scholarly journals Identifying Women at Risk for Polycystic Ovary Syndrome Using a Mobile Health App: Virtual Tool Functionality Assessment (Preprint)

2019 ◽  
Author(s):  
Erika Marie Rodriguez ◽  
Daniel Thomas ◽  
Anna Druet ◽  
Marija Vlajic-Wheeler ◽  
Kevin James Lane ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Correlation Coefficient ◽  
Risk Score ◽  
Polycystic Ovary ◽  
Pearson Correlation ◽  
Medical Professional ◽  
Mean Difference ◽  
Pearson Correlation Coefficient ◽  
Ovary Syndrome ◽  
Virtual Test

BACKGROUND Polycystic ovary syndrome (PCOS) is an endocrine disrupting disorder affecting about 10% of reproductive-aged women. PCOS diagnosis may be delayed several years and may require multiple physicians, resulting in lost time for risk-reducing interventions. Menstrual tracking apps are a potential tool to alert women of their risk while also prompting evaluation from a medical professional. OBJECTIVE The primary objective of this study was to develop and pilot test the irregular cycle feature, a predictive model that generated a PCOS risk score, in the menstrual tracking app, Clue. The secondary objectives were to run the model using virtual test subjects, create a quantitative risk score, compare the feature’s risk score with that of a physician, and determine the sensitivity and specificity of the model before empirical testing on human subjects. METHODS A literature review was conducted to generate a list of signs and symptoms of PCOS, termed variables. Variables were then assigned a probability and built into a Bayesian network. Questions were created based on these variables. A total of 9 virtual test subjects were identified using self-reported menstrual cycles and answers to the feature’s questions. Upon completion of the questionnaire, a Result Screen and Doctor’s Report summarizing the probability of having PCOS was displayed. This provided information about PCOS and data to facilitate diagnosis by a medical professional. To assess the accuracy of the feature, the same set of 9 virtual test subjects was assigned probabilities by the feature and the physician, who served as the gold standard. The feature recommended individuals with a score greater than or equal to 25% to follow-up with a physician. Differences between the feature and physician scores were evaluated using a t test and a Pearson correlation coefficient in 8 of the 9 virtual test subjects. A second iteration was conducted to assess the feature’s probability capabilities. RESULTS The irregular cycle feature’s first iteration produced 1 false-positive compared with the physician score and had an absolute mean difference of 15.5% (SD 15.1%) among the virtual test subjects. The second iteration had 2 false positives compared with the physician score and had an absolute mean difference of 18.8% (SD 13.6%). The feature overpredicted the virtual test subjects’ risk of PCOS compared with the physician. However, a significant positive correlation existed between the feature and physician score (Pearson correlation coefficient=0.82; <i>P</i>=.01). The second iteration performed worse, with a Pearson correlation coefficient of 0.73 (<i>P</i>=.03). CONCLUSIONS The first iteration of the feature outperformed the second and better predicted the probability of PCOS. Although further research is needed with a more robust sample size, this pilot study indicates the potential value for developing a screening tool to prompt high-risk subjects to seek evaluation by a medical professional. CLINICALTRIAL

scholarly journals Identifying Women at Risk for Polycystic Ovary Syndrome Using a Mobile Health App: Virtual Tool Functionality Assessment

10.2196/15094 ◽  
2020 ◽  
Vol 4 (5) ◽  
pp. e15094
Author(s):  
Erika Marie Rodriguez ◽  
Daniel Thomas ◽  
Anna Druet ◽  
Marija Vlajic-Wheeler ◽  
Kevin James Lane ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Correlation Coefficient ◽  
Risk Score ◽  
Polycystic Ovary ◽  
Pearson Correlation ◽  
Medical Professional ◽  
Mean Difference ◽  
Pearson Correlation Coefficient ◽  
Ovary Syndrome ◽  
Virtual Test

Background Polycystic ovary syndrome (PCOS) is an endocrine disrupting disorder affecting about 10% of reproductive-aged women. PCOS diagnosis may be delayed several years and may require multiple physicians, resulting in lost time for risk-reducing interventions. Menstrual tracking apps are a potential tool to alert women of their risk while also prompting evaluation from a medical professional. Objective The primary objective of this study was to develop and pilot test the irregular cycle feature, a predictive model that generated a PCOS risk score, in the menstrual tracking app, Clue. The secondary objectives were to run the model using virtual test subjects, create a quantitative risk score, compare the feature’s risk score with that of a physician, and determine the sensitivity and specificity of the model before empirical testing on human subjects. Methods A literature review was conducted to generate a list of signs and symptoms of PCOS, termed variables. Variables were then assigned a probability and built into a Bayesian network. Questions were created based on these variables. A total of 9 virtual test subjects were identified using self-reported menstrual cycles and answers to the feature’s questions. Upon completion of the questionnaire, a Result Screen and Doctor’s Report summarizing the probability of having PCOS was displayed. This provided information about PCOS and data to facilitate diagnosis by a medical professional. To assess the accuracy of the feature, the same set of 9 virtual test subjects was assigned probabilities by the feature and the physician, who served as the gold standard. The feature recommended individuals with a score greater than or equal to 25% to follow-up with a physician. Differences between the feature and physician scores were evaluated using a t test and a Pearson correlation coefficient in 8 of the 9 virtual test subjects. A second iteration was conducted to assess the feature’s probability capabilities. Results The irregular cycle feature’s first iteration produced 1 false-positive compared with the physician score and had an absolute mean difference of 15.5% (SD 15.1%) among the virtual test subjects. The second iteration had 2 false positives compared with the physician score and had an absolute mean difference of 18.8% (SD 13.6%). The feature overpredicted the virtual test subjects’ risk of PCOS compared with the physician. However, a significant positive correlation existed between the feature and physician score (Pearson correlation coefficient=0.82; P=.01). The second iteration performed worse, with a Pearson correlation coefficient of 0.73 (P=.03). Conclusions The first iteration of the feature outperformed the second and better predicted the probability of PCOS. Although further research is needed with a more robust sample size, this pilot study indicates the potential value for developing a screening tool to prompt high-risk subjects to seek evaluation by a medical professional.

scholarly journals Identifying Women at Risk for Polycystic Ovary Syndrome Using a Mobile Health Application

2019 ◽  
Author(s):  
Erika Rodriguez ◽  
Daniel Thomas ◽  
Anna Druet ◽  
Marija Vlajic Wheeler ◽  
Kevin Lane ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Medical History ◽  
Correlation Coefficient ◽  
Polycystic Ovary ◽  
Pearson Correlation ◽  
Medical Professional ◽  
Pearson Correlation Coefficient ◽  
Ovary Syndrome ◽  
Menstrual Cycles ◽  
Virtual Test

AbstractBackgroundPolycystic ovary syndrome (PCOS) is an endocrine disrupting disorder affecting at least 10 percent of reproductive-aged women. Women with PCOS are at increased risk for diabetes and cardiovascular disease. In North America and Europe, the diagnosis of PCOS may be delayed several years and may require multiple doctors resulting in lost time for risk-reducing interventions. Menstrual tracking applications are one potential tool to alert women of their risk for PCOS while also prompting them to seek evaluation from a medical professional.ObjectiveThe objective of this study was to develop the Irregular Cycles Feature (ICF), an adaptive questionnaire, on the mobile phone application (app) Clue® to generate a probability of a virtual test subject’s risk for PCOS. The secondary objective was to assess the accuracy of the ICF by comparing the probability of risk generated by the app to a probability generated by a physician.MethodsFirst, a literature review was conducted to generate a list of signs and symptoms of PCOS, termed variables. These include, but are not limited to, hirsutism, acne, and alopecia. Probabilities were assigned to each variable and built into a Bayesian network. The network served as the backbone of the ICF, which identified potential subjects through self-reported menstrual cycles and answers to medical history questions. Upon completion of the questionnaire, a Result Screen summarizing the virtual test subject’s probability of having PCOS is displayed. For each eligible virtual test subject, a Doctor’s Report containing information regarding tracked menstrual cycles and self-reported medical history is generated. Both of these documents share information about PCOS and detailed explanations for facilitating a diagnosis by a medical provider. Virtual test subjects were assigned probabilities by a) the ICF and b) a board-certified reproductive endocrinology/infertility physician-scientist, which served as the gold standard. The ICF was set to recommend individuals with a score greater than or equal to 25% to follow-up with their physician. Differences between the network and physician probability scores were assessed using a t-test and a Pearson correlation coefficient. An additional iteration was performed to improve the ICF’s prediction capability.ResultsThe first iteration of the ICF produced only one false positive compared to the physician screening score and had an absolute mean difference of 15.5% (SD= 15.1%) amongst virtual test subjects. Upon modification of the ICF, the second iteration had two false positives as compared to the physician screening score and had an absolute mean difference of 18.8% (SD = 13.6%). The majority of virtual test subjects had an ICF score that over predicted PCOS when compared to the physician. However, there was strong positive significant correlation between the ICF and the physician score (Pearson correlation coefficient= 0.69; p < 0.01). The second iteration performed worse with a Pearson correlation coefficient of 0.54; p > 0.01).ConclusionThe first iteration ICF, as compared to the second, was better able to predict the probability of PCOS and can potentially be used as a screening tool to prompt a high-risk subject to seek evaluation by a medical professional.

scholarly journals The role of serum β-trophin and endostatin in patients with polycystic ovary syndrome: Are they correlated?

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wei Gong ◽  
Aikmu Bilixzi ◽  
Xinmei Wang ◽  
Yanli Lu ◽  
Li Wan ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Influencing Factors ◽  
Polycystic Ovary ◽  
Pearson Correlation ◽  
Healthy Controls ◽  
Newly Diagnosed ◽  
Ovary Syndrome ◽  
Factors Affecting ◽  
Main Factors

Abstract Background It’s necessary to investigate the serum β-trophin and endostatin (ES) level and its influencing factors in patients with newly diagnosed polycystic ovary syndrome (PCOS). Methods Newly diagnosed PCOS patients treated in our hospital were selected, and healthy women who took physical examination during the same period as healthy controls. We detected and compared the related serum indicators between two groups, Pearson correlation were conducted to identify the factors associated with β-trophin and ES, and the influencing factors of β-trophin and ES were analyzed by logistic regression. Results A total of 62 PCOS patients and 65 healthy controls were included. The BMI, WHI, LH, FSH, TT, FAI, FBG, FINS, HOMA-IR, TC, TG, LDL, ES in PCOS patients were significantly higher than that of healthy controls, while the SHBG and HDL in PCOS patients were significantly lower than that of healthy controls (all p < 0.05). β-trophin was closely associated with BMI (r = 0.427), WHR (r = 0.504), FBG (r = 0.385), TG (r = 0.405) and LDL (r = 0.302, all p < 0.05), and ES was closely associated with BMI (r = 0.358), WHR (r = 0.421), FBG (r = 0.343), TC (r = 0.319), TG (r = 0.404, all p < 0.05). TG, BMI, WHR and FBG were the main factors affecting the serum β-trophin levels (all p < 0.05). FBG, TC and BMI were the main factors affecting the serum ES levels (all p < 0.05). The TG, β-trophin, ES level in PCOS patients with insulin resistance (IR) were significantly higher than that of those without IR (all p < 0.05). Conclusion Increased β-trophin is closely associated with increased ES in patients with PCOS, which may be the useful indicators for the management of PCOS.

scholarly journals Effects of probiotic and synbiotic supplementation on insulin resistance in women with polycystic ovary syndrome: a meta-analysis

2021 ◽  
Vol 49 (7) ◽  
pp. 030006052110317
Author(s):  
Chenyun Miao ◽  
Qingge Guo ◽  
Xiaojie Fang ◽  
Yun Chen ◽  
Ying Zhao ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Polycystic Ovary Syndrome ◽  
Confidence Interval ◽  
Polycystic Ovary ◽  
Serum Insulin ◽  
Meta Analysis ◽  
Mean Difference ◽  
Model Assessment ◽  
Ovary Syndrome ◽  
Synbiotic Supplementation

Objective This meta-analysis evaluated the effect of probiotics and synbiotics on insulin resistance in patients with polycystic ovary syndrome (PCOS). Methods A systematic search was performed to identify all relevant publications listed on the electronic databases (PubMed®, Web of Science, Embase® and China National Knowledge Infrastructure) between inception and 30 October 2020. All statistical analyses were performed on randomized controlled trials (RCTs) using RevMan version 5.3 software provided by the Cochrane Collaboration. Results A total of 486 patients from seven RCTs were included in the meta-analysis. Probiotic and synbiotic supplementation appeared to improve levels of homeostatic model assessment of insulin resistance (mean difference = –0.37; 95% confidence interval –0.69, –0.05) and serum insulin (standardized mean difference = –0.66; 95% confidence interval –1.19, –0.12). The results failed to show any influence of probiotic and synbiotic supplementation on body mass index, waist circumference, hip circumference and fasting blood sugar. Conclusions Probiotics and synbiotics appear to have a partially beneficial effect on indices of insulin resistance in patients with PCOS.

scholarly journals Effects of synbiotic supplementation on metabolic parameters and apelin in women with polycystic ovary syndrome: a randomised double-blind placebo-controlled trial

2018 ◽  
Vol 119 (4) ◽  
pp. 398-406 ◽  
Author(s):  
Elham Karimi ◽  
Ashraf Moini ◽  
Mehdi Yaseri ◽  
Nooshin Shirzad ◽  
Mahdi Sepidarkish ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Controlled Trial ◽  
Mean Difference ◽  
Metabolic Parameters ◽  
Ovary Syndrome ◽  
Double Blind ◽  
Double Blind Placebo ◽  
Synbiotic Supplementation

AbstractPolycystic ovary syndrome (PCOS) is one of the most common causes of infertility in women of reproductive age. Insulin resistance is a main pathophysiologic feature in these patients. According to some studies, the intake of probiotic bacteria may improve glucose homoeostasis. The aim of this study was to investigate the effect of synbiotics on metabolic parameters and apelin in PCOS patients. This randomised double-blind placebo-controlled trial was conducted on eighty-eight PCOS women aged 19–37 years old. The participants were randomly assigned to two groups receiving (1) synbiotic supplement (n44), and (2) placebo (n44) for 12 weeks. Fasting blood samples were taken at baseline and after 12 weeks. The two groups showed no difference in fasting blood sugar (adjusted mean difference: 0·60; 95 % CI −3·80, 5·00,P=0·727), plasma glucose fasting 2-h (adjusted mean difference 2·09; 95 % CI −9·96, 14·15,P=0·134), HbA1c (adjusted mean difference 0·06; 95 % CI −0·09, 0·22,P=0·959), homoeostatic model assessment-insulin resistance (HOMA-IR) (adjusted mean difference: 0·02; 95 % CI −0·99, 1·03,P=0·837), quantitative insulin sensitivity check index (QUICKI) (adjusted mean difference: −0·02; 95 % CI −0·33, 0·29,P=0·940) and C-reactive protein (CRP) (adjusted mean difference: 0·24; 95 % CI −1·61, 2·08,P=0·141) by the end of the intervention. A significant difference was observed in the mean apelin 36 before and after the intervention between synbiotic and placebo groups (adjusted mean difference: −4·05; 95 % CI −7·15, −0·96,P=0·004). A 12-week synbiotic supplementation has no significant beneficial effects on HOMA-IR and CRP in PCOS patients, whereas the level of apelin 36 significantly decreased.

scholarly journals Differential expression of long non-coding RNA Regulator of reprogramming and its molecular mechanisms in polycystic ovary syndrome

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Zhihong Zhang ◽  
Min Sang ◽  
Siqin Liu ◽  
Jing Shao ◽  
Yunjiang Cai
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Molecular Mechanisms ◽  
Polycystic Ovary ◽  
Pearson Correlation ◽  
Reproductive Age ◽  
Expression Level ◽  
Luciferase Reporter ◽  
Control Group ◽  
Ovary Syndrome ◽  
Gene Assay

Abstract Background Polycystic ovary syndrome (PCOS) is a common endocrine disease in women of reproductive age. Multiple studies have shown that long non-coding RNAs (lncRNA) and microRNAs (miRNA) play a role in PCOS. This study aimed to explore the role and molecular mechanism of lncRNA -Regulator of reprogramming (lncROR) in PCOS. Results Expression level of lncROR in PCOS patients was up-regulated, while level of miR-206 was down-regulated in comparison with control group (P < 0.001). Logistics regression analysis showed that lncROR and miR-206 were independent predictors of PCOS. The ROC curve showed that lncROR had a high diagnostic value for PCOS with an AUC value of 0.893. Pearson correlation coefficient indicated that the expression level of miR-206 was negatively correlated with the level of lncROR. CCK-8 assay and apoptosis assay revealed that downregulation of lncROR up-regulated the expression of miR-206, thereby inhibiting cell proliferation and promoting cell apoptosis. However, silencing the expression of miR-206 reversed the above effects caused by down-regulation of lncROR expression. Luciferase reporter gene assay suggested that there was a target relationship between lncROR and miR-206. VEGF was proved to be the target gene of miR-206. Conclusions Highly expressed lncROR indirectly up-regulated the expression of VEGF by down-regulating the expression of miR-206, thereby promoting the proliferation of KGN cells and inhibiting apoptosis, and further promoting the development of PCOS.

scholarly journals Frequency of Hyperprolactinemia in PCOS and its Association with Female Infertility

2019 ◽  
Vol 33 (4) ◽  
pp. 1-5
Author(s):  
Anam Rehman
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Pearson Correlation ◽  
Cross Sectional Study ◽  
Female Infertility ◽  
Health Concern ◽  
Serum Prolactin ◽  
Sectional Study ◽  
Ovary Syndrome ◽  
Cross Sectional

Introduction: Infertility is a critical health concern partially due to intricacy in its causes and striving for its prevention, diagnosis and treatment. Various researches have documented a close linkage between polycystic ovary syndrome and hyperprolactinemia. Aims & Objectives: This study was aimed to determine the frequency of hyperprolactinemia in PCOS and its association with infertility in PCOS subjects. Place and duration of study: It was a cross sectional study, conducted during April 2017 and September 2017 at Aziz Fatimah Hospital, Faisalabad, Pakistan. Material & Methods: It was a cross sectional study which was conducted at Aziz Fatimah Hospital, Faisalabad, Pakistan from April to September 2017. This study comprised of 88 female subjects of 17-35 years old who included PCOS subjects and age matched controls. Hyperprolactinemia was assessed by the measurement of serum prolactin levels which were measured by chemiluminescence immunoassay technique (CLIA). SPSS version 22 was used for the statistical analysis of the data. Results: Out of total 88 female participants, 61.4% of polycystic ovary syndrome subjects had hyperprolactinemia as compared to this 36.4% of controls had hyperprolactinemia. Pearson correlation also revealed significant positive association of hyperprolactinemia with infertility. Conclusion: Hyperprolactinemia was frequently seen in polycystic ovary syndrome females as well as raised BMI was also found. Raised prolactin levels are strongly associated with female infertility. Key words: Hyperprolactinemia, Infertilty, Polycystic Ovary Syndrome.

scholarly journals A genetic risk score is associated with polycystic ovary syndrome-related traits

2015 ◽  
Vol 31 (1) ◽  
pp. 209-215 ◽  
Author(s):  
Hyejin Lee ◽  
Jee-Young Oh ◽  
Yeon-Ah Sung ◽  
Hye Won Chung
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Risk Score ◽  
Genetic Risk ◽  
Polycystic Ovary ◽  
Genetic Risk Score ◽  
Ovary Syndrome

scholarly journals Mindfulness and Its Predictors in Women With Polycystic Ovary Syndrome

2020 ◽  
Vol 24 (3) ◽  
pp. 246-257
Author(s):  
Roghayeh Arbabi Moghaddam ◽  
◽  
Seyedeh Batool Hasanpoor-Azghady ◽  
Leila Amiri Farahani ◽  
Shima Haghani ◽  
...  
Keyword(s):  
Linear Regression ◽  
Polycystic Ovary Syndrome ◽  
Multiple Linear Regression ◽  
Polycystic Ovary ◽  
Pearson Correlation ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Multiple Linear Regression Analysis ◽  
Reproductive Age ◽  
Ovary Syndrome

Background: Polycystic Ovary Syndrome (PCOS) is the most common endocrine disorder in women of reproductive age which can cause many problems such as hyperandrogenic symptoms and fertility problems. Objective: The present study aimed to determine the relationship of mindfulness with hyperandrogenic symptoms and demographic and fertility factors in women with PCOS. Methods: This descriptive correlational study was conducted on 181 women with PCOS referred to Firoozabadi and Firoozgar hospitals in Tehran, Iran who were selected using a continuous sampling method and based on inclusion criteria from June 2018 to August 2019. Data were collected using a demographic/fertility form, the modified Ferriman-Gallwey Scale, Ludwig Hair Loss Scale, and Mindfulness Attention Awareness Scale (MAAS). Data were analyzed using independent t-test, one-way ANOVA, Kruskal-Wallis test, Pearson correlation test, and multiple linear regression analysis. Findings: The mean MAAS score of women was 68.61±9.88 and was significantly correlated with age (P=0.01), wife’s education (P=0.001), wife’s occupation (P=0.005), economic status (P=0.02), husband satisfaction with wife’s body and appearance (P=0.02), body mass index (P=0.01), and duration of marriage (P<0.001). According to the multiple linear regression model, the duration of marriage could predict 22% of the variance in overall MAAS score. Conclusion: Mindfulness is associated with some demographic variables, among which the marriage is its predictor. It is recommended to pay attention to the reported variables in preparation of counseling or educational programs, along with other treatments, for women with PCOS.

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