A method for glycated hemoglobin chromatographic detection for pregnancy risk assessment

Abstract

The present application relates to the technical field of glycated hemoglobin detection, and particularly relates to a glycated hemoglobin chromatographic detection method for pregnancy risk assessment, comprising the following steps: obtaining serum ferritin concentration, glycated hemoglobin concentration and C-reactive protein concentration of pregnant women; analyzing reference value weight of each pregnant woman according to distribution of serum ferritin concentration and C-reactive protein concentration of each pregnant woman and body mass index of each pregnant woman, and then obtaining data correction coefficient; obtaining serum ferritin reference value according to serum ferritin concentration, C-reactive protein concentration and glycated hemoglobin concentration of pregnant women; adjusting glycated hemoglobin concentration of a target pregnant woman according to data correction coefficient, serum ferritin reference value and serum ferritin concentration of the target pregnant woman, and then obtaining glycated hemoglobin detection result of the target pregnant woman. The present application can eliminate interference caused by iron deficiency or anemia, and make glycated hemoglobin detection result more accurate.

Description

Technical Field

[0001] This invention relates to the field of glycated hemoglobin detection technology, specifically to a chromatographic detection method for glycated hemoglobin used in pregnancy risk assessment. Background Technology

[0002] Gestational diabetes increases the risk of complications for both the mother and fetus, including macrosomia, premature birth, and gestational hypertension. Glycated hemoglobin (HbA1c) reflects the average blood glucose level of a pregnant woman over the past 2-3 months. Detecting HbA1c using chromatographic techniques allows for a rapid and accurate assessment of potential gestational diabetes risk in pregnant women, enabling preventative and interventional measures. Compared to traditional fasting blood glucose testing, HbA1c provides a more comprehensive picture of blood glucose control in pregnant women, has high clinical value, aids in pregnancy management, reduces adverse outcomes associated with gestational diabetes, and safeguards maternal and infant health.

[0003] The chromatographic detection method for glycated hemoglobin (HbA1c) commonly used for pregnancy risk assessment is as follows: First, a fasting venous blood sample is collected and preserved in an anticoagulant tube. Then, the blood sample is treated with a hemolysin and injected into a high-performance liquid chromatography (HPLC) system. Based on the residence time difference of hemoglobin molecules with different degrees of glycation in the mobile phase, HbA1c, HbA0, and other components are separated. The peak areas of each component in the chromatogram are then integrated to calculate the percentage concentration of glycated hemoglobin (HbA1c), and calibration is performed using the internal standard method or standard curve method. Based on the reference range standards for HbA1c concentration in pregnant women in the first, second, and third trimesters, as well as clinical experience, the physician determines whether the test results exceed the normal range, indicating pregnancy risks such as gestational diabetes, macrosomia, or placental dysfunction.

[0004] However, in chromatographic detection of glycated hemoglobin (HbA1c) used for pregnancy risk assessment, pregnant women often experience iron deficiency, which can lead to changes in the structure of hemoglobin, resulting in prolonged red blood cell lifespan (due to a decrease in newly generated red blood cells) and causing a false increase in HbA1c. This phenomenon is relatively common in pregnant women, especially in cases of iron deficiency anemia, and may affect the accuracy of HbA1c, resulting in lower precision in chromatographic detection of glycated hemoglobin. Summary of the Invention

[0005] To address the technical problem of low accuracy in glycated hemoglobin chromatographic detection due to iron deficiency in pregnant women, this invention aims to provide a glycated hemoglobin chromatographic detection method for pregnancy risk assessment. The specific technical solution adopted is as follows:

[0006] The serum ferritin concentration, glycated hemoglobin concentration, and C-reactive protein concentration of each pregnant woman were obtained, wherein the pregnant women included the target pregnant women and reference pregnant women under the same conditions;

[0007] Based on the distribution of serum ferritin and C-reactive protein concentrations and the body mass index of each pregnant woman, the reference value weight of each pregnant woman was analyzed. Combined with the differences in serum ferritin concentration and glycated hemoglobin concentration among different pregnant women, the data correction coefficient was obtained.

[0008] Based on the distribution of serum ferritin and C-reactive protein concentrations in the target pregnant woman and the glycated hemoglobin concentration of each reference pregnant woman, the serum ferritin reference value of the reference pregnant woman relative to the target pregnant woman is obtained.

[0009] Based on the data correction coefficient, serum ferritin reference value, and serum ferritin concentration of the target pregnant woman, the glycated hemoglobin concentration of the target pregnant woman is adjusted to obtain the glycated hemoglobin test result of the target pregnant woman.

[0010] Preferably, the step of analyzing the reference value weight of each pregnant woman based on the distribution of serum ferritin and C-reactive protein concentrations and the body mass index of each pregnant woman specifically includes:

[0011] For any pregnant woman, serum ferritin concentration and C-reactive protein concentration were standardized to obtain serum ferritin data and C-reactive protein data, respectively.

[0012] The product of the negative correlation coefficients of serum ferritin data and C-reactive protein data was used as the deficiency characteristic factor of the pregnant woman.

[0013] The reference value weight of the pregnant woman is obtained based on the pregnant woman's lack of characteristic factors and her body mass index.

[0014] Preferably, the step of obtaining the reference value weight of the pregnant woman based on her lacking characteristic factors and her body mass index specifically includes:

[0015] Based on the pregnant woman's height and weight, the pregnant woman's body mass index (BMI) is determined; based on the normalized result of the difference between the pregnant woman's BMI and the preset normal BMI and the lack of feature factors, the reference value weight of the pregnant woman is determined.

[0016] Preferably, the step of analyzing the reference value weight of each pregnant woman based on the distribution of serum ferritin and C-reactive protein concentrations and the body mass index of each pregnant woman, and combining the differences in serum ferritin concentration and glycated hemoglobin concentration among different pregnant women to obtain a data correction coefficient, specifically including:

[0017] By using the reference value weights of each pair of pregnant women, the differences in serum ferritin concentration and glycated hemoglobin concentration between each pair of pregnant women were weighted to obtain the degree of association contribution between each pair of pregnant women.

[0018] The sum of the correlation contributions of all pregnant women is used as the data correction coefficient.

[0019] Preferably, the step of weighting the differences in serum ferritin concentration and glycated hemoglobin concentration between each pair of pregnant women using the reference value of each pair of pregnant women to obtain the degree of association contribution between each pair of pregnant women specifically includes:

[0020] For any two pregnant women, the weight is the sum of the reference value weights of the two pregnant women; the absolute value of the difference between the glycated hemoglobin concentrations of the two pregnant women is the first difference coefficient; and the absolute value of the difference between the reciprocals of the serum ferritin concentrations of the two pregnant women is the second difference coefficient.

[0021] Using the weights, the ratio of the first difference coefficient and the second difference coefficient is weighted to obtain the degree of association contribution between any two pregnant women.

[0022] Preferably, obtaining the reference value of serum ferritin for the target pregnant woman based on the distribution of serum ferritin and C-reactive protein concentrations of the target pregnant woman and the glycated hemoglobin concentration of each reference pregnant woman specifically includes:

[0023] The glycated hemoglobin concentration of each pregnant woman was standardized to obtain glycated hemoglobin data for each pregnant woman;

[0024] Based on the missing characteristic factors of the target pregnant woman and the glycated hemoglobin data of each reference pregnant woman, the true data characteristic value of each reference pregnant woman relative to the target pregnant woman is obtained;

[0025] The reference value of serum ferritin for the target pregnant woman is obtained based on the actual data characteristic value corresponding to each reference pregnant woman and the serum ferritin concentration of the same reference pregnant woman.

[0026] Preferably, the step of obtaining the true data feature value of each reference pregnant woman relative to the target pregnant woman based on the lack of characteristic factors of the target pregnant woman and the glycated hemoglobin data of each reference pregnant woman specifically includes:

[0027] The product of the negative correlation coefficient of the lack of characteristic factors of the target pregnant woman and the negative correlation coefficient of the glycated hemoglobin data of each reference pregnant woman is used as the true data characteristic value of each reference pregnant woman relative to the target pregnant woman.

[0028] Preferably, the step of obtaining the serum ferritin reference value of the reference pregnant woman relative to the target pregnant woman based on the real data feature value corresponding to each reference pregnant woman and the serum ferritin concentration of the same reference pregnant woman specifically includes:

[0029] The proportion of the real data feature values ​​corresponding to each reference pregnant woman is used as the data weight for each reference pregnant woman. The serum ferritin concentration of each reference pregnant woman is weighted and summed using the data weight to obtain the serum ferritin reference value of the reference pregnant woman relative to the target pregnant woman.

[0030] Preferably, the step of adjusting the glycated hemoglobin concentration of the target pregnant woman based on the data correction coefficient, serum ferritin reference value, and serum ferritin concentration of the target pregnant woman to obtain the glycated hemoglobin test result of the target pregnant woman specifically includes:

[0031] The degree of data adjustment is determined based on the difference between the serum ferritin concentration of the target pregnant woman and the serum ferritin reference value, as well as the data correction coefficient.

[0032] The difference between the glycated hemoglobin concentration of the target pregnant woman and the data adjustment level is used as the correction value for the glycated hemoglobin concentration of the target pregnant woman, thus obtaining the glycated hemoglobin test result of the target pregnant woman.

[0033] Preferably, the step of determining the degree of data adjustment based on the difference between the target pregnant woman's serum ferritin concentration and the serum ferritin reference value, and a data correction coefficient, specifically includes:

[0034] The difference between the reciprocal of the serum ferritin concentration of the target pregnant woman and the reciprocal of the serum ferritin reference value is calculated, and the product of the data correction factor and this difference is used as the degree of data adjustment.

[0035] The embodiments of the present invention have at least the following beneficial effects:

[0036] This invention first collects serum ferritin, glycated hemoglobin (HbA1c), and C-reactive protein (CRP) concentrations from pregnant women. Simultaneously, during the correction process for the test results of the target pregnant woman, a reference sample (i.e., a reference pregnant woman) under the same conditions is set. Then, considering the relatively high HbA1c concentration variation in iron-deficient pregnant women, a data correction coefficient is used to reflect the linear relationship between differences in HbA1c concentration and serum ferritin concentration. Furthermore, combining the iron deficiency reflected in the HbA1c concentration of the reference sample with the iron deficiency data distribution of the target pregnant woman, the serum ferritin data performance that the target pregnant woman under the same conditions should have under normal circumstances is quantified, i.e., a serum ferritin reference value is obtained. Finally, a correction term for HbA1c concentration is obtained using the data correction coefficient, and the HbA1c concentration test value of the target pregnant woman is adjusted to obtain a more accurate HbA1c test result. Compared to existing technologies, this invention addresses the problems of iron deficiency in pregnant women leading to changes in hemoglobin structure, causing false increases in HbA1c, and abnormal hemoglobin synthesis potentially altering chromatographic peak shape and interfering with detection. This invention corrects glycated hemoglobin concentration by using chromatographic detection values ​​of serum ferritin concentration in pregnant women, eliminating interference caused by iron deficiency or anemia, and making glycated hemoglobin detection results more accurate. Attached Figure Description

[0037] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart of the steps of a chromatographic detection method for glycated hemoglobin for pregnancy risk assessment provided by the present invention;

[0039] Figure 2 This is a flowchart of the steps for obtaining the data correction coefficient provided by the present invention;

[0040] Figure 3 This is a flowchart of the steps for obtaining serum ferritin reference values ​​provided by the present invention. Detailed Implementation

[0041] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a glycated hemoglobin chromatographic detection method for pregnancy risk assessment proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0043] The following description, in conjunction with the accompanying drawings, details a specific scheme for the chromatographic detection method of glycated hemoglobin for pregnancy risk assessment provided by the present invention.

[0044] The main objective of this invention is to address the issue that iron deficiency in pregnant women can lead to prolonged red blood cell lifespan, resulting in elevated pseudoglycated hemoglobin (HbA1c). Furthermore, red blood cell lifespan is negatively correlated with iron reserves, while serum ferritin (SF) is positively correlated with the degree of iron deficiency; that is, the lower the SF level, the more severe the iron deficiency. Therefore, by analyzing the characteristics of serum ferritin data, a correction term can be constructed to adjust the HbA1c detection results, thus offsetting the false elevation caused by iron deficiency and obtaining more accurate HbA1c chromatographic detection results.

[0045] Please see Figure 1 The diagram illustrates a flowchart of a chromatographic detection method for glycated hemoglobin for pregnancy risk assessment provided by an embodiment of the present invention. The method includes the following steps:

[0046] Step S100: Obtain the serum ferritin concentration, glycated hemoglobin concentration, and C-reactive protein concentration for each pregnant woman, wherein the pregnant women include the target pregnant woman and the reference pregnant women under the same conditions.

[0047] First, identify the target pregnant women who need to undergo glycated hemoglobin chromatography. Then, select other pregnant women with the same conditions as the target pregnant women from the database as reference samples and record them as the reference dataset corresponding to the target pregnant women. The reference dataset includes several reference pregnant women.

[0048] Furthermore, serum ferritin, glycated hemoglobin, and C-reactive protein (CRP) concentrations were obtained for each pregnant woman. Serum ferritin and glycated hemoglobin concentrations were obtained using chromatographic detection, and CRP concentrations were measured using enzyme-linked immunosorbent assay (ELISA). The specific methods for obtaining these concentrations are well-known and will not be described in detail here. It should be understood that glycated hemoglobin concentration data are expressed as a percentage, serum ferritin concentration is generally expressed in μg / L, and CRP concentration data is generally expressed in mg / L.

[0049] It is important to note that gestational age directly affects a pregnant woman's iron requirements and metabolic status, and iron reserves and serum ferritin levels may vary significantly across different gestational weeks. Secondly, Body Mass Index (BMI) reflects a pregnant woman's physical condition and has a significant impact on serum ferritin concentration. Obese pregnant women may have lower serum ferritin concentrations due to plasma volume expansion, while pregnant women with low BMI may have abnormal gastrointestinal absorption, affecting iron absorption and storage. Therefore, when obtaining a reference dataset for the target users, comparisons should be made among pregnant women with the same gestational age and BMI value. This effectively eliminates the interference of these factors, ensuring the comparability of the determined serum ferritin reference analysis results, thus providing an accurate basis for individualized assessment.

[0050] It should be noted that Body Mass Index (BMI) is calculated based on the pregnant woman's height and weight, a well-known technique that will not be elaborated upon here. As a concrete example, a reference dataset is created by selecting other pregnant women from a hospital database who have the same gestational age, BMI, and age as the target pregnant woman.

[0051] In other embodiments, different dimensions of the conditional neighborhood range can be set. For example, the gestational age difference with the target pregnant woman can be at most one week, the BMI value difference can be at most 1, and the age difference can be at most two years. The implementer can set these ranges based on clinical experience and specific implementation circumstances. The reference dataset represents a collection of pregnant women with the same or similar physiological conditions as the target pregnant woman. It should be understood that the reference dataset is generally a normal reference sample without other complications.

[0052] Step S200: Based on the distribution of serum ferritin and C-reactive protein concentrations and the body mass index of each pregnant woman, analyze the reference value weight of each pregnant woman, and obtain the data correction coefficient by combining the differences in serum ferritin concentration and glycated hemoglobin concentration among different pregnant women.

[0053] Serum ferritin concentration is a biomarker for assessing iron reserves in the body, and low serum ferritin concentrations are commonly used to diagnose iron deficiency in pregnant women. However, serum ferritin is not solely a reflection of iron stores; it can also be falsely elevated in cases of infection, chronic disease, or inflammation. Therefore, this step involves constructing a data correction coefficient as a correction term to ensure that serum ferritin concentration accurately reflects iron deficiency status and to eliminate interference from other factors.

[0054] As a concrete example, such as Figure 2 As shown, the method for obtaining the data correction coefficient can be implemented by steps S201 to S203.

[0055] Step S201: Based on the distribution of serum ferritin concentration and C-reactive protein concentration of each pregnant woman, obtain the deficiency characteristic factors of each pregnant woman.

[0056] Specifically, for any pregnant woman, serum ferritin concentration and C-reactive protein concentration are standardized to obtain serum ferritin data and C-reactive protein data, respectively. The product of the negative correlation coefficients of serum ferritin data and C-reactive protein data is used as the lack-feature factor for the pregnant woman. It should be noted that the standardization method is a well-known technique; for example, Z-score standardization can be used to avoid the influence of different parameter dimensions on the feature analysis results. In other embodiments, implementers can process the data according to the specific implementation scenario.

[0057] Relying solely on low serum ferritin levels to diagnose iron deficiency can be inaccurate, especially in the presence of infection or chronic disease. Pregnancy is often accompanied by mild inflammation, particularly elevated C-reactive protein (CRP) levels. CRP is an acute-phase protein synthesized by the liver in inflammatory states, and its levels rise rapidly during an inflammatory response. Therefore, if both serum ferritin and CRP levels are low, it indicates the absence of significant inflammation, and the low serum ferritin level accurately reflects iron deficiency. Conversely, a high CRP level suggests the possible presence of infection or inflammation, which can influence the interpretation of serum ferritin levels and lead to false-negative iron deficiency.

[0058] Therefore, by analyzing the distribution of serum ferritin concentration and C-reflection protein concentration data for each pregnant woman, we can comprehensively analyze the true deficiency that serum ferritin concentration can represent for each pregnant woman and quantify the true degree of deficiency reflected by the serum ferritin concentration for each pregnant woman.

[0059] As a concrete example, taking the i-th pregnant woman as an example, the lack of characteristic factors of the i-th pregnant woman can be expressed by the formula: ;in, This indicates the lack of characteristic factors for the i-th pregnant woman. This represents the C-reactive protein data for the i-th pregnant woman. This represents the serum ferritin data for the i-th pregnant woman. This represents an exponential function with the natural constant e as its base.

[0060] Serum ferritin levels are positively correlated with the body's iron reserves; that is, the lower the serum ferritin value, the more severe the iron deficiency. The reciprocal of the serum ferritin value is used to represent the negative correlation coefficient of serum ferritin data, reflecting the original quantitative trend of the current iron deficiency in pregnant women.

[0061] Furthermore, considering the interference of non-iron deficiency elevation in serum ferritin, the negative correlation coefficient of C-reactive protein data was used. To achieve interference inhibition, C-reactive protein data are higher in the presence of inflammation. The smaller values ​​indicate that initial serum ferritin levels are significantly affected by inflammation, resulting in lower reliability of serum ferritin in reflecting a true deficiency, and correspondingly, smaller values ​​for deficiency characteristic factors. In the absence of inflammation, C-reactive protein data are also smaller. A larger value indicates that serum ferritin can accurately reflect iron reserves, and in this case, the value of the lacking characteristic factor is larger.

[0062] Following the same method, the deficiency trait factor for each pregnant woman can be obtained. This trait factor characterizes whether each pregnant woman's serum ferritin data accurately reflects the current degree of iron deficiency. A higher value indicates a greater degree of true iron deficiency, meaning the data is accurate and severe, requiring significant correction of the pregnant woman's glycated hemoglobin. A lower value indicates no iron deficiency, or that the serum ferritin data is unreliable due to inflammation, and does not require significant correction of glycated hemoglobin.

[0063] Furthermore, serum ferritin concentration must accurately reflect iron reserves, rather than being elevated due to other physiological factors such as inflammation or infection. Only when C-reactive protein levels are low and there is no inflammation can serum ferritin concentration accurately represent the iron reserve status of pregnant women, providing a reliable basis for correcting glycated hemoglobin concentration.

[0064] Step S202: Obtain the reference value weight of the pregnant woman based on the pregnant woman's lack of characteristic factors and body mass index.

[0065] It is important to note that a pregnant woman's body mass index (BMI) is also crucial. Obese pregnant women, due to increased plasma volume, may experience a relative decrease in serum ferritin concentration, thus affecting the accurate diagnosis of iron deficiency. In such cases, low serum ferritin concentration may not fully reflect actual iron reserves, but rather be due to a dilution effect caused by increased blood volume. Furthermore, pregnant women with low BMI may have gastrointestinal dysfunction (such as celiac disease), which can affect iron absorption and lead to iron deficiency. Therefore, excessively high or low BMI increases the measurement error of serum ferritin concentration, making it unable to accurately reflect the pregnant woman's iron deficiency status, and consequently affecting the correction of glycated hemoglobin concentration. Therefore, based on the analysis results excluding true iron deficiency caused by inflammation, further eliminating the interference of abnormal BMI on serum ferritin concentration, and comprehensively quantifying the data reference value reflected by each pregnant woman's performance, is essential.

[0066] Specifically, the pregnant woman's body mass index (BMI) is determined based on her height and weight. The reference value weight of the pregnant woman is determined based on the normalized result of the difference between her BMI and a preset normal BMI, and the absence of characteristic factors. The calculation method for the BMI is a well-known technique and will not be elaborated upon here. Meanwhile, the normal BMI for a pregnant woman needs to be determined by a doctor based on clinical experience and the gestational age, and will not be elaborated upon here either; its purpose is to measure the degree of deviation of the pregnant woman's BMI from the normal range at a corresponding gestational age.

[0067] As a concrete example, taking the i-th pregnant woman as an example, the reference value weight of the i-th pregnant woman can be expressed by the formula: ;in This represents the reference value weight of the i-th pregnant woman. This represents the normalized result of the absolute value of the difference between the body mass index (BMI) of the i-th pregnant woman and the preset normal BMI. This indicates the lack of characteristic factors for the i-th pregnant woman. This represents an exponential function with the natural constant e as its base.

[0068] It should be noted that the normalization method can adopt the minimax normalization method, which is a well-known technique and will not be discussed in detail here. Implementers can calculate according to the specific implementation scenario.

[0069] The larger the value of the reference value, the greater the deviation of the pregnant woman's body mass index from the normal range. In this case, the reference value of the pregnant woman's data is less valuable for the subsequent correction process, and the corresponding reference value weight is smaller. Therefore, the reference value weight of each pregnant woman represents the true usability of each pregnant woman for the subsequent calculation of correction items.

[0070] Step S203: Based on the reference value weight of each pregnant woman, and combined with the differences in serum ferritin concentration and glycated hemoglobin concentration among different pregnant women, a data correction coefficient is obtained.

[0071] Iron deficiency leads to prolonged red blood cell lifespan, thereby increasing the production of glycated hemoglobin. Therefore, pregnant women with iron deficiency have relatively high levels of glycated hemoglobin (HbA1c), which decreases when SF (serum ferritin) concentration decreases. An increase in the reciprocal of serum ferritin concentration (the fraction of which is lower than serum ferritin concentration) also leads to a false increase in glycated hemoglobin concentration. Therefore, There is a linear relationship between it and HbA1c, that is As the concentration increases, the HbA1c concentration also increases.

[0072] Based on this characteristic, by comparing the differences in glycated hemoglobin concentration between any two pregnant women and The differences between them, combined with the data availability of the corresponding pregnant women, quantify the current situation of pregnant women. The linear relationship between HbA1c and HbA1c is shown.

[0073] Specifically, the differences in serum ferritin concentration and glycated hemoglobin concentration between each pair of pregnant women were weighted using the reference value weights of each pair of pregnant women to obtain the degree of association contribution between each pair of pregnant women; the sum of the degree of association contribution of all pregnant women was used as the data correction coefficient.

[0074] More specifically, for any two pregnant women, the percentage of the sum of the reference value weights of the two pregnant women is used as the weight; the absolute value of the difference in glycated hemoglobin concentration between the two pregnant women is used as the first difference coefficient; the absolute value of the difference between the reciprocals of the serum ferritin concentration between the two pregnant women is used as the second difference coefficient; using the weights, the ratio of the first difference coefficient and the second difference coefficient is weighted to obtain the degree of association contribution between the two pregnant women.

[0075] As a concrete example, taking the a-th pregnant woman and the b-th pregnant woman as examples, the method for calculating the association contribution between the a-th pregnant woman and the b-th pregnant woman can be expressed by the formula:

[0076]

[0077] in, This indicates the degree of association contribution between pregnant woman a and pregnant woman b. This represents the reference value weight of the a-th pregnant woman. This represents the reference value weight of the b-th pregnant woman. This represents a dataset consisting of all pregnant women. This represents the glycated hemoglobin concentration of the a-th pregnant woman. This represents the glycated hemoglobin concentration of the b-th pregnant woman. This represents the serum ferritin concentration of the a-th pregnant woman. This represents the serum ferritin concentration of the b-th pregnant woman.

[0078] The weight represents the sum of the reference value weights of the two pregnant women, reflecting the relative reliability of their data. Using these weights to quantify the association's contribution indicates that a higher weight is assigned to the pregnant woman sample pair with reliable data (no inflammation, no BMI interference), allowing it to have a greater impact when calculating the data correction coefficient. This ensures that the data correction coefficient truly reflects the association between iron deficiency and a falsely elevated HbA1c concentration.

[0079] The coefficient of variation is the first difference, reflecting the difference in glycated hemoglobin concentration between the two pregnant women. The second difference coefficient reflects the differences between the two pregnant women. The difference between them. The ratio of the first difference coefficient to the second difference coefficient, as a correlation term, characterizes the linear association strength between the difference in iron deficiency between pregnant woman a and pregnant woman b and the spurious difference in their HbA1c concentration.

[0080] The association contribution between two pregnant women represents the percentage of a false increase in HbA1c concentration for each unit difference in iron deficiency level within a sample pair; that is, the proportion of association between the two. Based on this, each sample pair representing two different pregnant women corresponds to a specific association contribution. Furthermore, by synthesizing the association contributions of all sample pairs, a data correction coefficient is used to comprehensively quantify the association contribution. The linear relationship between HbA1c and HbA1c is constrained by data reliability to avoid interference from anomalous samples.

[0081] It should be noted that, in quantifying the degree of association contribution between any two pregnant women, there are limitations due to slight differences between individuals. The value of 0 is an extremely low probability event. Furthermore, considering that the false increase in HbA1c concentration is due to iron deficiency, if there is no difference in the degree of iron deficiency between the two pregnant women, then the false increase in HbA1c concentration should also be no different. In this case, the ratio calculation result is an indeterminate form of 0 / 0, not a meaningless value with a non-zero numerator and a zero denominator. Therefore, this embodiment directly uses the ratio of the differences in the two aspects to measure... The linear correlation between HbA1c and HbA1c.

[0082] Step S300: Based on the distribution of serum ferritin and C-reactive protein concentrations of the target pregnant woman and the glycated hemoglobin concentration of each reference pregnant woman, obtain the reference serum ferritin reference value of the reference pregnant woman relative to the target pregnant woman.

[0083] If other pregnant women have low glycated hemoglobin (HbA1c) concentrations and also low levels of iron deficiency, it suggests they are unlikely to be affected by iron deficiency leading to elevated HbA1c levels, indicating higher normality and data reliability. Based on this characteristic, the serum ferritin levels under normal conditions are quantified by analyzing the data characteristics of each reference pregnant woman.

[0084] As a concrete example, such as Figure 3 As shown, the method for obtaining serum ferritin reference values ​​can be implemented through steps S301 to S303.

[0085] Step S301: Standardize the glycated hemoglobin concentration of each pregnant woman to obtain glycated hemoglobin data for each pregnant woman.

[0086] In this embodiment, the Z-score standardization method is used for processing. The standardization method is a well-known technique and will not be described in detail here.

[0087] Step S302: Based on the lack of characteristic factors of the target pregnant woman and the glycated hemoglobin data of each reference pregnant woman, obtain the true data characteristic value of each reference pregnant woman relative to the target pregnant woman.

[0088] Specifically, the product of the negative correlation coefficient of the lack of characteristic factors of the target pregnant woman and the negative correlation coefficient of the glycated hemoglobin data of each reference pregnant woman is used as the true data characteristic value of each reference pregnant woman relative to the target pregnant woman.

[0089] As a concrete example, Lack of characteristic factors in target pregnant women The negative correlation coefficient, with Glycated hemoglobin data as the nth reference pregnant woman The negative correlation coefficient. The true data characteristic values ​​reflect the true comparability of each reference pregnant woman to the target pregnant woman's normal true reference value of serum ferritin, that is, the reliability of the data.

[0090] This indicates an iron deficiency background in pregnant women targeting specific populations. This excludes interference from iron deficiency in the reference pregnant woman. When the target pregnant woman lacks characteristic factors... The higher the value, the more severe the iron deficiency in the target pregnant woman. The smaller the value, the stricter the requirements for the normality of the reference pregnant woman's data, that is... The larger the value, the more reliable the data for the reference pregnant woman.

[0091] Step S303: Obtain the reference value of serum ferritin for the target pregnant woman based on the real data feature value corresponding to each reference pregnant woman and the serum ferritin concentration of the same reference pregnant woman.

[0092] Specifically, the proportion of the real data feature values ​​corresponding to each reference pregnant woman is used as the data weight for each reference pregnant woman. The serum ferritin concentration of each reference pregnant woman is weighted and summed using the data weight to obtain the serum ferritin reference value of the reference pregnant woman relative to the target pregnant woman.

[0093] As a concrete example, the method for obtaining the serum ferritin reference value for target pregnant women from the reference pregnant women can be expressed by the formula: ;in, This indicates the reference value for serum ferritin in pregnant women relative to the target pregnant women. This represents the true data feature value of the nth reference pregnant woman relative to the target pregnant woman, where N represents the total number of reference pregnant women. The serum ferritin concentration of the nth reference pregnant woman.

[0094] This represents the sum of the real data feature values ​​corresponding to all reference pregnant women, using... This represents the proportion of the actual data feature values ​​corresponding to each reference pregnant woman, reflecting the weight of the data's reliability for each reference pregnant woman. In other words, the data weight reflects the degree of data contribution of the corresponding reference pregnant woman, and using a weighted summation method allows for a more accurate measurement of the overall data result.

[0095] The reference values ​​for serum ferritin in target pregnant women represent normal serum ferritin levels consistent with the physiological characteristics of the target pregnant women and free from iron deficiency interference. It should be understood that the units of the serum ferritin reference values ​​are consistent with the units of serum ferritin concentration in pregnant women.

[0096] Step S400: Adjust the glycated hemoglobin concentration of the target pregnant woman according to the data correction coefficient, serum ferritin reference value and serum ferritin concentration of the target pregnant woman, and obtain the glycated hemoglobin test result of the target pregnant woman.

[0097] Iron deficiency in pregnant women can lead to prolonged red blood cell lifespan, causing a false increase in HbA1c concentration. Furthermore, red blood cell lifespan is negatively correlated with iron reserves, while serum ferritin (SF) is positively correlated with the degree of iron deficiency; that is, the lower the SF, the more severe the iron deficiency. The reciprocal of serum ferritin concentration... The larger it is, the more it can pass through. Linear relationship with HbA1c (data correction factor) ), construct a correction term for HbA1c concentration, i.e. Correcting the detection results of the target HbA1c concentration can offset the false increase caused by iron deficiency, resulting in more accurate HbA1c chromatographic detection results. Among these, This indicates the measured value of serum ferritin. This indicates the true value of serum ferritin.

[0098] Based on this characteristic, the degree of data adjustment is obtained by considering the difference between the target pregnant woman's serum ferritin concentration and the serum ferritin reference value, as well as the data correction coefficient. The difference between the target pregnant woman's glycated hemoglobin concentration and the data adjustment degree is used as the correction value for the target pregnant woman's glycated hemoglobin concentration, thus obtaining the target pregnant woman's glycated hemoglobin test result. Specifically, the method for obtaining the degree of data adjustment is as follows: calculate the difference between the reciprocal of the target pregnant woman's serum ferritin concentration and the reciprocal of the serum ferritin reference value, and multiply the data correction coefficient by this difference as the degree of data adjustment.

[0099] As a specific example, the formula for calculating the corrected value of the glycated hemoglobin concentration of the target pregnant woman can be expressed as:

[0100]

[0101] in, This indicates a correction value for the glycated hemoglobin concentration of the target pregnant woman. This indicates the glycated hemoglobin concentration of the target pregnant woman. Indicates the data correction factor. This indicates the serum ferritin concentration of the target pregnant woman. This indicates the reference value for serum ferritin in the target pregnant woman.

[0102] Serum ferritin reference values ​​characterize the true SF values ​​of target pregnant women under conditions of no iron deficiency and no physiological background interference. Data correction coefficients characterize the differences in HbA1c concentrations among target pregnant women. The linear relationship between the differences can be expressed as: ,in This indicates a difference in HbA1c concentration. express difference.

[0103] To the extent of data adjustment, The study precisely quantifies the degree of SF abnormality caused by iron deficiency in the target pregnant woman, then calculates the false increase in HbA1c concentration that needs to be offset using the data correction coefficient K, and finally subtracts the data adjustment degree from the true measured value of serum ferritin concentration in the target pregnant woman to obtain the true HbA1c concentration. This yields accurate glycated hemoglobin chromatography results, providing an accurate basis for doctors to assess pregnancy risks (such as gestational diabetes).

[0104] It should be noted that in other embodiments, the actual and corrected values ​​of the pregnant woman's glycated hemoglobin concentration are recorded simultaneously for doctors to use for reference and analysis. By constructing a mathematical model, interference caused by iron deficiency or anemia can be eliminated to a certain extent, thus effectively assisting doctors in their work.

[0105] Furthermore, the corrected HbA1c value can help assess pregnancy complications and long-term health risks. If gestational diabetes is not effectively controlled, it may develop into type 2 diabetes or other metabolic diseases postpartum. By monitoring the corrected HbA1c concentration, doctors can develop personalized intervention plans based on the pregnant woman's blood glucose control status, maintaining blood glucose within the normal range and reducing the incidence of fetal developmental abnormalities, premature birth, or other pregnancy complications. This part is not the focus of this application and is only briefly described. Implementers need to rely on doctors for relevant analysis based on the specific implementation situation, and will not be elaborated further here.

[0106] In summary, compared to existing technologies that address issues such as iron deficiency in pregnant women leading to alterations in hemoglobin structure and falsely elevated HbA1c levels, as well as abnormal hemoglobin synthesis potentially altering chromatographic peak shape and interfering with detection, this embodiment corrects glycated hemoglobin concentration by measuring the pregnant woman's serum ferritin concentration. This eliminates interference caused by iron deficiency or anemia, resulting in more accurate glycated hemoglobin test results and better assisting physicians in assessing pregnancy risks.

[0107] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A chromatographic detection method for glycated hemoglobin, characterized in that, The method includes: The serum ferritin concentration, glycated hemoglobin concentration, and C-reactive protein concentration of each pregnant woman were obtained. The pregnant women included the target pregnant woman and the reference pregnant women under the same conditions. The reference pregnant women under the same conditions were other pregnant women with the same gestational age, the same BMI value, and the same age as the target pregnant woman. Based on the distribution of serum ferritin and C-reactive protein concentrations and the body mass index of each pregnant woman, the reference value weight of each pregnant woman was analyzed. Combined with the differences in serum ferritin concentration and glycated hemoglobin concentration among different pregnant women, the data correction coefficient was obtained. Based on the distribution of serum ferritin and C-reactive protein concentrations in the target pregnant woman and the glycated hemoglobin concentration of each reference pregnant woman, the serum ferritin reference value of the reference pregnant woman relative to the target pregnant woman is obtained. Based on the data correction coefficient, serum ferritin reference value, and serum ferritin concentration of the target pregnant woman, the glycated hemoglobin concentration of the target pregnant woman is adjusted to obtain the glycated hemoglobin test result of the target pregnant woman. Specifically, the analysis of the reference value weight for each pregnant woman, based on the distribution of serum ferritin and C-reactive protein concentrations and her body mass index, includes: For any pregnant woman, serum ferritin concentration and C-reactive protein concentration were standardized to obtain serum ferritin data and C-reactive protein data, respectively. The product of the negative correlation coefficients of serum ferritin data and C-reactive protein data is taken as the deficiency characteristic factor of the pregnant woman, and the calculation formula is as follows: ;in, This indicates the lack of characteristic factors for the i-th pregnant woman. This represents the C-reactive protein data for the i-th pregnant woman. This represents the serum ferritin data for the i-th pregnant woman. This represents an exponential function with the natural constant e as its base. The reference value weights for the pregnant woman are derived based on her lacking trait factors and body mass index, including: Based on the pregnant woman's height and weight, her body mass index (BMI) is determined. Based on the normalized result of the difference between the pregnant woman's BMI and a preset normal BMI, and the lack of a characteristic factor, the pregnant woman's reference value weight is determined using the following formula: ;in This represents the reference value weight of the i-th pregnant woman. This represents the normalized result of the absolute value of the difference between the body mass index of the i-th pregnant woman and the preset normal body mass index; The method for obtaining the data correction coefficient is as follows: By weighting the reference value of each pair of pregnant women, the differences in serum ferritin concentration and glycated hemoglobin concentration between each pair of pregnant women are weighted to obtain the degree of association contribution between each pair of pregnant women. This includes: for any two pregnant women, using the percentage of the sum of the reference value weights of the two pregnant women as the weight; using the absolute value of the difference in glycated hemoglobin concentration between the two pregnant women as the first difference coefficient; using the absolute value of the difference between the reciprocals of the serum ferritin concentration between the two pregnant women as the second difference coefficient; and using the weights, weighting the ratio of the first difference coefficient and the second difference coefficient to obtain the degree of association contribution between the two pregnant women. The sum of the correlation contributions of all pregnant women is used as the data correction coefficient; The method for obtaining the serum ferritin reference value of the reference pregnant woman for the target pregnant woman is as follows: The glycated hemoglobin concentration of each pregnant woman was standardized to obtain glycated hemoglobin data for each pregnant woman; The product of the negative correlation coefficient of the lacking characteristic factor of the target pregnant woman and the negative correlation coefficient of the glycated hemoglobin data of each reference pregnant woman is used as the true data characteristic value of each reference pregnant woman relative to the target pregnant woman, where Lack of characteristic factors in target pregnant women The negative correlation coefficient, with Glycated hemoglobin data as the nth reference pregnant woman The negative correlation coefficient; The proportion of the real data feature value corresponding to each reference pregnant woman is used as the data weight of each reference pregnant woman. The serum ferritin concentration of each reference pregnant woman is weighted and summed using the data weight to obtain the serum ferritin reference value of the reference pregnant woman relative to the target pregnant woman. The specific method for obtaining the glycated hemoglobin test results of the target pregnant woman is as follows: The degree of data adjustment is determined based on the difference between the serum ferritin concentration of the target pregnant woman and the serum ferritin reference value, as well as the data correction coefficient. Specifically, this includes calculating the difference between the reciprocal of the serum ferritin concentration of the target pregnant woman and the reciprocal of the serum ferritin reference value, and using the product of the data correction coefficient and this difference as the degree of data adjustment. The difference between the glycated hemoglobin concentration of the target pregnant woman and the data adjustment level is used as the correction value for the glycated hemoglobin concentration of the target pregnant woman, thus obtaining the glycated hemoglobin test result of the target pregnant woman.

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