Urine and serum biomarkers associated with diabetic nephropathy

[0001] Diabetic nephropathy (DN) is a progressive kidney disease associated with long-term diabetes. It causes impaired fluid filtration and increased urinary albumin excretion, which ultimately leads to kidney failure.

[0002] DN shows no symptoms at an early stage. Therefore, the onset of this disease is difficult to detect. In fact, the current diagnosis of DN depends on the development of microalbuminuria, which appears when kidney damage is already present. The lack of an early diagnostic test prevents effective treatment of early stage DN. [0003] The identification of reliable biomarkers useful in the diagnosis of early stage DN is extremely important. Rao et al. (Proteomic identification of urinary biomarkers of diabetic nephropathy, 2007, Diabetes Care, 30 (3): 629-637) revealed the identification of biomarkers of urinary nephropathy from patients with type 2 diabetes by liquid chromatography combined with tandem mass spectrometry. However, Rao et al. they did not disclose that specific alpha-2-HS-glycoprotein fragments are useful as biomarkers for the diagnosis of diabetic nephropathy.

WO 03/019193 A1 discloses biomarkers useful for differentiating minimal lesions of nephrotic syndrome from focal segmental glomerulosclerosis, membranous nephrotropia, and membranous-proliferative glomerulonephritis. However, WO 03/019193 A1 does not disclose that specific alpha-2-HS glycoprotein fragments are useful as biomarkers for the diagnosis of diabetic nephropathy.

SUMMARY OF THE INVENTION [0004] The present invention is based on the unexpected discoveries that several proteins in urine and serum, and fragments thereof, alone or in combination, are present in a differentiated manner in patients with DN compared to individuals without DN. These protein molecules are therefore useful markers for the diagnosis of early stage DN.

[0005] In this regard, one aspect of the present invention provides a method of diagnosing DN in a subject. The method comprises at least two stages: (a) determining the suspected DN of the biomarker level in the subject and (b) assessing whether the patient has DN based on that biomarker level. An increase in biomarker level compared to that in an individual without DN indicates that the patient has DN.

[0006] The biomarker (i) used in this diagnosis method is a urine protein molecule which is an alpha-2-HS-glycoprotein fragment selected from the group consisting of VVSLGSPSGEVSHPRKT (SEQ ID NO: 1) and MGVVSLGSPSGEVSHPRKT (SEQ ID NO: 2). [0007] The method of the present invention preferably further comprises determining the level of one or more additional biomarkers (ii) to (iv).

[0008] The biomarker (ii) is a protein in urine that is a fragment of alpha-1 antitrypsin selected from the group consisting of KGKWERPFEVKDTEEEDF (SEQ ID NO: 3); MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO: 4), EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO: 5) and EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO: 6).

[0009] The biomarker (iii) is a protein molecule in urine which is an alpha-1 fragment of an acid glycoprotein, i.e. GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO: 7).

[0010] The biomarker (iv) is a serum protein molecule that is a fragment of osteopontin selected from the group consisting of

YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO: 8) and

KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO: 9).

[0011] The above-described method of diagnosis may further include, after the assessment step, a step of correlating the level of biomarkers with the state of DN (i.e. whether it is early or late stage). An increase in biomarker level relative to that of an individual without DN indicates late stage DN.

[0012] In another aspect, the present invention provides a method of assessing the efficacy of DN treatment in an individual (e.g., a human patient or laboratory animal). The method includes determining the subject's protein (i) levels before and after treatment, and assessing the effectiveness of the treatment based on the change in biomarker level after treatment. This method further preferably comprises determining in the subject the levels before and after treatment of one or more of the protein (ii) to (iv) molecules described above. If the biomarker level after treatment remains the same or decreases compared to the biomarker level before treatment, then the treatment is effective.

In yet another aspect, the present invention provides a method for determining the DN stage comprising at least four steps: (a) obtaining a urine sample and possibly a serum sample from an individual suspected of diabetic nephropathy, (b) determining the level of biomarker (i) in the sample (s) and preferably determining additionally the level of one or more biomarkers (ii) to (iv) as described above , (c) calculating the disease index based on the biomarker level and (d) assessing the stage of diabetic nephropathy in the subject based on the disease index compared to predetermined cut-off values, wherein an increase in disease index, compared to predetermined cut-off values, indicates that the subject is in late stage diabetic nephropathy. In this method, the calculation step can be performed using dorsal regression analysis, factor analysis, discriminant function analysis, and logistic regression analysis. Preferably, this method further comprises determining the level of one or more clinical factors selected from the group consisting of age, sex, HbA1c, albumin / creatinine ratio (ACR) and glomerular filtration rate (GFR).

[0013] In yet another aspect, the present invention provides a method of monitoring DN progress based on the level of biomarker (i) and optionally one or more of the clinical factors described above. Preferably, the method further comprises determining the level of one or more biomarkers (ii) to (iv) as described above. This method involves obtaining two urine samples and possibly two serum samples at an interval of 2 weeks to 12 months (e.g. 2-24 weeks or 3-12 months) from an individual suspected of having DN, determining in these samples the level of biomarker (i) and preferably additionally the level of one or more biomarkers (ii) to (iv), calculating disease indicators based on biomarker levels and optionally levels of one or more clinical factors and an assessment of the progression of DN in the subject based on disease indicators. The disease rate for samples obtained later higher than for samples obtained earlier indicates an exacerbation of DN.

[0014] The above-mentioned disease indicators can also be used to assess the effectiveness of DN treatment. Treatment is effective if the disease index after treatment remains unchanged or decreases compared to the disease index before treatment.

[0015] The present invention further provides a kit for use in any of the methods described above. The kit comprises an antibody (A) capable of binding to an alpha-2-HS-glycoprotein fragment selected from the group consisting of VVSLGSPSGEVSHPRKT (SEQ ID NO: 1) and MGVVSLGSPSGEVSHPRKT (SEQ ID NO: 2). Preferably, the kit further comprises at least one additional antibody selected from the group consisting of antibody (B), capable of binding to the antitrypsin alpha-1 fragment selected from the group consisting of KGKWERPFEVKDTEEEDF (SEQ ID NO: 3); MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO: 4), EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO: 5) and ED3

PQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO: 6), antibody (C), capable of binding to the alpha-1 fragment of the acid glycoprotein, namely GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ IDne with the antibody, and fragment), and , selected from the group consisting of YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO: 8) and KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO: 9). In one example, this kit only contains antibodies specific for the detected antigens (e.g., DN-related biomarkers) to implement one of the methods disclosed herein. That is, it consists essentially of such antibodies.

[0016] Also included within the scope of the present invention is an isolated antibody that specifically binds to a peptide selected from the group consisting of:

VVSLGSPSGEVSHPRKT (SEQ ID NO: 1) and MGVVSLGSPSGEVSHPRKT (SEQ ID NO: 2).

[0017] The term "isolated antibody" as used herein refers to an antibody that is substantially free of naturally associated molecules. More specifically, a preparation containing an antibody is considered to be an "isolated antibody" if the naturally associated molecules in the preparation constitute at most 20% of the dry matter. Purity can be measured by any suitable method, e.g. column chromatography, polyacrylamide gel electrophoresis and HPLC.

[0018] Any of the antibodies described above can be used to produce a kit useful in carrying out any of the methods of this invention. [0019] Details of one or more embodiments of the invention are set forth in the description below. Other features or advantages of the present invention will be apparent from the following drawings and detailed description of several embodiments, as well as from the appended claims. BRIEF DESCRIPTION OF THE DRAWING [0020] The drawing will first be described.

[0021] Fig. 1 is a diagram showing box plots for urine alpha-2-HS-glycoprotein (uDN2; see panel A), alpha-1-antitrypsin in urine (uDN5; see panel B), alpha-1 acid glycoprotein in urine urine (uGR3; see panel C) and serum osteopontin (sDNO; see panel D) in different groups of patients with DN. The upper and lower bounds of the boxes mean values ​​for 25% and 75%, and the median is indicated by a line across the box. The upper mustache means the highest value below the upper limit, which is the value for 75% plus 1.5 the interquartile range, and the lower mustache means the smallest value above the lower limit, which is the value for 25% minus 1.5 the interquartile range.

DETAILED DESCRIPTION OF THE INVENTION [0022] DN is a diabetes-related kidney disease. It has five stages of progress:

Stage 1: characterized by diabetes with GFR normal and normal albuminuria (ACR <30 mg / g);

Stage 2: characterized by glomerular hyperfiltration (above 120 ml / min / 1.73 ₂ m) and renal enlargement, which is accompanied by normal GFR and normal albuminuria (ACR <30 mg / g);

Stage 3: characterized by microalbuminuria;

Stage 4: characterized by overt albuminuria and a progressive decrease in GFR; and₂

Stage 5: characterized by a GFR lower than 15 ml / min / 1.73 m.

Most often, stages 1-3 are considered to be early stages, and stages 4 and 5 are considered to be late stages.

[0023] We have identified several DN-related biomarkers, especially DN at different stages. These biomarkers consist of fragments of the following four proteins, in urine or serum: (a) alpha-2-HS-glycoprotein (GenBank accession no. NP_001613; January 10, 2010); (b) alpha-1-antitrypsin (GenBank Accession No. AAB59495; January 10, 2010); (c) alpha-1 acid glycoprotein (GenBank accession no. EAW87416; January 10, 2010); and (d) osteopontin, which includes two isoforms known as secreted phosphoprotein 1a (GenBank Accession No. NP_001035147; 17 January 2010) and secreted phosphoprotein 1b (GenBank Accession No. NP_000573; January 10, 2010).

[0024] Fragments of these four proteins have a minimum length of 17 amino acids and a maximum length of 42 amino acids. For example, protein fragments (a), (b), (c) and (d) may contain up to 19, 34, 32 and 42 amino acid residues, respectively.

[0025] We have also found that disease indicators calculated on the basis of the levels of the above-mentioned fragments and possibly one or more clinical factors (age, sex, HbA1c, ACR and GFR) are also associated with DN at various stages.

[0026] Accordingly, one aspect of the present invention relates to a method for diagnosing DN using a biomarker based on protein (a) described above. Preferably, one or more protein-based biomarkers (b) to (d) are also used in this method. To implement this method, a urine sample and, if necessary, a serum sample from a suspected DN and the biomarker level (s) listed above in urine and serum may be determined using routine methods, e.g. spectrometry masses and immunological analysis. Where appropriate, clinical factors are determined routinely.

[0027] When the biomarker level is determined based on a single protein molecule, its level in an individual can be compared with a reference point to determine if that individual has DN. The reference point, representing the level of the same biomarker in an individual without DN, can be determined based on representative biomarker levels in groups of patients with DN and individuals without DN. For example, it could be a mid-point between average levels in these two groups. A biomarker level higher than the reference point indicates DN.

[0028] When biomarker levels are determined based on at least two protein molecules and optionally the level of at least one clinical factor, the levels of the protein molecules and the value (s) of the clinical factor (s) can be analyzed accordingly get the disease indicator (e.g. as a numerical value). The analysis is selected from the group consisting of discriminant function analysis, logistic regression analysis, dorsal regression analysis and factor analysis. The disease index is then compared to a reference point representing the level of the same biomarker in individuals without DN. The reference point can be determined by ordinary methods. For example, this may be an indicator obtained by analyzing the average levels of protein molecules and, if necessary, the average value (s) of the clinical agent (s) in subjects without DN using the same analysis. A disease indicator higher than the reference point indicates the presence of DN.

[0029] Another aspect of the present invention relates to a method for determining the DN stage based on the protein (a) -based biomarker described above. Preferably, one or more protein-based biomarkers (b) to (d) and optionally one or more of the clinical factors described above are additionally used in this method. To implement this method, the biomarker level in a DN patient, preferably represented as an indicator of disease, is compared to a set of predetermined cut-off values ​​that differentiate different stages of DN to determine the patient's DN stage. Cut-off values ​​can be determined by analyzing representative levels of the same biomarker in patients with different DN stages using the same analysis.

[0030] The following describes an example procedure for determining the abovementioned cutoff values ​​based on DN-bound biomarker at various stages:

(1) assigning patients with DN to different groups depending on their disease state (e.g. DN stages and risk factors);

(2) determining in each patient group the levels / values ​​of protein molecules and clinical factors;

(4) subjecting protein levels and clinical factor values ​​to appropriate analysis to determine the model (e.g. formula) for calculating disease index, and (6) determining the cut-off value for each disease stage based on the disease index (e.g. mean value) representing each group patients, as well as other relevant factors such as sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

[0031] Each of the models thus created can be evaluated for its diagnostic value by analysis (ROC) for plotting the operative characteristic curve to form the ROC curve. The optimal multivariate model provides a large area under the curve (AUC) in ROC analysis. See the models described in examples 1-3 below.

[0032] In yet another aspect, the present invention relates to a method of monitoring the progression of nephropathy in a subject based on a biomarker based on protein (a) described above. Preferably, one or more protein-based biomarkers (b) to (d) and, optionally, one or more of the clinical factors described above are also used in this method. More specifically, two urine and / or serum samples per subject may be obtained at an appropriate time interval (2 weeks to 12 months) and examined to determine biomarker levels. Then disease indicators are determined as described above. If the disease indicator representing the biomarker level in the sample (s) obtained later is greater than in the sample (s) obtained earlier, this indicates an exacerbation of DN in the subject.

[0033] This monitoring method can be applied to a human subject suffering from or at risk for DN. When the human subject is at risk or in the early stages of DN, the biomarker level can be tested once every 6 to 12 months to monitor the progression of DN. When the human subject is already in the late DN stage, it is preferable to test the biomarker level once every 3 to 6 months.

[0034] The monitoring method described above also applies to laboratory animals, in accordance with routine procedures, for testing DNs. The term "laboratory animal" as used herein refers to a vertebrate animal commonly used in animal studies, e.g., mouse, rat, rabbit, cat, dog, pig and non-human primates. Preferably, the laboratory animal is tested to determine the biomarker level once every 2 to 24 weeks.

[0035] Biomarkers can also be used to assess the efficacy of DN treatment in an individual in need of such treatment (i.e. a human patient with DN or a laboratory animal suffering from DN). In this method, before and after treatment, disease indicators calculated based on protein (a) -based biomarker levels and optionally one or more of the clinical factors described above are determined. Preferably, one or more protein-based biomarkers (b) to (d) are additionally used in this method. If the disease indicators remain the same or decrease during treatment, this indicates that the treatment is effective.

[0036] The present specification also discloses a kit useful in carrying out any of the methods described above. This kit contains an antibody (A) capable of binding to an alpha-2-HS-glycoprotein fragment selected from the group consisting of VVSLGSPSGEVSHPRKT (SEQ ID NO: 1) and MGVVSLGSPSGEVSHPRKT (SEQ ID NO: 2). Preferably, the kit further comprises at least one additional antibody selected from the group consisting of antibody (B), capable of binding to the antitrypsin alpha-1 fragment selected from the group consisting of KGKWERPFEVKDTEEEDF (SEQ ID NO: 3); MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO: 4), EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO: 5) and EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO: 6), an antibody (C) capable of binding to a fragment of alpha-1-acid glycoprotein, namely GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO: 7) and an antibody (D) capable of binding to an osteopontin fragment selected from the group consisting of YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO: 8) and KYPDAVATWLNPDPSQKQNLLAPQTLPSK) SE. In one example, this kit only contains antibodies specific for the detected antigens (e.g., DN-related protein molecules) to implement one of the methods disclosed herein. That is, the kit consists primarily of such antibodies.

[0037] The kit described above may contain two different antibodies (i.e., coating antibody and detection antibody) that bind to the same antigen. Typically, the detection antibody is conjugated to a molecule that emits a detectable signal either alone or by binding to another agent. The term "antibody" as used herein refers to an entire immunoglobulin or fragment thereof, such as Fab or F (ab ') 2, which retains antigen binding activity. It may be an naturally occurring or genetically engineered antibody (e.g., a single chain antibody, a chimeric antibody, or a humanized antibody).

[0038] The antibodies contained in the kit of this invention can be obtained from commercial suppliers. Alternatively, they can be prepared by conventional methods. See, for example, Harlow and Lane, (1988) Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, New York. To generate antibodies against a specific biomarker as mentioned above, a marker, optionally conjugated to a carrier protein (e.g. KLH), can be mixed with an adjuvant and injected into the host animal. Antibodies produced in the animal can then be purified by affinity chromatography. Commonly used host animals include rabbits, mice, guinea pigs and rats. The various adjuvants that can be used to increase the immune response are dependent on the host species and include Freund's adjuvant (complete and incomplete), mineral gels such as aluminum hydroxide, CpG, surfactants such as lysolecithin, pluronic polyols, polyanions , peptides, oil emulsions, keyhole limpet hemocyanin and dinitrophenol. Useful adjuvants for humans include BCG (Calmette-Guerin) and Corynebacterium parvum. Polyclonal antibodies, i.e. heterogeneous populations of antibody molecules, are present in the serum of the immunized animal.

[0039] Monoclonal antibodies, i.e. homogeneous populations of antibody molecules, can be prepared using standard hybridoma technique (see, for example, Kohler et al. (1975) Nature 256, 495; Kohler et al. (1976) Eur. J. Immunol. 6 , 511; Kohler et al (1976) Eur J Immunol 6, 292; and Hammerling et al (1981) Monoclonal Antibodies and T Cell Hybridomas, Elsevier, NY). In particular, monoclonal antibodies can be obtained by any technique that allows the production of antibody molecules in continuous cell lines in culture, as described in Kohler et al. (1975) Nature 256, 495 and U.S. Patent No. 4,376,110; hybridoma techniques from human B cells (Kosbor et al. (1983) Immunol Today 4, 72; Cole et al. (1983) Proc. Natl. Acad. Sci. USA 80, 2026 and EBV hybridoma techniques (Cole et al. (1983) Monoclonal Antibodies and Cancer Therapy, Alan R. Liss, Inc., pp. 77-96). Such antibodies may belong to any class of immunoglobulins, including IgG, IgM, IgE, IgA, IgD and any subclass thereof. Hybridomas producing monoclonal antibodies of the invention can be cultured in vitro or in vivo. The ability to produce high titers of monoclonal antibodies in vivo makes this production method particularly useful.

[0040] In addition, antibody fragments can be produced by known techniques.

For example, such fragments include, but are not limited to, F (ab ') 2 fragments that can be produced by pepsin digestion of the antibody molecule and Fab fragments that can be made by reducing the disulfide bridges in F (ab') 2 fragments.

[0041] Without further consideration, it is believed that one skilled in the art will be able, based on the above description, to utilize the present invention to its fullest extent. The following specific forms are therefore to be interpreted as illustrative only and not to limit the remainder of the disclosure in any way.

Example 1: Diagnosing DN based on urine alpha-2-HS-glycoprotein, urine alpha-1 antitrypsin, urine alpha-1 acid glycoprotein or serum osteopontin. Material and methods (i) Individuals [0042] 83 patients with diabetes ( referred to as "DM individuals") and 82 DN patients (referred to as "DN individuals") were recruited at the Tri-General Military Hospital in Taipei, Taiwan, in accordance with the standards set out by the American Diabetic Association, as well as described below:

DM: suffering from diabetes but not suffering from DN (see standards described below); DN: suffering from diabetes and secreting proteins in the urine of more than 1 g per day, having a biopsy DN or showing uremia.

[0043] All individuals were assigned to the training group and test group in a ratio of 7: 3.

(ii) Collection and processing of samples [0044] First morning urine samples and serum samples were taken from each of the subjects listed above. Peptides contained in urine samples were examined by mass spectrometry with laser desorption / ionization matrix assisted and time-of-flight analyzer (MALDI-TOF-MS) and using isobaric markers for relative and absolute quantitative assessment (iTRAQ).

[0045] Protein molecules, including alpha-2-HS-glycoprotein (DN2), alpha-1-antitrypsin (DN5), osteopontin (DNO) and alpha-1 acid glycoprotein (GR3) were tested to determine their concentrations in both samples urine as well as serum by ELISA. Briefly, urine samples were mixed with protease inhibitors and diluted 1: 100 with dilution buffer, and serum samples were diluted 1:10. Diluted samples were placed on ELISA plates in triplicate. DNO, DN2, DN5 and GR3 concentration levels were measured using a standard sandwich ELISA.

[0046] A 5-parameter standard curve was used to calculate the concentrations. Only standards and samples with% CV less than 15 were included, and those not meeting these criteria were repeated. Protein levels in urine samples were normalized to creatinine levels in the same urine samples that were measured using the Quantichrom Creatinine Assay assay (BioAssay Systems, (Hayward) California, USA).

(iii) Statistical analysis [0047] Data showing protein concentrations in urine and serum for each protein tested were statistically analyzed and showed efficacy, as demonstrated by auROC from 0.44-0.87, for their independent ability to distinguish individuals from DN from individuals with DM. For each individual, the correlation between the values ​​in the Spearman or Pearson analysis was determined depending on the results of the normality test. Comparisons of means or medians between groups were performed using Student's t-test or non-parametric Mann-Whitney test, when appropriate. Statistical significance was obtained when p <0.05. Statistics are presented as mean ± standard error of the mean (SEM) or as the median of [25%, 75%].

Results (i) Patient characteristics [0048] Tables 1 and 2 below present the characteristics of patients in the training group and test group and those in the DM and DN groups:

<td colspan="4">Table 1. Characteristics of patients in the training and test group</td>

<td></td><td>Training (n = 118)</td><td>Test (n = 47)</td><td>P value</td>

<td>Age, average (SD)</td><td> 59,94 (9,37)</td><td> 60,28 (9,48)</td><td> 0,8362</td>

<td>Women, n (%)</td><td> 83 (70)</td><td> 27 (57)</td><td> 0,16</td>

<td>MDRD_S_GFR, average (SD)</td><td> 86,56 (33,11)</td><td> 83,05 (43,96)</td><td> 0,5785</td>

<td>ACR (ug / mg), average (SD)</td><td> 737,82 (1465,47)</td><td> 1084,18 (2030,98)</td><td> 0,2239</td>

<td>TP / Cr in urine (mg / mg), average (SD)</td><td> 1,01 (2,01)</td><td> 1 (1,78)</td><td> 0,9963</td>

<td>Serum creatinine (mg / dl), average (SD)</td><td> 1,02 (0,87)</td><td> 1,34 (1,44)</td><td> 0,0903</td>

<td>HbA1c (%), average (SD)</td><td> 8,49 (1,5)</td><td> 8,29 (2,19)</td><td> 0,5356</td>

Markers (creatinine corrected), average (SD)

<td>uDNO (ng / mg)</td><td> 1452,71 (1416,7)</td><td> 1488,77 (1222,2)</td><td> 0,8687</td>

<td>sDNO (ng / ml)</td><td> 40,65 (34,52)</td><td> 38,35 (34,13)</td><td> 0,6926</td>

<td>uDN2 (ng / mg)</td><td> 4225,77 (9279,63)</td><td> 5999,64 (10305,95)</td><td> 0,2983</td>

<td>uDN5 (ng / mg)</td><td> 15951,12 (94956,78)</td><td> 45479,82 (199827,84)</td><td> 0,3228</td>

<td>uGR3 (ng / mg)</td><td> 32823,47 (62290,96)</td><td> 42709,23 (103787,54)</td><td> 0,5333</td>

Table 2. Patient characteristics in DM and DN groups

<td rowspan="2"></td><td colspan="3">Training (n = 118)</td><td colspan="3">Test (n = 47)</td>

<td>DM⁽ⁿ=⁶¹⁾</td><td>DN (n = 57)</td><td>P value</td><td>DM (N = 22)</td><td>DN (n = 25)</td><td>P value</td>

<td>Age, average</td><td> 57,11</td><td> 62,96 (9,8)</td><td> 0,0006</td><td> 59,09</td><td> 61,32</td><td> 0,4230</td>

<td>(SD)</td><td> (8,05)</td><td></td><td></td><td> (8,82)</td><td> (10,09)</td><td></td>

<td>Women, n (%)</td><td> 43 (70)</td><td> 40 (70)</td><td> 1,00</td><td> 12 (55)</td><td> 15 (60)</td><td> 0,93</td>

<td>MDRD_S_GFR,</td><td> 111,21</td><td> 60,18</td><td> &lt;0,0001</td><td> 115,6</td><td> 54,41</td><td> &lt;0,0001</td>

<td>average (SD)</td><td> (15,75)</td><td> (25,59)</td><td></td><td> (33,66)</td><td> (29,79)</td><td></td>

<td>ACR (ug / mg),</td><td> 11,35</td><td> 1515,26</td><td> &lt;0,0001</td><td> 9,63</td><td> 2029,78</td><td> 0,0004</td>

<td>average (SD)</td><td> (6,81)</td><td> (1815,72)</td><td></td><td> (5,61)</td><td> (2432,31)</td><td></td>

<td>TP / Cr in urine</td><td> 0,17</td><td> 1,9 (2,56)</td><td> &lt;0,0001</td><td> 0,17</td><td> 1,7 (2,18)</td><td> 0,0019</td>

<td>(mg / mg), average</td><td> (0,51)</td><td></td><td></td><td> (0,32)</td><td></td><td></td>

<td>(SD)</td><td></td><td></td><td></td><td></td><td></td><td></td>

<td>Creatinine in</td><td> 0,66</td><td> 1,42 (1,12)</td><td> &lt;0,0001</td><td> 0,67</td><td> 1,92 (1,79)</td><td> 0,0019</td>

<td>root canal (mg / dl),</td><td> (0,12)</td><td></td><td></td><td> (0,15)</td><td></td><td></td>

<td>average (SD)</td><td></td><td></td><td></td><td></td><td></td><td></td>

Table 2. Patient characteristics in DM and DN groups

<td colspan="3">Training (n = 118)</td><td colspan="3">Test (n = 47)</td>

<td>DM⁽ⁿ=⁶¹⁾</td><td>DN (n = 57)</td><td>P value</td><td>DM (N = 22)</td><td>DN (n = 25)</td><td>P value</td>

<td>HbA1c (%), mean 8.34</td><td> 8,7 (1,53)</td><td> 0,2311</td><td> 8,37</td><td> 8,22 (2,66)</td><td> 0,8238</td>

<td>nia (SD) (1.48)</td><td></td><td></td><td> (1,61)</td><td></td><td></td>

Markers (creatinine corrected), average (SD)

<td>uDNO (ng / mg)</td><td> 1422,18 (1105,46)</td><td> 1366,77 (1347,92)</td><td> 0,8083</td><td> 1769,54 (1260,15)</td><td> 1516,44 (1945,7)</td><td> 0,5953</td>

<td>sDNO (ng / ml)</td><td> 29,03</td><td> 46,17</td><td> 0,0026</td><td> 26,2</td><td> 64,52</td><td> 0,0010</td>

<td></td><td> (19,32)</td><td> (37,32)</td><td></td><td> (11,53)</td><td> (50,47)</td><td></td>

<td>uDN2 (ng / mg)</td><td> 1968,47</td><td> 8084,87</td><td> 0,0013</td><td> 968,79</td><td> 7348,69</td><td> 0,0074</td>

<td></td><td> (4218,58)</td><td> (13101,68)</td><td></td><td> (1144,47)</td><td> (10865,95)</td><td></td>

<td>uDN5 (ng / mg)</td><td> 390,24</td><td> 40036,86</td><td> 0,0467</td><td> 336,21</td><td> 71802,69</td><td> 0,1899</td>

<td></td><td> (1327,63)</td><td> (147186,58)</td><td></td><td> (568,08)</td><td> (264863,27)</td><td></td>

<td>uGR3 (ng / mg)</td><td> 3576,06</td><td> 67470,92</td><td> &lt;0,0001</td><td> 2447,77</td><td> 71693,1</td><td> 0,0003</td>

<td></td><td> (13562,8)</td><td> (105208,28)</td><td></td><td> (2742,38)</td><td> (82996,86)</td><td></td>

[0049] Statistically significant differences in GFR, ACR, serum protein and creatinine levels were observed in subjects with DN compared to subjects with DM. There was no difference in gender distribution between groups.

(ii) DN-related protein molecules [0050] By the urine proteomic analysis, it was found that the peptides listed in Table 3 below are present in different ways in urine samples from DM individuals and DN individuals:

Table 3. Peptides present differently in urine / serum and the proteins in which they are found

<td>Peptide sequences</td><td>corresponding proteins</td>

<td>VVSLGSPSGEVSHPRKT (SEQ ID NO: 1)</td><td>Alpha-2-HS-glycoprotein</td>

<td>MGVVSLGSPSGEVSHPRKT (SEQ ID NO: 2)</td><td>protein (DN2)</td>

<td>KGKWERPFEVKDTEEEDF (SEQ ID NO: 3)</td><td>Alpha-1-antytryp-</td>

<td>MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO: 4)</td><td>son (DN5)</td>

<td>EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO: 5)</td><td></td>

<td>EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO: 6)</td><td></td>

<td>YPDAVATWLNPDPSQKQ NLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO: 8)</td><td>osteopontin (BOTTOM)</td>

<td>GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO: 7)</td><td>Alpha-1 acid glycoprotein (GR3)</td>

[0051] Three protein molecules in urine, i.e. uDN2, uGR3 and uDN5, and one protein molecule in serum, i.e. sDNO, were found to be associated with DN by ELISA analysis. See Fig. 1, panels AD and Table 2 above. More specifically, uDN2, uDN5, uGR3 and sDNO levels were elevated in individuals with DN compared to individuals with DM (without DN), indicating that they are reliable DN markers. In addition, uDN5 and uGR3 levels in individuals with DN showing macroalbuminuria (ACR&gt; 300 mg / g) were higher than in individuals with DN showing microalbuminuria (ACR 30 mg / g to 300 mg / g). Makroal5 buminuria indicates late DN and microalbuminuria indicates early DN.

Example 2: Determination of DN stage based on a two-protein model from a combination of uDN2, uDN5, uGR3, uDNO and sDNO.

[0052] The combined levels of two of uDN2, uDN5, uGR3, uDNO and sDNO in individuals from DM and individuals from DN were subjected to discriminant function analysis, logistic regression analysis, and dorsal regression analysis. The results of this study indicate that any combination of two of the five proteins or fragments thereof can be used as reliable markers to determine DN stages.

[0053] The following is an exemplary two-protein model not part of this invention, ie uDN5 and uGR3, including equations for calculating disease indices based on the combined levels of these two protein molecules. The tables (i.e. Tables 4-9) summarizing cut-off, sensitivity, specificity, positive predictive values ​​(PPV) and negative predictive values ​​(NPV) as well as the area under the ROC curve (AUROC) for this two-protein model are also presented below.

Discriminant Function Analysis:

[0054]

Disease index = 0.3303 χ log₂[uDN5] (ng / mg) + 0.2732 χ log₂[uGR3] (ng / mg) + 5

Table 4. Cut-off values ​​representing the early and late stages of DN indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 11,227</td><td> 11,691</td><td> 11,227</td><td> 11,691</td>

<td>Sensitivity (%)</td><td> 93</td><td> 93</td><td> 96</td><td> 100</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 77</td><td> 83</td>

<td>PPV (%)</td><td> 90</td><td> 83</td><td> 83</td><td> 78</td>

<td>NPV (%)</td><td> 93</td><td> 96</td><td> 94</td><td> 100</td>

<td>AUROC</td><td> 0,95</td><td> 0,96</td><td> 0,98</td><td> 0,96</td>

Table 5. Cut-off values ​​representing stages 1-5 DN

<td>Training set (n = 118)</td><td>Test kit (n = 47)</td>

<td>Stadium 1 or 1-2 1-3 1-4 DN 2-5 or 3- or 4- or 5 5 5</td><td>1 or 1-2 1-3 1-4 2-5 resp. 3- or 4- or 5 5 5</td>

<td>Shut off 11.066 11.227 11.691 14.017 Sensitivity (%) 75 93 93 75</td><td> 11,066 11,227 11,691 14,017 84 96 100 100</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 75</td><td> 77</td><td> 83</td><td> 80</td>

<td>PPV (%)</td><td> 92</td><td> 90</td><td> 83</td><td> 21</td><td> 87</td><td> 83</td><td> 78</td><td> 18</td>

<td>NPV (%)</td><td> 69</td><td> 93</td><td> 96</td><td> 99</td><td> 71</td><td> 94</td><td> 100</td><td> 100</td>

<td>AUROC</td><td> 0,86</td><td> 0,95</td><td> 0,96</td><td> 0,95</td><td> 0,9</td><td> 0,98</td><td> 0,96</td><td> 0,91</td>

[0055] Logistic regression analysis:

Disease index = exp (Log_value) / (1 + exp (Log_value)), where

Log_value = -12.5332 + 0.7197 χ log₂[uDN5] (ng / mg) + 0.4941 χ log₂[UGR3] (ng / mg)

Table 6. Cut-off values ​​representing early and late DN stages indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 0,445</td><td> 0,676</td><td> 0,445</td><td> 0,676</td>

<td>Sensitivity (%)</td><td> 93</td><td> 93</td><td> 100</td><td> 100</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 82</td><td> 83</td>

<td>PPV (%)</td><td> 90</td><td> 83</td><td> 86</td><td> 78</td>

<td>NPV (%)</td><td> 93</td><td> 96</td><td> 100</td><td> 100</td>

<td>AUROC</td><td> 0,95</td><td> 0,96</td><td> 0,98</td><td> 0,97</td>

Table 7. Cut-off values ​​representing stages 1-5 DN

<td rowspan="2">Stage DN</td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test kit (n = 47)</td>

<td>1 or 2-5</td><td>1-2 or. 35</td><td>1-3 or. 45</td><td>1-4 or. 5</td><td>1 or 2-5</td><td>1-2 or. 35</td><td>1-3 or. 45</td><td>1-4 or. 5</td>

<td>cutoff</td><td> 0,383</td><td> 0,445</td><td> 0,676</td><td> 0,996</td><td> 0,383</td><td> 0,445</td><td> 0,676</td><td> 0,996</td>

<td>Sensitivity (%)</td><td> 75</td><td> 93</td><td> 93</td><td> 75</td><td> 84</td><td> 100</td><td> 100</td><td> 50</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 75</td><td> 82</td><td> 83</td><td> 80</td>

<td>PPV (%)</td><td> 92</td><td> 90</td><td> 83</td><td> 21</td><td> 87</td><td> 86</td><td> 78</td><td> 10</td>

<td>NPV (%)</td><td> 69</td><td> 93</td><td> 96</td><td> 99</td><td> 71</td><td> 100</td><td> 100</td><td> 97</td>

<td>AUROC</td><td> 0,86</td><td> 0,95</td><td> 0,96</td><td> 0,95</td><td> 0,9</td><td> 0,98</td><td> 0,97</td><td> 0,88</td>

[0056] Dorsal regression analysis:

Disease index = -1.77697 + 0.1520 χ log2 [uDN5] (ng / mg) + 0.2254 χ log2 [uGR3] (ng / mg)

Table 8. Cut-off values ​​representing early and late stages of DN indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 2,254</td><td> 2,606</td><td> 2,254</td><td> 2,606</td>

<td>Sensitivity (%)</td><td> 93</td><td> 93</td><td> 100</td><td> 94</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 77</td><td> 79</td>

<td>PPV (%)</td><td> 90</td><td> 83</td><td> 83</td><td> 74</td>

<td>NPV (%)</td><td> 93</td><td> 96</td><td> 100</td><td> 96</td>

<td>AUROC</td><td> 0,94</td><td> 0,96</td><td> 0,98</td><td> 0,96</td>

Table 9. Cut-off values ​​representing stages 1-5 DN

<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test kit (n = 47)</td>

<td>Stage</td><td>1 or</td><td> 1-2</td><td> 1-3</td><td> 1-4</td><td>1 or</td><td> 1-2</td><td> 1-3</td><td> 1-4</td>

<td>DN</td><td> 2-5</td><td>or. 3-</td><td>or. 4-</td><td>or. 5</td><td> 2-5</td><td>or. 3-</td><td>or. 4-</td><td>or. 5</td>

<td></td><td></td><td> 5</td><td> 5</td><td></td><td></td><td> 5</td><td> 5</td><td></td>

<td>cutoff</td><td> 2,185</td><td> 2,254</td><td> 2,606</td><td> 4,016</td><td> 2,185</td><td> 2,254</td><td> 2,606</td><td> 4,016</td>

<td>Sensitivity (%)</td><td> 75</td><td> 93</td><td> 93</td><td> 75</td><td> 84</td><td> 100</td><td> 94</td><td> 100</td>

<td>Specificity</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 75</td><td> 77</td><td> 79</td><td> 84</td>

<td> (%)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td>

<td>PPV (%)</td><td> 92</td><td> 90</td><td> 83</td><td> 21</td><td> 87</td><td> 83</td><td> 74</td><td> 22</td>

<td>NPV (%)</td><td> 69</td><td> 93</td><td> 96</td><td> 99</td><td> 71</td><td> 100</td><td> 96</td><td> 100</td>

<td>AUROC</td><td> 0,86</td><td> 0,94</td><td> 0,96</td><td> 0,95</td><td> 0,89</td><td> 0,98</td><td> 0,96</td><td> 0,91</td>

Three Protein Model [0057] The combined levels of three of uDN2, uDN5, uGR3, uDNO and sDNO in individuals from DM and individuals from DN were subjected to discriminant function analysis, logistic regression analysis, factor analysis and dorsal regression analysis. The results indicate that any combination of three proteins can be used as a reliable marker for determining the stages of DN.

[0058] The following is an exemplary three-protein model, ie uDN2, uDN5 and uGR3, including equations for calculating disease indices based on the combined levels of these three protein molecules. The tables (i.e. Tables

10-17) summarizing the cut-off, sensitivity, specificity, PPV, NPV and AUROC values ​​for this three-protein model.

Discriminant Function Analysis:

[0059]

Disease index = 0.3340 χ log₂[uDN5] (ng / mg) - 0.0142 χ log₂[uDN2] (ng / mg) + 0.2784 χ log2 [uGR3] (ng / mg) + 5

Table 10. Cut-off values ​​representing early and late stages of DN indicated by urinary albumin concentration

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 11,190</td><td> 11,663</td><td> 11,190</td><td> 11,663</td>

<td>tenderness (%)</td><td> 93</td><td> 93</td><td> 96</td><td> 100</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 77</td><td> 83</td>

<td>PPV (%)</td><td> 90</td><td> 83</td><td> 83</td><td> 78</td>

<td>NPV (%)</td><td> 93</td><td> 96</td><td> 94</td><td> 100</td>

<td>AUROC</td><td> 0,95</td><td> 0,96</td><td> 0,98</td><td> 0,96</td>

Table 11. Cut-off values ​​representing stages 1-5 DN

<td rowspan="2">Stage DN</td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test kit (n = 47)</td>

<td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td><td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td>

<td>cutoff</td><td> 11,064</td><td> 11,190</td><td> 11,663</td><td> 13,986</td><td> 11,064</td><td> 11,190</td><td> 11,663</td><td> 13,986</td>

<td>tenderness (%)</td><td> 75</td><td> 93</td><td> 93</td><td> 75</td><td> 84</td><td> 96</td><td> 100</td><td> 100</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 75</td><td> 77</td><td> 83</td><td> 82</td>

<td>PPV (%)</td><td> 92</td><td> 90</td><td> 83</td><td> 21</td><td> 87</td><td> 83</td><td> 78</td><td> 20</td>

<td>NPV (%)</td><td> 69</td><td> 93</td><td> 96</td><td> 99</td><td> 71</td><td> 94</td><td> 100</td><td> 100</td>

<td>AUROC</td><td> 0,87</td><td> 0,95</td><td> 0,96</td><td> 0,95</td><td> 0,9</td><td> 0,98</td><td> 0,96</td><td> 0,91</td>

Factor analysis:

[0060]

Disease index = 0.9190 χ log₂[uDN5] (ng / mg) + 0.6997 χ log₂[uDN2] (ng / mg) +0,9003 χ log2 [uGR3] (ng / mg)

Table 12. Cut-off values ​​representing the early and late stages of DN indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 26,356</td><td> 28,057</td><td> 26,356</td><td> 28,057</td>

<td>Sensitivity (%)</td><td> 84</td><td> 93</td><td> 88</td><td> 100</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 91</td><td> 86</td>

<td>PPV (%)</td><td> 89</td><td> 83</td><td> 92</td><td> 82</td>

<td>NPV (%)</td><td> 86</td><td> 96</td><td> 87</td><td> 100</td>

<td>AUROC</td><td> 0,93</td><td> 0,95</td><td> 0,99</td><td> 0,97</td>

Table 13. Cut-off values ​​representing stages 1-5 DN

<td rowspan="2">Stage DN</td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test kit (n = 47)</td>

<td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td><td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td>

<td>cutoff</td><td> 25,669</td><td> 26,356</td><td> 28,057</td><td> 36,464</td><td> 25,669</td><td> 26,356</td><td> 28,057</td><td> 36,464</td>

<td>tenderness (%)</td><td> 68</td><td> 84</td><td> 93</td><td> 75</td><td> 84</td><td> 88</td><td> 100</td><td> 50</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 88</td><td> 91</td><td> 86</td><td> 84</td>

<td>PPV (%)</td><td> 91</td><td> 89</td><td> 83</td><td> 21</td><td> 93</td><td> 92</td><td> 82</td><td> 12</td>

<td>NPV (%)</td><td> 63</td><td> 86</td><td> 96</td><td> 99</td><td> 74</td><td> 87</td><td> 100</td><td> 97</td>

<td>AUROC</td><td> 0,83</td><td> 0,93</td><td> 0,95</td><td> 0,95</td><td> 0,91</td><td> 0,99</td><td> 0,97</td><td> 0,86</td>

Logistic regression analysis:

[0061]

Disease index = exp (Log_value) / (1 + exp (Log_value)), where

Log_value = -11.2820 + 0.8810 χ log2 [uDN5] (ng / mg) - 0.3478 χ log2 [uDN2] (ng / mg) + 0.5576 χ log2 [uGR3] (ng / mg)

Table 14. Cut-off values ​​representing early and late stages of DN indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 0,462</td><td> 0,798</td><td> 0,462</td><td> 0,798</td>

<td>tenderness</td><td> 91</td><td> 88</td><td> 96</td><td> 94</td>

<td> (%)</td><td></td><td></td><td></td><td></td>

<td>Specificity</td><td> 90</td><td> 90</td><td> 82</td><td> 83</td>

<td> (%)</td><td></td><td></td><td></td><td></td>

<td>PPV (%)</td><td> 90</td><td> 82</td><td> 86</td><td> 77</td>

<td>NPV (%)</td><td> 92</td><td> 93</td><td> 95</td><td> 96</td>

<td>AUROC</td><td> 0,95</td><td> 0,96</td><td> 0,97</td><td> 0,95</td>

Table 15. Cut-off values ​​representing stages 1-5 DN

<td rowspan="2">Stage DN</td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test kit (n = 47)</td>

<td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td><td>1 or 2-5</td><td>. 1-2 resp. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td>

<td>cutoff</td><td> 0,361</td><td> 0,462</td><td> 0,798</td><td> 0,997</td><td> 0,361</td><td> 0,462</td><td> 0,798</td><td> 0,997</td>

<td>tenderness (%)</td><td> 75</td><td> 91</td><td> 88</td><td> 75</td><td> 90</td><td> 96</td><td> 94</td><td> 100</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 75</td><td> 82</td><td> 83</td><td> 82</td>

<td>PPV (%)</td><td> 92</td><td> 90</td><td> 82</td><td> 21</td><td> 88</td><td> 86</td><td> 77</td><td> 20</td>

<td>NPV (%)</td><td> 69</td><td> 92</td><td> 93</td><td> 99</td><td> 80</td><td> 95</td><td> 96</td><td> 100</td>

<td>AUROC</td><td> 0,88</td><td> 0,95</td><td> 0,96</td><td> 0,95</td><td> 0,89</td><td> 0,97</td><td> 0,95</td><td> 0,93</td>

Dorsal regression analysis:

[0062]

Disease rate = -1.2900 + 0.1800 χ log₂[uDN5] (ng / mg) - 0.1013 χ log₂[uDN2] (ng / mg) + 0.2505 5 χ log₂[UGR3] (ng / mg)

Table 16. Cut-off values ​​representing early and late stages of DN indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 2,122</td><td> 2,831</td><td> 2,122</td><td> 2,831</td>

<td>tenderness (%)</td><td> 95</td><td> 85</td><td> 100</td><td> 94</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 68</td><td> 86</td>

<td>PPV (%)</td><td> 90</td><td> 81</td><td> 78</td><td> 81</td>

<td>NPV (%)</td><td> 95</td><td> 92</td><td> 100</td><td> 96</td>

<td>AUROC</td><td> 0,95</td><td> 0,95</td><td> 0,97</td><td> 0,95</td>

Table 17. Cut-off values ​​representing stages 1-5 DN

<td>Training set (n = 118)</td><td>Test kit (n = 47)</td>

<td>Stadium 1 or 1-2 1-3 1-4 DN 2-5 or or. or. 5 3-5 4-5</td><td>1 or 1-2 1-3 1-4 2-5 resp. or. or. 5 3-5 4-5</td>

<td>Shut off 2.083 2.122 2.831 3.943 Sensitivity 78 95 85 75 (%)</td><td> 2,083 2,122 2,831 3,943 87 100 94 100</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 69</td><td> 68</td><td> 86</td><td> 82</td>

<td>PPV (%)</td><td> 92</td><td> 90</td><td> 81</td><td> 21</td><td> 84</td><td> 78</td><td> 81</td><td> 20</td>

<td>NPV (%)</td><td> 71</td><td> 95</td><td> 92</td><td> 99</td><td> 73</td><td> 100</td><td> 96</td><td> 100</td>

<td>AUROC</td><td> 0,88</td><td> 0,95</td><td> 0,95</td><td> 0,95</td><td> 0,89</td><td> 0,97</td><td> 0,95</td><td> 0,93</td>

Four Protein Model [0063] The combined levels of four of uDN2, uDN5, uGR3, uDNO and sDNO in DM and DN subjects were subjected to discriminant function analysis, logistic regression analysis, factor analysis, and dorsal regression analysis. The results indicate that any combination of four of the five proteins or fragments thereof can be used as a reliable marker for determining the stages of DN.

[0058] The following is an exemplary four-protein model, i.e. uDN2, uDN5, uGR3 and sDNO, including equations for calculating disease indices based on the combined levels of these four protein molecules. The following tables are also presented (i.e.

Tables 18-25) listing the cut-off, sensitivity, specificity, PPV, NPV and AUROC values ​​for this four-protein model.

Discriminant Function Analysis:

[0065]

Disease index = 0.2972 χ log₂[uDN5] (ng / mg) + 0.0159 χ log₂[uDN2] (ng / mg) + 0.2014 χ log₂[uGR3] (ng / mg) + 0.5688 χ log₂[sDNO] (ng / ml) + 5

Table 18. Cut-off values ​​representing early and late stages of DN indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 12,945</td><td> 13,520</td><td> 12,945</td><td> 13,520</td>

<td>tenderness (%)</td><td> 88</td><td> 95</td><td> 96</td><td> 100</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 82</td><td> 86</td>

<td>PPV (%)</td><td> 89</td><td> 83</td><td> 86</td><td> 82</td>

<td>NPV (%)</td><td> 89</td><td> 97</td><td> 95</td><td> 100</td>

<td>AUROC</td><td> 0,94</td><td> 0,96</td><td> 0,97</td><td> 0,97</td>

Table 19. Cut-off values ​​representing stages 1-5 DN

<td>Training set (n = 118)</td><td>Test kit (n = 47)</td>

<td>Stadia 1 or 1-2 1-3 1-4 DN 2-5 or or. or. 5 3-5 4-5</td><td>1 or 1-2 1-3 1-4 2-5 resp. or. or. 5 3-5 4-5</td>

<td>Shut off 12.888 12.945 13.520 15.560</td><td> 12,887 12,945 13,520 15,560</td>

<td>tenderness (%)</td><td> 73</td><td> 88</td><td> 95</td><td> 100</td><td> 81</td><td> 96</td><td> 100</td><td> 100</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 81</td><td> 82</td><td> 86</td><td> 82</td>

<td>PPV (%)</td><td> 91</td><td> 89</td><td> 83</td><td> 27</td><td> 89</td><td> 86</td><td> 82</td><td> 20</td>

<td>NPV (%)</td><td> 67</td><td> 89</td><td> 97</td><td> 100</td><td> 68</td><td> 95</td><td> 100</td><td> 100</td>

<td>AUROC</td><td> 0,87</td><td> 0,94</td><td> 0,96</td><td> 0,97</td><td> 0,93</td><td> 0,97</td><td> 0,97</td><td> 0,89</td>

[0066] Factor analysis:

Disease index = 0.9132 χ log₂[uDN5] (ng / mg) + 0.6950 χ log₂[uDN2] (ng / mg) + 0.9080 χ log₂[uGR3] (ng / mg) + 0.4549 χ log₂[SDNO] (ng / ml)

Table 20. Cut-off values ​​representing early and late stages of DN indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 28,459</td><td> 30,095</td><td> 28,459</td><td> 30,095</td>

<td>tenderness (%)</td><td> 82</td><td> 93</td><td> 92</td><td> 100</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 91</td><td> 83</td>

<td>PPV (%)</td><td> 89</td><td> 83</td><td> 92</td><td> 78</td>

<td>NPV (%)</td><td> 85</td><td> 96</td><td> 91</td><td> 100</td>

<td>AUROC</td><td> 0,93</td><td> 0,96</td><td> 0,99</td><td> 0,98</td>

Table 21. Cut-off values ​​representing stages 1-5 DN

<td rowspan="2">Stage DN</td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test kit (n = 47)</td>

<td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td><td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td>

<td>cutoff</td><td> 28,347</td><td> 28,459</td><td> 30,095</td><td> 38,624</td><td> 28,347</td><td> 28,459</td><td> 30,095</td><td> 38,624</td>

<td>tenderness (%)</td><td> 67</td><td> 82</td><td> 93</td><td> 75</td><td> 81</td><td> 92</td><td> 100</td><td> 50</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 94</td><td> 91</td><td> 83</td><td> 84</td>

<td>PPV (%)</td><td> 91</td><td> 89</td><td> 83</td><td> 21</td><td> 96</td><td> 92</td><td> 78</td><td> 12</td>

<td>NPV (%)</td><td> 62</td><td> 85</td><td> 96</td><td> 99</td><td> 71</td><td> 91</td><td> 100</td><td> 97</td>

<td>AUROC</td><td> 0,84</td><td> 0,93</td><td> 0,96</td><td> 0,95</td><td> 0,92</td><td> 0,99</td><td> 0,98</td><td> 0,86</td>

Logistic regression analysis:

[0067]

Disease index = exp (Log_value) / (1 + exp (Log_value)), where

Logit value = -13.7529 + 0.9460 χ log2 [uDN5] (ng / mg) - 0.3110 χ log2 [uDN2] (ng / mg) +

0.4957 χ log2 [uGR3] (ng / mg) + 0.4787 χ log2 [sDNO] (ng / ml)

Table 22. Cut-off values ​​representing the early and late stages of DN indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 0,423</td><td> 0,804</td><td> 0,423</td><td> 0,804</td>

<td>tenderness (%)</td><td> 91</td><td> 88</td><td> 96</td><td> 100</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 77</td><td> 86</td>

<td>PPV (%)</td><td> 90</td><td> 82</td><td> 83</td><td> 82</td>

<td>NPV (%)</td><td> 92</td><td> 93</td><td> 94</td><td> 100</td>

<td>AUROC</td><td> 0,96</td><td> 0,96</td><td> 0,97</td><td> 0,96</td>

Table 23. Cut-off values ​​representing stages 1-5 DN

<td rowspan="2">Stadia DN</td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test kit (n = 47)</td>

<td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td><td>1 or 2-5</td><td>. 1-2 resp. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td>

<td>cutoff</td><td> 0,341</td><td> 0,423</td><td> 0,804</td><td> 0,998</td><td> 0,341</td><td> 0,423</td><td> 0,804</td><td> 0,998</td>

<td>tenderness (%)</td><td> 75</td><td> 91</td><td> 88</td><td> 75</td><td> 90</td><td> 96</td><td> 100</td><td> 100</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 75</td><td> 77</td><td> 86</td><td> 82</td>

<td>PPV (%)</td><td> 92</td><td> 90</td><td> 82</td><td> 21</td><td> 88</td><td> 83</td><td> 82</td><td> 20</td>

<td>NPV (%)</td><td> 69</td><td> 92</td><td> 93</td><td> 99</td><td> 80</td><td> 94</td><td> 100</td><td> 100</td>

<td>AUROC</td><td> 0,89</td><td> 0,96</td><td> 0,96</td><td> 0,96</td><td> 0,91</td><td> 0,97</td><td> 0,96</td><td> 0,9</td>

Dorsal regression analysis:

[0068]

Disease index = -1.7588 + 0.1729 χ log2 [uDN5] (ng / mg) - 0.0971 χ log2 [uDN2] (ng / mg) + 0.2381 χ log2 [uGR3] (ng / mg) + 0.1312 χ log2 [sDNO] (ng / ml)

Table 24. Cut-off values ​​representing early and late DN stages indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 2,261</td><td> 2,854</td><td> 2,261</td><td> 2,854</td>

<td>tenderness</td><td> 91</td><td> 85</td><td> 96</td><td> 94</td>

<td> (%)</td><td></td><td></td><td></td><td></td>

<td>Specificity</td><td> 90</td><td> 90</td><td> 77</td><td> 90</td>

<td> (%)</td><td></td><td></td><td></td><td></td>

<td>PPV (%)</td><td> 90</td><td> 81</td><td> 83</td><td> 85</td>

<td>NPV (%)</td><td> 92</td><td> 92</td><td> 94</td><td> 96</td>

<td>AUROC</td><td> 0,95</td><td> 0,95</td><td> 0,97</td><td> 0,95</td>

Table 25. Cut-off values ​​representing stages 1-5 DN

<td rowspan="2">Stage DN</td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test kit (n = 47)</td>

<td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td><td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td>

<td>cutoff</td><td> 2,079</td><td> 2,261</td><td> 2,854</td><td> 3,950</td><td> 2,079</td><td> 2,261</td><td> 2,854</td><td> 3,950</td>

<td>tenderness (%)</td><td> 77</td><td> 91</td><td> 85</td><td> 75</td><td> 87</td><td> 96</td><td> 94</td><td> 100</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 69</td><td> 77</td><td> 90</td><td> 82</td>

<td>PPV (%)</td><td> 92</td><td> 90</td><td> 81</td><td> 21</td><td> 84</td><td> 83</td><td> 85</td><td> 20</td>

<td>NPV (%)</td><td> 70</td><td> 92</td><td> 92</td><td> 99</td><td> 73</td><td> 94</td><td> 96</td><td> 100</td>

<td>AUROC</td><td> 0,89</td><td> 0,95</td><td> 0,95</td><td> 0,95</td><td> 0,89</td><td> 0,97</td><td> 0,95</td><td> 0,93</td>

Five Protein Model [0069] Combined levels of uDN2, uDN5, uGR3, uDNO and sDNO in individuals from DM and individuals from DN were subjected to discriminant function analysis, logistic regression analysis, factor analysis and dorsal regression analysis. The results indicate that the combination of these five proteins or fragments thereof can be used as a reliable marker for determining DN stages.

[0058] Below are equations for calculating disease indices based on the combined levels of these five protein molecules, as well as tables (i.e., Tables 26-33) comparing the cutoff, sensitivity, specificity, NPV, PPV and AUROC values ​​for this five-protein model.

Discriminant Function Analysis:

[0071]

Disease index = 0.2780 χ log2 [uDN5] (ng / mg) + 0.0231 χ log2 [uDN2] (ng / mg) + 0.2236 χ log2 [uGR3] (ng / mg) + 0.6043 χ log2 [sDNO] (ng / ml) - 0.1513 χ log2 [uDNO] (ng / mg) + 5

Table 26. Cut-off values ​​representing early and late DN stages indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or. DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 11,818</td><td> 12,164</td><td> 11,818</td><td> 12,164</td>

<td>tenderness</td><td> 86</td><td> 98</td><td> 96</td><td> 100</td>

<td> (%)</td><td></td><td></td><td></td><td></td>

<td>Specificity</td><td> 90</td><td> 90</td><td> 86</td><td> 86</td>

<td> (%)</td><td></td><td></td><td></td><td></td>

<td>PPV (%)</td><td> 89</td><td> 83</td><td> 89</td><td> 82</td>

<td>NPV (%)</td><td> 87</td><td> 99</td><td> 95</td><td> 100</td>

<td>AUROC</td><td> 0,94</td><td> 0,97</td><td> 0,98</td><td> 0,98</td>

Table 27. Cut-off values ​​representing stages 1-5 DN

<td rowspan="2">Stadia DN</td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test kit (n = 47)</td>

<td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td><td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td>

<td>cutoff</td><td> 11,766</td><td> 11,818</td><td> 12,164</td><td> 14,432</td><td> 11,766</td><td> 11,818</td><td> 12,164</td><td> 14,432</td>

<td>tenderness (%)</td><td> 73</td><td> 86</td><td> 98</td><td> 100</td><td> 81</td><td> 96</td><td> 100</td><td> 100</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 88</td><td> 86</td><td> 86</td><td> 82</td>

<td>PPV (%)</td><td> 91</td><td> 89</td><td> 83</td><td> 27</td><td> 93</td><td> 89</td><td> 82</td><td> 20</td>

<td>NPV (%)</td><td> 67</td><td> 87</td><td> 99</td><td> 100</td><td> 70</td><td> 95</td><td> 100</td><td> 100</td>

<td>AUROC</td><td> 0,86</td><td> 0,94</td><td> 0,97</td><td> 0,98</td><td> 0,94</td><td> 0,98</td><td> 0,98</td><td> 0,91</td>

Factor analysis:

[0072]

Disease index = 0.9117 χ log2 [uDN5] (ng / mg) + 0.6949 χ log2 [uDN2] (ng / mg) +0.9095 χ log2 [uGR3] (ng / mg) + 0.4554 χ log2 [sDNO] (ng / ml) + 0.0384 χ log2 [uDNO] (ng / mg)

Table 28. Cut-off values ​​representing early and late DN stages indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 29,475</td><td> 30,541</td><td> 29,475</td><td> 30,541</td>

<td>tenderness (%)</td><td> 81</td><td> 93</td><td> 88</td><td> 100</td>

<td>Specificity</td><td> 90</td><td> 90</td><td> 91</td><td> 83</td>

<td> (%)</td><td></td><td></td><td></td><td></td>

<td>PPV (%)</td><td> 88</td><td> 83</td><td> 92</td><td> 78</td>

<td>NPV (%)</td><td> 83</td><td> 96</td><td> 87</td><td> 100</td>

<td>AUROC</td><td> 0,93</td><td> 0,96</td><td> 0,99</td><td> 0,98</td>

Table 29. Cut-off values ​​representing stages 1-5 DN

<td rowspan="2">Stadia DN</td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test kit (n = 47)</td>

<td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td><td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td>

<td>cutoff</td><td> 28,740</td><td> 29,475</td><td> 30,541</td><td> 39,042</td><td> 28,740</td><td> 29,475</td><td> 30,541</td><td> 39,042</td>

<td>tenderness (%)</td><td> 67</td><td> 81</td><td> 93</td><td> 75</td><td> 81</td><td> 88</td><td> 100</td><td> 50</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 94</td><td> 91</td><td> 83</td><td> 84</td>

<td>PPV (%)</td><td> 91</td><td> 88</td><td> 83</td><td> 21</td><td> 96</td><td> 92</td><td> 78</td><td> 12</td>

<td>NPV (%)</td><td> 62</td><td> 83</td><td> 96</td><td> 99</td><td> 71</td><td> 87</td><td> 100</td><td> 97</td>

<td>AUROC</td><td> 0,84</td><td> 0,93</td><td> 0,96</td><td> 0,95</td><td> 0,92</td><td> 0,99</td><td> 0,98</td><td> 0,86</td>

Logistic regression analysis:

[0073]

Disease index = exp (Log_value) / (1 + exp (Log_value)), where

Log_value = -11.4318 + 0.8188 χ log2 [uDN5] (ng / mg) - 0.5376 χ log2 [uDN2] (ng / mg) + 0.7561 χ log2 [uGR3] (ng / mg) + 0 , 3940 χ log2 [sDNO] (ng / ml)

- 0.1741 χ log2 [uDNO] (ng / mg)

<td colspan="5">Table 30. Cut-off values ​​representing early and late DN stages indicated on the basis of urinary albumin levels</td>

<td></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td></td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 0,436</td><td> 0,780</td><td> 0,436</td><td> 0,780</td>

<td>tenderness (%)</td><td> 91</td><td> 93</td><td> 96</td><td> 100</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 77</td><td> 86</td>

<td>PPV (%)</td><td> 90</td><td> 83</td><td> 83</td><td> 82</td>

<td>NPV (%)</td><td> 92</td><td> 96</td><td> 94</td><td> 100</td>

<td>AUROC</td><td> 0,96</td><td> 0,96</td><td> 0,97</td><td> 0,96</td>

Table 31. Cut-off values ​​representing stages 1-5 DN

<td colspan="5">Training set (n = 118)</td><td colspan="4">Test kit (n = 47)</td>

<td>Stadia DN</td><td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td><td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td>

<td>cutoff</td><td> 0,329</td><td> 0,436</td><td> 0,780</td><td> 0,997</td><td> 0,329</td><td> 0,436</td><td> 0,780</td><td> 0,997</td>

<td>tenderness (%)</td><td> 75</td><td> 91</td><td> 93</td><td> 100</td><td> 90</td><td> 96</td><td> 100</td><td> 100</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 75</td><td> 77</td><td> 86</td><td> 80</td>

<td>PPV (%)</td><td> 92</td><td> 90</td><td> 83</td><td> 27</td><td> 88</td><td> 83</td><td> 82</td><td> 18</td>

<td>NPV (%)</td><td> 69</td><td> 92</td><td> 96</td><td> 100</td><td> 80</td><td> 94</td><td> 100</td><td> 100</td>

<td>AUROC</td><td> 0,89</td><td> 0,96</td><td> 0,96</td><td> 0,96</td><td> 0,91</td><td> 0,97</td><td> 0,96</td><td> 0,91</td>

Dorsal regression analysis:

[0074]

Disease index = -1.3112 + 0.1648 χ log2 [uDN5] (ng / mg) - 0.0968 χ log2 [uDN2] (ng / mg) + 0.2468 5 χ log2 [uGR3] (ng / mg) + 0.1426 χ log2 [sDNO] (ng / ml) - 0.0552 χ log2 [uDNO] (ng / mg)

Table 32. Cut-off values ​​representing early and late DN stages indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 2,244</td><td> 2,729</td><td> 2,244</td><td> 2,729</td>

<td>tenderness (%)</td><td> 91</td><td> 88</td><td> 96</td><td> 100</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 82</td><td> 90</td>

<td>PPV (%)</td><td> 90</td><td> 82</td><td> 86</td><td> 86</td>

<td>NPV (%)</td><td> 92</td><td> 93</td><td> 95</td><td> 100</td>

<td>AUROC</td><td> 0,95</td><td> 0,95</td><td> 0,98</td><td> 0,97</td>

Table 33. Cut-off values ​​representing stages 1-5 DN

<td>Training set (n = 118)</td><td>Test kit (n = 47)</td>

<td>Stadia 1 or 1-2 1-3 1-4 DN 2-5 or or. or. 5 3-5 4-5</td><td>1 or 1-2 1-3 1-4 2-5 resp. or. or. 5 3-5 4-5</td>

<td>Cutoff 2.043 2.244 2.729 3.913</td><td> 2,043 2,244 2,729 3,913</td>

<td>tenderness (%)</td><td> 77</td><td> 91</td><td> 88</td><td> 100</td><td> 87</td><td> 96</td><td> 100</td><td> 100</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 69</td><td> 82</td><td> 90</td><td> 80</td>

<td>PPV (%)</td><td> 92</td><td> 90</td><td> 82</td><td> 27</td><td> 84</td><td> 86</td><td> 86</td><td> 18</td>

<td>NPV (%)</td><td> 70</td><td> 92</td><td> 93</td><td> 100</td><td> 73</td><td> 95</td><td> 100</td><td> 100</td>

<td>AUROC</td><td> 0,89</td><td> 0,95</td><td> 0,95</td><td> 0,96</td><td> 0,9</td><td> 0,98</td><td> 0,97</td><td> 0,93</td>

Example 3: Determination of DN stage based on the combination of uDN2, uDN5, uGR3 and age [0075] The following equations for calculating disease indicators determined by means of discriminant function analysis, factor analysis, logistic regression analysis and dorsal regression analysis, based on the complex biomarker level from three protein molecules, i.e. uDN2, uDN5 and uGR3, and one clinical factor, i.e. age. The tables (i.e. Tables 34-41) listing the cut-off, sensitivity, specificity, PPV, NPV and AUROC values ​​for this model.

Discriminant Function Analysis:

[0076]

Disease index = 0.3342 χ log2 [uDN5] (ng / mg) - 0.0201 χ log2 [uDN2] (ng / mg) + 0.2826 χ log2 [uGR3] (ng / mg) + 0.0059 χ Age (years) + 5

Table 34. Cut-off values ​​representing early and late DN stages indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 11,515</td><td> 12,088</td><td> 11,515</td><td> 12,088</td>

<td>tenderness (%)</td><td> 93</td><td> 93</td><td> 100</td><td> 100</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 77</td><td> 79</td>

<td>PPV (%)</td><td> 90</td><td> 83</td><td> 83</td><td> 75</td>

<td>NPV (%)</td><td> 93</td><td> 96</td><td> 100</td><td> 100</td>

<td>AUROC</td><td> 0,95</td><td> 0,96</td><td> 0,98</td><td> 0,97</td>

Table 35. Cut-off values ​​representing stages 1-5 DN

<td>Training set (n = 118)</td><td>Test kit (n = 47)</td>

<td>Stadium 1 or 1-2 1-3 1-4 DN 2-5 or or. or. 5 3-5 4-5</td><td>1 or 1-2 1-3 1-4 2-5 resp. or. or. 5 3-5 4-5</td>

<td>Cut-off 11.335 11.515 12.088 14.343</td><td> 11,353 11,515 12,088 14,343</td>

<td>tenderness (%)</td><td> 75</td><td> 93</td><td> 93</td><td> 75</td><td> 84</td><td> 100</td><td> 100</td><td> 100</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 75</td><td> 77</td><td> 79</td><td> 80</td>

<td>PPV (%)</td><td> 92</td><td> 90</td><td> 83</td><td> 21</td><td> 87</td><td> 83</td><td> 75</td><td> 18</td>

<td>NPV (%)</td><td> 69</td><td> 93</td><td> 96</td><td> 99</td><td> 71</td><td> 100</td><td> 100</td><td> 100</td>

<td>AUROC</td><td> 0,87</td><td> 0,95</td><td> 0,96</td><td> 0,95</td><td> 0,9</td><td> 0,98</td><td> 0,97</td><td> 0,9</td>

[0077] Factor analysis:

Disease index = 0.9184 χ log2 [uDN5] (ng / mg) + 0.7006 χ log2 [uDN2] (ng / mg) + 0.9005 χ log2 [uGR3] (ng / mg) + 0.1863 χ Age (patch)

Table 36. Cut-off values ​​representing early and late DN stages indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 38,341</td><td> 40,075</td><td> 38,341</td><td> 40,075</td>

<td>tenderness (%)</td><td> 82</td><td> 85</td><td> 96</td><td> 100</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 86</td><td> 83</td>

<td>PPV (%)</td><td> 89</td><td> 81</td><td> 89</td><td> 78</td>

<td>NPV (%)</td><td> 85</td><td> 92</td><td> 95</td><td> 100</td>

<td>AUROC</td><td> 0,93</td><td> 0,94</td><td> 0,99</td><td> 0,98</td>

Table 37. Cut-off values ​​representing stages 1-5 DN

<td rowspan="2">Stadia DN</td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test kit (n = 47)</td>

<td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td><td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td>

<td>number patients (%)</td><td> 73 (62)</td><td> 57 (48)</td><td> 41 (35)</td><td> 4 (3)</td><td> 31 (66)</td><td> 25(53)</td><td> 18 (38)</td><td> 2 (4)</td>

<td>cutoff</td><td> 38,341</td><td> 38,341</td><td> 40,075</td><td> 48,538</td><td> 38,341</td><td> 38,341</td><td> 40,075</td><td> 48,538</td>

<td>tenderness (%)</td><td> 66</td><td> 82</td><td> 85</td><td> 50</td><td> 81</td><td> 96</td><td> 100</td><td> 50</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 88</td><td> 86</td><td> 83</td><td> 89</td>

<td>PPV (%)</td><td> 91</td><td> 89</td><td> 81</td><td> 15</td><td> 93</td><td> 89</td><td> 78</td><td> 17</td>

<td>NPV (%)</td><td> 62</td><td> 85</td><td> 92</td><td> 98</td><td> 70</td><td> 95</td><td> 100</td><td> 98</td>

<td>AUROC</td><td> 0,82</td><td> 0,93</td><td> 0,94</td><td> 0,91</td><td> 0,9</td><td> 0,99</td><td> 0,98</td><td> 0,77</td>

Logistic regression analysis:

[0078]

Disease index = exp (Log_value) / (1 + exp (Log_value)), where

Log_value = -15.9748 + 0.8688 χ log2 [uDN5] (ng / mg) - 0.4966 χ log2 [uDN2] (ng / mg) + 0.6436 χ log2 [uGR3] (ng / mg) + 0 , 0879 χ Age (years)

Table 38. Cut-off values ​​representing early and late stages of DN indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 0,321</td><td> 0,889</td><td> 0,321</td><td> 0,889</td>

<td>tenderness (%)</td><td> 93</td><td> 80</td><td> 100</td><td> 94</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 77</td><td> 83</td>

<td>PPV (%)</td><td> 90</td><td> 80</td><td> 83</td><td> 77</td>

<td>NPV (%)</td><td> 93</td><td> 90</td><td> 100</td><td> 96</td>

<td>AUROC</td><td> 0,96</td><td> 0,95</td><td> 0,97</td><td> 0,95</td>

Table 39. Cut-off values ​​representing stages 1-5 DN

<td rowspan="2">Stadia DN</td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test kit (n = 47)</td>

<td>1 or 2-5</td><td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td><td>1 or 2-5</td><td>. 1-2 resp. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or. 5</td>

<td>cutoff</td><td> 0,301</td><td> 0,321</td><td> 0,889</td><td> 0,997</td><td> 0,301</td><td> 0,321</td><td> 0,889</td><td> 0,997</td>

<td>tenderness (%)</td><td> 75</td><td> 93</td><td> 80</td><td> 75</td><td> 87</td><td> 100</td><td> 94</td><td> 100</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 75</td><td> 77</td><td> 83</td><td> 89</td>

<td>PPV (%)</td><td> 92</td><td> 90</td><td> 80</td><td> 21</td><td> 87</td><td> 83</td><td> 77</td><td> 29</td>

<td>NPV (%)</td><td> 69</td><td> 93</td><td> 90</td><td> 99</td><td> 75</td><td> 100</td><td> 96</td><td> 100</td>

<td>AUROC</td><td> 0,89</td><td> 0,96</td><td> 0,95</td><td> 0,92</td><td> 0,88</td><td> 0,97</td><td> 0,95</td><td> 0,91</td>

Dorsal regression analysis:

[0079]

Disease index = -2.1690 + 0.1771 χ log2 [uDN5] (ng / mg) - 0.1074 χ log2 [uDN2] (ng / mg) + 0.2474 χ log2 [uGR3] (ng / mg) + 0.0168 χ Age (years)

Table 40. Cut-off values ​​representing early and late DN stages indicated on the basis of urinary albumin levels

<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test kit (n = 47)</td>

<td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td><td>DM or DN</td><td>DM, Microalbuminuria or macroalbuminuria</td>

<td>cutoff</td><td> 2,139</td><td> 2,880</td><td> 2,139</td><td> 2,880</td>

<td>tenderness (%)</td><td> 93</td><td> 85</td><td> 100</td><td> 89</td>

<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 73</td><td> 83</td>

<td>PPV (%)</td><td> 90</td><td> 81</td><td> 81</td><td> 76</td>

<td>NPV (%)</td><td> 93</td><td> 92</td><td> 100</td><td> 92</td>

<td>AUROC</td><td> 0,96</td><td> 0,95</td><td> 0,98</td><td> 0,96</td>

Table 41. Cut-off values ​​representing stages 1-5 DN

Training set (n = 118)

Test kit (n = 47)

<td>cutoff</td><td> 2,128</td><td> 2,139</td>

<td>tenderness (%)</td><td> 75</td><td> 93</td>

<td>Specificity (%)</td><td> 89</td><td> 90</td>

<td>PPV (%)</td><td> 92</td><td> 90</td>

<td>NPV (%)</td><td> 69</td><td> 93</td>

<td>AUROC</td><td> 0,89</td><td> 0,96</td>

Stadium 1 or 1-2 DN 2-5 resp.

3-5

<td>1-3 or. 4-5</td><td>1-4 or. 5</td><td>1 or 2-5</td>

<td> 2,880</td><td> 4,051</td><td> 2,128</td>

<td> 85</td><td> 75</td><td> 84</td>

<td> 90</td><td> 90</td><td> 69</td>

<td> 81</td><td> 21</td><td> 84</td>

<td> 92</td><td> 99</td><td> 69</td>

<td> 0,95</td><td> 0,92</td><td> 0,89</td>

<td>1-2 or. 3-5</td><td>1-3 or. 4-5</td><td>1-4 or.</td>

<td> 2,139</td><td> 2,880</td><td> 4,051</td>

<td> 100</td><td> 89</td><td> 100</td>

<td> 73</td><td> 83</td><td> 89</td>

<td> 81</td><td> 76</td><td> 29</td>

<td> 100</td><td> 92</td><td> 100</td>

<td> 0,98</td><td> 0,96</td><td> 0,92</td>

OTHER FORMS [0080] All the features disclosed in this description can be combined in any combination. Any feature disclosed in this description may be replaced by an alternative feature serving the same, equivalent or similar purpose. Thus, unless expressly stated otherwise, each feature disclosed is only an example of a general set of equivalent or similar features.

[0081] Based on the above description, a person skilled in the art can easily determine the most important properties of the present invention and can make various changes and modifications to the invention to suit different applications and conditions.

LIST OF SEQUENCES [0082] &lt;110&gt; Lin, Wei-Ya Yeh, Mary Ya-Ping Tseng, Tzu-Ling Cheng, Ping-Fu Hsu, Tsai-Wei Li, Hung-Yi Chen, Yi-Ting Lin, Yuh-Feng Chen , Giien-Shuen Li, Yen-Peng &lt;120&gt; Biomarkers in urine and serum associated with diabetic nephropathy &lt;130&gt; 70006-008001 &lt;150&gt; 61/147778 &lt;151&gt; 2009-01-28 &lt;160&gt; 9 &lt;170&gt; PatentIn version 3.5 &lt;210&gt; 1 &lt;211&gt; 17 &lt;212&gt; PRT &lt;213&gt; Artificial sequence &lt;220&gt;

&lt;223&gt; Fragment of alpha-2-HS-glycoprotein &lt;400&gt; 1

Val Val Ser Leu Gly Ser Pro Ser Gly Glu Val Ser His Pro Arg Lys 15 10 15

Thr &lt;210&gt; 2 &lt;211&gt; 19 &lt;212&gt; PRT &lt;213&gt; Artificial sequence &lt;220&gt;

&lt;223&gt; Alpha-2-HS-glycoprotein fragment &lt;400&gt; 2

Met Gly Val Val Ser Leu Gly Ser Pro Ser Gly Glu Val Ser His Pro 15 10 15

Arg Lys Thr &lt;210&gt; 3 &lt;211&gt; 18 &lt;212&gt; PRT &lt;213&gt; Artificial sequence &lt;220&gt;

&lt;223&gt; Antitrypsin &lt;400&gt; alpha-1 fragment 3

Lys Gly Lys Trp Glu Arg Pro Phe Glu Val Lys Asp Thr Glu Glu Glu 15 10 15

Asp Phe &lt;210&gt; 4 &lt;211&gt; 21 &lt;212&gt; PRT &lt;213&gt; Artificial sequence &lt;220&gt;

&lt;223&gt; Antitrypsin &lt;400&gt; alpha-1 fragment 4

Met Ile Glu Gin Asn Thr Lys Ser Pro Leu Phe Met Gly Lys Val Val 15 10 15

Asn Pro Thr Gin Lys &lt;210&gt; 5 &lt;211&gt; 32 &lt;212&gt; PRT &lt;213&gt; Artificial sequence &lt;220&gt;

&lt;223&gt; Antitrypsin &lt;400&gt; alpha-1 fragment 5

<td>Glu</td><td>Asp</td><td>Pro</td><td>Gin</td><td>Gly</td><td>Asp</td><td>ala</td><td>ala</td><td>Gin</td><td>lys</td><td>Thr</td><td>Asp</td><td>Thr</td><td>Cheese</td><td>His</td><td>His</td>

<td> 1</td><td></td><td></td><td></td><td> 5</td><td></td><td></td><td></td><td></td><td> 10</td><td></td><td></td><td></td><td></td><td> 15</td><td></td>

<td>Asp</td><td>Gin</td><td>Asp</td><td>His</td><td>Pro</td><td>Thr</td><td>phe</td><td>own</td><td>lys</td><td>background</td><td>Thr</td><td>Pro</td><td>own</td><td>Leu</td><td>ala</td><td>Glu</td>

<td></td><td></td><td></td><td> 20</td><td></td><td></td><td></td><td></td><td> 25</td><td></td><td></td><td></td><td></td><td> 30</td><td></td><td></td>

&lt;210&gt; 6 &lt;211&gt; 34 &lt;212&gt; PRT &lt;213&gt; Artificial sequence &lt;220&gt;

&lt;223&gt; Antitrypsin &lt;400&gt; alpha-1 fragment 6

<td>Glu</td><td>Asp</td><td>Pro</td><td>Gin</td><td>Gly</td><td>Asp</td><td>ala</td><td>ala</td><td>Gin</td><td>lys</td><td>Thr</td><td>Asp</td><td>Thr</td><td>Cheese</td><td>His</td><td>His</td>

<td> 1</td><td></td><td></td><td></td><td> 5</td><td></td><td></td><td></td><td></td><td> 10</td><td></td><td></td><td></td><td></td><td> 15</td><td></td>

<td>Asp</td><td>Gin</td><td>Asp</td><td>His</td><td>Pro</td><td>Thr</td><td>phe</td><td>own</td><td>lys</td><td>How much</td><td>Thr</td><td>Pro</td><td>own</td><td>Leu</td><td>ala</td><td>Glu</td>

<td></td><td></td><td></td><td> 20</td><td></td><td></td><td></td><td></td><td> 25</td><td></td><td></td><td></td><td></td><td> 30</td><td></td><td></td>

Phe Ala &lt;210&gt; 7 &lt;211&gt; 32 &lt;212&gt; PRT &lt;213&gt; Artificial sequence &lt;220&gt;

&lt;223&gt; A fragment of alpha-1 acid glycoprotein &lt;400&gt; 7

<td>Gly</td><td>Gin</td><td>Glu</td><td>His</td><td>phe</td><td>ala</td><td>His</td><td>Leu</td><td>Leu</td><td>How much</td><td>Leu</td><td>Arg</td><td>Asp</td><td>Thr</td><td>lys</td><td>Thr</td>

<td> 1</td><td></td><td></td><td></td><td> 5</td><td></td><td></td><td></td><td></td><td> 10</td><td></td><td></td><td></td><td></td><td> 15</td><td></td>

<td>Tyr</td><td>Underworld</td><td>Leu</td><td>ala</td><td>phe</td><td>Asp</td><td>val</td><td>own</td><td>Asp</td><td>Glu</td><td>lys</td><td>own</td><td>Trp</td><td>Gly</td><td>Leu</td><td>Cheese</td>

<td></td><td></td><td></td><td> 20</td><td></td><td></td><td></td><td></td><td> 25</td><td></td><td></td><td></td><td></td><td> 30</td><td></td><td></td>

&lt;210&gt; 8 &lt;211&gt; 42 &lt;212&gt; PRT &lt;213&gt; Artificial sequence &lt;220&gt;

&lt;223&gt; fragment of osteopontin &lt;400&gt; 8

<td>Tyr</td><td>Pro</td><td>Asp</td><td>ala</td><td>val</td><td>ala</td><td>Thr</td><td>Trp</td><td>Leu</td><td>own</td><td>Pro</td><td>Asp</td><td>Pro</td><td>Cheese</td><td>Gin</td><td>lys</td>

<td> 1</td><td></td><td></td><td></td><td> 5</td><td></td><td></td><td></td><td></td><td> 10</td><td></td><td></td><td></td><td></td><td> 15</td><td></td>

<td>Gin</td><td>own</td><td>Leu</td><td>Leu</td><td>ala</td><td>Pro</td><td>Gin</td><td>own</td><td>ala</td><td>val</td><td>Cheese</td><td>Cheese</td><td>Glu</td><td>Glu</td><td>Thr</td><td>own</td>

<td></td><td></td><td></td><td> 20</td><td></td><td></td><td></td><td></td><td> 25</td><td></td><td></td><td></td><td></td><td> 30</td><td></td><td></td>

<td>Asp</td><td>phe</td><td>lys</td><td>Gin</td><td>Glu</td><td>Thr</td><td>Leu</td><td>Pro</td><td>Cheese</td><td>lys</td><td></td><td></td><td></td><td></td><td></td><td></td>

<td></td><td></td><td> 35</td><td></td><td></td><td></td><td></td><td> 40</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td>

&lt;210&gt; 9 &lt;211&gt; 29 &lt;212&gt; PRT &lt;213&gt; Artificial sequence &lt;220&gt;

&lt;223&gt; Fragment of osteopontin &lt;400&gt; 9

Lys Tyr Pro Asp Ala Val Ala Thr Trp Leu Asn Pro Asp Pro Ser Gin 15 10 15

Lys Gin Asn Leu Leu Ala Pro Gin Thr Leu Pro Ser Lys

25