WO2015081391A1

WO2015081391A1 - Resistance to oxidative stress

Info

Publication number: WO2015081391A1
Application number: PCT/BE2014/000068
Authority: WO
Inventors: Paul Holvoet; Heinrich Huber; Maarten Hulsmans; Chantal Mathieu; Bart Van Der Schueren
Original assignee: Katholieke Universiteit Leuven Ku Leuven Research & Development
Priority date: 2013-12-05
Filing date: 2014-12-05
Publication date: 2015-06-11

Abstract

The disclosure relates to diagnosis and/or prognosis of type 2 diabetes (T2DM) in relation to obesity and prediction of response to treatment for T2DM. The determination of a patient's risk of developing T2DM and/or the prediction of a subject's response to treatment for T2DM is based on a biomarker profile comprising at least one of the genes COX4I1, COX10, PTGS1, PTGS2, and SOD2. The gene(s) may be measured in monocytes, or microvesicles derived from monocytes, or cells derived from white or brown adipose tissues.

Description

RESISTANCE TO OXIDATIVE STRESS

BACKGROUND

A. Technical Field

The application relates generally to biotechnology and more particularly to a new cluster of molecules that affect the oxidative stress and resistance to oxidation in association with obesity, metabolic syndrome (MetS) disorder and/or type 2 diabetes (T2DM) in white blood cells, particularly in monocytes. This cluster of molecules is used to determine the risk of diseases associated with activated monocytes such as obesity and obesity-related MetS disorder phenotype, characterized by dyslipidemia (low HDL cholesterol and high triglycerides), hypertension, and inflammation, evidenced by increased levels of high-sensitivity C-reactive protein (hs-CRP) (1). The application also relates to this cluster of molecules that affect the oxidative stress and resistance to oxidation in association with obesity and metabolic unhealthy state in white and brown adipose tissues and related to activation of macrophages in these tissues.

Several documents are cited throughout the text of this specification. Each of the documents herein (including any manufacturer's specifications, instructions etc.) is hereby incorporated by reference. However, there is no admission that any document cited is indeed prior art hereto.

B. Description of the Related Art

The epidemic of obesity is a global health issue across all age groups, especially in industrialized countries (American Obesity Association, 2006). According to WHO's estimate there are more than 300 million obese people (BMI>30) world-wide. Today, for example almost 65% of adult Americans (about 127 million) are categorized as being overweight or obese. There is also evidence that obesity is increasing problem among children. For example, in the USA, the percentage of overweight children (aged 5-14 years) has doubled in the last 30 years, from 15% to 32%.

Obesity's negative impact on health is well-documented and obesity has been associated with many adverse pathological conditions. Health consequences are categorized as being the result of either increased fat mass, which leads to osteoarthritis, obstructive sleep apnea, and/or social stigma or an increased number of fat cells which contributes diabetes, cancer, cardiovascular disease, non-alcoholic fatty liver disease(2). Mortality is increased in obesity(3). Obese patients also show alterations in response to insulin (IR), and are more likely than healthy patients to have a pro-inflammatory state and an increased tendency to thrombosis (pro-thrombotic state) (2). Central obesity, characterized by male-type or waist-predominant obesity in which there is a high waist-hip ratio or a high waist circumference), is also an important risk factor for metabolic syndrome (MetS). Metabolic syndrome, defined by the presence of at least three out of five symptoms or risk factors (i.e., central obesity, high blood pressure, elevated blood cholesterol/low HDL levels, elevated triglyceride levels, and insulin resistance), can lead to further health complications including developing cardiovascular disease and type 2 diabetes mellitus (T2DM) (4, 5).

Another related risk factor, particularly for cardiovascular disease, is subclinical chronic low-grade inflammation (6). Population studies showed a strong correlation between pro-inflammatory biomarkers (such as hc-CRP, interleukin-6 (IL-6), and tumor necrosis factor-a (TNF-a)) and perturbations in glucose homeostasis, obesity, and atherosclerosis (7). In addition, increased inflammation (8, 9) and oxidative stress (10-13) were found to be associated with MetS. For example, recent data suggest that increased oxidative stress in adipose tissue is an early instigator of MetS and that the redox state in adipose tissue is a potentially useful therapeutic target for the obesity- associated MetS (14). Oxidative damage of adipose tissues is associated with impaired adipocyte maturation, production of pro-inflammatory adipocytokines by dysfunctional adipocytes, and increased infiltration of activated macrophages into the adipose tissues of obese persons where they produce inflammatory chemokines (15). This enhanced infiltration is causatively related to the loss of insulin signaling, the development of insulin resistance and eventually, T2DM.

Although risk factors for MetS are well-studied and treatments have been developed for some symptoms of the disorders which characterize MetS, there are no cures. Lifestyle modifications such as losing weight, changing the diet, and following a regular exercise routine can reverse symptoms of MetS. In addition, there are medications available to address obesity, hypertension, elevated blood cholesterol, elevated triglyceride levels, and insulin resistance, although many medications must be taken chronically, and some have serious side effects. Moreover, many medications vary widely in their ability to affect clinically significant outcomes and to reduce mortality.

T2DM, in particular, can be controlled and managed but not cured. If other interventions such as weight loss or dietary changes are not effective, anti-diabetic medications are used. T2DM is thought to result from multiple factors, including insulin resistance and insufficient insulin production, and different factors are targeted by different classes of medications. Metformin is a first-line treatment, while additional medications such as sulfonylureas, biguanides, meglitinides, thiazolidinediones, DPP- 4 inhibitors, SGLT2 inhibitors, alpha-glucosidase inhibitors, and bile acid sequestrants may be additionally or alternatively be used. Combination therapies are also used, and insulin injections may be added to oral medications or used alone. Although T2D is never cured, symptoms may be improved and other health-related consequences of T2DM may be prevented. Accordingly, it is essential to monitor the development and progression of the disease, and to determine which medications are working effectively. Summary of the Disclosure

To this end, described herein are biomarker-based methods for analyzing a biological sample, identifying patients, assessing risk factors, and, for example, predicting responses to treatments for T2DM. The methods rely on analyses of expression of genes involved in oxidative stress and resistance to oxidation. The methods may also be used for analysis of patients with MetS, inflammation, and/or symptoms thereof.

One aspect of the present disclosure relates to a method for predicting a patient's response to a treatment for type 2 diabetes mellitus (T2DM), comprising: (a) obtaining a biological sample from the patient; (b) measuring expression of COX4I1 in the biological sample; and (c) comparing the expression of COX4I1 with reference measurements; wherein decreased expression of COX4I1 in the biological sample as compared to the reference measurements indicates that the patient will not respond to the treatment.

In some embodiments, the method further comprises measuring expression of COX10 in the biological sample and comparing the expression of COX10 with reference measurements, wherein decreased expression of COX10 in the biological sample as compared to the reference measurements indicates that the patient will not respond to the treatment.

Another aspect of the present disclosure relates to a method for predicting a patient's response to treatment for T2DM, comprising: (a) obtaining a biological sample from the patient; (b) measuring expression of COX4I1 and COX10 in the biological sample; and (c) comparing the expression of COX4I1 and COX10 with reference measurements; wherein decreased expression of COX4I1 and COX10 in the biological sample as compared to the reference measurements indicates that the patient will not respond to the treatment.

In some embodiments, the method further comprises measuring expression of PTGS1 in the biological sample, and comparing expression of PTGS1 with reference measurements, wherein decreased expression of PTGS1 in the biological sample as compared to the reference measurements indicates that the patient will not respond to treatment for metabolic syndrome. A further aspect of the present disclosure relates to a method for predicting a patient's response to treatment for T2DM, comprising: (a) obtaining a biological sample from the patient; (b) measuring expression of COX4I1, COXIO, and PTGSl in the biological sample; and (c) comparing the expression of COX4I1, COXIO, and PTGSl with reference measurements; wherein decreased expression of COX4I1, COXIO, and PTGSl in the biological sample as compared to the reference measurements indicates that the patient will not respond to the treatment.

In certain embodiments, the method further comprises measuring expression of at least one of PTGS2 and SOD2 in the biological sample, and comparing expression of PTGS2 and SOD2 with reference measurements, wherein increased expression of PTGS2 and SOD2 in the biological sample as compared to the reference measurements indicates that the patient will not respond to the treatment.

Still another aspect of the present disclosure relates to a method for predicting a patient's response to treatment for T2DM, comprising: (a) obtaining a biological sample from the patient; (b) measuring expression of COX4I1, COXIO, PTGSl, PTGS2,and SOD2 in the biological sample; and (c) comparing the expression of COX4I1, COXIO, PTGSl, PTGS2,and SOD2 with reference measurements; wherein decreased expression of C0X4I1, COXIO, and PTGSl and increased expression of PTGS2 and SOD2 in the biological sample as compared to the reference measurements indicates the patient will not respond to the treatment.

In some embodiments, the patient has metabolic syndrome. For example, the patient may have metabolic syndrome with inflammation characterized by high levels of C-reactive protein. In certain embodiments, the patient does not have metabolic syndrome.

The patient may be obese. In some embodiments, the patient has undergone treatment for obesity, for example, bariatric surgery. In certain embodiments, the patient is not obese.

In some embodiments, the biological sample is a blood sample or an adipose tissue sample. In certain embodiments, the biological sample is a white adipose tissue sample. In some embodiments, the biological sample is one or more adipocytes from white adipose tissue. In certain embodiments, the biological sample is one or more activated monocytes, for example macrophages, from white adipose tissue. The adipose tissue sample may be brown adipose tissue, for example, one or more adipocytes from brown adipose tissue. In some embodiments, the biological sample is one or more activated monocytes, for example macrophages, from brown adipose tissue. In certain embodiments, the biological sample is one or more monocytes. In some embodiments, the biological sample is one or more exosomes. For example, the exosomes may be monocyte-derived exosomes. In some embodiments, expression comprises gene expression. Expression may comprise RNA expression. BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will become more fully understood from the detailed description given herein below and the accompanying drawings which are given by way of illustration only and thus do not limit the disclosure.

Figure 1 shows a schematic overview of the proposed tool for diagnosis, prognosis and identification of preferable treatment (companion diagnostics). CD14⁺ monocytes/microvescicles (MVs) are isolated from a patient's blood sample using magnetic cell separation technology. Isolated CD14⁺ monocytes/MVs under baseline conditions are analyzed with qPCR for selected RNA molecules (diagnostic test). Isolated CD14⁺ monocytes are also seeded in a 96-well plate format and exposed to a stress inducer without (prognostic test) or with (companion diagnostic test) addition of a pharmacological agent. For both tests, the resistance to stress in these cells will be determined by measuring selected RNA molecules with qPCR technology. Interestingly, the isolation, exposure and downstream analysis can be miniaturized and integrated into a lab-on-a-chip device to facilitate and speed-up the work flow. Abbreviations: MVs, microvesicles; PBMCs, peripheral blood mononuclear cells; qPCR, quantitative real-time polymerase chain reaction. ^* Examples of stress inducers: ROS inducers (e.g. advanced glycation end-products, fatty acids, glucose, glucose oxidase, IL-6, insulin, leptin, oxidized LDL, platelet-activating factor, particulate matter, prostaglandins); ^£ Examples of pharmacological agents: adiponectin or an adiponectin mimetic, angiogenesis inhibitor and vascular endothelial growth factor A inhibitor, aspirin, 11-beta hydroxysteroid dehydrogenase inhibitor, calcineurin inhibitors carnitine acetyltransferase stimulant, CD4 antigen antagonist, Cll antigen antagonist, CD45 antigen antagonist, cytokine inhibitor, glinide, glucagon like peptide 1 receptor agonist, immunomodulator, insulin and insulin-like growth factor I stimulant, IL-6 receptor antagonist, IL-17 receptor antagonist, IRAK3 agonist, lipase inhibitor, metformin, neuropeptide Y2 receptor agonist, partial fatty acid oxidation inhibitor, Peroxisome proliferator-activated receptor agoniss, sodium channel antagonist, sodium-glucose transporter 2 inhibitor, somatotropin receptor antagonist, T cell activation inhibitor and thyroid hormone receptor beta agonist. ^# Examples of selected RNA molecules: COX4I1, COX10, GPX1, IRAK3, PTGS1, PTGS2, RUNX2, SOCS3, SOD2, and UCP2. Figure 2 shows a schematic overview of the relation of RNA expressions with obesity, the metabolic syndrome disorder phenotype (metabolic unhealthy state or MUH), and T2DM.

Figure 3 shows COX4I1 and PTGS2 in relation with future metabolic syndrome phenotype disorder. RNA expressions were measured at 4 months and baseline and differences were calculated for subjects with and without (controls) the metabolic syndrome disorder phenotype (MUH) at 7 years. ^*P < 0.05. Forward conditional multiple regression analysis confirmed that COX4I1 and PTGS2 at 4 months predicted the presence of MUH. They predicted 88% of controls (not having the metabolic syndrome disorder phenotype) and 100% of cases (having the metabolic syndrome disorder phenotype) correctly. Thus, overall prediction was 94%. Also the difference in PTGS2 expression, after adjusting for the difference in COX4I1, between 4 months and baseline predicted future metabolic syndrome disorder phenotype. It predicted 75% of controls (not having the metabolic syndrome disorder phenotype) and 89% of cases (having the metabolic syndrome disorder phenotype) correctly. Thus, overall prediction was 82%.

Figure 4 shows characteristics of lean C57BL/6J and obese diabetic DKO mice. Weight, glucose intolerance (AUC of IPGTT), insulin resistance (HOMA-IR) and blood levels of adiponectin, triglycerides and total cholesterol in lean C57BL/6J control mice (n = 10), placebo DKO (n = 13), diet restricted DKO mice (n = 11), and DKO mice treated with fenofibrate (n = 13) or rosiglitazone (n = 13) are shown. Data are mean+SD. ^** P<0.01, and ^*** P<0.001 compared to lean C57BL/6J mice; ^$$ P<0.01, and ^$$$ P<0.001 compared to placebo DKO. Figure 5 shows gene expressions in white visceral adipose tissues in lean and obese diabetic mice. RNA expressions of CoxlO, Cox4il, Gpxl, Irak3, Ptgsl and Sod3 in lean C57BL/6J control, placebo DKO, diet restricted DKO mice, and DKO mice treated with fenofibrate or rosiglitazone are shown. Data are meaniSD. ^** P<0.01, and "^*P<0.001 compared to lean C57BL/6J mice; ^$ P<0.01^$s P<0.01, and ^s$$ P<0.001 compared to placebo DKO. Figure 6 shows gene expressions in brown adipose tissues of C57BL6 and Ucpl KO mice (on C57BL6 background). RNA expressions of CoxlO, Cox4il, Gpxl, Irak3, Ptgsl and Sod3 in lean C57BL/6J and Ucpl KO mice are shown. Data are meaniSD. ^$ P<0.05^$s P<0.01 compared to C57BL6 control mice.

Figure 7 shows HOMA-IR, and plasma levels of adiponectin, triglycerides (TG) and total cholesterol (TC) in control mice and STZ-treated mice at 4, 8 and 12 weeks. Data are meaniSD. ^* P<0.05, ^** P<0.01, and ^*"P<0.001 compared to lean control C57BL/6J mice; ^$ P<0.05, ^s$ P<0.01, and ^$s$ P<0.001 compared to STZ mice at 4 weeks. Figure 8 shows markers of adipose tissue differentiation in control mice and STZ-treated mice at 4, 8 and 12 weeks. RIMA expressions of Glut4, Ppara, PparS and Ppary are shown. Data are meaniSD. ^*P<0.05, ^**P<0.01, and ^***P<0.001 compared to lean control C57BL/6J mice; ^$ P<0.05 and ^$$ P<0.01 compared to STZ mice at 4 weeks. Figure 9 shows RNA expressions of CoxlO, Cox4il, Gpxl, Irak3, Ptgsl and Sod3 in white visceral adipose tissues in in control mice and STZ-treated mice at 4, 8 and 12 weeks. Data are meaniSD. ^**P<0.01 compared to lean control C57BL/6J mice; ^$ P<0.05 compared to STZ mice at 4 weeks.

DETAILED DESCRIPTION A. Definitions

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, N.Y. 1994), Microarrays in Clinical Diagnostics (© 2005 Humana Press Inc.) provide one skilled in the art with a general guide to many of the terms used in the present application.

For purposes of the disclosure, the following terms are defined below.

Myeloid refers to the non-lymphocytic groups of white blood cells, including the granulocytes, monocytes and platelets.

Activated monocytes are monocytes that are associated with increased inflammation, often due to activation of the toll-like receptor (TLR)-2 (and/or -4), a decrease in the interleukin-1 receptor- associated kinase (IRAK)-3 (sometimes called IRAKM) and an increase in NFKB activity (16, 17), and/or an increased production of reactive oxygen species (ROS) and oxidative stress, often due to loss of antioxidant enzymes like superoxide dismutase (SOD1 or SOD3), and or gain of SOD2 (13, 18), and/or a loss of insulin signaling and IR, for example by loss of expression of the insulin receptor substrate (IRS)-l and -2 (19). Activation of monocytes renders them more prone to infiltration of in tissues (e.g. adipose, brain, vascular, pancreas, liver) often due to increased expression of the monocyte chemotactic protein 1 (MCP1 or otherwise called chemokine CC motif ligand or CCL2) (20). Once infiltrated, these activated monocytes are more prone to give rise to inflammatory Ml macrophages instead of anti-inflammatory M2 macrophages (21-24). In addition, they lost their capacity to activate their anti-inflammatory (e.g. increase in IRAK3) and antioxidative (e.g. increase in antioxidant SOD1 and/or SOD3 and decrease in SOD2 and ROS) mechanisms, and thus their capacity to switch their polarization from Ml to M2 in response to adiponectin (25, 26).

Glycemia concerns the presence of glucose in the blood. It is a medical term meaning that the blood glucose is elevated, typically above 100 mg/dl. Other terms are impaired glucose tolerance (IGT) or prediabetes.

Insulinemia concerns an abnormally large concentration of insulin in the blood.

Insulin resistance (IR) is the diminished ability of cells to respond to the action of insulin in transporting glucose (sugar) from the bloodstream into muscle and other tissues. IR typically develops with obesity and heralds the onset of T2DM. It is as if insulin is "knocking" on the door of muscle. The muscle hears the knock, opens up, and lets glucose in. But with IR, the muscle cannot hearthe knocking of the insulin (the muscle is "resistant"). The pancreas makes more insulin, which increases insulin levels in the blood and causes a louder "knock." Eventually, the pancreas produces far more insulin than normal and the muscles continue to be resistant to the knock. As long as one can produce enough insulin to overcome this resistance, blood glucose levels remain normal. Once the pancreas is no longer able to keep up, blood glucose starts to rise, initially after meals, eventually even in the fasting state. IR is an early feature and finding in the pathogenesis of T2DM. IR is the condition in which normal amounts of insulin are inadequate to produce a normal insulin response from fat, muscle and liver cells. IR in fat cells reduces the effects of insulin and results in elevated hydrolysis of stored triglycerides in the absence of measures which either increase insulin sensitivity or which provide additional insulin. Increased mobilization of stored lipids in these cells elevates free fatty acids in the blood plasma. IR in muscle cells reduces glucose uptake (and so local storage of glucose as (glycogen), whereas IR in liver cells reduces storage of glycogen, making it unavailable for release of glucose into the blood when blood insulin levels fall (normally only when blood glucose levels are at low storage: Both lead to elevated blood glucose levels. High plasma levels of insulin and glucose due to IR often lead to metabolic syndrome and T2DM, including its complications. In 2000, there were approximately 171 million people, worldwide, with diabetes. The numbers of diabetes patients will expectedly more than double over the next 25 years, to reach a total of 366 million by 2030 (WHO/IDF, 2004). The two main contributors to the worldwide increase in prevalence of diabetes are population ageing and urbanization, especially in developing countries, with the consequent increase in the prevalence of obesity (WHO/IDF, 2004). PPARy agonists and statins are frequently used to improve insulin sensitivity (27, 28). Diabetes, type 2 (T2DM) is one of the two major types of diabetes, the type in which the beta cells of the pancreas produce insulin but the body is unable to use it effectively because the cells of the body are resistant to the action of insulin. Although this type of diabetes may not carry the same risk of death from ketoacidosis, it otherwise involves many of the same risks of complications as type 1 diabetes (in which there is a lack of insulin). The aim of treatment is to normalize the blood glucose in an attempt to prevent or minimize complications. People with T2DM may experience marked hyperglycemia, but most do not require insulin injections. In fact, 80% of all people with T2DM can be treated with diet, exercise, and, if needed be, oral hypoglycemic agents (drugs taken by mouth to lower the blood sugar, such as metformin). T2DM requires good dietary control including the restriction of calories, lowered consumption of simple carbohydrates and fat with increased consumption of complex carbohydrates and fiber. Regular aerobic exercise is also an important method for treating T2DM diabetes since it decreases IR and helps burn excessive glucose. Regular exercise also may help lower blood lipids and reduce some effects of stress, both important factors in treating diabetes and preventing complications. T2DM is also known as insulin-resistant diabetes, non-insulin dependent diabetes, and adult-onset diabetes.

Dyslipidemia (From dys- + lipid (fat) + -emia (in the blood) = essentially, disordered lipids in the blood) is a disorder of lipoprotein metabolism. Dyslipidemias may be manifested by elevation of the triglyceride concentrations, and a decrease in the "good" high-density lipoprotein (HDL) cholesterol concentration in the blood. Dyslipidemia comes under consideration in many situations including diabetes, a common cause of lipidemia. For adults with diabetes, it has been recommended that the levels HDL-cholesterol, and triglyceride be measured every year. Optimal HDL-cholesterol levels are equal to or greater than 40 mg/dL (1.02 mmol/L), and desirable triglyceride levels are less than 150 mg/dL (1.7 mmol/L). PPARot agonists are used to treat dyslipidemia (29).

HDL-cholesterol concerns lipoproteins, which are combinations of lipids (fats) and proteins, are the form in which lipids are transported in the blood. The high-density lipoproteins transport cholesterol from the tissues of the body to the liver so it can be gotten rid of (in the bile). HDL-cholesterol is therefore considered the "good" cholesterol. The higher the HDL-cholesterol level, the lower the risk of coronary artery disease. Even small increases in HDL-cholesterol reduce the frequency of heart attacks. For each 1 mg/dl increase in HDL-cholesterol there is a 2 to 4% reduction in the risk of coronary heart disease. Although there are no formal guidelines, proposed treatment goals for patients with low HDL-cholesterol are to increase HDL-cholesterol to above 35 mg/dl in men and 45 mg/dl in women with a family history of coronary heart disease; and to increase HDL-cholesterol to approach 45 mg/dl in men and 55 mg/dl in women with known coronary heart disease. The first step in increasing HDL- cholesterol levels is life style modification. Regular aerobic exercise, loss of excess weight (fat), and cessation of cigarette smoking cigarettes will increase HDL-cholesterol levels. Moderate alcohol consumption (such as one drink a day) also raises HDL-cholesterol. When life style modifications are insufficient, medications are used. Medications that are effective in increasing HDL-cholesterol include nicotinic acid (niacin), gemfibrozil (Lopid), estrogen, and to a lesser extent, the statin drugs.

Triglycerides are the major form of fat. A triglyceride consists of three molecules of fatty acid combined with a molecule of the alcohol glycerol. Triglycerides serve as the backbone of many types of lipids (fats). Triglycerides come from the food we eat as well as from being produced by the body. Triglyceride levels are influenced by recent fat and alcohol intake, and should be measured after fasting for at least 12 hours. A period of abstinence from alcohol is advised before testing for triglycerides. Markedly high triglyceride levels (greater than 500mg/dl) can cause inflammation of the pancreas (pancreatitis). Therefore, these high levels should be treated aggressively with low fat diets and medications, if needed. The word "triglyceride" reflects the fact that a triglyceride consists of three ("tri-") molecules of fatty acid combined with a molecule of the alcohol glycerol ("-glyceride") that serves as the backbone in many types of lipids (fats).

Hypercholesterolemia is manifested by elevation of the total cholesterol due to elevation of the "bad" low-density lipoprotein (LDL) cholesterol in the blood. Optimal LDL-cholesterol levels for adults with diabetes are less than 100 mg/dL (2.60 mmol/L).

Low-density lipoprotein (LDL) belongs to the lipoprotein particle family. Its size is approx. 22 nm and its mass is about 3 million Daltons; but, since LDL particles contain a changing number of fatty acids, they actually have a mass and size distribution. Each native LDL particle contains a single apolipoprotein B-100 molecule (Apo B-100, a protein with 4536 amino acid residues) that circles the fatty acids, keeping them soluble in the aqueous environment. In addition, LDL has a highly- hydrophobic core consisting of polyunsaturated fatty acid known as linoleate and about 1500 esterified cholesterol molecules. This core is surrounded by a shell of phospholipids and unesterified cholesterol as well as a single copy of B-100 large protein (514 kD). Cholesterol is an animal sterol that is normally synthesized by the liver. The main types, low-density lipoprotein (LDL) and high-density lipoprotein (HDL) carry cholesterol from and to the liver, respectively. LDL-cholesterol concerns thus the cholesterol in low-density lipoproteins. Cholesterol is required in the membrane of mammalian cells for normal cellular function, and is either synthesized in the endoplasmic reticulum, or derived from the diet, in which case it is delivered by the bloodstream in low-density lipoproteins. These are taken into the cell by LDL receptor-mediated endocytosis in clathrin-coated pits, and then hydrolyzed in lysosomes. Ox-LDL-cholesterol concerns a LDL-cholesterol that has been bombarded by free radicals; it is thought to cause atherosclerosis; the ^'bad' cholesterol; a high level in the blood is thought to be related to various pathogenic conditions.

Hypertension or High blood pressure is defined as a repeatedly elevated blood pressure exceeding 140 over 90 mmHg - a systolic pressure above 140 with a diastolic pressure above 90. Chronic hypertension is a "silent" condition. Stealthy as a cat, it can cause blood vessel changes in the back of the eye (retina), abnormal thickening of the heart muscle, kidney failure, and brain damage. For diagnosis, there is no substitute for measurement of blood pressure. Not having your blood pressure checked (or checking it yourself) is an invitation to hypertension. No specific cause for hypertension is found in 95% of cases. Hypertension is treated with regular aerobic exercise, weight reduction (if overweight), salt restriction, and medications.

Metabolic syndrome (MetS) is a combination of medical disorders that increase the risk of developing cardiovascular disease and T2DM. It affects a large number of people, and prevalence increases with age. Some studies estimate the prevalence in the USA to be up to 25% of the population. MetS is also known as metabolic syndrome X, syndrome X, IR syndrome, Reaven's syndrome or CHAOS. MetS components were defined as detailed in the Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in adults (ATPIII) report: 1) waist circumference >102 cm in men and > 88 cm in women; 2) fasting triglycerides > 150 mg/dl (1.70 mmol/l); 3) HDL-cholesterol <40 mg/dl (1.03 mmol/l) in men and < 50 mg/dl (1.29 mmol/l) in women; 4) blood pressure≥ 130/85 mmHg or on anti-hypertensive medication; 5) fasting- glucose≥ 100 mg/dl (5.55 mmol/l) or on anti-diabetic medication (30). Recently, hs-CRP has been defined as an independent risk factor of T2DM and cardiovascular diseases. Persons with hs-CRP blood values of at least 3 mg/L are at higher risk. Therefore, persons with the MetS disorder phenotype are persons with at least three components out of six components.

The inflammatory state of a cell can be measured by determining well-known inflammatory parameters associated with said cell. These parameters include certain chemokines and cytokines, including but not limited to IFN-y, IL-1, IL-6, IL-8, and TNF-a. An increased inflammatory state of a cell refers to an increased amount of inflammatory parameters associated with said cell compared to a control cell. Similarly a normal or decreased inflammatory state of a cell refers to a similar or decreased amount, respectively, of inflammatory parameters associated with said cell compared to a control cell. Similarly, the oxidative stress state of a cell can be measured by determining well-known oxidative stress parameters, such as e.g. the amount of reactive oxygen species (ROS). An increased, normal or decreased oxidative stress state of a cell refers, respectively, to an increased, similar or decreased amount of oxidative stress parameters associated with said cell compared to a control cell.

Osteoarthritis is a type of arthritis caused by inflammation, breakdown, and eventual loss of cartilage in the joints. It is also known as degenerative arthritis. "Sample" or "biological sample" as used herein can be any organ, tissue, cell, or cell extract isolated from a subject, a cell-derived vesicle, such as a sample isolated from a mammal having a metabolic syndrome disorder or at risk for a metabolic syndrome disorder (e.g., based on family history or personal history). For example, a sample can include, without limitation, cells or tissue (e.g., from a biopsy or autopsy), peripheral blood, whole blood, red cell concentrates, platelet concentrates, leukocyte concentrates, blood cell proteins, blood plasma, platelet-rich plasma, a plasma concentrate, a precipitate from any fractionation of the plasma, a supernatant from any fractionation of the plasma, blood plasma protein fractions, purified or partially purified blood proteins or other components, serum, tissue or fine needle biopsy samples, or any other specimen, or any extract thereof, obtained from a patient (human or animal), test subject, healthy volunteer, or experimental animal. A subject can be a human, rat, mouse, non-human primate, etc. A sample may also include sections of tissues such as frozen sections taken for histological purposes. A "sample" may also be a cell or cell line created under experimental conditions, that is not directly isolated from a subject.

In a particular embodiment the sample is selected from the group consisting of (a) a liquid containing cells; (b) a tissue-sample; (c) a cell-sample; (d) a cell-derived vesicle; (e) a cell biopsy; more in particular the sample comprises hematopoietic cells or blood cells; even more in particular the sample comprises at least one myeloid cell or debris thereof. In an even further embodiment the sample comprises at least one of monocytes or peripheral blood mononuclear cells or debris thereof.

In addition, a sample can also be a blood-derived sample, like plasma or serum. In another particular embodiment, the RNAs of the disclosure can be quantified or qualified on isolated microvesicles, particularly on monocyte-derived microvesicles.

A "control" or "reference" includes a sample obtained for use in determining base-line expression or activity. Accordingly, a control sample may be obtained by a number of means including from subjects not having a metabolic syndrome disorder; from subjects not suspected of being at risk for developing a metabolic syndrome disorder; or from cells or cell lines derived from such subjects. A control also includes a previously established standard, such as a previously characterized pool of RNA or protein extracts from monocytes of at least 20 subjects without obesity, any of the MetS components or any of the other diseases as defined above. Accordingly, any test or assay conducted according to the invention may be compared with the established standard and it may not be necessary to obtain a control sample for comparison each time. A measurement made from a control or reference sample may be referred to as a reference measurement. The term "array" or "microarray" in general refers to an ordered arrangement of hybridizable array elements such as polynucleotide probes on a substrate. An "array" is typically a spatially or logically organized collection, e.g., of oligonucleotide sequences or nucleotide sequence products such as RNA or proteins encoded by an oligonucleotide sequence. In some embodiments, an array includes antibodies or other binding reagents specific for products of a candidate library. The array element may be an oligonucleotide, DNA fragment, polynucleotide, or the like, as defined below. The array element may include any element immobilized on a solid support that is capable of binding with specificity to a target sequence such that gene expression may be determined, either qualitatively or quantitatively.

When referring to a pattern of expression, a "qualitative" difference in gene expression refers to a difference that is not assigned a relative value. That is, such a difference is designated by an "all or nothing" valuation. Such an all or nothing variation can be, for example, expression above or below a threshold of detection (an on/off pattern of expression). Alternatively, a qualitative difference can refer to expression of different types of expression products, e.g., different alleles (e.g., a mutant or polymorphic allele), variants (including sequence variants as well as post-translationally modified variants), etc. In contrast, a "quantitative" difference, when referring to a pattern of gene expression, refers to a difference in expression that can be assigned a value on a graduated scale, (e.g., a 0-5 or 1- 10 scale, a + +++ scale, a grade 1 grade 5 scale, or the like; it will be understood that the numbers selected for illustration are entirely arbitrary and in no-way are meant to be interpreted to limit the invention). Microarrays are useful in carrying out the methods disclosed herein because of the reproducibility between different experiments. DNA microarrays provide one method for the simultaneous measurement of the expression levels of large numbers of genes. Each array consists of a reproducible pattern of capture probes attached to a solid support. Labeled RNA or DNA is hybridized to complementary probes on the array and then detected for instance by laser scanning. Hybridization intensities for each probe on the array are determined and converted to a quantitative value representing relative gene expression levels. See the patent / publications Nos. US6040138, US5800992, US6020135, US6033860, US6344316, US7439346, US7371516, US7353116, US7348181, US7347921, US7335762 , US7335470, US7323308, US7321829, US7302348, US7276592, US7264929, US7244559, US7221785, US7211390, US7189509, US7138506, US7052842, US7047141, and US7031845 which are incorporated herein by reference. High-density oligonucleotide arrays are particularly useful for determining the gene expression profile for a large number of RNA's in a sample.

A "DNA fragment" includes polynucleotides and/or oligonucleotides and refers to a plurality of joined nucleotide units formed from naturally-occurring bases and cyclofuranosyl groups joined by native phosphodiester bonds. This term effectively refers to naturally- occurring species or synthetic species formed from naturally-occurring subunits. "DNA fragment" also refers to purine and pyrimidine groups and moieties which function similarly but which have no naturally- occurring portions. Thus, DNA fragments may have altered sugar moieties or inter-sugar linkages. Exemplary among these are the phosphorothioate and other sulfur containing species. They may also contain altered base units or other modifications, provided that biological activity is retained. DNA fragments may also include species that include at least some modified base forms. Thus, purines and pyrimidines otherthan those normally found in nature may be so employed. Similarly, modifications on the cyclofuranose portions of the nucleotide subunits may also occur as long as biological function is not eliminated by such modifications. The term "polynucleotide," when used in singular or plural generally refers to any polyribonucleotide or polydeoxyribonucleotide, which may be unmodified RNA or DNA or modified RNA or DNA. Thus, for instance, polynucleotides as defined herein include, without limitation, single- and double-stranded DNA, DNA including single- and double-stranded regions, single- and double-stranded RNA, and RNA including single- and double-stranded regions, hybrid molecules comprising DNA and RNA that may be single-stranded or, more typically, double-stranded or include single- and double-stranded regions. In addition, the term "polynucleotide" as used herein refers to triple-stranded regions comprising RNA or DNA or both RNA and DNA. The strands in such regions may be from the same molecule or from different molecules. The regions may include all of one or more of the molecules, but more typically involve only a region of some of the molecules. One of the molecules of a triple-helical region often is an oligonucleotide. Thus, DNAs or RNAs with backbones modified for stability or for other reasons are "polynucleotides" as that term is intended herein. Moreover, DNAs or RNAs comprising unusual bases, such as inosine, or modified bases, such as tritiated bases, are included within the term "polynucleotides" as defined herein. In general, the term "polynucleotide" embraces all chemically, enzymatically and/or metabolically modified forms of unmodified polynucleotides, as well as the chemical forms of DNA and RNA characteristic of cells, including simple and complex cells.

The term "oligonucleotide" refers to a relatively short polynucleotide, including, without limitation, single-stranded deoxyribonucleotides, single- or double-stranded ribonucleotides, RNA: DNA hybrids and double-stranded DNAs. Oligonucleotides, such as single-stranded DNA oligonucleotides, are often synthesized by chemical methods, for example using automated oligonucleotide synthesizers that are commercially available. However, oligonucleotides can be made by a variety of other methods, including in vitro recombinant DNA- mediated techniques and by expression of DNAs in cells.

The terms "differentially expressed gene," "differential gene expression" and their synonyms, which are used interchangeably, refer to a gene whose expression is activated to a higher or lower level in a subject, relative to its expression in a normal or control subject. A differentially expressed gene may be either activated or inhibited at the nucleic acid level or protein level, or may be subject to alternative splicing to result in a different polypeptide product. Such differences may be evidenced by a change in mRNA levels, surface expression, secretion or other partitioning of a polypeptide, for example. Differential gene expression may include a comparison of expression between two or more genes, or a comparison of the ratios of the expression between two or more genes, or even a comparison of two differently processed products of the same gene, which differ between normal subjects and subjects suffering from a disease, or between various stages of the same disease. Differential expression includes both quantitative, as well as qualitative, differences in the temporal or cellular expression pattern in a gene or its expression products. As used herein, "differential gene expression" can be present when there is, for example, at least an about a one to about two-fold, or about two to about four-fold, or about four to about six-fold, or about six to about eight-fold, or about eight to about tenfold, or greater than about 11-fold difference between the expression of a given gene in a patient of interest compared to a suitable control. However, folds change less than one is not intended to be excluded and to the extent such change can be accurately measured, a fold change less than one may be reasonably relied upon in carrying out the methods disclosed herein. Differential expression includes changes in either a positive or negative direction, so that the expression of a biomarker in a biological sample obtained from a patient may be decreased or increased as compared to expression of a biomarker in reference or control samples. In some embodiments, the fold change may be greater than about five or about 10 or about 20 or about 30 or about 40.

The phrase "gene expression profile" (or "biomarker profile") as used herein, is intended to encompass the general usage of the term as used in the art, and generally means the collective data representing gene expression with respect to a selected group of one, two, or more genes, wherein the gene expression may be upregulated, downregulated, or unchanged as compared to a reference standard A gene expression profile is obtained via measurement of the expression level of many individual genes. The expression profiles can be prepared using different methods. Suitable methods for preparing a gene expression profile include, but are not limited to reverse transcription loop- mediated amplification ( T-LAMP), for instance one-step RT-LAMP, quantitative RT-PCR, Northern Blot, in situ hybridization, slot-blotting, nuclease protection assay, nucleic acid arrays, and immunoassays. The gene expression profile may also be determined indirectly via measurement of one or more gene products (whether a full or partial gene product) for a given gene sequence, where that gene product is known or determined to correlate with gene expression.

The phrase "gene product" is intended to have the meaning as generally understood in the art and is intended to generally encompass the product(s) of RNA translation resulting in a protein and/or a protein fragment. The gene products of the genes identified herein may also be used for the purposes of diagnosis or treatment in accordance with the methods described herein. A "reference gene expression profile" as used herein, is intended to indicate the gene expression profile, as defined above, for a pre-selected group which is useful for comparison to the gene expression profile of a subject of interest. For example, the reference gene expression profile may be the gene expression profile of a single individual known to not have an metabolic syndrome disorder phenotype or a propensity thereto (i.e. a "normal" subject) or the gene expression profile represented by a collection of RNA samples from '"normal" individuals that has been processed as a single sample. The "reference gene expression profile' ' may vary and such variance will be readily appreciated by one of ordinary skill in the art.

The phrase "reference standard" (or "reference measurement") as used herein may refer to the phrase "reference gene expression profile" or may more broadly encompass any suitable reference standard which may be used as a basis of comparison with respect to the measured variable. For example, a reference standard may be an internal control, the gene expression or a gene product of a "healthy" or '"normal" subject, a housekeeping gene, or any unregulated gene or gene product. The phrase is intended to be generally non-limiting in that the choice of a reference standard is well within the level of skill in the art and is understood to vary based on the assay conditions and reagents available to one using the methods disclosed herein.

"Gene expression profiling" as used herein, refers to any method that can analyze the expression of selected genes in selected samples.

The phrase "gene expression system" as used herein, refers to any system, device or means to detect gene expression and includes diagnostic agents, candidate libraries, oligonucleotide sets or probe sets. The terms "diagnostic oligonucleotide" or "diagnostic oligonucleotide set" generally refers to an oligonucleotide or to a set of two or more oligonucleotides that, when evaluated for differential expression their corresponding diagnostic genes, collectively yields predictive data.

Such predictive data typically relates to diagnosis, prognosis, selection of therapeutic agents, monitoring of therapeutic outcomes, and the like. In general, the components of a diagnostic oligonucleotide or a diagnostic oligonucleotide set are distinguished from oligonucleotide sequences that are evaluated by analysis of the DNA to directly determine the genotype of an individual as it correlates with a specified trait or phenotype, such as a disease, in that it is the pattern of expression of the components of the diagnostic oligonucleotide set, rather than mutation or polymorphism of the DNA sequence that provides predictive value. It will be understood that a particular component (or member) of a diagnostic oligonucleotide set can, in some cases, also present one or more mutations, or polymorphisms that are amenable to direct genotyping by any of a variety of well-known analysis methods, e.g., Southern blotting, RFLP, AFLP, SSCP, SNP, and the like.

The phrase "gene amplification" refers to a process by which multiple copies of a gene or gene fragment are formed in a particular cell or cell line. The duplicated region (a stretch of amplified DNA) is often referred to as "amplicon." Usually, the amount of the messenger RNA (mRNA) produced, i.e., the level of gene expression, also increases in the proportion of the number of copies made of the particular gene expressed.

A "gene expression system" refers to any system, device or means to detect gene expression and includes diagnostic agents, candidate libraries oligonucleotide, diagnostic gene sets, oligonucleotide sets, array sets, or probe sets.

As used herein, a "gene probe" refers to the gene sequence arrayed on a substrate.

As used herein, a "nucleotide probe" refers to the oligonucleotide, DNA fragment, polynucleotide sequence arrayed on a substrate. The terms "splicing" and "RNA splicing" are used interchangeably and refer to RNA processing that removes introns and joins exons to produce mature mRNA with continuous coding sequence that moves into the cytoplasm of a eukaryotic cell.

"Stringency" of hybridization reactions is readily determinable by one of ordinary skill in the art, and generally is an empirical calculation dependent upon probe length, washing temperature, and salt concentration. In general, longer probes require higher temperatures for proper annealing, while shorter probes need lower temperatures.

Hybridization generally depends on the ability of denatured DNA to re-anneal when complementary strands are present in an environment below their melting temperature. The higher the degree of desired homology between the probe and hybridizable sequence the higher is the relative temperature which can be used. As a result, it follows that higher relative temperatures would tend to make the reaction conditions more stringent, while lower temperatures less so. For additional details and explanation of stringency of hybridization reactions, see Ausubel et al., Current Protocols in Molecular Biology, Wiley Interscience Publishers, (1995) and in Current Protocols in Molecular Biology Copyright © 2007 by John Wiley and Sons, Inc., 2008.

As used herein, a "gene target" refers to the sequence derived from a biological sample that is labeled and suitable for hybridization to a gene probe affixed on a substrate and a "nucleotide target" refers to the sequence derived from a biological sample that is labeled and suitable for hybridization to a nucleotide probe affixed on a substrate. The term "treatment" refers to both therapeutic treatment and prophylactic or preventative measures, wherein the object is to prevent or slow down (lessen) the targeted pathologic condition or disorder. Those in need of treatment include those already with the disorder as well as those prone to have the disorder or those in whom the disorder is to be prevented. The practice of the disclosure will employ, unless otherwise indicated, conventional techniques of molecular biology (including recombinant techniques), microbiology, cell biology and biochemistry, which are within the skill of the art.

"Adipose tissue" refers to fat, body fat, adipocytes, and/or any loose connective tissue comprising mostly adipocytes. Adipose tissue may refer to either white or brown adipose tissue. Obesity is often defined as an excessive increase in white adipose tissue (WAT). WAT expands by increased adipocyte size (hypertrophy) and number (hyperplasia). The location and cellular mechanisms of WAT expansion greatly affect the pathogenesis of obesity (31) .Brown adipose tissue (BAT) dissipates energy as heat to maintain optimal thermogenesis and to contribute to energy expenditure in rodents and possibly humans. The energetic processes executed by BAT require a readily-available fuel supply, which includes glucose and fatty acids (FAs). FAs become available by cellular uptake, de novo lipogenesis, and multilocular lipid droplets in brown adipocytes. BAT also possesses a great capacity for glucose uptake and metabolism, and an ability to regulate insulin sensitivity. These properties make BAT an appealing target for the treatment of obesity, diabetes, and other metabolic disorders (32) . Metabolism in adipose tissues depends on activity of mitochondrial uncoupling proteins: UCP2, expressed ubiquitously; UCPl, exclusively in brown adipose tissue (BAT); UCP3, predominantly in muscle; UCP4 and BMCP (UCP5), in brain. UCP4 is the ancestral prototype from which the other UCPn diverged. Findings on the level of organism and reconstituted recombinant proteins demonstrated that UCPn exhibit a protonophoric function, documented by overexpression in mice, L6 myotubes, INS1 cells, muscle, and yeast. In a few cases (yeast), this protonophoric function was correlated with elevated fatty acid (FA) levels. Reconstituted UCPn exhibited nucleotide-sensitive FA induced H(+) uniport. Two mechanisms, local buffering or FA cycling were suggested as an explanation. A basic UCPn role with mild uncoupling is to accelerate metabolism and reduce reactive oxygen species. UCP2 (UCP3) roles were inferred from transcriptional up-regulation mediated by FAs via peroxisome proliferator-activated receptors, cytokines, leptin signaling via hypothalamic pathway, and by thyroid and beta2 adrenergic stimulation. The latter indicated a role in catecholamine-induced thermogenesis in skeletal muscle. UCP2 (UCP3) may contribute to body weight regulation, although obesity was not induced in knockout (KO) mice. An obesity reduction in middle-aged humans was associated with the less common allele of -866 G/A polymorphism in the ucp2 gene promoter enhancing the exon 8 insertion: deletion transcript ratio. Up-regulated UCP2 transcription by pyrogenic cytokines (tumour necrosis factor alpha (TNFalpha)) suggested a role in fever. UCP2 could induce type 2 diabetes as developed from obesity due to up-regulated UCP2 transcription by FAs in pancreatic beta-cells. UCPn might be pro-apoptotic as well as anti-apoptotic, depending on transcriptional and biochemical regulation (32, 33).

Visceral BAT includes the following: 1) Perivascular BAT around the aorta, common carotid artery, and brachiocephalic artery; in anterior mediastinum (paracardial) fat; and around epicardial coronary artery and cardiac veins as well as medium-sized muscular arteries and veins including the internal mammary and the intercostal artery branches from the subclavian and aorta. The intercostal veins drain blood from the chest and abdominal walls into the azygous veins, the left joining the main right azygous vein in the latter's thoracic cephalad course closely adjacent to the inferior vena cava before emptying into the superior vena cava). 2) Viscus BAT, defined as BAT surrounding a hollow muscular organ other than blood vessels, situated in variable amounts in the epicardium around the heart and in the esophago-tracheal groove, as well as greater omentum and transverse mesocolon in the peritoneal cavity. 3) BAT around solid organs, namely, kidney, adrenal, pancreas, liver, and splenic hilum including paravertebral fat, which was not examined in Heaton's series but can be seen on CT scans of the thorax adjacent to periaortic fat. It lies next to the intercostal artery from which a spinal branch supplies the spinal cord (34, 35). In view of the diverse locations and potential differences in responsiveness between BAT depots, it is likely that BAT will be shown to have much more subtle and thus previously overlooked functions and regulatory control mechanisms. Possible interventions may include 1) in vivo methods such as increasing BAT mass and thermogenesis in established depots or by differentiation (recruitment) from bright/beige progenitor cells located in WAT using systemically administered agents, 2) local injection of pharmacological compounds into BAT or WAT depots, 3) lowering ambient indoor temperature to values below thermoneutrality to trigger non-shivering thermogenesis (such as 16-17°C) if tolerated by people, 4) promoting skeletal muscle thermogenesis, and 5) increasing general mitochondrial uncoupling.

A "patient" (or "subject") refers to a person who requires medical care, is undergoing medical treatment, or who is awaiting medical care and treatment. A patient or subject may be under a physician's care for a particular disease or condition, or may be identified as requiring diagnostic and/or prognostic testing for a disease or conditions. The patient may be a human patient, either male or female. The patient may be any age, for example, an adult, a child, or an infant.

B. Description of the molecules The disclosure relates to molecules involved in oxidative stress and resistance to oxidation. Specific molecules may be used as biomarkers for disorders such as MetS, metabolically unhealthy (MUH) comprising MetS with inflammation, T2DM, and/or cardiovascular disorders. In some embodiments, the molecules are used to identify patients at risk for developing disorders. In some embodiments, the disorder is T2DM. In certain embodiments, the molecules are used to predict whether a patient will respond to treatment for a disorder. In some embodiments, the disorder is T2DM. The molecules are described below, and reference is made to the NCBI gene and protein databases, for example, the version as updated 9 Nov 2014.

(i) CYTOCHROME c OXIDASE, SUBUNIT IV, ISOFORM 1; COX4I1

Cytochrome c oxidase (COX) is the terminal enzyme of the mitochondrial respiratory chain. It is a multi- subunit enzyme complex that couples the transfer of electrons from cytochrome c to molecular oxygen and contributes to a proton electrochemical gradient across the inner mitochondrial membrane. The complex consists of 13 mitochondrial- and nuclear-encoded subunits. The mitochondrially-encoded subunits perform the electron transfer and proton pumping activities. The functions of the nuclear- encoded subunits are unknown but they may play a role in the regulation and assembly of the complex. COX subunit IV is the largest nucleus-encoded subunit of cytochrome c oxidase (COX; EC 1.9.3.1), the terminal enzyme complex of the mitochondrial electron transport chain. COX is an example of an unusual class of multisubunit enzyme complex found in both mitochondria and chloroplasts of eukaryotic cells. The novel feature of these complexes is their mixed genetic origin: in each complex, at least one of the polypeptide subunits is encoded in the genome of the organelle, with the remaining subunits encoded in the nucleus. Thus, 2 distinct genetic systems, each with its unique features and evolutionary constraints, must interact to produce these essential haloenzymes (36). In humans oxidative phosphorylation genes, such as COX4I1 and COIX10 (see below) were not found to be associated with I and T2DM (37).

The Homo sapiens COX4I1 mRNA sequence has been deposited in the NCBI database under the accession number NM 001861.3 (SEQ ID NO:l). Its protein has been deposited under the accession number NP_001852 (SEQ ID NO:2).

(ii) CYTOCHROME c OXIDASE ASSEMBLY PROTEIN COX10; COX10

The COX10 gene encodes a cytochrome c oxidase (COX) assembly protein involved in the mitochondrial heme biosynthetic pathway. COX10 catalyzes the farnesylation of a vinyl group at position C2, resulting in the conversion of protoheme (heme B) to heme O. The COX10 protein is required for the expression of functional COX (38). Funfschilling et al. (39) identified a metabolic component of axon-glia interactions by generating conditional CoxlO mutant mice, in which oligodendrocytes and Schwann cells fail to assemble stable mitochondrial COX (also known as mitochondrial complex IV). The Homo sapiens genomic sequence of COX10 has been deposited under the accession number NG_008034.1. Its mRNA has been deposited in the NCBI database under the accession number NM 001303.3 (SEQ ID NO:3). Its protein has been deposited under the accession number EAW89956 (isoform A) (SEQ ID NO:4) and EAW89957 (isoform B) (SEQ ID NO:5).

(iii) GLUTATHIONE PEROXIDASE; GPX1

Glutathione peroxidase (EC 1.11.1.9) catalyzes the reduction of organic hydroperoxides and hydrogen peroxide by glutathione and thereby protects against oxidative damage (40). De Haan et al. (41) demonstrated a role for GPX1 in protection against oxidative stress by showing that Gpxl -/- mice are highly sensitive to the oxidant paraquat. Lethality was detected within 24 hours in mice exposed to paraquat at 10 mg/kg, approximately 1/7 of the LD50 of wild-type controls. Shiomi et al. (42) created myocardial infarction by left coronary artery ligation in mice overexpressing Gpxl in the heart and wild-type mice. Although infarct size was comparable, the transgenic mice had an increased survival rate with decreased left ventricular dilatation, dysfunction, and end-diastolic pressure compared to wild-type mice. The improvement in left ventricular function was accompanied by a decrease in myocyte hypertrophy, apoptosis, and interstitial fibrosis in the noninfarcted left ventricle. They concluded that overexpression of Gpxl protects the heart against post-myocardial infarction remodeling and heart failure in mice. Gene polymorphisms of GPXl were found to be associated with peripheral neuropathy in subjects with diabetes mellitus (43), and with coronary artery disease in T2DM patients (44). Dietary selenium deficiency partially rescues T2DM-like phenotypes of Gpxl- overexpressing male mice (45). Mice lacking Gpxl were protected from high-fat-diet-induced I . The increased insulin sensitivity in Gpxl -/- mice was attributed to insulin-induced phosphatidylinositol-3- kinase (PI3K)/Akt signaling and glucose uptake in muscle and could be reversed by the antioxidant N- acetylcysteine. Increased insulin signaling correlated with enhanced oxidation of the PTP family member PTEN, which terminates signals generated by PI3K. These studies provided causal evidence for the enhancement of insulin signaling by ROS in vivo (46).

The Homo sapiens genomic sequence of GPXl has been deposited under the accession number NG_012264.1. Its mRNA has been deposited in the NCBI database under the accession number NM_000581.2 (SEQ ID NO:6). Its protein has been deposited under the accession number AAH70258.1 (SEQ ID NO:7).

(iv) INTERLEUKIN 1 RECEPTOR-ASSOCIATED KINASE 3; IRAK3

IRAK3 was found to be predominantly expressed in PBL and the monocytic cell lines U937 and THP-1, in contrast to the other IRAKs that are expressed in most cell types. Because of the restriction of expression of this IRAK to monocytic cells, the authors termed the protein IRAKM, now called IRAK3. The IRAK3 (or IRAKM or lnterleukin-1 Receptor-Associated Kinase 3 or lnterleukin-1 Receptor- Associated Kinase M) gene consists of 12 exons spanning a region of approximately 60 kb in chromosome 12ql4.3 (47). Like IRAK2, the expression of IRAK3 in THP-1 cells is upregulated in the presence of phorbol ester and ionomycin, which also induce differentiation of these cells into more mature macrophages (48). IRAK3 (IRAKM) is a member of the interleukine-1 receptor-associated kinase (IRAK) family. The IRAK family is implicated in the Toll-like receptor (TLR) and II-1R signaling pathway. IRAK3 interacts with the myeloid differentiation (MYD) marker MYD88 and TRAF6 signaling proteins in a manner similar to the other IRAKs. However, Kobayashi et al. (49) showed that IRAK3, in contrast to other IRAKs, is induced upon TLR stimulation but negatively regulates TLR signaling. IRAK3 -/- cells exhibited increased cytokine production upon TLR/IL1 stimulation and bacterial challenge, and Irakm -/- mice showed increased inflammatory responses to bacterial infection. Endotoxin tolerance, a protection mechanism against endotoxin shock, was significantly reduced in IRAKM -/- cells. Thus, the authors concluded that IRAK3 regulates TLR signaling and innate immune homeostasis. Data with Irakm -/- mice have revealed that IRAKM serves as a negative regulator of IL-1R/TLR signaling. Moreover, IRAK3 is a key inhibitor of inflammation in obesity and MetS (50). In addition, we found that IRAK3 is required forthe anti-inflammatory action of adiponectin. Finally, IRAK3 was not only a relevant inhibitor of NFKB signaling but was also involved in the atheroprotective action of PPAR agonists (51).

The Homo sapiens IRAK3 genomic sequence has been deposited under the accession number NG_021194.1. Its mRNA has been deposited in the NCBI database as under the accession number NM_001142523.1 (SEQ ID NO:8). Its protein has been deposited under the accession number NP_009130.2 (isoform A) (SEQ ID NO:9) and NP_001135995 (isoform B) (SEQ ID NO:10).

(v) PROSTAGLANDIN-ENDOPEROXIDE SYNTHASE 1; PTGSl

Prostaglandin-endoperoxide synthase (PTGS; EC 1.14.99.1; fatty acid cyclooxygenase; PGH synthase) is the key enzyme in prostaglandin biosynthesis. The cyclooxygenase activity of the enzyme is inhibited by nonsteroidal anti-inflammatory drugs (NSAID) such as aspirin and endomethacin. Two isoforms of PTGS has been identified: a constitutive isoform (PTGSl; COX1) and an inducible isoform (PTGS2, COX2; 600262) (52, 53). Aspirin insensitive thromboxane generation is associated with oxidative stress in T2DM (54). The Homo sapiens genomic sequence of PTGSl has been deposited under the accession number NG 032900.1. Its mRNA has been deposited in the NCBI database under the accession number NM_000962.3 (SEQ ID NO:ll). Its protein has been deposited under the accession number AAL33601.1 (SEQ ID NO:12).

(vi) PROSTAGLANDIN-ENDOPEROXIDE SYNTHASE 2; PTGS2 Whereas PTGSl is involved in production of prostaglandins for cellular housekeeping functions, PTGS2 is associated with biologic events such as injury, inflammation, and proliferation (55, 56). Morham et al. (57) noted that COX2 is induced at high levels in migratory and other responding cells by proinflammatory stimuli. COX2 is generally considered to be a mediator of inflammation. Leptin mediated ObRb receptor increases expression of adhesion intercellular molecules and COX2 on murine aorta tissue inducing endothelial dysfunction (58). HDL of patients with T2DM upregulated PTGS2 expression and prostacyclin 1-2 release in endothelial cells through HDL-associated sphingosine-1- phosphate (59).

The Homo sapiens genomic sequence of PTGS2 has been deposited under the accession number NG 028206.1. Its mRNA has been deposited in the NCBI database under the accession number NM_000963.2 (SEQ ID N0.13) . Its protein has been deposited under the accession number BAA05698.1 (SEQ ID NO:14).

(vii) RUNT-RELATED TRANSCRIPTION FACTOR 2; RUNX2

RUNX2 has a primary role in the differentiation of osteoblasts and hypertrophy of cartilage at the growth plate, cell migration, and vascular invasion of bone; is expressed in vascular endothelial cells, breast cancer cells, and prostate cancer cells; is linked to vascular calcification in atherosclerotic lesions; and is expressed in adult bone marrow, thymus, and peripheral lymphoid organs (60). Activation of the PI3K/Akt pathway by oxidative stress mediated high glucose-induced increase of adipogenic differentiation in primary rat osteoblasts, as evidenced by an increase in Runx2 among others, such as adipocyte fatty acid binding protein (61, 62). Delayed bone regeneration and low bone mass in a rat model of T2DM was found to be due to impaired osteoblast function, evidenced by a reduction in Runx2 (63). Advanced glycation end products-induced vascular calcification was found to be mediated by oxidative stress, associated with an increase in Runx2. But in circulating osteogenic precursor cells reduced molecular expression of the osteoblast regulator gene Runx2 was associated with increased expression of the oxidative stress markers p66(Shc) and SOD2 (64). Metformin induces osteoblast differentiation via orphan nuclear receptor SHP-mediated transactivation of Runx2 (65). Streptozotocin-induced diabetes increased expression of the receptor for activation of NFKB and decreased Runx2 expression in bone of rats (66). Obesity reduced bone density associated with activation of PPARy and suppression of Wnt/ -catenin in rapidly growing male rats; Runx2 was decreased (67).

The Homo sapiens genomic sequence of RUNX2 has been deposited under the accession number NG 008020.1. Its mRNA has been deposited in the NCBI database under the accession number NM 001015051.3 (SEQ ID NO:15). Its protein has been deposited under the accession number AAI08920.1 (SEQ ID NO: 16)and alternative protein (CCQ43044.1) (SEQ ID NO:17). (viii) SUPPRESSOR OF CYTOKINE SIGNALING 3; SOCS3

Suppressor of cytokine signaling (SOCS) proteins are key regulators of immune responses and exert their effects in a classic negative-feedback loop. SOCS3 is transiently expressed by multiple cell lineages within the immune system and functions predominantly as a negative regulator of cytokines that activate the JAK-STAT3 pathway (68). Members of SOCS family are involved in the pathogenesis of many inflammatory diseases. SOCS3 is predominantly expressed in T-helper type 2 cells, and Seki et al. (69) investigated its role in TH2-related allergic diseases. The Homo sapiens genomic sequence of SOCS3 has been deposited under the accession number NG_016851.1. Its mRNA has been deposited in the NCBI database under the accession number NM 003955.3 (SEQ ID NO:18). Its protein has been deposited under the accession number CAG46495.1 (SEQ ID NO:19). (ix) SUPEROXIDE DISMUTASE 2; SOD2

Mitochondrial superoxide dismutase-2 (SOD2; EC 1.15.1.1.) or Manganese SOD; MnSOD) is a mitochondrial matrix enzyme that scavenges oxygen radicals produced by the extensive oxidation- reduction and electron transport reactions occurring in mitochondria. In contrast with cytoplasmic SOD1, a homodimeric copper- and zinc-containing enzyme, SOD2 is tetrameric and contains manganese (70, 71). The SOD2 gene encodes an intramitochondrial free radical scavenging enzyme that is the first line of defense against superoxide produced as a byproduct of oxidative phosphorylation. Li et al. (72) inactivated the Sod2 gene in transgenic mice by homologous recombination. SOD2 expression was found to be increased in myotubes from obese non-diabetic subjects with a family history of T2DM (73). But, elevated free fatty acids and impaired adiponectin bioactivity reduced SOD2 protein in monocytes of T2DM patients (74). Thus, these findings are in contrast with the instant disclosure.

The Homo sapiens genomic sequence of SOD2 has been deposited under the accession number NG_008729.1. Its mRNA has been deposited in the NCBI database under the accession number NM_000636.2 (SEQ ID NO:20). Its protein has been deposited under the accession number AAH16934.1 (SEQ ID NO:21).

(x) UNCOUPLING PROTEIN 2 (MITOCHONDRIAL, PROTON CARRIER); UCP2

The mitochondrial protein called uncoupling protein (UCP1; 113730) plays an important role in generating heat and burning calories by creating a pathway that allows dissipation of the proton electrochemical gradient across the inner mitochondrial membrane in brown adipose tissue, without coupling to any other energy-consuming process. Fleury et al. (75) noted that this pathway has been implicated in the regulation of body temperature, body composition, and glucose metabolism. UCP2 is widely expressed in adult human tissues, including tissue rich in macrophages, and it is upregulated in white fat in response to fat feeding. Zhang et al. (76) assessed the role of UCP2 in regulating insulin secretion. Ucp2 -/- mice had higher islet ATP levels and increased glucose-stimulated insulin secretion, establishing that UCP2 negatively regulates insulin secretion. Of pathophysiologic significance, Ucp2 was markedly upregulated in islets of ob/ob mice, a model of obesity-induced diabetes. Ob/ob mice lacking Ucp2 had restored first-phase insulin secretion, increased serum insulin levels, and greatly decreased levels of glycemia. These results established UCP2 as a key component of beta-cell glucose sensing and as a critical link between obesity, beta-cell dysfunction, and T2DM. Because obesity and chronic hyperglycemia increase mitochondrial superoxide production, as well as UCP2 expression in pancreatic beta cells, Krauss et al. (77) hypothesized that a superoxide-UCP2 pathway could contribute importantly to obesity- and hyperglycemia-induced beta cell dysfunction. The Homo sapiens genomic sequence of UCP2 has been deposited under the accession number NG_011478.1. Its m NA has been deposited in the NCBI database under the accession number NMJD03355.2 (SEQ ID NO:22). Its protein has been deposited under the accession number AAC51336.1 (SEQ ID NO:23). C. PANELS OF BIOMARKERS

One aspect of the disclosure relates to a cluster of molecules which affect the oxidative stress and resistance to oxidation in association with obesity, metabolic syndrome (MetS) disorder and/or type 2 diabetes (T2DM) in white blood cells, particularly monocytes. For this cluster of molecules, a change in expression levels may indicate that a patient is in a metabolically unhealthy state. Expression of select molecules may be used as biomarkers for specific indications. For example, a panel of biomarkers may be a genetic signature of a disorder. In some embodiments, the biomarker panel comprises at least one of COX4I1, COX10, GPX1, IRAK3, PTGS1, PTGS2, RUNX2, SOCS3, SOD2, and UCP2. In some embodiments, the panel of biomarkers may be a genetic signature of T2DM.

In certain embodiments, the biomarker panel comprises COX4I1. A decrease in COX4I1 expression in a patient sample, as compared with a reference measurement, may indicate that the patient has obesity, T2DM, MetS, and/or inflammation. The decrease in COX4I1 may also indicate that the patient is at risk for developing T2DM, MetS, and/or inflammation, even if the patient does not yet have other symptoms of a disorder. The patient may be metabolically unhealthy. For example, a patient who is not obese may nonetheless be at risk for developing T2DM. Similarly, a patient may have 0-2 risk factors for MetS, and may be at risk for developing T2DM, MetS, and/or inflammation. Finally, decreased expression of COX4I1 may indicate that a patient will respond poorly or will not respond to treatments for symptoms of MetS, such as treatments for obesity or treatments for T2DM. In some embodiments, the decrease in COX4I1 expression in a patient sample, as compared with a reference measurement, indicates that the patient has T2DM. In certain embodiments, the decrease in COX4I1 indicates that the patient is at risk for developing T2DM, for example, when the patient has insulin resistance, but has not yet developed T2DM. Similarly, a patient may not have other risk factors for developing T2DM. In certain embodiments, the biomarker panel comprises COX4I1 and at least one of COX10, ¾Pfti, IRAK3, PTGS1, PTGS2, RUNX2, SOCS3, SOD2, and UCP2. In some embodiments, the biomarker panel comprises COX4I1 and PTGS2. A decrease in COX4I1 expression and an increase in PTGS2 expression in a patient sample, as compared with reference measurements, may indicate that a patient (1) has MetS and/or inflammation; (2) is at risk for developing MetS and/or inflammation; and/or (3) will respond poorly or will not respond to treatments for symptoms of MetS, such as treatments for obesity or treatments for T2DM. In some embodiments, the decrease in COX4I1 expression and increase in PTGS2 expression in a patient sample as compared with reference measurements indicates that the patient (1) has T2DM; (2) is at risk for developing T2SM; and/or (3) will respond poorly or will not respond to treatments for symptoms of T2DM.

In certain embodiments, the biomarker panel comprises COX4I1 and PTGS1. A decrease in COX4I1 and PTGS1 expression in the patient sample, as compared with reference measurements, may indicate that the patient (1) has T2DM; (2) is at risk for developing T2DM; and/or will respond poorly or will not respond to treatments for T2DM. In certain embodiments, the biomarker panel comprises COX4I1, PTGS1, PTGS2, and SOD2. A decrease in COX4I and PTGS1 expression, and an increase in PTGS2 and SOD2 expression in a patient sample, as compared with reference measurements, may indicate that the patient has obesity, T2DM, MetS, and/or inflammation. This patient may also be at risk for developing T2DM, MetS, and/or inflammation, even if the patient does not yet have other symptoms of a disorder. For example, a patient who is not obese may nonetheless be at risk for developing T2DM. Similarly, a patient may have 0-2 risk factors for MetS, and may be at risk for developing T2DM, MetS, and/or inflammation. Finally, decreased expression of COX4I1 and PTGS1 may indicate that a patient will respond poorly or will not respond to treatments for symptoms of MetS, such as treatments for obesity or treatments for T2DM. In certain embodiments, the biomarker panel comprises COX4I1, PTGS1, and COX10. A decrease in COX4I1, PTGS1, and COX10 expression in a patient sample, as compared with reference measurements, may indicate that the patient (1) has obesity, T2DM, MetS, and/or inflammation; (2) is at risk for developing obesity, T2DM, MetS, and/or inflammation; and/or (3) will respond poorly or will not respond to treatments for symptoms of MetS, such as treatments for obesity or treatments for T2DM. In some embodiments, a decrease in COX4I1, PTGS1, and COX10 expression in a patient sample, as compared with reference measurements, indicates that the patient (1) has T2DM; (2) is at risk for developing T2DM; and/or (3) will respond poorly or will not respond to treatments for T2DM. In certain embodiments, the biomarker panel comprises COX4I1, PTGSl, COXIO, and at least one οτ GPX1, I AK3, PTGS2, RUNX2, SOCS3 and SOD2. A decrease in COX4I1, PTGSl, and COXIO expression, in combination with at least one of a decrease in GPX1 expression, a decrease in IRAK3 expression, an increase in PTGS2 expression, an increase in RUNX2 expression, an increase in SOCS3 expression, and an increase in SOD2 expression in a patient sample, as compared with a reference measurement, may indicate that the patient (1) has obesity, T2DM, MetS, and/or inflammation; (2) is at risk for developing obesity, T2DM, MetS, and/or inflammation; and/or (3) will respond poorly or will not respond to treatments for symptoms of MetS, such as treatments for obesity or treatments for T2DM. Accordingly, in some embodiments the biomarker panel comprises C0X4I1, PTGSl, COXIO, GPX1, IRAK3, PTGS2, RUNX2, SOCS3 and SOD2. In certain embodiments, a decrease in COX4I1, PTGSl, and COXIO expression, in combination with at least one of a decrease in GPX1 expression, a decrease in IRAK3 expression, an increase in PTGS2 expression, an increase in RUNX2 expression, an increase in SOCS3 expression, and an increase in SOD2 expression in a patient sample, as compared with a reference measurement, indicates that the patient (1) has T2DM; (2) is at risk for developing T2DM,; and/or (3) will respond poorly (or will not respond) to treatments for symptoms of T2DM.

In some embodiments, the biomarker panel comprises COX4I1. In certain embodiments, the biomarker panel comprises COX4I1 and COXIO. The biomarker panel may comprise COX4I1, COXIO, and PTGSl. In some embodiments, the biomarker panel comprises COX4I1, COXIO, PTGSl, PTGS2, and SOD2. In these embodiments, decreased expression of COX4I1 or decreased expression of COX4I1 and COXIO, or decreased expression of COX4I1, COXIO, and PTGSl indicates that a patient has T2DM, is at risk for developing T2DM, and/or will respond poorly (or will not respond) to treatments for symptoms of T2DM. In certain embodiments, increased expression of PTGS2 and SOD2 (for example, in combination with decreased expression of COX4I1, with decreased expression of COX4I1 and COXIO, or with decreased expression of COX4I1, COXIO, and PTGSl) indicates that a patient is metabolically unhealthy, has T2DM, is at risk for developing T2DM, and/or will respond poorly (or will not respond) to treatments for symptoms of T2DM.

D. PATIENTS AT RISK

(i) Metabolic Syndrome

Patients at risk for developing MetS may not show symptoms of MetS, or may have none or few of the risk factors for developing MetS. Patients at risk may also have one or two risk factors but have not yet developed MetS. Similarly, patients may be at risk for developing MetS with inflammation, but do not yet show symptoms and/or have other risk factors. The biomarkers disclosed herein may be used to identify patients at risk for developing MetS and/or MetS with inflammation. One aspect of the disclosure relates to a method for identifying a patient at risk for developing metabolic syndrome, comprising obtaining a biological sample from the patient; measuring expression of COX4I1 in the biological sample; and comparing the expression of COX4I1 with reference measurements; wherein decreased expression of COX4I1 in the biological sample as compared to the reference measurements indicates the patient is at risk for developing metabolic syndrome. In some embodiments, the method comprises measuring expression of COX4I1 and at least one of COX10, GPX1, I AK3, PTGS1, PTGS2, RUNX2, SOCS3, SOD2, and UCP2 in the biological sample.

In some embodiments, the method comprises measuring expression of COX4I1 and PTGS2 in a patient sample. For example, the method may comprise obtaining a biological sample from the patient; measuring expression of COX4I1 and PTGS2 in the biological sample; and comparing the expression of COX4I1 and PTGS2 with reference measurements; wherein decreased expression of COX4I1 and increased expression of PTGS2 in the biological sample as compared to the reference measurements indicates the patient is at risk for developing metabolic syndrome.

In some embodiments, the method comprises measuring expression of COX4I1, PTGS2, and at least one of PTGS1 and SOD2 in a patient sample. Accordingly, the method may comprise obtaining a biological sample from the patient; measuring expression of COX4I1, PTGS2, PTGS1, and SOD2 in the biological sample; and comparing the expression of C0X4I1, PTGS2, PTGS1, and SOD2 with reference measurements; wherein decreased expression of COX4I1 and PTGS1 and increased expression of PTGS2 and SOD2 in the biological sample as compared to the reference measurements indicates the patient is at risk for developing metabolic syndrome.

In certain embodiments, the patients at risk of developing MetS or MetS with inflammation are obese. In other embodiments, the patients are not obese. In some embodiments, patients may have at least one of central obesity, high blood pressure, elevated blood cholesterol/low HDL levels, elevated triglyceride levels, and insulin resistance. Patients may have undergone treatment for one or more of these symptoms, such as bariatric surgery for treatment of obesity. In some embodiments, patients have undergone treatment for obesity. In some embodiments, patients have undergone bariatric surgery. Patients may not be diagnosed with MetS or MetS with inflammation, but are at risk. Similarly, patients may have been diagnosed previously, and due to treatment of symptoms no longer have MetS or MetS with inflammation, but are nonetheless at risk for developing MetS or MetS with inflammation again. In some embodiments, the methods described herein are suitable for diagnosing MetS and/or MetS with inflammation.

Type 2 Diabetes (T2DM) Patients at risk for developing T2DM may not show symptoms of T2DM, or may have none or few of the risk factors for developing T2DM. Patients may have been previously treated for T2DM and may be asymptomatic, but still at risk for developing T2DM. In some embodiments, the patients have prediabetes. In the prediabetic state, some but not all of the diagnostic criteria for diabetes are met, for example, the patient may suffer from impaired fasting glycemia (IFG), insulin resistance, or impaired glucose tolerance (IGT). The biomarkers disclosed herein may be used to determine if the patient is metabolically unhealthy. The biomarkers may be used to identify patients at risk for developing T2DM.

One aspect of the disclosure relates to a method for identifying a patient at risk for developing T2DM, comprising obtaining a biological sample from the patient; measuring expression of COX4I1 in the biological sample; and comparing the expression of COX4I1 with reference measurements; wherein decreased expression of COX4I1 in the biological sample as compared to the reference measurements indicates the patient is at risk for developing T2DM. In some embodiments, the method comprises measuring expression of COX4I1 and at least one of COXIO, GPX1, IRAK3, PTGS1, RUNX2, SOCS3, SOD2, and UCP2 in the biological sample. In certain embodiments, the method comprises measuring expression of COX4I1 and at least one of COXIO, PTGS1, PTGS2, and SOD2.

In some embodiments, the method comprises measuring expression of COX4I1 and COXIO in a patient sample. For example, the method may comprise obtaining a biological sample from the patient; measuring expression of COX4I1 and COXIO in the biological sample; and comparing the expression of COX4I1 and COXIO with reference measurements; wherein decreased expression of COX4I1 and COXIO in the biological sample as compared to the reference measurements indicates the patient is at risk for developing T2DM. In some embodiments, the method comprises obtaining a biological sample from the patient; measuring expression of COX4I1 and COXIO, and of PTGS2 and SOD2 in the biological sample; and comparing the expression of COX4I1 and COXIO, and PTGS2 and SOD2 with reference measurements; wherein decreased expression of COX4I1 and COXIO, and increased expression of PTGS2 and/or SOD2 in the biological sample as compared to the reference measurements indicates that the patient is at risk of developing T2DM.

In some embodiments, the method comprises measuring expression of COX4I1 and PTGS1 in a patient sample. For example, the method may comprise obtaining a biological sample from the patient; measuring expression of COX4I1 and PTGS1 in the biological sample; and comparing the expression of C0X4I1 and PTGS1 with reference measurements; wherein decreased expression of COX4I1 and PTGS1 in the biological sample as compared to the reference measurements indicates the patient is at risk for developing T2DM. In some embodiments, the method comprises obtaining a biological sample from the patient; measuring expression of COX4I1 and PTGSl, and of PTGS2 and SOD2 in the biological' sample; and comparing the expression of COX4I1 and PTGSl, and PTGS2 and SOD2 with reference measurements; wherein decreased expression of COX4I1 and PTGSl, and increased expression of PTGS2 and/or SOD2 in the biological sample as compared to the reference measurements indicates that the patient is at risk of developing T2DM.

In some embodiments, the method comprises measuring expression of COX4I1, COX10, and PTGSl in a patient sample. For example, the method may comprise obtaining a biological sample from the patient; measuring expression of COX4I1, COX10 and PTGSl in the biological sample; and comparing the expression of COX4I1, COX10 and PTGSl with reference measurements; wherein decreased expression of COX4I1, COX10 and PTGSl in the biological sample as compared to the reference measurements indicates the patient is at risk for developing T2DM. In some embodiments, the method comprises obtaining a biological sample from the patient; measuring expression of COX4I1, COX10 and PTGSl, and of PTGS2 and SOD2 in the biological sample; and comparing the expression of COX4I1, COX10 and PTGSl, and PTGS2 and SOD2 with reference measurements; wherein decreased expression of COX4I1, COX10 and PTGSl, and increased expression of PTGS2 and/or SOD2 in the biological sample as compared to the reference measurements indicates that the patient is at risk of developing T2DM.

In certain embodiments, the patients at risk of developing T2DM are obese. In other embodiments, the patients are not obese. In some embodiments, patients may have at least one of central obesity, high blood pressure, elevated blood cholesterol/low HDL levels, elevated triglyceride levels, and insulin resistance. Patients may have undergone treatment for one or more of these symptoms, such as bariatric surgery for treatment of obesity. In some embodiments, patients have undergone treatment for obesity. In some embodiments, patients have undergone bariatric surgery. Patients may be diagnosed with insulin resistance, hyperglycemia, prediabetes, or other irregularities in blood sugar regulation, but may not have T2DM. Patients may be at risk for developing T2DM. Similarly, patients may have been diagnosed with T2DM previously, and due to treatment of symptoms no longer have T2DM, but are nonetheless at risk for developing T2DM again. In some embodiments, the methods described herein are suitable for diagnosing T2DM.

Over 75% of hypertension cases are reported to be directly attributable to obesity, and the risk of developing hypertension is five to six times greater in obese adult Americans age 20 to 45 compared to non-obese individuals of the same age. Obesity and IR, and the interaction between these two components, are associated with a high cardiovascular risk (78, 79). Up to 90% of individuals with T2DM are overweight or obese. Obesity-related T2DM is a leading cause of morbidity and mortality in western societies, and is quickly approaching pandemic proportions (80). In addition to heart disease, obesity is reported to increase the risk of ischemic stroke independent of other risk factors, incTutfihg age and systolic blood pressure. The incidence of osteoarthritis increases with BMI and is associated with arthritis of the hand, hip, back and, in particular, the knee. Increased weight adds stress to bones and joints due to increased load. Lastly, there is evidence that some cancers (endometrial, breast and colon) are associated with obesity.

Although obesity and I , and the interaction between these two components, are associated with a high cardiovascular risk (78, 79), the severity of insulinemia and glycaemia during the diabetic phase can only to a minor extent explain this increased cardiovascular risk.

A possible pathogenic mechanism which links obesity with T2DM and with cardiovascular risk is monocyte activation. Indeed, obesity is associated with increased infiltration in the adipose tissue of activated monocytes/macrophages that also produce inflammatory chemokines (15).

Increased oxidative stress causes increased activated monocyte infiltration and is an early instigator of MetS. Several findings support this hypothesis. As an example, we demonstrated that MetS is associated with elevated levels of circulating ox-LDL, a systemic marker of oxidative stress. High triglycerides, low HDL-cholesterol, and high glucose and insulin predicted elevated levels of ox-LDL independent of LDL-cholesterol levels. The association between MetS and elevated levels of oxLDL has been confirmed in European and Japanese cohorts (81-83). Persons with high ox-LDL levels showed a greater disposition to myocardial infarction, adjusting for all established cardiovascular risk factors (10- 13, 84). Two other studies confirmed that elevated levels of circulating ox-LDL predict future cardiovascular events even after adjustment for traditional cardiovascular risk factors and C-reactive protein (85, 86). Recently, we have shown that persons with high ox-LDL showed a 4.5-fold greater disposition to future MetS after 5 years follow-up, adjusted for age, gender, race, study center, cigarette smoking, BMI, physical activity, and LDL-cholesterol, little changed by further adjustment for hs-CRP, and adiponectin. In particular, ox-LDL predicted the development of obesity, dyslipidemia and pre-diabetes. Several studies showed that ox-LDL can induce the activation of monocytes as evidenced by increased capacity of monocytes to infiltrate vascular tissues in response to ox-LDL-induced monocyte chemoattractant protein-1 by endothelial cells, by the ox-LDL-induced activation of toll-like receptor (TLR)-2 and 4-mediated pro-inflammatory response resulting in production of inflammatory cytokines, by the ox-LDL-induced NF-κΒ activation and by the ox-LDL-induced mitochondrial dysfunction resulting in a further enhancement of ROS production (87).

We hypothesized that the identification of a cluster of genes and associated proteins which are associated with monocyte/macrophage activation, as evidenced by their inflammation and oxidative stress state, and of which the expression pattern is improved by weight loss that significantly reduces cardiovascular risk could lead to a better estimate of the risk for cardiovascular disease for obese persons. We started from the observation in obese miniature pigs on an atherogenic diet that toll-like receptor 2 (TLR2) was over expressed in plaque macrophages isolated by laser capture micro dissection and correlated with atherosclerotic plaque complexity (88). Then, we performed micro array analysis of RNA extracted from monocytes of obese women. Because we found that TLR2 was over expressed, we searched for genes that correlated with TLR2. Structural modeling predicted a cluster of genes that besides TLR2 contains the following genes and associated proteins: IL1 receptor-associated kinase 3 (IRAK3), Tumor Necrosis Factor (TNF)-Associated Factor 6 (TRAF6), the myeloid differentiation marker MYD88, TNF-alpha-induced protein 3 and 6 (TNFAIP3; TNFAIP6), the Insulin Receptor Substrate 2 (IRS2), mitogen-activated protein kinase 13 (MAPK13), the Forkhead Box 03A (FOX03A), and superoxide dismutase 2 (SOD2). These genes and associated proteins form the backbone of a pathway that links the toll-like receptor-mediated inflammation with the protection against oxidative stress by means of SOD2. Earlier (WO2009121152) we presented evidence that some of these predicted molecules indeed are novel (bio)markers of cardiovascular risk in association with obesity, lipid homeostasis disorder related cardiovascular disease and/or an impaired glucose tolerance condition and that some are even causal biomarkers.

One aspect of the present disclosure relates to a method for predicting a subject's risk of developing type 2 diabetes mellitus (T2DM), wherein the subject has been diagnosed with metabolic syndrome, diagnosed with metabolic syndrome with inflammation characterized by high levels of C-reactive protein, is obese, has undergone treatment for obesity, has undergone bariatric surgery, or any combination thereof, the method comprising: obtaining a biological sample from the subject, wherein the biological sample is selected from the group consisting of a blood sample, an adipose tissue sample, a white adipose tissue sample, one or more adipocytes from white adipose tissue, one or more activated monocytes from white adipose tissue, macrophages, from white adipose tissue, brown adipose tissue, one or more adipocytes from brown adipose tissue, one or more activated monocytes from brown adipose tissue, macrophages from brown adipose tissue, one or more monocytes, one or more exosomes, monocyte-derived exosomes, and any combination thereof; preparing the biological sample for measurement of gene expression therein; measuring expression of COX4I1 in the thus prepared biological sample, wherein expression comprises gene expression; and comparing the expression of COX4I1 with a reference measurement; wherein decreased expression of COX4I1 in the biological sample as compared to the reference measurement indicates that the subject has an increased risk for developing T2DM. In some embodiments, the method further comprises measuring expression of COX10 in the biological sample and comparing the expression of COX10 with a reference measurement, wherein decreased expression of COX10 in the biological sample as compared to the reference measurement indicates that the subject has an increased risk for developing T2DM. A further aspect of the present disclosure relates to a method for predicting a subject's risk of developing type 2 diabetes mellitus (T2DM), wherein the subject has been diagnosed with metabolic syndrome, diagnosed with metabolic syndrome with inflammation characterized by high levels of C- reactive protein, is obese, has undergone treatment for obesity, has undergone bariatric surgery, or any combination thereof, the method comprising: obtaining a biological sample from the subject, wherein the biological sample is selected from the group consisting of a blood sample, an adipose tissue sample, a white adipose tissue sample, one or more adipocytes from white adipose tissue, one or more activated monocytes from white adipose tissue, macrophages, from white adipose tissue, brown adipose tissue, one or more adipocytes from brown adipose tissue, one or more activated monocytes from brown adipose tissue, macrophages from brown adipose tissue, one or more monocytes, one or more exosomes, monocyte-derived exosomes, and any combination thereof; preparing the biological sample for measurement of gene expression therein; measuring expression of C0X4I1 and COX10 in the thus prepared biological sample, wherein expression comprises gene expression; and comparing the expression of COX4I1 and COX10 with reference measurements; wherein decreased expression of COX4I1 and of COX10 in the biological sample as compared to the reference measurements indicates that the subject has an increased risk for developing T2DM.

In some embodiments, the method further comprises measuring expression of at least one of PTGS1 in the biological sample, and comparing expression of PTGS1 with a reference measurement, wherein decreased expression of PTGS1 and in the biological sample as compared to the reference measurements indicates that the subject has an increased risk for developing T2DM. Another aspect of the present disclosure relates to a method for predicting a subject's risk of developing T2DM, wherein the subject has been diagnosed with metabolic syndrome, diagnosed with metabolic syndrome with inflammation characterized by high levels of C-reactive protein, is obese, has undergone treatment for obesity, has undergone bariatric surgery, or any combination thereof, the method comprising: obtaining a biological sample from the subject, wherein the biological sample is selected from the group consisting of a blood sample, an adipose tissue sample, a white adipose tissue sample, one or more adipocytes from white adipose tissue, one or more activated monocytes from white adipose tissue, macrophages, from white adipose tissue, brown adipose tissue, one or more adipocytes from brown adipose tissue, one or more activated monocytes from brown adipose tissue, macrophages from brown adipose tissue, one or more monocytes, one or more exosomes, monocyte- derived exosomes, and any combination thereof; preparing the biological sample for measurement of gene expression therein; measuring expression of COX4I1, COX10 and PTGS1 in the thus prepared biological sample, wherein expression comprises gene expression; and comparing the expression of COX4I1, COX10 and, PTGS1 with reference measurements; wherein decreased expression of COX4I1, COX10 and PTGS1 in the biological sample as compared to the reference measurements indicates the subject has an increased risk for developing T2DM.

In some embodiments, the method further comprises measuring expression of at least one of PTGS2 and SOD2 in the biological sample, and comparing expression of PTGS2 and SOD2 with a reference measurement, wherein increased expression of PTGS2 and SOD2 in the biological sample as compared to the reference measurements indicates that the subject has an increased risk for developing T2DM.

Yet another aspect of the present disclosure relates to a method for predicting a subject's risk of developing T2DM, wherein the subject has been diagnosed with metabolic syndrome, diagnosed with metabolic syndrome with inflammation characterized by high levels of C-reactive protein, is obese, has undergone treatment for obesity, has undergone bariatric surgery, or any combination thereof, the method comprising: obtaining a biological sample from the subject, wherein the biological sample is selected from the group consisting of a blood sample, an adipose tissue sample, a white adipose tissue sample, one or more adipocytes from white adipose tissue, one or more activated monocytes from white adipose tissue, macrophages, from white adipose tissue, brown adipose tissue, one or more adipocytes from brown adipose tissue, one or more activated monocytes from brown adipose tissue, macrophages from brown adipose tissue, one or more monocytes, one or more exosomes, monocyte- derived exosomes, and any combination thereof; preparing the biological sample for measurement of gene expression therein; measuring expression of C0X4I1, COXlO and PTGS1, and PTGS2 and SOD2 in the thus prepared biological sample, wherein expression comprises gene expression; and comparing the expression of COX4I1, COX10 and PTGS1, and PTGS2 and SOD2 with reference measurements; wherein decreased expression of COX4I1, COX10 and PTGS1 and increased expression of PTGS2 and SOD2 in the biological sample as compared to the reference measurements indicates the subject has an increased risk for developing T2DM.

In certain embodiments of the methods described herein, expression comprises RNA expression. Still another aspect of the present disclosure relates to a method of analyzing a biological sample of a subject, wherein the subject is at risk of developing type 2 diabetes mellitus (T2DM), the method comprising: reacting the biological sample with a first compound to form a first complex, the first complex comprising a COX4I1 expression product and the first compound, and measuring expressfoh' of COX4I1 in the subject.

In some embodiments, the method further comprises reacting the biological sample with a second compound to form a second complex, the second complex comprising a COXIO expression product and the second compound, and measuring expression of COXIO in the subject.

In certain embodiments, the method further comprises reacting the biological sample with a third compound to form a third complex, the third complex comprising a PTGS1 expression product and the third compound, and measuring expression of PTGS1 in the subject.

In some embodiments, the method further comprises reacting the biological sample with a fourth compound to form a fourth complex, the fourth complex comprising a SOD2 expression product and the fourth compound, and measuring expression of SOD2 in the subject.

In certain embodiments, the method further comprises reacting the biological sample with a fifth compound to form a fifth complex, the fifth complex comprising a PTGS2 expression product and the fifth compound, and measuring expression of PTGS2 in the subject. A further aspect of the present disclosure relates to a method of analyzing a biological sample of a subject, wherein the subject is at risk of developing type 2 diabetes mellitus (T2DM), the method comprising: reacting the biological sample with a first compound to form a first complex, the first complex comprising a COX4I1 expression product and the first compound, and measuring expression of COX4I1 in the subject; reacting the biological sample with a second compound to form a second complex, the second complex comprising a COXIO expression product and the second compound, and measuring expression of COXIO in the subject; reacting the biological sample with a third compound to form a third complex, the third complex comprising a PTGS1 expression product and the third compound, and measuring expression of PTGS1 in the subject; reacting the biological sample with a fourth compound to form a fourth complex, the fourth complex comprising a SOD2 expression product and the fourth compound, and measuring expression of SOD2 in the subject; and reacting of the biological sample with a fifth compound to form a fifth complex, the fifth complex comprising a PTGS2 expression product and the fifth compound, and measuring expression of PTGS2 in the subject.

Another aspect of the present disclosure relates to a biomarker panel comprising a solid phase; a first compound bound to the solid phase, which first compound forms a first complex with a COX4I1 expression product. In some embodiments, the biomarker panel further comprises a second compound bound to the solid phase, which second compound forms a second complex with a Cl5 ¾J expression product. In some embodiments, the biomarker panel further comprises a third compound bound to the solid phase, which third compound forms a third complex with a PTGS1 expression product. In some embodiments, the biomarker panel further comprises a fourth compound bound to the solid phase, which fourth compound forms a fourth complex with a SOD2 expression product; and a fifth compound bound to the solid phase, which fifth compound forms a fifth complex with a PTGS2 expression product. In some embodiments, the biomarker panel further comprises a biological sample of a subject diagnosed as suffering from type 2 diabetes mellitus, said biological sample in contact with the first, second, third, and fourth compounds. In some embodiments, the biomarker panel further comprises at least one further compound bound to the solid phase, which further compound forms a further complex with an expression product of a gene selected from the group GPX1, IRAK3, RUNX2, SOCS3, and UCP2. In some embodiments, the biomarker panel further comprises discrete means for detecting each said complex.

E. PREDICTION OF RESPONSE TO TREATMENT (i) Metabolic Syndrome (MetS)

Many patients do not respond to treatments for MetS, indicating that one-size-fits-all treatment regimens for MetS or symptoms of MetS are unlikely to be found. Identification of patients who are likely, or conversely, not likely, to respond to treatments would enable doctors to tailor a specific treatment regimen to a specific patient. The biomarkers described herein may be used to predict a patient's response to treatment for MetS or MetS with inflammation.

One aspect of the disclosure relates to a method for predicting a patient's response to a treatment for at least one symptom of metabolic syndrome, comprising obtaining a biological sample from the patient; measuring expression of COX4I1 in the biological sample; and comparing the expression of COX4I1 with reference measurements; wherein decreased expression of C0X4I1 in the biological sample as compared to the reference measurements indicates that the patient will not respond to the treatment. In some embodiments, the method comprises measuring expression of COX4I1 and at least one of COX10, GPX1, IRAK3, PTGS1, PTGS2, RUNX2, SOCS3, SOD2, and UCP2 in the biological sample.

In some embodiments, the method comprises obtaining a biological sample from the patient; measuring expression of COX4I1 and PTGS2 in the biological sample; and comparing the expression of COX4I1 and PTGS2 with reference measurements; wherein decreased expression of COX4I1 and increased expression of PTGS2 in the biological sample as compared to the reference measurements indicates that the patient will not respond to the treatment. In some embodiments, the mettfocl' comprises measuring expression of COX4I1, PTGS2, and at least one of PTGSl and SOD2 in the biological sample.

Accordingly, a method for predicting a patient's response to a treatment for at least one symptom of metabolic syndrome may comprise obtaining a biological sample from the patient; measuring expression of COX4I1, PTGS2, PTGSl, and SOD2 in the biological sample; and comparing the expression of COX4I1, PTGS2, PTGSl, and SOD2 with reference measurements; wherein decreased expression of C0X4I1 and PTGSl and increased expression of PTGS2 and SOD2 in the biological sample as compared to the reference measurements indicates the patient will not respond to the treatment. In some embodiments, the patient is obese. In other embodiments, the patients are not obese. In some embodiments, patients may have at least one of central obesity, high blood pressure, elevated blood cholesterol/low HDL levels, elevated triglyceride levels, and insulin resistance. Patients may have undergone treatment for one or more of these symptoms, such as bariatric surgery for treatment of obesity. In some embodiments, patients have undergone treatment for obesity. In some embodiments, patients have undergone bariatric surgery. Patients may not be diagnosed with MetS or MetS with inflammation prior to starting treatment. Similarly, patients may have been diagnosed previously, and due to treatment of symptoms no longer have MetS or MetS with inflammation. Methods described herein may determine if these patients will respond to treatment for symptoms of MetS. (ii) Type 2 Diabetes

Like MetS, treatment of T2DM is unpredictable. Many patients do not respond to treatments for T2DM, indicating that one-size-fits-all treatment regimens for T2DM or symptoms of T2DM are unlikely to be found. Identification of patients who are likely, or conversely, not likely, to respond to T2DM treatments would enable doctors to tailor a specific treatment regimen(s) to a specific patient. The biomarkers described herein may be used to predict a patient's response to treatment for T2DM. The biomarkers may also be used to indicate whether a patient is responding to treatment for T2DM.

One aspect of the disclosure relates to a method for predicting a patient's response to treatment for T2DM, comprising obtaining a biological sample from the patient; measuring expression of COX4I1 in the biological sample; and comparing the expression of COX4I1 with reference measurements; wherein decreased expression of COX4I1 in the biological sample as compared to the reference measurements indicates that the patient will not respond to treatment for T2DM. In some embodiments, the method comprises measuring expression of COX4I1 and at least one of COXI ) GPX1, IRAK3, PTGSl, RUNX2, SOCS3, SOD2, and UCP2 in the biological sample.

In some embodiments, the method comprises obtaining a biological sample from the patient; measuring expression of COX4I1 and COXIO in the biological sample; and comparing the expression of COX4I1 and COXIO with reference measurements; wherein decreased expression of COX4I1 and COXIO in the biological sample as compared to the reference measurements indicates that the patient will not respond to treatment for T2DM. In some embodiments, the method comprises obtaining a biological sample from the patient; measuring expression of COX4I1 and COXIO, and of PTGS2 and SOD2 in the biological sample; and comparing the expression of COX4I1 and COXIO, and PTGS2 and SOD2 with reference measurements; wherein decreased expression of COX4I1 and COXIO, and increased expression of PTGS2 and/or SOD2 in the biological sample as compared to the reference measurements indicates that the patient will not respond to treatment for T2DM.

In some embodiments, the method comprises measuring expression of COX4I1 and PTGSl in a patient sample. For example, the method may comprise obtaining a biological sample from the patient; measuring expression of COX4I1 and PTGSl in the biological sample; and comparing the expression of COX4I1 and PTGSl with reference measurements; wherein decreased expression of COX4I1 and PTGSl in the biological sample as compared to the reference measurements indicates the patient will not respond to treatment for T2DM. In some embodiments, the method comprises obtaining a biological sample from the patient; measuring expression of COX4I1 and PTGSl, and of PTGS2 and SOD2 in the biological sample; and comparing the expression of COX4I1 and PTGSl, and PTGS2 and SOD2 with reference measurements; wherein decreased expression of COX4I1 and PTGSl, and increased expression of PTGS2 and/or SOD2 in the biological sample as compared to the reference measurements indicates that the patient will not respond to treatment forT2DM.

In some embodiments, the method comprises measuring expression of COX4I1, COXIO, and PTGSl in a patient sample. For example, the method may comprise obtaining a biological sample from the patient; measuring expression of COX4I1, COXIO and PTGSl in the biological sample; and comparing the expression of COX4I1, COXIO and PTGSl with reference measurements; wherein decreased expression of COX4I1, COXIO and PTGSl in the biological sample as compared to the reference measurements indicates the patient will not respond to treatment for T2DM. In some embodiments, the method comprises obtaining a biological sample from the patient; measuring expression of COX4I1, COXIO and PTGSl, and of PTGS2 and SOD2 in the biological sample; and comparing the expression of COX4I1, COXIO and PTGSl, and PTGS2 and SOD2 with reference measurements; wherein decreased expression of COX4I1, COXIO and PTGSl, and increased expression of PTGS2 and/or SOD2 in the biological sample as compared to the reference measurements indicates that the patieiYtifcril¹ not respond to treatment for T2DM.

In some embodiments, the patient is obese. In other embodiments, the patients are not obese. In some embodiments, patients may have at least one of central obesity, high blood pressure, elevated blood cholesterol/low HDL levels, elevated triglyceride levels, and insulin resistance. Patients may have undergone treatment for one or more of these symptoms, such as bariatric surgery for treatment of obesity. In some embodiments, patients have undergone treatment for obesity. In some embodiments, patients have undergone bariatric surgery. Patients may be diagnosed with insulin resistance, hyperglycemia, prediabetes, or other irregularities in blood sugar regulation, but may not have T2DM. Patients may be at risk for developing T2DM. Patients may not be diagnosed with T2DM prior to starting treatment. Similarly, patients may have been diagnosed previously, and due to treatment of symptoms no longer have T2DM. Methods described herein may determine if these patients will respond to treatment for symptoms of T2DM.

In some embodiments, patients have been diagnosed with T2DM and have been treated for T2DM, and the methods are used to determine if the patient is responding to treatment. For example, a patient undergoing treatment may be monitored periodically. If the biomarker profile indicates a metabolically unhealthy state, as described by the change in expression of the biomarkers described herein, the patient may not be responding to treatment and/or may predicted to not respond to prolongation of the current treatment. Thus, the methods described herein may be used in mid-course, before a treatment regimen is completed, in order to monitor whether the treatment is effective. In some embodiments, patients who have undergone a treatment such as bariatric surgery may be monitored 4 months after treatment, and the biomarker profile as described herein may be used to determine if the patients have responded to the treatment or if the patients remain in a metabolically unhealthy state. In certain embodiments, T2DM may be treated by lifestyle changes, surgery, and/or a medicament. In some embodiments, the lifestyle changes comprise weight loss, increased physical exercise, reduced dietary intake, and/or incorporation of a diet that minimizes diabetogenic foods or components. In certain embodiments, the surgery is bariatric surgery or any form of surgery that reduces the BMI of a patient. In some embodiments, T2DM is treated by a medicament selected from a biguanide, a sulfonylurea, a meglitinide, a D-Phenylalanine derivative, a thiazolidinedione (TZD), a PPAR agonist, a pioglitazone, a DPP-4 inhibitor, an alpha-glucosidase inhibitor, a bile acid sequestrant, insulin, and combinations thereof. For example, the medicament may be rosigltiazone. In some embodiments, the medicament is metformin. For example, a patient who has begun treatment with metformin may be monitored periodically for expression of the biomarkers disclosed herein, in order to deterntfrre^f¹ ' metformin is effective in returning a patient to a metabolically healthy state (for example, a state in which COX4I1, COXIO, and/or PTGSl levels are not decreased, and/or in which PTGS2 and SOD2 levels are not increased as compared with expression levels in a healthy control). In some embodiments, a patient with T2DM may be treated to the extent that diabetes diagnostic criteria (for example, the WHO diabetes diagnostic criteria in which a glucose tolerance test measuring plasma glucose 2 hours after an oral dose shows >11.1 mmol/l or >200mg/dl; or a fasting glucose test measuring fasting plasma glucose shows >7.0 mmol/l or >126 mg/dl) are no longer met. Patients whose condition is considered treated, managed, or in remission using this criteria may still be in a metabolically unhealthy state, and may still be susceptible to relapse. In some embodiments, the biomarkers described herein may be used to determine if a patient who has responded to treatment and who has been treated for T2DM is metabolically unhealthy and still at risk for developing T2DM and/or symptoms of T2DM again. A further aspect of the present disclosure relates to a method for predicting a subject's response to a treatment for type 2 diabetes mellitus (T2DM), wherein the subject has been diagnosed with T2DM, with metabolic syndrome, diagnosed with metabolic syndrome with inflammation characterized by high levels of C-reactive protein, is obese, has undergone treatment for obesity, has undergone bariatric surgery, or any combination thereof, the method comprising: obtaining a biological sample from the subject, wherein the biological sample is selected from the group consisting of a blood sample, an adipose tissue sample, a white adipose tissue sample, one or more adipocytes from white adipose tissue, one or more activated monocytes from white adipose tissue, macrophages, from white adipose tissue, brown adipose tissue, one or more adipocytes from brown adipose tissue, one or more activated monocytes from brown adipose tissue, macrophages from brown adipose tissue, one or more monocytes, one or more exosomes, monocyte-derived exosomes, and any combination thereof; preparing the biological sample for measurement of gene expression therein; measuring expression of COX4I1 in the thus prepared biological sample, wherein expression comprises gene expression; and comparing the expression of COX4I1 with a reference measurement; wherein decreased expression of COX4I1 in the biological sample as compared to the reference measurement indicates that the subject will not respond to the treatment for T2DM.

In some embodiments, the method further comprises measuring expression of COXIO in the biological sample and comparing the expression of COXIO with a reference measurement, wherein decreased expression of COXIO in the biological sample as compared to the reference measurement indicates that the subject will not respond to treatment for T2DM. Another aspect of the present dislcosure relates to a method for predicting a subject's response To treatment for type 2 diabetes mellitus (T2DM), wherein the subject has been diagnosed with T2DM, with metabolic syndrome, diagnosed with metabolic syndrome with inflammation characterized by high levels of C-reactive protein, is obese, has undergone treatment for obesity, has undergone bariatric surgery, or any combination thereof, the method comprising: obtaining a biological sample from the subject, wherein the biological sample is selected from the group consisting of a blood sample, an adipose tissue sample, a white adipose tissue sample, one or more adipocytes from white adipose tissue, one or more activated monocytes from white adipose tissue, macrophages, from white adipose tissue, brown adipose tissue, one or more adipocytes from brown adipose tissue, one or more activated monocytes from brown adipose tissue, macrophages from brown adipose tissue, one or more monocytes, one or more exosomes, monocyte-derived exosomes, and any combination thereof; preparing the biological sample for measurement of gene expression therein; measuring expression of COX4I1 and COX10 in the thus prepared biological sample, wherein expression comprises gene expression; and comparing the expression of COX4I1 and COX10 with reference measurements; wherein decreased expression of COX4I1 and of COX10 in the biological sample as compared to the reference measurements indicates that the subject will not respond to the treatment for T2DM.

In certain embodiments, the method further comprises measuring expression of at least one of PTGS1 in the biological sample, and comparing expression of PTGS1 with a reference measurement, wherein decreased expression of PTGS1 and in the biological sample as compared to the reference measurements indicates that the subject will not respond to treatment forT2DM.

Yet another aspect of the present disclosure relates to a method for predicting a subject's response to treatment for T2DM, wherein the subject has been diagnosed with T2DM, with metabolic syndrome, diagnosed with metabolic syndrome with inflammation characterized by high levels of C-reactive protein, is obese, has undergone treatment for obesity, has undergone bariatric surgery, or any combination thereof, the method comprising: obtaining a biological sample from the subject, wherein the biological sample is selected from the group consisting of a blood sample, an adipose tissue sample, a white adipose tissue sample, one or more adipocytes from white adipose tissue, one or more activated monocytes from white adipose tissue, macrophages, from white adipose tissue, brown adipose tissue, one or more adipocytes from brown adipose tissue, one or more activated monocytes from brown adipose tissue, macrophages from brown adipose tissue, one or more monocytes, one or more exosomes, monocyte-derived exosomes, and any combination thereof; preparing the biological sample for measurement of gene expression therein; measuring expression of COX4I1, COX10 and PTGS1 in the thus prepared biological sample, wherein expression comprises gene expression; and comparing the expression of COX4I1, COXIO and, PTGSl with reference measurements; wfferei i decreased expression of COX4I1, COXIO and PTGSl in the biological sample as compared to the reference measurements indicates the subject will not respond to the treatment for T2DM.

In some embodiments, the method further comprises measuring expression of at least one of PTGS2 and SOD2 in the biological sample, and comparing expression of PTGS2 and SOD2 with a reference measurement, wherein increased expression of PTGS2 and SOD2 in the biological sample as compared to the reference measurements indicates that the subject will not respond to treatment for T2DM.

Still another aspect of the present disclosure relates to a method for predicting a subject's response to treatment for T2DM, wherein the subject has been diagnosed with T2DM, with metabolic syndrome, diagnosed with metabolic syndrome with inflammation characterized by high levels of C-reactive protein, is obese, has undergone treatment for obesity, has undergone bariatric surgery, or any combination thereof, the method comprising: obtaining a biological sample from the subject, wherein the biological sample is selected from the group consisting of a blood sample, an adipose tissue sample, a white adipose tissue sample, one or more adipocytes from white adipose tissue, one or more activated monocytes from white adipose tissue, macrophages, from white adipose tissue, brown adipose tissue, one or more adipocytes from brown adipose tissue, one or more activated monocytes from brown adipose tissue, macrophages from brown adipose tissue, one or more monocytes, one or more exosomes, monocyte-derived exosomes, and any combination thereof; preparing the biological sample for measurement of gene expression therein; measuring expression of COX4I1, COXIO and PTGSl, and PTGS2 and SOD2 in the thus prepared biological sample, wherein expression comprises gene expression; and comparing the expression of COX4I1, COXIO and PTGSl, and PTGS2 and SOD2 with reference measurements; wherein decreased expression of COX4I1, COXIO and PTGSl and increased expression of PTGS2 and SOD2 in the biological sample as compared to the reference measurements indicates the subject will not respond to the treatment for T2DM. In some embodiments of the methods described herein, expression comprises NA expression.

A further aspect of the present disclosure relates to a method of analyzing a biological sample of a subject, wherein the subject has been diagnosed as suffering from type 2 diabetes mellitus (T2DM), the method comprising: reacting the biological sample with a first compound to form a first complex, the first complex comprising a COX4I1 expression product and the first compound, and measuring expression of COX4I1 in the T2DM subject. In some embodiments, the method further comprises reacting the biological sample with a second compound to form a second complex, the second complex comprising a COXIO expression product and the second compound, and measuring expression of COXIO in the T2DM subject.

In some embodiments, the method further comprises reacting the biological sample with a third compound to form a third complex, the third complex comprising a PTGS1 expression product and the third compound, and measuring expression of PTGS1 in the T2DM subject.

In certain embodiments, the method further comprises reacting the biological sample with a fourth compound to form a fourth complex, the fourth complex comprising a SOD2 expression product and the fourth compound, and measuring expression of SOD2 in the T2DM subject. In some embodiments, the method further comprises reacting the biological sample with a fifth compound to form a fifth complex, the fifth complex comprising a PTGS2 expression product and the fifth compound, and measuring expression of PTGS2 in the T2DM subject.

Another aspect of the present disclosure relates to a method of analyzing a biological sample of a subject, wherein the subject has been diagnosed as suffering from type 2 diabetes mellitus (T2DM), the method comprising: reacting the biological sample with a first compound to form a first complex, the first complex comprising a COX4I1 expression product and the first compound, and measuring expression of COX4I1 in the T2DM subject; reacting the biological sample with a second compound to form a second complex, the second complex comprising a COXIO expression product and the second compound, and measuring expression of COXIO in the T2DM subject; reacting the biological sample with a third compound to form a third complex, the third complex comprising a PTGS1 expression product and the third compound, and measuring expression of PTGS1 in the T2DM subject; reacting the biological sample with a fourth compound to form a fourth complex, the fourth complex comprising a SOD2 expression product and the fourth compound, and measuring expression of SOD2 in the T2DM subject; and reacting of the biological sample with a fifth compound to form a fifth complex, the fifth complex comprising a PTGS2 expression product and the fifth compound, and measuring expression of PTGS2 in the T2DM subject.

A further aspect of the present disclosure relates to a biomarker panel comprising: a solid phase; a first compound bound to the solid phase, which first compound forms a first complex with a COX4I1 expression product. In certain embodiments, the biomarker panel further comprises a second compound bound to ftie solid phase, which second compound forms a second complex with a COX10 expression product.

In some embodiments, the biomarker panel further comprises a third compound bound to the solid phase, which third compound forms a third complex with a PTGS1 expression product.

In certain embodiments, the biomarker panel further comprises a fourth compound bound to the solid phase, which fourth compound forms a fourth complex with a SOD2 expression product; and a fifth compound bound to the solid phase, which fifth compound forms a fifth complex with a PTGS2 expression product.

Accordingly, in some embodiments, the biomarker panel further comprises a biological sample of a subject diagnosed as suffering from type 2 diabetes mellitus, said biological sample in contact with the first, second, third, fourth, and fifth compounds.

In certain embodiments, the biomarker panel further comprises at least one further compound bound to the solid phase, which further compound forms a further complex with an expression product of a gene selected from the group GPX1, IRAK3, RUNX2, SOCS3, and UCP2.

In some embodiments, the biomarker panel further comprises discrete means for detecting each said complex.

F. PREPARATION AND USE OF PATIENT SAMPLES AND BIOMARKERS

In some embodiments of the disclosed methods, biological samples from patients are blood samples, or adipose tissue samples. In some embodiments, the blood samples comprise monocytes. Thus, the biological sample may be a monocyte preparation. Expression of biomarkers may comprise expression of the genes or gene products such as RNA. For example, measuring expression of biomarkers such as COX4I1, COX10, GPX1, IRAK3, PTGS1, PTGS2, RUNX2, SOCS3, SOD2, and UCP2 may comprise measuring expression of RNA, for example miRNA, of these genes. In certain embodiments, the reference samples are biological samples obtained from healthy control patients. The samples may also be biological samples obtained from the same patient at an earlier time point, for example, before treatment has begun and/or after symptoms of T2DM, MetS, or MetS with inflammation have improved. Accordingly, if taken at different time points, the biomarkers disclosed herein may be used to monitor the progression of a disorder in the same patient, or SSse^s' the efficacy of a treatment regimen. In some embodiments, the reference sample is a control sample. The control sample may be taken from a healthy control patient who does not suffer from T2DM, such as an age-matched control patient. The control sample may also comprise a pooled set of samples from healthy patients, or reference (i.e., control) measurements based on standards obtained from one or more healthy patients.

In some embodiments, the biological sample is a specific tissue, such as adipose tissue; a specific cell type, such as a monocyte; or a specific subcellular component, such as a microvesicle. In certain embodiments, the biological sample is an exosome. In certain embodiments, the same biomarkers are expressed in all biological samples. In some embodiments, one or more of the biomarkers described herein is expressed in all biological samples. In some embodiments, different subsets of biomarkers are expressed in specific types of biological samples.

In some embodiments, the biological sample is adipose tissue, for example, brown adipose tissue. In certain embodiments, a decrease in COX4I1, COX10, and PTGS1 in the brown adipose tissue as compared to reference measurements indicates that the patient is metabolically unhealthy. Metabolically unhealthy patients may be likely to develop T2DM. This biomarker profile of a metabolically unhealthy patient may also indicate that the patient will not respond to treatment for T2DM.

In certain embodiments, the biological sample is white adipose tissue, and decreased expression of COX4I1 and COX10 in the white adipose tissue as compared to reference measurements indicates that the patient is metabolically unhealthy. Metabolically unhealthy patients may be likely to develop T2DM. This biomarker profile of a metabolically unhealthy patient may also indicate that the patient will not respond to treatment for T2DM. In some embodiments, the biomarker profile in white adipose tissue further comprises decreased expression of Gpx, IRAK3, PTGS1, and SOD3, as compared with reference measurements.

In some embodiments, the biological sample obtained from a patient is adipose tissue, and a decrease in COX4I1, COX10, and IRAK3 expression in a biological sample, as compared with reference measurements, indicates that a patient is metabolically unhealthy. Metabolically unhealthy patients may be likely to develop T2DM. This biomarker profile of a metabolically unhealthy patient may also indicate that the patient will not respond to treatment for T2DM. In certain embodiments, the biomarker profile in adipose tissue further comprises decreased expression of IRAK3. In certain embodiments, the biomarker profile comprising decreased expression of C0X4I1 and COX10 (and optionally, decreased expression of IRAK3) indicates that a patient has T2DM, will likely develop TffiWi, will not respond to treatment for T2DM, and/or is not responding to treatment for T2DM.

In certain embodiments, the biological sample comprises monocyte-derived microvesicles, for example, exosomes, and a decrease in COX4I1 and PTGSl in the monocyte-derived microvesicles, as compared with reference measurements, indicates that a patient is metabolically unhealthy. Metabolically unhealthy patients may be likely to develop T2DM. This biomarker profile of a metabolically unhealthy patient may also indicate that the patient will not respond to treatment for T2DM. In certain embodiments, the biomarker profile comprising decreased expression of COX4I1 and PTGSl indicates that a patient has T2DM, will likely develop T2DM, will not respond to treatment for T2DM, and/or is not responding to treatment for T2DM.

(i) Isolation of monocyte-derived microvesicles from plasma samples

Plasma samples from patients are easy to collect and contain (micro)RNAs (89-92), which have diagnostic potential in MetS and cardiovascular disease (93, 94). The main physiological carrier of plasma (micro)RNAs are microvesicles (MVs) which are small vesicles shed from almost all cell types under both normal and pathological conditions (95, 96). The term 'microvesicles' comprises both exosomes and shedding microvesicles (also called ectosomes or microparticles) (97). Interestingly, MVs bear surface receptors/ligands of the original cells and have the potential to selectively interact with specific target cells. They are involved in cell-to-cell communication including the communication between adipocytes and macrophages and between circulating monocytes and vascular endothelial cells (92, 97). Due to the presence of specific surface receptors/ligands, peripheral blood MVs can be divided in origin-based subpopulations which can be used to determine (micro)RNA expression profiles in MVs derived from one specific cell type (97). In detail, peripheral blood MVs derived from mononuclear phagocyte cell lineage can be detected with anti-CD14, anti-CD16, anti-CD206, anti- CCR2, anti-CCR3 and anti-CCR5 antibodies (91). By labeling the antibodies with a fluorescent group or magnetic particles, these cell-specific MVs can be isolated using FACS or magnetic cell separation technology. Thus unexpected advantages of monocyte-derived exosomes are that they bear the same surface markers as monocytes (e.g. CD14), that they can be purified from plasma, be it fresh or after freezing-thawing cycle(s), using the same methods as used for the purification of (CD14+) monocytes from fresh blood, and that expressions of some RNAs are similar to these in monocytes from which they are derived, whereas expressions of others are different or not detectable. The latter data suggest that these RNAs with similar expressions as in the parent cells are more important for communication with other cell types than RNAs which are not contained in exosomes of parent cells. (ii) Preparation of Reagents Using Biomarkers

The biomarkers described herein may be used to prepare oligonucleotide probes and antibodies that hybridize to or specifically bind the biomarkers mentioned herein, and homologues and variants thereof. (iii) Probes and Primers

A "probe" or "primer" is a single-stranded DNA or NA molecule of defined sequence that can base pair to a second DNA or RNA molecule that contains a complementary sequence (the target). The stability of the resulting hybrid molecule depends upon the extent of the base pairing that occurs, and is affected by parameters such as the degree of complementarity between the probe and target molecule, and the degree of stringency of the hybridization conditions. The degree of hybridization stringency is affected by parameters such as the temperature, salt concentration, and concentration of organic molecules, such as formamide, and is determined by methods that are known to those skilled in the art. Probes or primers specific for the nucleic acid biomarkers described herein, or portions thereof, may vary in length by any integer from at least 8 nucleotides to over 500 nucleotides, including any value in between, depending on the purpose for which, and conditions under which, the probe or primer is used. For example, a probe or primer may be 8, 10, 15, 20, or 25 nucleotides in length, or may be at least 30, 40, 50, or 60 nucleotides in length, or may be over 100, 200, 500, or 1000 nucleotides in length. Probes or primers specific for the nucleic acid biomarkers described herein may have greater than 20-30% sequence identity, or at least 55-75% sequence identity, or at least 75-85% sequence identity, or at least 85-99% sequence identity, or 100% sequence identity to the nucleic acid biomarkers described herein. Probes or primers may be derived from genomic DNA or cDNA, for example, by amplification, or from cloned DNA segments, and may contain either genomic DNA or cDNA sequences representing all or a portion of a single gene from a single individual. A probe may have a unique sequence (e.g., 100% identity to a nucleic acid biomarker) and/or have a known sequence. Probes or primers may be chemically synthesized. A probe or primer may hybridize to a nucleic acid biomarker under high stringency conditions as described herein.

(iv) Diagnosis, prognosis and companion diagnostics

In a preferred embodiment, the invention involves methods to assess quantitative and qualitative aspects of the biomarker gene expression(s), e.g. (micro)RNAs. of which the increased or decreased expression as provided by the disclosure is indicative for the combination of oxidative stress and inflammation in the metabolic syndrome disorder phenotype in a subject associated with increased risk to develop T2DM and/or related cardiovascular diseases in said subject. Techniques well knownTn the art, e.g., quantitative or semi-quantitative RT PCR for instance real time RT PCR, for instance mRNA analysis by the fluorescence-based real-time reverse transcription polymerase chain reaction (qRT-PCR or RT-qPCR) or reverse transcription loop-mediated amplification (RT-LAMP), for instance one-step RT- LAMP, or real-time NASBA for detection, quantification and differentiation of the RNA and DNA targets (98), or Northern blot, can be used.

In a particular embodiment, the analysis techniques include the application of detectably-labeled probes or primers. The probes or primers can be detectably-labeled, either radioactively or nonradioactive^, by methods that are known to those skilled in the art, and their use in the methods according to the invention, involves nucleic acid hybridization, such as nucleic acid sequencing, nucleic acid amplification by the polymerase chain reaction (e.g., RT-PCR), single stranded conformational polymorphism (SSCP) analysis, restriction fragment polymorphism (RFLP) analysis, Southern hybridization, northern hybridization, in situ hybridization, electrophoretic mobility shift assay (EMSA), fluorescent in situ hybridization (FISH), and other methods that are known to those skilled in the art. By "detectably labeled" is meant any means for marking and identifying the presence of a molecule, e.g., an oligonucleotide probe or primer, a gene or fragment thereof, or a cDNA molecule. Methods for detectably-labeling a molecule are well known in the art and include, without limitation, radioactive labeling (e.g., with an isotope such as 32P or 35S) and nonradioactive labeling such as, enzymatic labeling (for example, using horseradish peroxidase or alkaline phosphatase), chemiluminescent labeling, fluorescent labeling (for example, using fluorescein), bioluminescent labeling, or antibody detection of a ligand attached to the probe. Also included in this definition is a molecule that is detectably labeled by an indirect means, for example, a molecule that is bound with a first moiety (such as biotin) that is, in turn, bound to a second moiety that may be observed or assayed (such as fluorescein-labeled streptavidin). Labels also include digoxigenin, luciferases, and aequorin. The disclosure relates to prognosis and diagnosis of metabolic syndrome disorder phenotype and/or T2DM in relation to obesity and prediction of the best therapy to increase resistance to oxidation and thus reduce risk of metabolic syndrome disorder phenotype and/or T2DM (companion diagnostics (summarized in Figure 1)).

(v) Treatment of disorders Detection of the biomarkers described herein may enable a medical practitioner to determine the appropriate course of action for a subject (e.g., further testing, drug or dietary therapy, surgery, no action, etc.) based on the diagnosis. Detection of the biomarkers described herein may also TieVp determine the presence or absence of a syndrome or disorder associated with activated monocytes, early diagnosis of such a syndrome or disorder, prognosis of such a syndrome or disorder, or efficacy of a therapy for such a syndrome or disorder. In alternative aspects, the biomarkers and reagents prepared using the biomarkers may be used to identify therapeutics for such a syndrome or disorder. The methods according to the invention allow a medical practitioner to monitor a therapy for a syndrome or disorder associated with activated monocytes in a subject, enabling the medical practitioner to modify the treatment based upon the results of the test.

In said aspect of the disclosure, it has for example been found that a syndrome or disorder associated with activated monocytes can be treated by administering to a subject in need thereof an effective amount of a therapeutic or a combination of therapeutics that increase(s) or decrease(s) the expression of RNAs (or their protein derivatives) in the monocytes or macrophages or any white blood cell. Said therapeutic may include an agent that increases the expression of COX4I1 (or its protein derivative), and/or RNA (or their protein derivatives) of PTGS1, , and decrease the expression of one or more RNAs (or their protein derivatives) selected form the group consisting of PTGS2, and SOD2.

Syndromes or disorders associated with activated monocytes include (1) non-insulin dependent diabetes mellitus (NIDDM), (2) hyperglycemia, (3) low glucose tolerance, (4) IR, (6) a lipid disorder, (7) dyslipidemia, (8) hyperlipidemia, (9) hypertriglyceridemia, (10) hypercholesterolemia, (11) low HDL levels, (12) high LDL levels, (13) atherosclerosis, (14), MetS, (15) AD and (16) NAFLD and NASH. Non-limiting examples of treatments are adiponectin or an adiponectin mimetic, angiogenesis inhibitor and vascular endothelial growth factor A inhibitor, aspirin, 11-beta hydroxysteroid dehydrogenase inhibitor, calcineurin inhibitors carnitine acetyltransferase stimulant, CD4 antigen antagonist, Cll antigen antagonist, CD45 antigen antagonist, cytokine inhibitor, glinide, glucagon like peptide 1 receptor agonist, immunomodulator, insulin and insulin-like growth factor I stimulant, IL-6 receptor antagonist, I L-17 receptor antagonist, IRAK3 agonist, lipase inhibitor, metformin, neuropeptide Y2 receptor agonist, partial fatty acid oxidation inhibitor, Peroxisome proliferator- activated receptor agonist, sodium channel antagonist, sodium-glucose transporter 2 inhibitor, somatotropin receptor antagonist, T cell activation inhibitor and thyroid hormone receptor beta agonist. The effective amount of a compound, which is required to achieve a therapeutic effect will, of course, vary with the type of therapeutic component, such as small molecules, peptides, etc.; the route of administration; the age and condition of the recipient; and the particular disorder or disease being treated. In all aspects hereof, the daily maintenance dose can be given for a period clinically desiraWe¹ in the patient, for example from 1 day up to several years (e.g. for the mammal's entire remaining life); for example from about (2 or 3 or 5 days, 1 or 2 weeks, or 1 month) upwards and/or for example up to about (5 years, 1 year, 6 months, 1 month, 1 week, or 3 or 5 days). Administration of the daily maintenance dose for about 3 to about 5 days or for about 1 week to about 1 year is typical. Nevertheless, unit doses should preferably be administered from twice daily to once every two weeks until a therapeutic effect is observed.

In addition, the disclosure provides the use of an agent according to any one of the different embodiments hereof in the preparation of a pharmaceutical composition. The compositions of the disclosure, for use in the methods of the disclosure, can be prepared in any known or otherwise effective dosage or product form suitable for use in providing topical or systemic delivery of the therapeutic compounds, which would include both pharmaceutical dosage forms as well as nutritional product forms suitable for use in the methods described herein.

The above-mentioned components may be administrated to induce an increase or a decrease of RNAs or their protein derivatives in myeloid cells in particular in blood monocytes. Such administration can be in any form by any effective route, including, for example, oral, parenteral, enteral, intraperitoneal, topical, transdermal (e.g., using any standard patch), ophthalmic, nasally, local, non-oral, such as aerosol, spray, inhalation, subcutaneous, intravenous, intramuscular, buccal, sublingual, rectal, vaginal, intra-arterial, and intrathecal, etc. Oral administration is preferred. Such dosage forms can be prepared by conventional methods well known in the art, and would include both pharmaceutical dosage forms as well as nutritional products.

A further aspect of the present disclosure relates to a method for treating a patient (or subject) with T2DM, the method comprising: identifying the subject likely to respond to a treatment for T2DM, wherein the subject has been determined to be likely to respond to treatment for T2DM by a method comprising: measuring expression of COX4I1 in a sample from the subject; and comparing the expression of COX4I1 in the subject sample to the expression of COX4I1, COX10 and PTGS1, and PTGS2 and SOD2 in a control sample taken from a control subject; wherein finding equivalent or increased expression of COX4I1 in the subject sample as compared to the control sample determines that the subject is likely to respond to treatment for T2DM; and treating the subject suffering from T2DM, wherein the treatment is selected from lifestyle changes, surgery, and a medicament. In some embodiments, the method further comprises measuring expression of COXIO in the sample from the subject; and comparing the expression of COXIO in the subject sample to the expression of COXIO in a control sample taken from a control subject; wherein finding equivalent or increased expression of COXIO in the subject sample as compared to the control sample determines that the subject is likely to respond to treatment for T2DM; and treating the subject suffering from T2DM, wherein the treatment is selected from lifestyle changes, surgery, and a medicament.

Still another aspect of the present dislcosure relates to a method for treating a subject with T2DM, the method comprising: identifying the subject likely to respond to a treatment for T2DM, wherein the subject has been determined to be likely to respond to treatment for T2DM by a method comprising: measuring expression of COX4I1 and COXIO in a sample from the subject; and comparing the expression of COX4I1 and COXIO in the subject sample to the expression of COX4I1 and COXIO in a control sample taken from a control subject; wherein finding equivalent or increased expression of COX4I1 and COXIO in the subject sample as compared to the control sample determines that the subject is likely to respond to treatment for T2DM; and treating the subject suffering from T2DM, wherein the treatment is selected from lifestyle changes, surgery, and a medicament.

In some embodiments, the method further comprises measuring expression of PTGSl in the sample from the subject; and comparing the expression of PTGSl in the subject sample to the expression of PTGSl in a control sample taken from a control subject; wherein finding equivalent or increased expression of PTGSl in the subject sample as compared to the control sample determines that the subject is likely to respond to treatment for T2DM; and treating the subject suffering from T2DM, wherein the treatment is selected from lifestyle changes, surgery, and a medicament.

Another aspect of the present disclosure relates to a method for treating a subject with T2DM, the method comprising identifying the subject likely to respond to a treatment for T2DM, wherein the subject has been determined to be likely to respond to treatment for T2DM by a method comprising measuring expression of COX4I1, COXIO and PTGSl in a sample from the subject; and comparing the expression of COX4I1, COXIO and PTGSl in the subject sample to the expression of COX4I1, COXIO and PTGSl in a control sample taken from a control subject; wherein finding equivalent or increased expression of COX4I1, COXIO and PTGSl in the subject sample as compared to the control sample determines that the subject is likely to respond to treatment for T2DM; and treating the subject suffering from T2DM, wherein the treatment is selected from lifestyle changes, surgery, and a medicament. In certain embodiments, the method further comprises measuring expression of PTGS2 and S002 m a^' sample from the subject; and comparing the expression of PTGS2 and SOD2 in the subject sample to the expression of PTGS2 and SOD2 in a control sample taken from a control subject; wherein finding equivalent or decreased expression of PTGS2 and SOD2 in the subject sample as compared to the control sample determines that the subject is likely to respond to treatment for T2DM; and treating the subject suffering from T2DM, wherein the treatment is selected from lifestyle changes, surgery, and a medicament.

A further aspect of the present disclosure relates to a method for treating a subject with T2DM, the method comprising: identifying the subject likely to respond to a treatment for T2DM, wherein the subject has been determined to be likely to respond to treatment for T2DM by a method comprising measuring expression of COX4I1, COXlO and PTGS1, and PTGS2 and SOD2 in a sample from the subject; and comparing the expression of COX4I1, COX10 and PTGS1, and PTGS2 and SOD2 in the subject sample to the expression of COX4I1, COX10 and PTGS1, and PTGS2 and SOD2 in a control sample taken from a control subject; wherein finding equivalent or increased expression of COX4I1, COX10 and PTGS1, and equivalent or decreased expression of PTGS2 and SOD2 in the subject sample as compared to the control sample determines that the subject is likely to respond to treatment for T2DM; and treating the subject suffering from T2DM, wherein the treatment is selected from lifestyle changes, surgery, and a medicament.

In some embodiments, the treatment is selected from a lifestyle change that is weight loss. In certain embodiments, the treatment is selected from a surgery that is bariatric surgery. In some embodiments, the treatment is a medicament is selected from a biguanide, a sulfonylurea, a meglitinide, a D- Phenylalanine derivative, a thiazolidinedione (TZD), a PPA agonist, a pioglitazone, a DPP-4 inhibitor, a alpha-glucosidase inhibitor, a bile acid sequestrant, insulin, and combinations thereof. For example, the medicament may be rosiglitazone. The treatment may be metformin.

EXAMPLES

Having provided a general disclosure, the following examples help to illustrate the general disclosure. These specific examples are included merely to illustrate certain aspects and embodiments of the disclosure, and they are not intended to be limiting in any respect. Certain general principles described in the examples, however, may be generally applicable to other aspects or embodiments tff^"tfi^"e disclosure.

Example 1: Biomarkers Associated with Obesity in monocytes

We tested the reproducibility of RNA analysis in monocytes of 13 healthy individuals. Blood samples were collected at week 0, week 1 and week 2. Blood monocytes were isolated, and RNA was extracted and expressions of COXIO, COX4I1, GPXl, IRAK3, PTGSl, PTGS2 and SOD2 were measured by RT-PCR. Mean areas under de curves (AUC) were 0.56 for COXIO, 0.54 for COX4I1, 0.53 for GPXl, 0.54 IRAK3, 0.53 for PTGSl, 0.62 for PTGS2 and 0.65 for superoxide dismutase SOD2 (AUC=0.50 means that there is no evidence that the data obtained with the test distinguish between groups). Mean within-group variability was 14% for COXIO, 12% for COX4I1, 17% for GPXl and IRAK3, 18% for PTGSl, and 24% for SOD2.

Compared with lean controls, obese persons without clinically diagnosed T2DM had more often MetS (30). They more were more often treated with diuretics. They had higher blood levels of leptin, insulin, IL-6, and hs-CRP. They also had higher DBP and higher HOMA-IR. They had lower levels of ADN and HDL-C. Compared with lean controls, obese patients with clinically diagnosed T2DM were older and had more often MetS. They were more frequently treated with statin, anti-hypertensive drugs like ACE inhibitors, angiotensin receptor blockers, β-blockers, calcium channel blockers or diuretics, and metformin and insulin. They had higher blood levels of leptin, IL-6, and hs-CRP. They also had higher DBP, combined with higher SBP. In addition, they had higher levels of glucose and insulin, and thus higher HOMA-IR, and higher TG. They had lower levels of ADN and HDL-C, and of LDL-C and ox-LDL. Lower LDL-C and ox-LDL was most likely due to more frequent statin use. Compared with obese persons without T2DM, obese patients with clinically diagnosed T2DM were older and had more often MetS. They were more frequently treated with statin, anti-hypertensive drugs, and metformin and insulin. They had higher blood levels of leptin, despite lower BMI. In contrast, ADN levels were not different. They had higher glucose, insulin, HOMA-IR, and higher SBP. They had lower HDL-C (Table 2A).

Previously, we performed microarray analysis of RNA extracts from monocytes of 14 obese women. We found 592 genes which were deregulated compared to age-matched controls. Especially genes which mediate cell-to-cell signaling and immune response were deregulated. They are known to be involved in the development of MetS, T2DM, cardiovascular, haematological, immunological and neurological diseases, and in cancer (50) (patent EP2260301). We validated the microarray data by qPCR in an independent cohort. Data showed a relation of low IRAK3 with obesity. IRAK3 was found to be down-regulated in monocytes of obese persons prior to T2DM and cardiovascular diseases, tow IRAK3 was associated with high TNFa, indicating high inflammation, and high SOD2, indicating oxidative stress. In here, we not only confirm the association between low IRAK3 and high SOD2, but also show a decrease in COX10 and GPX1 in relation with a decrease in IRAK3, further supporting its association with oxidative stress. In addition, we found a relation between a decrease in IRAK3 and an increase in SOCS3, an inhibitor of JAK/STAT pathways. Down-regulation of IRAK3 was also found to be associated with an increase in PTGS2, a regulator of prostaglandin synthesis that has been found to be associated with biologic events such as injury, inflammation, and cell proliferation. In contrast, PTGS1 was decreased. The expression of RUNX2, a Runt-related transcription factor which was found to be associated with vascular calcification and atherosclerosis, was higher in monocytes of obese persons. Finally, expression of UCP2, a regulator of mitochondrial uncoupling, was higher (Table 2B). PCA analysis of the data set separating lean controls vs. obese persons identified six components explaining ~ 80% of the variation in the data. The first component (IRAK3) takes up about 35% of variation. The highest loadings have IRAK3 and GPX1 with positive values and PTGS2, SOCS3, and RUNX2 with negative values. This could mean an inverse correlation of the latter to IRAK3 (and GPX1). The relation of IRAK3 with SOCS3 is seen in the network as part of the TLR2 signaling. The other relations are most probably indirect and may involve regulation of the transcription factor activity of RUNX2.

ROC curves were then used to determine the impact of genetic information on individual classification according to obesity. COX10, IRAK3, GPX1, PTGS2, SOCS3 and SOD2 were strongly related with obesity (Table 3A). The AUC of COX10, IRAK3, and GPX1 were above 0.80; these of PTGS2, SOCS3 and SOD2 were even above 0.90. We used the optimal cut points determined by ROC analysis to count the number of "positive" and "negative" participants among lean controls and obese persons, and to perform Fisher's exact test. The sensitivity and specificity of COX10, PTGS2 and SOD2 were higher than 80%. GPX1 had high sensitivity (above 80%) but lower specificity (below 80%); IRAK3 and SOCS3 had high specificity (above 90%) but low sensitivity (Table 3A).

In conclusion, obesity is associated with decreased expressions of COX10, GPX1, and IRAK3, and increased expressions of PTGS2, SOCS3, and SOD2 (Figure 2).

Compared with lean controls, obese patients with T2DM had also low COX10, GPX1, IRAK3, and PTGS1, and high PTGS2, RUNX2, SOCS3, and SOD2. In addition, COX4I1 was decreased. COX4I1, PTGS1, and UCP2 were lower in obese patients with than those without T2DM (Table 2B). AUC of COX4I1 and PTGS1 were higher than 0.70. Fisher's exact test, using cut points determined by ROC analysis, showed that sensitivity of COX4I1 and PTGS1 were above 70%, similar to that of glucose. More importantly, this test showed an additive value of COX4I1 and PTGS1 (OR increased form 6.6 and 4.8, respectively, to 20), of COX4I1 and glucose (OR increased from 6.6 and 9.5 to 45), and of PTGSl and gluco 'itW increased from 4.8 and 9.5 to 24). The sensitivity and specificity of the combination of COX4I1 and glucose, and of PTGSl and glucose was above 80% (Table 3B).

In addition, we performed stepwise, multiple logistic regression analysis including all genes that were differentially expressed in monocytes of obese persons. The combination of COX4I1, PTGSl, and PTGS2, and SOD2 predicted the best T2DM. It predicted 79% of controls (not having T2DM) and 81% of cases (having T2DM) correctly. Thus overall prediction was 80%. After adjusting for BMI and glucose only COX4I1 predicted T2DM. Finally, a model to predict diabetic patient using multi-layer perceptron (71.3% learning and 28.7 testing) and contained COX4I1, SOD2, PTGS2, and PTGSl. In conclusion, the T2DM state of an obese patient can be determined by counting the number of deregulated genes (or derived proteins) related to resistance to oxidation in his/her monocytes. Deregulation means low expression of COX4I1, PTGSl, and/or high expression of PTGS2, and SOD2 (Figure 2).

We further studied the association between gene expressions and hyperglycemia, combining lean and obese subjects (Table 4). Hyperglycemic patients were older, were more frequently obese and had more often MetS. They more frequently used statin, antihypertensive drugs, oral antidiabetics and insulin. Their BMI was slightly higher, and blood levels of leptin were similar. Hyperglycemic patients had higher glucose, insulin, HOMA-IR, TG, and IL-6, and SBP. They had lower ADN, HOMA%b, and HDL- C. They also had lower COX10, COX4I1, and PTGSl. ROC analysis and Fisher's exact test confirmed that COX10, COX4I1, and PTGSl were associated with hyperglycemia (Table 5). However, associations of COX4I1, and PTGSl with hyperglycemia were weaker than with T2DM. Interestingly, the latter revealed an additive value of the combination of COX4I1 and PTGSl (OR increased from 3.1 and 4.6, respectively, to 11), and of COX10 and PTGSl (OR increased from 2.9 and 4.6, respectively, to 7.9) and of the combination COX10, COX4I1, and PTGSl (OR of the combination 14).

In conclusion, the "prediabeticity" (or prediabetic state) of an obese patient can be determined by counting the number of deregulated genes (or derived proteins) related to resistance to oxidation in his/her monocytes. Deregulation means low expression of COX4I1 and PTGSl, and/or COX10.

We noticed that although COX4I1 and PTGSl were not different between lean controls and obese patients they both were different between obese with and without T2DM, and with and without prediabetes. Because the metabolic disorder phenotype was previously found to increase the risk of T2DM, we compared persons with and without the metabolic syndrome disorder phenotype (TaBfe' 6A). Patients with the metabolic syndrome disorder phenotype were older, were more frequently obese and had more often MetS and T2DM. They more frequently used statin, antihypertensive drugs, oral antidiabetics and insulin. Their BMI and blood levels of leptin were higher. They also had higher glucose, insulin, HOMA-IR, TG, and IL-6, hs-CRP, and SBP. They had lower ADN, and HDL-C. They also had lower COX10, COX4I1, GPX1, IRAK3, and PTGS1, and higher levels of PTGS2, SOCS3 and SOD2 (Table 6B)

ROC analysis and Fisher's exact test confirmed that they all were associated with the metabolic syndrome disorder phenotype (Table 7). In addition, we performed stepwise multiple logistic regression analysis including all genes which were differentially expressed in monocytes of obese persons. The combination of COX4I1, PTGS1, PTGS2, and SOD2 together with hs-CRP predicted the best the metabolic syndrome disorder phenotype. The model predicted 76% of controls (not having the metabolic syndrome disorder phenotype) and 92% of cases (having the metabolic syndrome disorder phenotype) correctly. Thus overall prediction was 84%. This model remained predicting the metabolic syndrome phenotype after adjusting for BMI and glucose. Finally, a model to predict diabetic patient using multi-layer perceptron (70.5% learning and 29.5 testing) and contained PTGS1, SOD2, COX4I1, and PTGS2.

In conclusion, the metabolic syndrome disorder phenotype can be determined by counting the number of deregulated genes (or derived proteins) related to resistance to oxidation in his/her monocytes. Deregulation means low expression of COX4I1, PTGS1, and/or high expression of PTGS2, and SOD2 (Figure 2).

Example 2: Prognosis of response to treatment

Bariatric surgery is a frequent treatment of morbid obesity. To further establish the relation of gene expressions in monocytes with obesity, we studied 17 morbid obese patients before and 4 months and 7 years after bariatric surgery (Table 8A).

Their characteristics were similar to those of our larger group of obese patients. Expressions of COX10, COX4I1, GPX1 and IRAK3 were lower; SOD2 was higher (Table 3B). Interestingly, a comparable expression profile was also detected in visceral adipose tissue of obese subjects compared with adipose tissue of lean controls. COX10 (0.88±0.16 vs. 1.12±0.23; P < 0.05; n=17 and 6, respectively), GPX1 (0.76±0.15 vs. 1.2110.23; P < 0.01) and IRAK3 (0.70±0.11 vs. 1.08±0.27; P < 0.01), but not PTGS1 (1.18±0.45 vs. 1.51±0.49; trend to lower value), were significantly lower in visceral adipose tissues from obese persons than in lean controls. In addition, COX4I1 (0.79±0.13 vs. 1.18±0.30; P < 0.05) was RJw¾r: Lower values were associated with impaired adipose tissue differentiation evidenced by lower expressions of adiponectin (ADIPOQ, 0.68±0.21 vs. 1.43±0.50; P < 0.05), glucose transporter (GLUT)-4 (0.37±0.19 vs. 1.3210.53; P < 0.01), peroxisome proliferator-activated receptor (PPAR)-a (0.65±0.12 vs. 1.0910.37; P < 0.01) and PPAR6 (0.83±0.12 vs. 0.99±0.10; P < 0.01), and a trend to lower PPARy (0.79±0.21 vs. 1.13±0.56).

At 4 months, weight loss and decrease of BMI was associated with a decrease in leptin, glucose, insulin, TG, and SBP, and DBP, and an increase in ADN. However, NA expressions were not changed (Table 8B). There was a trend to a decrease in number of patients with MetS (from 71 to 35%). At 7 years, weight loss was associated with a decrease of leptin, glucose, insulin, HOMA-IR and HOMA %b, TG, and ox-LDL, and an increase of ADN and HDL-C. Interestingly, glucose, insulin, HOMA-IR, ADN, TG, hs-CRP, and ox-LDL were not anymore different from these in lean controls; but BMI and leptin were still higher than in lean controls. Furthermore, the use of antihypertensive and oral antidiabetic drugs was not different after surgery (Table 8A). The increase in IRAK3, that was even higher than in lean controls, was associated with a decrease in PTGS2, RUNX2, and SOCS3, and an increase in GPX1. Importantly, weight loss did not normalize COX10 that even decreased further. In addition, COX4I1, a component of the terminal enzyme complex of the mitochondrial electron transport chain and regulator of mitochondrial oxidative stress was lower after follow-up. Finally, UCP2 was lower than before surgery than in lean controls (Table 8B). The number patients with MetS tended to increase again (from 35 to 53%).

In addition, the number of patients with the metabolic syndrome phenotype tended to decrease first (at 4 months) but tended to be higher again at 7 years. Therefore, we determined if RNA expressions at 4 months and/or the difference in RNA expressions between 4 months and baseline predicted the presence of the metabolic syndrome disorder phenotype at 7 years. Figure 3 shows that RNA expressions of COX4I1 and PTGS2 at 4 months, and the difference in PTGS2 were different between patients who did not or did develop the metabolic syndrome disorder phenotype (MUH). In obese patients who had T2DM COX10 decreased between 4 months and 7 years (-0.1210.18), whereas in patients who did not have T2DM COX10 increased (0.17±0.16; P = 0.01 for difference).

Stepwise multiple regression analysis confirmed that COX4I1 and PTGS2 at 4 months predicted the presence of MUH. They predicted 88% of controls (not having the metabolic syndrome disorder phenotype) and 100% of cases (having the metabolic syndrome disorder phenotype) correctly. Thus, overall prediction was 94%. Also the difference in PTGS2 expression, after adjusting for the difference in COX4I1, between 4 months and baseline predicted future metabolic syndrome disorder phenotype. It predicted 75% of controls (not having the metabolic syndrome disorder phenotype) and 89% of¾ases (having the metabolic syndrome disorder phenotype) correctly. Thus, overall prediction was 82%.

Several pharmacological treatments were found to be associated with improved expression of some but not all markers within our cluster. T2DM patients treated with a statin had lower levels of SOCS3 than T2DM patients who were not treated (2.32±0.85 vs. 3.02±1.04; P = 0.014). Patients treated with metformin had higher levels of COX10 (0.79±0.16 vs. 0.66±0.14; P = 0.004) and showed a trend to higher PTGSl (0.79±0.15 vs. 0.71±0.11; P = 0.054). Patients treated with oral antidiabetic drugs other than metformin had lower levels of SOD2 (2.01±0.45 vs. 2.60±1.46; P = 0.011). Patients treated with calcium antagonists had lower levels of PTGS2 (5.34+2.31 vs. 8.1415.52; P = 0.006), SOCS3 (2.1010.77 vs. 2.65+0.97; P = 0.027), and SOD2 (1.8310.83 vs. 2.74+1.44; P = 0.004). Importantly, none of these treatments were associated with an increase of COX4I1.

But, stepwise multiple regression analysis suggested also adverse associations with treatment in association with blood levels. The model contained blood levels (ADN, glucose, insulin, leptin, HDL-C, TG, IL-6, and hs-CRP), and treatment (ACE inhibitors, β-blocker, calcium channel antagonists, insulin, and metformin). COX10 was predicted by insulin treatment (-) and hs-CRP (-) (F ratio: 11.8; P<0.0001). C0X4I1 was predicted by β-blocker treatment (-) and insulin (-) (13.2; <0.0001). GPXl was predicted by leptin (-) and metformin treatment (-) (9.9; P=0.0001). IRAK3 was predicted by leptin (-) and TG (-) (13.8; PO.0001). PTGSl was predicted by β-blocker treatment (-), glucose (-) and insulin treatment (- ), and hs-CRP (11.1; P<0.0001). PTGS2 was predicted by insulin treatment (+) and hs-CRP (+) (6.0; P<0.01). SOCS3 was predicted by leptin (+) and hs-CRP (+) (23.8; P<0.0001). There were no significant predictors of RUNX2 and of SOD2. In aggregate, our data support a dysregulation of RNA expressions by factors related to hyperglycemia and IR (glucose and insulin), inflammatory markers (hs-CRP and leptin), and factors related to dyslipidemia (TG). In addition, dysregulation of RNA expression was found to be related to several treatments. In addition, stepwise multiple regression analysis was performed to determine the relation between RNA expressions within the cluster of selected genes. COX10 was predicted by COX4I1 (+), IRAK3 (+), and PTGSl (+) (F ratio: 39.0; P<0.0001). COX4I1 was predicted by COX10 (+), GPXl (+), IRAK3 (+), PTGSl (+), SOCS3 (-), and SOD2 (-) (33; P<0.0001). IRAK3 was predicted by GPXl (+), and by RUNX2 (-) and SOCS3 (-) (14.9; PO.0001). PTGSl was predicted by IRAK3 (+), COX10 (+), COX4I1 (+), and SOCS3 (-) (26.3; P<0.0001). PTGS2 was predicted by COX10 (-), and by SOCS3 (+) and SOD2 (+) (59.9; P<0.0001). RUNX2 was predicted by IRAK3 (-), SOD2 (+), and COX4I1 (-) (9.7; P<0.0001). SOCS3 was predicted by IRAK3 (-), PTGSl (-), and PTGS2 (+) (26.7; P<0.0001). SOD2 was predicted by COX10 (-), COX4I1 (+), and RUNX2 (+) and PTGS2 (51.0; P<0.0001). Thus, our data suggest interactions in the regulation of genes within our selected

combination of COX4I1 and PTGS1 gives us information about effects dependent on COXIO, IRAK3, GPXl, SOCS3, SOD2, glucose, insulin and treatment. Addition of I RAK3 will add information about the effects of GPXl, RUNX2, leptin, and TG. Addition of COXIO will add information about the effects of hs-CRP.

Therefore, the selected prognostic markers may also be useful for predicting response of other treatments than bariatric surgery.

Example 3: Deregulation of cluster of genes in adipose tissues of obese mice and reversal of this cluster by treatment with PPAR agonists and weight loss Figure 4 shows that obese diabetic DKO mice (C57BL6 background) had greater weight, lower glucose tolerance (evidenced by higher AUC of IPGTT) and insulin resistance (evidenced by higher HOMA-IR) compared to lean C57BL/6J control mice. They also had lower blood adiponectin and higher blood triglyceride and cholesterol levels. RNA expressions of CoxlO, Cox4/^'l, Gpxl, and Irak3, Ptgsl and Sod3 (predominant superoxide dismutase in adipose tissue) were lower in white visceral adipose tissues of DKO mice (Figure 5). Adipose tissue differentiation was impaired in obese diabetic mice, evidenced by lower expression of Ad 'ipoq (0.090±0.039vs. 1.03±0.26; P < 0.001), Glut4 (0.13±0.061 vs. 1.01+0.47), Ppara (0.21±0.072 vs. 1.01+0.47), Ppar6 (0.56±0.07 vs. 0.99±0.24) and Ppary (0.22±0.069 vs. 1.02±0.23) (P < 0.001 for all). This was associated with increased expression of Cc/2 (Mcpl), reflecting increased monocyte attraction (5.76±0.045 vs. 1.28±0.045; PO.001). Only diet restriction lowered weight. Only rosiglitazone improved glucose tolerance, decreased insulin resistance, and increased adiponectin; the latter even beyond levels in lean mice. Only diet restriction reduced triglycerides. None of the treatments reduced cholesterol (Figure 4).

Rosiglitazone treated mice had the highest expressions of CoxlO and Cox4il of all DKO mice; their expressions in diet-restricted mice were no longer different from these in lean control mice. Expressions of Gpxl, Irak3, Ptgsl and Sod3 in diet restricted and rosiglitazone treated mice were also not different from these in lean mice. Diet restricted mice had the highest Irak3 and Ptgsl (Figure 5). Effects of treatments on adipose tissue differentiation were different. Diet restriction and rosiglitazone treatment increased Adipoq (0.62±0.18 and 0.61±0.15 vs. 0.14+0.08), Glut4 (0.47±0.09 and 0.60±0.15 vs. 0.13±0.06), and Ppary (0.4710.09 and 0.60±0.15 vs. 0.13±0.06). They all increased Ppara, but diet restriction and rosiglitazone more than fenofibrate (1.38±0.27, 1.83±0.61, and 0.37±15). In addition, all treatments increased Ppar6 (0.9510.19, 0.7510.13, and 0.6610.10). Overall, these data indicate that diet restriction and rosiglitazone treatment more than fenofibrate improved adipose ti&ile¹ differentiation. This was associated with lower expression of Cc/2 (Mcpl) in diet restricted and rosiglitazone treated mice (2.35±0.86 and 3.05±0.031), reflecting lower monocyte attraction. Fenofibrate treatment did not decrease Cc/2 (4.32±0.023). Linear regression analysis showed that CoxlO and Cox4il were highly related (Rs=0.77; P < 0.001). CoxlO correlated strongly with Gpxl (R_s=0.57; P < 0.001), Irak3 (R_s=0.44; P < 0.001), Ptgsl (R_s=0.53; P < 0.001), and Sod3 (R_s=0.77; P < 0.001). Cox4il also correlated strongly with Gpxl (R_s=0.71; P < 0.001), Irak3 (R_s=0.44; P < 0.001), Ptgsl (R_s=0.48; P < 0.001), and Sod3 (R_s=0.34; P < 0.01). These data suggest that genes are clustered. All genes correlated with blood adiponectin: Rs values were 0.56 (P < 0.001) for CoxlO, 0.79 (P < 0.001) for Cox4il, 0.56 (P < 0.001) for Gpxl, 0.44 (P < 0.01) for Irak3, 0.31 for Ptgsl, and 0.60 for Sod3 (P < 0.001). These data confirmed their association with metabolic healthy state of adipose tissues. But only CoxlO, Cox4il and Gpxl were inversely related to glucose intolerance: Rs values were -0.51 (P < 0.001) for CoxlO, -0.66 (P < 0.001) for Cox4il, -0.52 (P < 0.001) for Gpxl. All genes except CoxlO were inversely related with blood triglycerides: -0.37 (P < 0.01) for Cox4il, -0.64 (P < 0.001) for Gpxl, -0.63 (P < 0.001) for Irak3, -0.50 for Ptgsl, and -0.37 for Sod3 (P < 0.001).

In aggregate, our observations in DKO mice show that obesity-induced lower ADN, hyperglycemia and insulin resistance is associated with increased resistance to oxidative stress evidenced by changed in expressions of markers selected on the basis of observations in monocytes. These data also indicate that our selected markers can be used to compare effects of several treatments on resistance to oxidative stress. These data further support interactions in the regulation of genes within our selected cluster.

On basis of these data, the selected markers may be useful to measure resistance to oxidation in other tissues than blood cells, more particular monocytes. Example 4: Brown adipose tissue (BAT) and impaired resistance to oxidative stress

Figure 6 shows that even in a thermoneutral environment impaired mitochondrial uncoupling due to Ucpl deletion was associated with decreased expression of CoxlO and Cox4il, and Ptgsl. At lower temperature, CoXlO and Cox4il were lower in Ucpl KO mice; Ptgsl was not different. The lower expressions of CoxlO and Cox4il in brown adipose tissues were independent of differences in Gpxl, Irak3, and Sod3. The decrease in CoxlO and Cox4il was associated with increased Mcpl indicating higher macrophage Ml polarization. Mcpl expressions at 30°C were 0.98±0.38 in C57BL6 and 3.60±1.05 in Ucpl KO mice; expressions at room temperature were 3.00+0.90 vs. 8.40±3.90 (P ANDVA^' < 0.001). Ucpl KO mice had lower blood adiponectin levels: 5.17±0.46 vs. 7.02+1.24 μg/ml at 30°C, and 4.70±1.28 vs. 7.34±2.44 μg/ml at room temperature. Lower adiponectin levels are indicative of metabolic unhealthy state in adipose tissues. These differences were independent of differences in weight, and cholesterol levels. Ucpl KO mice had higher insulin levels (36±15 vs. 15±4.2 mU/l) at 30°C; but insulin levels were similar at room temperature (66±27 vs. 66±34 mU/l). Glucose levels were not different. The differences in gene expressions in BAT were also independent of differences in gene expressions in WAT. For example, CoxlO expression was 1.00±0.044 in Ucpl KO mice compared to 1.00±0.20 in wild-type mice. Expressions of Cox4il were 1.00±0.11 and 1.00±0.11, respectively. On basis of all our data lowered expressions of CoxlO and of Cox4il, and possibly Ptgsl in the absence of decreased expressions of Gpxl, Irak3, and Sod3 may reflect mitochondrial uncoupling and metabolic unhealthy state of BAT. Lowered expressions of CoxlO and of Cox4il together with decreased expressions of Gpxl, Irak3, Ptgsl and Sod3 reflects metabolic unhealthy state of WAT.

Example 5: Deregulation of cluster of genes in adipose tissues of mice with T2DM induced by streptozotocyn and high-fat diet (HFD)

Streptozotocin (STZ) injection followed by HFD-feeding resulted in an increase of weight from 12.5±1.9 g at 4 weeks, to 16.7±1.2 g at 8 weeks and 22±6.2 at 12 weeks. The weight of control mice was higher at 4 weeks (21±1.2 g; P<0.05) and at 8 weeks (27.4±1.2 g; p<0.05) compared to STZ mice. Weight was similar at 12 weeks (24.2±2.3 g). The homeostatic model assessment of insulin resistance (HOMA-IR) of STZ mice was higher at 8 and 12 weeks compared to age-matched control mice. HOMA-IR of STZ mice at 12 weeks was higher than at 4 weeks. Adiponectin blood levels of STZ mice were higher at 4 weeks compared to control mice, but decreased at 8 and 12 weeks; but they were never lower than in control mice. Triglyceride (TG) levels were higher in STZ mice at 12 weeks, compared to control and STZ mice at 4 weeks. Total cholesterol (TC) levels were higher in STZ mice at 8 and 12 weeks, compared to control and STZ mice at 4 weeks. There were no age-dependent changes in HOMA-IR, adiponectin, TG and TC in control mice. In aggregate, these data indicate that differences in IR and diabetes between control and STZ mice were not due to overweight and reduction in adiponectin. Adipose tissue differentiation was impaired in STZ diabetic mice, evidenced by lower expression of Glut4 at 8 and 12 weeks, Ppara at 12 weeks, and Ppar6 at 8 and 12 weeks. Ppary and Adipoq were not different compared to control mice (Figure 7). Cc/2 (Mcpl) was increased, reflecting increased monocyte attraction, at 8 weeks (3.56±2.10 compared to 1.09±0.64 in control and 1.00±0.68 in STZ mice at 4 weeks; P<0.01 vs. control and P<0.05 vs. STZ mice at 4 weeks), and at 12 weeks (3.87±2.80; P<0.01 vs. control and P<0.05 vs. STZ mice at 4 weeks). There were no age-dependent changes in expressions of markers of adipose tissue differentiation and inflammation in control mice (Figure 8). CoxlO, xiii,¹ and Irak3 were lower in STZ diabetic mice at 12 weeks. CoxlO and Irak3 were also lower in STZ mice at 12 weeks compared to 4 weeks. Ptgsl in adipose tissue of STZ mice was higher than in control mice at 4 and 8 weeks, but then decreased to be lower at 12 compared to 4 weeks. Ptgsl at 12 weeks was, however, not lower than in control mice (Figure 9). Again, there were no age-dependent changes in expressions of markers of adipose tissue differentiation and inflammation in control mice.

Example 6: Biomarkers Associated with Obesity in monocyte-derived microvesicles

We measured COX4I1 and PTGSl in CD- 14+ monocyte-derived microvesicles isolated from plasma of lean control persons and obese patients (belonging to the obese patient cohort in whom we measured biomarkers in monocytes). From the latter, we obtained samples before bariatric surgery and at 4 months and 7 years after bariatric surgery. Expressions of COX4I1 were 10±0.97 in controls (n = 7) and 0.9710.69, 1.1210.57 and 0.50+0.23 in obese persons (n = 10) before, and at 4 months and 7 years after surgery respectively. Expressions in obese persons were lower in obese than in lean controls (ANOVA P < 0.001); and expression at 7 years after surgery was even lower than before surgery (P < 0.05). Expressions of PTGSl were 2119.4 in controls (n = 7) and 0.83+0.67, 0.87+0.40 and 0.4810.36 in obese persons (n = 10) before, and at 4 months and 7 years after surgery respectively. Expressions in obese persons were lower in obese than in lean controls (ANOVA P < 0.001); and expression at 7 years after surgery was even lower than before surgery (P < 0.05). For example, COX10 was not detected in exosomes.

MATERIALS AND METHODS

Materials

All chemicals were obtained from Sigma-Aldrich unless stated otherwise. Patients The first patient cohort comprised 24 lean control and 17 obese individuals. These 17 morbidly obese subjects were referred to our hospital for bariatric surgery. Before they were included, individuals were evaluated by an endocrinologist, an abdominal surgeon, a psychologist and a dietician. Only after multidisciplinary deliberation, the selected patients received a laparoscopic Roux-en-Y gastric bypass. A 30 ml fully divided gastric pouch was created and the jejunum, 30 cm distal of the ligament of Treitz, was anastomosed to it with a circular stapler of 25 mm. To restore intestinal transit, a fully stapte^d entero-entero anastomose was constructed 120 cm distal on the alimentary limb. In this way, the food passage was derived away from almost the whole stomach, the duodenum and the proximal jejunum (99-101). The metabolic syndrome (MetS) was defined according to the joint interim statement of 2009 (102). The samples were collected between March 29^ih, 2005, and February 28^ih, 2013. The second group consisted of 98 successive obese individuals. Inclusion was blinded; 31 of patients were found not to have T2DM; 67 were diagnosed with T2DM, according to the American Diabetes Association. The samples were collected at the Division of Endocrinology. All participants were without symptoms of clinical atherosclerotic cardiovascular disease. This study complies with the Declaration of Helsinki and the Medical Ethics Committee of the KU Leuven approved the study protocol. All human participants gave written informed consent.

Monocyte and microvesicle isolation

Blood samples were collected, and peripheral blood mononuclear cells (PBMCs) were prepared from the anti-coagulated blood using gradient separation on Histopaque-1077 after removal of the plasma fraction. Cells were washed three times in Ca²⁺- and Mg²⁺-free Dulbecco's (D)-PBS. PBMCs were incubated for 15 minutes at 4°C with CD14 microbeads at 20 μΙ/l x 10⁷ cells. The cells were washed once, re-suspended in 500 μΙ Ca²⁺- and Mg²⁺-free DPBS containing 0.5% BSA/1 x 10^s cells. The suspension was then applied to an LS column in a MidiMACS Separator (Miltenyi) (103, 104). We selected CD14⁺ monocytes because CD14 intensity expression on circulating monocytes was found to be associated with increased inflammation in patients with T2DM (105). Positive selection of CD14⁺ microvesicles derived from monocytes was performed, as for monocytes, but starting from 500 μΙ plasma (106-108). Data were Calibrated Normalized Relative Quantities global means on common targets.

Blood analysis

Blood samples were centrifuged to prepare plasma samples for analysis. Total and HDL-cholesterol and triglyceride levels were determined with enzymatic methods (Boehringer Mannheim). LDL- cholesterol levels were calculated with the Friedewald formula. Plasma glucose was measured with the glucose oxidase method (on Vitros 750XRC, Johnson & Johnson), and insulin with an immunoassay (Biosource Technologies). Ox-LDL (109) and IL-6 were measured with enzyme-linked immuno sorbent assay (Mercodia and R&D Systems). Hs-CRP (Beckman Coulter) was measured on an Immage 800 Immunochemistry System. Blood pressure was taken three times with the participant in a seafe¾' position after 5 minutes quiet rest. The average of the last two measurements was used for systolic and diastolic blood pressure.

RNA isolation, microarray and quantitative real-time PCR analysis Total RNA was extracted with TRIzol reagent (Invitrogen) and purified on (mi)RNeasy Mini Kit columns (Qiagen). RNA concentration and quality were assessed with the NanoDrop 2000 (Thermo Scientific), and RNA integrity was determined with the RNA 6000 Nano assay kit using the Agilent 2100 Bioanalyzer.

Microarray analysis of RNA expression was performed with lllumina's Sentrix Human-6 v2 Expression BeadChip Kit containing 46,713 probes/array targeting genes and known alternative splice variants from the RefSeq database release 17 and UniGene build 188. RNA was labeled, hybridized and scanned according to lllumina GLP standards by Aros AB laboratory. The raw data were normalized with the rank-invariant method (lllumina BeadStudio V2). This method uses a linear scaling of the populations being compared. The scaling factor is determined by rank-invariant genes. "Rank-invariant" genes are those genes whose expression values show a consistent order relative to other genes in the population. Of the 46,713 transcripts, 512 transcripts, which were mapped in the Ingenuity Pathway Analysis (IPA) program 5.5-802, were differentially expressed in monocytes of obese patients compared to lean controls at a P-value < 0.01. In order to build the most representative structural network related to mitochondrial oxidative stress, differentially transcripts were examined using the Signal Transduction Pathways (canonical) filter in the Genomatix Pathway System (Genomatix, Miinchen, Germany). The data discussed in this patent application have been deposited in NCBI's Gene Expression Omnibus (110) and are accessible through GEO Series accession number GSE32575 (www.ncbi.nlm.nih.gov/geo/querv/acc.cgi?acc=GSE32575). qPCR is a commonly used validation tool for confirming gene expression results obtained from microarray analysis. First-strand cDNA was generated from total RNA with the Superscript VILO cDNA synthesis kit (Invitrogen). qPCR was performed on a 7500 Fast Real-Time PCR system using Fast SYBRGreen master mix, according to the supplier protocols (Applied Biosystems). Oligonucleotides (Invitrogen) used as forward and reverse primers were designed using the "Primer Express" software (Applied Biosystems) and are summarized in Table 1. RNA expression levels were calculated with the delta-delta-quantification cycle method (AAC_q) described by Livak and Schmittgen (111). The C_q values for the gene of interest and the most stable housekeeping genes were determined for each sample to calculate AC_q,_Sampie (C_q, _gene of interest - mean Cq.housekee ing genes), thus normalizing the data and correcting for differences in amount among RNA samples. In detail, ACTB for mouse experiments, and HPRTl, fiA] TBP and YWHAZ for patient samples were selected as most stable housekeeping genes using GeNorm (112). The expression levels were related to untreated control cells or lean control individuals. Subsequently, C_q (AC_q,_Sam_Pie - AC_q, control) was determined, and the relative expression levels were calculated from 2^"MC .

Note that microarray and qPCR data often result in disagreement. It is well documented that both qPCR and microarray analysis have inherent pitfalls that may significantly influence the data obtained from each method (113). One of the microarray-related pitfalls is the fact that some oligonucleotide probes imprinted on the slide target the wrong gene (114). One of the important disagreements between microarray and qPCR analysis, was that the first identified IRAK3 as up-regulated, whereas the latter identified it as down-regulated. To make sure that primer sequences, used in qPCR, target the right gene, their specificity was validated by Basic Local Alignment Search Tool (BLAST) (115). Furthermore, cDNA clones (OriGene) for IRAK3 (and SOD2) were used to double check the primer specificity. PCR fragments were also validated for GC/AT ratio, length, and amplification specificity with dissociation curve analysis and agarose gel electrophoresis (116). In addition, in-house qPCR data were validated by an external qPCR service provider (www.biogazelle.com) using an independent third cohort comprising 115 patient samples (e.g. PTGS2: in-house (0.99±0.96) vs. external (1.1511.89, P = 0.110 (paired)); r_s = 0.91, P < 0.001).

Animal studies

Animal experiments conformed to the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health (NIH Publication No. 85-23, revised 1996). They were approved by the Institutional Animal Care and Research Advisory Committee of the KU Leuven (Permit Number: P087/2007). Breeding and genotyping of LDL-receptor deficient, leptin-deficient ob/ob, and DKO mice, on the C57BL/6J background, were performed as previously described (156-157). For comparison, age- and gender-matched lean C57BL/6J mice (n = 12) were used. Fenofibrate and rosiglitazone (Avandia) were purchased from Sigma-Aldrich and GlaxoSmithKline. DKO mice were treated with fenofibrate (n = 14), rosiglitazone (n = 13) or placebo (n = 26) for 12 weeks starting at the age of 12 weeks. Fenofibrate (50 mg kg ¹ day^"1) and rosiglitazone (10 mg kg ¹ day^"1) were added to standard diet (SD) containing 4% fat (Ssniff), placebo-treated mice received the grinded chow only. Food and water were available ad libitum. Food intake of the DKO mice was =5.7 g/day and was not affected by the treatments. Food intake of lean mice was about 50% of that of DKO mice. All mice were sacrificed by Nembutal overdose at the age of 24 weeks (51, 117-120). Ucpl KO mice were obtained from Wenner Gren Institute, Stockholm University, by courtesy of Dr. Barbara Cannon and Jan Nedergaard. We studied C57BC6¾ricf UcplKO mice at 30°c (thermoneutral; n=23 and 15, respectively) and at room temperature (20°C = thermodeficient; n=7 and 13, respectively) In order to study the relation of diabetes and obesity with NAFLD and NASH we use male STAM™ mice (Stelic, Tokyo, Japan) (121-123). In brief, pathogen-free 15-day-pregnant C57BL/6 mice were obtained from SLC, Japan (Japan). Male mice received a single subcutaneous injection of Streptozotocin (STZ; Sigma) 2 days after birth. STZ is an antibiotic that can cause pancreatic β-cell destruction, so it is widely used experimentally as an agent capable of inducing insulin-dependent diabetes mellitus (IDDM), also known as type 1 diabetes mellitus (T1DM). They were fed a high-fat diet (HFD; CLEA Japan, Japan) ad libitum after 4 weeks of age (day 28 ± 2). The composition was as follows: 24.5 % c; 5% egg white powder; 0.4% L-Cystine; 15.9 %beef tallow powder (80%); 20% Safflower (High Oleic acid); 5.5% cellulose: 5.5%; 8; 3% Maltodextrin; 6.9% lactose; 6.8% sucrose. Statistical analysis

More than two groups were compared with Kruskal-Wallis nonparametric one-way ANOVA (Kruskal- Wallis) followed by comparison by the Dunn's multiple comparison test or repeated measures ANOVA followed by the Bonferroni's multiple comparison test. Two groups were compared with an unpaired t-test with Welch's correction. ROC analysis was performed with MedCalc statistical software for biomedical research. Odds ratios were determined by Fisher exact test (GraphPad Prism 5). Stepwise multiple regression and multi-layer perceptron analysis was performed with the Statistical Package for the Social Sciences (SPSS for Windows; release 22). A P-value of less than 0.05 was considered statistically significant. Genomatix (Miinchen, Germany) performed Principle Component Analysis (PCA) that is mathematically defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate, the second greatest variance on the second coordinate and so forth. Reference List

Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J, et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999-2004). Arch Intern Med 2008;168:1617-24.

Bray GA. Medical consequences of obesity. J Clin Endocrinol Metab 2004;89:2583-9.

Manson JE, Willett WC, Stampfer MJ, Colditz GA, Hunter DJ, Hankinson SE, et al. Body weight and mortality among women. N Engl J Med 1995;333:677-85.

Grundy SM. Obesity, metabolic syndrome, and cardiovascular disease. J Clin Endocrinol Metab 2004;89:2595-600.

Grundy SM, Brewer HB, Jr., Cleeman Jl, Smith SCJr, Lenfant C. Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Arterioscler Thromb Vase Biol 2004;24:el3-el8.

Muntner P, He J, Chen J, Fonseca V, Whelton PK. Prevalence of non-traditional cardiovascular disease risk factors among persons with impaired fasting glucose, impaired glucose tolerance, diabetes, and the metabolic syndrome: analysis of the Third National Health and Nutrition Examination Survey (NHANES III). Ann Epidemiol 2004;14:686-95.

Duncan BB, Schmidt Ml, PankowJS, Ballantyne CM, Couper D, Vigo A, et al. Low-grade systemic inflammation and the development of type 2 diabetes: the atherosclerosis risk in communities study. Diabetes 2003;52:1799-805.

Ridker PM, Rifai N, Stampfer MJ, Hennekens CH. Plasma concentration of interleukin-6 and the risk of future myocardial infarction among apparently healthy men. Circulation 2000;101:1767-72.

Ridker PM, Buring JE, Cook NR, Rifai N. C-reactive protein, the metabolic syndrome, and risk of incident cardiovascular events: an 8-year follow-up of 14 719 initially healthy American women. Circulation 2003;107:391-7. 10. Holvoet P, Mertens A, Verhamme P, Bogaerts K, Beyens G, Verhaeghe R, et al. Circu alWg' oxidized LDL is a useful marker for identifying patients with coronary artery disease. Arterioscler Thromb Vase Biol 2001;21:844-8.

11. Holvoet P, Harris TB, Tracy RP, Verhamme P, Newman AB, Rubin SM, et al. Association of high coronary heart disease risk status with circulating oxidized LDL in the well-functioning elderly: findings from the Health, Aging, and Body Composition study. Arterioscler Thromb Vase Biol 2003;23:1444-8.

12. Holvoet P, Kritchevsky SB, Tracy RP, Mertens A, Rubin SM, Butler J, et al. The metabolic syndrome, circulating oxidized LDL, and risk of myocardial infarction in well- functioning elderly people in the health, aging, and body composition cohort. Diabetes

2004;53:1068-73.

13. Holvoet P, Lee DH, Steffes M, Gross M, Jacobs DRJr. Association between circulating oxidized low-density lipoprotein and incidence of the metabolic syndrome. JAMA 2008;299:2287-93. 14. Furukawa S, Fujita T, Shimabukuro M, Iwaki M, Yamada Y, Nakajima Y, et al. Increased oxidative stress in obesity and its impact on metabolic syndrome. J Clin Invest 2004;114:1752- 61.

15. Reilly MP, Lehrke M, Wolfe ML, Rohatgi A, Lazar MA, Rader DJ. Resistin is an inflammatory marker of atherosclerosis in humans. Circulation 2005;111:932-9. 16. Funderburg NT, Jadlowsky JK, Lederman MM, Feng Z, Weinberg A, Sieg SF. The Toll-like receptor 1/2 agonists Pam(3) CSK(4) and human beta-defensin-3 differentially induce interleukin-10 and nuclear factor-kappaB signalling patterns in human monocytes. Immunology 2011;134:151-60.

17. Standiford TJ, Kuick R, Bhan U, Chen J, Newstead M, Keshamouni VG. TGF-beta-induced IRAK- M expression in tumor-associated macrophages regulates lung tumor growth.

Oncogene 2011;30:2475-84.

18. Geeraert B, De Keyzer D., Davey PC, Crombe F, Benhabiles N, Holvoet P. Oxidized low-density lipoprotein-induced expression of ABCA1 in blood monocytes precedes coronary atherosclerosis and is associated with plaque complexity in hypercholesterolemic pigs. J Thromb Haemost 2007;5:2529-36. 19. Kirwan JP, Del Aguila LF. Insulin signalling, exercise and cellular integrity. Biochem Soc rans ^"

2003;31:1281-5.

20. Zeyda M, Gollinger K, Kriehuber E, Kiefer FW, Neuhofer A, Stulnig TM. Newly identified adipose tissue macrophage populations in obesity with distinct chemokine and chemokine receptor expression. Int J Obes (Lond) 2010;34:1684-94.

21. Swirski FK, Libby P, Aikawa E, Alcaide P, Luscinskas FW, Weissleder R, et al. Ly-6Chi monocytes dominate hypercholesterolemia-associated monocytosis and give rise to macrophages in atheromata. J Clin Invest 2007;117:195-205.

22. Tacke F, Alvarez D, Kaplan TJ, Jakubzick C, Spanbroek R, Llodra J, et al. Monocyte subsets differentially employ CCR2, CCR5, and CX3CR1 to accumulate within atherosclerotic plaques. J Clin Invest 2007;117:185-94.

23. Hirata Y, Tabata M, Kurobe H, Motoki T, Akaike M, Nishio C, et al. Coronary atherosclerosis is associated with macrophage polarization in epicardial adipose tissue. J Am Coll Cardiol 2011;58:248-55. 24. van Tits U, Stienstra R, van Lent PL, Netea MG, Joosten LA, Stalenhoef AF. Oxidized LDL enhances pro-inflammatory responses of alternatively activated M2 macrophages: a crucial role for Kruppel-like factor 2. Atherosclerosis 2011;214:345-9.

25. Ohashi K, Parker JL, Ouchi N, Higuchi A, Vita JA, Gokce N, et al. Adiponectin promotes macrophage polarization toward an anti-inflammatory phenotype. J Biol Chem 2010;285:6153-60.

26. Zacharioudaki V, Androulidaki A, Arranz A, Vrentzos G, Margioris AN, Tsatsanis C. Adiponectin promotes endotoxin tolerance in macrophages by inducing IRAK-M expression. J Immunol 2009;182:6444-51.

27. Betteridge DJ. Lipid control in patients with diabetes mellitus. Nat Rev Cardiol 2011;8:278-90. 28. Derosa G, Maffioli P. Peroxisome proliferator-activated receptor-gamma (PPAR-gamma) agonists on glycemic control, lipid profile and cardiovascular risk. Curr Mol Pharmacol 2012;5:272-81.

29. Rosenson RS. Fenofibrate: treatment of hyperlipidemia and beyond. Expert Rev Cardiovasc

Ther 2008;6:1319-30. 30. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman J I, Donato KA, et al. Harmonizi g-ffte^{1 '} metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and international association for the Study of Obesity.

Circulation 2009;120:1640-5.

31. Berry R, Jeffery E, Rodeheffer MS. Weighing in on adipocyte precursors. Cell Metab 2014;19:8-

20.

32. Townsend KL, Tseng YH. Brown fat fuel utilization and thermogenesis. Trends Endocrinol

Metab 2014;25:168-77.

33. Jezek P, Garlid KD. Mammalian mitochondrial uncoupling proteins. Int J Biochem Cell Biol

1998;30:1163-8.

34. Sacks H, Symonds ME. Anatomical locations of human brown adipose tissue: functional relevance and implications in obesity and type 2 diabetes. Diabetes 2013;62:1783-90. 35. Sacks HS, Fain JN, Bahouth SW, Ojha S, Frontini A, Budge H, et al. Adult epicardial fat exhibits beige features. J Clin Endocrinol Metab 2013;98:E1448-E1455.

36. Lomax Ml, Hewett-Emmett D, Yang TL, Grossman LI. Rapid evolution of the human gene for cytochrome c oxidase subunit IV. Proc Natl Acad Sci U S A 1992;89:5266-70.

37. Snogdal LS, Wod M, Grarup N, Vestmar M, Sparso T, Jorgensen T, et al. Common variation in oxidative phosphorylation genes is not a major cause of insulin resistance or type 2 diabetes. Diabetologia 2012;55:340-8.

38. Antonicka H, Leary SC, Guercin GH, Agar JN, Horvath R, Kennaway NG, et al. Mutations in

COX10 result in a defect in mitochondrial heme A biosynthesis and account for multiple, early-onset clinical phenotypes associated with isolated COX deficiency. Hum Mol Genet 2003;12:2693-702.

39. Funfschilling U, Supplie LM, Mahad D, Boretius S, Saab AS, Edgar J, et al. Glycolytic oligodendrocytes maintain myelin and long-term axonal integrity. Nature 2012;485:517-21. 40. Cohen HJ, Brown MR, Hamilton D, Lyons-Patterson J, Avissar N, Liegey P. GlutafffibrYe' peroxidase and selenium deficiency in patients receiving home parenteral nutrition: time course for development of deficiency and repletion of enzyme activity in plasma and blood cells. Am J Clin Nutr 1989;49:132-9. 41. de Haan JB, Bladier C, Griffiths P, Kelner M, O'Shea RD, Cheung NS, et al. Mice with a homozygous null mutation for the most abundant glutathione peroxidase, Gpxl, show increased susceptibility to the oxidative stress-inducing agents paraquat and hydrogen peroxide. J Biol Chem 1998;273:22528-36.

42. Shiomi T, Tsutsui H, Matsusaka H, Murakami K, Hayashidani S, Ikeuchi M, et al. Overexpression of glutathione peroxidase prevents left ventricular remodeling and failure after myocardial infarction in mice. Circulation 2004;109:544-9.

43. Tang TS, Prior SL, Li KW, Ireland HA, Bain SC, Hurel SJ, et al. Association between the rsl050450 glutathione peroxidase-1 (C > T) gene variant and peripheral neuropathy in two independent samples of subjects with diabetes mellitus. Nutr Metab Cardiovasc Dis 2012;22:417-25.

44. Ramprasath T, Murugan PS, Kalaiarasan E, Gomathi P, Rathinavel A, Selvam GS. Genetic association of Glutathione peroxidase-1 (GPx-1) and NAD(P)H:Quinone Oxidoreductase l(NQOl) variants and their association of CAD in patients with type-2 diabetes. Mol Cell Biochem 2012;361:143-50. 45. Yan X, Pepper MP, Vatamaniuk MZ, Roneker CA, Li L, Lei XG. Dietary selenium deficiency partially rescues type 2 diabetes-like phenotypes of glutathione peroxidase-1- overexpressing male mice. J Nutr 2012;142:1975-82.

46. Loh K, Deng H, Fukushima A, Cai X, Boivin B, Galic S, et al. Reactive oxygen species enhance insulin sensitivity. Cell Metab 2009;10:260-72. 47. Balaci L, Spada MC, Olla N, Sole G, Loddo L, Anedda F, et al. IRAK-M is involved in the pathogenesis of early-onset persistent asthma. Am J Hum Genet 2007;80:1103-14.

48. Wesche H, Gao X, Li X, Kirschning CJ, Stark GR, Cao Z. IRAK-M is a novel member of the

Pelle/interleukin-1 receptor-associated kinase (IRAK) family. J Biol Chem 1999;274:19403-10. 49. Kobayashi K, Hernandez LD, Galan JE, Janeway CAJr, Medzhitov R, Flavell RA. IRAK-M^Ta' negative regulator of Toll-like receptor signaling. Cell 2002;110:191-202.

50. Hulsmans M, Geeraert B, De KD, Mertens A, Lannoo M, Vanaudenaerde B, et al. Interleukin-l receptor-associated kinase-3 is a key inhibitor of inflammation in obesity and metabolic syndrome. PLoS One 2012;7:e30414.

51. Hulsmans M, Geeraert B, Arnould T, Tsatsanis C, Holvoet P. PPAR agonist-induced reduction of

Mcpl in atherosclerotic plaques of obese, insulin-resistant mice depends on adiponectin-induced Irak3 expression. PLoS One 2013;8:e62253.

52. Funk CD, Funk LB, Kennedy ME, Pong AS, Fitzgerald GA. Human platelet/erythroleukemia cell prostaglandin G/H synthase: cDNA cloning, expression, and gene chromosomal assignment. FASEB J 1991;5:2304-12.

53. Vane JR, Mitchell JA, Appleton I, Tomlinson A, Bishop-Bailey D, Croxtall J, et al. Inducible isoforms of cyclooxygenase and nitric-oxide synthase in inflammation. Proc Natl Acad Sci U S A 1994;91:2046-50. 54. Ames PR, Batuca JR, Muncy IJ, De La Torre IG, Pascoe-Gonzales S, Guyer K, et al. Aspirin insensitive thromboxane generation is associated with oxidative stress in type 2 diabetes mellitus. Thromb Res 2012;130:350-4.

55. Hla T, Neilson K. Human cyclooxygenase-2 cDNA. Proc Natl Acad Sci U S A 1992;89:7384-8.

56. Tazawa R, Xu XM, Wu KK, Wang LH. Characterization of the genomic structure, chromosomal location and promoter of human prostaglandin H synthase-2 gene. Biochem Biophys

Res Commun 1994;203:190-9.

57. Morham SG, Langenbach R, Loftin CD, Tiano HF, Vouloumanos N, Jennette JC, et al.

Prostaglandin synthase 2 gene disruption causes severe renal pathology in the mouse. Cell 1995;83:473-82. 58. Manuel-Apolinar L, Lopez-Romero R, Zarate A, Damasio L, Ruiz M, Castillo-Hernandez C, et al.

Leptin mediated ObRb receptor increases expression of adhesion intercellular molecules and cyclooxygenase 2 on murine aorta tissue inducing endothelial dysfunction. Int J Clin Exp Med 2013;6:192-6. 59. Tong X, Peng H, Liu D, Ji L, Niu C, Ren J, et al. High-density lipoprotein of patients with

diabetes mellitus upregulates cyclooxgenase-2 expression and prostacyclin 1-2 release in endothelial cells: relationship with HDL-associated sphingosine-l-phosphate. Cardiovasc Diabetol 2013;12:27. 60. Cohen MM, Jr. Perspectives on RUNX genes: an update. Am J Med Genet A 2009;149A:2629- 46.

61. Tada Y, Yano S, Yamaguchi T, Okazaki K, Ogawa N, Morita M, et al. Advanced glycation end products-induced vascular calcification is mediated by oxidative stress: functional roles of NAD(P)H-oxidase. Horm Metab Res 2013;45:267-72. 62. Zhang Y, Yang JH. Activation of the PI3K/Akt pathway by oxidative stress mediates high glucose-induced increase of adipogenic differentiation in primary rat osteoblasts. J Cell Biochem 2013:doi: 10.1002/jcb.24607.

63. Hamann C, Goettsch C, Mettelsiefen J, Henkenjohann V, Rauner M, Hempel U, et al. Delayed bone regeneration and low bone mass in a rat model of insulin-resistant type 2 diabetes mellitus is due to impaired osteoblast function. Am J Physiol Endocrinol

Metab 2011;301:E1220-E1228.

64. Manavalan JS, Cremers S, Dempster DW, Zhou H, Dworakowski E, Kode A, et al. Circulating osteogenic precursor cells in type 2 diabetes mellitus. J Clin Endocrinol Metab 2012;97:3240-50. 65. Jang WG, Kim EJ, Bae IH, Lee KN, Kim YD, Kim DK, et al. Metformin induces osteoblast differentiation via orphan nuclear receptor SHP-mediated transactivation of Runx2. Bone 2011;48:885-93.

66. Hie M, Tsukamoto I. Increased expression of the receptor for activation of NF-kappaB and decreased runt-related transcription factor 2 expression in bone of rats with streptozotocin-induced diabetes. Int J Mol Med 2010;26:611-8.

67. Chen JR, Lazarenko OP, Wu X, Tong Y, Blackburn ML, Shankar K, et al. Obesity reduces bone density associated with activation of PPARgamma and suppression of Wnt/beta- catenin in rapidly growing male rats. PLoS One 2010;5:el3704. 68. Hill G , Kuns D, Raffelt NC, Don AL, Olver SD, Markey KA, et al. SOCS3 regulates graft-ve suS- host disease. Blood 2010;116:287-96.

69. Seki Y, Inoue H, Nagata N, Hayashi K, Fukuyama S, Matsumoto K, et al. SOCS-3 regulates onset and maintenance of T(H)2-mediated allergic responses. Nat Med 2003;9:1047-54. 70. Beck Y, Oren R, Amit B, Levanon A, Gorecki M, Hartman JR. Human Mn superoxide dismutase cDNA sequence. Nucleic Acids Res 1987;15:9076.

71. Beckman G, Lundgren E, Tarnvik A. Superoxide dismutase isozymes in different human tissues, their genetic control and intracellular localization. Hum Hered 1973;23:338-45.

72. Li Y, Huang TT, Carlson EJ, Melov S, Ursell PC, Olson JL, et al. Dilated cardiomyopathy and neonatal lethality in mutant mice lacking manganese superoxide dismutase. Nat Genet

1995;11:376-81.

73. Aguer C, Pasqua M, Thrush AB, Moffat C, McBurney M, Jardine K, et al. Increased proton leak and SOD2 expression in myotubes from obese non-diabetic subjects with a family history of type 2 diabetes. Biochim Biophys Acta 2013;1832:1624-33. 74. Bauer S, Wanninger J, Neumeier M, Wurm S, Weigert J, Kopp A, et al. Elevated free fatty acids and impaired adiponectin bioactivity contribute to reduced SOD2 protein in monocytes of type 2 diabetes patients. Exp Mol Pathol 2011;90:101-6.

75. Fleury C, Neverova M, Collins S, Raimbault S, Champigny O, Levi-Meyrueis C, et al. Uncoupling protein-2: a novel gene linked to obesity and hyperinsulinemia. Nat Genet 1997;15:269-72.

76. Zhang CY, Baffy G, Perret P, Krauss S, Peroni O, Grujic D, et al. Uncoupling protein-2 negatively regulates insulin secretion and is a major link between obesity, beta cell dysfunction, and type 2 diabetes. Cell 2001;105:745-55.

77. Krauss S, Zhang CY, Scorrano L, Dalgaard LT, St-Pierre J, Grey ST, et al. Superoxide-mediated activation of uncoupling protein 2 causes pancreatic beta cell dysfunction. J Clin Invest

2003;112:1831-42.

78. Fagerberg B, Bondjers L, Nilsson P. Low birth weight in combination with catch-up growth predicts the occurrence of the metabolic syndrome in men at late middle age: the Atherosclerosis and Insulin Resistance study. J Intern Med 2004;256:254-9. 79. Sinaiko AR, Steinberger J, Moran A, Prineas RJ, Vessby B, Basu S, et al. Relation of bodyfnas's^{1 '} index and insulin resistance to cardiovascular risk factors, inflammatory factors, and oxidative stress during adolescence. Circulation 2005;111:1985-91.

80. Sjoholm A, Nystrom T. Inflammation and the etiology of type 2 diabetes. Diabetes Metab Res

Rev 2005.

81. Lapointe A, Couillard C, Piche ME, Weisnagel SJ, Bergeron J, Nadeau A, et al. Circulating oxidized LDL is associated with parameters of the metabolic syndrome in postmenopausal women. Atherosclerosis 2007;191:362-8.

82. Weinbrenner T, Schroder H, Escurriol V, Fito M, Elosua R, Vila J, et al. Circulating oxidized LDL is associated with increased waist circumference independent of body mass index in men and women. Am J Clin Nutr 2006;83:30-5.

83. Yamagishi S, Matsuoka H, Kitano S, Hibi N, Jinnouchi Y, Umei H, et al. Elevated circulating oxidized LDL levels in Japanese subjects with the metabolic syndrome. Int J Cardiol 2007;118:270-2. 84. Holvoet P, Vanhaecke J, Janssens S, Van de Werf F, Collen D. Oxidized LDL and malondialdehyde-modified LDL in patients with acute coronary syndromes and stable coronary artery disease. Circulation 1998;98:1487-94.

85. Johnston N, Jernberg T, Lagerqvist B, Siegbahn A, Wallentin L. Improved identification of patients with coronary artery disease by the use of new lipid and lipoprotein biomarkers. Am J Cardiol 2006;97:640-5.

86. Meisinger C, Baumert J, Khuseyinova N, Loewel H, Koenig W. Plasma oxidized low-density lipoprotein, a strong predictor for acute coronary heart disease events in apparently healthy, middle-aged men from the general population. Circulation 2005;112:651-7.

87. Hulsmans M, Holvoet P. The vicious circle between oxidative stress and inflammation in atherosclerosis. J Cell Mol Med 2010;14:70-8.

88. Holvoet P, Davey PC, De Keyzer D, Doukoure M, Deridder E, Bochaton-Piallat ML, et al. Oxidized low-density lipoprotein correlates positively with toll-like receptor 2 and interferon regulatory factor-1 and inversely with superoxide dismutase-1 expression: studies in hypercholesterolemia swine and THP-1 cells. Arterioscler Thromb Vast ^~Β¾Γ 2006;26:1558-65.

89. Valadi H, Ekstrom K, Bossios A, Sjostrand M, Lee JJ, Lotvall JO. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol 2007;9:654-9.

90. Zhang Y, Liu D, Chen X, Li J, Li L, Bian Z, et al. Secreted monocytic miR-150 enhances targeted endothelial cell migration. Mol Cell 2010;39:133-44.

91. Hunter MP, Ismail N, Zhang X, Aguda BD, Lee EJ, Yu L, et al. Detection of microRNA expression in human peripheral blood microvesicles. PLoS One 2008;3:e3694. 92. Hulsmans M, De Keyzer D, Holvoet P. MicroRNAs regulating oxidative stress and inflammation in relation to obesity and atherosclerosis. FASEB J 2011;25:2515-27.

93. Gupta SK, Bang C, Thum T. Circulating microRNAs as biomarkers and potential paracrine mediators of cardiovascular disease. Circ Cardiovasc Genet 2010;3:484-8.

94. Heneghan HM, Miller N, Kerin MJ. Role of microRNAs in obesity and the metabolic syndrome.

Obes Rev 2010;11:354-61.

95. Chen X, Ba Y, Ma L, Cai X, Yin Y, Wang K, et al. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res 2008;18:997- 1006.

96. Thery C, Zitvogel L, Amigorena S. Exosomes: composition, biogenesis and function. Nat Rev

Immunol 2002;2:569-79.

97. Hulsmans M, Holvoet P. MicroRNA-containing microvesicles regulating inflammation in association with atherosclerotic disease. Cardiovasc Res 2013:doi: 10.1093/cvr/cvtl61.

98. Loens K, BeckT, Goossens H, Ursi D, Overdijk M, Sillekens P, et al. Development of conventional and real-time NASBA for the detection of Legionella species in respiratory specimens. J Microbiol Methods 2006;67:408-15.

99. Cummings DE, Overduin J, Foster-Schubert KE. Gastric bypass for obesity: mechanisms of weight loss and diabetes resolution. J Clin Endocrinol Metab 2004;89:2608-15. 100. Klein S, Burke LE, Bray GA, Blair S, Allison DB, Pi-Sunyer X, et al. Clinical implications of dtfefrty with specific focus on cardiovascular disease: a statement for professionals from the American Heart Association Council on Nutrition, Physical Activity, and Metabolism: endorsed by the American College of Cardiology Foundation. Circulation 2004;110:2952-67.

101. Sjostrom L, Lindroos AK, Peltonen M, Torgerson J, Bouchard C, Carlsson B, et al. Lifestyle, diabetes, and cardiovascular risk factors 10 years after bariatric surgery. N Engl J Med 2004;351:2683-93.

102. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes

Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and international association for the Study of Obesity. Circulation 2009;120:1640-5. 103. Pickl WF, Majdic O, Kohl P, Stockl J, Riedl E, Scheinecker C, et al. Molecular and functional characteristics of dendritic cells generated from highly purified CD14+ peripheral blood monocytes. J Immunol 1996;157:3850-9.

104. Salio M, Cerundolo V, Lanzavecchia A. Dendritic cell maturation is induced by mycoplasma infection but not by necrotic cells. Eur J Immunol 2000;30:705-8. 105. Patino R, Ibarra J, Rodriguez A, Yague MR, Pintor E, Fernandez-Cruz A, et al. Circulating monocytes in patients with diabetes mellitus, arterial disease, and increased CD14 expression. Am J Cardiol 2000;85:1288-91.

106. Hong CS, Muller L, Whiteside TL, Boyiadzis M. Plasma exosomes as markers of therapeutic response in patients with acute myeloid leukemia. Front Immunol 2014;5:160. 107. Muller L, Hong CS, Stolz DB, Watkins SC, Whiteside TL. Isolation of biologically-active exosomes from human plasma. J Immunol Methods 2014;411:55-65.

108. Tauro BJ, Greening DW, Mathias RA, Ji H, Mathivanan S, Scott AM, et al. Comparison of ultracentrifugation, density gradient separation, and immunoaffinity capture methods for isolating human colon cancer cell line LIM1863-derived exosomes. Methods 2012;56:293-304. 109. Holvoet P, Macy E, Landeloos M, Jones D, Jenny NS, Van de WF, et al. Analytical perforrriaTTc¾' and diagnostic accuracy of immunometric assays for the measurement of circulating oxidized LDL. Clin Chem 2006;52:760-4.

110. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002;30:207-10.

111. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative

PCR and the 2(-Delta Delta C(T)) Method. Methods 2001;25:402-8.

112. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 2002;3:RESEARCH0034.

113. Morey JS, Ryan JC, Van Dolah FM. Microarray validation: factors influencing correlation between oligonucleotide microarrays and real-time PCR. Biol Proced Online 2006;8:175-93.

114. Knight J. When the chips are down. Nature 2001;410:860-1. 115. Ladunga I. Finding homologs to nucleotide sequences using network BLAST searches. Curr

Protoc Bioinformatics 2002;Chapter 3:Unit.

116. Ririe KM, Rasmussen RP, Wittwer CT. Product differentiation by analysis of DNA melting curves during the polymerase chain reaction. Anal Biochem 1997;245:154-60.

117. Mertens A, Verhamme P, Bielicki JK, Phillips MC, Quarck R, Verreth W, et al. Increased low- density lipoprotein oxidation and impaired high-density lipoprotein antioxidant defense are associated with increased macrophage homing and atherosclerosis in dyslipidemic obese mice: LCAT gene transfer decreases atherosclerosis. Circulation 2003;107:1640-6.

118. Verreth W, De KD, Pelat M, Verhamme P, Ganame J, Bielicki JK, et al. Weight-loss-associated induction of peroxisome proliferator-activated receptor-alpha and peroxisome proliferator-activated receptor-gamma correlate with reduced atherosclerosis and improved cardiovascular function in obese insulin-resistant mice. Circulation 2004;110:3259-69. 119. Verreth W, Ganame J, Mertens A, Bernar H, Herregods MC, Holvoet P. Peroxisome prolifeYSfftif- activated receptor-alpha,gamma-agonist improves insulin sensitivity and prevents loss of left ventricular function in obese dyslipidemic mice. Arterioscler Thromb Vase Biol 2006;26:922-8. 120. Verreth W, De KD, Davey PC, Geeraert B, Mertens A, Herregods MC, et al. Rosuvastatin restores superoxide dismutase expression and inhibits accumulation of oxidized LDL in the aortic arch of obese dyslipidemic mice. Br J Pharmacol 2007;151:347-55.

121. Cynis H, Kehlen A, Haegele M, Hoffmann T, Heiser U, Fujii M, et al. Inhibition of Glutaminyl

Cyclases alleviates CCL2-mediated inflammation of non-alcoholic fatty liver disease in mice. Int J Exp Pathol 2013;94:217-25.

122. Fujii M, Shibazaki Y, Wakamatsu K, Honda Y, Kawauchi Y, Suzuki K, et al. A murine model for non-alcoholic steatohepatitis showing evidence of association between diabetes and hepatocellular carcinoma. Med Mol Morphol 2013;46:141-52.

123. Klein T, Fujii M, Sandel J, Shibazaki Y, Wakamatsu K, Mark M, et al. Linagliptin alleviates hepatic steatosis and inflammation in a mouse model of non-alcoholic steatohepatitis. Med Mol Morphol 2013.

TABLES

Table 1. Primers used for qPCR analysis of human RNA extracts

Abbreviations: COX10, cytochrome c oxidase assembly homolog 10 (yeast); COX4I1, cytochr8me^uc' oxidase subunit IV isoform 1; GPX1, glutathione peroxidase 1; HPRT1, hypoxanthine phosphoribosyltransferase 1; IRAK3, interleukin-1 receptor-associated kinase 3; PTGS1, prostaglandin- endoperoxide synthase 1 (prostaglandin G/H synthase and cyclooxygenase); PTGS2, prostaglandin- endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase); RUNX2, runt-related transcription factor 2; SDHA, succinate dehydrogenase complex, subunit A, flavoprotein (Fp); SOCS3, suppressor of cytokine signaling 3; SOD2, superoxide dismutase 2, mitochondrial; TBP, TATA box binding protein; UCP2, uncoupling protein 2 (mitochondrial, proton carrier); YWHAZ, tyrosine 3- monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide. HPRT1, SDHA, TBP, and YWHAZ were used as housekeeping genes.

Table 2. Characteristics and gene expressions of lean controls and obese patients without ancl with T2D

Lean controls Obese Obese

without T2DM with T2DM

(n = 24) (n = 31) (n = 67)

A. Characteristics

Age (years) 40±13 42114 54+8.7***/$$$

Gender (n, % male) 9 (38) 4(13) 4 (6)^***

Smoker (n, %) 1(4) 0(0) 6(9)

Ex-smoker (n, %) 1(4) 0(0) 0(0)

Obesity (n, %) 0(0) 31(100)^*** 67 (100)^***

T2DM (n, %) 0(0) 0(0) 67 (100)^**V$$$

MetS (n, %) 1(4) 19(61)^*** 64 (96)^***/^5$$

Statin use (n, %) 1(4) 7(23) 48 (72)^***/$$$

Antihypertensive drug use (n, %) 6(25) 14 (45) 53 (79)^***/^$$$

ACEI or ARB (n, %) 2(8) 4(13) 32 (48)^***^^s$$

CACB (n, %) 1(4) 4(13) 16 (24)^*

Beta blocker (n, %) 4(17) 9(29) 31 (46)^*

Diuretics (n, %) 0(0) 9 (29)^** 28(42)^***

Oral antidiabetic drug use (n, %) 0(0) 0(0) 54 (81)^***/^$$$

Metformin (n, %) 0(0) 0(0) 52 (78)^***/^$$$

Insulin therapy (n, %) 0(0) 0(0) 43 (64)^***^^$

BMI (kg/m²) 23±2.7 4117.7^*** 38l5.0^***/^$

Leptin (ng/ml) 11.1±8.5 72140^*** 46i30^***/^$$

ADN ^g/ml) 10.9±6.3 5.0+2.9^*** 4.5+3.8^***

Glucose (mg/dl) 92±14 92+11 136+48^***/$$$

Insulin (mU/l) 10.1±6.2 14.4+7.7^* 34.4+40.2^***^^

HOMA-IR 1.310.8 1.8+1.0^* 4.4l4.5^***/$$$

HOMA %b 114.6169.1 136.2145.9 156.71172.2

TG (mg/dl) 102148 130165 153+68^***

LDL-C (mg/dl) 112+34 103+31 75_±28***/$$$

HDL-C (mg/dl) 63117 51+11^** 46ll2^***/$

SBP(mmHg) 129115 131116 139ll6^*$

DBP(mmHg) 7417.4 83+12^** 82+9.8^*** IL-6 (pg/ml) 2.4±1.1 5.312.7^*** 5.5+2.9^***

Hs-CRP (mg/l) 1.7±3.5 8.3111.2^** 6.7+7.5^***

Ox-LDL (IU/l) 54±20 60+22 48118^S$

B. Gene expressions

COX10 1.05±0.16 0.82+0.18^*** 0.76+0.17^***

COX4I1 1.05±0.15 1.0310.24 0.85+0.18^***/$$$

GPX1 0.98±0.40 0.5610.18^*** 0.58+0.21^***

IRAK3 1.03±0.21 0.7510.24^*** 0.80+0.18^***

PTGS1 1.01±0.19 0.86+0.18^** 0.77l0.14^***/$

PTGS2 1.00+0.96 8.78+7.18^*** 7.47+5.08^***

RUNX2 1.03±0.16 1.3310.51^** 1.19+0.47^*

SOCS3 1.10±0.59 2.9711.19^*** 2.52+0.95^***

SOD2 1.0510.24 2.5811.38^*** 2.53+1.38^***

UCP2 1.1210.20 1.26+0.24^* 1.1510.28^$

Data shown are means 1 SD. ^*P < 0.05, ^**P < 0.01 and ^***P < 0.001 obese persons with and without T2DM compared with lean controls; ^$P < 0.05, ^$$P < 0.01 and ^$S$P < 0.001 obese persons with T2DM compared with obese persons without T2DM. Abbreviations: ACEI, ACE inhibitor; ADN, adiponectin; ARB, Angiotensin Receptor blocker; BMI, body mass index; CACB, Calcium Channel blocker; C, cholesterol; DBP, diastolic blood pressure; HOMA-IR, homeostasis model assessment of insulin resistance; hs-CRP, high sensitivity C-reactive protein; IL, interleukin; MetS, metabolic syndrome; ox- LDL, oxidized LDL; SBP, systolic blood pressure; T2DM, type 2 diabetes mellitus; TG, triglycerides.

Table 3. Relation with obesity and T2DM

Gene AUC OR Sensitivity (%) Specificity (

A. Obesity

COXIO 0.88 (0.81-0.93) 33 (8.9-125) 83 (74-90) 88 (68-97)

GPXl 0.83 (0.75-0.89) 12 (4.2-32) 83 (74-90) 71 (49-87)

IRAK3 0.80 (0.71-0.86) 12 (2.8-56) 53 (43-63) 92 (73-99)

PTGSl 0.98 (0.94-1.00) 222 (42-1176) 97 (91-99) 88 (68-97)

SOCS3 0.92 (0.86-0.96) 64 (8.2-496) 73 (64-82) 96 (79-100)

SOD2 0.91 (0.85-0.96) 44 (13-155) 90 (82-95) 83 (63-95)

B. T2DM

COX4I1 0.77 (0.69-0.84) 6.6 (3.0-15) 76 (64-86) 67 (53-76)

PTGSl 0.74 (0.65-0.82) 4.8 (2.2-10) 72 (59-82) 65 (51-78)

Glucose ND 9.5 (4.1-22) 75 (63-84) 76 (63-87)

COX4I1 ND 20 (6.4-66) 86 (73-95) 76 (59-89)

& PTGSl (0 vs. 2)

COX4I1 ND 45 (12-171) 89 (75-96) 85 (69-95)

& glucose (0 vs. 2)

PTGSl ND 24 (7.4-75) 83 (69-92) 83 (66-93)

& glucose (0 vs. 2)

Values are AUC determined by ROC analysis, and OR, sensitivity and specificity determined with two- sided Fisher's exact test; 95% confidence intervals in parentheses. Cut-off value, determined by ROC analysis, is 0.92 for COXIO, 0.94 for COX4I1, 0.73 for GPXl, 0.94 for IRAK3, 0.84 for PTGSl, 1.96 for PTGS2, 2.03 for SOCS3, 1.16 for SOD2 and 100 mg/dl for glucose. Z-values are 11 for COXIO, 6.3 for COX4I1, 7.3 for GPXl, 6.2 for IRAK3, 5.4 for PTGSl, 41 for PTGS2, 16 for SOCS3, and 16 for SOD2. In addition, we performed stepwise multiple logistic regression analysis including all genes which were differentially expressed in monocytes of obese persons. The combination of COX4I1, PTGSl, and PTGS2, and SOD2 predicted the best T2DM. It predicted 79% of controls (not having T2DM) and 81% of cases (having T2DM) correctly. Thus overall prediction was 80%. After adjusting for BMI and glucose only COX4I1 predicted T2DM. In addition, a model to predict diabetic patient using multi-layer perceptron contained COX4I1, SOD2, PTGS2, and PTGSl. Abbreviations: AUC, area under the curve; ND, not determined; OR, odds ratio; T2DM, type 2 diabetes mellitus. Table 4. Characteristics and gene expressions of subjects without and with hyperglycemtt foY' prediabetes with cut-off value: 100 mg/dl)

Without With

(n = 59) (n = 63)

A. Characteristics

Age (years) 43±13 53±11^***

Gender (n, % male) 8 (14) 9 (14)

Smoker (n, %) 2 (3) 5 (8)

Ex-smoker (n, %) 1 (2) 0 (0)

Obesity (n, %) 40 (68) 58 (92)^***

T2DM (n, %) 17 (29) 50 (79)^***

MetS (n, %) 27 (46) 57 (90)^***

Statin use (n, %) 15 (25) 41 (65)^***

Antihypertensive drug use (n, %) 26 (44) 47 (75)^***

ACEI or ARB (n, %) 9 (15) 29 (46)^***

CACB (n, %) 6 (10) 15 (24)

Beta blocker (n, %) 18 (31) 26 (41)

Diuretics (n, %) 17 (29) 20 (32)

Oral antidiabetic drug use (n, %) 14 (24) 40 (63)^***

Metformin (n, %) 14 (24) 38 (60)^***

Insulin therapy (n, %) 11 (19) 32 (51)^***

BMI (kg/m²) 34±10 37±7^*

Leptin (ng/ml) 46±40 45±32

ADN (ug/ml) 7.3±5.6 4.6±3.7^**

Glucose (mg/dl) 85±9 145142^***

Insulin (mU/l) 16.1±17.5 32.4±39.8^**

HOMA-IR 1.9±1.8 4.2±4.5^***

HOMA %b 175.2±158.2 112.6±96.l"

TG (mg/dl) 124±62 149168^*

LDL-C (mg/dl) 93±33 86+34

HDL-C (mg/dl) 54±16 47+12^*

SBP (mmHg) 130±15 141116^***

DBP (mmHg) 79±11 83110

IL-6 (pg/ml) 4.2±2.9 5.4+2.7^* Hs-CRP (mg/l) 6.0±9.2 6.317.5

Ox-LDL (IU/1) 52±21 52+19

B. Gene expressions

COX10 0.89±0.21 0.79+0.18^**

COX4I1 0.9910.22 0.88+0.19^**

GPX1 0.6810.31 0.63+0.28

IRAK3 0.86+0.26 0.81+0.18

PTGS1 0.9010.20 0.78+0.15^*"

PTGS2 6.3916.53 6.66+5.31

RUNX2 1.19+0.42 1.20+0.47

SOCS3 2.25+1.23 2.45+1.08

SOD2 2.23+1.48 2.2611.27

UCP2 1.19+0.25 1.15+0.27

Data shown are means 1 SD. ^*P < 0.05, P < 0.01 and ^***P < 0.001 subjects with IGT compared with subjects without IGT. Abbreviations: ACEI, ACE inhibitor; ADN, adiponectin; ARB, Angiotensin Receptor blocker; BMI, body mass index; CACB, Calcium Channel blocker; C, cholesterol; DBP, diastolic blood pressure; HOMA-IR, homeostasis model assessment of insulin resistance; hs-CRP, high sensitivity C- reactive protein; IL, interleukin; MetS, metabolic syndrome; ox-LDL, oxidized LDL; SBP, systolic blood pressure; T2DM, type 2 diabetes mellitus; TG, triglycerides.

Table 5. Relation with hyperglycemia or prediabetes

Gene AUC OR Sensitivity (%) Specificity (%)

COX4I1 0.66 (0.57-0.74) 3.1 (1.5-6.6) 70 (57-81) 58 (44-70)

COXIO 0.64 (0.55-0.73) 2.9 (1.4-6.1) 67 (54-78) 59 (46-72)

PTGSl 0.70 (0.61-0.78) 4.6 (2.1-9.8) 67 (54-78) 69 (56-81)

COXIO &

COX4I1 (0 vs. ND 5.1 (2.0-13) 77 (61-88) 61 (45-75)

2)

COX4I1 &

ND 11 (3.7-34) 83 (66-93) 70 (53-83) PTGSl (0 vs. 2)

COXIO &

ND 7.9 (2.9-21) 74 (59-86) 73 (56-86) PTGSl (0 vs. 2)

COXIO &

COX4I1 & ND 14 (4.1-50) 83 (64-94) 75 (57-88)

PTGSl (0 vs. 3)

Values are AUC deternnined by ROC analysis, and OR, sensitivity and specificity determined with two- sided Fisher's exact test; 95% confidence intervals in parentheses. ^*P < 0.05, ^**P < 0.01 and ^***P < 0.001. Cut-off value, determined by ROC analysis, is 0.94 for COX4I1, 0.84 for COXIO, and 0.80 for PTGSl. Z- values are 3.2 for COX4I1, 2.8 for COXIO, and 4.2 for PTGSl. Abbreviations: AUC, area under the curve; ND, not determined; OR, odds ratio.

Table 6. Characteristics and gene expressions of subjects without and with metabolic syntfi¾me disorder phenotype (metabolic unhealthy)

Metabolic healthy Metabolic unhealthy

(n = 29) (n = 93)

A. Characteristics

Age (years) 38±14 51+11^***

Gender (n, % male) 9 (31) 8 (9)^**

Smoker (n, %) 1 (3) 6 (6)

Ex-smoker (n, %) 1 (3) 0 (0)

Obesity (n, %) 8 (28) 90 (97)^***

T2DM (n, %) 1 (3) 66 (71)^***

MetS (n, %) 0 (0) 84 (90)^***

Statin use (n, %) 3 (10) 53 (57)^***

Antihypertensive drug use (n, %) 7 (24) 66 (71)^***

ACEI or ARB (n, %) 1 (3) 37 (40)^***

CACB (n, %) 0 (0) 21 (23)^**

Beta blocker (n, %) 4 (14) 40 (43)^**

Diuretics (n, %) 3 (10) 34 (37)^**

Oral antidiabetic drug use (n, %) 0 (0) 54 (58)^***

Metformin (n, %) 0 (0) 52 (56)^***

Insulin therapy (n, %) 1 (3) 42 (45)^***

BMI (kg/m²) 27±8 38+7^***

Leptin (ng/ml) 23±26 53+36^***

ADN (pg/ml) 10.116.3 4.613.5^***

Glucose (mg/dl) 89±12 124+45^***

Insulin (mU/l) 11.819.6 28.5+35.3^***

HOMA-IR 1.511.2 3.614.0^***

HOMA %b 127.8+79.7 147.91146.2

TG (mg/dl) 96+40 150168^***

LDL-C (mg/dl) 106+32 84+33^**

HDL-C (mg/dl) 61+15 47+12^***

SBP (mmHg) 128+16 137+16^*

DBP (mmHg) 75+7 83111^***

IL-6 (pg/ml) 2.811.6 5.4+2.9^*** Hs-CRP (mg/l) 1.07±0.91 7.7618.98^***

Ox-LDL (IU/l) 53±20 52+20

B. Gene expressions

COX 10 0.9810.22 0.7910.17^***

COX4I1 1.0410.18 0.9010.21^***

GPX1 0.8910.41 0.58+0.20^***

IRAK3 0.97+0.25 0.8010.20^**

PTGS1 0.9810.19 0.80+0.16^***

PTGS2 2.8414.14 7.68+5.92^***

RUNX2 1.12+0.25 1.22+0.49

SOCS3 1.55+0.94 2.61+1.11^***

SOD2 1.48+1.06 2.49+1.37^***

UCP2 1.1210.24 1.19+0.26

Data shown are means 1 SD. ^*P < 0.05, ^**P < 0.01 and ^***P < 0.001 metabolic unhealthy subjects compared with metabolic healthy subjects. Abbreviations: ACEI, ACE inhibitor; ADN, adiponectin; ARB, Angiotensin Receptor blocker; BMI, body mass index; CACB, Calcium Channel blocker; C, cholesterol; DBP, diastolic blood pressure; HOMA-IR, homeostasis model assessment of insulin resistance; hs-CRP, high sensitivity C-reactive protein; IL, interleukin; MetS, metabolic syndrome; ox-LDL, oxidized LDL; SBP, systolic blood pressure; T2DM, type 2 diabetes mellitus; TG, triglycerides.

Table 7. Relation with metabolic syndrome disorder phenotype

Gene AUC OR Sensitivity (%) Specificity (%)

COXIO 0.77 (0.68-0.84) 6.6 (2.5-17) 68 (57-77) 76 (56-90)

COX4I1 0.71 (0.62-0.79) 5.8 (2.2-16) 60 (50-70) 79 (60-92)

GPXl 0.74 (0.66-0.82) 6.3 (2.6-16) 82 (72-89) 59 (39-76)

IRAK3 0.71 (0.62-0.78) 6.6 (2.4-18) 90 (82-95) 41 (24-61)

PTGSl 0.80 (0.72-0.87) 11 (3.9-32) 70 (60-79) 83 (64-94)

PTGS2 0.83 (0.76-0.90) 11 (4.3-31) 78 (69-86) 76 (56-90)

SOCS3 0.77 (0.69-0.85) 7.6 (3.0-19) 77 (68-85) 69 (49-85)

SOD2 0.79 (0.70-0.85) 10 (3.5-29) 68 (57-77) 83 (64-94)

Values are AUC determined by ROC analysis, and OR, sensitivity and specificity determined with two- sided Fisher's exact test; 95% confidence intervals in parentheses. Cut-off value, determined by ROC analysis, is 0.87 for COXIO, 0.91 for COX4I1, 0.73 for GPXl, 0.99 for IRAK3, 0.85 for PTGSl, 3.51 for PTGS2, 1.96 for SOCS3, and 1.73 for SOD2. Z-values are 5.2 for COXIO, 4.2 for COX4I1, 4.3 for GPXl, 3.5 for IRAK3, 6.3 for PTGSl, 6.7 for PTGS2, 5.1 for SOCS3, and 5.6 for SOD2.

In addition, we performed stepwise multiple logistic regression analysis including all genes which were differentially expressed in monocytes of obese persons. The combination of COX4I1, PTGSl, PTGS2, and SOD2 together with hs-CRP predicted the best the metabolic syndrome disorder phenotype. The model predicted 76% of controls (not having the metabolic syndrome disorder phenotype) and 92% of cases (having the metabolic syndrome disorder phenotype) correctly. Thus overall prediction was 84%. This model remained predicting the metabolic syndrome phenotype after adjusting for BMI and glucose. Abbreviations: AUC, area under the curve; ND, not determined; OR, odds ratio; T2DM, type 2 diabetes mellitus.

Table 8. Characteristics and gene expressions before and after bariatric surgery in obese patients

Obese patients (n = 17)

Lean controls Before bariatric 4 months after 7 years after

ANOVA

(n = 24) surgery bariatric surgery bariatric surgery

A. Characteristics

Age (years) 40±13 40±14 40±14 48±14^$$$£££ P< 0.001

Gender (n, % male) 9(38) 5(29) 5(29) 5(29) NA

Smoker (n, %) 1(4) 3(18) 3(18) 4(24) NA

Ex-smoker (n, %) 1(4) 4(24) 4(24) 5(29) NA

Obesity (n, %) 0(0) 17 (100)^*** 16 (94)^*** 13 (76)^*** NA

T2DM (n, %) 0(0) 9 (53)^*** 5 (29)^** 5 (29)^** NA

MetS (n, %) 1(4) 12 (71)^*** 6 (35)^* 9(53)^*** NA

Statin use (%) 1(4) 6 (35)^* 6 (35)^* 6 (35)^* NA

Antihypertensive drug use (n, %) 6(25) 6(35) 3(18) 4(24) NA

ACEI or ARB (n, %) 2(8) 3(18) 2(12) 1(6) NA

CACB (n, %) 1(4) 1(6) 0(0) 0(0) NA

Beta blocker (n, %) 4(17) 4(24) 1(6) 3(18) NA

Diuretics (n, %) 0(0) 4 (24)^* 2(12) 4 (24)^* NA

Oral antidiabetic drug use (n, %) 0(0) 8 (47)^*** 7 (41)^*** 4 (24)^* NA

Metformin (n, %) 0 (0) 7 (41)^*** 7 (41)*^** 4 (24)^* NA

Insulin therapy (n, %) 0 (0) 1 (6) 0 (0) 2 (12) NA

_37±6"V$5$ _33±4"/$$$

BMI (kg/m²) 23±2.7 45±7^*** P < 0.001

Leptin (ng/ml) 11.1+8.5 70±35*^** 27±20^$$$ 33+22*/$$$ P < 0.001

ADN (|ig/ml) 10.9±6.3 4.1+4.0*** 7.8±6.9^$$ 12.4±7.8^$5$/£££ P < 0.001

Glucose (mg/dl) 92±14 123+44^** 95±18^$$$ 99±20^$$ P < 0.001

Insulin (mU/l) 10.1±6.2 17.8±9.5 8.3±3.9^$ss 8.7+5.9^$$$ P < 0.001

HOMA-IR 1.3±0.8 2.4±1.2* 1.1±0.5^$$$ 1.2±0.8^$$$ P < 0.001

HOMA % b 114.6±69.1 119.3±71.6 97.1±41.9 84.4±30.4^$ P < 0.05

TG (mg/dl) 102±48 148±67 110±48^$ 99+49$$ P < 0.01

LDL-C (mg/dl) 112±34 94±32 88±27 88+20 NS

HDL-C (mg/dl) 63+17 50±12 47±10* 64+20^$$$/£££ P < 0.001

SBP (mmHg) 129±15 141±17 118±4^$$$ 131120 P < 0.001

59_±8**V$$$

DBP (mmHg) 74+7.4 87±12^* 79±8^£££ P < 0.001

IL-6 (pg/ml) 2.4±1.1 5.1±3.2"" 3.8±2.4 4.313.4 NS

Hs-CRP (mg/l) 1.7±3.5 5.7+6.0^*' 7.3±15.6 1.5+1.5 NS

Ox-LDL (IU/l) 54+20 76±21** 73±20* _49±10$$$/£££ P < 0.001

B. Gene expressions

COX10 1.05±0.16 0.91±0.19^* 0.94±0.19^* 0.78+0.14**^{* £} P < 0.05

COX4I1 1.05±0.15 1.09±0.26 1.1310.24 0.71+0.12^{***/$$$/£££} P < 0.001

GPX1 0.98±0.40 0.59+0.26*** 0.57+0.37** 0.87±0.28^{$$$ £££} P < 0.001

IRAK3 1.03±0.21 0.63+0.32**^* 0.76+0.23*** 1.57+0.38***^/$$$/£££ P < 0.001

PTGS1 1.01±0.19 0.9510.26 1.08±0.31 1.11±0.36 NS

SOD2 1.0510.24 2.07+1.03*** 1.99+1.03^** 1.04+0.21^$$$/£££ P < 0.001

Data shown are means 1 SD. *P < 0.05, *'P < 0.01 and ^***P < 0.001 compared with lean controls (Mann-Whitney test); ^$P < 0.05, ^$$P < 0.01 and ^$$$P < 0.001 compared with before bariatric surgery, and ^£P < 0.05, ^££P < 0.01 and ^£££P < 0.001 compared with 4 months after bariatric surgery (repeated measures ANOVA followed by the Bonferroni's multiple comparison test). Abbreviations: ACEI, ACE inhibitor; ADN, adiponectin; ARB, Angiotensin Receptor blocker; BM I, body mass index; CACB, Calcium Channel blocker; C, cholesterol; DBP, diastolic blood pressure; HOMA-IR, homeostasis model assessment of insulin resistance; hs-CRP, high sensitivity C-reactive protein; IL, interleukin; MetS, metabolic syndrome; NA, not applicable; NS, not significant; ox-LDL, oxidized LDL; SBP, systolic blood pressure; T2DM, type 2 diabetes mellitus; TG, triglycerides.

Claims

A method for predicting a patient's response to a treatment for type 2 diabetes mellitus (T2DM), comprising:

a. obtaining a biological sample from the patient;

b. measuring expression of COX4I1 in the biological sample; and

c. comparing the expression of COX4I1 with reference measurements;

wherein decreased expression of COX4I1 in the biological sample as compared to the reference measurements indicates that the patient will not respond to the treatment.

The method of claim 1, further comprising measuring expression of COXIO in the biological sample and comparing the expression of COXIO with reference measurements, wherein decreased expression of COXIO in the biological sample as compared to the reference measurements indicates that the patient will not respond to the treatment.

A method for predicting a patient's response to treatment for T2DM, comprising: a. obtaining a biological sample from the patient;

b. measuring expression of COX4I1 and COXIO in the biological sample;

c. comparing the expression of COX4I1 and COXIO with reference measurements;

wherein decreased expression of COX4I1 and COXIO in the biological sample as compared to the reference measurements indicates that the patient will not respond to the treatment.

The method of claim 3, further comprising measuring expression of PTGSl in the biological sample, and comparing expression of PTGSl with reference measurements, wherein decreased expression of PTGSl in the biological sample as compared to the reference measurements indicates that the patient will not respond to treatment for metabolic syndrome.

b. measuring expression of COX4I1, COXIO, and PTGSl in the biological sample; c. comparing the expression of COX4I1, COXIO, and PTGSl with reference measurements; wherein decreased expression of COX4I1, COX10, and PTGS1 in the biologiCa'F sample as compared to the reference measurements indicates that the patient will not respond to the treatment.

6. The method according to claim 5, further comprising measuring expression of at least one of PTGS2 and SOD2 in the biological sample, and comparing expression of PTGS2 and SOD2 with reference measurements, wherein increased expression of PTGS2 and SOD2 in the biological sample as compared to the reference measurements indicates that the patient will not respond to the treatment.

7. A method for predicting a patient's response to treatment for T2DM, comprising: a. obtaining a biological sample from the patient;

b. measuring expression of COX4I1, COX10, PTGS1, PTGS2,and SOD2 in the biological sample;

c. comparing the expression of COX4I1, COX10, PTGS1, PTGS2,and SOD2 with reference measurements;

wherein decreased expression of COX4I1, COX10, and PTGS1 and increased expression of PTGS2 and SOD2 in the biological sample as compared to the reference measurements indicates the patient will not respond to the treatment.

8. The method of any one of claims 1-7, wherein the patient has metabolic syndrome.

9. The method of claim 8, wherein the patient has metabolic syndrome with inflammation characterized by high levels of C-reactive protein.

10. The method of any one of claims 1-7, wherein the patient does not have metabolic syndrome.

11. The method of any one of claims 1-10, wherein the patient is obese.

12. The method of any one of claims 1-10, wherein the patient is not obese.

13. The method of any one of claims 1-11, wherein the patient has undergone treatment for obesity.

14. The method of claim 13, wherein the patient has undergone bariatric surgery.

15. The method of any one of claims 1-14, wherein the biological sample is a blood sample or an adipose tissue sample.

16. The method of claim 15, wherein the biological sample is a white adipose tissue sample.

17. The method of any one of claims 1-14, wherein the biological sample is one or more adipocytes from white adipose tissue.

18. The method of any one of claims 1-14, wherein the biological sample is one or more activated monocytes, for example macrophages, from white adipose tissue.

19. The method of claim 15, wherein the adipose tissue sample is brown adipose tissue.

20. The method of any one of claims 1-14, wherein the biological sample is one or more adipocytes from brown adipose tissue.

21. The method of any one of claims 1-14, wherein the biological sample is one or more activated monocytes, for example macrophages, from brown adipose tissue.

22. The method of any one of claims 1-14, wherein the biological sample is one or more monocytes.

23. The method of any one of claims 1-14, wherein the biological sample is one or more exosomes.

24. The method of claim 23, wherein the exosomes are monocyte-derived exosomes.

25. The method of any one of claims 1-24, wherein expression comprises gene expression.

26. The method of claim 25, wherein expression comprises RNA expression.