Introduction: Children with self-limited delayed puberty (DP) (constitutional delay) enter puberty after variable waiting times, and the factors associated with their eventual pubertal timing are not well understood. Methods: We conducted a retrospective study of 99 girls and 228 boys with self-limited DP at an academic medical center between 2000 and 2015. To define features and potential subtypes of self-limited DP, we performed group-based trajectory modeling on childhood growth and latent-variable factor analysis on clinical characteristics. We then conducted time-to-event analyses to identify associations with pubertal timing. Results: We identified two distinct growth trajectories in individuals with self-limited DP: one with stable and the other with declining height percentiles. Latent-variable factor analysis identified five factors underlying clinical variation that appear to correspond to genetic height potential, body mass index, childhood growth, parental pubertal delay, and medical issues (attention-deficit/hyperactivity disorder and inhaled glucocorticoid use). We observed correlations between pubertal timing and bone age (p = 0.01), childhood height (p = 0.004), and midparental target height (p < 0.001), but not with parental pubertal delay or with testosterone treatment in boys. Conclusions: By illustrating the heterogeneity within self-limited DP and identifying factors underlying this heterogeneity, our study suggests that there may be multiple causes of self-limited DP. However, our ability to determine when puberty will eventually occur remains limited. Dissecting self-limited DP into its component subtypes may inform future studies of the mechanisms contributing to pubertal delay as well as studies of the short- and long-term outcomes of self-limited DP.

Delayed puberty (DP) is a common concern in pediatrics, affecting 2–3% of all adolescents [1‒3]. The most common cause of DP is constitutional delay, a self-limited condition in which puberty eventually begins and progresses to achievement of adult reproductive endocrine function [4]. The clinical management of self-limited DP is hindered by uncertainty around when puberty will eventually start [4]. One management option is a “watch and wait” approach with reassurance and no medical intervention. Alternatively, sex-steroid treatment can safely induce development of secondary sex characteristics in both girls and boys, and it has been suggested that treatment may even accelerate the onset of puberty [1]. In some boys, testicular enlargement is observed during or just after testosterone therapy, indicating the action of endogenous gonadotropins and raising the possibility that sex-steroid treatment may “jump-start” puberty [5, 6]. Identifying the clinical features associated with pubertal timing in self-limited DP has the potential to inform our understanding of this condition and to guide clinical management and counseling [7].

One factor that may correlate with the timing of eventual pubertal onset is the pattern of childhood growth. Self-limited DP is often associated with a “constitutional delay of growth” pattern in childhood, before the pubertal growth spurt is a factor [8, 9], with childhood growth typically occurring along a percentile below that expected for midparental height (MPH) [10] and in some but not all cases, with a decline in height percentiles over time [8, 9]. Skeletal maturation is often proportionally slowed, such that predicted adult height based on bone age is generally concordant with the midparental target height.

To understand the natural history of self-limited DP, we have leveraged longitudinal data from a large retrospective cohort at a single referral center. We sought to characterize the key factors underlying self-limited DP by using data-driven methods to analyze clinical features including anthropometric measures, bone age X-rays to assess skeletal maturation, medical diagnoses potentially associated with DP (attention-deficit/hyperactivity disorder [ADHD], use of inhaled glucocorticoids, refs. [11, 12], family history, and sex-steroid treatment). We then tested for associations between these features and pubertal timing.

To characterize clinical features of individuals with self-limited DP and to associate these features with pubertal timing, we took three approaches: (1) group-based trajectory modeling to identify distinct patterns of growth in childhood, (2) latent-variable factor analysis to identify factors underlying clinical characteristics, family history, and conditions and treatments that have been associated with DP, and (3) time-to-event analyses to test whether testosterone treatment accelerates (“jump starts”) the onset of endogenous puberty in boys. The Boston Children’s Hospital (BCH) Institutional Review Board approved this study under protocol number IRB-P00017406 and waived informed consent.

Study Participants

Individuals with a diagnosis of self-limited DP were identified by review of medical records as previously described [4]. Briefly, self-limited DP was defined as (1) absence of breast development in girls 12 years or older [13‒16] and (2) testicular volume <4 mL in boys 13.5 years or older [17‒19]. Individuals found to have idiopathic hypogonadotropic hypogonadism were excluded [20]. Additional criteria for inclusion in specific analyses are described below.

Data Collection and General Statistical Methods

Medical history, parents’ heights, parental pubertal history, medications (including sex-steroid use), height, weight, pubertal status, bone age, and treating provider were electronically imported or manually entered from the BCH clinical data repository into a Research Electronic Data Capture (REDCap) database [21]. Methods for quality control, data cleaning, and data processing and transformation are described in detail in Supplementary Materials (see www.karger.com/doi/10.1159/000526590 for all online suppl. material). Briefly, height and BMI Z-score slopes were estimated using individual-level linear regression using available height measurements from age 4 to 10 years. To compare height Z-score across individuals, height and BMI Z-scores at the age of 7 years were interpolated/extrapolated.

We further collected data on ADHD and inhaled glucocorticoid use [22, 23]. Individuals with a diagnosis of ADHD and/or use of stimulant medications listed in the medical record were classified as having ADHD, and all others were classified as not having ADHD. A parental history of pubertal delay was defined by a reported history of delayed pubertal timing in either parent and/or maternal history of menarche ≥14 years.

Descriptive statistics were used to summarize patient characteristics, with mean and standard deviation for continuous variables and frequency and percentage for categorical variables. Paired t-tests were used to assess the difference between individuals’ height Z-scores and MPH Z-scores. All statistical analyses were conducted using SAS software (version 9.4, Cary, NC, USA). p values <0.05 were considered significant.

Group-Based Trajectory Modeling

To identify distinct trajectories of height in childhood, we applied group-based trajectory modeling using the PROC TRAJ procedure to those with at least three documented height measurements spanning at least 1 year from age 4 to 10 years. To account for variation in genetic height potential, we performed linear regression of height Z-scores at age of 7 years on MPH Z-scores and used the residuals from this regression in a second analysis using group-based trajectory modeling.

To compare demographic and clinical characteristics between subgroups identified by the analysis, Student’s independent-sample t-test was used for continuous outcomes and Fisher’s exact test for dichotomous outcomes. We used the PROC ICPHREG procedure to conduct interval-censored and informative right-censored time-to-event analysis on age at pubertal onset (see ref. [4] for the details of censoring). Proportional hazard regression models were used to compare hazard ratios for pubertal onset between different childhood growth trajectories, with a higher hazard ratio indicating earlier pubertal onset. Variables with p < 0.05 in univariate analysis were included in multivariate models.

Latent-Variable Factor Analysis

To identify factors influencing clinical characteristics, family history, and conditions and treatments associated with DP, we applied latent-variable factor analysis using the procedure PROC FACTOR with the varimax orthogonal rotation procedure (for simpler structure and interpretability). This method aggregates variables based on the degree of correlation between these variables. The number of factors retained was determined by eigenvalues >1, inspection of scree plots, and interpretability. In addition, Pearson correlation was used to assess correlations between the input variables used for latent-variable factor analysis.

Analyses of the Effect of Testosterone Treatment on Timing of Endogenous Puberty

To examine whether testosterone treatment accelerates pubertal onset, we analyzed time to pubertal onset as described above, defining the groups for comparison in three ways. First, we used the full cohort and compared all boys who had been treated with testosterone (treated boys) to all those who had not (untreated boys).

Second, we compared all treated boys to a subcohort of untreated boys matched for age at treatment, that is, who were prepubertal at the age at which the treated boys started treatment. Details of the process of selecting the matching untreated subcohort are given in online Supplementary Materials.

Third, we compared boys who were seen by providers grouped by their practices around testosterone treatment as a “fortuitous experiment” of earlier versus later testosterone treatment. We reasoned that provider assignment was essentially random with respect to baseline confounders and could be used as an instrument for causal inference. Details for assigning treating providers to the “early-treating” and “late-treating” groups are described in online supplementary materials. We then used nonparametric survival analysis (PROC ICLIFETEST) to compare the timing of pubertal onset between boys managed by “early-treating” versus “late-treating” providers.

Patient Characteristics

Reviewing medical records at BCH identified 99 girls and 228 boys with self-limited DP (Table 1). This cohort was shorter and had slower growth and slower skeletal maturation than reference populations from the CDC and the Brush Foundation [24, 25]. Compared to these reference populations, this cohort had lower height Z-score at age of 7 years (mean ± SD −1.20 ± 0.89) and bone age Z-score (−2.19 ± 1.10; p both <0.0001 for difference from zero; Table 1; online suppl. Fig. 1). There was also a decline in height Z-score across childhood (height Z-score slope −0.09 ± 0.12 per year, significantly different from zero, p < 0.0001). The lower height Z-scores at 7 years are partially attributable to lower-than-average MPH Z-scores for the cohort (mean ± SD −0.11 ± 0.74), but this does not entirely account for the difference between our cohort and the CDC reference population, as the individuals’ height Z-scores at 7 years were lower than their MPH Z-scores (mean ± SD for difference: −1.07 ± 0.82; p < 0.001).

Table 1.

Characteristics of the overall cohort and the subcohorts used in each analysis

 Characteristics of the overall cohort and the subcohorts used in each analysis
 Characteristics of the overall cohort and the subcohorts used in each analysis

Girls’ height Z-scores declined more rapidly than boys’ (slope −0.14 ± 0.12 vs. −0.06 ± 0.10 per year), and girls also had lower bone age Z-scores (−2.7 ± 1.1 vs. −1.9 ± 1.0, p < 0.001 for both). A high proportion of our cohort had ADHD (147 out of 327, 45%), a prevalence higher than that of the general US population (6.1–16.3%) [26], and 12% (39 out of 327) had a recorded history of inhaled glucocorticoid use. There were no significant differences in clinical characteristics between the overall cohort and the subcohorts used in each of our subsequent analyses (Table 1).

Identifying Distinct Growth Trajectories in Self-Limited DP

To identify potential subgroups within our cohort based on prepubertal growth trajectories, we performed group-based trajectory modeling of height Z-scores from age 4 to 10 years and identified three trajectories that appeared to represent differences in height and MPH, with no differences in height Z-score slope between trajectories (online suppl. Fig. 2). We repeated the analysis after adjustment for MPH and found that a model with two trajectories was the best fit (online suppl. Table 1). In the first trajectory, termed “stable height percentiles” (N = 121/150, 81%), height Z-scores remained stable over time, with an average change in the height Z-score from age 4 to 10 years of −0.04 ± 0.08 per year (Fig. 1, representative growth charts for each trajectory in Fig. 2). In contrast, the second “declining height percentiles” trajectory (N = 29/150, 19%) was characterized by height Z-scores that declined with age at −0.23 ± 0.07 per year (p < 0.001 for difference from “stable height percentiles” group; online suppl. Table 2). The declining height percentiles were unlikely to be due to growth hormone deficiency, as no individual in this cohort had IGF-1 below the reference range for prepubertal children (data not shown). There were disproportionately more girls in the “declining height percentiles” group than in the “stable height percentiles” group, and bone age Z-scores were lower (i.e., bone ages were more delayed) for the “declining height percentiles” group (p both <0.001; online suppl. Table 2).

Fig. 1.

Patterns of childhood growth from group-based trajectory modeling of height. Group-based trajectory modeling of height Z-score residuals after adjusting for MPH showed two distinct trajectories, with participants in group 1 exhibiting a minimal decline in height Z-score residuals and participants in group 2 exhibiting a more substantial decline in height Z-score residuals over time. Dashed lines indicate standard errors.

Fig. 1.

Patterns of childhood growth from group-based trajectory modeling of height. Group-based trajectory modeling of height Z-score residuals after adjusting for MPH showed two distinct trajectories, with participants in group 1 exhibiting a minimal decline in height Z-score residuals and participants in group 2 exhibiting a more substantial decline in height Z-score residuals over time. Dashed lines indicate standard errors.

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Fig. 2.

Representative growth charts by sex and growth pattern. Growth charts of a girl (a) and a boy (b) with stable height percentiles across childhood and of a girl (c) and a boy (d) with declining height percentiles across childhood. Arrows indicate midparental height (MPH).

Fig. 2.

Representative growth charts by sex and growth pattern. Growth charts of a girl (a) and a boy (b) with stable height percentiles across childhood and of a girl (c) and a boy (d) with declining height percentiles across childhood. Arrows indicate midparental height (MPH).

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Clinical Features of Individuals with Self-Limited DP

To further identify associations between clinical features within our cohort, we analyzed family history (MPH, parental pubertal delay), medical history (ADHD, inhaled glucocorticoid use), anthropometric measurements (height and BMI Z-scores at age of 7 years, height and BMI Z-score slopes from 4 to 10 years), and bone age Z-score. We assessed associations between these variables with pairwise correlation analyses and identified an expected correlation between MPH and height Z-score at 7 years (r = 0.47, p < 0.001; online suppl. Table 3). We also found correlations between height Z-score at 7 years and BMI Z-score at 7 years (r = 0.24, p = 0.006), between mean bone age Z-score and both height Z-score slope (r = 0.29, p = 0.003) and BMI Z-score at 7 years (r = 0.30, p = 0.002), and between BMI Z-score at 7 years and BMI Z-score slope (r = −0.21, p = 0.01).

We then conducted latent-variable factor analysis to identify factors underlying variation in these clinical variables and identified five factors (Table 2). Factor 1, which we term “genetic height potential,” had the largest contributions from height Z-score at 7 years and MPH Z-score. Factor 2, termed “BMI,” had the largest contributions from BMI Z-score at 7 years and BMI Z-score slope from age 4 to 10 years. Factor 3, termed “childhood growth tempo,” had the largest contributions from height Z-score slope and bone age Z-score. Factor 4, termed “parental pubertal delay,” had the largest contribution from presence/absence of a history of DP in at least one parent. Factor 5, termed “medical issues,” had the largest contributions from the presence/absence of ADHD and the use of inhaled glucocorticoids.

Table 2.

Factors and corresponding contributory variables identified by latent-variable factor analysis*

 Factors and corresponding contributory variables identified by latent-variable factor analysis*
 Factors and corresponding contributory variables identified by latent-variable factor analysis*

Variables Associated with Timing of Pubertal Onset

Having identified different trajectories of childhood growth underlying clinical variation in our cohort, we sought to assess whether any of these trajectories and clinical variables were associated with the timing of pubertal onset. After adjusting for sex, the only trajectories and variables associated with later pubertal onset were lower bone age Z-score (i.e., more delayed bone age), greater height Z-score at 7 years, and greater MPH Z-score (p = 0.01, p = 0.004, and p < 0.001, respectively; Table 3). In the multivariate model, only the association between higher MPH Z-score and later pubertal onset remained significant (p = 0.03).

Table 3.

Associations of time to pubertal onset with growth trajectories and clinical characteristics

 Associations of time to pubertal onset with growth trajectories and clinical characteristics
 Associations of time to pubertal onset with growth trajectories and clinical characteristics

Inferring Effects of Testosterone Treatment on Timing of Endogenous Puberty

Given the paucity of data from randomized trials on whether exogenous sex-steroid therapy accelerates (“jump starts”) puberty in self-limited DP, we used three analytic strategies to test whether testosterone treatment was associated with earlier onset of testicular enlargement in boys. (Because estradiol directly induces breast development, the indication of girls’ pubertal onset used in this study, we were unable to conduct a parallel analysis in girls.)

We first simply compared the age at pubertal onset between boys treated and not treated with testosterone, and we found that pubertal onset occurred later in boys treated with testosterone (Fig. 3a). However, this analysis was likely confounded by indication because the decision to treat with testosterone was almost certainly influenced by age; that is, an older boy who has not yet entered puberty is more likely than a younger boy to opt for treatment.

Fig. 3.

Effect of testosterone treatment on time to pubertal onset in boys with self-limited DP. Survival analysis of time to pubertal onset comparing (a) all boys treated versus not treated with testosterone, (b) testosterone-treated versus a subset of untreated boys matched by age, and (c) boys managed by “early-treating” versus “late-treating” providers. In the unmatched analysis (a), pubertal onset occurred later in boys treated with testosterone, but this difference was no longer observed after age-matching (b). Similarly, no difference in time to pubertal onset was seen between those managed by “early-treating” versus “late-treating” providers (c). Gray shading indicates standard errors.

Fig. 3.

Effect of testosterone treatment on time to pubertal onset in boys with self-limited DP. Survival analysis of time to pubertal onset comparing (a) all boys treated versus not treated with testosterone, (b) testosterone-treated versus a subset of untreated boys matched by age, and (c) boys managed by “early-treating” versus “late-treating” providers. In the unmatched analysis (a), pubertal onset occurred later in boys treated with testosterone, but this difference was no longer observed after age-matching (b). Similarly, no difference in time to pubertal onset was seen between those managed by “early-treating” versus “late-treating” providers (c). Gray shading indicates standard errors.

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To control for this possible confounding by age, we next compared testosterone-treated boys to untreated boys matched based on prepubertal status at the index age at which treatment was initiated. In other words, at the age at which the treated boy started testosterone treatment, the matching untreated boy was prepubertal and therefore also had the potential option of starting testosterone yet was not treated. No difference in the timing of pubertal onset between treated and matched untreated boys was revealed by this analysis (Fig. 3b). Because the decision to treat may have been influenced by additional information that led to an expectation that puberty would start later, such as a family history of DP or a delay in bone age, we compared the presence of parental history of DP and mean bone age Z-scores between the two groups and found no significant differences between the groups (p both >0.6); thus, confounding by severity was unlikely to have led the analysis to underestimate the effect of testosterone.

Our third approach addressed potential confounding bias by identifying a “fortuitous experiment” within our retrospective cohort. First, we noted that treatment practices vary among clinical providers, allowing us to classify providers as tending to treat with testosterone at earlier ages (“early-treating”) and tending to wait until later ages (“late-treating”; online suppl. Fig. 3). We then compared timing of pubertal onset between boys seen by “early-treating” versus “late-treating” providers and observed no differences in the timing of pubertal onset (Fig. 3c). The lack of an effect of provider group on pubertal onset suggests that there is no average effect of testosterone therapy on pubertal onset.

In this study of girls and boys with self-limited DP (constitutional pubertal delay), we used data-driven approaches to identify factors that underlie the clinical heterogeneity within the cohort and sought to identify associations with pubertal timing. We found that most individuals maintained a consistent height percentile across childhood, but a subset had height Z-scores that declined across childhood. Latent-variable factor analysis identified five factors – “genetic height potential,” “BMI,” “childhood growth,” “parental pubertal delay,” and “medical issues” – as the primary contributors to variation in our cohort. We observed correlations between pubertal onset and bone age, childhood height, and MPH, but not parental pubertal delay or, in boys, testosterone treatment. These findings highlight the wide variability within self-limited DP and identify key factors that contribute to this variability.

Self-limited DP is often associated with delayed growth and delayed bone age [27, 28]. In children presenting with DP, potential contributions to short stature (at least transiently) include: (1) genetics, reflected by lower MPH, (2) a “constitutional growth delay” pattern, with lower height relative to MPH, typically with a proportional delay in skeletal maturation (bone age), (3) declining growth percentiles during childhood, and (4) lack of a normally timed pubertal growth spurt [8, 9, 22]. Wehkalampi and colleagues previously showed that a decline in growth percentiles can start in early childhood, between the ages 3 and 8 years, before the expected time of the pubertal growth spurt [8, 9]. In our cohort, although childhood growth and bone age Z-scores were delayed overall, we observed considerable variability, such that not all individuals have heights shorter than expected for their genetic potential or delayed bone ages. These observations provide evidence that self-limited DP is a heterogeneous condition that may have multiple underlying mechanisms, and the application of quantitative methods to dissect DP into subtypes may disentangle these mechanisms.

In our latent-variable factor analysis, “medical issues,” which included the presence/absence of ADHD and use of inhaled glucocorticoids, emerged as a distinct factor. One potential and speculative explanation for the clustering of ADHD and inhaled glucocorticoid use in the same factor is that both may result in increased systemic glucocorticoid exposure. Use of ADHD medications has been shown to increase concentrations of circulating morning cortisol [29], and inhaled glucocorticoids, though intended to provide local effects without systemic absorption, may lead to systemic side effects, as potentially severe as secondary adrenal insufficiency [30].

We also sought to identify factors associated with timing of pubertal onset and found that more delayed bone age and greater genetic height potential (greater childhood height and MPH) were associated with later pubertal entry. While these associations raise the possibility of biological links between these factors and pubertal timing, we cannot exclude the possibility of ascertainment/referral bias [31]. For example, because patients with DP are often more concerned about height than about pubertal development per se, individuals who are taller due to greater genetic height potential may only seek specialist care if there is a greater degree of pubertal delay leading to a greater delay in growth. Furthermore, because each factor may be present to varying degrees within any one individual, this may have limited the statistical power to identify additional associations between individual factors and pubertal timing.

In addition, we tested the hypothesis that testosterone treatment accelerates (“jump starts”) the onset of puberty in boys. Sex steroids have been shown to accelerate the onset of puberty in conditions of endogenous gonadotropin-independent sex-steroid production, such as congenital adrenal hyperplasia [32] and autonomous gonadal activity [33], and in boys with Klinefelter Syndrome who were treated with oxandrolone [34]. Prior randomized studies of testosterone treatment in boys with constitutional delay reported higher testicular volumes in boys after testosterone treatment compared to those not treated [35, 36], suggesting that testosterone treatment can accelerate the onset and/or progression of puberty. In a survey of pediatric endocrine providers, most providers believed that sex-steroid therapy can “jump-start” pubertal onset in children with self-limited DP [7]. However, we found no evidence in our study that testosterone treatment results in earlier pubertal onset, although we cannot rule out the possibility that the decision to treat may have been influenced by additional information not captured by our study, such as subtle physical exam findings (as a speculative example: testicular volumes on the lower end of the prepubertal range). Furthermore, variation in the dose and duration of testosterone treatment could result in variability in responses, making it challenging to identify effects of testosterone treatment. To definitively investigate whether sex steroid treatment “jump starts” pubertal entry in individuals with self-limited DP, a prospective randomized clinical trial of treatment versus observation is needed.

This cohort consists of patients seen in a specialty clinic at a referral center, which may limit generalizability of our findings to the general population. Specifically, individuals with milder delays in puberty and/or childhood growth may not be referred for specialty evaluation, such that individuals in our cohort may have more severe delays in pubertal timing and/or growth. In addition, our results raise the possibility of referral bias based on sex/gender. In our cohort, girls had more severe growth delay (as indicated by slower childhood growth and more delayed bone ages) compared to boys. Thus, there may be a higher threshold for girls to be referred for DP, as has been suggested by other studies [22, 37‒39], such that only girls with more severely delayed growth are referred for specialist evaluation.

Additional limitations of our study include the retrospective design, which is susceptible to both selection bias, as noted above, and measurement bias. Only a subset of individuals within the cohort had all available clinical data for group-based trajectory modeling and latent-variable factor analysis; however, clinical characteristics of these and other subcohorts did not significantly differ from the overall cohort. Furthermore, data on ancestry, race, and/or ethnicity were not available, and we were therefore unable to analyze the effects of these variables.

By illustrating the heterogeneity within self-limited DP and identifying factors underlying this heterogeneity, our study suggests that there may be multiple causes of self-limited DP. This dissection of self-limited DP into its component subtypes can inform studies on the genetic and physiologic mechanisms contributing to self-limited DP and their adult consequences. This will ultimately allow us to predict when puberty will eventually occur, to provide timely sex-steroid treatment when indicated, and to mitigate any potential adverse consequences in this patient population.

We thank Ryan Ciarlo, Eric Denhoff, Selby Knudsen, Sinead Christiansen, Kara McLaughlin, Rebecca Shin, and Fu-Shiuan Lee for assistance with data abstraction and cleaning.

The Boston Children’s Hospital (BCH) Institutional Review Board approved this study under protocol number IRB-P00017406 and waived informed consent.

The authors disclose no conflicts of interest.

This work was supported by grant R01 HD0900071 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with additional support from the Harvard Catalyst | The Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, National Institutes of Health Award UL 1TR002541) and financial contributions from Harvard University and its affiliated academic health centers. Jia Zhu was supported by grant T32 DK007699 from the National Institute of Diabetes and Digestive and Kidney Diseases, a Pediatric Endocrine Society Research Fellowship Award, and an Endocrine Fellows Foundation Fellow Grant Award. Christina Mills Astley was supported by grant K23 DK120899 from the National Institute of Diabetes and Digestive and Kidney Diseases. Funding sources had no role in the preparation of the data or the manuscript.

Jia Zhu carried out data quality control, analysis, and interpretation and drafted the initial manuscript. Enju Liu and Henry A. Feldman contributed to data analysis and interpretation. Amalia Feld, Elfa Jonsdottir-Lewis., and Alexandria Shirey contributed to data abstraction, cleaning, and analysis. Christina M. Astley contributed to study design and analysis. Yee-Ming Chan conceptualized the study and coordinated and supervised data collection, analysis, and interpretation. All authors critically reviewed and revised the manuscript, approved the final version, and agree to be accountable for all aspects of the work.

The datasets generated during and/or analyzed during the current study are not publicly available due to their containing clinical information that could compromise the privacy of research participants but are available from the corresponding author [Yee-Ming Chan] upon reasonable request.

1.
Palmert
MR
,
Dunkel
L
.
Clinical practice. Delayed puberty
.
N Engl J Med
.
2012
;
366
(
5
):
443
53
.
2.
Wei
C
,
Crowne
EC
.
Recent advances in the understanding and management of delayed puberty
.
Arch Dis Child
.
2016
;
101
(
5
):
481
8
.
3.
Howard
SR
,
Dunkel
L
.
Delayed puberty-phenotypic diversity, molecular genetic mechanisms, and recent discoveries
.
Endocr Rev
.
2019
;
40
(
5
):
1285
317
.
4.
Jonsdottir-Lewis
E
,
Feld
A
,
Ciarlo
R
,
Denhoff
E
,
Feldman
HA
,
Chan
YM
.
Timing of pubertal onset in girls and boys with constitutional delay
.
J Clin Endocrinol Metab
.
2021
;
106
(
9
):
e3693
703
.
5.
Richman
RA
,
Kirsch
LR
.
Testosterone treatment in adolescent boys with constitutional delay in growth and development
.
N Engl J Med
.
1988
;
319
(
24
):
1563
7
.
6.
Dwyer
AA
,
Phan-Hug
F
,
Hauschild
M
,
Elowe-Gruau
E
,
Pitteloud
N
.
TRANSITION IN ENDOCRINOLOGY: Hypogonadism in adolescence
.
Eur J Endocrinol
.
2015
;
173
(
1
):
R15
24
.
7.
Zhu
J
,
Feldman
HA
,
Eugster
EA
,
Fechner
PY
,
Nahata
L
,
Thornton
PS
,
.
Practice variation in the management of girls and boys with delayed puberty
.
Endocr Pract
.
2020
;
26
(
3
):
267
84
.
8.
Wehkalampi
K
,
Vangonen
K
,
Laine
T
,
Dunkel
L
.
Progressive reduction of relative height in childhood predicts adult stature below target height in boys with constitutional delay of growth and puberty
.
Horm Res
.
2007
;
68
(
2
):
99
104
.
9.
Wehkalampi
K
,
Pakkila
K
,
Laine
T
,
Dunkel
L
.
Adult height in girls with delayed pubertal growth
.
Horm Res Paediatr
.
2011
;
76
(
2
):
130
5
.
10.
Tanner
JM
,
Davies
PS
.
Clinical longitudinal standards for height and height velocity for North American children
.
J Pediatr
.
1985
;
107
(
3
):
317
29
.
11.
Wolthers
OD
.
Growth problems in children with asthma
.
Horm Res
.
2002
;
57
(
Suppl 2
):
83
7
.
12.
Kelly
HW
,
Sternberg
AL
,
Lescher
R
,
Fuhlbrigge
AL
,
Williams
P
,
Zeiger
RS
,
.
Effect of inhaled glucocorticoids in childhood on adult height
.
N Engl J Med
.
2012
;
367
(
10
):
904
12
.
13.
Herman-Giddens
ME
,
Slora
EJ
,
Wasserman
RC
,
Bourdony
CJ
,
Bhapkar
MV
,
Koch
GG
,
.
Secondary sexual characteristics and menses in young girls seen in office practice: a study from the Pediatric Research in Office Settings Network
.
Pediatrics
.
1997
;
99
(
4
):
505
12
.
14.
Rosenfield
RL
,
Lipton
RB
,
Drum
ML
.
Thelarche, pubarche, and menarche attainment in children with normal and elevated body mass index
.
Pediatrics
.
2009
;
123
(
1
):
84
8
.
15.
Aksglaede
L
,
Sorensen
K
,
Petersen
JH
,
Skakkebaek
NE
,
Juul
A
.
Recent decline in age at breast development: the Copenhagen Puberty Study
.
Pediatrics
.
2009
;
123
(
5
):
e932
9
.
16.
Biro
FM
,
Greenspan
LC
,
Galvez
MP
,
Pinney
SM
,
Teitelbaum
S
,
Windham
GC
,
.
Onset of breast development in a longitudinal cohort
.
Pediatrics
.
2013
;
132
(
6
):
1019
27
.
17.
Sorensen
K
,
Aksglaede
L
,
Petersen
JH
,
Juul
A
.
Recent changes in pubertal timing in healthy Danish boys: associations with body mass index
.
J Clin Endocrinol Metab
.
2010
;
95
(
1
):
263
70
.
18.
Ma
HM
,
Chen
SK
,
Chen
RM
,
Zhu
C
,
Xiong
F
,
Li
T
,
.
Pubertal development timing in urban Chinese boys
.
Int J Androl
.
2011
;
34
(
5 Pt 2
):
e435
45
.
19.
Herman-Giddens
ME
,
Steffes
J
,
Harris
D
,
Slora
E
,
Hussey
M
,
Dowshen
SA
,
.
Secondary sexual characteristics in boys: data from the Pediatric Research in Office Settings Network
.
Pediatrics
.
2012
;
130
(
5
):
e1058
68
.
20.
Hoffman
AR
,
Crowley
WF
Jr
.
Induction of puberty in men by long-term pulsatile administration of low-dose gonadotropin-releasing hormone
.
N Engl J Med
.
1982
;
307
(
20
):
1237
41
.
21.
Harris
PA
,
Taylor
R
,
Thielke
R
,
Payne
J
,
Gonzalez
N
,
Conde
JG
.
Research electronic data capture (REDCap): a metadata-driven methodology and workflow process for providing translational research informatics support
.
J Biomed Inform
.
2009
;
42
(
2
):
377
81
.
22.
Sedlmeyer
IL
,
Palmert
MR
.
Delayed puberty: analysis of a large case series from an academic center
.
J Clin Endocrinol Metab
.
2002
;
87
(
4
):
1613
20
.
23.
Sorva
RA
,
Turpeinen
MT
.
Asthma, glucocorticoids and growth
.
Ann Med
.
1994
;
26
(
4
):
309
14
.
24.
Kuczmarski
RJ
,
Ogden
CL
,
Guo
SS
,
Grummer-Strawn
LM
,
Flegal
KM
,
Mei
Z
,
.
2000 CDC Growth Charts for the United States: methods and development
.
Vital Health Stat
.
2002
;
11
(
246
):
1
190
.
25.
Pyle
SI
,
Greulich
WW
.
Radiographic atlas of skeletal development of the hand and wrist
.
Stanford
:
Stanford University Press
;
1959
.
26.
Danielson
ML
,
Holbrook
JR
,
Bitsko
RH
,
Newsome
K
,
Charania
SN
,
McCord
RF
,
.
State-level estimates of the prevalence of parent-reported ADHD diagnosis and treatment among U.S. Children and adolescents, 2016 to 2019
.
J Atten Disord
;
2022
. Epub ahead of print. https://doi.org/10.1177/10870547221099961.
27.
Styne
DM
,
Grumbach
MM
.
Williams textbook of endocrinology
. 13 ed.
Elsevier
;
2015
. p.
1127
30
.
28.
Richmond
E
,
Rogol
AD
.
Causes of short stature [Internet]
.
UpToDate
;
2021
.
29.
Wang
LJ
,
Huang
YS
,
Hsiao
CC
,
Chen
CK
.
The trend in morning levels of salivary cortisol in children with ADHD during 6 months of methylphenidate treatment
.
J Atten Disord
.
2017
;
21
(
3
):
254
61
.
30.
Kapadia
CR
,
Nebesio
TD
,
Myers
SE
,
Willi
S
,
Miller
BS
,
Allen
DB
,
.
Endocrine effects of inhaled corticosteroids in children
.
JAMA Pediatr
.
2016
;
170
(
2
):
163
70
.
31.
Flor-Cisneros
A
,
Leschek
EW
,
Merke
DP
,
Barnes
KM
,
Coco
M
,
Cutler
GB
Jr
,
.
In boys with abnormal developmental tempo, maturation of the skeleton and the hypothalamic-pituitary-gonadal axis remains synchronous
.
J Clin Endocrinol Metab
.
2004
;
89
(
1
):
236
41
. https://doi.org/10.1210/jc.2002-021954.In
32.
Witchel
SF
.
Congenital adrenal hyperplasia
.
J Pediatr Adolesc Gynecol
.
2017
;
30
(
5
):
520
34
.
33.
Boyce
AM
,
Collins
MT
.
Fibrous Dysplasia/McCune-Albright syndrome: a rare, mosaic disease of Gα s activation
.
Endocr Rev
.
2020
;
41
(
2
):
bnz011
.
34.
Davis
SM
,
Lahlou
N
,
Cox-Martin
M
,
Kowal
K
,
Zeitler
PS
,
Ross
JL
.
Oxandrolone treatment results in an increased risk of gonadarche in prepubertal boys with klinefelter syndrome
.
J Clin Endocrinol Metab
.
2018
;
103
(
9
):
3449
55
.
35.
Soliman
AT
,
Khadir
MM
,
Asfour
M
.
Testosterone treatment in adolescent boys with constitutional delay of growth and development
.
Metabolism
.
1995
;
44
(
8
):
1013
5
.
36.
Wilson
DM
,
Kei
J
,
Hintz
RL
,
Rosenfeld
RG
.
Effects of testosterone therapy for pubertal delay
.
Am J Dis Child
.
1988
;
142
(
1
):
96
9
.
37.
Wehkalampi
K
,
Widen
E
,
Laine
T
,
Palotie
A
,
Dunkel
L
.
Patterns of inheritance of constitutional delay of growth and puberty in families of adolescent girls and boys referred to specialist pediatric care
.
J Clin Endocrinol Metab
.
2008
;
93
(
3
):
723
8
.
38.
Crowne
EC
,
Shalet
SM
,
Wallace
WH
,
Eminson
DM
,
Price
DA
.
Final height in girls with untreated constitutional delay in growth and puberty
.
Eur J Pediatr
.
1991
;
150
(
10
):
708
12
.
39.
Sedlmeyer
IL
,
Hirschhorn
JN
,
Palmert
MR
.
Pedigree analysis of constitutional delay of growth and maturation: determination of familial aggregation and inheritance patterns
.
J Clin Endocrinol Metab
.
2002
;
87
(
12
):
5581
6
.