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Objective To determine age-based criteria for polycystic ovary morphology. Design Cross-sectional, case-control design. Setting Outpatient setting. Subject(s) Women with polycystic ovary syndrome (PCOS) defined by hyperandrogenism and irregular menses (n = 544) and controls with regular menses and no evidence of hyperandrogenism (n = 666) participated. Parameters were tested in a second cohort of women with PCOS (n = 105) and controls (n = 32) meeting the same criteria. Intervention(s) Subjects underwent a pelvic ultrasound documenting ovarian volume and maximum follicle number in a single plane. Main Outcome Measure(s) A receiver operating characteristic curve was constructed to determine the ovarian volume and follicle number with the best sensitivity and specificity to define PCOS for age groups at approximately 5-year intervals from age 18 to >44 years. Result(s) The best sensitivity and specificity were obtained using a threshold volume of 12 mL and 13 follicles for ages ≤24 years, 10 mL and 14 follicles for ages 25–29 years, 9 mL and 10 follicles for ages 30–34 years, 8 mL and 10 follicles for ages 35–39 years, 10 mL and 9 follicles for ages 40–44 years, and 6 mL and 7 follicles for ages >44 years. Data from a second cohort confirmed the need to decrease volume and follicle number with increasing age to diagnose PCOS. Polycystic ovary morphology was most accurate at predicting the PCOS diagnosis for women ages 30–39 years. Conclusion(s) The ovarian volume and follicle number threshold to define polycystic ovary morphology should be lowered starting at age 30.

Aims: Variants in genes encoding metformin transport proteins and the ATM gene are associated with metformin response. We hypothesized that these gene variants contribute to variable metformin treatment response in polycystic ovary syndrome. Materials & methods: The discovery cohort (n = 38) was studied in an open-label study. Results were replicated in two additional cohorts (n = 26 and n = 131). Response was assessed after 3–6 months of treatment with metformin extended-release 1500–2000 mg/day. Results: The rs683369 variant was associated with less weight loss in the discovery cohort (p = 0.003), but these results were not replicated (p = 0.8). There were no differences in glucose parameters, testosterone levels or ovulatory frequency as a function of genotype. Conclusion: Variants in organic ion transporters do not explain the variable metformin response in polycystic ovary syndrome.

Women with polycystic ovary syndrome (PCOS) are at risk for metabolic syndrome, which may be exacerbated by smoking. We hypothesized that smoking worsens androgen levels and the metabolic profile in women with PCOS. PCOS smokers (n = 47) and non-smokers (n = 64) and control smokers (n = 30) and non-smokers (n = 28), aged 18–45 years, underwent anthropomorphic measurements, pelvic ultrasound and blood sampling. Smokers had higher cotinine (801 ± 83 versus

Objective To replicate variants in candidate genes associated with polycystic ovary syndrome (PCOS) in a population of European women with PCOS and control subjects. Design Case–control association analysis and meta-analysis. Setting Major academic hospital. Patient(s) Women of European ancestry with PCOS (n = 525) and controls (n = 472), aged 18–45 years. Intervention(s) Variants previously associated with PCOS in candidate gene studies were genotyped (n = 39). Metabolic, reproductive, and anthropomorphic parameters were examined as a function of the candidate variants. All genetic association analyses were adjusted for age, body mass index, and ancestry and were reported after correction for multiple testing. Main Outcome Measure(s) Association of candidate gene variants with PCOS. Result(s) Three variants, rs3797179 ( SRD5A1 ), rs12473543 ( POMC ), and rs1501299 ( ADIPOQ ), were nominally associated with PCOS. However, they did not remain significant after correction for multiple testing, and none of the variants replicated in a sufficiently powered meta-analysis. Variants in the FBN3 gene (rs17202517 and rs73503752) were associated with smaller waist circumferences, and variant rs727428 in the SHBG gene was associated with lower sex hormone-binding globulin levels. Conclusion(s) Previously identified variants in candidate genes do not seem to be associated with PCOS risk. Clinical Trial Registration Number NCT00166569.

Genome-wide association studies and replication analyses have identified (n = 5) or replicated (n = 10) DNA variants associated with risk for polycystic ovary syndrome (PCOS) in European women. However, the causal gene and underlying mechanism for PCOS risk at these loci have not been determined. We hypothesized that analysis of phenotype, gene expression and metformin response as a function of genotype would identify candidate genes and pathways that could provide insight into the underlying mechanism for risk at these loci. To test the hypothesis, subjects with PCOS (n = 427) diagnosed according to the NIH criteria (< 9 menses per year and clinical or biochemical hyperandrogenism) and controls (n = 407) with extensive phenotyping were studied. A subset of subjects (n = 38) underwent a subcutaneous adipose tissue biopsy for RNA sequencing and were subsequently treated with metformin for 12 weeks with standardized outcomes measured. Data were analyzed according to genotype at PCOS risk loci and adjusted for the false discovery rate. A gene variant in the THADA locus was associated with response to metformin and metformin was a predicted upstream regulator at the same locus. Genotype at the FSHB locus was associated with LH levels. Genes near the PCOS risk loci demonstrated differences in expression as a function of genotype in adipose including BLK and NEIL2 (GATA4 locus), GLIPR1 and PHLDA1 (KRR1 locus). Based on the phenotypes, expression quantitative trait loci (eQTL), and upstream regulatory and pathway analyses we hypothesize that there are PCOS subtypes. FSHB, FHSR and LHR loci may influence PCOS risk based on their relationship to gonadotropin levels. The THADA, GATA4, ERBB4, SUMO1P1, KRR1 and RAB5B loci appear to confer risk through metabolic mechanisms. The IRF1, SUMO1P1 and KRR1 loci may confer PCOS risk in development. The TOX3 and GATA4 loci appear to be involved in inflammation and its consequences. The data suggest potential PCOS subtypes and point to the need for additional studies to replicate these findings and identify personalized diagnosis and treatment options for PCOS.

Context: Although metformin is widely used to improve insulin resistance in women with polycystic ovary syndrome (PCOS), its mechanism of action is complex, with inconsistent effects on insulin sensitivity and variability in treatment response. Objective: The aim of the study was to delineate the effect of metformin on glucose and insulin parameters, determine additional treatment outcomes, and predict patients with PCOS who will respond to treatment. Design and Setting: We conducted an open-label, interventional study at an academic medical center. Subjects: Women with PCOS (n = 36) diagnosed by the National Institutes of Health criteria participated in the study. Interventions: Subjects underwent fasting blood sampling, an IV glucose tolerance test, dual-energy x-ray absorptiometry scan, transvaginal ultrasound, and measurement of human chorionic gonadotropin-stimulated androgen levels before and after 12 weeks of treatment with metformin extended release 1500 mg/d. Interval visits were performed to monitor anthropometric measurements and menstrual cycle parameters. Main Outcome Measures: Changes in glucose and insulin parameters, androgen levels, anthropometric measurements, and ovulatory menstrual cycles were evaluated. Results: Insulin sensitivity did not change despite weight loss. Glucose effectiveness (P = .002) and the acute insulin response to glucose (P = .002) increased, and basal glucose levels (P = .001) decreased after metformin treatment. T levels also decreased. Women with improved ovulatory function (61%) had lower baseline T levels and lower baseline and stimulated T and androstenedione levels after metformin treatment (all P < .05). Conclusions: Using an IV glucose tolerance test, which distinguishes improvements in glucose effectiveness and insulin sensitivity, metformin does not improve insulin sensitivity in women with PCOS but does improve glucose effectiveness. The improvement in glucose effectiveness may be partially mediated by decreased glucose levels. T levels also decreased with metformin treatment. Ovulation during metformin treatment was associated with lower baseline T levels and greater T and androstenedione decreases during treatment, but not with insulin or LH levels. Thus, the action of metformin in PCOS primarily affects glucose levels and steroidogenesis, which may be mediated by mechanisms that affect both pathways, such as inhibition of mitochondrial complex I.

Background Polycystic ovary syndrome (PCOS) is a heterogeneous disorder because of the variable criteria used for diagnosis. Therefore, International Classification of Diseases 9 (ICD-9) codes may not accurately capture the diagnostic criteria necessary for large scale PCOS identification. We hypothesized that use of electronic medical records text and data would more specifically capture PCOS subjects. Methods Subjects with PCOS were identified in the Partners Healthcare Research Patients Data Registry by searching for the term “polycystic ovary syndrome” using natural language processing (n = 24,930). A training subset of 199 identified charts was reviewed and categorized based on likelihood of a true Rotterdam PCOS diagnosis, i.e. two out of three of the following: irregular menstrual cycles, hyperandrogenism and/or polycystic ovary morphology. Data from the history, physical exam, laboratory and radiology results were codified and extracted from notes of definite PCOS subjects. Thirty-two terms were used to build an algorithm for identifying definite PCOS cases and applied to the rest of the dataset. The positive predictive value cutoff was set at 76.8 % to maximize the number of subjects available for study. A true positive predictive value for the algorithm was calculated after review of 100 charts from subjects identified as definite PCOS cases with at least two documented Rotterdam criteria. The positive predictive value was compared to that calculated using 200 charts identified using the ICD-9 code for PCOS (256.4; n = 13,670). In addition, a cohort of previously recruited PCOS subjects was submitted for algorithm validation. Results Chart review demonstrated that 64 % were confirmed as definitely PCOS using the algorithm, with a 9 % false positive rate. 66 % of subjects identified by ICD-9 code for PCOS could be confirmed as definitely PCOS, with an 8.5 % false positive rate. There was no significant difference in the positive predictive values using the two methods (p = 0.2). However, the number of charts that had insufficient confirmatory data was lower using the algorithm (5 % vs 11 %; p