Currently, there are no reliable predictors of risk of development and severity of acute kidney injury (AKI) in septic patients. The surfactant protein D (SP-D) polymorphism rs721917C/T is associated with a greater susceptibility to AKI in the Chinese population. Our aim was to evaluate the value of SP-D polymorphisms rs721917C/T and of plasma SP-D levels to predict the risk of development of AKI (defined with KDIGO criterion) in septic patients.
The study enrolled septic patients admitted to the Critical Care Department of two tertiary care hospitals. SP-D rs721917C/T polymorphisms were determined using the PCR-SSP method. Plasma SP-D and urine NGAL contents were measured using commercially available ELISA kits.
330 septic patients were included. Their SOFA scores were 12 ± 3. Patients with AKI (n = 156) had higher plasma SP-D levels (median: 153 ng/mL, range 111-198 ng/mL) and urinary NGAL levels (median: 575 ng/mL, range 423-727 ng/mL) than those without AKI (SP-D median: 124 ng/mL, range 81-159 ng/mL, P = 0.001; NGAL median: 484 ng/mL, range 429-573 ng/mL). Plasma SP-D levels of AKI patients were correlated with urinary NGAL contents (r = 0.853). In 32 patients receiving continuous renal replacement therapy (CRRT), plasma SP-D levels correlated with duration of CRRT (r = 0.448). The area under the receiver operating characteristic curve for plasma SP-D levels to predict AKI was 0.84. Patients with AKI had a higher rate of rs721917 CC genotype (AKI: 35% vs. non-AKI: 20%; P = 0.012), but a significantly lower rate of TT genotype (AKI: 19% vs. non-AKI: 26%; P = 0.005). SP-D rs721917 CC genotype was an independent predictor of AKI (P = 0.044) and mortality (P = 0.014).
Our study showed that increased plasma SP-D level is associated with a higher risk of AKI in patients with sepsis. The SP-D rs721917CC genotype is an independent and significant predictor of AKI development and mortality of septic patients. The SP-D rs721917C/T polymorphisms should be further studied as diagnostic and prognostic biomarkers to facilitate early recognition of AKI.
About The Expert
Jiao Liu
Jianying Yao
Lidi Zhang
Yizhu Chen
Hangxiang Du
Zhenliang Wen
Dechang Chen
References
PubMed