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. Author manuscript; available in PMC: 2021 May 24.
Published in final edited form as: Am J Med Genet A. 2019 Dec 19;182(2):303–313. doi: 10.1002/ajmg.a.61461

Turner syndrome in Diverse Populations

Paul Kruszka 1, Yonit A Addissie 1, Cedrik Tekendo-Ngongang 1, Kelly L Jones 2, Sarah K Savage 3, Neerja Gupta 4, Nirmala D Sirisena 5, Teresa E Aravena Cerda 6, Sheela Nampoothiri 7, Katta M Girisha 8, Siddaramappa Jagdish Patil 9, Saumya Shekhar Jamuar 10, Agustini Utari 11, Nydia Sihombing 11, Rupesh Mishra 12, Neer Shoba Chitrakar 12, Brenda Iriele 1, Ezana Lulseged 1, Andre Megarbane 13, Annette Uwineza 14, Milagros M Duenas Roque 15, Meow-Keong Thong 16, Angélica Moresco 17, María Gabriela Obregon 17, Tung Yuet Ling 18, Gary TK Mok 18, Nicole Fleischer 3, Godfrey Rwegerera 19, María Beatriz de Herreros 20, Jonathan Watts 21, Karen Fieggen 21, Dalia Farouk 22, Neveen A Ashaat 22, Brian HY Chung 18, Eden Badoe 23, Sultana MH Faradz 11, Mona El-Ruby 22, Vorasuk Shotelersuk 24, Ambroise Wonkam 21, Ekanem Nsikak Ekure 25, Antonio Richieri-Costa 26, Maximilian Muenke 1
PMCID: PMC8141514  NIHMSID: NIHMS1578084  PMID: 31854143

Abstract

Turner syndrome (TS) is a common multiple congenital anomaly syndrome resulting from complete or partial absence of the second X chromosome. TS presents with short stature, infertility secondary to ovarian dysgenesis, cardiac and renal anomalies, characteristic exam findings such as cubitus valgus, normal intelligence, and specific neurocognitive profile which includes visual spacial deficits and math difficulties. Clinical studies of TS have predominantly focused on individuals of European descent. In this study, we explore the phenotype of TS in diverse populations using clinical examination and facial analysis technology. Clinical data from 70 individuals and images from 108 individuals with TS from 18 different countries were analyzed. Individuals were grouped into categories of African descent (African), Asian, Latin American, Caucasian (European descent), and Middle Eastern. The most common phenotype features across all population groups were short stature (85%), cubitus valgus (77%) and, low posterior hair line 70%. Other phenotype features found in over half included small mandible (63%), narrow hyperconvex and deep set fingernails (56%), narrow maxilla (53%), and short fourth metacarpals (50%). Congenital heart disease was found in 32% of the cohort. Two facial analysis technology experiments were conducted: TS vs. general population and TS vs. Noonan syndrome. Across all ethnicities, facial analysis was very accurate in diagnosing TS from frontal facial images as measured by the area under the curve (AUC). An AUC of 0.903 (p<0.001) was found for TS versus general population controls and 0.925 (p<0.001) for TS versus individuals with Noonan syndrome. In summary, we present consistent clinical findings from global populations with TS and additionally demonstrate that facial analysis technology can accurately distinguish TS from the general population and Noonan syndrome.

INTRODUCTION

Turner syndrome (TS) is caused by complete or partial deletion of the second X chromosome and affects 1 in 2,000 females in a European population (Stochholm, Juul, Juel, Naeraa, & Gravholt, 2006). In a study from Nigeria, the incidence of live births was found to be similar at 1 in 2745 (Adeyokunnu, 1982). Mosaicism is common in Turner syndrome; in a large study of 902 individuals with Turner syndrome, 31% had mosaic karyotypes (Hook & Warburton, 1983)`. The phenotype characteristics of TS are short stature, characteristic physical exam findings, infertility secondary to ovarian dysgenesis, congenital heart disease, autoimmune disease and endocrine disorders, and a specific neurocognitive profile (Gravholt, Viuff, Brun, Stochholm, & Andersen, 2019). Although general intelligence is normal for individuals with TS, there is a higher prevalence of specific learning disabilities including visual-spatial and/or visual-perceptual abilities (Pavlidis, McCauley, & Sybert, 1995). Treatment begins in early childhood and includes multiple therapies such as growth hormone at ages 4–6 years and estrogen replacement at ages 11–12 (Gravholt et al., 2017). This therapeutic regimen will continue to change with scientific advances (Kruszka & Silberbach, 2019). Unfortunately, the average diagnosis is 15 years of age in a European population which can delay recommended treatments (Berglund et al., 2019; Gravholt et al., 2019), and the age of diagnosis is most likely later in underdeveloped countries. In a study of 11 participants with TS in Cameroon, 10 were diagnosed due to late puberty and one due to short stature and the average age of diagnosis was 18.4 ± 2.8 years (SD) (Wonkam et al., 2015).

Few studies have been done in underserved areas of the world such as sub-Saharan Africa (Wonkam et al., 2015). The ability to make early diagnoses in developing countries is important for counseling and treatment of girls with TS (Wonkam et al., 2015). Traditional reported facial features of TS include narrow maxilla, small mandible and inner canthal folds (Jones, Jones, & Campo, 2013); in this study we test the hypothesis that TS in diverse populations can be differentiate from the general population and Noonan syndrome using facial analysis technology and propose that facial analysis may be a tool for earlier diagnosis.

In previous studies, we have investigated genetic syndromes in diverse populations (Dowsett et al., 2019; Kruszka, Addissie, et al., 2017; Kruszka, Porras, Addissie, et al., 2017; Kruszka et al., 2018; Kruszka, Porras, Sobering, et al., 2017; Kruszka, Tekendo-Ngongang, & Muenke, 2019). In this study we compare clinical characteristics of girls and women with TS across diverse populations with respect to clinical characteristics and facial analysis technology.

METHODS

Patients

Individuals with TS were evaluated from 18 countries. All participants (Supplementary Table I) had TS diagnosed by both clinical evaluation and/or molecular diagnosis. The patients were grouped by geographic area of origin or ethnicity (African and African American, Asian, Latin American, and the Middle East). Local clinical geneticists examined patients for established clinical features found in TS.

Consent was obtained by local institutional review boards and the Personalized Genomics protocol at the National Institutes of Health (11-HG-0093). Exam findings from the current study and those from the medical literature are recorded in Table I.

Table 1.

Clinical findings

Present study Yeşilkaya et al. 2015 Ferguson-Smith 1965
African (n=17) Asian (n=30) Latin American (n=11) Middle East (n=12) All (n=70) (n=842) 45,X (n=117) 45,X/46,XX (n=38) combined (n=155)
average age 19 13 5 11 13
karyotype 53% (9/17) 57% (17/30) 100% (11/11) 100% (12/12) 70% (49/70)
 45,X 44% (4/9) 47% (8/17) 55% (6/11) 33% (4/12) 45% (22/49) 427 (50.7%)
 isochromosome 22% (2/9) 24% (4/17) 9% (1/11) 25% (3/12) 20% (10/49) 169 (20.1%)
 mosaic 44% (4/9) 29% (5/17) 18% (2/11) 67% (8/12) 39% (19/49) 114 (21.2%)
 46,X,+mar 11% (1/9) 0 9% (1/11) 0 4% (2/49) 10 (1.2%)
height < 3rd centile 88% (15/17) 97% (29/30) 45% (5/11) 100% (12/12) 85% (61/70) 84% (708/842) 100% (105/105) 80% (29/36) 95% (134/141)
congenital heart disease 23% (4/17) 40% (8/20) 20% (2/10) 41% (5/12) 32% (19/59) 25% (180/719) 21% (18/87) 7% (2/29) 17% (20/116)
 bicuspid aortic valve 6% (1/17) 15% (3/20) 10% (1/10) 0 9% (5/59) 8.6% (61/719)
 coarctation (aorta) 6% (1/17) 15% (3/20) 20% (2/10) 33% (4/12) 17% (10/59) 6.5% (46/719)
 aortic stenosis 0 15% (3/20) 0 0 5% (3/59) 5.4% (38/719)
growth hormone history**** 17% (3/17) 16% (4/25) 0% (0/3) 60% (6/10) 25% (13/51)
Age estrogen started***** 33% (3/9) 35% (7/20) 50% (1/2) 87% (7/8) 47% (18/38)
History of spontaneous menstruation 22% (2/9)* 5% (1/20)** 66% (2/3) 0% (0/8) 15% (5/32) 8% (7/83) 21% (7/34) 12% (14/117)
Pregnancy history 0% (0/9) 0% (0/20) N/A 0% (0/8) 0% (0/29)
Narrow maxilla (palate) 42% (6/14) 36% (7/19) 81% (9/11) 25% (3/12) 53% (25/47)
Small mandible 42% (6/14) 56% (13/23) 90% (10/11) 36% (4/11) 63% (33/52)
Inner canthal folds 28% (4/14) 29% (7/24) 63% (7/11) 27% (3/11) 40% (21/52)
Low posterior hairline 58% (10/17) 61% (16/26) 81% (9/11) 83% (10/12) 70% (45/64)
webbed posterior neck 41% (7/17) 48% (13/27) 63% (7/11) 80% (8/10) 55% (35/63) 54% (63/117) 16% (6/37) 45% (69/155)
cubitus valgus or other elbow anomaly 69% (9/13) 72% (18/25) 72% (8/11) 100% (12/12) 77% (47/61)
short fourth metacarpal 14% (2/14) 27% (5/18) 81% (9/11) 91% (11/12) 50% (27/54) 58% (34/59) 44% (11/25) 54% (45/84)
short fourth metatarsal 28% (4/14) 40% (8/20) 36% (4/11) 36% (4/11) 40% (20/49)
Congenital lymphedema 25% (3/12) 8% (2/23) 60% (6/10) 54% (6/11) 33% (17/51) 39% (37/94) 12% (3/25) 34% (40/119)
Type of renal anomaly (i.e. horseshoe kidney) 12% (2/16) 0% (0/14) 0% (0/7) 8% (1/12) 7% (3/38) 16.3% (117/714)
Excessive pigmented nevi 13% (2/15) 37% (9/24) 18% (2/11) 75% (9/12) 37% (22/59) 52% (32/62) 37% (11/30) 47% (43/92)
Narrow, hyperconvex, deep set nails, or hypoplastic 53% (7/13) 39% (9/23) 90% (10/11) 9% (1/11) 56% (27/48) 77% (20/26) 55% (10/18) 68% (30/44)
Hearing loss 15% (2/13) 13% (3/22) 54% (6/11) 9% (1/11) 25% (12/47) 10% (54/539)
Learning disorder 42% (6/14) 17% (4/23) 60% (3/5) 70% (7/10) 40% (20/49) 16.1% (47/291)
visual-spatial organization deficits 33% (4/12) 8% (1/12) 0% (0/5) 0% (0/10) 17% (5/29)
social cognition deficits (i.e. failure to appreciate subtle social cues) 25% (2/8) 0% (0/10) 66% (4/6) 50% (5/10) 37% (11/29)
Math problems 10% (1/10) 20% (1/5) 90% (9/10) 50% (18/36)
Type of mental health illness (i.e. depression or anxiety) 0% (0/7) 0% (0/1) 66% (6/9) 38% (10/26)
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*

One individual was mosaic and the other karyotype was unknown

**

this individual is mosaic 45 X(63)/46XX(10)

***

one individual is a short arm deletion on the X chromosome: 46,X,del(X)(p21.1)[20]; the other is mosaic: 45,X0 [27]/46,XX [3]

****

individual was considered eligible for growth hormone therapy if greater than 5 years

*****

individual was considered eligible for estrogen replacement therapy if greater than 11 years

Facial analysis technology

Facial analysis was performed using technology developed by the Face2Gene Research application (FDNA Inc., Boston, MA) as previously described (Gurovich et al., 2019; Kruszka, Hu, et al., 2019). Facial images were collected from girls and women with TS and the medical literature (Supplementary Table 2 lists source of images). A control group with Noonan syndrome, previously described by Kruszka et al. (Kruszka, Porras, Addissie, et al., 2017), and another unaffected control group was used for comparisons. Noonan syndrome was used as a control group due to the similarity in phenotypes between Noonan syndrome and TS. Controls were matched for age, gender, and ethnicity. Two binary classification experiments using the Face2Gene Research application (FDNA Inc., Boston, MA), were performed (Gurovich et al., 2019). All facial images were fully de-identified through the use of the DeepGestalt facial analysis (Gurovich et al., 2019).

RESULTS

Clinical results

Table 1 summarizes the phenotype of the present study and two large prior comparison studies from the medical literature (Ferguson-Smith, 1965; Yesilkaya et al., 2015). A total of 70 individuals had phenotype information available. The average age was 13 years with the Latin American cohort being the youngest at 5 years. The most common finding was short stature, defined by stature below 3rd centile using Centers for Disease Control graphs (https://www.cdc.gov/growthcharts/clinical_charts.htm), which was found in 85% (61/70) of individuals of all ethnicities (Table 1). Short stature was least prevalent in the Latin American group, most likely due to the average age of only 5 years. The next most frequent exam finding was cubitus valgus 77% (47/61) followed by low posterior hair line 70% (45/64). Other phenotype features found in over half included small mandible (63%), narrow hyperconvex and deep set fingernails (56%), narrow maxilla (53%), and short fourth metacarpals (50%). Figure 4 demonstrates short fourth metacarpals and fingernail anomalies in the present cohort. Congenital heart disease was found in 32% of the cohort which was comparable to Yesilkaya et al. (Yesilkaya et al., 2015).

Figure 4.

Figure 4.

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Hands of individuals with Turner syndrome. See supplementary Table 1 for age, country of origin, and karyotype.

There were no pregnancies and only 15% of the cohort had spontaneous menstruation, 3 of 5 of these individuals were mosaic, none were 45,X. Of the individuals eligible for estrogen replacement therapy (ERT), 47% (18/38) received this therapy, and the African group was least likely to receive ERT, 33% (3/9). Growth hormone was given to 25% of individuals greater than 5 years of age; however, if the Middle Eastern group is removed and only the African, Latin American, and Asian groups are considered, only 16% (7/45) received growth hormone therapy.

With regards to cognitive and mental health issues, 50% reported difficulty in math, 38% reported anxiety and/or depression, 37% reported social cognition deficits and 17% reported visual-spacial deficits (Table 1).

Facial analysis technology

We used facial analysis technology to test the hypothesis that an objective single syndrome classifier developed by Face2Gene (Gurovich et al., 2019) could discriminate between TS and individuals without TS, regardless of ethnicity or country of origin. Two different experiments were run, the first experiment compared TS to an unaffected female population and the second experiment compared TS to individuals with Noonan syndrome. The discrimination ability of this facial analysis technology was measured using the area under the curve (AUC) of the receiver operating characteristic curve. An AUC of 1 represents perfect separation between individuals with TS and controls and an AUC of 0.5 represents the worst separation, in other words, a random and indiscriminative test.

We collected 108 images of individuals with TS, average age was 9.8 years with a range of 2 days to 54 years (Table 2). Image with permissions to publish are shown in Figure 1–3 for the African, Asian, and Latin American cohorts, respectively. The images used for facial analysis technology for the Caucasian cohort came from the medical literature (Atton et al., 2015; Cassidy & Allanson, 2010; Chaput et al., 2013; Doswell, Visootsak, Brady, & Graham, 2006; Gamstorp, 1985; Hall, Sybert, Willamson, Fisher, & Reed, 1982; Hennekam, Gorlin, Allanson, & Krantz, 2010; Jones et al., 2013; Kunze, 2010; Loscalzo, 2008; Mazzocco & Ross, 2007; Muenke, Adeyemo, & Kruszka, 2016; Nabhan & Eugster, 2006; Nebesio & Eugster, 2007; Russell, 2001; Thiesen, Ilha, Borges, & Freitas, 2015) and the Turner Syndrome Research Registry (TSRR) (Prakash et al., 2019).

Table 2.

Facial analysis technology results.

African Asian Latin American Caucasian Combined
TS (n=17) vs. unaffected (n=19) TS (n=17) vs. NS (n=16) TS (n=33) vs. unaffected (n=34) TS (n=33) vs. NS (n=17) TS (n=35) vs. unaffected (n=35) TS (n=35) vs. NS (n=21) TS (n=23) vs. unaffected TS (n=23) vs. NS (n=21) TS (n=108) vs. unaffected (n=111) TS (n=108) vs. NS (n=75)
average age 13 years 12.3 years 5.3 years 10.5 years 9.8 years
age range 0.8–52 years 0.5–24 years 2 days – 15 years 2 days – 35 years 2 days – 52 years
mean AUC (STD) 0.81 (0.07) 0.72 (0.13) 0.97 (0.02) 0.97 (0.02) 0.89 (0.06) 0.90 (0.04) 0.93 (0.05) 0.89 (0.05) 0.91 (0.01) 0.93 (0.01)
aggregated AUC (p-value) 0.78 (0.03) 0.74 (0.14) 0.96 (0.001) 0.97 (0.005) 0.89 (<0.001) 0.89 (0.008) 0.92 (0.002) 0.87 (0.01) 0.903 (<0.001) 0.925 (<0.001)
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Figure 1.

Figure 1.

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Individuals of African descent with Turner syndrome See supplementary Table 1 for age, country of origin, and karyotype.

Figure 3.

Figure 3.

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Individuals of Latin American descent with Turner syndrome. See supplementary Table 1 for age, country of origin, and karyotype.

Comparing the features of all ethnicities of 108 individuals with TS with 111 age and ethnic matched controls resulted in an AUC of 0.903 (p<0.001), showing excellent ability to classify TS (Table 2). Similarly, when comparing TS with Noonan syndrome, excellent discrimination was seen with an AUC of 0.925 (p<0.001).

When performing the same discrimination testing on distinct populations, our results were varied. The Asian group had the best separation between cases and controls (Table 2). For Asian individuals with TS versus unaffected, the AUC was 0.96 (p=0.001) and for TS vs. Noonan syndrome in Asians, the AUC was 0.97 (p=0.005). The facial analysis technology performed worse on the African American group (Table 2). When comparing TS versus unaffected in individuals of African descent, the AUC was 0.78 (p=0.03) and the AUC for TS vs. Noonan syndrome was 0.74 (p=0.14). This may partly be explained by the number of participants used to train the algorithm, 33 in the Asian TS cohort and 17 in the African cohort. It should be noted that the Latin American cohort (n=35) was larger than the Asian cohort and the Asian cohort still had more favorable AUCs.

DISCUSSION

In this study we present 70 clinical examinations and of individuals from diverse populations affected by TS. We demonstrate that regardless of their ethnicity or country of origin, individuals with TS have a distinct phenotype consisting of short stature, amenorrhea, infertility, congenital heart disease and clinical exam findings that include cubitus valgus, low posterior hairline, webbing of the neck, and short 4th metacarpals (Table 1). Our findings are consistent with other large phenotype studies (Ferguson-Smith, 1965; Yesilkaya et al., 2015).

In 108 facial analysis technology evaluations, we show that facial analysis technology is accurate in discriminating individuals with TS from healthy controls and individuals with Noonan syndrome. The ability to discriminate TS from Noonan syndrome is particularly encouraging as TS and Noonan syndrome share phenotypic features and considering that the initial reports of Noonan syndrome used the terminology “TS” (Celermajer, Bowdler, & Cohen, 1968; Noonan, 1968; Nora & Sinha, 1968). The Face2Gene technology was most accurate in the Asian group (AUC 0.96 for TS vs. unaffected and 0.97 for TS vs. NS) and least accurate in the African American cohort (AUC 0.78 for TS vs. unaffected and 0.74 for TS vs. NS).

One factor that may have influenced a decreased accuracy (AUC) in the African group is the smaller number of participants. Recruiting individuals of African descent was more difficult in this study and highlights the importance of studies such as the present study that focuses on diversity. In the medical literature, there are simply a near absence of studies describing TS in African populations (Wonkam et al., 2015). In a recent review on TS, Gravholt et al. reinforce this point with “there is a paucity of studies from Africa, Asia and South America” (Gravholt et al., 2019).

A weakness of this study is ascertainment bias. Especially as many of the participants came from resource limited areas of the world, these individuals presented for medical attention secondary to significant medical problems such as structural heart disease. There is evidence from a previous study that incidentally prenatal diagnosed individuals (i.e. amniocentesis done for advance maternal age) had fewer phenotypic features, lower incidence of congenital heart disease, and more likely to be mosaic than clinically diagnosed individuals (Gunther et al., 2004). With the advent of non-invasive prenatal screening and possibly newborn screening in the future, more information on full phenotypic spectrum of TS will be available (Bianchi, 2019; Murdock et al., 2017).

In addition to the phenotypic and facial analysis data that this study presents, there is an evidence of treatment differences in the data. Given the relatively young age of this study with an average of 13 years, only 25% of individuals older than 5 years received growth hormone therapy which is recommended to start between ages 4 and 6 and 47% received estrogen replacement therapy which is recommended between ages 11 and 12 (Gravholt et al., 2017). We are encouraged that more studies are being done in diverse populations, and that increased attention is being focused on earlier diagnosis and potentially early treatment (Kruszka, Addissie, et al., 2017; Kruszka, Porras, Addissie, et al., 2017; Kruszka et al., 2018; Kruszka, Porras, Sobering, et al., 2017; Kruszka, Tekendo-Ngongang, et al., 2019).

In summary, we provide a summary of the phenotype of TS in diverse populations and demonstrate that facial analysis technology using Face2Gene is accurate in discriminating TS from the general population and Noonan syndrome.

Supplementary Material

Supplementary Table 1
Supplementaty Table 2

Figure 2.

Figure 2.

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Individuals of Asian descent with Turner syndrome See supplementary Table 1 for age, country of origin, and karyotype.

ACKNOWLEDGEMENTS

We are grateful to the individuals and their families who participated in our study. P.K., C.T-M., Y.A.A, and M.M. are supported by the Division of Intramural Research at the National Human Genome Research Institute, NIH. We thank the Chulalongkorn Academic Advancement Into Its 2nd Century Project. We are grateful for the Turner Syndrome Society of the United States (TSSUS) and the Turner Resource Network’s Turner Syndrome Registry for supporting this research.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 1
NIHMS1578084-supplement-Supplementary_Table_1.xlsx (11.8KB, xlsx)
Supplementaty Table 2
NIHMS1578084-supplement-Supplementaty_Table_2.xlsx (66.8KB, xlsx)

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