HRP20210511A1 - PROCEDURE FOR DETERMINATION OF MULTI-DAY AVERAGE CONCENTRATION OF ESTRADIOL IN BLOOD BASED ON THE COMPOSITION OF IgG GLYCOMA FROM BLOOD PLASMA - Google Patents

Abstract

The present invention discloses a method for determining estradiol (<U>E2</U>) by quantitative analysis of N-glycans bound to immunoglobulin G (IgG) from the blood plasma of women. Quantitative amounts of one or more IgG glycans are included in a numerical model that was previously obtained by statistical processing of the results of a study of variations in the quantitative composition of IgG glycans in blood plasma in relation to current <U>E2</U> concentrations, on a group of women aged 18 to 50 years old and who at the time of testing were not in any medical condition that is known to cause changes in IgG N-glycan and sex hormone concentrations, and secondarily, <U>E2</U> in the blood. As a result, the natural logarithm of <U>E2</U> concentration in the blood is obtained, from which the concentration of estradiol in the blood is easily calculated. The diagnostic procedure according to the invention is useful in determining the average multi-day concentration of <U>E2</U> in the blood. The subject invention discloses a procedure for determining estradiol (<U>E2</U>) by quantitative analysis of N-glycans bound to immunoglobulin G (IgG) from the blood plasma of women. The amounts of quantitative parts of one or more IgG glycans are inserted in a numerical model that was previously obtained by statistical processing of the results of a study of variations in the quantitative composition of IgG glycans in blood plasma in relation to current <U>E2 </U> concentrations, on a group of women aged 18 to 50 and who at the time of testing were not in any medical condition that is known to cause changes in IgG N-glycan and sex hormone concentrations, and secondarily, <U> E2</U> in the blood. As a result, the natural logarithm of <U>E2</U> concentration in the blood is obtained, from which the concentration of estradiol in the blood is easily calculated. The diagnostic procedure according to the invention is useful in determining the average multi-day concentration of <U>E2</U> in the blood.

Description

1.1 Field of the invention

The present invention relates to the procedure for determining the concentration of estradiol (E2) through the analysis of N-glycans bound to immunoglobulin G (IgG) from the blood plasma of a female subject.

1.2 Technical problem

The subject invention solves the technical problem of reliable determination of the multi-day average concentration of estradiol (E2) in the blood.

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It is known that existing diagnostic methods based on different immunochemical methods are not suitable for determining the multi-day average concentration of E2 due to the fact that its concentration varies significantly during the menstrual cycle, which is especially pronounced in the perimenopause period. The present invention solves the mentioned technical problem based on the quantitative analysis of IgG N-glycan of a female subject, aged 18 to 50, from a single blood sample, which is impossible with the use of existing immunochemical methods of quantitative E2 analysis.

1.3 Prior art

Glycans are complex carbohydrates mainly based on N-acetyl-glucosamine (■), fucose (▼), mannose (●), galactose (○) and N-acetyl-neuraminic acid (♦), which are often attached to proteins, typically N -glycosidic bond and involved in numerous physiological and pathological processes. Due to their involvement in a large number of biological processes, they are of great importance as biochemical indicators of the general state of health and various physiological and pathological conditions of the human organism; see literature reference 1:

1) G. Opdenakker, P. M. Rudd, C. P. Ponting, R. A. Dwek: Concepts and principles of glycobiology, FASEB J. 7 (1993) 1330-1337.

Immunoglobulin G (IgG) is the most abundant antibody in human blood plasma and plays an important role in the body's defense against various antigens. IgG is a glycoprotein whose stability and function are key to the glycans attached to its heavy chains. Glycosylation of IgG also depends on different physiological (age, sex, pregnancy) and pathological conditions (tumors, infections, autoimmune diseases). It is known in the state of the art that the glycosylation pattern of IgG changes with the age of an individual, and for example, by analyzing IgG N-glycans, it is possible to monitor the aging process itself and draw conclusions about the biological age of the subject; see literature references 2-5:

2) R. Parekh, I. Roitt, D. Isenberg, R. Dwek, T. Rademacher: Age-related galactosylation of the N-linked oligosaccharides of human serum IgG, J. Exp. Honey. 167 (1988) 1731-1736.

3) M. Pučić, A. Knežević, J. Vidič, B. Adamczyk, M. Novokmet, O. Polašek, O. Gornik, S. Supraha-Goreta, M. R. Wormald, I. Redžić, H. Campbell, A. Wright , N. D. Hastie, J. F. Wilson, I. Rudan, M. Wuhrer, P. M. Rudd, D. Josić, G. Lauc: High Throughput Isolation and Glycosylation Analysis of IgG-Variability and Heritability of the IgG Glycome in Three Isolated Human Populations, Molecular & Cellular Proteomics 10.10; doi:10.1074/mcp.M111.010090.

4) EP3011335B1; G. Lauc, M. Pučić-Baković, F. Vučković: Method for the analysis of N-glycans attached to immunoglobulin G from human blood plasma and its use; holder: Genos d.o.o. (HR); priority date: 20.06.2013.

5) J. Krištić, F. Vučković, C. Menni, L. Klarić, T. Keser, I. Beceheli, M. Pučić-Baković, M. Novokmet, M. Mangino, K. Thaqi, P. Rudan, N. Novokmet, J. Sarac, S. Missoni, I. Kolčić, O. Polašek, I. Rudan, H. Campbell, C. Hayward, Y. Aulchenko, A. Valdes, J. F. Wilson, O. Gornik, D. Primorac, V Zoldoš, T. Spector, G. Lauc: Glycans are a novel biomarker of chronological and biological ages, J. Gerontol. And Biol. Sci. Honey. Sci. 69 (2014) 779-789.

Estradiol (E2) is a female sex hormone from the group of estrogens, which is useful as a diagnostic indicator for the clinical assessment of diseases such as hypogonadism, hirsutism, polycystic ovarian syndrome (PCOS), amenorrhea, ovarian tumors, for monitoring the response to therapy with aromatase inhibitors in women, and for monitoring fertility treatment; see literature reference 6:

6) H. Ketha, A. Girtman, R. J. Singh: Estradiol assays – The path ahead, Steroids 99 (2015) 39-44.

The subject invention solves the described technical problem in a new and inventive way with the application of the known analytical methodology of quantitative analysis of IgG N-glycans and the hitherto unknown connection of their variation with the concentration of female sex hormones and estradiol (E2) in the blood.

1.4 The essence of the invention

The subject invention includes a procedure for determining the multi-day average concentration of estradiol (E2) through the analysis of N-glycans bound to immunoglobulin G from the blood plasma of women, designated by the abbreviations GP1 to GP24, of the general chemical structure I:

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where said procedure includes:

(i) quantitative analysis of one or more:

(a) fluorescently derivatized glycans obtained by release from immunoglobulin G (IgG) by the enzyme peptide-N4-(N-acetyl-beta-glucosaminyl)asparagine amidase (PNGase F); or

(b) free glycans or corresponding glycopeptides or glycoforms;

(ii) inclusion of the obtained quantitative proportions of said glycans in the numerical model that was previously obtained by statistical processing of the results of the study of variations in the quantitative proportion of IgG glycans in the blood plasma of a group of women, who at the time of testing were not in the menstrual phase or in any other medical condition for which is known to cause changes in the concentrations of sex hormones and, by extension, estradiol in the blood; you

(iii) calculation of the multi-day average concentration of estradiol (E2) in the blood of the subject based on a single analysis.

1.5 Brief description of the images

Figure 1. Typical chromatogram of 2-aminobenzamide (2AB)-derivatized IgG N-glycans obtained by ultra-high performance liquid chromatography (HILIC-UPLC) method described in the subject invention with 24 separate peaks which are hereinafter denoted by the abbreviations GP1-GP24.

Figure 2. Distribution of the number of women by age in the analyzed group; N(woman)= 70.

Figure 3: Scheme of the sampling protocol and experiments performed in the study. B = glycans with branching N-acetylglucosamine (GlcNAc); E2 = estradiol; F = glycans with core-linked fucose; G0 = galactose-free glycans; G1 = glycans with one galactose; G2 = glycans with two galactoses; MC = menstrual cycle; P = progesterone; S = glycans with sialic acid; T = testosterone.

Figure 4. Variability of glycan properties of IgG for each examined woman. Blue vertical lines represent the range of variability defined by the lowest and highest levels of agalactosylated (G0), monogalactosylated (G1), digalactosylated (G2), sialylated (S) and fucosylated (F) glycans, and glycans with branching GlcNAc (B) in the total N-glycome IgG for each woman during the 12-week duration of the study. Black vertical lines represent the extent of variability of the same glycan properties in the control sample (standard).

Figure 5. Menstrual cycle model. Application of the menstrual cycle model in determining the dynamics of the main glycan structure of the GP4 UPLC chromatogram of N-glycome IgG. N(samples) = 500.

Figure 6. Dynamics of IgG N-glycosylation during menstrual cycles. The gray curve describes the change in the level of six derived glycan properties of IgG: agalactosylation (G0), monogalactosylation (G1), digalactosylation (G2), sialinization (S), the presence of branching GlcNAc (B) and core fucose (F) over several consecutive menstrual cycles. Standardized glycan measurements enable comparison of the variability of different derived IgG glycan properties. Each point represents one sample. N(samples) = 500.

Figure 7. Dynamics of sex hormones and IgG N-glycosylation in the menstrual cycle. The black shaded curve describes the variability of six derived IgG glycan properties: agalactosylation (G0), monogalactosylation (G1), digalactosylation (G2), sialinization (S), the presence of branching GlcNAc (B) and core fucose (F), and the concentrations of the three sex hormones: of estradiol (E2; pmol/L), progesterone (P; nmol/L), and testosterone (T; nmol/L) through several consecutive menstrual cycles. The menstrual cycle is divided into two equal parts: the follicular (rectangle marked with a solid line) and the luteal (rectangle marked with a dashed line) phase. The analysis of the connection (association) of the variability of the derived glycan properties with the concentration of sex hormones can be found in Table 8. Standardized glycan measurements enable the comparison of the variability of the different derived glycan properties of IgG. Each point represents one sample. N(samples) = 500.

1.6 Detailed description of the invention

The subject invention includes a procedure for determining the multi-day average concentration of estradiol (E2) through the analysis of N-glycans bound to immunoglobulin G (IgG) from the blood plasma of women, designated by the abbreviations GP1 to GP24, of the general chemical structure I:

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Table 1. Structures of N-glycans bound to immunoglobulin G (IgG).

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characterized by the fact that said procedure includes the following steps:

(i) quantitative analysis of one or more:

(a) fluorescently derivatized glycans obtained by release from immunoglobulin G (IgG) by the enzyme peptide-N4-(N-acetyl-beta-glucosaminyl)asparagine amidase (PNGase F); or

(b) free glycans or corresponding glycopeptides or glycoforms;

by some of the suitable analytical techniques, whereby the quantitative proportions of glycans or corresponding glycopeptides are obtained by normalization, by dividing the area under the peak of each of the requested glycans by the total area of all peaks of glycans or glycoforms from the sample;

(ii) inclusion of the obtained quantitative proportions of said glycans in the numerical model that was previously obtained by statistical processing of the results of the study of variations in the quantitative proportion of IgG glycans in the blood plasma of a group of women, who at the time of testing were not in the menstrual phase or in any other medical condition for which is known to cause changes in the concentrations of sex hormones and, by extension, estradiol in the blood; you

(iii) calculation of the multi-day average concentration of estradiol (E2) in the blood of the subject based on a single analysis.

Said study of the variation of the quantitative proportion of IgG glycans in blood plasma from step (ii) was conducted on a group of women aged 18 to 50 years.

Said quantitative shares of GP1-GP24 glycans are included in the numerical model, whereby the logarithm of the mass concentration of estradiol (E2) in the blood is calculated:

Log c(E2) = -15.529•GP4 - 2.602•GP8 + 5.589•GP10 + 9.699•GP12 + 53.911•GP15 + 9.901•GP16 - 1.990•GP2•GP10 - 0.065•GP2•GP12 + 3.601•GP2•GP15 + 0.007 •GP2•GP16 + 0.465•(GP4)2 + 2.889•GP4•GP8 + 5.106•GP4•GP10 - 0.817•GP4•GP12 - 8.606•GP4•GP15 + 1.490•GP4•GP18 + 1.689•(GP8)2 - 9.048 •GP8•GP10 - 0.999• GP8•GP12 - 2.253•GP8•GP15 + 3.143•(GP10)2 + 0.712•GP10•GP12 - 3.505•GP10•GP15 - 4.753•GP10•GP16 + 1.128•GP10•GP18 - 4.584• GP12•GP15 + 1.138•GP12•GP16 - 1.355•GP12•GP18 - 0.598•GP12•GP22 - 0.904•GP12•GP23 - 4.638(GP15)2 + 0.287•GP15•GP16 - 3.049•GP15•GP18 + 2.492(GP16) 2 - 3,041•GP16•GP18

where the factors GP2, GP4, GP8, GP10, GP12, GP15, GP16, GP18, GP22 and GP23 represent the natural logarithm of the relative area under the peaks of the glycans of the same name GP2, GP4, GP8, GP10, GP12, GP15, GP16, GP18, GP22, GP23 from chromatogram of the chosen quantitative analytical technique.

The diagnostic procedure according to the invention in step (iii) includes obtaining the result of the concentration of estradiol in the blood expressed as the natural logarithm of the mass concentration of estradiol, Log c(E2), from which the value of its mass concentration, c, expressed in picomoles per milliliter (pmol/ mL).

Since glycans themselves do not absorb UV, they are not suitable for detection by standard UV-VIS or fluorescence (FLR) detectors used in various quantitative analysis techniques such as ultra-high performance liquid chromatography (UPLC). Therefore, after their release from immunoglobulin G (IgG) from the blood plasma, they are derivatized with suitable reagents that covalently bind to glycans, after which the corresponding glycan derivatives have UV absorption and are suitable for detection during quantitative analysis.

In this sense, the diagnostic procedure according to the invention is based on glycan derivatization with reagents selected from the group consisting of:

(i) combination of a suitable aromatic amine such as 2-aminobenzamide (2AB) or procainamide (PR) and a suitable reductant for reductive amination: 2-picoline borane complex (BH3•NC5H4-2-CH3; 2PB) or sodium cyanoborohydride (NaBH3CN);

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or

(ii) 2,5-dioxopyrrolidin-1-yl-[2N-[2-(N',N'-diethylamino)ethyl] carbamoyl]-quinolin-6-yl-carbamate (RF), known under the trade name RapiFluor- MS from Waters (USA):

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The application of the described derivatization reagents is described in the state of the art; see literature reference 7:

7) T. Keser, T. Pavić, G. Lauc, O. Gornik: Comparison of 2-Aminobenzamide, Procainamide and RapiFluor-MS as Derivatizing Agents for High-Throughput HILIC-UPLC-FLR-MS N-glycan Analysis, Front. Chem. 6 (2018) 321; doi: 10.3389/fchem.2018.00324.

Suitable analytical techniques for quantitative analysis are: ultra-high performance liquid chromatography (UPLC), MALDI-TOF mass spectrometry (matrix-assisted laser desorption/ionization time-of-flight), liquid chromatography coupled with mass spectrometry (LC-MS ) or capillary electrophoresis (CE), or other suitable quantitative analytical techniques. The description of the quantitative analysis of IgG N-glycans using different analytical techniques is described in the state of the art; see literature reference 4.

A study of monitoring the variability of N-glycans linked to immunoglobulin G in relation to the concentrations of sex hormones and, secondarily, estradiol (E2) during different phases of the menstrual cycle in women aged 18 to 50 years and the development of a diagnostic procedure according to the invention

Healthy adult women aged 18-50 participated in the study. Inclusion was based on data on previous menstrual cycles, health status and lifestyle habits of each person obtained through an elimination questionnaire. Inclusive criteria for participation in the study were: the person's age (between 18 and 50 years), and regular and normal menstrual cycles; see literature reference 8:

8) M. Mihm, S. Gangooly, S. Muttukrishna: The normal menstrual cycle in women, Anim. Reprod. Sci. 124 (2011) 229-236.

Only those women who had no diagnosed diseases known to affect changes in glycan patterns of IgG were included in the study. These are most often inflammatory diseases such as autoimmune diseases, various infections, and tumors; see literature reference 9:

9) I. Gudelj, G. Lauc, M. Pezer: Immunoglobulin G glycosylation in aging and diseases, Cell. Immunol. 333 (2018) 65-79.

Exclusive criteria for participation in the study were: pregnancy, breastfeeding, menopause, use of oral contraceptives, use of other hormonal preparations, cigarette smoking and alcohol use. A total of 70 healthy adult women between the ages of 19 and 48 were included in the study, see Figure 2.

Study protocol. In order to analyze the N-glycosylation of immunoglobulin G, blood plasma samples were collected. Sampling was carried out from September to November 2016, for twelve consecutive weeks, once a week (in the morning) at regular intervals of seven days, and regardless of the phase of the menstrual cycle of an individual woman. The detailed procedure for taking blood samples is described in Example 1.

Immunochemical method of determining the concentration of sex hormones

The concentrations of the sex hormones estradiol (E2), progesterone (P) and testosterone (T) were measured in plasma samples using a chemiluminescent microparticle immunoassay (CMIA) on an ARCHITECT® i1000SR (Abbott Diagnostics) automated system using commercial sets of reagents for hormone detection with variable test protocols of the specified manufacturer; see literary reference 10:

10) R. Stricker, R. Eberhart, M. C. Chevailler, F. A. Quinn, P. Bischof, R. Stricker: Establishment of detailed reference values for luteinizing hormone, follicle stimulating hormone, estradiol, and progesterone during different phases of the menstrual cycle on the Abbott ARCHITECT analyzer, Clin. Chem. Lab. Honey. 44 (2006) 883-887.

The detailed procedure for quantitative analysis of sex hormones and estradiol (E2) is described in Example 1.

Analysis of N-glycans from blood plasma

Isolation of a blood plasma sample from women for the purpose of estradiol (E2) diagnostics was carried out according to the methodology known in the state of the art; see literature reference 4. Also, isolation of IgG from blood plasma was performed according to a procedure known in the state of the art; see literature references 3 and 11:

11) I. Trbojević Akmačić, I. Ugrina, G. Lauc: Comparative Analysis and Validation of Different Steps in Glycomics Studies, Methods Enzymol. 586 (2017) 37-55.

The detailed procedure of sample processing and analysis of IgG N-glycans is described in Example 1. The sequence of exposure of glycan peaks GP1-GP24 in the described HILIC-UPLC-FLR method and the corresponding retention times (tR) are shown in Table 2.

A representative HPLC chromatogram of the separation of all subject N-glycans GP1-GP24 is shown in Figure 1.

Table 2. Retention times (tR) of 2-aminobenzamide (2-AB) labeled IgG glycans marked with the abbreviations GP1-GP24 obtained by the described UPLC-HILIC analytical method whose typical chromatogram is shown in Figure 1.

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Processing and analysis of study results

Removal of variations in glycan data due to series of experiments

In order to reduce the variation due to series of experiments (eng. batch effect), all samples of the same person, which were collected at 12 time points, were analyzed on the same plate. Therefore, plasma samples from a maximum of three to five people were randomly distributed on each plate so that the average age of the test subjects on each plate was equal. The plates also contained a standard plasma sample in pentaplicate, which was used to control non-biological variability, that is, technical variation of the method. With this analytical approach, variation between plates was avoided, and there was no need to perform the usual correction of glycan data for differences in batches (eng. batch correction).

Glycan data processing. Each chromatogram obtained from the analysis of N-glycans by IgG liquid chromatography was integrated in the same way and divided into 24 glycan peaks. Glycan data were first normalized to the total area (total chromatographic area). This means that the area of each glycan (chromatographic) peak is divided by the total area of the corresponding chromatogram to make the glycan measurements of different samples comparable. The amount of N-glycan in each peak is shown as a percentage of the total integrated area (% area); see literature reference 3. A set of about 20 manually integrated chromatograms was used as a template for automatic integration of all IgG N-glycan chromatograms in this study; see literature reference 12:

12) A. Agakova, F. Vučković, L. Klarić, G. Lauc, F. Agakov: Automated Integration of a UPLC Glycomic Profile, Methods Mol Biol. 1503 (2017) 217-233.

Determination of derived glycan properties of IgG

In addition to 24 directly measured glycan properties, 6 derived properties were calculated for IgG, which group glycans with certain structural properties for easier analysis and understanding of the biological processes in which they participate; see Table 3.

Derived properties represent relative representation:

(i) galactosylated glycans: G0 = galactose-free glycans; G1 = glycans with one galactose; G2 = glycans with two galactoses;

(ii) sialylated glycans: S = glycans with terminal sialic acid;

(iii) fucosylated glycans: F = glycans with core fucose; you

(iv) glycans with branching N-acetylglucosamine, GlcNAc: (B = bisecting) in the total N-glycome of IgG.

Table 3. Formulas for calculating the derived glycan properties of IgG.

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Relationship between the dynamics of sex hormones and the glycan properties of IgG in the menstrual cycle. The temporal shift in the dynamics of sex hormones and glycan properties of IgG is based on the comparison of their highest values (peak) during the menstrual cycle. The day of the menstrual cycle in which the highest concentration of an individual sex hormone and the highest level of an individual glycan property was observed was calculated according to formula (1):

MC Peak (X) = Peak (X) x MC Duration (1)

where is:

X1, X2 ∈ X; X1 = sex hormone, X2 = glycan property; MC duration = 30 days (average duration of the menstrual cycle in the study); MC = menstrual cycle. Example:

MC peak (E2) = 45% x 30 days = 0.45 x 30 days = 13.5 ~ 13th day MC

MC Peak (S) = 84% x 30 days = 0.84 x 30 days = 25.2 ~ 25th day MC

The time that passed from the day when the highest concentration of a particular sex hormone was observed to the day when the highest level of a specific glycan property was observed represents a time shift (MC Shift) in the dynamics of these properties during the menstrual cycle, and was calculated according to formula (2):

MC Offset (X1, X2) = MC Peak (X2) - MC Peak (X1) (2)

where is:

X1, X2 ∈ X; X1 = sex hormone, X2 = glycan property; MC duration = 30 days (average duration of the menstrual cycle in the study); MC = menstrual cycle. Example:

MC Shift (S, E2) = MC Peak (S) – MC Peak (E2) = 25th day – 13th day = 12 days

Statistical analysis of the obtained results. Data were analyzed and visualized using the R programming language (version 3.0.1). The dynamics of glycans and sex hormones in the menstrual cycle are approximated in the menstrual cycle model. The length of menstrual cycles in the study was standardized by dividing the duration of individual menstrual cycles by 100%, which enabled the positioning and comparison of glycan data within a common menstrual cycle model, independent of the duration of each individual menstrual cycle. Glycan measurements were standardized by dividing each measurement by its average value to allow comparison between different glycan properties. Analysis of the relationship between the menstrual cycle and glycan properties was performed using a linear mixed model. In the linear mixed model, the fixed variable was age, while the random variable was the subject (person). The assumed periodic pattern of longitudinal glycan measurements was modeled as a linear combination of sine and cosine functions for the phases of the menstrual cycle. The analysis of the relationship between the concentration of sex hormones and the derived glycan properties was performed using a linear mixed model. In the linear mixed model, the fixed variable was age, while the random variable was the subject (person). p values were corrected for multiple testing using the Benjamini-Hochberg method. p values less than 0.05 were considered statistically significant.

Characteristics of the test subjects. When including women in the research, basic general, anthropometric and health data related to the menstrual cycle of each person were collected. The description of the examined cohort is given in Table 4.

Table 4. Description of the characteristics of the subjects who were included in the study.

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Prior to inclusion in the study, the health status of the subjects was partially known, that is, it was known that the person does not suffer from an acute or chronic disease, does not use hormone replacement therapy, and is not in a state of pregnancy or menopause. In this way, selectivity was ensured in the selection of the investigated population so that only healthy people were included in the study, and in order to exclude the possible influence of the aforementioned health factors on IgG glycosylation; see literature reference 9. Continuous variables with normal distribution are presented as mean ± SD. Categorical variables are presented as percentages. WHtR (waist-to-height ratio) is the ratio of waist circumference to height as an indicator of fat distribution in the abdominal part of the body; see literature reference 13:

13) Q. Ibrahim, M. Ahsan: Measurement of Visceral Fat, Abdominal Circumference and Waist-hip Ratio to Predict Health Risk in Males and Females, Pak. J. Biol. Sci. 22 (2019) 168-173.

A total of 70 healthy women were included in the study. All test subjects stated that they have regular, normal menstrual cycles. The length of the menstrual cycles of the subjects before inclusion in the study lasted an average of 30 days with menstrual bleeding of 6 days. The test subjects got their first period at the age of 13 on average. It is evident from the results that the respondents are mostly highly educated persons or young students. The average age of the analyzed group is 27 years. Furthermore, all test subjects have healthy lifestyle habits, do not smoke or consume alcohol, and the vast majority of them have a healthy body weight with a normal amount of fat tissue in the abdominal part of the body (80%). Most of them are single, unmarried women who have not yet tried to start a family, which is why among the respondents there are people who have never experienced pregnancy (67%), childbirth (67%) or abortion (86%).

Study overview. Sampling took place over 12 consecutive weeks within three months during which blood was taken from each woman once a week and plasma was separated from it; see Figure 3. Details of the statistical processing of the study results are described in Example 2.

Biological variability of IgG glycans

In order to determine whether there were changes in IgG N-glycosylation during the study, it was first necessary to determine the biological variability of each glycan. For this purpose, in addition to the samples, one and the same sample of a known glycan profile (standard) was analyzed on all plates, the change of which represents the technical variability of the method. The biological glycan variability was then calculated as the ratio of the mean variability values of the sample of known glycan profile (standard) and the study samples for each of the 24 glycan peaks and multiplied by 100%. A ratio less than 100% means that the biological variability of the analyzed glycan peak, i.e. the glycan, is greater than the technical variability of the method. By comparing the variability of the glycan profiles of the standards with the glycan profiles of the samples, it was found that the biological variability exceeds the analytical variability in 14 of the 24 IgG glycan peaks monitored in the study; see Table 5 and Figure 1. Ratios of those glycan peaks with statistically significant biological variability are in bold. The structures of glycan peaks are shown in Figure 1.

Table 5. Biological variability of individual IgG glycans in the menstrual cycle.

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GP = glycan peak GP1-GP24.

Variability of derived glycan properties of IgG.

After the biological variability of individual IgG glycans was determined, the variability of glycan properties was determined in the same way, which groups glycans with similar structural properties, such as glycans with one galactose (G1), two galactoses (G2) or without galactose (G0), then glycans with sialic acid (S), branching N-acetylglucosamine (B) and IgG glycans with core-bound fucose (F), first for each person separately, and then at the group level. The range of changes in glycosylation properties within a single individual was most pronounced for sialylated (the largest difference between the lowest and highest values approximately 21%) and agalactosylated (the largest difference between the lowest and highest values approximately 16%) glycans, while fucosylated IgG glycans had the lowest intra-individual variation (greatest difference between the lowest and highest values less than 3%) during menstrual cycles; see Figure 4.

Mean values, medians, values of the first and third quartiles, and minimum and maximum values of derived glycan properties of IgG were calculated for the analyzed group of subjects. The level of individual glycan properties is given in Table 6.

Table 6. Level of certain derived glycan properties in plasma samples (n= 776) of the analyzed group of test subjects (N= 70) and control samples (n= 56) of the standard. The relative share of galactosylation is shown: G0 = agalactosylation, G1 = monogalactosylation, G2 = digalactosylation; sialinization (S); cleaving GlcNAc (B); and fucosylation (F); IgG in the total area of all glycan properties. Q1 = first quartile (25th percentile), Q3 = third quartile (75th percentile).

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IGS = derived glycosylation property made as % of total area; min = minimum; Q1 = first quartile (25th percentile); SV = mean value; MD = median; Q3 = third quartile (75th percentile); max = maximum.

Variability is expressed as the interquartile range of the first and third quartiles. In the analyzed group of test subjects, there were no major deviations in the level of derived properties compared to the values of derived glycan properties of IgG in the control samples. Fucosylation and monogalactosylation of IgG glycans had the lowest variability within the examined group, while the most variable was the glycosylation property associated with the level of agalactosylation of IgG glycans.

Menstrual cycles of the test subjects. Information about menstrual cycles was reported by the subjects through a questionnaire at each blood draw. The collected data were used to calculate the duration of menstrual cycles during the study. The description of the menstrual cycles of the subjects during the study is shown in Table 7.

Although all women reported having regular and normal menstrual cycles prior to inclusion in the study, our results revealed certain discrepancies. The most pronounced deviations were observed in relation to the length of menstrual cycles. During the study, the shortest menstrual cycle was only 20 days, while the longest lasted as long as 72 days.

Despite the great difference in the length of menstrual cycles, the analysis carried out showed that the majority of women (86%) have normal menstrual cycles lasting between 26 and 34 days with an average length of 30 days; see literature reference 8. 140 normal menstrual cycles (about 70% of the total number of recorded menstrual cycles) were selected for statistical processing, in which 500 plasma samples were collected from 60 women.

Table 7. Menstrual cycles of the subjects included in the study.

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Approximation of the menstrual cycle model. In order to carry out further statistical analyses, the data from the selected 140 menstrual cycles were grouped within a common menstrual cycle model, see Figure 5. Considering that the selected menstrual cycles have different durations (from 26 to 34 days), it was necessary to normalize them. Normalization of menstrual cycles was performed by dividing the duration of each menstrual cycle by 100%. In this way, glycan data and data on sex hormone concentrations measured at various time points within individual menstrual cycles could be easily positioned in a common model of the menstrual cycle and enable the necessary statistical analyses.

Variability of IgG N-glycosylation during the menstrual cycle. One of the main goals of this study was to determine whether the glycan profile of IgG changes with changes in sex hormones. Glycan data from 140 selected menstrual cycles were analyzed in a common menstrual cycle model. The analysis of longitudinal glycan measurements revealed periodic cyclic dynamics of the level of agalactosylated (G0), monogalactosylated (G1), digalactosylated (G2) and sialylated (S) glycans, as well as glycans with branched GlcNAc (B) in the total IgG glycome, while the level of fucosylated glycans remained unchanged during the menstrual cycle; see Figure 6.

The relationship of IgG N-glycosylation with the phases of the menstrual cycle. Since IgG N-glycosylation was found to change during the menstrual cycle, it was necessary to determine and describe in detail specific changes in glycan composition. Analysis of the dynamics of glycosylation profiles revealed two patterns with regard to the direction and extent of changes in the glycan properties of IgG in certain phases of the menstrual cycle. The results show that the group of glycans, which includes digalactosylated (G2) and sialinized (S) glycans, has the same pattern of changes and reaches its highest level in the lutein phase of the menstrual cycle. On the other hand, the group of glycans consisting of agalactosylated (G0) and monogalactosylated (G1) glycans, as well as glycans with branching GlcNAc (B), has its own common pattern of changes that is opposite to the previously described group, and reaches its highest level in the follicular phase of menstruation. cycles; see Figure 7.

The relationship between glycosylation of IgG and sex hormones during the menstrual cycle. Since it was established that the same changes in glycan properties always occur in certain phases of the menstrual cycle, it was necessary to examine whether these changes in N-glycosylation of IgG are in some way related to changes in the concentration of sex hormones during the menstrual cycle. Although there are studies on the possible influence of sex hormones, especially estrogen, on the sialinization and galactosylation of IgG, by comparing the patterns of the dynamics of sex hormones and the glycan properties of IgG during the menstrual cycle, it was revealed that the point of the menstrual cycle where the highest concentration of estradiol was observed does not coincide with the point of the menstrual cycle in which the presence of digalactosylated and sialylated glycans is the highest; see for comparison literature references 14 and 15:

14) C. Engdahl, A. Bondt, U. Harre, J. Raufer, R. Pfeifle, A. Camponeschi, M. Wuhrer, M. Seeling, I. L. Mårtensson, F. Nimmerjahn, G. Krönke, H. U. Scherer, H. Forsblad-d'Elia, G. Schett: Estrogen induces St6gal1 expression and increases IgG sialylation in mice and patients with rheumatoid arthritis: a potential explanation for the increased risk of rheumatoid arthritis in postmenopausal women, Arthritis Res. Ther. 20 (2018) 84.

15) A. Ercan, W. M. Kohrt, J. Cui, K. D. Deane, M. Pezer, E. W. Yu, J. S. Hausmann, H. Campbell, U. B. Kaiser, P. M. Rudd, G. Lauc, J. F. Wilson, J. S. Finkelstein, P. A. Nigrovic: Estrogens. regulate glycosylation of IgG in women and men. JCI Insight 2 (2017) e89703. doi: 10.1172/jci.insight.89703.

The day of the menstrual cycle in which the highest values (peak) of the glycan properties of IgG and sex hormones were recorded are shown in Table 8. The results show that the highest level of digalactosylation and sialinization of IgG is on approximately day 25 of the luteal phase, which is a time shift of 12 days after the highest estradiol concentration on approximately the 13th day of the follicular phase of the menstrual cycle. Furthermore, the highest level of IgG digalactosylation and sialinization occurs 9 days after the highest concentration of testosterone, which is approximately on day 16 of the menstrual cycle, and simultaneously with the highest concentration of progesterone in the luteal phase of the menstrual cycle; see Figure 7.

Table 8. Highest values (peaks) of derived glycan properties of IgG and sex hormones in the menstrual cycle.

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MC = menstrual cycle; GS = glycan property: G0, G1, G2, S, B, F; T = testosterone; E2 = estradiol; P = progesterone; uiv = at the same time.

The highest prevalence of agalactosylated, monogalactosylated and glycans with branched GlcNAc on IgG did not show coincidence with any of the points for which the highest concentrations of sex hormones were observed during the menstrual cycle.

Instead, the level of branching GlcNAc on IgG reaches its maximum on approximately day 9 of the menstrual cycle, and the level of agalactosylation and monogalactosylation of IgG is highest around day 10 of the follicular phase of the menstrual cycle, after the lutein-follicular phase which is the transitional phase between the two menstrual cycles, and which are generally characterized by the lowest levels of studied sex hormones; see literature reference 16:

16) B. G. Reed, B. R. Carr: The Normal Menstrual Cycle and the Control of Ovulation. 2018 Aug 5. In: K. R. Feingold, B. Anawalt, A. Boyce, G. Chrousos, W. W. de Herder, K. Dungan, A. Grossman, J. M. Hershman, J. Hofland, G. Kaltsas, C. Koch, P. Kopp, M. Korbonits, R. McLachlan, J.E. Morley, M. New, J. Purnell, F. Singer, C.A. Stratakis, D.L. Trence, D.P. Wilson (editors). Endotext [Internet]. South Dartmouth (MA): MDText.com, Inc.; 2000. PMID: 25905282.

The day of the menstrual cycle in which the highest mean value of the mentioned variables was recorded and their mutual time shift in the menstrual cycle were calculated according to formulas (1) and (2). Since the mismatch between the dynamics of sex hormones and the glycan properties of IgG in the menstrual cycle was discovered, it was necessary to investigate whether a connection could be found between these time-shifted events.

The analysis of the association of glycan properties of IgG with the level of sex hormones in the menstrual cycle is presented in Table 9.

Table 9. Correlation (association) of the concentration of sex hormones with the dynamics of changes in IgG N-glycosylation in the menstrual cycle.

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GS = derived glycan property: agalactosylated glycans (G0), monogalactosylated glycans (G1), digalactosylated glycans (G2), sialinized glycans (S), branched GlcNAc glycans (B), core-fucosylated glycans (F); MC = menstrual cycle; T = testosterone; E2 = estradiol; P = progesterone; the peak glycan property (GS) of IgG is the time point in the phase of the menstrual cycle expressed in percentages (%) in which the highest level of glycan IgG with similar structural properties, i.e. the analyzed glycan property, was observed. The peak of the hormone is the time point in the phase of the menstrual cycle expressed in percentages (%) in which the highest concentration of the analyzed sex hormone was observed.

The p value describes the statistical significance of the functional effect of individual hormones on the level of glycan property in the menstrual cycle. p values were adjusted for multiple testing using the Benjamini-Hochberg method. p values less than 0.05 (marked in bold) were considered statistically significant. The duration of one menstrual cycle is 100%. Follicular phase = 0% to 50% of the menstrual cycle, luteal phase – 50% to 100% of the menstrual cycle.

The results show that all properties describing IgG N-glycosylation have a statistically significant association with the concentration of estradiol (E2), progesterone and testosterone in the menstrual cycle.

Furthermore, the analysis shows that progesterone and estradiol have the same direction of functional effect on the glycosylation properties of IgG. It follows that estradiol (E2) is positively associated with sialinization, while progesterone, along with sialinization, is positively associated with digalactosylation of IgG in the menstrual cycle.

Furthermore, estradiol and progesterone are negatively associated with the production of IgG glycoforms containing branching GlcNAc or single galactose, while progesterone is negatively associated with IgG agalactosylation.

On the other hand, testosterone has a completely opposite direction of functional effect on IgG N-glycosylation compared to progesterone and estradiol. Namely, testosterone shows a negative functional effect on digalactosylation and sialinization of IgG N-glycans, and a positive effect on agalactosylation, monogalactosylation and fucosylation of IgG during the menstrual cycle.

The influence of the menstrual cycle on IgG glycosylation

Considering the detected changes in glycan properties during the menstrual cycle and their connection with the concentration of sex hormones, it was necessary to examine the actual extent of changes in the glycan properties of IgG during the menstrual cycle.

The range of variation of glycan properties of IgG during the menstrual cycle is shown in Table 10.

Table 10. Association between the menstrual cycle and the variability of IgG N-glycosylation.

[image]

GS = derived glycan property: agalactosylated glycans (G0), monogalactosylated glycans (G1), digalactosylated glycans (G2), sialinized glycans (S), branched GlcNAc glycans (B), core-fucosylated glycans (F); MC = menstrual cycle; SD = standard deviation; the peak glycan property (GS) of IgG is the time point in the phase of the menstrual cycle expressed in percentages (%) in which the highest level of glycan IgG with similar structural properties, i.e. the analyzed glycan property, was observed; the variability of the glycan property is the effect of a particular phase of the menstrual cycle on the derived glycan properties of IgG expressed in percentages (%) and standard deviations (SD); variability was calculated from the ratio of the mean values of the highest level (peak) and all measurements of the glycan property of IgG in a certain phase of the menstrual cycle; PersonVar is the variability of glycan properties arising from differences in IgG glycosylation between individuals; MCVar is the variability of the glycan property of IgG that occurs due to the menstrual cycle; The p value describes the statistical significance of the variability of glycan properties of IgG during the menstrual cycle; p values were adjusted for multiple testing using the Benjamini-Hochberg method. p values less than 0.05 (marked in bold) were considered statistically significant. The duration of one menstrual cycle is 100%. Follicular phase – 0% to 50% of the duration of the menstrual cycle, luteal phase – 50% to 100% of the duration of the menstrual cycle.

It is evident from the results that the extent of variability of glycan properties of IgG, which is related to the phase of the menstrual cycle, is very small and ranges from 0.5% to 1.1%. Glycan properties with structural features related to galactosylation and sialinization changed the most during the menstrual cycle - the variation of agalactosylation (G0) was 1.1%, and the variation of digalactosylation (G2) and sialinization (S) was 1.0%. Glycans with one galactose were moderately variable - monogalactosylation (G1) 0.8%, and glycans with branching N-acetylglucosamine (B) 0.5%, while the level of fucosylation (F) did not change during the menstrual cycle.

Furthermore, this study determined how much the menstrual cycle itself contributes to the overall variability of IgG N-glycosylation. Detailed analyzes show that the menstrual cycle can explain from 0.06% of the variability in the level of branched GlcNAc glycans (p= 0.01) to a maximum of 0.72% of the variability in the level of monogalactosylated glycans (p= 3.36 x 10-22) at IgG. In comparison, differences in the way an individual glycosylate IgG can explain a high 86% of the variation in the level of branching GlcNAc, while the level of agalactosylation between two individuals for the same reason can differ by as much as 98%. The results therefore show that changes in IgG N-glycosylation caused by the menstrual cycle itself account for less than 0.8% of the variability in the level of any IgG glycan property in the studied group of women.

Other aspects of processing the study results

Normalization and "batch" correction of data obtained after UHPLC analysis were performed with the aim of removing experimental error; normalization was performed by dividing the area of each individual chromatographic peak by the total area of the chromatogram.

Before the "batch" correction, the normalized data were log-transformed due to the right-skewness of the data distribution. Log-transformed data were corrected for "batch" using the "ComBat" method (R package sva) by which technical sources of variation were modeled as "batch" covariates; see literature reference 17:

17) J. T. Leek, W. E. Johanson, H. S. Parker, E. J. Fertig, A. E. Jaffe, Y. Zhang, J. D. Storey, L. C. Torres, Surrogate Variable Analysis. R package version 3.38.0 (2020).

The estimated "batch" effects were subtracted from the log-transformed measurements with the intention of correcting the experimental noise. The mean value of estradiol (E2) was calculated for each subject based on individual measurements, and the values were log-transformed. Before developing the machine learning model, 33% of the data was extracted to make up the data set that will be used for the final validation of the results. The remaining 67% of the data was used to develop a linear model. The glycans to be included in the final model were selected by the Stepwise Backward Selection method using the statsmodels module; see literature reference 18:

18) Seabold, Skipper, Josef Perktold: "statsmodels: Econometric and statistical modeling with python; Proceedings of the 9th Python in Science Conference (2010).

In this way, ten chromatographic peaks were selected, GP2, GP4, GP8, GP10, GP12, GP15, GP16, GP18, GP22 and GP23, for which the p-value was <0.001. Furthermore, with the aim of improving the prediction of the mean value of estradiol by the linear model, polynomial combinations of all glycans with the second degree were added to the specified data set (PolynomialFeatures class was used). The number of properties was then reduced to 35 by selecting the smallest number of properties after which R2 does not increase significantly using the SelectKBest class; "mutual_info_regression" was used as the selection function. All data were transformed in the specified manner. In order to predict the log-mean value of estradiol (E2) based on the values of the chromatographic peaks, a linear regression model was developed using machine learning. Also, the parameters for the model were determined using the GridSearchCV class with cross-validation using the ShuffleSplit class, so that the data were divided ten times randomly into fifths, one group of which was used for parameter validation. The following parameters were found to be the best by the mentioned procedure: 'copy_X': True, 'fit_intercept': False, 'normalize': True. The model with the specified parameters has R2 = 0.551 on the test sample of the cross-validation carried out in the previously described manner, while on the test sample (separated before developing the model) R2 = 0.547, the maximum error is 1.04 and the mean square error value is 0.09.

The final numerical model for calculating the multi-day average concentration of estradiol (E2) in the blood of women from the results of the quantitative analysis of IgG N-glycan is as follows:

Log c(E2) = -15.529•GP4 - 2.602•GP8 + 5.589•GP10 + 9.699•GP12 + 53.911•GP15 + 9.901•GP16 - 1.990•GP2•GP10 - 0.065•GP2•GP12 + 3.601•GP2•GP15 + 0.007 •GP2•GP16 + 0.465•(GP4)2 + 2.889•GP4•GP8 + 5.106•GP4•GP10 - 0.817•GP4•GP12 - 8.606•GP4•GP15 + 1.490•GP4•GP18 + 1.689•(GP8)2 - 9.048 •GP8•GP10 - 0.999• GP8•GP12 - 2.253•GP8•GP15 + 3.143•(GP10)2 + 0.712•GP10•GP12 - 3.505•GP10•GP15 - 4.753•GP10•GP16 + 1.128•GP10•GP18 - 4.584• GP12•GP15 + 1.138•GP12•GP16 - 1.355•GP12•GP18 - 0.598•GP12•GP22 - 0.904•GP12•GP23 - 4.638(GP15)2 + 0.287•GP15•GP16 - 3.049•GP15•GP18 + 2.492(GP16) 2 - 3,041•GP16•GP18

where the factors GP2, GP4, GP8, GP10, GP12, GP15, GP16, GP18, GP22 and GP23 represent the natural logarithm of the relative area under the peaks of the glycans of the same name GP2, GP4, GP8, GP10, GP12, GP15, GP16, GP18, GP22, GP23 from chromatogram of the chosen quantitative analytical technique.

The programming language Python version 3.7.6, Python package Scikit-learn and Jupyter notebook were used for data preparation and machine learning model development; see literature references 19 and 20:

19) Scikit-learn: Machine Learning in Python, Pedregosa... JMLR 12 (2011) 2825-2830.

20) T. Kluyver, B. Ragan-Kelley, F. Perez: Jupyter Notebooks – a publishing format for reproducible computational workflows. In: F. Loizides, B. Schmidt (Ed.): Positioning and Power in Academic Publishing; Players, Agents and Agendas. Clifton, VA: IOS Press (2016) 87-90.

The use of the diagnostic procedure according to the invention and the associated numerical model in clinical practice

The diagnostic procedure according to the invention is used to determine the average multi-day concentration of estradiol (E2) in the blood on the basis of a single blood analysis, which is impossible with the application of existing immunochemical methods of quantitative analysis of E2.

Alternatively, the diagnostic procedure according to the invention is used to determine the average concentration of estradiol (E2) in the blood during the past month.

Optionally, the diagnostic procedure according to the invention is used to determine the average concentration of estradiol (E2) in the blood during the past two months.

Alternatively, the diagnostic procedure according to the invention is used to determine the average concentration of estradiol (E2) in the blood during the past three months.

1.7 Examples of the implementation of the invention

General notes

The name of the IgG N-glycans, which are key to the present invention, is derived according to the rules of the Oxford nomenclature.

The chemicals, reagents and accessories used were obtained from the following suppliers: 2-aminobenzamide (2AB): Sigma-Aldrich, USA; 2-picoline borane (2PB): Sigma-Aldrich; acetonitrile HPLC grade: Scharlab; acetonitrile LC-MS: J. T. Baker; ammonium chloride (NH4Cl): Acros Organics; ammonium hydrogencarbonate (NH4HCO3): Acros Organics; dimethyl sulfoxide (DMSO): Sigma-Aldrich; ethanol: Carlo Erba; formic acid (HCOOH): Merck; Igepal CA-630: Sigma-Aldrich; potassium dihydrogen phosphate (KH2PO4): Sigma-Aldrich; potassium chloride (KCl): EMD Millipore; hydrochloric acid (HCl): Chemistry; sodium dodecyl sulfate (SDS): Sigma-Aldrich; sodium hydrogen phosphate (Na2HPO4): Acros Organics; sodium bicarbonate (NaHCO3): Merck; sodium hydroxide (NaOH): Chemistry; sodium chloride (NaCl): Carlo Erba; Acetic acid (CH3COOH): Merck; ammonia solution: Merck; tris(hydroxymethyl)aminomethane (Tris): Acros Organics; ultrapure water: Millipore; peptide-N-glycosidase F (PNGase F; 10 U/mL): Promega; ARC Estradiol RGT: Abbott Diagnostics; ARC Progesterone RGT: Abbott Diagnostics; ARC 2nd Gen Test RGT: Abbott Diagnostics; ARC Trigger solution: Abbott Diagnostics; ARC Pre-trigger solution: Abbott Diagnostics; GHP Acroprep 0.20 µm filter plate: Pall; GHP Acroprep 0.45 µm filter plate: Pall; 96-well sample collection plate, 1 and 2 mL volumes: Waters; Protein G plate: BIA Separations; protein G monolith plate (96 wells).

The research was conducted using the following devices: ARCHITECT® i1000SR analyzer, Abbott Diagnostics; ABgene PCR plates, Thermo Scientific; Acquity UPLC Glycan BEH amide column, 130 Å, 1.7 µm, 2.1 mm x 100 mm, Waters; Acquity UPLC H-Class Ultra High Performance Liquid Chromatography, Waters; ARC reaction tubes, Abbott Diagnostics; Centrifuge model 5840, Eppendorf; Digestor DIGIM 15 AFM, Schneider; Water purification system Direct-Q 3UV, Millipore; Analytical balance Explorer®, Ohaus; pH-meter FiveEasyTM, Mettler Toledo; Technical scale model JL1502-G, Mettler Toledo; Laboratory oven LAB. HOT AIR OVEN, M.R.C.; Laboratory incubator LAB. INCUBATOR, M.R.C.; Centrifuge miniSpin, Eppendorf; Magnetic mixer MR 3000 D, Heildoph; Spectrophotometer Nanodrop ND-8000, Thermo Scientific; Pipet-Lite XLS manual micropipettes single- and multi-channel of various volumes, Rainin; Circular shaker model 3023, GFL; Savant SC210A SpeedVac rotary vacuum concentrator and Savant solvent trap, Thermo Scientific; Refrigerated Vapor Traps RVT400 and vacuum pump OFP400, Thermo Scientific; Vacuum manifold and vacuum pump, Pall; Laboratory shaker Vortex-Genie 2, Scientific Industries.

The meanings of the abbreviations used are as follows:

PNGase F = peptide-N4-(N-acetyl-beta-glucosaminyl)asparagine amidase enzyme;

2AB = 2-amino-benzamide;

DMF = N,N-dimethylformamide, solvent;

EDTA = N,N,N',N'-ethylenediamine-tetraacetic acid, dipotassium salt;

EU = emission units (Eng. emission units);

GP = glycan peak (engl. glycan peak);

HILIC = liquid chromatography with hydrophilic interaction, comes from the English term "hydrophilic interaction liquid chromatography";

IgG = immunoglobulin G;

MC = menstrual cycle;

RT = retention time (tR); glycan retention time on the column (eng. retention time);

PR = procainamide;

RF = 2,5-dioxopyrrolidin-1-yl-[2N-[2-(N',N'-diethylamino)ethyl] carbamoyl]-quinolin-6-yl-carbamate (RapiFluor-MS);

SD = standard deviation;

SDS = sodium dodecyl sulfate, surfactant;

UPLC = ultra-high performance liquid chromatography, comes from the English term "ultra-high performance liquid chromatography."

Example 1. Study of monitoring the variability of N-glycans attached to immunoglobulin G in relation to the concentrations of sex hormones and, secondarily, estradiol (E2) during different phases of the menstrual cycle in women aged 18 to 50 years

A total of 70 healthy adult women aged 18-50 participated in the study; see Figure 2. Inclusion was carried out on the basis of data on previous menstrual cycles, health status and lifestyle habits of each person obtained through an elimination questionnaire. Inclusive criteria for participation in the study were: the person's age (between 18 and 50 years), and regular and normal menstrual cycles. Only those women without diagnosed diseases known to affect changes in glycan patterns of IgG were included in the study. All subjects signed an informed consent to participate in the research, and the study was approved by the Ethics Committees of the Faculty of Medicine, University of Zagreb and the Faculty of Medicine in Beijing, China. The study was conducted in accordance with the principles of the Declaration of Helsinki.

Study protocol. In order to analyze the N-glycosylation of immunoglobulin G, blood plasma samples were collected. Sampling was carried out from September to November 2016, for twelve consecutive weeks, once a week (in the morning) at regular intervals of seven days, and regardless of the phase of the menstrual cycle of an individual woman. At each visit, 5 mL of whole blood was collected from healthy subjects by venipuncture into tubes with EDTA anticoagulant (BD Vacutainer® K2EDTA REF 368861 tubes) as a routine procedure at the Jidong Petroleum Hospital in Hebei Province, China. Blood samples were incubated for 30 min at room temperature, followed by centrifugation at 1670 g for 10 min at 4 °C to separate plasma. Plasma samples were stored at -80 °C until further analysis. At each visit, the subjects filled out a short questionnaire related to their state of health and menstrual cycle since the last blood draw. The obtained data were used to determine the length of each woman's menstrual cycle for the duration of the study. In all blood plasma samples, the concentration of three sex hormones - estradiol (E2) as the most active form of estrogen, progesterone (P) and testosterone (T) was determined in order to enable the monitoring of the phases of menstrual cycles in women.

Immunochemical method of determining the concentration of sex hormones

The concentrations of the sex hormones estradiol (E2), progesterone (P) and testosterone (T) were measured in plasma samples using a chemiluminescent microparticle immunoassay (CMIA) on an ARCHITECT® i1000SR (Abbott Diagnostics) automated system using commercial sets of reagents for hormone detection with variable test protocols of the specified manufacturer; see literature reference 10. Before measuring sex hormone concentrations, the system must be calibrated using calibration solutions for the hormone whose concentration is being determined. The reaction mixture for determining the concentration of sex hormones is obtained by mixing a plasma sample, paramagnetic microparticles coated with antibodies to the hormone of interest, and a conjugate of the hormone of interest labeled with acridine in disposable ARC reaction tubes (Abbott Diagnostics). The hormone from the plasma is first bound to the microparticles coated with antibodies, and the acridinylated conjugates of the hormone of interest are bound to the remaining free antibodies on the microparticles, which are in contact with the pre-trigger solution (H2O2, φ = 1.32%) and the activation ( trigger solution; NaOH, c = 0.35 M) trigger the chemiluminescence reaction with the solution. Chemiluminescent signals are detected using ARHITECT and System optics and expressed in relative light units (relative light units, RLUs). The concentration of hormones in plasma is inversely proportional to the detected RLU units. This analysis was carried out in cooperation with the Endocrine Laboratory of the Hospital for Women's Diseases and Childbirth of the Zagreb Clinical Hospital Center.

Analysis of N-glycans from blood plasma. Isolation of a blood plasma sample from women for the purpose of estradiol (E2) diagnostics was carried out according to the methodology known in the state of the art; see literature reference 4. Also, isolation of IgG from blood plasma was performed according to a procedure known in the state of the art; see literature references 3 and 9.

Isolation of immunoglobulin G (IgG). Immunoglobulin G was isolated from blood plasma samples by affinity chromatography by binding to a protein G plate with 96 wells (BIA Separations) with the use of a vacuum device for filtering plates (vacuum manifold, Pall). All steps of IgG isolation take place at a pressure of about 380 mmHg, except for plasma sample application and IgG elution when a reduced pressure of about 200 mmHg is used. Isolation of immunoglobulin G from blood plasma is performed in a digester, and the used solutions are filtered through a 0.2 µm Supor PES filter (Nalgene) before use. Before applying the plasma samples, the protein G plate had to be prepared by successive washing with solutions, respectively: 2 mL of ultrapure water (18 MΩ/cm at 25°C), 2 mL of 1x concentrated buffer PBS pH 7.4 (137 mmol/L NaCl; 2 .7 mmol/L Na2HPO4; 9.7 mmol/L KH2PO4; 2.2 mmol/L KCl; titrated with NaOH to pH 7.4), 1 mL 0.1 mol/L HCOOH pH 2.5; 2 mL of 10x concentrated buffer PBS pH 6.6; and equilibrate by washing with 4 mL of 1x concentrated buffer PBS pH 7.4. In addition to the test samples (100 µL), five aliquots of the standard plasma sample (50 µL) were randomly placed on each plate, and one well was left empty as a negative control. Plasma samples were mixed and centrifuged for 10 min at 1479 g (centrifuge model 5840, Eppendorf). They were then diluted with 1x concentrated buffer PBS pH 7.4 in a ratio of 1:7 (v/v) and filtered through a 0.45 µm GHP AcroPrep 96-well filter plate (Pall) using a plate vacuum device (Pall). Filtered plasma samples were applied to a protein G plate and washed three times with 2 mL of 1x concentrated buffer PBS pH 7.4 to remove unbound proteins. Bound IgG was eluted from the protein G plate by washing with 1 mL of 0.1 mol/L HCOOH, and was neutralized with 170 µL of 1 mol/L NH4HCO3. The protein G plate was regenerated for reuse by washing with 1 mL of 0.1 mol/L HCOOH, 2 mL of 10x concentrated buffer PBS pH 6.6, 4 mL of 1x concentrated buffer PBS pH 7.4, and 1 mL of protein storage buffer G plates (ethanol φ = 20%, 20 mmol/L Tris; 0.1 mol/L NaCl; titrated with HCl to pH 7.4), after which another 1 mL of storage buffer was added and the plate was stored at 4 ° C.

Determination of immunoglobulin G concentration The concentration of IgG isolated from plasma was determined for each sample by measuring the absorbance at 280 nm using a Nanodrop ND-8000 spectrophotometer (Thermo Scientific). Part of the volume of the IgG eluate was separated and dried in a rotary vacuum concentrator SpeedVac Concentrator SC210A (Thermo Scientific). Eluates were stored at -20 °C until further use.

Denaturation and deglycosylation of immunoglobulin G in solution. Dried IgG samples were denatured by adding 30 µL of SDS (γ = 1.33%), and incubated for 10 min at 65 °C, after which they were cooled at room temperature for 30 min. 10 µL of Igepal CA-630 (Sigma Aldrich) solution (φ = 4%) was added to each sample to deactivate excess SDS, and the plate was incubated for 15 min at room temperature. Immunoglobulin G molecules were deglycosylated by adding 10 µL of 5x concentrated PBS buffer and 1.25 U PNGase F (Promega). PNGase F cleaves the N-glycosidic bond between the internal GlcNAc glycan and the asparagine residue of the protein part of the molecule. The N-glycan release reaction was carried out by incubating the samples for 18 h at 37 °C.

Fluorescence labeling with 2-aminobenzamide (2AB) and purification of 2AB-derivatized IgG N-glycans. Since glycans do not have chromophores in their structure, it is impossible to detect them by spectrophotometric methods. Free N-glycans are therefore labeled with the fluorescent dye 2-aminobenzamide (2AB). The marking procedure is carried out in a digester with always freshly prepared marking solution consisting of 2AB (γ = 19.2 mg/mL) and 2-picoline borane (2PB; γ = 44.8 mg/mL) dissolved in acetic acid solution (HAc) and dimethylsulfoxide (DMSO) in a ratio of 30:70 (v/v). 25 µL of the labeling solution was added to each sample, after which the samples were incubated for 2 h at 65 °C to carry out the N-glycan labeling reaction. After marking, it is necessary to remove impurities from the samples, such as proteins, excess fluorescent dye and other reagents, using liquid chromatography based on hydrophilic interactions with solid phase extraction (eng. hydrophilic interaction liquid chromatography - solid phase extraction, HILIC-SPE). . After cooling for 30 min at room temperature, 700 µL of chilled acetonitrile (φ = 100%, 4 °C) was added to each sample, and the samples were transferred to a GHP AcroPrep 0.2 µm filter plate. The filter plate had to be prepared beforehand by washing each well with 200 µL of ethanol (φ = 70%), 200 µL of ultrapure water and cooled acetonitrile (φ = 96%, 4 °C). Between each of these steps, the filter plate is emptied using a vacuum manifold, and the vacuum must not exceed 2 inHg. After transferring to a GHP AcroPrep 0.2 µm filter plate, the samples were incubated for 2 min at room temperature. During incubation, free 2AB labeled N-glycans bind to the hydrophilic polypropylene membrane of the plate. Each sample was then washed 4 times with 200 µL of chilled acetonitrile (φ = 96%, 4°C), followed by elution of glycans from the plate membrane. The elution of 2-AB labeled N-glycans is performed in two identical steps: 90 µL of ultrapure water is added to each well, followed by 15-minute incubation at room temperature with shaking on a circular shaker model 3023 (GIF), and eluate collection by centrifugation 5 min at 164 g in a centrifuge model 5840 (Eppendorf) in ABgene PCR plates. Purified fluorescently labeled IgG N-glycans are stored at -20 °C until further use.

Analysis of IgG N-glycans by liquid chromatography. Fluorescently labeled IgG N-glycans were analyzed by ultrahigh-performance liquid chromatography based on hydrophilic interactions (HILIC-UPLC) on an amide ACQUITY UPLC® Glycan BEH column (Waters, USA) with a length of 100 mm, a diameter of 2.1 mm and a particle size of 1.7 μm according to the previously established protocol; see literature reference 3. The aforementioned analysis was performed on an Acquity UPLC H-Class device (Waters) consisting of a quaternary solvent manager (QSM), a sample manager (SM) and a fluorescence ( FLR) detector. The instrument is controlled by the Empower 3 program, version 3471 (Waters, USA).

Glycan samples are prepared for analysis by mixing with acetonitrile (φ = 100%) in the ratio sample : ACN = 20 : 80 (v/v). For the mobile phase, ammonium formate (HCOONH4; c = 100 mM, pH 4.4) was used as solvent A and acetonitrile (φ = 100%) as solvent B, and the system was washed with acetonitrile solution (φ = 75%) between analyses. The samples were cooled to 10 °C before injection, and the separation on the column took place at 60 °C. Glycans are separated on the column based on their different interaction with the stationary or mobile phase, where the stationary phase is polar, and the mobile phase consists of a non-polar solvent with an increasing volume fraction of polar solvent. In the separation procedure, a linear gradient of 25-38% solvent A was used at a flow rate of 0.40 mL/min during the analytical procedure lasting 27 min. Separated glycans were detected using an FLR detector at the excitation and emission wavelengths for 2-AB (λex = 250 nm, λem = 428 nm). The system is calibrated using an external standard consisting of fluorescently labeled glucose oligomers (dextran ladder), where the glycan retention time on the column (eng. retention time, RT) is expressed in glucose units (eng. glucose units, GU). By separating the 2AB-labeled N-glycans of IgG using the described liquid chromatography procedure, a characteristic chromatogram of 24 glycan peaks is created; see literature reference 3.

The sequence of exposure of the peaks of glycans GP1-GP24 in the described HILIC-UPLC-FLR method and the corresponding retention times (tR) are shown in Table 2. A representative HPLC chromatogram of the separation of all subject N-glycans GP1-GP24 is shown in Figure 1.

Example 2. Processing and analysis of study results and formation of a numerical model for the determination of estradiol (E2) in the blood of women based on the quantitative analysis of IgG N-glycans

Removal of variations in glycan data due to series of experiments. In order to reduce the variation due to series of experiments (eng. batch effect), all samples of the same person, which were collected at 12 time points, were analyzed on the same plate. Therefore, plasma samples from a maximum of three to five people were randomly distributed on each plate so that the average age of the test subjects on each plate was equal. The plates also contained a standard plasma sample in pentaplicate, which was used to control non-biological variability, that is, technical variation of the method. With this analytical approach, variation between plates was avoided, and there was no need to perform the usual correction of glycan data for differences in batches (eng. batch correction).

Glycan data processing. Each chromatogram obtained from the analysis of N-glycans by IgG liquid chromatography was integrated in the same way and divided into 24 glycan peaks. Glycan data were first normalized to the total area (total chromatographic area). This means that the area of each glycan (chromatographic) peak is divided by the total area of the corresponding chromatogram to make the glycan measurements of different samples comparable. The amount of N-glycan in each peak is shown as a percentage of the total integrated area (% area); see literature reference 3. A set of about 20 manually integrated chromatograms was used as a template for automatic integration of all IgG N-glycan chromatograms in this study; see literature reference 12.

Determination of derived glycan properties of IgG. In addition to 24 directly measured glycan properties, 6 derived properties were calculated for IgG, which group glycans with certain structural properties for easier analysis and understanding of the biological processes in which they participate; see Table 3.

Relationship between the dynamics of sex hormones and the glycan properties of IgG in the menstrual cycle. The temporal shift in the dynamics of sex hormones and glycan properties of IgG is based on the comparison of their highest values (peak) during the menstrual cycle. The day of the menstrual cycle in which the highest concentration of an individual sex hormone and the highest level of an individual glycan property was observed was calculated according to formulas (1) and (2).

Statistical analysis of the obtained results

Data were analyzed and visualized using the R programming language (version 3.0.1). The dynamics of glycans and sex hormones in the menstrual cycle are approximated in the menstrual cycle model. The length of menstrual cycles in the study was standardized by dividing the duration of individual menstrual cycles by 100%, which enabled the positioning and comparison of glycan data within a common menstrual cycle model, independent of the duration of each individual menstrual cycle. Glycan measurements were standardized by dividing each measurement by its average value to allow comparison between different glycan properties. Analysis of the relationship between the menstrual cycle and glycan properties was performed using a linear mixed model. In the linear mixed model, the fixed variable was age, while the random variable was the subject (person). The assumed periodic pattern of longitudinal glycan measurements was modeled as a linear combination of sine and cosine functions for the phases of the menstrual cycle. The analysis of the relationship between the concentration of sex hormones and the derived glycan properties was performed using a linear mixed model. In the linear mixed model, the fixed variable was age, while the random variable was the subject (person). p values were corrected for multiple testing using the Benjamini-Hochberg method. p values less than 0.05 were considered statistically significant.

Characteristics of the test subjects. When including women in the research, basic general, anthropometric and health data related to the menstrual cycle of each person were collected. The description of the examined cohort is given in Table 4.

Prior to inclusion in the study, the health status of the subjects was partially known, that is, it was known that the person does not suffer from an acute or chronic disease, does not use hormone replacement therapy, and is not in a state of pregnancy or menopause. In this way, selectivity was ensured in the selection of the investigated population so that only healthy people were included in the study, and in order to exclude the possible influence of the aforementioned health factors on IgG glycosylation; see literature reference 9.

A total of 70 healthy women were included in the study. All test subjects stated that they have regular, normal menstrual cycles. The length of the menstrual cycles of the subjects before inclusion in the study lasted an average of 30 days with menstrual bleeding of 6 days. The test subjects got their first period at the age of 13 on average. It is evident from the results that the respondents are mostly highly educated persons or young students. The average age of the analyzed group is 27 years. Furthermore, all test subjects have healthy lifestyle habits, do not smoke or consume alcohol, and the vast majority of them have a healthy body weight with a normal amount of fat tissue in the abdominal part of the body (80%). Most of them are single, unmarried women who have not yet tried to start a family, which is why among the respondents there are people who have never experienced pregnancy (67%), childbirth (67%) or abortion (86%).

Study overview. Sampling took place over 12 consecutive weeks within three months during which blood was taken from each woman once a week and plasma was separated from it; see Figure 3. Seven subjects (10%) failed to complete the intended sampling protocol, and less than the planned 12 plasma samples were collected for them. Physical symptoms such as dizziness and pallor in the face, as well as a low level of iron in the blood, i.e. anemia, were listed as the reasons for giving up, while one respondent gave up due to personal reasons. Furthermore, during the study, one subject became ill with the flu, and two with a cold, which is why they used analgesics to alleviate the symptoms of the disease - lowering the temperature and eliminating pain, which is why their data were excluded from statistical processing. Ultimately, instead of the planned 840 samples, 806 plasma samples from 70 women were collected during the study. In 776 samples (96%), the IgG glycan profile was successfully analyzed, see Figure 1, and the concentration of sex hormones estradiol (E2), progesterone (P) and testosterone (T) was determined.

Biological variability of IgG glycans. In order to determine whether there were changes in IgG N-glycosylation during the study, it was first necessary to determine the biological variability of each glycan. For this purpose, in addition to the samples, one and the same sample of a known glycan profile (standard) was analyzed on all plates, the change of which represents the technical variability of the method. The biological glycan variability was then calculated as the ratio of the mean variability values of the sample of known glycan profile (standard) and the study samples for each of the 24 glycan peaks and multiplied by 100%. A ratio less than 100% means that the biological variability of the analyzed glycan peak, i.e. the glycan, is greater than the technical variability of the method. By comparing the variability of the glycan profiles of the standards with the glycan profiles of the samples, it was found that the biological variability exceeds the analytical variability in 14 of the 24 IgG glycan peaks monitored in the study; see Table 5 and Figure 1.

Variability of derived glycan properties of IgG. After the biological variability of individual IgG glycans was determined, the variability of glycan properties was determined in the same way, which groups glycans with similar structural properties, such as glycans with one galactose (G1), two galactoses (G2) or without galactose (G0), then glycans with sialic acid (S), branching N-acetylglucosamine (B) and IgG glycans with core-bound fucose (F), first for each person separately, and then at the group level. The range of changes in glycosylation properties within one individual was most pronounced for sialylated (the largest difference between the lowest and highest values approximately 21%) and agalactosylated (the largest difference between the lowest and highest values approximately 16%) glycans, while fucosylated IgG glycans had the lowest intra-individual variation (greatest difference between the lowest and highest values less than 3%) during menstrual cycles; see Figure 4.

Mean values, medians, values of the first and third quartiles, and minimum and maximum values of derived glycan properties of IgG were calculated for the analyzed group of subjects. The level of individual glycan properties is given in Table 6.

Variability is expressed as the interquartile range of the first and third quartiles. In the analyzed group of test subjects, there were no major deviations in the level of derived properties compared to the values of derived glycan properties of IgG in the control samples. Fucosylation and monogalactosylation of IgG glycans had the lowest variability within the examined group, while the most variable was the glycosylation property associated with the level of agalactosylation of IgG glycans.

Menstrual cycles of the test subjects. Information about the subject's menstrual cycles was reported via a questionnaire at each blood draw. The collected data were used to calculate the duration of menstrual cycles during the study. The description of the menstrual cycles of the subjects during the study is shown in Table 7.

For statistical processing, 140 normal menstrual cycles (about 70% of the total number of recorded menstrual cycles) were selected, in which 500 plasma samples were collected from 60 women.

Approximation of the menstrual cycle model. In order to carry out further statistical analyses, the data from the selected 140 menstrual cycles were grouped within a common menstrual cycle model, see Figure 5. Considering that the selected menstrual cycles have different durations (from 26 to 34 days), it was necessary to normalize them. Normalization of menstrual cycles was performed by dividing the duration of each menstrual cycle by 100%. In this way, glycan data and data on sex hormone concentrations measured at various time points within individual menstrual cycles could be easily positioned in a common model of the menstrual cycle and enable the necessary statistical analyses.

Variability of IgG N-glycosylation during the menstrual cycle. One of the main goals of this study was to determine whether the glycan profile of IgG changes with changes in sex hormones. Glycan data from 140 selected menstrual cycles were analyzed in a common menstrual cycle model. The analysis of longitudinal glycan measurements revealed periodic cyclic dynamics of the level of agalactosylated (G0), monogalactosylated (G1), digalactosylated (G2) and sialylated (S) glycans, as well as glycans with branched GlcNAc (B) in the total IgG glycome, while the level of fucosylated glycans remained unchanged during the menstrual cycle; see Figure 6.

The relationship of IgG N-glycosylation with the phases of the menstrual cycle. Since IgG N-glycosylation was found to change during the menstrual cycle, it was necessary to determine and describe in detail specific changes in glycan composition. Analysis of the dynamics of glycosylation profiles revealed two patterns with respect to the direction and extent of changes in the glycan properties of IgG in certain phases of the menstrual cycle. The results show that the group of glycans, which includes digalactosylated (G2) and sialinized (S) glycans, has the same pattern of changes and reaches its highest level in the lutein phase of the menstrual cycle. On the other hand, the group of glycans consisting of agalactosylated (G0) and monogalactosylated (G1) glycans, as well as glycans with branching GlcNAc (B), has its own common pattern of changes that is opposite to the previously described group, and reaches its highest level in the follicular phase of menstruation. cycles; see Figure 7.

The relationship between glycosylation of IgG and sex hormones during the menstrual cycle. Since it was established that the same changes in glycan properties always occur in certain phases of the menstrual cycle, it was necessary to examine whether these changes in N-glycosylation of IgG are in some way related to changes in the concentration of sex hormones during the menstrual cycle. The day of the menstrual cycle in which the highest values (peaks) of the glycan properties of IgG and sex hormones were recorded are shown in Table 8.

The day of the menstrual cycle in which the highest mean value of the mentioned variables was recorded and their mutual time shift in the menstrual cycle were calculated according to formulas (1) and (2).

Since the mismatch between the dynamics of sex hormones and the glycan properties of IgG in the menstrual cycle was discovered, it was necessary to investigate whether a connection could be found between these time-shifted events. The analysis of the association of glycan properties of IgG with the level of sex hormones in the menstrual cycle is presented in Table 9.

The influence of the menstrual cycle on IgG glycosylation. Given the detected changes in glycan properties during the menstrual cycle and their association with the concentration of sex hormones, it was necessary to examine the actual extent of changes in the glycan properties of IgG during the menstrual cycle. The range of variation of glycan properties of IgG during the menstrual cycle is shown in Table 10.

Other aspects of processing the study results

Normalization and "batch" correction of data obtained after UHPLC analysis were carried out with the aim of removing experimental error. Normalization was performed by dividing the area of each individual chromatographic peak by the total area of the chromatogram. Before the "batch" correction, the normalized data were log-transformed due to the right-skewness of the data distribution. Log-transformed data were corrected for "batch" using the "ComBat" method (R package sva) by which technical sources of variation were modeled as "batch" covariates; see literature reference 17. The estimated "batch" effects were subtracted from the log-transformed measurements with the intention of correcting the experimental noise. The mean value of estradiol (E2) was calculated for each subject based on individual measurements, and the values were log-transformed. Before developing the machine learning model, 33% of the data was extracted to make up the data set that will be used for the final validation of the results. The remaining 67% of the data was used to develop a linear model. The glycans to be included in the final model were selected by the Stepwise Backward Selection method using the statsmodels module; see literature reference 18. In this way, ten chromatographic peaks, GP2, GP4, GP8, GP10, GP12, GP15, GP16, GP18, GP22 and GP23 were selected for which the p-value was <0.001.

Furthermore, with the aim of improving the prediction of the mean value of estradiol by the linear model, polynomial combinations of all glycans with the second degree were added to the specified data set (PolynomialFeatures class was used). The number of properties was then reduced to 35 by selecting the smallest number of properties after which R2 does not increase significantly using the SelectKBest class; "mutual_info_regression" was used as the selection function. All data were transformed in the specified manner. In order to predict the log-mean value of estradiol (E2) based on the value of the chromatographic peaks, a linear regression model was developed using machine learning.

Also, the parameters for the model were determined using the GridSearchCV class with cross-validation using the ShuffleSplit class so that the data were divided ten times randomly into fifths, one group of which was used for parameter validation. The following parameters were found to be the best by the mentioned procedure: 'copy_X': True, 'fit_intercept': False, 'normalize': True. The model with the specified parameters has R2 = 0.551 on the test sample of the cross-validation carried out in the previously described manner, while on the test sample (separated before developing the model) R2 = 0.547, the maximum error is 1.04 and the mean square error value is 0.09.

Based on this analysis, a numerical model was created for determining the average daily concentration of estradiol (E2) in the blood of examined women aged 18 to 50, as described in the subject invention.

Conclusion

The present invention discloses a diagnostic method for determining the determination of estradiol (E2) in women aged 18 to 50, based on the quantitative analysis of N-glycans bound to immunoglobulin G (IgG) from her blood plasma, that is, a blood sample.

The development of said diagnostic method was made possible by the results of a study conducted on a group of women aged 18-50. This study showed that N-glycans bound to IgG, labeled GP1-GP24, and especially glycans GP2, GP4, GP8, GP10, GP12, GP15, GP16, GP18, GP22, GP23, change according to the concentration of E2 in the blood healthy women, within the specified age limits, and during their menstrual cycle. The type of interdependence was statistically processed based on the results of the conducted study, from which a numerical model was generated that enables the direct calculation of the logarithm of the mass concentration, Log c(E2) in the blood. From it, its mass concentration, c, expressed in picomoles per milliliter [pmol/mL], is easily calculated, based on a single analysis of the blood sample of the examined female person.

1.8 Industrial Applicability

The subject invention discloses a diagnostic procedure for determining estradiol (E2) in the blood of women. Therefore, the industrial applicability of the subject invention is unquestionable.

Claims (10)

1. The procedure for determining the multi-day average concentration of estradiol <E2> through the analysis of N-glycans bound to immunoglobulin G from the blood plasma of women, designated by the abbreviations GP1 to GP24, of the general chemical structure I: [image] [image] [image] [image] [image] characterized by the fact that said procedure includes: (i) quantitative analysis of one or more: (a) fluorescently derivatized glycans obtained by release from immunoglobulin G <IgG> using the enzyme peptide-N4-<N-acetyl-beta-glucosaminyl>asparagine amidase <PNGase F>; or (b) free glycans or corresponding glycopeptides or glycoforms; (ii) inclusion of the obtained quantitative proportions of said glycans in the numerical model that was previously obtained by statistical processing of the results of the study of variations in the quantitative proportion of IgG glycans in the blood plasma of a group of women, who at the time of testing were not in the menstrual phase or in any other medical condition for which is known to cause changes in the concentrations of sex hormones and, by extension, estradiol in the blood; you (iii) calculation of the multi-day average concentration of estradiol <E2> in the blood of the subject based on a single analysis. 2. The procedure according to claim 1, characterized in that the group of women from step (ii) is aged between 18 and 50 years. 3. The method according to claim 1 or 2, characterized in that the concentration of estradiol in step (iii) is expressed as the natural logarithm of the mass concentration of estradiol, Log c<E2>, in the blood, from which the value of its mass concentration, c, is then calculated. expressed in picomoles per milliliter <pmol/mL>. 4. The method according to claim 3, characterized in that the logarithm of the mass concentration of estradiol in the blood is calculated using a numerical model: Log c<E2> = -15.529•GP4 - 2.602•GP8 + 5.589•GP10 + 9.699•GP12 + 53.911•GP15 + 9.901•GP16 - 1.990•GP2•GP10 - 0.065•GP2•GP12 + 3.601•GP2•GP15 + 0.007 •GP2•GP16 + 0.465•(GP4)2 + 2.889•GP4•GP8 + 5.106•GP4•GP10 - 0.817•GP4•GP12 - 8.606•GP4•GP15 + 1.490•GP4•GP18 + 1.689•(GP8)2 - 9.048 •GP8•GP10 - 0.999• GP8•GP12 - 2.253•GP8•GP15 + 3.143•(GP10)2 + 0.712•GP10•GP12 - 3.505•GP10•GP15 - 4.753•GP10•GP16 + 1.128•GP10•GP18 - 4.584• GP12•GP15 + 1.138•GP12•GP16 - 1.355•GP12•GP18 - 0.598•GP12•GP22 - 0.904•GP12•GP23 - 4.638(GP15)2 + 0.287•GP15•GP16 - 3.049•GP15•GP18 + 2.492(GP16) 2 - 3,041•GP16•GP18 where the factors GP2, GP4, GP8, GP10, GP12, GP15, GP16, GP18, GP22 and GP23 represent the natural logarithm of the relative area under the peaks of the glycans of the same name GP2, GP4, GP8, GP10, GP12, GP15, GP16, GP18, GP22, GP23 from chromatogram of the chosen quantitative analytical technique. 5. The method according to any of the preceding claims, characterized in that the glycans are derivatized with reagents selected from the group consisting of: (i) a combination of a suitable aromatic amine such as 2-amino benzamide <2AB> or procainamide (PR) and a suitable reductant for reductive amination such as 2-picoline borane complex <BH3•NC5H4-2-CH3> or sodium cyanoborohydride <NaBH3CN> ; [image] or (ii) 2,5-dioxopyrrolidin-1-yl-<2N-<2-<N',N'-diethylamino>ethyl> carbamoyl>-quinolin-6-yl-carbamate <RF>: [image] . 6. The method according to any of the preceding claims, characterized in that the analytical technique is chosen from the group consisting of: ultrahigh efficiency liquid chromatography <UPLC>; MALDI-TOF mass spectrometry; liquid chromatography coupled with mass spectrometry <LC-MS>; or capillary electrophoresis <CE>. 7. Use of the method according to any one of the preceding claims for determining the average multi-day concentration of estradiol in the blood. 8. Use of the method according to any of the previous claims for determining the average concentration of estradiol in the blood during the past month. 9. Use of the method according to any one of the preceding claims for determining the average concentration of estradiol in the blood during the past two months. 10. Use of the method according to any one of the preceding claims for determining the average concentration of estradiol in the blood during the past three months.