Application of iga immune activity index time series in diagnosis of iga nephropathy

Abstract

Provided is an application of an IgA immune activity index time series in diagnosis of IgA nephropathy. An IgA nephropathy marker comprises at least one of the mean value, abnormally high percentage, baseline change rate mean value, absolute baseline change rate mean value, standard deviation, mean radius, skewness and kurtosis of IgA immune activity indexes of an IgA immune activity index time series. The IgA immune activity index time series comprises IgA immune activity indexes of n samples of a subject that are collected every time interval St within a certain time period, and n≥2. An IgA immune activity index refers to the concentration of IgA immune complexes in serum / plasma. The monitoring of IgA immune activity indexes can provide early diagnosis and early intervention for patients at risk of IgA nephropathy, and can also assess the risk of patients with IgA nephropathy progressing to end-stage kidney disease.

Description

Application of IgA immune activity index time series in the diagnosis of IgA nephropathy Technical Field

[0001] The present invention relates to the field of biomedicine technology, and in particular to the application of an IgA immune activity index time series in diagnosing IgA nephropathy. Background Art

[0002] IgA nephropathy (IgAN) is caused by the accumulation and retention of IgA complexes in the kidneys, with circulating IgA complexes in the serum being a significant inducing factor. IgA nephropathy arises when, in at-risk patients, an upper respiratory or intestinal infection initiates an IgA immune response. The intermediate IgA immune complexes are not effectively cleared, and some accumulate in the kidneys, triggering a local immune response and leading to renal damage.

[0003] Patents "Application of Reagents for Detecting IgA Immune Complexes" (Publication No.: CN114814201A) and "Method and Apparatus for Calculating the IgA Immune Activity Index of a Sample" (Publication No.: CN115078277A) describe methods for detecting IgA immune complexes in peripheral blood using an enzyme-linked immunosorbent assay (ELISA) to quantify IgA immune activity. While IgA immune activity has a good auxiliary diagnostic value compared to the gold standard of renal biopsy for diagnosing IgA nephropathy, the specificity of a one-time test of IgA immune activity compared to the gold standard is approximately 80%, resulting in a low sensitivity and thus being unable to replace the gold standard of renal biopsy.

[0004] Therefore, there is a need to provide a more sensitive method for diagnosing IgA nephropathy.

[0005] Summary of the Invention

[0006] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides an IgA nephropathy marker.

[0007] The present invention also provides an IgA nephropathy diagnostic system.

[0008] The present invention also provides use of the above-mentioned IgA nephropathy marker or the above-mentioned IgA nephropathy diagnostic system in the preparation of a product for diagnosing or assisting in the diagnosis of IgA nephropathy.

[0009] The present invention also provides use of the above-mentioned IgA nephropathy marker or the above-mentioned IgA nephropathy diagnostic system in screening drugs for preventing and / or treating IgA nephropathy.

[0010] The present invention also provides a method for assessing the risk of IgA nephropathy that is not related to disease diagnosis and / or treatment.

[0011] The present invention also provides a computer-readable storage medium.

[0012] The present invention also provides a computer device.

[0013] An IgA nephropathy marker according to the first embodiment of the present invention includes at least one of information A1) to A8) of the IgA immune activity index time series:

[0014] A1) Average value of IgA immune activity index;

[0015] A2) abnormally high percentage of IgA immune activity index;

[0016] A3) Average rate of change from baseline in IgA immune activity index;

[0017] A4) Average absolute change rate from baseline in IgA immune activity index;

[0018] A5) standard deviation of IgA immune activity index;

[0019] A6) Mean radius of IgA immune activity index;

[0020] A7) Skewness of IgA immune activity index;

[0021] A8) Kurtosis of IgA immune activity index;

[0022] The IgA immune activity index time series is the IgA immune activity index of n samples of the subject; the samples are within a certain time period, at intervals of S t The collected sample; n≥2; the IgA immune activity index refers to the concentration of IgA immune complexes in serum / plasma.

[0023] The IgA nephropathy marker according to the embodiment of the present invention has at least the following beneficial effects:

[0024] The mean, abnormally high percentage, mean baseline change rate, mean standard deviation of the absolute baseline change rate, mean radius, skewness, and kurtosis of the IgA immune activity index time series have greater specificity and sensitivity than single-point IgA immune activity indicators, providing an excellent reference for the diagnosis, prognosis, medication efficacy evaluation, and disease tracking of IgA nephropathy. Monitoring the IgA immune activity index can facilitate early diagnosis and intervention for patients at risk of IgA nephropathy and can also be used to predict the risk of IgA nephropathy patients developing end-stage kidney disease (ESKD, such as uremia).

[0025] According to some embodiments of the present invention, the IgA nephropathy marker has the use of A1) and / or A2):

[0026] A1) Predicting the presence or risk of IgA nephropathy;

[0027] A2) Predict the risk of IgA nephropathy progressing to end-stage renal disease.

[0028] According to some embodiments of the present invention, the baseline in the baseline change rate average and the baseline absolute change rate average is an average value of the IgA immune activity index at at least a previous time point.

[0029] According to a second aspect of the present invention, an IgA nephropathy diagnostic system includes:

[0030] a detection unit, the detection unit being used to detect the IgA immune activity index of n samples of the subject to obtain a time series of the IgA immune activity index;

[0031] The sample is within a certain period of time, every time interval S t The samples collected; n≥2;

[0032] The IgA immune activity index refers to the concentration of IgA immune complexes in serum / plasma;

[0033] A data acquisition unit, configured to acquire at least one of the information A1) to A8) of the IgA immune activity index time series:

[0034] A1) Average value of IgA immune activity index;

[0035] A2) abnormally high percentage of IgA immune activity index;

[0036] A3) Average rate of change from baseline in IgA immune activity index;

[0037] A4) Average absolute change rate from baseline in IgA immune activity index;

[0038] A5) standard deviation of IgA immune activity index;

[0039] A6) Mean radius of IgA immune activity index;

[0040] A7) Skewness of IgA immune activity index;

[0041] A8) Kurtosis of IgA immune activity index.

[0042] According to some embodiments of the present invention, the detection module includes at least one of an ELISA kit, an electrochemiluminescence detection kit, a turbidimetry kit, an ELISA detection device, an immunochromatographic detection device, an electrochemiluminescence detection device, and a turbidimetry detection device.

[0043] According to some embodiments of the present invention, the IgA nephropathy diagnostic system further includes an analysis unit, which is used to analyze whether the subject suffers from IgA nephropathy or the risk of suffering from IgA nephropathy or developing end-stage renal disease based on the information acquired by the data acquisition module.

[0044] According to some embodiments of the present invention, analyzing whether a subject suffers from IgA nephropathy or is at risk of suffering from IgA nephropathy includes: comparing at least one information from A1) to A8) with a preset threshold to determine whether the subject suffers from IgA nephropathy or is at risk of suffering from IgA nephropathy.

[0045] According to the third aspect of the present invention, the use of the above-mentioned IgA nephropathy marker or the above-mentioned IgA nephropathy diagnostic system in the preparation of products for diagnosing or assisting in the diagnosis of IgA nephropathy.

[0046] According to some embodiments of the present invention, the product is used to analyze whether a subject suffers from IgA nephropathy or the risk of suffering from IgA nephropathy or developing end-stage renal disease from IgA nephropathy.

[0047] According to the fourth aspect of the present invention, the above-mentioned IgA nephropathy marker or the above-mentioned IgA nephropathy diagnostic system is used in screening drugs for preventing and / or treating IgA nephropathy.

[0048] According to a fifth aspect of the present invention, a method for assessing the risk of IgA nephropathy for non-disease diagnosis and / or treatment comprises the following steps:

[0049] S1. Determine at least one of the information A1) to A8) of the time series of the subject's IgA immune activity index:

[0050] A1) Average value of IgA immune activity index;

[0051] A2) abnormally high percentage of IgA immune activity index;

[0052] A3) Average rate of change from baseline in IgA immune activity index;

[0053] A4) Average absolute change rate from baseline in IgA immune activity index;

[0054] A5) standard deviation of IgA immune activity index;

[0055] A6) Mean radius of IgA immune activity index;

[0056] A7) Skewness of IgA immune activity index;

[0057] A8) Kurtosis of IgA immune activity index;

[0058] The IgA immune activity index time series is the IgA immune activity index of n samples of the subject; the samples are within a certain time period, at intervals of S t Samples collected; n≥2; the IgA immune activity index refers to the concentration of IgA immune complexes in serum / plasma;

[0059] S2. Based on the information, analyzing whether the subject suffers from IgA nephropathy or the risk of suffering from IgA nephropathy or developing end-stage renal disease from IgA nephropathy.

[0060] According to some embodiments of the present invention, analyzing whether a subject suffers from IgA nephropathy or is at risk of developing IgA nephropathy includes: comparing at least one of the information A1) to A8) with a preset threshold to determine whether the subject suffers from IgA nephropathy or is at risk of developing IgA nephropathy or end-stage renal disease.

[0061] According to a sixth aspect of an embodiment of the present invention, a computer-readable storage medium stores computer-executable instructions for executing the above-mentioned method for assessing the risk of IgA nephropathy.

[0062] According to a seventh aspect of the present invention, a computer device includes a memory and a processor; the memory stores a computer program, and when the computer program is executed, the above-mentioned method for assessing the risk of IgA nephropathy can be implemented.

[0063] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 shows the simulated time series trajectory of the IgA immune activity index of the IgA nephropathy group, other renal disease groups, and healthy subjects obtained by random sampling according to the Puasson distribution at a single time point;

[0065] Figure 2 shows the representative ROC curves obtained from the basic analysis. Figure 2A shows the ROC curve corresponding to time point T0. Since the initial stage was the earliest stage of IgA nephropathy (three years before the diagnosis of renal puncture), the IgA activity had almost no diagnostic effect. Figure 2B shows the ROC curve corresponding to time point T0. 11 The corresponding ROC curve shows excellent diagnostic performance because this time point simulates the IgA immune activity at the time of renal puncture diagnosis. Figure 2C shows the ROC curve combining all IgA activity index detection values ​​over the three years. This situation is closest to the actual situation because in actual application, patients may be at all stages of IgA nephropathy at the time of sampling.

[0066] Figure 3 shows representative ROC curves obtained based on the analysis of the comprehensive index of IgA immune activity time; Figure 3A shows the ROC curve corresponding to the average value, Figure 3B shows the ROC curve corresponding to the average value of the baseline change rate, Figure 3C shows the ROC curve corresponding to the abnormally high percentage (with a judgment value of 11.51), and Figure 3D shows the ROC curve corresponding to the average value of the baseline absolute change rate;

[0067] Figure 4 shows the ROC curve of the “mean + baseline change rate” bivariate model. DETAILED DESCRIPTION

[0068] The following will clearly and completely describe the concept and technical effects of the present invention in conjunction with the embodiments to fully understand the purpose, features and effects of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of the present invention.

[0069] If the specific conditions are not specified in the examples, the experiments were carried out under conventional conditions or those recommended by the manufacturer. All reagents or instruments used, if the manufacturer is not specified, are commercially available conventional products.

[0070] In the description of the present invention, the terms "comprises" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to the process, method, product or apparatus.

[0071] 1. “IgA immune activity index time series” refers to a given subject and a given observation and return time interval (S t , such as one week, one month, three months or longer; the interval between return visits is generally required to be at least one week) and the number of return visits (n+1, n+1≥2; including the initial diagnosis), starting from the time point T0 of the initial diagnosis, peripheral blood is collected at regular intervals, and the IgA immune activity index of the peripheral blood serum is detected according to the method described in Example 1 of "Application of Reagents for Detecting IgA Immune Complexes" (CN114814201A), and the detection values ​​are recorded; the corresponding time point sequence is (T0, T1, T2, ..., T n ), where T i =T0+iS t ,i=0,1,2,...,n;the corresponding IgA immune activity index time series is (A0,A1,A2,...,A nThe subjects under observation can be generally healthy people or those with abnormal urine routine or renal function.

[0072] During the development of IgA nephropathy, starting from the early mild symptoms, the IgA immune activity index detected in the peripheral blood is tracked and collected at predetermined time intervals. Its trajectory curve (i.e., a two-dimensional curve depicted by time as the X-axis and the IgA immune activity index as the Y-axis) is very different from the trajectory curves of the IgA immune activity index of healthy people and other kidney disease patients. The mathematical induction of this difference can be used as a characteristic indicator for the diagnosis of IgA nephropathy.

[0073] 2. The IgA immune activity index time series analysis obtained the IgA immune activity time comprehensive index is as follows:

[0074] (1) Basic statistics, such as mean (μ, A i is the IgA immune activity index at the i+1th time point, n+1 is the total number of time points), maximum value, minimum value, median value, standard deviation (σ), and deviation coefficient.

[0075] (2) Other statistical quantities, such as skewness, kurtosis, etc. Then, the K-order kinetic energy formula (K is a given positive integer) of the IgA immune activity index time series is as follows:

[0076] Where μ is the mean value and n+1 is the total number of time points;

[0077] When K = 1, this formula defines the mean radius;

[0078] When K = 2, this formula defines the variance (square of the standard deviation);

[0079] When K = 3, this formula defines the skew;

[0080] When K=4, this formula defines the kurtosis.

[0081] (3) Change rate of IgA activity index (P i ) is the relative percentage change of the IgA immune activity index at each time point relative to the IgA immune activity index at the previous time point, and its calculation formula is as follows:

[0082] Average rate of change The calculation formula is as follows:

[0083] (4) Average absolute rate of change The calculation formula is as follows:

[0084] (5) Baseline change rate (Q i ) is the relative percentage change at each time point relative to the baseline (IgA immune activity index Q0 at the first time point). The calculation formula is as follows:

[0085] Average baseline change rate The calculation formula is as follows:

[0086] It should be noted that to maintain baseline stability, the average of the IgA immune activity index at the first few time points can also be used as the baseline. The baseline change rate represents the cumulative degree of change in the patient's index since the beginning of observation. If the baseline change rate is continuously positive, it indicates that the body's circulating IgA complexes continue to exceed the baseline, which may indicate a greater risk of IgA nephropathy.

[0087] (6) Average absolute rate of change of baseline The calculation formula is as follows:

[0088] (7) Abnormally high percentage (P A ) is the percentage of abnormally high IgA activity index times series over all sampling times. Its significance lies in the following: for IgA nephropathy, abnormally high IgA activity indicates that the kidneys are experiencing a significant immune shock and potential re-injury (or worsening) during this period. The abnormally high percentage can be used to determine the frequency of abnormally high IgA activity in a patient throughout the entire process of tracking IgA activity. Its calculation formula is as follows:

[0089] Among them, C is the preset positive judgment value; if A i ≥C,Ind(A i ≥C)=1; if A i <C,Ind(Ai≥C)=0。

[0090] 3. Prediction model: IgA immune activity time comprehensive index - renal puncture to confirm IgA nephropathy.

[0091] The above-mentioned comprehensive index of IgA immune activity time was used to predict IgA nephropathy diagnosed by renal puncture.

[0092] The objective function was binary: IgA nephropathy confirmed by renal puncture was positive (=1), and other renal diseases and healthy controls were negative (=0). Univariate models were established, and receiver operating characteristic (ROC) curves or multivariate linear regression models were used. The prediction models were also evaluated by ROC curves.

[0093] In order to verify that the above-mentioned comprehensive index of IgA immune activity time can be used as a diagnostic indicator for IgA nephropathy, given the difficulty and time cost of collecting data sets, a simulated data set of time series was first generated based on the statistical properties of the actual detection data.

[0094] We first conducted a preliminary analysis of the statistical characteristics of non-time-series IgA immune activity index values. Table 1 lists the mean and standard deviation of the actual values ​​at a single time point for three groups of people: IgA nephropathy, other renal diseases (non-IgAN), and healthy volunteers. The IgA nephropathy and other renal diseases (non-IgAN) groups primarily consisted of samples collected before renal biopsy confirmation.

[0095] Table 1

[0096] Table 1 shows that the IgA activity index in healthy individuals is 8.17 ± 6.26 (mean plus or minus two standard deviations); in IgA nephropathy, it is 13.06 ± 9.18; and in other renal diseases (non-IgAN), it is 10.95 ± 8.82. On average, IgA nephropathy has an IgA activity index 60% higher than in healthy individuals and 19% higher than in other renal diseases (non-IgAN).

[0097] When simulating time series, it is assumed that the data of healthy people are the initial time point, that is, T0; and the data of IgA nephropathy group and other renal disease (non-IgAN) group are the end time point, that is, T n Other time points are from T0 to T n The linear interpolation is done by adding the standard deviation for distortion. Since the distribution of detected values ​​is long on the right and bounded by 0 on the left, reflecting the asymmetry of the distribution, the Poisson distribution model is used for random sampling.

[0098] For IgA nephropathy patients, assume that during the development of IgA nephropathy, their IgA immune activity index increases from the average level of healthy people to the average level before renal puncture. Assume that the time series is three years, and the test is performed every three months, for a total of 12 times, that is, the length of each simulated time series is 12. To determine the time series of a case of IgA nephropathy, randomly select the Poisson distribution N (m i ,σ i ) 11 is the end point of the time series, where m i =13.06,σ i =4.59, and then select the Puasson distribution of healthy people (corresponding to m i =8.17,σ i =3.13) is the starting value, which satisfies the requirement A 11 >A0, obtain m by uniform interpolation 11 ,m10 ,...,m2, the corresponding standard deviation also changes from σ 11 =4.59 to σ0=3.13 uniformly interpolated to obtain σ 11 ,σ 10 ,...,σ2, then A k Selected from the Poisson distribution N(m k ,σ k ), k = 10, 9, ..., 1. This is repeated 50 times to represent the time series of IgA immune activity index of 50 people in the IgA nephropathy group.

[0099] The same method was used to simulate a time series of IgA immune activity indices for 50 individuals in a group with other kidney diseases (non-IgAN). For the healthy group and the group with other kidney diseases (non-IgAN), since IgA immune activity does not change over time in practice, interpolation was not required. Instead, 50 cases were randomly sampled using the corresponding Poisson distribution. This time series accurately simulated the actual situation. The simulated time series is shown in Figure 1.

[0100] It should be noted that these simulation sequences only utilize the demographic characteristics of the population listed in Table 1 and have nothing to do with individual cases in the population group. The number of selected cases (50) can also be any other larger number.

[0101] The simulated data set obtained above was used to first perform a time series comprehensive index analysis. Then, the comprehensive index was used as a diagnostic marker for IgA nephropathy to establish univariate and multivariate detection models, and the model results were evaluated.

[0102] Basic analysis refers to the current detection mode, which does not consider the time factor. That is, each time point is treated as a separate data set, and ROC is used to build a prediction model separately, or the data set of all time points is combined and ROC is used to build a model. Time points T0, T1, ..., T 11 These correspond to the sampling times of month 0, month 3, ..., and month 33, respectively. At a given time point, only all the detection values ​​corresponding to that time were used as input to establish a univariate model using ROC to predict IgA nephropathy and calculate the corresponding evaluation index.

[0103] The evaluation results are shown in Table 2. Representative ROC curves are shown in Figure 2.

[0104] Table 2

[0105] Note: AUC is the area under the curve, FPR is the false positive rate, TPR is the true positive rate, ACC is the accuracy, and PPV is the positive predictive value.

[0106] The data showed that the auc of the baseline T0 was 0.56; the end point T11 Before renal puncture, the AUC was 0.97. Furthermore, the ROC was established by combining the detection values ​​at all time points, which is equivalent to 600 independent samples per each group (50 × 12 = 600). The AUC was 0.73, which is closer to the actual application scenario. Because in the actual testing process, different kidney patients may be at different time points of their condition, which is equivalent to any reasonable subset of the above combined set.

[0107] Based on the IgA immune activity time comprehensive index, a univariate model was established using ROC. The IgA immune activity time comprehensive index in (1)-(7) above was calculated for each time series, and the ROC curves were drawn.

[0108] The model evaluation results are shown in Table 3. Representative ROC curves are shown in Figure 3.

[0109] Table 3

[0110] Note: AUC is the area under the curve, FPR is the false positive rate, TPR is the true positive rate, ACC is the accuracy, and PPV is the positive predictive value.

[0111] The ROC curve for the average time series of the IgA immune activity index showed a specificity of 93%, a sensitivity of 78%, and an AUC of 0.91. The ROC curve for the average rate of change from baseline in the IgA immune activity index showed a specificity of 88%, a sensitivity of 78%, and an AUC of 0.92. The ROC curve for the percentage of abnormally high IgA immune activity showed a specificity of 75%, a sensitivity of 84%, and an AUC of 0.87. The AUC for the average absolute rate of change from baseline was 0.87.

[0112] The mean IgA immune activity index, mean baseline change rate, mean baseline absolute change rate, frequency of abnormally high detection, standard deviation, mean radius, skewness, and kurtosis all demonstrated good diagnostic efficacy for IgA nephropathy. Their accuracy exceeded that of the near-real-world model (AUC = 0.73) that combined all time points, as shown in Figure 2C.

[0113] Therefore, by continuously tracking the patient's IgA immune activity indicators, the accuracy of diagnosing IgA nephropathy can be improved.

[0114] A multivariate linear regression model was constructed based on a comprehensive index of IgA immune activity over time. Linear models were constructed using combinations of the mean IgA activity value, the mean baseline rate of change, the percentage of abnormally high activity, and the mean baseline absolute rate of change. The bivariate model (mean + mean baseline rate of change) achieved an AUC of 0.96, while the combination (mean + mean baseline rate of change + mean baseline absolute rate of change) had the same AUC. Furthermore, the combination (mean + mean baseline rate of change + percentage of abnormally high activity) achieved an AUC of 0.97, showing little change. However, a closer look at the model parameters revealed a negative weighting coefficient for the percentage of abnormally high activity, contradicting the univariate model's belief that the percentage of abnormally high activity was a better indicator. This suggests that the mean baseline rate of change or the mean already adequately captures all information about the percentage of abnormally high activity.

[0115] Regarding the results of the “mean + mean baseline change rate” bivariate model, the model parameters are shown in Table 4 , and the ROC curve is shown in Figure 4 , with the corresponding AUC = 0.96, and the specificity and sensitivity are both 90%.

[0116] Table 4

[0117] By tracking and collecting the time series of the IgA immune activity index of people with early kidney disease (people with early kidney disease characteristics, including mild urine protein, hematuria, abnormal renal function indicators, etc.), peripheral blood is collected once every three months to detect the IgA immune activity index. The clinical trial period is three years. The end time of each patient's follow-up is when the renal puncture is confirmed, and the renal puncture result is IgA nephropathy or non-IgA nephropathy or three years. Based on the average value, abnormal high percentage, baseline change rate average value, baseline absolute change rate average value, standard deviation, mean radius, skewness, and kurtosis of the time series of the IgA immune activity index, a predictive IgA nephropathy risk model is established to predict the risk of developing IgA nephropathy. The ROC results of the predictive IgA nephropathy risk model showed that the AUC was greater than 0.7, the specificity was greater than 75%, and the sensitivity was greater than 75%.

[0118] By tracking and collecting the time series of the IgA immune activity index of patients diagnosed with IgA nephropathy by renal puncture, peripheral blood was collected every three months to detect the IgA immune activity index. The clinical trial period is five years. The follow-up time for each patient is to reach the ESKD stage or five years later. Based on the mean value, abnormal high percentage, mean baseline change rate, mean baseline absolute change rate, standard deviation, mean radius, skewness, and kurtosis of the time series of the IgA immune activity index, a predictive IgA nephropathy risk model was established to predict the risk of IgA nephropathy developing into end-stage kidney disease (ESKD, such as uremia). The ROC results of the predictive IgA nephropathy risk model showed an AUC greater than 0.7, a specificity greater than 75%, and a sensitivity greater than 75%.

[0119] The embodiments of the present invention are described in detail above in conjunction with the embodiments, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge scope of ordinary technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. An IgA nephropathy marker, characterized in that At least one of the information A1) to A8) including the time series of IgA immune activity index: A1) Average value of IgA immune activity index; A2) abnormally high percentage of IgA immune activity index; A3) the mean value of the baseline change rate of IgA immune activity index; A4) Average absolute change rate of IgA immune activity index from baseline; A5) Standard deviation of IgA immune activity index; A6) Mean radius of IgA immune activity index; A7) Skewness of IgA immune activity index; A8) Kurtosis of IgA immune activity index; The IgA immune activity index time series is the IgA immune activity index of n samples of the subject; the samples are within a certain period of time, at intervals S t The sample collected; n≥2; the IgA immune activity index refers to the concentration of IgA immune complexes in serum / plasma.

2. An IgA nephropathy diagnostic system, characterized in that: include: A detection unit, the detection unit is used to detect the IgA immune activity index of n samples of the subject to obtain an IgA immune activity index time series; The samples are collected within a certain period of time at intervals S t The samples collected; n≥2; The IgA immune activity index refers to the concentration of IgA immune complexes in serum / plasma; A data acquisition unit, the data acquisition unit is used to acquire at least one information of A1) to A8) of the IgA immune activity index time series: A1) Average value of IgA immune activity index; A2) abnormally high percentage of IgA immune activity index; A3) the mean value of the baseline change rate of IgA immune activity index; A4) Average absolute change rate of IgA immune activity index from baseline; A5) Standard deviation of IgA immune activity index; A6) Mean radius of IgA immune activity index; A7) Skewness of IgA immune activity index; A8) Kurtosis of IgA immune activity index.

3. The IgA nephropathy diagnostic system according to claim 2, characterized in that: The detection module includes at least one of a qPCR kit, an ELISA kit, an immunoblotting detection kit, an immunohistochemistry detection kit, an immunochromatography detection kit, an electrochemiluminescence detection kit, a qPCR instrument, an ELISA detection device, an immunoblotting detection device, an immunohistochemistry detection device, an immunochromatography detection device, and an electrochemiluminescence detection device.

4. The IgA nephropathy diagnostic system according to claim 2, characterized in that: The IgA nephropathy diagnostic system further comprises an analysis unit, which is used to analyze whether the subject suffers from IgA nephropathy or has a risk of suffering from IgA nephropathy or developing end-stage renal disease from IgA nephropathy based on the information acquired by the data acquisition module.

5. Use of the IgA nephropathy marker according to claim 1 or the IgA nephropathy diagnostic system according to any one of claims 2 to 4 in the preparation of products for diagnosing or assisting in the diagnosis of IgA nephropathy.

6. Use of the IgA nephropathy marker according to claim 1 or the IgA nephropathy diagnostic system according to any one of claims 2 to 4 in screening drugs for preventing and / or treating IgA nephropathy.

7. A method for assessing the risk of IgA nephropathy without disease diagnosis and / or treatment, characterized in that: The following steps are involved: S1. Determine at least one of the information A1) to A8) of the time series of the IgA immune activity index of the subject: A1) Average value of IgA immune activity index; A2) abnormally high percentage of IgA immune activity index; A3) the mean value of the baseline change rate of IgA immune activity index; A4) Average absolute change rate of IgA immune activity index from baseline; A5) Standard deviation of IgA immune activity index; A6) Mean radius of IgA immune activity index; A7) Skewness of IgA immune activity index; A8) Kurtosis of IgA immune activity index; The IgA immune activity index time series is the IgA immune activity index of n samples of the subject; the samples are within a certain period of time, at intervals S t The sample collected; n≥2; the IgA immune activity index refers to the concentration of IgA immune complexes in serum / plasma; S2. Based on the information, analyzing whether the subject suffers from IgA nephropathy or the risk of suffering from IgA nephropathy or developing end-stage renal disease from IgA nephropathy.

8. The method for assessing the risk of IgA nephropathy according to claim 7, characterized in that: Analyzing whether the subject suffers from IgA nephropathy or the risk of suffering from IgA nephropathy includes: comparing at least one of the information in A1) to A8) with a preset threshold to determine whether the subject suffers from IgA nephropathy or the risk of suffering from IgA nephropathy or developing end-stage renal disease from IgA nephropathy.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions for executing the method for assessing the risk of IgA nephropathy according to claim 7 or 8.

10. A computer device comprising a memory and a processor; the memory stores a computer program, characterized in that: When the computer program is executed, the method for assessing the risk of IgA nephropathy as claimed in claim 7 or 8 can be implemented.

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