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Metabolomic profiling reveals extensive adrenal suppression due to inhaled corticosteroid therapy in asthma

Nature Medicine volume 28, pages 814–822 (2022)Cite this article

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An Author Correction to this article was published on 20 July 2022

This article has been updated

Abstract

The application of large-scale metabolomic profiling provides new opportunities for realizing the potential of omics-based precision medicine for asthma. By leveraging data from over 14,000 individuals in four distinct cohorts, this study identifies and independently replicates 17 steroid metabolites whose levels were significantly reduced in individuals with prevalent asthma. Although steroid levels were reduced among all asthma cases regardless of medication use, the largest reductions were associated with inhaled corticosteroid (ICS) treatment, as confirmed in a 4-year low-dose ICS clinical trial. Effects of ICS treatment on steroid levels were dose dependent; however, significant reductions also occurred with low-dose ICS treatment. Using information from electronic medical records, we found that cortisol levels were substantially reduced throughout the entire 24-hour daily period in patients with asthma who were treated with ICS compared to those who were untreated and to patients without asthma. Moreover, patients with asthma who were treated with ICS showed significant increases in fatigue and anemia as compared to those without ICS treatment. Adrenal suppression in patients with asthma treated with ICS might, therefore, represent a larger public health problem than previously recognized. Regular cortisol monitoring of patients with asthma treated with ICS is needed to provide the optimal balance between minimizing adverse effects of adrenal suppression while capitalizing on the established benefits of ICS treatment.

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Fig. 1: Overall study design.
Fig. 2: Plasma steroid metabolite associations in EPIC-Norfolk cohort and MGBB-Asthma cohort.
Fig. 3: Quantification of cortisol in the EMR-Cortisol cohort.

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Data availability

The EPIC-Norfolk data can be requested by bona fide researchers for specified scientific purposes via the study website (https://www.mrc-epid.cam.ac.uk/research/studies/epic-norfolk/). Requests for the other datasets can be made by researchers via a data use agreement for specific scientific inquiries. Datasets for the other cohorts are subject to controlled access. These restrictions apply because of the sensitive nature of patient data and the possibility of identifying individuals via the use of electronic medical records in conjunction with omic data. To create a data use agreement, contact the corresponding author. The corresponding author will respond to requests within 10 days.

Code availability

All code for data processing and analyses are available via GitHub at:

https://github.com/CDNMBioinformatics/PartnersBiobank_asthma_metabolomics

https://github.com/CDNMBioinformatics/RPDR

https://github.com/CDNMBioinformatics/RPDR_ACTH

https://github.com/CDNMBioinformatics/PEGASUS_Scripts

Change history

References

  1. Global Initiative for Asthma. Global strategy for asthma management and prevention. https://ginasthma.org/ (2021).

  2. Masoli, M., Fabian, D., Holt, S. & Beasley, R. The global burden of asthma: executive summary of the GINA Dissemination Committee report. Allergy 59, 469–478 (2004).

    Article  PubMed  Google Scholar 

  3. Becker, A. B. & Abrams, E. M. Asthma guidelines: the Global Initiative for Asthma in relation to national guidelines. Curr. Opin. Allergy Clin. Immunol. 17, 99–103 (2017).

    Article  PubMed  Google Scholar 

  4. Centers for Disease Control and Prevention. Data, Statistics, and Surveillance: Asthma Surveillance Data. https://www.cdc.gov/asthma/asthmadata.htm

  5. Greally, M., Jagoe, W. S. & Greally, J. The genetics of asthma. Ir. Med. J. 75, 403–405 (1982).

    CAS  PubMed  Google Scholar 

  6. Dold, S., Wjst, M., von Mutius, E., Reitmeir, P. & Stiepel, E. Genetic risk for asthma, allergic rhinitis, and atopic dermatitis. Arch. Dis. Child. 67, 1018–1022 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Jenkins, M. A., Hopper, J. L. & Giles, G. G. Regressive logistic modeling of familial aggregation for asthma in 7,394 population-based nuclear families. Genet. Epidemiol. 14, 317–332 (1997).

    Article  CAS  PubMed  Google Scholar 

  8. Sharma, S. et al. The genomic origins of asthma. Thorax 69, 481–487 (2014).

    Article  PubMed  Google Scholar 

  9. Louisias, M., Ramadan, A., Naja, A. S. & Phipatanakul, W. The effects of the environment on asthma disease activity. Immunol. Allergy Clin. North Am. 39, 163–175 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Reinke, S. N. et al. Metabolomics analysis identifies different metabotypes of asthma severity. Eur. Respir. J. 49, 1601740 (2017).

  11. Kelly, R. S. et al. Asthma metabolomics and the potential for integrative omics in research and the clinic. Chest 151, 262–277 (2017).

    Article  PubMed  Google Scholar 

  12. Kelly, R. S. et al. Plasma metabolite profiles in children with current asthma. Clin. Exp. Allergy 48, 1297–1304 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. McGeachie, M. J. et al. The metabolomics of asthma control: a promising link between genetics and disease. Immun. Inflamm. Dis. 3, 224–238 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Adamko, D. J., Sykes, B. D. & Rowe, B. H. The metabolomics of asthma: novel diagnostic potential. Chest 141, 1295–1302 (2012).

    Article  CAS  PubMed  Google Scholar 

  15. Bush, A. Translating asthma: dissecting the role of metabolomics, genomics and personalized medicine. Indian J. Pediatr. 85, 643–650 (2018).

    Article  PubMed  Google Scholar 

  16. Snowden, S., Dahlen, S. E. & Wheelock, C. E. Application of metabolomics approaches to the study of respiratory diseases. Bioanalysis 4, 2265–2290 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. Checkley, W. et al. Identifying biomarkers for asthma diagnosis using targeted metabolomics approaches. Respir. Med. 121, 59–66 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Pite, H., Morais-Almeida, M. & Rocha, S. M. Metabolomics in asthma: where do we stand? Curr. Opin. Pulm. Med. 24, 94–103 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Crompton, G. A brief history of inhaled asthma therapy over the last fifty years. Prim. Care Respir. J. 15, 326–331 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Duplantier, J. E., Nelson, R. P. J., Morelli, A. R., Good, R. A. & Kornfeld, S. J. Hypothalamic–pituitary–adrenal axis suppression associated with the use of inhaled fluticasone propionate. J. Allergy Clin. Immunol. 102, 699–700 (1998).

    Article  CAS  PubMed  Google Scholar 

  21. Guilbert, T. W. et al. Long-term inhaled corticosteroids in preschool children at high risk for asthma. N. Engl. J. Med. 354, 1985–1997 (2006).

    Article  CAS  PubMed  Google Scholar 

  22. Allen, D. B. Effects of inhaled steroids on growth, bone metabolism and adrenal function. Expert Rev. Respir. Med. 1, 65–74 (2007).

    Article  CAS  PubMed  Google Scholar 

  23. Lapi, F., Kezouh, A., Suissa, S. & Ernst, P. The use of inhaled corticosteroids and the risk of adrenal insufficiency. Eur. Respir. J. 42, 79–86 (2013).

    Article  CAS  PubMed  Google Scholar 

  24. Gurnell, M., Heaney, L. G., Price, D. & Menzies-Gow, A. Long-term corticosteroid use, adrenal insufficiency and the need for steroid-sparing treatment in adult severe asthma. J. Intern. Med. 290, 240–256 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Todd, G. R., Wright, D. & Ryan, M. Acute adrenal insufficiency in a patient with asthma after changing from fluticasone propionate to budesonide. J. Allergy Clin. Immunol. 103, 956–957 (1999).

    Article  CAS  PubMed  Google Scholar 

  26. Drake, A. J. et al. Symptomatic adrenal insufficiency presenting with hypoglycaemia in children with asthma receiving high dose inhaled fluticasone propionate. BMJ 324, 1081–1082 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Todd, G. R. G. et al. Acute adrenal crisis in asthmatics treated with high-dose fluticasone propionate. Eur. Respir. J. 19, 1207–1209 (2002).

    Article  CAS  PubMed  Google Scholar 

  28. Macdessi, J. S. et al. Adrenal crises in children treated with high-dose inhaled corticosteroids for asthma. Med. J. Aust. 178, 214–216 (2003).

    Article  PubMed  Google Scholar 

  29. Santiago, A. H. & Ratzan, S. Acute adrenal crisis in an asthmatic child treated with inhaled fluticasone proprionate. Int. J. Pediatr. Endocrinol. 2010, 749239 (2010).

  30. Smith, R. W. et al. Prevalence of hypothalamic–pituitary–adrenal axis suppression in children treated for asthma with inhaled corticosteroid. Paediatr. Child Health 17, e34–e39 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Hay, C. M. & Spratt, D. I. Adrenal insufficiency in a woman secondary to standard-dose inhaled fluticasone propionate therapy. Endocrinol. Diabetes Metab. Case Rep. 2014, 130080 (2014).

    PubMed  PubMed Central  Google Scholar 

  32. Keeley, D. Inhaled corticosteroids for asthma: guidance is inconsistent. BMJ 367, l6934 (2019).

    Article  PubMed  Google Scholar 

  33. Meyers, D. A., Bleecker, E. R., Holloway, J. W. & Holgate, S. T. Asthma genetics and personalised medicine. Lancet Respir. Med. 2, 405–415 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Keskin, O. et al. Genetic associations of the response to inhaled corticosteroids in asthma: a systematic review. Clin. Transl. Allergy 9, 2 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Hernandez-Pacheco, N., Pino-Yanes, M. & Flores, C. Genomic predictors of asthma phenotypes and treatment response. Front. Pediatr. 7, 6 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Vijverberg, S. J. H., Farzan, N., Slob, E. M. A., Neerincx, A. H. & Maitland-van der Zee, A. H. Treatment response heterogeneity in asthma: the role of genetic variation. Expert Rev. Respir. Med. 12, 55–65 (2018).

    Article  CAS  PubMed  Google Scholar 

  37. Cazzola, M., Rogliani, P., Calzetta, L. & Matera, M. G. Pharmacogenomic response of inhaled corticosteroids for the treatment of asthma: considerations for therapy. Pharmgenomics. Pers. Med. 13, 261–271 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Figueiredo, R. G., Costa, R. S., Figueiredo, C. A. & Cruz, A. A. Genetic determinants of poor response to treatment in severe asthma. Int. J. Mol. Sci. 22, 4251 (2021).

  39. Hawcutt, D. B. et al. Susceptibility to corticosteroid-induced adrenal suppression: a genome-wide association study. Lancet Respir. Med. 6, 442–450 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Frey, L. J. Data integration strategies for predictive analytics in precision medicine. Per. Med. 15, 543–551 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Donovan, B. M., Bastarache, L., Turi, K. N., Zutter, M. M. & Hartert, T. V. The current state of omics technologies in the clinical management of asthma and allergic diseases. Ann. Allergy Asthma Immunol. 123, 550–557 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Akbaraly, T. et al. Association of circulating metabolites with healthy diet and risk of cardiovascular disease: analysis of two cohort studies. Sci. Rep. 8, 8620 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Bakkeheim, E., Mowinckel, P., Carlsen, K. H., Burney, P. & Lødrup Carlsen, K. C. Reduced basal salivary cortisol in children with asthma and allergic rhinitis. Acta Paediatr. 99, 1705–1711 (2010).

    Article  CAS  PubMed  Google Scholar 

  44. Shin, Y. S. et al. The impact of asthma control on salivary cortisol level in adult asthmatics. Allergy Asthma Immunol. Res. 6, 463–466 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Dorsey, M. J., Cohen, L. E., Phipatanakul, W., Denufrio, D. & Schneider, L. C. Assessment of adrenal suppression in children with asthma treated with inhaled corticosteroids: use of dehydroepiandrosterone sulfate as a screening test. Ann. Allergy Asthma Immunol. 97, 182–186 (2006).

    Article  CAS  PubMed  Google Scholar 

  46. Svendsen, U. G. et al. High-dose inhaled steroids in the management of asthma. A comparison of the effects of budesonide and beclomethasone dipropionate on pulmonary function, symptoms, bronchial responsiveness and the adrenal function. Allergy 47, 174–180 (1992).

    Article  CAS  PubMed  Google Scholar 

  47. Boe, J., Bakke, P., Rodolen, T., Skovlund, E. & Gulsvik, A. High-dose inhaled steroids in asthmatics: moderate efficacy gain and suppression of the hypothalamic–pituitary–adrenal (HPA) axis. Research Council of the Norwegian Thoracic Society. Eur. Respir. J. 7, 2179–2184 (1994).

    Article  CAS  PubMed  Google Scholar 

  48. Afilalo, M. et al. Efficacy of inhaled steroids (beclomethasone dipropionate) for treatment of mild to moderately severe asthma in the emergency department: a randomized clinical trial. Ann. Emerg. Med. 33, 304–309 (1999).

    Article  CAS  PubMed  Google Scholar 

  49. Chang, C. C. & Tam, A. Y. Suppression of adrenal function in children on inhaled steroids. J. Paediatr. Child Health 27, 232–234 (1991).

    Article  CAS  PubMed  Google Scholar 

  50. Kannisto, S., Korppi, M., Remes, K. & Voutilainen, R. Adrenal suppression, evaluated by a low dose adrenocorticotropin test, and growth in asthmatic children treated with inhaled steroids. J. Clin. Endocrinol. Metab. 85, 652–657 (2000).

    CAS  PubMed  Google Scholar 

  51. Ahmet, A., Kim, H. & Spier, S. Adrenal suppression: a practical guide to the screening and management of this under-recognized complication of inhaled corticosteroid therapy. Allergy Asthma Clin. Immunol. 7, 13 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Aalbers, R., Vogelmeier, C. & Kuna, P. Achieving asthma control with ICS/LABA: a review of strategies for asthma management and prevention. Respir. Med. 111, 1–7 (2016).

    Article  PubMed  Google Scholar 

  53. O’Byrne P., Fabbri L. M., Pavord I. D., Papi A., Petruzzelli S. & Lange, P. Asthma progression and mortality: the role of inhaled corticosteroids. Eur. Respir. J. 54, 1900491 (2019).

  54. Cho, M. H. & Tantisira, K. G. Adrenal insufficiency and ICS: genetics takes a breath. Lancet Respir. Med. 6, 407–408 (2018).

    Article  PubMed  Google Scholar 

  55. Wenzel, S. E. Asthma phenotypes: the evolution from clinical to molecular approaches. Nat. Med. 18, 716–725 (2012).

    Article  CAS  PubMed  Google Scholar 

  56. Suissa, S., Ernst, P., Benayoun, S., Baltzan, M. & Cai, B. Low-dose inhaled corticosteroids and the prevention of death from asthma. N. Engl. J. Med. 343, 332–336 (2000).

    Article  CAS  PubMed  Google Scholar 

  57. Wade, M. et al. Technical details influence the diagnostic accuracy of the 1 microg ACTH stimulation test. Eur. J. Endocrinol. 162, 109–113 (2010).

    Article  CAS  PubMed  Google Scholar 

  58. Holt, S. et al. Dose–response relation of inhaled fluticasone propionate in adolescents and adults with asthma: meta-analysis. BMJ 323, 253–256 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Sannarangappa, V. & Jalleh, R. Inhaled corticosteroids and secondary adrenal insufficiency. Open Respir. Med. J. 8, 93–100 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Day, N. et al. EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer. Br. J. Cancer 80, 95–103 (1999).

    PubMed  Google Scholar 

  61. Yu, S. et al. Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources. J. Am. Med. Inform. Assoc. 22, 993–1000 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Kelly, R. S. et al. Metabolomic profiling of lung function in Costa-Rican children with asthma. Biochim. Biophys. Acta. Mol. Basis Dis. 1863, 1590–1595 (2017).

    Article  CAS  PubMed  Google Scholar 

  63. The Childhood Asthma Management Program (CAMP): design, rationale, and methods. Childhood Asthma Management Program Research Group. Control. Clin. Trials 20, 91–120 (1999).

  64. Long-term effects of budesonide or nedocromil in children with asthma. The Childhood Asthma Management Program Research Group. N. Engl. J. Med. 343, 1054–1063 (2000).

  65. Strunk, R. C. et al. Long-term budesonide or nedocromil treatment, once discontinued, does not alter the course of mild to moderate asthma in children and adolescents. J. Pediatr. 154, 682–687 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Kelly, R. S. et al. An integrative transcriptomic and metabolomic study of lung function in children with asthma. Chest 154, 335–348 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  67. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).

  68. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B Methodol. 57, 289–300 (1995).

  69. Haynes, W. Tukey’s test. In: Encyclopedia of Systems Biology (eds Dubitzky, W., Wolkenhauer, O., Cho, K.-H. & Yokota, H.) 2303–2304 (Springer, 2013).

  70. Neary, N. & Nieman, L. Adrenal insufficiency: etiology, diagnosis and treatment. Curr. Opin. Endocrinol. Diabetes Obes. 17, 217–223 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Pazderska, A. & Pearce, S. H. Adrenal insufficiency—recognition and management. Clin. Med. 17, 258–262 (2017).

    Article  Google Scholar 

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Acknowledgements

The external funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Effort from P.K., J.A.L.S. and S.T.W. is supported by P01HL132825 from the National Heart, Lung, and Blood Institute, National Institutes of Health (NIH/NHLBI). I.D.S. is funded by the Medical Research Council (MC_UU_00006/1-Etiology and Mechanisms). Effort from R.K., D.I.S., M.C., K.M., M.S. and J.A.L.S. is supported by R01HL123915 from the NIH/NHLBI and W81XWH-17-1-0533 from the US Department of Defense. Effort from R.S.K. is supported by K01HL146980 from the NIH/NHLBI. Effort from S.H.C. is supported by K01HL153941 from the NIH/NHLBI. Effort from M.H. and J.A.L.S. is supported by R01HL141826 from the NIH/NHLBI. Effort from A.D. is supported by K01HL130629 from the NIH/NHLBI. Effort from A.D. and J.A.L.S. is supported by 1R01HL152244 from the NIH/NHLBI. Effort from M.M. and J.A.L.S. is supported by R01HL155742 from the NIH/NHLBI. Effort from H.K. is supported by the Jane and Aatos Erkko Foundation, the Paulo Foundation and the Pediatric Research Foundation. Effort from K.L.S. is supported by K08HL148178 from the NIH/NHLBI. Effort from M.M. is supported by R01HL139634 from the NIH/NHLBI. Effort from A.W. is supported by K23HL151819 from the NIH/NHLBI and the Thrasher Research Fund Award (15115). Effort from A.C.W. is supported by 1R01HD085993 from the National Institute of Child Health and Human Development. Effort from Y.V. is supported by K23AI130408 from the National Institute of Allergy and Infectious Diseases. Effort from J.A.L.S., S.T.W. and E.W.K. is supported by NIH U01HG008685. Effort from P.Z. and C.E.W. was supported by the Japan Society for the Promotion of Science KAKENHI Grant (JP19K21239), the Japanese Environment Research and Technology Development Fund (5-1752), the Gunma University Initiative for Advanced Research, the Japan-Sweden Research Cooperative Program between the Japan Society for the Promotion of Science and the Swedish Foundation for International Cooperation in Research and Higher Education (JPJSBP-1201854), the Swedish Heart Lung Foundation (HLF 20180290 and HLF 20200693) and the Swedish Research Council (2016-02798). The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 MC-UU_12015/1 and MC_UU_00006/1) and Cancer Research UK (C864/A14136). Metabolite measurements in the EPIC-Norfolk study were supported by the MRC Cambridge Initiative in Metabolic Science (MR/L00002/1) and the Innovative Medicines Initiative Joint Undertaking under European Medical Information Framework grant agreement number 115372. We are grateful to all the participants who have been part of the project and to the many members of the study teams at the University of Cambridge who have enabled this research. We thank the staff and participants of the EPIC-Norfolk, Mass General Brigham Biobank and Childhood Asthma Management Program studies for their contributions. Figure 1 was created with BioRender (https://biorender.com/).

Author information

Author notes

  1. These authors contributed equally: Priyadarshini Kachroo, Isobel D. Stewart.

Authors and Affiliations

  1. Department of Medicine, Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Priyadarshini Kachroo, Rachel S. Kelly, Meryl Stav, Kevin Mendez, Amber Dahlin, Djøra I. Soeteman, Su H. Chu, Mengna Huang, Margaret Cote, Hanna M. Knihtilä, Kathleen Lee-Sarwar, Michael McGeachie, Alberta Wang, Scott T. Weiss & Jessica A. Lasky-Su

  2. MRC Epidemiology Unit, University of Cambridge, Cambridge, UK

    Isobel D. Stewart, Nicholas J. Wareham & Claudia Langenberg

  3. Center for Health Decision Science, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Djøra I. Soeteman

  4. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA

    Hanna M. Knihtilä

  5. Harvard Pilgrim Health Care Institute and Department of Population Medicine, Harvard Medical School, Boston, MA, USA

    Ann Chen Wu

  6. Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Yamini Virkud

  7. Gunma University Initiative for Advanced Research (GIAR), Gunma University, Maebashi, Japan

    Pei Zhang & Craig E. Wheelock

  8. Department of Medical Biochemistry and Biophysics, Division of Physiological Chemistry 2, Karolinska Institute, Stockholm, Sweden

    Pei Zhang & Craig E. Wheelock

  9. Department of Medicine, Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Elizabeth W. Karlson

  10. Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm, Sweden

    Craig E. Wheelock

  11. Broad Institute, Cambridge, MA, USA

    Clary Clish

  12. Computational Medicine, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany

    Claudia Langenberg

Authors
  1. Priyadarshini Kachroo
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  2. Isobel D. Stewart
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  3. Rachel S. Kelly
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  24. Jessica A. Lasky-Su
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Contributions

P.K. and I.D.S. had full access to the data and take responsibility for the data integrity and accuracy of the analysis. J.A.L.S. and C.L. contributed to conceptualization of the study. P.K. performed the quality control and statistical downstream data analyses for Mass General Brigham Biobank (MGBB) cohorts and comparison to the EPIC-Norfolk datasets. P.K. also performed the replication in the Childhood Asthma Management Program. I.D.S. performed the quality control and regression analysis for the EPIC-Norfolk cohort. M.S. contributed to the data pulls and acquisition from MGBB. A.W. contributed to ascertainment of the inhaled medications in MGBB. K.M. contributed to the downstream analyses. D.I.S. performed the cost-effectiveness analysis. P.K. and J.A.L.S. prepared the original draft of the manuscript. P.K., J.A.L.S., R.S.K., I.D.S., C.L., K.M., A.D., S.H.C., M.H., M.M., M.C., H.M.K., K.L.S., A.W., A.C.W., Y.V. and C.E.W. contributed to the statistical interpretation and critical revision of the manuscript. P.Z., C.E.W. and C.C. contributed to the metabolomic data generation. J.A.L.S., S.T.W., C.L., N.J.W. and E.W.K. contributed to funding acquisition. All authors reviewed the final manuscript.

Corresponding author

Correspondence to Jessica A. Lasky-Su.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Daniel Hawcutt, Warwick Dunn, Mohsen Sadatsafavi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Michael Basson was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Plasma metabolites in EPIC-Norfolk cohort.

a. Manhattan plot of metabolites sorted by their pathways on x-axis and negative log10 of P-value on the y-axis. The cut off horizontal lines on the y-axis highlight the metabolites significantly associated with asthma outcome at a P-value <0.05 and at the Bonferroni threshold (n = 35 metabolites, P-value<5.14 × 10-5). The legend key shape and color show the direction of effect for the metabolites and the main pathway they belong to, respectively. b. Volcano Plot showing the effect size of the metabolites with OR on the x-axis and negative log10 of P-value on the y-axis. The metabolites colored in red are significant at a Bonferroni threshold of P-value<5.14 × 10-5. Multivariable logistic regression models were used to obtain odds ratio and p-values comparing asthma cases with controls (A, B).

Extended Data Fig. 2 Principal steroid hormone biosynthesis pathways with mineralocorticoid, glucocorticoid and androgen metabolites highlighting the replicated metabolites between EPIC-Norfolk cohort and Mass General Brigham Biobank.

Our annotated metabolites colored in red have been mapped to these pathways with their precursors or intermediates. Abbreviations: CRH: Corticotropin releasing hormone; ACTH: Adrenocorticotropic hormone.

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Supplementary Tables 1–6. Extended Data figures have been uploaded as separate files through the online portal as required in the checklist.

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Kachroo, P., Stewart, I.D., Kelly, R.S. et al. Metabolomic profiling reveals extensive adrenal suppression due to inhaled corticosteroid therapy in asthma. Nat Med 28, 814–822 (2022). https://doi.org/10.1038/s41591-022-01714-5

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