Predicting the Risk of Proteinuria Based on Blood Plasma Fourier-Transform Infrared Spectroscopy and Machine Learning Algorithms From Pregnant Women and Newborns.

Proteinuria, defined by an abnormally elevated concentration of proteins in the urine, serves as a critical biomarker for the early detection and diagnosis of preeclampsia. Elevated proteinuria levels adversely affect both maternal and foetal health, increasing the risk of preterm birth and low birth weight. This study focused on the identification of proteinuria in blood samples through Fourier-transform infrared (FTIR) spectroscopy as a diagnostic tool for real-time diagnosis. Blood plasma collected during birth was evaluated in four study groups: pregnant controls (n = 12), pregnant women with proteinuria (n = 13), newborn controls (n = 12), newborns with proteinuria (n = 14). First, attenuated total reflectance-FTIR spectra of plasma samples were collected for subsequent spectral smoothing, vector normalisation and machine-learning classification with various supervised machine learning algorithms, including several types of Support Vector Machine, K-Nearest Neighbours and decision trees. We obtained (76.1 ± 3.1)% accuracy, (72.1 ± 4.8)% sensitivity and (80.4 ± 3.8)% specificity for pregnant classification, and (90.6 ± 1.5)% accuracy, (83.3 ± 2.4)% sensitivity and (98.9 ± 1.6)% specificity for newborn classification. The vibrational modes corresponding to the predominant biochemical components in blood plasma of pregnant women and newborns were listed. Sensitivity and specificity results demonstrated that FTIR can be a promising tool for early diagnosis and monitoring of individual proteinuria. Although the proposed spectroscopic approach demonstrated promising diagnostic performance, the findings should be interpreted in light of clinical conditions that could affect the biochemical profile. Further studies involving larger and more heterogeneous populations are required to validate the proposed methodology.