Because growth patterns and rates differ between sexes, the Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO) created separate growth charts for girls and boys (1–3). The CDC 2000 growth charts for US children 2–19 years old, WHO 2006 growth charts for US children 0–2 years old and, in many countries the 1990 United Kingdom reference charts for children 0–5 years old, have been considered “reference” or “standard” for at least 15 years (3–5). However, scenarios exist in which this binary “girl versus boy” approach may not be ideal, including in care of non-binary youth or transgender youth undergoing transition consistent with their gender identity.

An increasing number of transgender youth are seeking medical care to establish treatment regimens geared towards developing physical characteristics congruent with experienced gender (6).

These interventions include gonadotropin releasing hormone agonists (GnRHa) to suppress puberty and reduce endogenous sex hormone production of one’s natal sex, and hormonal therapies (i.e., testosterone and estradiol) to induce secondary sex characteristics consistent with affirmed gender identity (7). Such therapies can impact anthropometrics, including linear growth, body mass index (BMI), and body composition (8, 9). For example, testosterone may increase muscle mass and BMI, while estradiol can lower waist-to-hip ratios due to hip fat deposition (10–12). Overall, the understanding of how hormonal interventions impact changes in pubertal growth spurt patterns is still in its infancy (13–15).

To monitor growth in non-binary youth or transgender youth and their response to medical interventions, currently clinicians have limited tools. It has been proposed that use of growth charts corresponding to both affirmed gender and genetic sex should/might be considered (10). For example, when evaluating growth parameters in a transgender male or female, the clinician must plot their height, height velocity, weight, and BMI on both the girls and boys CDC 2000 charts. Since all these growth parameters are sex dependent, assessments as to whether the growth response is appropriate become arbitrary as the determination is made by looking at the plot points of these parameters on both the male and female charts side-by-side. Because of the current challenges in monitoring growth in transgender youths, and the extended time it will take to develop gender-specific charts based on longitudinally collected data, an intermediate tool is needed.

Our group previously developed pubertal- and chronological-age adjusted growth charts and reference ranges using cross-sectional data from nationally representative US health surveys (e.g., National Health Examination Surveys (NHES), National Health and Nutrition Examination Surveys (NHANES) and Hispanic Health and Nutrition Examination Surveys (HHANES)) to assess how puberty affects anthropometrics including height, weight, and BMI (16–18). For example, we showed that considering race/ethnicity differences in pubertal timing affects shortness, tallness, and overweight/obesity prevalence quantification (16–18). Towards this end and utilizing similar techniques including use of pooled data from large US cross-sectional nationally-representative samples, we have now developed age-adjusted, sex non-specific growth charts and reference ranges for height, weight, and BMI, and z-score calculators for these parameters. In addition to sharing our growth charts and reference ranges, we exemplify how they can be used in clinical practice. Our goal in creating these tools is two-fold: to age-smooth growth across sexes in order to help clinicians monitor how one is growing as a “child” (sex non-specific) as opposed to specifically as a “girl or boy,” and as a research tool to monitor growth parameters until long-term auxological data can be systematically collected.

Data from 39,119 participants (49.5% female; 66.7% non-Hispanic White (NHW); 21.7% non-Hispanic Black (NHB)) were included in the development of our growth charts, reference ranges, and z-score calculators (see Table 1 ). Analytic sample sizes used to estimate our growth charts were large and robust.

To illustrate the clinical utility of our age-adjusted sex non-specific height, weight, and BMI growth charts, we overlaid them on the CDC 2000 girls and boys growth charts. Of note, when modeled using GAMLSS methods, the combined data (girls and boys) have a broader distribution resulting in a new set of percentiles that differ from those for either sex-specific cohort. Because of this, at certain age points, especially before puberty, the height and weight of children plotted on the sex non-specific growth chart may appear slightly taller and/or heavier than on the CDC 2000 boy and girl charts. As shown in Figures 1 – 3 , the respective curves are nearly superimposable up to around 10 years of age (e.g., 10-12 years old) in terms of height and around 8 years of age (e.g., 8-10 years old) in terms of weight and BMI, after which differences become more apparent. For example, in a 5 year old, the median (p50) height on our age-adjusted sex non-specific curve is 111 cm, compared to 108 cm on the girl and 109 cm on the boy CDC 2000 charts. However, in a 15 year old the p50 height on our curve is 167 cm, compared to 162 cm and 170 cm on the CDC girl and boy charts, respectively.

As for median near adult height, our age-adjusted sex non-specific height curve shows p50 of 169 cm (range p3: 151 cm, p97: 186 cm), compared to 163 cm (p3: 151 cm, p97: 176 cm) and 177 cm (p3: 163 cm, p97: 190 cm) on the girl and boy CDC 2000 curves, respectively. A comparison of clinically useful percentiles (height/weight: 3^rd, 25^th, 50^th, 75^th, 97^th; BMI: 5^th, 25^th, 50^th, 85^th, 95^th) from our age-adjusted sex non-specific curves with those from the CDC 2000 girl and boy curves can be seen in Supplemental Tables 1-3 .

Although one generally desires to target an appropriate near adult height for one’s chosen gender, this is often an unreasonable expectation, particularly in a transgender male (assigned female at birth) youth. Figure 4 (transgender male, assigned female at birth, height chart) illustrates how using these sex non-specific charts can be useful in the clinical setting compared to the common practice of comparing male and female charts side-by side for clinical decision-making. Figure 5 (transgender female, assigned male at birth, BMI chart) illustrates differences in BMI percentiles between the sexes that may lead to misclassification of weight-related disorders in a transgender youth.

Finally, we created age-adjusted sex non-specific z-score calculators for height, weight, and BMI (http://tsaheight2020.shinyapps.io/gender0growthcharts). This website also includes comparative age-adjusted, sex non-specific growth curves, their CDC 2000 girl and boy counterpart curves, and data tables.

Using data from large multi-ethnic cross-sectional populations of US youth, we created age-adjusted, sex non-specific growth charts; reference ranges for height, weight, and BMI; and z-score calculators that age-smooth differences between sexes to assess how a youth is growing “as a child.” While challenging to specifically quantify, and with some differences among height, weight, and BMI, our growth charts are largely superimposable with the CDC 2000 girl and boy charts for about the first 10 years of life, after which divergence becomes more apparent, largely coinciding with general pubertal onset (34, 35). Therefore, clinical utility of these growth charts may have less impact during the pre-pubertal years, however become more important during pubertal onset and thereafter.

While the most commonly used US growth charts have been the WHO (for 0-2 year olds) and CDC 2000 (for 2-19 year olds) charts over the last at least 15 years, clinicians and researchers have realized the necessity of creating additional growth charts to address arisen needs among specific populations. For example, there are now specialized growth charts for Down, Turner, Noonan, and Williams syndromes (36–39). Additionally, pediatric severe obesity (defined as BMI ≥1.2 times the 95^th BMI percentile (40)) charts have been generated from CDC 2000 data to better document varying degrees of obesity (41). Of note, creation of these additional charts has largely been made possible through the use of newer statistical techniques and/or larger numbers of youth within these subpopulations available for analysis, and more continue to be developed (31, 32).

The prevalence of youth identifying as transgender has been increasing (42). While difficult to determine, estimates suggest 1.2–4.1% of adolescents report a gender identity different from their genetic sex, with a similar number being variant in gender expression (43). Concurrently, the number of youth presenting for transgender care is growing with a steadily increasing demand for services in multidisciplinary clinics on several continents (6). Methods for tracking growth parameters in this population are lacking, and while it has been proposed that such youth be dually-tracked on girl and boy growth charts, this approach has limitations (i.e., may misclassify diagnoses of weight-related disorders; lead to difficulties predicting near adult height) (7, 10, 44). Therefore, there is a need to track how one is growing as a “transgender child” compared to specifically as a “girl” or “boy.” Our growth charts and z-score calculators can serve as an intermediate reference between the male-specific or female-specific data points until longitudinal growth data are available for the creation of transgender-specific growth charts. Further, as we do not yet know attitudes toward height growth in non-binary young people, sex non-specific charts can serve as a way to monitor overall growth and weight status, and to collect longitudinal data that can be linked to their attitudes/perceptions of growth.

There are a number of clinical applications whereby use of age-adjusted, sex non-specific growth charts may be helpful. For one, differences in BMI percentiles between sexes may misclassify diagnoses of weight-related disorders (i.e., overweight/obesity, underweight) in transgender youth (10). Kidd et al. presents examples illustrating this, including a 16 year old transgender male (assigned female at birth) adolescent on GnRHa and testosterone who would be classified as overweight on the girl but obese on the boy growth chart (10). Similarly, we present ( Figure 5 ) a transgender male (assigned female at birth) adolescent who, at 17 years of age had a BMI in the normal weight category (83^rd percentile) on the CDC 2000 girls growth chart, however, in the overweight category (86^th percentile) on the sex non-specific growth chart. Longitudinal studies linking body composition measures of transgender youth to age-adjusted, sex non-specific BMI percentiles versus BMI percentiles for one’s sex and gender may help to more accurately assess a transgender youth’s weight classification. As the diagnosis of pediatric obesity accompanies medical and psychological sequelae and is associated with increased healthcare utilization, accurate diagnosis is imperative (18, 45–47). Further, if a child or adolescent is not diagnosed with overweight/obesity when they indeed have this, opportunities for earlier intervention and prevention of complications may be missed (18).

We do not believe that use of our age-adjusted, sex non-specific growth charts would impact decisions on when to start pubertal blockade, as early pubertal suppression leads to better psychological and physical outcomes (7). That said, our growth curves may help in guiding medical therapy to potentially augment height. For example, although one generally desires to target an appropriate near adult height for one’s chosen gender, this is often an unreasonable expectation, particularly in a transgender male (assigned female at birth) child during medical intervention with pubertal suppression and cross-hormone sex therapy.

Figure 4 shows how using these sex non-specific growth charts can be a more practical tool compared to the common practice of comparing the girl and boy charts side-by-side for clinical decision-making in terms of near adult height prediction. Prior to starting puberty blockers at 11 years old, the growth patterns of this transgender male (assigned female at birth) child were similar on the sex non-specific, CDC 2000 boy, and CDC 2000 girl growth charts at around the 50th percentile. However, after three years of pubertal suppression, his growth patterns began to diverge on the CDC 2000 boy and girl charts, a difference that became more pronounced after starting testosterone therapy. On the female chart, growth plots increased from the 25^th toward the 50^th percentile and, after starting testosterone, further increased to the 85^th percentile. On the male chart, growth plots decreased from the 25^th to the 10^th percentile after starting testosterone, and increased to only the 20^th percentile by age 18 years compared to the 85^th on the female chart. This level of divergence makes it difficult to assess the impact of testosterone on growth solely by comparing the boy and girl CDC 2000 growth charts. In contrast, on the sex non-specific charts, after three years on pubertal suppression, growth decreased from the 50^th to the 25^th percentile and, after initiating testosterone, increased back toward the 50^th percentile where he was growing prior to pubertal suppression. Therefore, the growth patterns seen on the sex non-specific chart provide an easily interpretable growth trajectory and target for medical interventions that can be used until longitudinal growth charts specific for transgender children are developed.

Our study has several strengths. Creation of our growth charts and z-score calculators was done using data from large nationally representative multi-ethnic cross-sectional cohorts (19–24). Specifically, we utilized the same datasets and similar statistical techniques used to create CDC 2000 growth charts, allowing for a direct comparison between our charts and those most commonly used in the US (25, 26). Further, our large sample size afforded us adequate statistical power to create these growth charts.

Our study also has limitations. Data used to create our weight and BMI charts were from 1963-1994, largely predating the obesity epidemic. Therefore, these growth charts may be different if more contemporaneous datasets were used. Pediatric severe obesity was rare from 1963-1994, whereas now it is the fastest growing pediatric obesity category (48). In terms of whether the use of more contemporaneous height data would also affect our growth charts (i.e., whether children are now comparatively taller), we suspect this not to be the case based upon current analyses of NHANES data from our group suggesting no secular trends (unpublished). Finally, we note that our z-scores have wider variability compared to those from CDC 2000 resulting from combining girls and boys in our analyses, with differences in pubertal growth spurt timing and long bone growth between sexes.

Although we believe that creation of our age-adjusted, sex non-specific growth charts is an important step towards addressing a critical need in the transgender population, we recognize the imperative need for future research and longitudinal data collection leading to separate growth charts and z-score calculators for transgender male and female youth. This will require significant time and effort. For example, NHANES data for the CDC 2000 growth charts were collected from 1960-1994. Further, it may be helpful to have sex non-specific height velocity charts and z-score calculators given their importance and practicality for tracking changes in height over shorter time durations (49). This is especially important given that height velocity may be altered by GnRHa and/or cross-sex hormonal therapy use (44, 50). Given the inherent limitations of creating height velocity charts using cross-sectional data, development of these charts would be best done using longitudinal cohorts and data registries tracking individuals’ growth over time (51).

In conclusion, we developed age-adjusted, sex non-specific growth charts that may eventually be used in scenarios in which standard “girls versus boys” growth charts may not be ideal. Presently, our sex non-specific growth charts should be considered a research tool that needs validation before they can be applied to clinical practice. Given the increasing prevalence of youth seeking transgender care and recognized limitations of current approaches, a critical need has arisen in terms of tracking growth parameters in these individuals. Until longitudinal data, including body composition measures, are available in this population, our growth charts may help to assess a transgender youth’s growth trajectory and weight classification, and expectations surrounding this.

Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs.

The studies involving human participants were reviewed and approved by CDC/National Center for Health Statistics institutional review board. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

EB contributed to the research concept and design, statistical analyses, and data analysis and interpretation, wrote the first draft of the manuscript, and made critical revisions for intellectual content. BM contributed to the research concept and design, data interpretation, and manuscript write-up, and made critical revisions for intellectual content. OA and MJ contributed to the research concept and design, data interpretation, and manuscript write-up; performed statistical analyses, and made critical revisions for intellectual content. AR contributed to data interpretation, manuscript write-up, and critical revisions for intellectual content. KS developed the research concept and design, contributed to data analysis, interpretation, and manuscript write-up; and made critical revisions for intellectual content. All authors contributed to the article and approved the submitted version.