JP2014032046A - Marker for identification of diabetic renal diseases, and use thereof - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a novel index to enable early identification of diabetic renal diseases, and a use thereof.SOLUTION: This invention provides a marker for identification of diabetic renal diseases comprising one compound, or a combination of two or more compounds, selected from the group consisting of creatinine, aspartic acid, γ-butyrobetaine, citrullination, symmetric dimethylarginine, kynurenine, azelaic acid and galactaric acid.

Description

  The present invention relates to disease markers and uses thereof. Specifically, the present invention relates to a biomarker that enables differentiation of diabetic nephropathy and a method of differentiating diabetic nephropathy.

  Diabetic nephropathy, which is the leading primary dialysis-introduced disease, is a renal disease that requires the most countermeasures in chronic kidney disease (suppression of new patients and suppression of progression of existing patients). Since an effective therapeutic effect can be expected if the stage can be accurately differentially diagnosed, the diagnostic technique is an extremely important elemental technique in diabetic nephropathy countermeasure medicine.

  In the prevention and treatment of diabetic nephropathy, the differentiation of disease state and the estimation of disease stage are in great demand. In the prior art, in addition to the history of diabetes, changes in urinary findings (appearance of proteinuria / microalbuminuria) and eGFR (glomerular filtration rate: an index that represents renal function) are calculated based on serum creatinine concentration and age / sex. And diabetic nephropathy were differentiated and staged (Non-patent Document 1) based on the fundus examination findings (observation of fundus vascular lesions). However, these conventional techniques have a problem that diabetic nephropathy cannot be easily and reliably distinguished from other renal diseases and a stage that cannot be accurately estimated. On the other hand, research on new biomarkers in urine and blood has been observed recently, but new biomarkers that have replaced conventional technologies have not yet been put into practical use, and urinary protein, albumin, and serum creatinine concentrations are still present. Diagnosis based on is the de facto standard. As an example of exploratory research for urinary biomarkers, Dihazi and colleagues report three urinary proteins useful for distinguishing diabetic nephropathy patients from type 2 diabetic patients by SELDI / TOFMS. (Non-Patent Document 2). Mischak et al. Have also found a pattern that can distinguish between healthy individuals and type 2 diabetics by profiling urinary polypeptides using a technique that combines capillary electrophoresis and mass spectrometry (CE-MS) (non-patent literature). 3).

CKD clinical practice guidelines based on evidence 2009, p87-p104, Chapter 8 Diabetic nephropathy Dihazi H, Muller GA, Lindner S, et al. (2007) Characterization of diabetic nephropathy by urinary proteomic analysis: identification of a processed ubiquitin form as a differentially excreted protein in diabetic nephropathy patients. Clin Chem 53: 1636-1645 Mischak H, Kaiser T, Walden M, et al. (2004) Proteomic analysis for the assessment of diabetic renal damage in humans.Clin Sci (Lond) 107: 485-495 Xia JF, Liang QL, Hu P, Wang YM, Li P, Luo GA (2009) Correlations of six related purine metabolites and diabetic nephropathy in Chinese type 2 diabetic patients.Clin Biochem 42: 215-220 Jiang Z, Liang Q, Luo G, Hu P, Li P, Wang Y (2009) HPLC-electrospray tandem mass spectrometry for simultaneous quantitation of eight plasma aminothiols: application to studies of diabetic nephropathy.Talanta 77: 1279-1284 Zhang J, Yan L, Chen W, et al. (2009) Metabonomics research of diabetic nephropathy and type 2 diabetes mellitus based on UPLC-oaTOF-MS system. Anal Chim Acta 650: 16-22

  The reason why the “problem in which diabetic nephropathy cannot be detected unless the pathogenesis is sufficiently advanced” and “the problem in which the stage cannot be accurately estimated” is included in the conventional technology Because there is. In other words, in the prior art, renal dysfunction is diagnosed by detecting a phenomenon that appears as a result of the structural abnormality of the kidney tissue, so that the pathogenesis progresses sufficiently and the structural abnormality of the kidney tissue develops sufficiently. Otherwise, the onset of diabetic nephropathy could not be detected. Furthermore, the prior art generally detects renal dysfunction, and it was impossible to differentiate diabetic nephropathy. On the other hand, as described above, there is an attempt to identify a biomarker for diabetic nephropathy in urine. However, a substance in urine has a large variation in its abundance (concentration) and is not suitable for high-precision discrimination.

  Then, this invention makes it a subject to provide the new parameter | index which enables the early differentiation of diabetic nephropathy, and its use.

  In view of the above problems, the present inventors have focused on blood metabolites and attempted to extract and identify a new index that enables early differentiation of diabetic nephropathy. As a result, blood metabolites from diabetic nephropathy patients can be converted into diabetic kidneys using a technology that can comprehensively analyze in vivo metabolites using a capillary electrophoresis device and a mass spectrometer (so-called metabolomic analysis technology, metabolomics). We have succeeded in extracting an index metabolite group (biomarker) useful for differentiating the disease stage (non-onset, microalbuminuria, macroalbuminuria). That is, by comprehensively qualitatively and quantitatively analyzing metabolites contained in the blood of diabetic nephropathy patients at each stage and comparing the metabolite profiles in blood with each other, 289 contained in the blood of diabetic nephropathy patients. From the metabolites of the species, 19 substances were successfully extracted as biomarkers reflecting the pathology and stage of diabetic nephropathy. As a result of further investigation and multiple logistic regression analysis using these biomarkers, the area under the receiver operating characteristic curves (AUC: 0.844-0.927) is highly accurate. It was possible to classify. That is, it was confirmed that these biomarkers are extremely effective for differentiating diabetic nephropathy.

  By the way, there are several examples of metabolome analysis performed for the purpose of searching for a biomarker of diabetic nephropathy, but many of them are limited to specific targets (for example, Non-Patent Documents 4 and 5). ), And few report the results of exhaustive analyses. Zhang et al. Report the results of comprehensive analysis using LC-MS, but those identified as biomarker candidates for diabetic nephropathy are leucine, dihydrosphingosine and phytosphingosine (Non-patent Document 6). It is different from the compound that we have successfully extracted and identified.

The invention described below is mainly based on the above-described results obtained by the inventors' extensive studies.
[1] Diabetic kidney consisting of one compound or a combination of two or more compounds selected from the group consisting of creatinine, aspartic acid, γ-butyrobetaine, citrulline, symmetric dimethylarginine, kynurenine, azelaic acid and galactaric acid Marker for disease identification.
[2] A method for distinguishing diabetic nephropathy using the amount of the marker according to [1] as an index.
[3] The discrimination method according to [2], including the following steps (1) and (2):
(1) measuring the marker in the blood sample;
(2) A step of determining the presence or absence of diabetic nephropathy and / or the stage of diabetic nephropathy based on the measurement result.
[4] The discrimination method according to [3], wherein a capillary electrophoresis-mass spectrometer is used for the measurement in step (1).
[5] The discrimination method according to [2], which is used for discrimination of any of the following (a) to (c):
(A) Differentiation of non-diabetic nephropathy from diabetic nephropathy;
(B) Differentiation between the absence of diabetic nephropathy and diabetic nephropathy with microalbuminuria;
(C) Differentiation between non-diabetic nephropathy, diabetic nephropathy with microalbuminuria and diabetic nephropathy with macroalbuminuria.

Diabetes patient background. Comparison is made among diabetic nephropathy-free patients (asymptomatic), microalbuminuria patients, and macroalbuminuria patients. Data in the table are mean ± standard deviation. * Indicates that there is a significant difference from diabetic nephropathy-free patients. † indicates that there is a significant difference from patients with microalbuminuria. The numbers in parentheses are the number of patients whose clinical laboratory values are unknown. A list of metabolites that showed a significant difference in the duration of diabetic nephropathy. The Kruskal-Wallis test was used for the calculation of the p value. Correlation between the concentration of each metabolite and eGFR (estimated glomerular filtration rate). Correlation between the concentration of each metabolite and UACR (urine albumin / creatinine ratio). Ability to distinguish the presence or absence of diabetic nephropathy onset of each metabolite. ROC (receiver operating characteristic curve) and AUC (area under curve) for differentiating diabetic nephropathy using 5 metabolites (including 2 unidentified metabolites) value. Multiple logistic regression model variables (corresponding to FIG. 6). ROC curve and AUC value for differentiating diabetic nephropathy using 4 types of identified metabolites. Multiple logistic regression model variables (corresponding to FIG. 8). Different combinations and differentiation capabilities of identified metabolites. 1: creatinine, 2: aspartic acid, 3: γ-butyrobetaine, 4: citrulline, 5: symmetric dimethylarginine (SDMA), 6: kynurenine, 7: azelaic acid, 8: galactaric acid Correlation coefficient and p-value between metabolites. Upper row; correlation coefficient, lower row; p-value, *; p <0.05, **; p <0.01, ***; p <0.001

1. Diabetic Nephropathy Differentiation Marker The first aspect of the present invention relates to a diabetic nephropathy differentiation marker (hereinafter sometimes abbreviated as “differentiation marker”). The “diabetic nephropathy differentiation marker” is a biomolecule that serves as an identification / specific index that reflects the disease state / stage of a person who has developed or can develop diabetic nephropathy. The marker for differentiating diabetic nephropathy is useful for determining the onset and stage of nephropathy in diabetic patients.

  The differentiation marker of the present invention comprises a blood metabolite. Specifically, the differential marker of the present invention is a compound selected from the group consisting of creatinine, aspartic acid, γ-butyrobetaine, citrulline, symmetric dimethylarginine (SDMA), kynurenine, azelaic acid and galactaric acid, It consists of a combination of two or more compounds. In the present specification, “in blood” means existing in blood. Therefore, each marker molecule of the present invention which is a blood metabolite can be detected in a blood sample (serum, plasma, etc.).

As shown in Examples described later, in addition to the above 8 compounds, 11 compounds shown below were also correlated with the stage of diabetic nephropathy. Therefore, in one embodiment of the present invention, these 11 compounds are used as a marker for differentiation, similarly to the above 8 compounds. Specifically, these compounds present in the blood are each used alone, or two or more compounds selected from a total of 19 compounds of these compounds and the above 8 compounds are used as differential markers. Note that m / z (ratio of mass to charge) is a physical quantity displayed on the horizontal axis of the mass spectrum obtained as a result of mass analysis.
Blood metabolite with m / z 129.067 (composition formula C ₅ H ₈ N ₂ O ₂ ) (referred to as “ID17” for convenience)
Metabolite in blood whose m / z is 156.139 (composition formula C ₉ H ₁₇ NO) (referred to as “ID51” for convenience)
Metabolite in blood whose m / z is 158.154 (composition formula C ₉ H ₁₉ NO) (referred to as “ID52” for convenience)
Blood metabolite with m / z 243.184 (referred to as “ID96” for convenience)
Blood metabolite with m / z of 244.106 (referred to as “ID97” for convenience)
Blood metabolite with m / z of 276.128 (referred to as “ID114” for convenience)
Blood metabolite with m / z 302.197 (referred to as “ID127” for convenience)
Blood metabolite with m / z of 316.213 (referred to as “ID134” for convenience)
Metabolite in blood whose m / z is 96.960 (composition formula H ₂ SO ₄ ) (referred to as “ID152” for convenience)
Metabolite in blood whose m / z is 103.014 (composition formula C ₂ H ₄ N ₂ O ₃ ) (referred to as “ID158” for convenience)
A metabolite in blood whose m / z is 149.049 (composition formula C ₆ H ₆ N ₄ O) (referred to as “ID202” for convenience)

  As shown in the below-mentioned Examples, all of the identified 8 compounds and unidentified 11 compounds showed high discrimination ability. This fact means that these compounds are useful even when used alone. On the other hand, an improvement in discrimination accuracy (sensitivity and specificity) was recognized by using two or more compounds in combination. In consideration of this fact, it is preferable to use a combination of two or more compounds as a differentiation marker. Various combinations can be employed. A specific example of combination and its discrimination ability are shown in FIG. In addition, when using two or more compounds together, it is good to select the compound to be used in consideration of the correlation coefficient between each compound. In general, accuracy or accuracy is improved by combining markers with small correlation coefficients. The correlation between each compound is shown in FIG.

Among the combinations illustrated in FIG. 10, the following combinations are preferable. Among these combinations, (1), (2), (4), (5), (6), (7), (10), (11), (12), and (16) are particularly preferable.
(1) Creatinine, aspartic acid, γ-butyrobetaine, citrulline, SDMA, kynurenine, azelaic acid and galactaric acid
(2) Creatinine, aspartic acid, γ-butyrobetaine, citrulline, SDMA, kynurenine, azelaic acid, galactaric acid
(3) Aspartic acid, γ-butyrobetaine, citrulline, SDMA, kynurenine, azelaic acid, galactaric acid
(4) Creatinine, γ-butyrobetaine, citrulline, SDMA, kynurenine, azelaic acid, galactaric acid
(5) Creatinine, aspartic acid, citrulline, SDMA, kynurenine, azelaic acid, galactaric acid
(6) Creatinine, aspartic acid, γ-butyrobetaine, SDMA, kynurenine, azelaic acid, galactaric acid
(7) Creatinine, aspartic acid, γ-butyrobetaine, citrulline, kynurenine, azelaic acid, galactaric acid
(8) Creatinine, aspartic acid, γ-butyrobetaine, citrulline, SDMA, azelaic acid, galactaric acid
(9) Creatinine, aspartic acid, γ-butyrobetaine, citrulline, SDMA, kynurenine, galactaric acid
(10) Creatinine, aspartic acid, γ-butyrobetaine, citrulline, SDMA, kynurenine, azelaic acid
(11) Creatinine, aspartic acid, γ-butyrobetaine, SDMA, azelaic acid, galactaric acid
(12) Creatinine, aspartic acid, citrulline, kynurenine, azelaic acid, galactaric acid
(13) Creatinine, γ-butyrobetaine, citrulline, SDMA, kynurenine, azelaic acid
(14) γ-butyrobetaine, citrulline, SDMA, kynurenine, azelaic acid, galactaric acid
(15) Creatinine, aspartic acid, γ-butyrobetaine, SDMA, azelaic acid
(16) Aspartic acid, γ-butyrobetaine, SDMA, azelaic acid, galactaric acid
(17) Creatinine, citrulline, SDMA, azelaic acid, galactaric acid
(18) γ-butyrobetaine, citrulline, kynurenine, azelaic acid, galactaric acid
(19) Creatinine, citrulline, azelaic acid, galactaric acid
(20) Creatinine, aspartic acid, SDMA, galactaric acid
(21) Aspartic acid, SDMA, azelaic acid, galactaric acid
(22) Creatinine, azelaic acid, galactaric acid
(23) SDMA, galactaric acid

2. Diabetic Nephropathy Differentiation Method The second aspect of the present invention provides a differentiation method for diabetic nephropathy as a typical use of the differentiation marker of the present invention. The differentiation method of the present invention is to distinguish between those who have not developed diabetic nephropathy and those who have developed (identification / extraction of those who have not developed or those who have developed), the pathology of diabetic nephropathy / It can be used for identification by stage (for example, identification / extraction of a person in a specific disease state / stage). The discrimination method of the present invention is used, for example, in any of the following (a) to (c).
(A) Differentiation between non-diabetic nephropathy and diabetic nephropathy (b) Differentiation between non-diabetic nephropathy and diabetic nephropathy with microalbuminuria (c) No diabetic nephropathy Between diabetic nephropathy with microalbuminuria and diabetic nephropathy with macroalbuminuria

  Here, diabetic nephropathy is based on clinical features (urinary protein, creatinine clearance), etc. Phase 1 (early nephropathy), Phase 2 (early nephropathy), Phase 3 A (early nephropathy early stage) ), Stage B (late stage of overt nephropathy), stage 4 (renal failure stage), and stage 5 (dialysis therapy stage). The state before reaching the first stage or the first stage corresponds to “non-diabetic nephropathy”. Similarly, the second stage corresponds to "diabetic nephropathy with micro (album) albuminuria" and the third stage A and later corresponds to "diabetic nephropathy with macroalbuminuria (apparent proteinuria)" To do. According to the classification of the Japan Diabetes Society and the Japanese Society of Nephrology (JNPR Journal 2002; 44 (1): i), if the UACR (urine albumin / creatinine ratio) is 30-299 mg / g Microalbuminuria is diagnosed, and if it is 300 mg / g or more, macroalbuminuria is diagnosed.

In the discrimination method of the present invention, for example, the following steps (1) and (2) are performed.
(1) A step of measuring a marker for differentiation in a blood sample (2) A step of determining the presence or absence of diabetic nephropathy and / or the stage of diabetic nephropathy based on the measurement result

Step (1)
In step (1), a blood sample derived from a subject is prepared, and a differentiation marker present therein is measured. In the present specification, “measuring the differentiation marker” is synonymous with clarifying the content (concentration) of the differentiation marker in the sample. The subject is typically a diabetic patient or a potential diabetic (who may have diabetes). However, it is possible to make other persons the subject.

  One or more compounds selected from the group consisting of creatinine, aspartic acid, γ-butyrobetaine, citrulline, SDMA, kynurenine, azelaic acid and galactaric acid are used as the differentiation marker. In another embodiment, the group consisting of creatinine, aspartic acid, γ-butyrobetaine, citrulline, SDMA, kynurenine, azelaic acid, galactaric acid, ID17, ID51, ID52, ID96, ID97, ID114, ID127, ID134, ID152, ID158 and ID202 One or more compounds selected from the above are used.

  When two or more compounds are used in combination as a differentiation marker, various combinations (for example, those shown in FIG. 10) can be employed.

  In the present invention, the amount of differentiation marker in blood is measured. As the blood sample, whole blood, serum, plasma or the like can be used, but preferably serum or plasma is used. It is preferable to pre-treat the blood sample prior to measurement. In the case of CE-TOFMS, which is a measurement method employed in Examples described later, for example, serum or plasma is treated by the following procedure. In addition, what is necessary is just to perform the preparation of the serum from whole blood by a conventional method (typically coagulation | solidification by standing at normal temperature and subsequent centrifugation process). The same applies to the preparation of plasma (typically the addition of a coagulant and subsequent centrifugation).

  First, a blood sample (serum or plasma) is mixed with an alcohol solvent such as methanol or ethanol to deactivate the enzyme in the blood sample. The alcohol solvent is not particularly limited, but preferably methanol is used. Further, an alcohol solvent with high purity is preferable, and for example, an alcohol solvent for LC / MS is used. Next, an organic solvent and water are added to and mixed with a specimen mixed with an alcohol solvent, and then an organic layer containing a fat-soluble substance (phospholipid or the like) is removed by layer separation. The organic solvent used here is not particularly limited as long as it is suitable for layer separation. Preferably, chloroform, dichloromethane, dichloroethane or the like is used. A special grade or an organic solvent for analysis may be used. As for water, one having a low impurity content is preferable, and for example, distilled water, deionized water, MilliQ (registered trademark) water or the like is used. Of these, MilliQ water is particularly preferred. Centrifugation, standing, etc. can be adopted as the method of layer separation.

  It is preferable to remove the protein from the aqueous phase obtained by layer separation. Deproteinization can be performed, for example, by ultrafiltration.

  The method for measuring the differentiation marker is not particularly limited. Examples of measurement methods include capillary electrophoresis-mass spectrometry (CE-MS), high performance liquid chromatography (LC), gas chromatography (GC), chip LC, chip CE, and mass spectrometer (MS) GC-MS method, LC-MS method or CE-MS method combining various methods, various MS measurement methods, NMR methods, methods of measuring metabolites after derivatizing them into fluorescent substances or UV absorbers, and creating antibodies This is a method (immunological measurement method) for measuring by ELISA method or the like. Among these, CE-MS is a particularly preferable measurement method. According to CE-MS, a plurality of compounds can be detected together. ID17, ID51, ID52, ID96, ID97, ID114, ID127, ID134, ID152, ID158 and ID202 (unidentified metabolites) can also be measured.

  In measurement methods such as CE-MS and high-performance liquid chromatography, after separating the compound in the blood sample (separation of the sample), the content of the target compound (ie, the marker for differentiation) contained in a given fraction is determined. (Content measurement). In the case of CE-MS, a step of separating a blood sample using a capillary electrophoresis apparatus and a step of measuring the content of a differentiation marker in a specific fraction obtained by electrophoresis (a fraction containing a differentiation marker) Will be done. And a mass spectrometer is used for the measurement of content of the marker for discrimination. When a plurality of compounds are employed as the differentiation marker, the content of the corresponding compound is measured for each of the plurality of fractions. Strict quantification is not essential, and the amount of marker for discrimination may be measured semi-quantitatively.

  In addition to the description in this specification, the operation method and measurement conditions of the measuring instrument for measuring the differentiation marker in the blood sample, refer to the instruction manual of the measuring device or past reports as necessary. Just decide. As a specific example of measurement conditions, the case of CE-TOFMS will be described below.

  First, a blood sample that has been pretreated as described above is prepared. Preferably, an internal standard is added at the pretreatment stage. The internal standard is a standard for determining the electrophoresis time and content of the differentiation marker. There is no particular limitation as long as it does not affect the electrophoresis and mass spectrometry of the differentiation marker. For example, methionine sulfone or 10-camphorsulfonic acid (CSA) can be used as an internal standard. The capillary is preferably a fused silica capillary. In order to obtain a high resolution, the inner diameter of the capillary is preferably 100 μm or less, and the total length of the capillary is preferably about 50 cm to 150 cm. The method for identifying the target fraction (fraction containing a marker for differentiation) in each fraction obtained by capillary electrophoresis is not particularly limited. For example, a method using a relative time with the electrophoresis time of the internal standard, a method of measuring the electrophoresis time of the compound in advance using a sample of a compound as a differentiation marker, and the like can be employed. When the target fraction can be identified, the content of the discrimination marker (compound having the corresponding m / z) in the fraction is measured as the peak area. In that case, it is good to correct with the peak area of the internal standard. On the other hand, if a calibration curve created using a standard is used, the concentration, that is, the content of the discrimination marker in the blood sample can also be obtained from the measured peak area.

Step (2)
In step (2), the presence or absence of the onset of diabetic nephropathy and / or the stage of diabetic nephropathy is determined based on the measurement result. The reference value or cut-off value used for the determination may be determined in consideration of the type and state of the sample to be used, the discrimination target, the required accuracy (reliability), and the like. For example, when determining the onset of diabetic nephropathy, the amount of a marker for differentiation (reference value) in the blood of a person who has not developed diabetic nephropathy (asymptomatic patient) is determined in advance. Keep it. And it can be determined that diabetic nephropathy has developed when the amount of the marker for differentiation in the blood of the subject shows a significant difference from the reference value. It can determine similarly about the stage (stage) of diabetic nephropathy. The reference value is set as a specific amount or a specific amount range.

  Among the compounds constituting the marker of the present invention, blood concentrations of creatinine, aspartic acid, γ-butyrobetaine, citrulline, SDMA, kynurenine, ID17, ID97, ID152, ID158 and ID202 are proportional to the onset and progression of diabetic nephropathy The blood concentrations of azelaic acid, galactaric acid, ID51, ID52, ID96, ID114, ID127 and ID134 are inversely related to the onset and progression of diabetic nephropathy. Therefore, in the former compound group, the amount in the blood is significantly higher in diabetic nephropathy patients than in asymptomatic patients and increases with progression of the stage. In contrast, in the latter group, the amount in the blood is significantly less in diabetic nephropathy patients compared to asymptomatic patients and decreases with progression of stage. In other words, an increase in the amount of blood in the former compound group is an indicator of the onset and stage progression of diabetic nephropathy, and a decrease in the amount of blood in the latter compound group is an indicator of diabetic nephropathy. It is an indicator of onset and stage progression.

The determination is not limited to qualitative. That is, semi-quantitative or quantitative determination may be made. In the case of quantitative determination, for example, as shown below, “possibility of developing diabetic nephropathy (%)” is set in advance for each range of measured values, and from the measured values, “diabetic nephropathy” is set. The possibility of developing (%) ”is determined.
Measured value <a: Possibility of developing diabetic nephropathy is greater than 80% a ≦ Measured value <b: Possibility of developing diabetic nephropathy 20% to 80%
b <Measured value: The possibility of developing diabetic nephropathy is less than 20% In this example, it is divided into three stages, but the number of divisions can be arbitrarily set. Examples of the number of sections are 2 to 10, preferably 2 to 5.

A specific example of determination (example in the case of measurement by CE-TOFMS) is shown below.
(1) Calculation of the probability of morbidity when a combination of γ-butyrobetaine, SDMA, ID114, ID127 and azelaic acid is used as a differential marker About five blood metabolites (γ-butyrobetaine, SDMA, ID114, ID127 and azelaic acid) The results of multiple logistic regression analysis are shown in FIG. When the probability of not having diabetic nephropathy (DM) is p, the probability of having diabetic nephropathy (DN) is 1-p, and the ROC calculation formula is as follows.
log (p / (1-p)) = 0.309-92.3 × γ-Butyrobetaine-6.12 × 10 ² × SDMA + 8.79 × 10 ² × ID114 + 44.0 × ID 127-2.4 × 10 ² × Azelaic acid

  The value of each metabolite to be substituted into the formula is the Rel area value (the area value of the target compound normalized by the area value of the internal standard). For example, if the Rel areas of γ-butyrobetaine, SDMA, ID114, ID127, and azelaic acid are 0.0291, 0.00631, 0.00466, 0.0969, 0.0155, respectively, the probability of being DM is 16.8%, that is, the probability of being DN is 83.2% It becomes.

(2) Calculation of morbidity probability when a combination of aspartic acid, SDMA, azelaic acid and galactaric acid is used as a differential marker Multiple logistic regression of four blood metabolites (aspartic acid, SDMA, azelaic acid and galactaric acid) The result of the analysis is shown in FIG. When these metabolites are used as markers for differentiation, the probability p of not developing diabetic nephropathy (DM) and the probability 1-p of diabetic nephropathy (DN) are the same as in (1). It can be calculated by the following ROC calculation formula.
log (p / (1-p)) = 1.58-22.4 x aspartic acid-3.07 x 10 ² x SDMA + 39.7 x azelaic acid + 74.9 x galactaric acid

  By the way, when a combination of a plurality of compounds is employed as a differentiation marker, sensitivity and specificity usually vary depending on the type and number of compounds to be combined. Therefore, an optimal combination of compounds may be selected according to the purpose. For example, a highly sensitive combination is suitable for screening tests. In contrast to this, a combination with high specificity is suitable for a test that requires a more reliable determination (for example, a secondary test or a tertiary test). By combining judgment methods with different balances of sensitivity and specificity, it is possible to improve efficiency and improve accuracy or reliability.

  It should be noted that the determination in step (2) can be performed automatically / mechanically without depending on the determination by a person having expertise such as a doctor or a laboratory technician, as is apparent from the determination criteria.

In order to find an effective index (biomarker) for differentiation of diabetic nephropathy, the following examination was performed.
1. Method (1) Sample collection Samples with different stages (stages) of diabetic nephropathy with consent, among patients who are hospitalized or admitted to Chubu Rosai Hospital (Aichi Prefecture) A total of 78 sera were collected, 32 microalbuminuria and 26 macroalbuminuric). The background of the patient is shown in FIG.

(2) Extraction of metabolites from blood 450 μL of methanol containing an internal standard was added to 50 μL of a plasma sample and stirred, and then 500 μL of chloroform and 200 μL of pure water were further added and stirred well. After stirring, the mixture was centrifuged at 4 ° C. and 5,000 rpm for 5 minutes, and 600 μL of the separated aqueous phase was deproteinized using an ultrafiltration filter having a fractional molecular weight of 5 kDa. The filtrate was centrifuged to dryness, redissolved with 50 μL of pure water, and subjected to CE-MS analysis.

(3) Measurement of plasma metabolites using capillary electrophoresis-time-of-flight mass spectrometer (CE-TOFMS)
CE-TOFMS was used to comprehensively measure metabolites in the serum samples of patients without diabetic nephropathy, patients with microalbuminuria, and patients with microalbuminuria.

(3-1) Cationic Metabolite Measurement Conditions (Soga, T., et al., J. Proteome Res. 2. 488-494, 2003; Soga, T. et al., J. Biol. Chem. 281 , 16768-16776, 2006)
(A) Analysis conditions for capillary electrophoresis (CE) A fused silica capillary (inner diameter 50 μm, outer diameter 350 μm, total length 100 cm) was used as the capillary. As the buffer, 1 M formic acid (pH about 1.8) was used. The applied voltage was +30 kV, and the capillary temperature was 20 ° C. Samples were injected for 3 seconds at 50 mbar using the pressure method.

(B) Analysis conditions of time-of-flight mass spectrometer (TOFMS) The positive ion mode was used, the ionization voltage was set to 4 kV, the fragmentor voltage was set to 75 V, the skimmer voltage was set to 50 V, and the OctRFV voltage was set to 125 V. Nitrogen was used as the drying gas, and the temperature was set to 300 ° C. and the pressure was set to 10 psig. A 50% methanol solution was used as the sheath liquid, and hexakis (2,2-difluorotoxyl) phosphazene was mixed at 0.1 μM for mass calibration, and the solution was fed at 10 μL / min. All data obtained were automatically calibrated using the mass number of the dimer isotope (m / z 66.0632) of hexakis (2,2-difluorotoxyl) phosphazene (m / z 622.0290) and methanol.

(3-2) Anionic Metabolite Measurement Conditions (a) Analysis Conditions for Capillary Electrophoresis (CE) A COSMO (+) capillary (inner diameter 50 μm, outer diameter 350 μm, total length 100 cm) was used as the capillary. As the buffer, 50 mM ammonium acetate (pH 8.5) was used. The applied voltage was -30 kV, and the capillary temperature was 20 ° C. The sample was injected for 30 seconds at 50 mbar using the pressure method.

(B) Analysis conditions of time-of-flight mass spectrometer (TOFMS) The negative ion mode was used, the ionization voltage was set to 3.5 kV, the fragmentor voltage was set to 100 V, the skimmer voltage was set to 50 V, and the OctRFV voltage was set to 200 V. Nitrogen was used as the drying gas, and the temperature was set to 300 ° C. and the pressure was set to 10 psig. As the sheath liquid, a 50% methanol solution containing 5 mM ammonium acetate was used. For mass calibration, hexakis (2,2-difluorotoxyl) phosphazene was mixed to a concentration of 0.1 μM and fed at 10 μL / min. All data obtained were autocalibrated using the acetic acid adduct ion of reserpine (m / z 680.0355) and the mass number of the dimer isotope of acetic acid (m / z 120.0384).

(4) Statistical analysis In order to eliminate bias, the relative peak area (Rel area) with respect to the internal standard was used for statistical processing. GraphPad Prism 5.0 (GraphPad Software, Inc., San Diego, CA, USA) was used for data analysis. The Kruskal-Wallis test and Dunn's post test assessed statistically significant differences between diabetic nephropathy-free patients, microalbuminuria patients, and macroalbuminuria patients. Moreover, the correlation of the relative peak area (Rel area) of UACR, eGFR, and each metabolite was investigated using the Spearman's rank correlation test (Spearman's rank correlation test). Two methods were used to create a multiple logistic regression model (MLR model). First, the normalized data is subjected to OPLS-DA (orthogonal partial least-squares discriminant analysis) using SIMCA-P software (Version 12.0, Umetrics, Umea, Sweden) to build a model and eliminate diabetic nephropathy. We decided to identify effective metabolites between patients with onset and those with macroalbuminuria. On the other hand, the stepwise variable selection method (forward direction and reverse direction) was executed with the threshold set to p <0.25. The generalization ability of the constructed MLR model was evaluated by cross-validation method. The optimistic bias was eliminated with the bootstrap method.

2. result
By metabolomic analysis using CE-TOFMS, 289 metabolites were obtained from the serum of diabetic nephropathy patients. As a result of statistical analysis using these peaks, 19 compounds were found to show significant differences between groups with different stages of diabetic nephropathy (asymptomatic, microalbuminuria, macroalbuminuria). It was possible to identify (FIG. 2). Of these, 8 substances could be identified by comparison with standard reagents. The identified substances were creatinine, aspartic acid, γ-butyrobetaine, citrulline, SDMA, kynurenine, azelaic acid, galactaric acid.

  The correlation between the concentration of each identified metabolite and the index used to estimate the renal function conventionally used was examined. The results are shown in FIG. 3 (correlation with eGFR (estimated glomerular filtration rate)) and FIG. 4 (correlation with UACR (urine albumin / creatinine ratio)). All metabolites showed significant correlation with both eGFR and UACR, and were considered to be suitable biomarker candidates for estimation of renal function decline. Azelaic acid and galactaric acid have been shown to have inverse correlations with other metabolites, and these substances are particularly promising as highly specific biomarkers.

  Next, regarding the 19 candidate marker substances obtained this time, diagnosis of diabetic nephropathy-free patients and albuminuria patients (including both microalbumin and macroalbuminuria) using each metabolite alone The ability was evaluated (FIG. 5). Even when the metabolite was used alone, the AUC value was 0.765 (ID 202) to 0.643 (citrulline), and it was possible to determine the presence or absence of diabetic nephropathy.

  In order to further improve the discrimination ability, multiple logistic regression analysis, which is one of the multivariate analysis methods, was performed, and a model in which a plurality of these marker candidates were combined was created. The results of multiple logistic regression analysis using five types of biomarker candidates are shown in FIG. 6, and the model variables at that time are shown in FIG. Similarly, FIG. 8 shows the results of using four types of identified metabolites, and FIG. 9 shows the variables of the model at that time. Thus, the discrimination ability improved as a result of combining a plurality of marker candidates.

  As described above, 19 blood metabolites effective in differentiating diabetic nephropathy were successfully identified. These alone are useful for differentiation (determination) of the stage of diabetic nephropathy. In particular, it is useful for extraction of patients who develop diabetic nephropathy (that is, screening for the development of diabetic nephropathy). On the other hand, as shown in FIGS. 6 to 9, two or more may be used in combination for improving the discrimination ability. In addition, the specific example of a combination and its discrimination ability are shown in FIG.

  The differentiation marker of the present invention enables early diagnosis of diabetic nephropathy. The differentiation marker of the present invention is particularly useful for discriminating between patients with no onset of diabetic nephropathy and those with diabetic nephropathy. According to the differentiation marker of the present invention, patients who develop diabetic nephropathy can be identified and extracted at an early stage after onset. In addition, if the differentiation marker of the present invention is used, a constant result can be obtained only by a blood test (that is, without a urine test or a doctor's findings), which is simple and less burdensome on the patient. The information provided by the differentiation method using the differentiation marker of the present invention is useful for grasping the stage and pathology of diabetic nephropathy, determining a more appropriate treatment policy, and the like.

  The present invention is not limited to the description of the embodiments and examples of the invention described above. Various modifications may be included in the present invention as long as those skilled in the art can easily conceive without departing from the description of the scope of claims. The contents of papers, published patent gazettes, patent gazettes, and the like specified in this specification are incorporated by reference in their entirety.

Claims (5)

  For distinguishing diabetic nephropathy consisting of one compound or a combination of two or more compounds selected from the group consisting of creatinine, aspartic acid, γ-butyrobetaine, citrulline, symmetric dimethylarginine, kynurenine, azelaic acid and galactaric acid marker.   A method for distinguishing diabetic nephropathy using the amount of the marker according to claim 1 as an index. The differentiation method according to claim 2, comprising the following steps (1) and (2):
(1) measuring the marker in the blood sample;
(2) A step of determining the presence or absence of diabetic nephropathy and / or the stage of diabetic nephropathy based on the measurement result.   The identification method according to claim 3, wherein a capillary electrophoresis-mass spectrometer is used for the measurement in step (1). The discrimination method according to claim 2, which is used for discrimination in any of the following (a) to (c):
(A) Differentiation of non-diabetic nephropathy from diabetic nephropathy;
(B) Differentiation between the absence of diabetic nephropathy and diabetic nephropathy with microalbuminuria;
(C) Differentiation between non-diabetic nephropathy, diabetic nephropathy with microalbuminuria and diabetic nephropathy with macroalbuminuria.

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