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Context Hierarchical clustering (HC) identifies subtypes of polycystic ovary syndrome (PCOS). Objective This work aimed to identify clinically significant subtypes in a PCOS cohort diagnosed with the Rotterdam criteria and to further characterize the distinct subtypes. Methods Clustering was performed using the variables body mass index (BMI), luteinizing hormone (LH), follicle-stimulating hormone, dehydroepiandrosterone sulfate, sex hormone–binding globulin (SHBG), testosterone, insulin, and glucose. Subtype characterization was performed by analyzing the variables estradiol, androstenedione, dehydroepiandrosterone, cortisol, anti-Müllerian hormone (AMH), total follicle count (TFC), lipid profile, and blood pressure. Study participants were girls and women who attended our university hospital for reproductive endocrinology screening between February 1993 and February 2021. In total, 2502 female participants of European ancestry, aged 13 to 45 years with PCOS (according to the Rotterdam criteria), were included. A subset of these (n = 1067) fulfilled the National Institutes of Health criteria (ovulatory dysfunction and hyperandrogenism). Main outcome measures included the identification of distinct PCOS subtypes using cluster analysis. Additional clinical variables associated with these subtypes were assessed. Results Metabolic, reproductive, and background PCOS subtypes were identified. In addition to high LH and SHBG levels, the reproductive subtype had the highest TFC and levels of AMH (all P <.001). In addition to high BMI and insulin levels, the metabolic subtype had higher low-density lipoprotein levels and higher systolic and diastolic blood pressure (all P <.001). The background subtype had lower androstenedione levels and features of the other 2 subtypes. Conclusion Reproductive and metabolic traits not used for subtyping differed significantly in the subtypes. These findings suggest that the subtypes capture distinct PCOS causal pathways. [ABSTRACT FROM AUTHOR]

Context Several challenges still exist to adopt the anti-müllerian hormone (AMH) as a marker of polycystic ovary morphology, as included in the recently updated international guideline. Although different evaluations of age- and assay-specific reference ranges have been published in the past few years, these studies have mainly been conducted in normo-ovulatory or infertile women. Objective To develop an age-specific percentile distribution of AMH in patients with polycystic ovary syndrome (PCOS) measured by 3 different assays. Design Retrospective cross-sectional study. Patients A total of 2725 women aged 20 to 40 years with PCOS diagnosis were included. Interventions Serum AMH measurement by the Gen II (Beckman Coulter), the picoAMH (Ansh Labs), and the Elecsys (Roche) assays. Main outcome measures Age-specific percentile curves for all the assays and correlations between AMH, clinical, hormonal, and ultrasound characteristics. Results Age-related nomograms for the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of AMH were calculated using the Lambda-Mu-Sigma method for all the assays. AMH levels were significantly different between PCOS phenotypes. AMH levels were positively correlated to LH, LH/FSH ratio, testosterone, androstenedione, free androgen index, mean follicular number, and mean ovarian volume. Conclusion To our knowledge, this is the first study reporting age-specific percentile nomograms of serum AMH levels measured by the Gen II, the picoAMH, and the Elecsys assays in a large population of women with PCOS. These findings may help to interpret AMH levels in patients with PCOS and facilitate the use of AMH as a diagnostic tool across age ranges. [ABSTRACT FROM AUTHOR]

Given all its systemic adaptive requirements, pregnancy shares several features with physical exercise. In this pilot study, we aimed to assess the physiological response to submaximal cardiopulmonary exercise testing (CPET) in early pregnancy. In 20 healthy, pregnant women (<13 weeks gestation) and 20 healthy, non-pregnant women, we performed a CPET with stationary cycling during a RAMP protocol until 70% of the estimated maximum heart rate (HR) of each participant. Hemodynamic and respiratory parameters were non-invasively monitored by impedance cardiography (PhysioFlow ® ) and a breath-by-breath analyzer (Oxycon TM ). To compare both groups, we used linear regression analysis, adjusted for age. We observed a similar response of stroke volume, cardiac output (CO) and HR to stationary cycling in pregnant and non-pregnant women, but a slightly lower 1-min recovery rate of CO (-3.9 [-5.5;-2.3] vs. -6.6 [-8.2;-5.1] L min -1  min -1 ; p = .058) and HR (-38 [-47; -28] vs. -53 [-62; -44] bpm/min; p = .065) in pregnant women. We also observed a larger increase in ventilation before the ventilatory threshold (+6.2 [5.4; 7.0] vs. +3.2 [2.4; 3.9] L min -1  min -1 ; p < .001), lower P ET CO 2 values at the ventilatory threshold (33 [31; 34] vs. 36 [34; 38] mmHg; p = .042) and a larger increase of breathing frequency after the ventilatory threshold (+4.6 [2.8; 6.4] vs. +0.6 [-1.1; 2.3] breaths min -1  min -1 ; p = .015) in pregnant women. In conclusion, we observed a slower hemodynamic recovery and an increased ventilatory response to exercise in early pregnancy., (© 2020 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society.)