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Record W2894074216 · doi:10.1016/j.ebiom.2018.09.026

Screening a large pediatric cohort with GH deficiency for mutations in genes regulating pituitary development and GH secretion: Frequencies, phenotypes and growth outcomes

2018· article· en· W2894074216 on OpenAlexaff
Werner Blum, Jürgen Klammt, Serge Amselem, Heike Pfäffle, Marie Legendre, Marie‐Laure Sobrier, Marie‐Pierre Luton, Christopher J. Child, Christine Jones, Alan G. Zimmermann, Charmian A. Quigley, Gordon B. Cutler, Cheri Deal, Jan Lebl, Ron G. Rosenfeld, John S. Parks, Roland Pfäffle

Bibliographic record

VenueEBioMedicine · 2018
Typearticle
Languageen
FieldMedicine
TopicGrowth Hormone and Insulin-like Growth Factors
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersNational Heart, Lung, and Blood InstituteWomen's Health Initiative
KeywordsShort statureSanger sequencingGrowth hormone deficiencyMutationInternal medicineGenetic testingHypopituitarismBiologyGenotypingMedicineGeneticsEndocrinologyOncologyGenotypeGeneGrowth hormoneHormone

Abstract

fetched live from OpenAlex

Background Pituitary development and GH secretion are orchestrated by multiple genes including GH1 , GHRHR , GLI2 , HESX1 , LHX3 , LHX4 , PROP1 , POU1F1 , and SOX3 . We aimed to assess their mutation frequency and clinical relevance in children with severe GH deficiency (GHD). Methods The Genetics and Neuroendocrinology of Short Stature International Study (GeNeSIS; Clinical Trial Registry Number: NCT01088412) was a prospective, open-label, observational research program for pediatric patients receiving GH treatment, conducted in 30 countries between 1999 and 2015. The study included a sub-study to investigate mutations in the genes listed above. PCR products from genomic blood cell DNA were analyzed by Sanger sequencing. DNA variants were classified as pathogenic according to the recommendations of the American College of Medical Genetics and Genomics. Demographic, auxologic, and endocrine data at baseline and during GH treatment were documented and related to the genotyping results. Findings The analysis comprised 917 patients. In 92 patients (10%) 33 mutations were found, 16 previously described and 17 novel (52%). Mutation carriers were significantly younger, shorter, and more slowly growing than non-carriers. In general, their peak values in GH stimulation tests were very low; however, in 15/77 (20%) patients with GH1 , PROP1 , and SOX3 mutations they were only moderately diminished (3-6 μg/L). Two patients with a GH1 mutation developed TSH deficiency and one ADH deficiency. Using logistic multi-regression analysis, significant indicators of a mutation were combined pituitary hormone deficiency, greater patient-parent height difference (SDS), low GH peak, and young age. Final height SDS gain in mutation carriers (mean ± SD 3.4 ± 1.4) was greater than in non-carriers (2.0 ± 1.4; P < .001) and in patients with non-GHD short stature. Interpretation DNA testing for mutations in children with severe GHD shows a positive finding in approximately 10%. Phenotypes of mutation carriers can be variable. The benefit for clinical practice justifies DNA testing as an important component in the diagnostic work-up of patients with severe GHD. Fund Eli Lilly and Company, Indianapolis, IN, USA. ClinicalTrials.com registration: NCT01088412.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.256
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations58
Published2018
Admission routes1
Has abstractyes

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