CN111308001A - Metabolism marker of human macular neovascular diseases and application thereof - Google Patents
Metabolism marker of human macular neovascular diseases and application thereof Download PDFInfo
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Abstract
The invention provides a metabolic marker of human macular neovascular diseases and application thereof. Specifically, the invention provides a biomarker set, wherein the biomarker set comprises a plurality of metabolic biomarkers, and can be used for evaluating the risk of the macular neovascular disease of a subject to be detected or diagnosing the macular neovascular disease of the subject to be detected. The invention also provides a kit containing the biomarker set and application of the biomarker set in risk assessment and diagnosis of macular neovascular diseases.
Description
Technical Field
The invention relates to the field of biomedicine, in particular to a metabolic marker of human macular neovascular diseases and application thereof.
Background
Macular neovascular disease is a group of diseases characterized by choroidal neovascularisation in the macular region, including: age-related macular degeneration, polypoid choroidal vasculopathy, pathologic high myopia, and the like.
Age-related macular degeneration is the leading cause of blindness in patients over the age of 55 years; by 2040 years, it will affect about 17 billion population, 11 billion of which are asian population; abnormal neovascularization from age-related macular degeneration can cause fluid and lipid leakage under the macula, fibrous scarring, and ultimately severe visual impairment. Polypoid choroidal vasculopathy is also found in asian populations and is often recognized as a subtype of age-related macular degeneration; the generation of new blood vessels is related to abnormal branched vascular network, and polypoid blood vessels are expanded at the end of the blood vessels. Pathologically high myopia is most commonly seen in east asian countries and is characterized by abnormal choroidal vessels and growing eyeballs. All of the above macular neovascular diseases have similar pathologic changes of neovascularization, which can cause serious visual impairment and irreversible blindness, and thus, such diseases have become the focus and difficulty for the diagnosis and treatment of ophthalmologists.
To date, the diagnosis of macular neovascular disease has relied heavily on imaging examinations, such as: optical Coherence Tomography (OCT), blood flow OCT (octa), Fundus Fluorography (FFA), indocyanine green angiography (ICGA), and the like. OCT and OCTA are non-invasive tests, and can quickly and effectively provide high-definition images of fundus retina, choroid and even sclera; in FA and ICGA examinations, fluorescein sodium or indocyanine green contrast agent is injected into the vein of a patient and is commonly used for detecting the blood circulation of the retina and choroid of the fundus oculi of the patient. However, since the pathological changes of the macular neovascular diseases are very similar, some diseases are still difficult to be accurately diagnosed even if all the above detection means are applied. Therefore, there is an urgent need to find other diagnostic means for macular neovascular diseases.
Therefore, the method for identifying the metabolic markers of the macular neovascular diseases by combining a gas chromatography-time-of-flight mass spectrometry technology and a pattern recognition method is urgently needed in the field, a new diagnosis and typing means is provided for the diseases, and a foundation is laid for further clarifying the occurrence and development mechanisms of the diseases.
Disclosure of Invention
The invention aims to identify the metabolic markers of macular neovascular diseases by combining a gas chromatography-time-of-flight mass spectrometry technology and a pattern recognition method, provides a new diagnosis and typing means for the diseases, and lays a foundation for further clarifying the occurrence and development mechanisms of the diseases.
In a first aspect of the invention there is provided a set of biomarkers comprising two or more biomarkers selected from the group consisting of: l-sorbose, D-tagatose and ribonolactone.
In another preferred example, the biomarker panel is used for diagnosing macular neovascular disease.
In another preferred embodiment, the biomarker panel is a biomarker panel for diagnosing AMD disease, comprising biomarkers selected from the group consisting of: l-sorbose, D-tagatose, or a combination thereof.
In another preferred embodiment, the biomarker panel is a biomarker panel for diagnosing AMD disease, further comprising biomarkers selected from the group consisting of: glycerol, L-2-amino-3- (1-pyrazolyl) propionic acid, 5-hydroxylysine, or a combination thereof.
In another preferred embodiment, the biomarker panel is a biomarker panel for diagnosing AMD disease, further comprising biomarkers selected from the group consisting of: ribonolactone, petroselinic acid, or a combination thereof.
In another preferred embodiment, the biomarker panel is a biomarker panel for diagnosing AMD disease, further comprising biomarkers selected from the group consisting of: hexanoic acid, 4-hydroxyphenylpyruvic acid, L-arabinose, L-glutamic acid, D-maltose, or a combination thereof.
In another preferred embodiment, the biomarker panel is a biomarker panel for diagnosing AMD disease, further comprising biomarkers selected from the group consisting of: pipecolic acid, maleic acid, oleic acid, hypoxanthine, linoleic acid, L-cysteine, or combinations thereof.
In another preferred example, the biomarker panel is a biomarker panel for diagnosing AMD disease, further comprising biomarkers selected from table 1;
TABLE 1
In another preferred embodiment, the biomarker panel is a biomarker panel for diagnosing PCV disease, comprising biomarkers selected from the group consisting of: l-sorbose, ribonolactone, or a combination thereof.
In another preferred embodiment, the biomarker panel is a biomarker panel for diagnosing PCV disease, further comprising biomarkers selected from the group consisting of: petroselinic acid, D-tagatose, myristic acid, or a combination thereof.
In another preferred embodiment, the biomarker panel is a biomarker panel for diagnosing PCV disease, further comprising biomarkers selected from the group consisting of: heptadecanoic acid, L-2-amino-3- (1-pyrazolyl) propionic acid, or a combination thereof.
In another preferred embodiment, the biomarker panel is a biomarker panel for diagnosing PCV disease, further comprising a biomarker selected from the group consisting of α -ketoisovalerate, glycerol, 4-hydroxyphenylpyruvate, D-maltose, phenylpyruvic acid, 5-hydroxylysine, dodecanoic acid, or a combination thereof.
In another preferred embodiment, the biomarker panel is a biomarker panel for diagnosing PCV disease, further comprising biomarkers selected from the group consisting of: palmitic acid, hypoxanthine, linoleic acid, stearic acid, pipecolic acid, pyruvic acid, or a combination thereof.
In another preferred example, the biomarker panel is a biomarker panel for diagnosing PCV disease, further comprising biomarkers selected from table 2;
TABLE 2
In another preferred example, the biomarker panel is a biomarker panel for diagnosing PM disease, comprising ribonolactones.
In another preferred embodiment, the set of biomarkers is a set of biomarkers for diagnosing a PM disease, further comprising L-2-amino-3- (1-pyrazolyl) propanoic acid.
In another preferred example, the biomarker panel is a biomarker panel for diagnosing PM disease, further comprising biomarkers selected from the group consisting of: glycerol, D-maltose, or a combination thereof.
In another preferred example, the biomarker panel is a biomarker panel for diagnosing PM disease, further comprising biomarkers selected from the group consisting of: pyruvic acid, hypoxanthine, linoleic acid, palmitic acid, oleic acid, petroselinic acid, stearic acid, maleic acid, or a combination thereof.
In another preferred example, the biomarker panel is a biomarker panel for diagnosing PM disease, further comprising a biomarker selected from table 3;
TABLE 3
In another preferred embodiment, the set comprises two or more biomarkers selected from the following table:
in another preferred embodiment, the set comprises biomarkers b1-b 20.
In another preferred embodiment, the set comprises the biomarkers bm and one or more biomarkers selected from the subset X consisting of the biomarkers b1-b (m-1) and b (m +1) -b20, m being an integer and 1. ltoreq. m.ltoreq.20, preferably 1. ltoreq. m.ltoreq.7, more preferably 1. ltoreq. m.ltoreq.5.
In another preferred embodiment, m is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20;
wherein, when m is 1, the biomarker b1-b (m-1) is not present;
when m is 2, the biomarker b1-b (m-1) represents a biomarker b 1;
when m is an integer of 3 to 20, the subset X consists of biomarkers b1 to b (m-1) and biomarkers b (m +1) to b 20;
when m is 19, the biomarkers b (m +1) to b20 represent the biomarker b 20;
when m is 20, the biomarkers b (m +1) -b20 are not present.
In another preferred embodiment, the set comprises biomarkers b1-b3, and one or more biomarkers selected from subset Y, wherein subset Y consists of biomarkers b4-b 20.
In another preferred embodiment, said set comprises the biomarkers b1-b2, b4-b6 and one or more biomarkers selected from the subset Z, wherein the subset Z consists of the biomarkers b3, b7-b 20.
In another preferred embodiment, said set comprises biomarkers b1-b7 and one or more biomarkers selected from subset Z, wherein subset Z consists of biomarkers b8-b 20.
In another preferred embodiment, said set comprises biomarkers selected from the group consisting of:
b1-b7。
in another preferred embodiment, said set comprises biomarkers selected from the group consisting of:
b1-b2、b4-b6。
in another preferred embodiment, said set comprises biomarkers selected from the group consisting of:
b1-b3。
in another preferred embodiment, the set comprises biomarkers b1, b3 and one or more biomarkers selected from subset Y, wherein subset Y consists of biomarkers b2, b4-b 20.
In another preferred embodiment, said set comprises biomarkers b1-b3, b7, b16 and one or more biomarkers selected from subset Z, wherein subset Z consists of biomarkers b4-b6, b8-b15, b17-b 20.
In another preferred embodiment, said set comprises the biomarkers b1-b4, b7, b16-b17 and one or more biomarkers selected from the subset Z, wherein the subset Z consists of the biomarkers b5-b6, b8-b15, b18-b 20.
In another preferred embodiment, said set comprises biomarkers selected from the group consisting of:
b1-b4、b7、b16-b17。
in another preferred embodiment, said set comprises biomarkers selected from the group consisting of:
b1-b3、b7、b16。
in another preferred embodiment, said set comprises biomarkers selected from the group consisting of:
b1、b3。
in another preferred embodiment, the set comprises biomarker b3 and one or more biomarkers selected from subset Y, wherein subset Y consists of biomarkers b1-b2, b4-b 20.
In another preferred embodiment, said set comprises biomarkers b3-b4 and one or more biomarkers selected from subset Z, wherein subset Z consists of biomarkers b1-b2, b5-b 20.
In another preferred embodiment, said set comprises biomarkers selected from the group consisting of:
b3-b4。
in another preferred embodiment, said set comprises biomarkers selected from the group consisting of:
b3。
in another preferred embodiment, the biomarker or set of biomarkers is derived from a blood, plasma, or serum sample.
In another preferred embodiment, an increase in one or more biomarkers selected from subset H, as compared to a reference value, indicates that the subject is at risk for or has macular neovascular disease,
wherein subset H comprises biomarkers b5, b6, b7, b11, b13, b14, b16, b17, b 19.
In another preferred embodiment, the subset H comprises the biomarkers b5, b7, b16, b 17.
In another preferred embodiment, the subset H comprises the biomarkers b5, b7, b 16.
In another preferred embodiment, the subset H comprises the biomarkers b5, b7, b11, b13, b 14.
In another preferred embodiment, the subset H comprises the biomarkers b5, b 7.
In another preferred embodiment, the subset H comprises the biomarkers b5, b7, b14, b16, b17, b 19.
In another preferred embodiment, the subset H comprises the biomarkers b7, b16, b 17.
In another preferred embodiment, said subset H comprises the biomarker b 6.
In another preferred embodiment, a decrease in one or more biomarkers selected from subset K, as compared to a reference value, indicates that the subject is at risk for or has macular neovascular disease,
wherein subset K comprises biomarkers b1, b2, b3, b4, b6, b8, b9, b10, b12, b15, b18, b 20.
In another preferred example, the subset K comprises the biomarkers b1, b2, b3, b4, b 6.
In another preferred embodiment, the subset H comprises the biomarkers b1, b2, b 3.
In another preferred example, said subset H comprises the biomarkers b1, b2, b3, b4, b6, b8, b9, b10, b12, b 15.
In another preferred embodiment, the subset H comprises the biomarkers b1, b2, b3, b4, b 6.
In another preferred example, said subset H comprises the biomarkers b1, b2, b3, b4, b6, b9, b12, b18, b 20.
In another preferred embodiment, the subset H comprises the biomarkers b1, b2, b3, b 4.
In another preferred embodiment, the subset H comprises the biomarkers b3, b4, b 12.
In another preferred embodiment, the subset H comprises the biomarkers b3, b 4.
In another preferred embodiment, the individual biomarkers are identified by mass spectrometry, preferably by a combination of chromatographic mass spectrometry, such as gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS).
In another preferred embodiment, the set is used for evaluating the risk of the macular neovascular disease of the subject to be tested or diagnosing the macular neovascular disease of the subject to be tested.
In another preferred embodiment, said assessing the risk of a subject suffering from a macular neovascular disease comprises early screening of the subject for a macular neovascular disease.
In another preferred embodiment, the macular neovascular disease includes age-related macular degeneration, polypoid choroidal vasculopathy, and pathologic high myopia.
In a second aspect, the present invention provides a reagent combination for risk assessment or diagnosis of macular neovascular disease, the reagent combination comprising reagents for detecting each biomarker in the collection according to the first aspect of the invention.
In another preferred embodiment, the reagents comprise substances for mass spectrometry detection of the individual biomarkers of the collection according to the first aspect of the invention.
According to a third aspect of the invention there is provided a kit comprising a collection according to the first aspect of the invention and/or a combination of reagents according to the second aspect of the invention.
In another preferred embodiment, each biomarker in the collection according to the first aspect of the invention is used as a standard.
In another preferred embodiment, the kit further comprises an instruction which describes a reference data set of the levels of the respective biomarkers in the collection according to the first aspect of the invention from patients with macular neovascular disease and/or healthy controls.
In a fourth aspect, the present invention provides a use of a biomarker panel for preparing a kit for assessing a risk of developing macular neovascular disease in a subject or diagnosing macular neovascular disease in a subject, wherein the biomarker panel comprises one or more biomarkers selected from the group consisting of: l-sorbose, D-tagatose and ribonolactone.
In another preferred embodiment, the macular neovascular disease is selected from the group consisting of: AMD, PVC, PM, or combinations thereof.
In another preferred example, when used to assess the risk of AMD disease in a subject or to diagnose AMD disease in a subject, the biomarker panel comprises biomarkers selected from the group consisting of: l-sorbose, D-tagatose, or a combination thereof.
In another preferred example, when used to assess the risk of AMD disease in a subject or to diagnose AMD disease in a subject, the biomarker panel further comprises a biomarker selected from the group consisting of: glycerol, L-2-amino-3- (1-pyrazolyl) propionic acid, 5-hydroxylysine, or a combination thereof.
In another preferred example, when used to assess the risk of AMD disease in a subject or to diagnose AMD disease in a subject, the biomarker panel further comprises a biomarker selected from the group consisting of: ribonolactone, petroselinic acid, or a combination thereof.
In another preferred example, when used to assess the risk of AMD disease in a subject or to diagnose AMD disease in a subject, the biomarker panel further comprises a biomarker selected from the group consisting of: hexanoic acid, 4-hydroxyphenylpyruvic acid, L-arabinose, L-glutamic acid, D-maltose, or a combination thereof.
In another preferred example, when used to assess the risk of AMD disease in a subject or to diagnose AMD disease in a subject, the biomarker panel further comprises a biomarker selected from the group consisting of: pipecolic acid, maleic acid, oleic acid, hypoxanthine, linoleic acid, L-cysteine, or combinations thereof.
In another preferred example, when used to assess the risk of AMD disease in a subject or to diagnose AMD disease in a subject, the biomarker panel further comprises a biomarker selected from table 1.
In another preferred example, when used for assessing the risk of or diagnosing PVC disease in a subject, the set of biomarkers comprises biomarkers selected from the group consisting of: l-sorbose, ribonolactone, or a combination thereof.
In another preferred example, when used for assessing the risk of or diagnosing PVC disease in a subject, the biomarker panel further comprises a biomarker selected from the group consisting of: petroselinic acid, D-tagatose, myristic acid, or a combination thereof.
In another preferred example, when used for assessing the risk of or diagnosing PVC disease in a subject, the biomarker panel further comprises a biomarker selected from the group consisting of: heptadecanoic acid, L-2-amino-3- (1-pyrazolyl) propionic acid, or a combination thereof.
In another preferred example, when used for assessing the risk of or diagnosing a PVC disease in a subject, the biomarker panel further comprises a biomarker selected from the group consisting of α -ketoisovalerate, glycerol, 4-hydroxyphenylpyruvate, D-maltose, phenylpyruvic acid, 5-hydroxylysine, dodecanoic acid, or a combination thereof.
In another preferred example, when used for assessing the risk of or diagnosing PVC disease in a subject, the biomarker set further comprises a biomarker selected from the group consisting of: palmitic acid, hypoxanthine, linoleic acid, stearic acid, pipecolic acid, pyruvic acid, or a combination thereof.
In another preferred example, when used to assess a subject's risk of developing a PVC disease or a subject's PVC disease diagnosis, the biomarker panel further comprises a biomarker selected from table 2.
In another preferred example, when used to assess risk of PM disease in a subject or to diagnose PM disease in a subject, the biomarker panel comprises ribonolactones.
In another preferred example, when used for assessing the risk of or diagnosing a PM disease in a subject, the set of biomarkers further comprises L-2-amino-3- (1-pyrazolyl) propanoic acid.
In another preferred example, when used for assessing the risk of PM disease in a subject or for a diagnosis of PM disease in a subject, the biomarker panel further comprises a biomarker selected from the group consisting of: glycerol, D-maltose, or a combination thereof.
In another preferred example, when used for assessing the risk of PM disease in a subject or for a diagnosis of PM disease in a subject, the biomarker panel further comprises a biomarker selected from the group consisting of: pyruvic acid, hypoxanthine, linoleic acid, palmitic acid, oleic acid, petroselinic acid, stearic acid, maleic acid, or a combination thereof.
In another preferred example, when used to assess a subject's risk of PM disease or a subject's PM disease diagnosis, the biomarker panel further comprises a biomarker selected from table 3.
In another preferred embodiment, said evaluating or diagnosing comprises the steps of:
(1) providing a sample from a subject, and detecting the level of each biomarker in said collection in the sample;
(2) comparing the level measured in step (1) with a reference data set or a reference value (e.g., a reference value for a healthy control);
preferably, the reference data set comprises the levels of each biomarker in the collection as derived from a patient with macular neovascular disease and a healthy control;
preferably, the reference data set comprises the levels of each biomarker in the collection as derived from AMD patients and healthy controls;
said reference data set comprising levels of individual biomarkers as in said set derived from PVC disease patients and healthy controls;
the reference data set comprises the levels of each biomarker in the collection as derived from PM patients and healthy controls.
In another preferred embodiment, the sample is selected from the group consisting of: blood, plasma, and serum.
In another preferred embodiment, the comparing the level measured in step (1) with a reference data set further comprises the step of establishing a multivariate statistical model to output the probability of disease, preferably, the multivariate statistical model is an orthonormal partial least squares regression discriminant analysis model.
In another preferred embodiment, if the probability of disease is greater than or equal to 0.5, the subject is determined to be at risk for or suffering from AMD disease.
In another preferred embodiment, one or more biomarkers selected from subset H is increased when compared to a reference value, indicating that the subject is at risk for or has macular neovascular disease,
wherein subset H comprises biomarkers b5, b6, b7, b11, b13, b14, b16, b17, b 19.
In another preferred embodiment, one or more biomarkers selected from subset H is increased when compared to a reference value, indicating that the subject is at risk for or has AMD disease,
wherein subset H comprises biomarkers b5, b 7.
In another preferred example, the subset H further comprises the biomarkers b11, b13, b 14.
In another preferred example, if the probability of disease is greater than or equal to 0.5, the subject is determined to be at risk of or suffering from PCV disease.
In another preferred embodiment, one or more of the biomarkers selected from subset H is increased when compared to a reference value, indicating that the subject is at risk for or has PCV disease,
wherein subset H comprises biomarkers b7, b16, b17, b 19.
In another preferred example, the subset H further comprises the biomarkers b5, b 14.
In another preferred example, if the probability of disease is greater than or equal to 0.5, the subject is determined to be at risk of or suffering from PM disease.
In another preferred embodiment, one or more biomarkers selected from subset H is increased when compared to a reference value, indicating that the subject is at risk for or suffering from PM disease,
wherein subset H comprises biomarker b 6.
In another preferred embodiment, a decrease in one or more biomarkers selected from subset K, compared to a reference value, indicates that the subject is at risk for or has macular neovascular disease,
wherein subset K comprises biomarkers b1, b2, b3, b4, b6, b8, b9, b10, b12, b15, b18, b 20.
In another preferred embodiment, the subset H comprises the biomarkers b1, b2, b3, b4, b 6.
In another preferred embodiment, the subset H comprises the biomarkers b1, b2, b 3.
In another preferred embodiment, a decrease in one or more biomarkers selected from subset K, as compared to a reference value, indicates that the subject is at risk for or has AMD,
wherein subset K comprises biomarkers b1, b2, b3, b4, b6, b8, b9, b10, b12, b 15.
In another preferred embodiment, the subset H comprises the biomarkers b1, b2, b3, b4, b 6.
In another preferred embodiment, the subset H comprises the biomarkers b1, b2, b 3.
In another preferred embodiment, a decrease in one or more biomarkers selected from subset K, compared to a reference value, indicates that the subject is at risk for or has PCV disease,
wherein subset K comprises biomarkers b1, b2, b3, b4, b6, b9, b12, b18, b 20.
In another preferred embodiment, the subset H comprises the biomarkers b1, b2, b3, b 4.
In another preferred embodiment, the subset H comprises the biomarkers b1, b2, b 3.
In another preferred embodiment, a decrease in one or more biomarkers selected from subset K, as compared to a reference value, indicates that the subject is at risk for or suffering from PM disease,
wherein subset K comprises biomarkers b3, b4, b 12.
In another preferred embodiment, the subset H comprises the biomarkers b3, b 4.
In another preferred embodiment, said subset H comprises the biomarker b 3.
In another preferred embodiment, the level of each biomarker is detected by mass spectrometry, preferably by a combination of chromatographic mass spectrometry, such as gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS).
In another preferred embodiment, before step (1), the method further comprises a step of processing the sample.
The fifth aspect of the present invention provides a method for evaluating the risk of developing macular neovascular disease of a subject to be tested or diagnosing macular neovascular disease of the subject to be tested, comprising the steps of:
(1) providing a sample derived from a subject, and detecting the level of each biomarker in a collection of samples, said collection comprising one or more biomarkers selected from the group consisting of: l-sorbose, D-tagatose, and ribonolactone;
(2) comparing the level measured in step (1) with a reference data set or a reference value (e.g., a reference value for a healthy control);
preferably, the reference data set comprises the levels of each biomarker in the collection as derived from macular neovascular disease patients and healthy controls.
In another preferred embodiment, the reference data set comprises the levels of each biomarker in the collection as derived from patients with AMD disease and healthy controls.
In another preferred embodiment, said reference data set comprises the levels of individual biomarkers as in said set derived from patients with PCV disease and healthy controls.
In another preferred embodiment, said reference data set comprises the levels of each biomarker in said collection as derived from a PM disease patient and a healthy control.
In a sixth aspect, the present invention provides a method of screening a candidate compound for the treatment of a macular neovascular disease, comprising the steps of:
(1) in the test group, administering a test compound to a subject to be tested, and detecting the level of each biomarker in the pool of samples derived from the subject in the test group at V1; in a control group, administering a blank control (including vehicle) to a subject to be tested, and detecting the level V2 of each biomarker in the collection in a sample derived from the subject in the control group;
(2) comparing the level V1 and the level V2 detected in the previous step to determine whether the test compound is a candidate compound for the treatment of macular neovascular disease, wherein the panel comprises one or more biomarkers selected from the group consisting of: l-sorbose, D-tagatose and ribonolactone.
In another preferred example, the subject to be tested is a patient with macular neovascular disease.
In another preferred embodiment, if the level V1 of one or more biomarkers selected from subset H is significantly lower than the level V2, it is indicated that the test compound is a candidate compound for the treatment of macular neovascular disease,
wherein subset H comprises biomarkers b5, b6, b7, b11, b13, b14, b16, b17, b 19.
In another preferred embodiment, the subset H comprises the biomarkers b5, b7, b16, b 17.
In another preferred embodiment, the subset H comprises the biomarkers b5, b7, b 16.
In another preferred embodiment, the phrase "substantially lower than" means that the ratio of level V1/level V2 is 0.8 or less, preferably 0.6 or less, and more preferably 0.4 or less.
In another preferred embodiment, if the level of one or more biomarkers selected from subset K, V1, is significantly higher than the level of V2, it is indicated that the test compound is a candidate compound for the treatment of macular neovascular disease,
wherein subset K comprises biomarkers b1, b2, b3, b4, b6, b8, b9, b10, b12, b15, b18, b 20.
In another preferred embodiment, the subset H comprises the biomarkers b1, b2, b3, b4, b 6.
In another preferred embodiment, the subset H comprises the biomarkers b1, b2, b 3.
In another preferred embodiment, said "significantly higher" means that the ratio of level V1/level V2 is ≥ 1.2, preferably ≥ 1.5, more preferably ≥ 1.8.
In a seventh aspect, the present invention provides the use of a biomarker panel for screening candidate compounds for the treatment of macular neovascular disease and/or for assessing the therapeutic effect of a candidate compound on macular neovascular disease, wherein the biomarker panel comprises one or more biomarkers selected from the group consisting of: l-sorbose, D-tagatose and ribonolactone.
In an eighth aspect, the present invention provides a method for establishing a mass spectrometric model for assessing the risk of or diagnosing macular neovascular disease, said method comprising the step of identifying a differentially expressed substance in a blood sample between a patient and a healthy control, wherein said differentially expressed substance comprises one or more biomarkers from a set of biomarkers, wherein said set of biomarkers comprises one or more biomarkers selected from the group consisting of: l-sorbose, D-tagatose and ribonolactone.
It is to be understood that within the scope of the present invention, the above-described features of the present invention and those specifically described below (e.g., in the examples) may be combined with each other to form new or preferred embodiments. Not to be reiterated herein, but to the extent of space.
Drawings
Figure 1 shows the demographic and clinical characteristics of the study.
Fig. 2 shows principal component analysis score maps of the first two and three principal components of the age-related macular degeneration group and the cataract group.
FIG. 3 shows the results of 200 displacement tests on the orthonormal partial least squares discriminant analysis scores of the age-related macular degeneration group and the control group.
Fig. 4 shows metabolic markers of 3 subtypes of macular neovascular disease.
FIG. 4A is a heat map of different metabolic markers of different macular neovascular diseases, the color changing from blue to brown indicating an increase in metabolic markers; FIG. 4B is a Venn diagram of metabolic markers associated with three disease subtypes of age-related macular degeneration, polypoid choroidal vasculopathy, and pathological high myopia; FIG. 4C is a sparse partial least squares discriminant analysis model score plot based on 131 metabolic markers.
FIG. 5 ROC curve analysis based on the metabolic marker panel. FIG. 5A is a ROC curve analysis of AMD versus control groups; FIG. 5B is a ROC curve analysis of PCV group versus control group; FIG. 5C is a ROC curve analysis of PM versus control.
Detailed Description
The present inventors have conducted extensive and intensive studies and, for the first time, have unexpectedly found biomarkers for macular neovascular disease. Specifically, the invention discovers a biomarker set, the biomarker set comprises a plurality of biomarkers of the macular neovascular diseases, can be used for evaluating the risk of the macular neovascular diseases of the object to be detected or diagnosing the macular neovascular diseases of the object to be detected, has the advantages of high sensitivity and high specificity, and has important application value. On this basis, the inventors have completed the present invention.
Term(s) for
The terms used herein have meanings commonly understood by those of ordinary skill in the relevant art. However, for a better understanding of the present invention, some definitions and related terms are explained as follows:
according to the present invention, the term "macular neovascular disease" is a group of diseases characterized by choroidal neovascularisation in the macular region, including: age-related macular degeneration (AMD), Polypoid Choroidal Vasculopathy (PCV), pathological high myopia (PM), and the like. AMD is mostly expressed as vitreous membrane wart and retinal pigment epithelium damage, PCV is a macular degeneration with higher incidence rate in Asian population, and is characterized in that the pathological features of PM include paint crack, lamellar atrophy, thinning of choroid and choroid capillary vessel, etc. the PCV is an abnormal branch-shaped choroid vascular network and polypoidal choroid vascular dilatation focus at the periphery thereof displayed by subretinal orange-red nodular lesion and indocyanine green angiography.
As used herein, the terms "BCVA", "best corrected visual acuity" are used interchangeably and all refer to best corrected vision.
As used herein, the terms "CRT", "central retinal thickness" are used interchangeably and refer to the central retinal thickness.
As used herein, the terms "CRV", "central retinal volume" are used interchangeably and refer to the central retinal volume.
According to the present invention, the term "biomarker panel" refers to one biomarker, or a combination of two or more biomarkers.
According to the present invention, Mass Spectrometry (MS) can be divided into ion trap mass spectrometry, quadrupole mass spectrometry, orbitrap mass spectrometry and time-of-flight mass spectrometry with deviations of 0.2amu, 0.4amu, 3ppm and 5ppm, respectively. In the present invention, MS data is obtained using time-of-flight mass spectrometry.
According to the invention, the level of the biomarker substance is indicated by a mass spectrometry signal area normalization value.
According to the present invention, the reference set refers to a training set.
According to the present invention, the training set and the validation set have the same meaning, as is known from the prior art. In one embodiment of the invention, the training set refers to the set of biomarker levels in biological samples of CHD patients and healthy controls. In one embodiment of the invention, a validation set refers to a data set used to test the performance of a training set. In one embodiment of the invention, the level of the biomarker may be represented as an absolute value or a relative value according to the method of determination. For example, when mass spectrometry is used to determine the level of a biomarker, the intensity of the peak may represent the level of the biomarker, which is the level of a relative value; when PCR is used to determine the level of a biomarker, the copy number of the gene or the copy number of a gene fragment may represent the level of the biomarker.
In one embodiment of the invention, the reference value refers to a reference value or normal value of a healthy control. It will be clear to those skilled in the art that, in the case of a sufficiently large number of samples, a range of normal values (absolute values) for each biomarker can be obtained by testing and calculation methods. Therefore, when the levels of the biomarkers are detected by methods other than mass spectrometry, the absolute values of the levels of the biomarkers can be directly compared with normal values, thereby evaluating the risk of developing macular neovascular diseases (especially age-related macular degeneration, polypoid choroidal vasculopathy, and pathologic high myopia), and diagnosing or early diagnosing the macular neovascular diseases (especially age-related macular degeneration, polypoid choroidal vasculopathy, and pathologic high myopia). Statistical methods may also be used in the present invention.
According to the present invention, the term "biomarker", also referred to as "biological marker", refers to a measurable indicator of the biological state of an individual. Such biomarkers can be any substance in an individual as long as they are related to a particular biological state (e.g., disease) of the subject, e.g., nucleic acid markers (e.g., DNA), protein markers, cytokine markers, chemokine markers, carbohydrate markers, antigen markers, antibody markers, species markers (species/genus markers) and functional markers (KO/OG markers), and the like. Biomarkers are measured and evaluated, often to examine normal biological processes, pathogenic processes, or therapeutic intervention pharmacological responses, and are useful in many scientific fields.
According to the present invention, the term "individual" refers to an animal, in particular a mammal, such as a primate, preferably a human.
According to the present invention, the term "plasma" refers to the liquid component of whole blood. Depending on the separation method used, the plasma may be completely free of cellular components and may also contain varying amounts of platelets and/or small amounts of other cellular components.
According to the present invention, terms such as "a," "an," and "the" do not refer only to a singular entity, but also include the general class that may be used to describe a particular embodiment.
It should be noted that the explanation of the terms provided herein is only for the purpose of better understanding the present invention by those skilled in the art, and is not intended to limit the present invention.
Evaluation method
The invention calculates the weighted comprehensive score by the formula S-W1S 1+ W2S2+ WiS3+ … … WnSn;
wherein, W1 and W2 … … Wn are weights;
s1, S2 … … Sn are scores for each marker.
In a preferred embodiment, S is from the normal populationsubject＝W1S1+W2S2+WiS3+……WnSn。
S of susceptible populationsubject’＝W1’S1’+W2’S2’+Wi’S3’+……Wn’Sn’。
Where normal is 0 and abnormal is 1.
An exception is noted when the Fold change > a predetermined threshold (e.g., 1.2, 1.5, 1.8), or an exception is noted when the Fold change < a predetermined threshold (e.g., 0.8, 0.6, 0.4).
The results show that S is comparable to that of the normal populationsubjectBy comparison, S in susceptible populationsubject' significantly higher S than that of the normal populationsubjectThen the disease risk is indicated.
The result shows that the marker of the invention can obviously improve the accuracy of the diagnosis of the macular neovascular diseases (especially AMD, PCV, PM).
The main advantages of the invention include:
(a) the blood plasma metabolite is used as a biomarker for early screening, prejudging and preventing of the macular neovascular diseases (especially age-related macular degeneration, polypoid choroidal vasculopathy and pathologic high myopia), has the advantages of high sensitivity and high specificity, and has important application value.
(b) The plasma as the biomarker detection sample has the advantages of convenient material acquisition, simple operation steps, continuous in vitro detection and the like.
(c) The biomarker is used for early screening, predicting and preventing the macular neovascular diseases (especially age-related macular degeneration, polypoid choroidal vasculopathy and pathologic high myopia) and has the characteristic of good repeatability.
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. The experimental procedures, in which specific conditions are not noted in the following examples, are generally carried out under conventional conditions or conditions recommended by the manufacturers. Unless otherwise indicated, percentages and parts are by weight.
Unless otherwise specified, reagents and materials used in examples of the present invention are commercially available products.
Example 1
Patient recruitment team
A total of 328 patients were enrolled in the ophthalmic center of the first human hospital in shanghai, of which 24.7% (81) of the control group, 26.8% (88) of the age-related macular degeneration patients, 31.1% (102) of the polypoid choroidal vasculopathy patients, and 17.4% (57) of the pathologic high myopia patients. We applied OCT to all patients to detect their macular thickness and volume.
The results are shown in FIG. 1, the demographic and clinical characteristics of the study.
Example 2
Metabolic manifestations of the various subtypes of macular neovascular disease
The blood samples of three different subtypes of patients with the control group and the macular neovascular disease are subjected to metabonomic analysis by utilizing a gas chromatography-time-of-flight mass spectrometry technology, and 131 metabolites are identified. And the metabolic phenotypes of three different subtypes of macular neovascular disease were further clarified using pattern recognition techniques.
The results are shown in FIGS. 2-4. FIG. 2: principal component analysis score maps of the first two and three principal components of the age-related macular degeneration group and the cataract group. The samples from AMD group and the control group have a certain segregation tendency on the score chart, which indicates that the AMD and the control group have difference in the whole metabolism. FIG. 3 shows the results of the Orthonormal Partial Least Squares Discriminant Analysis (OPLSDA) scores and model 200 permutation tests for the age-related macular degeneration group and the control group. On the OPLSDA score plot, AMD and control were completely isolated and the results of 200 displacement tests showed that the OPLSDA model fit well without overfitting. Fig. 4 shows metabolic markers of 3 subtypes of macular neovascular disease. FIG. 4A is a heat map of different metabolic markers of different macular neovascular diseases, the color changing from blue to brown indicating an increase in metabolic markers; FIG. 4B is a Venn diagram of metabolic markers associated with three disease subtypes of age-related macular degeneration, polypoid choroidal vasculopathy, and pathological high myopia; FIG. 4C is a sparse partial least squares discriminant analysis model score plot based on 131 metabolic markers. Table 1: metabolites that were statistically different in the macular neovascular disease group compared to the control group. FIG. 5A is the ROC curve analysis result of AMD group and control group based on the metabolic marker group, the AUC value is 0.967, the specificity is 89.8%, and the sensitivity is 95.1%; FIG. 5B is the result of ROC curve analysis based on PCV group and control group of metabolic marker group, the AUC value is 0.959, the specificity is 87.3%, and the sensitivity is 92.6%; FIG. 5C shows the result of ROC curve analysis based on the PM group and the control group of the metabolic markers, the AUC value is 0.916, the specificity is 78.9%, and the sensitivity is 90.1%. The ROC curve analysis result shows that the metabolic marker group obtained based on the plasma metabolome research has good distinguishing effect on the macular neovascular diseases and the control.
TABLE 4 novel metabolites with statistical differences in macular neovascular disease (AMD) and control groups compared to
TABLE 5 novel metabolites statistically different between macular neovascular disease group (PCV) and control group compared to
TABLE 6 novel metabolites statistically different in macular neovascular disease group (PM) and control group compared to
TABLE 8 AMD, PM, PCV prediction rates (taking ribonolactone as an example)
TABLE 9 prediction rates for AMD, PM, and PCV (D-tagatose as an example)
TABLE 10 AMD, PM, PCV prediction rates (sorbose as an example)
The data in tables 4-10 show that the prediction rate of macular neovascular disease can be greatly improved by using a single marker, such as ribonolactone, sorbose, D-tagatose, in the present invention.
Therefore, the combined use of the markers of the invention can improve the accuracy of the diagnosis of the macular neovascular disease.
All documents referred to herein are incorporated by reference into this application as if each were individually incorporated by reference. Furthermore, it should be understood that various changes and modifications of the present invention can be made by those skilled in the art after reading the above teachings of the present invention, and these equivalents also fall within the scope of the present invention as defined by the appended claims.
Claims (10)
1. A set of biomarkers, wherein said set comprises two or more biomarkers selected from the group consisting of: l-sorbose, D-tagatose and ribonolactone.
2. A reagent combination for use in the risk assessment or diagnosis of a macular neovascular disease, comprising reagents for detecting a biomarker in the panel of claim 1.
3. A kit comprising the collection of claim 1 and/or the combination of reagents of claim 2.
4. Use of a biomarker panel for the preparation of a kit for assessing the risk of or diagnosis of macular neovascular disease in a subject, wherein the biomarker panel comprises one or more biomarkers selected from the group consisting of: l-sorbose, D-tagatose and ribonolactone.
5. A method for assessing the risk of developing macular neovascular disease in a subject or diagnosing macular neovascular disease in a subject, comprising the steps of:
(1) providing a sample derived from a subject, and detecting the level of each biomarker in a collection of samples, said collection comprising one or more biomarkers selected from the group consisting of: l-sorbose, D-tagatose, and ribonolactone;
(2) comparing the level measured in step (1) with a reference data set or a reference value (e.g., a reference value for a healthy control);
preferably, the reference data set comprises the levels of each biomarker in the collection as derived from macular neovascular disease patients and healthy controls.
6. A method of screening a candidate compound for treatment of a macular neovascular disease comprising the steps of:
(1) in the test group, administering a test compound to a subject to be tested, and detecting the level of each biomarker in the pool of samples derived from the subject in the test group at V1; in a control group, administering a blank control (including vehicle) to a subject to be tested, and detecting the level V2 of each biomarker in the collection in a sample derived from the subject in the control group;
(2) comparing the level V1 and the level V2 detected in the previous step to determine whether the test compound is a candidate compound for the treatment of macular neovascular disease, wherein the panel comprises one or more biomarkers selected from the group consisting of: l-sorbose, D-tagatose and ribonolactone.
7. The method of claim 6, wherein if the level V1 of one or more biomarkers selected from subset H is significantly lower than the level V2, the test compound is a candidate compound for the treatment of macular neovascular disease,
wherein subset H comprises biomarkers b5, b6, b7, b11, b13, b14, b16, b17, b 19.
8. The method of claim 6, wherein if the level V1 of one or more biomarkers selected from subset K is significantly higher than the level V2, the test compound is a candidate compound for the treatment of macular neovascular disease,
wherein subset K comprises biomarkers b1, b2, b3, b4, b6, b8, b9, b10, b12, b15, b18, b 20.
9. Use of a biomarker panel for screening candidate compounds for treatment of macular neovascular disease and/or for assessing the therapeutic effect of a candidate compound on macular neovascular disease, wherein the biomarker panel comprises one or more biomarkers selected from the group consisting of: l-sorbose, D-tagatose and ribonolactone.
10. A method of establishing a mass spectrometric model for assessing the risk of or diagnosing macular neovascular disease, comprising the step of identifying a differentially expressed substance in a blood sample between a patient and a healthy control, wherein the differentially expressed substance comprises one or more biomarkers from a set of biomarkers, wherein the set of biomarkers comprises one or more biomarkers selected from the group consisting of: l-sorbose, D-tagatose and ribonolactone.
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