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      Characterisation of lung function trajectories and associated early-life predictors in an Australian birth cohort study

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          Abstract

          Background

          There is growing evidence that lung function in early-life predicts later lung function. Adverse events over the lifespan might influence an individual's lung function trajectory, resulting in poor respiratory health. The aim of this study is to identify early-life risk factors and their impact on lung function trajectories to prevent long-term lung impairments.

          Methods

          Our study included participants from the Raine Study, a prospective pregnancy cohort, with at least two spirometry measurements. Lung function trajectories from the 6- to 22-year follow-ups were characterised using finite mixture modelling. Multinomial logistic regression analyses were used to evaluate the association between early-life predictors and lung function trajectories.

          Main results

          A total of 1512 participants (768 males, 744 females), representing 53% of the whole cohort, were included in this analysis. Four lung function trajectories of forced expiratory volume in 1 s (FEV 1), forced vital capacity (FVC) and FEV 1/FVC (z-scores) were identified. FEV 1 and FVC trajectories were categorised as: “very low”, “low”, “average” and “above average”, respectively. Based on their shape, lung function trajectories of FEV 1/FVC were categorised as “very low”, “low–average”, “average–low” and “average”. Asthma and maternal smoking were identified as risk factors for low lung function trajectories in this cohort, as well as early-life exposure to PM 2.5Absorbance.

          Conclusions

          Early-life risk factors may influence lung function trajectories over time. Nonetheless, identifying children with a high risk of having low lung function trajectories should be prioritised to prevent deficits in later life.

          Abstract

          Early-life risk factors may influence lung function trajectories over time. Nonetheless, identifying children with a high risk of having low lung function trajectories should be prioritised to prevent deficits in later life. https://bit.ly/3oYbgzr

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          Most cited references40

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          Standardisation of spirometry.

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            Multi-ethnic reference values for spirometry for the 3-95-yr age range: the global lung function 2012 equations.

            The aim of the Task Force was to derive continuous prediction equations and their lower limits of normal for spirometric indices, which are applicable globally. Over 160,000 data points from 72 centres in 33 countries were shared with the European Respiratory Society Global Lung Function Initiative. Eliminating data that could not be used (mostly missing ethnic group, some outliers) left 97,759 records of healthy nonsmokers (55.3% females) aged 2.5-95 yrs. Lung function data were collated and prediction equations derived using the LMS method, which allows simultaneous modelling of the mean (mu), the coefficient of variation (sigma) and skewness (lambda) of a distribution family. After discarding 23,572 records, mostly because they could not be combined with other ethnic or geographic groups, reference equations were derived for healthy individuals aged 3-95 yrs for Caucasians (n=57,395), African-Americans (n=3,545), and North (n=4,992) and South East Asians (n=8,255). Forced expiratory value in 1 s (FEV(1)) and forced vital capacity (FVC) between ethnic groups differed proportionally from that in Caucasians, such that FEV(1)/FVC remained virtually independent of ethnic group. For individuals not represented by these four groups, or of mixed ethnic origins, a composite equation taken as the average of the above equations is provided to facilitate interpretation until a more appropriate solution is developed. Spirometric prediction equations for the 3-95-age range are now available that include appropriate age-dependent lower limits of normal. They can be applied globally to different ethnic groups. Additional data from the Indian subcontinent and Arabic, Polynesian and Latin American countries, as well as Africa will further improve these equations in the future.
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              Group-based trajectory modeling in clinical research.

              Group-based trajectory models are increasingly being applied in clinical research to map the developmental course of symptoms and assess heterogeneity in response to clinical interventions. In this review, we provide a nontechnical overview of group-based trajectory and growth mixture modeling alongside a sampling of how these models have been applied in clinical research. We discuss the challenges associated with the application of both types of group-based models and propose a set of preliminary guidelines for applied researchers to follow when reporting model results. Future directions in group-based modeling applications are discussed, including the use of trajectory models to facilitate causal inference when random assignment to treatment condition is not possible.
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                Author and article information

                Journal
                ERJ Open Res
                ERJ Open Res
                ERJOR
                erjor
                ERJ Open Research
                European Respiratory Society
                2312-0541
                January 2022
                21 March 2022
                : 8
                : 1
                : 00072-2022
                Affiliations
                [1 ]Wal-yan Respiratory Centre, Children's Lung Health, Telethon Kids Institute, Nedlands, WA, Australia
                [2 ]Unit of Epidemiology and Medical Statistics, Dept of Diagnostic and Public Health, University of Verona, Verona, Italy
                [3 ]Children's Health and Environment Program, Child Health Research Centre, University of Queensland, South Brisbane, QLD, Australia
                [4 ]Royal Darwin Hospital, Darwin, NT, Australia
                [5 ]Centre for Ecosystem Management, School of Science, Edith Cowan University, Joondalup, WA, Australia
                [6 ]School of Population and Global Health, The University of Western Australia, Perth, WA, Australia
                [7 ]School of Allied Health, Curtin University, Perth, WA, Australia
                Author notes
                Corresponding author: Francesca Sanna ( francesca.sanna@ 123456telethonkids.org.au )
                Author information
                https://orcid.org/0000-0001-9910-1036
                https://orcid.org/0000-0001-7214-4431
                https://orcid.org/0000-0003-3747-2960
                https://orcid.org/0000-0002-1201-6785
                Article
                00072-2022
                10.1183/23120541.00072-2022
                8943283
                35350282
                0fc3f289-0107-485f-9273-136c83555168
                Copyright ©The authors 2022

                This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. For commercial reproduction rights and permissions contact permissions@ersnet.org

                History
                : 7 February 2022
                : 14 February 2022
                Funding
                Funded by: Raine Foundation
                Award ID: Priming grant
                Funded by: National Health and Medical Research Council, doi 10.13039/501100000925;
                Award ID: Early Career Fellowship
                Categories
                Original Research Articles
                Asthma
                2
                9
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