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Modelling lung function trajectories through life course
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DOI：10.1016/j.anai.2025.10.024.png)
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Abstract
En 中文
Lung function development reflects a complex interplay of biological, environmental, and social factors, with early-life disruptions linked to long-term respiratory and systemic outcomes. In this review, we assess how lung function trajectories have been modelled across the literature, combining a systematic search with a bibliometric analysis of 1,919 longitudinal studies. We find that research remains largely concentrated around spirometry and classical statistical approaches, with limited engagement with alternative lung function measures or more flexible modelling frameworks. Although interest in newer methods has grown, the use of machine learning algorithms is often disconnected from clinical application, and approaches such as Bayesian and functional data analysis, which sit in the “Goldilocks” zone between predictive power and interpretability, are almost completely absent. Alongside this methodological conservatism, we observe a striking global asymmetry in authorship and research output, with high-income countries dominating publications and low- and middle-income countries consistently underrepresented and most likely positioned in secondary roles. These patterns constrain the relevance and reach of current research, limiting its capacity to reflect diverse populations and contexts. We argue that progress will require broader engagement with underused measurement and modelling strategies, and a commitment to more equitable, interdisciplinary collaboration across settings and disciplines.
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