CN111812318A - New application of blood inositol as kidney prognosis diagnosis marker of diabetic nephropathy patient - Google Patents
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Abstract
The invention provides an application of blood inositol in a kidney prognosis diagnosis marker of a patient with type 2 diabetes and diabetic nephropathy, wherein the blood inositol content of the patient is used for predicting the kidney function reduction degree of the patient and judging the speed of the kidney of the patient for performing end-stage renal insufficiency (ESRD); the aforementioned blood inositol includes all inositol isomers. The invention also provides application of the serum inositol in preparing a long-term prognosis diagnostic kit for diagnosing type 2 diabetes mellitus combined with Diabetic Nephropathy (DN) patients. The invention can effectively diagnose the renal long-term prognosis of Diabetic Nephropathy (DN) by absolutely quantifying blood inositol, and has important significance for diagnosis and treatment of the diabetic nephropathy.
Description
Technical Field
The invention relates to the field of gene biological preparations, in particular to a new application of blood inositol as a kidney long-term prognosis diagnostic marker of a patient with type 2 diabetes mellitus and diabetic nephropathy.
Background
With the increasing incidence rate and the aging population of metabolic diseases such as diabetes, obesity and the like in China, the incidence rate of Diabetic Nephropathy (DN) in China is continuously increased, and the DN becomes one of the most main causes of terminal renal insufficiency in China at present. The method has very important significance in clarifying the occurrence and development mechanism of chronic kidney diseases, and discovering and verifying noninvasive biomarkers for early diagnosis and prognosis judgment of DN patients.
At present, the biomarker for early diagnosis and prognosis judgment of diabetic nephropathy patients which is most commonly used clinically is microalbuminuria. However, microalbuminuria is not a perfect biomarker, and there are still many disadvantages in the practical clinical application process. The level of microalbuminuria in a patient is affected clinically by many other physiological and pathological factors, such as elevated body temperature, diet, body weight, and the like. Secondly, the level of microalbuminuria is stable, such as ACEI and ARB antihypertensive drugs, which can reduce the level of microalbuminuria and influence the judgment of kidney lesion of DN patients; and there is no good correlation between the annual rate of decline in the level of microalbuminuria in patients with DN (eGFR) not well in the early stages of DN. In view of the above reasons, there is a great need in the clinical field to find suitable biomarkers that can reflect the renal prognosis of Diabetic Nephropathy (DN), particularly in patients with early DN.
In recent years, a great deal of research is devoted to the research of non-invasive biomarkers of DN patients by using the hematuria samples of the DN patients and using the biological means of the system, such as proteomics, metabonomics and the like. In previous work, the inventor discovers metabolites which are obviously changed in blood and urine of early stage DN patients by using blood and urine samples of early stage diabetic nephropathy patients and adopting a high-flux meteorological mass spectrometry method (GC/MS), wherein the inositol content in the blood (which is only used as a detection index of renal injury at present) is obviously increased in patients with type 2 diabetes mellitus combined with micro albuminuria compared with normal people, and the inositol content in the blood is suggested to have the potential of being used as a biomarker of type 2 diabetes mellitus combined with diabetic nephropathy patients.
However, the correlation of blood inositol levels with other clinical indicators in type 2 diabetes-associated Diabetic Nephropathy (DN) patients, with the progression of renal function in the patient, with the renal prognosis, and with its specificity and sensitivity as a non-invasive biomarker for the prognosis of the renal has not been demonstrated.
Disclosure of Invention
The invention aims to provide a new application of detecting blood inositol. In particular to a new application of blood inositol as a kidney prognosis diagnosis marker of Diabetic Nephropathy (DN) patients.
The technical scheme of the invention is as follows: use of blood inositol as a renal prognostic diagnostic marker in type 2 diabetes mellitus (DN) patients in whom the patient's blood inositol content can be used to predict the extent of decline in the patient's renal function and to determine the rate at which the kidney undergoes end-stage renal insufficiency (ESRD). The aforementioned blood inositol includes all inositol isomers (fig. 1).
The prepared diabetic nephropathy kidney prognosis diagnostic kit is specially used for detecting blood inositol and is applied to the preparation of the diabetic nephropathy kidney prognosis diagnostic kit by detecting the blood inositol.
The detection of the blood inositol is used for performing the kidney prognosis diagnosis of the diabetic nephropathy by constructing ELISA kit detection (enzyme linked immunosorbent assay kit and enzyme linked immunosorbent assay kit).
The new application refers to the new application of the blood inositol serving as a noninvasive biomarker for the kidney prognosis diagnosis of patients with type 2 diabetes mellitus combined with Diabetic Nephropathy (DN) and the preparation of a biological kit for the kidney prognosis diagnosis of the patients with DN.
The research of the inventor of the present invention proves that: the content of blood inositol differs in patients with diabetic nephropathy who are positive for microalbuminuria and who are not accompanied by microalbuminuria, as well as in the blood of normal people. Using the cohort of independent diabetic nephropathy patients, it was demonstrated that the levels of blood inositol correlated with the patient's proteinuria, serum creatinine, blood Cystatin C and eGFR levels, and that the survival curves demonstrated that diabetic nephropathy patients had serum inositol greater than 8.6umol/L with poor renal prognosis. Serum inositol elevation remains an independent risk factor for poor kidney prognosis in diabetic nephropathy patients after correction of blood pressure, blood glucose, gender and age confounders.
The above features may find use in serum inositol levels as a prognostic renal marker in Diabetic Nephropathy (DN) patients.
Has the advantages that: based on the technical scheme of the invention, the long-term prognosis of the kidney of the patient with type 2 diabetes accompanied with renal Disease (DN) can be effectively judged by utilizing the content of inositol in serum, and the reduction speed of the renal function of the patient with DN is predicted. Based on the DNA, the kit for the renal long-term prognosis diagnosis of the DN patient based on the blood inositol content is established, and has extremely important significance for improving the diagnosis and treatment level of DN.
Drawings
FIG. 1 shows the results of the kidney survival curves for different blood inositol levels by the Kaplan-Meier curve.
FIG. 2ROC curve analysis shows the results of the area under the curve for blood inositol content diagnosis DN patient ESRD.
FIG. 3Pearson correlation analysis results showing the relationship between blood inositol levels and annual rate of decline of eGFR in DN patients.
FIG. 4 shows a plurality of blood inositols described above, including all inositol isomer formulae.
Detailed Description
In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.
Example one application of blood inositol content as a renal prognosis biomarker in type 2 diabetes mellitus patients with renal disease
Blood inositol is obtained by sugar catabolism in human body, is one of important raw materials for synthesizing phosphatidylinositol kinase in vivo, is mainly metabolized in renal tubules at the proximal end of kidney, and is metabolized into calcium gluconate by inositol oxygenase in the renal tubules, and is discharged from urine. In the application of blood inositol as a kidney long-term prognosis biomarker of type 2 diabetes mellitus combined Diabetic Nephropathy (DN) patients, the specific detection method comprises the following steps:
(1) meteorological mass spectrometry detection method
Diluting the inositol standard product to a certain concentration, detecting the diluted inositol standard product in a mass spectrometer, and obtaining the peak area and the flight time of the diluted inositol standard product. The peak area and the concentration of the compound have a good linear relation, and the absolute quantitative concentration of the serum inositol is calculated by utilizing the ratio of the peak area corresponding to the sample to be detected to the peak area.
This example utilizes an independent prospective DN patient cohort study finding,the amount of inositol in the blood was closely related to the 24-hour urinary protein quantification, estimated glomerular filtration rate (eGFR) level, urea nitrogen level, blood creatinine level, blood cystatinC level and blood albumin level in DN patients (P)<0.05, table 1). Blood inositol was found using a kaplan-meier curve by four classifications of blood inositol levels in DN patients>8.60 μ M DN, the risk of ESRD in the kidney during follow-up is significantly higher than the blood inositol content<8.60 μ M DN patients (χ)2＝6.615,P＝0.01)。
The inventors further calculated the area under the ROC curve (AUROC) for blood inositol content, urinary protein quantification and eGFR level diagnosis DN patients with end-stage renal insufficiency (ESRD), respectively, using the ROC curve (the ROC curve is a curve plotted according to a series of different two classification methods (cut-off values or decision thresholds) with true positive rate (sensitivity) as ordinate and false positive rate (1-specificity) as abscissa), as shown in fig. 2, and the AUROC for ESRD for blood inositol content diagnosis DN patients with AUROC of 0.731.
Meanwhile, through Pearson correlation analysis, a remarkable negative correlation exists between the blood inositol content of DN patients and the annual reduction rate of eGFR of the patients, and R is2＝-0.460，P<0.001 (fig. 3). After correcting for DN patients' gender, age, blood pressure, blood lipids, blood glucose levels, baseline eGFR and proteinuria levels, blood inositol remains an independent risk factor for DN patients with poor renal prognosis (table 2, HR ═ 1.140, P ═ 0.002).
In conclusion, the above embodiments of the present invention show that the microRNA-30 family has significant effects in the diagnosis of Focal Segmental Glomerulosclerosis (FSGS) patients and the application of the FSGS family in the preparation of drugs for treating podocyte injury, and has an extremely important meaning for the diagnosis and treatment of chronic kidney diseases.
Table 1 shows the correlation results between the amount of inositol in the blood and the clinical index of DN patients.
Table 2 presents the results of Cox regression analysis showing that blood inositol is an independent risk factor for poor renal prognosis in DN patients.
TABLE 1
In statistics, the calculation method of the square value of R is as follows: r-squared value ═ sum of regression squares (ssreg)/sum of squares (sstotal), where sum of regression squares ═ sum of total squares-sum of residuals squares (ssresid). In statistics, linear regression analysis is carried out on variables, when a least square method is adopted for zhi parameter estimation, the R square is the ratio of the sum of regression mean squares dao to the sum of total deviations squares, the ratio which can be explained by the sum of regression squares in the sum of total deviations squares is represented, the larger the ratio is, the better the model is, and the more accurate the model is, the more remarkable the regression effect is. The R square is between 0 and 1, the closer to 1, the better the regression fitting effect is, and generally, the model fitting goodness of more than 0.8 is considered to be higher.
TABLE 2
The P value refers to the amount of likelihood that the difference between the two compared is due to an opportunity. The smaller the P value, the more reasonable is the difference between the objects to be compared. For example, P <0.05 means that the results show a difference of less than 5% in probability due to chance, or that others repeat the same study under the same conditions with less than 5% probability of concluding the contrary.
P >0.05 is said to be "not significant"; p < ═ 0.05 was called "significant", and P < ═ 0.01 was called "very significant".
Comparing the survival of the two groups of patients with HR has the following advantages:
1. in some studies, more than 50% of the patients in the test or control group may have not had an endpoint event or deletion at the end of the study, in which case median survival time is unavailable;
2. survival data always obeys bias distribution, and the distribution state of the whole survival data is compared by only using median survival time;
3. the median survival time is subtracted to compare the survival conditions of two groups of patients, unbalanced covariates in the baseline can not be adjusted, the obtained effect estimation value is influenced by confounding factors, and the HR is used to adjust the influence of the confounding factors through a multivariate Cox model to obtain an unbiased effect estimation value.
HR, 0.76; 95% CI, 0.65-0.89; p <0.001 means that the risk rate is 0.76 and less than 1, the risk rate is protective factor, the 95% confidence interval value is 0.65-0.89, and p is less than 0.001, which indicates that the method has very significant statistical significance.
CI refers to a Confidence Interval (CI), and estimates the total of a certain event. The confidence interval is the range within which the overall parameter (mean or rate) is estimated with a certain probability, which is the range of possibilities for determining the value of an unknown parameter with a predetermined probability (1-a, usually 95% or 99%), which is referred to as the confidence interval or confidence interval for the estimated parameter value. E.g., 95% confidence interval, is to randomly sample n from the estimated population and calculate a confidence interval from each sample, wherein theoretically there is a 95% probability that the estimated parameter will be included.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.
Claims (4)
1. The application of blood inositol in a kidney prognosis diagnosis marker of a patient with type 2 diabetes mellitus and diabetic nephropathy is characterized in that the blood inositol content of the patient is used for predicting the degree of kidney function reduction of the patient and judging the speed of the kidney of the patient for performing end-stage renal insufficiency (ESRD); the aforementioned blood inositol includes all inositol isomers.
2. Application of blood inositol in preparing diabetic nephropathy kidney prognosis diagnostic kit.
3. The application of the kit as claimed in claim 2, wherein the detection of the blood inositol is performed by constructing an ELISA kit to prepare a diabetic nephropathy renal prognosis kit, and the kit is specially used for detecting the blood inositol and is applied to the preparation of the diabetic nephropathy renal prognosis kit by detecting the blood inositol.
4. The use of claim 2, wherein the diabetic nephropathy has a serum inositol level of greater than 8.6umol/L and a poor renal prognosis.
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Cited By (3)
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| CN113552369A (en) * | 2021-07-23 | 2021-10-26 | 江苏省中医院 | Use of protein marker combination for diagnosis of type 2 diabetes and type 2 diabetic nephropathy |
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Cited By (5)
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| CN112730849A (en) * | 2021-01-14 | 2021-04-30 | 山西医科大学第二医院 | Application of endogenous orphan enkephalin as serum biomarker for diabetes mellitus combined with asymptomatic myocardial ischemia |
| CN112946303A (en) * | 2021-02-23 | 2021-06-11 | 江苏省中医院 | Application of TAG54:2-FA18:1 and composition thereof in diagnosis of diabetes and diabetic nephropathy |
| CN112946303B (en) * | 2021-02-23 | 2023-10-20 | 江苏省中医院 | TAG54:2-FA18:1 and application of composition thereof in diagnosis of diabetes and diabetic nephropathy |
| CN113552369A (en) * | 2021-07-23 | 2021-10-26 | 江苏省中医院 | Use of protein marker combination for diagnosis of type 2 diabetes and type 2 diabetic nephropathy |
| CN113552369B (en) * | 2021-07-23 | 2023-10-20 | 江苏省中医院 | Use of protein markers in combination for diagnosis of type 2 diabetes mellitus, type 2 diabetic nephropathy |
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