WO2012116074A1

WO2012116074A1 - Biomarkers of insulin sensitivity

Info

Publication number: WO2012116074A1
Application number: PCT/US2012/026136
Authority: WO
Inventors: K. Sreekumaran Nair
Original assignee: Mayo Foundation For Medical Education And Research
Priority date: 2011-02-22
Filing date: 2012-02-22
Publication date: 2012-08-30

Abstract

This document provides methods and materials for assessing insulin sensitivity in a mammal (e.g., human). For example, the document relates to methods and materials useful for diagnosing, assessing and monitoring progression of insulin resistance in a mammal.

Description

DESCRIPTION

BIOMARKERS OF INSULIN SENSITIVITY

STATEMENT AS TO FEDERALLY SPONSORED RESEARCH

Funding for the work described herein was provided by the federal government under grant number ROl DK41973 and ULRR024150 awarded by the National Institute of Health. The federal government has certain rights in the invention.

PRIORITY CLAIM

This application claims benefit of priority to U.S. Provisional Application Serial No. 61/445,379, filed February 22, 201 1, the entire contents of which are hereby incorporated by reference.

BACKGROUND

1. Technical Field

This document relates to methods and materials for assessing insulin sensitivity in a mammal {e.g., human). For example, this document provides methods and materials for assessing insulin sensitivity in a mammal comprising measuring the levels of biomarkers in a sample, where the levels of the biomarkers indicate presence, absence or degree of insulin sensitivity. For example, this document provides methods and materials for diagnosing a mammal as having normal insulin sensitivity or being insulin resistant. In some aspects, the document relates to methods and kits useful for diagnosing, assessing and monitoring progression of insulin resistance in a mammal.

2. Background Information

Diabetes mellitus, or simply, diabetes, is a group of diseases characterized by high blood glucose levels that result from defects in the body's ability to produce and/or use insulin. The main types include Type 1 diabetes, Type 2 diabetes, and gestational diabetes. Type 1 diabetes is a form of diabetes mellitus that results from autoimmune destruction of insulin-producing beta cells of the pancreas. Type 2 diabetes is characterized by increased hepatic glucose output, increased peripheral resistance to insulin action, and impaired insulin secretion. Gestational diabetes is a type of diabetes that is defined as any degree of glucose intolerance with onset or first recognition during pregnancy.

Insulin is the principal hormone that regulates uptake of glucose from the blood into most cells and inhibits the production of glucose from the liver. Therefore deficiency of insulin or the insensitivity of its action plays a central role in all forms of diabetes mellitus.

Insulin resistance (IR) is a condition in which the body's cells become less sensitive to the glucose-lowering effects of the hormone insulin. Insulin resistance results from inherited and acquired influences. Hereditary causes include mutations of insulin receptor, glucose transporter, and signaling proteins. The common underlying causes of insulin resistance are largely unidentified. Environmental factors such as physical inactivity, abdominal obesity, diet (e.g., high calorie intake), medications (e.g., Cortisol), hyperglycemia (glucose toxicity), increased free fatty acids, and the aging process. The most common type of insulin resistance is associated with overweight and obesity resulting in a condition known as metabolic syndrome. Type 2 diabetes occurs commonly in association with other disorders such as hypertension, dyslipedemia (includes high LD1 cholesterol, low HDL cholesterol, and high triglycerides) and hypercoagulability. All these problems usually occur in association with obesity, especially abdominal obesity. Existence of some or all these problems together are commonly known as metabolic syndrome and insulin resistance is a common factor that occurs in this syndrome.

In a normal metabolism, the elevated blood glucose level makes beta (β) cells in the Islets of Langerhans located in the pancreas release insulin into the blood. The insulin in turn makes insulin-sensitive tissues in the body absorb glucose and inhibits the production of glucose from liver thereby lowering the blood glucose level. The beta cells reduce insulin output as the blood glucose level falls, which maintains blood glucose at approximately 5 mmol/L (mM) (90 mg dL). In an insulin-resistant person, normal levels of insulin do not have the same effect in controlling blood glucose levels. During the compensated phase on insulin resistance insulin levels are higher, and blood glucose levels are still maintained. Therefore, in most people with insulin resistance there are normal levels of glucose but high levels of insulin. If compensatory insulin secretion fails, then either fasting (impaired fasting glucose) or postprandial (impaired glucose tolerance) glucose concentrations increase. Eventually, Type 2 diabetes occurs when glucose levels become higher throughout the day as the resistance increases and compensatory insulin secretion fails.

In pre-diabetes, insulin becomes less effective at helping tissues metabolize glucose. Pre-diabetics may be detectable as early as 20 years before diabetic symptoms become evident. Studies have shown that although patients show very few symptoms, long-term physiological damage is already occurring at this stage. Up to 60% of these individuals will progress to Type 2 diabetes within 10 years. Many well conducted studies have shown that measures to improve insulin sensitivity such as increased physical activity and low calories diet can prevent or delay the onset of diabetes in people who have high chance of becoming diabetic. Diabetic complications involving cardio vascular system, kidneys, and the nervous system are all related not only the control of glucose and other associated problems but also to the duration of diabetes. Diagnosis of people with insulin resistance will allow targeting of these individuals for interventions to delay or prevent diabetes.

Insulin resistance plays a central role in the development of numerous diseases but it is not readily detectable using many of the clinical measurements for pre-diabetic conditions. Therefore there is an unmet need for diagnostic biomarkers and tests that can assess insulin sensitivity to identify insulin resistance and determine the risk of disease progression in subjects with insulin resistance. Insulin resistance biomarkers and diagnostic tests can better identify and determine the risk of diabetes development in a pre-diabetic subject, can monitor disease development and progression and/or regression, can allow new therapeutic treatments to be developed and can be used to test therapeutic agents for efficacy on reversing insulin resistance and/or preventing insulin resistance and related diseases.

SUMMARY

As described herein, blood samples were collected from subjects with impaired fasting glucose or Type 2 Diabetes (T2DM) not receiving any anti-diabetic medications and were randomized in a double blinded design to receive either placebo or a combination of metformin and pioglitazone. Amino acids and their metabolite concentrations were determined in the blood samples. The results provided herein demonstrate that amino acids and their metabolites act as biomarkers of insulin sensitivity. This can allow physicians to develop a clinical assay to determine insulin sensitivity and monitor loss of insulin sensitivity and efficacy of treatments for restoring and maintaining insulin sensitivity.

In general, one aspect of this document features a method of assessing insulin sensitivity in a mammal, the method comprising, (a) obtaining a biological sample from a mammal, (b) analyzing the biological sample from the mammal to determine the level(s) of one or more biomarkers selected from the group consisting of lysine, ethanolamine, beta- amino isobutyric acid, alpha-amino adipic acid, and alpha-amino-N-butyric acid, and (c) comparing the level(s) of the one or more biomarkers in the sample to insulin sensitive and/or insulin resistant reference levels of the one or more biomarkers in order to assess the presence, absence or degree of insulin sensitivity in the mammal. The method can further comprise analyzing the biological sample from the mammal to determine the level(s) of one or more additional biomarkers selected from the group consisting of citrulline, glutamic acid, serine, glycine and arginine. The biological sample can be a body fluid. The body fluid can be a urine sample, a blood sample, or a plasma sample.

In one aspect, this document features a method of diagnosing a mammal as having normal insulin sensitivity or being insulin resistant, the method comprising, (a) obtaining a biological sample from a mammal, (b) analyzing the biological sample from the mammal to determine the level of citrulline, and (c) comparing the level of citrulline in the sample to insulin sensitive and/or insulin resistant reference levels of citrulline in order to diagnose the mammal as having normal insulin sensitivity or insulin resistance. The method can further comprise analyzing the biological sample from the mammal to determine the level(s) of one or more additional biomarkers selected from the group consisting of lysine, ethanolamine, beta-amino isobutyric acid, alpha-amino adipic acid, and alpha-amino-N-butyric acid.

In one aspect, this document features a method of monitoring the progression or regression of insulin resistance in a mammal, the method comprising, (a) analyzing a biological sample from a mammal to determine the level of citrulline and one or more biomarkers for insulin sensitivity selected from the group consisting of ethanolamine, beta- amino isobutyric acid, alpha-amino adipic acid, and alpha-amino-N-butyric acid, and (b) comparing the level of citrulline and one or more biomarkers in the sample to insulin sensitive and/or insulin resistant reference levels in order to monitor the progression or regression of insulin resistance in a mammal. The method can further comprise, determining the level of an additional biomarker selected from the group consisting of glutamic acid, serine, glycine and arginine. In another aspect, this document provides kits for assessing insulin sensitivity in a mammal. The kit can comprise reagents suitable for determining levels of a plurality of biomarkers in a test sample, wherein the plurality of biomarkers comprises two or more of lysine, ethanolamine, beta-amino isobutyric acid, alpha-amino adipic acid, and alpha-amino- N-butyric acid; optionally one or more control samples comprising predetermined levels of the same biomarkers, wherein a comparison of the levels of the biomarkers in the test sample with the levels in the control samples indicates presence, absence, or degree of the insulin sensitivity. The kit of can further comprise reagents suitable for determining the levels of additional biomarkers selected from the group consisting of citrulline, glutamic acid, serine, glycine and arginine.

Unless otherwise defined, all 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 pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

Other features and advantages of the invention will be apparent from the following detailed description, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. The invention may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.

FIG. 1. Effects of 12 weeks of either insulin sensitizer therapy (metformin plus pioglitazone, N=13) or placebo (N=12) on amino acid/amino acid metabolite concentrations in obese insulin resistant adults. The values are shown as the median (box split) and mean (+) percent changes. In addition, the lower (bottom of the box) and upper (top of the box) quartiles, the 10^th and 90^th percentiles (lines), and the outlier values (·) are also presented. All treatment effects are significant at p<0.05. DETAILED DESCRIPTION

Twenty five million US adults are affected by diabetes with an additional seventy nine million adults with prediabetes according to recent estimates by the US Center for Disease Control (CDC, 2011). Moreover, type 2 diabetes (T2D) represents the disease of the 21^st century world-wide due to its rapid increase in prevalence and its concomitant increase in diabetes-related comorbidities. Like hypertension, T2D is a silent killer. Indeed, approximately 25% of individuals with T2D are undiagnosed in USA and much higher in Asian and South Americans (CDC, 2011). Moreover, by the time of the initial diagnosis of T2D (e.g., fasting blood glucose > 126 mg/dL) both the microvascular and macrovascular effects of T2D have begun to take a toll (Kohner et al, 1998; Hillier and Pedula, 2003). Therefore, vigorous efforts are currently underway to develop biomarkers of insulin resistance that may predict T2D risk earlier in its disease progression.

Elevations in branch chain amino acids (BCCA) and amino acid (AA) metabolites have been proposed to contribute to the development of obesity-related insulin resistance (Newgard et al, 2009). Subsequently, elevations in isoleucine, tyrosine, and phenylalanine have been shown to predict future diabetes risk in two independent cohorts (Framingham Offspring Study and the Malmo Diet and Cancer Study) (Wang et al, 2011). Taken together these studies indicate that AA/AA metabolites could serve as potential biomarkers for both current insulin-resistance as well as future diabetes risk. Consistent with this notion, it has recently been reported that the rapid improvement in insulin sensitivity following gastric bypass is associated with reductions in circulating AA/AA metabolite concentrations Laferrere et al, 2011). Many previous studies have shown that insulin is a key regulator of AA/AA metabolites including the appearance rate of amino acids and oxidation (Fukagawa et al, 1985; Schauder et al, 1983; Meek et al, 1998; Gelfand and Barrett, 1987). Although it is not clear whether reduced insulin action on glucose metabolism may result in alteration in AA/AA metabolite levels, insulin deprivation profoundly affects plasma amino acid levels (Nair et al, 1995). If reduced insulin sensitivity (insulin resistance) to glucose metabolism also results in alterations in AA/AA metabolite levels this may offer an opportunity to use the AA/AA metabolites as biomarkers of insulin sensitivity.

The over-arching goal of the experiments described herein was to interrogate whether AA/AA metabolite concentrations may serve as reliable biomarkers of insulin resistance/sensitivity as well as play a more direct role in modulating insulin resistance/sensitivity in humans. Firstly, the inventor determined the independent and combined effects of fasting AA/AA metabolite concentrations on whole-body insulin sensitivity (S|) after adjusting for age, sex, and BMI in healthy adults. Secondly, he determined whether ethnic differences in whole-body Si were associated with concomitant differences in AA/AA metabolite concentrations. Finally, he reported the pharmacologically enhancing whole-body S| with insulin sensitizers (metformin and pioglitazone) on AA/AA metabolite concentrations among insulin-resistant adults.

Thus, this document relates to methods and materials for assessing insulin sensitivity in a mammal (e.g., human). For example, this document provides methods and materials for assessing insulin sensitivity in a mammal comprising measuring the levels of biomarkers in a sample, where the levels of the biomarkers indicate presence, absence or degree of insulin sensitivity. For example, this document provides methods and materials for diagnosing a mammal as having normal insulin sensitivity or being insulin resistant. In some aspects, the document relates to methods and kits useful for diagnosing, assessing and monitoring progression of insulin resistance in a mammal.

As described herein, this document relates to methods and materials for assessing insulin sensitivity in a mammal. For example, this document provides methods and materials for assessing insulin sensitivity in a mammal. A mammal can be any type of mammal including, without any limitation, a mouse, rat, dog, cat, horse, sheep, goat, cow, pig, monkey, or human.

A biological sample can be any biological specimen {e.g., a blood sample) useful for determining or measuring the levels of the biomarkers. Typically, a sample contains one or more biomarkers. The sample can be a body fluid. Examples of body fluids can include blood, serum, plasma, or urine.

As used herein, insulin sensitivity refers to the ability of cells to respond to the effects of insulin to regulate the uptake and utilization of glucose. Insulin sensitivity ranges from normal (insulin sensitive) to Insulin Resistant (IR). Insulin resistant refers to the condition when cells become resistant to the effects of insulin, or when the amount of insulin produced is insufficient to maintain a normal glucose level.

This document relates to the discovery of a plurality of biomarkers that are useful for assessing insulin sensitivity in a mammal. As described herein, the levels of one or more of the biomarkers selected from the group consisting of citrulline, glutamic acid, ethanolamine, serine, beta-amino isobutyric acid, glycine, alpha amino adipic acid, arginine and alpha- amino butyric acid can be assessed in a sample to determine insulin sensitivity in a mammal. Any combination of the biomarkers listed above can be used. In some cases, a mammal can be diagnosed as insulin sensitive based on high concentrations of serine and glycine, and low concentrations of lysine, phenylalanine, tyrosine, arginine, a-amino adipic acid, glutamic acid, citrulline, ethanolamine, amino adipic acid, and alpha-amino-N-butyric acid.

For example, the methods can include providing a biological sample from the mammal; determining a level of one or more, e.g., two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, or all nine biomarkers in the sample; and comparing the levels of the biomarkers with reference levels of the same biomarkers.

The level(s) of the biomarkers are compared to a reference {e.g., insulin sensitive and/or insulin resistant reference levels) wherein the levels of the biomarkers in comparison to the reference is indicative of whether or not the mammal has normal insulin sensitivity and/or should be diagnosed with an insulin sensitivity disorder (e.g., insulin resistance). For example, an "insulin sensitive reference level" of a biomarker means a level of a biomarker that is indicative of normal insulin sensitivity in a mammal, and an "insulin resistance reference level" of a biomarker means a level of a biomarker that is indicative of a diagnosis of insulin resistance in a mammal.

A "reference level" of a biomarker may be an absolute or relative amount or concentration of the biomarker, a presence or absence of the biomarker, a range of amount or concentration of the biomarker, a minimum and/or maximum amount or concentration of the biomarker, a mean amount or concentration of the biomarker, and/or a median amount or concentration of the biomarker; and, in addition, "reference levels" of combinations of biomarkers may also be ratios of absolute or relative amounts or concentrations of two or more biomarkers with respect to each other. A "reference level" may also be a "standard curve reference level" based on the levels of one or more biomarkers determined from a population and plotted on appropriate axes to produce a reference curve (e.g., a standard probability curve). Reference levels may also be tailored to specific techniques that are used to measure levels of biomarkers in biological samples (e.g., LC-MS, GC-MS, NMR, enzyme assays, etc.), where the levels of biomarkers may differ based on the specific technique that is used.

In some cases, the reference comprises predetermined values for a plurality of biomarkers (e.g., each of the plurality of biomarkers). The predetermined value can take a variety of forms. It can be a level of a biomarker in a control mammal (e.g., a mammal with an insulin sensitivity disorder (i.e., an affected mammal) or a mammal without such a disorder (i.e., a normal mammal)). It can be a level of a biomarker in a fasting mammal. It can be a level in the same mammal, e.g., at a different time point. A predetermined value that represents a level(s) of a biomarker referred to herein as a predetermined level. A predetermined level can be single cut-off value, such as a median or mean. It can be a range of cut-off (or threshold) values, such as a confidence interval.

Mammals associated with predetermined values are typically referred to as control mammals (or controls). A control mammal may or may not have an insulin sensitivity disorder (e.g., insulin resistance). In some cases it may be desirable that control mammal is insulin resistant, and in other cases it may be desirable that a control mammal is insulin sensitive. Thus, in some cases the level of a biomarker in a mammal being greater than or equal to the level of the biomarker in a control mammal is indicative of a clinical status (e.g., indicative of an insulin sensitivity disorder diagnosis). In other cases the level of a biomarker in a mammal being less than or equal to the level of the biomarker in a control mammal is indicative of a clinical status. The amount of the greater than and the amount of the less than is usually of a sufficient magnitude to, for example, facilitate distinguishing a mammal from a control mammal using the disclosed methods. Typically, the greater than, or the less than, that is sufficient to distinguish a mammal from a control mammal is a statistically significant greater than, or a statistically significant less than. In cases where the level of a biomarker in a mammal being equal to the level of the biomarker in a control mammal is indicative of a clinical status, the "being equal" refers to being approximately equal (e.g., not statistically different).

The predetermined value can depend upon a particular population of mammals selected. For example, an apparently healthy population will have a different 'normal' range of biomarkers than will a population of mammals which have, or are likely to have, an insulin sensitivity disorder. Accordingly, the predetermined values selected may take into account the category (e.g., healthy, at risk, diseased) in which a mammal falls. Appropriate ranges and categories can be selected with no more than routine experimentation by those of ordinary skill in the art.

In some cases a predetermined value of a biomarker is a value that is the average for a population of healthy mammals (e.g., human subjects who have no apparent signs and symptoms of an insulin sensitivity disorder). The predetermined value will depend, of course, on the particular biomarker selected and even upon the characteristics of the population in which the mammal lies.

The levels of the biomarkers from a biological sample from a mammal can be obtained by any art recognized method. Typically, the level is determined by measuring the level of the biomarker in a body fluid (clinical sample), e.g. blood, plasma, or urine. The level can be determined by any method known in the art, e.g. immunoassays, enzymatic assays, mass-spectrometry (MS), tandem-mass-spectrometry (MS-MS), Liquid chromatography- mass spectrometry (LC-MS), high performance liquid chromatography (HPLC), Ultra- Performance Liquid Chromatography (UPLC), nuclear magnetic resonance (NMR) spectroscopy, infrared (IR) spectroscopy, gas chromatography (GC) or other known techniques for determining the presence and/or quantity of a metabolite. Conventional methods include sending clinical sample(s) to a commercial laboratory for measurement or the use of commercially available assay kits.

In some cases, the methods described in this document are useful for diagnosing a mammal as being insulin sensitive or insulin resistant. The methods described herein may be combined with the results of clinical measurements useful in clinical determination of disorders or conditions associated with insulin sensitivity. For example, glucose disposal rates (Rd, M-value, or glucose infusion rate to maintain similar glucose levels during the hyperinsulinemic euglycemic clamp), body weight measurements, waist circumference measurements, BMI determinations, waist/hip ratio, triglycerides measurements, cholesterol (HDL, LDL) measurements, LDL/HDL ratio, triglyceride/HDL ratio, age, family history of diabetes (T1D and or T2D), family history of cardiovascular disease, Peptide YY measurements, C-peptide measurements, Hemoglobin AIC measurements and estimated average glucose, (eAG), adiponectin measurements, fasting plasma glucose measurements (e.g., oral glucose tolerance test, fasting plasma glucose test), free fatty acid measurements, fasting plasma insulin and pro-insulin measurements, systolic and diastolic blood pressure measurements, and urate measurements.

In another aspect, this document provides a method of monitoring the progression or regression of insulin resistance in a mammal. Monitoring the progression or regression of insulin resistance (e.g., increase or decrease in insulin sensitivity) can be determined for many reasons. For example, it may be desirable to determine progression of insulin resistance (e.g., decrease in insulin sensitivity) of a mammal to select appropriate treatment. In one aspect of the document, the methods further include selecting a treatment (i.e., a treatment for insulin resistance) for the mammal based on the comparison of the levels of the biomarkers with the insulin resistance reference levels for said biomarkers. In some embodiments, the methods further include administering the selected treatment to the mammal. In some cases, the method can further comprise determining the levels of biomarkers at different time points to determine efficacy of the selected treatment in a mammal. In some embodiments, the treatment can comprise administering to the mammal an effective amount of at least one anti- diabetes compound, and/or instructing the subject to adopt at least one lifestyle change. Examples of anti-diabetes compounds are metformin, rosiglitazone and pioglitazone. Examples of lifestyle changes are dietary changes, exercise routine, and weight-loss surgery.

Other reasons for determining the progression or regression of insulin resistance (e.g., increase or decrease in insulin sensitivity) include monitoring treatment response (e.g., response to a particular drug or therapy regimen) and predicting phenotype (e.g., the likelihood of developing diabetes). In some cases, determining the progression or regression of insulin resistance (e.g. increase or decrease in insulin sensitivity) can be used to optimize the dosages of a particular drug given to the mammal. Thus, the applications of the invention are numerous and are not limited to the specific examples described herein.

The document also provides kits for evaluating biomarkers in a mammal. The kits of the invention can take on a variety of forms. Typically, the kits will include reagents suitable for determining levels of one or more of the biomarkers disclosed herein (e.g., citrulline, glutamic acid, ethanolamine, serine, beta-amino isobutyric acid, glycine, alpha-amino adipic acid, arginine and alpha-amino butyric acid). For example, the kits may contain, one or more control samples. For example, the control samples can be specific for levels of one or more biomarkers that correspond to insulin sensitive and/or insulin resistant reference levels. Typically, a comparison between the levels of the biomarkers in the sample from the mammal and the levels of the biomarkers in the control samples is indicative of a clinical status (e.g., diagnosis, likelihood assessment, insulin sensitivity, glucose control capacity, etc.). Also, the kits, in some cases, will include written information (indicia) providing a reference (e.g., predetermined values), wherein a comparison between the levels of the biomarkers in the subject and the reference (pre-determined values) is indicative of a clinical status.

Also, the kits, in some cases, will include written information (indicia) providing a reference (e.g., predetermined values), wherein a comparison between the levels of the biomarkers in the subject and the reference (pre-determined values) is indicative of a clinical status. In some cases, the kits comprise software useful for comparing biomarker levels with a reference (e.g., a prediction model). Usually the software will be provided in a computer readable format such as a compact disc, but it also may be available for downloading via the internet. However, the kits are not so limited and other variations with will apparent to one of ordinary skill in the art. The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

EXAMPLES

The following examples are included to demonstrate particular embodiments of the invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventor to function well in the practice of the invention. However, those of skill in the art, should in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention.

Example 1 - Methods

Study Parameters. The participants in Study I, II and III were recruited from the local community to participate one of five protocols approved by the Mayo Clinic Institutional Review Board. Participants provided informed consent prior to participation. Participants were excluded if they were currently taking antidiabetic medications. Body composition, whole-body S|, and fasting AA and AA metabolite concentrations were assessed in the Mayo Clinic's Center for Translational Science Activities' Clinic Research Unit (CRU) as described below. All measurements were made in the fasting-state. Body composition was assessed by dual-energy x-ray absorptiometry (Lunar DPX-L; Lunar Radiation, Madison, WI) outpatient visit. Subsequently, following three days of a standardized weight maintaining diet, participants were admitted overnight to the CRU and studied the following morning.

Whole-body Si was defined as the steady-state glucose infusion rate (GIR, μηιοΙ/Κ^- FFM min) achieved during the last 2h of an 8h hyperinsulinemic-euglycemic clamp. Insulin was infused at a rate of 1.5 mU/kgFFM/min. A mixture of amino acids (either 10% Travasol, Baxter Healthcare Corporation, Deerfield, IL, 5.4% NephrAmine, B. Braun Medical Inc., Bethlehem, PA, or Trophamine, B. Braun Medical Inc., Bethlehem, PA) was infused to prevent insulin-induced hypoaminoacidemia. Arterialized venous blood was used to measure glucose levels every 10 min with either a Beckman glucose analyzer (Beckman Coulter, Fullerton, CA) or a GM9 Analox glucose analyzer (Analox Instruments, London, UK). The GIR (40% solution) was adjusted to maintain euglycemia [~5.0 mmol/L (90 mg/dL)] during the 8h constant infusion of insulin. Fasting AA and AA metabolite concentrations were measured in plasma using the following UPLC-MS method. Plasma samples were spiked with internal standards then deproteinized with cold methanol followed by centrifugation at 10,000 g for 5 minutes. The supernatant was immediately derivatized with 6-aminoquinolyl- N-hydroxysuccinimidyl carbamate according to Waters' MassTrak kit. A 10-point calibration standard curve underwent similar derivatization procedure after the addition of internal standards. Both derivatized standards and samples were analyzed on a triple quadrupole mass spectrometer coupled with an Ultra Pressure Liquid Chromatography system. Data acquisition was done using select ion monitor (SRM). Concentrations of 42 analytes of each unknown were calculated against each perspective calibration curve.

Study I. To be eligible to participate in Study I participants had to have completed an 8h hyperinsulinemic-euglycemic clamp and have their fasting concentrations of AA and AA metabolites measured by UPLC-MS. 1 13 non-diabetic and 9 newly diagnosed T2D participants were included in Study I.

Study II. Thirteen non-diabetic Asian Indians and 13 age, sex, and BMI matched Northern European Americans were recruited to participate in a study that examined whether Asian Indians have any metabolic differences that may render them more susceptible to insulin-resistance and type 2 diabetes (Nair et al., 2008). A detailed description of this study and primary results have previously been reported (Nair et al, 2008). To be eligible to participate in Study II, participants had to have completed an 8h hyperinsulinemic- euglycemic clamp and have their fasting concentrations of AA and AA metabolites measured by UPLC-MS. In contrast to study I, no AA were infused during the hyperinsulinemic- euglycemic clamp performed in study II.

Study III. A subset of participants that were included in Study I also completed Study III. Study III was a 12 week double-blind, placebo-controlled study of the effects of insulin sensitizers (metformin and pioglitazone) on whole-body Si and AA metabolism. To be eligible to participate in Study III participants had to have completed a hyperinsulinemic- euglycemic clamp and have their fasting concentrations of AA/AA metabolites measured by Ultra Pressure Liquid Chromatography Mass Spectrometry (UPLC-MS) at baseline and following the 12 week intervention. 25 participants with untreated pre-diabetes or diabetes (fasting glucose 100-180 mg/dL) were included in Study III. Participants were randomized to receive either 45 mg of pioglitazone per day plus 1 g of metformin twice per day (n=12) or placebo (n=13) for 12 weeks. Statistical Methods. All statistical analyses were conducted using SAS software (SAS Version 9.1, Cary, NC). All data were examined for departures from normality and transformations were employed as needed. In Studies I and II, Spearman correlation coefficients (p) were used to test for univariate association among AA, AA metabolites, QUICKI, and steady-state GIR. Results are presented as medians with interquartile ranges. In Studies I and II, unpaired, Mann- Whitney tests were used to test for differences between groups (e.g., males vs. females, Asian Indians vs. Northern European Americans). In Study III, unpaired, Mann- Whitney tests were used to test for differences between treatment groups with respect to the pre- to postinternvention changes scores (Δ). AA and AA metabolites with significant Spearman correlation coefficients (p) were included in a stepwise multiple regression analysis. Age, sex, BMI, and AA infusion, were also included into the model as fixed effects. Statistical significance was set at <x=0.05.

Example 2 - Results

Association between Fasting AA/AA Metabolite Concentrations and Measures of Whole-body Si (Study I). In a cross-sectional analysis, associations between AA AA metabolite concentrations and measures of whole-body Si were determined. By design, the participants had a wide range of age (18-80 yr), body composition (7.2-56.8% fat), and whole-body Si(steady-state GIR, 0.9 - 104.2 μπιοΙ/kgFFM/min). Table 1 presents the participants' anthropometric, metabolic, and AA/AA metabolite data stratified by sex.

First, the inventor examined the association between fasting AA/AA metabolite concentrations and the quantitative insulin sensitivity index (QUICKI) (Katz et al, 2000), which is a surrogate measure of whole-body Si that is based on fasting measures of glucose and insulin. Subsequently, the inventor examined the association between fasting AA/AA metabolite concentrations and the steady-state GIR during a hyperinsulinemic-euglycemic clamp (DeFronzo et al, 1979), which also included the concurrent infusion of amino acids to prevent insulin-induced hypoaminoacidemia. Table 6 presents the Spearman-rank correlations between the fasting AA/AA metabolite concentrations and the two measures of whole-body Si. Forty percent (13/32) of the AA/AA metabolite concentrations were significantly associated with QUICKI, of which 85% were inverse associations ranging from r = -0.452 (a-amino adipic acid, pO.0001) to r = -0.181 (isoleucine, p=0.047). Likewise, forty-one percent (13/32) of the AA/AA metabolite concentrations were significantly associated with the steady-state GIR, of which 69% were inverse associations ranging from r = -0.505 (a-amino adipic acid, pO.0001) to r = -0.181 (aspartic acid, p=0.0460). Since differences in the composition of the AA mixtures that were infused during the different hyperinsulinemic-euglycemic clamp protocols could have affected the steady-state GIR, the inventor conducted a sub-analysis that included eighty five participants that were infused with 10% Travasol during their hyperinsulinemic-euglycemic clamp study. Table 6 also presents the Spearman-rank correlations between the fasting AA AA metabolite concentrations and the two measures of whole-body S] for this subanalysis. Twenty-two percent (7/32) of the AA/AA metabolite concentrations were significantly associated with the QUICKI, of which 100% were inverse associations ranging from r = -0.401 (a-amino adipic acid, p=0.0002) to r = -0.231 (phenylalanine, p=0.035). Forty-one percent (12/32) of the AA/AA metabolite concentrations were significantly associated with the steady-state GIR, of which 69% were inverse associations ranging from r = -0.549 (a-amino adipic acid, pO.0001) to r = -0.234 (serine, p=0.045).

Next, the inventor examined whether sex differences affect the association between the AA/AA metabolite concentrations and the two measures of whole-body S|. There were no significant differences observed between males and females for neither QUICKI nor the steady-state GIR. In contrast, thirty-one percent (10/32) of the AA/AA metabolite concentrations were significantly lower females compared to the males (Table 1). In men, forty-one percent (13/32) of the AA/AA metabolite concentrations were significantly associated with QUICKI, of which 77% were inverse associations ranging from r = -0.638 (cc- amino adipic acid, pO.0001) to r = -0.298 (lysine, p=0.028). In men, twenty-five percent (8/32) of the AA/AA metabolite concentrations were significantly associated with the steady- state GIR, of which 63% were inverse associations ranging from r = -0.658 (a-amino adipic acid, pO.0001) to r = -0.316 (aspartic acid, p=0.018). In women, nineteen percent (6/32) of the AA/AA metabolite concentrations were significantly associated with QUICKI, of which 67% were inverse associations ranging from r = -0.344 (alanine, p=0.005) to r = -0.292 (a- amino-N-butyeric acid, p=0.017). In women, twenty-five percent (8/32) of the AA/AA metabolite concentrations were significantly associated with the steady-state GIR, of which 75% were inverse associations ranging from r = -0.406 (a-amino adipic acid, p=0.0007) to r = -0.259 (alanine, p=0.036).

Table 2 presents the results of the multivariate modeling for the prediction of whole- body Si (i.e., steady-state GIR) from AA/AA metabolite concentrations adjusted for age, sex, and BMI as well as the type of AA infusion employed during the hyperinsulinemic- euglycemic clamp. The final model explained ~70% of the variance in whole-body Si. Five AA/AA metabolites were retained in the model. Ethanolamine and glycine were positive predictors of whole-body S|, whereas glutamic acid, a-amino adipic acid, and phenylalanine were negative predictors of whole-body Si after adjusting for age, sex, BMI, and type of AA infusion. The individual AA/AA metabolites explained between 4 and 11% of the variance in whole-body SI. Of interest, a-amino adipic acid and phenylalanine explained as much or more of the variance in whole-body Si than BMI. Moreover, neither age nor sex were significant predictors of whole-body S| in the present cohort.

Effects of Race/Ethnicity on Whole-body Si and Fasting AA /AA Metabolite Concentrations (Study II). Table 3 presents anthropometric, metabolic, and AA AA metabolite concentration data stratified by ethnicity obtained from the 13 non-diabetic Asian Indians and the 13 age, sex, and BMI matched non-diabetic Northern European Americans. As the inventors has previously reported, whole-body Si was significantly lower in the Asian Indians than the Northern European Americans (Nair et al, 2008). Specifically, Asian Indians had higher fasting glucose and insulin concentrations compared to the Northern European Americans, which was associated with lower measures of whole-body Si (e.g., QUICKI and steady-state GIR) in the Asian Indians compared to the Northern European Americans.

Based on the result from Study I and those from previous reports in the literature (Newgard et al, 2009; Wang et al, 2011; LaFerrere et al, 2011) the inventor expected that the AA/AA metabolite concentrations would be higher in the insulin-resistant Asian Indians compared to the insulin-sensitive Northern European Americans. Unexpectedly, sixteen percent (5/32) of the AA/AA metabolite concentrations measured were significantly lower in the Asian Indians compared to the Northern European Americans (Table 3). Moreover, none of the AA/ AA metabolite concentrations that were measured was higher in the Asian Indians compared to the Northern European Americans. Since ethnic differences in body composition and/or diet could have contributed to the lower AA/AA metabolite concentrations that were observed in the Asian Indians compared to the Northern European Americans, the inventor examined whether the relationship between AA/AA metabolite concentrations and the two measures of whole-body S| was also present among Asian Indians. Thirteen percent (4/32) AA/AA metabolite concentrations were inversely correlated with the steady-state GIR (1-methylhistidine: p = -0.783, p = 0.013; ethanolamine p = -0.665, p = 0.013; tyrosine p = -0.626, p = 0.022; and valine p = -0.654, p = 0.015) among the Asian Indians. Moreover, the sum of the BCAA (valine, leucine, and isoleucine) and Aromatic AA (phenylalanine and tyrosine) were also inversely correlated with the steady-state GIR (p = - 0.593, p = 0.033 and p = -0.533, p = 0.061, respectively) among Asian Indians. Effects of Insulin Sensitizers on Whole-body Si and Fasting AA AA Metabolite Concentrations (Study III). Table 4 presents the baseline anthropometric, metabolic, and AA/AA metabolite data stratified by treatment group. At baseline there were no significant differences between treatment groups for any of the anthropometric, metabolic, or AA/AA metabolite data (all p>0.05). As expected, 12 weeks of insulin sensitizer therapy increased whole-body S_\ (Table 5). Moreover, 12 weeks of insulin sensitizer therapy increased the concentrations of serine and glycine, while concomitantly decreasing the concentration of lysine, phenylalanine, tyrosine, arginine, a-amino adipic acid, glutamic acid and citrulline (Table 5 and FIG. 1).

Table 1. Study I's participants' anthropometric, metabolic, and AA/AA metabolite data stratified by sex. Data presented as median (interquartile range).

Table 2. Multivariate regression analysis (n=122).

Standardized Partial

B P-Value R²

Model 0.695

Intercept 82.090 0.000 <0.0001 -

Age -0.012 -0.013 0.814 <0.001

BMI -0.087 -0.216 0.002 0.087

Sex 2.270 0.059 0.310 0.009

Travasol 13.848 0.330 0.0007 0.099

Nephramine -6.101 -0.128 0.186 0.016

Ethanolamine 1.570 0.155 0.019 0.048

Glycine 0.054 0.200 0.001 0.090

Glutamic Acid -0.045 -0.133 0.029 0.042 a-Amino adipic Acid -1 1.727 -0.228 0.001 0.093

Phenylalanine -0.475 -0.252 0.0003 0.1 13

Age expressed in years.

Model set sex to 1 for men and 0 for women.

Amino acid and amino acid metabolite concentrations expressed as μπιοΐ per liter. Partial R² represents the partial squared correlation coefficient derived from the multiple regression model.

Table 3. Study II's participants' anthropometric, metabolic, and AA/AA metabolite data stratified by ethnicity. Data presented as median (interquartile range).

Table 4. Study Ill's participants' anthropometric, metabolic, and AA/AA metabolite data at baseline stratified by treatment group. Data presented as median (interquartile range).

Table 5. The change in anthropometric, metabolic, and AA/AA metabolite data in response to 12 weeks of insulin sensitizer therapy or placebo. Data presented as median (interquartile range)

Table 6. Spearman rank correlations (p) between amino acid/amino acid metabolite concentrations and measures of whole body insulin sensitivity observed in participants in Study I.

a: All subjects; b: Only subjects receive Travasol

* : PO.05; **: PO.01 ;†: PO.001 ;††: PO.0001

QUICKI: Quantitative Insulin Sensitivity Check Index (l/(log(insulin)+log(gIucose)) GIR: Steady-state glucose infusion rate during a hyperinsulinemic euglycemic clamp, which included a concurrent infusion of amino acids to prevent insulin-induce hypoaminoacidemia. Table 7. Spearman rank correlations (p) between amino acid/amino acid metabolite concentrations and measures of whole body insulin sensitivity observed in participants in Study II stratified by ethnicity.

*: PO.05

QUICKI: Quantitative Insulin Sensitivity Check Index (l/(log(insulin)+log(glucose)) GIR: Steady-state glucose infusion rate during a hyperinsulinemic euglycemic clamp.

Example 3 - Discussion

The current study demonstrates that elevations in several AA/AA metabolites are associated with lower whole-body Si. In particular, glutamic acid, a-amino adipic-acid, and phenylalanine were negative predictors of whole-body Si independent of age, sex, and BMI. In contrast, ethanolamine and glycine were positive predictors of whole-body Si independent of age, sex, and BMI. In addition, the inventor also observed that the concentrations of several AA/AA metabolites were lower in females compared to males, and in Asian Indians compared to Northern European Americans. While the inventor observed sex- and ethnicity- related differences in the absolute concentrations of several AA/AA metabolites, the observed associations between measures of whole-body Si and the concentrations of the AA/AA metabolites were similar within the males and females, as well as within the Asian Indians and the Northern European Americans. Importantly, the inventor demonstrated that 12 weeks of insulin sensitizer therapy (metformin plus pioglitazone) induced improvements in whole- body Si and resulted in concomitant reductions in 25% of the concentrations of the AA/AA metabolites measured. Pharmacological improvement of whole-body Si, while concomitantly increasing serine and glycine and decreasing concentrations of lysine, phenylalanine, tyrosine, arginine, a- amino adipic acid, glutamine, and citrulline.

The present results confirm that elevations in the concentrations of several AA/AA metabolites are associated with lower levels of whole-body Si (Newgard et al, 2009; Wang et al, 2011; Laferrere et al, 2011). In contrast to previous reports (Newgard et al, 2009; Laferrere et al., 2011) that have indicated that elevations in BCAA are associated with lower levels of whole-body Si (Newgard et al, 2009; Fiehn et al, 2010), the present results indicate that the BCAA were not significant predictors of whole-body Si, particularly after adjusting for age, sex, and BMI (Table 2). However, elevations in the aromatic AA were associated with lower whole-body Si, which is consistent with the recent finding that elevations in the aromatic AA predict the development of type 2 diabetes mellitus (Wang et al, 2011). Moreover, the current study demonstrated that 12 weeks of insulin sensitizer therapy significantly reduced the concentrations of the aromatic AA (FIG. 1, which occurred in the absence of significant changes in the concentrations of the BCAA in obese insulin resistant adults. Taken together, these results indicate that the aromatic AA may be a better biomarker of whole-body Si than BCAA.

In addition to elevations in the aromatic AA, a novel finding is that elevations in a- adipic acid were also associated with lower levels of whole-body Si. Importantly, a-amino adipic acid remained a significant negative predictor of whole-body S| after adjusting for age, sex, and BMI. Moreover, 12 weeks of insulin sensitizer therapy reduced the concentration of a-amino adipic acid, while concomitantly increasing whole-body Si in obese insulin resistant adults, a-amino adipic acid (also known as 2-amino adipic acid) is an oxidized metabolite of lysine, which has been reported to be a biomarker of protein oxidation in individuals with T2D (Sell et al, 2007). Mechanistically, lysine is initially oxidized to allysine (also known as a-amino adipic-6-semialdehyde) by a Strecker-type reaction in the presence of hyperglycemia (Akagawa et ai, 2002), which can be further oxidized to a-amino adipic acid in the presence of high concentrations of H₂0₂ and low concentrations of glutathione (Fan et al., 2009). Indeed, a-amino adipic acid concentrations are elevated in both animals (Wijekoon et al, 2004) and humans (Sell et al, 2007) with diabetes. Therefore, elevations in a-amino adipic acid in insulin-resistant people likely results from an initial conversion of lysine to allysine as a result of their glucotoxic environment, which is concomitantly, converted to a-amino adipic acid in the presence of high concentrations H2O2 and/or low concentrations glutathione.

The current results indicated that glutamic acid was inversely associated with whole- body Si. Importantly, glutamic acid remained as a negative predictor of whole-body Si after adjusting for age, sex, and BMI. In humans, elevations in glutamic acid and/or glutamate in insulin resistant states have been attributed to concomitant increases in BCAA catabolism Newgard et al, 2009; Tai et al, 2010). Elevations in glutamic acid and/or glutamate in insulin resistant states likely provide gluconeogic precursors, which may facilitate the concomitant development of fasting hyperglycemia. An important observation in the current study is that on 12 weeks of insulin sensitizer therapy reduced the concentrations of glutamic acid in insulin resistant adults (Table 5 and FIG. 1, which coincided with reductions in fasting glucose (Table 5). Indeed, metformin has been shown to reduce gluconeogenesis (Stumoll et al, 1995) causing the reduction of hepatic glucose production. Taken together, these data support a hypothesis that elevations in glutamic acid and/or glutamate may serve as a biomarker for hepatic insulin resistance.

These results also indicated that elevations in glycine and ethanolamine were positively associated with whole-body S|. Moreover, both glycine and ethanolamine remained significant positive predictors of whole-body SI after adjusting for age, sex, and BMI. It has been reported that glycine and ethanolamine concentrations are higher in obese non-diabetic compared to obese T2D African American women (Fiehn et al, 2010). Likewise, Newgard and colleagues Newgard et al, 2009) reported that in insulin sensitive lean adults compared to insulin resistant obese adults have higher levels of glycine. It has also been reported that plasma glycine concentrations are lower in obese insulin resistant ZDF fatty rats compared to their lean littermates (Wijekoon et al, 2004) as well as in obese (ob/ob) mice compared to their lean littermates (She et al, 2007). Again, twelve weeks of insulin sensitizer therapy resulted in elevations in the concentration of glycine (Table 5 and FIG. 1 in obese insulin resistant adults. However, unexpectedly, 12 weeks of insulin sensitizer therapy resulted in a reduction in the concentration of ethanolamine (Table 5 and FIG. 1 in obese insulin resistant adults. Therefore, glycine may be a more robust positive predictor of whole-body insulin sensitivity than ethanolamine.

Next, the inventor examined whether there were sex differences in the AA/AA metabolite concentrations. Table 1 demonstrates that several of the AA/AA metabolite concentrations were lower in females compared to males despite having similar levels of whole-body Si. The present results corroborate the recent report that females have lower AA/AA metabolites compared to males that was observed in the Cooperative Research in the Region of Augsburg (KORA) cohort (Mittelstrass et al., 201 1). Taken together, these results indicate that sex should be taken into consideration when examining the association between AA/AA metabolites and clinical outcomes including whole-body Sj. Therefore, the inventor examined the associations between the concentrations of the AA/AA metabolites and the measures of whole-body Si stratified by sex. The data demonstrates that the associations between AA/AA metabolites and measures of whole-body Si showed similar trends in males and females.

Since the inventor has previously reported that non-diabetic Asian Indians are severely insulin resistant compared to non-diabetic Northern European Americans matched for age, sex, and BMI (Nair et al, 2008), the inventor took this opportunity to determine whether ethnic differences in the concentrations of AA/AA metabolites could explain the ethnic differences in whole-body Si. Table 3 reveals that several of the AA/AA metabolite concentrations were lower in the Asian Indians compared to their Northern European American counterparts. The underlying mechanism(s) by which the AA/AA metabolites are lower in Asian Indians compared to Northern European Americans remains to be determined. Lower dietary intake of protein and / or lower lean body mass could potentially contribute to the lower concentrations of the AA/AA metabolites in the Asian Indians compared to the Northern European Americans. These results indicate that ethnicity should also be taken into consideration when examining the association between AA AA metabolites and clinical outcomes including whole-body Si. The data demonstrates that the associations between the AA/AA metabolites and the measures of whole-body Si showed similar trends. Of interest, the BCAA and the aromatic AA were negatively associated with the measures of whole-body Si in the Asian Indians. Similarly, a recent report also indicated that aromatic AA, particularly tyrosine, were elevated in insulin resistant Asian Indians compared to insulin sensitive Asian Indians (Tai et al., 2010). However, Tai and colleagues did not observe a significant difference in the concentrations of the BCAA between the insulin resistant and insulin sensitive Asian Indians. Taken together, these results indicate that the aromatic AA may be a more robust biomarker of insulin resistance than the BCAA among Asian Indians.

As the inventor has discussed, elevations in several key AA AA metabolites likely contribute to the development of insulin resistance. Likewise, reductions in a select group of AA/AA metabolites may also contribute to the development of insulin resistance. Since cross-sectional studies are not able to draw definitive conclusions with respect to the cause and effect relationship between the differences in the concentration of AA/AA metabolites and the presence of insulin resistance, the inventor examined the effect of 12 weeks of insulin sensitizer therapy. Twelve weeks of insulin sensitizer therapy significantly increased whole- body Si (Table 5 and FIG. 1). Moreover, insulin sensitizer therapy significantly increased the concentrations of serine and glycine, while concomitantly reducing the concentrations of lysine, phenylalanine, tyrosine, arginine, ethanolamine, a-amino adipic acid, glutamic acid, and citrulline (Table 5 and FIG. 1). Based on previous work in the literature, one would have predicted that pharmacologically-induced improvements in whole-body Sj would have elicited concomitant reductions in branch chain AA (Newgard et al, 2009; Laferrere et ah, 2011). However, despite marked improvement in Si insulin sensitizer therapy had no effect on the BCAA concentrations. As noted previously, insulin sensitizer therapy resulted in reductions in concentrations of the Aromatic AA (Table 5 and FIG. 1). Again, indicating that the aromatic AA may be a more robust biomarker of insulin resistance.

In addition to the reductions in the concentrations of aromatic AA, glutamic acid, and a-amino adipic acid that would be predicted base on the cross-sectional analyses, the inventor also observed a significant reductions in the concentrations of both arginine and citrulline. The underlying mechanism(s) of the insulin-sensitizer induced reductions in arginine and citrulline remain to be determined. Arginine and citrulline are key components of two metabolic pathways: 1) nitric oxide biosynthesis and 2) the urea cycle. Chronic stimulation of the urea cycle in response to an increase in response to AA catabolism could potentially lead to a relative depletion in plasma concentrations of arginine and citrulline. Future investigations are warranted to investigate the underlying mechanism(s) of the insulin- sensitizer induced reductions in arginine and citrulline as well as the physiological significance.

Finally, the finding that infusion of the NephrAmine AA mixture resulted in lower whole-body Si compared to the infusion of the Travasol AA mixture may be due to the higher concentrations of leucine, valine, and phenylalanine. Moreover, the participants who received the NephrAmine AA mixture were recruited to participate in Study II based on the presence of impaired fasting glucose. Inasmuch, the NephrAmine-effect on whole-body Si may be due to a cohort effect.

In summary, the present results indicate that phenylalanine, glutamic acid, and a- amino adipic acid are negative predictors of whole-body Si independent of age, sex, and BMI. Moreover, the present data highlight that sex- and ethnic-differences exist with respect to the absolute concentrations of several AA/AA metabolites, therefore both sex and ethnicity should be taken into account when examining the association between AA/AA metabolites and clinical outcomes. Finally, 12 weeks of insulin sensitizer therapy significantly reduces the concentatrations of phenylalanine, glutamic acid, and a-amino adipic acid as well as other physiologically relevant AA/AA metabolites.

OTHER EMBODIMENTS

It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

REFERENCES

The following references, to the extent that they provide exemplary procedural or other details supplementary to those set forth herein, are specifically incorporated herein by reference.

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Claims

1. A method of assessing insulin sensitivity in a mammal, the method comprising:

(a) obtaining a biological sample from a mammal;

(b) analyzing the biological sample from the mammal to determine the level(s) of one or more biomarkers selected from the group consisting of lysine, ethanolamine, beta-amino isobutyric acid, alpha-amino adipic acid, and alpha- amino-N-butyric acid; and

(c) comparing the level(s) of the one or more biomarkers in the sample to insulin sensitive and/or insulin resistant reference levels of the one or more biomarkers in order to assess the presence, absence or degree of insulin sensitivity in the mammal.

2. The method of claim 1, further comprising analyzing the biological sample from the mammal to determine the level(s) of one or more additional biomarkers selected from the group consisting of citrulline, glutamic acid, serine, glycine and arginine.

3. The method of claim 1 , wherein the biological sample is a body fluid.

4. The biological sample of claim 3, wherein the body fluid is an urine sample, a blood sample, or a plasma sample.

5. A method of monitoring the progression or regression of insulin resistance in a mammal, the method comprising:

(a) analyzing a biological sample from a subject to determine the level(s) of citrulline and one or more biomarkers for insulin sensitivity selected from the group consisting of lysine, ethanolamine, beta-amino isobutyric acid, alpha-amino adipic acid, and alpha-amino-N-butyric acid, and

(b) comparing the level(s) of citrulline and the one or more biomarkers in the sample to insulin sensitive and/or insulin resistant reference levels in order to monitor the progression or regression of insulin resistance in a mammal.

6. The method of claim 5, further comprising determining the level of an additional biomarker selected from the group consisting of glutamic acid, serine, glycine and arginine.

7. A kit for assessing insulin sensitivity in a mammal, the kit comprising: reagents suitable for determining levels of a plurality of biomarkers in a test sample, wherein the plurality of biomarkers comprises two or more of lysine, ethanolamine, beta-amino isobutyric acid, alpha-amino adipic acid, and alpha- amino-N-butyric acid; optionally one or more control samples comprising predetermined levels of the same biomarkers, wherein a comparison of the levels of the biomarkers in the test sample with the levels in the control samples indicates presence, absence, or degree of the insulin sensitivity.

8. The kit of claim 7, further comprising reagents suitable for determining the levels of additional biomarkers selected from the group consisting of citrulline, glutamic acid, serine, glycine and arginine.