Noonan syndrome in diverse populations

Paul Kruszka, Antonio R Porras, Yonit A Addissie, Angélica Moresco, Sofia Medrano, Gary T K Mok, Gordon K C Leung, Cedrik Tekendo-Ngongang, Annette Uwineza, Meow-Keong Thong, Premala Muthukumarasamy, Engela Honey, Ekanem N Ekure, Ogochukwu J Sokunbi, Nnenna Kalu, Kelly L Jones, Julie D Kaplan, Omar A Abdul-Rahman, Lisa M Vincent, Amber Love, Khadija Belhassan, Karim Ouldim, Ihssane El Bouchikhi, Anju Shukla, Katta M Girisha, Siddaramappa J Patil, Nirmala D Sirisena, Vajira H W Dissanayake, C Sampath Paththinige, Rupesh Mishra, Eva Klein-Zighelboim, Bertha E Gallardo Jugo, Miguel Chávez Pastor, Hugo H Abarca-Barriga, Steven A Skinner, Eloise J Prijoles, Eben Badoe, Ashleigh D Gill, Vorasuk Shotelersuk, Patroula Smpokou, Monisha S Kisling, Carlos R Ferreira, Leon Mutesa, Andre Megarbane, Antonie D Kline, Amy Kimball, Emmy Okello, Peter Lwabi, Twalib Aliku, Emmanuel Tenywa, Nonglak Boonchooduang, Pranoot Tanpaiboon, Antonio Richieri-Costa, Ambroise Wonkam, Brian H Y Chung, Roger E Stevenson, Marshall Summar, Kausik Mandal, Shubha R Phadke, María G Obregon, Marius G Linguraru, Maximilian Muenke, Paul Kruszka, Antonio R Porras, Yonit A Addissie, Angélica Moresco, Sofia Medrano, Gary T K Mok, Gordon K C Leung, Cedrik Tekendo-Ngongang, Annette Uwineza, Meow-Keong Thong, Premala Muthukumarasamy, Engela Honey, Ekanem N Ekure, Ogochukwu J Sokunbi, Nnenna Kalu, Kelly L Jones, Julie D Kaplan, Omar A Abdul-Rahman, Lisa M Vincent, Amber Love, Khadija Belhassan, Karim Ouldim, Ihssane El Bouchikhi, Anju Shukla, Katta M Girisha, Siddaramappa J Patil, Nirmala D Sirisena, Vajira H W Dissanayake, C Sampath Paththinige, Rupesh Mishra, Eva Klein-Zighelboim, Bertha E Gallardo Jugo, Miguel Chávez Pastor, Hugo H Abarca-Barriga, Steven A Skinner, Eloise J Prijoles, Eben Badoe, Ashleigh D Gill, Vorasuk Shotelersuk, Patroula Smpokou, Monisha S Kisling, Carlos R Ferreira, Leon Mutesa, Andre Megarbane, Antonie D Kline, Amy Kimball, Emmy Okello, Peter Lwabi, Twalib Aliku, Emmanuel Tenywa, Nonglak Boonchooduang, Pranoot Tanpaiboon, Antonio Richieri-Costa, Ambroise Wonkam, Brian H Y Chung, Roger E Stevenson, Marshall Summar, Kausik Mandal, Shubha R Phadke, María G Obregon, Marius G Linguraru, Maximilian Muenke

Abstract

Noonan syndrome (NS) is a common genetic syndrome associated with gain of function variants in genes in the Ras/MAPK pathway. The phenotype of NS has been well characterized in populations of European descent with less attention given to other groups. In this study, individuals from diverse populations with NS were evaluated clinically and by facial analysis technology. Clinical data and images from 125 individuals with NS were obtained from 20 countries with an average age of 8 years and female composition of 46%. Individuals were grouped into categories of African descent (African), Asian, Latin American, and additional/other. Across these different population groups, NS was phenotypically similar with only 2 of 21 clinical elements showing a statistically significant difference. The most common clinical characteristics found in all population groups included widely spaced eyes and low-set ears in 80% or greater of participants, short stature in more than 70%, and pulmonary stenosis in roughly half of study individuals. Using facial analysis technology, we compared 161 Caucasian, African, Asian, and Latin American individuals with NS with 161 gender and age matched controls and found that sensitivity was equal to or greater than 94% for all groups, and specificity was equal to or greater than 90%. In summary, we present consistent clinical findings from global populations with NS and additionally demonstrate how facial analysis technology can support clinicians in making accurate NS diagnoses. This work will assist in earlier detection and in increasing recognition of NS throughout the world.

Keywords: Africa; Asia; Latin America; Middle East; Noonan syndrome; diverse populations; facial analysis technology.

© 2017 Wiley Periodicals, Inc.

Figures

Figure 1
Figure 1
Facial landmarks on a Noonan syndrome patient. Inner facial landmarks are represented in red, while external landmarks are represented in blue. Blue lines indicate the calculated distances. Green circles represent the corners of the calculated angles. Texture features are extracted only from the inner facial landmarks.
Figure 2
Figure 2
Frontal and lateral facial profiles of individuals of African descent with Noonan syndrome. Gender, age, and country of origin found in Supplementary Table I. a(Ndiaye et al., 2014) b(Lee and Sakhalkar 2014)
Figure 3
Figure 3
Frontal and lateral facial profiles of Asian individuals with Noonan syndrome. Gender, age, and country of origin found in Supplementary Table I. c(Aoki et al., 2013) d(Edwards et al., 2014) e(Addissie et al., 2015) f(Yaoita et al., 2016)
Figure 4
Figure 4
Frontal and lateral facial profiles of Latin Americans with Noonan syndrome. Gender, age, and country of origin found in Supplementary Table I.
Figure 5
Figure 5
Sequential photos of individuals with Noonan syndrome at different ages. Gender, age, and country of origin found in Supplementary Table I.
Figure 6
Figure 6
Facial and torso profiles of individuals of African descent with Noonan syndrome. Gender, age, and country of origin found in Supplementary Table I.
Figure 7
Figure 7
Facial and torso profiles of individuals of Asian individuals with Noonan syndrome. Gender, age, and country of origin found in Supplementary Table I.
Figure 8
Figure 8
Facial and torso profiles of individuals of Latin American individuals with Noonan syndrome. Gender, age, and country of origin found in Supplementary Table I.

Source: PubMed

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