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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Background: Although inhaled corticosteroids (ICS) are the first-line therapy for patients with persistent asthma, many patients continue to have exacerbations. We developed machine learning models to predict the ICS response in patients with asthma. Methods: The subjects included asthma patients of European ancestry (n = 1371; 448 children; 916 adults). A genome-wide association study was performed to identify the SNPs associated with ICS response. Using the SNPs identified, two machine learning models were developed to predict ICS response: (1) least absolute shrinkage and selection operator (LASSO) regression and (2) random forest. Results: The LASSO regression model achieved an AUC of 0.71 (95% CI 0.67–0.76; sensitivity: 0.57; specificity: 0.75) in an independent test cohort, and the random forest model achieved an AUC of 0.74 (95% CI 0.70–0.78; sensitivity: 0.70; specificity: 0.68). The genes contributing to the prediction of ICS response included those associated with ICS responses in asthma (TPSAB1, FBXL16), asthma symptoms and severity (ABCA7, CNN2, PTRN3, and BSG/CD147), airway remodeling (ELANE, FSTL3), mucin production (GAL3ST), leukotriene synthesis (GPX4), allergic asthma (ZFPM1, SBNO2), and others. Conclusions: An accurate risk prediction of ICS response can be obtained using machine learning methods, with the potential to inform personalized treatment decisions. Further studies are needed to examine if the integration of richer phenotype data could improve risk prediction.

Details

Title
Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma
Author
Mei-Sing Ong 1   VIAFID ORCID Logo  ; Sordillo, Joanne E 1 ; Dahlin, Amber 2 ; McGeachie, Michael 2 ; Tantisira, Kelan 3 ; Wang, Alberta L 2 ; Lasky-Su, Jessica 2 ; Brilliant, Murray 4 ; Kitchner, Terrie 5 ; Roden, Dan M 6   VIAFID ORCID Logo  ; Weiss, Scott T 2 ; Ann Chen Wu 1   VIAFID ORCID Logo 

 PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA 02215, USA; [email protected] (J.E.S.); [email protected] (A.C.W.) 
 Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA; [email protected] (A.D.); [email protected] (M.M.); [email protected] (A.L.W.); [email protected] (J.L.-S.); [email protected] (S.T.W.) 
 Division of Pediatric Respiratory Medicine, Department of Pediatrics, University of California San Diego and Rady Children’s Hospital, San Diego, CA 92123, USA; [email protected] 
 Marshfield Clinic Research Institute, Marshfield, WI 54449, USA; [email protected] (M.B.); [email protected] (T.K.); Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; [email protected] 
 Marshfield Clinic Research Institute, Marshfield, WI 54449, USA; [email protected] (M.B.); [email protected] (T.K.) 
 Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA; [email protected] 
First page
246
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
20754426
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3003332524
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.