Informing a PCOS Lifestyle Program: Mapping Behavior Change Techniques to Barriers and Enablers to Behavior Change Using the Theoretical Domains Framework.

This article aimed to identify the behavior change techniques (BCTs) based on facilitators and barriers to lifestyle management in women with polycystic ovary syndrome (PCOS) according to the behavior change wheel (BCW). This qualitative study design using inductive thematic analysis following semistructured interviews (n = 20) identified barriers and enablers to lifestyle management. These were then mapped to Capability, Opportunity, Motivation-Behavioral Model (COM-B) constructs and the corresponding Theoretical Domains Framework (TDF) domains. This study included women with PCOS residing in Australia. Main outcome measures include intervention functions, policy categories, and BCTs described in the BCW. Twenty-three BCTs were recognized to influence behavior change in women with PCOS. Factors were categorized into the subcomponents of the COM-B: psychological capability (e.g., lack of credible information), physical capability (e.g., managing multiple health conditions), physical opportunity (e.g., limited access to resources), social opportunity (e.g., adequate social support), reflective motivation (e.g., positive health expectancies following behavior change), and automatic motivation (e.g., emotional eating). Future research should use this work to guide PCOS lifestyle intervention development and then test intervention effectiveness through an experimental phase to provide empirical evidence for wider use and implementation of tailored, theory-informed PCOS lifestyle programs as part of evidence-based PCOS management.

Cardiometabolic risks in PCOS: a review of the current state of knowledge.

INTRODUCTION: Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting up to 18% women of reproductive age. It is associated with a range of metabolic, reproductive, and psychological features. Current evidence indicates a role of PCOS in the development of metabolic and increased cardiovascular risk factors (CVRF) with implications for compromised cardiovascular endpoint disease, which may have a considerable impact on health and health care costs. AREAS COVERED: Existing studies examining long-term cardiometabolic health in PCOS are heterogeneous with inconsistent findings. In the current review, we aim to explore and critically review retrospective, prospective, meta-analysis and review articles relating to PCOS on cardiometabolic risk factors and clinical consequences to summarize the evidence, note evidence gaps, and suggest implications for future research. EXPERT COMMENTARY: Although there is an established association between PCOS and metabolic health, implications on cardiac health are more uncertain with associations observed for CVRF and subclinical disease, yet limited and conflicting data on actual cardiovascular endpoints. There is a lack of population-based long-term studies examining cardiometabolic morbidity and mortality in PCOS with a need for further research to progress toward a better understanding of the long-term cardiometabolic impacts in women with PCOS.

Metabolic syndrome in polycystic ovary syndrome: a systematic review, meta-analysis and meta-regression.

INTRODUCTION: Women with polycystic ovary syndrome (PCOS) have increased risk of metabolic syndrome. The relative contribution of clinical, demographic or biochemical factors to metabolic syndrome in PCOS is not known. A literature search was conducted in MEDLINE, CINAHL, EMBASE and clinical trial registries. Of 4530 studies reviewed, 59 were included in the systematic review and 27 in the meta-analysis and meta-regression. In good and fair quality studies, women with PCOS had an overall increased prevalence of metabolic syndrome (odds ratio, OR 3.35, 95% confidence interval, CI 2.44, 4.59). Increased prevalence of metabolic syndrome occurred in overweight or obese women with PCOS (OR 1.88, 95% 1.16, 3.04) but not in lean women (OR 1.45, 95% CI 0.35, 6.12). In meta-regression analyses, the markers of metabolic syndrome diagnostic criteria (waist circumference, high-density lipoprotein cholesterol, triglyceride, blood pressure), BMI, glucose tolerance (2-hr oral glucose tolerance test) and surrogate markers of insulin resistance (HOMA-IR) but not markers of reproductive dysfunction (sex hormone binding globulin, testosterone, PCOS phenotypes) contributed significantly to the heterogeneity in the prevalence of metabolic syndrome. Women with PCOS have increased risk of metabolic syndrome which was associated with obesity and metabolic features but not with indices of hyperandrogenism.

Ethnicity, obesity and the prevalence of impaired glucose tolerance and type 2 diabetes in PCOS: a systematic review and meta-regression.

BACKGROUND: Our prior meta-analyses demonstrated an increased prevalence of impaired glucose tolerance (IGT) and type 2 diabetes mellitus (T2DM) with polycystic ovary syndrome (PCOS), but with substantial clinical heterogeneity. OBJECTIVE AND RATIONALE: We aimed to update our previous review to quantify the prevalence of IGT and T2DM in PCOS with only quality studies (good and fair quality). We also aimed to examine the contribution of parameters including ethnicity, obesity and method of diagnosing T2DM in explaining the observed heterogeneity in IGT and T2DM prevalence in PCOS. SEARCH METHODS: We conducted a literature search (MEDLINE, CINAHL, EMBASE, clinical trial registries and hand-searching) up to June 2016 to identify studies reporting the prevalence of dysglycemia (IGT and T2DM) in women with and without PCOS. We included studies where women with PCOS (defined according to original National Institute of Health) were compared to women without PCOS for the end-points of the prevalence of IGT or T2DM. We excluded case reports, case series, editorials, and narrative reviews. Studies where PCOS was diagnosed by self-report, or where IGT or T2DM were measured by fasting glucose, only were excluded. We assessed the methodological quality of the included studies using a priori criteria based on the Newcastle-Ottawa Scaling (NOS) for non-randomized studies. Data are presented as odds ratio (OR) (95% CI) with random-effects meta-analysis by Mantel-Haenszel methods. We assessed the contribution of demographic and clinical factors to heterogeneity using subgroup and meta-regression analysis. OUTCOMES: We reviewed 4530 studies and included 40 eligible studies in the final analysis. On meta-analysis of quality studies, women with PCOS had an increased prevalence of IGT (OR = 3.26, 95% CI: 2.17-4.90) and T2DM (OR = 2.87, 95% CI: 1.44-5.72), which differed by ethnicity (for IGT, Asia: 5-fold, the Americas: 4-fold and Europe: 3-fold), was higher with obesity, and doubled among studies using self-report or administrative data for diagnosing diabetes. The ethnicity-related difference retained its significance for Asia and Europe in BMI-matched subgroups. Clear contributors to heterogeneity did not emerge in meta-regression. WIDER IMPLICATIONS: Our findings underscore the importance of PCOS as a cause of dysglycemia with a higher prevalence of IGT and T2DM. They support the relevance of ethnicity and obesity and emphasize the need for accurate diagnostic methods for diabetes. PROSPERO REGISTRATION NUMBER: CRD42017056524.

Weight management practices associated with PCOS and their relationships with diet and physical activity.

STUDY QUESTION: Do weight management practices differ in women with and without PCOS? SUMMARY ANSWER: Women in the general population with self-reported PCOS are more likely to be using healthy weight management practices and alternative non-lifestyle measures for weight management than women without PCOS. WHAT IS KNOWN ALREADY: Lifestyle management is the first-line treatment in PCOS. However, the specific weight management practices used by women with PCOS and their effect on diet and physical activity are unclear. STUDY DESIGN, SIZE, DURATION: The study was a population-based observational cross-sectional study involving women in the 1973-1978 cohort (n = 7767 total; n = 556 with PCOS, n = 7211 without PCOS). PARTICIPANTS/MATERIALS, SETTING, METHODS: Women with and without self-reported PCOS were included. Self-reported outcome measures included healthy lifestyle-related or alternative non-lifestyle-related (e.g. laxatives or smoking) weight management practices, dietary intake and physical activity. MAIN RESULTS AND THE ROLE OF CHANCE: Women with PCOS were more likely to be following both healthy [reducing meal or snack size (odds ratio (OR) 1.50, 95% CI 1.14, 1.96, P = 0.004) and reducing fat or sugar intake (OR 1.32, 95% CI 1.03, 1.69, P = 0.027) or following a low glycaemic index diet (OR 2.88, 95% CI 2.30, 3.59, P < 0.001)] and alternative [smoking (OR 1.60, 95% CI 1.02, 2.52, P = 0.043) or use of laxative, diet pills, fasting or diuretics (OR 1.45, 95% CI 1.07, 1.97, P = 0.017)] weight management practices than women without PCOS. In PCOS, the use of a range of healthy weight management practices was associated with increases in physical activity (P < 0.001), diet quality (P < 0.001), percentage protein intake (P < 0.001) and decreases in glycaemic index (P < 0.001), and percentages of fat (P = 0.001), saturated fat (P < 0.001) or fibre (P = 0.003). Use of alternative weight management practices was associated with decreases in diet quality. LIMITATIONS, REASONS FOR CAUTION: Limitations include the use of self-reported data for PCOS, height, weight, diet, physical activity and weight management behaviours. WIDER IMPLICATIONS OF THE FINDINGS: In PCOS, we should focus on improving healthy weight practices across both diet quality and quantity, and on assessing alternative weight practices and their potential adverse effect on dietary intake. STUDY FUNDING/COMPETING INTEREST(S): L.M. is supported by a South Australian Cardiovascular Research Development Program Fellowship (ID AC11S374); a program collaboratively funded by the National Heart Foundation, the South Australian Department of Health and the South Australian Health and Medical Research Institute. H.T. is supported by the NHMRC. S.A.M. is supported by an NHMRC Career Development Fellowship Level 2, ID1104636 and was previously supported by an ARC Future Fellowship (2011-2015, FT100100581). The authors declare no conflict of interest. TRIAL REGISTRATION NUMBER: Not applicable.

The association between Polycystic Ovary Syndrome (PCOS) and metabolic syndrome: a statistical modelling approach.

OBJECTIVE: Polycystic ovary syndrome (PCOS) affects 12-21% of women. Women with PCOS exhibit clustering of metabolic features. We applied rigorous statistical methods to further understand the interplay between PCOS and metabolic features including insulin resistance, obesity and androgen status. DESIGN: Retrospective cross-sectional analysis. PATIENTS: Women with PCOS attending reproductive endocrine clinics in South Australia for the treatment of PCOS (n = 172). Women without PCOS (controls) in the same Australian region (n = 335) from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab), a national population-based study (age- and BMI-matched within one standard deviation of the PCOS cohort). MEASUREMENTS: The factor structure for metabolic syndrome for women with PCOS and control groups was examined, specifically, the contribution of individual factors to metabolic syndrome and the association of hyperandrogenism with other metabolic factors. RESULTS: Women with PCOS demonstrated clustering of metabolic features that was not observed in the control group. Metabolic syndrome in the PCOS cohort was strongly represented by obesity (standardized factor loading = 0·95, P < 0·001) and insulin resistance factors (loading = 0·92, P < 0·001) and moderately by blood pressure (loading = 0·62, P < 0·001) and lipid factors (loading = 0·67, P = 0·002). On further analysis, the insulin resistance factor strongly correlated with the obesity (r = 0·70, P < 0·001) and lipid factors (r = 0·68, P < 0·001) and moderately with the blood pressure factor (loading = 0·43, P = 0·002). The hyperandrogenism factor was moderately correlated with the insulin resistance factor (r = 0·38, P < 0·003), but did not correlate with any other metabolic factors. CONCLUSIONS: PCOS women are more likely to display metabolic clustering in comparison with age- and BMI-matched control women. Obesity and insulin resistance, but not androgens, are independently and most strongly associated with metabolic syndrome in PCOS.

Contraception use and pregnancy outcomes in women with polycystic ovary syndrome: data from the Australian Longitudinal Study on Women's Health.

STUDY QUESTION: Do contraception use, pregnancy outcome and number of children differ in women with and without polycystic ovary syndrome (PCOS)? SUMMARY ANSWER: Women with PCOS were less likely to report use of contraception and more likely to report a miscarriage, whilst number of children was similar between groups. WHAT IS KNOWN ALREADY: The oral contraceptive pill is used in the management of PCOS, but the patterns of contraception use in women with PCOS is not known. In women with PCOS who undergo assisted reproduction, the risk of pregnancy loss appears higher, yet pregnancy loss and family size among community-based women with PCOS is not known. STUDY DESIGN, SIZE AND DURATION: This is a cross-sectional analysis of a longitudinal cohort study. Mailed survey data were collected at five time points (years 1996, 2000, 2003, 2006 and 2009). Data from respondents to Survey 4 (2006), aged 28-33 (n = 9145, 62% of the original cohort aged 18-23 years) were analysed. PARTICIPANTS/MATERIALS, SETTING, METHODS: This study was conducted in a general community setting. Data from participants who responded to the questions on PCOS, contraception and pregnancy outcome were analysed. The main outcome measures were self-reported PCOS, body mass index (BMI), contraception use, pregnancy loss and number of children. MAIN RESULTS AND THE ROLE OF CHANCE: In women aged 28-33 years, women with PCOS were less likely to be using contraception (61 versus 79%, P < 0.001) and more likely to be trying to conceive (56 versus 45%, P < 0.001), compared with women not reporting PCOS. A greater proportion of women with PCOS reported pregnancy loss (20 versus 15%, P = 0.003). PCOS was not independently associated with pregnancy loss; however, BMI was independently associated with pregnancy loss in the overweight and obese groups (OR 1.2, 95% CI 1.04-1.4, P = 0.02 and OR 1.4, 95% CI 1.1-1.6, P = 0.001, respectively). Fertility treatment use was also independently associated with pregnancy loss (adjusted OR 3.2, 95% CI 2.4-4.2, P < 0.001). There was no significant difference in number of children between women with and without PCOS. LIMITATIONS, REASON FOR CAUTION: PCOS, contraception use and pregnancy outcome data were self-reported. Attrition occurred, but is reasonable compared with similar longitudinal cohort studies. WIDER IMPLICATIONS OF THE FINDINGS: This community-based cohort aged 28-33 years provides insights into the contraceptive use, pregnancy loss and family size of a large cohort of unselected women. Women reporting PCOS had lower rates of contraception use and were more likely to be currently trying to conceive, suggesting that they may be aware of potential fertility challenges, yet in those not planning to conceive, contraceptive use was low and further education may be required. Despite prior reports of higher rates of pregnancy loss in PCOS, usually from infertility services, in this community-based population, PCOS was not independently associated with pregnancy loss, yet independent risk factors for pregnancy loss included higher BMI, were higher in PCOS. The number of children per woman was similar in the both groups, albeit with more infertility treatment in PCOS. This may reassure women with PCOS that with access to fertility treatment, family sizes appear similar to women not reporting PCOS.

Gestational diabetes and type 2 diabetes in reproductive-aged women with polycystic ovary syndrome.

CONTEXT: Polycystic ovary syndrome (PCOS) affects 6%-21% of women. PCOS has been associated with an increased risk of dysglycemia including gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM). OBJECTIVE: The objective of the study was to assess the prevalence of dysglycemia and the impact of obesity in young reproductive-aged women with and without PCOS in a community-based cohort. DESIGN: This was a cross-sectional analysis of data from a large longitudinal study (the Australian Longitudinal Study on Women's Health). SETTING: The setting for the study was the general community. PARTICIPANTS: Women were randomly selected from the national health insurance database. Standardized data collection occurred at five survey time points (years 1996, 2000, 2003, 2006, and 2009). Data from survey 4 (2006, n = 9145, 62% of original cohort aged 18-23 y) were examined for this study. MAIN OUTCOME MEASURES: Self-reported PCOS, GDM, and T2DM were measured. RESULTS: In women aged 28-33 years, PCOS prevalence was 5.8% [95% confidence interval (CI) 5.3%-6.4%]. The prevalence of GDM (in women reporting prior pregnancy) and T2DM was 11.2% and 5.1% in women with PCOS and 3.8% and 0.3% in women without PCOS, respectively (P for both < .001). PCOS was associated with an increased odds of GDM and T2DM. After adjusting for age, body mass index, hypertension, smoking, and demographic factors, the odds of GDM (odds ratio 2.1, 95% CI 1.1-3.9, P = .02) and T2DM (odds ratio 8.8, 95% CI 3.9-20.1, P < .001) remained increased in women reporting PCOS. CONCLUSIONS: In a large community-based cohort of reproductive-aged women, PCOS was independently associated with a higher risk of GDM and T2DM, independent of body mass index. Aggressive screening, prevention, and management of dysglycemia is clearly warranted in women with PCOS.