Urine and serum biomarkers associated with diabetic nephropathy
Abstract
A method for diagnosing diabetic nephropathy in a subject, comprising: determining the level of a biomarker in a urine sample from a subject suspected of suffering from diabetic nephropathy, in which the biomarker is a fragment of alpha-2- HS-glycoprotein selected from the group consisting of VVSLGSPSGEVSHPRKT (SEQ ID NO: 1) and MGVVSLGSPSGEVSHPRKT (SEQ ID NO: 2), and assess whether the subject has a diabetic nephropathy based on the biomarker level; in which an increase in the level of the biomarker, compared to the level in a subject without diabetic nephropathy, indicates that the subject has diabetic nephropathy.
Term
3.3 yearsto projected expiry
Projected expiry 27 January 2030, counted from filing; an application has no term until it is granted.
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32 claims: 9 independent, 23 dependent
- 1ES 2 552 467 T3 REIVINDICACIONES 1. - Un método para diagnosticar la nefropatía diabética en un sujeto, que comprende:determinar el nivel de un biomarcador en una muestra de orina procedente de un sujeto sospechoso de padecer una nefropatía diabética, en el que el biomarcador es un fragmento de la alfa-2-HS-glicoproteína seleccionado del grupo que consiste en VVSLGSPSGEVSHPRKT (SEQ ID NO:1) y MGVVSLGSPSGEVSHPRKT (SEQ ID NO:2), y evaluar si el sujeto presenta una nefropatía diabética básandose en el nivel del biomarcador;en el que un aumento en el nivel del biomarcador, comparado con el nivel en un sujeto sin nefropatía diabética, indica que el sujeto presenta una nefropatía diabética.
- 2- El método de la reivindicación 1, que comprende además determinar el nivel de un segundo biomarcador en la muestra de orina procedente del sujeto, en el que el segudno biomarcador es un fragmento de la alfa-1 antitripsina seleccionado del grupo que consiste en KGKWERPFEVKDTEEEDF (SEQ ID NO:3), MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4), EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO:5) y EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6).
- 3- El método de la reivindicación 1 o 2, que comprende además determinar el nivel de un biomarcador en la muestra de orina procedente del sujeto, en el que el biomarcador es GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO:7).
- 4- El método de las reivindicaciones 1 a 3, que comprende además determinar el nivel de un fragmento de osteopontina en una muestra de suero procedente del sujeto, en el que el fragmento de osteopontina se selecciona del grupo que consiste en YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8) y KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO:9).
- 5- El método de las reivindicaciones 1 a 4, que comprende además, después de la etapa de evaluación, correlacionar el nivel del biomarcador con el estado de la nefropatía diabética, en el que un aumento en el nivel del biomarcador, con relación al nivel en un sujeto sin nefropatía diabética, indica que el sujeto se encuentra en el estadio tardío de la nefropatía diabética.
- 6- Un método para evaluar la eficacia de un tratamiento para la nefropatía diabética en un sujeto, que comprende:determinar el nivel de un biomarcador antes del tratamiento en una muestra de orina procedente del sujeto, en el que el biomarcador es un fragmento de la alfa-2-HS-glicoproteína seleccionado del grupo que consiste en VVSLGSPSGEVSHPRKT (SEQ ID NO:1) y MGVVSLGSPSGEVSHPRKT (SEQ ID NO:2), determinar el nivel del biomarcador después del tratamiento en una muestra de orina procedente del sujeto, y evaluar la eficacia del tratamiento basándose en el cambio en el nivel del biomarcador después del tratamiento, en el que si el nivel del biomarcador después del tratamiento es igual o menor que el nivel del biomarcador antes del tratamiento, esto indica la eficacia del tratamiento.
- 7- El método de la reivindicación 6, que comprende además determinar el nivel de un segundo biomarcador en la muestra de orina procedente del sujeto, en el que el segundo biomarcador es un fragmento de la alfa-1 antitripsina seleccionado del grupo que consiste en KGKWERPFEVKDTEEEDF (SEQ ID NO:3), MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4), EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO:5) y EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6).
- 8- El método de la reivindicación 6 o 7, que comprende además determinar el nivel de un tercer biomarcador en la muestra de orina del sujeto, en el que el tercer biomarcador es GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO:7).
- 9- El método de las reivindicaciones 6 a 8, que comprende además determinar el nivel de un fragmento de osteopontina en una muestra de suero procedente del sujeto, en el que el fragmento de osteopontina se selecciona del grupo que consiste en YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8) y KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO:9).
- 10- El método de las reivindicaciones 6 a 9, en el que el sujeto es un paciente humano.
- 11- El método de las reivindicaciones 6 a 9, en el que el sujeto es un animal de laboratorio.
- 12- Un método para determinar el estadio de la nefropatía diabética en un sujeto, que comprende:determinar el nivel de un biomarcador en una muestra de orina procedente de un sujeto sospechoso de padecer una nefropatía diabética y, opcionalmente, uno o más factores clínicos, en el que el biomarcador es un fragmento de la ES 2 552 467 T3 alfa-2-HS-glicoproteína seleccionado del grupo que consiste en VVSLGSPSGEVSHPRKT (SEQ ID NO:1) y MGVVSLGSPSGEVSHPRKT (SEQ ID NO:2), y los factores clínicos se seleccionan del grupo que consiste en la edad, el género, HbA1c, la proporción de albúmina/creatinina y la velocidad de filtración glomerular;calcular una puntuación de enfermedad basándose en el nivel del biomarcador;y evaluar el estadio de la nefropatía diabética del sujeto basándose en la puntuación de enfermedad, comparado con valores de corte predeterminados, en el que un aumento en la puntuación de la enfermedad, comparado con unos valores de corte predeterminados, indica que el sujeto se encuentra en un estadio tardío de la nefropatía diabética.
- 13- El método de la reivindicación 12, que comprende además determinar el nivel de un segundo biomarcador en la muestra de orina procedente del sujeto, en el que el segundo biomarcador es un fragmento de la alfa-1 antitripsina seleccionado del grupo que consiste en KGKWERPFEVKDTEEEDF (SEQ ID NO:3), MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4), EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO:5) y EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6).
- 14- El método de la reivindicación 12 o 13, que comprende además determinar el nivel de un tercer biomarcador en la muestra de orina procedente del sujeto, en el que el tercer biomarcador es GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO:7).
- 15- El método de las reivindicaciones 12 a 14, que comprende además determinar el nivel de un fragmento de osteopontina en una muestra de suero procedente del sujeto, en el que el fragmento de osteopontina se selecciona del grupo que consiste en YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8) y KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO:9).
- 16- El método de las reivindicaciones 12 a 15, en el que la puntuación de enfermedad se calcula mediante un análisis seleccionado del grupo que consiste en un análisis de la regresión Ridge, un análisis de factores, un análisis de la función discriminante, y un análisis de la regresión logística.
- 17- Un método para controlar el avance de la nefropatía diabética en un sujeto, que comprende:obtener una primera muestra de orina de un sujeto sospechoso de padecer una nefropatía diabética;obtener una segunda muestra de orina del sujeto de 2 semanas a 12 meses después;determinar en la primera y la segunda muestra el nivel de un biomarcador, en el que el biomarcador es un fragmento de la alfa-2-HS-glicoproteína seleccionado del grupo que consiste en VVSLGSPSGEVSHPRKT (SEQ ID NO:1) y MGVVSLGSPSGEvShPRKT (SEQ ID NO:2), y los factores clínicos se seleccionan del grupo que consiste en la edad, el género, HbA1c, la proporción de albúmina/creatinina y la velocidad de filtración glomerular;calcular una primera puntuación de enfermedad y una segunda puntuación de enfermedad basándose en los niveles del biomarcador y, opcionalmente, uno o más factores clínicos, en la primera y la segunda muestra, respectivamente;y evaluar el avance de la enfermedad en el sujeto, en el que si la segunda puntuación de enfermedad es mayor que la primera puntuación de enfermedad, esto es indicativo de la exacerbación de la nefropatía diabética.
- 18- El método de la reivindicación 17, que comprende además determinar el nivel de un segundo biomarcador en la muestra de orina procedente del sujeto, en el que el segundo biomarcador es un fragmento de la alfa-1 antitripsina seleccionado del grupo que consiste en KGKWERPFEVKDTEEEDF (SEQ ID NO:3), MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4), EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO:5) y EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6).
- 19- El método de la reivindicación 17 o 18, que comprende además determinar el nivel de un tercer biomarcador en la muestra de orina procedente del sujeto, en el que el tercer biomarcador es GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO:7).
- 20- El método de las reivindicaciones 17 a 19, que comprende además determinar el nivel de un fragmento de osteopontina en una muestra de suero procedente del sujeto, en el que el fragmento de osteopontina se selecciona del grupo que consiste en YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8) y KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO:9).
- 21- El método de las reivindicaciones 17 a 20, en el que la puntuación de enfermedad se calcula mediante un análisis seleccionado del grupo que consiste en un análisis de la regresión Ridge, un análisis de factores, un análisis de la función discriminante, y un análisis de la regresión logística.
- 22- Un método para evaluar la eficacia de un tratamiento para la nefropatía diabética en un sujeto, que comprende:ES 2 552 467 T3 obtener una primera muestra de orina del sujeto antes del tratamiento, obtener una segunda muestra de orina del sujeto después del tratamiento, determinar en las muestras los niveles de un biomarcador, en el que el biomarcador es un fragmento de la alfa-2HS-glicoproteína seleccionada del grupo que consiste en VVSLGSPSGEVSHPRKT (SEQ ID NO:1) y MGVVsLgSPSGEVSHPRKT (SEQ ID nO:2), y los factores clínicos se seleccionan del grupo que consiste en la edad, el género, HbA1c, la proporción de albúmina/creatinina y la velocidad de filtración glomerular;calcular una primera puntuación de enfermedad y una segunda puntuación de enfermedad basándose en los niveles de los biomarcadores y, opcionalmente, uno o más factores clínicos, en la primera y la segunda muestra, respectivamente;y evaluar la eficacia del tratamiento en el sujeto, en el que si la segunda puntuación de enfermedad es igual o menor que la primera puntuación de enfermedad, esto indica la eficacia del tratamiento.
- 23- El metodo de la reivindicación 22, que comprende además determinar el nivel de un segundo biomarcador en la muestra de orina procedente del sujeto, en el que el segundo biomarcador es un fragmento de la alfa-1 antitripsina seleccionado del grupo que consiste en KGKWERPFEVKDTEEEDF (SEQ ID NO:3), MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4), EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO:5) y EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6).
- 24- El método de la reivindicación 22 o 23, que comprende además determinar el nivel de un tercer biomarcador en la muestra de orina procedente del sujeto, en el que el tercer biomarcador es GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO:7).
- 25- El método de las reivindicaciones 22 a 24, que comprende además determinar el nivel de un fragmento de osteopontina en una muestra de suero procedente del sujeto, en el que el fragmento de osteopontina se selecciona del grupo que consiste en YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8) y KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO:9).
- 26- El método de las reivindicaciones 22 a 25, en el que la puntuación de enfermedad se calcula mediante un análisis seleccionado del grupo que consiste en un análisis de la regresión Ridge, un análisis de factores, un análisis de la función discriminante, y un análisis de la regresión logística.
- 27- Un anticuerpo aislado que se une específicamente a un péptido seleccionado del grupo que consiste en VVSLGSPSGEVSHPRKT (SEQ ID NO:1) y MGVVSLGSPSGEVSHPRKT (SEQ ID NO:2).
- 28- Un kit para el diagnóstico de la nefropatía diabética, que comprende un anticuerpo que es capaz de unirse a un fragmento de la alfa-2-HS-glicoproteína seleccionado del grupo que consiste en VVSLGSPSGEVSHPRKT (SEQ ID NO:1) y MGVVSLGSPSGEVSHPRKT (SEQ ID NO:2).
- 29- El kit de la reivindicación 28, que comprende además un anticuerpo, que es capaz de unirse a un fragmento de la alfa-1 antitripsina seleccionado del grupo que consiste en KgkWeRPFEVKDTEEEDF (SEQ ID NO:3), MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4), EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO:5) y EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6).
- 30- El kit de la reivindicación 28 o 29, que comprende además un anticuerpo, que es capaz de unirse a GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO:7).
- 31- El kit de las reivindicaciones 28 a 30, que comprende además un anticuerpo, que es capaz de unirse a un fragmento de osteopontina seleccionado del grupo que consiste en YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8) y KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO:9).
- 32- El kit de las reivindicaciones 28 a 31, en el que los anticuerpos son moléculas de inmunoglobulina completas.
Independent claims32
536 paragraphs in 21 sections, as filed
ES 2 552 467 T3
DESCRIPTION
Urine and serum biomarkers associated with diabetic nephropathy
Background of the invention
Diabetic nephropathy (DN) is a progressive kidney disease associated with long-established diabetes mellitus. It causes abnormal fluid flitration and increased urinary albumin excretion, ultimately leading to kidney failure.
DN shows no symptoms in its early development. Therefore, it is difficult to detect the incipience of this disease. In fact, the current diagnosis of dN depends on the development of microalbuminuria, which appears when kidney damage has already occurred. The lack of an early diagnostic test prevents effective treatment of early-stage DN.
It is very important to identify reliable biomarkers that are useful for diagnosing DN in early stages. Rao et al. (Proteomic identification of urinary biomarkers of diabetic nephropathy, 2007, Diabetes Care, 30 (3): 629-637) describe the identification of biomarkers of nephrotpathy in urine from type 2 diabetic patients using liquid chromatography-mass spectrometry. in tandem. However, Rao et al. they do not indicate that certain specific fragments of alpha-2-HS-glycoprotein are useful as biomarkers for the diagnosis of diabetic nephropathy.
WO 03/019193 A1 describes biomarkers useful for differentiating minimal change nephrotic syndrome from focal segmental glomerulosclerosis, membranous nephrotropy and membranoproliferative glomerulonephritis. However, WO 03/019193 A1 does not indicate that certain specific fragments of alpha-2-HS-glycoprotein are useful as biomarkers for the diagnosis of diabetic nephropathy.
Summary of the invention
The present invention is based on the unexpected discoveries that a number of urine and serum proteins, and their fragments, alone or in combination, are differentially present in patients with DN, compared to subjects without DN. Therefore, these protein molecules are useful markers for the diagnosis of DN in early stages.
Accordingly, one aspect of this invention includes a method of diagnosing DN in a subject. This method includes at least two steps: (a) determining, in a subject suspected of having DN, the level of a biomarker, and (b) evaluating whether the subject has DN based on the level of the biomarker. An increase in the level of the biomarker, compared to the level in a subject without DN, indicates that the subject has DN.
The biomarker (i) used in this diagnostic method is a urine protein molecule that is a fragment of alpha-2-HS-glycoprotein selected from the group consisting of VVSLGSPSGEVSHPRKT (SEQ ID NO: 1) and MGVVSLGSPSGEVSHPRKT (SEQ ID NO: 2).
The method of the present invention preferably further comprises determining the level of one or more additional biomarkers (ii) to (iv).
Biomarker (ii) is a urine protein molecule that is a fragment of alpha-1 antitrypsin selected from the group consisting of KGKWERPFEVKDTEEEDF (SEQ ID NO: 3), MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO: 4), EDPQGDAAQKTDTSE ID NO: 5) and
EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO: 6).
Biomarker (iii) is a urine protein molecule that is a fragment of alpha-1 acid glycoprotein, specifically GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO: 7).
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 KYPDAVATWLNPDPSQKQNLPSK (SEQ ID NO: 9LAPQTL).
The diagnostic method described above may also include, after the evaluation step, a step of correlating the level of the biomarker with the state of the DN (ie, whether it is in the early or late stage). An increase in the level of the biomarker relative to the level in a subject without DN is indicative of a late-stage DN.
In another aspect, the present invention includes a method of evaluating the efficacy of a DN treatment in a subject (eg, a human patient or a laboratory animal). This method includes determining in the subject the levels before and after the treatment of the protein molecule (i), and evaluating the efficacy of the treatment based on the change in the level of the biomarker after the treatment. The method preferably further comprises determining in the subject the levels before and after treatment of one or more of the protein molecules (ii) to (iv) described above. If the level of the biomarker after treatment remains the same
ES 2 552 467 T3 same or decreases, compared to the level of the biomarker before treatment, this indicates that the treatment is effective. In another aspect, this invention includes a method for determining a stage of DN, which includes at least four stages: (a) obtain a urine sample and, optionally, a serum sample from the subject suspected of suffering from diabetic nephropathy, (b) determine in the sample or samples the level of biomarker (i) and also preferably determine the level of one or more of the biomarkers (ii) to (iv) described above, (c) calculate a disease score based on the level of the biomarker, and (d) assessing the subject's stage of diabetic nephropathy based on the disease score, compared to predetermined cut-off values, which would indicate that the subject is in a late stage of diabetic nephropathy. In this method, the calculation step can be performed by a Ridge regression analysis, a factor analysis, a discriminant function analysis, and a logistic regression analysis. Preferably, the method further comprises determining the level of one or more clinical factors selected from the group consisting of age, gender, HbA1c, albumin / creatinine ratio (ACR), and glomerular filtration rate (GFR).
In another aspect, the present invention provides a method of monitoring DN progression 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 of the biomarkers (ii) to (iv) described above. This method includes obtaining two urine samples and, optionally, two serum samples, within 2 weeks to 12 months (for example, 2-24 weeks or 3-12 months) from a subject suspected of having DN. , determine in the samples the level of the biomarker (i) and, furthermore, preferably the level of one or more of the biomarkers (ii) to (iv), calculate the disease scores based on the levels of the biomarkers, and optionally, on the levels of one or more clinical factors, and assessing the progression of DN in the subject based on disease scores. A disease score for samples collected at a later point in time higher than that for samples collected earlier is indicative of an exacerbation of DN.
The disease scores mentioned above can also be used to assess the efficacy of a DN treatment. Treatment is effective if the disease score after treatment remains unchanged or decreases compared to the disease score before treatment.
The present invention also provides a kit for use in any of the methods described above. This kit includes an antibody (A) capable of binding to a fragment of an alpha-2-HS-glycoprotein selected from the group consisting of VVSLGSPSGEVSHPRKT (SEQ ID NO: 1) and MGVVSLGSPSGEVSHPRKT (SEQ ID NO: 2). Preferably, the kit also comprises at least one other antibody selected from the group consisting of an antibody (B) capable of binding to an alpha-1 antitrypsin 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, specifically GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID of a fragment of NO: 7), and of osteopontin selected from the group consisting of YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO: 8) and KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO: 9). In one example, this kit contains only antibodies specific for the antigens to be detected (eg, biomarkers associated with DN) to practice one of the methods described herein. Specifically, it consists mainly of said antibodies.
Also within the scope of this 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).
The term "an isolated antibody" used herein refers to an antibody substantially free of the molecules associated with it in nature. More specifically, a preparation containing the antibody is considered to be "an isolated antibody" when the molecules associated with the antibody in nature present in the preparation constitute a maximum of 20% by dry weight. Purity can be measured by any appropriate method, eg, column chromatography, polyacrylamide gel electrophoresis, and HPLC.
Any of the antibodies described above can be used to make a kit useful for the practice of any of the methods of this invention.
The details of one or more embodiments of the invention are set forth in the following description. Other features or advantages of the present invention will be apparent from the following figure and the detailed description of various embodiments, and also from the appended claims.
Brief description of the drawing
First, the drawing is described.
Figure 1 is a diagram showing a bar graph for urine alpha-2-HS-glycoprotein (uDN2;
ES 2 552 467 T3 see panel A), urine alpha-1 antitrypsin (uDN5; see panel B), urine alpha-1 acid glycoprotein (uGR3; see panel C), and osteopontin from serum (sDNO; see panel D) in various groups of DN patients. The upper and lower limits of the bars mark the values of 25% and 75%, the medians being the lines that cross the bars. The upper line marks the largest value below the upper perimeter, which is the value of 75% plus 1.5 of the interquartile range, and the lower line marks the smallest value above the lower perimeter, which is the value of 25% minus 1.5 of the interquartile range.
Detailed description of the invention
DN is a kidney disorder associated with diabetes. It has five phases of progress:
Stage 1: characterized by diabetes mellitus with normal GFR and normal albuminuria (ACR <30 mg / g);
Stage 2: characterized by glomerular hyperfiltration (greater than 120 ml / minute / 1.73 m<sup>2</sup>) and renal enlargement, 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 GRF; Y
Stage 5: characterized by a GRF less than 15 ml / minute / 1.73 m<sup>2</sup>.
Stages 1-3 are usually considered the early stage and stages 4 and 5 are considered the late stage.
The inventors have identified a number of biomarkers associated with DN, especially DN at different stages. These biomarkers are composed of fragments of the following four proteins, in urine or serum: (a) alpha-2-HS-glycoprotein (GenBank accession # NP_001613, January 10, 2010) (b) alpha-1 antitrypsin (GenBank accession number AAB59495, January 10, 2010) ( c) alpha-1 acid glycoprotein (GenBank accession # EAW87416, Jan 10, 2010), and (d) osteopontin, which includes two isoforms known as secreted phosphoprotein 1a (GenBank accession # NP_001035147, 17 -January-2010) and secreted phosphoprotein 1b (GenBank Accession # NP_000573, 10-January-2010).
The 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) can contain up to 19, 34, 32, and 42 amino acid residues, respectively.
The inventors have also discovered that disease scores calculated based on the levels of the aforementioned fragments and optionally one or more clinical factors (age, gender, HbA1c, ACR and GFR) are also associated with DN at different stages.
Accordingly, one aspect of the present invention relates to a DN diagnostic method that employs the biomarker based on protein (a) described above. The method also preferably employs one or more of the biomarkers based on proteins (b) to (d). To practice this method, a urine sample is collected, and where necessary a serum sample, from a subject suspected of having DN, and the urine and serum levels of the biomarker (s) listed above can be determined by standard methods. , for example, mass spectrometry and immunological analysis. If applicable, clinical factors are determined by standard methods.
When the level of a biomarker based on a single protein molecule is determined, its level in a subject can be compared to a benchmark to determine whether the subject has DN. The reference point, which represents the level of the same biomarker in a subject without DN, can be determined based on representative levels of the biomarker in groups of patients with DN and in subjects without DN. For example, it may be the middle point between the average levels of these two groups. A level of a biomarker higher than the reference point is indicative of DN.
When the levels of the biomarkers based on at least two protein molecules and optionally the level of the least one clinical factor are determined, the levels of the protein molecules and the value or values of the clinical factor (s) may be subjected to a suitable analysis to generate a disease score (for example, represented by a number). The analysis is selected from the group consisting of a discriminant function analysis, a logistic regression analysis, a Ridge regression analysis, and a factor analysis. The disease score is then compared to a benchmark that represents the level of the same biomarker in subjects without DN. The reference point can be determined by conventional methods. For example, it can be a score obtained by analyzing the average of the levels of the protein molecules and, when necessary, the average of the values of the clinical factor (s) in subjects without DN with the same analysis. A disease score greater than the baseline is indicative of the presence of DN.
Another aspect of this invention relates to a method for determining a DN stage based on the biomarker
ES 2 552 467 T3 based on protein (a) described above. Preferably, the method also employs one or more of the biomarkers based on proteins (b) to (d) and, optionally, one or more of the clinical factors described above. To practice this method, the level of a biomarker from a patient with DN, preferably represented by a disease score, is compared to a set of predetermined cut-off values that distinguish between different stages of DN to determine the stage of the subject's DN. . Cut-off values can be determined by analyzing representative levels of the same biomarker in patients with different stages of DN using the same analysis.
An example of a procedure to determine the cut-off values mentioned above is described below based on a biomarker associated with DN at different stages:
(1) assign DN patients to different groups according to their disease conditions (eg, DN stages and risk factors);
(2) determine in each group of patients the levels / values of protein molecules and clinical factors;
(4) subjecting protein levels and clinical factor values to appropriate analysis to establish a model (for example, a formula) for calculating a disease score, and (6) determining a cut-off value for each stage of disease. disease based on a disease score (e.g. average value) representing each group of patients, as well as other relevant factors such as sensitivity, specificity, the positive predictive value (PPV) and the negative predictive value (NPV).
Any of the models generated in this way can be evaluated for its diagnostic value using a receiver operating characteristic (ROC) analysis to create a ROC curve. An optimal multivariate model provides a large area under the curve (AUC) in ROC analysis. See the models described in the following examples 1-3.
In another aspect, this invention relates to a method of monitoring the progression of nephropathy in a subject based on the protein (a) -based biomarker described above. Preferably, the method also employs one or more of the biomarkers based on proteins (b) to (d) and, optionally, one or more of the clinical factors described above. More specifically, two urine samples and / or serum samples can be obtained from a subject within a suitable time frame (2 weeks to 12 months) and studied for biomarker levels. The disease scores are then determined as described above. If the disease score representing the level of the biomarker in the sample or samples obtained at a later time is higher than that of the sample or samples obtained at an earlier time, this indicates the exacerbation of DN in the subject.
The control method can be applied to a human subject suffering from or who is at risk of suffering from DN. When the human subject is at risk for DN or is in the early stage of DN, the level of the biomarker can be studied once every 6 to 12 months to monitor the progression of DN. When the human subject is already in the late stage of DN, it is preferred that the level of the biomarker be studied once every 3 to 6 months.
The control method described above can also be applied to laboratory animals, following the usual procedures, to study DN. The term "a laboratory animal" used herein refers to a vertebrate animal that is commonly used in animal experimentation, for example, a mouse, a rat, a rabbit, a cat, a dog, a pig, and a non-human primate. Preferably, a laboratory animal is tested for the level of the biomarker once every 2 to 24 weeks.
Biomarkers can also be used to assess the efficacy of a DN treatment in a subject in need of it (ie, a human DN patient or a laboratory animal suffering from DN). In this method, disease scores based on levels of protein (a) -based biomarkers and optionally one or more clinical factors described above are determined before and after treatment. Preferably, the method also employs one or more of the biomarkers based on proteins (b) to (d). If the disease scores remain unchanged or decrease throughout the treatment, this indicates that the treatment is effective.
Also described herein is a kit useful for practicing any of the methods described above. This kit contains an antibody (A) capable of binding to a fragment of alpha-2-HS-glycoprotein 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 other antibody selected from the group consisting of an antibody (B) capable of binding to an alpha-1 antitrypsin 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, specifically GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID
NO: 7), and an antibody (D) capable of binding to a fragment of osteopontin selected from the group consisting of YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO: 8) and
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KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO: 9). In one example, this kit contains only antibodies specific for the antigens to be detected (eg, DN-associated protein molecules) to practice one of the methods described herein. Specifically, the kit consists mainly of said antibodies.
The kit described above can include two different antibodies (ie, a coating antibody and a detector antibody) that bind to the same antigen. Generally, the detector antibody is conjugated to a molecule that emits a detectable signal by itself or by binding to another agent. The term "antibody" used herein refers to a whole immunoglobulin or one of its fragments, such as Fab or F (ab ') 2, that retains antigen-binding activity. It can be natural or genetically modified (eg, a single chain antibody, a chimeric antibody, or a humanized antibody).
The antibodies included in the kit of this invention can be obtained from commercial vendors. 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 produce antibody against a particular biomarker, as listed above, the marker, optionally coupled with a carrier protein (eg, KLH), can be mixed with an adjuvant and injected into a host animal. Antibodies produced in the animal can then be purified by affinity chromatography. Host animals commonly used include rabbits, mice, guinea pigs, and rats. The various adjuvants that can be used to enhance the immune response depend 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 human adjuvants include BCG (Bacillus Calmette-Guerin) and Corynebacterium parvum. Polyclonal antibodies, that is, heterogeneous populations of antibody molecules, are present in the serum of the immunized animal.
Monoclonal antibodies, that is, homogeneous populations of antibody molecules, can be prepared using conventional hybridoma technology (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 provides for the production of antibody molecules by continuous cell lines in culture, as described in Kohler et al. (1975), Nature, 256, 495; and US Patent No. 4,376,110; the human B-cell hybridoma technique (Kosbor et al. (1983), Immunol. Today, 4, 72; Cole et al. (1983), Proc. Natl. Acad. Sci. USA, 80, 2026; and the technique hybridoma-EBV (Cole et al. (1983), Monoclonal Antibodies and Cancer Therapy, Alan R. Liss, Inc., pp 77-96). These antibodies can belong to any class of immunoglobulin, including IgG, IgM, IgE, IgA, IgD, and any of their subclasses. The hybridoma that produces the 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 it a particularly useful production method.
Furthermore, antibody fragments can be generated by known techniques. For example, these fragments include, but are not limited to, F (ab ') 2 fragments that can be produced by a pepsin digestion of an antibody molecule, and Fab fragments that can be generated by reducing the disulfide bridges of F (ab') fragments. )two.
It is believed that, without further explanation, those skilled in the art, based on the foregoing description, will utilize the present invention to its fullest extent. Therefore, the following specific embodiments are to be considered merely illustrative and not limiting of the remainder of the description in any way.
Example 1: Diagnosis of DN based on urine alpha-2-HS-glycoprotein, urine alpha-1 antitrypsin, urine alpha-1 acid glycoprotein, or serum osteopontin
Materials and methods (i) Subjects
83 patients with diabetes mellitus (called "subjects with DM") and 82 patients with DN (called "subjects with DN") were recruited at the Tri-General Military Hospital in Taipei, Taiwan, following the guidelines indicated by the American Diabetic Association and also described below:
DM: have diabetes mellitus but do not have DN (see guidelines described below);
DN: they have diabetes mellitus and secrete urinary proteins at a level greater than 1 g daily, have a DN demonstrated by biopsy, or have uremia.
All subjects were assigned to a training group and a test group in a ratio of 7: 3.
(ii) Sample collection and processing
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Samples of the first morning urine and serum samples were collected from each of the subjects mentioned above. The peptides contained in the urine samples were studied by means of urinary matrix assisted laser ionization / desorption time-of-flight mass spectrometry (MALDITOF-MS) and by isobaric markers for relative and absolute quantification (iTRAQ).
Protein molecules, which include alpha-2-HS-glycoprotein (DN2), alpha-1 antitrypsin (DN5), osteopontin (DNO), and alpha-1 acid glycoprotein (GR3), were studied for their concentrations in urine and serum samples by ELISA. Briefly, urine samples were mixed with protease inhibitors and diluted 1: 100 with dilution buffer and serum samples were diluted 1:10. The diluted samples were plated on ELISA plates in triplicate. The levels of DNO, DN2, DN5 and GR3 concentrations were measured by the conventional sandwich ELISA method.
A 5-parameter standard curve was used to calculate the concentration. Only standards and samples with% CV less than 15 were included, and those that did not meet the criteria were repeated. Protein levels in urine samples were normalized against creatinine levels in the same urine samples, which were measured with the QuantiChrom creatinine assay (BioAssay Systems (Hayward), California, USA).
(iii) Statistical analysis
The data indicating the urine and serum protein concentrations of each protein studied were statistically analyzed and represented by auROC from 0.44-0.87 for its independent ability to distinguish DN subjects from DM subjects.
For each subject, the correlation between the values was determined by a Spearman or Pearson analysis depending on the test results for normality. Comparisons of the mean or median of the groups were made by Student's t-test or the nonparametric Mann-Whitney test, as appropriate. Statistical significance was obtained when p <0.05. Statistical results were presented as the mean ± standard error of the mean (SEM) or as the median with [25%, 75%].
Results (i) Patient characteristics
The following tables 1 and 2 show the characteristics of the patients in the training group and in the test group and those in the DM and DN groups.
Table 1 - Characteristics of the patients in the training and test groups
<td></td><td>Training (n = 118)</td><td>Trial (n = 47)</td><td>P value</td>
<td>Age, mean (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, mean (SD)</td><td> 86,56 (33,11)</td><td> 83,05 (43,96)</td><td> 0,5785</td>
<td>ACR (ug / ml), mean (SD)</td><td> 737,82 (1465,47)</td><td> 1084,18 (2030,98)</td><td> 0,2239</td>
<td>Urine TP / Cr (mg / mg), mean (SD)</td><td> 1,01 (2,01)</td><td> 1 (1,78)</td><td> 0,9963</td>
<td>Serum creatinine (mg / dl), mean (SD)</td><td> 1,02 (0,87)</td><td> 1,34 (1,44)</td><td> 0,0903</td>
<td>HbA1c (%), mean (SD)</td><td> 8,49 (1,5)</td><td> 8,29 (2,19)</td><td> 0,5356</td>
<td colspan="4">Markers (adjusted for creatinine), mean (SD)</td>
<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>
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Table 2 - Characteristics of the patients in the DM and DN groups.
<td rowspan="2"></td><td colspan="3">Training (n = 118)</td><td colspan="3">Trial (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, mean (SD)</td><td> 57,11 (8,05)</td><td> 62,96 (9,8)</td><td> 0,0006</td><td> 59,09 (8,82)</td><td> 61,32 (10,09)</td><td> 0,4230</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, average</td><td> 111,21</td><td> 60,18</td><td> <0,0001</td><td> 115,6</td><td> 54,41</td><td> <0,0001</td>
<td>(FROM)</td><td> (15,75)</td><td> (25,59)</td><td></td><td> (33,66)</td><td> (29,79)</td><td></td>
<td>ACR (ug / ml), mean (SD)</td><td> 11,35 (6,81)</td><td> 1515,26</td><td> <0,0001</td><td> 9,63 (5,61)</td><td> 2029,78</td><td> 0,0004</td>
<td></td><td></td><td> (1815,72)</td><td></td><td></td><td> (2432,31)</td><td></td>
<td>Urine TP / Cr (mg / mg), mean (SD)</td><td> 0,17 (0,51)</td><td> 1,9 (2,56)</td><td> <0,0001</td><td> 0,17 (0,32)</td><td> 1,7 (2,18)</td><td> 0,0019</td>
<td>Serum creatinine (mg / dl), mean (SD)</td><td> 0,66 (0,12)</td><td> 1,42 (1,12)</td><td> <0,0001</td><td> 0,67 (0,15)</td><td> 1,92 (1,79)</td><td> 0,0019</td>
<td>HbA1c (%), mean (SD)</td><td> 8,34 (1,48)</td><td> 8,7 (1,53)</td><td> 0,2311</td><td> 8,37 (1,61)</td><td> 8,22 (2,66)</td><td> 0,8238</td>
<td colspan="7">Markers (adjusted for creatinine), mean (SD)</td>
<td>uDNO (ng / mg)</td><td> 1422,18</td><td> 1366,77</td><td> 0,8083</td><td> 1769,54</td><td> 1516,44</td><td> 0,5953</td>
<td></td><td> (1105,46)</td><td> (1347,92)</td><td></td><td> (1260,15)</td><td> (1945,7)</td><td></td>
<td>sDNO (ng / ml)</td><td> 29,03</td><td> 46,17</td><td> 0,0026</td><td> 26,2 (11,53)</td><td> 64,52</td><td> 0,0010</td>
<td></td><td> (19,32)</td><td> (37,32)</td><td></td><td></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> <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>
Statistically significant differences were observed in GFR, ACR, protein, and serum creatinine levels in DN subjects versus DM subjects. There are no differences in the gender distribution between the 5 groups.
(ii) DN-associated protein molecules
By proteomic analysis of urine, the peptides listed in Table 3 below were found to be differentially presented in urine samples from subjects with DM and subjects with DN.
Table 3 - Differentially presented urine / serum peptides and peptides in which they are located
<td>Peptide sequences</td><td>Corresponding proteins</td>
<td>VVSLGSPSGEVSHPRKT (SEQ ID NO: 1)</td><td rowspan="2">alpha-2-HS-glycoprotein (DN2)</td>
<td>MGVVSLGSPSGEVSHPRKT (SEQ ID NO: 2)</td>
<td>KGKWERPFEVKDTEEEDF (SEQ ID NO: 3)</td><td rowspan="4">alpha-1 antitrypsin (DN5)</td>
<td>MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO: 4)</td>
<td>EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO: 5)</td>
<td>EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO: 6)</td>
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<td>Peptide sequences</td><td>Corresponding proteins</td>
<td>YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO: 8)</td><td>osteopontin (DNO)</td>
<td>GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO: 7)</td><td>alpha-1 acid glycoprotein (GR3)</td>
Through an ELISA assay, three urine protein molecules, namely uDN2, uGR3 and uDN5, and one serum protein molecule, specifically sDNO, were found to be associated with DN (see Figure 1, panels AD and table 2 above). More specifically, it was found that the levels of uDN2, uGR3, uDN5 and sDNO appear elevated in subjects with DN, compared to subjects with DM (they do not have DN), which indicates that they are reliable markers of DN. Furthermore, the levels of uDN5 and uGR3 in DN subjects presenting macroalbumiuria (ACR> 300 mg / g) were higher than levels in DN subjects presenting microalbumiuria (ACR 30 mg / g to 300 mg / g). Macroalbumiuria is an indicator of late-stage DN, and microalbumiuria indicates early-stage DN.
Example 2: Clinical staging of DN based on a combination of two proteins model uDN2, uDN5, uGR3, uDNO and sDNO
The combined levels of two of uDN2, uDN5, uGR3, uDNO, and sDNO in DM subjects and DN subjects were subjected to discriminant function analysis, logistic regression analysis, and Ridge regression analysis. The results of this study indicate that any combination of two of the five proteins or their fragments can be used as a reliable marker to determine the stage of DN.
Below is an example of a two protein model that is not part of this invention, namely uDN5 and uGR3, which includes the equations for calculating disease scores based on the combined levels of these two protein molecules. Also shown below are tables (specifically, Table 4-9) that list cutoff values, sensitivities, specificities, positive predictive values (PPV) and negative predictive values (NPV), and area under the ROC curve (AUROC) for this two-protein model.
Discriminant function analysis:
Disease score = 0.3303 x log2 [uDN5] (ng / mg) + 0.2732 x log2 [uGR3] (ng / mg) + 5
Table 4 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</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 that represent the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</td><td> 11,066</td><td> 11,227</td><td> 11,691</td><td> 14,017</td><td> 11,066</td><td> 11,227</td><td> 11,691</td><td> 14,017</td>
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<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>Sensitivity (%)</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> 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>
Logistic regression analysis:
Disease score = exp (Logit_value) / (1 + exp (Logit_value)), where Logit_value = -12.5332 + 0.7197 x log2 [uDN5] (ng / mg) + 0.4941 x log2 [uGR3] (ng / mg)
Table 6 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</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 that represent the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</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>
Ridge regression analysis:
Disease score = -1.7697+ 0.1520 x Iog2 [uDN5] (ng / mg) + 0.2254 x log2 [uGR3] (ng / mg)
Table 8 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
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<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</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 that represent the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</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>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
The combined levels of three of uDN2, uDN5, uGR3, uDNO, and sDNO in subjects with DM and subjects with DN were subjected to a discriminant function analysis, a logistic regression analysis, a factor analysis, and an analysis. of the Ridge regression. The results indicate that any combination of three proteins can be used as a reliable marker to determine the clinical staging of DN.
Below is an example of a three protein model, namely uDN2, uDN5, and uGR3, which includes the equations to calculate disease scores based on the combined levels of these three protein molecules. Also shown below are tables (specifically, Tables 10-17) listing cutoffs, sensitivities, specificities, PPVs and NPVs, and AUROC for this three protein model.
Discriminant function analysis:
Disease score = 0.3340 x log2 [uDN5] (ng / mg) - 0.0142 x log2 [uDN2] (ng / mg) + 0.2784 x log2 [uGR3] (ng / mg) + 5
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Table 10 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 11,190</td><td> 11,663</td><td> 11,190</td><td> 11,663</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 11 - Cut-off values representing the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</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>Sensitivity (%)</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:
Disease score = 0.9190 x log2 [uDN5] (ng / mg) + 0.6997 x log2¡uDN2] (ng / mg) + 0.9003 x log2 [uGR3] (ng / mg)
Table 12 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</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>
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<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</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 that represent the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</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>Sensitivity (%)</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:
Disease score = exp (Logit_value) / (1 + exp (Logit_value)), where Logit_value = -11.2820 + 0.8810 x log2 [uDN5] (ng / mg) - 0.3478 x log2 [uDN2] (ng / mg) + 0.5576 x log2 [uGR3] (ng / mg)
Table 14 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 0,462</td><td> 0,798</td><td> 0,462</td><td> 0,798</td>
<td>Sensitivity (%)</td><td> 91</td><td> 88</td><td> 96</td><td> 94</td>
<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 82</td><td> 83</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>
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Table 15 - Cut-off values representing the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</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>Sensitivity (%)</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>
Ridge regression analysis:
Disease score = -1.2900 + 0.1800 x Iog2 [uDN5] (ng / mg) - 0.1013 x Iog2 [uDN2] (ng / mg) + 0.2505 x log2 [uGR3] (ng / mg)
Table 16 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 2,122</td><td> 2,831</td><td> 2,122</td><td> 2,831</td>
<td>Sensitivity (%)</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 the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</td><td> 2,083</td><td> 2,122</td><td> 2,831</td><td> 3,943</td><td> 2,083</td><td> 2,122</td><td> 2,831</td><td> 3,943</td>
<td>Sensitivity (%)</td><td> 78</td><td> 95</td><td> 85</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> 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>
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Four protein model:
The combined levels of four of uDN2, uDN5, uGR3, uDNO, and sDNO in subjects with DM and subjects with DN were subjected to a discriminant function analysis, a logistic regression analysis, a factor analysis, and a factor analysis. of the Ridge regression. The results indicate that any combination of four proteins of the five proteins or their fragments can be used as a reliable marker to determine the stages of DN.
Below is an example of a four protein model, namely uDN2, uDN5, uGR3, and sDNO, which includes the equations to calculate disease scores based on the combined levels of these four protein molecules. Also shown below are tables (specifically, Tables 18-25) listing cutoffs, sensitivities, specificities, PPVs and NPVs, and AUROC for this four protein model.
Discriminant function analysis:
Disease score = 0.2972 x log2 [uDN5] (ng / mg) + 0.0159 x log2 [uDN2] (ng / mg) + 0.2014 x log2 [uGR3] (ng / mg) + 0.5688 x log2 [sDNO] (ng / ml) + 5
Table 18 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 12,945</td><td> 13,520</td><td> 12,945</td><td> 13,520</td>
<td>Sensitivity (%)</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 that represent the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</td><td> 12,887</td><td> 12,945</td><td> 13,520</td><td> 15,560</td><td> 12,887</td><td> 12,945</td><td> 13,520</td><td> 15,560</td>
<td>Sensitivity (%)</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>
Factor analysis:
Disease score = 0.9132 x log2 [uDN5] (ng / mg) + 0.6950 x log2¡uDN2] (ng / mg) + 0.9080 x log2 [uGR3] (ng / mg) + 0.4549 x log2 [sDNO] (ng / ml)
Table 20 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
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<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 28,459</td><td> 30,095</td><td> 28,459</td><td> 30,095</td>
<td>Sensitivity (%)</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 that represent the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</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>Sensitivity (%)</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:
Disease score = exp (Logit_value) / (1 + exp (Logit_value)), where Logit_value = -13.7529 + 0.9460 x log2 [uDN5] (ng / mg) - 0.3110 x log2 [uDN2] (ng / mg) + 0.4957 x log2 [uGR3] (ng / mg) + 0.4787 x log2 [sDNO] (ng / ml)
Table 22 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 0,423</td><td> 0,804</td><td> 0,423</td><td> 0,804</td>
<td>Sensitivity (%)</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>
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Table 23 - Cut-off values representing the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</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>Sensitivity (%)</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>
Ridge regression analysis:
Disease score = -1.7588 + 0.1729 x Iog2 [uDN5] (ng / mg) - 0.0971 x Iog2 [uDN2] (ng / mg) + 0.2381 x log2 [uGR3] (ng / mg) + 0.1312 x log2 [sDNO] (ng / ml)
Table 24 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 2,261</td><td> 2,854</td><td> 2,261</td><td> 2,854</td>
<td>Sensitivity (%)</td><td> 91</td><td> 85</td><td> 96</td><td> 94</td>
<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 77</td><td> 90</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 that represent the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</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>Sensitivity (%)</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>
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Five protein model:
The combined levels of uDN2, uDN5, uGR3, uDNO, and sDNO in subjects with DM and in subjects with DN were subjected to a discriminant function analysis, a logistic regression analysis, a factor analysis, and an analysis of the Ridge regression. The results indicate that the combination of these five proteins or their fragments can be used as a reliable marker to determine the stages of DN.
Below are equations to calculate disease scores based on the combined levels of these five protein molecules, as well as tables (specifically, Tables 26-33) that list cutoffs, sensitivities, specificities, the PPVs and NPVs, and the AUROC for this five-protein model.
Discriminant function analysis:
Disease score = 0.2780 x log2 [uDN5] (ng / mg) + 0.0231 x log2 [uDN2] (ng / mg) + 0.2236 x log2 [uGR3] (ng / mg) + 0.6043 x log2 [sDNO] (ng / ml) - 0.1513 x log2 [uDNO] (ng / mg) + 5
Table 26 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 11,818</td><td> 12,164</td><td> 11,818</td><td> 12,164</td>
<td>Sensitivity (%)</td><td> 86</td><td> 98</td><td> 96</td><td> 100</td>
<td>Specificity (%)</td><td> 90</td><td> 90</td><td> 86</td><td> 86</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 the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</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>Sensitivity (%)</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:
Disease score = 0.9117 x log2 [uDN5] (ng / mg) + 0.6949 x log2¡uDN2] (ng / mg) + 0.9095 x log2 [uGR3] (ng / mg) + 0.4554 x log2 [sDNO] (ng / ml) + 0.0384 x log2 [uDNO] (ng / mg)
Table 28 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
ES 2 552 467 T3
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 29,475</td><td> 30,541</td><td> 29,475</td><td> 30,541</td>
<td>Sensitivity (%)</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>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 the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</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>Sensitivity (%)</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:
Disease score = exp (Logit_value) / (1 + exp (Logit_value)), where Logit value = -11.4318 + 0.8188 x log2 [uDN5] (ng / mg) - 0.5376 x log2 [uDN2 ] (ng / mg) + 0.7561 x log2 [uGR3] (ng / mg) + 0.3940 x log2 [sDNO] (ng / ml) - 0.1741 x log2 [uDNO] (ng / mg)
Table 30 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 0,436</td><td> 0,780</td><td> 0,436</td><td> 0,780</td>
<td>Sensitivity (%)</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>
ES 2 552 467 T3
Table 31 - Cut-off values that represent the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</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>Sensitivity (%)</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>
Ridge regression analysis:
Disease score = -1.3112 + 0.1648 x Iog2 [uDN5] (ng / mg) - 0.0968 x Iog2 [uDN2] (ng / mg) + 0.2468 x log2 [uGR3] (ng / mg) + 0.1426 x log2 [sDNO] (ng / ml) - 0.0552 x log2 [uDNO] (ng / mg)
Table 32 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 2,244</td><td> 2,759</td><td> 2,244</td><td> 2,729</td>
<td>Sensitivity (%)</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 that represent the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</td><td> 2,043</td><td> 2,244</td><td> 2,729</td><td> 3,913</td><td> 2,043</td><td> 2,244</td><td> 2,729</td><td> 3,913</td>
<td>Sensitivity (%)</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>
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Example 3: Clinical staging of DN based on a combination of uDN2, uDN5, uGR3, and age
Below are the equations to calculate disease scores determined by a discriminant function analysis, a factor analysis, a logistic regression analysis, and a Ridge regression analysis, based on the level of a biomarker composed of three protein molecules, specifically, uDN2, uDN5, and uGR3, and a clinical factor, specifically, age. Tables (specifically, Tables 34-41) that list cutoff values, sensitivities, specificities, PPVs, NPVs, and AUROC for this model are also shown below.
Discriminant function analysis:
Disease score = 0.3342 x log2 [uDN5] (ng / mg) - 0.0201 x log2 [uDN2 (ng / mg) 0.2862 x log2 [uGR3] (ng / mg) 10 + 0.059 x age (years ) + 5
Table 34 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 11,515</td><td> 12,088</td><td> 11,515</td><td> 12,088</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> 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 the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</td><td> 11,353</td><td> 11,515</td><td> 12,088</td><td> 14,343</td><td> 11,353</td><td> 11,515</td><td> 12,088</td><td> 14,343</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> 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>
Factor analysis:
Disease score = 0.9184 x log2 [uDN5] (ng / mg) + 0.7006 x log2¡uDN2] (ng / mg) + 0.9005 x log2 [uGR3] (ng / mg) + 0.1863 x age (years)
ES 2 552 467 T3
Table 36 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 38,341</td><td> 40,075</td><td> 38,341</td><td> 40,075</td>
<td>Sensitivity (%)</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 the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>No. of 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>cut</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>Sensitivity (%)</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:
Disease score = exp (Logit_value) / (1 + exp (Logit_value)), where Logit_value = -15.9748 + 0.8688 x log2 [uDN5] (ng / mg) - 0.4966 x log2 [uDN2] (ng / mg) + 0.6436 x log2 [uGR3] (ng / mg) + 10 0.0879 x age (years)
Table 38 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 0,321</td><td> 0,889</td><td> 0,321</td><td> 0,889</td>
<td>Sensitivity (%)</td><td> 93</td><td> 80</td><td> 100</td><td> 94</td>
ES 2 552 467 T3
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</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 the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</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>Sensitivity (%)</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>
Ridge regression analysis:
Disease score = -2.1690 + 0.1771 x Iog2 [uDN5] (ng / mg) - 0.1074 x Iog2 [uDN2] (ng / mg) + 0.2474 x log2 [uGR3] (ng / mg) + 0.0168 x age (years)
Table 40 - Cut-off values representing the early and late stages of DN indicated by the levels of albumin in the urine
<td rowspan="2"></td><td colspan="2">Training set (n = 118)</td><td colspan="2">Test set (n = 47)</td>
<td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td><td>DM vs. DN</td><td>DM, microalbuminuria versus macroalbuminuria</td>
<td>cut</td><td> 2,139</td><td> 2,880</td><td> 2,139</td><td> 2,880</td>
<td>Sensitivity (%)</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>
ES 2 552 467 T3
Table 41 - Cut-off values representing the stages of DN 1-5
<td></td><td colspan="4">Training set (n = 118)</td><td colspan="4">Test set (n = 47)</td>
<td>DN stage</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td><td>1 fr. 2-5</td><td>1-2 fr. 3-5</td><td>1-3 fr. 4-5</td><td>1-4 fr. 5</td>
<td>cut</td><td> 2,128</td><td> 2,139</td><td> 2,880</td><td> 4,051</td><td> 2,128</td><td> 2,139</td><td> 2,880</td><td> 4,051</td>
<td>Sensitivity (%)</td><td> 75</td><td> 93</td><td> 85</td><td> 75</td><td> 84</td><td> 100</td><td> 89</td><td> 100</td>
<td>Specificity (%)</td><td> 89</td><td> 90</td><td> 90</td><td> 90</td><td> 69</td><td> 73</td><td> 83</td><td> 89</td>
<td>PPV (%)</td><td> 92</td><td> 90</td><td> 81</td><td> 21</td><td> 84</td><td> 81</td><td> 76</td><td> 29</td>
<td>NPV (%)</td><td> 69</td><td> 93</td><td> 92</td><td> 99</td><td> 69</td><td> 100</td><td> 92</td><td> 100</td>
<td>AUROC</td><td> 0,89</td><td> 0,96</td><td> 0,95</td><td> 0,92</td><td> 0,89</td><td> 0,98</td><td> 0,96</td><td> 0,92</td>
Other realizations
All of the features described in this specification can be combined in any combination. Each feature described in this specification can be replaced by an alternative feature that meets the same goal, an equivalent goal, or the like. Thus, unless expressly stated otherwise, each feature described is just one example of a generic set of equivalent or similar features.
From the foregoing description, those skilled in the art can easily determine the essential features of the present invention, and can make various changes and modifications to the invention to adapt it to various uses and conditions.
Contents21
38 members in 15 offices
Priority claims3
| Document | Office | Kind | Date |
|---|---|---|---|
| 147778P | United States of America | – | |
| 14777809 | United States of America | P | |
| 2010000097 | Canada | W |
Members38
| Document | Office | Kind | |
|---|---|---|---|
| CA2748937A1 | Canada | A1 | |
| US2010197033A1 | United States of America | A1 | |
| WO2010085879A1 | World Intellectual Property Organization (WIPO) | A1 | |
| TW201033616A | Taiwan Province of China | A | |
| US2011079077A1 | United States of America | A1 | |
| US2011086371A1 | United States of America | A1 | |
| EP2391654A1 | European Patent Office (EPO) | A1 | |
| CN102300877A | China | A | |
| MX2011007811A | Mexico | A | |
| JP2012516431A | Japan | A | |
| EP2391654A4 | European Patent Office (EPO) | A4 | |
| US8465980B2 | United States of America | B2 | |
| US8476077B2 | United States of America | B2 | |
| EP2623517A1 | European Patent Office (EPO) | A1 | |
| US2013252267A1 | United States of America | A1 | |
| TWI449910B | Taiwan Province of China | B | |
| CN102300877B | China | B | |
| JP5689426B2 | Japan | B2 | |
| EP2391654B1 | European Patent Office (EPO) | B1 | |
| EP2623517B1 | European Patent Office (EPO) | B1 | |
| DK2391654T3 | Denmark | T3 | |
| PT2391654E | Portugal | E | |
| PT2623517E | Portugal | E | |
| DK2623517T3 | Denmark | T3 | |
| ES2552467T3This record | Spain | T3 | |
| ES2552557T3 | Spain | T3 | |
| EP2623517B8 | European Patent Office (EPO) | B8 | |
| PL2391654T3 | Poland | T3 | |
| PL2623517T3 | Poland | T3 | |
| HUE025795T2 | Hungary | T2 | |
| HUE025796T2 | Hungary | T2 | |
| CA2748937C | Canada | C | |
| CY1116893T1 | Cyprus | T1 | |
| CY1117159T1 | Cyprus | T1 | |
| BRPI1007443A2 | Brazil | A2 | |
| US10101340B2 | United States of America | B2 | |
| BRPI1007443B1 | Brazil | B1 | |
| BRPI1007443B8 | Brazil | B8 |
Numbers
- Publication
- 2552467
- Application
- 10735454
Titles2
- Spanish
- Biomarcadores de la orina y el suero asociados con la nefropatía diabética
- English
- Biomarkers of urine and serum associated with diabetic nephropathy
Classification
- CPC, 8
- G01N33/6893
- G01N2333/4728
- G01N2333/52
- G01N2333/71
- G01N2333/8125
- G01N2800/347
- G01N2800/52
- G01N2800/56
- IPC, 8
- C07K16 38
- C07K14 47
- C07K14 81
- C07K16 18
- C40B30 00
- G01N33 53
- G01N33 573
- G01N33 68