KR101698394B1 - Kit and Method for Diagnosing Diabetes mellitus using PBMC Metabolites - Google Patents

Abstract

The present invention provides a kit for diagnosing diabetes by analyzing a metabolite of PBMC and a method for providing information necessary for diagnosis of diabetes. By measuring and analyzing the levels of the PBMC metabolites identified in the present invention, pre-diabetic or type 2 diabetes can be diagnosed.

Description

[0001] The present invention relates to a kit for diagnosing diabetes mellitus using PBMC metabolism,

The present invention relates to a diagnostic kit for diabetes using a peripheral blood mononuclear cell (PBMC) metabolite and a method for providing information necessary for diagnosis of diabetes.

Unlike type 1 diabetes, which is called childhood diabetes, type 2 diabetes is a cause of insulin secretion due to lack of exercise, obesity or stress, but it occurs when insulin is not functioning and blood glucose control fails Stumvoll M, et al., Lancet 365: 1333-46 (2005)). According to statistics from the Center for Disease Control and Prevention in 2008, the prevalence of diabetes mellitus is 9.1% in people over 30 years old, and the prevalence rate increases sharply when it is over 40 years old, reaching 20% in their 50s. These sudden increases in prevalence are presumed to be caused by changes in environmental factors such as nutritional status improvement and lack of exercise.

In Korea, non-insulin-dependent type 2 diabetes accounts for 90-95% of all diabetic patients. In developed countries as well as in developing countries, people gradually lose physical activity and obesity increases, leading to the development of type 2 diabetes It is increasing at an alarming rate. The proportion of provinces in Korean diet has increased from 7.2% in 1969 to 18.5% in 2007 (South Korea's National Health and Nutrition Examination Survey 2008) , The mortality rate rapidly increased from 7.4% in 1988 to 22.9% in 2007 (South Korea National Statistical Office. Annual Report on the Cause of Death Statistics (2007)).

There are several reports of metabolic patterns in prediabates or diabetic patients. Non-patent document 1 reports that impaired fasting glucose (IFG) and type 2 diabetes can be characterized by abnormal characteristics of amino acids, fatty acids, glycerol phospholipid and sphingomyelin metabolism. Non-patent document 2 also reports the relationship between branched-chain keto acid metabolites in plasma and urine, 3-methyl-2-oxovaleric acid and IFG.

Numerous papers and patent documents are referenced and cited throughout this specification. The disclosures of the cited papers and patent documents are incorporated herein by reference in their entirety to better understand the state of the art to which the present invention pertains and the content of the present invention.

 Xu F, Tavintharan S, Sum CF, Woon K, Lim SC, Ong CN. J Clin Endocrinol Metab 2013; 98: E1060-E1065.  Trimmer J, Bell JT, Yong Woo, Milburn M, Palmer CNA, Frayling TM, Trimmer J, Lee JS, Gieger C, Mohney RP, Brosnan MJ, Suhre K, Soranzo N, Spector TD. Diabetes 2013; 62: 4270-343 4276.

The present inventors have made efforts to develop a kit capable of diagnosing diabetes through nontargeted metabolite studies. As a result, the metabolism of PBMC and plasma in patients newly diagnosed with pre-diabetes or type 2 diabetes was analyzed to identify eleven PBMC metabolites significantly increased in diabetic patients than in normal individuals to complete the present invention .

Accordingly, it is an object of the present invention to provide a diabetic diagnostic kit.

It is another object of the present invention to provide a method for providing information necessary for diagnosis of diabetes.

Other objects and advantages of the present invention will become more apparent from the following detailed description of the invention, claims and drawings.

According to one aspect of the present invention, the present invention provides a method for the treatment and / or prophylaxis of leucine, phenylalanine, tyrosine, methionine, valine, tryptophan, oleamide, L-pyroglutamic acid, palmitic amide, lysoPC (16: 0). ≪ / RTI > In another aspect, the present invention provides a diagnostic kit for a diabetes mellitus comprising a quantification device for a PBMC metabolite selected from the group consisting of:

The present inventors have made efforts to develop a kit capable of diagnosing diabetes through non-target metabolite studies. As a result, we analyzed the metabolism of PBMC and plasma in newly diagnosed patients with pre - diabetes or type 2 diabetes and identified eleven PBMC metabolites that were significantly increased in diabetic patients than in normal individuals. In addition, the present inventors identified seven plasma metabolites significantly increased or decreased in diabetic patients than in normal individuals.

The present inventors selected PBMCs and plasma metabolites that were increased or decreased according to the variable importance in the projection (VIP), and finally metabolized with a VIP value of 1 or more was finally selected. Specifically, valine, leucine, methionine, phenylalanine, tyrosine, tryptophan, pyroglutamic acid, palmitic amide, oleamide, lysoPC (16: 0) and lysoPC (18: 0) It is a metabolite of PBMC metabolite showing a big difference from normal control group.

As used herein, the term "PBMC metabolite" refers to a metabolite obtained from a PBMC, and the term "plasma metabolite" refers to a metabolite obtained from a liquid sample of blood origin.

As used herein, the term "diagnosis" is intended to include determining the susceptibility of an object to diabetes, determining whether an object currently has diabetes, prognosis of a diabetes- ), Or therametrics (e.g., monitoring the status of an object to provide information about the therapeutic efficacy).

(18: 2), lysoPC (18: 1), and lysoPC (18: 2), lysoPC (17: 0), lysoPC 0). ≪ / RTI > In addition to the PBMC metabolism, the present inventors further identified plasma metabolites significantly increased or decreased in diabetic patients compared to normal subjects, and further accurate determination of the plasma metabolites is possible for more accurate and reliable diagnosis of diabetes .

According to one embodiment of the invention, the diabetes diagnosed by the kit of the invention is type 2 diabetes.

As used herein, the term "Type 2 diabetes" refers to diabetes that develops when insulin is normally secreted, but insulin fails to function, and is also referred to as adult type or non-insulin dependent diabetes. Type 2 diabetes occurs when a cell does not respond effectively to insulin produced in the pancreas, which is called insulin resistance (IR). Patients with insulin resistance initially produce additional insulin to maintain normal blood glucose levels. Eventually, insulin resistance progresses and the pancreas can not cope with insulin requirements, leading to elevated blood glucose levels.

According to one embodiment of the present invention, diabetes diagnosed by the kit of the present invention is prediabetes.

As used herein, the term "pre-diabetic" refers to a condition in which blood sugar is higher than normal but requires additional information until confirmed as diabetes, and is used synonymously with pre-diabetes. Most people go through a pre-diabetes process before being diagnosed with type 2 diabetes. Although elevated blood glucose levels in pre-diabetes mellitus are caused by insulin resistance problems, it is true that pre-diabetes does not automatically progress to diabetes, but it is highly likely to progress to diabetes, and pre-diabetes is a risk factor for heart disease.

According to an embodiment of the present invention, the pre-diabetes represents impaired fasting glucose (IFG) or impaired glucose tolerance (IGT). The impaired glucose tolerance refers to the case where the fasting blood glucose level is less than 126 mg / dL and the blood glucose level is between 140-199 mg / dL 2 hours after the glucose load. The fasting glucose tolerance test indicates that the fasting glucose level is 100-125 mg / dL .

According to one embodiment of the present invention, the quantitative device for measuring or identifying the concentration of PBMC and / or plasma metabolites contained in the kit of the present invention is a chromatography / mass spectrometer. The PBMCs and / or plasma metabolites of the present invention can be separated from each other in chromatography depending on the difference in mobility, and the molecular weight information as well as the elemental composition can be confirmed using the information obtained through the mass spectrometer .

Chromatography which can be used in the present invention can be carried out by liquid-solid chromatography (LSC), paper chromatography (PC), thin-layer chromatography (TLC), gas- (GSC), liquid-liquid chromatography (LC), foam chromatography (Foam Chromatography), emulsion chromatography (EC), gas-liquid chromatography Including, but not limited to, liquid chromatography, liquid chromatography, ion chromatography (Ion Chromatography), gel filtration chromatography (GFC), or Gel Permeation Chromatography (GPC) May be used for any quantitative chromatography commonly used in the art.

According to one embodiment of the present invention, the chromatography used in the present invention is ultra performance liquid chromatography (UPLC).

Mass analyzers usable in the present invention include MALDI-TOF MS, Q-TOF MS, and UPLC-LTQ-Orbitrap MS.

According to another aspect of the present invention, the present invention provides a method for providing information necessary for the diagnosis of diabetes, comprising the steps of:

(a) measuring a metabolite level in a peripheral blood mononuclear cell (PBMC) isolated from a subject, wherein the metabolite is leucine, phenylalanine, tyrosine, methionine, valine, tryptophan, oleamide, L-pyroglutamic acid ), Palmitic amide, lysoPC (16: 0) and lysoPC (18: 0);

(b) comparing the level of the measured PBMC metabolism to the measured value in a normal control sample.

According to one embodiment of the present invention, diabetes can be diagnosed when the level of the PBMC metabolite is higher than the value measured in the normal control sample.

According to one embodiment of the present invention, in step (a), proline, valine, lysoPC (16: 0), lysoPC (17: 0), lysoPC (18: 2), lysoPC : 0), and in step (b), the level of the measured plasma metabolite can be compared to the measured value in a normal control sample.

In this case, diabetes can be diagnosed when the level of the proline and valine is higher than that of the normal control sample, and diabetes can be diagnosed when the level of lysoPC is lower than that of the normal control sample .

The features and advantages of the present invention are summarized as follows:

(I) The present invention provides a kit for diagnosing diabetes by analyzing PBMC metabolites and a method for providing information necessary for diabetes diagnosis.

(Ii) By measuring and analyzing the levels of the PBMC metabolites identified in the present invention, pre-diabetic or type 2 diabetes can be diagnosed.

Figure 1 shows the results of the identification of PBMC and plasma metabolites significantly changed in each subgroup. A: Score plot of PBMC metabolites from PLS-DA models classified as NFG (n = 65) and IFG or T2D group (n = 65). B: Score plot of plasma metabolites from PLS-DA models classified as NFG (n = 65) and IFG or T2D group (n = 65). C and D: S-plot for covariance [ p ] and reliability correlation [ p (corr)] obtained from the PLS-DA model.
Figure 2 shows a correlation matrix between metabolites and biochemical characteristics in each group. All biochemical features were tested by algebraic transformation. Correlations were obtained by deriving Spearman correlation coefficient. PBMC metabolites, plasma metabolites, and biochemical characteristics were described on the right side of the heat map. Red indicates a positive correlation, and purple indicates a negative correlation.

Hereinafter, the present invention will be described in more detail with reference to Examples. It is to be understood by those skilled in the art that these embodiments are only for describing the present invention in more detail and that the scope of the present invention is not limited by these embodiments in accordance with the gist of the present invention .

Example

Experimental Method

Subject

Yonsei University Nutritional Genetics / Nutritional Genomics The participant of this study was selected from the participants of clinical nutrition research conducted by the National Research Lab. Between January and June 2013, participants from non-obese persons (18.5 ≤ BMI <30 kg / m ² ) who were 30-70 years old were recruited from a person who underwent regular health screening at the National Health Insurance Corporation Ilsan Hospital Health Examination Center . Based on health screening data, a person with fasting glucose deficiency (IFG; 100 ≤ fasting glucose <126 mg / dL) or newly diagnosed with type 2 diabetes (T2D; fasting glucose ≥ 126 mg / dL) was selected Respectively. Those who took lipid-lowering drugs, drugs or supplements known to affect lipid metabolism, or probiotics were excluded during the past month. In addition, those who are diagnosed with dyslipidemia, hypertension, liver disease, kidney disease, cardiovascular disease, cerebrovascular disease, pancreatitis, or cancer, those who are diagnosed as having a cure or who are diagnosed with alcohol abuse, pregnant or breastfeeding Respectively. Sixty-five nondiabetic individuals with prediabates or newly diagnosed T2D (30-70 years) participated in the study and 65 sex-matched healthy individuals participated in the experiment as controls. The general dietary macronutrient composition of each participant was a typical diet consumed by a significant number of Koreans. This diet represents about 63% energy from carbohydrates, about 21% energy from fat and about 16% energy from protein. All participants were informed about the purpose of the study and agreed upon, and the protocol of the study was approved by the Institutional Review Board of Yonsei University and Ilsan Hospital.

Anthropometric parameters, blood pressure and blood sampling

We measured body weight and weight with clothes and shoes undressed and calculated BMI in kg / m ² . Waist and hip circumference and WHR (waist to hip ratio) were calculated. The blood pressure of the left arm of the participant was measured while sitting after 20 minutes rest using an automatic blood pressure meter (FT-200S, Jawon Medical). After fasting for 12 hours, venous blood samples were collected in EDTA-treated whole blood and serum tubes. Blood samples were centrifuged to obtain plasma and serum, and stored at -70 ° C.

(HOMA) of fasting glucose, insulin, insulin resistance, and hemoglobin A _1c

Fasting blood glucose levels were analyzed by the hexokinase method using a Hitachi 7600 automatic analyzer. Insulin levels were measured using a radioimmunoassay kit (DIAsource Immuno Assays SA). The insulin resistance (IR) was calculated by the homeostasis-model assessment (HOMA) using the formula "HOMA-IR = [fasting insulin (μIU / mL) x fasting glucose (mmol / L)] / 22.5". Hemoglobin A _1c (HbA _1c ) was measured by immunoturbidimetric analysis.

hole Analysis of clear lipid profiles and free fatty acids

Fasting total cholesterol and triglyceride levels were analyzed using a Hitachi 7600 automatic analyzer (Hitachi Ltd.). The remaining HDL-cholesterol in the supernatant fractions was determined by enzymatic methods after other lipoprotein precipitation. LDL-cholesterol concentrations were calculated indirectly for participants with a serum triglyceride level <400 mg / dL using the Predebalt formula: LDL cholesterol = total cholesterol - [HDL cholesterol + (triglyceride / 5)] . LDL-cholesterol concentrations were measured directly on a Hitachi 7600 automated analyzer for participants with serum triglyceride levels ≥400 mg / dL. Free fatty acids (FFA) were analyzed by acyl CoA synthetase-acyl-CoA oxidase (ACS-ACOD) enzyme assay using a Hitachi 7600 automatic analyzer.

Serum-sensitive C-reactive protein, Lp-PLA ₂  Evaluation of active, plasma malondialdehyde, LDL particle size and oxidized LDL

High-Sensitivity C-Reactive Protein (hs-C) was amplified by ADVIA 2400 clinical chemistry system (Siemens Ltd.) using a high-sensitivity CRP-Latex (II) X2 kit (Denka-Seiken Co., CRP) were measured. Lp-PLA ₂ activity was measured using a modified high-throughput radiometric activity assay (Wilensky RL, et al., Nat Med 2008; 14: 1059-1066). Plasma malondialdehyde (MDA) was measured from thiobarbituric acid-reactive substances (TBARS) using a TBARS assay kit (ZeptoMetrix Co.). LDL particles were separated by sequential flotation ultracentrifugation. The distribution of particle sizes (1.019-1.063 g / mL) was measured on a non-denaturing gel-pore-gradient lipoprotein system (CBS) containing a linear 2-16% acrylamide gradient (CBS Scientific Company) Scientific Company). Thioglobulin (17 nm), ferritin (12.2 nm) and catalase (10.4 nm) standards conjugated to latex beads (30 nm) were used to estimate the relative band mobility. The gel was scanned using a GS-800 calibrated imaging densitometer (GS-800 Calibrated Imaging Densitometer; Bio-Rad Laboratories). Enzyme immunoassay kit (Mercodia AB) the use of plasma and measuring the oxidized (ox) -LDL, the derived color reaction Wallac Victor ² multi-label instrument; a ^{(Wallac Victor 2 multilabel counter Perkin-} Elmer Life Sciences) 450 nm The color reaction results were read.

Urine 8-epi-prostaglandin F _2α  And TNF-a measurement

After fasting for 12 hours, urine was collected in a polyethylene container containing 1% butylhydroxytoluene, and the container was immediately wrapped with aluminum foil and stored at -20 ° C. Urinary Isoprostane ELISA kit (Oxford Biomedical Research Inc.) using 8- epi-prostaglandin _{F 2α (8-epi-prostaglandin} F 2α; 8-epi-PGF 2α) levels were measured. Creatinine Jaffe method was used to measure urine creatine levels. Serum TNF-α levels were measured using a Bio-Plex Reagent kit and Bio-Plex (Bio-Rad Laboratories, Inc.).

Overall (non-target) plasma metabolism profiling

Preparation and analysis of samples

Prior to analysis, 800 μl of 80% acetonitrile was added to 100 μl of plasma, mixed by vortexing, and centrifuged at 10,000 rpm for 5 minutes at 4 ° C. The supernatant was dried with N ₂ , dissolved in 10% methanol, vortexed, and centrifuged at 10,000 rpm for 5 minutes at 4 ° C. The supernatant was then transferred to a new vial.

Ultra High Performance Liquid Chromatography (UPLC)

Plasma extraction samples (4 μl) were injected into a coupled in-line Acquity UPLC-BEH-C18 column (2.1 × 50 mm, 1.7 μm; Waters) coupled with UPLC-LTQ-Orbitrap XL (Thermo Fisher Scientific). Samples injected with water containing 0.1% formic acid were equilibrated. The sample was eluted for 20 minutes using an acetonitrile gradient containing 0.1% formic acid at a flow rate of 0.35 ml / min. Metabolites were separated by UPLC (Thermo Fisher Scientific) and analyzed using LTQ-Orbitrap-XL (Thermo Fisher Scientific). The mass spectrometer was operated in an ESI-positive mode. The spray voltage was set at 5 kV. The flow rates of nitrogen sheath gas and auxiliary gas were 50 and 5 (arbitrary units). The capillary voltage (V), tube-lens voltage (V) and capillary temperature (캜) were kept constant at 35V, 80V and 370 ° C, respectively. Orbitrap data were collected in the m / z 50-1,000 range. The MS / MS spectra of the metabolites were measured using a collision-energy ramp at 55-65 eV and analyzed using Excalibur 2.1 and MS Frontier software (Thermo Fisher Scientific).

Data processing and identification of metabolites

All MS data, including retention time, m / z and ionic strength, were extracted using SIEVE software (Thermo Fisher Scientific) and the resulting MS data results were combined into a matrix. The SIEVE parameters were set as follows: m / z range 50-1,000; m / z width 0.02; Retention time width 2.5; m / z resistance 0.005. Metabolites were searched using the following databases: ChemSpider (www.chemspider.com), Human Metabolome (www.hmdb.ca), Lipid MAPS (www.lipidmaps.org), KEGG (www.genome.jp/kegg ) And MassBank (www.massbank.jp). Metabolites selected based on retention time and mass spectra were compared to standard samples.

Statistical analysis

SPSS v. Statistical analysis was performed using 21.0 (IBM SPSS Statistics 21). Skewed variables were transformed logarithmically for statistical analysis. For descriptive purposes, the mean values are presented as untransformed values. Results are expressed as mean ± standard error (SE). A two-tailed P-value <0.05 was considered statistically significant. Differences in biochemical variables between the two groups were tested using Student's independent t -test. A GLM (General Linear Model) test was applied to compare the parameter changes between the two groups by calibrating to baseline time values. In order to investigate the relationship between variables over time, simple and partial correlation coefficients were used. R package 'fdrtool' was used to calculate the corrected q -value (false discovery rate). A heat map was generated to visualize and evaluate the correlation between metabolites and biochemical characteristics.

Multivariate statistical analyzes were performed using SIMCA-P + software version 12.0 (Umetrics). Use Partial Least-Squares Discriminant Analysis (PLS-DA) to visualize score plots or S -plots using the first or second PLS element as a classification method to model differences between patients with IFG or T2D and healthy controls Respectively. To verify the validity of the model, seven-fold validation was applied to the PLS-DA model and the reliability of the model was rigorously tested with a permutation test (n = 200). The chi-square goodness-of-fit was quantified as R ² Y , and the predictive power was quantified as Q ² Y. In general, R ² Y shows how well the data is mathematically generated in the training set and varies between 0-1 (1 means that the model is perfectly matched). A model with Q ² Y ≥ 0.5 was considered to have good predictive power.

Experiment result

Clinical features and nutrient intake

Clinical and biochemical characteristics of healthy controls with normal fasting blood glucose levels and patients newly diagnosed with IFG or T2D are shown in Table 1. [ There were no significant differences in clinical characteristics such as sex ratio (25/40 vs. 25/40), education level, smoking, drinking, total calories and nutrient intake between the two groups. The patient group was older, had a higher body weight, had higher diastolic blood pressure, serum triglyceride and FFA than the control group. HbA _1c , insulin, HOMA-IR, hs-CRP, circulating plasma, and PBMC Lp-PLA ₂ were measured by age, BMI, blood pressure and serum lipid profile Activity, ox-LDL, 8-epi-PGF _2α , MDA, and TNF-α levels. In addition, the patient group showed a smaller LDL particle size than the control group (Table 1).

Each value is mean ± standard error.

^∮ is tested through logarithmic transformation.

The P value is derived from an independent t-test.

P ₁ is adjusted by age, BMI, WHR, smoking, drinking, blood pressure, triglyceride, total cholesterol, HDL, LDL and FFA.

UPLC - LTQ - Orbitrap  Using a mass spectrometer PBMC  And plasma Metabolism  profile

Nontargeted Metabolic Pattern Analysis

Mass spectrometry (MS) data of PBMC metabolites were analyzed using a PLS-DA score plot (FIG. 1A). The two-factor PLS-DA score plot of PBMC metabolism showed clear clustering and distinct separation that each healthy control was distinct from the patient population. The two groups can be clearly distinguished from each other by a first element t (1) or the second element t (2) based on the model with R ² X (cum), and R ² Y (cum) of 0.682 in 0.256 , Which shows that the data is well fitted. The Q ² Y (cum) value of 0.608 provides an estimate of the predictive ability of this model. The PLS-DA model was verified using a permutation test, which showed a R ² Y slice value of 0.258 and a Q ² Y slice value of -0.185.

MS data of plasma metabolites were analyzed using a PLS-DA score plot (FIG. 1B). A two-factor PLS-DA score plot of plasma metabolism showed clear clustering and distinct separation that each healthy control was distinct from the patient population. The two groups by 0.12 of the R ² X (cum) and 0.708 of the R ² Y (cum) a first element t (1) or the second element t (2) based on the model having to be clearly distinguished from each other , Which shows that the data is well fitted. The Q ² Y (cum) value of 0.414 provides an estimate of the predictive ability of this model. The PLS-DA model was verified using a permutation test, which showed a R ² Y slice value of 0.486 and a Q ² Y slice value of -0.0496. S-plots of p (1) and p (corr) (1) were derived using centroid scaling to identify metabolites that contribute to differences between normal control and patient groups (Figure 1 C and D). The S-plot showed that metabolites with higher or lower p (corr) values are more appropriate to distinguish between the two groups.

Identification of PBMC Metabolites

Based on the parameters of variable importance in the projection (VIP), metabolites that play an important role in distinguishing between 1,948 PBMC metabolites were selected. The VIP value of> 1.0 was judged to be more related to the difference between the groups. Based on the VIP value of> 1.0, 36 metabolites were selected. Of these, 11 metabolites were previously identified and 25 metabolites were unknown. Eleven kinds of PBMC metabolites (VIP > 1.0) are shown in Table 2. The normalized peak intensities of the 6 amino acids (valine, leucine, methionine, phenylalanine, tyrosine, tryptophan) were significantly higher in PBMC of the patient group than the control group ( q value <0.001). Significantly higher levels of L-pyroglutamic acid, two fatty acid amides (palmitic amide, oleamide), and diriso phosphatidylcholine (lysoPC 16: 0, lysoPC 18: 0) 2).

Each value is mean ± standard error.

Identification of plasma metabolites

Based on the Variable Importance Scale (VIP) parameters, 4,164 plasma metabolites were screened for metabolites that play an important role in the differentiation between groups. Gt; 1.0 &lt; / RTI > value was considered to be more relevant than generating the difference between the groups. Based on the VIP value of> 1.0, 81 kinds of metabolites were selected. Among them, 12 kinds of metabolites and PC were previously identified and 69 kinds of metabolites were unknown. Twelve plasma metabolites and PCs are shown in Table 3. The normalized peak intensities of the two amino acids (proline, valine) were significantly higher in the plasma of patients than in the control group ( q value <0.001). The five lysoPCs (C16: 0, C17: 0, C18: 2, C18: 1, C18: 0) were significantly lower in the plasma of patients than in the control group (Table 3).

Each value is mean ± standard error.

¹ PCs were not successfully isolated by C18-UPLC in this study, but were detected by Obitrap MS. Thus, the amounts of all detected PCs were combined with each other.

Fasting blood glucose, plasma and PBMC Lp-PLA ₂  Activity, biochemical parameters, and correlation between major PBMC metabolites

Correlation matrices of fasting glucose, plasma and PBMC Lp-PLA ₂ activity, biochemical parameters, and major metabolites of PBMCs and plasma were calculated for all study participants (n = 130) (Fig. 2). In all participants, fasting blood glucose, plasma and PBMC Lp-PLA ₂ activity, biochemical parameters, and major metabolites of PBMC and plasma were highly correlated. For example, fasting plasma glucose levels were measured using HOMA-IR, ox-LDL, plasma and PBMCs Lp-PLA ₂ , hs-CRP, TNF-α, 8-epi-PGF _2α , valine of PBMC, pyroglutamic acid, leucine, methionine, And tyrosine; Plasma concentrations of proline, valine, leucine and phenylalanine were positively correlated. Fasting blood glucose showed a negative correlation with LDL particle size and plasma lysoPC (C17: 0, C18: 2).

In the control group, fasting blood glucose showed a positive correlation with PBMC valine and tryptophan. Plasma Lp-PLA ₂ activity was positively correlated with PBMC Lp-PLA ₂ (r = 0.273, P = 0.032), phenylalanine, tyrosine and lysoPC 18: 0 (r = 0.251, P = 0.048). The activity of PBMC Lp-PLA ₂ was measured by HOMA-IR, hs-CRP, leucine, methionine, phenylalanine, tyrosine, palmitic amide, oleamide, lysoPC 16: 0 (r = 0.273, P = 0.030) (r = 0.266, P = 0.035); Positive correlation was observed with plasma valine, leucine, phenylalanine, oleamide, and lysoPC (C16: 0, C17: 0, C18: 2, C18: 0, C20: 4). Fasting blood glucose showed a negative correlation with LDL particle size.

In patients, plasma Lp-PLA ₂ activity of ox-LDL (r = 0.464, P <0.001), 8-epi-PGF 2α (r = 0.500, P <0.001), TNF-α and PBMC Lp-PLA ₂ (r = 0.518, P <0.001), respectively. PBMC Lp-PLA ₂ activity, hs-CRP, TNF-α, ox-LDL (r = 0.445, P <0.001), the PBMC phenylalanine, palmitate tick amide, oleamide, lysoPC 16: 0 (r = 0.421, P < 0.001), and lysoPC 18: 0 (r = 0.415, P <0.001) (Fig. 2).

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the same is by way of illustration and example only and is not to be construed as limiting the scope of the present invention. Accordingly, the actual scope of the present invention will be defined by the appended claims and their equivalents.

Claims (10)

A kit for the diagnosis of diabetes comprising a quantification device for a peripheral blood mononuclear cell (PBMC) metabolite selected from the group consisting of phenylalanine, tyrosine, methionine, valine, tryptophan, L-pyroglutamic acid and palmitic amide. The kit according to claim 1, wherein the kit comprises a group consisting of proline, valine, lysoPC (16: 0), lysoPC (17: 0), lysoPC (18: 2), lysoPC Lt; RTI ID = 0.0 &gt; a &lt; / RTI &gt; plasma metabolite. The kit according to claim 1, wherein the diabetes is type 2 diabetes. The kit according to claim 1, wherein the diabetes is prediabetes. The kit according to claim 1 or 2, wherein the quantification device is a chromatography / mass spectrometer. A method for providing information necessary for the diagnosis of diabetes, comprising the steps of:
(a) measuring a metabolite level in a peripheral blood mononuclear cell (PBMC) isolated from a subject, wherein the metabolite is selected from the group consisting of phenylalanine, tyrosine, methionine, valine, tryptophan, L-pyroglutamic acid, Amide; &lt; / RTI &gt;
(b) comparing the level of the measured PBMC metabolism to the measured value in a normal control sample. 7. The method of claim 6, wherein the level of the PBMC metabolism is higher than the value measured in a normal control sample, diagnosing diabetes. 7. The method according to claim 6, wherein said step (a) comprises the step of contacting proline, valine, lysoPC (16: 0), lysoPC (17: 0), lysoPC (18: 2), lysoPC (18: Wherein the level of the plasma metabolite selected from the group consisting of: (1) is further measured, and in step (b) the level of the measured plasma metabolite is compared with the measured value in a normal control sample. 9. The method of claim 8, wherein the level of proline and valine is higher than the value measured in a normal control sample. The method according to claim 8, wherein the level of lysoPC is lower than the value measured in a normal control sample, the diagnosis is diabetes.

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