{"title":"婴儿期症状轨迹对 BILD 和 PASTURE 队列中后续喘息和哮喘的预测:动态网络分析","authors":"","doi":"10.1016/S2589-7500(24)00147-X","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Host and environment early-life risk factors are associated with progression of wheezing symptoms over time; however, their individual contribution is relatively small. We hypothesised that the dynamic interactions of these factors with an infant's developing respiratory system are the dominant factor for subsequent wheeze and asthma.</div></div><div><h3>Methods</h3><div>In this dynamic network analysis we used data from term healthy infants from the Basel-Bern Infant Lung Development (BILD) cohort (435 neonates aged 0–4 weeks recruited in Switzerland between Jan 1, 1999, and Dec 31, 2012) and replicated the findings in the Protection Against Allergy Study in Rural Environments (PASTURE) cohort (498 infants aged 0–12 months recruited in Germany, Switzerland, Austria, France, and Finland between Jan 1, 2002, and Oct 31, 2006). BILD exclusion criteria for the current study were prematurity (<37 weeks), major birth defects, perinatal disease of the neonate, and incomplete follow-up period. PASTURE exclusion criteria were women younger than 18 years, a multiple pregnancy, the sibling of a child was already included in the study, the family intended to move away from the area where the study was conducted, and the family had no telephone connection. Outcome groups were subsequent wheeze, asthma, and healthy. The first outcome was defined as ever wheezed between the age of 2 years and 6 years. Week-by-week correlations of the determining factors with cumulative symptom scores (CSS) were calculated from weeks 2 to 52 (BILD) and weeks 8 to 52 (PASTURE). The complex dynamic interaction between the determining factors and the CSS was assessed via dynamic host–environment correlation network, quantified by a simple descriptor: trajectory function <em>G(t)</em>. Wheeze outcomes at age 2–6 years were compared in 335 infants from BILD and 437 infants from PASTURE, and asthma outcomes were analysed at age 6 years in a merged cohort of 783 infants.</div></div><div><h3>Findings</h3><div>CSS was significantly different for wheeze and asthma outcomes and became increasingly important during infancy in direct comparison with all determining factors. Weekly symptoms were tracked for groups of infants, showing a non-linear increase with time. Using logistic regression classification, <em>G(t)</em> distinguished between the healthy group and wheeze or asthma groups (area under the curve>0·97, p<0·0001; sensitivity analysis confirmed significant CSS association with wheeze [BILD p=0·0002 and PASTURE p=0·068]) and <em>G(t)</em> was also able to distinguish between the farming and non-farming exposure groups (p<0·0001).</div></div><div><h3>Interpretation</h3><div>Similarly to other risk factors, CSS had weak sensitivity and specificity to identify risks at the individual level. At group level however, the dynamic host–environment correlation network properties (<em>G(t)</em>) showed excellent discriminative ability for identifying groups of infants with subsequent wheeze and asthma. Results from this study are consistent with the 2018 <em>Lancet</em> Commission on asthma, which emphasised the importance of dynamic interactions between risk factors during development and not the risk factors per se.</div></div><div><h3>Funding</h3><div>The Swiss National Science Foundation, the Kühne Foundation, the EFRAIM study EU research grant, the FORALLVENT study EU research grant, and the Leibniz Prize.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":null,"pages":null},"PeriodicalIF":23.8000,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Symptom trajectories in infancy for the prediction of subsequent wheeze and asthma in the BILD and PASTURE cohorts: a dynamic network analysis\",\"authors\":\"\",\"doi\":\"10.1016/S2589-7500(24)00147-X\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Host and environment early-life risk factors are associated with progression of wheezing symptoms over time; however, their individual contribution is relatively small. We hypothesised that the dynamic interactions of these factors with an infant's developing respiratory system are the dominant factor for subsequent wheeze and asthma.</div></div><div><h3>Methods</h3><div>In this dynamic network analysis we used data from term healthy infants from the Basel-Bern Infant Lung Development (BILD) cohort (435 neonates aged 0–4 weeks recruited in Switzerland between Jan 1, 1999, and Dec 31, 2012) and replicated the findings in the Protection Against Allergy Study in Rural Environments (PASTURE) cohort (498 infants aged 0–12 months recruited in Germany, Switzerland, Austria, France, and Finland between Jan 1, 2002, and Oct 31, 2006). BILD exclusion criteria for the current study were prematurity (<37 weeks), major birth defects, perinatal disease of the neonate, and incomplete follow-up period. PASTURE exclusion criteria were women younger than 18 years, a multiple pregnancy, the sibling of a child was already included in the study, the family intended to move away from the area where the study was conducted, and the family had no telephone connection. Outcome groups were subsequent wheeze, asthma, and healthy. The first outcome was defined as ever wheezed between the age of 2 years and 6 years. Week-by-week correlations of the determining factors with cumulative symptom scores (CSS) were calculated from weeks 2 to 52 (BILD) and weeks 8 to 52 (PASTURE). The complex dynamic interaction between the determining factors and the CSS was assessed via dynamic host–environment correlation network, quantified by a simple descriptor: trajectory function <em>G(t)</em>. Wheeze outcomes at age 2–6 years were compared in 335 infants from BILD and 437 infants from PASTURE, and asthma outcomes were analysed at age 6 years in a merged cohort of 783 infants.</div></div><div><h3>Findings</h3><div>CSS was significantly different for wheeze and asthma outcomes and became increasingly important during infancy in direct comparison with all determining factors. Weekly symptoms were tracked for groups of infants, showing a non-linear increase with time. Using logistic regression classification, <em>G(t)</em> distinguished between the healthy group and wheeze or asthma groups (area under the curve>0·97, p<0·0001; sensitivity analysis confirmed significant CSS association with wheeze [BILD p=0·0002 and PASTURE p=0·068]) and <em>G(t)</em> was also able to distinguish between the farming and non-farming exposure groups (p<0·0001).</div></div><div><h3>Interpretation</h3><div>Similarly to other risk factors, CSS had weak sensitivity and specificity to identify risks at the individual level. At group level however, the dynamic host–environment correlation network properties (<em>G(t)</em>) showed excellent discriminative ability for identifying groups of infants with subsequent wheeze and asthma. Results from this study are consistent with the 2018 <em>Lancet</em> Commission on asthma, which emphasised the importance of dynamic interactions between risk factors during development and not the risk factors per se.</div></div><div><h3>Funding</h3><div>The Swiss National Science Foundation, the Kühne Foundation, the EFRAIM study EU research grant, the FORALLVENT study EU research grant, and the Leibniz Prize.</div></div>\",\"PeriodicalId\":48534,\"journal\":{\"name\":\"Lancet Digital Health\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":23.8000,\"publicationDate\":\"2024-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Lancet Digital Health\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S258975002400147X\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lancet Digital Health","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S258975002400147X","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
Symptom trajectories in infancy for the prediction of subsequent wheeze and asthma in the BILD and PASTURE cohorts: a dynamic network analysis
Background
Host and environment early-life risk factors are associated with progression of wheezing symptoms over time; however, their individual contribution is relatively small. We hypothesised that the dynamic interactions of these factors with an infant's developing respiratory system are the dominant factor for subsequent wheeze and asthma.
Methods
In this dynamic network analysis we used data from term healthy infants from the Basel-Bern Infant Lung Development (BILD) cohort (435 neonates aged 0–4 weeks recruited in Switzerland between Jan 1, 1999, and Dec 31, 2012) and replicated the findings in the Protection Against Allergy Study in Rural Environments (PASTURE) cohort (498 infants aged 0–12 months recruited in Germany, Switzerland, Austria, France, and Finland between Jan 1, 2002, and Oct 31, 2006). BILD exclusion criteria for the current study were prematurity (<37 weeks), major birth defects, perinatal disease of the neonate, and incomplete follow-up period. PASTURE exclusion criteria were women younger than 18 years, a multiple pregnancy, the sibling of a child was already included in the study, the family intended to move away from the area where the study was conducted, and the family had no telephone connection. Outcome groups were subsequent wheeze, asthma, and healthy. The first outcome was defined as ever wheezed between the age of 2 years and 6 years. Week-by-week correlations of the determining factors with cumulative symptom scores (CSS) were calculated from weeks 2 to 52 (BILD) and weeks 8 to 52 (PASTURE). The complex dynamic interaction between the determining factors and the CSS was assessed via dynamic host–environment correlation network, quantified by a simple descriptor: trajectory function G(t). Wheeze outcomes at age 2–6 years were compared in 335 infants from BILD and 437 infants from PASTURE, and asthma outcomes were analysed at age 6 years in a merged cohort of 783 infants.
Findings
CSS was significantly different for wheeze and asthma outcomes and became increasingly important during infancy in direct comparison with all determining factors. Weekly symptoms were tracked for groups of infants, showing a non-linear increase with time. Using logistic regression classification, G(t) distinguished between the healthy group and wheeze or asthma groups (area under the curve>0·97, p<0·0001; sensitivity analysis confirmed significant CSS association with wheeze [BILD p=0·0002 and PASTURE p=0·068]) and G(t) was also able to distinguish between the farming and non-farming exposure groups (p<0·0001).
Interpretation
Similarly to other risk factors, CSS had weak sensitivity and specificity to identify risks at the individual level. At group level however, the dynamic host–environment correlation network properties (G(t)) showed excellent discriminative ability for identifying groups of infants with subsequent wheeze and asthma. Results from this study are consistent with the 2018 Lancet Commission on asthma, which emphasised the importance of dynamic interactions between risk factors during development and not the risk factors per se.
Funding
The Swiss National Science Foundation, the Kühne Foundation, the EFRAIM study EU research grant, the FORALLVENT study EU research grant, and the Leibniz Prize.
期刊介绍:
The Lancet Digital Health publishes important, innovative, and practice-changing research on any topic connected with digital technology in clinical medicine, public health, and global health.
The journal’s open access content crosses subject boundaries, building bridges between health professionals and researchers.By bringing together the most important advances in this multidisciplinary field,The Lancet Digital Health is the most prominent publishing venue in digital health.
We publish a range of content types including Articles,Review, Comment, and Correspondence, contributing to promoting digital technologies in health practice worldwide.