Method of assessing female's risk of having PCOS, as well as products and uses relating thereto

Abstract

To provide a "decision support system" helping a physician to identify women with high risk of having PCOS; and especially, a less error-prone and objective method for assessing a female's risk of having PCOS.SOLUTION: The present invention relates to a method of assessing a female's risk of having polycystic ovary syndrome (PCOS), a kit for use in assessing a female's risk of having PCOS, the use of a marker combination in the assessment of a female's risk of having PCOS, a computer system for use in a method according to the present invention, as well as a computer program and a computer-readable storage medium that comprise instructions which, when executed by a computer, cause the computer to carry out the method of the present invention.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to methods of assessing the risk of having polycystic ovary syndrome (PCOS) in females, kits for use in assessing the risk of having PCOS in females, the use of combinations of markers in assessing the risk of having PCOS in females, computer systems for use in the methods according to the invention, and computer programs and computer readable storage media comprising instructions which, when executed by a computer, cause the computer to carry out the methods of the invention. [Background technology]

[0002] Polycystic ovary syndrome (PCOS) is one of the most common endocrine and metabolic disorders, affecting 8–13% of reproductive-age women, with up to 70% remaining undiagnosed. PCOS is a heterogeneous disorder defined by a combination of signs and symptoms of androgen excess and ovarian insufficiency. Women with PCOS exhibit diverse characteristics, including psychological (anxiety, depression, body image), reproductive (irregular menstrual cycles, hirsutism, infertility, and pregnancy complications), and metabolic (insulin resistance (IR), metabolic syndrome, prediabetes, type 2 diabetes mellitus (DM2), and cardiovascular risk factors) (Escobar-Morreale, HF 2018; International evidence-based guideline for the assessment and management of polycystic ovary syndrome 2018). For a definitive diagnosis of PCOS, other conditions or diseases such as pregnancy, non-classical adrenal hyperplasia (NCAH), androgen-secreting tumors, Cushing's syndrome, thyroid disorders, or hyperprolactinemia should be excluded. Diagnostic tests that can be used to exclude other diseases include, for example: 17α-hydroxyprogesterone (17-OHP) to rule out NCAH (Nordenstrom and Falhammar 2018) Prolactin to eliminate hyperprolactinemia Cortisol to rule out patients with Cushing's syndrome Thyroid-stimulating hormone (TSH) to rule out thyroid disorders

[0003] PCOS can be caused by a combination of genetic, epigenetic and environmental factors, including heredity.

[0004] Currently, there are no specific PCOS medications available. Treatment is symptom-directed and tailored to individual needs. Therapeutic approaches target hyperandrogenism, irregular cycles, and associated metabolic disorders. The 2018 International Evidence-Based Guidelines for the Evaluation and Management of Polycystic Ovary Syndrome provide information to support clinical decision-making and patient management.

[0005] The most widely used definition of PCOS is the so-called Rotterdam criteria. PCOS is indicated if at least two of the following criteria are present: (i) irregular cycles and ovulatory dysfunction (oligo-anovulation, OA), (ii) clinical and / or biochemical hyperandrogenism (HA), and (iii) polycystic ovarian morphology (PCOM) (PCOS Consensus Workshop Group, Fertil Steril 2004;81:19-25). PCOM is typically determined using an intravaginal ultrasound transducer with a frequency bandwidth of 8 MHz or more, according to the International Evidence-Based Guideline for PCOS 2018. The threshold for PCOM is considered to be either a follicle count of >20 per ovary and / or an ovarian volume of >10 ml, which ensures the absence of corpora lutea, cysts, or dominant follicles. When older ultrasound techniques are used, the threshold for PCOM may be an ovarian volume of >10 ml or a follicle count of >12 per ovary. However, the need to consider the results of multiple diagnostic tests and clinical examinations requires specific expertise, making it very difficult for less specialized physicians (such as general practitioners) to diagnose PCOS in clinical routine. For example, determining PCOM by transvaginal ultrasound requires an appropriate ultrasound device and subjective analysis of ultrasound images by the physician. Furthermore, results may also depend on the specific ultrasound device used to assess PCOM. As a result, a diagnosis of PCOS based on the Rotterdam Criteria always involves at least one subjective, device- and operator-dependent measurement, which is prone to error.

[0006] Another method for detecting PCOS is to measure anti-Müllerian hormone (AMH) levels in subjects. AMH is a glycoprotein hormone whose expression is important for sex differentiation at specific times during fetal development. Furthermore, AMH, produced by granulosa cells of growing follicles, normally correlates with the number of follicles in the ovary. Therefore, serum AMH levels can be a surrogate biomarker for ovarian follicle count (AFC), determined by transvaginal ultrasound. Several studies have suggested serum AMH as a biochemical marker for PCOM. Small studies have proposed an AMH threshold for PCOM in women with PCOS (Nicholas et al. 2014; Pigny et al. 2016). However, according to the International Evidence-Based Guidelines for the Assessment and Management of Polycystic Ovary Syndrome 2018, serum AMH levels should not be used as a substitute for the detection of PCOM or the diagnosis of PCOS.

[0007] An additional method for detecting PCOS is the 3-item PCOS Criteria System (Indran (Sahmay et al., 2014) proposed a system for the diagnosis of PCOS based on the presence of two of three factors: (i) oligomenorrhea (defined as a mean menstrual cycle length >35 days); (ii) suprathreshold AMH; and (iii) hyperandrogenism, defined as either suprathreshold testosterone and / or the presence of hirsutism (mFG score ≥5). Alternatively, AMH has been suggested in combination with hyperandrogenemia and oligomenorrhea (Sahmay et al., 2014) or in combination with SHBG (Calzada et al., 2019).

[0008] Russian Application No. 2629720 proposes a method for predicting the risk level of developing polycystic ovary syndrome (PCOS) in adolescent girls, taking into account a large list of indicators from the fields of anamnesis, clinical signs, laboratory findings, and ultrasound findings. In total, these fields contain 33 different values, all of which are determined by the adolescent girl's patient and family members and then weighted by a coefficient of 1, 2, or 3 points depending on their importance. The weighted values ​​are summed, and the resulting sum of points indicates a low, medium, or high level of risk of developing PCOS.

[0009] To date, there is no universal test available to indicate PCOS. To evaluate women suspected of having PCOS, additional hormones, such as luteinizing hormone (LH) and follicle-stimulating hormone (FSH), are often tested. However, the diagnostic utility of the LH:FSH ratio for diagnosing PCOS appears low, as only a small proportion of women with PCOS have significantly elevated LH:FSH ratios (Cho et al. 2005). In fact, the range of LH:FSH ratios seen in women diagnosed with PCOS is wide (Malini and George 2018).

[0010] Furthermore, there is a need for a "decision support system" to assist physicians in identifying women at high risk of having PCOS. In particular, there is a need for an objective, less error-prone method for assessing the risk of having PCOS in women. This method is preferably simple to perform and does not involve subjective assessment. Summary of the Invention

[0011] It is therefore an object of the present invention to provide a more accurate and / or objective method of assessing the risk of having PCOS in women that is less prone to error than state of the art methods, preferably a computer-implemented method. In a first aspect, the present invention provides a method for assessing the risk of having polycystic ovary syndrome (PCOS) in a female, comprising: a) - OA values ​​reflecting the length of a female menstrual cycle and / or the number of female menstrual cycles per year, wherein an increase in the OA value relative to the OA values ​​of a healthy reference population indicates an abnormal menstrual cycle length and / or number; - an HA value reflecting the androgen status of a female, wherein an increase in the HA value relative to the HA value of a healthy reference population indicates increased androgen levels in the female; -Anti-Müllerian hormone (AMH) value corresponding to the amount or concentration of AMH in a sample obtained from a female; providing a dataset comprising: b) processing the dataset provided in step a) with a processing unit, wherein the processing comprises combining the values ​​of the dataset provided in step a) into one combined value; c) comparing the composite value obtained in step b) with a corresponding composite value established in a reference population, wherein an increase in the composite value in a female relative to the composite value of a healthy reference population indicates an increased risk of PCOS; and d) indicating the risk of having PCOS in women via an indication unit The present invention relates to a method, including:

[0012] As demonstrated by the examples, the method of the present invention can be used to assess the risk of female PCOS. In the examples, a regression model was established using a reference population including healthy women (controls) and women diagnosed with PCOS (cases). For this purpose, women's (cases and controls) data on menstrual cycle, total testosterone (TT) concentration ratio, sex hormone-binding globulin (SHBG) concentration, and AMH concentration were collected into a dataset and converted into OA, HA, and AMH values. Optionally, the dataset also included female weight and height, and female age, which were converted into WEIGHT and AGE values, respectively. These values ​​were combined into a single value indicating the risk of female PCOS. A weighted logistic regression model was established using Monte Carlo cross-validation (MCCV) with case-control status as the endpoint. The results show that OA has the greatest impact on PCOS risk, followed by AMH and the free androgen index (FAI; FAI = total testosterone × 100 / SHBG). The small standard deviation indicates very stable regression coefficients across MCCV runs. The inventors have also found that the selection of appropriate variables is important, as other variables (e.g., follicle count, LH, and FSH) were found to be less suitable in the above method. It is clear that PCOS is a syndrome characterized by a constellation of symptoms, each of which may or may not be present in a single female, and if present, may be present to different degrees. However, when looking at the composite values ​​according to the present invention, it has been demonstrated that females diagnosed with PCOS have an increased composite value when compared to females without PCOS.

[0013] The examples further provide a scoring system that allows for classification of subjects into low, moderate, or high risk of PCOS. In addition to the numerical score, this also allows for visualization of the risk of having PCOS in women (e.g., red, yellow, green).

[0014] Each value included in the dataset may be determined at a numerical level (cycle length / number, hormone concentration or amount, weight, and age). In contrast to current methods in which ovarian morphology is usually characterized by a physician, the method of the present invention for assessing a patient's risk status for PCOS does not include required values ​​that need to be subjectively determined (i.e., OA, HA, and AMH values, optionally combined with AGE and WEIGHT values), thereby reducing sensitivity to errors. Furthermore, the method of the present invention allows for standardized large-scale testing of women. Furthermore, the dataset processed in step b) does not include data from people other than the evaluated female (e.g., family members, such as the mother), or data that reflect the past rather than the present (i.e., time before sexual maturity), such as the female's birth weight. Such parameters are prone to error. The inclusion of such parameters may result in false-negative characterization. In this regard, reference is made to Russian Patent Application No. 2629720. A female who fulfills all diagnostic criteria for PCOS: oligo-anovulation (OA) and hyperandrogenemia (HA) and polycystic ovarian morphology (PCOM) if she has no (family) history of PCOS and shows no further clinical symptoms (hirsutism or acne) would be erroneously classified as low risk by the method of Russian Patent Application No. 2629720.

[0015] By applying the method of the present invention, the risk of females having PCOS can be assessed even more accurately, whereby the dataset provided in step a) includes not only OA value, HA value and AMH value, but also WEIGHT value reflecting the female's weight and / or AGE value reflecting the female's age.Therefore, in a preferred embodiment of the present invention, the dataset provided in step a) includes OA value, HA value, AMH value and AGE value.More preferably, the dataset provided in step a) includes OA value, HA value, AMH value, AGE value and WEIGHT value.

[0016] If a female is identified as being at high risk for PCOS, she may be offered differential diagnosis or monitoring for PCOS, including any of the above, e.g., ovarian analysis by ultrasound to detect PCOM, and to rule out or confirm PCOS. Additionally, other disorders, such as nonclassical adrenal hyperplasia (NCAH), androgen-secreting tumors, Cushing's syndrome, thyroid disorders, or hyperprolactinemia, should be ruled out. Furthermore, clinical symptoms of PCOS, such as infertility, and PCOS-affected disorders / diseases, such as insulin resistance and / or diabetes, can be evaluated and treated in an appropriate manner based on the diagnosis of PCOS.

[0017] As detailed above, the method according to the first aspect of the invention may be used to assess the risk of a female having PCOS.

[0018] In this regard, the term "assessing the risk of a female having PCOS" refers to an analysis of the likelihood that a female tested by the method of the first embodiment has or will develop (preferably have) PCOS. The result of the analysis may be qualitative or quantitative. This means that the result may be that the female either has or does not have a risk of having PCOS (qualitative), or the risk may be further defined as, for example, high, moderate, or low (quantitative). In the latter case, the female's risk may be defined by a percentage value specifying a risk ranging from 0% to 100%, or a numerical value, such as a risk score having a value within a given range, such as 0 to 2 (0 = low; 1 = moderate; 2 = high) or 0 to 9 (0 to 2 = low; 3 to 5 = moderate; 6 to 9 = high).

[0019] The term "female" refers to the sex of the organism that provides the oocyte. The female may be of any species; for example, the female may preferably be a female mammal, such as a human, horse, cat, or dog. More preferably, the female is human. In a preferred embodiment, the female is human. If the female is a mammal, such as a human, it is characterized by two X chromosomes. The female may be at sexual maturity. Preferably, the female is of reproductive age, i.e., after the onset of fertility and before menopause. If the female is human, it may be between 10 and 60 years old, preferably between 13 and 55 years old, more preferably between 15 and 50 years old, and most preferably between 18 and 45 years old.

[0020] Females tested by the method according to the first aspect of the invention may not have PCOS at all, may have PCOS, or may develop any form or degree of PCOS in the future, such as women with mild or severe symptoms of PCOS. Furthermore, females tested by the method according to the first aspect of the invention may have any phenotype of PCOS or any combination of symptoms associated with PCOS. Symptoms or phenotypes are well known to those skilled in the art.

[0021] Step a) according to the method of the first aspect of the present invention comprises , - OA values ​​reflecting the length of a female menstrual cycle and / or the number of female menstrual cycles per year, wherein an increase in the OA value relative to the OA values ​​of a healthy reference population indicates an abnormal menstrual cycle length and / or number; - an HA value reflecting the androgen status of a female, wherein an increase in the HA value relative to the HA value of a healthy reference population indicates increased androgen levels in the female; -Anti-Müllerian hormone (AMH) value corresponding to the amount or concentration of AMH in a sample obtained from a female; providing a dataset including:

[0022] The term "OA value" refers to a value that reflects the length of a female's menstrual cycle and / or the number of menstrual cycles per year of a female, and reflects oligomenorrhea and / or anovulation (OA). An increase in the OA value relative to the OA value of a reference population indicates abnormal menstrual cycle length and / or number, and indicates an increased risk of having PCOS. Usually, the OA value directly or indirectly correlates with the length of a female's menstrual cycle and / or the number of menstrual cycles per year of a female, and oligomenorrhea and / or anovulation. A female with a normal length of menstrual cycle and a normal number of menstrual cycles per year is called asymptomatic with respect to OA. Oligomenorrhea and / or anovulation is one symptom of PCOS.

[0023] The term "female menstrual cycle" refers to the regular natural changes that normally occur in the female reproductive system due to the rise and fall of hormones. These hormonal fluctuations generally result in the growth and thickening of the endometrium and the development of an egg. An egg may be released from the ovary on about day 14 of the cycle. If pregnancy does not occur, the endometrium is shed in a state known as menstruation.

[0024] The term "menstruation" refers to the discharge of blood, secretions, and mucosal tissue (known as menstruation) from the lining of the uterus through the vagina. The blood may be liquid or clotted.

[0025] The length of a female menstrual cycle can be determined as follows. The first day of menstruation is generally considered to be the first day of the female menstrual cycle, The female menstrual cycle is normally considered to end on the last day before the start of the next menstruation.

[0026] Generally, the human female menstrual cycle lasts 21 to 35 days. Normally, a female has 11 to 13 menstrual cycles per year (more than 3 years after menarche and not using any form of hormonal contraception). (For adult women). The term "oligomenorrhea" refers to a condition in which a woman's cycle is repeatedly longer than 35 days. Repeated means that it occurs frequently or consistently and is not due to other conditions, such as a disease other than PCOS, such as anorexia. Preferably, in oligomenorrhea, a woman's menstrual cycle lasts longer than 40 days, more preferably longer than 50 days, and most preferably longer than 60 days. In oligomenorrhea, a woman's menstrual cycle may even last up to 90 days. It is well known in the art that cycle length can vary from cycle to cycle in an individual female. Thus, oligomenorrhea can also refer to a condition in which a woman has fewer than 8, preferably fewer than 6, and more preferably fewer than 4 menstrual cycles per year.

[0027] The term "anovulation" usually refers to a state in which the ovaries do not release any oocytes during a female's menstrual cycle. A female being assessed for risk of having PCOS can be determined to suffer from anovulation if she does not release oocytes for at least one female menstrual cycle, preferably at least three female menstrual cycles, more preferably at least six female menstrual cycles, and most preferably at least nine female menstrual cycles per year. Furthermore, a female being assessed for risk of having PCOS can be determined to suffer from anovulation if she does not release oocytes for at least six months, preferably at least nine months, and more preferably at least one year.

[0028] Information about oligomenorrhea or anovulation can be easily collected by asking the female or by monitoring the female.For example, by keeping a calendar in which details about the start and end of menstruation are entered, this information is usually very accurate.Oligomenorrhea and / or anovulation is converted into an increased OA value compared to the OA value of a female with normal menstrual cycle.Therefore, an increased OA value compared to the OA value of a reference population is considered to indicate abnormal menstrual cycle length and / or frequency, which indicates an increased risk of having PCOS.

[0029] As detailed above, the values ​​defining normal and abnormal menstrual cycle length and frequency are well known in the art. The values ​​defining normal menstrual cycle length and frequency can be obtained from a healthy reference population or standard publications. The term "healthy reference population" refers to a population of apparently healthy women who are not suffering from PCOS or any disease that affects the female menstrual cycle or the amount or concentration of sex hormones.

[0030] For example, the length and / or frequency of menstrual cycles of females who 1) belong to a healthy reference population or 2) are affected by PCOS are analyzed. This provides a broad data set of values ​​related to the health status of females, e.g., apparently healthy females or women with PCOS who have PCOS symptoms of various forms or severities, and allows cut-off values ​​to be established for distinguishing between groups.

[0031] Alternatively, no threshold may be applied, but a continuous correlation may be used instead, i.e., high deviations of the menstrual cycle from a healthy reference population may correspond to high OA values, and low deviations of the menstrual cycle from the reference population may correspond to low OA values.

[0032] Additionally, values ​​used to determine OA values, such as female cycle length, may be subjected to any mathematical operation before the OA value is determined. Suitable mathematical operations are well known to those skilled in the art and include, for example, addition, subtraction, multiplication, division, or logarithms.

[0033] Furthermore, the OA value (X OA ) can be determined, for example, by grouping the values ​​(e.g., by forming percentiles) and determining a cutoff value.

[0034] For example, if menstrual cycle length and / or frequency are grouped into two groups, the group of a healthy reference population consisting of females without PCOS will have X OA = 0 etc. X OA The minimum number of while the group of females with any form of oligomenorrhea or anovulation may be assigned an X such as 1 or 2.OA Any other number of .alpha.

[0035] For human females, the OA value (X OA ) can be determined, for example, as follows: X OA =2, if females suffer from oligomenorrhea and anovulation (e.g., absent menstrual cycles; OR mean menstrual cycle length >35 days, preferably >40 days, more preferably >50 days, most preferably >60 days); or if the female has fewer than 8 cycles per year, more preferably fewer than 6 cycles per year, and most preferably fewer than 4 cycles per year; X OA = 0, if the female is asymptomatic (i.e., not affected by oligomenorrhea and anovulation).

[0036] In the methods of the present invention, data relating to the female menstrual cycle are converted into OA values ​​taking into account a threshold or cut-off value selected to distinguish between groups (eg symptomatic and asymptomatic).

[0037] Furthermore, when menstrual cycle length and / or frequency are classified into groups of 3 or more (e.g., asymptomatic, with symptomatic oligomenorrhea, and with symptomatic anovulation), asymptomatic females are required to have a minimum number (X OA =0, etc.), while females with oligomenorrhea or even anovulation may be assigned a higher number (e.g., X = 0, X = 1, X = 2, X = 3, X = 4, X = 5, X = 6, X = 7, X = 8, X = 9, X = 10, X = 11, X = 12, X = 13, X = 14, X = 15, X = 16, X = OA =1 or 2) can be assigned.

[0038] Most preferably, the OA value is a categorical variable for oligo / anovulation (OA) (yes, no). Olig / anovulation is assumed when menstrual cycle lengths are repeatedly reported to be greater than 35 days (see Example 1).

[0039] The term "HA value" refers to a value that reflects the androgen status of a woman, and an increase in the HA value relative to the HA value of a healthy reference population indicates an increase in the androgen level of a woman, which is considered a symptom of PCOS. Usually, the HA value is directly correlated with the androgen status of a woman. A woman with a normal female androgen status is called asymptomatic with respect to HA. An increase in the level of androgen in a woman is one possible symptom of PCOS.

[0040] Elevated levels of androgens in females are called "hyperandrogenemia (HA)." Phenotypic symptoms can include, for example, acne, seborrhea (inflamed skin), scalp hair loss, increased body or facial hair, and menorrhea or amenorrhea. Hyperandrogenemia can be a diagnostic feature of PCOS, potentially including both clinical (hirsutism, alopecia, and acne) and biochemical hyperandrogenemia conditions. Hyperandrogenemia can be caused, inter alia, by PCOS.

[0041] In the present invention, HA levels directly correlate with female androgen status.

[0042] The term "androgen" refers to any natural or synthetic steroid hormone that regulates the development and maintenance of male characteristics in vertebrates by binding to androgen receptors. Androgens are typically synthesized in the testes, ovaries, and adrenal glands. Androgens generally increase in both boys and girls during puberty. Androgens are also precursors to estrogens in both men and women. Examples of androgens involved in the female menstrual cycle are dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulfate (DHEA-S), androstenedione, testosterone, and dihydrotestosterone (DHT).

[0043] In general, testosterone is the primary androgen in men. Humans produce much smaller amounts of testosterone, which affects the growth and maintenance of female reproductive tissues and bone mass. Using the Elecsys Testosterone II Assay as an example, the normal concentration of total testosterone in blood for women aged 20 to 49 years is considered to be 0.08 to 0.48 ng / mL (equivalent to 0.29 to 1.67 nmol / L in blood). However, the concentration of testosterone produced in the body can vary daily and throughout the day; for example, testosterone concentrations are usually highest in the morning. Furthermore, testosterone concentrations may depend on age or health history. It is also well known in the art that reference ranges depend on the assay and methodology used; reference ranges for apparently healthy individuals are determined by the provider and provided in the assay instructions / method sheet / package insert.

[0044] In the method according to the first aspect of the present invention, the androgen status of a female is determined by measuring a biochemical parameter, namely androgens, in a sample from the female, preferably by determining the amount or concentration of free testosterone (FT) in the sample obtained from the female, or the ratio of the amount or concentration of total testosterone (TT) to the amount or concentration of sex hormone binding globulin (SHBG) in the sample obtained from the female (TT / SHBG), optionally multiplied by a constant a (a*TT / SHBG), in particular 100 (100*TT / SHBG).

[0045] The term "total testosterone" refers to all three types of testosterone in the blood: testosterone bound to another molecule (e.g., a protein such as albumin or sex hormone-binding globulin (SHBG)), and testosterone that is not bound to other molecules, especially proteins (free testosterone). Normally, human testosterone circulates in the bloodstream, loosely bound primarily to serum albumin and sex hormone-binding globulin (SHBG). Only a small fraction of testosterone is unbound, or "free," and therefore biologically active, able to enter cells and activate their receptors. Furthermore, testosterone that is weakly bound to albumin is also bioavailable and can be readily taken up by the body's tissues.

[0046] Generally, total testosterone tests do not distinguish between bound and unbound testosterone and determine the total amount of testosterone. Methods for measuring total testosterone are well known to those skilled in the art. For example, a needle can be used to draw blood from a vein in the arm or hand. Suitable methods for detecting total testosterone include immunoassays and / or mass spectrometry, such as liquid chromatography-mass spectrometry (LCMS) / mass spectrometry, enzyme-linked immunosorbent assay (ELISA), electrochemiluminescence immunoassay (ECLIA), and extraction / chromatography immunoassay, preferably electrochemiluminescence immunoassay (ECLIA), such as Roche's Elecsys®. Due to the variable testosterone concentration throughout the day, this test is usually performed in the morning and may be repeated several times to obtain more accurate values, which can then be statistically evaluated, for example, by forming an average or median value, or by other statistical methods well known to those skilled in the art.

[0047] Furthermore, in the method according to the first aspect of the present invention, the androgen status of a female can be determined, for example, by determining the free androgen index (FAI), which is generally intended to provide a guide to free testosterone levels (Vermeulen et al. 1999).

[0048] Preferably, the FAI is determined to determine the HA value. The term "free androgen index (FAI)" refers to a ratio used to determine abnormal androgenic status in humans. The ratio, calculated on a mole / mole basis, is the total testosterone concentration divided by the sex hormone binding globulin (SHBG) concentration, then multiplied by a constant, usually 100: FAI %: Total testosterone / SHBG x 100

[0049] Methods for measuring total testosterone are described above.

[0050] SHBG is a glycoprotein that binds to androgens and estrogens, thereby inhibiting the function of these hormones. Therefore, the bioavailability of sex hormones is affected by the concentration of SHBG. Methods for measuring SHBG are well known to those skilled in the art. For example, a needle can be used to draw blood from a vein in the arm or hand. Methods for detecting SHBG include, for example, immunoassays and / or mass spectrometry, such as liquid chromatography-mass spectrometry (LCMS) / mass spectrometry, enzyme-linked immunosorbent assay (ELISA), electrochemiluminescence immunoassay (ECLIA), and extraction / chromatography immunoassay, preferably electrochemiluminescence immunoassay (ECLIA), such as Roche's Elecsys®.

[0051] For example, using the Elecsys SHBG assay, premenopausal adult females have blood SHBG concentrations of 32-128 nmol / L, while adolescent females have blood SHBG concentrations of 36-125 nmol / L. At age 50 or older, female SHBG concentrations are approximately 27-128 nmol / L.

[0052] Increased levels of androgens (e.g., increased amounts or concentrations of free testosterone (FT) in a sample obtained from a female, or the ratio of the amount or concentration of total testosterone (TT) to the amount or concentration of sex hormone-binding globulin (SHBG) in a sample obtained from a female) translate into increased HA values ​​relative to the HA values ​​of individuals with normal androgen status / levels. Thus, increased HA values ​​compared to the HA values ​​of a reference population are considered to indicate increased androgen levels in a female, and thus indicate an increased risk of having PCOS.

[0053] As detailed above, values ​​defining normal and abnormal androgen status are well known in the art. Values ​​defining normal androgen status can be obtained from standard publications or from reference populations (see also above regarding OA values).

[0054] For human females, the HA value (X HA ) can be determined, for example, based on FAI or FT. A serial correlation can be applied, i.e., a high FAI or FT can correspond to a high HA value, and a low FAI or FT can correspond to a low HA value.

[0055] Furthermore, the values ​​used to determine an HA value, such as FAI or FT, may be subjected to any mathematical operation before the HA value is determined. Suitable mathematical operations are well known to those skilled in the art and include, for example, addition, subtraction, multiplication, division, or logarithms.

[0056] Furthermore, the HA value (X HA ) can be determined, for example, by grouping the values ​​(e.g., by forming percentiles) and determining cutoff values. Cutoff values, groupings, value transformations, etc. can be defined as described above for OA values.

[0057] For human females, the HA value (X HA ) can be determined, for example, as follows: X HA =2, if females exhibit elevated androgen levels (e.g., FAI > threshold (threshold may be, for example, 5.5%, preferably 6%, more preferably 6.5%, most preferably 7%) and / or total testosterone > threshold (threshold may be, for example, 48 ng / dl, preferably 52 ng / dl, more preferably 56 ng / dl, most preferably 60 ng / dl)); X HA = 0, if females are asymptomatic (i.e., do not exhibit increased androgen levels) ).

[0058] Most preferably, the HA value is a numerical variable of HA. Hyperandrogenism (HA) is derived as the free androgen index (FAI) calculated from serum testosterone (nmol / l) and serum sex hormone binding globulin (SHBG) (nmol / l) levels on a mole / mole basis, where FAI = testosterone / SHBG * 100 (see Example 1).

[0059] The term "AMH value" refers to the amount or concentration of anti-Mullerian hormone (AMH) in a sample obtained from a female. An increased AMH value compared to the AHM value of a reference population indicates an increased amount or concentration of anti-Mullerian hormone (AMH) in a subject, which is considered a PCOS symptom. Usually, the AMH value is directly correlated with the amount or concentration of anti-Mullerian hormone (AMH) in a sample obtained from a female. A female with a normal amount or concentration of AHM is called PCOS-asymptomatic with respect to AMH. An increased amount or concentration of AMH in a female is one possible symptom of PCOS.

[0060] The term "quantity" refers to a standard defined amount of a substance that measures the size of a collection of elementary entities such as atoms, molecules, electrons, and other particles. Quantity is sometimes called chemical quantity. The International System of Units (SI) defines the amount of a substance as proportional to the number of elemental particles present. The SI unit of quantity of substance is the mole, which has the unit symbol mol.

[0061] The "concentration" of a substance is the amount of a component divided by the total volume of the mixture. Several types of mathematical descriptions can be distinguished: mass concentration, molar concentration, number concentration, and volume concentration. The term concentration can apply to any kind of chemical mixture, but most often refers to the solute and solvent in a solution. There are variants of molar (volume) concentration, such as ordinary concentration and osmotic concentration.

[0062] The term "sample" refers to any type of fluid or tissue obtained from a female. The sample may be any sample suitable for measuring a marker according to the present invention, such as AMH or androgens, and may refer to a biological sample obtained for the purpose of in vitro evaluation. The sample may be particularly relevant to an individual and may contain material from which specific information about the individual can be determined, calculated, or inferred. Exemplary samples include blood, serum, plasma, or urine.

[0063] Preferably, the sample is a blood sample, more preferably selected from the group consisting of serum, plasma and whole blood.

[0064] The sample can be obtained by any method known to those skilled in the art for obtaining a sample from a female's body, for example, a needle can be used to draw blood from a vein in the arm or hand of the female.

[0065] Methods for detecting the amount or concentration of AMH in a sample from a female are well known to those skilled in the art. For example, the amount or concentration of AMH in serum can be detected using immunoassay and / or mass spectrometry, such as liquid chromatography-mass spectrometry (LCMS) / mass spectrometry, enzyme-linked immunosorbent assay (ELISA), electrochemiluminescence immunoassay (ECLIA) and extraction / chromatography immunoassay, preferably electrochemiluminescence immunoassay (ECLIA), such as Roche's Elecsys®.

[0066] Normally, women (humans) have age-dependent AMH concentrations. The results are shown in Table 1. [Table 1]

[0067] The corresponding amount of AMH can be calculated by multiplying the concentration value by the volume.

[0068] An increase in the amount or concentration of AMH in a sample obtained from a female can be converted into an increased AMH value relative to the AMH value of a female with a normal AMH amount or concentration. Thus, an increase in the AMH value relative to the AMH value of a reference population can be considered to indicate an increase in the amount or concentration of AMH in the female, which indicates an increased risk of having PCOS.

[0069] As detailed above, values ​​defining normal and abnormal AMH are well known in the art. Values ​​defining normal AMH can be obtained from standard publications or from reference populations (see also above regarding OA values).

[0070] In human females, the AMH value (X AMH ) can be determined, for example, based on the amount or concentration of AMH. A continuous correlation can be applied, i.e., a high AMH amount or concentration can correspond to a high AMH value, and a low AMH amount or concentration can correspond to a low AMH value.

[0071] Furthermore, the values ​​used to determine the AMH value, such as the amount or concentration of AMH, may be subjected to any mathematical operation before the AMH value is determined. Suitable mathematical operations are well known to those skilled in the art and include, for example, addition, subtraction, multiplication, division, or logarithms.

[0072] Furthermore, AMH value (X AMH ) can be determined, for example, by grouping the values ​​(e.g., by forming percentiles) and determining cutoff values. Cutoff values, groupings, value transformations, etc. can be defined as described above for OA values.

[0073] In human females, the AMH value (X AMH ) can be determined, for example, as follows: X AMH =2, if females exhibit elevated AMH levels (e.g., serum AMH >3.5 ng / ml, preferably >4 ng / ml, more preferably >4.5 ng / ml, most preferably preferably above 5ng / ml); X AMH = 0, if the female was asymptomatic (i.e., did not show increased AHM levels).

[0074] Most preferably, the AMH value is a numerical variable of AHM expressed as the level of serum AMH (nmol / l) (see Example 1).

[0075] The data set of step a) may further comprise other values, for example relating to further conditions of the female body or to substances present in the female body (hormones, etc.).

[0076] Suitable values ​​of the data set of step a) may also relate to further substances present in the female body, such as estrogens, androgens (e.g. testosterone (e.g. FT and / or bioavailable testosterone), dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulfate (DHEA-S), androstenedione and / or dihydrotestosterone (DHT)).

[0077] Suitable values ​​of the data set of step a) relating to further body conditions of the female may for example relate to the weight of the female, the age of the female and / or phenotypic characteristics.

[0078] Further characteristics that may be taken into consideration are genetics, female lifestyle habits such as smoking or physical exercise, or environmental conditions.

[0079] Step b) according to the method of the first aspect of the present invention , processing the dataset provided in step a) in a processing unit, wherein the processing includes combining the values ​​of the dataset provided in step a) into one combined value.

[0080] The term "processing unit" refers to a component capable of implementing a method coded by executable code, such as an electronic circuit that performs operations on some external data source, typically a memory or some other data stream. For example, a processing unit may be a computer or a mobile device such as a smartphone.

[0081] The term "processing" refers to the collection and modification of data or items of information in any way to generate meaningful information. The processing unit may also have an app (application) or computer program installed on it. The app may further support the processing and may also provide a graphical or text-based user interface.

[0082] In step b) of the method of the first aspect of the present invention, the processing comprises combining the values ​​of the datasets provided in step a) (OA values, HA values ​​and AHM values, and optionally WEIGHT and / or AGE values) into one composite value. Combining the values ​​of the datasets provided in step a) into one composite value can be performed using any mathematical operation known to those skilled in the art. Furthermore, combining the values ​​of the datasets provided in step a) into one composite value can also involve the application of any statistical analysis known to those skilled in the art. Preferably, the OA values, HA values ​​and AHM values ​​are combined by addition, so that the composite value is the sum of these values.

[0083] Also preferably, combining the values ​​of the datasets provided in step a) into one composite value can include the application of weighting factors to increase or decrease the influence of a single value on the composite value. This means that one of the values ​​can be given a higher weight than the other values. The weighting factors can be obtained by mathematically analyzing a reference population of females without PCOS (healthy reference population) and / or females with PCOS. Preferably, the weighting factors The weighting factors may be, or may be, derived by mathematically analyzing populations of females with and without PCOS. More preferably, the weighting factors are derived by analyzing populations of females with and without PCOS with a weighted regression model, in particular a weighted logistic regression model. Suitable methods are illustrated in the Examples.

[0084] For example, the values ​​(OA, HA, AHM, and optionally AGE and / or WEIGHT) of females 1) belonging to a healthy reference population or 2) suffering from PCOS are analyzed / collected. The weighting coefficients of the variables can be determined by established mathematical procedures. This can result in a broad data set of PCOS risk probabilities associated with females, e.g., those apparently healthy or with various forms or severities of PCOS, allowing for the establishment of cutoff values ​​for distinguishing between groups (see FIG. 3). The group of females suffering from PCOS can be further subdivided according to the severity of symptoms in women with PCOS, resulting in three or more groups, such as no PCOS, PCOS with mild symptoms, and PCOS with severe symptoms, allowing for further differentiation. An exemplary procedure is described in the Examples.

[0085] For example, at least one, two or three of the values ​​provided in step a) are weighted by applying a weighting factor. Preferably, the values ​​provided in step a) are weighted by applying a weighting factor.

[0086] Preferably, in step b) of the first aspect of the present invention, the composite value is a weighted composite value obtained by weighted calculation of the values ​​provided in step a).

[0087] For example, the OA value, HA value, and AMH value are each weighted by a weighting factor W OA , W HA and W AMH It may be further processed by a weighting factor W OA , W HA and W AMH W can be determined by comparing females from a reference population without PCOS to females from a population with PCOS. OA may reflect the frequency or significance of oligomenorrhea and / or anovulation in PCOS patients. A high frequency or significance of oligomenorrhea and / or anovulation is associated with a high W OA Although it may be correlated with low W, the low frequency or significance of oligomenorrhea and / or anovulation OA It can be correlated. HAmay reflect the frequency or significance of increased androgen levels in PCOS patients. HA Although the low frequency or significance of increased androgen levels may correlate with low W HA A significant increase in the amount or concentration of AMH may be associated with a high W HA Although it may be correlated with low W, the low significance of AMH amount or concentration AMH can be correlated with.

[0088] When weighting factors are applied to each value (OA, HA and AHM values), the data set provided in step a) can be combined, for example, using the following algorithm (PCOS Scoring algorithm): Score = X OA *W OA +X HA *W HA +X AMH *W AMH In the formula, X OA , X HA and X AMH can be defined as above, and W OA , W HA and W AMH is X OA , X HA and X AMH is a weighting coefficient for weighting the

[0089] As shown in the example, the weighting factor can be defined taking into account: W OA >W AMH and W OA >W HA .

[0090] To determine the score, use X OA , X HA , X AMH , W OA , W HA and / or W AMH Any interaction between the weighting factors is possible. For example, this includes combining weighting factors using any interaction such as: Score = XOA *W OA +X HA *W HA +X AMH *W AMH +W OA HA *X H A *X OA

[0091] Furthermore, these interactions may include any mathematical operation known to those skilled in the art, such as addition, subtraction, multiplication, division, or logarithmization.

[0092] Step c) according to the method of the first aspect of the present invention comprises: Comparing the composite value obtained in step b) with a corresponding composite value established in a reference population, wherein an increase in the composite value in a female relative to the composite value in the reference population indicates an increased risk of PCOS.

[0093] The term "reference population" refers to a population that may include apparently healthy females who do not suffer from PCOS, particularly any disease that affects the female menstrual cycle or the amount or concentration of sex hormones, as well as females who suffer from PCOS or any disease that affects the female menstrual cycle or the amount or concentration of sex hormones. Preferably, the reference population may be selected to include at least 20, 30, 50, 100, 200, 500, or 1000 individuals. Selecting appropriate individuals for the reference population is within the skill of a medical practitioner.

[0094] Preferably, the composite value obtained in step b) and the composite value of the reference population have been obtained using the same mathematical procedure, for example using the same algorithm.

[0095] For example, a composite value for a reference population can be calculated as described above for females assessed for risk of having PCOS. The composite values ​​of all females belonging to this healthy reference population can then be evaluated and combined into a composite value for the healthy reference population by statistical analysis, such as by determining the mean or median of the composite values ​​of all females belonging to this healthy reference population. Further suitable methods for statistical analysis are well known to those skilled in the art.

[0096] The composite value obtained in step b) can be considered to be higher (increased) than the composite value established in a healthy reference population if it is significantly higher than the composite value established in the reference population. Statistical procedures for assessing whether two values ​​are significantly different from each other are well known to those skilled in the art.

[0097] For example, the composite value obtained in step b) may be considered higher (increased) than the composite value established in a healthy reference population if it is at least 1.1-fold, preferably 1.5-fold, more preferably 2.0-fold, and most preferably 2.5-fold higher than the composite value established in a healthy reference population.

[0098] Furthermore, the composite value obtained in step b) may be considered higher (increased) than the composite value established in a healthy reference population if it has an absolute value that is at least 0.5, preferably at least 1.0, more preferably at least 2.0, most preferably at least 2.5 higher than the composite value established in a healthy reference population.

[0099] Furthermore, the composite value obtained in step b) may be considered to be higher (increased) than the composite value established in a healthy reference population if it increases by at least 10%, in particular at least 25%, in particular at least 50%, in particular at least 75%, in particular at least 100%, in particular at least 150%, in particular at least 200% compared to the composite value established in a healthy reference population.

[0100] Preferably, in step c), the weighted composite value is compared with a corresponding weighted composite value of a reference population, an increase in the weighted composite value of a female indicating an increased risk of having PCOS, in particular the weighting coefficients being obtained or obtainable by analyzing a population of females with PCOS and / or a population of females without PCOS.

[0101] For example, the composite value obtained in step b) may be further processed by a weighting factor. This weighting factor can be determined by analyzing a population of females with PCOS and / or a population of females without PCOS. For example, this may be the case when it is known that females assessed as having PCOS may have similar effects compared to PCOS (e.g., similar OA, HA, AMH values) and have conditions that need to be corrected for PCOS diagnosis.

[0102] Alternatively, and preferably, analysis of the reference population can yield PCOS risk probabilities (see FIG. 3) associated with female health status, e.g., apparently healthy, or females with various forms or severity of PCOS, allowing for the establishment of cutoff values ​​for distinguishing between groups. Risk probability thresholds can be defined to allow for grouping of females with or without a high likelihood of having PCOS. The group of females with PCOS can be further subdivided according to the severity of PCOS, resulting in three or more groups, such as no PCOS, PCOS with mild symptoms, and PCOS with severe symptoms, allowing for further differentiation. An exemplary procedure is described in the Examples.

[0103] Generally, the threshold or cutoff represents a suitable value for distinguishing females without PCOS from females with PCOS. Furthermore, if more than one threshold is applied, the thresholds may represent suitable values ​​for distinguishing females without PCOS from females with various symptoms, forms or severities of PCOS.

[0104] An appropriate threshold can be chosen depending on the desired sensitivity and specificity. Sensitivity and specificity are statistical measures of the performance of a binary classification test, also known in statistics as the classification function: Sensitivity (also called true positive rate) measures the proportion of such correctly identified positives (e.g., the proportion of women with PCOS who have symptoms of PCOS or are correctly identified as having the disease). Specificity (also called the true negative rate) measures the proportion of such correctly identified negatives (e.g., the proportion of healthy females correctly identified as not having PCOS or symptoms of PCOS).

[0105] A threshold can be set to increase either sensitivity or specificity. Values ​​above such a threshold can be considered increased. Preferably, the threshold is selected to match the diagnostic question of interest.

[0106] Furthermore, when considered alone, all values ​​of the dataset of step a) are only considered to be indicative of the risk of having PCOS. Each increment of a single value compared to a healthy reference population may not automatically be considered as confirmation of having PCOS. Finally, by collectively processing the dataset provided in step a) into a composite value according to step b) and comparing the composite value obtained in step b) with the corresponding composite value established in the reference population, the risk of having PCOS in women can be finally assessed.

[0107] Thus, a single value in the dataset of step a) may be increased compared to the reference population, while other values ​​are not increased compared to a healthy reference population, but the significance of the increased value may be decreased by its / their weighting factor and / or the significance of the non-elevated values ​​may be increased by its / their weighting factor(s), and thus the female may not be considered to have PCOS.

[0108] However, not all values ​​in the data set of step a) are elevated compared to a healthy reference population, but at the same time other values ​​are not elevated compared to the reference population, e.g. It may also be possible that a female is considered to have PCOS because the significance of the incremented value(s) is increased by the / their weighting factor(s) and / or the significance of the non-incremented value(s) is decreased by the / their weighting factor(s).

[0109] Step d) according to the method of the first aspect of the present invention comprises, including indicating the risk of having PCOS in women via an indication unit.

[0110] The term "indicative of" describes an indication or display of the risk of having PCOS in females assessed by the method according to the first aspect of the present invention.

[0111] Risk may be indicated in a variety of ways, such as by words, numbers, scales, or colors, or any other way known to those skilled in the art.

[0112] For example, risk may be indicated by simply displaying words with the meaning "yes" and "no", or "PCOS" and "no PCOS" in any language.

[0113] The risk of having PCOS can also be displayed using a number, such as a low number (e.g., "0") for no risk of having PCOS, or a high number (e.g., "100" or "10") for the highest risk of having PCOS. Percentile numbers, such as 0% and 100%, may also be applicable. Furthermore, the numbers can also display various degrees of risk of having PCOS, whereby, for example, all numbers between the lowest and highest numbers on a continuous range may be appropriate (e.g., all values ​​between "0" and "100"). The numbers may also be displayed using a scale such as that used in a car's speedometer.

[0114] Furthermore, risk can be indicated using colors for different results of the method according to the first aspect of the present invention. For example, green indicates a low risk of having PCOS, and red indicates a high risk of having PCOS. Furthermore, colors can also indicate different degrees of risk of having PCOS, such as green for low risk, yellow for moderate risk, and red for high risk. Furthermore, different degrees may be indicated by a continuous color transition, for example, from green to yellow to red. In each of these examples, the colors are interchangeable.

[0115] For example, the above exemplary X OA , X HA and X AMH Considering the values, the risk score classification can be as follows: High risk score (red): 6 to 9 points Moderate risk score (yellow): 3 to 5 points Low risk score (green): 0-2 points

[0116] Alternatively, the risk score classification may be selected based on the degree of risk, for example, as follows: High risk score (red): 67% to 100% Moderate risk score (yellow): 34% to 66% Low risk score (green): 0% to 33%

[0117] Alternatively, the risk score classification may be determined by mathematically optimizing the risk classification threshold using pre-specified threshold(s) for sensitivity and / or specificity or risk decile, for example using a prediction curve (Pepe et al. 2008).

[0118] The term "indication unit" refers to an electronic device or part of an electronic device that displays the results of the operation of the processing unit. Suitable indication units are well known to those skilled in the art. For example, the indication unit may be any kind of visual display integrated into an electronic device, such as a computer or a mobile device, such as a smartphone. Furthermore, the indication unit may be any kind of visual display that can be connected to a computer or a mobile device, e.g., a smartphone, but is considered a separate device.

[0119] For example, the method according to the first aspect of the present invention can be easily implemented during a visit to a doctor. During this visit, the female may be asked about the length of her menstrual cycle. Furthermore, after drawing a blood sample, androgen levels, such as the amount of total testosterone and SHBG, and the amount or concentration of AMH can be determined by clinical testing. The doctor can then easily input the results (menstrual cycle length (see definition above), the amount or concentration of androgens, e.g., the amount or concentration of total testosterone and SHBG or FAI, and the amount or concentration of AMH) into a computer program or application. Furthermore, if total testosterone and SHBG data are provided, the program can also calculate FAI. The program or application can then automatically determine the OA, HA, and AMH values ​​based on the method according to the first aspect of the present invention, compare these values ​​with those of a reference population, take into account potential weighting factors, and, as a result, indicate the female's risk of having PCOS. This indication can simply be a number or any kind of color code, as described above. The doctor can then simply enter the numbers retrieved from the female and the laboratory into the program or application. No further estimation or analysis is required. Further diagnosis, such as diabetes testing, can be performed according to the results of the method of the first aspect of the present invention as described above. Furthermore, treatment, such as the administration or discontinuation of birth control pills, can be initiated immediately to regularize the female's menstrual cycle.

[0120] Alternatively, the method of the first aspect may be performed in part by the patient or in part by a medical professional, such as a physician, who may input their cycle length and / or phenotypic symptoms of HA (e.g., skin or hair symptoms) into an app or software program. If these deviate from the healthy reference, the app or software program may suggest that the patient consult a physician for further evaluation. During such a visit and after laboratory testing of androgen levels and AMH values ​​as described above, the physician can easily input the results (androgen amounts or concentrations, e.g., total testosterone and SHBG or FAI amounts or concentrations, and AMH amounts or concentrations) into the computer program or application. As described above, the software program or app can then automatically determine OA, HA, and AMH values ​​based on the method of the first aspect of the present invention, compare these values ​​with those of a healthy reference population, take into account potential weighting factors, and, as a result, indicate the risk of female PCOS.

[0121] In a preferred embodiment, the dataset of step a) of the method of the first aspect of the present invention comprises: - a WEIGHT value reflecting the body weight of a female, where an increase in the WEIGHT value relative to the WEIGHT value of a normal weight population indicates an increase in body weight, and / or - Further includes AGE values ​​that reflect female age and correlate with age-dependent hormone levels.

[0122] Particularly preferably, the dataset of step a) of the method of the first aspect of the present invention further comprises OA values, HA values, AMH values ​​and AGE values, even more preferably OA values, HA values, AMH values, AGE values ​​and WEIGHT values.

[0123] The data set of step a) of the method of the first aspect of the invention may contain additional values ​​reflecting further conditions of the female's body or health. Correlating factors and corresponding values ​​are well known to those skilled in the art. For example, values ​​may reflect the weight (WEIGHT value) and / or age (AGE value) of a female.

[0124] In a preferred embodiment, the dataset of step a) of the method of the first aspect of the present invention further comprises WEIGHT values ​​reflecting the body weight of the female, and an increased WEIGHT value relative to the WEIGHT values ​​of a reference population indicates increased weight, which is one possible symptom of PCOS.

[0125] The term "WEIGHT value" refers to a value that reflects a deviation (increase) from a normal weight for a female (i.e., the weight of a corresponding normal weight female). An increased WEIGHT value relative to the WEIGHT value of a reference population indicates abnormal weight, a symptom observed in some females with PCOS. Usually, WEIGHT value is directly correlated with body weight. Females with normal weight are referred to as asymptomatic with respect to WEIGHT value. Overweight / obesity is one possible symptom of PCOS.

[0126] The term "body weight" refers to the mass or weight of a female. Suitable methods for determining a female's weight are well known to those skilled in the art, such as a personal scale. Typically, the weight is measured without the female wearing any items (such as clothing, shoes, and accessories). For example, body weight may vary throughout the day because the amount of water in the body may not be constant due to activities such as drinking, urination, or exercise. Therefore, if there is significant variation, several measurements can be performed and combined by statistical analysis, such as forming a median or mean value. Further suitable methods for statistical analysis are well known to those skilled in the art.

[0127] The term "normal weight" refers to the standard weight values ​​for healthy, reproductive-age females. The term "normal weight population" refers to a population of healthy, non-obese, reproductive-age females.

[0128] Weight gain translates to an increased WEIGHT value compared to the WEIGHT value of a normal weight female. Thus, an increased WEIGHT value compared to the WEIGHT value of a reference population is considered to indicate weight gain in a female and indicates an increased risk of having PCOS.

[0129] Values ​​defining normal weight, underweight and overweight / obesity are well known in the art. Values ​​defining normal body weight can be obtained from standard publications or from healthy reference populations (see also above regarding OA values).

[0130] For human females, the WEIGHT value (X WEIGHT ) can be determined, for example, based on the degree of overweight or obesity of females. A serial correlation can be applied, i.e., high overweight or obesity can correspond to high WEIGHT values, while low overweight or obesity can correspond to low WEIGHT values.

[0131] Furthermore, the values ​​used to determine a WEIGHT value, such as a measure of overweight or obesity, may be subjected to any mathematical operation before the WEIGHT value is determined. Suitable mathematical operations are well known to those skilled in the art and include, for example, addition, subtraction, multiplication, division, or logarithms.

[0132] Furthermore, the WEIGHT value (X WEIGHT ) may be determined, for example, by grouping the values ​​(e.g., by forming percentiles) and determining threshold and / or cutoff values. Thresholds, cutoff values, groupings, value transformations, etc. may be defined as described above for OA values.

[0133] WEIGHT value (X WEIGHT ) can be determined, for example, as follows: X 体重 =2, if the female is overweight; X 体重 =0, if females were normal weight;

[0134] In addition to the ideal weight for a female, a female's body size or measurements of specific parts of the female body may be taken into account to calculate a WEIGHT value, such as by including body mass index (BMI), Broca's index, Ponderal index, waist-to-hip ratio, waist-to-height ratio, or waist circumference. The female's body size or measurements of specific parts of the female body can be measured by methods known to those skilled in the art, such as using a measuring stick or tape measure. For example, considering BMI, a woman over the age of 19 is considered underweight with a BMI of less than 19, normal weight with a BMI of 10-24, overweight with a BMI of 25-29, and obese with a BMI of more than 30 (see, e.g., https: / / www.bmi-rechner.net / bmi-tabelle.htm).

[0135] If the BMI is at least 25, preferably at least 26, more preferably at least 27, more preferably at least 28, and most preferably at least 29, then the WEIGHT value for a female can be considered higher than the WEIGHT value for a healthy reference population.

[0136] In a further preferred embodiment, the data set of step a) of the method of the first aspect of the invention further comprises an AGE value reflecting the age of the female.

[0137] The age of the female may also affect other parameters determined.For example, hormone concentrations in females may change with age, as shown for AMH in Table 1 above.Therefore, it may be an option to introduce AGE values ​​to correct the age-dependent variation of other values, such as AMH values.In this way, all other values, such as AMH values, or only specific values, may be corrected.

[0138] The term "AGE value" refers to a value that reflects the age of a female. This can be easily retrieved by asking a female. Usually, the AGE value correlates directly or indirectly with the age of a female.

[0139] In this preferred embodiment, when weighting factors are applied to each value (OA, HA and AHM values, and AGE value, and optionally WEIGHT value), the data sets provided in step a) can be combined, for example, using the following algorithm (PCOS scoring algorithm): Score = X OA *W OA +X HA *W HA +X AMH *W AMH +X AGE *W AGE or Score = X OA *W OA +X HA *W HA +X AMH *W AMH +X AGE *W AGE +X WEIGHT *W WEIGHT , In the formula, X OA , X HA , X AMH , X WEIGHT and X AGE can be defined as above, and W OA , W HA , W AMH , W WEIGHT and W AGE is X OA , X HA , X AMH , X WEIGHT and X AGE is a weighting coefficient for weighting the

[0140] To determine the score, use X OA , X HA , X AMH , X WEIGHT , X AGE W OA , W HA , W AMH , W WEIGHT and / or W AGE Any interaction between is possible. Such interaction terms can also be included in the equation.

[0141] Furthermore, these interactions may include any mathematical operation known to those skilled in the art, such as addition, subtraction, multiplication, division, or logarithmization.

[0142] In a preferred embodiment, the data set provided in step a) is used to obtain the composite value. The only values ​​processed in step b) are the OA value, the HA value, the AMH value, the WEIGHT value and the AGE value, optionally combined with the PHE value.

[0143] In another preferred embodiment, the HA value of the dataset provided in step a) of the method of the first aspect of the invention is: the amount or concentration of free testosterone (FT) in a sample obtained from a female; or It corresponds to the ratio of the amount or concentration of total testosterone (TT) to the amount or concentration of sex hormone binding globulin (SHBG) in a sample obtained from a female (TT / SHBG), optionally multiplied by a constant a (a*TT / SHBG), in particular multiplied by 100 (100*TT / SHBG).

[0144] In a preferred embodiment, the HA value of the dataset provided in step a) of the method of the first aspect of the invention corresponds to the amount or concentration of free testosterone (FT) in a sample obtained from a female.

[0145] The term "free testosterone" refers to testosterone in the blood that is not bound to any protein. Using the Elecsys Testosterone Assay as an example, women aged 20-49 years have bioavailable "free" testosterone levels of 0.059-0.756 nmol / L.

[0146] A suitable method for detecting the amount or concentration of free testosterone (FT) in a sample is, for example, mass spectrometry, e.g., liquid chromatography mass spectrometry (LCMS) / mass spectrometry. For example, a needle can be used to draw blood from a vein in the arm or hand.

[0147] If the HA values ​​of the dataset provided in step a) of the method of the first aspect of the invention correspond to the amount or concentration of free testosterone (FT) in a sample obtained from a female, then the HA value (X HA ) can be determined, for example, according to a threshold value.

[0148] For example, X corresponds to the amount or concentration of free testosterone (FT) in a healthy female sample. HA values ​​of 0, whereas all X values ​​of females with higher amounts or concentrations of free testosterone (FT) may be assigned a value of 0. HA may be grouped and assigned another number, preferably 2.

[0149] When free testosterone is used to determine the HA value, the HA value (X HA ) can be determined, for example, as follows: X HA =2, if free testosterone > threshold (threshold may be, for example, 1.1 ng / dl, preferably 1.3 ng / dl, more preferably 1.5 ng / dl, most preferably 2.0 ng / dl); X HA = 0, if females are asymptomatic (i.e., do not show an increase in free testosterone).

[0150] In another preferred embodiment, the HA value of the dataset provided in step a) of the method of the first aspect of the invention corresponds to the ratio of the amount or concentration of total testosterone (TT) to the amount or concentration of sex hormone binding globulin (SHBG) in a sample obtained from a female (TT / SHBG), optionally multiplied by a constant a (a*TT / SHBG), in particular multiplied by 100 (100*TT / SHBG).

[0151] This preferred embodiment presents two options for determining the HA value, which have already been explained above: 1) Total testosterone / SHBG × 100 (which corresponds to the FAI calculation above), and 2) (Total testosterone / SHBG × 100) * a (also corresponds to the weighted FAI calculation above). This means that the constant a is WHA Corresponds to.

[0152] In a further preferred embodiment, in the method according to the first aspect of the invention, -Composite value is the threshold 高 females are at higher risk if > -Composite value is the threshold 中等度 and the threshold 高 If the risk is below 100%, females are at moderate risk; and -Composite value is the threshold 中等度 If the risk is below 100%, females are at low risk.

[0153] Furthermore, the risk of female PCOS can be determined using one or more thresholds or cut-off values.To determine these thresholds, for example, 1) a healthy reference group without PCOS and 2) a composite value of females belonging to the reference group with various degrees and forms of PCOS are determined.This can result in various cut-off values ​​or thresholds for healthy females and females with different risks / degrees of PCOS.

[0154] These composite values ​​can then be grouped, for example, into three groups, preferably based on statistical methods well known to those skilled in the art. In a next step, a threshold value for each group can be determined, for example the lowest or highest composite value for each group.

[0155] The composite value may be grouped into three or more groups, each group representing a different risk of having PCOS, for example, the composite value may be grouped into three groups: low risk, moderate risk, and high risk of having PCOS.

[0156] The group at lowest risk of having PCOS is up to the group at moderate risk of having PCOS (e.g., threshold 中等度 The group representing the highest risk of having PCOS may be the highest composite value below a threshold (threshold) that includes the highest composite value of the group representing a moderate risk of having PCOS. 高 ) Females may have a composite value below the threshold. 高If the composite value is above the threshold, there is a high risk. 中等度 is greater than or equal to the threshold 高 If the composite value is below the threshold, females are at moderate risk. 中等度 If the risk is below 0.5, the female may be considered to be at low risk.

[0157] In a further preferred embodiment, one or more values ​​of a (healthy) reference population and / or a composite value of a reference population and / or weighting factors for the weighted calculation are retrieved from a database, which may be any kind of database containing patient data on symptoms indicative of PCOS and which is well known to those skilled in the art.

[0158] In a preferred embodiment of the first aspect of the present invention, the dataset of step a) further comprises PHE values ​​reflecting one or more phenotypic traits known to be indicative of PCOS, wherein an increase in the PHE value relative to the PHE value of a healthy reference population is indicative of the presence of one or more phenotypic traits known to be indicative of PCOS as reflected by the increased PHE value, in particular the phenotypic traits being polycystic ovarian morphology (PCOM) and / or clinical hyperandrogenemia, more particularly acne, seborrhea, alopecia, and / or hirsutism.

[0159] Preferably, the data set of step a) further comprises 1, 2, 3 or 4 values ​​of the group consisting of PHE values, whereby any combination of these values ​​may be possible.

[0160] Preferably, the dataset of step a) further comprises PHE values ​​reflecting one or more phenotypic traits known to be indicative of PCOS, wherein an increase in the PHE value relative to the PHE value of a healthy reference population indicates the presence of one or more phenotypic traits known to be indicative of PCOS as reflected by the increase in PHE value, in particular the phenotypic traits are polycystic ovarian morphology (PCOM) and and / or hyperandrogenemia, more particularly acne, seborrhea, alopecia, and / or hirsutism.

[0161] The term "PHE value" reflects one or more phenotypic characteristics known to be indicative of PCOS. Typically, the PHE value correlates directly or indirectly with one or more phenotypic characteristics known to be indicative of PCOS.

[0162] The term "phenotypic trait" refers to any feature of the female phenotype known to be indicative of PCOS. For example, these phenotypic traits include polycystic ovarian morphology (PCOM) and / or clinical hyperandrogenism, such as acne, seborrhea, alopecia, and / or hirsutism. Preferably, these phenotypic traits include polycystic ovarian morphology (PCOM) and / or clinical hyperandrogenism, more preferably acne, seborrhea, alopecia, deepening of the voice, and / or hirsutism.

[0163] These phenotypic characteristics of clinical hyperandrogenism can be diagnosed simply by questioning the female or are evident after a brief physical examination of the female's body.

[0164] Typically, the reference population exhibits none, or less than one, of these phenotypic characteristics known to be indicative of PCOS.

[0165] The presence of the above phenotypic traits translates into increased PHE values ​​compared to the PHE values ​​of normal-appearing females. Thus, increased PHE values ​​compared to the PHE values ​​of a reference population are considered to indicate the presence of the phenotypic traits in females, which indicates an increased risk of having PCOS.

[0166] For human females, the PHE value (X PHE ) can be determined, for example, based on the female phenotype. A serial correlation can be applied, i.e., a phenotype exhibiting many characteristics of the female phenotype known to be indicative of PCOS may then correspond to a high PHE value, whereas a phenotype exhibiting few characteristics of the female phenotype known to be indicative of PCOS may correspond to a low PHE value.

[0167] Furthermore, the values ​​used to determine the PHE value, such as the number or type of phenotype, may be subjected to any mathematical operation before the PHE value is determined. Suitable mathematical operations are well known to those skilled in the art and include, for example, addition, subtraction, multiplication, division, or logarithms.

[0168] Furthermore, the PHE value (X PHE ) may be determined, for example, by grouping the values ​​(e.g., by forming percentiles) and determining threshold and / or cutoff values. Thresholds, cutoff values, groupings, value transformations, etc. may be defined as described above for OA values.

[0169] In humans, the PHE value (X PHE ) can be determined, for example, as follows: X PHE = 2, if the female suffers from at least one phenotypic trait known to be indicative of PCOS, preferably at least two phenotypic traits known to be indicative of PCOS, more preferably at least three phenotypic traits known to be indicative of PCOS, and most preferably at least four phenotypic traits known to be indicative of PCOS; X PHE = 0, if the female is asymptomatic, i.e., does not reflect any phenotypic characteristics known to be indicative of PCOS.

[0170] In addition to the accumulation of phenotypic traits, PHE values ​​also indicate the severity of each of these phenotypic traits, e.g. For example, one may consider the severity of acne suffered by females.

[0171] Furthermore, the PHE values ​​may be weighted when combined with the values ​​of the dataset provided in step a) of the first aspect of the invention, as described above for the dataset provided in step a).

[0172] Preferably, the dataset of step a) further comprises additional values ​​that allow to exclude other diseases such as non-classical adrenal hyperplasia (NCAH), androgen-secreting tumors, Cushing's syndrome, thyroid disorders or hyperprolactinemia.

[0173] Therefore, 17alpha-hydroxyprogesterone (17-OHP) levels may be included to rule out nonclassical adrenal hyperplasia (NCAH).

[0174] Therefore, values ​​for androstenedione and dehydroepiandrosterone sulfate (DHEAS) (determined, for example, using the Roche Elecsys androstenedione or DHEA-S assays, respectively) can be included to rule out androgen-secreting tumors.

[0175] Therefore, to rule out Cushing's syndrome, cortisol (e.g., Roche The value of the serotonin level (as determined using the Elecsys Cortisol II assay) may also be included.

[0176] Therefore, a thyroid stimulating hormone (TSH) (eg, Roche Elecsys TSH) level may be included to rule out thyroid disorders.

[0177] Therefore, a value for prolactin (measured, for example, using the Roche Elecsys Prolactin II assay) may be included to rule out hyperprolactinemia.

[0178] In another preferred embodiment, the method according to the first aspect of the invention further comprises determining one or more values ​​of the dataset provided in step a), in particular one or more value(s) corresponding to the amount or concentration of the one or more hormone(s), in particular by measuring the amount or concentration of the one or more hormone(s) in a sample, in particular a female sample.

[0179] In a further preferred embodiment, the amount or concentration of one or more hormone(s) in the female sample is determined by immunoassay and / or mass spectrometry.

[0180] Suitable methods for determining the amount or concentration of one or more hormones are well known to those skilled in the art.For example, suitable methods for detecting the amount or concentration of hormones include immunoassay and / or mass spectrometry, such as liquid chromatography-mass spectrometry (LCMS) / mass spectrometry, enzyme-linked immunosorbent assay (ELISA), electrochemiluminescence immunoassay (ECLIA) and extraction / chromatography immunoassay, preferably electrochemiluminescence immunoassay (ECLIA), such as Roche's Elecsys®.Furthermore, when the amount or concentration of two or more hormones, such as two, three, four or more hormones, is determined, they may be determined separately in different measurements, or may be determined together in one or more measurements.

[0181] In a second aspect, the present invention relates to a kit for use in a method of assessing the risk of having PCOS in a female, the kit comprising detecting in a sample obtained from the female: -(i) the amount or concentration of FT, or (ii) the amount or concentration of TT and the amount of SHBG or concentration; - amount or concentration of AMH; and - optionally, the amount or concentration of one or more additional hormones indicative of PCOS The device is provided with a reagent necessary for specifically measuring In particular, the kit is for use in the method of the first aspect of the invention, In particular in the method of the first aspect, the method further comprises determining one or more values ​​of the dataset provided in step a), in particular one or more value(s) corresponding to the amount or concentration of the one or more hormone(s), in particular by measuring the amount or concentration of the one or more hormone(s) in a sample from a female, and / or The amount or concentration of one or more hormone(s) in the female sample is measured by immunoassay and / or mass spectrometry.

[0182] A kit for use in a method for assessing a female's risk of having PCOS includes: -(i) the amount or concentration of FT, or (ii) the amount or concentration of TT and the amount or concentration of SHBG; - amount or concentration of AMH; and - Optionally, with the reagents necessary to specifically measure the amount or concentration of one or more additional hormones indicative of PCOS.

[0183] In the context of the kits of the present invention, the term "reagent" refers to a substance or compound that is added to a sample that makes it possible to indicate the amount or concentration of a particular component in the sample.

[0184] In the context of the kit of the present invention, the term "specifically measure" means detecting the precise amount or concentration of a clearly defined molecule. For specific measurement, a sample obtained from a female can be incubated with a reagent under conditions suitable for the formation of a binder-marker complex. Such suitable incubation conditions are well known to those skilled in the art, and therefore, it is not necessary to specify such conditions.

[0185] Preferably, the kit comprises: -(i) FT or (ii) TT and SHBG, and - Provides reagents for specific measurement of AMH.

[0186] Also preferably, the kit comprises: -(i) FT or (ii) TT and SHBG, and - Equipped with a reagent specifically for measuring AMH.

[0187] More preferably, the kit comprises: -TT and SHBG, and - Equipped with reagents for specifically measuring AMH.

[0188] Even more preferably, the kit comprises: -TT and SHBG, and - Equipped with a reagent specifically for measuring AMH.

[0189] The kit may also contain estrogen, androgen (other than FT, TT, TT / SHBG (FAI) and SHBG), dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulfate (DHEA-S), androstenedione and / or dihydroterpenoids. Further reagents may be provided for specifically measuring other molecules, such as dihydrotestosterone (DHT). Further suitable reagents will be known to those skilled in the art.

[0190] In the context of the kits of the present invention, the term "reagent" may refer to a protein molecule (such as an antibody), a nucleic acid molecule (such as any form of deoxyribonucleic acid (DNA) or ribonucleic acid (RNA)), or another biochemical, organic, or inorganic substance that can interact with the molecule to be specifically measured in the sample.

[0191] Furthermore, the reagents can be linked to detectable reporter moieties or labels, such as enzymes, dyes, radionuclides, luminescent groups, fluorescent groups, or biotin, e.g., fluorescent markers that can be used in immunoassay analysis. Any reporter moiety or label can be used with the reagents of the kit according to the second aspect of the present invention, so long as its signal can be directly related to or proportional to the amount of binding agent remaining on the support after washing. The amount of any second binding agent that remains bound to the solid support can then be determined using a method appropriate for the specific detectable reporter moiety or label. In the case of radioactive groups, scintillation counting or autoradiography is generally appropriate. Antibody-enzyme conjugates can be prepared using a variety of coupling techniques (for a review, see, e.g., Scouten, W.H., Methods in Enzymology 135:30-65, 1987). Spectroscopic methods can be used to detect dyes (including, e.g., colorimetric products of enzymatic reactions), luminescent groups, and fluorescent groups. Biotin can be detected using avidin or streptavidin, conjugated to a different reporter group (usually a radioactive or fluorescent group or an enzyme). Enzyme reporter groups can generally be detected by the addition of substrate (generally for a specific period of time), followed by spectroscopic, spectrophotometric, or other analysis of the reaction products. The standards and standard additions can be used to determine the level of antigen in a sample, using techniques well known to those skilled in the art.

[0192] The reagent may also be a substance that can further bind to the matrix of a column used in chromatography for purification and / or further analysis (such as mass spectrometry), and the reagent may further be linked to a test strip.

[0193] Preferably, the reagent is an antibody. Suitable antibodies for measuring the amount or concentration of one of the molecules to be specifically measured in a sample obtained from the above-mentioned females are well known to those skilled in the art.

[0194] The term "antibody" may include polyclonal antibodies, monoclonal antibodies, fragments thereof such as F(ab')2 and Fab fragments, as well as any naturally occurring or recombinantly produced binding partner that is a molecule that specifically binds to one of the molecules to be specifically measured in a sample. Any antibody fragment that retains the above criteria of a specific binder can be used. Antibodies are produced by state-of-the-art procedures, for example as described in Tijssen 1990. In addition, those skilled in the art are well aware of immunosorbent-based methods that can be used for the specific isolation of antibodies. By these means, the quality of polyclonal antibodies and therefore their performance in immunoassays can be improved (Tijssen 1990). Preferably, the antibody is a monoclonal antibody.

[0195] For the results disclosed in this invention, polyclonal antibodies produced in, for example, goats can be used. However, obviously, polyclonal antibodies from different species, such as rats, rabbits, or guinea pigs, as well as monoclonal antibodies, can also be used. Monoclonal antibodies are ideal tools in the development of assays for clinical routine, since they can be produced in any quantity required with consistent properties.

[0196] Preferably, the reagent can be used in an electrochemiluminescence immunoassay, more preferably, the reagent can be used in an electrochemiluminescence immunoassay. It is an antibody that can be used in chemiluminescence immunoassay.

[0197] Furthermore, the kit may contain two or more reagents, such as two, three, four, or more different reagents, preferably two different reagents that interact with one molecule to be specifically measured in a sample. For example, if the molecule to be specifically measured is measured by electrochemiluminescence immunoassay, the kit may contain two different antibodies that bind to the same molecule to be measured. Preferably, the two different antibodies that bind to the same molecule do not compete for binding sites on the molecule and bind to this molecule at different positions. Furthermore, both antibodies may be linked to different detectable reporter moieties or labels.

[0198] Samples that can be tested using the kit according to the second aspect of the present invention are typically obtained from females as described above.

[0199] Suitable methods for measuring the amount or concentration of one or more hormones in a female sample are well known to those skilled in the art or are described above. Preferred methods for measuring the amount or concentration of one or more hormones in a female sample are liquid chromatography mass spectrometry (LCMS) / mass spectrometry, enzyme-linked immunosorbent assay (ELISA), electrochemiluminescence immunoassay (ECLIA) and extraction / chromatography immunoassay, preferably electrochemiluminescence immunoassay (ECLIA), such as Elecsys® by Roche, and mass spectrometry.

[0200] In one example of an electrochemiluminescence immunoassay based on a ruthenium complex and tripropylamine (TPA), a first antibody may be conjugated to biotin, and a second antibody may be conjugated to the ruthenium complex. During the incubation step with the molecule to be measured, both antibodies may bind to the same molecule to form a sandwich complex. After incubation, the sandwich complex may be contacted with immobilized streptavidin, such as streptavidin linked to microparticles, allowing the sandwich complex to bind to the streptavidin microparticles via the interaction between biotin and streptavidin. The microparticles linked to the sandwich complex may then be brought into a measurement cell, where they may be immobilized by magnetically interacting with the surface of an electrode. After removal of unbound material, a voltage is applied to the electrode, inducing the emission of chemiluminescence, which can be detected with a photomultiplier tube.

[0201] In one example of an enzyme-linked immunosorbent assay (ELISA), a sample can be incubated in a microwell plate, and the wells are coated with a first antibody against the molecule to be measured. In the next step, after incubation and washing, a second antibody against the molecule to be measured, which is linked to biotin, can be added. After further incubation and washing, streptavidin-horseradish peroxidase (HRP) can be added. After a final incubation and wash, the substrate tetramethylbenzidine (TMB) can be added to the sample. Finally, an acidic stop solution can be added. Measurement can be performed by dual-wavelength absorbance measurement between 450 nm and 600-630 nm. The measured absorbance is usually directly proportional to the concentration of AMH in the sample. Using a set of AMH calibrators, a calibration curve of absorbance versus AMH concentration can be plotted. The AMH concentration in the sample can then be calculated from this calibration curve.

[0202] Specifically, the measuring step can be carried out as follows: the sample can be contacted with a first reagent (which can be immobilized, for example, on a solid phase) under conditions that allow binding of the substance to be measured to the first reagent. Unbound reagent can be removed by a separation step (for example, one or more washing steps). A second reagent (for example, a labeling agent) can be added to detect the bound first reagent and allow its binding and quantification. Unbound second reagent can be removed. The amount of the second reagent, which is proportional to the amount of the substance to be measured, can be determined, for example, based on the label. Quantification can be performed, for example, based on a calibration curve constructed for each assay by plotting the measured values ​​against the concentration of each calibrator. The concentration or amount of the substance to be identified in the sample can then be read from the calibration curve.

[0203] Preferably, the kit for use in the method of the first aspect of the invention is further characterized, said method further comprising determining one or more values ​​of the dataset provided in step a), in particular one or more value(s) corresponding to the amount or concentration of one or more hormone(s), in particular by measuring the amount or concentration of one or more hormone(s) in a sample, in particular a female sample; and / or The amount or concentration of one or more hormone(s) in the female sample is measured by immunoassay and / or mass spectrometry.

[0204] The kit may further comprise a buffer and / or salt for adjusting pH and reaction and measurement conditions. Furthermore, the kit may comprise a stabilizer for supporting the stability of the reagents and / or hormones during specific measurement of, for example, (i) the amount or concentration of FT, or (ii) the amount or concentration of TT and the amount or concentration of SHBG, the amount or concentration of AMH, and the amount or concentration of one or more additional hormones indicative of PCOS. Suitable buffers, salts, and stabilizers are well known to those skilled in the art. Furthermore, sodium azide may be added to all liquid solutions in the kit, such as reagents or buffer solutions.

[0205] The kit may also include all the equipment necessary to take a blood sample from a female, such as a blood sample container, a needle, and a device to connect the container and the needle. Preferably, the kit may include a syringe.

[0206] Generally, a physician or physician's assistant can draw blood from a female. The blood can then be sent to a laboratory where the sample is measured using the kit on a designated analyzer and the data is sent to the physician. However, the kit may also be applied by the physician or physician's assistant themselves. The kit may be applied during a physician's outpatient, routine, or home visit.

[0207] All components of the kit may be packaged separately in individual containers. However, it is also possible that two or more components of the kit may be packaged together in one or more containers.

[0208] The kit may further comprise a label containing, for example, instructions on how to use the kit or a description of the contents of the kit, however, this information may also be provided in any other form, such as on a storage medium such as a CD-ROM or USB stick.

[0209] In a third aspect, the present invention provides a method for assessing the risk of having PCOS in women, using OA values, optionally in combination with WEIGHT and / or AGE values. (i) FT or TT / SHBG, and (ii) AMH wherein a female has an increased risk of PCOS if the combined value of the amount or concentration or ratio of the markers and the OA value is increased relative to a combined value established in a reference population, and optionally the combined marker further comprises one or more additional hormones indicative of PCOS.

[0210] The term "marker" refers to a clinical or biological characteristic that can be objectively measured and provides information about the risk of having or developing PCOS. A marker may indicate how likely a patient is to recover health during the course of PCOS and PCOS treatment. A marker may also be intended to objectively assess the overall outcome of a patient. Typically, Markers are measured and evaluated at the time of diagnosis, and the presence or absence of the marker can be useful in selecting females for treatment.

[0211] Suitable markers for detecting the risk of having female PCOS are typically, for example, estrogens and / or androgens (e.g., testosterone (e.g., FT, TT, TT / SHBG(FAI), SHBG and / or bioavailable testosterone), dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulfate (DHEA-S), androstenedione and / or dihydrotestosterone (DHT)). Further suitable markers for detecting the risk of having female PCOS are well known to those skilled in the art.

[0212] Preferably, the markers for detecting the risk of having PCOS in women are: (i) FT or TT / SHBG, and (ii) AMH is combined with the OA value.

[0213] Also preferably, the markers for detecting the risk of having female PCOS are: (i) FT or TT / SHBG, and (ii) AMH in combination with OA, WEIGHT and / or AGE values.

[0214] More preferably, the markers for detecting the risk of having female PCOS are: (i) FT or TT / SHBG, and (ii) AMH in combination with an OA value, where the combination of markers further includes one or more additional hormones indicative of PCOS.

[0215] More preferably, the markers for detecting the risk of having female PCOS are: (i) FT or TT / SHBG, and (ii) AMH in combination with an OA value, and a WEIGHT value and / or an AGE value, the combination of markers further comprising one or more additional hormones indicative of PCOS.

[0216] Further hormones indicative of PCOS are listed above as suitable markers for detecting the risk of having PCOS in women, and may include, for example, estrogens, androgens (other than FT, TT, TT / SHBG (FAI) and SHBG), dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulfate (DHEA-S), androstenedione and / or dihydrotestosterone (DHT).

[0217] Furthermore, if the combined amount or concentration or ratio of the markers and the OA value is increased relative to the combined value established in a reference population, the female may have an increased risk of PCOS.

[0218] The composite value of the amount or concentration or ratio of the marker and the OA value can be processed as described above for processing composite values.

[0219] The use of the marker combination of the third aspect of the present invention can make it possible to determine the risk of females having PCOS.In particular, if the combined value of the amount or concentration or ratio of the markers as defined above and the OA value is increased relative to the combined value established in a healthy reference population, the female may have an increased risk of PCOS.

[0220] The combined value of the amount or concentration or ratio of female markers and OA value is compared with the combined value of the amount or concentration or ratio of markers and OA value of a reference population.If the combined value of the amount or concentration or ratio of female markers and OA value is increased compared to the combined value of the amount or concentration or ratio of markers and OA value of a healthy reference population, this may indicate an increased risk of female PCOS.

[0221] Generally, the amount or concentration or ratio of female marker and the combined value of OA value are directly or indirectly correlated with the risk of having PCOS.Usually, the combined value of the amount or concentration or ratio of female marker and OA value increases compared with the combined value of the amount or concentration or ratio of marker and OA value of healthy reference population, the greater the risk of having this female PCOS.

[0222] The combined value of the amount or concentration or ratio of female marker and OA value can be considered higher than the combined value of the amount or concentration or ratio of marker and the OA value of healthy reference population if it is significantly higher than the combined value of the amount or concentration or ratio of marker and the OA value of healthy reference population.The statistical procedures for assessing whether two values ​​are significantly different from each other are well known to those skilled in the art, such as Student's t-test or chi-square test.

[0223] In a fourth aspect, the present invention relates to a computer system for use in the method of the first aspect, the computer system comprising: a) a data set unit containing computer instructions, - OA values ​​reflecting the length of a female menstrual cycle and / or the number of female menstrual cycles per year, wherein an increase in the OA value relative to the OA values ​​of a healthy reference population indicates an abnormal menstrual cycle length and / or number; - an HA value reflecting the androgen status of a female, wherein an increase in the HA value relative to the HA value of a healthy reference population indicates an increase in androgen levels in the female; - an AMH value corresponding to the amount or concentration of AMH in a sample obtained from the female; and b) a processing unit comprising computer instructions for processing the dataset of step a), the processing comprising combining the values ​​of the dataset provided in step a) into one composite value; c) a reference data unit containing computer instructions, (i) storing and / or retrieving a reference dataset containing one or more reference values ​​established in a reference population and processing the reference dataset into a composite value for the reference population; or (ii) To store and / or retrieve composite values ​​of a reference population a reference data unit containing computer instructions for (d) comparing the composite value obtained in step b) with the corresponding composite value of step c), wherein an increase in the female composite value relative to the composite value of the reference population indicates an increased risk of PCOS; e) Indication units that indicate the risk of having PCOS in women; Includes.

[0224] The term "computer instructions" as used in segments a), b), and c) of the fourth aspect of the present invention describes a set of machine language instructions that a particular processor can understand and execute.

[0225] Furthermore, according to segment c) of the computer system according to the fourth aspect of the present invention, the reference data unit comprises: (i) storing and / or retrieving a reference dataset containing one or more reference values ​​established in a reference population and processing the reference dataset into a composite value for the reference population; or (ii) To store and / or retrieve composite values ​​of a reference population The computer instructions include:

[0226] The term "reference dataset" refers to a collection of reference values ​​comprising one or more reference values ​​established in a reference population, such as a reference population comprising females without PCOS and females with any form of PCOS, preferably a healthy reference population without PCOS. This reference dataset can be further processed into a composite value of the reference population, or a composite value of a reference population comprising healthy females without PCOS and females with any form of PCOS, preferably a composite value of the reference population. Processing into a composite value can be performed as described above.

[0227] Further features of this fourth aspect of the invention are described above or will be known to those skilled in the art.

[0228] In a preferred embodiment, the computer system according to the fourth aspect of the invention is further characterized in the same way as any embodiment of the first aspect of the invention.

[0229] In a fifth aspect, the present invention relates to a computer program comprising instructions which, when executed by a computer, cause the computer to perform steps a), b), c) and d) of any of the methods of the first aspect.

[0230] The computer program, when executed, is capable of causing a computer to carry out the method according to the first aspect of the invention as described above.

[0231] The computer program may be directly loadable into the internal memory of a digital computer and may contain software code portions suitable for carrying out the method according to the first aspect of the invention when the product is run on a computer.

[0232] The computer program may preferably be a computer program stored on a machine-readable storage medium such as RAM, ROM, or on a removable and / or portable storage medium such as, but not limited to, a CD-ROM, flash memory, DVD, BlueRay, FlashDisk, storage card or USB stick. The computer program may also be provided on a server from which it is downloaded via a data network such as the Internet or another transfer system such as a telephone line or a wireless transfer connection. Additionally or alternatively, the computer program may be a network of computer-implemented computer programs, such as on a client / server system or a cloud computing system, on an embedded system comprising the computer program, or on an electronic device such as a smartphone or personal computer on which the computer program is stored, loaded, executed, exercised or developed.

[0233] In a sixth aspect, the present invention relates to a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to perform steps a), b), c) and d) of any of the methods of the first aspect.

[0234] The term "computer-readable storage medium" refers to any computer-readable medium that can store data such as executable code.

[0235] The products and uses according to the second, third, fourth and fifth aspects may be further defined as specified for the method of the first aspect of the invention. In particular, with regard to the terms used in the second, third, fourth and fifth aspects of the present disclosure, reference is made to the terms, examples and specific embodiments used in the first aspect of the present disclosure, which are also applicable to the other aspects of the present disclosure.

[0236] In further embodiments, the present invention relates to the following aspects: 1. A method for assessing the risk of having polycystic ovary syndrome (PCOS) in a female, comprising: a) - OA values ​​reflecting the length of the female's menstrual cycle and / or the number of the female's menstrual cycles per year, wherein an increase in the OA value relative to the OA values ​​of a healthy reference population indicates an abnormal menstrual cycle length and / or number; - an HA value reflecting the androgen status of said female, wherein an increase in said HA value relative to said HA value of a healthy reference population indicates increased androgen levels in said female; - an anti-Müllerian hormone (AMH) value corresponding to the amount or concentration of AMH in a sample obtained from said female; providing a dataset comprising: b) processing the dataset provided in step a) with a processing unit, said processing comprising combining the values ​​of the dataset provided in step a) into one composite value; c) comparing the composite value obtained in step b) with the corresponding composite value established in a reference population, wherein an increase in the female composite value relative to the composite value of a healthy reference population indicates an increased risk of PCOS; and d) indicating via an indicator unit the risk of having PCOS in said female. A method comprising:

[0237] 2. The data set of step a) a WEIGHT value reflecting the female's body weight, wherein an increase in the WEIGHT value relative to the WEIGHT value of a normal weight population indicates an increase in body weight, and / or - an AGE value reflecting the age of the female.

[0238] 3. The HA value is - the amount or concentration of free testosterone (FT) in a sample obtained from said female; or The method according to aspect 1 or 2, wherein the ratio of the amount or concentration of total testosterone (TT) to the amount or concentration of sex hormone binding globulin (SHBG) in the sample obtained from said female (TT / SHBG) optionally multiplied by a constant a (a*TT / SHBG), in particular multiplied by 100 (100*TT / SHBG).

[0239] 4. The method according to any of aspects 1 to 3, wherein in step b) the composite value is a weighted composite value obtained by weighted calculation of the values ​​provided in step a), and in step c) the weighted composite value is compared with the corresponding weighted composite value of a reference population, wherein an increase in the weighted composite value of the female indicates an increased risk of having PCOS, and in particular the weighting coefficients have been obtained or can be obtained by analyzing a reference population comprising healthy females and / or females diagnosed with PCOS.

[0240] 5. said composite value being a threshold value 高 if the female has a high risk of said composite value being a threshold value 中等度 and the threshold 高 if the HIV infection rate is below 100%, the female is at moderate risk; and said composite value being a threshold value 中等度 5. The method of any one of aspects 1 to 4, wherein if the HIV-1 expression level is below 1, then the female has a low risk.

[0241] 6. One or more values ​​of said reference population and / or said composite value and / or weight of said reference population A method according to any one of aspects 1 to 5, wherein the weighting factors for the calculation are retrieved from a database.

[0242] 7. The method according to any of aspects 1 to 6, wherein said dataset of step a) further comprises a PHE value reflecting one or more phenotypic traits known to be indicative of PCOS, wherein an increase in PHE value relative to said PHE value of a healthy reference population indicates the presence of one or more phenotypic traits known to be indicative of PCOS reflected by the increased PHE value, in particular said phenotypic traits being polycystic ovarian morphology (PCOM) and / or hyperandrogenemia, more particularly acne, seborrhea, alopecia, and / or hirsutism.

[0243] 8. The method according to any of aspects 1 to 7, wherein the sample is a blood sample, in particular selected from the group consisting of serum, plasma and whole blood.

[0244] 9. The method of any one of aspects 1 to 8, wherein the female is a human.

[0245] 10. The method according to any of aspects 1 to 9, further comprising determining one or more values ​​of said dataset provided in step a), in particular one or more value(s) corresponding to the amount or concentration of one or more hormone(s), in particular by measuring the amount or concentration of one or more hormone(s) in a sample from said female.

[0246] 11. The method according to aspects 1 to 10, wherein the amount or concentration of one or more of said hormone(s) in said female sample is measured by immunoassay and / or mass spectrometry.

[0247] 12. A kit for use in a method for assessing the risk of having PCOS in a female, said kit comprising: -(i) the amount or concentration of said FT, or (ii) the amount or concentration of said TT and the amount or concentration of said SHBG; - the amount or concentration of AMH; and - optionally, the amount or concentration of one or more further hormones indicative of PCOS The device is provided with a reagent necessary for specifically measuring In particular, the kit is for use in a method according to aspect 10 or 11.

[0248] 13. OA values, optionally in combination with WEIGHT and / or AGE values, in assessing the risk of having PCOS in women; (i) FT or TT / SHBG, and (ii) AMH wherein said female has an increased risk of PCOS if the combined value of the amount or concentration or ratio of said markers and said OA value is increased relative to said combined value established in a reference population, and optionally said combination of markers further comprises one or more further hormones indicative of PCOS.

[0249] 14. A computer system for use in the method according to any one of aspects 1 to 11, wherein the computer system comprises: a) a data set unit containing computer instructions, - OA values ​​reflecting the length of the female's menstrual cycle and / or the number of the female's menstrual cycles per year, wherein an increase in the OA value relative to the OA values ​​of a healthy reference population indicates an abnormal menstrual cycle length and / or number; - an HA value reflecting the androgen status of said female, wherein an increase in the HA value relative to said HA value of a healthy reference population indicates an increase in androgen levels in said female; value, and - an AMH value corresponding to the amount or concentration of AMH in a sample obtained from said female; a dataset unit comprising computer instructions for providing a dataset comprising: b) a processing unit comprising computer instructions for processing the dataset of step a), said processing comprising combining the values ​​of the dataset provided in step a) into one composite value; and c) a reference data unit containing computer instructions, (i) storing and / or retrieving a reference dataset comprising one or more of said reference values ​​established in a reference population, and processing said reference dataset into a composite value for said reference population; or (ii) storing and / or retrieving composite values ​​of said reference population; a reference data unit containing computer instructions for (d) comparing the composite value obtained in step b) with the corresponding composite value of step c), wherein an increase in the female composite value relative to the composite value of a healthy reference population indicates an increased risk of PCOS; e) an indicator unit indicating the risk of having PCOS in said female; 2. A computer system comprising:

[0250] 15. The computer system of embodiment 14, further characterized as described in any of embodiments 2 to 11.

[0251] 16. A computer program comprising instructions that, when executed by a computer, cause the computer to perform steps a), b), c), and d) of the method of any one of aspects 1 to 11.

[0252] 17. A computer-readable storage medium comprising instructions that, when executed by a computer, cause the computer to perform steps a), b), c), and d) of the method of any one of aspects 1 to 11.

[0253] In general, the present disclosure is not limited to the particular methodology, protocols, and reagents described herein, as these may vary. Furthermore, the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present disclosure. As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Similarly, the words "comprise," "contain," and "include" are to be construed as inclusive rather than exclusive.

[0254] Unless otherwise defined, all technical and scientific terms and any acronyms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Although any methods and materials similar or equivalent to those described herein can be used in the practice presented herein, particular methods and materials are described herein.

[0255] The present disclosure is further illustrated by the following figures and examples, however, it is understood that unless specifically indicated otherwise, the figures and examples are included for illustrative purposes only and are not intended to limit the scope of the present disclosure. [Brief explanation of the drawings]

[0256] [Figure 1] Figure 1 shows the ROC curve obtained from weighted logistic regression evaluated using 200 Monte Carlo cross-validation runs on 1955 cases and 1642 controls based on all variables of the PCOS risk score (age, BMI, OA, FAI, and AMH; area under the curve (AUC): 0.976) compared with the single variables OA (AUC: 0.898), FAI (AUC: 0.765), or AMH (AUC: 0.838) alone. [Figure 2]Figure 2 shows the ROC curve obtained from a weighted logistic regression evaluated using 200 Monte Carlo cross-validation runs on 1955 cases and 1642 controls based on all variables of the PCOS risk score (age, BMI, OA, FAI, and AMH; AUC: 0.976) and the combination of AMH and FAI (AUC: 0.877) or AMH and SHBG (AUC: 0.873). [Figure 3] Figure 3 shows a histogram of PCOS risk probability obtained from weighted logistic regression using age and BMI and OA, FAI, and AMH using 1955 cases and 1642 controls. Crosses and circles indicate risk for control and case subjects, respectively. Vertical lines indicate PCOS risk classification derived using prediction curves for 80% sensitivity and specificity. [Figure 4A] Figure 4 shows the mean regression coefficients (and mean SD) of weighted logistic regression models obtained from 200 Monte Carlo cross-validation experiments using 1955 cases and 1642 controls and the variables age and BMI (A), or age (B) and OA, FAI, and AMH. [Figure 4B] Figure 4 shows the mean regression coefficients (and mean SD) of weighted logistic regression models obtained from 200 Monte Carlo cross-validation experiments using 1955 cases and 1642 controls and the variables age and BMI (A), or age (B) and OA, FAI, and AMH. [Figure 5A] FIG. 5 shows the prediction curves of PCOS risk scores for 1955 cases and 1642 controls based on the variables age, BMI, OA, FAI and AMH (A) or age, OA, FAI and AMH (B). [Figure 5B] FIG. 5 shows the prediction curves of PCOS risk scores for 1955 cases and 1642 controls based on the variables age, BMI, OA, FAI and AMH (A) or age, OA, FAI and AMH (B). [Figure 6]Figure 6 shows the ROC curves for predicting case-control status for a second independent sample set consisting of 200 cases and 44 controls for the single variable PCOS risk score and PCOS risk alone. Figure 6 visualizes the performance of the PCOS risk score based on 44 controls and 200 cases described in Example 3. The ROC curves and AUCs demonstrated that the composite variable PCOS risk score (age, BMI, OA, FAI, and AMH) was superior to using the single variables OA, FAI, or AMH alone. The AUC for the PCOS risk score was 0.99, indicating very good separation between cases and controls, followed by OA (AUC: 0.96), AMH (AUC: 0.76), and FAI (AUC: 0.90). The performance of the PCOS risk score for a combination of four variables (PCOS risk variables OA, FAI, AMH, and AGE) was also evaluated (see Table 9). [Figure 7] Figure 7 shows the ROC curves for predicting case-control status for a second sample set consisting of 200 cases and 44 controls for the PCOS risk score using the combined variables of age, BMI, OA, FAI, AMH, and AMH+FAI and AMH+SHBG. Figure 7 shows the performance of the PCOS risk score including all variables (age, BMI, OA, FAI, and AMH) compared with the combined variables of AMH and FAI or AMH and SHBG (as suggested by Calzada et al.). The highest AUC was found for the PCOS risk score (AUC: 0.99). AUCs of 0.90 were for the combined variables of AMH+FAI (AUC: 0.90) and AMH+SHBG (AUC: 0.90). The ROC curves and AUCs show that the composite variable of PCOS risk score (age, BMI, OA, FAI and AMH; AUC: 0.99) is superior to the use of AMH in combination with either FAI (AUC: 0.90) or AMH and SHBG (AUC: 0.90). [Figure 8]Figure 8 shows the ROC curve for predicting case-control status of a second sample set consisting of 200 cases and 44 controls for the biochemical detection of hyperandrogenemia (HA). FAI was compared with LH, FSH, and the LH / FSH ratio. The highest AUC was found for FAI (AUC: 0.90), followed by the LH / FSH ratio (AUC: 0.85). [Figure 9] Figure 9 shows a histogram of PCOS risk probability for a second sample set consisting of 200 cases and 44 controls. PCOS risk weights were derived using weighted logistic regression with 200 MCCV runs and 100 replicates using age, BMI, OA, FAI, and AMH in a sample set of 1,866 cases and 1,675 controls. Vertical lines indicate thresholds of low = 0.2 and high = 0.8, resulting in a sensitivity of 96.0% and a specificity of 90.9%. Crosses and circles indicate the risk for control and case subjects, respectively. [Example]

[0257] Example 1: Derivation of a PCOS risk score A total of N=1642 controls and N=1955 cases were used to derive a PCOS risk score based on a combination of numerical variables for age, BMI, FAI, and AMH, and a categorical variable for oligoanovulation (OA) (yes, no). Additionally, PCOS risk scores were derived using a four-variable combination, i.e., OA, age, FAI, and AMH, but excluding BMI, and a three-variable combination, i.e., OA, FAI, and AMH, but excluding BMI and age.

[0258] Cases A total of 1955 women aged 20-45 years who were diagnosed with PCOS and were not using contraceptives were included. OA was based on irregular menstrual cycles and / or cycle length. Hyperandrogenism (HA) was calculated as the free androgen index (FAI) based on serum testosterone (nmol / l) and serum sex hormone-binding globulin (SHBG) (nmol / l) levels: FAI = Testosterone / SHBG*100

[0259] Patients were assessed for PCOM by an ovarian volume of ≥10 ml and / or an above-threshold follicle count (AFC).

[0260] Additionally, serum anti-Müllerian hormone (AMH) was measured using the Elecsys AMH Plus immunoassay.

[0261] The PCOS cases included patients with PCOS who represented the four phenotypes according to the Rotterdam criteria (PCOS Consensus Workshop Group, Fertil Steril 2004;81:19-25).

[0262] Phenotype A (oligoanovulation (OA)+, hyperandrogenism (HA)+, poly- Cystic ovarian morphology (PCOM)+) ●Phenotype B (OA+, HA+, PCOM-) ●Phenotype C (OA-, HA+, PCOM+) ●Phenotype D (OA+, HA-, PCOM+)

[0263] Control In deriving the PCOS risk score, the control group consisted of 1642 healthy women aged 20-45 years who did not have PCOS.

[0264] Information obtained from cases where age and body mass index (BMI), ovarian follicle count (AFC), and serum AMH values ​​were available, and testosterone and SHBG levels to represent FAI were used. Simulations were based on reference range values ​​for healthy women to reflect the expected distribution of these variables in healthy subjects based on reference range studies. Simulations were performed by sampling from the expected distribution of SHBG and testosterone in healthy women.

[0265] The following table lists the statistics of the variables for cases and controls. [Table 2]

[0266] Of note, due to the study design, there was a difference in age between cases and controls.

[0267] PCOS Risk Score The proposed PCOS risk score calculates a patient's risk of having PCOS, ranging from 0 to 1, with higher values ​​indicating a higher risk of having PCOS.

number

[0268] A weighted logistic regression model was established with case-control status as the endpoint in Monte Carlo cross-validation (MCCV) with 200 runs (Xu & Liang 2001).

[0269] For the derivation of PCOS risk, the variables Age, BMI (any), and OA, as well as FAI, were included, whereas the variables AMH, testosterone, and SHBG were included as log-transformed variables. To account for the imbalance between the number of cases and controls, a weighted logistic regression model was applied to derive PCOS risk. This meant that a weight was assigned to each subject, which was considered within the logistic regression model estimation (Hastie et al. (2009)). The weights were selected by applying the costs of misclassifying cases and controls, respectively, according to Elkan (2001).

[0270] MCCV: For each run of MCCV, the dataset was randomly split into a training set and a test set (80% and 20%, respectively) while maintaining the proportion of cases and controls. On the current training set, models were built and performance was assessed by the area under the ROC curve (AUC) using the respective test sets. Estimated overall performance of the logistic regression model was given as the mean AUC. Mean sensitivity and specificity were calculated to estimate model performance for cases and controls separately. The stability of the regression model was assessed by providing the mean of the regression coefficients along with the standard deviation (SD) and coefficient of variation (CV).

[0271] The mean regression coefficient of each variable from the MCCV was used as the weight for PCOS risk.

number

[0272] PCOS risk, i.e., the probability of having PCOS, was estimated by:

number

[0273] PCOS risk was classified as low, moderate, and high as follows: Risk thresholds were derived using the prediction curve proposed by Pepe et al. (2007), assuming that a sensitivity of at least 80% and a specificity of 80% would be achieved.

[0274] result Figure 1 visualizes the performance of the PCOS risk score based on 1,642 controls and 1,955 cases. The ROC curve and AUC demonstrate superior performance for the PCOS risk score with variables (age, BMI, OA, FAI, and AMH) compared with the single variables OA, FAI, or AMH alone. The AUC for the PCOS risk score was 0.98, indicating very good separation between cases and controls, followed by OA (AUC: 0.90), AMH (AUC: 0.84), and FAI (AUC: 0.77). The performance of three- and four-variable combinations (PCOS risk variables OA, FAI, AMH, and AGE, or OA, FAI, or AMH) was also evaluated (see Table 7). The ROC curve and AUC demonstrated that the combined variables for PCOS risk (age, OA, FAI, and AMH) were superior to using OA, FAI, or AMH alone. The AUC for PCOS risk reached 98%, indicating that very good separation between cases and controls could be achieved, followed by OA (90%) and AMH (84%). The AUC for FAI only reached approximately 77%. A three-variable model (OA, AMH, and FAI combined vs. OA, FAI, or AMH alone) yielded a PCOS risk score AUC of 0.970.

[0275] Figure 2 shows the performance of the PCOS risk score including all variables (age, BMI, OA, FAI, and AMH) compared with the combination of AMH and FAI or the combination of AMH and SHBG (as suggested by Calzada et al.). The ROC curves and AUCs are for the variables in the PCOS risk score (age, BMI, OA, FAI, and AMH). The results show that the use of AMH in combination with either FAI or SHBG is superior to the use of AMH in combination with either FAI or SHBG. The performance of the model was also evaluated for all PCOS risk variables (except BMI) or for OA, FAI, or AMH alone (data not shown). ROC curves and AUCs demonstrated that the PCOS risk variables (age, OA, FAI, and AMH) were superior to the use of AMH dichotomized using a cutoff of 5.03 ng / ml along with either FAI or SHBG. The AUC for PCOS risk reached 98%, indicating very good separation between PCOS cases and controls without PCOS, followed by AMH (dichotomized using a cutoff of 5.03 ng / ml) plus SHBG at 82%. The AUC for AMH (dichotomized using a cutoff of 5.03 ng / ml) plus FAI only reached approximately 76%.

[0276] The stability of the weighted logistic regression model was evaluated using 200 MCCV runs and is shown in Figure 4 and Tables 3 and 4. The results show that OA has the greatest impact on PCOS risk, followed by AMH and FAI. The small standard deviations indicate very stable regression coefficients across MCCV runs. [Table 3] [Table 4]

[0277] Estimated PCOS risk probabilities obtained from weighted logistic regression using age and BMI, as well as OA, FAI, and AMH, are displayed by case-control status in Figure 3. The histogram shows a clear separation between cases and controls, with cases having a high estimated risk and controls having a low risk. Few subjects are considered to be at moderate risk of PCOS, approximately 50%. The dashed lines indicate the thresholds for grouping women as low, moderate, and high risk (from left to right), indicating 80% sensitivity and 80% specificity. Similar results were obtained by weighted logistic regression using 3- and 4-variable models (see Table 7).

[0278] Example 2: Risk threshold investigation The PCOS risk derived by weighted logistic regression assigns the majority of cases at high risk of having PCOS, while controls are estimated to be at low risk (see Tables 5 and 6). [Table 5] [Table 6]

[0279] Overall, PCOS risk without BMI results in a risk classification equivalent to that of including BMI in PCOS risk.

[0280] When risk groups were classified based on the prediction curves, the risk threshold for low risk (specificity ≥ 80%) was 7% and the risk threshold for high risk (sensitivity ≥ 80%) was 78%. Based on these thresholds, the low-risk group included approximately 39% of women, the moderate-risk group 16%, and the high-risk group 45% based on 1955 cases and 1642 controls (Figures 5A and 5B). [Table 7]

[0281] Example 3: Evaluation of PCOS risk scores The performance of the PCOS risk score was evaluated in a second independent sample set of 200 cases and 44 controls.

[0282] Control The controls consisted of 44 healthy women aged 18-38 years without PCOS. The median age was 25.5 years (standard deviation = 5.02), and most had a normal body mass index (BMI, median = 21.9 kg / m). 2, standard deviation = 1.88). All women included in this control group had regular menstrual cycles based on information on menstrual cycle length and / or cycle duration. Serum anti-Müllerian hormone (AMH) was measured using the Elecsys AMH Plus immunoassay. Hyperandrogenism (HA) was derived as the free androgen index (FAI) based on serum testosterone (nmol / L) and serum sex hormone-binding globulin (SHBG) (nmol / L) levels: FAI = Testosterone / SHBG*100

[0283] Testosterone and SHBG levels to represent FAI were determined by Elecsys Testosterone II (nmol / L) and Elecsys SHBG immunoassay (nmol / L).

[0284] The three serum assays were performed on serum samples collected on days 1-3 of the menstrual cycle.

[0285] Cases Two hundred women aged 20-41 years who were not using contraceptives and were diagnosed with PCOS were included in this study. Oligomenorrhea and / or anovulation (OA) was determined based on irregular menstrual cycles and / or cycle length. Hyperandrogenism (HA) was determined as the free androgen index (FAI) based on serum testosterone (nmol / L) and serum sex hormone-binding globulin (SHBG) (nmol / L): FAI = Testosterone / SHBG*100

[0286] Patients were evaluated for PCOM by an ovarian volume of ≥10 mL and / or an above-threshold follicle count (AFC) based on transvaginal ultrasound. Serum anti-Müllerian hormone (AMH) was measured using the Elecsys AMH Plus immunoassay.

[0287] The PCOS cases included patients with PCOS who represented the four phenotypes according to the Rotterdam criteria (PCOS Consensus Workshop Group, Fertil Steril 2004;81:19-25).

[0288] The following table lists the baseline characteristics of cases and controls: [Table 8]

[0289] Sensitivity and specificity of the PCOS risk score: Different thresholds were applied to an independent second sample set of 200 cases and 44 controls. Best results were achieved with fixed risk probability thresholds of 0.2 and 0.8, with a sensitivity of 96.0% and A specificity of 90.9% was obtained (see Figure 9).

[0290] Additionally, the table below lists the area under the ROC curve (AUC) for different variable combinations applied to an independent second dataset, which demonstrates the superior performance of the PCOS risk score combination. [Table 9]

[0291] References Calzada et al. “AMH in combination with SHBG for the diagnosis of polycystic ovary syndrome”;J Obstet Gynaecol.2019,17:1-7. Cho et al. “The biological variation of the LH / FSH ratio in normal women and those with Polycystic Ovarian Syndrome”;2005 Endocrine Abstracts 2005;9 P80. Escobar-Morreale H.F.,“Polycystic ovary syndrome:definition,aetiology,diagnosis and treatment”;Nature Reviews Endocrinology 2018;Vol 14,270-284. Indran et al.“Simplified 4-item criteria for polycystic ovary syndrome:A bridge too far?”;Clin.Endocrinol.(Oxf).2018;doi:10.1111 / cen.13755. International evidence-based guideline for the assessment and management of polycystic ovary syndrome 2018. Mahajan,Nalini,&Kaur,Jasneet.2019.Establishing an Anti-Muellerian hormone cut-off for diagnosis of polycystic ovarian syndrome in women of reproductive age-bea ring Indian ethnicity using the automated Anti-Muellerian hormone assay.Journal of Human Reproductive Sciences,12(2),104-113. Malini and George “Evaluation of different ranges of LH:FSH ratios in polycystic ovarian syndrome(PCOS)-Clinical based case control study” General and Comparative Endocrinology 2018;260:51-57. Mireya Calzada,Natividad Loepez,Jose A.Noguera,Jaime Mendiola,Ana I.Hernandez,S hiana Corbalan,Maria Sanchez&Alberto M.T orres(2019):AMH in combination with SHBG for the diagnosis of polycystic ovary syndrome,Journal of Obstetrics and Gynaecology,JOURNAL OF OBSTETRICS AND GYNAECOLOGY Nicholas et al.“The utility of Anti-Muellerian Hormone in diagnosing Polycystic Ovary Syndrome amongst women presenting to an infertility clinic”;Hum Reprod.Abstract ESHRE 2014. Nordenstrom A.and Falhammar H.,“MANAGEMENT OF ENDOCRINE DISEASE:Diagnosis and management of the patient with non-classic CAH due to 21-hydroxylase deficiency.”;Eur J Endocrinol.2018;pii:EJE-18-0712.R2.doi:10.1530 / EJE-18-0712. Pepe et al.,“Integrating the Predictiveness of a Marker with Its Performance as a Classifier”;Am J Epidemiol.2008,167(3):362-368. Pigny et al.“Comparative assessment of five serum antimuellerian hormone assays for the diagnosis of polycystic ovary syndrome.”;Fertil Steril.2016;105(4):1063-1069.e3. R Core Team.2015.R:A Language and Environment for Statistical Computing.R Foundation for Statistical Computing,Vienna,Austria(https: / / www.r-project.org / ) Sahmay et al.“Diagnosis of Polycystic Ovary Syndrome:AMH in combination with clinical symptoms”;J Assist Reprod Genet.2014;31(2):213-220. Scouten,W.H.,“A survey of enzyme coupling techniques.”;Methods in Enzymology 135:30-65,1987. Tijssen,P.,Practice and theory of enzyme immunoassays,Elsevier Science Publishe rs B.V.,Amsterdam(1990),the whole book,especially pages 43-78 and pages 108-115 Vermeulen,A.,L.Verdonck,and J.Kaufman,A critical evaluation of simple methods for the estimation of free testosterone in serum.Journal of Clinical Endocrinology&Metabolism,1999.84(10):p.3666-3672. Xu,Qing-Song,&Liang,Yi-Zeng.2001.Monte Carlo cross validation.Chemometrics and Intelligent Laboratory Systems,56(1),1-11. The Rotterdam ESHRE / ASRM-Sponsored PCOS Consensus Workshop Group.Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome.Fertil Steril 2004;81:19-25.

Claims

1. 1. A method for assessing a female's risk of having polycystic ovary syndrome (PCOS), comprising: a) - OA values ​​reflecting the length of the female's menstrual cycle and / or the number of the female's menstrual cycles per year, wherein an increase in the OA value relative to the OA values ​​of a healthy reference population indicates an abnormal menstrual cycle length and / or number; - an HA value reflecting the androgen status of the female, wherein an increase in the HA value relative to the HA value of a healthy reference population indicates an increase in the androgen level in the female; - an anti-Müllerian hormone (AMH) value corresponding to the amount or concentration of AMH in the sample obtained from said female; providing a dataset comprising: b) processing the dataset provided in step a) with a processing unit, said processing comprising combining the values ​​of the dataset provided in step a) into one composite value; c) comparing the composite value obtained in step b) with a corresponding composite value established in a reference population, wherein an increase in the female composite value relative to the composite value of a healthy reference population indicates an increased risk of PCOS; and d) indicating said female's risk of having PCOS via an indicating unit A method comprising:

2. The data set of step a) is a WEIGHT value reflecting the body weight of said female, wherein an increase in WEIGHT value relative to said WEIGHT value of a normal weight population indicates an increase in body weight, and / or - AGE value reflecting the age of the female The method of claim 1 further comprising:

3. The HA value is - the amount or concentration of free testosterone (FT) in a sample obtained from said female, or the ratio of the amount or concentration of total testosterone (TT) to the amount or concentration of sex hormone binding globulin (SHBG) in the sample obtained from said female (TT / SHBG), optionally multiplied by a constant a (a*TT / SHBG), in particular by 100 (100*TT / SHBG); 3. The method according to claim 1 or 2, which corresponds to

4. 4. The method according to any one of claims 1 to 3, wherein in step b) the composite value is a weighted composite value obtained by weighted calculation of the values ​​provided in step a), and wherein in step c) the weighted composite value is compared with the corresponding weighted composite value of a reference population, an increase in the weighted composite value of the female indicating an increased risk of having PCOS, in particular the weighting coefficients being obtained or obtainable by analyzing a reference population comprising healthy females and / or females diagnosed with PCOS.

5. - the composite value is a threshold 高 if the female has a high risk of - the composite value is a threshold 中等度 and the threshold 高 if the IL-16 expression level is below 0, the female is at moderate risk; and - the composite value is a threshold 中程度 If the HIV infection rate is below 100%, the female has a low risk. The method according to any one of claims 1 to 4.

6. The method according to any one of claims 1 to 5, wherein the one or more values ​​of the reference population and / or the composite value of the reference population and / or weighting factors for the weighted calculation are retrieved from a database.

7. 7. The method according to any one of claims 1 to 6, wherein the dataset of step a) further comprises PHE values ​​reflecting one or more phenotypic traits known to be indicative of PCOS, wherein an increase in PHE value relative to the PHE values ​​of a healthy reference population is indicative of the presence of one or more phenotypic traits known to be indicative of PCOS reflected by the increased PHE value, in particular said phenotypic traits being polycystic ovarian morphology (PCOM) and / or hyperandrogenemia, more particularly acne, seborrhea, alopecia and / or hirsutism.

8. The method according to any one of claims 1 to 7, wherein the sample is a blood sample, in particular selected from the group consisting of serum, plasma and whole blood.

9. The method of any one of claims 1 to 8, wherein the female is a human.

10. 10. The method according to any one of claims 1 to 9, further comprising determining one or more values ​​of the dataset provided in step a), in particular one or more value(s) corresponding to the amount or concentration of one or more hormone(s), in particular by measuring the amount or concentration of one or more hormone(s) in a sample from said female.

11. The method of any one of claims 1 to 10, wherein the amount or concentration of one or more hormone(s) in the female sample is measured by immunoassay and / or mass spectrometry.

12. 1. A kit for use in a method of assessing the risk of having PCOS in a female, the kit comprising: - (i) the amount or concentration of FT, or (ii) the amount or concentration of TT and the amount or concentration of SHBG; - amount or concentration of AMH; and - optionally the amount or concentration of one or more further hormones indicative of PCOS The device is provided with a reagent necessary for specifically measuring In particular, the kit is for use in the method according to claim 10 or 11.

13. OA values, optionally in combination with WEIGHT and / or AGE values, in assessing the risk of having PCOS in women; (i) FT or TT / SHBG, and (ii) AMH wherein said female has an increased risk of PCOS if the combined value of the amount or concentration or ratio of said markers and said OA value is increased relative to said combined value established in a reference population, and optionally further comprising one or more additional hormones wherein said combination of markers is indicative of PCOS.

14. A computer system for use in the method of any one of claims 1 to 11, said computer system comprising: a) a data set unit containing computer instructions, - OA values ​​reflecting the length of a female menstrual cycle and / or the number of female menstrual cycles per year, wherein an increase in the OA value relative to the OA values ​​of a healthy reference population indicates an abnormal menstrual cycle length and / or number; - HA value reflecting the androgen status of said female, said HA of a healthy reference population an HA value, wherein an increase in the HA value relative to the A value indicates an increase in androgen levels in the female; - an AMH value corresponding to the amount or concentration of AMH in the sample obtained from said female; a dataset unit comprising computer instructions for providing a dataset comprising: b) a processing unit comprising computer instructions for processing the dataset of step a), said processing comprising combining the values ​​of the dataset provided in step a) into one composite value; and c) a reference data unit containing computer instructions, (i) storing and / or retrieving a reference dataset comprising one or more of said reference values ​​established in a reference population and processing said reference dataset into a composite value for said reference population; or (ii) storing and / or retrieving composite values ​​of said reference populations; a reference data unit containing computer instructions for (d) comparing the composite value obtained in step b) with the corresponding composite value of step c), wherein an increase in the female composite value relative to the composite value of a healthy reference population indicates an increased risk of PCOS; e) an indicator unit that indicates the risk of a female having PCOS; 2. A computer system comprising:

15. The computer system according to claim 14, further characterized as claimed in any one of claims 2 to 11.

16. 12. A computer program comprising instructions which, when executed by a computer, cause the computer to carry out steps a), b), c) and d) of the method of any of claims 1 to 11.

17. A computer-readable storage medium comprising instructions that, when executed by a computer, cause the computer to perform steps a), b), c) and d) of the method of any of claims 1 to 11.

Images