{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T19:38:47Z","timestamp":1792093127510,"version":"4.4.0"},"reference-count":41,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T00:00:00Z","timestamp":1775952000000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["949906"],"award-info":[{"award-number":["949906"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pediatric Allergy Immunology"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Allergic diseases often develop jointly during early childhood. Potential disease trajectories and relevant early\u2010life factors have been described, yet existing prediction approaches mostly focus on single allergic diseases cross\u2010sectionally. Models addressing allergic multimorbidity and disease trajectories are lacking. We aim to predict allergic disease trajectories from birth up to adolescence using early\u2010life factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Preceding research using data from 4646 adolescents of the German birth cohorts GINIplus and LISA identified seven allergic disease trajectories up to the age of 15\u2009years. A set of predictors comprising parental and perinatal factors, early allergic or respiratory symptoms, lifestyle and environmental factors was used with an XGBoost machine learning approach to perform multiclass classification. In a subsample (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u20092109), polygenic risk scores (PRS) for asthma, allergic rhinitis, atopic dermatitis, and any allergy were added to the predictor set.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our approach revealed moderate classification success (multiclass area under the curve (AUC)\u2009=\u20090.69). A macro\u2010averaged sensitivity of 0.26 and specificity of 0.89 were obtained. The most important predictors were early\u2010life skin rash, respiratory symptoms, and air pollution. In the sub\u2010analysis, the PRS were among the factors with high importance, but the prediction performance in external test data was not improved.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our prediction success was comparable to established prediction scores while accounting for multiple allergic disease trajectories and using solely early\u2010life factors. This study cannot yet provide reliable individual\u2010level prediction in a clinical setting but can inform development of future work on this.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/pai.70341","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T03:41:06Z","timestamp":1776051666000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Prediction of allergic disease trajectories from birth up to adolescence"],"prefix":"10.1111","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6884-6640","authenticated-orcid":false,"given":"Miriam","family":"Leskien","sequence":"first","affiliation":[{"name":"Institute of Epidemiology Helmholtz Zentrum M\u00fcnchen\u2010German Research Center for Environmental Health  Neuherberg Germany"},{"name":"Institute for Medical Information Processing, Biometry, and Epidemiology LMU Munich  Munich Germany"},{"name":"German 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