期刊
GENETICS IN MEDICINE
卷 23, 期 5, 页码 927-933出版社
ELSEVIER SCIENCE INC
DOI: 10.1038/s41436-020-01073-x
关键词
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资金
- Cystic Fibrosis Foundation [STRUG17PO]
- Canadian Institutes of Health Research [MOP 258916, MOP 117978, MOP 388348, MOP167282]
- Cystic Fibrosis Canada [2626]
- SickKids Foundation
- CF Canada
- Natural Sciences and Engineering Research Council of Canada [RGPIN-2015- 03742, 250053-2013]
- Government of Canada through Genome Canada [OGI-148]
- Government of Ontario
- Institut National de la Sante et de la Recherche Medicale
- Agence Nationale de la Recherche [R09186DS]
- DGS
- Association Agir Informer Contre la Mucoviscidose
- GIS-Institut des Maladies Rares
- CANSSI Ontario STAGE (Strategic Training for Advanced Genetic Epidemiology) program at the University of Toronto
- CFIT Program
- Assistance Publique Hopitaux de Paris
- Universite Pierre et Marie Curie Paris
- DGS, Association Vaincre La Mucoviscidose
- Chancellerie des Universites (Legs Poix)
By studying genetic and clinical indicators in CF patients, a model for predicting CFRD was successfully constructed and validated in different studies. Results showed that factors such as sex, CFTR severity score, and specific genetic variants were strong predictors, with the model performing well in different populations.
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.
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