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Journal of Autism and Developmental Disorders

, Volume 47, Issue 12, pp 3983–3993 | Cite as

Typical Pubertal Timing in an Australian Population of Girls and Boys with Autism Spectrum Disorder

  • Tamara May
  • Ken C. Pang
  • Michele A. O’Connell
  • Katrina Williams
Original Paper

Abstract

Secondary data analyses from the Longitudinal Study of Australian Children Kindergarten cohort were performed to understand any alterations in pubertal timing in Autism Spectrum Disorder (ASD) in a population sample. Timing of parent-reported pubertal events (ages 8–9, 10–11, 12–13 years), and self-report (14–15 years; N = 3454 no ASD, N = 94 with ASD) included breast development, menses, skin changes, growth spurt, body hair, deepening voice and facial hair. Survival analyses and Cox regression controlling for covariates showed no evidence of altered pubertal onset amongst males with ASD. In contrast to some past studies, there was also no difference in pubertal timing in females with ASD. These exploratory findings suggest typical puberty timing in a population representative group of young people with ASD.

Keywords

Autism Spectrum Disorder Puberty Adrenarche 

Introduction

Autism Spectrum Disorder (ASD) is a developmental disorder characterised by difficulties with social-communication and patterns of restricted and repetitive behaviours (American Psychiatric Association 2013). ASD is found in around 1.5–2.5% of the Australian population (Randall et al. 2016). For around 20% of individuals with ASD there are known genetic causes, but for the remainder, the aetiology remains unclear (Abrahams and Geschwind 2008). The prevalence of ASD in males is around 2–4 times higher than females, a consistent finding suggesting males have more underlying vulnerabilities or risk factors than do females or there are other factors involved such as under diagnosis of females (American Psychiatric Association 2013).

Due to the higher proportion of males with ASD, theories relating to the influence of sex chromosomes and sex hormones in ASD have been proposed. Androgens, given their male sexual differentiating effects, could potentially play a key role. The androgen theory of ASD proposes that elevated levels of prenatal androgens, which masculinise the brain at sensitive periods of gestation and postnatally, play a role in the development of ASD resulting in a hyper-masculinisation of the brain, or an “extreme” male brain (Baron-Cohen 2002). Males have a higher exposure than females to androgens during sexual differentiation of the gonads in utero. This theory has been indirectly explored by measuring foetal testosterone pre- or peri-natally and correlating these levels with later measures of autistic behaviour in non-ASD samples. Some studies published in this area to date have shown small magnitude correlations between elevated foetal testosterone levels and later autistic traits (Whitehouse et al. 2012, 2010), while others show no association (Kung et al. 2016). The androgen theory has also claimed indirect support with findings in females with ASD of more testosterone related medical conditions and less heterosexual sexual attraction, which could potentially relate to exposure to excess androgens (Gilmour et al. 2012; Ingudomnukul et al. 2007). Finally, Baron-Cohen (2002) also interpreted possible alterations in pubertal timing in ASD (see below) as further potential support for the extreme male brain theory.

Typical secondary sex characteristics result from the hormonal changes of both adrenarche and true puberty, two separate, but often concurrent, biological processes. Adrenarche describes the developmental maturation of adrenal androgen production that occurs in both sexes. Adrenal androgens such as dehydroepiandrosterone (DHEA) underlie bodily changes including pubic and axillary hair, and increased oil production in the skin and body odour. Adrenarche typically begins around 8 years of age in boys and girls but biochemical awakening may be evident from 6 years. True puberty (gonadarche) results from activation and functional maturation of the hypothalamic–pituitary–gonadal (HPG) axis with subsequent testicular production of testosterone in boys and ovarian production of oestradiol in girls. The first signs of true puberty are testicular volume enlargement in boys and breast development in girls. The physical manifestations of true puberty usually follow those of adrenarche, but the two processes can be disassociated. Breast development prior to age 8 years and testicular volume ≥4 ml at age less than 9 years in boys are considered ‘precocious’. Typically, the pubertal growth spurt occurs in early-mid puberty in females, whereas it is later in males. Menarche is a late sign of true puberty in females occurring on average 2.5 years after onset of breast development.

In the general population, timing of puberty has been associated with a broad range of genetic and environmental variables. For example, timing of a child’s puberty has been associated with parents’ pubertal onset (Sedlmeyer and Palmert 2002), with a trend for maternal age of menarche to predict their daughters’ age of menarche (Graber et al. 1995). Environmental associations which may be associated with earlier puberty include a lack of presence of a male parent or male siblings in the household (for females), migration and obesity (Karapanou and Papadimitriou 2010; Parent et al. 2003; Willemsen and Dunger 2015). Variations in pubertal timing are also known for specific chronic diseases (Soliman et al. 2014). Regardless of the cause, alterations in the timing of puberty, both early and delayed, can have significant negative effects on the affected individual and has been linked with an increased risk of poor health and psychosocial outcomes (Mensah et al. 2013).

Studies exploring the timing of puberty in ASD have been limited and findings have varied (Table 1). In females with ASD age of menarche has been explored by six studies. Two found late menarche with two small clinical samples (Harper and Collins 1979; Knickmeyer et al. 2006). Another in a non-ASD population cohort found later age of menarche in those with high autistic traits measured at age 2, (Whitehouse et al. 2011). A recent study of university students also found associations between later age of menarche and autism traits (Hergüner and Hergüner 2016).

Table 1

Summary of studies exploring timing of puberty in males and females with Autism

 

Authors

Study type

Sample

Puberty related findings

+ Early

− Late

Females

Harper and collins (1979)

Uncontrolled clinically referred

N = 28 females with autism, aged 8–28 years; IQ not reported

Delayed menarche (N = 9) 14.43 years

Delayed secondary sex characteristics (pubic hair & breast development)

Mouridesen and Larsen (1989)

Uncontrolled clinically referred case study

N = 1 girl with autism aged 6 years; IQ = 88

Precocious puberty (breast development, pubic hair)

+

Yoshimura et al. (2005)

Uncontrolled clinically referred case studies

N = 3 females with autism; age range 6–9 years; IQ not reported

Precocious puberty (breast development, menarche, public hair)

+

Knickmeyer et al. (2006)

Retrospective self-report Clinically/community referred

N = 38 females with ASD, mean age 31 years, varied IQ levels; N = 38 age matched female controls mean age 32 years

Later menarche (13 years 4 months ASD, 12 years 7 months controls)

Whitehouse et al. (2011)

Prospective, population representative sample

N = 383 females; 17 years of age at last follow-up; Divided into low, typical and high autistic traits measured at 2 years

Later age of menarche in high autistic traits girls (N = 70, 13.07years) than in typical (N = 216

12.72 years) or low (N = 47, 12.66 years)

Pohl et al. (2014)

Retrospective self-report; self-referred online community database

N = 415 females with ASD, Mean age 36; N = 415 female controls mean age 39; IQ not reported

Early puberty

Early growth spurt

No difference menarche timing

+

Herguner and Herguner (2016)

Retrospective self-report, community referred

Normally developing female students, mean age 19.6 years; mean age at menarche 13.3years

Later age of menarche correlated with autistic traits

Males

Tordjman et al. (1997)

Uncontrolled Clinically referred

Males with ASD N = 12, aged 6–10 years); IQ not reported

4/12 males showed precocious puberty

+

In contrast, one of the first studies on this topic identified precocious puberty in three females with ASD (Harper and Collins 1979). A large online study of women with and without ASD also found higher levels of precocious puberty and early growth spurt in women with ASD according to retrospective self-report (Pohl et al. 2014), similar to prior small case studies reporting precocious puberty in girls with ASD (Mouridsen and Larsen 1989; Yoshimura et al. 2005). However, the measure used did not separate precocious puberty by characteristics related to adrenarche or true puberty (Pohl et al. 2014). In males with ASD there has only been one report of precocious puberty in a small clinical group (Tordjman et al. 1997). In this study and those already mentioned in which cases were ascertained clinically, referral biases towards more severe cases with additional comorbidities may have impacted on puberty timing. Some studies have also used retrospective self-report which may reduce the accuracy of the results (Knickmeyer et al. 2006; Pohl et al. 2014).

Other studies of puberty in ASD have focused on consequences of puberty rather than timing. There have been reports of an aggravation of symptoms such as more aggressive and hyperactive behaviour around puberty, as well as deterioration in intellectual and communication skills, particularly in females (Gillberg and Schaumann 1982; Gillberg and Steffenburg 1987). Given these associations with important functional and behavioural changes, as well as the possible theoretical implications, further knowledge about puberty timing in ASD is needed.

The purpose of this exploratory study was to explore the timing of puberty in a population representative sample of children with and without ASD, differentiating indicators of adrenarche from true puberty. Using the prospective nationally representative Longitudinal Study of Australian Children (LSAC) the specific aims were to explore whether there was: (1) an altered timing of adrenarche onset in girls with ASD relative to girls without ASD; (2) early adrenarche in boys with ASD relative to boys without ASD; (3) alterations in true puberty in boys and girls with ASD relative to those without, and, (4) evidence of alterations in pubertal timing in parents of children with ASD.

Method

Study Design

LSAC was designed to be representative of the Australian population. It employs a prospective cross-sequential design that follows two cohorts of children, initially aged 0–1 years (Birth “B” cohort; N = 4983) and 4–5 year olds (Kindergarten “K” cohort; N = 5,107) in 2004. Stratification was used to ensure proportional geographic representation for states/territories and capital city statistical division/rest of state areas. The sample was stratified by state, capital city statistical division/balance of state and two strata based on the size of the target population in the postcode. Postcodes were selected with probability proportional to size selection where possible, and with equal probability for small population postcodes. Children from both cohorts were selected from the same postcodes. Some remote postcodes were excluded from the design, and the population estimates were adjusted accordingly. LSAC is generally representative of the Australian population, although it slightly under-represents single parent families and non-English speaking families, and families living in rental properties.

This study utilised data from the Kindergarten cohort at four time points, waves 3–6, when the children were aged 8–9, 10–11, 12–13 and 14–15 years. Retention in the K cohort at Wave 6 was N = 3,454.

Measures

ASD status was determined using parent report when the children were aged 14–15 years. During parent interview by a trained study researcher the primary caregiver was asked: ‘Does your child have any of these ongoing conditions?’ from which parents could respond “yes” or “no” to ‘Autism, Aspergers, or other autism spectrum’. If parents answered yes, the child was classified as having ASD.

Puberty Measures

Onset of puberty in LSAC was measured using adapted items from the Pubertal Development Scale for parent report (Petersen et al. 1988). Parents reported on their child at 8–9, 10–11 and 12–13 years by answering the following: “Some children are approaching puberty by 8–9/10–11/12–13 years of age. We’re interested to know if you’ve noticed any early signs in your own child. Have you noticed any of the following things in this child yet?” Table 2 contains the list of pubertal indicators. At 14–15 years of age children self-reported on these measures using an audio computer-assisted self-interview (ACASI). This method allows for sensitive content to be answered by the young person with anonymity. Measures were grouped into a dichotomous variable as “none/barely” and “Definite/seems complete”. For boys true puberty indicators were growth spurt, voice deepening and facial hair. As breast development, menses and growth spurt happen in sequence for girls, a combined true puberty indicator was not computed and breast development was used as the true puberty indicate in girls. Adrenarche indicators for both boys and girls were body odour and body hair (pubic/armpits).

Table 2

Puberty questions asked of parents and young people across the time points

Gender

Question

8–9a

10–11a

12–13a

14–15a

Boys and girls

Skin changes, like acne, pimples or blackheads

X

X

X

X

Body hair (armpits and/or dark pubic hair)

X

X

X

X

Child’s growth in height (growth spurt)

 

X

X

X

Adult type body odour

X

   

Girls

Breast growth

 

X

X

X

Has your child ever menstruated (had her period?)

(Yes/no response option)

 

X

X

X

Boys

Have you noticed a deepening of your child’s voice?

 

X

X

X

Has your child begun to grow hair on his face?

 

X

X

X

aYears of age of the young people at the time point

Mother’s Puberty

Mothers of children were also asked to retrospectively report their own age of menarche (when the children were 14–15 years) and their pubertal timing, which was assessed by asking, “Timing of children’s puberty can be related to their parents’ own puberty. If you think of the age at which your own puberty began, do you feel that in comparison to your peers you were?”, and providing the following response options: “Way ahead of most other kids; Ahead; About the same age as other kids; Behind; Way behind most other kids.”, which were grouped into three categories (Way ahead/Ahead, About the same, Behind/Way behind).

Cognitive and Language Functioning

Language functioning was assed using the short version of the Peabody Picture Vocabulary Test Third-edition (PPVT-III) (Dunn and Dunn 1997) at 8 years of age. Cognitive functioning was assessed using the Matrix Reasoning subtest from the Wechsler Intelligence Scale for Children IV (WISC-IV) (Wechsler 2003) at 10 years of age which has a mean of 10 and a standard deviation of 3.

Body Mass Index (BMI) was collected by the trained researchers at each wave of LSAC data collection used in the current study by measuring height and weight of the children, using international definitions for classifying BMI (kg/m2).

Covariates

A number of covariates that may impact on the timing of puberty were investigated in the analyses. The following were included: socioeconomic disadvantage status, body mass index (BMI) as defined above, whether English was the main language spoken at home (as a measure of ethnicity), parent education level, and whether the child was living in a two parent family. Neighbourhood socioeconomic disadvantage was measured using the Socio-Economic Indexes for Areas Disadvantage Index (SEIFA) corresponding to the family’s postcode of residence (ABS 2013).

Procedure

At each LSAC wave, trained interviewers conducted face-to-face interviews with the primary caregiver in the home, supplemented by direct assessments of children and administration of parent surveys. The LSAC study is approved by the Australian Institute of Family Studies Ethics Committee, and parents provided written informed consent. Permission was granted to use the LSAC data for addressing the current study aims.

Data Analyses

Summary statistics were used to compare the demographic characteristics of those with and without ASD. Survival analyses were used such that once the puberty indicator became “Definite/seems complete” the ‘event’ had occurred and the corresponding age (in months) was the event time. Cox regression was used to explore any group differences in the model with the covariates included. Post-hoc power analyses were performed using G*Power. For the Cox regression analyses with six predictors (5 covariates and ASD group) a sample of 73 males with ASD would allow for detection of a medium to large difference and for 21 girls with ASD a large difference. Tests for the equality of survivor functions between the ASD and non-ASD groups (without covariates) were also performed. Survey methods were used where appropriate to account for the unequal probability of participant selection into the sample, non-response and sample attrition, and the multi-stage, clustered sampling design (Soloff et al. 2006). Analyses were conducted in Stata version 14.0.

Results

Demographic Information

Demographic details for the ASD and non-ASD group at 14–15 years of age are detailed in Table 3. There were 94 children with parent-reported ASD in the K cohort, 73 boys and 21 girls, at ages 14–15 years. There was a greater proportion of males in the ASD group compared to the non-ASD group. Children with ASD had significantly lower scores on the cognitive and language measures. The proportion of those in the ASD group scoring below the second percentile on these measures was 4% for the language task (3% in the non-ASD group) and 15% for the cognitive task (2% in the non-ASD group) indicating a relatively high-functioning ASD group. Children with ASD were more socioeconomically disadvantaged than their non-ASD peers. Otherwise, the groups were similar for number of children in the family, English language spoken at home, indigenous status, remote location, single parent family, BMI, and parents’ age at birth.

Table 3

Sample characteristics for children with and without ASD in the K cohort at 14–15 years

Measure

ASD (N = 94)

Non-ASD (N = 3454)

p

Child age in months, mean (SD)

178.9 (3.8)

179.2 (4.1)

0.616

Male %

77.5

50.8

<0.001*

Number of children at home, mean (SD)

2.3 (1.1)

2.5 (1.1)

0.108

English main language spoken at home (%)

95.5

86.8

0.072

Indigenous (%)

3.1

2.6

0.773

Remote/very remote location (%)

2.5

3.0

0.809

Single parent family (%)

28.6

19.6

0.064

Maternal age at childbirth, mean (SD)

29.9 (6.5)

30.6 (5.3)

0.376

Paternal age at childbirth, mean (SD)

32.3 (6.8)

33.3 (6.1)

0.209

Primary caregiver did not complete high school (%)

60.3

49.6

0.050

BMI for age z scoreb

0.4 (1.2)

0.4 (1.1)

0.988

Neighbourhood disadvantage, mean (SD)c

985.3 (74.0)

1008.1 (74.0)

0.006*

Cognitive functioning (Matrices), mean (SD)a

9.1 (3.8)

10.7 (2.9)

0.008*

Language functioning (PPVT)a

76.3 (6.0)

78.3 (4.8)

0.027

ASD autism spectrum disorder, SD standard deviation, PPVT peabody picture vocabulary test

*p < .05

aAll proportions are weighted and adjusted for LSAC sample design.

bBased on Centre for Disease Control growth charts

cLower scores indicate more disadvantage

Puberty Indicators

Tables 4 and 5 show the proportions and 95% confidence intervals for the indicators of puberty in girls and boys with and without ASD. There were no significant differences between survivor functions for boys and girls with and without ASD for any of the individual and combined puberty indicators (Girls: skin changes χ2 = 1.28, p = .25; body hair χ2 = 1.36, p = .24; breast growth χ2 = 0.57, p = .45; menses χ2 = 0.38, p = .54; growth spurt χ2 = 0.57, p = .45; combined adrenarche χ2 = 1.27, p = .26. Boys: skin changes χ2 = 1.5, p = .22; body hair χ2 = 2.68, p = .10; growth spurt χ2 = 0.06, p = .81; facial hair χ2 = 0.29, p = .58; voice deepening χ2 = 0.15, p = .70; combined adrenarche χ2 = 2.75, p = .10; combined true puberty χ2 = 0.27, p = .60). Figure 1 shows the Kaplan Meier survival curves for boys and girls for the combined adrenarche indicator, true puberty for boys and breast growth for girls (as the first indicator of true puberty in girls). There were no indications of precocious puberty in boys or girls. Figure 1a shows a small difference in curves with a higher proportion of girls with ASD than girls without with earlier adrenarche onset, which was not statistically significant.

Table 4

Adjusted proportion with 95% confidence intervals of indicators of puberty in girls with and without ASD and ASD status comparisons

 

8–9 years

10–11 years

12–13 years

14–15a years

None/barely

Definite/seems complete

None/barely

Definitely started/seems complete

None/barely

Definitely started/seems complete

None/barely

Definitely started/seems complete

Adult type body odour

 Girls ASD

91.5 (78.0, 100)

8.5 (0, 22.0)

NA

NA

NA

NA

NA

NA

 Non-ASD girls

91.4 (89.9, 93.0)

8.5 (7.0, 10.1)

NA

NA

NA

NA

NA

NA

Body hair (armpits and/or dark pubic hair)

 Girls ASD

95.0 (84.3, 100)

5.0 (0, 15.8)

51.9 (27.5, 76.3)

48.1 (23.7, 72.5)

11.6 (0, 26.8)

88.4 (73.2, 100)

0

100

 Non-ASD girls

96.3 (95.2, 97.3)

3.7 (2.7, 4.8)

68.8 (66.4, 71.2)

31.2 (28.8, 33.6)

24.7 (22.4, 27.0)

75.3 (73.0, 77.6)

4.7 (3.5, 5.9)

95.3 (94.1, 96.5)

Adrenarche combined (body odour, body hair)

 Girls ASD

91.5 (65.5, 98.4)

8.5 (1.6, 34.5)

51.9 (27.5, 76.3)

48.1 (23.7, 72.5)

11.6 (0, 26.8)

88.4 (73.2, 100)

0

100

 Non-ASD girls

89.6 (87.9, 91.1)

10.4 (8.8, 12.1)

68.8 (66.4, 71.2)

31.2 (28.8, 33.6)

24.7 (22.4, 27.0)

75.3 (73.0, 77.6)

4.7 (3.5, 5.9)

95.3 (94.1, 96.5)

Skin changes

 Girls ASD

86.7 (66.6, 100)

13.3 (0, 33.4)

64.9 (45.0, 84.9)

35.0 (15.1, 55.0)

32.2 (6.9, 57.5)

67.8 (42.5, 93.1)

4.2 (0, 13.7)

95.7 (86.3, 100)

 Non-ASD girls

94.7 (93.5, 96.0)

5.3 (4.0, 6.5)

78.7 (76.5, 81.0)

21.3 (19.0, 23.5)

44.4 (41.7, 46.9)

55.6 (53.1, 58.2)

19.7 (17.5, 21.8)

80.3 (78.2, 82.3)

Breast growth

 Girls ASD

90.1 (74.3, 100)

9.9 (0, 25.7)

53.4 (31.4, 75.4)

46.6 (24.6, 68.6)

12.5 (0, 27.8)

87.5 (72.2, 100)

17.6 (0, 40.0)

82.4 (60.1, 100)

 Non-ASD girls

93.3 (91.8, 94.8)

6.7 (5.2, 8.2)

58.0 (55.4, 60.7)

42.0 (39.3, 44.6)

19.9 (17.6, 22.1)

80.1 (77.9, 82.4)

12.8 (11.1, 14.6)

87.2 (85.4, 88.9)

Growth spurt

 Girls ASD

NA

NA

41.4 (19.1, 63.6)

58.6 (36.4, 80.9)

16.3 (0.1, 32.4)

83.7 (67.6, 99.9)

20.7 (0.7, 40.7)

79.3 (59.3, 99.2)

 Non-ASD girls

NA

NA

39.1 (36.4, 41.9)

60.9 (58.1, 63.6)

21.5 (19.3, 23.8)

78.5 (76.2, 80.7)

20.5 (18.2, 22.8)

79.5 (77.2, 81.8)

 Menstruating

  

Yes

No

Yes

No

Yes

No

 Girls ASD

NA

NA

14.5 (3.7, 42.9)

85.5 (57.1, 96.3)

64.7 (36.4, 85.5)

35.3 (14.5, 63.6)

96.6 (73.7, 99.6)

3.4 (3.5, 26.3)

 Non-ASD girls

NA

NA

4.6 (3.5, 6.0)

95.4 (94.0, 96.5)

56.1 (53.4, 58.8)

43.9 (41.2, 46.6)

94.6 (93.2, 95.7)

5.4 (4.3, 6.8)

Non-ASD girls N = 1488–1633; Girls with ASD N = 18–21 due to missing responses

aChild self report; NA not asked

Table 5

Adjusted proportion with 95% confidence intervals of indicators of puberty in boys with and without ASD and ASD status comparisons

 

8–9 years

10–11 years

12–13 years

14-15a years

None/barely

Definite/seems complete

None/barely

Definitely started/seems complete

None/barely

Definitely started/seems complete

None/barely

Definitely started/seems complete

Adult type body odour

 Boys ASD

99.2 (97.7, 100)

0.7 (0, 2.3)

NA

NA

NA

NA

NA

NA

 Non-ASD boys

96.2 (95.2, 97.2)

2.8 (2.8, 4.8)

NA

NA

NA

NA

NA

NA

Body Hair (armpits and/or dark pubic hair)

 Boys ASD

99.1 (97.3, 100)

0.9 (0,2.7)

92.3 (85.7, 99.0)

7.7 (9.6, 14.3)

58.3 (45.8, 70.8)

41.7 (29.2, 54.2)

17.6 (7.9, 27.4)

82.4 (72.6, 92.1)

 Non-ASD boys

99.1 (98.6, 99.6)

0.9 (0.4,1.4)

89.5 (87.6, 91.4)

10.5 (8.6, 12.4)

62.7 (60.1, 65.3)

37.3 (34.7, 39.9)

18.3 (16.0, 20.5)

81.7 (79.5, 83.9)

Adrenarche combined (body odour, body hair)

 Boys ASD

98.3 (93.6, 99.6)

1.7 (0.4, 6.4)

92.3 (82.4, 96.9)

7.7 (3.1, 17.6)

58.3 (45.6, 70.1)

41.7 (29.9, 54.4)

17.6 (9.8, 29.5)

82.4 (70.5, 90.1)

 Non-ASD boys

95.7 (94.5, 96.6)

4.3 (3.3, 5.5)

89.5 (87.4, 91.3)

10.5 (8.7, 12.6)

62.7 (60.1, 65.3)

37.3 (34.7, 39.9)

18.3 (16.2, 20.6)

81.7 (79.4, 83.8)

Skin changes

 Boys ASD

97.6 (94.5, 100)

2.4 (0, 5.5)

88.2 (79.6, 96.8)

11.8 (3.2, 20.3)

69.6 (58.5, 80.6)

30.4 (19.4, 41.5)

18.7 (8.1, 29.2)

81.3 (70.8, 91.9)

 Non-ASD boys

96.8 (95.8, 97.8)

3.2 (2.2, 4.2)

92.4 (91.0, 93.8)

7.6 (6.2, 9.0)

67.2 (64.7, 69.8)

32.8 (30.2, 35.3)

25.0 (22.8, 27.2)

75.0 (72.8, 77.2)

Deepening voice

 Boys ASD

NA

NA

97.1 (93.6, 100)

2.9 (0, 6.4)

86.5 (78.0, 95.0)

13.5 (5.0, 22.0)

25.6 (14.1, 37.2)

74.4 (62.8, 85.9)

 Non-ASD Boys

NA

NA

98.5 (97.7, 99.3)

1.5 (0.7, 2.3)

75.8 (73.5, 78.1)

24.2 (21.9, 26.5)

24.6 (22.4, 26.8)

75.4 (73.2, 77.6)

Facial hair

 Boys ASD

NA

NA

98.0 (94.1, 100)

1.9 (0, 5.9)

91.4 (83.5, 99.4)

8.6 (0.6, 16.5)

53.3 (40.0, 66.5)

46.7 (33.5, 60.0)

 Non-ASD boys

NA

NA

99.4 (99.0, 99.8)

0.6 (0.2, 0.9)

88.1 (86.3, 89.8)

11.9 (10.2, 13.7)

50.5 (48.0, 52.9)

49.5 (47.1, 52.0)

Growth spurt

 Boys ASD

NA

NA

49.9 (36.5, 63.3)

50.1 (36.7, 63.5)

49.0 (35.2, 62.8)

51.0 (37.2, 64.8)

24.4 (12.5, 36.4)

75.6 (63.6, 87.5)

 Non-ASD boys

NA

NA

53.0 (50.0, 56.0)

47.0 (44.0, 49.9)

43.7 (41.1, 46.4)

56.3 (53.6, 58.9)

22.8 (20.5, 25.2)

77.2 (74.8, 79.5)

True puberty combined (growth spurt, voice deepening, facial hair)

 Boys ASD

NA

NA

46.8 (34.2, 59.8)

53.2 (40.2, 65.8)

47.8 (34.4, 61.5)

52.2 (38.5, 65.6)

7.3 (3.0, 16.6)

92.7 (83.4, 97.0)

 Non-ASD boys

NA

NA

52.3 (49.3, 55.3)

47.7 (44.7, 50.7)

41.1 (38.4, 43.8)

58.9 (56.2, 61.6)

10.8 (9.2, 12.6)

89.2 (87.4, 90.8)

ASD boys, N = 54–69; Non-ASD boys N = 1488–1642, due to missing responses

aChild self report; NA not asked

Fig. 1

Kaplan–Meier survival curves for puberty onset for the combined features of adrenarche in girls (a) and boys (b), Breast growth in girls (c), and combined features of true puberty in boys (d), for children with and without autism spectrum disorder by years of age

Separate Cox regression analyses were conducted for boys and girls for the puberty indicators including the covariates. ASD status was not a significant factor in predicting onset of any pubertal indicator. For the covariates, increased BMI was associated with an increased proportion of children with early onset of skin changes, body hair and the combined features of adrenarche for both boys and girls. For girls higher BMI and having a single parent family was associated with a higher proportion with earlier onset of breast growth and growth spurt. Earlier menses onset for a higher proportion of girls was associated with higher BMI, being from a single parent family and not having English as the main language spoken at home. For boys increased BMI and lower parent education level were each associated with an increased proportion with early onset of growth spurt and the combined true puberty indicator. Earlier onset of facial hair growth for boys was also associated with higher BMI.

Maternal Puberty Indicators

There was no difference in the age of menarche in mothers of girls (p = .71) and boys (p = .59) with or without ASD (ASD boys M = 13.0years, SD = 1.4, Non-ASD boys M = 12.9, SD = 2.0; ASD girls M = 12.7, SD = 1.6; Non-ASD girls M = 12.8, SD = 2.0). Unadjusted logistic regression analyses (given retrospective parent demographic factors were not available) showed there was no difference in mother reported timing of their own puberty for boys with or without ASD, p = .31, and girls with or without ASD, p = .53.

Discussion

This exploratory study aimed to explore indicators of puberty and their onset in a population based sample of boys and girls with ASD relative to their non-ASD peers using data prospectively collected over four time points from ages 8 to 14 years. Prior studies have yielded conflicting results with some indicating that girls with either ASD or autistic traits may experience either earlier or later menarche than typical, and that boys with ASD may experience earlier puberty. Contrary to these findings, results from this population representative sample showed statistically comparable pubertal timing for girls and boys with parent reported ASD to children not reported to have ASD. There was no indication of early or delayed puberty in girls with ASD, or early puberty in boys with ASD. The current study also explored maternal age of menarche and timing of puberty given the association between mother and child pubertal timing (Graber et al. 1995). Again, there were no differences in the age of onset of puberty or menarche for mothers with children with or without ASD.

The strength of the current study is the use of a prospective population based sample of children with and without ASD that did not have a specific emphasis on puberty which may otherwise have biased responses (potentially resulting in participants with more atypical pubertal timing). Past studies indicating delayed (Harper and Collins 1979; Knickmeyer et al. 2006) or early puberty (Mouridsen and Larsen 1989; Pohl et al. 2014; Yoshimura et al. 2005) in females with ASD have used non-population based samples and there has only been one report of precocious puberty in males with ASD which was a small clinical report (Tordjman et al. 1997). These studies may have referral biases towards more severe cases and potentially with additional medical comorbidities which may influence the timing of puberty rather than ASD per se.

Previous authors (Pohl et al. 2014) have found higher rates of precocious puberty and early growth spurt as well as higher levels of hyperandrogenism (although lower reported polycystic ovary syndrome) in women with ASD compared with controls and have postulated that prenatal androgen exposure may provide a link between these findings. We found no difference in timing of either adrenarche or signs of true puberty in a prospective cohort with parent-reported autism relative to controls and our data therefore do not support this theory. Some prior studies exploring facets of the androgen theory of ASD have also failed to find support in the areas of cognitive abilities (Falter et al. 2008), gender identity (Pasterski et al. 2014) and in the association between androgens in utero and later autistic traits itself (Kung et al. 2016). As shown in Table 1, many previous studies that reported altered timing of puberty in ASD included physical changes arising from both adrenal androgen effects (adrenarche) as well as those of true puberty; these are however, separate physiological events. In girls, breast development, pubertal growth spurt and timing of menarche relate predominantly to true puberty (oestrogen exposure); alterations in their timing would be unlikely to relate to the theory of androgen excess, which in females would only explain earlier features of adrenarche. As per prior large population based studies earlier pubertal onset was associated with higher BMI, and in girls being from a single parent family which may relate to the paternal investment theory through the presence or absence of a male in the home (Ellis 2004; Karapanou and Papadimitriou 2010; Parent et al. 2003; Willemsen and Dunger 2015).

There are a number of limitations to consider in this research. Firstly, parent report of puberty may be inaccurate; in particular chest wall adiposity may have been interpreted as breast development in some with higher BMI. However, there is a lack of non-invasive, easy to assess markers of pubertal onset in large population based cohorts. We speculate that it may also be possible that parents of children with ASD are more involved in daily living tasks such as dressing and bathing their children which may give them more exposure to early signs of puberty. This could be a potential confounding factor given previous studies in ASD cohorts suggesting earlier puberty in both boys and girls with ASD relative to population norms or control groups. Secondly, there were a small number of young people with ASD in the sample, particularly females with ASD (n = 18–21). Power analyses suggest the sample of 73 males with ASD would have allowed for detection of a medium to large difference in regression analyses. For females only a large difference compared to typically developing females would have been detected. If the difference in pubertal timing is small then our sample size would have been insufficient. Hence, these findings should be taken as preliminary and highlight both the value but also limitations of using population studies with low prevalence disorders.

Thirdly, the study was limited by the use of parent-reported rather than clinician confirmed ASD. However, past community studies suggest that parents are typically accurate in reporting ASD status (Warnell et al. 2015). The prevalence of parent-reported ASD was around 2.9% in the K cohort at 14 years which may suggest a broader spectrum was identified compared with the expected 1% from the narrow autism phenotype (Lundström et al. 2015). Finally, it is also the case, more notably for boys where puberty starts later and last longer than in girls, that at the oldest assessment time point of 14–15 years old, some had not yet completed puberty; this did not differ with ASD status however (Table 5). Proportions who had attained menarche at age 14–15years were also similar in the ASD and non-ASD cohorts in this study.

The current findings have implications for both clinicians and families. Given typical onset of puberty found in this population representative group of both boys and girls with ASD, sexuality, puberty and hygiene education needs to occur at similar times as for children without ASD. Where there is concern regarding atypical pubertal timing in young people with (or without) ASD medical referral and assessment is warranted.

In conclusion, the present population representative study indicated similar timing of puberty for children with ASD compared to children without. This information is important for clinicians to be aware of when working with families of children with ASD and also contributes to the exploration of theories attempting to explain the male preponderance of ASD.

Notes

Acknowledgments

This article uses confidential unit record files from the LSAC survey. The LSAC was initiated and funded by the Commonwealth Department of Families, Housing, Community Services, and Indigenous Affairs and was managed by the Australian Institute of Family Studies. The findings and views reported in this article are those of the authors and should not be attributed to either the Commonwealth Department of Families, Housing, Community Services, and Indigenous Affairs, or the Australian Institute of Family Studies. We thank all the families participating in the LSAC study. We wish to thank the William Collie Trust, University of Melbourne, and the Lorenzo and Pamela Galli Charitable Trust, for their support of authors Dr May and Professor Williams, and the Melbourne Children’s Clinician Scientist Fellowship scheme for its support of Dr Pang.

Author Contributions

TM conceived of the secondary analysis and drafted the manuscript and performed the statistical analysis; KP, KW, and MO participated in the interpretation of the data and helped to draft the manuscript. All authors read and approved the final manuscript.

Compliance with Ethical Standards

Conflict of interest

The authors report no conflicts of interest.

References

  1. Abrahams, B. S., & Geschwind, D. H. (2008). Advances in autism genetics: On the threshold of a new neurobiology. Nature Reviews Genetics, 9(5), 341–355.CrossRefPubMedPubMedCentralGoogle Scholar
  2. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5). Washington, DC: American Psychiatric Publications.CrossRefGoogle Scholar
  3. Australian Bureau of Statistics. (2013). 2033.0. 55.001-Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, 2011. Australia: Australian Bureau of Statistics Canberra.Google Scholar
  4. Baron-Cohen, S. (2002). The extreme male brain theory of autism. Trends in Cognitive Sciences, 6(6), 248–254.CrossRefPubMedGoogle Scholar
  5. Dunn, L., & Dunn, L. (1997). Peabody picture vocabulary test. (3rd ed.). Circle Pines, MN: American Guidance Service.Google Scholar
  6. Ellis, B. J. (2004). Timing of pubertal maturation in girls: An integrated life history approach. Psychological Bulletin, 130(6), 920.CrossRefPubMedGoogle Scholar
  7. Falter, C. M., Plaisted, K. C., & Davis, G. (2008). Visuo-spatial processing in autism—testing the predictions of extreme male brain theory. Journal of Autism and Developmental Disorders, 38(3), 507–515.CrossRefPubMedGoogle Scholar
  8. Gillberg, C., & Schaumann, H. (1982). Infantile autism and puberty. Journal of Autism and Developmental Disorders, 11(4), 365–371.CrossRefGoogle Scholar
  9. Gillberg, C., & Steffenburg, S. (1987). Outcome and prognostic factors in infantile autism and similar conditions: A population-based study of 46 cases followed through puberty. Journal of Autism and Developmental Disorders, 17(2), 273–287.CrossRefPubMedGoogle Scholar
  10. Gilmour, L., Schalomon, P. M., & Smith, V. (2012). Sexuality in a community based sample of adults with autism spectrum disorder. Research in Autism Spectrum Disorders, 6(1), 313–318.CrossRefGoogle Scholar
  11. Graber, J. A., Brooks-Gunn, J., & Warren, M. P. (1995). The antecedents of menarcheal age: heredity, family environment, and stressful life events. Child Development, 66(2), 346–359.CrossRefPubMedGoogle Scholar
  12. Harper, J., & Collins, J. (1979). Physical growth and development in a sample of autistic girls from New South Wales. Journal of Paediatrics and Child Health, 15(2), 110–112.CrossRefGoogle Scholar
  13. Hergüner, A., & Hergüner, S. (2016). Association between age at menarche and autistic traits in Turkish university students. American Journal of Human Biology, 28(1), 44–47.CrossRefPubMedGoogle Scholar
  14. Ingudomnukul, E., Baron-Cohen, S., Wheelwright, S., & Knickmeyer, R. (2007). Elevated rates of testosterone-related disorders in women with autism spectrum conditions. Hormones and Behavior, 51(5), 597–604.CrossRefPubMedGoogle Scholar
  15. Karapanou, O., & Papadimitriou, A. (2010). Determinants of menarche. Reproductive Biology and Endocrinology, 8(1), 1.CrossRefGoogle Scholar
  16. Knickmeyer, R. C., Wheelwright, S., Hoekstra, R., & Baron-Cohen, S. (2006). Age of menarche in females with autism spectrum conditions. Developmental Medicine & Child Neurology, 48(12), 1007–1008.CrossRefGoogle Scholar
  17. Kung, K. T., Spencer, D., Pasterski, V., Neufeld, S., Glover, V., O’Connor, T. G.,. .. Hines, M. (2016). No relationship between prenatal androgen exposure and autistic traits: Convergent evidence from studies of children with congenital adrenal hyperplasia and of amniotic testosterone concentrations in typically developing children. Journal of Child Psychology and Psychiatry, 57(12), 1455–1462.CrossRefPubMedGoogle Scholar
  18. Lundström, S., Reichenberg, A., Anckarsäter, H., Lichtenstein, P., & Gillberg, C. (2015). Autism phenotype versus registered diagnosis in Swedish children: Prevalence trends over 10 years in general population samples. BMJ (Clinical Research ed.), 350, h1961.Google Scholar
  19. Mensah, F. K., Bayer, J. K., Wake, M., Carlin, J. B., Allen, N. B., & Patton, G. C. (2013). Early puberty and childhood social and behavioral adjustment. Journal of Adolescent Health, 53(1), 118–124.CrossRefPubMedGoogle Scholar
  20. Mouridsen, S. E., & Larsen, F. W. (1989). Pervasive developmental disorder and idiopathic precocious puberty in a 5-year-old girl. Journal of Autism and Developmental Disorders, 19(2), 351–353.CrossRefPubMedGoogle Scholar
  21. Parent, A.-S., Teilmann, G., Juul, A., Skakkebaek, N. E., Toppari, J., & Bourguignon, J.-P. (2003). The timing of normal puberty and the age limits of sexual precocity: Variations around the world, secular trends, and changes after migration. Endocrine Reviews, 24(5), 668–693.CrossRefPubMedGoogle Scholar
  22. Pasterski, V., Gilligan, L., & Curtis, R. (2014). Traits of autism spectrum disorders in adults with gender dysphoria. Archives of Sexual Behavior, 43(2), 387–393.CrossRefPubMedGoogle Scholar
  23. Petersen, A. C., Crockett, L., Richards, M., & Boxer, A. (1988). A self-report measure of pubertal status: Reliability, validity, and initial norms. Journal of Youth and Adolescence, 17(2), 117–133.CrossRefPubMedGoogle Scholar
  24. Pohl, A., Cassidy, S., Auyeung, B., & Baron-Cohen, S. (2014). Uncovering steroidopathy in women with autism: A latent class analysis. Molecular Autism, 5(1), 1.CrossRefGoogle Scholar
  25. Randall, M., Sciberras, E., Brignell, A., Ihsen, E., Efron, D., Dissanayake, C., & Williams, K. (2016). Autism spectrum disorder: Presentation and prevalence in a nationally representative Australian sample. Australian and New Zealand Journal of Psychiatry, 50(3), 243–253.CrossRefPubMedGoogle Scholar
  26. Sedlmeyer, I. L., & Palmert, M. R. (2002). Delayed puberty: Analysis of a large case series from an academic center. The Journal of Clinical Endocrinology & Metabolism, 87(4), 1613–1620.CrossRefGoogle Scholar
  27. Soliman, A., De Sanctis, V., & Elalaily, R. (2014). Nutrition and pubertal development. Indian Journal of Endocrinology and Metabolism, 18(Suppl 1), S39-S47. doi:  10.4103/2230-8210.145073.PubMedCentralGoogle Scholar
  28. Soloff, C., Lawrence, D., Misson, S., Johnstone, R., & Slater, J. (2006). Wave 1 weighting and non-response. LSAC Technical Paper, 3.Google Scholar
  29. Tordjman, S., Ferrari, P., Sulmont, V., Duyme, M., & Roubertoux, P. (1997). Androgenic activity in autism. American Journal of Psychiatry, 154(11), 1626a–1627.CrossRefGoogle Scholar
  30. Warnell, F., George, B., McConachie, H., Johnson, M., Hardy, R., & Parr, J. (2015). Designing and recruiting to UK autism spectrum disorder research databases: Do they include representative children with valid ASD diagnoses? BMJ Open, 5(9), e008625.CrossRefPubMedPubMedCentralGoogle Scholar
  31. Wechsler, D. (2003). Wechsler intelligence scale for children–Fourth Edition (WISC-IV). San Antonio, TX: The Psychological Corporation.Google Scholar
  32. Whitehouse, A. J., Mattes, E., Maybery, M. T., Dissanayake, C., Sawyer, M., Jones, R. M., ... Hickey, M. (2012). Perinatal testosterone exposure and autistic-like traits in the general population: A longitudinal pregnancy-cohort study. Journal of Neurodevelopmental Disorders, 4(1), 1.CrossRefGoogle Scholar
  33. Whitehouse, A. J., Maybery, M. T., Hart, R., Mattes, E., Newnham, J. P., Sloboda, D. M., ... Hickey, M. (2010). Fetal androgen exposure and pragmatic language ability of girls in middle childhood: Implications for the extreme male-brain theory of autism. Psychoneuroendocrinology, 35(8), 1259–1264.CrossRefPubMedGoogle Scholar
  34. Whitehouse, A. J., Maybery, M. T., Hickey, M., & Sloboda, D. M. (2011). Brief report: autistic-like traits in childhood predict later age at menarche in girls. Journal of Autism and Developmental Disorders, 41(8), 1125–1130.CrossRefPubMedGoogle Scholar
  35. Willemsen, R. H., & Dunger, D. B. (2015). Normal Variation in Pubertal Timing: Genetic Determinants in Relation to Growth and Adiposity Puberty from Bench to Clinic (Vol. 29, pp. 17–35): Basel: Karger Publishers.CrossRefGoogle Scholar
  36. Yoshimura, K., Naiki, Y., Horikawa, R., & Tanaka, T. (2005). Three patients with autism and central precocious puberty. Clinical Pediatric Endocrinology, 14(Supplement24), S24_55–S24_57.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Tamara May
    • 1
    • 2
    • 3
  • Ken C. Pang
    • 2
    • 3
    • 4
    • 5
    • 6
  • Michele A. O’Connell
    • 3
    • 7
  • Katrina Williams
    • 2
    • 3
    • 8
  1. 1.School of PsychologyDeakin UniversityBurwoodAustralia
  2. 2.Department of PaediatricsUniversity of MelbourneParkvilleAustralia
  3. 3.Murdoch Childrens Research InstituteParkvilleAustralia
  4. 4.The Walter and Eliza Hall Institute of Medical ResearchParkvilleAustralia
  5. 5.Department of Adolescent MedicineRoyal Children’s HospitalParkvilleAustralia
  6. 6.Department of PsychiatryUniversity of MelbourneParkvilleAustralia
  7. 7.Department of Endocrinology & DiabetesRoyal Children’s HospitalParkvilleAustralia
  8. 8.Developmental Medicine Royal Children’s HospitalParkvilleAustralia

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