AUTHOR=Lu Hongxiang , Wen Dalin , Sun Jianhui , Du Juan , Qiao Liang , Zhang Huacai , Zeng Ling , Zhang Lianyang , Jiang Jianxin , Zhang Anqiang 

TITLE=Polygenic Risk Score for Early Prediction of Sepsis Risk in the Polytrauma Screening Cohort

JOURNAL=Frontiers in Genetics

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.545564

DOI=10.3389/fgene.2020.545564

ISSN=1664-8021

ABSTRACT=Background: Increasing genetic variants associated with sepsis have been identified by candidate-gene and genome-wide association studies, but single variant conferred minimal alterations in risk prediction. Our aimed to evaluate whether a weighted genetic risk score (wGRS) that aggregate information from multiple variants could improve risk discrimination of traumatic sepsis.
Methods: 64 genetic variants potential relating to sepsis were genotyped in Chinese trauma cohort. Genetic variants with mean decrease accuracy (MDA)>1.0 by random forest algorithms were selected to construct the multilocus wGRS. Area under curve (AUC) and Net reclassification improvement (NRI) were adopted to evaluate the discriminatory and reclassification ability of wGRS.
Results: Seventeen variants were extracted to construct the wGRS in 883 trauma patients. The wGRS was significantly associated with sepsis after trauma (OR=2.19, 95%CI=1.53-3.15, P=2.01×10-5) after adjusted by age, sex, and ISS. Patients with higher wGRS have an increasing incidence of traumatic sepsis (Ptrend=6.81×10-8), higher SOFA (Ptrend=5.00×10-3) and APACHE II score (Ptrend=1.00×10-3). The AUC of risk prediction model incorporating wGRS into the clinical variables was 0.768 (95%CI=0.739-0.796), with an increase of 3.40% (P=8.00×10-4) versus clinical factors-only model. Furthermore, the NRI increased 25.18% (95%CI=17.84-32.51%) (P=6.00×10-5).
Conclusions: Our finding indicated that genetic variants could enhance the predictive power of risk model for sepsis and highlighted the application among trauma patients, suggesting that the sepsis risk assessment model will be a promising screening and prediction tool for high risk population.