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• Bridging the gap between lung function trajectories and the clinic
  Rachel M Scott, Jack Grenville, George W Nava and James Dodd
  Published on: 16 July 2024

• Published on: 16 July 2024

  Bridging the gap between lung function trajectories and the clinic

  • Rachel M Scott, Respiratory academic clinical fellow Academic Respiratory Unit, University of Bristol, UK
  • Other Contributors:
    • Jack Grenville, Associate respiratory research fellow
    • George W Nava, Respiratory academic clinical fellow
    • James Dodd, Associate Professor in Respiratory Medicine

  We read with great interest this latest valuable addition by Zhang et al. to the growing evidence describing lung function trajectories. Although a relatively small cohort, this study has remarkable retention of participants with lung function measurements from the age of 3 to 45 years, bridging the existing gap in the literature between birth cohort and mid-adult life studies. The authors identify ten FEV1 trajectories, notably more than previous studies, by using a best fitting model with an upper limit of twelve trajectories. Trajectories which rise and fall are of interest as potential targets for public health intervention. Whilst the parallel course of most trajectories identified thus far by this and other cohorts do not inspire confidence in modifiability, their 10-class model does reveal additional decline and catch-up groups not identified by a 6-class model in the supplement. This raises the question as to whether there has been an oversimplification in lung function trajectory modelling in previous analyses, which select between just three and six classes[1–4].

  Our interest was particularly sparked by data in supplementary figure S8 where individual lung function trajectories are displayed by class, in which FEV1 in the ‘persistently low’ trajectory demonstrated considerable variability. For clinicians, this individual variability is the hallmark of asthma, especially when combined with the strong association of childhood airway hyper-responsiveness. Th...

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  We read with great interest this latest valuable addition by Zhang et al. to the growing evidence describing lung function trajectories. Although a relatively small cohort, this study has remarkable retention of participants with lung function measurements from the age of 3 to 45 years, bridging the existing gap in the literature between birth cohort and mid-adult life studies. The authors identify ten FEV1 trajectories, notably more than previous studies, by using a best fitting model with an upper limit of twelve trajectories. Trajectories which rise and fall are of interest as potential targets for public health intervention. Whilst the parallel course of most trajectories identified thus far by this and other cohorts do not inspire confidence in modifiability, their 10-class model does reveal additional decline and catch-up groups not identified by a 6-class model in the supplement. This raises the question as to whether there has been an oversimplification in lung function trajectory modelling in previous analyses, which select between just three and six classes[1–4].

  Our interest was particularly sparked by data in supplementary figure S8 where individual lung function trajectories are displayed by class, in which FEV1 in the ‘persistently low’ trajectory demonstrated considerable variability. For clinicians, this individual variability is the hallmark of asthma, especially when combined with the strong association of childhood airway hyper-responsiveness. This suggests a potential target for intensification of asthma management in this group and the possibility of inducing a ‘catch-up’ phase.

  In summary we commend this paper which we feel highlights the need to clinically phenotype abnormal trajectories more thoroughly. To date lung function trajectory studies have relied on questionnaire-based self-reported diagnoses of asthma and chest infections. We would argue this is insufficient and should be enriched with objectives physiological measures and biomarkers, especially given the clinical difficulty of diagnosing asthma[5]. To introduce meaningful interventions we need the information we would rely on as clinicians: clinical records of respiratory disease and treatment, markers of type 2 inflammation, assessment of small airways involvement, and even thoracic imaging.

  1 Belgrave DCM, Granell R, Turner SW, et al. Lung function trajectories from pre-school age to adulthood and their associations with early life factors: a retrospective analysis of three population-based birth cohort studies. Lancet Respir Med. 2018;6:526–34.
  2 Berry CE, Billheimer D, Jenkins IC, et al. A Distinct Low Lung Function Trajectory from Childhood to the Fourth Decade of Life. Am J Respir Crit Care Med. 2016;194:607–12.
  3 Bui DS, Lodge CJ, Burgess JA, et al. Childhood predictors of lung function trajectories and future COPD risk: a prospective cohort study from the first to the sixth decade of life. Lancet Respir Med. 2018;6:535–44.
  4 Wang G, Hallberg J, Faner R, et al. Plasticity of Individual Lung Function States from Childhood to Adulthood. Am J Respir Crit Care Med. ;207:406–15.
  5 Kavanagh J, Jackson DJ, Kent BD. Over- and under-diagnosis in asthma. Breathe. 2019;15:e20–7.

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  Conflict of Interest:
  None declared.

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