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Correction to: Cystic fibrosis–related diabetes onset can be predicted using biomarkers measured at birth

Authors :
Julie Avolio
Lorna Kosteniuk
Mays Merjaneh
Emmanuelle Brochiero
Pearce G. Wilcox
Yu-Chung Lin
Nancy Morrison
Katherine Keenan
Mark A. Chilvers
April Price
Melinda Solomon
Damien Adam
Dimas Mateos-Corral
Felix Ratjen
Jennifer Pike
Bradley S. Quon
Scott M. Blackman
Vanessa McMahon
Anne L. Stephenson
Joe Reisman
Terry Viczko
Mary Jackson
Natalie Henderson
Naim Panjwani
Nathalie Vadeboncoeur
Émilie Maille
Caroline Burgess
Katie Griffin
Harriet Corvol
Danny Veniott
Lori Fairservice
Shaikh Iqbal
Angela Hillaby
Raquel Consunji-Araneta
Christine Donnelly
Guillaume Côté-Maurais
Emma Karlsen
Clare Smith
Elizabeth Tullis
Andrea Dale
Winnie Leung
Paula Barrett
Lei Sun
Mary Jane Smith
Daniel Hughes
Stéphanie Bégin
Janna Brusky
Candice Bjornson
Lynda Lazosky
Richard van Wylick
Michael D. Parkins
Yves Berthiaume
Lara Bilodeau
Johanna M. Rommens
Lisa J. Strug
Jennifer Itterman
Valerie Levesque
Fan Lin
Jiafen Gong
Garry R. Cutting
Source :
Genetics in Medicine
Publication Year :
2021
Publisher :
Elsevier BV, 2021.

Abstract

Cystic fibrosis (CF), caused by pathogenic variants in the CF transmembrane conductance regulator (CFTR), affects multiple organs including the exocrine pancreas, which is a causal contributor to cystic fibrosis-related diabetes (CFRD). Untreated CFRD causes increased CF-related mortality whereas early detection can improve outcomes.Using genetic and easily accessible clinical measures available at birth, we constructed a CFRD prediction model using the Canadian CF Gene Modifier Study (CGS; n = 1,958) and validated it in the French CF Gene Modifier Study (FGMS; n = 1,003). We investigated genetic variants shown to associate with CF disease severity across multiple organs in genome-wide association studies.The strongest predictors included sex, CFTR severity score, and several genetic variants including one annotated to PRSS1, which encodes cationic trypsinogen. The final model defined in the CGS shows excellent agreement when validated on the FGMS, and the risk classifier shows slightly better performance at predicting CFRD risk later in life in both studies.We demonstrated clinical utility by comparing CFRD prevalence rates between the top 10% of individuals with the highest risk and the bottom 10% with the lowest risk. A web-based application was developed to provide practitioners with patient-specific CFRD risk to guide CFRD monitoring and treatment.

Details

ISSN :
10983600
Volume :
23
Database :
OpenAIRE
Journal :
Genetics in Medicine
Accession number :
edsair.doi.dedup.....a58bc4c6d23bddeb54bbae662e77180d
Full Text :
https://doi.org/10.1038/s41436-021-01281-z