Application of volatilomic analysis by electronic nose for the detection of women with preeclampsia at high risk of developing chronic kidney disease.

BACKGROUND

Pre-eclampsia is a systemic disorder of pregnancy. Nowadays, there is no single clinical tool to identify women at risk of developing CKD after pre-eclampsia. The objective of this study was to create a statistical predictive model for chronic kidney disease (CKD) risk screening in patients with pre-eclampsia and persistent albuminuria by detecting global metabolite patterns in urine through the Cyranose® 320 electronic nose.

METHODS

The study included 22 pregnant women without risk factors for pre-eclampsia, 25 pregnant women with risk factors for pre-eclampsia, and 25 patients with diagnostic criteria for pre-eclampsia and 23 with CKD at the time of the study. There were analyzed urine samples by an electronic nose.

RESULTS

A natural variation between the study groups was verified through a PERMANOVA with a significant difference (F = 6.37, p < 0.0003). The statistical predictive model, performed through a Canonical analysis of principal coordinated (CAP), allowed correct classification of 68.4 % between all groups with a statistically significant difference (p = 0.0001). This study achieved discrimination between groups based on the metabolomic pattern present in urine.

CONCLUSIONS

The generated model can be a potential tool in the timely detection of patients with preeclampsia who are at high risk of developing chronic kidney disease.