WO2020105562A1

WO2020105562A1 - Method for evaluating risk of type 2 diabetes using blood metabolites as an index

Info

Publication number: WO2020105562A1
Application number: PCT/JP2019/044926
Authority: WO
Inventors: Mitsuhiro Yanagida; Takayuki Teruya
Original assignee: Okinawa Institute Of Science And Technology School Corporation
Priority date: 2018-11-20
Filing date: 2019-11-15
Publication date: 2020-05-28

Abstract

It is an object of the present invention to provide a novel method capable of evaluating risk of Type 2 diabetes. The present invention is related to a method for evaluating risk of Type 2 diabetes using blood metabolites as an index, wherein the blood metabolite comprises at least one metabolite selected from the group consisting of NADP+, (iso)Valeryl-carnitine, 2-Hydroxybutyrate, 3-Hydroxybutyrate, Acetyl-carnitine, Proline, ADP, Urate, Sedoheptulose-7-phosphate, N-Acetyl-aspartate, Glycerophosphoethanolamine, 6-Phosphogluconate, Quinolinic acid, N-Acetyl-glucosamine, UMP, Adenine, Succinate, Cytidine, Phosphoenolpyruvate, Fructose-6-phosphate, Keto(iso)leucine, Glyceraldehyde-3-phosphate, Pentose-phosphate, UDP-glucuronate, N-Methyl-adenosine, UTP, Citrulline, S-Adenosyl-methionine, Dimethyl-arginine, Indoxyl-sulfate, Kynurenine, UDP-glucose, Dimethyl-guanosine, N2-Acetyl-arginine, Glutathione disulfide, Phenylalanine, AMP, Serine, Threonine, 1,5-Anhydroglucitol, Taurine, Trimethyl-lysine, and Pseudouridine.

Description

METHOD FOR EVALUATING RISK OF TYPE 2 DIABETES USING BLOOD METABOLITES AS AN INDEX

The present invention relates to a method for evaluating risk of Type 2 diabetes using blood metabolites as an index.

Diabetes is a disease that affects body’s ability to produce or use insulin. Type 2 diabetes (hereafter T2D) occurs when the body does not produce enough insulin, or when the cells cannot use insulin properly, which is called insulin resistance. If insulin is little or not produced, excess sugar remains in blood. In Type 1 diabetes, pancreas does not produce insulin so that this is called juvenile diabetes. T2D is diagnosed later in life and called a life-style related disease. 90-95 percent of people with diabetes have this type. Development of diabetes is affected by genetic and environmental factors. If hyperglycemia continues over a long period of time, micro-capillaries are damaged, causing complications such as heart disease, retinopathy, nephropathy, neurological disorders, and so on.

Therefore, both treating and preventing T2D is a global priority and there is growing interest in finding useful markers that indicate changes in metabolic function prior to the actual onset of T2D. The worldwide explosion of T2D has been attributed to the increased prevalence of obesity. On the other hand, T2D with lean/normal body mass index (abbreviated BMI), which was once a minority, has recently explosively increased in the developing countries, especially in Asia (NPL 1, 2). However, investigation on the cause and the mechanism of lean diabetes (L-T2D) is much less than that of Ob-T2D (NPL 3). It is believed that genetic, acquired and behavioral factors are behind the development of diabetes, but due to complexity of the disease, it is not well understood what principal metabolic effects lead to the development of diabetes.

Human activities are supported by blood. Blood circulates oxygen and numerous compounds, and eliminates unnecessary substances. Hundreds of metabolites are known. What blood metabolites tell us? Many metabolites are markers for health and diseases. Diverse health- and disease-related information have been collected through abundance of metabolites. As samples of the subjects are easily collected, blood metabolites should be more exploited for detailed and thorough analysis. Actually currently available information on blood cells metabolites is scarce: data of plasma metabolites are the great majority. In addition, blood metabolites are often analyzed without great precaution regarding metabolites stability. Blood samples are often kept without quenching such as immediate transfer to -40°C.

Ronald CW Ma, & Juliana CN Chan (2013) Nathaniel J Colemana, Jadwiga Miernik, Louis Philipson, & Leon Fogelfeld (2014) Lean versus obese diabetes mellitus patients in the United States minority population. Journal of diabetes and its Complications 28(4):500-505. Amrutha M George, Amith G Jacob, Leon Fogelfeld (2015) Lean diabetes mellitus: An emerging entity in the era of obesity. World Journal of Diabetes 6(4):613-620.

Diabetes is currently diagnosed based on blood glucose concentration and HbA1c, a glycated hemoglobin, in blood. The risk of Type 2 diabetes, which accounts for 90% of diabetes cases, is generally evaluated in a non-molecular way, considering such factors as diet, alcohol consumption, abdominal girth, etc. People identified as being at high risk before onset can implement appropriate health management measures such as lifestyle improvements and/or early medical intervention, based on the characteristics of their individual metabolic profiles. Early biomarkers for diabetes have been studied in large cohort studies, but individual differences in basal metabolism due to differences in age or BMI make it difficult to identify reliable markers. In other words, in order to discover early biomarkers of diabetes, the influence of age and BMI on metabolic profiles must be taken into account.

We employed analysis of metabolites by non-targeted metabolomics that provide comprehensive information of metabolites abundance. For targeted analysis, metabolites for analysis are pre-determined so that abundance changes of untargeted metabolites may be overlooked. Although non-targeted analysis is far more time consuming than targeted LC-MS, it is worth trying for the discovery of compounds’ abundance change. Previous T2D metabolomic studies mainly involve over-weight/obese subjects and few studies focused on lean/normal-weight T2D subjects. This study focuses on assessing blood metabolomics of T2D subjects with and without obesity. Based on healthy lean people as control, by comparative analysis of blood metabolites abundance in non-diabetic obese, lean-T2D and obese-T2D people, we identified characteristic changes in each group and the change common to T2D with or without obesity. T2D patients from non-T2D people were distinguished by principal component analysis of 15 metabolites, identified as T2D markers common to T2D with or without obesity. These data suggest that the quantitative information of T2D-related metabolites identified in the present invention opens a way to facilitate the early-stage or preliminary judgment for medical diagnosis. To our knowledge, this is the first report characterizing the blood metabolomic profiles of lean-T2D and Ob-T2D, respectively.

In this work, we present a novel method for evaluating the risk of T2D in which a blood metabolite is used as an indicator. The method of the present invention is easy and accurate.

The present inventions are as follows.
(1)
A method for evaluating risk of Type 2 diabetes using blood metabolites as an index, wherein the blood metabolite comprises at least one metabolite selected from the group consisting of NADP+, (iso)Valeryl-carnitine, 2-Hydroxybutyrate, 3-Hydroxybutyrate, Acetyl-carnitine, Proline, ADP, Urate, Sedoheptulose-7-phosphate, N-Acetyl-aspartate, Glycerophosphoethanolamine, 6-Phosphogluconate, Quinolinic acid, N-Acetyl-glucosamine, UMP, Adenine, Succinate, Cytidine, Phosphoenolpyruvate, Fructose-6-phosphate, Keto(iso)leucine, Glyceraldehyde-3-phosphate, Pentose-phosphate, UDP-glucuronate, N-Methyl-adenosine, UTP, Citrulline, S-Adenosyl-methionine, Dimethyl-arginine, Indoxyl-sulfate, Kynurenine, UDP-glucose, Dimethyl-guanosine, N2-Acetyl-arginine, Glutathione disulfide, Phenylalanine, AMP, Serine, Threonine, 1,5-Anhydroglucitol, Taurine, Trimethyl-lysine, and Pseudouridine.
(2)
The method for evaluating risk of Type 2 diabetes using blood metabolites as an index according to (1), wherein the blood metabolite comprises at least one metabolite selected from the group consisting of NADP+, Sedoheptulose-7-phosphate, N-Acetyl-aspartate, Glycerophosphoethanolamine, 6-Phosphogluconate, Quinolinic acid, UMP, Adenine, Phosphoenolpyruvate, Glyceraldehyde-3-phosphate, Pentose-phosphate, UDP-glucuronate, Serine, Threonine, S-Adenosyl-methionine, UDP-glucose, Dimethyl-guanosine, N2-Acetyl-arginine, Taurine and Pseudouridine.
(3)
The method for evaluating risk of Type 2 diabetes using blood metabolites as an index according to (2), wherein the blood metabolite comprises at least one metabolite selected from the group consisting of UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine, Glyceraldehyde-3-phosphate, 6-phosphogluconate, Pseudouridine, Quinolinic acid and S-Adenosyl-methionine.
(4)
The method for evaluating risk of Type 2 diabetes using blood metabolites as an index according to any one of (1) to (3), wherein the blood metabolites comprise at least UDP-glucuronate and UMP.
(5)
The method for evaluating risk of Type 2 diabetes according to (4), wherein the blood metabolites comprise at least UDP-glucuronate, UMP and Dimethyl-guanosine.
(6)
The method for evaluating risk of Type 2 diabetes according to (5), wherein the blood metabolites comprise at least UDP-glucuronate, UMP, Dimethyl-guanosine and UDP-glucose.
(7)
The method for evaluating risk of Type 2 diabetes according to (6), wherein the blood metabolites comprise at least UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose and N2-Acetyl-arginine.
(8)
The method for evaluating risk of Type 2 diabetes according to (7), wherein the blood metabolites comprise at least UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine and Glyceraldehyde-3-phosphate.
(9)
The method for evaluating risk of Type 2 diabetes according to (8), wherein the blood metabolites comprise at least UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine, Glyceraldehyde-3-phosphate and 6-phosphogluconate.
(10)
The method for evaluating risk of Type 2 diabetes according to (9), wherein the blood metabolites comprise at least UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine, Glyceraldehyde-3-phosphate, 6-phosphogluconate and Pseudouridine.
(11)
The method for evaluating risk of Type 2 diabetes according to (10), wherein the blood metabolites comprise at least UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine, Glyceraldehyde-3-phosphate, 6-phosphogluconate, Pseudouridine and Quinolinic acid.
(12)
The method for evaluating risk of Type 2 diabetes according to (11), wherein the blood metabolites comprise at least UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine, Glyceraldehyde-3-phosphate, 6-phosphogluconate, Pseudouridine, Quinolinic acid and S-Adenosyl-methionine.
(13)
An apparatus for evaluating risk of Type 2 diabetes which comprises means for input and means for evaluating, wherein the data of blood metabolites of the subject are input to the means for input, risk of Type 2 diabetes is evaluated by comparing the data of the subject and the data of the population, and the blood metabolites comprise at least one metabolite selected from the group consisting of NADP+, (iso)Valeryl-carnitine, 2-Hydroxybutyrate, 3-Hydroxybutyrate, Acetyl-carnitine, Proline, ADP, Urate, Sedoheptulose-7-phosphate, N-Acetyl-aspartate, Glycerophosphoethanolamine, 6-Phosphogluconate,Quinolinic acid, N-Acetyl-glucosamine, UMP, Adenine, Succinate, Cytidine, Phosphoenolpyruvate, Fructose-6-phosphate, Keto(iso)leucine, Glyceraldehyde-3-phosphate, Pentose-phosphate, UDP-glucuronate, N-Methyl-adenosine, UTP, Citrulline, S-Adenosyl-methionine, Dimethyl-arginine, Indoxyl-sulfate, Kynurenine, UDP-glucose, Dimethyl-guanosine, N2-Acetyl-arginine, Glutathione disulfide, Phenylalanine, AMP, Serine, Threonine, 1,5-Anhydroglucitol, Taurine, Trimethyl-lysine, and Pseudouridine.
(14)
The apparatus for evaluating risk of Type 2 diabetes according (13), wherein the blood metabolites comprise at least UDP-glucuronate and UMP.
(15)
A kit for evaluating risk of Type 2 diabetes by using blood metabolites comprising at least one metabolite selected from the group consisting of NADP+, (iso)Valeryl-carnitine, 2-Hydroxybutyrate, 3-Hydroxybutyrate, Acetyl-carnitine, Proline, ADP, Urate, Sedoheptulose-7-phosphate, N-Acetyl-aspartate, Glycerophosphoethanolamine, 6-Phosphogluconate,Quinolinic acid, N-Acetyl-glucosamine, UMP, Adenine, Succinate, Cytidine, Phosphoenolpyruvate, Fructose-6-phosphate, Keto(iso)leucine, Glyceraldehyde-3-phosphate, Pentose-phosphate, UDP-glucuronate, N-Methyl-adenosine, UTP, Citrulline, S-Adenosyl-methionine, Dimethyl-arginine, Indoxyl-sulfate, Kynurenine, UDP-glucose, Dimethyl-guanosine, N2-Acetyl-arginine, Glutathione disulfide, Phenylalanine, AMP, Serine, Threonine, 1,5-Anhydroglucitol, Taurine, Trimethyl-lysine, and Pseudouridine as an index which contains blood collection tubes and blood metabolite compounds as detection standard.
(16)
The kit for evaluating risk of Type 2 diabetes according to (15), wherein the blood metabolites comprise at least UDP-glucuronate and UMP.
(17)
A method of screening substances which reduce risk of Type 2 diabetes comprising the step of measuring a blood metabolite, wherein the blood metabolites comprise at least one metabolites selected from the group consisting of NADP+, (iso)Valeryl-carnitine, 2-Hydroxybutyrate, 3-Hydroxybutyrate, Acetyl-carnitine, Proline, ADP, Urate, Sedoheptulose-7-phosphate, N-Acetyl-aspartate, Glycerophosphoethanolamine, 6-Phosphogluconate,Quinolinic acid, N-Acetyl-glucosamine, UMP, Adenine, Succinate, Cytidine, Phosphoenolpyruvate, Fructose-6-phosphate, Keto(iso)leucine, Glyceraldehyde-3-phosphate, Pentose-phosphate, UDP-glucuronate, N-Methyl-adenosine, UTP, Citrulline, S-Adenosyl-methionine, Dimethyl-arginine, Indoxyl-sulfate, Kynurenine, UDP-glucose, Dimethyl-guanosine, N2-Acetyl-arginine, Glutathione disulfide, Phenylalanine, AMP, Serine, Threonine, 1,5-Anhydroglucitol, Taurine, Trimethyl-lysine, and Pseudouridine.
(18)
The method of screening substances which reduce risk of Type 2 diabetes according to (17), wherein the blood metabolites comprise at least UDP-glucuronate and UMP.

We performed non-targeted, quantitative metabolomic analysis in blood of lean controls (LC), Obese, non-diabetic (Ob), lean, Type 2 diabetic (L-T2D), and obese, Type 2 diabetic (Ob-T2D) individuals (Fig. 1). Two statistical tests (Student's t test and Mann-Whitney U test) showed that 55 of 124 compounds identified were involved in diabetes and/or obesity (Fig. 4). Fifty-five compounds comprise 12 obesity-related metabolites and 43 diabetes-related metabolites. Of the 43 diabetes-related compounds, 14 showed significant differences regardless of whether subjects were obese or not, and 29 showed different responses depending on whether subjects are obese or not. Furthermore, among 14 compounds which change with diabetes regardless of obesity, 7 compounds (6-phosphogluconate, fructose-6-phosphate, glyceraldehyde-3-phosphate, N-acetyl-glucosamine, phosphoenolpyruvate, pentose-phosphate, UDP-glucuronate) were glucose metabolites. Among the 18 metabolites that showed a change only in Lean-T2D, 12 compounds were amino acids and their metabolites (dimethyl-arginine, indoxyl-sulfate, kynurenine, N-acetyl-arginine, S-adenosyl-methionine, threonine, citrulline, glutathione disulfide, phenylalanine, serine, taurine, trimethyl-lysine). On the other hand, among 11 compounds that showed a change only in Ob-T2D, 5 compounds involve fatty acid metabolism (2-hydroxybutyrate, 3-hydroxybutyrate, acetyl-carnitine, (iso)valeryl-carnitine, glycerophosphoethanolamine). Seven of 12 obesity-related compounds were short-chain fatty acid-related metabolites (butyryl-carnitine, carnitine, aminobutyrate, propionyl-carnitine, valine, isoleucine, leucine). Principal component analysis using these 55 diabetes and/or obese-related compounds data classified four subject groups clearly (Fig. 9). This result shows that metabolite profiles vary depending on whether subjects are obese. Thus, by using quantitative data regarding diabetes-related compounds, it is possible to evaluate the onset level of type 2 diabetes precisely, considering the influence of the subject BMI on blood metabolites.

Group composition of 18 subjects. Individual detailed data from 10 non-diabetic (LC, Ob) and 8 diabetic subjects (L-T2D, Ob-T2D) are shown. The procedures of collecting samples are described in Materials and methods. Data of their age, gender, BMI (body mass index) and HbA1c are shown. HbA1c values are 5.2~5.8 and 5.1~5.8 for non-diabetic lean control (LC) and obese (Ob), respectively, whilst HbA1c values are 8.7~12.6 and 6.9~9.8 for obese diabetics (Ob-T2D) and lean diabetics (L-T2D), respectively. Characteristics of 18 subjects. Characterizations of 4 subject groups (LC, Ob, L-T2D, and Ob-T2D) are indicated by dot plot of age (yr), BMI (body mass index), and HbA1c. A. Average of age and gender composition (F, female; M, male) are shown below the dot plot in each subject group. B. Average values of BMI are shown below the dot plot. C. Average values of HbA1c, the numerical indicator of diabetes, are shown below the dot plot. Blood metabolites identified in the present study. In this non-targeted LC-MS metabolomic study, 124 metabolites were identified and quantified for all of the subjects. They were classified into fourteen categories. Quantification of all metabolites was carried out by measuring the peak area of individual metabolites described previously (rank of high, medium, and low indicated by (H), (M), and (L), respectively (1, 2). Fifty-five blood metabolites related to Obese, Obese-T2D and Lean-T2D significantly differ from LC (lean control). The metabolite abundances were compared by using Student’s t-test and Mann-Whitney U test. Compounds with the p-values below p<0.05 are shown. Compounds underlined are enriched in RBC (1, 2). The number of compounds in each category is shown. The numerical values indicate the ratio of the average peak area in two groups. The arrows indicate either increase↑ or decrease↓ of metabolites. The number of arrows represents the degree of the fold-change (single, 1.0~1.5; double, 1.5~2.0; triple, greater than 2.0). Dot plot profiling of category A 12 metabolites abundances for 18 subjects. Dot plot profiling of category A 8 metabolites is shown in Fig.5-1, and that of category A 4 metabolites is shown in Fig.5-2. Peak area of the non-diabetic lean control group (LC, n=5), non-diabetic obese group (Ob, n=5), lean T2D group (L-T2D, n=4), and obese T2D group (Ob-T2D, n=4) are plotted. All category A compounds significantly increased in Ob subjects. Asterisk (*) and hash (#) represent p-value (single, p<0.05; double, p<0.01) by t-test and U-test, respectively. Dot plot profiling of category B 11 metabolites abundances for 18 subjects. Dot plot profiling of category B 8 metabolites is shown in Fig.6-1, and that of category B 3 metabolites is shown in Fig.6-2. T-test and U-test showed that Obese-T2D subjects showed statistically significant differences (mostly increase) for these compounds. Asterisk (*) and hash (#) represent p-value (single, p<0.05; double, p<0.01) by t-test and U-test, respectively. Dot plot profiling of category C 14 metabolites abundances for 18 subjects. Dot plot profiling of category C 8 metabolites is shown in Fig.7-1 and that of category C 6 metabolites is shown in Fig.7-2. Category C metabolites significantly changed their metabolites abundance in both L-T2D and Ob-T2D, diabetic patients with or without obesity. These metabolites mostly increased except four. Ten of 14 metabolites were enriched in RBCs, strongly suggesting that diabetes is very much implicated in RBC metabolism. Asterisk (*) and hash (#) represent p-value (single, p<0.05; double, p<0.01; triple, p<0.001) by t-test and U-test, respectively. Dot plot profiling of category D 18 metabolites abundances for 18 subjects. Dot plot profiling of category D 8 metabolites is shown each in Fig.8-1 and Fig.8-2, and that of category D 2 metabolites is shown in Fig.8-3. Category D metabolites mostly decreased (only one exception is UTP). Only five of them were enriched in RBC (1, 2). Asterisk (*) and hash (#) represent p-value (single, p<0.05; double, p<0.01) by t-test and U-test, respectively. PCA plot using 55 obese- and/or T2D-related metabolites. Non-diabetic lean control group (LC, n=5), non-diabetic obese group (Ob, n=5), lean T2D group (L-T2D, n=4), and obese T2D group (Ob-T2D, n=4) are shown as empty circles, empty triangles, black circles, and black triangles, respectively. Top 10 PCA loadings in PC1 and PC2 are shown the table below the PCA plot. PC1 score data analyzed in each metabolite categories. PC1 score data of category A and category B are shown in Fig.10-1, and category C and category D are shown in Fig. 10-2. Subjects are sorted in ascending order of the value for factor loading. Group and number in each subject correspond to Fig. 1. Non-diabetic and diabetic subjects are shown in empty and black bars, respectively.

Before the present invention is described in detail, it is to be understood that this invention is not limited to the particular methodology, apparatuses, and systems described, as such methodology, apparatuses and systems can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention.

Unless defined otherwise or the context clearly dictates otherwise, 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 belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the invention, the preferred methods and materials are now described.

All publications mentioned herein are hereby incorporated by reference for the purpose of disclosing and describing the particular materials and methodologies for which the reference was cited. The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the invention is not entitled to antedate such disclosure by virtue of prior invention.

Definitions
The term "risk of T2D" is used herein to refer to a trend level of T2D. The term "risk of T2D refers to a value that indicates whether the subject's blood metabolic profile is close or far from T2D patients. In particular, the “risk of T2D” refers whether or not the patient has type 2 diabetes, and whether or not there is a risk of suffering from diabetes in the future (how close to the state of diabetes) in the present invention.

The term "blood metabolite" is used herein to refer to a low molecular compound involved in biological metabolic activity contained in blood constituents.

It is understood that aspects and embodiments of the invention described herein include "consisting" and/or "consisting essentially" of aspects and embodiments.

Other objects, advantages and features of the present invention will become apparent from the following specification taken in conjunction with the accompanying drawings.

A method for evaluating risk of Type 2 diabetes
According to the present invention, the risk of Type 2 diabetes (T2D) is evaluated by using a specific blood metabolite in a subject as an indicator. By measuring the amount of a specific blood metabolite in the whole blood of the subject, the risk of T2D of the subject can be evaluated. There are three types of T2D markers disclosed in the present invention: obese-T2D (Ob-T2D) markers, lean-T2D (L-T2D) markers, and common T2D markers not related to obesity. Diabetes risk can be evaluated using only common T2D markers. However, T2D risk can be detected at an early stage or make more detailed patient's condition clear as shown below by further examining the Ob-T2D or L-T2D markers according to the subject's BMI.

As the blood metabolite in the present invention, it is preferable that the compound has a large difference in blood content between the non-diabetic and diabetic group. The blood metabolite comprises at least one metabolite selected from the group consisting of NADP+, (iso)Valeryl-carnitine, 2-Hydroxybutyrate, 3-Hydroxybutyrate, Acetyl-carnitine, Proline, ADP, Urate, Sedoheptulose-7-phosphate, N-Acetyl-aspartate, Glycerophosphoethanolamine, 6-Phosphogluconate, Quinolinic acid, N-Acetyl-glucosamine, UMP, Adenine, Succinate, Cytidine, Phosphoenolpyruvate, Fructose-6-phosphate, Keto(iso)leucine, Glyceraldehyde-3-phosphate, Pentose-phosphate, UDP-glucuronate, N-Methyl-adenosine, UTP, Citrulline, S-Adenosyl-methionine, Dimethyl-arginine, Indoxyl-sulfate, Kynurenine, UDP-glucose, Dimethyl-guanosine, N2-Acetyl-arginine, Glutathione disulfide, Phenylalanine, AMP, Serine, Threonine, 1,5-Anhydroglucitol, Taurine, Trimethyl-lysine, and Pseudouridine. In order to obtain a more accurate evaluation result of T2D risk, it is preferable to analyze plural blood metabolites. It is more preferable to analyze plural blood metabolites as many as possible, and it is most preferable to analyze all blood metabolites shown above.

N-Acetyl-aspartate, Glycerophosphoethanolamine, Cytidine, Glyceraldehyde-3-phosphate, Pentose-phosphate, UDP-glucuronate, Citrulline, S-Adenosyl-methionine, Dimethyl-arginine, Indoxyl-sulfate, Kynurenine, UDP-glucose, Dimethyl-guanosine, N2-Acetyl-arginine, 1,5-Anhydroglucitol, Glutathione disulfide, Phenylalanine, AMP, Serine, Threonine, Taurine, Trimethyl-lysine, and Pseudouridine are lower in T2D patients. Therefore when the content of these blood compounds is lower than standard, the risk of T2D of the subject is judged to be high.

On the other hand, (iso)Valeryl-carnitine, 2-Hydroxybutyrate, 3-Hydroxybutyrate, Acetyl-carnitine, NADP+, Proline, ADP, Urate, Sedoheptulose-7-phosphate, 6-Phosphogluconate, N-Acetyl-glucosamine, Quinolinic acid, UMP, Adenine, Phosphoenolpyruvate, Succinate, Fructose-6-phosphate, Keto(iso)leucine, N-Methyl-adenosine and UTP are higher in T2D patients. Therefore when the content of these compounds is higher than standard, the risk of T2D of the subject is judged to be high.

Preferably, the blood metabolite comprises at least one metabolite selected from the group consisting of NADP+, Sedoheptulose-7-phosphate, N-Acetyl-aspartate, Glycerophosphoethanolamine, 6-Phosphogluconate, Quinolinic acid, UMP, Adenine, Phosphoenolpyruvate, Glyceraldehyde-3-phosphate, Pentose-phosphate, UDP-glucuronate, Serine, Threonine, S-Adenosyl-methionine, UDP-glucose, Dimethyl-guanosine, N2-Acetyl-arginine, Taurine and Pseudouridine. In order to obtain a more accurate evaluation result of T2D risk, it is preferable to analyze plural blood metabolites. It is more preferable to analyze plural blood metabolites as many as possible, and it is most preferable to analyze all blood metabolites shown above.

More preferably, the blood metabolite comprises at least one metabolite selected from the group consisting of 6-Phosphogluconate, UMP, UDP-glucuronate, S-Adenosyl-methionine, Quinolinic acid, UDP-glucose, Dimethyl-guanosine, N2-Acetyl-arginine, Glyceraldehyde-3-phosphate, and Pseudouridine. In order to obtain a more accurate evaluation result of T2D risk, it is preferable to analyze plural blood metabolites. It is more preferable to analyze plural blood metabolites as many as possible, and it is most preferable to analyze all blood metabolites shown above.

It is preferable to use UDP-glucuronate and UMP as an index in the method of the present invention for evaluating risk of Type 2 diabetes.

It is also preferable to use UDP-glucuronate, UMP and Dimethyl-guanosine as an index in the method of the present invention for evaluating risk of Type 2 diabetes.

It is also preferable to use UDP-glucuronate, UMP, Dimethyl-guanosine and UDP-glucose as an index in the method of the present invention for evaluating risk of Type 2 diabetes.

It is also preferable to use UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose and N2-Acetyl-arginine as an index in the method of the present invention for evaluating risk of Type 2 diabetes.

It is also preferable to use UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine and Glyceraldehyde-3-phosphate as an index in the method of the present invention for evaluating risk of Type 2 diabetes.

It is also preferable to use UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine, Glyceraldehyde-3-phosphate and 6-phosphogluconate as an index in the method of the present invention for evaluating risk of Type 2 diabetes.

It is also preferable to use UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine, Glyceraldehyde-3-phosphate, 6-phosphogluconate and Pseudouridine as an index in the method of the present invention for evaluating risk of Type 2 diabetes.

It is also preferable to use UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine, Glyceraldehyde-3-phosphate, 6-phosphogluconate, Pseudouridine and Quinolinic acid as an index in the method of the present invention for evaluating risk of Type 2 diabetes.

It is also preferable to use UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine, Glyceraldehyde-3-phosphate, 6-phosphogluconate, Pseudouridine, Quinolinic acid and S-Adenosyl-methionine as an index in the method of the present invention for evaluating risk of Type 2 diabetes.

Glucose metabolites (Sedoheptulose-7-phosphate and/or Glycerophosphoethanolamine), purine/pyrimidine metabolites (NADP+, ADP and/or Urate), amino acid metabolites (2-Hydroxybutyrate, Proline and/or N-Acetyl-aspartate), and/or fatty acid metabolites ((iso)Valeryl-carnitine, 3-Hydroxybutyrate and/or Acetyl-carnitine) can be used as Ob-T2D markers for evaluation of T2D risk for obese people in the present invention. Preferably, at least purine/pyrimidine metabolites (NADP+, ADP or Urate) are used as Ob-T2D markers for evaluation of T2D risk for obese people in the present invention. More preferably, at least glucose metabolites (Sedoheptulose-7-phosphate or Glycerophosphoethanolamine), purine/pyrimidine metabolites (NADP+, ADP or Urate), amino acid metabolites (2-Hydroxybutyrate, Proline or N-Acetyl-aspartate), and fatty acid metabolites ((iso)Valeryl-carnitine, 3-Hydroxybutyrate or Acetyl-carnitine) are used as Ob-T2D markers for evaluation of T2D risk for obese people in the present invention. Most preferably, 2 glucose metabolites (Sedoheptulose-7-phosphate and Glycerophosphoethanolamine), 3 purine/pyrimidine metabolites (NADP+, ADP and Urate), 3 amino acid metabolites (2-Hydroxybutyrate, Proline and N-Acetyl-aspartate), and 3 fatty acid metabolites ((iso)Valeryl-carnitine, 3-Hydroxybutyrate and Acetyl-carnitine) are used as Ob-T2D markers for evaluation of T2D risk for obese people in the present invention.

Glucose metabolites (UDP-glucose and/or 1,5-Anhydroglucitol), purine/pyrimidine metabolites (UTP, AMP, Pseudouridine and/or Dimethyl-guanosine), and/or amino acid metabolites (Citrulline, Glutathione disulfide, Phenylalanine, Serine, Taurine, Trimethyl-lysine, Threonine, Dimethyl-arginine, Indoxyl-sulfate, Kynurenine, N2-Acetyl-arginine and/or S-Adenosyl-methionine) can be used as L-T2D markers for evaluation of T2D risk for lean/normal-weight people in the present invention. Preferably, at least amino acid metabolites (Citrulline, Glutathione disulfide, Phenylalanine, Serine, Taurine, Trimethyl-lysine, Threonine, Dimethyl-arginine, Indoxyl-sulfate, Kynurenine, N2-Acetyl-arginine or S-Adenosyl-methionine) are used as L-T2D markers for evaluation of T2D risk for lean/normal-weight people in the present invention. More preferably, glucose metabolites (UDP-glucose and/or 1,5-Anhydroglucitol), purine/pyrimidine metabolites (UTP, AMP, Pseudouridine and/or Dimethyl-guanosine), and amino acid metabolites (Citrulline, Glutathione disulfide, Phenylalanine, Serine, Taurine, Trimethyl-lysine, Threonine, Dimethyl-arginine, Indoxyl-sulfate, Kynurenine, N2-Acetyl-arginine and/or S-Adenosyl-methionine) are used as L-T2D markers for evaluation of T2D risk for lean/normal-weight people in the present invention. Most preferably, 2 glucose metabolites (UDP-glucose and 1,5-Anhydroglucitol), 4 purine/pyrimidine metabolites (UTP, AMP, Pseudouridine and Dimethyl-guanosine), and 12 amino acid metabolites (Citrulline, Glutathione disulfide, Phenylalanine, Serine, Taurine, Trimethyl-lysine, Threonine, Dimethyl-arginine, Indoxyl-sulfate, Kynurenine, N2-Acetyl-arginine and S-Adenosyl-methionine) are used as L-T2D markers for evaluation of T2D risk for lean/normal-weight people in the present invention.

Glucose metabolites (6-phosphogluconate, N-Acetyl-glucosamine, Phosphoenolpyruvate, Fructose-6-phosphate, Glyceraldehyde-3-phosphate, Pentose-phosphate and/or UDP-glucuronate), purine/pyrimidine metabolites (N-Methyl-adenosine, UMP, Adenine and/or Cytidine), amino acid metabolites (quinolinic acid and/or keto(iso)leucine), and/or organic acid (succinate) can be used as T2D markers not related to obesity for evaluation of T2D risk for both lean/normal-weight and obese people in the present invention. Preferably, at least glucose metabolites (6-phosphogluconate, N-Acetyl-glucosamine, Phosphoenolpyruvate, Fructose-6-phosphate, Glyceraldehyde-3-phosphate, Pentose-phosphate and/or UDP-glucuronate) are used as T2D markers not related to obesity for evaluation of T2D risk for both lean/normal-weight and obese people in the present invention. More preferably, glucose metabolites (6-phosphogluconate, N-Acetyl-glucosamine, Phosphoenolpyruvate, Fructose-6-phosphate, Glyceraldehyde-3-phosphate, Pentose-phosphate and/or UDP-glucuronate), purine/pyrimidine metabolites (N-Methyl-adenosine, UMP, Adenine and/or Cytidine), amino acid metabolites (quinolinic acid and/or keto(iso)leucine), and organic acid (succinate) are used as T2D markers not related to obesity for evaluation of T2D risk for both lean/normal-weight and obese people in the present invention. Most preferably, 7 glucose metabolites (6-phosphogluconate, N-Acetyl-glucosamine, Phosphoenolpyruvate, Fructose-6-phosphate, Glyceraldehyde-3-phosphate, Pentose-phosphate and UDP-glucuronate), 4 purine/pyrimidine metabolites (N-Methyl-adenosine, UMP, Adenine and Cytidine), 2 amino acid metabolites (quinolinic acid and keto(iso)leucine), and organic acid (succinate) are used as T2D markers not related to obesity for evaluation of T2D risk for both lean/normal-weight and obese people in the present invention.

The method for evaluating the risk of T2D of the present invention comprises (i) a step of preparing a sample, (ii) a step of analysis and (iii) a step of evaluating the risk of T2D.

(i) a step of preparing a sample
Metabolomic samples can be prepared as reported previously (1, 2). All blood samples are drawn in a hospital laboratory to ensure rapid sample preparation. Briefly, venous blood samples for metabolomics analysis are taken into 5 mL heparinized tubes. Immediately, 0.1~1.0 mL blood were quenched in 30~70% methanol (preferably 50~60%) of 5~10 times volume of the blood at -20°C~-80°C (preferably at -40°C~-80°C). This quick quenching step immediately after blood sampling ensured accurate measurement of many labile metabolites. The use of whole blood samples also allowed us to observe cellular metabolite levels that might otherwise have been affected by lengthy cell separation procedures.

Two internal standards (10 nmol of HEPES and PIPES) are added to each sample. After brief vortexing, samples are transferred to Amicon Ultra 10-kDa cut-off filters (Millipore, Billerica, MA, USA) to remove proteins and cellular debris. After sample concentration by vacuum evaporation, each sample is re-suspended in 40 μL of 50% acetonitrile, and 1 μL is used for each injection into the LC-MS system.

(ii) a step of analysis
The content of blood metabolite in the sample of the subject is analyzed in this step. LC-MS data are preferably to be obtained using a Ultimate 3000 DGP-3600RS (Thermo Fisher Scientific, Waltham, MA, USA) coupled to an LTQ Orbitrap mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA), as previously described (1-3). Briefly, LC separation is performed on a ZIC-pHILIC column (Merck SeQuant, Umea, Sweden; 150 mm x 2.1 mm, 5 μm particle size). The HILIC column is quite useful for separating many hydrophilic blood metabolites, which are previously not assayed by others (4). Acetonitrile (A) and 10 mM ammonium carbonate buffer, pH 9.3 (B) are used as the mobile phase, with a gradient elution from 80-20% A in 30 min, at a flow rate of 100 μL mL-1. Peak areas of metabolites of interest are measured using MZmine 2 software (5, 6). Detailed data analytical procedures and parameters have been described previously (3).

(iii) a step of evaluating the risk of Type 2 diabetes
We analyze 124 blood compounds confirmed by standards or MS/MS analysis (1-3). For each metabolite we choose a singly charged, [M+H]+ or [M-H]-, peak (Table 2). Metabolites are classified into 3 groups (H, M, and L), according to their peak areas. H denotes compounds with high peak areas (>10⁸ AU), M with medium peak areas (10⁸ ~10⁷ AU) and L with low peak areas (<10⁷ AU) (Fig. 3).

The method for evaluating the risk of T2D of the present invention is not particularly limited as long as it uses the above metabolite as an index. For interpreting metabolic data, principal component analysis (PCA) can be applied for synthesizing and integrating many parameters of metabolite levels for individuals. We attempted to establish the index that represents the T2D risk. Our present invention may be epoch-making as the method based on measuring T2D markers, include non-sugar metabolites, at the molecular level and further integrate many kinds of metabolites information on the simple score (Fig. 10).

Apparatus
The present invention provides an apparatus for evaluating the risk of T2D. The apparatus uses the method of the present invention above.

The apparatus for evaluating the risk of T2D of the present invention comprises means for input and means for evaluating, wherein data of blood metabolites of the subject are input to the means for input, and the risk of T2D is evaluated by comparing the data of the subject with the data of the population. Said method section can be referred for details of the method of the present invention used by the apparatus.

System
The present invention provides a system for evaluating the risk of T2D. The risk of T2D is evaluated by the method of the present invention above, or the apparatus of the present invention above. Said method section and the apparatus section can be referred for details of the system of the present invention.

Methods
The present invention provides a method of evaluating substances which affect the risk of T2D comprising the step of measuring a blood metabolite, wherein the blood metabolite comprises at least one metabolite selected from the group consisting of NADP+, (iso)Valeryl-carnitine, 2-Hydroxybutyrate, 3-Hydroxybutyrate, Acetyl-carnitine, Proline, ADP, Urate, Sedoheptulose-7-phosphate, N-Acetyl-aspartate, Glycerophosphoethanolamine, 6-Phosphogluconate, Quinolinic acid, N-Acetyl-glucosamine, UMP, Adenine, Succinate, Cytidine, Phosphoenolpyruvate, Fructose-6-phosphate, Keto(iso)leucine, Glyceraldehyde-3-phosphate, Pentose-phosphate, UDP-glucuronate, N-Methyl-adenosine, UTP, Citrulline, S-Adenosyl-methionine, Dimethyl-arginine, Indoxyl-sulfate, Kynurenine, UDP-glucose, Dimethyl-guanosine, N2-Acetyl-arginine, Glutathione disulfide, Phenylalanine, AMP, Serine, Threonine, 1,5-Anhydroglucitol, Taurine, Trimethyl-lysine, and Pseudouridine. The substances found by this evaluation method can be widely used as anti-diabetic foods, drinks, supplements, pharmaceuticals, cosmetics and the like. The section of "A method for evaluating the risk of Type 2 diabetes" can be referred for details of the step of measuring a blood metabolite.

Kit
The present invention provides a kit for evaluating the risk of T2D by using the methods of the present invention, comprising blood collection tubes and blood metabolite compounds as detection standard. The kit of the present invention may comprise any constituent elements besides the blood collection tube and the like. The blood metabolite compounds as detection standard can be selected from the group consisting of NADP+, (iso)Valeryl-carnitine, 2-Hydroxybutyrate, 3-Hydroxybutyrate, Acetyl-carnitine, Proline, ADP, Urate, Sedoheptulose-7-phosphate, N-Acetyl-aspartate, Glycerophosphoethanolamine, 6-Phosphogluconate, Quinolinic acid, N-Acetyl-glucosamine, UMP, Adenine, Succinate, Cytidine, Phosphoenolpyruvate, Fructose-6-phosphate, Keto(iso)leucine, Glyceraldehyde-3-phosphate, Pentose-phosphate, UDP-glucuronate, N-Methyl-adenosine, UTP, Citrulline, S-Adenosyl-methionine, Dimethyl-arginine, Indoxyl-sulfate, Kynurenine, UDP-glucose, Dimethyl-guanosine, N2-Acetyl-arginine, Glutathione disulfide, Phenylalanine, AMP, Serine, Threonine, 1,5-Anhydroglucitol, Taurine, Trimethyl-lysine, and Pseudouridine.

Metabolomics provides comprehensive information on the state of body metabolism. To identify metabolic disturbances in the type 2 diabetes (T2D) subjects, whole blood samples of lean or obese diabetes subjects (L-T2D or Ob-T2D, respectively) were compared with healthy lean (normal weight) control (LC) and obese (Ob) by non-targeted LC-MS (liquid chromatography - mass spectroscopy) metabolomics. Among 124 metabolites routinely identified and quantified, 55 of metabolites showinged significant differences from LC, which enabled to separate metabolites into 4 subgroups (categories A, obese related compounds; B, obese-T2D related metabolites; C, common between lean- and obese-T2D related metabolites, and D, lean-T2D related metabolties; summary results are shown in Fig. 4).

Fifty-five category A-D metabolites contained well-known obese- or diabetes-related compounds consistent with our present results. Twelve A metabolites contain 3 carnitines, free-carnitine, propionyl-carnitine, and butyryl-carnitine (7, 8), 5 amino acids, valine, leucine, isoleucine, tryptophan, and tyrosine (7, 9). Aminobutyrate, hypoxanthine, malate, and N2-acetyl-lysine are novel obese markers to our knowledge. Eleven B metabolites contain (iso)valeryl-carnitine (8), 3 organic acids, 2-hydroxybutyrate, 3-hydroxybutyrate, urate (10, 11), and one amino acid, proline (12). Acetyl-carnitine, NADP+, ADP, sedoheptulose-7-phosphate, N-acetyl-aspartate, and glycerophosphoethanolamine are novel obese-T2D markers. Fourteen C metabolites contain 7 sugar metabolites, glyceraldehyde-3-phosphate (10) as known T2D-related metabolties, while 6-phosphogluconate, N-acetyl-glucosamine, phosphoenolpyruvate, fructose-6-phosphate, pentose-phosphate, UDP-glucuronate as novel T2D markers. N-methyl-adenosine, UMP, adenine, and cytidine are also novel T2D markers. Quinolinic acid (13), succinate (14), and keto(iso)leucine (10) are other reported T2D-related metabolites. Eighteen D metabolites contain many new findings as L-T2D markers, UTP, glutathione disulfide, phenylalanine, AMP, taurine, trimethyl-lysine, pseudouridine, dimethyl-arginine, indoxyl-sulfate, kynurenine, UDP-glucose, dimethyl-guanosine, N2-acetyl-arginine, and S-adenosyl-methionine, while citrulline (15), glutathione disulfide (16) serine, threonine (17), and 1,5-anhydroglucitol (18) are previously reported T2D-related metabolites. These 55 compounds may be rather important to understand the development of diabetes.

Other aspect emerged from the present study is that metabolites specifically implicated in Ob-T2D and L-T2D are very different. Understanding how these metabolites are implicated in diabetes may help not only early diagnosis but also develop more effective prevention or treatment methods tailored to individual metabolic changes.

The summary of the Examples is as follows. We employed analysis of metabolites by non-targeted metabolomics that provide comprehensive information of metabolites abundance. For targeted analysis, metabolites for analysis are pre-evaluated so that abundance changes of untargeted metabolites may be overlooked. Although non-targeted analysis is far more time consuming than targeted LC-MS, it is worth trying for the discovery of compounds’ abundance change. Previous T2D metabolomic studies mainly involve over-weight/obese subjects and few studies focused on lean/normal-weight T2D subjects. This study focuses on assessing blood metabolomics of T2D subjects with and without obesity. Based on healthy lean people as control, by comparative analysis of blood metabolites abundance in non-diabetic obese, lean-T2D and obese-T2D people, we identified characteristic changes in each group and the change common to T2D with or without obesity. To our knowledge, this is the first report characterizing the blood metabolomic profiles of lean-T2D and Ob-T2D, respectively.

Ethics statement
Written, informed consent was obtained from all donors, in accordance with the Declaration of Helsinki. All experiments were performed in compliance with relevant Japanese laws and institutional guidelines. All protocols were approved by the institutional ethical committee of University of the Ryukyus and the Human Subjects Research Review Committee of the Okinawa Institute of Science and Technology Graduate University (OIST).

Whole blood metabolomics in four subgroups
To identify metabolites affected by obesity and/or diabetes, LC-MS analysis was conducted for whole blood samples from 18 subjects, which consist of 10 non-diabetics, 9 diabetics, and 2 others diagnosed by Japanese standards. Samples were obtained from four subgroups, LC, Ob, L-T2D and Ob-T2D (Table 1 and Fig. 1). Non-diabetic lean control LC has normal weight range (BMI=20.6~21.1), while Ob is non-diabetic obese with BMI=29.1~34.2. Type 2 diabetes lean subject is L-T2D, and their BMI and HbA1c, respectively, are 19.3~23.1, and 8.7~12.6. Diabetic obese Ob-T2D has BMI=28.4~35.8 and HbA1c=6.9~9.8. In Fig. 2, age distributions (39~79 yr), gender (10 females and 8 males), the values of HbA1c (normal 5.1~5.8; diabetes 7.2~12.6) are graphically shown.

Blood samples from diabetes subjects were collected at the Ryukyu University Hospital, Nishihara-cho, Okinawa, while non-diabetic blood samples were obtained from healthy volunteers in the Onna Clinic, Onna-Son, Okinawa. The Thermo Scientific LTQ Orbitrap XL was used for non-targeted comprehensive metabolomic analysis. We quantified 124 whole blood metabolites for all the subjects by LC-MS (listed in Table 2 and Fig. 3). Metabolites are classified into 14 subgroups. 53 metabolites enriched in RBC are underlined; ~43% (53/124x100) of RBC-enriched metabolites thus contained anti-oxidants, coenzymes, nucleotides-sugar derivatives and sugar phosphates etc. (1, 2).

Statistical tests revealed 55 obese- or Type 2 diabetes-related metabolites
The quantitative level of metabolites were obtained and compared with LC, Ob, Ob-T2D and L-T2D. Statistical assessments for relative abundances of metabolites were evaluated by two methods, namely, parametric Student’s t-test and non-parametric Mann-Whitney U-test (abbreviated t-test and U-test, respectively). These two methods are distinct and complementary. In t-test and U-test, it was found that total 55 metabolites showed significant changes relative to the control abundance of LC (p<0.05, Table 3). Summary results are shown in Fig. 4. The directions of arrows indicate either the increase (upward) or the decrease (downward) from LC. The number indicates the ratio of increase or the decrease. Twenty-three of them underlined are enriched in RBCs. Eleven [H] were abundant, while 14 [M] were mediumly abundant. Remaining [L] were low in quantity.

Obesity-related metabolites (category A)
Twelve metabolites identified by t-test or U-test increased in obese subjects (Fig. 5). P-values (<0.05) obtained from dot plot data showed that Obese-related compounds consisted of 5 standard amino acids (valine, tryptophan, tyrosine, leucine, isoleucine), 1 acetylated amino acid (N2-acetyl-lysine), 3 carnitines (propionyl-carnitine, butyryl-carnitine, carnitine), 2 organic acid (aminobutyrate, malate), and 1 nucleobase (xanthine). In them, 6 compounds (propionyl-carnitine, valine, leucine, aminobutyrate, hypoxanthine, isoleucine) also increased in Ob-T2D (Fig. 5).

Metabolites related to obese diabetes (category B)
Statistical tests revealed the abundance change of 11 metabolites in Ob-T2D subjects (Fig. 6); eight metabolites increased, while three metabolites decreased in Ob-T2D. They consist of 2 organic acids (2-hydroxybutyrate, 3-hydroxybutyrate), 2 carnitines ((iso)valeryl-carnitine, acetyl-carnitine), 1 standard amino acid (proline), 1 acetylated amino acid (N-acetyl-aspartate), 1 coenzyme (NADP+), 1 sugar-phosphate (sedoheptulose-7-phosphate), 1 nucleotide (ADP), 1 nucleobase (urate), and 1 ethanolamine (glycerophosphoethanolamine). These may be implicated in lipid transport/degradation and gluconeogenesis, strongly suggesting the resemblance to metabolic change caused by fasting (3).

Common metabolites changed in obese and lean diabetes (category C)
T-test and U-test indicated the common change for 14 metabolites in obese and lean diabetes, which consisted of 5 sugar-phosphates (6-phosphogluconate, phopsphoenolpyruvate, fructose-6-phosphate, glyceraldehyde-3-phosphate, pentose-phosphate), 1 acetylated-sugar (N-acetyl-glucosamine), 1 nucleotide-sugar (UDP-glucuronate), 1 nucleotide (UMP), 2 nucleoside (N-methyl-adenosine, cytidine), 1 nucleobase (adenine), 2 amino acid metabolites (quinolinic acid, keto(iso)leucine), and 1 organic acid (succinate) (Fig. 4). In Fig. 7, dot plot data of these 14 metabolites are shown. Ten compounds increased and 5 declined, in both types of diabetic subjects regardless obese or not. Interestingly, the great majority (10/14) of category C is enriched in RBC (2), containing sugar phosphates. Sugar metabolism in RBC might be impared in T2D. Judging from the nature of metabolites category C may be involved in sugar, amino acid and purine/pyrimidine metabolism, in general, in energy metabolism.

Metabolites declined in lean diabetes (category D)
Eighteen metabolites were identified by the decrease (an exception was UTP that increased) in lean diabetic subjects (Fig. 4). These metabolites consisted of 3 standard amino acids (phenylalanine, serine, threonine), 1 acetylated amino acid (N2-acetyl-arginine), 2 methylated amino acids (trimethyl-lysine, dimethyl-arginine), 1 sugar (1,5-anhydroglucitol), 2 nucleotide (UTP, AMP), 1 nucleotide-sugar (UDP-glucose), 2 nucleosides (pseudouridine, dimethyl-guanosine), 1 antioxidant (glutathione disulfide), and 5 amino acid metabolites (citrulline, taurine, indoxyl-sulfate, kynurenine, S-adenosyl-methionine). In Fig. 8, dot plot distributions are shown for all of 18 metabolites. The majority (94%, 17/18) compounds decreased, but one increased in L-T2D.

Application of principal component analysis to synthesize the risk assessment for T2D
We have succeeded in developing a method to quantify the risk of T2D using principal component analysis (PCA) of age-related metabolites. We demonstrate the method as a new index for diverse patterns of onset of T2D.
Abundance data of 55 metabolites were used for principal component analysis (PCA). The two-dimensional PCA plot (PC1 vs. PC2) accounted for 60% of the total variance (Figure 9). Strikingly, four distinct clusters made up of subjects LC, Ob, Ob-T2D, L-T2D were clearly seen. Seven of eight T2D subjects (black circles and triangles) reside on the negative side of PC1, only one exception is early T2D subject, indicated by an arrow, whilst six of nine obese and six of nine lean subjects (empty circles and triangles) reside on the positive and negative sides of PC2 axis, respectively. The top 10 ranked factor loadings (Table 2) indicate that the PC1 (X-axis) are mainly reflected the abundance information of lean-T2D related category D compounds (6/10), while PC2 (Y-axis) includes the information of obese related category A compounds (5/10), the short-chain acylcarnitines and branched-chain amino acids, in other words, sugar substitutes metabolites (Figure 4 and 9). The factor loading of the individual compounds is shown in Table 4.

We then constructed PCA by using the metabolite data in each category (Fig. 10). When subjects were arranged in the order of PC1 score, non-obese subjects showed relatively low scores and obese subjects showed high scores in category A consisting of obesity-related compounds. In category B, which consisted of Ob-T2D-related metabolites, all Ob-T2D subjects showed positive scores, but also included non-diabetic obese people. In contrast, in the D group consisting of lean-T2D-related metabolites, L-T2D subjects accounted for the top score. This would be the result of finding a sufficient number of compounds with high affinity for L-T2D. Strikingly, In group C, which consists of common T2D related compounds, non-diabetics and diabetics clearly separated with positive and negative scores. Moreover, an early T2D subject, indicated by an arrow, had the lowest scores among T2D subjects (Fig. 10). These results demonstrate that it is possible to evaluate the onset level of Type 2 diabetes, considering the influence of subject BMI on blood metabolites by proper selecting and analyzing quantitative data of diabetes-related compounds. To our knowledge, the method presented here is novel and will serve greatly as the synthetic evaluation of T2D risk.

Throughout this application, various references in Table 5-1 and 5-2 describe the state of the art to which this invention pertains. The disclosures of these references are hereby incorporated by reference into the present disclosure.

Claims

A method for evaluating risk of Type 2 diabetes using blood metabolites as an index, wherein the blood metabolites comprise at least UDP-glucuronate and UMP.
The method for evaluating risk of Type 2 diabetes according to claim 1, wherein the blood metabolites comprise at least UDP-glucuronate, UMP and Dimethyl-guanosine.
The method for evaluating risk of Type 2 diabetes according to claim 2, wherein the blood metabolites comprise at least UDP-glucuronate, UMP, Dimethyl-guanosine and UDP-glucose.
The method for evaluating risk of Type 2 diabetes according to claim 3, wherein the blood metabolites comprise at least UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose and N2-Acetyl-arginine.
The method for evaluating risk of Type 2 diabetes according to claim 4, wherein the blood metabolites comprise at least UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine and Glyceraldehyde-3-phosphate.
The method for evaluating risk of Type 2 diabetes according to claim 5, wherein the blood metabolites comprise at least UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine, Glyceraldehyde-3-phosphate and 6-phosphogluconate.
The method for evaluating risk of Type 2 diabetes according to claim 6, wherein the blood metabolites comprise at least UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine, Glyceraldehyde-3-phosphate, 6-phosphogluconate and Pseudouridine.
The method for evaluating risk of Type 2 diabetes according to claim 7, wherein the blood metabolites comprise at least UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine, Glyceraldehyde-3-phosphate, 6-phosphogluconate, Pseudouridine and Quinolinic acid.
The method for evaluating risk of Type 2 diabetes according to claim 8, wherein the blood metabolites comprise at least UDP-glucuronate, UMP, Dimethyl-guanosine, UDP-glucose, N2-Acetyl-arginine, Glyceraldehyde-3-phosphate, 6-phosphogluconate, Pseudouridine, Quinolinic acid and S-Adenosyl-methionine.
An apparatus for evaluating risk of Type 2 diabetes which comprises means for input and means for evaluating, wherein the data of blood metabolites of the subject are input to the means for input, risk of Type 2 diabetes is evaluated by comparing the data of the subject and the data of the population, and the blood metabolites comprise at least UDP-glucuronate and UMP.
A kit for evaluating risk of Type 2 diabetes by using blood metabolites comprising at least UDP-glucuronate and UMP as an index which contains blood collection tubes and blood metabolite compounds as detection standard.
A method of screening substances which reduce risk of Type 2 diabetes comprising the step of measuring a blood metabolite, wherein the blood metabolites comprise at least UDP-glucuronate and UMP.