Identification of Biomarkers for Preeclampsia Based on Metabolomics

Metabolomic prediction of severe maternal and newborn complications in preeclampsia

INTRODUCTION

Preeclampsia (PreE) remains a major source of maternal and newborn complications. Prenatal prediction of these complications could significantly improve pregnancy management.

OBJECTIVES

Using metabolomic analysis we investigated the prenatal prediction of maternal and newborn complications in early and late PreE and investigated the pathogenesis of such complications.

METHODS

Serum samples from 76 cases of PreE (36 early-onset and 40 late-onset), and 40 unaffected controls were collected. Direct Injection Liquid Chromatography-Mass Spectrometry combined with Nuclear Magnetic Resonance (NMR) spectroscopy was performed. Logistic regression analysis was used to generate models for prediction of adverse maternal and neonatal outcomes in patients with PreE. Metabolite set enrichment analysis (MSEA) was used to identify the most dysregulated metabolites and pathways in PreE.

RESULTS

Forty-three metabolites were significantly altered (p < 0.05) in PreE cases with maternal complications and 162 metabolites were altered in PreE cases with newborn adverse outcomes. The top metabolite prediction model achieved an area under the receiver operating characteristic curve (AUC) = 0.806 (0.660-0.952) for predicting adverse maternal outcomes in early-onset PreE, while the AUC for late-onset PreE was 0.843 (0.712-0.974). For the prediction of adverse newborn outcomes, regression models achieved an AUC = 0.828 (0.674-0.982) in early-onset PreE and 0.911 (0.828-0.994) in late-onset PreE. Profound alterations of lipid metabolism were associated with adverse outcomes.

CONCLUSION

Prenatal metabolomic markers achieved robust prediction, superior to conventional markers for the prediction of adverse maternal and newborn outcomes in patients with PreE. We report for the first-time the prediction and metabolomic basis of adverse maternal and newborn outcomes in patients with PreE.

Advances in pathogenesis of preeclampsia

PURPOSE

Preeclampsia is a major cause of health problems for both pregnant women and unborn babies worldwide. However, the underlying causes of preeclampsia are not fully understood, leading to limited effective treatments. The goal of this study is to enhance our knowledge of its causes, devise prevention strategies, and develop treatments.

METHODS

We performed a systematic literature search. Six models regarding the pathogenesis of preeclampsia are discussed in this review.

RESULTS

This review focuses on the latest advancements in understanding preeclampsia's origins. Preeclampsia is a complex condition caused by various factors, processes, and pathways. Reduced blood flow and oxygen to the uterus and placenta, heightened inflammatory reactions, immune imbalances, altered genetic changes, imbalanced blood vessel growth factors, and disrupted gut bacteria may contribute to its development.

CONCLUSION

Preeclampsia is thought to result from the interplay of these factors.

Investigating genetic links between blood metabolites and preeclampsia

BACKGROUND

Observational studies have revealed that metabolic disorders are closely related to the development of preeclampsia (PE). However, there is still a research gap on the causal role of metabolites in promoting or preventing PE. We aimed to systematically explore the causal association between circulating metabolites and PE.

METHODS

Single nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) of 486 blood metabolites (7,824 participants) were extracted as instrumental variables (P < 1 × 10- 5), GWAS summary statistics for PE were obtained from FinnGen consortium (7,212 cases and 194,266 controls) as outcome, and a two-sample Mendelian randomization (MR) analysis was conducted. Inverse variance weighted (IVW) was set as the primary method, with MR-Egger and weighted median as auxiliary methods; the instrumental variable strength and confounding factors were also assessed. Sensitivity analyses including MR-Egger, Cochran's Q test, MR-PRESSO and leave-one-out analysis were performed to test the robustness of the MR results. For significant associations, repeated MR and meta-analysis were performed by another metabolite GWAS (8,299 participants). Furthermore, significantly associated metabolites were subjected to a metabolic pathway analysis.

RESULTS

The instrumental variables for the metabolites ranged from 3 to 493. Primary analysis revealed a total of 12 known (e.g., phenol sulfate, citrulline, lactate and gamma-glutamylglutamine) and 11 unknown metabolites were associated with PE. Heterogeneity and pleiotropy tests verified the robustness of the MR results. Validation with another metabolite GWAS dataset revealed consistency trends in 6 of the known metabolites with preliminary analysis, particularly the finding that genetic susceptibility to low levels of arachidonate (20:4n6) and citrulline were risk factors for PE. The pathway analysis revealed glycolysis/gluconeogenesis and arginine biosynthesis involved in the pathogenesis of PE.

CONCLUSIONS

This study identifies a causal relationship between some circulating metabolites and PE. Our study presented new perspectives on the pathogenesis of PE by integrating metabolomics with genomics, which opens up avenues for more accurate understanding and management of the disease, providing new potential candidate metabolic molecular markers for the prevention, diagnosis and treatment of PE. Considering the limitations of MR studies, further research is needed to confirm the causality and underlying mechanisms of these findings.

CD24+ decidual stromal cells: a novel heterogeneous population with impaired regulatory T cell induction and potential association with recurrent miscarriage

OBJECTIVE

To explore the heterogeneity of CD24+ decidual stromal cells (DSCs) in patients with recurrent miscarriages (RMs).

DESIGN

We have discerned that the expression of CD24 serves to differentiate two stable and functionally distinct lineages of DSCs. The heterogeneity of CD24+ DSCs has been scrutinized, encompassing variances in stromal markers, transcriptional profiles, metabolic activity, and immune regulation.

SETTING

Department of Reproductive Immunology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University; Shanghai Institute of Immunity and Infection, Chinese Academy of Science.

PATIENTS

A total of 129 early decidual samples were obtained, comprising 36 from healthy donors and 93 from patients with RMs. Blood samples were collected before the surgical procedure. Paraffin-embedded segments from 20 decidual samples of patients with RMs were obtained.

INTERVENTIONS

None.

MAIN OUTCOME MEASURES

The flow cytometry was used to quantify the expression of CD24+ DSCs in both healthy donors and patients with RMs, although it also evaluated the cellular heterogeneity. To ascertain the transcriptomic profiles of CD24+ DSCs by reanalyzing our single-cell transcriptomic data. Additionally, to measure the metabolomic activity of CD24+ DSCs from patients with RMs, ultraperformance liquid chromatography-mass spectrometry was employed. Through the implementation of a coculture system, we unraveled the role of CD24+ DSCs in immune regulation.

RESULTS

Patients with RMs exhibit a notable enrichment of CD24+ DSCs, revealing a pronounced heterogeneity characterized by variations in stromal markers and transcriptional profiles. The heightened enrichment of CD24+ DSCs may play a pivotal role in triggering decidual inflammation and dysfunction in decidualization. Furthermore, CD24+ DSCs showed diverse metabolic activities and impeded the induction of naïve CD4+ T cells into regulatory T cells through the abundant secretion of 3-hydroxyisovaleric acid. Finally, our investigations have revealed that intraperitoneal administration of 3-hydroxyisovaleric acid in mouse models can elevate the risk of RM.

CONCLUSION

We have successfully identified a disease-associated subset of CD24+ decidual stromal cells that could potentially contribute to the development of RM through the impairment of decidual immune tolerance. Targeting these specific CD24+ DSCs might hold promising prospects for therapeutic interventions in the clinical management of RM.

Maternal Acylcarnitine Disruption as a Potential Predictor of Preterm Birth in Primigravida: A Preliminary Investigation

Preterm birth, defined as any birth before 37 weeks of completed gestation, poses adverse health risks to both mothers and infants. Despite preterm birth being associated with several risk factors, its relationship to maternal metabolism remains unclear, especially in first-time mothers. Aims of the present study were to identify maternal metabolic disruptions associated with preterm birth and to evaluate their predictive potentials. Blood was collected, and the serum harvested from the mothers of 24 preterm and 42 term births at 28-32 weeks gestation (onset of the 3rd trimester). Serum samples were assayed by untargeted metabolomic analyses via liquid chromatography/mass spectrometry (QTOF-LC/MS). Metabolites were annotated by inputting the observed mass-to-charge ratio into the Human Metabolome Database (HMDB). Analysis of 181 identified metabolites by PLS-DA modeling using SIMCA (v17) showed reasonable separation between the two groups (CV-ANOVA, p = 0.02). Further statistical analysis revealed lower serum levels of various acyl carnitines and amino acid metabolites in preterm mothers. Butenylcarnitine (C4:1), a short-chain acylcarnitine, was found to be the most predictive of preterm birth (AUROC = 0.73, [CI] 0.60-0.86). These observations, in conjuncture with past literature, reveal disruptions in fatty acid oxidation and energy metabolism in preterm primigravida. While these findings require validation, they reflect altered metabolic pathways that may be predictive of preterm delivery in primigravida.

Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study

OBJECTIVES

To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites.

DESIGN

Case-cohort design within a prospective cohort study.

SETTING

Cambridge, UK.

POPULATION OR SAMPLE

A total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB.

METHODS

An untargeted metabolomic analysis of maternal serum samples at 12, 20, 28 and 36 weeks of gestation was performed. We applied six supervised machine learning methods and a weighted Cox model to measurements at 28 weeks of gestation and sPTB, followed by feature selection. We used logistic regression with elastic net penalty, followed by best subset selection, to reduce the number of predictive metabolites further. We applied coefficients from the chosen models to measurements from different gestational ages to predict sPTB and sETB.

MAIN OUTCOME MEASURES

sPTB and sETB.

RESULTS

We identified 47 metabolites, mostly lipids, as important predictors of sPTB by two or more methods and 22 were identified by three or more methods. The best 4-predictor model had an optimism-corrected area under the receiver operating characteristics curve (AUC) of 0.703 at 28 weeks of gestation. The model also predicted sPTB in 12-week samples (0.606, 95% CI 0.544-0.667) and 20-week samples (0.657, 95% CI 0.597-0.717) and it predicted sETB in 36-week samples (0.727, 95% CI 0.606-0.849). A lysolipid, 1-palmitoleoyl-GPE (16:1)*, was the strongest predictor of sPTB at 12 weeks of gestation (0.609, 95% CI 0.548-0.670), 20 weeks (0.630, 95% CI 0.569-0.690) and 28 weeks (0.660, 95% CI 0.599-0.722), and of sETB at 36 weeks (0.739, 95% CI 0.618-0.860).

CONCLUSIONS

We identified and internally validated maternal serum metabolites predictive of sPTB. A lysolipid, 1-palmitoleoyl-GPE (16:1)*, is a novel predictor of sPTB and sETB. Further validation in external populations is required.

Dynamic changes in blood lipid levels and their associations with hypertensive disorders of pregnancy in twin pregnancy: A retrospective study

BACKGROUND

Little knowledge on the association of blood lipid levels with hypertensive disorders of pregnancy (HDP) in twin pregnancy.

OBJECTIVE

To investigate the association of blood lipid levels with HDP in twin pregnancy.

METHODS

This is a retrospective study in the Beijing Birth Cohort on patients followed between January 2014 and November 2021. A total of 2628 women pregnant with twins were included and divided into HDP (n = 565) and normal blood pressure (NBP, n = 2063) groups. HDP subtypes included gestational hypertension (GH, n = 190) and preeclampsia (PE, n = 375). Dynamic changes in blood lipid profiles and their associations with HDP were assessed.

RESULTS

Compared to NBP group, higher triglyceride (TG) and low-density lipoprotein  cholesterol (LDL-c) in the first (T1) and second trimesters (T2) existed in women with PE. In addition, TG increased significantly from T1 to T2, and high-density lipoprotein cholesterol (HDL-c) decreased significantly since T2 in women with PE, especially in women with early-onset PE and severe PE. Elevated TG and LDL-c were associated with HDP, mainly PE. In a subgroup analysis, higher TG or LDL-c increased the risk of HDP for underweight, overweight and primipara women.

CONCLUSIONS

In twin pregnancy, women with PE had higher TG and LDL-c, and elevated TG and LDL-c were associated with PE. A significant increase in TG or decrease in HDL-c were more prone to PE, especially early-onset PE and severe PE. It is helpful to monitor blood lipid levels in women pregnant with twins, especially in underweight, overweight, and primipara women.

Statistical methods and resources for biomarker discovery using metabolomics

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

Bioactive lipid mediators in plasma are predictors of preeclampsia irrespective of aspirin therapy

There are few early biomarkers to identify pregnancies at risk of preeclampsia (PE) and abnormal placental function. In this cross-sectional study, we utilized targeted ultra-performance liquid chromatography-ESI MS/MS and a linear regression model to identify specific bioactive lipids that serve as early predictors of PE. Plasma samples were collected from 57 pregnant women prior to 24-weeks of gestation with outcomes of either PE (n = 26) or uncomplicated term pregnancies (n = 31), and the profiles of eicosanoids and sphingolipids were evaluated. Significant differences were revealed in the eicosanoid, (±)11,12 DHET, as well as multiple classes of sphingolipids; ceramides, ceramide-1-phosphate, sphingomyelin, and monohexosylceramides; all of which were associated with the subsequent development of PE regardless of aspirin therapy. Profiles of these bioactive lipids were found to vary based on self-designated race. Additional analyses demonstrated that PE patients can be stratified based on the lipid profile as to PE with a preterm birth linked to significant differences in the levels of 12-HETE, 15-HETE, and resolvin D1. Furthermore, subjects referred to a high-risk OB/GYN clinic had higher levels of 20-HETE, arachidonic acid, and Resolvin D1 versus subjects recruited from a routine, general OB/GYN clinic. Overall, this study shows that quantitative changes in plasma bioactive lipids detected by ultra-performance liquid chromatography-ESI-MS/MS can serve as an early predictor of PE and stratify pregnant people for PE type and risk.

Clinical Value of Serum SIRT1 Combined with Uterine Hemodynamics in Predicting Disease Severity and Fetal Growth Restriction in Preeclampsia

Objective

The sirtuin regulator 1-related enzyme (SIRT1) has been shown to play an important role in various pathophysiological processes. Our aim was to investigate the effect and correlation of serum SIRT1 combined with uterine hemodynamic parameters on disease severity and fetal uterine growth restriction in the progression of preeclampsia and to evaluate its clinical value as a potential marker.

Methods

A total of 100 patients with preeclampsia who were hospitalized in Qufu Normal University Hospital from June 2017 to June 2021 were selected as the research objects. According to the severity, they were divided into the mild (62 cases) and severe groups (38 cases), and according to whether the fetal growth restriction was combined or not, they were divided into the combined fetal growth restriction group (56 cases) and the uncomplicated fetal growth restriction group (44 cases). Serum SIRT1 expression and uterine artery hemodynamic parameters were detected, and Spearman analysis was used to evaluate the association of serum SIRT1 expression and uterine artery hemodynamic parameters (the peak-to-trough ratio of arterial blood velocity, the pulsatility index, and the resistance index) with disease severity (systolic blood pressure, diastolic blood pressure, and random urinary protein levels) and fetal growth restriction (femoral length, biparietal diameter, head circumference, and neonatal weight); unsupervised principal component analysis (PCA), supervised partial least-squares discrimination analysis (PLS-DA), cluster heat map analysis, the receiver operating characteristic (ROC) curve, and the area under curve (AUC) were used to evaluate the diagnostic value of serum SIRT1 expression combined with uterine artery hemodynamic parameters in the severity of disease and fetal growth restriction in patients with preeclampsia.

Results

Compared with patients with mild preeclampsia, serum SIRT1 expression was lower in patients with severe preeclampsia (p < 0.0001), the arterial blood flow velocity peak-to-trough ratio, pulsatility index, and resistance index were higher (p < 0.001; p < 0.0001); and serum SIRT1 expression and uterine artery hemodynamic parameters were closely related to disease severity (p < 0.001; p < 0.0001). In addition, the expression of serum SIRT1 in patients with preeclampsia combined with fetal growth restriction was lower than patients without preeclampsia (p < 0.0001); the peak-to-trough ratio of arterial blood flow velocity, the pulsatility index, and the resistance index were higher (p < 0.0001); and serum SIRT1 expression and uterine artery hemodynamics were closely related to fetal growth restriction (p < 0.0001). Unsupervised PCA analysis and supervised PLS-DA analysis showed that patients with different severity of disease and patients with or without fetal growth restriction were similar within the groups, and there were significant differences between the groups; cluster heat map analysis showed that the mild and severe groups were stratified clustering, and the combined fetal growth restriction group and the uncombined group were hierarchically clustered; ROC curve showed that the AUC of serum SIRT1 expression combined with uterine artery hemodynamic parameters was 0.776 in identifying the severity of preeclampsia and 0.956 in identifying the preeclampsia complicated by fetal growth restriction.

Conclusion

Serum SIRT1 combined with uterine hemodynamic parameters in preeclampsia is closely related to disease severity and fetal growth restriction and is expected to become a potential biomarker for early clinical intervention in patients.

Circulating Growth Differentiation Factor 15 and Preeclampsia: A Meta-Analysis

Growth differentiation factor 15 (GDF-15) has been suggested as a potential biomarker of preeclampsia. However, previous studies evaluating circulating GDF-15 in women with preeclampsia showed inconsistent results. A meta-analysis was performed accordingly in this study. Observational studies comparing circulating GDF-15 between women with preeclampsia normal pregnancy were identified by search of electronic databases including PubMed, Embase, Web of Science, Wanfang, and CNKI. The Newcastle-Ottawa Scale (NOS) was used for assessing the quality of the studies. A randomized-effect model incorporating the possible between-study heterogeneity was used to pool the results. Eleven observational studies including 498 women with preeclampsia and 2349 women with normal pregnancy contributed to the meta-analysis. Results showed that compared to controls of women with normal pregnancy at least matched for gestational ages, women with preeclampsia had significantly higher circulating GDF-15 at the diagnosis [standard mean difference (SMD): 0.66, 95% confidence interval (CI): 0.16 to 1.17, p=0.01, I2=93%]. Subgroup analysis showed consistent results in women with preterm and term preeclampsia in Asian and non-Asian women and in studies with different quality scores, which were not statistically significant between subgroups (p for subgroup difference>0.05). Moreover, a higher level of GDF-15 was also found before the diagnosis in women who subsequently developed preeclampsia than those who did not (SMD: 1.32, 95% CI: 0.45 to 2.18, p=0.003, I2=89%). In conclusion, a higher circulating GDF-15 is observed in women with preeclampsia even before the diagnosis of the disease.

Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia-A Prospective Study

(1) Background: Preeclampsia (PE) prediction in the first trimester of pregnancy is a challenge for clinicians. The aim of this study was to evaluate and compare the predictive performances of machine learning-based models for the prediction of preeclampsia and its subtypes. (2) Methods: This prospective case-control study evaluated pregnancies that occurred in women who attended a tertiary maternity hospital in Romania between November 2019 and September 2022. The patients' clinical and paraclinical characteristics were evaluated in the first trimester and were included in four machine learning-based models: decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), and their predictive performance was assessed. (3) Results: Early-onset PE was best predicted by DT (accuracy: 94.1%) and SVM (accuracy: 91.2%) models, while NB (accuracy: 98.6%) and RF (accuracy: 92.8%) models had the highest performance when used to predict all types of PE. The predictive performance of these models was modest for moderate and severe types of PE, with accuracies ranging from 70.6% and 82.4%. (4) Conclusions: The machine learning-based models could be useful tools for EO-PE prediction and could differentiate patients who will develop PE as early as the first trimester of pregnancy.

First-trimester preterm preeclampsia prediction with metabolite biomarkers: differential prediction according to maternal body mass index

BACKGROUND

Prediction of preeclampsia risk is key to informing effective maternal care. Current screening for preeclampsia at 11 to 13 weeks of gestation using maternal demographic characteristics and medical history with measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor can identify approximately 75% of women who develop preterm preeclampsia with delivery at <37 weeks of gestation. Further improvements to preeclampsia screening tests will likely require integrating additional biomarkers. Recent research suggests the existence of distinct maternal risk profiles. Therefore, biomarker evaluation should account for the possibility that a biomarker only predicts preeclampsia in a specific maternal phenotype.

OBJECTIVE

This study aimed to verify metabolite biomarkers as preterm preeclampsia predictors early in pregnancy in all women and across body mass index groups.

STUDY DESIGN

Observational case-control study drawn from a large prospective study on the early prediction of pregnancy complications in women attending their routine first hospital visit at King's College Hospital, London, United Kingdom, in 2010 to 2015. Pregnant women underwent a complete first-trimester assessment, including the collection of blood samples for biobanking. In 11- to 13-week plasma samples of 2501 singleton pregnancies, the levels of preselected metabolites implicated in the prediction of pregnancy complications were analyzed using a targeted liquid chromatography-mass spectrometry method, yielding high-quality quantification data on 50 metabolites. The ratios of amino acid levels involved in arginine biosynthesis and nitric oxide synthase pathways were added to the list of biomarkers. Placental growth factor and pregnancy-associated plasma protein A were also available for all study subjects, serving as comparator risk predictors. Data on 1635 control and 106 pregnancies complicated by preterm preeclampsia were considered for this analysis, normalized using multiples of medians. Prediction analyses were performed across the following patient strata: all subjects and the body mass index classes of <25, 25 to <30, and ≥30 kg/m². Adjusted median levels were compared between cases and controls and between each body mass index class group. Odds ratios and 95% confidence intervals were calculated at the mean ±1 standard deviation to gauge clinical prediction merits.

RESULTS

The levels of 13 metabolites were associated with preterm preeclampsia in the entire study population (P<.05) with particularly significant (P<.01) associations found for 6 of them, namely, 2-hydroxy-(2/3)-methylbutyric acid, 25-hydroxyvitamin D3, 2-hydroxybutyric acid, alanine, dodecanoylcarnitine, and 1-(1Z-octadecenyl)-2-oleoyl-sn-glycero-3-phosphocholine. Fold changes in 7 amino acid ratios, all involving glutamine or ornithine, were also significantly different between cases and controls (P<.01). The predictive performance of some metabolites and ratios differed according to body mass index classification; for example, ornithine (P<.001) and several ornithine-related ratios (P<.0001 to P<.01) were only strongly associated with preterm preeclampsia in the body mass index of <25 kg/m² group, whereas dodecanoylcarnitine and 3 glutamine ratios were particularly predictive in the body mass index of ≥30 kg/m² group (P<.01).

CONCLUSION

Single metabolites and ratios of amino acids related to arginine bioavailability and nitric oxide synthase pathways were associated with preterm preeclampsia risk at 11 to 13 weeks of gestation. Differential prediction was observed according to body mass index classes, supporting the existence of distinct maternal risk profiles. Future studies in preeclampsia prediction should account for the possibility of different maternal risk profiles to improve etiologic and prognostic understanding and, ultimately, clinical utility of screening tests.

Targeted Metabolomics Revealed a Sex-Dependent Signature for Metabolic Syndrome in the Mexican Population

Metabolic syndrome (MetS) is a group of several metabolic conditions predisposing to chronic diseases. Individuals diagnosed with MetS are physiologically heterogeneous, with significant sex-specific differences. Therefore, we aimed to investigate the potential sex-specific serum modifications of amino acids and acylcarnitines (ACs) and their relationship with MetS in the Mexican population. This study included 602 participants from the Health Workers Cohort Study. Forty serum metabolites were analyzed using a targeted metabolomics approach. Multivariate regression models were used to test associations of clinical and biochemical parameters with metabolomic profiles. Our findings showed a serum amino acid signature (citrulline and glycine) and medium-chain ACs (AC14:1, AC10, and AC18:10H) associated with MetS. Glycine and AC10 were specific metabolites representative of discrimination according to sex-dependent MetS. In addition, we found that glycine and short-chain ACs (AC2, AC3, and AC8:1) are associated with age-dependent MetS. We also reported a significant correlation between body fat and metabolites associated with sex-age-dependent MetS. In conclusion, the metabolic profile varies by MetS status, and these differences are sex-age-dependent in the Mexican population.

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

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Endothelial dysfunction misleads blood marker discovery by differential expression.

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Blood-derived surrogate transcriptome of target-tissue avoids the false discovery.

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ITGA5 implies polymicrobial infection of maternal-fetal interface in preeclampsia.

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ITGA5 and IRF6 implies viral co-infection in early-onset preeclampsia.

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ITGA5, IRF6, and P2RX7 differ imminent preeclampsia from COVID-19 infection.

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 tissues was determined by that in maternal blood, utilizing four datasets (n = 1354) 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 tissues. We selected the best predictive model by the area under the receiver operating characteristic (AUROC) using a dataset for developing the model, and well-replicated in datasets both with and without an intervention. To identify eligible blood biomarkers that predicted any-onset preeclampsia from the datasets but that were not positive in the COVID-19 dataset (n = 47), we compared several methods of predictor discovery: (1) the best prediction model; (2) gene sets of 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, multiomics 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-α5 (ITGA5), interferon regulatory factor-6 (IRF6), and P2X purinoreceptor-7 (P2RX7) from the prediction model, which was the only method that significantly discovered 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 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 C). Greater proportions of predicted events among LOPE were cluster A (82.85 % vs 70.53 %) compared to early-onset preeclampsia (EOPE). The biological relevance by multiomics 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 genes in maternal blood that were not extremely expressed, including the proposed blood biomarkers. The predictive performance and biological relevance should be validated in future experiments.