Chorioamnionitis alters lung surfactant lipidome in newborns with respiratory distress syndrome

Giambelluca, Sonia; Verlato, Giovanna; Simonato, Manuela; Vedovelli, Luca; Bonadies, Luca; Najdekr, Lukáš; Dunn, Warwick B.; Carnielli, Virgilio P.; Cogo, Paola

doi:10.1038/s41390-021-01371-3

Cited by 10 publications

(11 citation statements)

References 37 publications

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“…In a study of preterm infants with RDS, exposure to severe chorioamnionitis led to a poor response to surfactant, 96 possibly by altering lung surfactant lipid profiles. 97 A meta-analysis of 244,000 infants concluded that chorioamnionitis increased the risk of BPD. 84 However, reports from the Canadian Neonatal Network and by Laughon et al found no association of BPD with chorioamnionitis.…”

Section: Introductionmentioning

confidence: 99%

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Chorioamnionitis and neonatal outcomes

et al. 2021

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Chorioamnionitis or intra-uterine inflammation is a frequent cause of preterm birth. Chorioamnionitis can affect almost every organ of the developing fetus. Multiple microbes have been implicated to cause chorioamnionitis, but “sterile” inflammation appears to be more common. Eradication of micro-organisms has not been shown to prevent the morbidity and mortality associated with chorioamnionitis as inflammatory mediators account for continued fetal and maternal injury. Mounting evidence now supports the concept that the ensuing neonatal immune dysfunction reflects the effects of inflammation on immune programming during critical developmental windows, leading to chronic inflammatory disorders as well as vulnerability to infection after birth. A better understanding of microbiome alterations and inflammatory dysregulation may help develop better treatment strategies for infants born to mothers with chorioamnionitis.

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Section: Introductionmentioning

confidence: 99%

“…In a study of preterm infants with RDS, exposure to severe chorioamnionitis led to a poor response to surfactant, 96 possibly by altering lung surfactant lipid profiles. 97 …”

Section: Introductionmentioning

confidence: 99%

Chorioamnionitis and neonatal outcomes

et al. 2021

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“…Naive Bayes (NB) is a method that searches to describe the probability of occurrence of an event. Taking into account the Bayes theorem [37], the probability of occurrence of an event A is sought; since event B has already occurred, it is also known as conditional probability, and it is exemplified in the following Equation (6). Thus, the probability is determined of a sample belonging to one class or another.…”

Section: Classification Methodsmentioning

confidence: 99%

“…Lipidomics and metabolomics have been described in the literature as optimal approaches to find differences in a particular physiological state. For example, a study of the metabolic profile in urine of preterm newborns of mothers with and without chorioamnioitis was carried out, where they found differences between the metabolites that were able to distinguish those born to mothers who suffered from this pathology or not [ 6 ]. Other authors [ 7 ] have worked with the metabolic profile in postmortem brains and identified changes in the urea as well as changes in carbohydrate and protein metabolism.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model Based on Lipidomic Profile Information to Predict Sudden Infant Death Syndrome

Villagrana-Bañuelos

Galván-Tejada

et al. 2022

Healthcare

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Sudden infant death syndrome (SIDS) represents the leading cause of death in under one year of age in developing countries. Even in our century, its etiology is not clear, and there is no biomarker that is discriminative enough to predict the risk of suffering from it. Therefore, in this work, taking a public dataset on the lipidomic profile of babies who died from this syndrome compared to a control group, a univariate analysis was performed using the Mann–Whitney U test, with the aim of identifying the characteristics that enable discriminating between both groups. Those characteristics with a p-value less than or equal to 0.05 were taken; once these characteristics were obtained, classification models were implemented (random forests (RF), logistic regression (LR), support vector machine (SVM) and naive Bayes (NB)). We used seventy percent of the data for model training, subjecting it to a cross-validation (k = 5) and later submitting to validation in a blind test with 30% of the remaining data, which allows simulating the scenario in real life—that is, with an unknown population for the model. The model with the best performance was RF, since in the blind test, it obtained an AUC of 0.9, specificity of 1, and sensitivity of 0.8. The proposed model provides the basis for the construction of a SIDS risk prediction computer tool, which will contribute to prevention, and proposes lines of research to deal with this pathology.

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“…In neonates who were born at <30 weeks of gestation, with or without exposure to maternal chorioamnionitis and had RDS, tracheal aspirates collected within the first 24 h of life were studied using LC-MS-based untargeted lipidomics. 72 This study described changes in the lipidomic fingerprints in the fluid of the lung epithelial lining collected from preterm neonates with RDS who had exposures to chorioamnionitis compared with neonates who did not have a clinical presentation of RDS. 72 The authors suggested that the identified tracheal aspirate lipid mediators (Table 4 ) in neonates with RDS exposed to chorioamnionitis could be associated with later adverse respiratory outcomes.…”

Section: Omics and Machine-learning Approaches To Common Sustained In...mentioning

confidence: 99%

Omics approaches: interactions at the maternal–fetal interface and origins of child health and disease

et al. 2022

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Immunoperinatology is an emerging field. Transdisciplinary efforts by physicians, physician‐scientists, basic science researchers, and computational biologists have made substantial advancements by identifying unique immunologic signatures of specific diseases, discovering innovative preventative or treatment strategies, and establishing foundations for individualized neonatal intensive care of the most vulnerable neonates. In this review, we summarize the immunobiology and immunopathology of pregnancy, highlight omics approaches to study the maternal–fetal interface, and their contributions to pregnancy health. We examined the importance of transdisciplinary, multiomic (such as genomics, transcriptomics, proteomics, metabolomics, and immunomics) and machine-learning strategies in unraveling the mechanisms of adverse pregnancy, neonatal, and childhood outcomes and how they can guide the development of novel therapies to improve maternal and neonatal health. Impact Discuss immunoperinatology research from the lens of omics and machine-learning approaches. Identify opportunities for omics-based approaches to delineate infection/inflammation-associated maternal, neonatal, and later life adverse outcomes (e.g., histologic chorioamnionitis [HCA]).

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Chorioamnionitis alters lung surfactant lipidome in newborns with respiratory distress syndrome

Cited by 10 publications

References 37 publications

Chorioamnionitis and neonatal outcomes

Chorioamnionitis and neonatal outcomes

Machine Learning Model Based on Lipidomic Profile Information to Predict Sudden Infant Death Syndrome

Omics approaches: interactions at the maternal–fetal interface and origins of child health and disease

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