Exploratory serum mass spectral fingerprinting and machine learning for case-control classification of preeclampsia

Preeclampsia is a pregnancy-specific hypertensive disorder associated with maternal and perinatal morbidity and mortality and can increase the risk of vascular diseases after pregnancy. However, a timely and unequivocal diagnosis in the first weeks allows access to appropriate medical follow-up.

In this exploratory approach, we aim to evaluate the analytical potential of MALDI-TOF spectral fingerprints combined with machine learning algorithms for discriminating serum samples from patients with preeclampsia from those with normotensive pregnancies. The dataset comprised 164 spectra of serum samples from 67 women with preeclampsia and 97 negative controls, which were processed using the Filter-Assisted Sample Preparation (FASP) protocol and analyzed by mass spectrometry.

Spectral data analysis was subjected to a machine learning algorithm that demonstrated high performance in classifying cases and controls, with an overall accuracy of 88% and sensitivities of 0.90 and 0.85 for predicting positive cases and negative controls, respectively.

The proposed analytical workflow demonstrated promising classification performance, highlighting the potential of MALDI-TOF spectral pattern recognition as a rapid screening approach for disease-associated signatures.