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Enregistrement W1983611341 · doi:10.1002/ajmg.a.35704

Special section. Syndrome‐specific growth charts

2012· article· en· W1983611341 sur OpenAlexaff
Judith G. Hall, Judith Allanson, Karen W. Gripp, Anne Slavotinek

Notice bibliographique

RevueAmerican Journal of Medical Genetics Part A · 2012
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGenetic Syndromes and Imprinting
Établissements canadiensBC Children's HospitalChildren's Hospital of Eastern OntarioUniversity of British Columbia
Organismes subventionnairesBarth Syndrome Foundation
Mots-clésGeneticistTerminologyIdentification (biology)NormativeDiseaseMedical geneticsPsychologyMedicineBiologyGeneticsPathologyPolitical science

Résumé

récupéré en direct d'OpenAlex

Interestingly, new data from around the world suggest that the health and nutrient status of the mother and breast feeding influence growth in addition to ethnicity and other genetic and environmental factors [WHO, 2006]. One of the greatest challenges for the clinical geneticist, when seeing a family, is to provide natural history information for the particular disorder, syndrome, or disease. As geneticists become skilled at describing physical features, improved terminology, and better measurements are becoming available for the clinical setting [Allanson et al., 2009]. As developmental biologists begin to identify the multiple genes involved in growth and development, better understanding of the changes leading to abnormal growth will likely be identified. Nevertheless, the physician, the affected individual, and the family will continue to seek “normal” or normative curves for these disorders since they are a key part of the ongoing assessment of individuals with a particular condition. This Special Section of the Journal is aimed at increasing awareness of the importance of having comprehensive growth information for each of the many disorders in clinical genetics. The handbook that we have edited over the last 20 years has been improving with every edition [Hall et al., 1989; Hall et al., 2007]. The articles in this issue of the American Journal of Medical Genetics will make it even more useful since they characterize the growth patterns in specific conditions and, often, in specific populations. With the advances in the understanding of developmental genetics, identification of specific genes and pathways will allow for more and more specific therapies for rare disorders. As therapies for these syndromes become available, it will be important to be able to compare those treated with a population without therapy (see El-Bassyouni et al. regarding Turner syndrome in this issue). The study of growth in the general population is remarkably complex and involves multiple modifiers. Little wonder then that it has been challenging to accumulate data on rare disorders. This Special Section in this issue of the Journal comprises 14 articles, most providing normative growth curves for various syndromes; some of the articles providing ethnicity-specific growth curves. The articles report on 10 different syndromes, all well known to the practicing pediatrician, clinical geneticist, and genetic counselor. The opening two articles feature growth curves in children with Down syndrome (DS). The article by Affifi et al. includes growth charts of children in the first 3 years of life; the data come from children without the common medical complications of DS, including structural heart defects. Curves for weight, length, and occipito-frontal-circumference (OFC) will be useful in the evaluation of infants with DS. The article by Beyhan et al. presents growth curves of individuals with DS between birth and age 18 years. Associated major malformations were recorded. Both articles lay the groundwork for the upcoming growth curves in U.S. children with DS being compiled by the Centers for Disease Control and Prevention. The next three articles provide growth curves for individuals with the 22q11.2 deletion syndrome; the first two by Tarquinio et al. and Habel and colleagues are derived from United States data, while the article by Guzman et al. presents data derived from Chilean infants with the syndrome from birth to age 24 months. Bassyouni et al. summarize anthropometric measurements in 93 patients with full 45,X or 45,X mosaic karyotypes. None of the individuals has been treated with growth hormone, thus these measurements provide a valuable resource for future comparison of populations of treated and untreated girls with Turner syndrome. The next two articles feature growth curves for individuals with disorders in the RAS/MAPK pathway. The study by Sammon et al. presents normative growth curves for individuals with Costello syndrome at various ages. Measurements obtained about? Do you mean after growth hormone exposure in a small number of the studied individuals were excluded from the analysis. Given the notable manifestation of growth deficiency in infants with Costello syndrome, we anticipate that these data will constitute an important resource. Malaquis et al. provide growth standards for patients with Noonan syndrome and so-called “Noonan-like syndromes” who have mutations in the RAS/MAPK pathway. As height in individuals with Noonan syndrome is correlated to the specific affected gene in this heterogeneous condition, future work may focus on comparing and contrasting cohorts stratified by mutated gene. Lee et al. present growth charts for individuals with Smith–Lemli–Opitz syndrome. These collaborators provide longitudinal data from 78 patients from near birth to age 16 years. Their study represents the largest cohort available of anthropometric data on this important syndrome. The timing of this is particularly relevant, as the upcoming issue in the American Journal of Medical Genetics Part C, Seminars in Medical Genetics, is devoted to Smith–Lemli–Opitz and related syndromes. The next two articles deal with anthropometric measurements in two skeletal dysplasias familiar to the clinical geneticist: osteogenesis imperfecta subtypes (not sure if this is best, but not sure also about The osteogenesis imperfectas) and the Majewski osteodysplastic primordial dwarfism type 2 syndrome (MOPD 2). In MOPD 2, length, weight, and OFC curves for a relatively large series of pericentrin (PCNT) mutation positive patients with this rare condition are provided. The curves will be helpful in the diagnosis of this complex condition. The ensuing two articles concern important but less common conditions, Barth syndrome and Meier-Gorlin syndrome, and provide useful clinical details for these conditions beyond the growth curves. Roberts et al. present data from the Barth syndrome registry and provide growth curves for weight and length compared to the normative population; other clinical data derived from the registry, including developmental milestones and age of diagnosis of cardiomyopathy, are provided. The article by de Munnik and colleagues presents a large series of patients with Meier-Gorlin syndrome, providing growth data, as well as information on secondary sexual development. These data will be invaluable for clinicians of all specialties following children with these two rare syndromes. The final article in the series is unique. Rosenbloom and Butler describe the experiences at two medical centers in different stages of incorporating electronic growth charts into medical records using Prader–Willi syndrome as an illustration. A recent article in the pediatric literature from this group [Butler et al., 2011] provides updated growth charts in individuals with PWS. In addition to the articles in this Special Section, Boghossian et al. [2012] submitted their article on growth curves in trisomy 21, 18, and 13 earlier in 2011. The article was meant to be included in this Special Section but was published before our series. This article, along with all 14 articles summarized above, will appear together in a Virtual Issue of the American Journal of Medical Genetics Part A, which will be online and freely accessible in 2013 on the Wiley Blackwell Interscience web page of the AJMG at http://www.interscience.wiley.com/ajmg. In this way, all of these charts are available for printing or electronic inclusion into patients' charts. The authors appreciate the assistance of John C. Carey in the finalization of the manuscript.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,565
Score d'incertitude au seuil0,650

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,253
Écart entre enseignants0,241 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2012
Routes d'admission1
Résumé présentoui

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