Blood biomarkers representing maternal-fetal interface tissues used to predict early-and late-onset preeclampsia but not COVID-19 infection (preprint)

ABSTRACT

Background: A well-known blood biomarker (soluble fms-like tyrosinase-1 [sFLT-1]) for preeclampsia, i.e., a pregnancy disorder, was found to predict severe COVID-19, including in males. True biomarker may be masked by more-abrupt changes related to endothelial instead of placental dysfunction. This study aimed to identify blood biomarkers that represent maternal-fetal interface tissues for predicting preeclampsia but not COVID-19 infection. Methods: The surrogate transcriptome of the tissues was determined by that in maternal blood, utilizing four datasets (n=1,354) which were collected before the COVID-19 pandemic. Applying machine learning, a preeclampsia prediction model was chosen between those using blood transcriptome (differentially expressed genes [DEGs]) and the blood-derived surrogate for the tissues. We selected the most predictive model by the area under receiver operating characteristic (AUROC) using a dataset for developing the model, and well-replicated in datasets either with or without intervention. To identify eligible blood biomarkers that predicted any-onset preeclampsia from the datasets but did not predict positives in the COVID-19 dataset (n=47), we compared several methods of predictor discovery (1) the best prediction model; (2) gene sets by standard pipelines; and (3) a validated gene set for predicting any-onset preeclampsia during the pandemic (n=404). We chose the most predictive biomarkers from the best method with the significantly largest number of discoveries by a permutation test. The biological relevance was justified by exploring and reanalyzing low- and high-level, multi-omics information. Results: A prediction model using the surrogates developed for predicting any-onset preeclampsia (AUROC of 0.85, 95% confidence interval [CI] 0.77 to 0.93) was the only that was well-replicated in an independent dataset with no intervention. No model was well-replicated in datasets with a vitamin D intervention. None of the blood biomarkers with high weights in the best model overlapped with blood DEGs. Blood biomarkers were transcripts of integrin-5 (ITGA5), interferon regulatory factor-6 (IRF6), and P2X purinoreceptor-7 (P2RX7) from the prediction model, which was the only method that significantly discovered the eligible blood biomarkers (n=3/100 combinations, 3.0%; P=.036). Most of the predicted events (73.70%) among any-onset preeclampsia were cluster A as defined by ITGA5 (Z-score [≥]1.1), but were only a minority (6.34%) among positives in the COVID-19 dataset. The remaining were the predicted events (26.30%) among any-onset preeclampsia or those among COVID-19 infection (93.66%) if IRF6 Z-score was [≥]-0.73 (clusters B and C), in which none was the predicted events among either late-onset preeclampsia (LOPE) or COVID-19 infection if P2RX7 Z-score was <0.13 (cluster B). Greater proportion of predicted events among LOPE were cluster A (82.85% vs. 70.53%) compared to early-onset preeclampsia (EOPE). The biological relevance by multi-omics information explained the biomarker mechanism, polymicrobial infection in any-onset preeclampsia by ITGA5, viral co-infection in EOPE by ITGA5-IRF6, a shared prediction with COVID-19 infection by ITGA5-IRF6-P2RX7, and non-replicability in datasets with a vitamin D intervention by ITGA5. Conclusions: In a model that predicts preeclampsia but not COVID-19 infection, the important predictors were maternal-blood genes that were not extremely expressed, including the proposed blood biomarkers. The predictive performance and biological relevance should be validated in future experiments.