KR101118555B1 - Composition for distinction of obese tendency and a informational method for obese tendency - Google Patents

Abstract

The present invention relates to a composition for screening obesity constitution. More specifically, the present invention provides a composition for screening obesity constitution and using obesity constitution as a biomarker by using metabolome analysis as a biomarker of metabolites that are excessively excreted in the urine of an individual with obese constitution than in a conventional constitution. It is about a method.

Obesity, Taurine, Betaine, Metabolomixes

Description

Composition for distinction of obese tendency and a informational method for obese tendency}

The present invention relates to a biomarker for screening obese constitution isolated from a urine sample. More specifically, the present invention provides a composition for screening obesity constitution and composition comprising a composition that reacts to the urine of an individual having a tendency to become easily obese when eating a general diet or a high fat diet in response to metabolites that are excreted in excess of the urine of a general subject. It relates to a method of providing information about the constitution.

Obesity is growing rapidly year by year in the world. Obesity is recognized as a risk factor for diabetes, cardiovascular disease, liver disease and kidney disease. In order to reduce the associated risks, it is necessary to investigate the causes of weight gain (eg lifestyle and habits).

Obesity studies induced by high fat diet (HFD) intake in animal models have been found to be more suitable than using single-gene variation obesity models, since the former is more suitable for application to human obesity. to be. In addition, human obesity is mostly known to be due to the interaction between various environmental factors and complex genes, not single gene mutations.

On the other hand, meta bolrom (metabolome) is a cell, tissue, or living body refers to the total metabolites present in the solution and, ¹ H- nuclear magnetic resonance ⁽¹ H-NMR) and multivariate statistical chemometric (chemometric) technique that combines the analysis method Metabolomic profiling and the characterization of several biological fluids have been used. Shearer et al. Measured the effect of diet-induced insulin resistance on metabolome in experimental animals (C57BL / 6J mouse) using target metabolomic profiling. Jane et al. Used mice commonly used in genetic research (C57BL / 6, 129S6, BALB / c, DBA / 2, and C3H), using traditional physiological, biochemical and hormonal studies and metabolologic techniques based on ¹ H-NMR. Metabotypic results were obtained after feeding high fat diet (40%) in Esau. Using microarray and qRT-PCR techniques, genotyping and gene expression analysis of the adipose tissue and hypothalamus of the solid and underweight groups in mice fed high fat diets were performed. Another study performed gas chromatography and mass spectrometry on urine samples from Sprague-Dawley rats to reveal the difference in metabolic profiling between the diabetic model and the normal group experimentally induced by streptozotocin. Li et al. Also found that dietary involvement is closely related to urine metabolite profiles in rats.

The present inventors have thought that the metabolomics analysis method can be used to screen constitutions that are prone to obesity, and provide information on whether obesity is constitution, and to search for biomarkers that can select obesity constitution. The present invention has been completed.

An object of the present invention is to provide a method for providing information on whether or not obese constitution using a urine sample and to provide a composition for screening obese constitution.

In order to achieve the object of the present invention as described above, the present invention is for screening obese constitution that can detect a metabolite as a biomarker in the urine of the individual having a tendency to become obese than in the urine of the general subject To provide a composition.

In another aspect, the present invention provides a method for providing information on whether the presence of the biomarker is obese constitution.

Hereinafter, the present invention will be described in detail.

As used herein, the term "selection" refers to identifying a particular constitution that is distinguished from other constitutions. For the purposes of the present invention, selection is the selection of constitutions that tend to be obese.

As used herein, the term "obesity" refers to a body obesity index (or body mass index of 25 or more (30 or more for Westerners), where the body mass index is the weight (kg) in m ) Divided by the square of.

As used herein, the term "biomarker" refers to a metabolite that is excessively excreted in the urine of a subject with an obesity tendency as compared to a subject with a general constitution as a substance that can distinguish an obese constitution from a general constitution. The metabolites include, but are not limited to, organic acids, amino acids, sugars, and the like.

The present inventors have analyzed the urine of rats fed a normal and high fat diet by metabolomix method to determine the amount of betaine, taurine, acetone / acetoacetate, phenylacetylglycine, pyruvate, lactate and citrate excreted in the urine. It turns out that it depends on the physiological constitution. It means that these parameters are especially related to obesity due to physiological constitution and high fat diet. In particular, these metabolites showed a significant difference between the high weight gain group and the low weight gain group among individuals fed the normal and high fat diets. Therefore, these metabolites are biomarkers for screening obesity. Can be used as

Betaine is known to prevent and treat cirrhosis in rats, and is known to reduce liver cholesterol and total lipids in rats fed high cholesterol feed. Taurine plays an important role in cell and tissue homeostasis, such as stabilization of membranes and proteins, antioxidant activity, and alteration of ion flow. Taurine also acts as a neuromodulator or osmoregulator. In addition, taurine is known to act as an insulin-positive agent, such as influx of glucose into tissues and activate glycogen synthesis in the liver. Like other amino acids (for example, glutamine), taurine is thought to increase energy storage in the body. However, the fact that these components are detected in large quantities in the urine of individuals particularly prone to obesity has been newly discovered by the present inventors, and such components may be useful for preselecting obesity.

Accordingly, the biomarker for screening obesity constitution of the present invention is a substance selected from the group containing taurine, betaine, acetone / acetoacetate, phenylacetylglycine, pyruvate, lactate and citrate.

In addition, the composition for screening obesity constitution of the present invention is a composition that reacts with the biomarker. The biomarkers are known materials, and compositions that react with such materials can utilize commercially available compositions.

Preferably, the composition may include an oxidizing or reducing enzyme for the biomarkers.

As used herein, the term "biological sample" or "sample" refers to blood and other liquid samples of biological origin. Preferably, the biological sample or sample is urine. The sample may be pretreated before use for detection. For example, it can include filtration, distillation, extraction, concentration, inactivation of disturbing components, addition of reagents, and the like.

In addition, the composition may include a label, a dissolving agent, and a cleaning agent capable of generating a detectable signal. In addition, when the labeling substance is an enzyme, it may include a substrate capable of measuring enzyme activity and a reaction terminator.

The label capable of generating a detectable signal enables qualitative or quantitative measurement of the substance to be detected, examples of which include enzymes, fluorescent substances, ligands, luminants, microparticles, redox molecules and radioisotopes. Elements and the like can be used. Enzymes include β-glucuronidase, β-D-glucosidase, urease, peroxidase, alkaline phosphatase, acetylcholinesterase, glycosidase, hexokinase, malate dehydrogenase, and glucose-6 Phosphate dehydrogenase, invertase and the like can be used. As the fluorescent substance, fluorescein, isothiocyanate, rhodamine, phycoerythrin, phycocyanin, allophycocyanin, fluorine isothiocyanate and the like can be used. Ligands include biotin derivatives, and luminescent materials include acridinium esters, luciferin, and luciferase. The microparticles include colloidal gold and colored latex, and the redox molecules include ferrocene, ruthenium complex, biologen, quinone, Ti ion, Cs ion, diimide, 1,4-benzoquinone and hydroquinone. Radioisotopes include ³ H, ¹⁴ C, ³² P, ³⁵ S, ³⁶ Cl, ⁵¹ Cr, ⁵⁷ Co, ⁵⁸ Co, ⁵⁹ Fe, ⁹⁰ Y, ¹²⁵ I, ¹³¹ I, ¹⁸⁶ Re, and the like. However, any of those which can be used for biochemical analysis other than those exemplified above may be used.

In addition, the diagnostic composition of the present invention may be provided in an immobilized state using various methods known on a suitable carrier or support, in order to increase the speed and convenience of diagnosis. Examples of suitable carriers or supports include agarose, cellulose, nitrocellulose, dextran, Sephadex, Sepharose, liposomes, carboxymethyl cellulose, polyacrylamides, polyesters, companion rocks, filter papers, ion exchange resins, plastic films, plastic tubes , Glass, polyamine-methyl vinyl-ether-maleic acid copolymers, amino acid copolymers, ethylene-maleic acid copolymers, nylons, cups, flat packs and the like. The support may have any possible form, for example spherical (bead), cylindrical (test tube or inner surface of the well), planar (sheet, test strip).

Preferably, the obese constitution screening composition of the present invention may be provided in a kit or microarray form. Herein, the kit or micro array preferably has a positive reaction when a substance to be detected is detected to be above a reference value. More preferably, a positive response is detected when it is detected twice or more than the reference value.

In another aspect, the present invention provides a method for providing information on whether or not obese constitution including the step of confirming whether the biomarker is detected above the reference value in the urine sample. Preferably, the method is characterized in that it is determined that the biomarker is obese constitution when more than twice the reference value.

According to the present invention, there is provided a composition for screening obesity constitution that can screen for obesity constitution and a method for providing information on whether or not obesity constitution. By the composition and the method, it is expected that an obesity-prone individual may be selected in advance, and thus, an appropriate diet or exercise therapy may be selected, thereby effectively preventing obesity.

Hereinafter, the present invention will be described in more detail with reference to Examples. However, the following examples are illustrative of the present invention, and the content of the present invention is not limited by the examples.

≪ Example 1 >

Sample Preparation

Contains D ₂ O (99.9%, 0.05% 3- (trimethylsilyl) -propion-2,2,3,3- d 4 acid and sodium salt (TSP) purchased from Sigma (St. Louis, MO, USA) ), Sodium azide, phosphate buffer was prepared. NaOD was purchased from Cortec (Paris, France) and diethyl ether from Deoksan Chemical (Seoul, Korea).

Five-week-old Sprague-Dawley rats were placed in a plastic cage in a room maintained at a temperature of 21 ± 2 ° C. and a relative humidity of 50 ± 5%, respectively, for 12 hours light and 12 hours dark. All rats were fed a weekly diet (Purina Rodent Chow 5001) for 1 week before dividing into normal and high fat diet groups (n = 10 / group).

High fat diets were formulated by replacing carbohydrates with lard (17%, w / w) and corn oil (3%, w / w) so that fat provided 40% of the total energy content of the feed. The composition of the experimental feed is shown in Table 1. Feed intake was recorded daily and body weight was monitored between 10 am and 11 am once a week.

The experiments of the present invention followed the Guide for the Care and Use of Laboratory Animals created by the Institute of Laboratory Animal Resources of the National Research Council, Yonsei University Approved by Animal Experiment Ethics Committee.

After eight weeks of dietary and high-fat diets in each group, the animals were individually placed in plastic metabolic cages and urine collected. Twenty-four hours of urine was collected in a tube containing 500 μl of 1% sodium azide at 12 hour intervals (8 pm-8 am, 8 am-8 pm) for three consecutive days. After the collection was completed, the amount of urine was measured with a volumetric pipette and the pH was also measured. Urine samples were centrifuged at 3000 x g for 10 minutes and the supernatant obtained was stored at -70 ° C until analysis.

After feeding for 11 weeks, the animals were divided into four groups according to feed type and weight gain: general diet (ND) underweight gain (n = 5), normal diet increase in solids (n = 4, one) Died at 10 weeks), high fat diet (HFD) underweight (n = 5) and high fat diet solids (n = 5). Underweight and solid weight groups were divided into two groups based on the median weight gain. After fasting overnight the rats were anesthetized with diethyl ether. Blood was taken from the abdominal aorta and placed in a vacuum tube, and serum was collected by centrifugation at 1000 × g for 15 minutes at 4 ° C.

Remove the fat layer from 4 tissues (epididymal, perirenal, retroperitoneal and mesenteric) where liver and visceral fat have accumulated and rinse with 0.1 M phosphate buffer (pH 7.4). Weighed. The serum and liver samples were stored at −70 ° C. until analysis.

Composition of experimental feed fed to rats (g / kg) ingredient ND HFD Casein 200 200 DL-methionine 3 3 Corn starch 150 111 Sugar 500 370 cellulose 50 50 Corn oil 50 30 lard - 170 Mixed minerals ^a 35 42 Mixed vitamin ^b 10 12 Choline biartrate 2 2 cholesterol - 10 tert-butylhydroquinone ^c 0.01 0.04 Total (g) 1,000 1,000 Fat, calories% 11.5 39.0 Total calories, KJ / kg feed 16,439 19,315

^a AIN-76A Mixed Mineral for Rodent Feed

^b AIN-76A Mixed Vitamins for Rodent Feed

^c Antioxidants added at 0.01 g / 50 g lipid

<Example 2>

Sample analysis

Plasma concentrations of total cholesterol, high density lipoprotein (HDL) cholesterol, free fatty acids (FFA), sugars and triglycerides were measured enzymatically using a commercial kit (BioClinical System, Korea). The plasma concentration of LDL + VLDL cholesterol was calculated by subtracting the HDL cholesterol concentration from the total cholesterol concentration. Liver lipids were extracted according to the method of Folch et al. (Folch J, Less M, Sloane Stanley GH (1957) A simple method for the isolation and purification of total lipids from animal tissues.J Biol Chem 226: 497-509). Lipid residue was dissolved in 1 ml of ethanol. Concentrations in liver lipid extracts of cholesterol, triglycerides and FFA were measured using the same enzyme kits used for plasma analysis.

All results obtained from biochemical analysis were expressed as mean ± standard error (SEM) of 4 or 5 rats in each group. Statistical evaluation was performed using one-way batch variance analysis and Duncan's multiple range tests. The intensity of the spectral data of major metabolites selected through variable importance plot (VIP) partial least squares-discriminant analysis (PLS-DA) analysis was determined as a significant limit calibrated by the independent t test (Bonferroni's calibration method). 0.025) was then expressed as the mean ± SEM of the remaining 3 rats except for one or two animals that showed extreme weight gain in each group. Samples outside the Hotelling's T2 region of the PLS-DA model value curve showing experimental deviations were also excluded when performing statistical analysis.

<Example 3>

Urine ^One H-NMR measurement And data analysis

Urine samples were selected between 8 am and 8 am for three consecutive days. Frozen urine samples were dissolved in a water bath at 40 ° C, 0.35 ml of each urine sample was taken into an Eppendorf tube and shaken for 5 seconds. KH ₂ PO ₄ was added to the buffer in D ₂ O. The pH of D ₂ O used for NMR was adjusted to 6.0 using 520 μl of 1 N NaOD. 0.3 ml of this prepared urine mixture contains 0.2 ml of D ₂ O (0.05% 3- (trimethylsilyl) -propion-2,2,3,3- d 4 acid, sodium salt; TSP is the internal standard of D ₂ O Pipette into 5-mm NMR tube (Norell, NJ) and shake for several seconds to mix thoroughly.

All spectra were measured using a NMR spectrometer (Avance 600 FT-NMR, Bruker, Germany) at a temperature of 298 K with a triple-axis inverse (TXI) cold probe at a quantum NMR frequency of 600.13 MHz. Spectra were recorded using zgpr as the presaturation pulse sequence for water molecule inhibition. 128 scans were recorded for each sample with the following parameters: 0.155 Hz / point, pulse width 4.0 μs (30 °), relaxation delay 2 seconds.

Spectral zones ranging from δ = 0.52 to 10.00 were partitioned to 0.04 ppm width using Chenomx NMR software (version 5.1, Chenomx, USA) to make a total of 218 zones per NMR spectrum. The range of δ = 4.6 to 4.9 was excluded from the analysis and urea containing zones (δ = 5.5-6.0) because of the water residual peak in the aqueous extract. Spectrum data was converted to Microsoft Excel format and SIMCA-P software (version 12.0, Umetrics, Umea, Sweden) was used for multivariate statistical analysis.

In this analysis, all spectral data were processed around the mean without scaling. PLS-DA uses a classification based on PLS, where the response variable is category 1 (dummy variables representing those categories) that represents the hierarchical membership of the statistical unit. PLS-DA was applied to the ¹ H-NMR spectrum using SIMCA-P software. PLS-DA aims to find a model that classifies the hierarchy of observations based on its X variables. The PLS-DA model was validated by substitution representing R ² Y and Q ² Y values. Samples located outside the Hotellings T2 area of the PLS-DA value curve were considered experimental deviations and were excluded from subsequent analysis.

<Example 4>

Biochemical Parameter Analysis Results

Table 2 shows the biochemical parameters. Body weight gain was highest in the high fat diet group, but there was no significant difference between the normal diet and the high fat diet group. The unusual result was that the feed intake was not different between the increase in the normal diet and the increase in the high fat diet. The food efficiency ratio (FER) of weight gain divided by feed intake was the highest in the high fat diet solids group, and there was no difference between the high fat diet low weight group and the normal diet solids group.

Visceral fat weights of the epididymis, perirenal and posterior peritoneal regions were highest in the high fat diet. The weights of epididymis, peri-renal and mesenteric sites did not differ between the normal diet groups, but the peritoneal fat weight in the normal diet increased solids was significantly higher than the normal diet increased, and the high fat diet did not differ from the low weight gain group. The total weight of visceral fat was highest in the high fat diet group, the lowest in the normal diet group, and the low in the normal diet group and the high fat diet group, although they had different types of feed. Did.

Triglycerides were higher in the high fat diet increase group than in the high fat diet increase weight group. The sum of the concentrations of low-density lipoprotein (LDL) and very-low-density lipoprotein (VLDL) cholesterol was not different in group 4. The concentration of free fatty acids was higher in the high-fat diet group than in the normal diet-increased group, but the blood glucose level was not different between the 4 groups.

GOT (glutamic oxaloacetic transaminase) and GPT (glutamic pyruvic transaminase) were higher in the high-fat diets than in the normal diets. The liver was the heaviest in the high fat diet, followed by the low fat diet, the normal diet, and the normal diet. Hepatic triglyceride levels were higher in the high-fat diet solids group than in the normal diet group, and liver cholesterol and free fatty acid concentrations were significantly higher in the high-fat diet group than in the normal diet group.

Body weight gain, visceral fat layer weight and serum and liver biochemical data in rats fed two experimental diets group ND Underweight (n = 5) ND increase in solids (n = 4) HFD low weight gain (n = 5) HFD solid increase (n = 5) 11 weeks weight gain (g) 345 ± 7.3 c 388 ± 9.4 bc 396 ± 10.9 b 488 ± 25.2 a Feed Intake (g / day) 18.9 ± 0.2 b 20.4 ± 0.3 ab 19.5 ± 0.5 b 21.6 ± 0.9a FER ^a 0.22 ± 0.004 c 0.23 ± 0.009 bc 0.24 ± 0.008 b 0.27 ± 0.008 a Visceral fat layer weight (mg / g body weight) Epididymis 13.1 ± 1.2 c 16.4 ± 1.2 bc 18.7 ± 0.5 b 22.9 ± 1.3 a Around the kidneys 4.5 ± 0.5 b 5.1 ± 0.3 b 4.9 ± 0.3 b 6.7 ± 0.5 a Posterior peritoneum 14.6 ± 1.5 c 21.6 ± 1.2 b 20.5 ± 2.0 bc 28.4 ± 2.8 a mesentery 9.7 ± 1.0 b 13.2 ± 1.6 b 10.9 ± 1.3 ab 16.6 ± 1.1 a Sum 56.1 ± 4.0 c 76.1 ± 4.3 b 71.7 ± 5.9 b 103.0 ± 9.1 a serum Triglycerides (mmol / L) 0.9 ± 0.2 b 1.3 ± 0.2 ab 0.8 ± 0.1 b 1.5 ± 0.2 a Total Cholesterol (mmol / L) 2.5 ± 0.3 ab 3.4 ± 0.5 a 2.1 ± 0.1 b 2.7 ± 0.1 ab HDL cholesterol (mmol / L) 1.2 ± 0.2 ab 1.6 ± 0.2 a 0.7 ± 0.02 c 0.9 ± 0.03 bc LDL + VLDL Cholesterol (mmol / L) 1.3 ± 0.2 1.8 ± 0.3 1.4 ± 0.1 1.8 ± 0.1 FFA (μEq / L) 668 ± 98 ab 822 ± 93 a 576 ± 49 b 681 ± 28 ab Glucose (mmol / L) 9.6 ± 1.4 11.9 ± 0.9 11.1 ± 0.3 11.9 ± 0.9 GOT (IU / mL) 29.7 ± 4.6 b 28.9 ± 2.0 b 40.5 ± 3.5 a 45.7 ± 2.6 a GPT (IU / mL) 19.8 ± 4.5 b 20.6 ± 1.9 b 49.3 ± 10.6 a 45.5 ± 9.7 a liver Liver weight (g / 100 g weight) 2.4 ± 0.07 d 3.1 ± 0.18 c 4.55 ± 0.19 b 4.97 ± 0.16 a Triglycerides (μmol / g liver) 13.7 ± 2.7 c 28.5 ± 4.1 b 46.3 ± 2.1 ab 38.8 ± 7.4 a Cholesterol (μmol / g liver) 7.7 ± 1.3 b 23.1 ± 7.0 b 55.5 ± 2.6 a 48.8 ± 10.9 a FFA (μEq / L) 8.6 ± 1.5 b 14.9 ± 2.1 b 28.1 ± 1.9 a 25.5 ± 5.8 a

Each value is mean ± SEM (n = 4 or 5).

Each letter on the same line represents a significant difference between experimental groups with P <0.05.

^a food efficiency ratio (FER) = weight gain during the experiment (g / day) / feed intake during the experiment (g / day)

Example 5

Urine Metabolite ^One H-NMR Spectrum and PLS-DA Model

1 is a representative spectrum of ¹ H-NMR of the urine of one of the general diet low weight gain group. The following signals were identified by comparison with chemical shifts of standard compounds using the Kenomex NMR software suite (version 5.1, Chenomx, USA). Spectra were measured by ¹ H-NMR at a frequency of 600.13 MHz showing high peak resolution: leucine δ = 0.91-0.96 (m); Lactate δ = 1.34 (d, J = 6.6 Hz); Acetone / acetoacetate δ = 2.30 (s); Pyruvate δ = 2.38 (s); Citrate δ = 2.58 (d, J = 16.2 Hz) and 2.72 (d, J = 16.4 Hz); Creatinine δ = 3.06 (s); Taurine δ = 3.27 (t, J = 6.6 Hz) and 3.44 (t, J = 6.6 Hz); Betain δ = 3.90 (s); Phenylacetylglycine δ = 7.32-7.39 (m) and 7.42 (t, J = 7.2 Hz); And hiprate δ = 7.53-7.58 (m) and 7.64 (t, J = 7.4 Hz).

The PLS-DA value curve shows the clustering of the first and second PLS components.

The PLS-DA value curve (FIG. 2A) shows the separation between the low dietary weight gain group and the normal dietary solid gain group along the axis corresponding to the first and second PLS components. These two components accounted for 90.4% of the total variation. The goodness of fit of the model was quantified by R ² Y and the predictive ability was expressed by Q ² Y. In general, R ² Y -showing how well the data in the training set is mathematically reproduced-varies from 0 to 1, where 1 refers to a model with perfect fit. Models with Q ² Y values of 0.5-0.9 and 0.9-1.0 are also considered to have good and good predictive power, respectively.

As shown in Table 3, this PLS-DA model M1 has a high R ² Y value (0.905) and a Q ² Y value (0.799). This shows that each rat in group 2 consumed the same general diet but could be metabolically divided into different physiological constitutions. However, the PLS-DA model using the high fat diet low weight gain group and the high fat diet solid gain group has low validity (data not shown), indicating that the metabolite change is a physiological constitution between rats fed a high fat diet. It is not suitable for prediction.

Figure 2b shows that the normal diet low weight gain group and high fat diet solids increase group can be distinguished for the PLS components 1 and 2 accounting for 90.6% of the total variation. This model (M2) has high quality ( R ² Y = 0.903 and Q ² Y = 0.812) as shown in Table 3, showing that several endogenous metabolites changed in rats fed a high fat diet.

Validation data of the PLS-DA model Model Number of ingredients R ² Y Q ² Y M1 ^a 3 0.905 0.799 M2 ^b 3 0.903 0.812

^a ND underweight vs ND solids

^b ND underweight vs HFD solids

<Example 6>

Substitution test

Permutation tests were performed to validate the PLS-DA model. This can be seen from the statistical significance of the estimated predictive power of the model by comparing the R ² Y and Q ² Y values of the original model with that of the reordered model. The reordered model is created each time the Y data is randomly replaced. Models with R ² Y intercept values less than 0.3-0.4 and Q ² Y intercept values less than 0.05 are known to be valid models. As can be seen in FIG. 3A, the PLS-DA model M1 has a suitable R ² Y intercept value of 0.178 and a Q ² Y intercept value of −0.399. For the PLS-DA model M2, the R ² Y intercept value is 0.377 and the Q ² Y intercept value is acceptable as −0.200, as can be seen in FIG. 3B.

The VIP value is the weighted sum of the PLS weights taking account of the explained Y deviation in each dimension. In general, a VIP limit of about 0.7-0.8 is in effect, and therefore a deviation of VIP> 0.75 is considered to be useful for distinguishing each sample in these PLS-DA models. The VIP values of several metabolites designated using the Kenomex NMR software suite are shown in Table 4. Among these metabolites, betaine, taurine, acetone / acetoacetate, phenylacetylglycine, pyruvate, lactate and citrate were selected as key metabolites for sample identification in each model. The inventors performed an independent t test (P <0.025 with significance limit) to determine the relative concentrations of the major metabolites used to distinguish each group selected by VIP analysis. The results are shown in FIG. 4 where the metabolites for each model are plotted on the X axis according to its VIP value (VIP> 0.75) and the relative concentrations of the metabolite normalized to creatinine concentration on the Y axis. 4A shows that betaine, taurine, acetone / acetoacetate, phenylacetylglycine, pyruvate, lactate and citrate show a significant difference between the normal diet (ND) weight gain group and the general diet increase group solids. . These compounds also serve to distinguish between the normal diet low weight gain group and the high fat diet solid gain group in model M2 (FIG. 4B).

VIP values of key compounds for selection in curves derived from PLS-DA model compound
Model (number of members) M1 (3) M2 (3) Leucine 0.521 0.523 Lactate 0.754 0.830 Acetone / Acetoacetate 0.914 0.838 Pyruvate 0.833 0.779 Citrate 0.752 0.778 Taurine 1.382 2.776 Betaine 1.589 4.520 Phenylacetylglycine 0.913 0.881 Hippurate 0.578 0.571

1 is a representative representation of ¹ H-NMR of a rat urine sample.

FIG. 2 is a PLS-DA value curve obtained using ¹ H-NMR data for three models: a) ND underweight vs. ND solids, b) ND underweight vs. HFD solids. Each urine sample was three and excluded outside the Hotellings area.

FIG. 3 is a validation curve: a) ND underweight vs. ND solids, b) ND underweight vs. HFD solids. The Y axis indicates R ² Y (up triangle) and Q ² Y (square) for each model, and the X axis indicates the correlation coefficient between the circle and the substituted response data. The Y intercept of the curves for R ² Y and Q ² Y in each model was expressed as a number.

Figure 4 shows the concentration of metabolites normalized to creatinine concentrations: a) ND underweight vs ND solids, b) ND underweight vs. HFD solids. Independent t tests were performed to assess the statistical significance between each group. Error bars are indicated by SEM * P <0.025 for each group.

Claims (7)

Biomass for screening obesity, characterized in that it is excessively released from the urine of individuals with obesity constitution, selected from the group comprising taurine, betaine, acetone, acetoacetate, phenylacetylglycine, pyruvate, lactate and citrate Marker. Obesity constitution screening composition, characterized in that in response to the biomarker according to claim 1. Obesity constitution screening kit comprising the composition of claim 2. The kit for screening obesity constitution according to claim 3, wherein the kit is configured to display a positive response when the biomarker is above a reference value. The kit for screening obesity constitution according to claim 4, wherein the kit shows a positive reaction when the biomarker is 2 times or more than the reference value. A method of providing information on obesity constitution including a step of confirming whether a biomarker according to claim 1 is detected above a reference value in a urine sample. The method of claim 6, wherein the biomarker is determined to be obese when the biomarker is twice or more than a reference value.

Images