Irregular Cycles, Ovulatory Disorders, and Cardiometabolic Conditions in a US-Based Digital Cohort.

Importance: Polycystic ovary syndrome (PCOS), characterized by irregular menstrual cycles and hyperandrogenism, is a common ovulatory disorder. Having an irregular cycle is a potential marker for cardiometabolic conditions, but data are limited on whether the associations differ by PCOS status or potential interventions. Objective: To evaluate the association of PCOS, time to regularity since menarche (adolescence), and irregular cycles (adulthood) with cardiometabolic conditions. Design, Setting, and Participants: This cross-sectional study used a large, US-based digital cohort of users of the Apple Research application on their iPhone. Eligibility criteria were having ever menstruated, living in the US, being at age of consent of at least 18 years (or 19 years in Alabama and Nebraska or 21 years in Puerto Rico), and being able to communicate in English. Participants were enrolled between November 14, 2019, and December 13, 2022, and completed relevant surveys. Exposures: Self-reported PCOS diagnosis, prolonged time to regularity (not spontaneously establishing regularity within 5 years of menarche), and irregular cycles. Main Outcomes and Measures: The primary outcome was self-reported cardiometabolic conditions, including obesity, prediabetes, type 1 and 2 diabetes, high cholesterol, hypertension, metabolic syndrome, arrhythmia, congestive heart failure, coronary artery disease, heart attack, heart valve disease, stroke, transient ischemic attack (TIA), deep vein thrombosis, and pulmonary embolism measured using descriptive statistics and logistic regression to estimate prevalence odds ratios (PORs) and 95% CIs. Effect modification by lifestyle factors was also estimated. Results: The study sample (N = 60 789) had a mean (SD) age of 34.5 (11.1) years, with 12.3% having PCOS and 26.3% having prolonged time to regularity. Among a subset of 25 399 participants who completed the hormonal symptoms survey, 25.6% reported irregular cycles. In covariate-adjusted logistic regression models, PCOS was associated with a higher prevalence of all metabolic and several cardiovascular conditions, eg, arrhythmia (POR, 1.37; 95% CI, 1.20-1.55), coronary artery disease (POR, 2.92; 95% CI, 1.95-4.29), heart attack (POR, 1.79; 95% CI, 1.23-2.54), and stroke (POR, 1.66; 95% CI, 1.21-2.24). Among participants without PCOS, prolonged time to regularity was associated with type 2 diabetes (POR, 1.24; 95% CI, 1.05-1.46), hypertension (POR, 1.09; 95% CI, 1.01-1.19), arrhythmia (POR, 1.20; 95% CI, 1.06-1.35), and TIA (POR, 1.33; 95% CI, 1.01-1.73), and having irregular cycles was associated with type 2 diabetes (POR, 1.36; 95% CI, 1.08-1.69), high cholesterol (POR, 1.17; 95% CI, 1.05-1.30), arrhythmia (POR, 1.21; 95% CI, 1.02-1.43), and TIA (POR, 1.56; 95% CI, 1.06-2.26). Some of these associations were modified by high vs low body mass index or low vs high physical activity. Conclusions and Relevance: These findings suggest that PCOS and irregular cycles may be independent markers for cardiometabolic conditions. Early screening and intervention among individuals with irregular menstrual cycles may be beneficial.

Benzophenone-3 and ovarian reserve.

OBJECTIVE: To evaluate the association between urinary benzophenone-3 concentrations and measures of ovarian reserve (OR) among women in the Environment and Reproductive Health (EARTH) Study seeking fertility treatment at Massachusetts General Hospital in Boston, Massachusetts. DESIGN: Prospective cohort study. METHODS: Women from the EARTH cohort contributed spot urine samples before assessment of OR outcomes. Antral follicle count (AFC) and day-3 follicle stimulating hormone (FSH) levels were evaluated as part of standard infertility workups during unstimulated menstrual cycles. Quasi-Poisson and linear regression models were used to evaluate the association of specific gravity (SG)-adjusted urinary benzophenone-3 concentrations with AFC and FSH, respectively, with adjustment for age and physical activity. In secondary analyses, models were stratified by age. Sensitivity analyses assessed for confounding by season by restricting to women with exposure and outcome measured in the same season and stratifying by summer vs. non-summer months and for confounding by sunscreen use by restricting to women who filled out product questionnaires and adjusting for and stratifying by average sunscreen use score. RESULTS: The study included 142 women (mean age ± SD, 36.1 ± 4.6; range, 22-45 years) enrolled between 2009 and 2017 with both urinary benzophenone-3 and AFC and 57 women with benzophenone-3 and FSH measurements. Most women were white (78%) and highly educated (49% with a graduate degree). Women contributed a mean of 2.7 urine samples (range, 1-10) with 37% contributing 2 or more samples. Benzophenone-3 was detected in 98% of samples. Geometric mean (GM) SG-corrected urinary benzophenone-3 concentration was 85.9 µ g/L (geometric standard deviation 6.2). There were no associations of benzophenone-3 with AFC and day-3 FSH in the full cohort. In stratified models, a 1-unit increase in log GM benzophenone-3 was associated with AFC 0.91 (95% CI, 0.86, 0.97) times lower among women ≤35 years old and was associated with FSH 0.73 (95% CI, 0.12, 1.34) IU/L higher among women >35 years old. Effect estimates from models stratified by season and sunscreen use were null. CONCLUSION: In main models, urinary benzophenone-3 was not associated with OR. However, younger may be vulnerable to potential effects of benzophenone-3 on AFC. Further research is warranted.

First trimester plasma PER- AND Polyfluoroalkyl Substances (PFAS) and blood pressure trajectories across the second and third trimesters of pregnanacy.

BACKGROUND: Evidence suggests that exposure to per- and polyfluoroalkyl substances (PFAS) increases risk of high blood pressure (BP) during pregnancy. Prior studies did not examine associations with BP trajectory parameters (i.e., overall magnitude and velocity) during pregnancy, which is linked to adverse pregnancy outcomes. OBJECTIVES: To estimate associations of multiple plasma PFAS in early pregnancy with BP trajectory parameters across the second and third trimesters. To assess potential effect modification by maternal age and parity. METHODS: In 1297 individuals, we quantified six PFAS in plasma collected during early pregnancy (median gestational age: 9.4 weeks). We abstracted from medical records systolic BP (SBP) and diastolic BP (DBP) measurements, recorded from 12 weeks gestation until delivery. BP trajectory parameters were estimated via Super Imposition by Translation and Rotation modeling. Subsequently, Bayesian Kernel Machine Regression (BKMR) was employed to estimate individual and joint associations of PFAS concentrations with trajectory parameters - adjusting for maternal age, race/ethnicity, pre-pregnancy body mass index, income, parity, smoking status, and seafood intake. We evaluated effect modification by age at enrollment and parity. RESULTS: We collected a median of 13 BP measurements per participant. In BKMR, higher concentration of perfluorooctane sulfonate (PFOS) was independently associated with higher magnitude of overall SBP and DBP trajectories (i.e., upward shift of trajectories) and faster SBP trajectory velocity, holding all other PFAS at their medians. In stratified BKMR analyses, participants with ≥ 1 live birth had more pronounced positive associations between PFOS and SBP velocity, DBP magnitude, and DBP velocity - compared to nulliparous participants. We did not observe significant associations between concentrations of the overall PFAS mixture and either magnitude or velocity of the BP trajectories. CONCLUSION: Early pregnancy plasma PFOS concentrations were associated with altered BP trajectory in pregnancy, which may impact future cardiovascular health of the mother.

Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records.

Introduction: Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis. Methods: This is a retrospective cohort study from a SafetyNet hospital's electronic health records (EHR) from 2003-2016. The study population included 30,601 women aged 18-45 years without concurrent endocrinopathy who had any visit to Boston Medical Center for primary care, obstetrics and gynecology, endocrinology, family medicine, or general internal medicine. Four prediction outcomes were assessed for PCOS. The first outcome was PCOS ICD-9 diagnosis with additional model outcomes of algorithm-defined PCOS. The latter was based on Rotterdam criteria and merging laboratory values, radiographic imaging, and ICD data from the EHR to define irregular menstruation, hyperandrogenism, and polycystic ovarian morphology on ultrasound. Results: We developed predictive models using four machine learning methods: logistic regression, supported vector machine, gradient boosted trees, and random forests. Hormone values (follicle-stimulating hormone, luteinizing hormone, estradiol, and sex hormone binding globulin) were combined to create a multilayer perceptron score using a neural network classifier. Prediction of PCOS prior to clinical diagnosis in an out-of-sample test set of patients achieved an average AUC of 85%, 81%, 80%, and 82%, respectively in Models I, II, III and IV. Significant positive predictors of PCOS diagnosis across models included hormone levels and obesity; negative predictors included gravidity and positive bHCG. Conclusion: Machine learning algorithms were used to predict PCOS based on a large at-risk population. This approach may guide early detection of PCOS within EHR-interfaced populations to facilitate counseling and interventions that may reduce long-term health consequences. Our model illustrates the potential benefits of an artificial intelligence-enabled provider assistance tool that can be integrated into the EHR to reduce delays in diagnosis. However, model validation in other hospital-based populations is necessary.

Inositol for Polycystic Ovary Syndrome: a systematic review and meta-analysis to inform the 2023 update of the International Evidence-Based PCOS Guidelines.

CONTEXT: Insulin resistance is common in women with Polycystic Ovary Syndrome (PCOS). Inositol may have insulin sensitising effects; however, its efficacy in the management of PCOS remains indeterminate. OBJECTIVE: To inform the 2023 International Evidence-based Guidelines in PCOS, this systematic review and meta-analysis evaluated the efficacy of inositol, alone or in combination with other therapies, in the management of PCOS. DATA SOURCES: Medline, PsycInfo, EMBASE, All EBM, and CINAHL from inception until August 2022. STUDY SELECTION: Thirty trials (n=2230; 1093 intervention, 1137 control), with 19 pooled in meta-analyses were included. DATA EXTRACTION: Data were extracted for hormonal, metabolic, lipids, psychological, anthropometric, reproductive outcomes and adverse effects by one reviewer, independently verified by a second. DATA SYNTHESIS: Thirteen comparisons were assessed, with three in meta-analyses. Evidence suggests benefits for myo-inositol or D-chiro-inositol (DCI) for some metabolic measures and potential benefits from DCI for ovulation but inositol may have no effect on other outcomes. Metformin may improve waist-hip ratio and hirsutism compared to inositol but there is likely no difference for reproductive outcomes, and the evidence is very uncertain for BMI. Myo-inositol likely causes fewer gastrointestinal adverse events compared with metformin; however, these are typically mild and self-limited. CONCLUSIONS: The evidence supporting the use of inositol in the management of PCOS is limited and inconclusive. Clinicians and their patients should consider the uncertainty of the evidence together with individual values and preferences when engaging in shared decision-making regarding the use of inositol for PCOS.

Evaluation of Menstrual Cycle Tracking Behaviors in the Ovulation and Menstruation Health Pilot Study: Cross-Sectional Study.

BACKGROUND: Menstrual cycle tracking apps (MCTAs) have potential in epidemiological studies of women's health, facilitating real-time tracking of bleeding days and menstrual-associated signs and symptoms. However, information regarding the characteristics of MCTA users versus cycle nontrackers is limited, which may inform generalizability. OBJECTIVE: We compared characteristics among individuals using MCTAs (app users), individuals who do not track their cycles (nontrackers), and those who used other forms of menstrual tracking (other trackers). METHODS: The Ovulation and Menstruation Health Pilot Study tested the feasibility of a digitally enabled evaluation of menstrual health. Recruitment occurred between September 2017 and March 2018. Menstrual cycle tracking behavior, demographic, and general and reproductive health history data were collected from eligible individuals (females aged 18-45 years, comfortable communicating in English). Menstrual cycle tracking behavior was categorized in 3 ways: menstrual cycle tracking via app usage, that via other methods, and nontracking. Demographic factors, health conditions, and menstrual cycle characteristics were compared across the menstrual tracking method (app users vs nontrackers, app users vs other trackers, and other trackers vs nontrackers) were assessed using chi-square or Fisher exact tests. RESULTS: In total, 263 participants met the eligibility criteria and completed the digital survey. Most of the cohort (n=191, 72.6%) was 18-29 years old, predominantly White (n=170, 64.6%), had attained 4 years of college education or higher (n= 209, 79.5%), and had a household income below US $50,000 (n=123, 46.8%). Among all participants, 103 (39%) were MCTA users (app users), 97 (37%) did not engage in any tracking (nontrackers), and 63 (24%) used other forms of tracking (other trackers). Across all groups, no meaningful differences existed in race and ethnicity, household income, and education level. The proportion of ever-use of hormonal contraceptives was lower (n=74, 71.8% vs n=87, 90%, P=.001), lifetime smoking status was lower (n=6, 6% vs n=15, 17%, P=.04), and diagnosis rate of gastrointestinal reflux disease (GERD) was higher (n=25, 24.3% vs n=12, 12.4%, P=.04) in app users than in nontrackers. The proportions of hormonal contraceptives ever used and lifetime smoking status were both lower (n=74, 71.8% vs n=56, 88.9%, P=.01; n=6, 6% vs n=11, 17.5%, P=.02) in app users than in other trackers. Other trackers had lower proportions of ever-use of hormonal contraceptives (n=130, 78.3% vs n=87, 89.7%, P=.02) and higher diagnostic rates of heartburn or GERD (n=39, 23.5% vs n=12, 12.4%, P.03) and anxiety or panic disorder (n=64, 38.6% vs n=25, 25.8%, P=.04) than nontrackers. Menstrual cycle characteristics did not differ across all groups. CONCLUSIONS: Our results suggest that app users, other trackers, and nontrackers are largely comparable in demographic and menstrual cycle characteristics. Future studies should determine reasons for tracking and tracking-related behaviors to further understand whether individuals who use MCTAs are comparable to nontrackers.

Air pollution and pregnancy.

Increased fossil fuel usage and extreme climate change events have led to global increases in greenhouse gases and particulate matter with 99% of the world's population now breathing polluted air that exceeds the World Health Organization's recommended limits. Pregnant women and neonates with exposure to high levels of air pollutants are at increased risk of adverse health outcomes such as maternal hypertensive disorders, postpartum depression, placental abruption, low birth weight, preterm birth, infant mortality, and adverse lung and respiratory effects. While the exact mechanism by which air pollution exerts adverse health effects is unknown, oxidative stress as well as epigenetic and immune mechanisms are thought to play roles. Comprehensive, global efforts are urgently required to tackle the health challenges posed by air pollution through policies and action for reducing air pollution as well as finding ways to protect the health of vulnerable populations in the face of increasing air pollution.

Glucagon-like peptide-1 receptor agonists and safety in the preconception period.

PURPOSE OF REVIEW: Glucagon-like peptide-1 (GLP-1) receptor agonists (RAs) are becoming increasingly popular for the treatment of type II diabetes and obesity. Body mass index (BMI) thresholds at in vitro fertilization (IVF) clinics may further drive the use of these medications before infertility treatment. However, most clinical guidance regarding optimal time to discontinue these medications prior to conception is based on animal data. The purpose of this review was to evaluate the literature for evidence-based guidance regarding the preconception use of GLP-1 RA. RECENT FINDINGS: 16 articles were found in our PubMed search, 10 were excluded as they were reviews or reported on animal data. Included were 3 case reports detailing pregnancy outcomes in individual patients that conceived while on a GLP-1 RA and 2 randomized controlled trials (RCTs) and a follow-up study to one of the RCTs that reported on patients randomized to GLP-1 RA or metformin prior to conception. No adverse pregnancy or neonatal outcomes were reported. SUMMARY: There are limited data from human studies to guide decision-making regarding timing of discontinuation of GLP-1 RA before conception. Studies focused on pregnancy and neonatal outcomes would provide additional information regarding a safe washout period. Based on the available literature a 4-week washout period prior to attempting conception may be considered for the agents reviewed in this publication.

Predicting polycystic ovary syndrome (PCOS) with machine learning algorithms from electronic health records.

Introduction: Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis. Methods: This is a retrospective cohort study from a SafetyNet hospital's electronic health records (EHR) from 2003-2016. The study population included 30,601 women aged 18-45 years without concurrent endocrinopathy who had any visit to Boston Medical Center for primary care, obstetrics and gynecology, endocrinology, family medicine, or general internal medicine. Four prediction outcomes were assessed for PCOS. The first outcome was PCOS ICD-9 diagnosis with additional model outcomes of algorithm-defined PCOS. The latter was based on Rotterdam criteria and merging laboratory values, radiographic imaging, and ICD data from the EHR to define irregular menstruation, hyperandrogenism, and polycystic ovarian morphology on ultrasound. Results: We developed predictive models using four machine learning methods: logistic regression, supported vector machine, gradient boosted trees, and random forests. Hormone values (follicle-stimulating hormone, luteinizing hormone, estradiol, and sex hormone binding globulin) were combined to create a multilayer perceptron score using a neural network classifier. Prediction of PCOS prior to clinical diagnosis in an out-of-sample test set of patients achieved AUC of 85%, 81%, 80%, and 82%, respectively in Models I, II, III and IV. Significant positive predictors of PCOS diagnosis across models included hormone levels and obesity; negative predictors included gravidity and positive bHCG. Conclusions: Machine learning algorithms were used to predict PCOS based on a large at-risk population. This approach may guide early detection of PCOS within EHR-interfaced populations to facilitate counseling and interventions that may reduce long-term health consequences. Our model illustrates the potential benefits of an artificial intelligence-enabled provider assistance tool that can be integrated into the EHR to reduce delays in diagnosis. However, model validation in other hospital-based populations is necessary.

Menstrual cycle length variation by demographic characteristics from the Apple Women's Health Study.

Menstrual characteristics are important signs of women's health. Here we examine the variation of menstrual cycle length by age, ethnicity, and body weight using 165,668 cycles from 12,608 participants in the US using mobile menstrual tracking apps. After adjusting for all covariates, mean menstrual cycle length is shorter with older age across all age groups until age 50 and then became longer for those age 50 and older. Menstrual cycles are on average 1.6 (95%CI: 1.2, 2.0) days longer for Asian and 0.7 (95%CI: 0.4, 1.0) days longer for Hispanic participants compared to white non-Hispanic participants. Participants with BMI ≥ 40 kg/m2 have 1.5 (95%CI: 1.2, 1.8) days longer cycles compared to those with BMI between 18.5 and 25 kg/m2. Cycle variability is the lowest among participants aged 35-39 but are considerably higher by 46% (95%CI: 43%, 48%) and 45% (95%CI: 41%, 49%) among those aged under 20 and between 45-49. Cycle variability increase by 200% (95%CI: 191%, 210%) among those aged above 50 compared to those in the 35-39 age group. Compared to white participants, those who are Asian and Hispanic have larger cycle variability. Participants with obesity also have higher cycle variability. Here we confirm previous observations of changes in menstrual cycle pattern with age across reproductive life span and report new evidence on the differences of menstrual variation by ethnicity and obesity status. Future studies should explore the underlying determinants of the variation in menstrual characteristics.

Use of personal care products during pregnancy and birth outcomes - A pilot study.

Prenatal exposure to endocrine disrupting chemicals (EDCs) from personal care products may be associated with birth outcomes including preterm birth and low birth weight. There is limited research examining the role of personal care product use during pregnancy on birth outcomes. Our pilot study consisted of 164 participants in the Environmental Reproductive and Glucose Outcomes (ERGO) study (Boston, MA), with data on self-reported personal care product use at four study visits throughout pregnancy (product use in the 48 h before a study visit and hair product use in the month before a study visit). We used covariate-adjusted linear regression models to estimate differences in mean gestational age at delivery, birth length, and sex-specific birth weight-for-gestational age (BW-for-GA) Z-score based on personal care product use. Hair product use in the past month prior to certain study visits was associated with decreased mean sex-specific BW-for-GA Z-scores. Notably, hair oil use in the month prior to study visit 1 was associated with a lower mean BW-for-GA Z-score (V1: -0.71, 95% confidence interval: -1.12, -0.29) compared to non-use. Across all study visits (V1-V4), increased mean birth length was observed among nail polish users vs. non-users. In comparison, decreased mean birth length was observed among shave cream users vs. non-users. Liquid soap, shampoo, and conditioner use at certain study visits were significantly associated with higher mean birth length. Suggestive associations were observed across study visits for other products including hair gel/spray with BW-for-GA Z-score and liquid/bar soap with gestational age. Overall, use of a variety of personal care products throughout pregnancy was observed to be associated with our birth outcomes of interest, notably hair oil use during early pregnancy. These findings may help inform future interventions/clinical recommendations to reduce exposures linked to adverse pregnancy outcomes.

Abnormal uterine bleeding patterns determined through menstrual tracking among participants in the Apple Women's Health Study.

BACKGROUND: Use of menstrual tracking data to understand abnormal bleeding patterns has been limited because of lack of incorporation of key demographic and health characteristics and confirmation of menstrual tracking accuracy. OBJECTIVE: This study aimed to identify abnormal uterine bleeding patterns and their prevalence and confirm existing and expected associations between abnormal uterine bleeding patterns, demographics, and medical conditions. STUDY DESIGN: Apple Women's Health Study participants from November 2019 through July 2021 who contributed menstrual tracking data and did not report pregnancy, lactation, use of hormones, or menopause were included in the analysis. Four abnormal uterine bleeding patterns were evaluated: irregular menses, infrequent menses, prolonged menses, and irregular intermenstrual bleeding (spotting). Monthly tracking confirmation using survey responses was used to exclude inaccurate or incomplete digital records. We investigated the prevalence of abnormal uterine bleeding stratified by demographic characteristics and used logistic regression to evaluate the relationship of abnormal uterine bleeding to a number of self-reported medical conditions. RESULTS: There were 18,875 participants who met inclusion criteria, with a mean age of 33 (standard deviation, 8.2) years, mean body mass index of 29.3 (standard deviation, 8.0), and with 68.9% (95% confidence interval, 68.2-69.5) identifying as White, non-Hispanic. Abnormal uterine bleeding was found in 16.4% of participants (n=3103; 95% confidence interval, 15.9-17.0) after accurate tracking was confirmed; 2.9% had irregular menses (95% confidence interval, 2.7-3.1), 8.4% had infrequent menses (95% confidence interval, 8.0-8.8), 2.3% had prolonged menses (95% confidence interval, 2.1-2.5), and 6.1% had spotting (95% confidence interval, 5.7-6.4). Black participants had 33% higher prevalence (prevalence ratio, 1.33; 95% confidence interval, 1.09-1.61) of infrequent menses compared with White, non-Hispanic participants after controlling for age and body mass index. The prevalence of infrequent menses was increased in class 1, 2, and 3 obesity (class 1: body mass index, 30-34.9; prevalence ratio, 1.31; 95% confidence interval, 1.13-1.52; class 2: body mass index, 35-39.9; prevalence ratio, 1.25; 95% confidence interval, 1.05-1.49; class 3: body mass index, >40; prevalence ratio, 1.51; 95% confidence interval, 1.21-1.88) after controlling for age and race/ethnicity. Those with class 3 obesity had 18% higher prevalence of abnormal uterine bleeding compared with healthy-weight participants (prevalence ratio, 1.18; 95% confidence interval, 1.02-1.38). Participants with polycystic ovary syndrome had 19% higher prevalence of abnormal uterine bleeding compared with participants without this condition (prevalence ratio, 1.19; 95% confidence interval, 1.08-1.31). Participants with hyperthyroidism (prevalence ratio, 1.34; 95% confidence interval, 1.13-1.59) and hypothyroidism (prevalence ratio, 1.17; 95% confidence interval, 1.05-1.31) had a higher prevalence of abnormal uterine bleeding, as did those reporting endometriosis (prevalence ratio, 1.28; 95% confidence interval, 1.12-1.45), cervical dysplasia (prevalence ratio, 1.20; 95% confidence interval, 1.03-1.39), and fibroids (prevalence ratio, 1.14; 95% confidence interval, 1.00-1.30). CONCLUSION: In this cohort, abnormal uterine bleeding was present in 16.4% of those with confirmed menstrual tracking. Black or obese participants had increased prevalence of abnormal uterine bleeding. Participants reporting conditions such as polycystic ovary syndrome, thyroid disease, endometriosis, and cervical dysplasia had a higher prevalence of abnormal uterine bleeding.

Environmental Exposures and Anti-Müllerian Hormone: A Mixture Analysis in the Nurses' Health Study II.

BACKGROUND: Previous studies have linked environmental exposures with anti-Müllerian hormone (AMH), a marker of ovarian reserve. However, associations with multiple environment factors has to our knowledge not been addressed. METHODS: We included a total of 2,447 premenopausal women in the Nurses' Health Study II (NHSII) who provided blood samples during 1996-1999. We selected environmental exposures linked previously with reproductive outcomes that had measurement data available in NHSII, including greenness, particulate matter, noise, outdoor light at night, ultraviolet radiation, and six hazardous air pollutants (1,3-butadiene, benzene, diesel particulate matter, formaldehyde, methylene chloride, and tetrachloroethylene). For these, we calculated cumulative averages from enrollment (1989) to blood draw and estimated associations with AMH in adjusted single-exposure models, principal component analysis (PCA), and hierarchical Bayesian kernel machine regression (BKMR). RESULTS: Single-exposure models showed negative associations of AMH with benzene (percentage reduction in AMH per interquartile range [IQR] increase = 5.5%, 95% confidence interval [CI] = 1.0, 9.8) and formaldehyde (6.1%, 95% CI = 1.6, 10). PCA identified four major exposure patterns but only one with high exposure to air pollutants and light at night was associated with lower AMH. Hierarchical BKMR pointed to benzene, formaldehyde, and greenness and suggested an inverse joint association with AMH (percentage reduction comparing all exposures at the 75th percentile to median = 8.2%, 95% CI = 0.7, 15.1). Observed associations were mainly among women above age 40. CONCLUSIONS: We found exposure to benzene and formaldehyde to be consistently associated with lower AMH levels. The associations among older women are consistent with the hypothesis that environmental exposures accelerate reproductive aging.

Covid-19 vaccination and menstrual cycle length in the Apple Women's Health Study.

COVID-19 vaccination may be associated with change in menstrual cycle length following vaccination. We estimated covariate-adjusted differences in mean cycle length (MCL), measured in days, between pre-vaccination cycles, vaccination cycles, and post-vaccination cycles within vaccinated participants who met eligibility criteria in the Apple Women's Health Study, a longitudinal mobile-application-based cohort of people in the U.S. with manually logged menstrual cycles. A total of 9652 participants (8486 vaccinated; 1166 unvaccinated) contributed 128,094 cycles (median = 10 cycles per participant; inter-quartile range: 4-22). Fifty-five percent of vaccinated participants received Pfizer-BioNTech's mRNA vaccine, 37% received Moderna's mRNA vaccine, and 8% received the Johnson & Johnson/Janssen (J&J) vaccine. COVID-19 vaccination was associated with a small increase in MCL for cycles in which participants received the first dose (0.50 days, 95% CI: 0.22, 0.78) and cycles in which participants received the second dose (0.39 days, 95% CI: 0.11, 0.67) of mRNA vaccines compared with pre-vaccination cycles. Cycles in which the single dose of J&J was administered were, on average, 1.26 days longer (95% CI: 0.45, 2.07) than pre-vaccination cycles. Post-vaccination cycles returned to average pre-vaccination length. Estimated follicular phase vaccination was associated with increased MCL in cycles in which participants received the first dose (0.97 days, 95% CI: 0.53, 1.42) or the second dose (1.43 days, 95% CI: 1.06, 1.80) of mRNA vaccines or the J&J dose (2.27 days, 95% CI: 1.04, 3.50), compared with pre-vaccination cycles. Menstrual cycle change following COVID-19 vaccination appears small and temporary and should not discourage individuals from becoming vaccinated.

Optimization of assisted reproductive technology outcomes in patients with polycystic ovarian syndrome: updates and unanswered questions.

PURPOSE OF REVIEW: Narrative review of recent literature on optimization of assisted reproduction technology outcomes in patients with polycystic ovarian syndrome (PCOS). RECENT FINDINGS: The key areas of focus include pre cycle treatment with the goal of cohort synchronization, methods of ovulation suppression and trigger medication. There is no definitive evidence that precycle treatment with combined oral contraceptives (COCs) or progestins improve or negatively impact in vitro fertilization outcomes in patients with PCOS. The reviewed evidence supports consideration of progestins as suppression of premature ovulation in patients with PCOS as an alternative to gonadotropin releasing hormone (GnRH) antagonist if a freeze all protocol is planned. There is limited prospective evidence in PCOS populations regarding use of a dual trigger using GnRH agonist and human chorionic gonadotropin (hCG). SUMMARY: This review has implications for clinical practice regarding ovarian stimulation protocols for patients with PCOS. We also identified areas of research need including the further exploration of the value of pre cycle COC or progestin use in a PCOS population, also the use of GnRH agonist in combination with hCG in a well defined PCOS population and using GnRH agonist trigger alone as a control.

Digital Global Recruitment for Women's Health Research: Cross-sectional Study.

BACKGROUND: With the increased popularity of mobile menstrual tracking apps and boosted Facebook posts, there is a unique opportunity to recruit research study participants from across the globe via these modalities to evaluate women's health. However, no studies to date have assessed the feasibility of using these recruitment sources for epidemiological research on ovulation and menstruation. OBJECTIVE: The objective of this study was to assess the feasibility of recruiting a diverse sample of women to an epidemiological study of ovulation and menstruation (OM) health (OM Global Health Study) using digital recruitment sources. The feasibility and diversity were assessed via click and participation rates, geographic location, BMI, smoking status, and other demographic information. METHODS: Participants were actively recruited via in-app messages using the menstrual tracking app Clue (BioWink GmbH) and a boosted Facebook post by DivaCup (Diva International Inc.). Other passive recruitment methods also took place throughout the recruitment period (eg, email communications, blogs, other social media). The proportion of participants who visited the study website after viewing and clicking the hypertext link (click rates) in the in-app messages and boosted Facebook post and the proportion of participants who completed the surveys per the number of completed consent and eligibility screeners (participation rates) were used to quantify the success of recruiting participants to the study website and study survey completion, respectively. Survey completion was defined as finishing the pregnancy and birth history section of the OM Global Health Study questionnaire. RESULTS: The recruitment period was from February 27, 2018, through January 24, 2020. In-app messages and the boosted Facebook post were seen by 104,000 and 21,400 people, respectively. Overall, 215 participants started the OM Global Health Study survey, of which 140 (65.1%), 39 (18.1%), and 36 (16.8%) participants were recruited via the app, the boosted Facebook post, and other passive recruitment methods, respectively. The click rate via the app was 18.9% (19,700 clicks/104,000 ad views) and 1.6% via the boosted Facebook post (340 clicks/21,400 ad views.) The overall participation rate was 44.6% (198/444), and the average participant age was 21.8 (SD 6.1) years. In terms of geographic and racial/ethnic diversity, 91 (44.2%) of the participants resided outside the United States and 147 (70.7%) identified as non-Hispanic White. In-app recruitment produced the most geographically diverse stream, with 44 (32.8%) of the 134 participants in Europe, 77 (57.5%) in North America, and 13 (9.8%) in other parts of the world. Both human error and nonhuman procedural breakdowns occurred during the recruitment process, including a computer programming error related to age eligibility and a hacking attempt by an internet bot. CONCLUSIONS: In-app messages using the menstrual tracking app Clue were the most successful method for recruiting participants from many geographic regions and producing the greatest numbers of started and completed surveys. This study demonstrates the utility of digital recruitment to enroll participants from diverse geographic locations and provides some lessons to avoid technical recruitment errors in future digital recruitment strategies for epidemiological research.

Covid-19 vaccination and menstrual cycle length in the Apple Women's Health Study.

Background: COVID-19 vaccination may be associated with change in menstrual cycle length following vaccination. Methods: We conducted a longitudinal analysis within a subgroup of 14,915 participants in the Apple Women's Health Study (AWHS) who enrolled between November 2019 and December 2021 and met the following eligibility criteria: were living in the U.S., met minimum age requirements for consent, were English speaking, actively tracked their menstrual cycles, and responded to the COVID-19 Vaccine Update survey. In the main analysis, we included tracked cycles recorded when premenopausal participants were not pregnant, lactating, or using hormonal contraceptives. We used conditional linear regression and multivariable linear mixed-effects models with random intercepts to estimate the covariate-adjusted difference in mean cycle length, measured in days, between pre-vaccination cycles, cycles in which a vaccine was administered, and post-vaccination cycles within vaccinated participants, and between vaccinated and unvaccinated participants. We further compared associations between vaccination and menstrual cycle length by the timing of vaccine dose within a menstrual cycle (i.e., in follicular or luteal phase). We present Bonferroni-adjusted 95% confidence intervals to account for multiple comparisons. Results: A total of 128,094 cycles (median = 10 cycles per participant; interquartile range: 4-22) from 9,652 participants (8,486 vaccinated; 1,166 unvaccinated) were included. The average within-individual standard deviation in cycle length was 4.2 days. Fifty-five percent of vaccinated participants received Pfizer-BioNTech's mRNA vaccine, 37% received Moderna's mRNA vaccine, and 7% received the Johnson & Johnson/Janssen vaccine (J&J). We found no evidence of a difference between mean menstrual cycle length in the unvaccinated and vaccinated participants prior to vaccination (0.24 days, 95% CI: -0.34, 0.82).Among vaccinated participants, COVID-19 vaccination was associated with a small increase in mean cycle length (MCL) for cycles in which participants received the first dose (0.50 days, 95% CI: 0.22, 0.78) and cycles in which participants received the second dose (0.39 days, 95% CI: 0.11, 0.67) of mRNA vaccines compared with pre-vaccination cycles. Cycles in which the single dose of J&J was administered were, on average, 1.26 days longer (95% CI: 0.45, 2.07) than pre-vaccination cycles. Post-vaccination cycles returned to average pre-vaccination length. Estimates for pre vs post cycle lengths were 0.14 days (95% CI: -0.13, 0.40) in the first cycle following vaccination, 0.13 days (95% CI: -0.14, 0.40) in the second, -0.17 days (95% CI: -0.43, 0.10) in the third, and -0.25 days (95% CI: -0.52, 0.01) in the fourth cycle post-vaccination. Follicular phase vaccination was associated with an increase in MCL in cycles in which participants received the first dose (0.97 days, 95% CI: 0.53, 1.42) or the second dose (1.43 days, 95% CI: 1.06, 1.80) of mRNA vaccines or the J&J dose (2.27 days, 95% CI: 1.04, 3.50), compared with pre-vaccination cycles. Conclusions: COVID-19 vaccination was associated with an immediate short-term increase in menstrual cycle length overall, which appeared to be driven by doses received in the follicular phase. However, the magnitude of this increase was small and diminished in each cycle following vaccination. No association with cycle length persisted over time. The magnitude of change associated with vaccination was well within the natural variability in the study population. Menstrual cycle change following COVID-19 vaccination appears small and temporary and should not discourage individuals from becoming vaccinated.

Untargeted metabolomics reveals that multiple reproductive toxicants are present at the endometrium.

BACKGROUND: Recent epidemiologic research shows many environmental chemicals exhibit endocrine disrupting effects on the female reproductive system. Few studies have examined exposure at reproductive organs. Our aim was to perform a preliminary untargeted metabolomic characterization of menstrual blood, a novel biofluid, to identify environmental toxins present in the endometrium and evaluate the suitability of this sample type for exposome research. METHODS: Whole blood menstrual samples were collected from four women using a menstrual cup. Samples were analyzed for small molecules that include both environmental chemicals and endogenous metabolites using untargeted liquid chromatography with high-resolution mass spectrometry (LC-HRMS). Principal component analysis (PCA) and ANOVA was used to identify differences within and between individuals' menstrual blood metabolomic profiles, and the influence of the sample processing method. To assess the presence of environmental exposures, LC-HRMS chemical profiles were matched to the ToxCast chemical database, which includes 4557 commonly used commercial chemicals. Select compounds were confirmed by comparison to reference standards. RESULTS: PCA of metabolome profiles showed analysis of menstrual blood samples were highly reproducible, with high variability in detected metabolites between participants and low variability between analytical replicates of an individual's sample. Endogenous metabolites detected in menstrual blood samples achieved good coverage of the human blood metabolome. We found 1748 annotations for environmental chemicals, including suspected reproductive toxicants such as phenols, parabens, phthalates, and organochlorines. Storage temperature for the first 24 h did not significantly influence global metabolomic profiles. CONCLUSION: Our results show chemical exposures linked to reproductive toxicity and endocrine disruption are present in menstrual blood, a sampling medium for the endometrium.