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Figure 1.  Study Flow Diagram
Study Flow Diagram

PCOS indicates polycystic ovary syndrome.

aThe exclusion criteria included studies of patients with gestational diabetes.

Figure 2.  Forest Plot Showing Prevalence of Polycystic Ovary Syndrome in Patients With Pediatric Type 2 Diabetes
Forest Plot Showing Prevalence of Polycystic Ovary Syndrome in Patients With Pediatric Type 2 Diabetes

Size of boxes is proportional to weight of each study. Solid lines represent confidence interval for the prevalence value reported in each study. Dotted line represents the pooled estimate.

Figure 3.  Forest Plot Showing Prevalence of Polycystic Ovary Syndrome (PCOS) in Patients With Pediatric Type 2 Diabetes in Studies Following PCOS Definition in Adolescence
Forest Plot Showing Prevalence of Polycystic Ovary Syndrome (PCOS) in Patients With Pediatric Type 2 Diabetes in Studies Following PCOS Definition in Adolescence

Size of boxes is proportional to weight of each study. Solid lines represent confidence interval for the prevalence value reported in each study. Dotted line represents the pooled estimate.

Table.  Characteristics of Included Studies
Characteristics of Included Studies
1.
Dabelea  D, Mayer-Davis  EJ, Saydah  S,  et al; SEARCH for Diabetes in Youth Study.  Prevalence of type 1 and type 2 diabetes among children and adolescents from 2001 to 2009.   JAMA. 2014;311(17):1778-1786. doi:10.1001/jama.2014.3201PubMedGoogle ScholarCrossref
2.
Nadeau  K, Dabelea  D.  Epidemiology of type 2 diabetes in children and adolescents.   Endocr Res. 2008;33(1-2):35-58. doi:10.1080/07435800802080138PubMedGoogle ScholarCrossref
3.
Dabelea  D, Bell  RA, D’Agostino  RB  Jr,  et al; Writing Group for the SEARCH for Diabetes in Youth Study Group.  Incidence of diabetes in youth in the United States.   JAMA. 2007;297(24):2716-2724. doi:10.1001/jama.297.24.2716PubMedGoogle Scholar
4.
Dabelea  D, Hanson  RL, Bennett  PH, Roumain  J, Knowler  WC, Pettitt  DJ.  Increasing prevalence of type II diabetes in American Indian children.   Diabetologia. 1998;41(8):904-910. doi:10.1007/s001250051006PubMedGoogle ScholarCrossref
5.
Dabelea  D, Pettitt  DJ, Jones  KL, Arslanian  SA.  Type 2 diabetes mellitus in minority children and adolescents: an emerging problem.   Endocrinol Metab Clin North Am. 1999;28(4):709-729. doi:10.1016/S0889-8529(05)70098-0PubMedGoogle ScholarCrossref
6.
Chen  L, Magliano  DJ, Zimmet  PZ.  The worldwide epidemiology of type 2 diabetes mellitus: present and future perspectives.   Nat Rev Endocrinol. 2011;8(4):228-236. doi:10.1038/nrendo.2011.183PubMedGoogle ScholarCrossref
7.
Mayer-Davis  EJ, Lawrence  JM, Dabelea  D,  et al; SEARCH for Diabetes in Youth Study.  Incidence trends of type 1 and type 2 diabetes among youths, 2002-2012.   N Engl J Med. 2017;376(15):1419-1429. doi:10.1056/NEJMoa1610187PubMedGoogle ScholarCrossref
8.
Divers  J, Mayer-Davis  EJ, Lawrence  JM,  et al.  Trends in incidence of type 1 and type 2 diabetes among youths: selected counties and Indian reservations, United States, 2002-2015.   MMWR Morb Mortal Wkly Rep. 2020;69(6):161-165. doi:10.15585/mmwr.mm6906a3PubMedGoogle ScholarCrossref
9.
Kim  G, Divers  J, Fino  NF,  et al.  Trends in prevalence of cardiovascular risk factors from 2002 to 2012 among youth early in the course of type 1 and type 2 diabetes: the SEARCH for Diabetes in Youth Study.   Pediatr Diabetes. 2019;20(6):693-701. doi:10.1111/pedi.12846PubMedGoogle Scholar
10.
Reynolds  K, Saydah  SH, Isom  S,  et al.  Mortality in youth-onset type 1 and type 2 diabetes: the SEARCH for Diabetes in Youth study.   J Diabetes Complications. 2018;32(6):545-549. doi:10.1016/j.jdiacomp.2018.03.015PubMedGoogle ScholarCrossref
11.
Cioana  M, Deng  J, Hou  M,  et al.  Prevalence of hypertension and albuminuria in pediatric type 2 diabetes: a systematic review and meta-analysis.   JAMA Netw Open. 2021;4(4):e216069. doi:10.1001/jamanetworkopen.2021.6069PubMedGoogle Scholar
12.
Christensen  SB, Black  MH, Smith  N,  et al.  Prevalence of polycystic ovary syndrome in adolescents.   Fertil Steril. 2013;100(2):470-477. doi:10.1016/j.fertnstert.2013.04.001PubMedGoogle ScholarCrossref
13.
Naz  MSG, Tehrani  FR, Majd  HA,  et al.  The prevalence of polycystic ovary syndrome in adolescents: a systematic review and meta-analysis.   Int J Reprod Biomed. 2019;17(8):533-542. doi:10.18502/ijrm.v17i8.4818PubMedGoogle Scholar
14.
Fauser  BCJM, Tarlatzis  BC, Rebar  RW,  et al.  Consensus on women’s health aspects of polycystic ovary syndrome (PCOS): the Amsterdam ESHRE/ASRM-Sponsored 3rd PCOS Consensus Workshop Group.   Fertil Steril. 2012;97(1):28-38.e25. doi:10.1016/j.fertnstert.2011.09.024PubMedGoogle ScholarCrossref
15.
Ibáñez  L, Oberfield  SE, Witchel  S,  et al.  An international consortium update: pathophysiology, diagnosis, and treatment of polycystic ovarian syndrome in adolescence.   Horm Res Paediatr. 2017;88(6):371-395. doi:10.1159/000479371PubMedGoogle ScholarCrossref
16.
Legro  RS, Arslanian  SA, Ehrmann  DA,  et al; Endocrine Society.  Diagnosis and treatment of polycystic ovary syndrome: an Endocrine Society clinical practice guideline.   J Clin Endocrinol Metab. 2013;98(12):4565-4592. doi:10.1210/jc.2013-2350PubMedGoogle ScholarCrossref
17.
Witchel  SF, Oberfield  S, Rosenfield  RL,  et al.  The diagnosis of polycystic ovary syndrome during adolescence.   Horm Res Paediatr. 2015;83:376-389. doi:10.1159/000375530PubMedGoogle ScholarCrossref
18.
Peña  AS, Witchel  SF, Hoeger  KM,  et al.  Adolescent polycystic ovary syndrome according to the international evidence-based guideline.   BMC Med. 2020;18(1):72. doi:10.1186/s12916-020-01516-xPubMedGoogle ScholarCrossref
19.
Tay  CT, Hart  RJ, Hickey  M,  et al.  Updated adolescent diagnostic criteria for polycystic ovary syndrome: impact on prevalence and longitudinal body mass index trajectories from birth to adulthood.   BMC Med. 2020;18(1):389. doi:10.1186/s12916-020-01861-xPubMedGoogle ScholarCrossref
20.
Baillargeon  JP, Iuorno  MJ, Nestler  JE.  Insulin sensitizers for polycystic ovary syndrome.   Clin Obstet Gynecol. 2003;46(2):325-340. doi:10.1097/00003081-200306000-00011PubMedGoogle ScholarCrossref
21.
Diamanti-Kandarakis  E, Dunaif  A.  Insulin resistance and the polycystic ovary syndrome revisited: an update on mechanisms and implications.   Endocr Rev. 2012;33(6):981-1030. doi:10.1210/er.2011-1034PubMedGoogle ScholarCrossref
22.
Carreau  AM, Baillargeon  JP.  PCOS in adolescence and type 2 diabetes.   Curr Diab Rep. 2015;15(1):564. doi:10.1007/s11892-014-0564-3PubMedGoogle ScholarCrossref
23.
Arslanian  SA, Lewy  VD, Danadian  K.  Glucose intolerance in obese adolescents with polycystic ovary syndrome: roles of insulin resistance and beta-cell dysfunction and risk of cardiovascular disease.   J Clin Endocrinol Metab. 2001;86(1):66-71. doi:10.1210/jcem.86.1.7123PubMedGoogle Scholar
24.
Hudnut-Beumler  J, Kaar  JL, Taylor  A,  et al.  Development of type 2 diabetes in adolescent girls with polycystic ovary syndrome and obesity.   Pediatr Diabetes. 2021;22(5):699-706. doi:10.1111/pedi.13206PubMedGoogle ScholarCrossref
25.
Çoban  ÖG, Tulacı  ÖD, Adanır  AS, Önder  A.  Psychiatric disorders, self-esteem, and quality of life in adolescents with polycystic ovary syndrome.   J Pediatr Adolesc Gynecol. 2019;32(6):600-604. doi:10.1016/j.jpag.2019.07.008PubMedGoogle ScholarCrossref
26.
Fazleen  NE, Whittaker  M, Mamun  A.  Risk of metabolic syndrome in adolescents with polycystic ovarian syndrome: a systematic review and meta-analysis.   Diabetes Metab Syndr. 2018;12(6):1083-1090. doi:10.1016/j.dsx.2018.03.014PubMedGoogle ScholarCrossref
27.
Sari  SA, Celik  N, Uzun Cicek  A.  Body perception, self-esteem, and comorbid psychiatric disorders in adolescents diagnosed with polycystic ovary syndrome.   J Pediatr Adolesc Gynecol. 2020;33(6):691-696. doi:10.1016/j.jpag.2020.08.018PubMedGoogle ScholarCrossref
28.
Balen  AH, Rutherford  AJ.  Managing anovulatory infertility and polycystic ovary syndrome.   BMJ. 2007;335(7621):663-666. doi:10.1136/bmj.39335.462303.80PubMedGoogle ScholarCrossref
29.
Benson  J, Severn  C, Hudnut-Beumler  J,  et al.  Depression in girls with obesity and polycystic ovary syndrome and/or type 2 diabetes.   Can J Diabetes. 2020;44(6):507-513. doi:10.1016/j.jcjd.2020.05.015PubMedGoogle ScholarCrossref
30.
Lewy  VD, Danadian  K, Witchel  SF, Arslanian  S.  Early metabolic abnormalities in adolescent girls with polycystic ovarian syndrome.   J Pediatr. 2001;138(1):38-44. doi:10.1067/mpd.2001.109603PubMedGoogle ScholarCrossref
31.
Dunaif  A, Finegood  DT.  Beta-cell dysfunction independent of obesity and glucose intolerance in the polycystic ovary syndrome.   J Clin Endocrinol Metab. 1996;81(3):942-947. doi:10.1210/jcem.81.3.8772555PubMedGoogle Scholar
32.
Dunaif  A, Segal  KR, Futterweit  W, Dobrjansky  A.  Profound peripheral insulin resistance, independent of obesity, in polycystic ovary syndrome.   Diabetes. 1989;38(9):1165-1174. doi:10.2337/diab.38.9.1165PubMedGoogle ScholarCrossref
33.
Ibáñez  L, Ong  KK, López-Bermejo  A, Dunger  DB, de Zegher  F.  Hyperinsulinaemic androgen excess in adolescent girls.   Nat Rev Endocrinol. 2014;10(8):499-508. doi:10.1038/nrendo.2014.58PubMedGoogle ScholarCrossref
34.
Teede  HJ, Misso  ML, Costello  MF,  et al; International PCOS Network.  Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome.   Hum Reprod. 2018;33(9):1602-1618. doi:10.1093/humrep/dey256PubMedGoogle ScholarCrossref
35.
Samaan  MC, Cioana  M, Banfield  L,  et al. The prevalence of comorbidities in pediatric type 2 diabetes mellitus: a systematic review—PROSPERO CRD42018091127. March 19, 2018. Accessed January 5, 2022. http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42018091127
36.
Stroup  DF, Berlin  JA, Morton  SC,  et al.  Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group.   JAMA. 2000;283(15):2008-2012. doi:10.1001/jama.283.15.2008PubMedGoogle ScholarCrossref
37.
Hoy  D, Brooks  P, Woolf  A,  et al.  Assessing risk of bias in prevalence studies: modification of an existing tool and evidence of interrater agreement.   J Clin Epidemiol. 2012;65(9):934-939. doi:10.1016/j.jclinepi.2011.11.014PubMedGoogle ScholarCrossref
38.
OCEBM Levels of Evidence Working Group. The Oxford 2011 levels of evidence. Oxford Centre for Evidence-Based Medicine. 2011. Accessed January 7, 2022. http://www.cebm.net/index.aspx?o=5653
39.
Borenstein  M, Hedges  LV, Higgins  JPT, Rothstein  HR.  A basic introduction to fixed-effect and random-effects models for meta-analysis.   Res Synth Methods. 2010;1(2):97-111. doi:10.1002/jrsm.12PubMedGoogle ScholarCrossref
40.
Barendregt  JJ, Doi  SA, Lee  YY, Norman  RE, Vos  T.  Meta-analysis of prevalence.   J Epidemiol Community Health. 2013;67(11):974-978. doi:10.1136/jech-2013-203104PubMedGoogle ScholarCrossref
41.
Deeks  J, Higgins  J, Altman  D, eds. Chapter 10: Analysing data and undertaking meta-analyses. In:  Cochrane Handbook for Systematic Reviews of Interventions. Version 6.0. Cochrane; 2019. Accessed January 5, 2022. https://training.cochrane.org/handbook/archive/v6/chapter-10
42.
Viechtbauer  W.  Conducting meta-analyses in R with the metafor package.   J Stat Softw. 2010;36(3):1-48. doi:10.18637/jss.v036.i03Google ScholarCrossref
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RStudio: Integrated development for R version 1.1.383. RStudio, Inc; 2016. Accessed January 5, 2022. https://www.rstudio.com/
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R Foundation for Statistical Computing. R: a language and environment for statistical computing, version 3.4.3. 2017. Accessed January 5, 2022. https://cran.r-project.org/bin/windows/base/old/3.4.3/
45.
Amed  S, Hamilton  JK, Sellers  EAC,  et al.  Differing clinical features in Aboriginal vs. non-Aboriginal children presenting with type 2 diabetes.   Pediatr Diabetes. 2012;13(6):470-475. doi:10.1111/j.1399-5448.2012.00859.xPubMedGoogle ScholarCrossref
46.
Amutha  A, Datta  M, Unnikrishnan  R, Anjana  RM, Mohan  V.  Clinical profile and complications of childhood- and adolescent-onset type 2 diabetes seen at a diabetes center in south India.   Diabetes Technol Ther. 2012;14(6):497-504. doi:10.1089/dia.2011.0283PubMedGoogle ScholarCrossref
47.
Balasanthiran  A, O’Shea  T, Moodambail  A,  et al  Type 2 diabetes in children and young adults in East London: an alarmingly high prevalence.   Pract Diabetes. 2012;29(5):193-198a. doi:10.1002/pdi.1689Google ScholarCrossref
48.
Pérez-Perdomo  R, Pérez-Cardona  CM, Allende-Vigo  M, Rivera-Rodríguez  MI, Rodríguez-Lugo  LA.  Type 2 diabetes mellitus among youth in Puerto Rico, 2003.   P R Health Sci J. 2005;24(2):111-117.PubMedGoogle Scholar
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Zdravkovic  V, Daneman  D, Hamilton  J.  Presentation and course of type 2 diabetes in youth in a large multi-ethnic city.   Diabet Med. 2004;21(10):1144-1148. doi:10.1111/j.1464-5491.2004.01297.xPubMedGoogle ScholarCrossref
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Shield  JPH, Lynn  R, Wan  KC, Haines  L, Barrett  TG.  Management and 1 year outcome for UK children with type 2 diabetes.   Arch Dis Child. 2009;94(3):206-209. doi:10.1136/adc.2008.143313PubMedGoogle ScholarCrossref
51.
Reinehr  T.  Type 2 diabetes mellitus in children and adolescents.   World J Diabetes. 2013;4(6):270-281. doi:10.4239/wjd.v4.i6.270PubMedGoogle ScholarCrossref
52.
Kaczmarek  C, Haller  DM, Yaron  M.  Health-related quality of life in adolescents and young adults with polycystic ovary syndrome: a systematic review.   J Pediatr Adolesc Gynecol. 2016;29(6):551-557. doi:10.1016/j.jpag.2016.05.006PubMedGoogle ScholarCrossref
53.
Sadeeqa  S, Mustafa  T, Latif  S.  Polycystic ovarian syndrome-related depression in adolescent girls: a review.   J Pharm Bioallied Sci. 2018;10(2):55-59. doi:10.4103/JPBS.JPBS_1_18PubMedGoogle ScholarCrossref
54.
Patel  SS, Truong  U, King  M,  et al.  Obese adolescents with polycystic ovarian syndrome have elevated cardiovascular disease risk markers.   Vasc Med. 2017;22(2):85-95. doi:10.1177/1358863X16682107PubMedGoogle ScholarCrossref
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Rosenfield  RL, Ehrmann  DA.  The pathogenesis of polycystic ovary syndrome (PCOS): the hypothesis of PCOS as functional ovarian hyperandrogenism revisited.   Endocr Rev. 2016;37(5):467-520. doi:10.1210/er.2015-1104PubMedGoogle ScholarCrossref
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Rosenfield  RL, Mortensen  M, Wroblewski  K, Littlejohn  E, Ehrmann  DA.  Determination of the source of androgen excess in functionally atypical polycystic ovary syndrome by a short dexamethasone androgen-suppression test and a low-dose ACTH test.   Hum Reprod. 2011;26(11):3138-3146. doi:10.1093/humrep/der291PubMedGoogle ScholarCrossref
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Ehrmann  DA, Sturis  J, Byrne  MM, Karrison  T, Rosenfield  RL, Polonsky  KS.  Insulin secretory defects in polycystic ovary syndrome: relationship to insulin sensitivity and family history of non-insulin-dependent diabetes mellitus.   J Clin Invest. 1995;96(1):520-527. doi:10.1172/JCI118064PubMedGoogle ScholarCrossref
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Mannerås-Holm  L, Leonhardt  H, Kullberg  J,  et al.  Adipose tissue has aberrant morphology and function in PCOS: enlarged adipocytes and low serum adiponectin, but not circulating sex steroids, are strongly associated with insulin resistance.   J Clin Endocrinol Metab. 2011;96(2):E304-E311. doi:10.1210/jc.2010-1290PubMedGoogle ScholarCrossref
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Shenep  L, Al-Zubeidi  H.  Characteristics and ethnic variations of adolescents with polycystic ovarian syndrome at a tertiary care center.   J Pediatr Endocrinol. 2017;2(2):1019. Accessed January 10, 2022. http://austinpublishinggroup.com/pediatric-endocrinology/download.php?file=fulltext/jpe-v2-id1019.pdfGoogle Scholar
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Original Investigation
Diabetes and Endocrinology
February 15, 2022

Prevalence of Polycystic Ovary Syndrome in Patients With Pediatric Type 2 Diabetes: A Systematic Review and Meta-analysis

Author Affiliations
  • 1Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
  • 2Division of Pediatric Endocrinology, McMaster Children’s Hospital, Hamilton, Ontario, Canada
  • 3Michael G. De Groote School of Medicine, McMaster University, Hamilton, Ontario, Canada
  • 4Health Sciences Library, McMaster University, Hamilton, Ontario, Canada
  • 5College of Medicine, King Saud bin Abdulaziz University for Health Sciences, Division of Endocrinology, Department of Pediatrics, Ministry of the National Guard Health Affairs, Riyadh, Saudi Arabia
  • 6Department of Pediatrics, Division of Pediatric Endocrinology, King Abdullah bin Abdulaziz University Hospital, Princess Noura University, Riyadh, Saudi Arabia
  • 7Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
  • 8Department of Anesthesia, McMaster University, Hamilton, Ontario, Canada
  • 9Centre for Evaluation of Medicines, St Joseph’s Health Care, Hamilton, Ontario, Canada
  • 10Biostatistics Unit, St Joseph’s Healthcare, Hamilton, Ontario, Canada
JAMA Netw Open. 2022;5(2):e2147454. doi:10.1001/jamanetworkopen.2021.47454
Key Points

Question  What is the prevalence of polycystic ovary syndrome (PCOS) among adolescents with type 2 diabetes (T2D)?

Findings  In this systematic review and meta-analysis involving 470 girls across 6 studies, the prevalence of PCOS was 19.58%, a prevalence that is substantially higher than that of PCOS in the general adolescent population.

Meaning  These findings suggest that PCOS is a common morbidity in girls with T2D, and it is critical that active screening for PCOS in girls with T2D is initiated at diabetes diagnosis and follows international evidence-based guidelines for diagnosing PCOS in adolescents.

Abstract

Importance  The prevalence of pediatric type 2 diabetes (T2D) is increasing globally. Girls with T2D are at risk of developing polycystic ovary syndrome (PCOS), but the prevalence of PCOS among girls with T2D is unknown.

Objective  To determine the prevalence of PCOS in girls with T2D and to assess the association of obesity and race with this prevalence.

Data Sources  In this systematic review and meta-analysis, MEDLINE, Embase, CINAHL, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Science: Conference Proceedings Citation Index–Science, and the gray literature were searched from inception to April 4, 2021.

Study Selection  Two reviewers independently screened for studies with observational study design that recruited 10 or more participants and reported the prevalence of PCOS in girls with T2D.

Data Extraction and Synthesis  Risk of bias was evaluated using a validated tool, and level of evidence was assessed using the Oxford Centre for Evidence-Based Medicine criteria. A random-effects meta-analysis was performed. This study follows the Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guideline.

Main Outcomes and Measures  The main outcome of this systematic review was the prevalence of PCOS in girls with T2D. Secondary outcomes included assessing the associations of obesity and race with PCOS prevalence.

Results  Of 722 screened studies, 6 studies involving 470 girls with T2D (mean age at diagnosis, 12.9-16.1 years) met the inclusion criteria. The prevalence (weighted percentage) of PCOS was 19.58% (95% CI, 12.02%-27.14%; I2 = 74%; P = .002). Heterogeneity was moderate to high; however, it was significantly reduced after excluding studies that did not report PCOS diagnostic criteria, leading to a calculated prevalence (weighted percentage) of 24.04% (95% CI, 15.07%-33.01%; I2 = 0%; P = .92). Associations with obesity and race could not be determined because of data paucity.

Conclusions and Relevance  In this meta-analysis, approximately 1 in 5 girls with T2D had PCOS, but the results of this meta-analysis should be considered with caution because studies including the larger numbers of girls did not report the criteria used to diagnose PCOS, which is a challenge during adolescence. The associations of obesity and race with PCOS prevalence among girls with T2D need further evaluation to help define at-risk subgroups and implement early assessment and treatment strategies to improve management of this T2D-related comorbidity.

Introduction

Over the past 3 decades, type 2 diabetes (T2D) has made the transition from being an adult disease to being a pediatric disorder.1-8 T2D in youth is an aggressive disease with multiple associated comorbidities and poor response to current therapies; it is also associated with higher morbidity and mortality rates than adult-onset T2D.9-11

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder that occurs in 1.14% to 11.04% of adolescent girls globally.12,13 The diagnostic criteria for PCOS during adolescence include the combination of menstrual irregularities according to time since menarche and clinical or biochemical hyperandrogenism after excluding other possible causes.14-18 Pelvic ultrasonography is not recommended for PCOS diagnosis in girls who are less than 8 years since menarche according to international evidence-based guidelines,18 because it is associated with overdiagnosis of PCOS.19 Insulin resistance and compensatory hyperinsulinemia are present in 44% to 70% of women with PCOS,20,21 suggesting that they are more likely to develop T2D.22-24

PCOS is also associated with a range of cardiometabolic diseases, including hypertension and dyslipidemia, as well as mental health disorders and future infertility.25-28 Importantly, girls with T2D and PCOS are at an increased risk of depression.29 However, although PCOS is associated with a range of conditions that are related to obesity, the association of PCOS with obesity is not well understood. PCOS is more common in adolescents with obesity,12 yet insulin resistance is at times present in patients with PCOS regardless of their body mass index (BMI).30-32

In addition, pediatric T2D disproportionately affects female patients, and its rates are increased among minoritized racial and ethnic groups.1,2,4,5 Determining the scale of PCOS in T2D and the association of obesity and race with PCOS genesis can inform personalized screening and treatment strategies in this population.33,34 The objectives of this systematic review and meta-analysis were to determine the prevalence of PCOS in girls with T2D and to assess the association of obesity and race with PCOS prevalence.

Methods
Systematic Review Protocol and Registration

This systematic review and meta-analysis has been registered with PROSPERO.35 Institutional review board approval and informed consent were not sought because the data were anonymous and publicly available, in accordance with 45 CFR §46. The manuscript was developed and reported in accordance with the Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guideline.36

Search Strategy and Eligibility Criteria

Searches in MEDLINE, Embase, CINAHL, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews were developed by a senior health sciences librarian (L.B.) (eTable 1, eTable 2, eTable 3, and eTable 4 in the Supplement). Gray literature searches were conducted in ClinicalTrials.gov, Cochrane Central Registry of Controlled Trials, and Web of Science Conference Proceedings Citation Index–Science (eTable 5 in the Supplement). In addition, we searched the reference lists of eligible articles at the full-text screening stage for additional papers that fulfill the inclusion criteria.

The databases were initially searched from inception to February 4, 2019, and updated searches were run on February 20, 2020, and April 4, 2021. There were no language restrictions, but searches were limited to human studies. Terms for pediatrics and T2D were combined with language referencing PCOS, prevalence, and observational study design. Where a conference abstract was considered for inclusion, we searched the databases for a full-text publication and contacted the principal investigator if the publication could not be located.

Studies were included if they reported PCOS in girls diagnosed with T2D at age 18 years or younger. The studies included cross-sectional, retrospective, and prospective cohort studies, with a sample size of 10 or more patients, which reported the prevalence of PCOS in patients with T2D. We included all studies reporting on PCOS regardless of whether PCOS definition was reported.

The exclusion criteria included studies of patients with gestational diabetes. When encountering studies with serial reporting of data, we planned to include the report with the largest sample size.

Study Selection, Data Abstraction, and Quality Appraisal

Title, abstract, and full-text screening, data abstraction, risk of bias, and level of evidence assessments were performed by 2 independent reviewers in 3 teams (M.C., A.N., M.H., Y.Q., S.S.J.C., and A.J.R). Disagreements were resolved through discussion, or by a third reviewer (M.C.S.) if they persisted.

Data abstractions were done using a standardized form. We collected data including study title, author name, publication year, country, study design, age at diabetes diagnosis, age at study participation, duration of diabetes, sample size, and prevalence of obesity in participants, where available. We also extracted data on PCOS definition and total and race-based prevalence of PCOS. We contacted the principal investigators to collect any missing data.

Risk of bias was evaluated using a validated tool for prevalence studies.37 The tool assesses the internal and external validity of the studies, rating overall risk of bias as low (score >8), moderate (score 6-8), or high (score ≤5).

Level of evidence was assessed using the Oxford Centre for Evidence-Based Medicine criteria.38 The scale rates the appropriateness of each study to answer the research question, taking into account study design, study quality, imprecision, indirectness, and inconsistency.38

Statistical Analysis

We performed a meta-analysis using a random-effects model when 2 or more studies reporting the prevalence of PCOS used similar design, methods, and populations.39,40 If studies could not be included in the meta-analysis, the results were reported as a narrative summary and tabulated. Prevalence values were calculated using raw proportions of the number of girls with PCOS and T2D divided by the total number of girls diagnosed with T2D. Study weights were calculated from the inverse of the variance of prevalence value. All studies were then pooled according to weight, and a pooled prevalence value was determined. Because no studies had prevalence values close to 0% or 100%, we did not use transformations in our calculations.40

The primary outcome for this review was the pooled prevalence of PCOS with a 95% CI. Both inconsistency index (I2) and χ2 test P values were used to quantify heterogeneity, with I2 > 75% and P < .10 considered as significant cutoffs for heterogeneity.41

We had originally planned to perform subgroup analyses by race if 10 or more studies were included in the meta-analysis. However, these analyses could not be completed because of the limited number of eligible studies. Because of the number of included studies that did not report PCOS diagnostic criteria, we also conducted a post hoc sensitivity analysis excluding these studies to examine their impact on prevalence and heterogeneity. The meta-analysis was conducted using the metafor package in RStudio statistical software version 1.1.383 and R statistical software version 3.4.3 (R Project for Statistical Computing).42-44

Results
Study Selection

Of 722 screened articles, 6 studies45-50 involving 470 girls met our inclusion criteria (Figure 1). The Table reports the characteristics of the included studies. Five were retrospective cohort studies,45-49 and 1 was a prospective cohort study.50 The mean (SD) age at diagnosis of T2D ranged from 12.9 to 16.1 years, and the mean duration of T2D ranged from inclusion at diagnosis of T2D to 5.9 years after diagnosis.

Prevalence of PCOS

The prevalence (weighted percentage) of PCOS across the included studies was 19.58% (95% CI, 12.02%-27.14%) (Figure 2).45-50 Heterogeneity was moderate to high (I2 = 74%; P = .002).

Prevalence of PCOS by Diagnostic Criteria

There were variations in the PCOS definition in the included studies. The PCOS diagnostic criteria used in the included studies are summarized in eTable 6 in the Supplement. Common diagnostic criteria across the different guidelines included persistent oligomenorrhea and clinical and/or biochemical hyperandrogenism. Three studies used these criteria to make a PCOS diagnosis (Table).47,49,50 The remaining studies reported PCOS diagnosis according to medical records review or clinical symptoms; however, the exact criteria were not defined.45,46,48 In addition, none of the studies reported time to menarche, which is important for establishing PCOS diagnosis.14-18

We conducted a sensitivity analysis excluding studies that did not report PCOS diagnostic criteria (87 girls) (Figure 3). The pooled PCOS prevalence (weighted percentage) increased to 24.04% (95% CI, 15.07%-33.01%) with a substantial reduction in heterogeneity (I2 = 0%; P = .92).47,49,50

Prevalence of PCOS by Race

Only 2 studies45,46 reported the prevalence of PCOS by race. The prevalence was 17.00% in White individuals (36 girls),45 23.10% in Indian individuals (195 girls),46 and 2.00% in Indigenous individuals in Canada (64 girls).45

Obesity and PCOS Prevalence

Although we originally aimed to determine the association of obesity and PCOS, none of the included studies provided information on the prevalence of obesity. Thus, the association between PCOS and obesity could not be evaluated.

Risk of Bias

Two studies had low risk of bias,49,50 3 studies had moderate risk of bias,45-47 and 1 had high risk of bias (eTable 7 in the Supplement).48 In 3 studies,46,47,49 the study population was not representative of the national population, and in 3 other studies45,47,48 the sampling frame was not representative of the target population. Cases were not selected using random selection or census data in 2 studies.45,48 One study48 had nonresponse bias and it was unclear what numerator and denominator were used to calculate PCOS prevalence.

In 3 included studies,45,46,48 the diagnostic criteria used for PCOS assessment were unclear. In 1 study,48 it was not clear whether all patients were assessed for PCOS using the same methods.

Level of Evidence

Studies had a level of evidence of 1 (1 study),46 2 (3 studies),47,49,50 or 3 (2 studies).45,48 Level of evidence was rated down for studies that did not use random sampling,45,48 and for those that did not have an adequate sample size.47,49,50

Discussion

The prevalence of pediatric T2D is increasing globally, and the majority of these patients are female.6,7,51 PCOS is a comorbidity of T2D that is associated with substantial metabolic, cardiovascular, and psychological consequences.23,26,52-54 Thus, the timely assessment and management of PCOS in this high-risk population is critical.22 On the basis of studies with mostly moderate risk of bias, this systematic review and meta-analysis demonstrated that approximately 1 in 5 girls with T2D have PCOS. This figure is substantially higher than PCOS prevalence among the general female adolescent population, which is estimated at 1.14% to 11.04%.12,13 There was moderate-to-high heterogeneity in the results of the studies included, although most of the heterogeneity may be attributable to the inclusion of studies that did not clearly report the PCOS diagnostic criteria. The prevalence of PCOS by race was reported in single studies, which precluded generalizability, and the association of obesity with PCOS could not be estimated because of the lack of data.

The association between PCOS and T2D in adults is bidirectional.55-57 Insulin resistance plays a central role in the pathogenesis of PCOS, and studies in adolescents have shown that girls with PCOS have decreased insulin sensitivity and compensatory hyperinsulinemia.23,30,58 Insulin increases the sensitivity of the pituitary gland to hypothalamic gonadotropin-releasing hormone, which, in turn, stimulates the production of luteinizing hormone.59 Both insulin and luteinizing hormone act synergistically on the ovarian theca cells to upregulate androgen production,21,55 which, in turn, reduces adipose tissue adiponectin secretion and insulin sensitivity and upregulates insulin production.60 In addition, insulin increases androgen production within the subcutaneous adipose tissue via the upregulation of the aldo-keto reductase 1C3 activity.61

Another mechanism that may lead to both insulin resistance and hyperandrogenism is lipotoxicity.22 Increased ovarian exposure to fatty acids can lead to the overproduction of androgens,62,63 and the enhanced delivery of fatty acids to nonadipose tissues is key to the development of insulin resistance and T2D.64

Although earlier studies suggested that obesity-related insulin resistance and hyperinsulinemia can contribute to PCOS pathogenesis,15 insulin resistance in patients with PCOS may be present independently of BMI.30-32 Obesity seems to increase the risk of PCOS only slightly65 and might represent a referral bias for PCOS.12 Lipotoxicity is a potential mechanism in the development of both T2D and PCOS, and it can occur independently of obesity.15,66 In addition, adipose tissue dysfunction is seen in both PCOS and T2D,22 as women with PCOS have larger subcutaneous adipocytes for the same degree of total adiposity and BMI, and adipocyte hypertrophy is strongly correlated with insulin resistance and T2D.67,68 Further studies are needed to clarify the association of obesity with PCOS pathogenesis in girls with T2D.

Because of the scarcity of studies reporting race-specific data, we could not address the association of race with PCOS prevalence comprehensively. However, our data demonstrate that Indian girls had the highest prevalence, followed by White girls, and then Indigenous girls in Canada. In a retrospective study69 assessing PCOS prevalence in 250 adolescents without T2D, 60 patients were African American (65%) and 24 patients (26%) were White. African American patients had a higher BMI and hemoglobin A1c and less dyslipidemia compared with White individuals.69 A systematic review70 in adult women reported that Chinese women have the lowest prevalence of PCOS, followed by White, Middle Eastern, and Black women. More studies are needed to evaluate the prevalence of PCOS in girls with T2D across different racial groups to aid the development of personalized screening and management strategies.

It is important for PCOS to be diagnosed early to prevent the development of ensuing complications when untreated. PCOS in adolescence is associated with features of the metabolic syndrome, including hypertension, hyperglycemia, and dyslipidemia.26 In addition, adolescents with PCOS have higher prevalence of cardiovascular risk factors,23 including higher carotid intima thickness, β stiffness index, and reduced arterial compliance compared with patients with obesity and no PCOS.54 Psychiatric comorbidities are also prevalent in PCOS, such as anxiety (18%), depression (16%), and attention-deficit/hyperactivity disorder (9%).53 Health-related quality of life is substantially reduced in patients with PCOS, with body weight concerns, menstrual irregularity, and a sense of lack of control over health being important contributors.52,71 It is critical that PCOS in T2D is managed with a focus on biopsychosocial well-being to achieve positive health outcomes.

Limitations

The limitations of this systematic review include that none of the studies had PCOS as a primary outcome. There was also a lack of a unified approach to diagnosing PCOS across studies and no reporting of the timing of menarche, which may have contributed to the high heterogeneity observed in the meta-analysis. Two of the largest studies did not report the criteria used for PCOS diagnosis.45,46 However, this systematic review had a comprehensive search strategy across several databases, including the gray literature, which includes all available evidence to date on this outcome.

The results of this study reflect the lack of consensus and difficulty in diagnosing PCOS in adolescents. The European Society of Human Reproduction and Embryology/American Society of Reproductive Medicine, the Pediatric Endocrine Society, and the International Consortium of Paediatric Endocrinology guidelines suggest that ultrasonography showing increased ovarian size could be used to aid in diagnosis, but other guidelines are more conservative in using these findings to diagnose PCOS.14-18 In addition, the European Society of Human Reproduction and Embryology/American Society of Reproductive Medicine guidelines state that biochemical hyperandrogenism needs to be present, and not just clinical signs of hyperandrogenism, whereas other guidelines state that either is sufficient for diagnosing PCOS.14-18 There is a need for a consensus to establish the pediatric criteria for diagnosing PCOS in adolescents to ensure accurate diagnosis and lower the misclassification rates.

Given these limitations, the results should be interpreted with caution. Larger multiethnic, longitudinal cohort studies evaluating PCOS prevalence in girls with T2D and using standardized criteria for defining PCOS are urgently needed.

Conclusions

This study found that in girls with T2D, approximately 1 in 5 had PCOS. Identifying PCOS in this population is critical to allow for early screening and management of PCOS and its associated health concerns. Future studies are urgently needed to define the impact of obesity and race on PCOS prevalence in this population and to ensure the development of personalized assessment and treatment strategies.

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Article Information

Accepted for Publication: December 15, 2021.

Published: February 15, 2022. doi:10.1001/jamanetworkopen.2021.47454

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2022 Cioana M et al. JAMA Network Open.

Corresponding Author: M. Constantine Samaan, MD, MSc, Division of Pediatric Endocrinology, McMaster Children’s Hospital, 1200 Main St W, 3A-57, Hamilton, ON L8N 3Z5, Canada (samaanc@mcmaster.ca).

Author Contributions: Ms Cioana and Dr Samaan had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Cioana, Deng, Banfield, Thabane, Samaan.

Acquisition, analysis, or interpretation of data: Cioana, Deng, Nadarajah, Hou, Qiu, Chen, Rivas, Alfaraidi, Alotaibi, Thabane, Samaan.

Drafting of the manuscript: Cioana, Deng, Alfaraidi, Samaan.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Cioana, Deng, Qiu, Thabane, Samaan.

Administrative, technical, or material support: Cioana, Deng, Rivas, Banfield, Alotaibi, Samaan.

Supervision: Banfield, Alfaraidi, Thabane, Samaan.

Conflict of Interest Disclosures: None reported.

Meeting Presentation: This article was presented as a poster presentation at the Pediatric Endocrine Society Annual Meeting; April 30, 2021; virtual meeting.

Additional Contributions: Mr Parm Pal Toor (undergraduate student at McMaster University) helped with the updated search of this systematic review; he was not compensated for this work.

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