Mapping the evolving landscape: a bibliometric analysis of macrophages research in bronchopulmonary dysplasia (1990–2025)—a Web of Science Core Collection-based study

Introduction

Bronchopulmonary dysplasia (BPD) is a chronic lung disease that primarily affects preterm infants, especially those with extremely low birth weight (1). Despite advances in neonatal respiratory care, BPD remains difficult to prevent and treat, reflecting an incomplete understanding of its multifactorial pathogenesis, which involves disrupted alveolar and vascular development and sustained inflammation (2-4). Within this framework, macrophages have emerged as key regulators of injury, repair, and developmental remodeling in the immature lung (5,6).

In recent years, technological and conceptual advances—including refined animal models and high-dimensional profiling such as single-cell transcriptomics—have accelerated macrophage-focused BPD research and expanded the field across molecular mechanisms, immunophenotyping, and translational applications (5,7). While this growth has produced a large and diverse literature, it has also led to dispersion across disciplines and journals, making it challenging to delineate the field’s major thematic pillars, identify how research priorities have shifted over time, and understand which countries, institutions, and author groups function as collaboration hubs that drive translation.

Traditional narrative reviews are well suited to synthesizing mechanistic findings and providing expert interpretation, but they are inherently selective and are not designed to quantitatively map the “intellectual structure” of a research field (8). In contrast, bibliometric analysis applies quantitative methods to publication metadata to characterize research output, knowledge clusters, co-occurrence patterns, and collaboration networks at scale (9). This approach can answer questions that narrative reviews typically cannot, such as how thematic clusters connect to each other, which topics are emerging (or fading) based on burst dynamics, and whether the collaboration architecture of the field supports cross-disciplinary and international translation. For macrophage-related BPD research—where progress depends on integrating developmental biology, immunology, omics, and neonatal clinical studies—understanding these structural features may help identify under-connected areas and opportunities to strengthen bench-to-bedside pathways.

Bibliometric analysis provides an effective means to map knowledge structure, collaboration patterns, and thematic trends in scientific research (10). Therefore, in this study, we performed a bibliometric analysis of macrophage-related BPD research based on the Web of Science Core Collection (WoSCC) (1990–2025), aiming to quantify publication and citation trends, map country-, institution-, and author-level collaboration networks, and identify evolving thematic hotspots and emerging topics. We present this article in accordance with the BIBLIO reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2026-0236/rc).

Methods

Search strategies and data collection

A comprehensive literature search was conducted in the WoSCC database. The search formula was as follows (11): TS=(Bronchopulmonary Dysplasia OR Dysplasia, Bronchopulmonary OR Ventilator-Induced Lung Injuries OR Ventilator Induced Lung Injury) AND TS=(Macrophage* OR Macrophagocyte*). The WoSCC was selected because it provides standardized citation indexing and complete bibliographic records (e.g., cited references) that are required by common bibliometric workflows (e.g., co-citation, bibliographic coupling, and collaboration network analyses). We recognize that PubMed and Scopus index additional biomedical content; however, differences in indexing rules and export formats may introduce duplicate handling and citation-metadata inconsistencies across sources. Therefore, to ensure a single, reproducible citation dataset, we used WoSCC as the primary database and interpreted findings as representing the WoSCC-indexed literature.

Inclusion criteria were: (I) original research articles; (II) published in English; (III) publication period from January 1990 to December 2025; and (IV) indexed in WoSCC. Exclusion criteria included reviews, editorials, meeting abstracts, and non-original articles. Literature retrieval was conducted on a single day (2025-12-05) to ensure consistency and avoid database updates. Two authors independently performed data export and screening based on document type and language filters, and discrepancies were resolved by discussion. Extracted data included publication and citation counts, titles, author and institutional information, countries/regions, keywords, and journal details for bibliometric analysis. The full records and cited references were exported from WoSCC in plain-text format and imported into the analysis software for cleaning and de-duplication within WoSCC exports (e.g., harmonizing author and institution name variants).

Statistical analysis

To conduct a comprehensive bibliometric analysis, a combination of advanced analytical tools was utilized: R package “bibliometrix” (v.4.3.3), VOSviewer (v.1.6.20), and CiteSpace (v.6.1.R3). This multi-tool approach enabled thorough statistical analysis and visualization of the literature landscape. Specifically, R package “bibliometrix” was employed for its capabilities in document analysis, quantifying publications and citations, and mapping international collaborations, thereby providing a macroscopic view of research trends. CiteSpace was instrumental in constructing keyword timelines to analyze the frequency and emergence of terms over time, offering insights into evolving research foci. This tool proved particularly valuable for identifying pivotal points in the literature and highlighting influential studies (12). Meanwhile, VOSviewer excelled in handling large-scale data and was used to perform network analyses. This included examining collaborative patterns among authors and institutions, which was essential for understanding the structure and dynamics of the research community. The graphical capabilities of VOSviewer enabled us to visualize complex networks clearly, facilitating the identification of key players and collaborative hubs in the field (13). Network maps were generated using default settings unless otherwise specified. For keyword analyses, author keywords and Keywords Plus were merged after standardization (e.g., singular/plural forms and synonymous terms), and a minimum occurrence threshold was applied as described in the corresponding figure legends. In CiteSpace, burst detection was used to identify rapidly increasing keyword usage over time, reflecting emerging research interests.

Citation impact was evaluated using the latest available journal impact factor (IF), Journal Citation Reports (JCR), as well as bibliometric indexes including the h-index, g-index, and m-index. To improve transparency for readers, definitions and interpretive notes for the bibliometric indicators used in this study are summarized in Table S1 (14-16).

Results

An overview of publications

A comprehensive search of the WoSCC database yielded 641 initial records published between January 1, 1990 and December 5, 2025 (Figure 1). After excluding reviews (n=32), editorial material (n=1), meeting abstracts (n=17), and other non-original articles (n=4), 587 original research articles were included in the final bibliometric analysis.

Figure 1 Flowchart of the literature screening process.

The annual publication output has grown steadily, with fewer than 5 articles per year before 1995, followed by a notable rise thereafter. This growth can be divided into two phases: a gradual increase (1990–2000), and a rapid, fluctuating expansion from 2001 onwards, peaking at 30 articles in 2020. Since 1990, the publication trend has fit a linear growth model (y = 0.6907x + 3.527, R²=0.7692) (Figure 2).

Figure 2 Annual number of publications.

Analysis of the countries

The majority of the literature originated from authors in the USA, China, and Germany, contributing 37.0%, 21.0%, and 7.7% of the total publications (TP), respectively. Collectively, these three countries accounted for nearly two-thirds of all reports. Additionally, studies from Ireland, Switzerland, and the Netherlands exhibited the highest average citations per article, with values of 79.0, 68.2, and 54.9, respectively. Researchers from 19 countries contributed to this research area. The top three countries by publication productivity, based on corresponding authors, were the USA [217], China [123], and Germany [45] (Figure 3A). Among the top productive nations, the Netherlands (77.3%) led in terms of multiple country publications (MCP) ratio. Notably, the USA also ranked first in articles [777] and total citations (TC =10,992), followed by China (articles =428, TC =2,049) and Germany (articles =192, TC =1,878) (Table 1). Among the 20 countries involved in international collaborations with a minimum of 3 articles, the USA had the highest number of collaborations with other countries [92], followed by the Netherlands [35] and Germany [34] (Figure 3B).

Figure 3 Analysis of countries. (A) Distribution of corresponding author’s publications by country. (B) Visualization map depicting the collaboration among different countries. MCP, multiple country publications; SCP, single country publications.

Analysis of the institutions

The top ten institutions, ranked by article count, were predominantly located in North America, with the University of Toronto (104 articles), Harvard University (78 articles), and Harvard University Medical Affiliates (52 articles) leading the list (Figure 4A). Among the 141 institutions involved in international collaborations with a minimum of three articles, National Yang-Ming University had the highest number of collaborations with other institutions (total link strength =46), followed by the University of Toronto [39] and Harvard University [37] (Figure 4B).

Figure 4 Analysis of institutions. (A) Top ten institutions by article count and rank. (B) Visualization map depicting the collaboration among different institutions.

Analysis of journals

Among a total of 469 journals, American Journal of Physiology-Lung Cellular and Molecular Physiology was identified as the most productive and influential journal, with the highest h-index [37], the greatest number of TP [70], and the most total citations (TC =1,602). Other prominent journals included Pediatric Research (h-index =25, TP =48, TC =873) and American Journal of Respiratory and Critical Care Medicine (h-index =26, TP =31, TC =1,477). Notably, American Journal of Respiratory and Critical Care Medicine possesses the highest impact factor among the top journals (IF =19.4), followed by Anesthesiology (IF =9.1) and Free Radical Biology and Medicine (IF =8.2) (Table 2).

A co-occurrence network analysis was performed on 42 journals with at least three occurrences. The three key journals with the highest total link strength in the co-occurrence network were American Journal of Physiology-Lung Cellular and Molecular Physiology [288], Pediatric Research [180], and American Journal of Respiratory and Critical Care Medicine [162], highlighting their central roles in scholarly communication within this topic (Figure 5A). Additionally, a coupling network was established for the same 42 journals, based on the frequency of shared references. The three leading journals by total link strength in the coupling network were American Journal of Physiology-Lung Cellular and Molecular Physiology [8,148], Pediatric Research [4,128], and American Journal of Respiratory and Critical Care Medicine [4,111] (Figure 5B).

Figure 5 Visualization map depicting the co-occurrence networks of journals and coupling networks. (A) The co-occurrence networks of journals. (B) The coupling networks of journals.

Analysis of the authors

A total of 233 authors contributed to this research area, with their publication and citation profiles summarized in Table 3. The most productive author was Li, Li-Fu, who published the most articles (11 publications, TP rank 1), and also demonstrated a strong academic influence with an H-index of 10 and a g-index of 11. Jankov Robert P. also exhibited notable productivity (10 publications, TP rank 2), with an H-index of 10 and a total of 373 citations (TC rank 6). Kantores Crystal ranked third in terms of publications (8 articles) and showed extensive collaborative activity, having the highest number of collaborations with other authors [40], followed by Jankov Robert P. [40] and Li Li-Fu [38]. In terms of academic impact, Kramer Boris W. stood out with the third highest total citations [424] and an h-index of 6, while Job Alan H. also had a high citation count [419]. Among the 233 authors involved in international collaborations with a minimum of 3 articles, Kantores Crystal has the highest total link strength [40], followed by Jankov, Robert P. [40] and Li, Li-Fu [38] (Figure 6).

Figure 6 Visualization map depicting the collaboration among different authors.

Analysis of the keywords

Keyword co-existence network analysis

A comprehensive keyword co-occurrence analysis was performed to reveal the research hotspots and thematic evolution within this field (Figure 7A). Five major clusters were identified, each representing a distinct research theme: cluster 1 (red): cell death and inflammatory mechanisms. This cluster encompasses keywords related to cell activation, apoptosis, cell death, cytokine expression, and inflammatory responses. It highlights studies focusing on the cellular and molecular mechanisms underlying lung injury and inflammation, with terms such as “activation”, “apoptosis”, “expression”, “oxidative stress”, and “mechanical ventilation”. Cluster 2 (green): animal models and pathogenesis. This cluster centers on experimental research using animal models, especially relating to BPD and pulmonary hypertension. Representative keywords include “bronchopulmonary dysplasia”, “animal models”, “angiogenesis”, “macrophages”, “chronic lung disease”, and “hyperoxia”. Cluster 3 (blue): growth, development, and gene expression. The third cluster highlights themes of growth, gene expression, developmental biology, and oxygen exposure, focusing on mechanisms such as “growth”, “gene expression”, “hyperoxia”, “VEGF”, and “exposure”. Cluster 4 (yellow): acute lung injury and immunity. This cluster is characterized by keywords related to acute lung injury, immune responses, and host defense, including “acute lung injury”, “innate immunity”, “endothelial cells”, “host-defense”, and “phagocytosis”. Cluster 5 (purple): clinical features and inflammatory factors. This cluster assembles terms regarding clinical manifestations and inflammatory mediators in neonatal lung disease, such as “chronic lung disease”, “colony-stimulating factor”, “hyaline-membrane disease”, “premature infants”, and “tumor necrosis factor”. These clusters were interconnected rather than independent. Overlay and burst analyses indicate that early studies focused on clinical terms and single cytokines (e.g., tumor necrosis factor, chronic lung disease, hyaline membrane disease), whereas later work increasingly linked inflammation to signaling pathways (such as NF-κB), gene expression programs, developmental processes [growth/angiogenesis/vascular endothelial growth factor (VEGF)], and vascular complications (pulmonary hypertension). Overall, the field has shifted from describing macrophage-related cytokines as markers of injury to exploring macrophage-driven gene regulation and developmental remodeling as potential therapeutic targets.

Figure 7 Analysis of keywords. (A) Keyword co-occurrence network showing main thematic clusters. (B) Temporal evolution of keyword focus. (C) Keyword burst analysis highlighting emerging topics.

The temporal overlay visualization (Figure 7B) demonstrates the dynamic evolution of research topics. Early research (yellow nodes) concentrated on fundamental mechanisms such as “mechanical ventilation”, “activation”, and “cytokines”. In contrast, more recent research (blue/green nodes) has shifted towards “bronchopulmonary dysplasia”, “pulmonary hypertension”, “growth”, and “gene expression”, indicating an increasing focus on developmental biology and long-term outcomes in neonatal lung disease.

Analysis of burst keywords

The burst detection analysis (Figure 7C) identifies keywords with strong citation bursts, reflecting periods of intense research interest. Early bursts (1994–2006) were observed for terms such as “tumor necrosis factor” (1994–2003), “chronic lung disease” (1996–2006), and “hyaline membrane disease” (1996–2006), indicating early focus on inflammatory mediators and clinical features. Mid-period (2007–2015) bursts included “apoptosis” (2007–2009), “responses” (2009–2012), and “respiratory distress syndrome” (2011–2012). In recent years, the field has shifted towards molecular and cellular mechanisms, as reflected by bursts for “mice” (2013–2023), “gene expression” (2013–2015), and “growth” (2016–2025). The most recent and ongoing (2016–2025) bursts highlight emerging hotspots, including “nf kappa b” (2016–2025), “cells” (2018–2025), “pulmonary hypertension” (2018–2025), “inflammation” (2021–2025), and “bronchopulmonary dysplasia” (2023–2025). These burst sequences support a thematic transition from “single-mediator inflammation” to “pathway- and program-level regulation” (e.g., NF-κB-linked immune signaling and gene expression), alongside increasing attention to vascular comorbidity (pulmonary hypertension) and developmental endpoints.

Discussion

Our bibliometric analysis provides a comprehensive overview of research trends, collaboration networks, and thematic evolution in the field of BPD and related neonatal lung diseases over the past two decades. Importantly, the observed patterns are not only a “map” of keywords and publications but also a reflection of clinical and methodological constraints in neonatology: limited access to human lung tissue, ethical barriers to repeated invasive sampling, and the need for experimentally tractable systems. These realities help explain why mechanistic pathway terms, animal models, and multi-omics themes increasingly structure the field, and why translation often depends on cross-cohort validation and clinically aligned endpoints.

The USA and China dominate global output, with American institutions such as the University of Pennsylvania and Harvard Medical School leading both in productivity and international collaboration. These research advantages are rooted not only in substantial governmental and private funding but also in the existence of large-scale multicenter clinical cohorts and translational research platforms that facilitate rapid bench-to-bedside translation (17). In contrast, Chinese institutions have shown notable growth, often leveraging national policy support and increased investment in neonatal intensive care infrastructure, though their international collaboration rates are relatively lower (18). Notably, European institutions such as Erasmus University Medical Center have made significant contributions to multi-omics and personalized medicine approaches, reflecting divergent research emphases shaped by clinical resources and regulatory environments (19).

Beyond these descriptive differences, our network patterns likely reflect structural factors that influence collaboration. For example, rapid increases in publication volume can be driven by expanded neonatal intensive care unit (NICU) capacity and policy-supported research investment, whereas lower international collaboration may be related to practical barriers such as differences in neonatal data governance/ethics review processes, heterogeneity in case definitions and follow-up practices, language and dissemination norms, and incentive structures that prioritize domestic authorship leadership. These factors are especially important for BPD because biomarker and multi-omics findings require replication across diverse populations and care protocols to be clinically generalizable (18,19).

Journal analysis reveals that high-impact journals such as The Journal of Pediatrics and Pediatric Research are not only prolific in this field but also shape its direction by prioritizing mechanistic studies, translational research, and large-scale clinical trials. These journals maintain rigorous peer-review standards and favor manuscripts with robust methodological designs and translational potential, as evidenced by their high citation rates and central positions in journal coupling and co-citation networks (20). For authors, our network analysis demonstrates that leading researchers such as Li Li-Fu and Jankov Robert P. serve as key connectors within the collaboration network, often bridging basic and clinical research teams. High H-indices and extensive collaboration networks are associated with both innovative research output and sustained academic influence. Previous studies have highlighted that authors with diverse interdisciplinary collaborations tend to produce more innovative and highly cited work in neonatal lung research (21). Furthermore, the structural analysis of the co-authorship network indicates a moderate clustering coefficient and high centrality for a handful of authors, underscoring the field’s reliance on a few key opinion leaders.

Critically, this “hub-and-spoke” structure can be a double-edged sword: centralized hubs can accelerate standard-setting and multicenter coordination, but they may also concentrate methodological choices, such as preferred models, endpoints, and platforms, and slow diffusion of alternative hypotheses or region-specific cohorts. Broadening participation through shared protocols and multi-site validation studies would strengthen reproducibility and improve the likelihood that mechanistic discoveries translate into clinically useful interventions (21).

Thematic insights from keyword cluster analysis

Cell death and inflammatory mechanisms

The prominence of cell death and inflammation-related terms arises from persistent efforts to unravel the cellular and molecular events driving BPD and related neonatal lung injuries. The high prevalence of ventilator-induced injury and the central role of inflammation in neonatal outcomes have made these mechanisms a primary research focus. Recent experimental studies have elucidated how pathways such as NF-κB and oxidative stress orchestrate both acute and chronic lung pathology, directly linking external insults to apoptosis and sustained pro-inflammatory signaling (22). Advances in translational science have further encouraged exploration of targeted modulation of these pathways through small molecules or biologics, which attenuate alveolar simplification and fibrosis in animal models—paving the way for early-phase clinical trials. The integration of single-cell transcriptomics has enabled the identification of cell subpopulations uniquely susceptible to inflammatory injury, suggesting that future interventions may need to be cell-type or developmental-stage-specific (23). This thematic shift from single cytokines toward regulated programs suggests the field is increasingly prioritizing causal, testable control points (pathways and transcriptional states) over descriptive biomarkers. However, it also increases the burden of proof: pathway signals must be validated across cohorts and anchored to clinically meaningful endotypes to avoid over-interpreting mechanistic associations (22,23).

Animal models and pathogenesis

The emergence of animal models as a major cluster reflects the need to replicate the multifactorial insults experienced by preterm infants, such as hyperoxia, infection, and mechanical ventilation. Genetically engineered mice and advanced imaging techniques have been central to dissecting basic pathophysiology and testing interventions (24). Animal models are indispensable for defining therapeutic windows, validating drug candidates, and understanding the developmental timing of injury and repair. Multi-omics profiling in these models has revealed novel regulators of pulmonary vascular and alveolar development, including microRNAs and epigenetic modifiers, which are now being validated in human cohorts (25). This translational continuum is further supported by the growing number of stem cell and gene therapy trials based on robust preclinical evidence. The prominence of “mice” also implies reliance on hyperoxia-driven murine paradigms, which—while experimentally powerful—may incompletely capture the immune phenotype and exposure complexity of extremely preterm infants. Differences in immune maturation, macrophage ontogeny, and the clinical mixture of insults (variable ventilation, infection/inflammation, antenatal exposures, and supportive-care practices) can shift inflammatory set-points and treatment responses. This mismatch may partially explain why some interventions show strong effects in preclinical models yet translate variably in heterogeneous clinical settings, reinforcing the need for careful model-to-clinic alignment and human-cohort validation of proposed targets (24,25).

Growth, development, and gene expression

The increasing focus on growth and gene regulation is driven by the field’s shift toward precision medicine and early prevention. Single-cell and spatial transcriptomics technologies have enabled detailed mapping of lung development and identification of key drivers of arrested alveolarization (26). These advances facilitate early biomarker discovery and help predict long-term respiratory morbidity based on perinatal gene expression signatures (27). Integrating genomic with clinical datasets in newborn cohorts has revealed that certain genetic variants and epigenetic modifications confer increased risk for BPD and pulmonary hypertension, providing new avenues for risk stratification and targeted intervention (28). By emphasizing developmental programs (rather than injury markers alone), this cluster aligns with the clinical need to identify infants at highest risk early enough to intervene—before irreversible alveolar and vascular remodeling is established.

Acute lung injury and immunity

The centrality of acute lung injury and immune responses in the clustering reflects ongoing challenges in neonatal intensive care, where immune dysregulation frequently dictates clinical outcomes. Research focus has been shaped by the recognition that both the phenotype and timing of immune cell activation determine the balance between tissue repair and chronic lung disease (5). Studies have further clarified the dual role of macrophages and neutrophils in both injury and repair, as well as the impact of perinatal exposures such as chorioamnionitis and sepsis on immune “training” and subsequent lung response (29,30). This nuanced understanding of immune plasticity now informs the design of immunomodulatory therapies aimed at promoting regeneration while minimizing harm.

Clinical features and inflammatory factors

The clinical cluster’s prominence reflects the continued need for reliable biomarkers and the translation of molecular insights into bedside practice. Recent multicenter observational studies highlight that early measurement of cytokines, colony-stimulating factors, and other inflammatory mediators can predict BPD severity and progression, including the risk of pulmonary hypertension (31). The integration of longitudinal biomarker tracking with imaging and lung function testing is refining dynamic risk stratification models, allowing for more individualized and adaptive management (32). The convergence of clinical and molecular research in this cluster underscores the field’s ongoing commitment to actionable, patient-centered solutions. This cluster underscores that biomarker utility depends not only on statistical association but also on feasibility (timing, specimen type), reproducibility across centers, and actionable thresholds linked to specific management decisions.

Burst keywords

Early Phase (1994–2006). During the early period, keywords such as “tumor necrosis factor,” “chronic lung disease,” and “hyaline membrane disease” experienced citation bursts. This focus reflects the field’s initial emphasis on characterizing inflammatory mediators and clinical features central to BPD pathogenesis. At this stage, research was largely descriptive, aiming to define disease phenotypes and identify the most relevant clinical and cytokine markers.

Mid-period (2007–2015). The mid-phase is marked by bursts in keywords like “apoptosis”, “responses”, “respiratory distress syndrome”, “mice”, and “gene expression”. The prominence of “mice” (2013–2023) underscores the widespread use of advanced animal models to dissect disease mechanisms, supporting the field’s reliance on reverse translation—validating human findings in genetically engineered models (33). Concurrent bursts in “gene expression” (2013–2015) and “growth” (2016–2025) highlight the adoption of transcriptomic and developmental biology approaches, which have facilitated the identification of key regulatory genes and pathways underlying abnormal lung maturation (34). The emergence of “NF kappa B” (2016–2025) as a burst keyword points to a growing focus on inflammatory signaling, with molecular and pharmacological studies dissecting its role in disease progression (35).

Recent and Ongoing Phase (2016–2025). In the most recent period, keywords such as “growth”, “NF kappa B”, “cells”, “pulmonary hypertension”, “inflammation”, and “bronchopulmonary dysplasia” have dominated. This shift indicates a broadening of research scope from molecular mechanisms toward translational and clinical applications. The focus on “cells” (2018–2025) and “pulmonary hypertension” (2018–2025) reflects increased attention to cellular heterogeneity and long-term complications. Meanwhile, “inflammation” (2021–2025) and “bronchopulmonary dysplasia” (2023–2025) highlight the integration of basic and clinical research, including the development of multi-omics-based risk models and exploration of targeted therapies such as anti-inflammatory agents and cell-based interventions (36). Collectively, these bursts suggest that the field is moving from descriptive markers toward integrative, endotype-oriented frameworks. At the same time, the dominance of model-driven mechanistic keywords (e.g., “mice”, “NF kappa B”, “gene expression”) implies that clinical translation will depend on rigorous external validation, harmonized phenotyping, and better alignment between experimental exposures and the heterogeneous realities of preterm intensive care. These needs are consistent with the emergence of recent clinical trials and prospective cohorts aimed at bridging mechanistic insights with clinically testable interventions (37).

Limitations

However, this study has several limitations. First, citation-based metrics (e.g., total citations, H-index, centrality) reflect visibility and scholarly uptake rather than clinical importance; highly cited topics may be methodologically convenient or broadly discussed without proving translational benefit in preterm infants. Citation-based metrics may not fully reflect the clinical importance or translational value of individual studies. Restricting the analysis to English-language publications may have excluded relevant research published in other languages. This may also bias geographic interpretations (e.g., under-representing locally influential studies and domestic guidelines) and can distort apparent collaboration patterns. Additionally, bibliometric methods emphasize quantitative analysis and may not adequately assess study quality or real-world impact. For example, our approach cannot distinguish between robust prospective cohort studies versus smaller exploratory reports, nor can it determine whether mechanistic findings—particularly those derived from hyperoxia-driven murine models—are reproducible across centers or applicable to heterogeneous clinical BPD endotypes. Results may vary across bibliographic sources (e.g., WoSCC vs. Scopus) due to differences in indexing, author name disambiguation, and journal inclusion, which can affect country and institution rankings and collaboration metrics.

Future research should address these gaps by incorporating multilingual databases and linking bibliometric findings with clinical or experimental outcomes. Concretely, future work could harmonize search strategies across multiple databases, perform sensitivity analyses using alternative inclusion criteria, and integrate bibliometrics with evidence-level assessment (e.g., study design classification or trial-phase mapping) to better connect “influential” themes with “actionable” evidence.

Conclusions

This bibliometric analysis indicates that research on macrophages in BPD has evolved in parallel with changing clinical priorities and advances in experimental and omics technologies, with major hotspots centered on cell death and inflammatory mechanisms, animal models and pathogenesis, growth and gene regulation, acute lung injury and immunity, and clinically oriented studies. To facilitate translation, future work should prioritize harmonized BPD phenotyping (including endotypes) and standardized outcome definitions across cohorts, strengthen alignment between preclinical models and the developmental timing and exposure patterns relevant to preterm infants, and undertake multicenter human validation of candidate macrophage-related biomarkers and pathways using consistent sampling timepoints and assay pipelines. In addition, broader international and cross-disciplinary collaboration will be important to improve reproducibility across populations and NICU practices and to support prospective studies and early-phase clinical trials.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the BIBLIO reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2026-0236/rc

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2026-0236/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2026-0236/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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