mean of 120min and 180min values), all in mIU/L. Multiply the result by 10⁴To arrive at a unit of 10^-2*1²/(IU*min*kg)。

The resulting scatter plot shown in fig. 8 represents the correlation between the expression (Δ Ct) of some genes of the panel and the 2 insulin sensitivity indices (ISI and M) obtained with hyperinsulinemic normal glucose clamps. Of the 10 genes analyzed, only CD36 showed a consistent and significant correlation with ISI and M, while TNFa showed only a trend of correlation with ISI. The populations analyzed included Normoglycemic (NGT), prediabetic (impaired glucose tolerance, IGT) and diabetic (T2D) individuals with the following characteristics:

table 2: demographic data and baseline characteristics

Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, the description and examples should not be construed as limiting the scope of the invention. The disclosures of all patent and scientific literature cited herein are expressly incorporated by reference in their entirety.

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Claims (16)

1. A method for detecting the presence of low grade inflammation in a patient, the method comprising: determining an expression pattern of a set of genes selected from the group consisting of the genes of Table 1 in a whole blood test sample taken from the patient, wherein an alteration in the expression pattern of the set of genes relative to a healthy control sample is indicative of the presence of low grade inflammation in the patient.

2. A method of identifying a patient who may benefit from treatment with an insulin sensitizer, the method comprising: determining an expression pattern of a set of genes selected from the group consisting of the genes of Table 1 in a whole blood test sample taken from the patient, wherein a change in the expression pattern of the set of genes relative to a non-insulin resistant control sample indicates that the patient may benefit from insulin sensitizer treatment.

3. A method of monitoring the effectiveness of an insulin sensitizer therapy administered to a patient, the method comprising: determining an expression pattern of a set of genes selected from the group consisting of the genes of Table 1 in a whole blood test sample taken from the patient, wherein a change in the expression pattern of the set of genes relative to a non-insulin resistant control sample indicates that the insulin sensitizer treatment is ineffective.

4. The method of any one of the preceding claims, wherein the set of genes comprises one or more genes selected from the group consisting of insulin resistance protein, leptin, FoxP3, CD79A, and CTLA 4.

5. The method of claim 4, wherein the gene set comprises 2, 3, 4, or all 5 of insulin resistance protein, leptin, FoxP3, CD79A, and CTLA 4.

6. The method of any one of the preceding claims, wherein the set of genes comprises one or more genes selected from the group consisting of IL1R1, CD36, TNFRSF10, and ICOS.

7. The method of claim 7, wherein the set of genes comprises 2, 3, or all 4 of IL1R1, CD36, TNFRSF10, and ICOS.

8. The method of any one of the preceding claims, wherein the set of genes comprises one or more genes selected from the group consisting of CEACAM8, insulin resistance protein, TNFa, IL6, ILR1, TLR4, TNFRSF1A, TNFRSF1B, MIF, CMKLR1, NFkB, CD36, and ICOS.

9. The method of claim 9, wherein the set of genes comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or all 13 of CEACAM8, insulin resistance protein, TNFa, IL6, ILR1, TLR4, TNFRSF1A, TNFRSF1B, MIF, CMKLR1, NFkB, CD36, and ICOS.

10. The method of any one of the preceding claims, wherein the patient is suffering from a metabolic disease.

11. The method of claim 10, wherein the metabolic disease is type2diabetes, obesity, insulin resistant states, nonalcoholic steatohepatitis (NASH), or nonalcoholic fatty liver disease (NAFLD).

12. The method of any one of the preceding claims, wherein determining an expression pattern comprises determining mRNA expression levels.

13. The method of claim 12, wherein the mRNA expression level is measured using qRT-PCR.

14. The method of any one of the preceding claims, wherein the change in the expression level of the set of genes in the test sample is at least about a 1.5-fold difference relative to a control sample.

15. A method of monitoring the effectiveness of an insulin sensitizer therapy administered to a patient, said method comprising the steps of

a) Determining the expression pattern of a set of genes selected from the group consisting of the genes of Table 1 in a whole blood test sample taken from said patient,

b) comparing the expression pattern of said set of genes with the expression pattern of said set of genes in a reference sample of whole blood taken from said patient prior to treatment with an insulin sensitizer, and

c) determining that the treatment is effective when the expression pattern of the genes in the test sample is more similar to the expression pattern of the non-insulin resistant control sample than the expression pattern of the genes in the reference sample.

16. A method substantially as hereinbefore described, particularly with reference to the foregoing examples.