# Patent

## Title
Metabolic biomarkers for diabetes and insulin resistance

## Abstract

Disclosed are small molecule metabolites useful as biomarkers for the evaluation and treatment of pre diabetes, diabetes and insulin resistance.

## Background
CROSS REFERENCE TO RELATED APPLICATIONS
This application claims priority under 35 USC §§119 and 120 to U.S. Provisional Application Ser. No. 61/254,304, filed Oct. 22, 2009.


FIELD OF THE INVENTION
The present invention relates to the identification and use of small molecule metabolites that mark insulin resistance, pre-diabetes and diabetes.
SUMMARY OF THE INVENTION
An embodiment of the invention is the identification of small molecule metabolites that mark diabetes, diabetes risk, insulin resistance, or other metabolic disorders associated with changes in insulin activity as discovered in an analysis of metabolite patterns in plasma samples derived from type 2 diabetic (T2D) human subjects.
Another embodiment of the invention is the combination of newly identified small molecule metabolites with known metabolites to mark metabolic perturbation, diabetes risk, insulin resistance, or other metabolic disorders associated with changes in insulin activity.
Another embodiment of the invention is the use of small molecule metabolites associated with T2D as biomarkers in clinical tests of animal biofluids as a prognostic or diagnostic indicator of diabetes, insulin resistance, or other metabolic disorders associated with changes in insulin activity.
A further embodiment is the use of small molecule metabolites to evaluate responses to pharmacologic, nutritional, or other modalities designed to combat metabolic diseases involving insulin resistance and poor blood sugar control, including, e.g., diabetes, diabetes risk level, insulin resistance, or other metabolic disorders associated with changes in insulin activity.
DEFINITIONS
As used herein:
“MS” refers to Mass Spectrometry.
“Biomarker” refers to small molecule metabolites that mark diabetic, pre diabetic, insulin resistance or other conditions associated with poor blood sugar control or altered insulin activity.
“Treatment” refers to both therapeutic treatment and prophylactic or preventative measures, wherein the object is to reverse, 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.
A “pharmaceutically acceptable carrier” includes any and all solvents, dispersion media, coatings, antibacterial and antifungal agents, isotonic and absorption delaying agents, and the like, compatible with pharmaceutical administration (Gennaro, 2000). Preferred examples of such carriers or diluents include, but are not limited to, water, saline, Finger's solutions, dextrose solution, and 5% human serum albumin. Liposomes and non-aqueous vehicles such as fixed oils may also be used. Except when a conventional media or agent is incompatible with an active compound, use of these compositions is contemplated. Supplementary active compounds can also be incorporated into the compositions.
The term “subject” as used herein relates to animals, preferably to mammals such as mice, rats, sheep, dogs, cats, horses, monkeys, or cows and, also preferably, to humans.
The term “comparing” refers to assessing whether the results of the determination described herein in detail, i.e. the results of the qualitative or quantitative determination of the at least one metabolite, are identical or similar to reference results or differ there from.


DETAILED DESCRIPTION OF THE INVENTION
Abnormal long-chain fatty acid (LCFA) combustion in skeletal muscle is associated with insulin resistance, but specific signaling moieties that link LCFA β-oxidation to insulin signaling remain controversial. No clinically-relevant biomarkers are available that specifically reflect muscle LCFA catabolism, but these would be useful for T2DM risk assessment, diagnostics, and to understand disease etiology. Use of metabolomic approaches to identify plasma metabolites specifically or robustly altered by changes in muscle LCFA β-oxidation forms provides assessment of the clinically relevant biomarkers disclosed herein. Furthermore, comprehensive analysis of the plasma metabolite profile comparing type 2 diabetics vs. non-diabetics has revealed unique signatures of metabolites that are altered under this condition, and that track blood sugar control markers.
An embodiment of the invention is the identification of biomarkers that are associated with T2D in clinical tests of samples of human or animal biofluids, including but not limited to plasma, serum and urine. Wherein these biomarkers serve as prognostic or diagnostic indicators of pre-diabetes, diabetes, insulin resistance metabolic disorders associated with changes in insulin activity.
A further embodiment of the invention is the use of small molecule metabolites that mark diabetes as discovered in an analysis of metabolite patterns in plasma samples derived from type 2 diabetics (T2D) using MS analysis. The increase or decrease in the concentration of select biomarkers in T2D subjects compared to non diabetic control subjects matched for body mass and other characteristics can be used to mark T2D. Correlation to T2D may be based on single biomarkers or a group/subset of a group of biomarkers.
An additional embodiment of the invention includes clinical tests useful to treat or prevent disease by evaluating biomarker concentration in response to pharmacologic, nutritional, or other interventions that are designed to combat metabolic diseases involving insulin resistance and conditions associated with poor blood sugar control. More particularly, biomarkers elucidated via methodologies of the instant invention find utility related to broad areas of disease therapeutics. Such therapeutic avenues include, but are not limited to:
1) utilisation and recognition of said biomarkers, variants or moieties thereof as direct therapeutic modalities, either alone or in conjunction with an effective amount of a pharmaceutically effective carrier; 2) validation of therapeutic modalities or disease preventative agents as a function of biomarker presence or concentration; 4) use of biomarkers or moieties thereof as a means of elucidating therapeutically viable agents, and 5) synthesis of molecular structures related to said biomarkers, moieties or variants thereof which are constructed and arranged to therapeutically intervene in the disease process.
The metabolites are small molecule compounds, such as substrates for enzymes of metabolic pathways, intermediates of such pathways or the products obtained by a metabolic pathway. Metabolic pathways are well known in the art and may vary between species. Preferably, said pathways include at least citric acid cycle, respiratory chain, photosynthesis, photorespiration, glycolysis, gluconeogenesis, hexose monophosphate pathway, oxidative pentose phosphate pathway, production and R-oxidation of fatty acids, urea cycle, amino acid biosynthesis pathways, protein degradation pathways such as proteasomal degradation, amino acid degrading pathways, biosynthesis or degradation of. lipids, polyketides (including e.g. flavonoids and isoflavonoids), isoprenoids (including eg. terpenes, sterols, steroids, carotenoids, xanthophylls), carbohydrates, phenylpropanoids and derivatives, alcaloids, benzenoids, indoles, indole-sulfur compounds, porphyrines, anthocyans, hormones, vitamins, cofactors such as prosthetic groups or electron carriers, lignin, glucosinolates, purines, pyrimidines, nucleosides, nucleotides and related molecules such as tRNAs, microRNAs (miRNA) or mRNAs. Accordingly, small molecule compound metabolites are preferably composed of the following classes of compounds: alcohols, alkanes, alkenes, alkines, aromatic compounds, ketones, aldehydes, carboxylic acids, esters, amines, imines, amides, cyanides, amino acids, peptides, thiols, thioesters, phosphate esters, sulfate esters, thioethers, sulfoxides, ethers, or combinations or derivatives of the aforementioned compounds. The small molecules among the metabolites may be primary metabolites which are required for normal cellular function, organ function or animal growth, development or health. Moreover, small molecule metabolites further comprise secondary metabolites having essential ecological function, e.g. metabolites which allow an organism to adapt to its environment. Furthermore, metabolites are not limited to said primary and secondary metabolites and further encompass artificial small molecule compounds. Small molecule metabolites of the invention are found in Table 1.
In a further preferred embodiment of the method of the present invention, said at least one metabolite or metabolites is selected from the group delineated as different in T2D vs. non-diabetic subjects in Table 1, including but not exclusive: Metabolite 206604, 223513, 228939, 225539, 226851, 210286, 199794, 228143, oleic acid, 199777, 241310, 270003, 213143, 208655, 212208, 227352, 226853, 281329, 213193, gluconic acid, 223505, 228315, fructose, 222049, 228147, palmitoleic acid, 228911, 281216, 3,6-anhydrogalactose, 281257, 225555, 231713, 213304, glucuronic acid, 227367, 236890, heptadecanoic acid, inulobiose, leucine, 223402, 281229, 2-hydroxybutanoic acid, 223506. Other metabolites altered in diabetes and/or that track blood sugar control indices include, as examples, down-regulated metabolites in the plasma of T2D (Table 1).
Each of said metabolites is a suitable biomarker by its own for the diseases referred to herein. However, most preferably, a group of biomarkers including biomarkers of one of the aforementioned groups is to be determined by the method of the present invention. A group of biomarkers consists, preferably, of at least two, at least three, at least four and, preferably, up to all of the aforementioned biomarkers. Further, it has been found in accordance with the studies underlying the present invention that the aforementioned group of metabolites is particularly well suited as biomarkers for diabetes, insulin resistance, poor blood sugar control or predisposition thereof in female subjects. Accordingly, more preferably, the subject referred to in connection with the aforementioned preferred embodiment is a female.
Metabolites lacking full structural identification (“unknowns”) are unambiguously described by BinBase (BB) numbers and full mass spectra, quantifier ions and retention indices. These data are publically available and queryable against all 24,000 samples in BinBase. In the absence of specific nomenclature identification of the biomarker against known chemical standards, identification and concentration of the unnamed biomarker may be accomplished via analysis of the abundance of the relevant MS peak signatures.
A pharmaceutical composition of the agonist or antagonist is formulated to be compatible with its intended route of administration, including intravenous, intradermal, subcutaneous, oral (e.g., inhalation), transdermal (i.e., topical), transmucosal, and rectal administration. Solutions or suspensions used for parenteral, intradermal, or subcutaneous application can include: a sterile diluent such as water for injection, saline solution, fixed oils, polyethylene glycols, glycerine, propylene glycol or other synthetic solvents; antibacterial agents such as benzyl alcohol or methyl parabens; antioxidants such as ascorbic acid or sodium bisulfite; chelating agents such as ethylenediaminetetraacetic acid (EDTA); buffers such as acetates, citrates or phosphates, and agents for the adjustment of tonicity such as sodium chloride or dextrose. The pH can be adjusted with acids or bases, such as hydrochloric acid or sodium hydroxide. The parenteral preparation can be enclosed in ampules, disposable syringes or multiple dose vials made of glass or plastic.
GC-TOF MS was used to compare the plasma metabolome (254 unique small molecules) of overnight-fasted obese African-American women with or without an uncoupling protein 3 (UCP3) g/a polymorphism (n=28/genotype), and to also determine if metabolites differed between non-diabetic (n=12; 6 each genotype) and T2DM (n=44; 22 each genotype) subjects. Preliminary analysis revealed 23 metabolites increased significantly by ≧2-fold (p<0.05) in T2DM. As expected, plasma glucose, LCFAs, and β-hydroxybutyrate were significantly increased in T2DM, and the plasma branch-chain amino acid leucine was increased by 47% (p≦0.01). T2DM plasma fructose was  ̃200% of non-diabetics, suggestive of increased endogenous fructose generation. Interestingly, 83% of the molecules elevated in T2DM represent unique, unidentified T2DM-associated factors. Only 2 plasma molecules were reduced significantly by ≧50% in T2DM. Multivariate and correlation statistics have been employed to understand if these altered metabolites, in combination with our previously-published data on acylcarnitine patterns in these subjects (Adams et al., J. Nutr., 2009), yield a “metabolic signature” that segregates/clusters type 2 diabetics or that is predictive of persons with disrupted muscle LCFA catabolism. This toolset may be useful to predict T2DM risk, to track efficacy of modalities to thwart T2DM, and to understand the basic biology underlying T2DM and UCP3 function.
The aforementioned samples are, preferably, pretreated before they are used for the method of the present invention. As described in more detail below, said pre-treatment may include treatments required to release or separate the compounds or to remove excessive material or waste. Suitable techniques comprise centrifugation, extraction, fractioning, purification and/or enrichment of compounds. Moreover, other pre-treatments are carried out in order to provide the compounds in a form or concentration suitable for compound analysis. For example, if gas-chromatography coupled mass spectrometry is used in the method of the present invention, it will be required to derivatize the compounds prior to the said gas chromatography. Suitable and necessary pretreatments depend on the means used for carrying out the method of the invention and are well known to the person skilled in the art. Pre-treated samples as described before are also comprised by the term “sample” as used in accordance with the present invention.
Moreover, the at least one metabolite can also be determined by a specific chemical or biological assay. Said assay shall comprise means which allow to specifically detect the at least one metabolite in the sample. Preferably, said means are capable of specifically recognizing the chemical structure of the metabolite or are capable of specifically identifying the metabolite based on its capability to react with other compounds or its capability to elicit a response in a biological read out system (e.g., induction of a reporter gene). Means which are capable of specifically recognizing the chemical structure of a metabolite are, preferably, antibodies or other proteins which specifically interact with chemical structures, such as receptors or enzymes. Specific antibodies, for instance, may be obtained using the metabolite as antigen by methods well known in the art. Antibodies as referred to herein include both polyclonal and monoclonal antibodies, as well as fragments thereof, such as Fv, Fab and F(ab)2 fragments that are capable of binding the antigen or hapten.
The term “reference” refers to results, i.e. data of characteristic features of the at least one metabolite, which can be correlated to diabetes, insulin resistance, poor blood sugar control, pre diabetes or a predisposition there for. Such reference results are, preferably, obtained from a sample from a subject known to suffer from diabetes or a subject known to have predisposition therefor. The reference results may be obtained by applying the method of the present invention. Alternatively, but nevertheless also preferred, the reference results may be obtained from sample of a subject known not to suffer from diabetes or a subject known not to have a predisposition therefore, i.e. a healthy subject with respect to diabetes and, more preferably, other diseases as well. More over, the reference, also preferably, could be a calculated reference, most preferably the average or median, for the relative or absolute amount of a metabolite of a population of individuals comprising the subject to be investigated. The absolute or relative amounts of the metabolites of said individuals of the population can be determined as specified elsewhere herein. How to calculate a suitable reference value, preferably, the average or median, is well known in the art. The population of subjects referred to before shall comprise a plurality of subjects, preferably, at least 5, 10, 50, 100, 1,000 or 10,000 subjects. It is to be understood that the subject to be diagnosed by the method of the present invention and the subjects of the said plurality of subjects are of the same species.
More preferably, the reference results, i.e. values for at least one characteristic features of the at least one metabolite, will be stored in a suitable data storage medium such as a database and are, thus, also available for future diagnoses. This also allows efficiently diagnosing predisposition for a disease because suitable reference results can be identified in the database once it has been confirmed (in the future) that the subject from which the corresponding reference sample was obtained (indeed) developed diabetes.
As described above, in a preferred embodiment of the method of the present invention, said determining of the at least one metabolite comprises mass spectrometry (MS). Mass spectrometry as used herein encompasses all techniques which allow for the determination of the molecular weight (i.e. the mass) or a mass variable corresponding to a compound, i.e. a metabolite, to be determined in accordance with the present invention. Preferably, mass spectrometry as used herein relates to GC-MS, LC-MS, direct infusion mass spectrometry, FT-ICR-MS, CE-MS, HPLC-MS, quadrupole mass spectrometry, any sequentially coupled mass spectrometry such as MS-MS or MS-MS-MS, ICP-MS, Py-MS, TOF or any combined approaches using the aforementioned techniques. How to apply these techniques is well known to the person skilled in the art. Moreover, suitable devices are commercially available. More preferably, mass spectrometry as used herein relates to LC-MS and/or GC-MS, i.e. to mass spectrometry being operatively linked to a prior chromatographic separation step. More preferably, mass spectrometry as used herein encompasses quadrupole MS. Most preferably, said quadrupole MS is carried out as follows: a) selection of a mass/charge quotient (m/z) of an ion created by ionisation in a first analytical quadrupole of the mass spectrometer, b) fragmentation of the ion selected in step a) by applying an acceleration voltage in an additional subsequent quadrupole which is filled with a collision gas and acts as a collision chamber, selection of a mass/charge quotient of an ion created by the fragmentation process in step b) in an additional subsequent quadrupole, whereby steps a) to c) of the method are carried out at least once and analysis of the mass/charge quotient of all the ions present in the mixture of substances as a result of the ionisation process, whereby the quadrupole is filled with collision gas but no acceleration voltage is applied during the analysis.
More preferably, said mass spectrometry is liquid chromatography (LC) MS and/or gas chromatography (GC) MS.
Materials & Methods
Subject Group
A detailed description of the human cohorts used in the analysis of metabolite differences between non-diabetics and type 2 diabetics may be found in Adams et al. (J. Nutrition, 139: 1073-1081, 2009). Briefly: Archived plasma samples derived from BMI- and age-matched overweight to obese type 2 diabetic (n=44) and non-diabetic (n=12) Gullah-speaking African-American women with or without a UCP3 g/a missense polymorphism were evaluated (subject characteristics are given in Table 1). Volunteers were recruited as part of the Project Sugar Study described in detail elsewhere (McLean, D. C. et al., American Journal of Physiol. Anthropology 127: 427-38, 2005; Sale, M. M. et al., Diabetes, 2008). Considering that this subject population is of a single sex, displays extraordinarly low genetic admixture, lives in a relatively small geographical space, and has a common dietary intake pattern, we anticipate that this group is well-suited for metabolomics studies since biological signal-to-noise should be low in terms of metabolite signatures. Studies were approved by the Institutional Review Boards of the Medical University of South Carolina, University of Alabama at Birmingham, and the University of California, Davis, and all subjects provided informed consent. Blood was obtained between  ̃08:00-09:00 by arm venipuncture into EDTA-treated collection tubes after an overnight fast (no food or drink since 20:00 the night before). Plasma was frozen at −20° C. for 1-7 days before transport to −80° C. freezers for longer-term storage. Subjects were instructed to avoid any unusual activity and intentional exercise in the 3 days leading up to the study, and were instructed to continue to eat their habitual diet without unusual deviations. Patients with diabetes did not take doses of oral agents on the evening before and on the morning of study. Patients treated with insulin could take regular or rapid acting insulin at dinner the night before the study but were instructed to withhold any intermediate- or long-acting insulin on the evening before, and to avoid insulin injections on the morning of the study.
Analytical Methods
Plasma Extraction: For preparing the extraction mixture, degassing devices (such as vacuum/ultrasonic bath, or pure argon or nitrogen gas bombs) and a liquid cooling system must be available. A freshly prepared, chilled (−15° C.) and degassed mixture of acetone and isopropanol is prepared at a ratio of 2:1 (v/v). For each solvent, the highest quality (e.g. >99% ultra-pure HPLC-MS gradient grade purity) is used and stored at room temperature in the dark. A pH measurement device ensured neutrality of solvents. Volumes are measured using calibrated pipettes whose accuracies are subjected to quality control routines at least once every six months. An ice bath and liquid nitrogen dewars are used for temporarily storing samples during the process. Large twisters are useful to operate in nitrogen dewars. Extraction is performed in a micro centrifuge tube shaker.
Derivatisation:
A speed vacuum concentrator or lyophiliser is used for drying extracts to complete dryness. A mixture of 40 mg/mL of methoxyamine. HCl in pyridine (p.a. quality) is freshly prepared using an ultrasonicator. In case ATAS (NL) liners are used, pyridine must be exchanged against dimethylformamide as polar, aprotic and basic solvent. N-methyl-N-trimethylsilyltrifluoroacetamide (MSTFA) is used from freshly opened 1-mL bottles. Reagents and solvents are stored in a desiccator in the dark. Derivatizations are carried out in thermoshakers that are set to 45° C. and 37° C. for the first and second reaction step, resp.
Mass Spectrometric Analysis:
GC-MS analysis is carried out on a quadrupole or a time-of-flight mass spectrometer equipped with autosampler and electron impact ionization. Samples are injected in randomized order or appropriate block designs. For each injection sequence, the analysis of quality control samples is a prerequisite (e.g. reagent blanks, method blanks, reference compound mixture, reference design sample). Low bleeding injector septa or septum free injector systems are prerequisite. Standard 10 μL gas chromatography injection needles are mounted into the autosampler. Chromatography is carried out on a 30 m long, 0.32 mm I.D. and 0.25 μm (35%-Phenyl)-methylpolysiloxane column. The GC oven must be temperature programmable up to 360° C. The mass spectrometer must be capable of a data acquisition rate of at least 20 s−1 and a mass range of at least 83-500 Da. Raw GC-MS data files are transferred to servers. Long-term data safety is ensured by back up routines on DVDs or by mirrored server space. Data analysis is carried out on office personal computers using the vendor's GC-TOF software that is able to carry out multi-target analysis, including compound identity checks based on mass spectral and retention index matching (e.g. ChromaTOF 2.25). The software must be capable of quantitation by area and height on user defined ion traces.
Brief Summary of Typical Protocol:
Protein Precipitation and Metabolite Extraction
1. Take out 30 μl sample aliquots one by one and add internal standards, e.g. U-13C-Sorbitol (200 ng per vial) for normalization, vortex for 10 s.
2. Add 0.4 mL of cold extraction solvent mixture (−15° C., degassed) to each and vortex vigorously for 20 s.
3. Shake the samples in batches of 10 for 5 min in a 4° C. room. When taking out the samples, place them in an ice bath.
4. Centrifuge samples at 20,800 rcf for 2 min.
5. Collect the liquid supernatant of each sample and store in a clean micro centrifuge tubes. The metal balls can be re-used after cleaning. The cell debris pellet can be discarded.
6. Repeat steps 1-5 until all samples are extracted.
7. For storage, extracts must be degassed with a gentle stream of nitrogen or argon gas for 1 min prior to tube closure. Tubes can then be stored in the dark at −80° C. for about four weeks.
8. Dry the extracts in a speed vacuum concentrator or a lyophilizer to complete dryness.
9. For storage, deoxygenate samples with a gentle stream of nitrogen or argon gas for 1 min before closing the tubes. Tubes can then be stored in the dark at −80° C. for at least four weeks.
Derivatization
1. Take out dried samples from store and allow them to warm up to room temperature for at least 15 min before start of derivatization.
2. Add 10 μL of methoxyamine solution (40 mg/mL in dimethylformamide) to each dried extract, and immediately close tubes afterwards.
3. Shake extracts for 90 min at 28° C.
4. Add 180 μL silylating agent (MSTFA) to each tube, and immediately close tubes afterwards.
5. Shake samples for 30 min at 37° C.
6. Transfer sample reaction solutions to glass vials suitable for the GC-MS autosampler. Immediately close each sample with crimps that contain a teflon rubber seal. Wait two hours before injecting the first sample into the GC-MS.
Data Acquisition by GC-MS
1. The mass spectrometer must be tuned according to the manufacturer's manuals for optimal parameters for ion lenses, detector voltage and other settings. Usually, this can be performed in autotune operation.
2. Change or clean the liner every sample, otherwise data for lipids and aromatic compounds will not be reliable.
3. Check that manufacturere's recommended maintenance routines have all been carried out.
4. Inject 1 μL (1.5 μL for ATAS liners) of each sample in splitless, depending on the metabolite concentrations and eventual signal-to-noise ratios in the GC-MS profiles. Injection temperature is set to 230° C. Injection programs have to include syringe washing steps before and after the injection, a sample pumping step for removal of small air bubbles and an air buffer for complete sample removal during injection.
5. Separate metabolites using a GC temperature ramping program. Reasonable values are: GC start conditions at 80° C., 2 min isothermal, ramp with 5° C./min up to 330° C., 5 min isothermal, cool down to initial conditions. The ion source should be turned off during the solvent delay.
6. Detect metabolites by setting the ion source filament energy to 70 eV. Scan a mass range of at least 83-500 Da, or 40-500 Da, if low mass-to-charge (m/z) fragment ions are to be recorded. At least two scans per second should be recorded in full scan mode.
7. Transfer raw GC-MS profile chromatograms to a server station.
Data Analysis
1. For raw data processing, use appropriate software. First choice is the GC-MS manufacturer's software. For general quadrupole mass spectrometers, data deconvolution by the freely available software AMDIS is recommended.







TABLE 1







Identities of plasma metabolites with concentration differences in the 


plasma of adult women with type 2 diabetes mellitus (n = 44) compared 


to age- and weight-matched adult women without diabetes (n = 12). 


Shown are those metabolites that were 150% or 75% of non-diabetic 


control values, or that were significantly different at p < 0.1 (unpaired 


Student's t-test).*











Absolute 





Concentration





Difference in 

Diabetic, 



type 2 Diabetics 

% of 



vs. non-diabetics

Non-


Metabolite
(peak area)

Diabetic













206604
1203158
0.004492
2107.4


3-hydroxybutanoic 
36034
0.048683
437.5


acid (β-hydroxybutyrate 





ketone body)





228939
184317
0.541997
351.3


223513
7951
8.49E−07
318.8


225539
903
6.92E−06
305.4


226851
4383
2.72E−07
294.0


210286
7365
2.59E−06
281.7


199794
43640
1.53E−07
280.5


228143
1639
4.74E−05
270.6


oleic acid
14430
0.00105
263.3


199777
170571
1.69E−09
249.4


241310
915
1.12E−06
247.5


270003
3498
1.94E−06
244.1


204465
3537
0.550693
236.6


213143
1420
9.94E−10
229.8


212208
4904
0.000607
218.1


208655
2276
4.96E−05
216.4


227352
1826
3.55E−07
212.8


226853
1271
0.002346
210.8


228315
2274
0.464227
210.4


213193
667
3.51E−08
207.1


281329
691
0.001038
206.2


gluconic acid
2724
1.33E−07
206.0


223505
2022
1.66E−05
202.6


fructose
257992
3.43E−08
201.2


222049
4723
0.001433
200.3


228147
415
1.88E−06
187.1


palmitoleic acid
5092
0.016313
181.0


281216
2151
0.032345
179.9


228911
20101
0.000579
178.6


231713
217
0.00199
175.1


3,6-anhydrogalactose
1246
1.92E−06
175.0


281257
2974
1.83E−06
172.8


225555
3249
0.114927
172.6


213304
772
0.00012
167.6


glucuronic acid
1106
0.03242
164.4


227367
251
0.001415
163.6


sucrose
589
0.505118
156.8


glucose
596395
5.14E−06
156.4


281229
596
0.05042
155.9


236890
9571
0.106175
154.4


heptadecanoic acid
6274
0.038575
153.9


inulobiose 2
529
0.005449
152.4


leucine
52273
0.011352
147.4


223402
51982
0.011212
147.3


2-hydroxybutanoic acid
45283
0.040355
145.0


223506
1663
0.004005
140.7


274531
662
0.090695
136.4


2-deoxyerythritol
2703
0.001577
132.7


linoleic acid
2113
0.092294
131.9


palmitic acid
23477
0.04879
131.2


phosphoethanolamine
515
0.067484
131.0


uridine
254
0.02067
129.9


2-ketoisocaproic acid
1306
0.045047
127.1


226935
7606
0.04324
126.7


xylose
1139
0.014629
126.0


cystine
7558
0.061529
124.8


histidine
10558
0.01981
123.5


217797
1411
0.015875
121.2


serine
18981
0.070326
120.9


Total branched-chain amino 
117028
0.057367
120.2


acids (BCAAs) + meta-





bolites (valine, leucine, 





isoleucine, ketoisocaproic 





acid)





Total BCAA (valine, 
115722
0.058833
120.1


leucine, isoleucine)





stearic acid
119485
0.018492
120.0


220169
−3029
0.083012
86.2


tyrosine
−27674
0.046953
85.8


213733
−509
0.040706
82.8


lysine
−31386
0.031029
81.6


226841
−4797
0.047908
80.8


200427
−9056
0.018543
80.5


204425
−2887
0.002215
79.4


213526
−4401
0.000825
78.3


ethanolamine
−105773
0.020283
78.0


200429
−8024
0.021525
77.8


215682
−8589
0.013975
77.3


221431
−4106
0.014618
77.2


213961
−3643
0.040719
76.3


229105
−6652
0.061982
74.8


224033
−2089
0.147945
74.7


226864
−854
0.090138
74.3


226849
−3029
0.155581
74.1


226916
−2997
0.305944
74.1


240018
−559
0.11936
73.8


arachidonic acid
−9346
0.006359
73.4


220010
−3402
0.003713
72.8


glycine
−90613
0.019996
72.2


223548
−643
0.004802
72.0


223973
−1903
0.025398
71.9


240264
−726
0.069733
71.9


235373
−2979
0.212995
71.8


227358
−2761
0.130707
71.8


202572
−2274
0.497109
71.5


226859
−8115
0.192243
70.4


223576
−1716
0.161669
69.5


glycerol-alpha-phosphate
−7358
0.002141
69.2


226911
−2525
0.109537
68.5


putrescine
−1843
0.242229
67.8


281193
−1104
0.110333
67.6


217870
−19766
0.055383
66.6


226923
−1900
0.060475
66.4


231161
−209
1.46E−05
65.5


213697
−626
0.044689
65.1


240017
−579
0.028334
64.5


223527
−2093
0.0505
64.3


281132
−559
8.21E−05
60.2


270508
−1394
0.000188
59.2


lactic acid
−402727
0.034438
46.2


281134
−1983
5.59E−07
45.8


241403
−5610
0.034246
25.0





*Metabolites with numerical identities only are unnamed metabolites; numbers refer to the arbitrary nomenclature derived from the laboratory of Oliver Fiehn (Univ. of CA, Davis: http://eros.fiehnlab.ucdavis.edu:8080/binbase-compound/).













TABLE 2







Plasma metabolites with significantly-altered concentrations in non-


diabetic obese African-American women harboring a UCP3 g/a 


missense allele.














Relevant
g/a to



g/g genotype 
g/a genotype 
Metabolic
g/g



(n = 6)
(n = 6)
Pathway
Ratio





Increased in 






non-diabetic






g/a:






BB226860
2552 ± 669
 5851 ± 1302
unknown
2.29*


BB219174
6966 ± 988
10253 ± 1074
unknown
1.47*


Decreased in 






non-diabetic






g/g:






BB223521
3569 ± 340
2516 ± 318
unknown
0.71*


phosphoric 
1540725 ± 
1065028 ± 
acid/base 
0.69**


acid
106083
88171
balance?



BB223506
4884 ± 343
3288 ± 483
unknown
0.67*


BB281189
432579 ± 45901
285112 ± 14032
unknown
0.66**


inulobiose
1223 ± 152
795 ± 87
carbo-
0.65*





hydrate



BB228147
579 ± 67
374 ± 49
unknown
0.65*


BB211382
36484 ± 3617
21651 ± 3493
unknown
0.59**


cysteine
24519 ± 4380
13827 ± 1928
amino acid
0.56*


2-oxoglu-
2209 ± 240
1182 ± 130
TCA
0.54**


tarate (α-


cycle/trans-



ketogluta-


amination



rate)






BB281112
18342 ± 2886
 9189 ± 2265
unknown
0.50*


BB228144
3874 ± 659
1910 ± 524
unknown
0.49*


BB239966
2358 ± 418
1158 ± 308
unknown
0.49*


glutamic 
48146 ± 7473
21048 ± 1427
amino acid
0.44**


acid 






(glutamate)






BB222169
25526 ± 4397
10743 ± 4133
unknown
0.42*





‡Values are quantifier peak height means SEM; see Supplemental Materials for full list of metabolites including those whose concentration differences were not statistically significant;


*p ≦ 0.05;


**p ≦ 0.01













TABLE 3







Identifiable plasma metabolites with significantly-altered concentrations in 


obese non-diabetic vs. type 2 diabetic African-American women.















Diabetic/





Relevant 
Non-



Non-Diabetic
Diabetic 
Metabolic
Diabetic



(n = 12)
(n = 43)
Pathway
Ratio














Increased in 






Diabetes:






3-hydroxybutanoic 
10676 ± 
47424 ± 
lipid/fatty acid
4.44*


acid (β-hydroxy-
1455
9450




butryrate)






oleic acid
8837 ± 
23377 ± 
lipid/fatty acid
2.65***



1105
2189




gluconic acid
2570 ± 230
5317 ± 229
carbohydrate
2.07****


fructose
255053 ± 
517922 ± 
carbohydrate
2.03****



34001
18549




palmitoleic acid
6286 ± 
11400 ± 
lipid/fatty acid
1.81*



1444
1018




3,6-anhydro-
1660 ± 147
2920 ± 116
carbohydrate
1.76****


galactose






glucuronic acid
1718 ± 151
2844 ± 264
carbohydrate
1.66*


glucose
1057532 ± 
1644213 ± 
carbohydrate
1.56****



90953
56650




heptadecanoic 
11630 ± 554
17911 ± 
lipid/fatty acid
1.54*


acid

1564




inulobiose
1009 ± 106
1546 ± 92
carbohydrate
1.53**


leucine
110271 ± 
164281 ± 
amino acid
1.49**



14147
9806




2-hydroxybu- 
100560 ± 
146853 ± 
amino acid
1.46*


tanoic acid (α-
21376
9844




hydroxybu-






tryrate)






2-deoxyerythritol
8270 ± 727
10950 ± 383
lipid/fatty 
1.32**





alcohol



palmitic acid
75185 ± 
98294 ± 
lipid/fatty acid
1.31*



5308
6003




2-ketoisocaproic 
4809 ± 462
6169 ± 309
amino acid
1.28*


acid (α-






ketoisocaproate)






uridine
850 ± 51
1085 ± 51
pyrimidine
1.28*


cystine
30534 ± 
38496 ± 
amino acid
1.26*



3583
1818




xylose
4388 ± 290
5479 ± 221
carbohydrate/
1.25*





pentose 






phosphate



histidine
44969 ± 
56071 ± 
amino acid
1.25**



2332
2178




stearic acid
598153 ± 
719217 ± 
lipid/fatty 
1.20*



31201
24664
acid



Decreased in 






Diabetes:






benzylalcohol
17762 ± 
15741 ± 405
phenolic 
0.89*



1062

metabolite or



benzoic acid
37841 ± 
32968 ± 
phenolic 
0.88*



2445
1066
metabolite or



lysine
170439 ± 
141626 ± 
amino acid
0.83*



13635
6008




ethanolamine
479789 ± 
380214 ± 
choline 
0.79*



42252
19511
precursor



arachidonic acid
35123 ± 
26058 ± 
lipid/fatty acid
0.74**



3669
1410




glycine
326074 ± 
239650 ± 
amino acid
0.74*



41720
16035




glycerol-3-
23920 ± 
16571 ± 
glycero-
0.69**


phosphate 
2430
1018
phospholipid



(glycerol-α-






phosphate)





‡Values are quantifier peak height means ± SEM; see Supplemental Materials for information on unknown metabolites significantly changed in T2DM;


*p ≦ 0.05;


**p ≦ 0.01;


***p ≦ 0.001;


****p ≦ 0.0001, (unpaired t-test)













TABLE 4







Metabolite (BB) & Ret index.













quant



BB name
ret index
ion














206604
653437
147



3-hydroxybutanoic acid
278929
191



223513
808133
98



228939
253563
154



225539
798935
387



226851
788409
373



210286
701682
217



199794
681176
361



maltose
946608
361



228143
673081
187



oleic acid
778858
339



199777
675254
217



241310
782338
373



270003
736904
217



213143
873684
446



208655
688305
217



212208
705463
147



227352
927021
132



226853
966330
272



281329
705694
245



213193
865396
446



gluconic acid
693140
333



223505
810335
246



228315
553878
299



fructose
642325
307



222049
701919
117



228147
699907
261



palmitoleic acid
706298
129



228911
632392
307



281216
605436
144



3,6-anhydrogalactose
589230
231



281257
612012
231



225555
685994
266



231713
692917
244



213304
930533
156



glucuronic acid
666743
333



227367
995564
217



sucrose
913309
271



236890
628019
217



glucose
657634
160



heptadecanoic acid
750645
117



inulobiose 2
930708
204



leucine
345953
158



223402
345709
158



281229
986303
310



2-hydroxybutanoic acid
258175
131



223506
802836
246



taurine
557250
326



arachidic acid
856486
117



224552
494599
239



274531
706670
331



218761
336333
116



2-deoxyerythritol
422939
103



linoleic acid
777102
337



281143
617834
328



palmitic acid
711066
313



ornithine
527822
142



227322
364439
142



proline
364232
142



2-ketoisocaproic acid
310629
200



226935
983996
310



2-oxogluconic acid
542152
201



uridine
856953
258



phosphoethanolamine
604454
100



tocopherol gamma
1026222
223



cystine
804143
218



shikimic acid
607609
204



xylose
542808
103



histidine
663393
154



273236
1108221
311



serine
394650
204



204465
474367
140



217797
655403
273



cysteine
499495
220



adipic acid
475399
111



stearic acid
787358
117



valine
313224
144



239843
890991
290



myristic acid
634543
285



226845
795371
232



alanine
243537
116



226922
696043
139



methionine
483425
176



palmitic acid butyl ester
768467
129



aconitic acid
586574
229



lignoceric acid
977850
117



268506
301113
216



alpha mannosylglycerate
633295
217



281131
1071318
371



281108
857519
362



cysteine-glycine
715639
220



asparagine
553791
116



273450
462401
193



281112
802983
232



kynurenine
769271
218



281158
500924
240



phenylalanine
538016
218



levoglucosan
569799
204



227364
572785
314



239883
869427
290



281200
816855
445



228144
800275
290



228583
302763
216



241572
1082682
299



aspartic acid
480543
232



erythritol
471274
205



N-methylalanine
286258
130



glycolic acid
229810
177



glutamic acid
527101
246



226850
385813
183



228612
756779
230



228018
563074
275



4-hydroxyproline
481319
140



phosphoric acid
344674
299



281080
944566
429



indole-3-acetate
685195
202



uric acid
731185
441



225403
562683
243



226888
580679
170



269969
410322
279



citric acid
617288
273



281118
1037677
309



223495
538501
170



223536
521629
156



2-hydroxyvaleric acid
310750
131



226867
735540
290



behenic acid
919675
117



isothreonic acid
489846
292



creatinine
502434
115



211382
773139
147



isoleucine
356931
158



1-hexadecanol
679338
299



isonicotinic acid
367074
180



2-aminoadipic acid
572700
260



threonine
409403
117



281348
241383
151



glutamine
600452
156



pyrophosphate
548254
451



269151
341172
130



inositol myo-
729867
305



221569
425557
156



240439
584307
170



221572
444313
170



281271
711045
451



213353
695971
293



lathosterol
1095135
255



281128
498007
156



223521
1166398
283



tryptophan
779834
202



224788
493150
228



224551
501237
228



229268
407022
263



228401
594044
170



pseudo uridine
813829
217



glycerol
343749
205



1-monoolein
952993
129



229108
281476
234



1-monostearin
959625
399



241369
194932
172



224037
606591
170



226907
716792
387



fumaric acid
390708
245



238566
611453
170



239966
806614
290



indole-3-lactate
764543
202



235678
530661
329



281120
231838
97



228809
428316
124



tocopherol beta
1022809
222



pelargonic acid
399163
117



citrulline
620728
157



281186
760555
315



234579
378003
315



228897
379191
218



229073
254699
228



226865
552707
411



224521
352767
218



pyrazine 2,5-dihydroxy
396061
241



281178
258298
144



224547
538202
275



223597
1204498
297



mannitol
665209
103



2-phenylpropanol
319992
193



269983
494553
263



272306
253512
193



231657
286430
187



281107
206299
119



200411
595016
116



224849
535229
275



239859
797156
218



lactic acid
215380
191



281125
566380
94



urea
331223
171



226909
861795
290



281328
278237
220



228927
313497
165



benzylalcohol
281322
165



227298
374409
211



227353
306967
218



201887
1200427
316



227743
627324
156



threonic acid
497167
292



218520
232023
147



tocopherol alpha
1067178
237



glyceric acid
373972
189



211916
440480
100



224539
566862
296



226855
511474
270



cholesterol
1077630
129



217838
289054
214



281363
710284
290



281185
226102
95



228900
243069
125



217866
237804
228



benzoic acid
338043
179



216428
505415
223



281111
236617
112



arginine + ornithine
619420
142



281124
490965
291



281145
216493
97



202599
570779
173



219174
506055
173



223905
257174
248



281119
883595
359



268590
282017
140



281346
429630
218



221574
354121
85



tyrosine
671085
218



227365
405467
114



226908
737073
213



224526
334987
234



215490
315917
130



242405
291859
139



lauric acid
547810
117



226910
793299
173



228885
250227
85



224522
279422
174



281268
448141
174



229203
840930
94



211898
413647
270



225272
416106
182



223717
477769
155



226848
827200
202



239882
682727
228



238358
640923
239



281133
209890
120



monopalmitin-1-glyceride
901207
371



220169
304229
174



239873
732710
391



lysine
663816
156



223634
471218
140



204344
246354
147



239593
614313
269



223578
397473
272



281249
692691
261



213733
597844
271



226842
544485
171



224808
458884
181



226846
442001
258



227391
527630
170



alpha ketoglutaric acid
507734
198



224571
371691
140



glycerol-beta-phosphate
574994
211



200427
452933
154



229962
951858
397



215504
512079
373



217842
447001
265



281172
589910
140



224529
549948
275



281106
485548
232



239995
655244
287



281122
234713
105



222058
477647
366



219021
335520
228



226927
824957
445



240265
672366
139



226843
551430
171



227345
395995
186



226841
759286
232



281166
934476
290



222169
975088
144



223500
516216
269



199773
755246
215



226844
641635
301



ethanolamine
342787
174



224792
436689
155



204425
448206
350



200429
438443
174



281147
329324
106



oxalic acid
259625
147



281189
226160
105



241114
425547
238



215682
433076
174



239852
712000
373



conduritol-beta-epoxide
704220
318



221431
427636
174



234491
489261
226



281150
388815
112



281148
783627
331



217867
400245
154



223708
354552
273



223938
492335
184



213526
284610
201



226858
356536
241



227366
283401
159



succinic acid
370518
247



215355
338105
188



221562
273779
98



228309
595001
156



226903
833661
313



200624
421877
373



223541
365519
98



213961
897509
91



281109
628352
98



223492
384421
142



226860
690521
139



210891
265181
130



217784
431379
239



228203
457545
152



226916
510239
224



226849
541089
388



223499
675563
285



224881
486165
309



240018
904978
290



arachidonic acid
833292
91



229105
297744
100



231590
321781
114



226864
691552
211



glycine
364262
174



223973
1028825
243



241403
468354
212



224798
464635
329



226280
369173
98



220010
1027429
227



240264
455266
320



226859
926164
290



224033
605457
98



223548
749189
294



223778
226081
159



235373
407853
258



281187
574112
185



227358
308098
142



glycerol-alpha-phosphate
588999
299



226911
914309
290



220009
459742
172



putrescine
588249
174



281182
518697
228



226923
743658
290



281193
557891
144



223576
467132
329



231161
575302
362



223527
373284
158



223566
624258
156



213697
456893
232



217870
401556
142



240017
351090
294



281132
608773
276



270508
1084500
227



202572
613924
217



281134
781899
324

## Claims

1. A method for screening a subject for insulin resistance, diabetes, diabetes risk, pre-diabetes, or diseases involving altered insulin activity comprising:
a. obtaining a plasma, tissue, or biofluid sample from the subject,
b. determining relative or absolute abundance of a metabolite or metabolites present in the sample by performing gas chromatogram mass spectrometric analysis on the sample for the presence or absence of metabolites, wherein a scan of the mass range is 40-500 Da,
c. comparing the peak areas or heights found in the scan to differential peak areas or heights of the metabolites 3 hydroxybutanoic acid, oleic acid, gluconic acid, fructose, palmitoleic acid, (3,6) andhydrogalactose, glucuronic acid, sucrose, glucose, heptadecanoic acid, inulobiose 2, leucine, 2-hydroxybutanoic acid, 2-deoxyerythritol, linoleic acid, palmitic acid, phosphoethanolamine, uridine, 2-ketoisocaproic acid, xylose, cystine, histidine, serine, valine, leucine, isoleucine, stearic acid, tyrosine, lysine, ethanolamine, arachidonic acid, glycerol-alpha-phosphate, putrescine, lactic acid 206604, 228939, 223513, 225539, 226851, 210286, 199794, 228143, 199777, 270003, 241310, 213143, 208655, 212208, 227352, 226853, 281329, 213193, 223505, 228315, 222049, 228147, 228911, 281216, 281257, 225555, 231713, 213304, 227367, 236890, 223402, 281229, 223506 found in subjects with insulin resistance, diabetes or pre-diabetic conditions.

2. The method of claim 1 wherein an enzymatic or colorimetric analysis is used in addition to gas chromatogram to determine relative or absolute abundance of a metabolite or metabolites.

3. A method for assessing therapeutic efficacy of a drug, or pharmacologic, nutritional, or other modalities used to treat or prevent insulin resistance, diabetes, and diseases associated with altered insulin activity conditions in a subject comprising:
a. administering a drug or therapeutic modality to the subject,
b. obtaining a plasma, tissue, or other biofluid sample from the subject,
c. determining the relative or absolute abundance of the metabolites 3 hydroxybutanoic acid, oleic acid, qluconic acid, fructose, palmitoleic acid, (3,6) andhydroqalactose, qlucuronic acid, sucrose, glucose, heptadecanoic acid, inulobiose 2, leucine, 2-hydroxybutanoic acid, 2-deoxyerythritol, linoleic acid, palmitic acid, phosphoethanolamine, uridine, 2-ketoisocaproic acid, xylose, cystine, histidine, serine, valine, leucine, isoleucine, stearic acid, tyrosine, lysine, ethanolamine, arachidonic acid, glycerol-alpha-phosphate, putrescine, lactic acid, 206604, 228939, 223513, 225539, 226851, 210286, 199794, 228143, 199777, 270003, 241310, 213143, 208655, 212208, 227352, 226853, 281329, 213193, 223505, 228315, 222049, 228147, 228911, 281216, 281257, 225555, 231713, 213304, 227367, 236890, 223402, 281229, 223506 by performing gas chromatogram mass spectrometric analysis on the sample for the presence or absence of metabolites, wherein a scan of the mass range is 40-500 Da,
d. comparing the peak areas or heights found in the scan to differential peak areas or heights of metabolites found in subjects with insulin resistance or diabetic or pre-diabetic conditions wherein a significant increase or decrease in differential peak areas or heights of metabolites associated with insulin resistance, diabetes, or pre-diabetes conditions indicates therapeutic efficacy.

4. The method of claim 3 wherein an enzymatic or colorimetric analysis is used in addition to a gas chromatogram to determine relative or absolute abundance of the metabolites.