Open AccessAssociation of genetic polymorphisms with acute kidney injury after cardiac surgery in a Southeast Asian population
Author(s) -
Kah Ming Eddy Saw,
Rui Ge Roderica Ng,
Siew Pang Chan,
Yi Hui Ang,
Lian Kah Ti,
Tsong Huey Sophia Chew
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0213997
Subject(s) - medicine , acute kidney injury , cardiopulmonary bypass , population , allele , cardiac surgery , cardiology , bioinformatics , gene , genetics , biology , environmental health
Genetic polymorphisms are important in explaining the wide interpatient variability that exists in the development of acute kidney injury (AKI) post cardiac surgery. We hypothesised that polymorphisms in 4 candidate genes, namely angiotensin-converting enzyme (ACE), apolipoprotein-E (ApoE), interleukin-6 (IL-6), and tumour necrosis factor-alpha (TNF-α) are associated with AKI. Methods 870 patients who underwent cardiac surgery in Singapore were analysed. All patients who fulfilled stage 1 KDIGO criteria and above were considered to have AKI. This was investigated against various demographic, clinical and genetic factors. Results Increased age, history of hypertension, anaemia and renal impairment remained important preoperative risk factors for AKI. Intraoperatively, longer cardiopulmonary bypass (CPB) time and the use of intra-aortic balloon pump (IABP) were shown to be associated with AKI. Among the genetic factors, ACE-D allele was associated with an increased risk of AKI while IL6-572C allele was associated with a decreased risk of AKI. Conclusion ACE-D allele was associated with the development of AKI similar to other studies. On the other hand, IL6-572C was shown to have a protective role against the development of AKI, contradictory to studies done in the Caucasian population. This contradictory effect of IL6-572C is a result of a complex interplay between the gene and population specific modulating factors. Our findings further underscored the necessity of taking into account population specific differences when developing prediction models for AKI.