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Article Contents

What does this mean for those with pcos, introduction, materials and methods, acknowledgments, author contributions, disclosures, data availability, recommendations from the 2023 international evidence-based guideline for the assessment and management of polycystic ovary syndrome.

This article is simultaneously published in Fertility and Sterility, Journal of Clinical Endocrinology and Metabolism, European Journal of Endocrinology and Human Reproduction .

Participants of the International PCOS Network are listed in the Appendix.

• Article contents
• Figures & tables
• Supplementary Data

Helena J Teede, Chau Thien Tay, Joop J E Laven, Anuja Dokras, Lisa J Moran, Terhi T Piltonen, Michael F Costello, Jacky Boivin, Leanne M Redman, Jacqueline A Boyle, Robert J Norman, Aya Mousa, Anju E Joham, on behalf of the International PCOS Network, Recommendations From the 2023 International Evidence-based Guideline for the Assessment and Management of Polycystic Ovary Syndrome, The Journal of Clinical Endocrinology & Metabolism , Volume 108, Issue 10, October 2023, Pages 2447–2469, https://doi.org/10.1210/clinem/dgad463

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What is the recommended assessment and management of those with polycystic ovary syndrome (PCOS), based on the best available evidence, clinical expertise, and consumer preference?

International evidence-based guidelines address prioritized questions and outcomes and include 254 recommendations and practice points, to promote consistent, evidence-based care and improve the experience and health outcomes in PCOS.

The 2018 International PCOS Guideline was independently evaluated as high quality and integrated multidisciplinary and consumer perspectives from six continents; it is now used in 196 countries and is widely cited. It was based on best available, but generally very low to low quality, evidence. It applied robust methodological processes and addressed shared priorities. The guideline transitioned from consensus based to evidence-based diagnostic criteria and enhanced accuracy of diagnosis, whilst promoting consistency of care. However, diagnosis is still delayed, the needs of those with PCOS are not being adequately met, evidence quality was low and evidence-practice gaps persist.

The 2023 International Evidence-based Guideline update reengaged the 2018 network across professional societies and consumer organizations with multidisciplinary experts and women with PCOS directly involved at all stages. Extensive evidence synthesis was completed. Appraisal of Guidelines for Research and Evaluation-II (AGREEII)-compliant processes were followed. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework was applied across evidence quality, feasibility, acceptability, cost, implementation and ultimately recommendation strength and diversity and inclusion were considered throughout.

This summary should be read in conjunction with the full Guideline for detailed participants and methods. Governance included a six-continent international advisory and management committee, five guideline development groups, and paediatric, consumer, and translation committees. Extensive consumer engagement and guideline experts informed the update scope and priorities. Engaged international society-nominated panels included paediatrics, endocrinology, gynaecology, primary care, reproductive endocrinology, obstetrics, psychiatry, psychology, dietetics, exercise physiology, obesity care, public health and other experts, alongside consumers, project management, evidence synthesis, statisticians and translation experts. Thirty-nine professional and consumer organizations covering 71 countries engaged in the process. Twenty meetings and five face-to-face forums over 12 months addressed 58 prioritized clinical questions involving 52 systematic and 3 narrative reviews. Evidence-based recommendations were developed and approved via consensus across five guideline panels, modified based on international feedback and peer review, independently reviewed for methodological rigour, and approved by the Australian Government National Health and Medical Research Council (NHMRC).

The evidence in the assessment and management of PCOS has generally improved in the past five years, but remains of low to moderate quality. The technical evidence report and analyses (∼6000 pages) underpins 77 evidence-based and 54 consensus recommendations, with 123 practice points. Key updates include: i) further refinement of individual diagnostic criteria, a simplified diagnostic algorithm and inclusion of anti-Müllerian hormone (AMH) levels as an alternative to ultrasound in adults only; ii) strengthening recognition of broader features of PCOS including metabolic risk factors, cardiovascular disease, sleep apnea, very high prevalence of psychological features, and high risk status for adverse outcomes during pregnancy; iii) emphasizing the poorly recognized, diverse burden of disease and the need for greater healthcare professional education, evidence-based patient information, improved models of care and shared decision making to improve patient experience, alongside greater research; iv) maintained emphasis on healthy lifestyle, emotional wellbeing and quality of life, with awareness and consideration of weight stigma; and v) emphasizing evidence-based medical therapy and cheaper and safer fertility management.

Overall, recommendations are strengthened and evidence is improved, but remain generally low to moderate quality. Significantly greater research is now needed in this neglected, yet common condition. Regional health system variation was considered and acknowledged, with a further process for guideline and translation resource adaptation provided.

The 2023 International Guideline for the Assessment and Management of PCOS provides clinicians and patients with clear advice on best practice, based on the best available evidence, expert multidisciplinary input and consumer preferences. Research recommendations have been generated and a comprehensive multifaceted dissemination and translation programme supports the Guideline with an integrated evaluation program.

This effort was primarily funded by the Australian Government via the National Health Medical Research Council (NHMRC) (APP1171592), supported by a partnership with American Society for Reproductive Medicine, Endocrine Society, European Society for Human Reproduction and Embryology, and the European Society for Endocrinology. The Commonwealth Government of Australia also supported Guideline translation through the Medical Research Future Fund (MRFCRI000266). HJT and AM are funded by NHMRC fellowships. JT is funded by a Royal Australasian College of Physicians (RACP) fellowship. Guideline development group members were volunteers. Travel expenses were covered by the sponsoring organizations. Disclosures of interest were strictly managed according to NHMRC policy and are available with the full guideline, technical evidence report, peer review and responses ( www.monash.edu/medicine/mchri/pcos ). Of named authors HJT, CTT, AD, LM, LR, JBoyle, AM have no conflicts of interest to declare. JL declares grant from Ferring and Merck; consulting fees from Ferring and Titus Health Care; speaker's fees from Ferring; unpaid consultancy for Ferring, Roche Diagnostics and Ansh Labs; and sits on advisory boards for Ferring, Roche Diagnostics, Ansh Labs, and Gedeon Richter. TP declares a grant from Roche; consulting fees from Gedeon Richter and Organon; speaker's fees from Gedeon Richter and Exeltis; travel support from Gedeon Richter and Exeltis; unpaid consultancy for Roche Diagnostics; and sits on advisory boards for Roche Diagnostics. MC declares travels support from Merck; and sits on an advisory board for Merck. JBoivin declares grants from Merck Serono Ltd.; consulting fees from Ferring B.V; speaker's fees from Ferring Arzneimittell GmbH; travel support from Organon; and sits on an advisory board for the Office of Health Economics. RJN has received speaker's fees from Merck and sits on an advisory board for Ferring. AJoham has received speaker's fees from Novo Nordisk and Boehringer Ingelheim. The guideline was peer reviewed by special interest groups across our 39 partner and collaborating organizations, was independently methodologically assessed against AGREEII criteria and was approved by all members of the guideline development groups and by the NHMRC.

Building on the 2018 International Evidence-based Guideline for the Assessment and Management of Polycystic Ovary Syndrome (PCOS), this Guideline updates and expands clinical questions, aiming to ensure that women with PCOS receive optimal, evidence-based care that meets their needs and improves health outcomes. The guideline and translation program were developed with full consumer participation at all stages including priority topics and outcomes for those with PCOS. The aim is to support women and their healthcare providers to optimize diagnosis, assessment and management of PCOS. There is an emphasis on improved education and awareness of healthcare professionals, partnership in care, and empowerment of women with PCOS. Personal characteristics, preferences, culture and values are considered, in addition to resource availability across different settings. With effective translation, the Guideline will address priorities identified by women with PCOS, upskill healthcare professionals, empower consumers, improve care and outcomes, identify key research gaps, and promote vital future research.

Polycystic ovary syndrome (PCOS) is the most common endocrinopathy affecting reproductive-aged women, with impacts across the lifespan from adolescence to post menopause. PCOS prevalence is between 10% to 13% as confirmed in the guideline process ( 1 , 2 ). PCOS aetiology is complex; clinical presentation is heterogeneous with reproductive, metabolic, and psychological features ( 1 , 2 ). Women internationally experience delayed diagnosis and dissatisfaction with care ( 3-5 ). Clinical practice in the assessment and management of PCOS remains inconsistent, with ongoing key evidence-practice gaps. Following on from the 2018 International Evidence-based Guideline for the Assessment and Management of Polycystic Ovary Syndrome ( 6 , 7 ), independently evaluated as high quality, this extensive update integrates current literature with previous systematic reviews and extends to new clinical questions prioritized by consumers. Ultimately, we aim to update, extend and translate rigorous, comprehensive evidence-based guidelines for diagnosis, assessment and treatment, to improve the lives of those with PCOS worldwide.

To do so, the Guideline leverages substantive government and society investment and brings together extensive consumer engagement and international collaboration with leading societies and organizations, multidisciplinary experts, and primary care representatives. This comprehensive evidence-based Guideline is constructed from a rigorous, Appraisal of Guidelines for Research and Evaluation-II (AGREEII)-compliant, evidence-based guideline development process. It provides a single source of international evidence-based recommendations to guide clinical practice with the opportunity for adaptation in relevant health systems. Together with an extensive translation program, the aim is to reduce worldwide variation in care and promote high quality clinical service provision to improve health outcomes and quality of life in women with PCOS. The Guideline is supported by a multifaceted international translation programme with co-designed resources to enhance the skills of healthcare professionals and to empower women with PCOS, with an integrated comprehensive evaluation program. Here, we summarize recommendations from the 2023 International Evidence-based Guideline for the Assessment and Management of PCOS.

Best practice evidence-based guideline development methods were applied and are detailed in the full Guideline and the technical report, which are available online ( www.monash.edu/medicine/mchri/pcos ) ( 8 ). In brief, extensive healthcare professional and consumer or patient engagement informed the Guideline priority areas. International society-nominated panels from across three leading entities, four partner organizations and thirty-two collaborating entities included consumers and experts in paediatrics, endocrinology, gynaecology, primary care, reproductive endocrinology, psychology, dietetics, exercise physiology, sleep, bariatric/ metabolic surgery, public health, other co-opted experts, project management, evidence synthesis and translation. Governance included an international advisory and a management committee, five guideline development groups (GDGs) with 56 members, and paediatric, consumer, and translation committees. The five GDGs covered i) Screening, diagnostic and risk assessment and life stage; ii) Psychological features and models of care; iii) Lifestyle management; iv) Management of nonfertility features; and v) Assessment and management of infertility. The leading entities; the Australian National Health and Medical Research Council (NHMRC) Centres for Research Excellence in Women's Health in Reproductive Life and in Polycystic Ovary Syndrome, led by Monash University, partnered with the American Society for Reproductive Medicine, the Endocrine Society, the European Society of Endocrinology and the European Society of Human Reproduction and Embryology and collaborated with 32 other entities. With international meetings over 12 months fifty-five prioritized clinical questions involved 52 systematic and three narrative reviews, generating evidence-based and consensus recommendations with accompanying practice points. Committee members nominated by partner and collaborator organizations provided international peer review, and independent experts reviewed methods which were then submitted to NHMRC for independent review. The target audience includes multidisciplinary healthcare professionals, consumers or patients, policy makers, and educators. The Guideline includes a focus on equity, cultural and ethnic diversity, avoidance of stigma and inclusivity (see full guideline for details).

Processes aligned with all elements of the AGREE-II tool for quality guideline assessment ( 9 ), with extensive evidence synthesis and meta-analysis. Integrity assessment was integrated into guideline evidence synthesis processes and followed the Research Integrity in Guideline Development (RIGID) framework, with studies assessed against criteria from the Research Integrity Assessment (RIA) tool and the Trustworthiness in RAndomised Controlled Trials (TRACT) checklist ( 10-12 ). Evidence synthesis methods are outlined in the full guideline and followed best practice ( 9 , 13 , 14 ) Guideline recommendations are presented by category, terms used, evidence quality and Grading of Recommendations, Assessment, Development and Evaluation (GRADE) framework considerations. Category includes evidence-based (sufficient evidence in PCOS) or consensus (insufficient evidence in PCOS, also evidence in general or relevant populations was considered) recommendations and accompanying practice points (implementation considerations) ( Table 1 ).

Categories of PCOS guideline recommendations

Abbreviation: PCOS, polycystic ovary syndrome.

The terms include “should”, “could” and “should not”, which are informed by the nature of the recommendation (evidence or consensus), the GRADE framework and the evidence quality and are independent descriptors reflecting GDG judgement. They refer to overall interpretation and practical application of the recommendation, balancing benefits and harms. “Should” is used where benefits of the recommendation exceed harms and where the recommendation can be trusted to guide practice. Conditional recommendations are reflected using the terms “could” or “should/could consider” which are used where evidence quality was limited or available studies demonstrate little clear advantage of one approach over another, or the balance of benefits to harms was unclear. “Should not” applies when there is a lack of appropriate evidence, or harms may outweigh benefits.

Evidence quality was categorized according to the GRADE framework, with judgments about the quality of the included studies and/or synthesized evidence incorporating risk of bias, inconsistency, indirectness, imprecision and any other considerations (eg, publication bias) that may influence evidence quality. These judgments considered study number and design, statistical data and importance of outcomes ( Table 2 ). The quality of evidence reflects the confidence that the estimate of the effect is adequate to support each recommendation ( 13 ), largely determined by the expert evidence synthesis team. GRADE acknowledges that evidence quality is a continuum; any discrete categorization involves some arbitrary decisions; nevertheless, the advantages of simplicity, transparency, and clarity outweigh these limitations ( 13 ).

Quality (certainty) of evidence categories (adapted from GRADE)

Abbreviation: GRADE, Grading of Recommendations, Assessment, Development, and Evaluation.

The GRADE framework enabled structured and transparent consideration across evidence quality, feasibility, acceptability, cost, implementation, and ultimately recommendation strength ( 13 ) and was completed at face to face guideline group meetings for all clinical questions ( Table 3 ) ( 15 ).

The grading of recommendations, assessment, development, and evaluation (GRADE) framework recommendation strength

Notably, certainty of evidence varied across outcomes within each question. Here evidence certainty reflects the lowest certainty for the critical outcomes. Evidence was often stronger for the top ranked outcome, and high quality randomized controlled trials (RCTs) were often present, despite overall low quality of evidence. These nuances were considered by the GDG for all question as per the technical report, with any apparent discrepancy between recommendation strength and evidence certainty justified in the full Guideline. Finally, we note that this is a living Guideline with annual evidence review in rapidly evolving areas.

The recommendations ( Table 4 ) apply the category, descriptive terms, GRADE of the recommendations and the quality of the evidence. The full Guideline, technical evidence and administrative reports are available online ( www.monash.edu/medicine/mchri/pcos ). The Guideline outlines the clinical need for the question, the clinical question, the evidence summary, the recommendations and practice points, and a summary of the justification developed by the GDGs using the GRADE framework. Extensive international peer review from across the 39 organizations was then considered by each GDG and recommendations were reconsidered applying the GRADE framework if justified. The comprehensive evidence reviews, profiles, and GRADE frameworks supporting each recommendation can be found in the Technical Report. The administrative report on guideline development, disclosure of interest process and declarations, peer review feedback and responses can also be found online. Here, we present the evidence-based and consensus recommendations and practice points ( Table 4 ). This summary, the full Guideline and technical reports are supported by a comprehensive co-designed translation program to optimize dissemination and impact with resources freely available online ( www.monash.edu/medicine/mchri/pcos ).

Recommendations for the assessment and management of polycystic ovary syndrome (PCOS). © Monash University on behalf of the NHMRC Centre for Research Excellence in Women's Health in Reproductive Life, 2023.

See Table 1 for the definition of CR, EBR, and PP.

© International evidence-based guideline for the assessment and management of polycystic ovary syndrome 2023, Helena Teede et al. Monash University (monash.edu/medicine/mchri/pcos), 2023, by permission of Monash University, on behalf of the NHMRC Centre for Research Excellence in Women's Health in Reproductive Life. This image/content is not covered by the terms of the Creative Commons licence of this publication. For permission re reuse, please contact the rights holder.

Two algorithms are provided to support recommendations on diagnosis ( Fig. 1 ) and infertility management ( Fig. 2 ).

Algorithm 1—Diagnostic algorithm for polycystic ovary syndrome (PCOS). © Monash University on behalf of the NHMRC Centre for Research Excellence in Women's Health in Reproductive Life, 2023. International evidence-based guideline for the assessment and management of polycystic ovary syndrome 2023, Helena Teede et al. Monash University ( monash.edu/medicine/mchri/pcos ), 2023, by permission of Monash University, on behalf of the NHMRC Centre for Research Excellence in Women's Health in Reproductive Life. This image/content is not covered by the terms of the Creative Commons licence of this publication. For permission re reuse, please contact the rights holder. * Exclusion of other causes = TSH, prolactin, 17-OH progesterone, FSH or if clinically indicated exclude other causes (eg, Cushing's syndrome, adrenal tumours). For hypogonadotrophic hypogonadism, usually due to low body fat or intensive exercise, exclude clinically and with LH and FSH levels. TSH, thyroid stimulating hormone.

Algorithm 2—Infertility algorithm for polycystic ovary syndrome (PCOS). © Monash University on behalf of the NHMRC Centre for Research Excellence in Women's Health in Reproductive Life, 2023. International evidence-based guideline for the assessment and management of polycystic ovary syndrome 2023, Helena Teede et al. Monash University ( monash.edu/medicine/mchri/pcos ), 2023, by permission of Monash University, on behalf of the NHMRC Centre for Research Excellence in Women's Health in Reproductive Life. This image/content is not covered by the terms of the Creative Commons licence of this publication. For permission re reuse, please contact the rights holder. Central blue pathway follows best practice evidence and is preferred.

The International Evidence-based Guideline for the Assessment and Management of PCOS and the related translation program aims to provide a high quality, reliable source of international evidence-based recommendations to guide consistent clinical practice and to empower women with evidence-based information. All recommendations were formulated after an assessment of the best available evidence, multidisciplinary clinical expertise, consumer preferences and structured review by five GDGs. The guideline provides 77 evidence-based and 54 consensus recommendations, with 123 practice points underpinned by a technical report on evidence synthesis and GRADE detailed considerations (∼6000 pages). The evidence has generally improved over the past five years but remains of low to moderate quality, requiring significant research investment into this neglected, yet common condition.

Key recommendations and updates include that PCOS should be diagnosed using the 2018 International Evidence-based Guideline criteria, which built on the consensus based 2003 Rotterdam criteria. This requires the presence of two of the following: i) clinical/biochemical hyperandrogenism; ii) ovulatory dysfunction; and iii) polycystic ovaries on ultrasound; and here in 2023, alternatively anti-Müllerian hormone (AMH) can now be used instead of ultrasound. Exclusion of other aetiologies. Importantly, where irregular menstrual cycles and hyperandrogenism are present, diagnosis is simplified and ultrasound or AMH are not required for diagnosis. In adolescents, both hyperandrogenism and ovulatory dysfunction are required, with ultrasound and AMH not recommended due to poor specificity. AMH was highlighted as a rapidly evolving area in 2018 and evidence is now strong enough to make this new recommendation. This will significantly change practice and offers women a low cost, convenient option, without evidence of overdiagnosis.

Insulin resistance is recognized as a key feature of PCOS, yet routinely available measures of insulin resistance are inaccurate and clinical measurement is not currently recommended. Once diagnosed, assessment and management should address reproductive, metabolic, cardiovascular, dermatologic, sleep, and psychological features. A lifelong health plan is recommended including a focus on healthy lifestyle, prevention of excess weight gain, optimization of fertility and preconception risk factors, and prevention and treatment of diverse clinical features. These include metabolic risk factors, diabetes, cardiovascular disease, and sleep disorders, which are all increased in PCOS. PCOS should be considered a high-risk condition in pregnancy with women identified and monitored. An increased premenopausal risk of endometrial cancer should also be recognized, whilst absolute risks remain low.

Symptoms of depression and anxiety are significantly increased and should be screened for in all women with PCOS, with psychological assessment and therapy as indicated. Greater awareness of psychological features including eating disorders and impacts on body image and quality of life is needed.

Dissatisfaction with PCOS diagnosis and care is high and significant improvement in education and awareness is strongly recommended for women and healthcare professionals including high quality, evidence-based resources. Shared decision making and self-empowerment are fundamental and integrated models of care should be codesigned, funded and evaluated.

Supported healthy lifestyle remains vital throughout the lifespan in PCOS, with a strong focus on overall health, prevention of weight gain and, if required, on weight management. Recognizing the benefits of many diet and physical activity regimens, there is no one specific regimen that has benefits over others in PCOS. Weight bias and stigma should be minimized and healthcare professionals should seek permission to weigh women, with explanation of weight-related risks.

Combined oral contraceptive pills are the first line pharmacological treatment for menstrual irregularity and hyperandrogenism, with no specific recommended preparation and a preference for lower ethinyl estradiol dose preparations and those with less side-effects. Metformin is recommended primarily for metabolic features and has greater efficacy than inositol, which offers limited clinical benefits in PCOS. Metformin is not routinely recommended for use in pregnant women with PCOS. Mechanical laser therapy is effective for hair reduction in some subgroups, whilst anti-androgens have a limited role where other therapies are ineffective or contraindicated. Anti-obesity agents and bariatric/metabolic surgery may be considered based on general population guidelines, balancing potential for benefits and side effects.

Letrozole is the preferred first line pharmacological infertility therapy, with clomiphene in combination with metformin; gonadotrophins or ovarian surgery primarily having a role as second line therapy. In vitro fertilization (IVF) could be offered, potentially with in vitro maturation, as third line therapy, where other ovulation induction therapies have failed and in the absence of an absolute indication for IVF in women with PCOS and anovulatory infertility. Given the underlying risk for pregnancy complications in PCOS, single embryo transfer should be preferred.

Overall, evidence in PCOS is low to moderate quality. Based on high prevalence and significant health impact, greater priority, education, models of care, funding, and research are recommended. Guideline translation will be extensive including multilingual education outputs and evidence-based resources for consumers (the ASKPCOS app), healthcare professionals and policy makers.

The guideline recommendations are protected under copyright, however a clear process for adaption of guideline recommendations to regional context is available by contacting the author for correspondence online ( www.monash.edu/medicine/mchri/pcos ). The translation program will be free and internationally accessible, building on the existing range of codesigned resources including the patient focused, evidence-based PCOS APP (AskPCOS), used in 186 countries and based on a rigorously developed question prompt list. Multi-faceted patient codesigned resources will aim to enhance health literacy with comprehensive PCOS-related health information available in multiple formats and in 15–20 languages. Internationally accessible resources include education modules for healthcare professionals at different career stages and disciplines, healthcare professional accredited courses, practice resources and tools, webinars with international expert panels, and e-health information resources that will be available online ( www.monash.edu/medicine/mchri/pcos ). Importantly, the Guideline and translation of the Guideline is expected to improve patient experiences through the provision of timely and accurate diagnosis, of accessible evidence-based information and of improved multi-disciplinary support. Ultimately, this international initiative may serve as an exemplar for large scale collaborative engagement, pooling of resources, avoidance of duplication and inconsistency with consensus-based statements, and codesign of best quality consistent guidelines with processes for local adaption and healthcare impact. Key elements include extensive collaboration, broad stakeholder representation, consumer partnership, distributive leadership, adequate funding, robust project management and governance, adherence to best practice and integrated comprehensive translation, and evaluation. We sincerely thank the partner and collaborating organizations, consumer groups and members of the GDGs for their substantive commitment to the international partnership to optimize health outcomes for women with this common, heterogeneous, and much neglected condition.

We gratefully acknowledge contribution of our partners and collaborating organizations:

The Australian National Health and Medical Research Council (NHMRC) Centre for Research Excellence in Women's Health in Reproductive Life (CRE WHiRL) (APP1171592), Centre for Research Excellence in Polycystic Ovary Syndrome (CRE PCOS) (APP1078444) and the members of these Centres who coordinated this international guideline effort.

Our partner and co-funding organizations are:

American Society for Reproductive Medicine (ASRM)

Endocrine Society (ENDO)

European Society for Endocrinology (ESE)

European Society of Human Reproduction and Embryology (ESHRE)

Collaborating and engaged societies and consumer providing in-kind support include:

Androgen Excess and Polycystic Ovary Syndrome Society (AEPCOS)

Asia Pacific Paediatric Endocrine Society (APPES)

Asia Pacific Initiative on Reproduction (ASPIRE)

Australia and New Zealand Society for Paediatric Endocrinology and Diabetes (ANZSPED)

Australian Diabetes Society (ADS)

Brazilian Society of Endocrinology and Metabolism (SBEM)

British Fertility Society (BFS)

Canadian Society of Endocrinology and Metabolism (CSEM)

Dietitians Association Australia (DA)

Endocrine Society Australia (ESA)

European Society for Paediatric Endocrinology (ESPE)

Exercise and Sports Science Australia (ESSA)

Fertility Society Australia and New Zealand (FSA)

International Federation of Fertility Societies (IFFS)

International Federation of Gynecology and Obstetrics (FIGO)

International Society of Endocrinology (ISE)—40 partner societies

Italian Society of Gynaecology and Obstetrics

Japanese Society for Paediatric Endocrinology (JSPE)

Latin American Society for Paediatric Endocrinology (SLEP)

Nordic Federation of Societies of Obstetrics and Gynaecology (NFOG)

PCOS Challenge Inc: The National Polycystic Ovary Syndrome Association

PCOS Society of India

PCOS Vitality

Paediatric Endocrine Society (PES)

Royal Australasian College of Physicians (RACP)

Royal Australian New Zealand College of Obstetricians and Gynaecologists (RANZCOG)

Royal Australian and New Zealand College of Radiologists (RANZCR)

Royal College of Obstetricians and Gynaecologists (RCOG)

Society for Endocrinology

South African Society of Gynaecology and Obstetrics (SASOG)

Victorian Assisted Reproductive Technology Association (VARTA)

Other relevant organizations are welcome to apply to partner in guideline translation.

The Australian National Health Medical Research Council (NHMRC) (APP1171592) primarily funded this work. The American Society for Reproductive Medicine, Endocrine Society, the European Society of Human Reproduction and Embryology and the European Society for Endocrinology provided partnership funding. Collaborating organizations provided in-kind support. The Commonwealth Government of Australia also supported Guideline Translation through the Medical Research Future Fund (MRFCRI000266). HJT and AM are funded by NHMRC fellowships and CTT by an RACP fellowship.

HJT led the guidelines from funding, engaging partners, coordinating processes, prioritizing clinical questions, co-chairing guideline meetings, coordinating peer review responses and leading writing, approval and publication processes. Listed authors held senior leadership roles as chair or deputy chair of the five GDGs or leadership of the evidence team with roles from the management committee, chair/ co-chair of GDG or the early career evidence network, involvement at all stages, responding to feedback, providing input into and endorsing the guideline. All other included authors were actively engaged as partner nominees and multidisciplinary GDG or consumer experts. The evidence synthesis network was led by CTT AM, across search strategies, training, Covidence processes, quality appraisal and GRADE, meta-analysis, evidence integrity processes (with BM) and preparing the technical report. The listed members of this network led evidence synthesis across the clinical questions and had input into the technical report.

Disclosures of interest were declared at the outset and updated throughout the guideline process, aligned with National Health Medical Research Council (NHMRC) guideline processes. These are available online ( www.monash.edu/medicine/mchri/pcos ). Of named authors HJT, CTT, AD, LM, LR, JBoyle, AM have no conflicts of interest to declare. JL declares grant from Ferring and Merck; consulting fees from Ferring and Titus Health Care; speaker's fees from Ferring; unpaid consultancy for Ferring, Roche Diagnostics and Ansh Labs; and sits on advisory boards for Ferring, Roche Diagnostics, Ansh Labs, and Gedeon Richter. TP declares a grant from Roche; consulting fees from Gedeon Richter and Organon; speaker's fees from Gedeon Richter and Exeltis; travel support from Gedeon Richter and Exeltis; unpaid consultancy for Roche Diagnostics; and sits on advisory boards for Roche Diagnostics. MC declares travels support from Merck; and sits on an advisory board for Merck. JBoivin declares grants from Merck Serono Ltd.; consulting fees from Ferring B.V; speaker's fees from Ferring Arzneimittell GmbH; travel support from Organon; and sits on an advisory board for the Office of Health Economics. RJN has received speaker's fees from Merck and sits on an advisory board for Ferring. AJoham has received speaker's fees from Novo Nordisk and Boehringer Ingelheim.

All data extracted and analyzed in the guideline is available in a repository and can be accessed via https://doi.org/10.26180/23625288.v1

Members of the PCOS Network:

The international advisory panel, guideline technical team, paediatric, consumer and translation committees, the Indigenous cultural advisor and the extended early career support network who assisted with evidence synthesis, can be found online ( www.monash.edu/medicine/mchri/pcos ).

Guideline Development Members and Key Contributors (in Addition to Listed Authors)

Wiebke Arlt, University of Birmingham, UK

Ricardo Azziz, University of Alabama at Birmingham, USA

Adam Balen, Leeds Teaching Hospital; British Fertility Society, UK

Lisa Bedson, Repromed, Australia

Lorna Berry, Polycystic Ovary Syndrome Association of Australia, Australia

Jacky Boivin, Cardiff University, UK

Leah Brennan, Latrobe University, Australia

Wendy Brown, Monash University, Australia

Tania Burgert, University Missouri—Kansas School of Medicine, USA

Maureen Busby, PCOS Vitality, Ireland

Carolyn Ee, Western Sydney University, Australia

Rhonda M. Garad, Monash University, Australia

Melanie Gibson-Helm, Te Tātai Hauora o Hine, Victoria University of Wellington; NZ

Cheryce Harrison, Monash University, Australia

Roger Hart, The University of Western Australia; City Fertility, Australia

Kim Hopkins, PCOS Challenge: National Polycystic Ovary Syndrome Association, USA

Angelica Lindén Hirschberg, Karolinska Institutet, Karolinska University Hospital, Sweden

Tuong Ho, HOPE Research Centre, My Duc Hospital, Vietnam

Kathleen Hoeger, University of Rochester, USA

Cailin Jordan, Genea Hollywood Fertility, Australia

Richard S. Legro, Penn State Clinical and Translational Institute, USA

Rong Li, Peking University Third Hospital, China

Marla Lujan, Cornell University, USA

Ronald Ma, Chinese University of Hong Kong, Hong Kong /China

Darren Mansfield, Monash and Epworth Health, Monash University, Australia

Kate Marsh, Northside Nutrition & Dietetics, Australia

Edgar Mocanu, Rotunda Hospital, Ireland

Ben Mol, Monash University, Australia

Rachel Mormon, Verity—PCOS Charity, UK

Sharon Oberfield, Columbia University Medical Center, USA

Malika Patel, University of Cape Town; Groote Schuur Hospital, South Africa

Loyal Pattuwage, Cochrane Australia, Monash University, Australia

Alexia Peña, The Robinson Research Institute at the University of Adelaide, Australia

Leanne Redman, Pennington Biomedical Research Center, USA

Luk Rombauts, Monash University, Australia

Daniela Romualdi, Fondazione Policlinico Universitario Agostino Gemelli, Italy

Duru Shah, PCOS Society of India; Centre for Women's Health and Fertility, India

Poli Mara Spritzer, Federal University of Rio Grande Do Sul, Brazil

Elisabet Stener-Victorin, Karolinska Institutet, Sweden

Fahimeh Ramezani Tehrani, Shahid Beheshti University of Medical Sciences, Iran

Shakila Thangaratinam, University of Birmingham, UK

Mala Thondan, Harp Family Medical, Australia

Eszter Vanky, Norwegian University of Science and Technology; Norway

Chandrika Wijeyaratne, University of Colombo, Sri Lanka

Selma Witchel, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, USA

Dongzi Yang, Reproductive Medical Centre, Sun Yat-Sen Memorial Hospital, China

Bulent Yildiz, Hacettepe University, Turkey

International Early Career Evidence Synthesis Network Leads

Simon Alesi, Monash University, Australia

Snigdha Alur-Gupta, University of Rochester, USA

Jodie Avery, University of Adelaide, Australia

Mahnaz Bahri Khomami, Monash University, Australia

Jamie Benham, University of Calgary, Canada

Hugh Bidstrup, Australian Catholic University, Australia

Su Jen Chua, Monash University, Australia

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• Review Article
• Published: 07 July 2021

Epigenetic inheritance of polycystic ovary syndrome — challenges and opportunities for treatment

• Elisabet Stener-Victorin   ORCID: orcid.org/0000-0002-3424-1502 1 &
• Qiaolin Deng 1 , 2  

Nature Reviews Endocrinology volume  17 ,  pages 521–533 ( 2021 ) Cite this article

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• Endocrine reproductive disorders
• Pathogenesis
• Polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is the main cause of female infertility worldwide and is associated with a substantially increased lifetime risk of comorbidities, including type 2 diabetes mellitus, psychiatric disorders and gynaecological cancers. Despite its high prevalence ( ~ 15%) and substantial economic burden, the aetiology of PCOS remains elusive. The genetic loci linked to PCOS so far account for only ~10% of its heritability, which is estimated at 70%. However, growing evidence suggests that altered epigenetic and developmental programming resulting from hormonal dysregulation of the maternal uterine environment contributes to the pathogenesis of PCOS. Male as well as female relatives of women with PCOS are also at an increased risk of developing PCOS-associated reproductive and metabolic disorders. Although PCOS phenotypes are highly heterogenous, hyperandrogenism is thought to be the principal driver of this condition. Current treatments for PCOS are suboptimal as they can only alleviate some of the symptoms; preventative and targeted treatments are sorely needed. This Review presents an overview of the current understanding of the aetiology of PCOS and focuses on the developmental origin and epigenetic inheritance of this syndrome.

Polycystic ovary syndrome (PCOS) is a common heritable disorder strongly linked to hyperandrogenism and hyperinsulinaemia.

Disentangling the genetic and non-genetic contributions to the transmission of PCOS will require further investigation.

PCOS-like phenotypic traits are transgenerationally inherited in female offspring of androgen-exposed or anti-Müllerian hormone-exposed dams up to the F 3 generation, indicating long-lasting effects of an aberrant maternal–fetal environment.

Studies in mouse models of PCOS demonstrate that epigenetic modulation connects early-life exposures to subsequent phenotypes and contributes to the development and familial transmission of PCOS.

Inheritance through epigenetic mechanisms opens a path towards novel treatment strategies for PCOS-like phenotypic traits.

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Acknowledgements

The authors’ research is supported by the Swedish Medical Research Council (Project No. 2018-02435: E.S.V., 2018-02557: Q.D.), Novo Nordisk Foundation (NNF19OC0056647: E.S.V.), the Strategic Research Programme in Diabetes at Karolinska Institutet (E.S.V.), Adlerbert Research Foundation (E.S.V.), Åke Wibergs Stiftelse (Q.D.) and faculty funding at Karolinska Institutet (Q.D.).

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Stener-Victorin, E., Deng, Q. Epigenetic inheritance of polycystic ovary syndrome — challenges and opportunities for treatment. Nat Rev Endocrinol 17 , 521–533 (2021). https://doi.org/10.1038/s41574-021-00517-x

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A better understanding of PCOS offers fresh hope for new treatments

New insights into polycystic ovary syndrome are revealing more about the causes of this common but misunderstood whole-body condition, and these could lead to new treatments

By Alice Klein

26 January 2023

I WAS 19, my face raging with acne, when my dermatologist started asking me questions that seemed to have nothing to do with my skin. “Are your periods regular? Do you have any excess body hair?” he asked. “You may have polycystic ovary syndrome,” he concluded. I had no idea what he was talking about. “It can make it difficult to have children,” he said as he saw me out.

Reeling, I went to my family doctor, who ordered blood tests and an ultrasound of my ovaries that confirmed I had polycystic ovary syndrome, or PCOS. But she admitted she didn’t know much about it, leaving me confused and miserable about this mysterious condition I had suddenly been saddled with.

Many of my friends have recounted similar experiences. Despite PCOS being the most common hormonal condition among women aged 18 to 45 and a leading cause of infertility, it has been hard for us to get a straight answer about what it actually is or what to do about it.

Seventeen years on from my diagnosis, however, the tide is turning. Researchers are finally piecing together the causes of PCOS and it is being taken seriously as a condition that doesn’t just affect the ovaries, but also has cardiovascular, metabolic and psychological repercussions. As a result, the condition is even set to get a different name later this year (see “Misleading moniker”). And what’s more, this clearer understanding is opening up routes to new treatments.

The first doctors to characterise PCOS were Irving Stein and Michael Leventhal at Northwestern University in Chicago. In 1935, they published a report on…

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Comorbidities in women with polycystic ovary syndrome: a sibling study

• Beata Vivien Boldis 1 , 2 , 3 , 4 ,
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Polycystic ovary syndrome (PCOS) has previously been associated with several comorbidities that may have shared genetic, epigenetic, developmental or environmental origins. PCOS may be influenced by prenatal androgen excess, poor intrauterine or childhood environmental factors, childhood obesity and learned health risk behaviors. We analyzed the association between PCOS and several relevant comorbidities while adjusting for early-life biological and socioeconomic conditions, also investigating the extent to which the association is affected by familial risk factors.

This total-population register-based cohort study included 333,999 full sisters, born between 1962 and 1980. PCOS and comorbidity diagnoses were measured at age 17-45 years through national hospital register data from 1997 to 2011, and complemented with information on the study subjects´ early-life and social characteristics. In the main analysis, sister fixed effects (FE) models were used to control for all time-invariant factors that are shared among sisters, thereby testing whether the association between PCOS and examined comorbidities is influenced by unobserved familial environmental, social or genetic factors.

Three thousand five hundred seventy women in the Sister sample were diagnosed with PCOS, of whom 14% had obesity, 8% had depression, 7% had anxiety and 4% experienced sleeping, sexual and eating disorders (SSE). Having PCOS increased the odds of obesity nearly 6-fold (adjusted OR (aOR): 5.9 [95% CI:5.4-6.5]). This association was attenuated in models accounting for unobserved characteristics shared between full sisters, but remained considerable in size (Sister FE: aOR: 4.5 [95% CI: 3.6-5.6]). For depression (Sister FE: aOR: 1.4 [95% CI: 1.2-1.8]) and anxiety (Sister FE: aOR: 1.5 [95% CI: 1.2-1.8), there was a small decrease in the aORs when controlling for factors shared between sisters. Being diagnosed with SSE disorders yielded a 2.4 aOR (95% CI:2.0-2.6) when controlling for a comprehensive set of individual-level confounders, which only decreased slightly when controlling for factors at the family level such as shared genes or parenting style. Accounting for differences between sisters in observed early-life circumstances influenced the estimated associations marginally.

Having been diagnosed with PCOS is associated with a markedly increased risk of obesity and sleeping, sexual and eating disorders, also after accounting for factors shared between sisters and early-life conditions.

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Introduction

Polycystic ovary syndrome (PCOS), a subtype of ovarian dysfunction (OD), is a heterogenous disorder that has a prevalence of 4-20% among premenopausal women, depending on the criteria used for diagnosis [ 1 , 2 , 3 ]. Despite PCOS showing a similar prevalence to diabetes mellitus (DM) [ 4 ], it still remains one of the most poorly understood, undertreated and misclassified syndromes with long-term health consequences [ 5 , 6 , 7 ].

PCOS is associated with numerous comorbidities, including obesity, depression, anxiety, sleep apnea and eating and sexual disorders [ 8 , 9 , 10 , 11 , 12 ]. Obesity, metabolic disorders and insulin resistance are often observed among women with the most severe phenotypes of PCOS [ 13 , 14 ]. Excess androgen among women with PCOS facilitates abdominal and visceral adiposity, which is commonly seen among patients with insulin resistance [ 15 , 16 ]. This surplus of androgen may also lead to psychiatric disorders such as depressive disorder and generalized anxiety disorder [ 17 ].

Previous studies have reported a higher risk for eating disorder among women with PCOS [ 18 , 19 ], supporting the idea that hyperandrogenism can intensify food cravings, over-eating and bulimic behavior [ 20 , 21 ]. There may also be an association between obstructive sleep apnea (OSA) and PCOS, since OSA is also associated with obesity and depressive disorders. Since the endocrine system has an important role in the regulation of comorbidities characterized by metabolic disturbances, as well as PCOS, it is likely that these conditions cluster together. This suggests a complex relationship between the different conditions [ 22 ]. OSA is also associated with depression, which could potentially cause a lower quality of life for people with PCOS [ 20 , 23 ]. Finally, sexual dysfunction among women has been linked to obesity, depression and anxiety. Due to these co-existing comorbidities, lower sexual satisfaction may occur among PCOS patients [ 9 , 24 , 25 ]. Still, PCOS screening among women with these comorbidities is not fully stipulated by existing guidelines [ 26 ], possibly resulting in undiagnosed cases and cumulative negative health outcomes [ 27 ]. The first aim of this study is to investigate the association between PCOS and several comorbidities; obesity, depression, anxiety and sleeping, sexual and eating disorders (SSE).

The etiology of PCOS is still ambiguous with a range of conceivable genetic and environmental contributing factors [ 28 , 29 ]. Women have a higher risk of developing PCOS when having a first-degree female relative with the syndrome [ 30 , 31 ], and one third of sisters of women with PCOS meet the diagnostic criteria of the syndrome as well [ 30 ]. In our prior research, we found that the risk for PCOS increased almost 3-fold when the mother, and by nearly 5-fold when a sister, had already been diagnosed with PCOS [ 32 ]. Further evidence suggests that male first-degree relatives of PCOS women have similar endocrine and metabolic risks [ 33 ], co-occurring with typical phenotype of male-patterned baldness [ 33 ]. Therefore, building on our earlier research finding familial clustering of the trait [ 32 ], the second and main aim of this present study is to explore the extent to which the associations between PCOS and examined comorbidities is affected by familial confounding. This is done through a sister fixed effects (FE) approach, exploiting variation between full sisters, cancelling out the influence of time-invariant factors – observed and unobserved – that are shared between sisters.

PCOS has been suggested to be influenced by intrauterine development [ 28 , 34 , 35 ]. In particular, it has been suggested that hyperandrogenic fetal ovaries might reprogram developmental processes that can lead to adult PCOS phenotype [ 34 ]. As outlined in our previous publication [ 32 ], we found evidence for the influence of early-life factors such as gestational age and one-minute Apgar score. Therefore, to further elaborate on our previous findings, the third aim of our study is to quantify the association between PCOS and examined comorbidities when controlling for differences in early-life conditions between full sisters.

Study population

This study was part of the Swedish Interdisciplinary Panel (SIP) project with an individual-level database, administered at the Centre for Economic Demography, Lund University. Through the unique Swedish personal identification number assigned at birth or immigration, several national registers, such as the Swedish Medical Birth Register (MBR), the Swedish National Patient Register (NPR), Total Population Register (TPR), Register of Participation in Education (UREG) and the Multi-Generational Register have been linked together, allowing for a uniquely detailed and longitudinal description of the health and socioeconomic characteristics of the population. We extracted a population consisting of all women born between 1962 and 1980 ( N = 1,352,019), linking them to their biological parents and siblings.

Multiple births and women with a missing link to either biological parent were excluded ( n =330, 378). We further excluded those who died or emigrated before the age of 17 or before the start of the follow-up period ( n = 45,467), and those who were outside of the follow-up period (1997-2011) ( n = 50,396). After excluding those with missing information on the explanatory variables and those without at least one full biological sister, two sub-samples were created. One consisted of women with at least one full sister (Sister sample: n = 333,999) and another with additional restriction to women with information on characteristics measured at birth, retrieved from the MBR, implying only Swedish-born women born between 1973-1980 are included (MBR Sister sample: n = 77,034). The flow chart in Fig.  1 illustrates how the analytical samples were generated.

Flow diagram of the study sample creation

The follow-up period starts on January 1, 1997, as International Classification of Disease (ICD) 10 codes started being used in the same year in Sweden, with the sample restricted to women observed between the ages of 17 and 45 years. The study population was followed continuously until whichever happened first: turning 45 years of age, death, emigration, or the end of our follow-up period on December 31, 2011. Figure 2 depicts the samples used in different parts of analysis and also illustrates the periods of availability of relevant data in Swedish administrative registers.

Data availability from Swedish national registers and sampling windows

Outcome: comorbidity variables

Using the NPR, we defined the following potential comorbidities, using the ICD-10 diagnostic codes: obesity (E66), depression (F32 – Depressive episode, and F33 – Recurrent depressive disorder), anxiety (F41 – Other anxiety disorder), and SSE disorders (F51 – Non-organic sleeping disorder, F52 – Sexual dysfunction not caused by organic dysfunction or disease, F50 – Eating disorders). Each condition was defined as a binary variable, indicating whether the individual had been diagnosed at any time during the follow-up period and not necessarily subsequent to the PCOS diagnosis.

Exposure: PCOS and exclusion criteria

PCOS was identified through the NPR [ 36 ] at any time during the follow-up period as the binary exposure, including both inpatient and outpatient diagnoses (E28), with the vast majority of the diagnoses coming from outpatient data (99.7%). The aggregated ICD-10 code E28 was used, due to limited access to the more detailed PCOS diagnosis code (ICD-10: E28.2). Women diagnosed with conditions that could cause similar symptoms to PCOS, including Turner syndrome (Q96), Malignant neoplasm of ovary (C56), Suprarenal tumor (C74), Adrenogenital syndrome (E25), Cushing disease (E24) and Pituitary hypersecretion (E22), have been excluded to ensure specificity, similar to previous literature [ 37 ]. Both main and contributing diagnoses were considered. In our main analysis, we only included women who were never diagnosed with any of the exclusion criteria, regardless of whether these happened before or after the PCOS diagnosis.

Family characteristics

Highest maternal and paternal education attainment were obtained from UREG and categorized as primary/secondary or university, and were used as proxies for their daughters’ socioeconomic status in childhood. Mother’s, father’s and offspring’s country of birth was obtained from the TPR and grouped as a three-level variable to Sweden; Europe, North America and Oceania; and Africa, Asia and South America. Birth order was created from maternal live birth and grouped as first born, second born and third born or higher. Mother’s age at birth was grouped into less than or equal to 18 years of age, between 19 and 35 years of age and greater than or equal to 36 years of age.

Adult characteristics

The index individual’s highest attained education was obtained from UREG and categorized as Primary/Secondary or University. Civil status was extracted from the TPR and used to distinguish between married/in a registered partnership or not married/not in a registered partnership, using the civil status of the highest observed age between the ages of 17 to 45 years. Additionally, the final models were adjusted for the index woman’s residence at the time of the follow-up, where residence status was grouped into today’s 21 counties.

Early-life factors

Birthweight, one-minute Apgar score and gestational age were extracted from the MBR. Birthweight was categorized into 500-g groups (≤2,499g, 2,500-2,999g, 3,000g-3,499g, 3,500-3,999g, 4,000-4,499g and ≥4,500g). Gestational age was measured in completed weeks of gestation and grouped as extremely preterm (<28 weeks), very preterm (28-32 weeks), moderate to late preterm (33-36 weeks), full-term (37-41 weeks) and post-term birth (≥42 weeks). Gestational age was based on self-reported first dates of the last menstrual period or on results of ultrasound examinations. One-minute Apgar score was categorized as 10, 9, 8 and less than or equal to 7, as a standardized assessment of health signs of newborns immediately after birth [ 38 ].

Statistical analysis

Statistical analyses were performed using STATA/MP 17.0 (StataCorp). The main analyses were restricted to individuals with non-missing information on all covariates included in the models.

In order to address the first aim of the study, we treated the sisters as unrelated individuals and thus used unconditional logistic regression to examine the association between PCOS and each comorbidity condition in the Sister sample, while controlling for previously outlined covariates. Covariates were added to the model group-wise, from controls for year of birth to family background characteristics and adult characteristics. Additionally, the same models were run on a sample that was not restricted to full sisters.

Addressing the second aim, comparing the associations between PCOS and examined comorbidity conditions among full sisters, a FE logistic regression was used (also known as conditional logistic regression). This accounts for unobserved, time-invariant factors coming from the shared family environmental, social or genetic factors that could affect both PCOS and the selected comorbidity.

A potential downside to this approach pertains to it requiring within-sister combination variation in the outcome. Thus, at least one (but not every) sister has to be diagnosed with the examined comorbidity, meaning that only discordant sisters are included in the analysis. This restriction reduced the sample for the examination of all outcomes; obesity ( n = 7,994 families, n = 17,732 women), depression ( n = 15,232 families, n = 33,709 women), anxiety ( n = 12,597 families, n = 27,881 women), SSE disorders ( n = 5,129 families, n = 11,351 women).

For each selected comorbidity, we compared the results from unconditional logistic regression models and sister FE models on the Sister and MBR Sister sample, respectively. The FE approach adjusts for unmeasured factors that are shared between the sisters, such as genes which may cause a predisposition to disease or other factors linked to sharing an environment during the upbringing, such as parenting style, and attitudes towards exercise and diet. While we are unable to quantify the relative importance of each of the aforementioned factors, the method allows for obtaining the association after adjusting for shared factors between sisters.

To investigate our third aim, to what extent the association between PCOS and the selected comorbidities changes when controlling for early-life factors such as birthweight, gestational age and one-minute Apgar score, we restricted our sample to full sisters born in Sweden between 1973-1980 and with information on their selected birth characteristics available (MBR Sister sample).

Sensitivity analysis

Due to discordancy in the outcome variable and also the fact that each PCOS diagnosed woman needed to have a sister, there is a risk that the sample is selected and thereby yielding results with limited external validity in a larger representative sample. We address this concern by reporting results from models estimated on the sibling FE sample but without aforementioned FE. In addition, we compare these results to the corresponding results of a sample without restrictions to sisters. We argue that this provides a good approximation of the degree to which the sibling FE sample can be used to understand associations in a population of outcome-concordant siblings. Additionally, we estimate random intercept models on the Sister sample and Sister MBR sample. Random intercept models are less restrictive in terms of both study sample and the ability to obtain parameter estimates for independent variables that do not display any variation within sibling combinations. The disadvantage, however, is that the appropriateness of the method requires that there is no correlation between the random effects and the independent variables. We therefore used the Hausman (1978) [ 39 ] specification test to detect violations of the random effects approach assumptions.

The diagnostic codes for SSE disorders were combined, due to the generally low prevalence of these conditions (0.64%, 0.40% and 0.64% in the total sample, respectively), and were treated as one binary variable in the main analyses. Therefore, a separate analysis for each of the disorders was conducted.

Descriptive characteristics

Descriptive characteristics of the study population are presented in Table 1 for two samples of women that we used in our analyses: one sample with women born in Sweden in 1962-1980 with at least one full sister (Sister sample, n = 333,999), and another sample of women born in Sweden in 1973-1980 with at least one full sister (MBR Sister sample, n = 77,034). Descriptive characteristics of additional sub-samples such as: without restriction to sisters ( n = 857,757), concordant sisters without PCOS ( n = 326,422), concordant sisters with PCOS ( n = 137), and discordant sisters ( n = 7,440), are shown in Appendix Table A. 1 .

From a total of 333,999 women aged 17 to 45 years and followed from 1997-2011, 3,570 were diagnosed with PCOS, of which 14% were diagnosed with obesity, 8% with depression, 7% with anxiety and 4% with SSE disorders during follow-up. The prevalence of the comorbidities among cases of PCOS remained very similar in the MBR Sister sample (Fig.  3 ).

Prevalence of comorbidity conditions among women with and without PCOS

Main analysis

Associations between pcos and the selected comorbidities.

The minimally adjusted odds ratio (mORs), only adjusted for birth year, associated with PCOS in Model 1 (Sister sample, Table 2 ) was 5.88 (95% CI: 5.34-6.47) for being diagnosed with obesity, 1.54 (95% CI: 1.37-1.74) for depression, 1.69 (95% CI: 1.49-1.93) for anxiety and 2.35 (95% CI: 1.98-2.79) for SSE disorders. Extending the models to account for other risk factors only changed the associations marginally, with adjusted ORs (aOR) of 5.92, 1.58, 1.72 and 2.36 in the fully adjusted Model 3. Results from otherwise identical models but not restricted to full sisters (Table A. 2 ), confirm the robustness of these findings. Thus, restricting the sample to full sisters is not driving observed associations between PCOS and examined comorbidities, something that applies to both the mORs and the aORs.

The influence of familial risk factors

We investigated to what extent the association between being diagnosed with PCOS and each comorbidity can be attributed to factors shared between sisters. We present the results from sibling FE models in Table 2 , with the corresponding results from random effect models presented in the  Appendix (Table A. 3 ), yielding similar conclusions. A strong positive association between PCOS and obesity was still observed in the sibling FE model (Sister FE Model 3: aOR: 4.47 [95% CI: 3.60-5.56]), although is attenuated compared to the results from the Sister model.

For depression (Sister FE Model 3: aOR: 1.44 [95% CI: 1.18-1.75]) and anxiety (Sister FE Model 3: aOR: 1.47 [95% CI: 1.20-1.80]), there was a small decrease in the aORs when also controlling for time-invariant factors shared between sisters. Being diagnosed with SSE disorders among women with PCOS yielded a 2.37 aOR in the Sister sample (Model 3: aOR: 2.37 [95% CI:1.99-2.80]), which decreased slightly when controlling for shared factors at the family level (Sister FE Model 3: aOR: 2.29 [95% CI: 1.69-3.10]).

Importance of early-life factors

The observed associations between PCOS and comorbidities where we also adjust for birthweight, gestational age and one-minute Apgar score are presented in Table 3 . The estimates in Model 1 are rather similar to those obtained in the larger sister sample and in the without restriction to sisters sample (Appendix. Table A. 2 ), showing that the minimally adjusted associations between PCOS and the examined outcomes are similar in the more restricted MBS Sister sample. The association between PCOS an obesity is particularly accentuated in the MBR Sister Unconditional Models (Table 3 ), suggesting a six-fold increase in the odds (aOR) when not accounting for familial confounding. In case of all comorbidities, the results from Model 2 and 3 (MBR Sister Unconditional, Table 3 ) show that adjusting for early-life factors did not affect estimates considerably, suggesting that early-life factors are not strong predictors for the association between PCOS and associated comorbidities. However, when accounting for familiar confounding (MBR Sister FE, Table 3 ) this association is attenuated considerably, yet still results in a four-fold increase in the odds of being diagnosed with obesity (aOR).

Estimates of ORs calculated for SSE disorders separately were similar to those with the combined SSE variables (Appendix. Table A. 4 ). When comparing the FE and random intercept models, we see that the random intercept model estimates consistently are slightly smaller in size but yields qualitatively similar results (Appendix. Table A. 3 .). The results of the Hausman test, which compared the fitting of the fixed and random effects models, rejected the null hypothesis in the Sister sample (Appendix. Table A. 3 .), confirming that the FE estimators were better fitting our data.

This study makes a unique contribution to the literature on comorbidities in women with PCOS by using family FE models to examine the relationship between PCOS and obesity, depression, anxiety and SSE disorders. After adjusting for a range of shared family environmental, social or genetic risk factors, we found that women with a diagnosis of PCOS have 4-fold increased odds of being diagnosed with obesity, 1.4-fold higher odds for depression or anxiety and almost 2-fold higher odds for SSE disorders, compared to a sister without PCOS.

Our finding on the strong association between PCOS and obesity is consistent with earlier epidemiological studies [ 13 , 14 , 40 ]. However, the direction of the association is still unclear, and most likely there are also underlying factors which affect the development of both PCOS and obesity. Similarly, a recent nationwide Swedish study on PCOS and psychiatric comorbidities, has found that women with PCOS had an 1.5-fold increased odds for having depressive and anxiety disorders [ 12 ]. These findings resonate well with our results on increased odds for depression and anxiety disorders among women with PCOS which remained robust across all models. We found an even stronger association between PCOS and SSE disorders, as much as 2.4 higher odds for having SSE as comorbidities among women with PCOS, in our Total sample. A similar pattern of results was obtained in a recent meta-analysis of 36 studies which found PCOS to be associated with an increased risk of sleeping and eating disorders and low sexual satisfaction [ 8 ].

Since previous studies have found evidence for a genetic component of PCOS based on familial clustering of the trait [ 41 , 42 ], our underlying hypothesis was that there are several plausible factors at the sibling level that can impact on both PCOS and the studied comorbidities. This could be shared environmental factors, lifestyle, or a combination of genetic and environmental influences.

Even after accounting for unobserved, fixed characteristics that might influence both PCOS and the comorbidity conditions, our findings indicate that many of the previously observed associations persist, and these associations cannot be solely attributed to familial confounding. Although the sister FE approach effectively addresses shared risk factors among sisters, it does not capture variations unique to each sister. Consequently, we have made adjustments for various risk factors, available from our data, including birth year, birth order, maternal age at birth, birthweight, gestational age, one-minute Apgar score, individual educational attainment, civil status, and county of residence, aiming to account for some of the unmeasured differences in risk factors between sisters.

In our effort to minimize residual variability using observational data and focusing on full sisters who share the same biological parents, we observed that controlling for the available early-life factors had minimal impact on the previously reported coefficient estimates. This suggests that these observed early-life factors are not important confounding variables to the associations between PCOS and the examined comorbidities in our data.

Strengths and limitations

The large, nationally representative Swedish register data allows for analyses with substantial statistical power even for relatively low-prevalence diseases in the registers such as PCOS. The high quality of the Swedish national registers with prospectively collected data [ 43 ] and a nationwide coverage also reduce the risk of selection, recall and information bias. However, this type of data source is prone to its own limitations, such as that they do not cover information on health risks or health-seeking behaviors.

Since data used for research purposes are further limited in detail in order to protect personal integrity, the research data extract do not contain the full information from the patient registers. We only had access to the more aggregated three-digit ICD-10 codes from the Swedish NPR, meaning that the E28 Ovarian dysfunction code was used throughout the analysis instead of the more detailed E28.2 Polycystic ovary syndrome code. As previously reported [ 12 ], the prevalence of PCOS is lower in the Swedish NPR than it could be expected on a population level. This could have led to measurement error. Due to information sharing between sisters, this may however be less of a concern in families where at least one sister has been diagnosed, which are the ones that are included in our FE analysis. As previously described by March et al. [ 3 ], the prevalence of PCOS is dependent on the diagnostic criteria used, finding that it can range from 8.7% up to 17.8%, depending on whether the National Institute of Health or the Rotterdam criteria were used for diagnosis. Since outpatient data is constrained by the inherent limitation of uncertainty in the diagnostic criteria used, many patients may only get diagnosed if a comorbidity condition such as obesity or sub-fertility is also present which they would primarily seek help for. This could also be the reason for the lower prevalence of PCOS in the Swedish NPR.

There was a paradigm shift in PCOS diagnosis during the follow-up period with the introduction of the Rotterdam criteria in 2003 [ 14 ] meaning that some of the index women and their sisters had been diagnosed before the shift while others were diagnosed subsequently. Additionally, outpatient specialist care was added to the Swedish NPR from 2001, and before that only inpatient care diagnoses were recorded. This could have caused a left censoring in the analysis as well as a higher concentration of more severe PCOS cases before 2001.

PCOS is associated with an increased risk of obesity, depression, anxiety and eating, sleeping and sexual disorders. This association remains, net of familial confounding and observable characteristics. Early screening for comorbidities in women with PCOS and screening for PCOS in women with comorbidities is justified and early intervention may increase the quality of life in women with PCOS.

Availability of data and materials

Swedish law prohibits the distribution of and unauthorized access to the data used for this study, stored and analyzed on a secure server managed by Statistics Sweden ( https://www.scb.se/en/services/ordering-data-and-statistics/ordering-microdata/mona--statistics-swedens-platform-for-access-to-microdata/about-mona/ ).

Abbreviations

Adjusted odds ratio

Confidence interval

Diabetes mellitus

Fixed effect

International Classification of Disease

Minimally adjusted odds ratio

Obstructive sleep apnea

Ovarian dysfunction

• Polycystic ovary syndrome

Register of Participation in Education

Sleeping, sexual and eating disorders

Swedish Interdisciplinary Panel

Swedish Medical Birth Register

Swedish National Patient Register

Total Population Register

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Acknowledgements

The authors acknowledge data access through the Centre for Economic Demography, Lund University.

Open access funding provided by Stockholm University. This research was supported by Swedish Research Council for Health, Working Life and Welfare (FORTE grant number: 2018-00211, PI Grünberger). The funding bodies had no role in design of the study, data collection, analysis, interpretation of data or in writing the manuscript.

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Beata Vivien Boldis, Anton Nilsson & Jonas Helgertz

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BVB developed the conceptual framework and drafted the manuscript. BVB had full access to the data and conducted the data management and the statistical analysis with input from JH. All co-authors assisted in the conceptualization of the study, advised on the statistical analyses, reviewed the manuscript for important intellectual content and approved the final version. All authors gave important intellectual input during the manuscript writing.

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Boldis, B.V., Grünberger, I., Cederström, A. et al. Comorbidities in women with polycystic ovary syndrome: a sibling study. BMC Women's Health 24 , 221 (2024). https://doi.org/10.1186/s12905-024-03028-9

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Multi-omics insights and therapeutic implications in polycystic ovary syndrome: a review

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Polycystic ovary syndrome (PCOS) is a common gynecological disease that causes adverse effects in women in their reproductive phase. Nonetheless, the molecular mechanisms remain unclear. Over the last decade, sequencing and omics approaches have advanced at an increased pace. Omics initiatives have come to the forefront of biomedical research by presenting the significance of biological functions and processes. Thus, multi-omics profiling has yielded important insights into understanding the biology of PCOS by identifying potential biomarkers and therapeutic targets. Multi-omics platforms provide high-throughput data to leverage the molecular mechanisms and pathways involving genetic alteration, epigenetic regulation, transcriptional regulation, protein interaction, and metabolic alterations in PCOS. The purpose of this review is to outline the prospects of multi-omics technologies in PCOS research by revealing novel biomarkers and therapeutic targets. Finally, we address the knowledge gaps and emerging treatment strategies for the management of PCOS. Future PCOS research in multi-omics at the single-cell level may enhance diagnostic and treatment options.

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Luyao Ren, Leming Shi & Yuanting Zheng

A brief insight into the etiology, genetics, and immunology of polycystic ovarian syndrome (PCOS)

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The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

3-Hydroxyl-3-methyl glutaryl coenzyme-A reductase

α-1-β-GlycoproteinAMH Anti-müllerian hormone

Apolipoprotein A-II

Apolipoprotein C3

Apoprotein A-IV

Chromobox homolog 2

Clomiphene citrate

C-terminal binding protein 1 antisense

Cumulus granulosa cells

Data-independent acquisition

Dickkopf-related protein 1

DNA adenine methyltransferase identification

Deoxyribonucleic acid

DNA methyltransferases

Follicle-stimulating hormone receptor

Follicular fluid

Food and Drug Administration

Glucagon-like peptide-1

Gonadotropin-releasing hormone

Granulosa cells

Histone deacetylases

Homeostatic model assessment for insulin resistance

Insulin resistance

In vitro fertilization

Long interspersed nucleotide element-1

Luteinized granulosa cells

Luteinizing hormone

Luteinizing hormone receptor

Mass spectrometry

Matrix-assisted laser desorption/ionization time-of-flight/time-of-flight

Methyl-CpG binding protein 2

Mitochondrial DNA

mitogen-activated protein kinase kinase kinase 15

National Health and Nutrition Examination Survey

Next-generation sequencing

Non-coding RNA

Nuclear magnetic resonance

Phosphatidyl ethanolamine-binding protein 1

Polycystic ovary syndrome

Polymerase chain reaction

Proteasome activator complex subunit 1

Randomized controlled trial

Ribonucleic acid

Surface-enhanced laser desorption/ionization

Translationally controlled tumor protein

Triosephosphate isomerase 1

Two-dimensional difference gel electrophoresis

Two-dimensional gel electrophoresis coupled to mass spectrometry

Type 2 diabetes mellitus

Whole genome sequencing

Zinc-alpha-2-glycoprotein

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Acknowledgements

The authors would like to thank the management of the Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India, for providing the necessary facilities and encouragement to carry out this work.

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Babu, A., Ramanathan, G. Multi-omics insights and therapeutic implications in polycystic ovary syndrome: a review. Funct Integr Genomics 23 , 130 (2023). https://doi.org/10.1007/s10142-023-01053-9

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Exploring the molecular mechanisms by which per- and polyfluoroalkyl substances induce polycystic ovary syndrome through in silico toxicogenomic data mining

Affiliations.

• 1 Department of Preventive Medicine, Fujian Provincial Key Laboratory of Environment factors and Cancer, Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou, Fujian Province 350122, China.
• 2 Ganzhou Ganxian District Maternity and Child Health Hospital, Ganzhou, Jiangxi Province 341100, China.
• 3 School of Public Health, Fudan University, Shanghai 200032, China.
• 4 Research Center for Environment and Female Reproductive Health, the Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China.
• 5 Department of Preventive Medicine, Fujian Provincial Key Laboratory of Environment factors and Cancer, Key Laboratory of Environment and Health, School of Public Health, Fujian Medical University, Fuzhou, Fujian Province 350122, China. Electronic address: [email protected].
• PMID: 38537477
• DOI: 10.1016/j.ecoenv.2024.116251

The pathogeny of polycystic ovary syndrome (PCOS) is intricate, with endocrine disruptors (EDCs) being acknowledged as significant environmental factors. Research has shown a link between exposure to per- and polyfluoroalkyl substances (PFAS) and the development and progression of PCOS, although the precise mechanism is not fully understood. This study utilized toxicogenomics and comparative toxicogenomics databases to analyze data and investigate how PFAS mixtures may contribute to the development of PCOS. The results indicated that 74 genes are associated with both PFAS exposure and PCOS progression. Enrichment analysis suggested that cell cycle regulation and steroid hormone synthesis may be crucial pathways through which PFAS mixtures participate in the development of PCOS, involving important genes such as CCNB1 and SRD5A1. Furthermore, the study identified transcription factors (TFs) and miRNAs that may be involved in the onset and progression of PCOS, constructing regulatory networks encompassing TFs-mRNA interactions and miRNA-mRNA relationships to elucidate their regulatory roles in gene expression. By utilizing data mining techniques based on toxicogenomic databases, this study provides relatively comprehensive insights into the association between exposure factors and diseases compared to traditional toxicology studies. These findings offer new perspectives for further in vivo or in vitro investigations and contribute to understanding the pathogenesis of PCOS, thereby providing valuable references for identifying clinical treatment targets.

Keywords: PCOS; PFAS; Toxicogenomic data-mining; molecular mechanisms.

Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.

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Is It Healthier to Eat Your Vegetables Before Your Carbs?

“Nutrient sequencing” is said to regulate blood sugar. We asked experts if the science holds up.

Credit... Eric Helgas for The New York Times

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By Nikki Campo

• April 2, 2024

Q: I’ve heard that it’s best for my health to eat a salad before dinner. But if I’m eating vegetables regardless, does the order really matter?

It’s a popular internet health hack: Eat foods in the “right” order — vegetables first, proteins and fats second, carbohydrates last — and you’ll significantly reduce your resulting spike in blood sugar, which can therefore reduce cravings, fatigue and health risks like Type 2 diabetes, proponents say.

Past research on the topic , sometimes referred to as nutrient or meal sequencing, has concluded that it can indeed benefit blood sugar, especially for those with Type 2 diabetes or pre-diabetes.

For everyone else, it’s not as cut-and-dried, said Dr. Alpana Shukla, a physician and researcher at Weill Cornell Medicine in New York City who has studied food order. Though there are some reasons to consider giving it a try, she said.

What does the research suggest?

Existing studies on the benefits of meal sequencing are small, but the results are consistent, experts say.

In one 2023 review of 11 studies , for instance, researchers concluded that people who saved carbohydrate-rich foods for the end of a meal, after vegetables and proteins, had significantly lower blood sugar levels than when they consumed them first.

In one 2019 study of 15 people with pre-diabetes, Dr. Shukla and her colleagues asked the participants to eat a meal of skinless grilled chicken, salad and ciabatta in three different orders on three different days: ciabatta first, followed 10 minutes later by the chicken and salad; chicken and salad first, followed by the ciabatta; and salad first, followed by the chicken and ciabatta.

Researchers measured participants’ blood sugar levels right before they ate, and every 30 minutes for three hours after each meal. They found that when the participants ate the chicken and salad before the bread, their blood sugar spikes following the meal were about 46 percent lower than when they ate the bread first.

Researchers aren’t entirely sure why this might be. One theory is that eating fats, fiber and proteins first delays stomach emptying , which could slow the absorption of sugars from the carbohydrates into the bloodstream, Dr. Shukla said.

Barbara Eichorst, vice president of health care programs at the American Diabetes Association, said it makes sense for people with Type 2 diabetes or pre-diabetes to consume vegetables and proteins first during meals, since, unlike carbohydrates, vegetables and proteins don’t rapidly turn into sugar and cause high blood glucose spikes.

For those who have Type 2 diabetes, some limited research even suggests that this blood sugar lowering effect could be comparable with certain diabetes medications, said Nicola Guess, a clinical dietitian and researcher at the University of Oxford in Britain. Though more research is needed on the topic.

Should everyone eat like this?

Research has also shown that eating carbohydrates last in a meal can reduce blood sugar spikes in people who don’t have diabetes . But the experts said that healthy people usually don’t need to micromanage their blood sugar in this way.

A properly functioning body will bring blood sugar levels back down to normal within hours after eating a meal, said Dr. Vijaya Surampudi, an endocrinologist at UCLA Health.

Nonetheless, since proteins, fats and fiber-rich vegetables take longer to digest than simple carbohydrates, saving carbs for last can help people feel fuller for longer, said Dr. Domenico Tricò, an assistant professor of internal medicine at the University of Pisa in Italy who studies food order.

Research also suggests that eating this way can stimulate the gut to produce more of a satiety hormone called glucagon-like peptide 1, or GLP-1. (The diabetes drug Ozempic is designed to mimic this hormone.)

“GLP-1 slows digestion and tells your brain you’re not hungry,” Dr. Surampudi said. But some experts said it’s not clear if the small increases in this hormone from meal sequencing alone (compared with the large increase you’d get from a drug like Ozempic) would make a big difference in how sated you feel.

If you tend to feel sluggish after meals, front-loading them with vegetables or protein could help, Dr. Shukla and Dr. Surampudi said.

Some research also suggests that saving carbohydrates for the end of a meal can make you more likely to fill up on vegetables and protein and eat fewer simple carbohydrates, which tend to have fewer nutrients and more calories, Dr. Shukla said.

The bottom line, the experts said, is that while meal sequencing is one of many healthy eating strategies, it’s not something to stress about. Dietary trends like these sometimes result in making people anxious, which can lead to disordered eating.

“If it’s easy for you, then you should go for it,” Dr. Tricò said. But if not, just aim for high-quality food you enjoy. Loading up on vegetables at every meal is more important than focusing too intently on the order of your food, Dr. Guess said.

Nikki Campo is a freelance writer in North Carolina.

A Guide to Better Nutrition

How much salt is too much? Should I cut back? We asked experts these and other questions about sodium .

Patients were told for years that cutting calories would ease the symptoms of polycystic ovary syndrome. But research suggests dieting may not help at all .

We asked a nutrition expert how she keeps up healthy habits without stressing about food. Here are seven tips  she shared for maintaining that balance.

There are many people who want to lose a few pounds for whom weight loss drugs are not the right choice. Is old-fashioned dieting a good option ?

Salmon is good for you, but choosing the right type to eat isn’t so easy. Here are answers to all your questions about this nutritional powerhouse .

Read these books to shift into a healthier way of thinking about food .

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The Prevalence of Polycystic Ovary Syndrome: A Brief Systematic Review

Ritu deswal.

Center for Medical Biotechnology, MD University, Rohtak, Haryana, India

Vinay Narwal

1 Department of Biochemistry, MD University, Rohtak, Haryana, India

Chandra S. Pundir

Background:.

Polycystic ovary syndrome (PCOS), the major endocrinopathy among reproductive-aged women, is not yet perceived as an important health problem in the world. It affects 4%–20% of women of reproductive age worldwide. The prevalence, diagnosis, etiology, management, clinical practices, psychological issues, and prevention are some of the most confusing aspects associated with PCOS.

The exact prevalence figures regarding PCOS are limited and unclear. The aim of this review is to summarize comprehensively the current knowledge on the prevalence of PCOS.

Materials and Methods:

Literature search was performed through PubMed, ScienceDirect, Cochrane Library, and Google Scholar (up to December 2019). All relevant articles published in English language were identified following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Our analysis yielded 27 surveys with a pooled mean prevalence of 21.27% using different diagnostic criteria. The proportion of women with PCOS also increased in the last decade.

Conclusion:

The current review summarizes and interprets the results of all published prevalence studies and highlights the burden of the syndrome, thereby supporting early identification and prevention of PCOS in order to reverse the persistent upward trend of prevalence.

I NTRODUCTION

Stein and Leventhal were the first to describe polycystic ovary syndrome (PCOS) more comprehensively in 1935.[ 1 ] With varied clinical manifestations, unknown etiology, complex pathophysiology, and poor diagnosis, it has produced considerable scientific debate.[ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ] The diagnosis of PCOS remains a controversy in clinical endocrinology. In order to create an extensive and descriptive definition for the diagnosis of PCOS, the National Institutes of Health (NIH) criteria came into existence in 1990.[ 12 ] Then, in 2003, a workshop in Rotterdam formulated a new diagnostic criterion named Rotterdam criteria.[ 13 ] This criterion requires the presence of two conditions out of the three: (1) oligomenorrhea/anovulation, (2) clinical/biochemical hyperandrogenism, and (3) polycystic ovaries (≥12 follicles in each ovary measuring 2–9 mm). In 2006, the Androgen Excess Society (AES) revised the diagnostic criteria. The AES requires the specific presence of clinical/biochemical hyperandrogenism in combination with either oligoanovulation or polycystic ovaries.[ 12 , 13 ] The process of standardization of diagnosis confronts certain obstacles. First, in early menarche, ovulation is often irregular. Thus, anovulation cannot be considered as a definite evidence of the existence of the syndrome.[ 14 ] Second, transvaginal ultrasonography is not routinely performed in adolescents, which restricts ovary visualization and therefore excludes any invasive diagnosis of polycystic ovarian morphology. Third, there is a lack of consensus on the biochemical levels of hyperandrogenism, and there is limited information regarding normal levels of androgens in adolescents. Therefore, determining androgen abnormality is a complex task. Fourth, multifollicular ovaries, which may be present normally in adolescent girls, are hard to extricate from polycystic ones. Thus, the Pediatric Endocrine Society has recommended certain guidelines for differential diagnosis of PCOS in adults and adolescence. The appropriate consensus (persistent hyperandrogenic oligoanovulation) based on age and stage appropriate standards for early diagnosis and management of PCOS is summarized in [ Supplementary Table 1 ].[ 15 ]

Supplementary Table 1

Differential diagnostic guidelines for diagnosis of polycystic ovary syndrome in adults and adolescence

AUB=Abnormal uterine bleeding, HA=Hyperandrogenism, NIH=National Institutes of Health, RC=Rotterdam criteria, AES=Androgen excess society

Multiple genetic and environmental factors play an important role in occurrence of PCOS. The consequences of this multifaceted disorder extend beyond the reproductive system affecting metabolic, cardiovascular, immune, and psychological health of affected women. Over the past decade, genome-wide association studies (GWASs) have greatly advanced the understanding of PCOS pathophysiology by identifying several critical genes involved in steroidogenesis, hypothalamic–pituitary pathways, gonadotrophin action, insulin action and secretion, adipose tissue disturbances, homeostasis, lipid metabolism, and chronic inflammation are considered as the most promising genes involved in PCOS. Some of these genes are LHR, FSHR, INSR, ERB, THADA, and HMGA2.[ 16 , 17 , 18 , 19 , 20 ] Azziz[ 21 ] reviewed the etiology of PCOS implicating genes involved in modulation of gonadotropin and neuroendocrine action, ovarian androgen biosynthesis, and possible insulin action, providing clues to the evolutionary path and potential evolutionary advantages of PCOS. The overexpression of DENND 1A isoform produced a PCOS theca phenotype, and causal mechanisms and balancing selection were inferred from genetic associations with PCOS.[ 22 , 23 ] Women with PCOS have considerable varied symptomatology across life span. Physical, biochemical, and radiographic evaluations along with medical history provide confirmatory PCOS-related evidences. Hallmark features of PCOS include anovulation, hyperandrogenism, and polycystic ovaries. Other major manifestations of PCOS are as follows: Luteinizing hormone hypersecretion, metabolic disturbances, hyperinsulinemia, insulin resistance, glucose intolerance, dyslipidemia, hirsutism, acne, obesity, diabetes mellitus type II, and infertility. Various long-term complications include cardiovascular events, endometrial cancer, and psychological disorders such as stress and depression.[ 24 , 25 , 26 ] Table 1 represents various symptomatologies associated with the disorder. In recent years, the geographic variations of PCOS prevalence have been studied worldwide. The prevalence of PCOS is frequently quoted between 2% and 26%.[ 27 ] The differences in diagnostic criteria, sample heterogeneity, socioeconomic level, medical care access, prevalence of influential risk factors, health and education/awareness were among the possible causes of substantial geographic disparities in the prevalence rate.[ 28 ] Based on ancestral or geographical segregation, the world's populations vary in physical, social, and behavioral features due to natural selection and environmental adaptations, the conditions which then strongly influences the phenotype of the disease. It is now evident that race and ethnicity affect clinical presentation of PCOS due to different genetic and environmental predisposition to endocrine and metabolic aberrations. As reported in 2017, it was found that Hispanic PCOS women presented a higher degree of hyperandrogenism and metabolic aberrations as compared to non-Hispanic women.[ 29 ]

Clinical features associated with polycystic ovary syndrome

GAGS=Global acne grading system, T=Testosterone, NCEP=National Cholesterol Education Program, TG=Triglycerides, LDL=Low-density lipoproteins, C=Cholesterol, BP=Blood pressure, BMI=Basal metabolic rate, HOMA-IR=Homeostatic model assessment-insulin resistance, BAI=Body adiposity index, BDI=Body density index, AN=Acanthosis nigricans, CVD=Cardiovascular disease

The need to improve the clinical and therapeutic management of PCOS patients has become increasingly evident in the last decade. Many treatment possibilities exist to correct the severity of clinical manifestations of PCOS patients. Every physician should be able to choose the most protocol in relation to PCOS and the possible prospect of a pregnancy. Table 2 includes the appropriate therapeutic techniques with pharmacological therapies in order to treat PCOS.[ 30 , 31 , 32 , 33 , 34 ] The key strategies for better management of PCOS included the need for specific biological markers, the use of more precise techniques for measuring circulating androgens, understanding the risk factor consequences of PCOS, and finally, treatment strategies based on individual-specific phenotype needs.

Different approaches for treatment of polycystic ovary syndrome

GLP1=Glucagon-like peptide 1, FSH=Follicle-stimulating hormone, LH=Luteinizing hormone, hCG=Human chorionic gonadotropin

We therefore aimed to collate different prevalence studies conducted till date in order to explore key variables that may influence prevalence estimates. The present study highlighted past to present-day accepted guidelines used for PCOS diagnosis. This review also stressed on current treatment and screening guidelines used with specific emphasis on potential new therapies that can be used for better management of PCOS.

M ATERIALS AND M ETHODS

Search strategy.

Two reviewers carried out a systemic computer-assisted literature search of all major databases including MEDLINE, PubMed, ScienceDirect, ISI Web of Knowledge, Embase, Google Scholar, and Wiley. The following search terms were entered as medical subject headings for finding studies reporting the prevalence of PCOS: The search strategy used a combination of different terms “prevalence of PCOS,” “epidemiology of PCOS,” “PCOS in reproductive age,” and “polycystic ovary syndrome.” References in the identified studies were also investigated to identify additional studies. Any discrepancies regarding data extraction were resolved by mutual consensus.

Eligibility criteria

Inclusion criteria.

Studies meeting the following criteria were included: (1) cross-sectional, case–control, or cohort studies including PCOS women aged 15–45 years and age-matched controls of any ethnicity; (2) PCOS was diagnosed based on either Rotterdam, NIHCD, AES criteria, or all three; (3) studies containing original data (independent of other studies); (4) design where the prevalence of PCOS with sample size was presented; and (5) publications in full text written in English.

Exclusion Criteria

The studies were excluded, if these (1) contained data overlapping data with other studies (2) reported in language other than English (3)epidemiological studies reporting prevalence in family members of affected cases (4)letters, abstracts and conference proceedings ,which are not fully published in peer reviewed journals or published with limited access.

Data extraction

A data extraction form consists of information needed for the study (name of first author, year of publication, country, study design, study population size and description, age group, diagnostic criteria used, and prevalence rates). 95% confidence intervals (CIs) were calculated from the available data. The analysis was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.[ 35 ]

Quality assessment

The quality of included studies was assessed by QUADAS tool[ 36 ] (quality assessment for studies of diagnostic accuracy). The quality scoring checklist includes the following: (i) objective clearly stated, (ii) study design clearly described, (iii) patient selection criteria clearly defined, (iv) details of control selection, (v) sample size, (vi) method of PCOS diagnosis was provided, (vii) inclusion and exclusion criteria, (viii) prevalence clearly provided, (ix) confounding variables measured in the analysis, and (x) statistical analysis appropriately described. Studies scoring >7, 4–6, and <4 are rated as good, fair, and poor quality, respectively.

Figure 1 outlines the detailed study screening and selection process. Database search yielded 2167 initial citations. All irrelevant studies (1136) were excluded. The studies describing other aspects of PCOS (polymorphism, prevalence of a particular comorbidity only, clinical trials, and reviews) were also excluded (551). A total of 480 articles had their full text reviewed for inclusion. Four hundred and seventeen articles were excluded after full-text review. Out of 68 included studies, 41 studies were omitted due to incomplete information. Twenty-seven prevalence involving 32,125 participants were therefore selected for inclusion in the review.

Flowchart of study screening and selection procedure

Baseline characteristics of studies

Table 3 provides a comprehensive portrait of the prevalence studies of PCOS across the globe including all three international diagnostic criteria.[ 27 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ] The present review represented data based on random cross-sectional, prospective, cohort, case–control, and observational studies using all three different diagnostic criteria. The Rotterdam criteria are the most common one, included in 19 studies. The second most used criterion was NIH (11 studies). Twenty studies adopted cross-sectional study design. Only five studies include a large sample size (>1000). Southern China, Iran, and the USA reported a prevalence of 2.2%, 3%, and 4%, respectively. Beijing, Palestine, Brazil, Sri Lanka, the UK, Greek, and Spain found a prevalence rate in the range of 5%–10%. Australia, Turkey, and Denmark reported a higher prevalence (15%–20%). The prevalence rates differ with different criteria used. The Rotterdam criteria are the most acceptable diagnostic, as it includes broader evidences (oligomenorrhea/amenorrhea, clinical/biochemical hyperandrogenism plus polycystic ovaries) of PCOS. Studies adopting Rotterdam criteria as diagnostic methodology report higher prevalence rates when compared with the other two methods [ Figure 2 ]. Today, 1 in every 10 women is diagnosed with PCOS across the world. Until the late 1990s, the studies regarding the prevalence of PCOS were rare. Most of the studies were carried out on small sample size. The number of random community surveys is also limited. Fourteen studies were conducted in Asia, with India being the country presenting maximum number of researches (five). Seven studies were conducted in Europe, two in Australia, one in Africa, two in North America, and one in South America [ Figure 3 ].

Prevalence (%) of PCOS using different diagnostic criteria. PCOS=Polycystic ovary syndrome, NIH=National Institutes of Health, AES=Androgen excess society

Scenario of prevalence studies in the world

Prevalence studies of PCOS across the globe with different diagnostic criteria

CI=Confidence interval, NIH=National Institutes of Health, RC=Rotterdam criteria, AES=Androgen excess society, CLT=Chronic lymphocytic thyroiditis, QS=Quality score

Table 4 shows a comparison between the results of various PCOS-associated parameters using three different methods. The total number of PCOS patients included in these studies was 3434, 838, and 410 using RC, NIH, and AES criteria, respectively. Statistically significant differences were observed in polycystic ovaries on ultrasound (0.003%), hirsutism (0.001), and obesity (0.001) among PCOS cases when all three diagnostic criteria were compared. As expected, overall PCOS cases had higher percentages of girls with oligomenorrhea. Infertility was significantly higher in women with polycystic ovary morphology (21.70%) using AES criteria, while Rotterdam criteria reported the presence of the same in minority of women (6%). Hirsutism was present among 58.12% of cases (Rotterdam diagnosis) and 52.68% of cases (AES diagnosis). Degree of hirsutism was less in women diagnosed with NIH criteria (25.77%). However, there was no statistical difference found in the prevalence of insulin resistance and metabolic syndrome profile of these women. PCOS is present in both obese and lean females. Rotterdam criteria report a low prevalence of insulin resistance (8.04%) as the condition was found to be more prevalent in obese PCOS cases.

Different characteristics of polycystic ovary syndrome patients

Distributions were compared using analysis of variance. Categorical variables were compared using Pearson’s Chi-square test. P <0.05 is considered statistically significant. RC=Rotterdam criteria, MI=Menstrual irregularities

D ISCUSSION

PCOS is associated with multiple reproductive, reproductive, and psychological complications which are of serious concern. PCOS represents a significant socioeconomic burden to health care. It was during the mid-nineteenth century that headway was made in the understanding of PCOS by Stein and Leventhal. In India, it took almost a century for the prevalence of PCOS to come in the forefront in medical literature. To address this issue, few nationally representative surveys have been conducted in India from 2010 to 2014, reporting the variation in prevalence rate from 6% to 46.8%. Ganie et al . published the first Indian case–control study using Rotterdam criteria in 2010, which reported a high prevalence rate of 46.8% as the study was conducted in 176 chronic lymphocytic thyroiditis (CLT) patients.[ 64 ] Nidhi et al ., in 2011, conducted a prospective study involving 460 girls of 15–18 years from a residential college in South India and reported a prevalence rate of 9.13%.[ 54 ] A 2017 study conducted by Gupta et al . in 500 college girls aged 17–24 reported a prevalence rate of 8.2%.[ 37 ] Later, during 2017, Choudhary A et al . showed a higher prevalence of 41% in 170 women with menstrual irregularities by NIH criteria. Another study conducted in Mumbai among 600 girls of 15–24 years reported an estimated prevalence of 22.5%.[ 40 ] A meta-analysis conducted by Ding et al ., in 2017, reviewed the prevalence of PCOS across different ethnic groups and concluded that Caucasian females are less likely to develop PCOS compared with middle east and non white female populations.[ 65 ] Accordingly, the prevalence of PCOS varies among different countries worldwide. Iran, China, and the USA reported a prevalence of 3%, 2.2%, and 4.7%, respectively. Brazil, Beijing, Sri Lanka, Palestine, Greece, the UK, and Spain found a prevalence rate in the range of 5%–10%. Denmark, Turkey, and Australia reported a higher prevalence range (15%–20%). In 2018, Wolf et al . reported the prevalence of PCOS in Mexico also.[ 66 ] In 2019, Ganie et al . concluded the prevalence of PCOS in India ranging from 3.7%–22.5% depending on the population studied and criteria used for diagnosis.[ 67 ] A report from this laboratory showed that overall 71% of the women with PCOS resided in urban regions, while 29% in rural regions in the Haryana state of India.[ 68 ] The discrepancies might be partly attributed to small sample sizes, socioeconomic differences, clinical heterogeneity, low statistical power, differing ethnic backgrounds among various populations, geographic variations, and interactions with other environmental plus genetic factors. Until today, five different GWASs have identified 16 candidate genes/loci associated with PCOS. These findings implicated the role of genes involved in gonadotropin action (LHR and FSHR), insulin signaling and type 2 diabetes (INSR, THADA, HMGA2), cell proliferation (YAP1 and SUMO1P1), and chromatin remodeling (TOX3) in the pathogenesis of PCOS.[ 15 , 16 , 17 , 18 , 19 ] Shim et al ., 2015, conducted pathway-based GWAS to elucidate significant biological pathways and candidate genes involved in pathogenesis of PCOS.[ 20 ] The study identified three top rank pathways (ovulation, insulin secretion, and calcium signaling) associated with PCOS. INSR gene was observed in all three pathways. Variations in INSR gene could result in abnormal insulin regulation and disordered glucose homeostasis which enhances insulin resistance, type 2 diabetes, and obesity deteriorating metabolic profile of PCOS. To offer novel insights into the etiology, pathogenesis, and treatment of PCOS, future population-based prospective case–control studies in compliance with family-based linkage studies involving a large number of individuals in various populations are clearly warranted. CLT is known as chronic lymphocytic thyroiditis. Ganie et al .[ 64 ] have reported that 170 girls (46 years age) with euthyroid CLT had higher hirsutism score, the lower number of annual menstrual cycles as well as higher insulin resistance score as compared to control girls, under the high prevalence of PCOS.

C ONCLUSION AND F UTURE P ERSPECTIVES

It is undoubtedly one of the most perplexing disorders posing threat to women's health, probably due to various manifestations of the disorder and lack of uniformly accepted diagnostic criteria. The pathogenesis of PCOS remains elusive, with contributions from insulin resistance, adipose tissue dysfunction, abnormal steroidogenesis, and hypothalamic–pituitary–ovarian dysregulation. Genetic variants and epigenetic environmental factors probably contribute to the dysregulation of these varied systems and raise new avenues of research investigation in the rapidly evolving field of PCOS. Despite rigorous research, certain questions are still unanswered so far. (i) As no single candidate gene has emerged as a convincing biomarker, so the future studies could be focused on selecting the appropriate genes as biomarkers for PCOS (ii) designing different therapeutic approaches to ameliorate additional complications such as metabolic syndrome, endometrial cancer, cardiovascular diseases, and mental health issues in later life; (iii) formulation of epigenetic studies to untangle the nature and nurture of the syndrome; (iv) need for globally agreed upon consensus on optimal diagnosis and management of PCOS; (v) conducting the large epidemiological studies worldwide to address the accurate burden of PCOS (vi) study of genetic polymorphism at wide scale to optimize individualized treatment; and (vii) increased awareness of PCOS and associated comorbidities to helps in early detection and management of PCOS. The possible roles of autoimmune phenomenon in the etiopathogenesis of PCOS and overexpression of certain genes of gonadotropin and neuroendocrine action, ovarian androgen biosynthesis, and insulin action in etiology of PCOS are suggested.

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