Menstrual pattern in polycystic ovary syndrome and hypothalamic-pituitary-ovarian axis immaturity in adolescents: a systematic review and meta-analysis

Figures & data

Table 1. Modified PI(E)COS strategy for the selection of descriptors used in the search for articles in the databases.

P	Population/participants	Teenagers Descriptors: Adolescent OR Teenager
I (E)	Intervention/exposure	Description of menstrual patterns Descriptors: Menstruation OR Menstrual Cycle OR Menstruation Disturbances OR Follicular Phase OR Luteal Phase
C	Comparison	AUB due to HPO axis immaturity and PCOS Descriptors: (Hypothalamo-Hypophyseal System OR Polycystic Ovary Syndrome) AND Rotterdam
O	Outcomes	Changes in menstrual pattern in terms of duration, volume, frequency, and/or regularity of bleeding Descriptors: Dysmenorrhea OR Menorrhagia OR Menarche OR Metrorrhagia OR Oligomenorrhea OR Amenorrhea OR Follicular Phase OR Luteal Phase OR Anovulation
S	Study	Cohort, case-control, observational, cross-sectional studies

AUB: Abnormal Uterine Bleeding; HPO: Hypothalamic-Pituitary-Ovarian;

Figure 1. Articles selection process.

Table 2. Menstrual pattern data extracted from selected studies.

Author, year, place, and type of study	Sample profile (n)	PCOS prevalence Menstrual pattern of PCOS group	Menstrual pattern of NPCOS group
Nidhi et al. 2011, India [33] Cross-sectional study	University population (semi-urban and rural areas) n = 460	PCOS: 9.13% Phenotype A: 26.19% Phenotype B: 2.38% Phenotype C: 2.38% Phenotype D: 69.05%	13% with persistent menstrual irregularity
Hickey et al. 2011, Australia [37] Cross-sectional study	Community population n = 232	PCOS: 21.1% Amenorrhea: 8%	5.3 % with persistent menstrual irregularity
Rehme et al. 2013, Brazil [35] Cross-sectional study	Urban population n = 44	Menstrual irregularity: 100% Amenorrhea: 72.7% Oligomenorrhea: 27.3% Phenotype A: 40.9% Phenotype B: 45.5% Anovulation and HA: 13.6%	NI
Siklar et al. 2014, Turkey [38] Cross-sectional study	Urban population n = 53	Menstrual irregularity: 87% Phenotype A: 49% Phenotype B: 19% Phenotype C: 13% Phenotype D: 19%	NI
Balaji et al. 2014, India [32] Cross-sectional study	Urban and rural population n = 126	PCOS: 53% Menstrual irregularity: 96%	29% with persistent menstrual irregularity
Kumari et al. 2015, India [36] Cross-sectional study	Girls attending a gynecology clinic n = 102	PCOS: 18.6% Oligomenorrhea: 78.9% Hypomenorrhea: 10.5% Polymenorrhea: 5.3%	1,29 % with persistent menstrual irregularity
Ybarra et al. 2017, Brazil [29] Cross-sectional study	Urban population n = 49	PCOS: 26.5%	20% with menstrual irregularity
Kaewnin et al. 2017, Thailand [31] Cross-sectional study	University students n = 548	PCOS: 5.29% Phenotype A: 41.38% Phenotype B: 3.44% Phenotype C: 27.59% Phenotype D: 27.59% Menstrual irregularity: 65%	28.71% with menstrual irregularity
Fruzzetti et al. 2020, Italy [34] Cross-sectional study	Urban population n = 109	Phenotype A: 73.4% Phenotype B: 21.1% Phenotype C: 4.6% Phenotype D: 0.9%	NI

NI: not informed; PCOS: polycystic ovary syndrome; NPCOS: non PCOS (HPO axis immaturity).

Table 3. Age, age at menarche, and BMI of the adolescents in the PCOS and NPCOS groups. Article quality rating by the NOS scale.

	Number of participants		Age; Age at Menarche (years) Mean ± SD		BMI (kg/m²) Mean ± SD
Author, Year, and Place	PCOS	NPCOS	PCOS	NPCOS	PCOS	NPCOS	NOS rating
Nidhi et al. 2011, India [33]	42	418	15.67 ± 0.78*	–	20.24 ± 2.55	–	6 stars
Hickey et al. 2011, Australia [37]	48	179	15.4 ± 0.62; 12.4 ± 1.1	15.2 ± 0.43; 12.6 ± 1.2	24.5 ± 5.7	22.3 ± 3	8 stars
Rehme et al. 2013, Brazil [35]	44	–	16.7 ± 2.2; 11.8 ± 1.4	–	30.3 ± 6.6	–	5 stars
Siklar et al. 2014, Turkey [38]	53	–	15.27 ± 1.34; 12.16 ± 1.13	–	25.32 ± 5.6	–	6 stars
Balaji et al. 2014, India [32]	67	59	16.0 ± 2.0; 12.0 ± 1.0	17.0 ± 2.0; 12.0 ± 1.0	22.0 ± 3.0	21.0 ± 2.0	7 stars
Kumari et al. 2015, India [36]	19	83	–	–	–	–	7 stars
Ybarra et al. 2017, Brazil [29]	9	40	15.3 ± 2*	14.6 ± 2.2*	35.5 ± 5.6	34.4 ± 6.5	8 stars
Kaewnin al. 2017, Thailand [31]	29	519	18.69 ± 0.47; 12.55 ± 1.06	18.66 ± 0.49; 12.37 ± 1.32	20.82 ± 3.60	20.70 ± 2.89	9 stars
Fruzzetti et al. 2020, Italy [34]	109	–	17.13 ± 1.3; 12.06 ± 1.25	–	26.24 ± 6.04	–	8 stars

BMI: Body Mass index; SD: standard deviation; 95% CI: 95% confidence interval; NOS: Newcastle-Ottawa Scale; PCOS: polycystic ovary syndrome; NPCOS: non PCOS (HPO axis immaturity).

* No data for age at menarche.

Figure 2. Analysis of age at menarche comparing the PCOS and NPCOS groups.

Figure 3. Analysis of the BMI comparing the PCOS and NPCOS groups.

Table 4. Meta-analysis of age at menarche and BMI of analyzed adolescent groups (PCOS and NPCOS).

Age at menarche		Age at Menarche (years) Mean ± SD		Effect estimate	p value	Test
		PCOS	NPCOS	SMD (95% CI)		I^2 (%)
Study (author, year)
Hickey et al. 2011 [37]		12.4 ± 1.1	12.6 ± 1.2	−0.170 (−0.488 − 0.149)	0.790	–
Balaji et al. 2014 [32]		12.0 ± 1.0	12.0 ± 1.0	0.000 (−0.350 − 0.350)		0.0%
Kaewnin al., 2017 [31]		12.55 ± 1.06	12.37 ± 1.32	0.138 (−0.236 − 0.512)		p 0.464
	Grouped			−0.027 (−0.227 − 0.172)
Body Mass Index		BMI (kg/m²) Mean ± SD		Effect estimate _	p value	Test
		PCOS	NPCOS	SMD (95% CI)		I^2 (%)
Study (Author, year)
Hickey et al. 2011 [37]		24.5 ± 5.7	22.3 ± 3	0.590 (0.267 − 0.913)	0.012
Balaji et al. 2014 [32]		22.0 ± 3.0	21.0 ± 2.0	0.387 (0.034 − 0.741)		40.2%
Ybarra et al. 2017 [29]		35.5 ± 5.6	34.4 ± 6.5	0.173 (−0.551 − 0.897)		p 0.171
Kaewnin al. 2017 [31]		20.82 ± 3.60	20.70 ± 2.89	0.041 (−0.333 − 0.415)
	Grouped			0.334 (0.073 − 0.595)

SMD: Standardized Mean Difference, 95% CI: 95% Confidence Interval.

Balaji S, Amadi C, Prasad S, et al. Urban rural comparisons of polycystic ovary syndrome burden among adolescent girls in a hospital setting in India. Biomed Res Int. 2015;2015:158951.

 PubMed Web of Science ®Google Scholar

Fruzzetti F, Baldari F, Palla G, et al. Comparison of PCOS phenotypes in adolescent and young adult mediterranean women with possible PCOS. J Endocrinol Invest. 2021;44(5):995–1000. doi: 10.1007/s40618-020-01394-3.

 PubMed Web of Science ®Google Scholar

Hickey M, Doherty DA, Atkinson H, et al. Clinical, ultrasound and biochemical features of polycystic ovary syndrome in adolescents: implications for diagnosis. Human Rep. 2011;26(6):1469–1477. doi: 10.1093/humrep/der102.

 PubMed Web of Science ®Google Scholar

Rehme MFB, Pontes AG, Goldberg TBL, et al. Manifestações clínicas, bioquímicas, ultrassonográficas e metabólicas da síndrome dos ovários policísticos em adolescentes [Clinical manifestations, biochemical, ultrasonographic and metabolic of polycystic ovary syndrome in adolescents]. Rev Bras Ginecol Obstet. 2013;35(6):249–254. doi: 10.1590/s0100-72032013000600003.

 PubMedGoogle Scholar

Nidhi R, Padmalatha V, Nagarathna R, et al. Prevalence of polycystic ovarian syndrome in Indian adolescents. J Pediatr Adolesc Gynecol. 2011; 24(4):223–227. doi: 10.1016/j.jpag.2011.03.002.

 PubMed Web of Science ®Google Scholar

Kumari S, Pankaj S, Kavita K, et al. Study of adolescent girls with menstrual irregularities for polycystic ovaries and insulin resistance. Jemds. 2015;4(32):5472–5483. doi: 10.14260/jemds/2015/802.

 Google Scholar

Ybarra M, Franco RR, Cominato L, et al. Polycystic ovary syndrome among obese adolescents. Gynecol Endocrinol. 2018;34(1):45–48. doi: 10.1080/09513590.2017.1359250.

 PubMed Web of Science ®Google Scholar

Kaewnin J, Vallibhakara O, Arj-Ong Vallibhakara S, et al. Prevalence of polycystic ovary syndrome in Thai university adolescents. Gynecol Endocrinol. 2018;34(6):476–480.

 PubMed Web of Science ®Google Scholar

Sıklar Z, Berberoğlu M, Çamtosun E, et al. Diagnostic characteristics and metabolic risk factors of cases with polycystic ovary syndrome during adolescence. J Pediatr Adolesc Gynecol. 2015;28(2):78–83. doi: 10.1016/j.jpag.2014.05.006.

 PubMed Web of Science ®Google Scholar

Data availability statement

The data that support the findings of this study were derived from the following resources available in the public domain: PubMed (https://www.ncbi.nlm.nih.gov/pubmed), EMBASE (https://www.embase.com), Web of Science (https://isiknowledge.com), Virtual Health Library (http://bvsalud.org), and Scopus (https://www.scopus.com)