ArticleRespiratory research2023
Leveraging transcriptomics to develop bronchopulmonary dysplasia endotypes: a concept paper.
Article in Respiratory research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Urine Metabolomics to Predict Bronchopulmonary Dysplasia: A Single-Center Prospective Study.Pediatric pulmonology · 2026Article
- Advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infants.Frontiers in pediatrics · 2025Review
- Effect Analysis of Extracellular Vesicles in the Treatment of Bronchopulmonary Dysplasia via Different Drug Delivery and Administration Routes.International journal of nanomedicine · 2025Review
- Bridging the Gap: Exploring Bronchopulmonary Dysplasia through the Lens of Biomedical Informatics.Journal of clinical medicine · 2024Review
- Endotypes of Paediatric Cough-Do They Exist and Finding New Techniques to Improve Clinical Outcomes.Journal of clinical medicine · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
impactBronchopulmonary dysplasia has multiple definitions that are currently based on phenotypic characteristics. Using an unsupervised machine learning approach, we created BPD subclasses (e.g., endotypes) by clustering whole microarray data. T helper 17 cell differentiation was the most significant pathway differentiating the BPD endotypes.
introductionBronchopulmonary dysplasia (BPD) is the most common complication of extreme prematurity. Discovery of BPD endotypes in an unbiased format, derived from the peripheral blood transcriptome, may uncover patterns underpinning this complex lung disease.
methodsAn unsupervised agglomerative hierarchical clustering approach applied to genome-wide expression of profiling from 62 children at day of life five was used to identify BPD endotypes. To identify which genes were differentially expressed across the BPD endotypes, we formulated a linear model based on least-squares minimization with empirical Bayes statistics.
resultsFour BPD endotypes (A, B,C,D) were identified using 7,319 differentially expressed genes. Across BPD endotypes, 5,850 genes had a p value < 0.05 after multiple comparison testing. Endotype A consisted of neonates with a higher gestational age and birthweight. Endotypes B-D included neonates between 25 and 26 weeks and a birthweight range of 640 to 940 g. Endotype D appeared to have a protective role against BPD compared to Endotypes B and C (36% vs. 62% vs. 60%, respectively). The most significant pathway focused on T helper 17 cell differentiation.
conclusionBioinformatic analyses can help identify BPD endotypes that associate with clinical definitions of BPD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.