Detection Of Polycystic Ovary Syndrome Research Articles

To evaluate the effect of a structured training program about polycystic ovarian syndrome on the knowledge and perception of nursing students of a government nursing teaching institute in Northern India

ABSTRACT Background: The education levels of females have been found to aid in the early detection of polycystic ovary syndrome (PCOS). Early diagnosis and treatment are crucial in mitigating the long-term consequences of PCOS. Nursing students are poised to become the next generation of healthcare professionals, equipped with valuable knowledge that can be utilized to increase awareness among their peers and patients, particularly about this critical subject matter. Objective: We conducted this study to assess the current level of knowledge and perception regarding PCOS among nursing students and to evaluate the impact of structured training interventions on their baseline knowledge and perception level. Methods: It was a single-group pre-post interventional study done on the nursing students of Govt. Nursing College. A pre-tested questionnaire was used as a study tool. Intervention in the form of a structured training program was imparted to the participants. Results: The pre-test group exhibited a lower mean knowledge score (M = 22.5758, SD = 11.02290) than the post-test group (M = 31.3889, SD = 14.50417). A significant difference was observed in the mean knowledge scores (t = −8.107070, P = 0.00001). The pre-test group exhibited a lower mean perception score (M = 35.3535, SD = 3.79637) than the post-test group (M = 36.6616, SD = 3.79576). There was a significant difference in the mean perception score (t = −5.883333, P = 0.00001). Conclusions: This study has shown the positive impact of relevant training on the knowledge and perception of nursing students about PCOS.

A Novel Invasive Weed Optimization and its Variant for the Detection of Polycystic Ovary Syndrome.

This study intends to provide a novel Invasive Weed Optimization (IWO) algorithm for the detection of Polycystic Ovary Syndrome (PCOS) from ultrasound ovarian images. PCOS is an intricate anarchy described by hyperandrogenemia and irregular menstruation. Indian women are increasingly finding reproductive disorders, namely PCOS. The women having PCOS grow more small follicles in their ovaries. The radiologists take a look into women's ovaries by use of ultrasound scanning equipment to manually count the number of follicles and their size for fertility treatment. These may lead to error diagnosis. This paper proposed an automatic follicle detection system for identifying PCOS in the ovary using IWO. The performance of IWO is improved in Modified Invasive Weed Optimization (MIWO). This algorithm imitates the biological weeds' behavior. The MIWO is employed to obtain the optimal threshold by maximizing the between-class variance of the modified Otsu method. The efficiency of the proposed method has been compared with the well-known optimization technique called Particle Swarm Optimization (PSO) and with IWO. Experimental results proved that the MIWO finds an optimal threshold higher than that of IWO and PSO.

P-658 Identification of novel biomarkers for the detection of polycystic ovary syndrome (PCOS) in adolescents and adult women

Abstract Study question Can novel biomarkers be identified to distinguish between PCOS-positive cases and healthy PCOS-negative controls in women as early as adolescence? Summary answer Meteorin-like protein (METRNL), fibroblast growth factor binding protein-1 (FGFBP1) and leukotriene A4 hydrolase (LTA4H) identified PCOS as early as adolescence, independent of PCOS phenotype. What is known already International evidence-based guidelines recommend use of the Rotterdam criteria for PCOS diagnosis. Despite this guidance, diagnosing PCOS can be extremely challenging due to the heterogeneity of clinical presentation, and further complicated in adolescence and young adulthood due to overlap between signs of physiologic changes during puberty and common symptoms of PCOS (e.g., menstrual irregularity, excess male hormones/androgens, multiple follicles in ovaries). Timely diagnosis of PCOS allowing for appropriate monitoring, follow up and early intervention may reduce the risk of associated comorbidities. An unmet need exists for biomarkers/alternative tools to support earlier diagnosis of PCOS in adolescents/adult women of reproductive age. Study design, size, duration This was a retrospective study (October 2020–June 2023) using banked samples from women with PCOS (confirmed per Rotterdam criteria) and healthy controls who had participated in previous studies sponsored by/in collaboration with Roche Diagnostics. Candidate biomarkers identified by exploratory proteomics analysis (Olink Proximity Extension Assay) were verified by ELISA and receiver operating characteristic-area under the curve (ROC-AUC) analysis for their ability to discriminate PCOS cases and controls in women aged 15-45 years. Participants/materials, setting, methods Top-performing biomarkers from Olink discovery (n = 51 PCOS phenotype A; n = 37 controls) were selected for further validation based on AUC >0.970, biological relevance for PCOS and commercial ELISA availability. Serum biomarker concentrations/biomarker ratios were determined and ROC-AUC analysis conducted in two validation cohorts (Cohort 1: n = 90 cases [phenotype A, n = 30; B, n = 20; C, n = 20; D, n = 20]; n = 47 controls; Cohort 2: n = 240 cases [15-19 years, n = 70; 20-24 years, n = 99; 25-40 years, n = 71]; n = 48 controls, median 26.0 years). Main results and the role of chance Serum METRNL concentrations were numerically lower, and FGFBP1 and LTA4H concentrations numerically higher, in PCOS cases compared with controls. All three biomarkers distinguished PCOS cases and controls, regardless of PCOS phenotype A-D (Cohort 1: AUCs 0.91-0.99 [METRNL], 0.84-0.88 [FGFBP1] and 0.76-0.90 [LTA4H]), and across different age groups in women of reproductive age (Cohort 2: 15-19, 20-24 and 25-40 years; AUCs 0.88, 0.91 and 0.96 [METRNL], respectively; 0.90, 0.88 and 0.91 [FGFBP1], respectively; 0.74, 0.70 and 0.78 [LTA4H], respectively). Compared with results for the individual biomarkers, combining FGFBP1 with METRNL, and LTA4H with METRNL, as biomarker ratios further improved the ability to distinguish PCOS cases and controls, independent of PCOS phenotype A-D (Cohort 1: AUCs 0.95-1.00 [FGFBP1:METRNL] and 0.97-0.99 [LTA4H:METRNL]) and across age groups (Cohort 2: 15-19, 20-24 and 25-40 years; AUCs 0.91, 0.96 and 0.98 [FGFBP1:METRNL], respectively, and 0.91, 0.91 and 0.97 [LTA4H:METRNL], respectively). Findings were not impacted by adjustment for baseline body mass index. METRNL, FGFBP1 and LTA4H are linked to biological mechanisms associated with clinical manifestations and underlying characteristics of PCOS, i.e., metabolic pathways, inflammation and ovarian function. While METRNL has been described previously in adult PCOS, FGFBP1 and LTA4H findings are novel in this context. Limitations, reasons for caution These findings in retrospective samples warrant validation in additional independent prospective cohorts, particularly in adolescents and young adult women. Wider implications of the findings Reliable diagnosis of PCOS in adolescents and young adult women represents an unmet medical need. METRNL, FGFBP1 and LTA4H have potential use as biomarkers to support the detection of PCOS in adolescents and adult women, potentially shortening time to diagnosis and allowing for earlier and targeted intervention. Trial registration number not applicable

Establishment and Analysis of an Artificial Neural Network Model for Early Detection of Polycystic Ovary Syndrome Using Machine Learning Techniques.

To identify novel gene combinations and to develop an early diagnostic model for Polycystic Ovary Syndrome (PCOS) through the integration of artificial neural networks (ANN) and random forest (RF) methods. We retrieved and processed gene expression datasets for PCOS from the Gene Expression Omnibus (GEO) database. Differential expression analysis of genes (DEGs) within the training set was performed using the "limma" R package. Enrichment analyses on DEGs using gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), and immune cell infiltration. The identification of critical genes from DEGs was then performed using random forests, followed by the developing of new diagnostic models for PCOS using artificial neural networks. We identified 130 up-regulated genes and 132 down-regulated genes in PCOS compared to normal samples. Gene Ontology analysis revealed significant enrichment in myofibrils and highlighted crucial biological functions related to myofilament sliding, myofibril, and actin-binding. Compared with normal tissues, the types of immune cells expressed in PCOS samples are different. A random forest algorithm identified 10 significant genes proposed as potential PCOS-specific biomarkers. Using these genes, an artificial neural network diagnostic model accurately distinguished PCOS from normal samples. The diagnostic model underwent validation using the independent validation set, and the resulting area under the receiver operating characteristic curve (AUC) values was consistent with the anticipated outcomes. Utilizing unique gene combinations, this research created a diagnostic model by merging random forest techniques with artificial neural networks. The AUC indicated a notably superior performance of the diagnostic model.

A systematic review and future research agenda on detection of polycystic ovary syndrome (PCOS) with computer-aided techniques

Polycystic Ovary Syndrome (PCOS) is among the most prevalent endocrinological abnormalities seen in reproductive female bodies posing serious health hazards. The correctness of interpreting this condition depends heavily on the wide spectrum of associated symptoms and the doctor's expertise, making real-time clinical detection quite challenging. Thus, investigations on computer-aided PCOS detection systems have recently been explored by several researchers worldwide as a potential replacement for manual assessment. This review study's objective is to analyze the relevant research works on computer-assisted methods for automatically identifying PCOS through a systematic literature review (SLR) methodology as well as investigate the research limitations and explore potential future research scopes in this domain. 28 articles have been selected using the PRISMA approach based on a set of inclusion-exclusion criteria for conducting the review. The data synthesis of the selected articles has been conducted using six data exploration themes. As outcomes, the SLR explored the topical association between the studies; their research profiles; objectives; data size, type, and sources; methodologies applied for the detection of PCOS; and lastly the research outcomes along with their evaluation measures and performances. The study also highlights areas for future research directions examining the study gaps to enhance the current efforts for autonomous PCOS identification; such as integrating advanced techniques with the current methods; developing interactive software systems; exploring deep learning and unsupervised machine learning techniques; enhancing datasets and country context; and investigating more unknown factors behind PCOS. Thus, this SLR provides a state-of-the-art paradigm of autonomous PCOS detection which will support significantly efficient clinical assessment, diagnosis and treatment of PCOS.

Diagnostic Accuracy of Anti-Mullerian Hormone for Polycystic Ovary Syndrome

Objective: To determine the diagnostic accuracy of Anti-Mullerian Hormone in Polycystic ovary syndrome detection, keeping Rotterdam criteria as a gold standard.
 Study Design: Cross-sectional study.
 Place and Duration of Study: Armed Forces Institute of Pathology, Rawalpindi and Rehman Medical Institute, (RMI)Peshawar, Pakistan from Jul 2018 to Jun 2019.
 Methodology: The study included one hundred and forty clinically suspected patients of polycystic ovary syndrome (PCOS) of 16-45 years of age. Blood samples were analyzed for serum Anti-Mullerian Hormone analysis on Architect ci 8200 System using the Chemiluminescent immunoassay technique. AMH was considered positive for Polycystic ovary syndrome at a threshold of 4.9ng/ml. In addition, AMH's diagnostic accuracy was evaluated, considering the Rotterdam criteria of PCOS as a gold standard.
 Results: The mean age and mean BMI of the study subjects were 26.73±5.07 years and 30.43±4.83kg/m2, respectively. The mean follicle-stimulating and Luteinizing hormones were 4.34±0.68mIU/ml and 5.24±1.43mIU/ml, respectively. The mean Anti-Mullerian hormone of all patients was 7.79±4.62ng/ml. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), the likelihood ratio of a positive test (LR+) and Likelihood ratio of a Negative Test (LR-) of AntiMullerian hormone were 91.8%, 74.7%, 73.7%, 92.2%, 3.63%, 0.11%, respectively and overall diagnostic accuracy was 82.1%.
 Conclusion: Anti-Mullerian hormone can effectively determine the existence of Polycystic ovary syndrome among women of reproductive age and serve as a sensitive diagnostic tool for Polycystic ovary syndrome.

Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique

Polycystic ovary syndrome (PCOS) is the most frequent endocrinological anomaly in reproductive women that causes persistent hormonal secretion disruption, leading to the formation of numerous cysts within the ovaries and serious health complications. But the real-world clinical detection technique for PCOS is very critical since the accuracy of interpretations being substantially dependent on the physician's expertise. Thus, an artificially intelligent PCOS prediction model might be a feasible additional technique to the error prone and time-consuming diagnostic technique. In this study, a modified ensemble machine learning (ML) classification approach is proposed utilizing state-of-the-art stacking technique for PCOS identification with patients' symptom data; employing five traditional ML models as base learners and then one bagging or boosting ensemble ML model as the meta-learner of the stacked model. Furthermore, three distinct types of feature selection strategies are applied to pick different sets of features with varied numbers and combinations of attributes. To evaluate and explore the dominant features necessary for predicting PCOS, the proposed technique with five variety of models and other ten types of classifiers is trained, tested and assessed utilizing different feature sets. As outcomes, the proposed stacking ensemble technique significantly enhances the accuracy in comparison to the other existing ML based techniques in case of all varieties of feature sets. However, among various models investigated to categorize PCOS and non-PCOS patients, the stacking ensemble model with ‘Gradient Boosting’ classifier as meta learner outperforms others with 95.7% accuracy while utilizing the top 25 features selected using Principal Component Analysis (PCA) feature selection technique.

New approaches to early detection of polycystic ovary syndrome in obese women

Objectives: development of an algorithm for the early detection of comorbid pathology polycystic ovary syndrome (PCOS) in obese women.Materials and methods. The study included 56 women aged 28.02 ± 2.25 years with a diagnosis of obesity who had no history of PCOS. The examination was carried out by a multidisciplinary group of doctors consisting of a family doctor, a gynecologist and an endocrinologist.The patient’s history of life and diseases was studied, an anthropometric еxamination and a survey according to the modified Ferriman–Gallwey scale were performed with a further ultrasound examination and a gynecologist consultation.Results. The majority of patients (55.36%) had acne, hirsutism and seborrhea occurred in 41.07 and 26.76% of women, respectively, and 14.29% of patients had alopecia. Often, the patient had not one, but several symptoms at the same time.According to the modified Ferriman–Gallwey scale, the majority of women (58.92%) had no manifestations of hirsutism along with obesity, 17.8% had moderate manifestations of hirsutism, 16.07 and 7.14% had mild and moderate hirsutism, respectively.The patients had no complaints and did not know whether they had PCOS. PCOS were diagnosed in 31 (55.36%) women after ultrasound examination and gynecologist consultation. It was also found that patients with longer obesity and a higher body mass index were more likely to have a diagnosis of PCOS (ρ = 0.77). The largest number of PCOS patients was among women with a body mass index over 40 kg/m2, i.e. with the III degree of obesity.Conclusions. The algorithm we developed for the diagnosis of PCOS in obese patients allowed us to detect this disease early in 55.36% of cases. We consider it appropriate to recommend screening for PCOS to all obese patients of reproductive age. This tactic will help maintain the fertility of this category of patients.

Deep Learning Algorithm for Automated Detection of Polycystic Ovary Syndrome Using Scleral Images.

The high prevalence of polycystic ovary syndrome (PCOS) among reproductive-aged women has attracted more and more attention. As a common disorder that is likely to threaten women’s health physically and mentally, the detection of PCOS is a growing public health concern worldwide. In this paper, we proposed an automated deep learning algorithm for the auxiliary detection of PCOS, which explores the potential of scleral changes in PCOS detection. The algorithm was applied to the dataset that contains the full-eye images of 721 Chinese women, among which 388 are PCOS patients. Inputs of the proposed algorithm are scleral images segmented from full-eye images using an improved U-Net, and then a Resnet model was applied to extract deep features from scleral images. Finally, a multi-instance model was developed to achieve classification. Various performance indices such as AUC, classification accuracy, precision, recall, precision, and F1-score were adopted to assess the performance of our algorithm. Results show that our method achieves an average AUC of 0.979 and a classification accuracy of 0.929, which indicates the great potential of deep learning in the detection of PCOS.

DEODORANT: a novel approach for early detection and prevention of polycystic ovary syndrome using association rule in hypergraph with the dominating set property

Present online health discussion forums generate the bountiful amount of digitized data through health-blogs, posts, tweets, and chats in the social media. People post queries, health issues they undergo along with the symptoms, diagnosis, and clinical reports to get direction for preventive measures and medical relief. As a case study, this work focus on detection of Polycystic Ovary Syndrome, a prevalent condition that affects a woman’s hormone levels. This PCOS problem has been investigated as it forms high-risk factor for infertility, heart disease, diabetes, stroke and many such diseases. We propose a novel model named as DEODORANT (Detection and prEvention of polycystic Ovary synDrome using assOciation rule hypeRgrAph and domiNating set properTy) to derive prospective use of real-time mining data. The unstructured data collected from various media sources are preprocessed using NLTK and association rules are derived by applying apriori algorithm. These association rules are represented in hypergraph and then regenerated as line graph to make it suitable for cluster construction. Spectral clustering is performed on line graph to partition into clusters of hypergraphs. By applying dominating set property on the resultant hypergraph, required inferences can be elicited. From the experimental results, support value of the outcome derived from the dominating set of each cluster has exhibited the symptoms and the causes with percentage ranking. It is evident that they get aligned with precise result portraying real statistics. This type of analysis will empower doctors and health organizations to keep track of the diseases, their symptoms for early detection and safe recovery.

Adjusting antimüllerian hormone levels for age and body mass index improves detection of polycystic ovary syndrome

To examine whether accounting for a woman's age and body mass index (BMI) would improve the ability of antimüllerian hormone (AMH) to distinguish between women with (cases) and without (controls) polycystic ovarian syndrome (PCOS). An opportunistic case-control dataset of reproductive age women having evaluations for PCOS as defined by National Institutes of Health criteria. Two medical centers in the United States enrolled women. Serum samples were analyzed for relevant analytes. Women were between 18 and 39 years of age when samples and clinical information were collected. Residual samples had been stored for 2-17 years. AMH was measured via immunoassay. None; this was an observational study. Detection and false-positive rates for PCOS were computed for AMH results expressed as multiples of the median (MoM) both before and after adjustment for the woman's age and BMI. Using unadjusted AMH MoM results, 168 cases (78%) cases were at or beyond the 90th centile of controls (2.47 MoM). After accounting for each woman's age and BMI, 188 (87%) of those women were beyond the 90th centile of controls (2.20 MoM), a significant increase (P = .015). The adjusted AMH MoM levels fitted logarithmic normal distributions well (mean, standard deviation for controls and cases of 0.0000, 0.2765 and 0.6884, 0.2874, respectively) and this allowed for computation of patient-specific PCOS risks. Accounting for the woman's age and BMI resulted in significantly higher AMH-based detection rates for PCOS at a 10% false-positive rate, and patient-specific PCOS risks could be computed.

Genetic Clustering for Polycystic Ovary Syndrome Detection in Women of Reproductive Age

Now a days, hormonal disorder causing Polycystic Ovary Syndrome (PCOS) is been observed in most of the women of reproductive age. PCOS causes enlarged ovaries with small cysts on the outer edges. Women with PCOS may have irregularity in menstrual periods or excess male hormone (androgen) levels. The ovaries may develop numerous small collections of follicles (cysts) and fail to regularly release eggs. Symptoms of PCOS include irregular periods, excess androgen, polycystic ovaries, abnormal Body Mass Index, disturbed levels of hormones (Luteinizing Hormone, Follicle-stimulating Hormone, Dehydroepiandros- terone), poor insulin resistance. There is a need to design and develop an optimized system to analyze the sonogram in correlation with the physical symptoms for detection of PCOS at early stage which may result in proper treatment and reduced health loss. This article presents work-in-progress of our proposed research on Intelligent System to detect PCOS. The performance analysis of various Machine learning algorithms like Artificial Neural Network, K- nearest Neighbor and Linear Regression to detect PCOS is presented. Whereas, optimized Genetic Clustering for optimization of classification results is proposed. Basic Genetic Algorithm (GA) and other hybrid GA’s will be used for comparing the optimal results. The classification results are optimized with 89% accuracy

Active contour with modified Otsu method for automatic detection of polycystic ovary syndrome from ultrasound image of ovary

Polycystic ovary syndrome (PCOS) disorder is identified by the presence of a number of follicles present in the ovary of female reproductive system. Ultrasound imaging of the ovary contains essential information about the size, number of follicles and its position. In real time, the detection of PCOS is a difficult task for radiologists due to the various sizes of follicles and is highly connected with blood vessels and tissues. This often results in error diagnosis. For preprocessing various standard filtering techniques are applied on ovary image. Based on the performance, appropriate filter is chosen to remove the noise from the image. This paper presents an effectual active contour with modified Otsu threshold value to automated discovery of follicles from the ultrasound images. The performances of the proposed method illustrate the betterments of the proposed approach over other techniques.

A Detailed Research on Detection of Polycystic Ovary Syndrome from Ultrasound Images of Ovaries

A common endocrine disorder named polycystic ovarian syndrome (PCOS) usually occurs to several women’s during their reproductive age. This type of disease leads to infertility which represent with amenorrhea and hirsutism. The combination of clinical, endocrinological, and biochemical abnormalities are known to be PCOS which particularly related with the metabolism of estrogen and androgens. The periphery of ovaries is identified with the most common immature follicles which are less than 10mm. Analyzing this kind of situations in women is a major challenge which now doctors used ultrasound images which have the necessary details like number of follicles, size, and position. For real-time analysis of PCOS is a major task as follicles contains different sizes and highly connected with tissues and blood vessels which results in error prone. Several researchers have proposed different techniques for analyzing the PCOS using the ultrasound images of ovaries. In this study, we analyze the important factors and techniques used in detection of PCOS by using the ultrasound images taken from the women’s ovary and compared the results of existing works.

New markers for the detection of polycystic ovary syndrome

New markers for the detection of polycystic ovary syndrome

An ultrasensitive electrochemical immunosensor for detection of sex hormone binding globulin

A novel electrochemical immunosensor was fabricated for detection of polycystic ovary syndrome (PCOS) by immobilizing horseradish peroxidase (HRP) labeled sex hormone binding globulin (SHBG) antibody (Ab) onto chitosan (CHIT), copper nanoparticles (CuNPs), iron nanoparticles (Fe3O4NPs) and graphene oxide nanoparticles (GrONPs) modified gold (Au) electrode. This HRP-SHBG Ab/CHIT/CuNPs/Fe3O4NPs/GrONPs modified Au electrode as working electrode, Ag/AgCl as standard electrode and Pt wire as auxiliary electrode were connected through potentiostat. The working electrode was characterized by scanning electron microscopy (SEM), cyclic voltammetry (CV), Fourier transform infrared spectroscopy (FTIR) and electrochemical impedance spectra (EIS) during its construction. The immunosensor possessed a dynamic linear range (0.01–250 nM) with a low limit of detection (LOD) of 0.01 nM. The evaluation study of immunosensor displayed a good analytical recovery of 98.03%, when SHBG antigen concentration was added in the serum sample. In addition, within and between batch coefficients of variation for working electrode were 0.115% and 0.111%, respectively. The immunosensor exhibited a correlation coefficient of (R2 = 0.99), which was underlying in agreement with standard method. The immunosensor lost 10% of its initial activity after its regular use for 180 days, while being stored at 4 °C.

Serum Prostate-Specific Antigen Level in Women With Polycystic Ovary Syndrome: A Systematic Review and Meta-analysis.

We undertook a comprehensive search of all published original articles evaluating the diagnostic value of prostate-specific antigen (PSA) for the detection of polycystic ovary syndrome (PCOS). Finally, 25 studies with a total of 1865 participants (1104 cases and 761 controls) met the inclusion criteria. Mean serum PSA levels were significantly higher in PCOS women compared to healthy women [Standardized mean difference (SMD)=1.73 ng/ml, 95% CI=1.22 to 2.25, p<0.001]. Only 5 studies reported accuracy indicators and were included in the bivariate random-effects model; with a total of 509 samples that included 332 (65.22%) confirmed PCOS women. The sensitivity of serum PSA for the diagnosis of PCOS varied between 66.3 and 90.6%, and, the specificity ranged from 62.5-86.7%. Meta-analysis revealed a pooled sensitivity of 72.3% (95% CI 67.1-77.00%) and specificity of 74.6% (95% CI 67.5-80.8%) with an area under the summary ROC curve equal to 0.82 (95% CI 0.75-0.83). The summary estimates were 2.92 (95% CI 1.98-4.31) for positive LR and 0.37 (95% CI 0.26-0.51) for negative LR. The pooled estimate of diagnostic odds ratio (DOR) of serum PSA was 9.01 (95% CI 4.35-18.64). Considering the methodological weaknesses existed in studies, lack of sufficient data regarding the accuracy indicators, and the complexity of the exact pathophysiology of PSA secretion in women, the precise position of PSA, as a diagnostic test for the detection of PCOS, in clinical practice is undetermined, and further studies are still needed.

Treatment of adolescent girls with polycystic ovary syndrome and insulin resistance

Aim. Polycystic ovary syndrome is a common cause of infertility. In addition, polycystic ovary syndrome is often associated with metabolic complications. In most women hyperandrogenic manifestations occur during puberty. Early detection of polycystic ovary syndrome and associated metabolic problems can solve the problem of puberty and prevent infertility, metabolic syndrome, and diabetes mellitus type 2 in women of reproductive age. One of the objectives of the study was to evaluate the effectiveness of the simultaneous use of a combined oral preparation (ethinyl estradiol + drospirenone) and metformin in adolescent girls with polycystic ovary syndrome.Materials and methods. 113 adolescent girls with hyperandrogenism from 14 to 19 years were included. 32 of these girls aged 14–18 years with polycystic ovary syndrome were treated with medication ethinyl estradiol + drospirenone. Of these 32 patients, 20 girls with insulin resistance were obtained simultaneously with metformin. The following methods were used: examination, evaluation of hair (Ferriman-Gallwey score), pelviс ultrasound, determination of hormone status (LH, FSH, PRL, 17-OHP, E2 , TSH, DHEA-s, SHBG, T), glucose, insulin, and glucose tolerance test. The diagnostic technique used the Sultan C. criteria of polycystic ovary syndrome (2004).Results.A reduction in the frequency characteristics after treatment was shown: of laboratory hyperandrogenism 90.6% , dermopathy 65.6% , insulin resistance 43.8%, hyperinsulinemia 18.8%, ultrasonic signs of PCOS 34.4%.Conclusions. The study results confirm the efficacy of the treatment of polycystic ovary syndrome with insulin resistance during puberty by Low-dose combined oral preparation (0.03 mg ethinyl estradiol and 3 mg drospirenone) in combination with metformin.

Surface-enhanced Raman scattering for the detection of polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is a multi-factorial heterogeneous syndrome that affects many women of reproductive age. This work demonstrates how the surface-enhanced Raman scattering (SERS) technique can be used to differentiate between PCOS and non-PCOS patients. We determine that the use of SERS, in conjunction with partial least squares (PLS) and principal component analysis (PCA), allows us to detect PCOS in patient samples. Although the role of chemerin in the pathogenesis of PCOS patients is not clear, this work enables us to measure their chemerin levels using the PLS regression method.

Rapid and easy assessment of insulin resistance contributes to early detection of polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is frequently observed in women of reproductive age, and is associated with disturbances in both reproductive and metabolic function. Insulin resistance (IR) is key to the pathophysiology of PCOS, and early detection may improve outcomes in this patient group. Rapid and straightforward laboratory tests may contribute towards early detection. A retrospective chart review of 185 women presenting for the first time to a gynecology clinic was carried out. Of this group, 77 met the inclusion criteria. The sample was divided according to insulin sensitivity (IS) given by the Matsuda Index, and the two groups were compared using correlation analysis. Furthermore, the sensitivity and specificity of the Matsuda, homeostasis model assessment of IR (HOMA-IR) and quantitative insulin sensitivity check index (QUICKI) indexes were compared. Although bodu mass index (BMI) was higher in the insulin resistant group than the insulin sensitive group, the mean age of the IR group was actually lower. HOMA-IR and QUICKI correlated well with the Matsuda index in both groups. The HOMA-IR test showed the highest sensitivity and specificity in the detection of IR when compared to the Matsuda Index, and no added benefit was derived from using a combination of both QUICKI and HOMA- 1R. In a group of 77 women diagnosed with PCOS, 49 (63.6%) had IR according to the Matsuda index. The HOMA-IR index, which is based on fasting serum insulin and glucose, correlated closely with the Matsuda index, indicating it may be a reliable substitute in the detection and subsequent early intervention required to improve outcomes in PCOS.