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

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

Research output: Contribution to journalArticlepeer-review


Purpose: 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. Methods: 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. Results: 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. Conclusion: 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.

Original languageEnglish (US)
Pages (from-to)927-933
Number of pages7
JournalGenetics in Medicine
Issue number5
StatePublished - May 2021

ASJC Scopus subject areas

  • Genetics(clinical)


Dive into the research topics of 'Cystic fibrosis–related diabetes onset can be predicted using biomarkers measured at birth'. Together they form a unique fingerprint.

Cite this