KR101303825B1 - A Kit diagnosing Type 2 diabetes using plasma metabolites - Google Patents

Abstract

The present invention analyzes plasma metabolites comprising lyso-phosphatidyl choline (C14: 0), lyso-phosphatidyl choline (C16: 1) and lyso-phosphatidylethanolamine (C18: 1) to diagnose type 2 diabetes. The kit relates to a diagnosis.
The present invention can be usefully used for diagnosis of type 2 diabetes by simultaneously analyzing the concentrations of various markers.

Description

A kit diagnosing type 2 diabetes using plasma metabolites}

The present invention provides a device for quantifying plasma metabolites selected from the group consisting of lyso-phosphatidyl choline (C14: 0), lyso-phosphatidyl choline (C16: 1) and lyso-phosphatidylethanolamine (C18: 1). It relates to a diabetes diagnosis kit comprising.

As the 21st century enters an aging age, where dietary life is more abundant than ever before and the average life expectancy is long due to the development of medical technology, interest in longevity of health is gradually increasing. Changes in dietary habits, such as high-fat diets, along with economic growth, are disrupting the balance of living bodies, and the number of patients with obesity and diabetes has increased significantly regardless of gender or age.

Obesity is a cause of high blood pressure, diabetes, cardiovascular disease and various cancers, and medical expenses for obesity-related diseases are increasing day by day. Has become of great concern.

Unlike type 1 diabetes, which is called pediatric diabetes, type 2 diabetes is caused by a lack of exercise, obesity, or stress, which causes insulin secretion to be controlled smoothly, but insulin fails to function and blood glucose control fails. Stumvoll M, et al., Lancet 365: 1333-46 (2005)). According to the statistics of the Centers for Disease Control and Prevention in 2008, the prevalence of diabetes reached 9.1% among people aged 30 and over, and the prevalence increased rapidly after 40 years, reaching 20% in their 50s. This rapid increase in prevalence is presumably due to changes in environmental factors such as improved nutrition and lack of exercise.

In Korea, insulin-independent type 2 diabetes accounts for 90-95% of all diabetic patients. In addition to developed countries, people in developing countries are gradually losing physical activity and increasing obesity. It is increasing at a speed. In the last decade, the share of fat in Korean diets has increased from 7.2% in 1969 to 18.5% in 2007 (South Korea Ministry of Health and Social Affairs.The Third Korea National Health & Nutrition 4 Examination Survey (2008)). The death rate increased rapidly from 7.4% in 1988 to 22.9% in 2007 (South Korea National Statistical Office.Annual Report on the Cause of Death Statistics (2007)).

Recent metabolite studies have shown that human plasma metabolites are associated with glycemic loads caused by insulin resistance, type 2 diabetes and prediabetes (Zhang, X. et al., J. 9 Proteome . Res . , 8 (11), 5188-95 (2009), Huffman, KM et al., Diabetes Care . , 32 (9), 1678-83 (2009), Bain, JR Diabetes . , 58 (11), 16 2429-43 (2009), Zhao, X. et al., J. Physiol . Endocrinol . Metab . , 296 (2), E384-93 (2009)).

Recent plasma metabolite studies, however, are aimed at developing methods for diagnosing diabetes or improving the effectiveness of the drug. In particular, a comprehensive understanding of the differences between inflammatory markers, oxidative markers and arterial stiffness markers and metabolic changes between early diabetics and healthy people has not been studied yet.

The present inventors have attempted to develop new diabetes diagnosis kits by revealing correlations between inflammatory markers, oxidative markers and arterial stiffness as well as metabolic changes between early diabetics and normal individuals.

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.

The present invention sought to develop a type 2 diabetes diagnostic kit that analyzes the plasma metabolites from patients newly diagnosed with type 2 diabetes and analyzes the correlation between their levels and inflammation, oxidative stress and vascular stiffness. As a result, plasma analysis using UPLC / Q-TOF MS revealed 19 metabolites [(leucine, lysine, phenylalanine, 8 acylcanine, 6 lysophosphatidylcholine, lysoPC) and two lysophosphatidylethanolamines (lysoPEs) were higher than normal levels, whereas serine and lyso-phosphatidylethanolamines (C18: 1) were lower than normal. As a result of confirming the relationship between normal people and diabetic patients through various measured values such as analyzed metabolites, the present invention was completed by confirming the correlation between inflammation, oxidative stress and arterial stiffness in the determination of type 2 diabetes. Was done.

Accordingly, it is an object of the present invention to provide a kit for diagnosing type 2 diabetes through correlation with plasma metabolite analysis and inflammation, oxidative stress and vascular stiffness.

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 invention, the invention is selected from the group consisting of lyso-phosphatidyl choline (C14: 0), lyso-phosphatidyl choline (C16: 1) and lyso-phosphatidylethanolamine (C18: 1) It provides a diabetic diagnostic kit comprising a device for quantifying plasma metabolites.

The present inventors have developed a diagnostic kit for diagnosing type 2 diabetes by analyzing plasma metabolites from patients newly diagnosed with type 2 diabetes and analyzing the correlation between their levels and inflammation, oxidative stress and vascular stiffness. Efforts have been made to earnest research. As a result, it was found that there is a consistent correlation between plasma metabolites and inflammation, oxidative stress, and vascular stiffness, which are indicators of the risk of diabetes, between normal and diabetic patients. .

According to the present invention, in the diabetic disease group, blood glucose, triglyceride, oxidized-LDL, inflammatory marker hs-CRP, interleukin-6 (IL-6), tumor necrosis factor- (TNF-), insulin resistance indicator HOMA- The values of IR, oxidative stress marker, and brachial-ankle pulse wave rate (ba-PWV, used for vascular stiffness index) were higher than controls. In addition, leucine, lysine, phenylalanine, eight acylcanines (propionyl-carnitine, octanoyl-carnitine, dicanoyl-carnitine, dodicano) through analysis of plasma metabolites using UPLC / Q-TOF MS. Yl-carnitine, palmityl-carnitine, heptadicanoyl-carnitine, linoleyl-carnitine and basinyl-carnitine), 6 lyso-phosphatidyl choline (C14: 0, C16: 1, C18: 1 , C18: 3, C20: 5 and C22: 6) and two lyso-phosphatidylethanolamines (C18: 2 and C22: 6) appear higher than normal levels, while serine and lyso-phosphatidylethanolamine (C18 : 1) was lower than the control group.

As used herein, the term “plasma metabolite” refers to a metabolite obtained from a liquid sample of blood origin, and preferably, the blood origin is whole blood, more preferably plasma. Preferably, whole blood may be pretreated to detect the plasma metabolites. For example, it may include filtration, distillation, extraction, separation, concentration, inactivation of interference components, addition of reagents, and the like. In addition, the metabolites may include substances produced by metabolic and metabolic processes or substances generated by chemical metabolism by biological enzymes and molecules.

According to a preferred embodiment of the present invention, the diagnostic kit of the present invention is leucine, lysine, phenylalanine, serine, lyso-phosphatidyl choline (C14: 0, C16: 1, C18: 1, C18: 3, C20: 5 and C22). : 6)), lyso-phosphatidylethanolamine (C18: 1, C18: 2 and C22: 6), propionyl-carnitine, octanoyl-carnitine, dicanoyl-carnitine, dodicanoyl-carnitine, And a quantitative device for plasma metabolites selected from the group consisting of palmityl-carnitine, heptadicanoyl-carnitine, linoleyl-carnitine and basinyl-carnitine.

According to the present invention, lyso-phosphatidyl choline (C14: 0), lyso-phosphatidyl choline (C16: 1) and lyso-phosphatidylethanolamine (C18: 1), which are indicator metabolites of diabetes, newly identified by the present inventors In addition, by including the quantitative information on the metabolites in addition, more accurate and consistent diagnosis of diabetes is possible.

As used herein, the term “diagnosis” refers to determining the susceptibility of an object to a particular disease or condition, determining whether an object currently has a particular disease or condition (eg, metabolic abnormalities or diabetes mellitus). Identification), determining the prognosis of an object with a particular disease or condition, or terrametrics (e.g., monitoring the condition of an object to provide information about treatment efficacy). do.

According to a preferred embodiment of the present invention, the diabetes diagnosed with the diagnostic kit of the present invention is type 2 diabetes.

As used herein, the term “type 2 diabetes” refers to diabetes that occurs when insulin is normally secreted but does not function properly, and is also referred to as adult diabetes or insulin-independent diabetes. Type 2 diabetes occurs when cells do not respond effectively to the insulin produced by the pancreas. This condition is called insulin resistance. Insulin-resistant patients initially produce more insulin to maintain normal blood sugar, which eventually leads to insulin resistance, which causes the pancreas to not meet its insulin requirements, resulting in elevated blood sugar.

According to a preferred embodiment of the present invention, the diabetes diagnosed with the diagnostic kit of the present invention is early diabetes.

As used herein, the term “early diabetes” includes conditions in which blood glucose is higher than normal but additional information is needed before confirmation of diabetes. Most people have an early diabetes course before they are confirmed with type 2 diabetes. The rise in blood sugar levels in early diabetes is due to insulin resistance problems, but it is true that early diabetes does not automatically progress to diabetes, but the risk of developing diabetes is high, and early diabetes is a risk factor for developing heart disease. Like people with type 2 diabetes, people with early diabetes tend to be overweight, have high blood pressure, and have abnormal cholesterol levels. According to the present invention, the present invention can detect increased inflammation, oxidative stress and arterial stiffness in the early diabetes process.

According to a preferred embodiment of the present invention, the quantitative apparatus of the present invention is a chromatography / mass spectrometer.

Chromatography used in the present invention is liquid-solid chromatography (Liquid-Solid Chromatography, LSC), paper chromatography (Paper Chromatography, PC), thin-layer chromatography (Thin-Layer Chromatography, TLC), gas-solid chromatography ( Gas-Solid Chromatography (GSC), Liquid-Liquid Chromatography, LLC, Foam Chromatography (FC), Emulsion Chromatography (EC), Gas-Liquid Chromatography (Gas- Liquid Chromatography (GLC), Ion Chromatography (IC), Gel Filtration Chromatograhy (GFC) or Gel Permeation Chromatography (GPC), including but not limited to Any conventionally used quantitative chromatography can be used. Preferably, the chromatography used in the present invention is liquid-solid chromatography, more preferably ultra performance liquid chromatography (UPLC).

Preferably, the mass spectrometer used in the present invention is MALDI-TOF MS or Q-TOF MS, more preferably Q-TOF MS.

The blood glucose metabolism of the present invention is separated from each component in UPLC according to the different mobility, and using the information obtained through the Q-TOF MS to confirm the components as well as accurate molecular weight information as well as the structural information (elemental composition).

According to a preferred embodiment of the present invention, leucine, lysine, phenylalanine, lyso-phosphatidyl choline (C14: 0, C16: 1, C18: 1, C18: 3, C20: 5 and C22: 6)), lyso-phosphatidylethanolamine (C18: 2 and C22: 6), propionyl-carnitine, octanoyl-carnitine, dicanoyl-carnitine, dodicanoyl-carnitine, palmityl-carnitine Increased diabetes when heptadicanoyl-carnitine, linoleyl-carnitine and basinyl-carnitine show increased concentrations and serine and lyso-phosphatidylethanolamine (C18: 1) show decreased concentrations Indicates the risk of the disease.

As used herein, the term "increase (in blood glucose metabolite concentration)" means that blood glucose metabolite concentration is measurably increased as compared to healthy people with normal fasting blood glucose, preferably 70% or more. More preferably 30% or more.

As used herein, the term " reduced blood glucose metabolite concentration " means that the blood glucose metabolite concentration is measurably reduced as compared to healthy people with normal fasting blood glucose, preferably 40% or more. More preferably 20% or more.

According to the invention, leucine, lysine, phenylalanine, lyso-phosphatidyl choline (C14: 0, C16: 1, C18: 1, C18: 3, C20: 5 and C22: 6) in the plasma metabolites of the present invention), Lyso-phosphatidylethanolamine (C18: 2 and C22: 6), propionyl-carnitine, octanoyl-carnitine, dicanoyl-carnitine, dodicanoyl-carnitine, palmityl-carnitine, heptadicanoyl -Carnitine, linoleyl-carnitine and basinyl-carnitine showed significantly increased concentrations in the diabetic group compared to the control group, and serine and lyso-phosphatidylethanolamine (C18: 1) showed significantly decreased concentrations. (Table 4).

According to the invention, dicanoyl-carnitine, lyso-phosphatidyl choline (C14: 0, C16: 0, C16: 1, C18: 0, C18: 1, C18: 2 and C22: 6) and lyso-phosphatidyl Ethanolamine (C18: 1) is a plasma metabolite with a variable importance in the projection (VIP) value of 1 or higher, indicating a high association with the control.

In addition, according to the present invention, among the plasma metabolites having VIP values of 1 or more, dicanoyl-canitine, lyso-phosphatidyl choline (C14: 0, C16: 1, C18: 1 and C22: 6) and lyso-phosphatidylethanol Amine (C18: 1) is a major factor in distinguishing the control and diabetic groups on the PLS-DA score plot (Table 4), while oleamide is not significantly increased in diabetic patients (P = 0.124), but the VIP level is 6.592. It is the most important plasma metabolite to distinguish between the control group and the diabetic group.

According to the present invention, dicanoyl-carnitine is positively correlated with oxidation-LDL, 8-epi-prostaglindine F ₂ (8-epi-PGF ₂ ), IL-6, TNF- and brachial-ankle pulse wave rates. In addition, the brachial-ankle pulse wave rate is positively correlated with lyso-phosphatidyl choline (C14: 0 and C16: 1) and negatively correlated with lyso-phosphatidylethanolamine (C18: 1). There is.

According to the present invention, 8-epi-PGF ₂ has a positive correlation with Lp-PLA ₂ , brachial-ankle pulse wave rate and lyso-phosphatidyl choline (C14: 0 and C16: 1), and lyso-phosphatidyl choline (C14: 0, C16: 1 and C18: 1) are myristic acid in serum phospholipid (C14: 0) (r = 0.427, P = 0.003), palmitoleic acid (C16: 1) (r = 0.415, P = Positively correlated with the ratio of 0.005) and oleic acid (C18: 1ω9) (r = 0.376, P = 0.011), respectively, and lyso-phosphatidyl choline (C22: 6) and lyso-phosphatidylethanolamine (C22: 6 ) (r = 0.518, P <0.001) and lyso-phosphatidyl choline (C18: 2) and lyso-phosphatidylethanolamine (C18: 2) (r = 0.310, P = 0.024) respectively. .

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

(a) The present invention analyzes a plasma metabolite comprising lyso-phosphatidyl choline (C14: 0), lyso-phosphatidyl choline (C16: 1) and lyso-phosphatidylethanolamine (C18: 1) Provided are kits for diagnosing type diabetes.

(b) The present invention can be usefully used for diagnosis of type 2 diabetes by simultaneously analyzing the concentrations of various markers.

FIG. 1A is a diagram showing a score plot of PLS-DA classifying a newly diagnosed patient with type 2 diabetes (▲) and a healthy person (■) with normal fasting serum glucose concentration.
Figure 1b is a diagram showing the S-plot of the covariance [p] and the reliability relationship [p (corr)] of the PLS-DA model.

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

Test subject

In the present invention, the test participants recruited 26 patients newly diagnosed with type 2 diabetes (fasting blood glucose concentration of 126 mg / dL or more), and the control subjects were National Health Insurance Corporation Ilsan Hospital. We recruited 27 patients with fasting blood glucose levels below 126 mg / dL. The age of the subjects was 35-65 years and the body mass index (BMI) of type 2 diabetics and control subjects was matched. The criteria for excluded persons are as follows: Subjects diagnosed with vascular disease, kidney disease, liver disease, and acute or chronic inflammation. None of the subjects took drugs or supplements. Before undertaking the investigation, all participants were informed of the purpose of this study and agreed to, and the protocol of the study conducted was approved by the Yonsei University Institutional Review Board.

Anthropology  Parameters and Blood Collection

Participants' height and weight were measured without clothes and shoes in the morning. Body mass index was calculated by dividing body weight by the height squared (kg / m ² ). Waist and hip circumferences were measured to calculate waist-to-hip ratio (WHR). Blood pressure was measured in the patient's left arm after 20 minutes of rest using an automatic blood pressure monitor (TM-2654, A & D, Tokyo, Japan). Venous blood samples were collected in EDTA-treated tubes or normal tubes after 12 hours overnight fasting. Centrifugation gave plasma or serum and stored at −70 ° C. until analysis.

Dietary Intake  evaluation

General meal information of subjects was obtained using both 24-hour recall and semi-quantitative food frequency questionnaire (SQFFQ) (Shim JS, et al., Kor J Community Nutr . 7: 484-494 (2002)). Detailed calorie and nutrient content analysis of ingested food was done using the Nutritional Evaluation Program of Korea Nutrition Association (CAN-pro 3.0.). Total energy expenditure (TEE) (kcal / day) was calculated using basal metabolic rate for 24 hours, activity pattern including physical activity and specific dynamic metabolic rate of food. Basal metabolic rate for each subject was calculated as the Harris-Benedict equation (The American Dietetic Association Handbook of clinical dietetics 2 nd ed New Haven, CT:.... Yale University Press 5-39 (1992)).

Serum Glucose Levels, Insulin Levels, and Insulin Resistance Indicators

Fasting blood glucose levels were measured using a Beckman glucose analyzer (Beckman Instruments, Irvine, Calif.) By the glucose oxidase method. Insulin was measured by radioimmunoassay using a kit purchased from Immuno Nucleo Corporation (Stillwater, MN, USA). Insulin resistance was calculated by homeostasis model assessment (HOMA) using the following formula:

IR = [fasting insulin (μIU / ml) X fasting blood glucose (mmol / l)] / 22.5].

Serum fat profile

Total cholesterol and triglycerides of fasting serum were measured on a commercially available Hitachi 7150 Autoanalyzer (Hitachi Ltd., Tokyo, Japan). After precipitation of serum chylomicron using dextran sulfate magnesium, the concentrations of LDL and HDL cholesterol in the supernatant were measured by enzymatic method. LDL cholesterol was indirectly calculated using the Friedewald formula below for subjects with serum triglyceride concentrations below 400 mg / dL (4.52 mol / L).

LDL cholesterol = total cholesterol-[HDL cholesterol + (triglyceride / 5)]

Lipid Peroxidation: Plasma MDA , Oxidation LDL  And urine 8- epi - PGF ₂

Plasma malondialdehyde (MDA) was measured using a TBARS kit (thiobarbituric acid-reactive substances assay kit, Zepto-Metrix Co.k Buffalo, NY). The resulting color response was read at 540 nm wavelength of a Wallac Victor ² multilabel instrument (Perkin Elmer Life Sciences, Turku, Finland). Plasma oxidized LDL (ox-LDL) was measured using an enzyme immunoassay (Mercodia, Uppsala, Sweden). The color response shown was read at 450 nm wavelength of the Wallac Victor ² multilabel meter.

Urine was collected in a polyethylene tube containing 1% butylated hydroxytoluene after 12 hours fasting to examine urine 8-epi-PGF _2a . The tubes were wrapped in aluminum foil immediately after sample collection and stored at -70 ° C until used for analysis. 8-epi-PGF _2a was measured using an enzyme immunoassay analyzer (BIOXYTECH Uurinary 8-epi-PGF 2 TM Assay Kit, OXIS International Inc., Portland, OR, USA), the resulting color reaction was Wallac Victor ² Measurements were made at 650 nm using a multilabel meter. Urine creatine was measured using alkaline pepoxidation (Jeffe) reaction. Urine 8-epi-PGF _2a The concentration is expressed as pmol / mmol creatine.

Inflammation Marker : Serum hs - CRP , IL -1β, IL -6 and TNF -density

Serum concentrations of high sensitivity C-reactive protein (hs-CRP) were determined by the high-sensitivity CRP-Latex (II) X2 kit, Seiken Laboratories Ltd., Tokyo, Japan After the reaction was performed using an automatic analyzer (Express ⁺ autoanalyzer, Chiron Diagnostics Co.k Walpole, MA). Serum IL-1β, IL-6 and TNF-concentrations were reacted using Fluorokine MultiAnalyte Profiling (MAP) cytokine multiple kits and analytes (R & D Systems, Minneapolis, MN) and then bio-plex human cytokine panel (Bio-Rad, Hercules). , CA).

plasma Lp - PLA ₂  Active and Adiponectin  density

The activity of Lp-PLA ₂ (lipoprotein-associated phospholipase A2), known as platelet-activating factor acetylhydrolase, has been described by Jeong et al. (Jeong TS, et al., Bioorg) . Med Chem Lett . Mar 1; 15 (5): 1525-7 (2005)).

Plasma adiponectin concentrations were measured using an enzyme immunoassay (Human Adiponectin ELISA kit, B-Bridge International Inc., CA, USA). The analytical results were measured using Victor ² at 450 nm and wavelength correction was fixed at 540 nm.

Upper arm Ankle pulse wave velocity brachial - ancle wave velocity , ba - PWV )

The brachial-ankle pulse wave velocity was reacted using Kim et al. (Kim, OY et al., Atherosclerosis . 208 (2), 581-6 (2010)), followed by an automatic waveform analyzer, model VP-1000; Nippon Colin. Ltd., Komaki, Japan). Participants were allowed to lie down and rest for 5 minutes. Electrocardiogram electrodes are placed on both wrists and a microphone for the phonogram is placed at the left end of the rib. The brachial-ankle pulse wave velocity is recorded using a semiconductor pressure sensor (1200-Hz sample acquisition frequency) and calculated using the equation (La-Lb) / ΔTba. The distance from the sternum to the elbow is La, and the distance to the ankle is Lb. La = [0.2195 × height of object (cm)] 2.0734 and Lb = [0.8129 × height of object (cm)] + 12.328.

The time difference ΔTba between the arm and ankle distances is defined by the pulse transport time between the arm and tibia arterial blood pressure waveforms. The mean brachial-ankle pulse wave velocity values from the left and right body parts were used for analysis (left and right brachial-ankle pulse rate correlations: r ² = 0.925, p <0.001).

Fatty Acid Composition in Serum Phospholipids

Fatty acid composition in serum phospholipids was determined by Folch et al. (Folch J, et al., J Biol ) using thin-layer chromatography. Chem . 226: 497-509 (1957) and Lepage et al. (Lepage G, et al., J Lipid Res . 27: 114-120 (1986)) were analyzed by a modified method. Fatty acid methyl esters were analyzed using gas chromatography (HP 7890A, Agilent technologies, Santa Clara, Calif.). Fatty acids were analyzed by comparing the residence time on gas chromatograms with standard fatty acid methyl esters. The ratio of each fatty acid set the total fatty acid volume to 100% and calculated the relative volume for the peak portion of the individual fatty acids to the total portion as a percentage.

UPLC / Q- TOF MS  Of plasma samples by analysis Metabolism  Profiling

Sample Preparation and Analysis

400 μl of the mixture mixture (acetonitrile: methanol: ethanol = 1: 1: 1, v / v / v) was mixed with 100 μl of plasma, and then centrifuged at 13,000 rpm for 10 minutes at 4 ° C. The supernatant was evaporated to dryness and then dissolved in 80% methanol. 10 μl of sample was applied to UPLC / Q-TOF MS (Water, Milford, Mass.). Plasma extracts were applied to an Acquity UPLC BEH C18 column (2.1 × 50 mm, 1.7 μm; Waters) and equilibrated with water containing 0.1% formic acid. Samples were eluted in a concentration gradient acetonitrile containing 0.1% formic acid, and metabolites were separated using UPLC and analyzed by Q-TOF MS. Q-TOF was operated in ESI-positive mode. Capillary and sampling cone voltages are 3 kV and 30 V, respectively, the dissolution rate is 700 L / h at 250 ° C., and the source temperature is 120 ° C. MS data was collected in the range of m / z 50-1000 including 0.2-s scan time and 0.02-s thermal stress time. MS / MS spectra of metabolites were collected by a collision energy ramp of 10-30 eV. Accurate mass and composition for precursor and debris ions were calculated by MassLynx (Waters).

Data processing and Metabolism  analysis

MS-data related information including retention time, m / z and ionic strength was extracted from MarkerLynx software (Waters). MarkerLynx parameters include 5% peak width, 1 second height, 120 counts of intensity threshold, 0.04 amu mass window, 0.15 minutes retention time window, and noise rejection level. (noise elimination level) 6 and mass tolerance 0.04 Da. Peak integration was performed using Apex Track integration. Metabolites were identified through databases of Chemspider (www.chemspider.com) and Human Metabolome (www.hmdb.ca). The compounds identified were matched with standards based on retention time and mass spectra. Standards were purchased from Sigma (St. Louis, MO), Crystal Chem (Chicago, IL) and Avanti Polar Lipid (Alabaster, AL). MS / MS fragment data of the identified compounds were obtained in matched collision energy ramp (10-30 eV) and standards.

Statistical analysis

Statistical analysis was performed using SPSS version 12.0 for Windows (SPSS, Chicago, IL, USA). The normal distribution was examined using the Kolmogorov-Smirnov test, and the distorted variables were transformed logarithmically for statistical analysis. Mean values are given as unconverted values. Results were expressed as mean ± SE and considered to have statistical significance if the two-tailed value was P <0.05. Differences in clinical variability, including plasma metabolite mass intensity, between the two groups were performed using the Mann-Whitney U-test.

Multivariate winter analyzes were performed using SIMCA-P + software version 12.0 (Umetrics, Ume, Sweden).

In order to determine the number of required elements, a crossover test was performed for 7 groups of crossover tests. Partial least-squares discriminant analysis (PLS-DA), which visualizes the score plot or S-plot using the first or second PLS elements, was used as a classification method to model the distinction between diabetes and control subjects. The goodness of the fit was quantified by R ₂ Y, while the predictive ability was expressed by Q ₂ Y. In general, R ₂ Y, which shows how well the training set is generated mathematically, is between 0-1 and 1 means that the model is a perfect match. Furthermore, when Q ₂ Y is 0.5 or more, it is considered to have excellent predictive capability.

Experiment result

Clinical features and Dietary Intake

The dietary intake and clinical characteristics of healthy control subjects with normal fasting glucose levels and newly diagnosed patients with type 2 diabetes are shown in Table 1. Age, body mass index, smoking, drinking, serum insulin, total cholesterol, LDL cholesterol, HDL cholesterol and serum GPT (glutamic pyruvic transaminase) did not differ significantly between type 2 diabetics and controls. Patients with type 2 diabetes had higher body fat (P = 0.008) ratio, wide waist circumference (P = 0.031), high serum glucose levels (P <0.001), high HOMA-IR (P <0.001), and high serum neutrality compared to controls. Fat levels (P = 0.001) and low GPT levels. In addition, diabetics tended to have elevated serum free fatty acids (P = 0.089) levels. There was no difference in calorie uptake from total energy intake (TEI), total energy expenditure (TEE), or macronutrients between the two groups. However, the ratio of total energy consumption to total energy consumption was significantly higher in the diabetic group than in the control group (P = 0.031).

Clinical Characteristics and Dietary Intake of Control and Type 2 Diabetics - Control group
(n = 27) diabetes
Patient (n = 26) P-value Age (yr) 50.0 ± 1.27 49.2 ± 1.40 0.728 Body mass index (kg / m ² ) 24.6 ± 0.40 25.1 ± 0.35 0.364 Body fat (%) 21.0 ± 0.88 23.9 ± 0.67 0.008 Chest measurement (cm) 86.2 ± 1.01 89.0 ± 0.74 0.031 ¹ WHR 0.91 + - 0.01 0.92 ± 0.01 0.314 Smoker, n (%) 9 (47.4) 10 (52.6) 0.779 Drinker, n (%) 18 (43.9) 23 (56.1) 0.099 Systolic Blood Pressure (mmHg) 120.2 ± 2.17 132.4 ± 2.44 0.001 Diastolic blood pressure (mmHg) 73.8 ± 1.67 82.5 ± 1.67 0.001 Fasting Blood Sugar (mg / dL) ^∮ 91.0 ± 1.21 165.2 ± 7.21 <0.001 Insulin (μU / mL) ^∮ 8.34 ± 0.48 8.95 ± 0.82 0.993 ² HOMA-IR 1.88 ± 0.12 3.69 ± 0.42 <0.001 Free fatty acid (uEq / L) ^∮ 483.2 ± 37.3 634.1 ± 54.2 0.089 Triglycerides (mg / dL) ^∮ 102.0 ± 10.3 169.0 ± 15.7 0.001 Total cholesterol (mg / dL) 194.3 ± 4.80 189.3 ± 5.69 0.373 LDL Cholesterol (mg / dL) 121.0 ± 5.73 111.7 ± 3.83 0.227 HDL Cholesterol (mg / dL) ^∮ 52.8 ± 2.77 48.0 ± 2.52 0.196 GOT 24.0 ± 1.00 21.9 ± 1.29 0.046 GPT 23.0 ± 1.68 25.8 ± 2.11 0.344 ^† Estimated Daily Nutrition Intake  Total energy intake (kcal / d) 2452.2 ± 26.2 2521.1 ± 38.9 0.200  Carbohydrate (g / d) 379.7 ± 4.23 392.0 ± 5.57 0.105  Protein (g / d) 106.7 ± 1.87 107.7 ± 2.64 0.993  Fat (g / d) 58.4 ± 0.83 58.5 ± 1.50 0.644  Alcohol (g / d) 13.3 ± 2.60 19.9 ± 4.11 0.387 Total energy consumption (kcal / d) 2512.7 ± 31.2 2525.1 ± 36.5 0.908 Total energy intake / total energy consumption 1.01 ± 0.01 1.05 ± 0.01 0.031

Each value is expressed as mean ± standard error; ^검사 Check through log conversion.

¹ Waist circumference / hip circumference

² HOMA-IR = [fasting insulin (μU / ml) x fasting blood glucose (mmol / L)] / 22.5

P values were determined by independent t- test.

GOT: glutamic oxaloacetic transaminase, GPT: glutamic pyruvic transaminase

^† Nutrition intake was calculated using the Korean Food Code Database.

Lipid peroxide, inflammation Marker  And arterial stiffness

Type 2 diabetes patients oxidation -LDL high blood plasma compared to the control group (P = 0.021), high plasma MDA (P = 0.028), high urinary _{8-epi-PGF 2 α (} P = 0.001) and high serum levels of hs -CRP (P = 0.027), high serum IL-6 (P0.011), high serum TNF- (P = 0.031) and low serum adiponectin (P <0.001) levels (Table 2). Diabetic patients tended to have higher serum Lp-PLA ₂ activity (P = 0.083) compared to the control group (P <0.001) and the brachial-ankle pulse wave values were significantly higher.

Lipid Peroxides, Inflammatory Markers, and Brachial-Ankle Pulse Wave Rates - Control group
(n = 27) diabetes
Patient (n = 26) P-value Oxidized LDL (U / L) ^∮ 44.9 ± 3.66 55.9 ± 3.59 0.021 MDA (nmol / mL) ^∮ 9.80 ± 0.44 11.8 ± 0.91 0.028 8-epi-PGF _{2 α} (g / mg creatine) ^∮ 1135.9 ± 54.7 1650.9 ± 110.2 0.001 Lp-PLA ₂ Activity (nmol / mL / min) 34.7 ± 2.23 39.9 ± 2.18 0.083 hs-CRP (mg / dL) ^∮ 0.53 ± 0.09 1.03 ± 0.17 0.027 IL-1β (ρg / mL) ^∮ 1.06 ± 0.11 1.11 ± 0.11 0.426 IL-6 (pg / mL) ^∮ 2.68 ± 0.17 5.04 ± 1.48 0.011 TNF- (g / mL) ^∮ 10.1 ± 0.64 13.2 ± 1.41 0.031 Adiponectin (μg / ml) ^∮ 5.62 ± 0.50 3.10 ± 0.27 <0.001 Brachial-ankle pulse wave velocity (cm / sec) ^∮ 1258.2 ± 34.9 1519.6 ± 41.1 <0.001

Each value is expressed as mean ± standard error; ^검사 Check through log conversion.

P values were determined by independent t- test.

Fatty Acid Composition in Serum Phospholipids

Type 2 diabetic patients contained higher percentages of dodecanoic acid (12: 0, P = 0.032) and myristic acid (14: 0, P = 0.001) in serum phospholipids compared to controls (Table 3). Diabetic patients showed lower γ-linolenic acid levels (18: 3ω6, P = 0.057) and higher linolenic acid levels (18: 3ω6, P = 0.067) in serum phospholipids. Desaturase δ-9 (18: 1ω9 / 18: 0), δ-9 (16: 1ω7 / 16: 0), and δ-6 (18: 3ω6 / 18: 2ω6) in serum phospholipids between two groups And δ-5 (20: 4ω6 / 20: 3ω6) were not significantly different.

Fatty Acid Composition in Black Phospholipids - Control group
(n = 27) diabetes
Patient (n = 26) Dodecanoic Acid (c12: 0) 0.21 ± 0.01 0.39 ± 0.07 * Mystic Acid (c14: 0) 0.42 ± 0.02 0.69 ± 0.07 ** Palmitic acid (c16: 0) 34.7 ± 0.58 33.5 ± 1.04 Stearic acid (c18: 0) ^∮ 18.0 ± 0.34 18.9 ± 0.70 Palmitoleic acid (c16: 1) ^∮ 0.66 ± 0.05 0.70 ± 0.06 Oleic acid (c18: 1 ω9) 6.94 ± 0.32 7.19 ± 0.42 Oleic acid (c18: 1 ω7) 1.78 ± 0.09 1.61 ± 0.08 Linoleic acid (c18: 2 ω6) 13.1 ± 0.44 11.9 ± 0.63 γ-linolenic acid (c18: 3 ω6) ^∮ 0.17 ± 0.01 0.15 ± 0.03 Aicosadienoic acid (c20: 2 ω6) 0.41 + - 0.05 0.56 ± 0.17 Dihomo-γ-linolenic acid (c20: 3 ω6) 1.50 ± 0.08 1.59 ± 0.11 Arachidonic acid (c20: 4 ω6) 4.90 ± 0.30 4.87 ± 0.35 α-linolenic acid (c18: 3 ω3) ^∮ 0.15 + - 0.01 0.19 ± 0.02 Eicosapentaenoic acid (c20: 5 ω3) ^∮ 1.16 ± 0.11 1.39 ± 0.17 Docosahexaenoic acid (c22: 6 ω3) ^∮ 2.85 ± 0.21 2.98 ± 0.29 δ-9 desaturating enzyme (18: 1 ω9 / 18: 0) 0.388 ± 0.02 0.393 ± 0.03 δ-9 desaturase (16: 1 ω7 / 16: 0) 0.019 ± 0.00 0.022 ± 0.00 δ-6 desaturating enzyme (18: 3 ω6 / 18: 2 ω6) 0.013 ± 0.00 0.013 ± 0.00 δ-5 desaturase (20: 4 ω6 / 20: 3 ω6) 3.456 ± 0.25 3.116 ± 0.18

Each value is expressed as mean ± standard error; ^검사 Check through log conversion.

P values were determined by independent t- test. ^* P <0.05, ^** P <0.01

UPLC -Q- TOF MS Plasma Metabolism Profiling Based on

ratio- target  Metabolic Pattern Analysis

To account for the maximum distinction between defined population samples in the data set, 382 variants obtained in the plasma of healthy and newly diagnosed people with diabetes were analyzed by PLS-DA. The PLS-DA score plot (FIG. 1A) distinguishes healthy people from newly diagnosed people with diabetes along the axis corresponding to the first two PLS elements. Our model tells us that the variation in X (R ₂ X = 0.570) is 57% and the variation in Y (class) (R ₂ Y = 0.800) is 80% and Y (Q) for two factor models. ₂ Y = 0.752) predicted that the mutation is 75.2%. A permutation test with 200 permutations was performed to show that the R ₂ intercept was 0.249 and the Q ₂ intercept was -0.297. The score plot showed clear distinction between diabetic patients in the healthy population, and the S-plot (FIG. 1b) distinguishes between healthy controls and diabetic patients according to the axis corresponding to the covariates [p] and the confidence relationship [p (corr)]. Showed that each metabolite contributed.

plasma Metabolic fold change  shame

Normalized intensity for the total metabolites (382 metabolites in plasma) detected by UPLC-Q-TOF was statistically analyzed by nonparametric t test. All metabolites except some metabolites are significantly affected by the early onset of type 2 diabetes. UPLC-Q-TOF results are shown in Table 4.

Plasma metabolic profiles, including lyso-phosphatidylcholine, lyso-phosphatidylethanolamine, acylcanitine and amino acids, were determined by plasma analysis using UPLC / Q-TOF MS to control and type 2 diabetic patients with normal fasting glucose levels. There was a big difference between them. Three amino acids (leucine, lysine and phenylalanine), eight acylcanines (propionyl-, octanoyl-, dicanoyl-, palmityl-, heptadicanoyl-, linoleyl- and basinyl-cany) in diabetics Tin), six lyso-phosphatidylcholines [lysoPCs (C14: 0, C16: 1, C18: 1, C18: 3, C20: 5 and C22: 6)] and two lyso-phosphatidylethanolamines [lysoPE ( C18: 2 and C22: 6)] were higher than normal, while serine and lysoPE (C18: 1) were lower than normal.

Dicanoyl-carnitine, lysoPCs (C14: 0, C16: 1, C18: 1 and C22: 6) and lysoPE (C18: 1) with a variable importance in the projection (VIP) value of 1 or higher were higher than the control group. Relevance is shown (Table 4), and they also distinguish between normal and diabetic patients on the PLS-DA score plot as major plasma metabolites (Table 4). Oleamide does not increase significantly in diabetics (P = 0.124), but has a VIP value of 6.592, which is the most important plasma metabolite to distinguish between normal and diabetic patients.

No name Molecular Weight Molecular Formula (mDa) Type 2 Diabetic / Control Group Ratio P value VIP One L-Leucine 132.1025 C ₆ H ₁₃ NO ₂ (0.0) 1.55 <0.001 0.164 2 L-lysine 147.1134 C ₆ H ₁₄ NO ₂ (0.2) 2.33 <0.001 0.406 3 L-phenylalanine 166.0868 C ₉ H ₁₁ NO ₂ (4.0) 1.89 <0.001 0.501 4 L-serine 106.0504 C ₃ H7NO ₃ (7.7) 0.30 <0.001 0.203 5 Propionyl-Carnitine 218.1392 C ₁₀ H ₁₉ NO ₄ (0.1) 1.55 0.002 0.082 6 Octanoyl-Carnitine 288.2175 C ₁₅ H ₂₉ NO ₄ (0.5) 4.77 <0.001 0.682 7 Dicanoyl-Carnitine 316.2488 C ₁₇ H ₃₃ NO ₄ (0.5) 3.71 <0.001 1.122 8 Dodecanoyl-Carnitine 344.2801 C ₁₉ H ₃₇ NO ₄ (0.2) 3.90 <0.001 0.273 9 Palmityl-Carnitine 400.3427 C ₂₃ H ₄₅ NO ₄ (0.7) 4.78 <0.001 0.489 10 Heptadicanoyl-carnitine 414.3583 C ₂₄ H ₄₇ NO ₄ (4.4) 3.39 0.001 0.435 11 Renorail-Carnitine 424.3427 C ₂₅ H ₄₅ NO ₄ (2.5) 2.01 0.006 0.108 12 Basinyl-Carnitine 426.3583 C ₂₅ H ₄₇ NO ₄ (1.6) 5.13 <0.001 0.657 13 LysoPC (14: 0) 468.3090 C ₂₂ H ₄₆ NO ₇ P (3.0) 2.25 <0.001 1.144 14 LysoPC (16: 0) 496.3403 C ₂₄ H ₅₀ NO ₇ P (0.2) 1.04 0.545 1.927 15 LysoPC (16: 1) 494.3247 C ₂₄ H ₄₈ NO ₇ P (0.8) 2.13 <0.001 1.580 16 LysoPC (18: 0) 524.3716 C ₂₆ H ₅₄ NO ₇ P (1.1) 0.90 0.051 1.872 17 LysoPC (18: 1) 522.3560 C ₂₆ H ₅₂ NO ₇ P (0.4) 1.19 0.008 1.699 18 LysoPC (18: 2) 520.3403 C ₂₆ H ₅₀ NO ₇ P (0.3) 1.11 0.200 1.251 19 LysoPC (18: 3) 518.3247 C ₂₆ H ₄₈ NO ₇ P (0.7) 1.69 0.047 0.367 20 LysoPC (20: 2) 548.3716 C ₂₈ H ₅₄ NO ₇ P (2.0) 0.87 0.262 0.077 21 LysoPC (20: 4) 544.3403 C ₂₈ H ₅₀ NO ₇ P (0.6) 1.17 0.062 0.569 22 LysoPC (20: 5) 542.3247 C ₂₈ H ₄₈ NO ₇ P (0.0) 1.85 0.001 0.377 23 LysoPC (22: 6) 568.3403 C ₃₀ H ₅₀ NO ₇ P (0.3) 1.46 0.002 2.033 24 LysoPE (18: 1) 480.3090 C ₂₃ H ₄₆ NO ₇ P (0.2) 0.55 0.003 1.799 25 LysoPE (18: 2) 478.2934 C ₂₃ H ₄₄ NO ₇ P (0.2) 1.99 <0.001 0.255 26 LysoPE (22: 6) 526.2934 C ₂₇ H ₄₄ NO ₇ P (0.1) 2.04 <0.001 0.549 27 Oleamide 282.2797 C ₂₈ H ₃₅ NO (0.5) 1.30 0.124 6.952

P values were determined by independent t- test.

Lipid peroxide, inflammation Marker And Plasma of Fatty Acids in Rats, Arterial Stiffness and Serum Phospholipids Metabolic  relation

Correlation analyzes were performed for major plasma metabolites as determined by UPLC-Q-TOF. Dicanoyl-carnitine was oxidized-LDL (r = 0.285, P = 0.047), 8-epi-PGF ₂ (r = 0.317, P = 0.036), IL-6 (r = 0.403, P = 0.009), TNF- There was a positive correlation with (r = 0.328, P = 0.036) and brachial-ankle pulse wave velocity (r = 0.548, P = 0.001). The brachial-ankle pulse wave rate is positively correlated with lysoPC C14: 0 (r = 0.543, P = 0.001) and lysoPC C16: 1 (r = 0.559, P = 0.001) and lysoPE C18: 1 (r = -0.444 , P = 0.011). 8-epi-PGF ₂ positively correlates with lysoPC C14: 0 (r = 0.439, P = 0.003) and lysoPC C16: 1 (r = 0.352, P = 0.019) and Lp-PLA ₂ (r = 0.313 , P = 0.044) and brachial-ankle pulse velocity (r = 0.409, P = 0.034) are also positively correlated. LysoPC C22: 6 correlated positively with IL-6 (r = 0.324, P = 0.039). Oleamide was positively correlated with TNF- (r = 0.328, P = 0.036) and oleic acid (C18: 1ω9) (r = 0.291, P = 0.05) in serum phospholipids. Lyso-phosphatidyl choline (C14: 0, C16: 1 and C18: 1) was measured for myristic acid (C14: 0) (r = 0.427, P = 0.003), palmitoleic acid (C16: 1) (r) in serum phospholipids. = 0.415, P = 0.005) and oleic acid (C18: 1ω9) (r = 0.376, P = 0.011) were positively correlated. Lyso-phosphatidyl choline (C22: 6) and lyso-phosphatidyl ethylamine (C22: 6) (r = 0.518, P <0.001) and lyso-phosphatidyl choline (C18: 2) and lyso-phosphatidyl ethyl There was a positive correlation between amines (C18: 2) (r = 0.310, P = 0.024), respectively.

Claims (10)

A quantitative device for plasma metabolism selected from the group consisting of lyso-phosphatidyl choline (C14: 0), lyso-phosphatidyl choline (C16: 1) and lyso-phosphatidylethanolamine (C18: 1) Diabetes Diagnosis Kit.
The method of claim 1, wherein the diagnostic kit is leucine, lysine, phenylalanine, serine, lyso-phosphatidyl choline (C18: 1), lyso-phosphatidyl choline (C18: 3), lyso-phosphatidyl choline (C20: 5) , Lyso-phosphatidyl choline (C22: 6), lyso-phosphatidylethanolamine (C18: 2), lyso-phosphatidylethanolamine (C22: 6), propionyl-carnitine, octanoyl-carnitine, dicanoyl A quantitative device for plasma metabolism selected from the group consisting of carnitine, dodicanoyl-carnitine, palmityl-carnitine, heptadicanoyl-carnitine, linoleyl-carnitine and vacinyl-carnitine Diagnostic kit, characterized in that it further comprises.
The diagnostic kit of claim 1, wherein the diabetes is diabetes, which increases inflammation, oxidative stress, and vascular stiffness.
4. The diagnostic kit of claim 3, wherein the diabetes is type 2 diabetes.
4. The diagnostic kit of claim 3, wherein the diabetes is early diabetes.
The diagnostic kit according to claim 1 or 2, wherein the quantitative device is a chromatography / mass spectrometer.
The diagnostic kit of claim 1, wherein when the concentrations of lyso-phosphatidyl choline (C14: 0) and lyso-phosphatidyl choline (C16: 1) are increased, the diagnostic kit is characterized by an increased risk of diabetes disease.
The diagnostic kit of claim 1, wherein when the concentration of lyso-phosphatidylethanolamine (C18: 1) is decreased, the diagnostic kit is characterized by an increased risk of diabetes disease.
The method of claim 2, wherein the leucine, lysine, phenylalanine, lyso-phosphatidyl choline (C18: 1), lyso-phosphatidyl choline (C18: 3), lyso-phosphatidyl choline (C20: 5), lyso-phosphatidyl Choline (C22: 6), Lyso-phosphatidylethanolamine (C18: 2), Lyso-phosphatidylethanolamine (C22: 6), Propionyl-carnitine, Octanoyl-carnitine, Dicanoyl-carnitine, Dodi Diagnosis characterized by an increased risk of diabetic disease when the concentrations of cannoyl-carnitine, palmityl-carnitine, heptadicanoyl-carnitine, linoleyl-carnitine and basinyl-carnitine are increased Kit.
3. The diagnostic kit of claim 2, wherein when the serine concentration decreases, it represents an increased risk of diabetes disease.

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