The systematic use of metabolomic epidemiology, biobanks, and electronic medical records for precision medicine initiatives in asthma: findings suggest new guidelines to optimize treatment

doi:10.21203/rs.3.rs-268507/v1

The application of large-scale metabolomic profiling provides new opportunities to realize the potential of omics-based precision medicine with regard to asthma. We leveraged over 14,000 individuals from four distinct epidemiological studies. We identified and independently replicated seventeen steroid metabolites that were significantly reduced in individuals with prevalent asthma. Importantly steroid levels were reduced among all individuals with asthma regardless of medication use; however, the largest reduction was associated with inhaled corticosteroids use (ICS) that was further confirmed in a four-year ICS clinical trial. Cortisol levels extracted from electronic medical records confirmed that cortisol is reduced among asthmatics taking ICS over the entire 24-hour period, compared with all other groups. Clinical-grade adrenal suppression in asthmatics on ICS, resulting from substantial reductions in steroid metabolites, represents a larger public health problem than previously recognized. Regular cortisol testing may identify at-risk individuals, enabling personalized treatment modifications and improving overall patient care.

Pulmonology

Chemical Biology

asthma

metabolomics

precision medicine

biobanks

inhaled corticosteroids

steroid resistance

adrenal suppression

Asthma imparts a tremendous global health and economic burden, affecting over 350 million people worldwide^1–4, with annual asthma-related costs surpassing $80 billion/year in the USA. Any improvement in treatment will therefore have significant public health ramifications. While several genetic variants have been determined to influence an individual’s asthma liability^5–8, asthma also has substantial environmental triggers⁹ and the majority of cases arise from complex interactions between both factors.

Metabolomics, the systematic analysis of small molecules in a biological sample, provides an integrated profile of biological status, reflecting the ‘net results’ of genetic, transcriptomic, proteomic, and environmental interactions, making it ideally suited to the study of asthma. The clinical potential of asthma metabolomics is well-demonstrated through the improvement in predictive accuracy of asthma phenotypes and the improved understanding of underlying biology it has provided relative to genetics or environmental factors alone⁸. While a number of metabolomic signatures of asthma-relevant phenotypes have been reported to date⁸; there are several drawbacks to these studies that have limited their overall clinical impact, including a lack of independent replication^10–13 and small sample sizes ¹¹. The identification and validation of metabolic signatures for asthma phenotypes using multiple large, well-characterized epidemiological cohorts and clinical trials with comprehensive coverage of the global metabolome is imperative for the translational potential of metabolomics to be fully realized^11,13–21.

As a composite measure that captures environmental exposures, the metabolome can inform our understanding of the biological impact that specific medications, such as inhaled corticosteroids (ICS), may have on individuals with asthma. Long-term use of ICS among moderate to severe asthmatics is common. While several studies have examined the biological impact that prolonged ICS use may have, significant limitations in study design have limited their overall utility, resulting in conflicting findings^22–26. Moreover, the current guidelines on ICS use are inconsistent on dosing and effective symptom control²⁷, resulting in confusion.

Metabolomic findings from epidemiological studies may be further enhanced for clinical translation by integrating them with Electronic Medical Records (EMR)²⁸, as many validated and clinically approved ‘non-omic’ biomarkers and clinical tests have quantifiable relationships with metabolites and metabolomic pathways^29,30. In this study, we utilized metabolomics to investigate prevalent asthma and the impact of ICS use, leveraging multiple cohorts in combination with clinical tests available via EMR to explore a metabolomic-driven precision medicine approach for optimized asthma management.

Subjects

We applied an analytic framework leveraging over 14,000 individuals from four epidemiological studies to discover and validate asthma associated metabolites and provide clinical recommendations that optimize ICS treatment recommendations: 1) Discovery cohort: European Prospective Investigation of Cancer (EPIC-Norfolk, n=10,754); 2) Replication cohort: Mass General Brigham Biobank-Asthma (MGBB-Asthma, n=610); 3) ICS randomized clinical trial: Childhood Asthma Management Program (CAMP, n=1,120); and 4) Biobank/EMR-based adrenal suppression cohort: (MGBB-Cortisol, n=2,235). Clinical characteristics of the EPIC-Norfolk, MGBB and CAMP subjects are summarized in Table 1. EPIC-Norfolk asthmatics (N=661) and controls (N=10,093) differed significantly by smoking status (P=0.003) and marginally by gender (P=0.08). Asthma cases (N=287) in MGBB-Asthma significantly differed from controls (N=323) by sex (P<0.001), race (P=0.002), ICS intake (P<0.001) and BMI (P<0.001).

Metabolite associations in the EPIC-Norfolk cohort

We identified 973 metabolites that remained for analysis after quality control. In EPIC-Norfolk, 35 (3.6%) were significantly associated with prevalent asthma after multiple testing corrections using Bonferroni threshold (Table S3). Thirty-four of these metabolites were significantly reduced in asthmatics compared with controls (range of ORs=0.65-0.81; range of P-values=1.4x10^-27-1.5x10^-7) and were annotated to canonical curated pathways for corticosteroids, pregnenolone, and androgenic steroids (Figure S1A). Glycosyl-N-palmitoyl-sphingosine (OR=1.19, P=7.6x10^-6); a sphingolipid had increased levels in asthmatics (Table S3, Figure S1B).

Replication in MGBB-Asthma cohort

Seventeen of the 35 significant metabolites were replicated in MGBB-Asthma at an FDR threshold of 5%; 15 of which also met the more stringent Bonferroni threshold (Table 2). All 17 metabolites were reduced in asthmatics compared to controls (Figure 2A) and were annotated to major steroid hormone biosynthesis sub-pathways, specifically corticosteroid, pregnenolone, and androgenic steroid pathways (Table 2, Figure S2).

Metabolite-ICS associations in MGBB-Asthma and CAMP

In MGBB all corticosteroid, androgenic, and pregnenolone steroids were lower in both asthmatics on and off ICS when compared with controls (Table S4, Figure 2B). These reductions were larger and more significant among asthmatics on ICS than among asthmatics off ICS; however, steroid reductions were still evident in asthmatics off ICS when compared with controls. While progestin steroids also demonstrated this general trend in all comparisons between asthmatics and controls, the reduction was not as robust (Table S5-S8, further details in supplement).

Cortisone and cortisol levels in CAMP collected at the end of the four-year trial also demonstrated reduced concentrations in subjects randomized to ICS as compared to those receiving nedocromil or placebo (cortisol: b=-0.87; 95% CI=-1.50, -0.24; P=6.8x10³ and cortisone: b==-0.60; 95% CI=-1.20, -0.01; P=0.04). Importantly, there was no significant difference at baseline (pre-ICS randomization) between the groups (cortisol: Estimate=0.09; 95% CI=-0.37, 0.55; P=0.70 and Cortisone: Estimate=0.19; 95% CI=-0.30, 0.68; P=0.45). These results are consistent with systemic suppression of the steroid pathway metabolites in asthmatics with prolonged ICS use.

Clinical Quantification of Cortisol: MGBB-Cortisol

The MGBB-Cortisol subjects (N=2,235) are summarized in Table 1. The asthmatics and controls differed by sex (P=3.9x10^-14), race (P=4.3x10^-5), ICS intake (P<2.2x10^-16) and adrenal insufficiency diagnosis (P=0.03). We determined that asthmatics with ICS intake had the lowest minimum cortisol levels throughout a 24-hour period. Pairwise comparisons between the subgroups demonstrated that asthmatics on ICS had significantly lower minimum cortisol levels compared to asthmatics without ICS intake (b=-1.74 mcg/dL; 95% CI=-2.90, -0.59; P=3.3x10^-3) and controls without ICS intake (b=-2.86 mcg/dL; 95% CI=-3.96, -1.76; P=3.7x10^-7) throughout the 24-hour period with the largest difference occurring in early morning, the time when individuals are most vulnerable to asthma attacks^31,32 (Figure 3). A comparison with controls on ICS was marginally significant (P=0.055); however, this group may have other comorbid and confounding diagnoses that make this relationship difficult to assess.

The Research Patient Data Registry (RPDR) identified 16,929 individuals with asthma and 23.4% (3,969) met the criteria for regular ICS use, as defined for MGBB-Asthma. Of the 1,021 (25.7%) asthmatics with ICS use that were tested for adrenal insufficiency, 31.0% (n=316) met the clinical diagnostic criteria for adrenal suppression (defined as a peak level<500 nmol/L)³³.

In this study, we used four independent cohorts to demonstrate the translational utility of metabolomics. We identified significant and robust reductions in steroid metabolites among asthma cases using ICS that place them at serious risk for adrenal suppression with as many as thirty percent reaching clinically diagnosable levels. There were three key findings. First, seventeen steroid metabolites had substantially reduced levels in prevalent asthma cases compared with controls, including marked reductions in the two primary hypothalamic-pituitary-adrenal axis (HPA) steroid hormones that are biomarkers for adrenal suppression³⁴, DHEA-S and cortisol. Second, we observed that this reduction in steroids was primarily, but not exclusively driven by ICS use in asthmatics, suggesting that the reduced steroid levels represent both a fundamental characteristic of the asthma phenotype and a result of ICS use. Third, not only were cortisol levels consistently reduced among asthmatics on ICS compared with all other groups throughout an entire 24-hour diurnal period, but this group had the largest variation in levels throughout the day, with a steep reduction in cortisol levels during the early morning hours, a peak time for asthma exacerbations to occur^31,32 Prior studies have correlated low cortisol with decreased asthma control³⁵, suggesting that this reduction may pose a further threat during this vulnerable time^31,32. The global reduction in cortisol levels was so pronounced that on average, peak cortisol levels among asthmatics on ICS during an entire 24-hour period, were lower than the average minimum cortisol levels among any of the other groups at any time.

To date, multiple studies have investigated the potential adverse side effects of ICS use for asthma; however, the overall utility of these studies to assess adrenal suppression has been hampered by small or modest samples sizes, short trial periods^22,25, and a limited range of ICS dose^22,36, with few resounding conclusions; a more comprehensive interrogation is therefore recommended²⁷. While acknowledged as a potential harmful side effect, clinical suppression of the HPA-axis from ICS therapy alone has been considered unusual with minimal long-term systemic ramifications on adrenal function^22,23 that may only be apparent at high doses^25,26. In contrast to the existing scientific literature, in our population there was a consistent, pronounced, and clinically relevant reduction (as defined by a pronounced in increase in adrenal suppression diagnoses) in adrenal function over a range of ICS use and dose. The utilization of 25 years of EMRs from individuals diagnosed with asthma and ICS use, cortisol measurements from a 4-year ICS trial, and extensive clinical-grade cortisol testing from over 2,000 individuals enables a robust evaluation of the long-term impact of ICS use on adrenal function and suggests increased rates of adrenal and sub-adrenal suppression among asthmatics as a result of ICS use.

The majority of moderate to severe asthmatics use ICS as the first line of treatment to improve control of persistent asthma. It remains an integral part of their long-term treatment protocol^37,38 and one of the most effective and efficacious treatments to date. In the short term, these benefits likely outweigh the long-term side effects. However, patient treatment is optimized with proactive monitoring of circulating steroid levels to prevent permanent adrenal suppression. We observed that 31 percent of asthma cases with ICS use tested met the clinical criteria for an adrenal suppression diagnosis. While this is likely an overestimate due to potential selection bias in ordering tests for adrenal suppression when clinically suspected, if these were the only diagnosable cases out of all the entire RPDR, including the other 74.3 percent of asthmatics using ICS who were not tested, this would still suggest that eight percent of all asthmatics using ICS have cortisol levels low enough to classify as adrenal insufficiency diagnosis.

The degree of pronounced steroid suppression upon extended ICS use among asthma cases suggests that proactive monitoring of cortisol levels has the potential to identify and prevent adrenal suppression through altered treatment approaches. This becomes even more imperative, as cases of adrenal suppression are often problematic to diagnose, symptoms can be missed due to the range of presentation of the disease, and the broad range of negative side effects can even result in life-threatening complications that are critical to avoid^27,38–42. The institution of regular cortisol testing enables the identification of marked decreases in steroid levels prior to significant and potentially permanent long-term complications from clinical-grade adrenal suppression. Moreover, yearly monitoring is inexpensive (adrenocorticotropic hormone test (ACTH) simulation test)⁴³ and manageable in most primary care settings and may decrease the overall public health burden of this ICS adverse effect. Individual optimization of treatment protocols does not necessarily imply omitting ICS use in all cases, but potentially changing the dosage, the frequency, or supplementation with additional medications may result in more efficacious outcomes in some individuals, while preventing adverse adrenal suppression ramifications in others.

While other omic data types are touted for their potential use in precision medicine, this study demonstrates the expediency to clinical translation that metabolomic profiling offers. Because a large number of clinical tests currently in use are measured metabolites, there are often cases where the metabolites of interest from untargeted analyses are already measured in clinical practice. This was the case in the present study, in which the observed reduction of multiple steroid metabolites led to the exploration of the use of cortisol testing to assess adrenal suppression in asthmatics on ICS. This approach enables investigators to more efficiently translate metabolomic findings into clinical practice than for other omic data types that would require substantial follow-up time and resources.

Despite the strengths of the reported findings, several limitations should be noted. First, our discovery cohort (EPIC-Norfolk) did not have information on ICS use. In MGBB, we created a robust algorithm to identify ICS use. We acknowledge that there is likely misclassification; however, this misclassification would result in a bias towards the null hypothesis and the effects we identified would remain highly significant. Therefore, we do not believe that this impacted the overall conclusions of the study. Moreover, we verified our findings in an RCT, which represents a more robust study design. Second, the CAMP RCT utilized children and while at the end of the trial, they were mostly adolescents; this differs from the other cohorts that utilized primarily adults. It is important to note that there may be important differences between ICS response in adults and adolescents that should be studied in more detail. Third, metabolomic profiling was performed in a different laboratory for CAMP and did not have the broader range of steroid metabolites. Despite these differences, we were able to validate and further refine our findings over the four populations we utilized. Finally, while it is important to realize the potential of large EMR databases, it is equally important to recognize that this information is derived from an overrepresentation of individuals with illness and may bias the data or result in confounding by indication. Acknowledging this limitation, we excluded individuals with common relevant comorbid conditions, such as COPD.

In conclusion, our results suggest regular monitoring of steroid levels among asthma cases with long-term ICS use, which is not currently commonplace, is merited to identify the optimal clinical regimen for individuals with asthma at risk of serious adrenal suppression and determine the clinical impact of low cortisol levels in this population. This could potentially improve overall health and reduce health care spending. Integrating metabolomics data from epidemiological studies with existing clinical biomarkers obtained via EMRs may enhance the interpretation of metabolomic data as it relates to health and current medical practices, in addition to enhancing comprehension both on the side of researchers and of clinicians.

Statistical Analysis

Overview of Analytic Approach

A detailed summary of the four cohorts utilized in this study, including cohort generation, details on asthma diagnosis and ICS use, metabolomic profiling and quality control procedures is provided in the supplement (Methods & Table S1-S2). An illustration of our analytic strategy is presented in Figure 1. Briefly, we utilized a discovery and replication approach to identify metabolites associated with prevalent asthma, using the EPIC-Norfolk (discovery) and biobank-derived Mass General Brigham Biobank-Asthma (MGBB-Asthma) (replication) cohorts. As these findings implicated potential involvement of steroid-associated metabolites, we then assessed the impact of ICS use on the replicated asthma-associated metabolites within MGBB-Asthma cohort and further evaluated the impact of ICS use on steroid metabolites using cortisol and cortisone measures from the four-year double-blind longitudinal ICS randomized controlled trial (RCT), Childhood Asthma Management Program (CAMP)^44,45. Using cortisol as a biomarker for subclinical steroid suppression from ICS use, we queried the MGBB EMR to identify individuals with cortisol measures to create the MGBB-Cortisol cohort and assessed the impact of ICS use on these cortisol measures. Some analyses in the EPIC-Norfolk cohort were done in Stata. All other analyses were performed in R version 3.6.3⁴⁶. The EPIC-Norfolk study was approved by the Norwich Local Ethics Committee (REC Ref. 78 98CN01). The research work for Biobank cohorts was approved by the IRB of Mass General Brigham (# 2015P000983, #2014P001109). For CAMP data, all study procedures were approved by the Institutional Review Board of the Brigham and Women’s Hospital (the Partners Human Research Committee (PHRC), # 2002P000331). All study participants provided written consent at enrollment.

Discovery and Replication Analyses for Metabolite-Asthma Associations

Multivariable logistic regression models were employed in EPIC-Norfolk to assess the association between log-transformed plasma concentrations of each metabolite with asthma affection status. Models were adjusted for age, sex, body mass index (BMI) and smoking status (current, former and never). We did not adjust for ethnicity, as the EPIC-Norfolk population is mostly White (99.7%). To correct for multiple testing, we applied a stringent Bonferroni significance threshold (0.05/number of statistical tests in EPIC-Norfolk). In MGBB-Asthma, multivariable logistic regression models adjusted for age, sex, race, BMI and smoking status were used to replicate the associations between the Bonferroni significant metabolites and asthma case status. An association was considered “replicated” if: 1) The effect estimate (Odds Ratio) is in the same direction as the initial association finding and 2) The False Discovery Rate (FDR)⁴⁷ is <5%. To quantify the relative reduction in steroid metabolite levels based on ICS use, asthmatics were stratified into four sub-groups: 1) no asthma/no ICS use; 2) no asthma/ICS use; 3) asthma/no ICS use; 4) asthma/ICS use. Multivariable logistic regression models using pairwise comparisons adjusted for age, gender, race, and BMI were utilized to compare metabolite levels between groups. All models were adjusted for age, gender, race, and BMI.

Establishment of a temporal relationship between cortisone, cortisol, ICS using the CAMP RCT

We utilized multivariable linear regression models and individuals from CAMP to further assess the relationship between cortisol and cortisone levels in children randomized to ICS (budesonide) verses those randomized to nedocromil or placebo. Considering both baseline and the end of the four-year clinical trial, the models were adjusted for age, gender, race, BMI, and an interaction variable between age and randomized ICS-use for the end of the trial model, as the children were in various stages of puberty and puberty directly influences steroid levels.

Clinical Quantification of Cortisol: MGBB-Cortisol

We investigated minimum cortisol levels (mcg/dL) recorded in MGBB-Cortisol subjects throughout a 24-hour period stratifying on asthma status and ICS use. To account for differences in sample availability across the 24-hour period, subjects were binned into three time categories based on sample collection time: 4:00am-12:00pm, 12:00pm-6:00pm and 6:00pm-4:00am. The mean cortisol levels were subjected to smoothing interpolation using loess curve regression fitting. Tukey’s HSD⁴⁸ test was used to identify significant differences between the asthma/ICS subgroupsPairwise comparisons between the subgroups were also performed using generalized linear models, adjusted for collection time, age, gender and race.

Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

Requests for raw data, analyzed data and materials will be reviewed by the cohort and contact PIs for the studies to determine if the request is subject to intellectual property or confidentiality obligations. Data and materials that can be shared will be released using a Material Transfer Agreement. Appropriate IRB approvals may be required to access de-identified data in particular data from electronic medical health records. 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/). Data will either be shared through an institutional data sharing agreement or arrangements will be made for analyses to be conducted remotely without the need for data transfer.

Code availability

The R Code for data processing and analyses can be made available for research purposes upon request from the corresponding author.

Author Contributions: PK and IDS had full access to the data and take responsibility for the data integrity and accuracy of the analysis. JALS and CL contributed to conceptualization of the study; PK performed the quality control and statistical downstream data analyses for Mass General Brigham Biobank (MGBB) cohorts and comparison to the EPIC-Norfolk datasets. PK also performed the replication in the Childhood Asthma Management Program (CAMP). IDS performed the quality control and regression analysis for the EPIC-Norfolk cohort. MS contributed to the data pulls and acquisition from MGBB. AW contributed to ascertainment of the inhaled medications in MGBB. KM contributed to the downstream analyses. DIS performed the cost-effectiveness analysis. PK and JALS prepared the original draft of the manuscript; PK, JALS, RSK, IDS, CL, KM, AD, SHC, MH, MM, MC, HMK, KLS, AW, ACW, YV, and CEW contributed to the statistical interpretation and critical revision of the manuscript; PZ, CEW, and CC contributed to the metabolomic data generation. JALS, STW, CL, NJW, EWK contributed to funding acquisition; all authors reviewed the final manuscript.

Conflict of Interest disclosures: There are no conflicts of interests to declare.

Funding/Support and acknowledgements: Effort from PK, JALS and STW is supported by P01HL132825. Effort for RK and JALS is supported by R01HL123915 and W81XWH-17-1-0533. Effort for MH and JALS is supported by R01HL141826. Effort for AD is supported by K01HL130629. Effort for RSK is supported by K01HL146980 from the NHLBI. Effort for HK is supported by the Jane and Aatos Erkko Foundation, the Paulo Foundation, and the Pediatric Research Foundation. Effort for KLS is supported by K08HL148178 from NHLBI. Effort for MM is supported by R01HL139634 from NIH/NHLBI. Effort for YV is supported by K23AI130408 from NIAID. Effort for JALS, STW, EWK is supported by NIH U01HG008685. This work was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant (JP18H06121, JP19K21239) and the Japanese Environment Research and Technology Development Fund (No. 5-1752). We acknowledge the Gunma University Initiative for Advanced Research (GIAR), the STINT Foundation, the Swedish Heart Lung Foundation (HLF 20170734, HLF 20180290), and the Swedish Research Council (2016-02798). AD is supported by R01HL152244 to NHLBI. The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 and MC-UU_12015/1) 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 EMIF grant agreement no. 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 CAMP studies for their contributions.

Role of the Funder/Sponsor: 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.

Global Initiative for Asthma. Global strategy for asthma management and prevention, 2018. Available at: .
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).
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).
(2018)., C. gov. CDC - Asthma - Data and Surveillance - Asthma Surveillance Data.
Greally, M., Jagoe, W. S. & Greally, J. The genetics of asthma. Ir. Med. J. 75, 403–405 (1982).
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).
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).
Sharma, S. et al. The genomic origins of asthma. Thorax 69, 481–487 (2014).
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).
Reinke, S. N. et al. Metabolomics analysis identifies different metabotypes of asthma severity. Eur. Respir. J. 49, (2017).
Kelly, R. S. et al. Asthma Metabolomics and the Potential for Integrative Omics in Research and the Clinic. Chest 151, 262–277 (2017).
Kelly, R. S. et al. Plasma metabolite profiles in children with current asthma. Clin Exp Allergy 48, 1297–1304 (2018).
McGeachie, M. J. et al. The metabolomics of asthma control: a promising link between genetics and disease. Immunity, Inflamm. Dis. 3, 224–238 (2015).
Adamko, D. J., Sykes, B. D. & Rowe, B. H. The metabolomics of asthma: novel diagnostic potential. Chest 141, 1295–1302 (2012).
Luxon, B. A. Metabolomics in asthma. Adv Exp Med Biol 795, 207–220 (2014).
Snowden, S., Dahlen, S. E. & Wheelock, C. E. Application of metabolomics approaches to the study of respiratory diseases. Bioanalysis 4, 2265–2290 (2012).
Amaral, A. F. S. Metabolomics of asthma. The Journal of allergy and clinical immunology 133, 1497–9, 1499.e1 (2014).
Bush, A. Translating Asthma: Dissecting the Role of Metabolomics, Genomics and Personalized Medicine. Indian J. Pediatr. 85, 643–650 (2018).
Pecak, M., Korosec, P. & Kunej, T. Multiomics Data Triangulation for Asthma Candidate Biomarkers and Precision Medicine. OMICS 22, 392–409 (2018).
Checkley, W. et al. Identifying biomarkers for asthma diagnosis using targeted metabolomics approaches. Respir. Med. 121, 59–66 (2016).
Pite, H., Morais-Almeida, M. & Rocha, S. M. Metabolomics in asthma: where do we stand? Curr. Opin. Pulm. Med. 24, 94–103 (2018).
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).
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).
Allen, D. B. Effects of inhaled steroids on growth, bone metabolism and adrenal function. Expert Rev Respir Med 1, 65–74 (2007).
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).
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).
Keeley, D. Inhaled corticosteroids for asthma: guidance is inconsistent. BMJ (Clinical research ed.) 367, l6934 (2019).
Frey, L. J. Data integration strategies for predictive analytics in precision medicine. Per. Med. 15, 543–551 (2018).
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. Off. Publ. Am. Coll. Allergy, Asthma, Immunol. 123, 550–557 (2019).
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).
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).
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).
Gunnarsson, C. et al. Health Care Burden in Patients With Adrenal Insufficiency. J. Endocr. Soc. 1, 512–523 (2017).
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. Off. Publ. Am. Coll. Allergy, Asthma, Immunol. 97, 182–186 (2006).
Aburuz, S., Heaney, L. G., Millership, J., Gamble, J. & McElnay, J. A cross-sectional study evaluating the relationship between cortisol suppression and asthma control in patients with difficult asthma. Br. J. Clin. Pharmacol. 63, 110–115 (2007).
Chang, C. C. & Tam, A. Y. Suppression of adrenal function in children on inhaled steroids. J Paediatr Child Heal. 27, 232–234 (1991).
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).
O’Byrne P, Fabbri LM, Pavord ID, Papi A, Petruzzelli S, L. P. Asthma progression and mortality: The role of inhaled corticosteroids. Eur. Respir. J. (2019).
RWH, H. Inhaled corticosteroid-phobia and childhood asthma: Current understanding and management implications. Paediatr Respir Rev. (2019).
Kwda, A. et al. Effect of long term inhaled corticosteroid therapy on adrenal suppression, growth and bone health in children with asthma. BMC Pediatr. 19, 411 (2019).
Dahl, R. Systemic side effects of inhaled corticosteroids in patients with asthma. Respir. Med. 100, 1307–1317 (2006).
Ye, Q., He, X.-O. & D’Urzo, A. A Review on the Safety and Efficacy of Inhaled Corticosteroids in the Management of Asthma. Pulm. Ther. 3, 1–18 (2017).
Centers for Medicare and Medicaid Services: Physician Fee Schedule. (2020).
The Childhood Asthma Management Program (CAMP): design, rationale, and methods. Childhood Asthma Management Program Research Group. Control. Clin. Trials 20, 91–120 (1999).
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).
Team, R. C. R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. (2017).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B Met 289–300 (1995).
Haynes, W. Tukey’s Test. in Encyclopedia of Systems Biology (eds. Dubitzky, W., Wolkenhauer, O., Cho, K.-H. & Yokota, H.) 2303–2304 (Springer New York, 2013). doi:10.1007/978-1-4419-9863-7_1212

Table 1. Clinical characteristics of subjects in the large discovery epidemiologic cohort (EPIC-Norfolk) and replication in two biobank/electronic medical record (EMR)-based cohorts (MGBB-Asthma, MGBB-Cortisol) and an inhaled corticosteroid (ICS)-randomized clinical trial (CAMP (Childhood Asthma Management Program))

*Discovery cohort: EPIC-Norfolk*
Number of subjects	All subjects (n = 10,754)	Asthmatics (n = 661)	Non asthmatics (n = 10,093)	P-value*
Age, mean (SD)	59.73 (8.96)	59.24 (9.05)	59.76 (8.95)	0.142
BMI kg/m², mean (SD)	26.19 (3.71)	26.10 (3.86)	26.20 (3.70)	0.529
Sex, n (%)				0.076
Female	5,759 (53.6)	376 (56.9)	5,383 (53.3)
Male	4,995 (46.4)	285 (43.1)	4,710 (46.7)
Race, n (%)				0.181
African American	9 (0.1)	1 (0.2)	8 (0.1)
White	10,684 (99.7)	655 (99.5)	10,029 (99.8)
Asian	9 (0.1)	1 (0.2)	8 (0.1)
Other	9 (0.1)	1 (0.2)	8 (0.1)
Inhaled steroid intake, n (%)	NA	NA	NA	NA
No
Yes
Smoking, n (%)
No	5,012 (46.6)	308 (46.6)	4,704 (46.6)	0.003
Yes	5,742 (53.4)	353 (53.4)	5,389 (53.4)
*Replication cohort: MGBB-Asthma*
Number of subjects	All subjects (n = 610)	Asthmatics (n = 287)	Non asthmatics (n = 323)	P-value*
Age, mean (SD)	32.7 (5.3)	33.1 (6.6)	32.4 (3.7)	0.110
BMI kg/m², mean (SD)	25.6 (6.5)	28.3 (8.0)	23.2 (3.1)	<2.2x10^-16
Sex, n (%)				2x10^-10
Female	359 (58.9)	208 (72.5)	151 (46.7)
Male	251 (41.1)	79 (27.5)	172 (53.3)
Race, n (%)				9.8x10^-3
African American	51 (8.4)	34 (11.8)	17 (5.3)
White	475 (77.9)	222 (77.4)	253 (78.3)
Asian	28 (4.6)	10 (3.5)	18 (5.6)
Other	56 (9.2)	21 (7.3)	35 (10.8)
Inhaled corticosteroid intake, n (%)				<2.2x10^-16
No	413 (67.7)	90 (31.4)	323 (100)
Yes	172 (28.2)	172 (59.9)	0 (0)
Smoking, n (%)				1.2x10^-3
No	482 (79)	210 (73.2)	272 (84.2)
Yes	128 (21)	77 (26.8)	51 (15.8)
*Validation cohort: Randomized controlled trial of ICS use - CAMP*
Number of subjects	All subjects (n = 1041)	Baseline	End of trial	P-value*
Number of subjects	All subjects (n = 1041)	(n = 560 at both time points)		P-value*
Age, mean (SD)	8.8 (2.1)	8.8 (2.1)	12.8 (2.1)	<2.2x10^-16
BMI kg/m², mean (SD)	18.1 (3.5)	18.0 (3.3)	21.2 (4.5)	<2.2x10^-16
Sex, n (%)				NA
Female	420 (40.3)	201 (35.9)	201 (35.9)
Male	621 (59.7)	359 (64.1)	359 (64.1)
Race, n (%)				NA
African American	138 (13.3)	82 (14.6)	82 (14.6)
White	711 (68.3)	395 (70.5)	395 (70.5)
Hispanic	98 (9.4)	56 (10)	56 (10)
Other	94 (9.0)	27 (4.8)	27 (4.8)
Treatment, Steroid use (%)				NA
Budenoside	311 (29.9)	151 (27)	151 (27)
Nedocramil + Placebo	730 (70.1)	409 (73)	409 (73)
*Validation cohort: Clinical cohort of MGBB-Cortisol*
Number of subjects	All subjects (n = 2,235)	Asthmatics (n = 383)	Non asthmatics (n = 1,852)	P-value*
Age, mean (SD)	56.1 (16.2)	55.2 (16)	56.3 (16.3)	0.200
Sex, n (%)				3.9x10^-14
Female	1377 (61.6)	302 (78.9)	1075 (58)
Male	858 (38.4)	81 (21.1)	777 (42.2)
Race, n (%)				4.3x10^-5
African American	122 (5.5)	34 (8.9)	88 (4.8)
White	1914 (85.6)	302 (78.9)	1612 (87)
Hispanic	49 (2.2)	16 (4.2)	33 (1.8)
Asian	48 (2.1)	6 (1.6)	42 (2.3)
Other	102 (4.6)	25 (6.5)	77 (4.2
Inhaled steroid intake, n (%)				<2.2x10^-16
No	2079 (93)	268 (70)	1811 (97.8)
Yes	156 (7)	115 (30)	41 (2.2)
Adrenal Insufficiency diagnosis, n (%)				0.030
No	1640 (73.4)	263 (68.7)	1377 (74.4)
Yes	595 (26.6)	120 (31.3)	475 (25.6)

Missing data: In MGBB-Asthma, BMI was missing for seven subjects; Inhaled steroid intake was missing for 25 subjects

* Significance of difference was evaluated using chi-squared test for categorical variables and two-sample t-test for continuous variables. In CAMP, the categorical variables sex, race and treatment did not change with time point.

Abbreviations: BMI, body mass index; SD, standard deviation; CAMP, Childhood Asthma Management Program

Table 2. Plasma metabolites significantly associated with asthma in EPIC-Norfolk with replication in MGBB-Asthma.

Metabolite	Metabolite Subclass	*EPIC-Norfolk (N=10,754)**				MGBB-Asthma (N=610)
Metabolite	Metabolite Subclass	OR	95% CI	P-value*	OR	95% CI	P-value^‡
Dehydroisoandrosterone sulfate (DHEA-S)	Androgenic Steroid	0.65	0.60, 0.70	1.4x10^-27	0.38	0.25, 0.55	1.1x10^-6
Steroid conjugate*	Corticosteroid	0.68	0.61, 0.72	2.9x10^-21	0.60	0.44, 0.81	1.1x10^-3
Cortisone	Corticosteroid	0.72	0.67, 0.77	7.8x10^-20	0.46	0.31, 0.66	6.2x10^-5
21-hydroxypregnenolone disulfate	Pregnenolone Steroid	0.71	0.65, 0.77	4.5x10^-16	0.47	0.33, 0.63	2.9x10^-6
Epiandrosterone sulfate	Androgenic Steroid	0.74	0.69, 0.80	1.1x10^-13	0.41	0.29, 0.57	1.0x10^-7
16a-hydroxy DHEA 3-sulfate	Androgenic Steroid	0.81	0.66, 0.79	2.5x10^-5	0.64	0.48, 0.85	1.9x10^-3
Pregnenolone sulfate	Pregnenolone Steroid	0.73	0.66, 0.79	3.2x10^-12	0.38	0.27, 0.52	3.4x10^-9
Androsterone sulfate	Androstane Steroid	0.77	0.72, 0.83	2.1x10^-11	0.45	0.33, 0.62	1.1x10^-6
Cortisol	Corticosteroid	0.78	0.72, 0.84	3.3x10^-11	0.53	0.37, 0.74	2.9x10^-4
pregnenetriol disulfate*	Pregnenolone Steroid	0.73	0.69, 0.82	3.2x10^-12	0.60	0.45, 0.79	4.6x10^-4
17-hydroxypregnenolone sulfate*	Pregnenolone Steroid	0.74	0.68, 0.82	7.4x10^-10	0.48	0.36, 0.63	1.9x10^-7
etiocholanolone glucuronide	Androstane Steroid	0.79	0.71, 0.84	1.5x10^-7	0.49	0.37, 0.63	1.7x10^-7
5-androstenetriol disulfate*	Androstane Steroid	0.79	0.72, 0.86	1.5x10^-7	0.50	0.38, 0.65	3.1x10^-7
Tetrahydrocorticosterone glucuronide^‡‡	Corticosteroid	0.81	0.74, 0.88	9.9x10^-7	0.74	0.58, 0.94	0.015
Steroid conjugate*^‡‡	Corticosteroid	0.68	0.75, 0.90	2.9x10^-21	0.73	0.55, 0.95	0.020
5alpha-androstan-3beta,17alpha-diol disulfate	Androgenic Steroid	0.81	0.74, 0.89	1.8x10^-5	0.56	0.44, 0.71	1.1x10^-6
16a-hydroxy-DHEA-disulfate*	Androgenic Steroid	0.81	0.73, 0.89	2.5x10^-5	0.54	0.42, 0.70	2.6x10^-6

max N = 10,754^* for EPIC-Norfolk, as N varies for each metabolite in this cohort

^‡ The P-value thresholds for declaring significance was Bonferroni in both EPIC-Norfolk (P<5.14x10^-5) and MGBB-Asthma (P<2x10^-3)

^‡‡ Replicated metabolites in MGBB-Asthma at FDR threshold only

Abbreviations: MGBB: Mass General Brigham Biobank

Metabolite names with an asterisk * indicate that the metabolite has not been confirmed based on an analytical standard, but we are confident in its identity as confirmed by Metabolon

Yes there is potential Competing Interest. JALS has been a consultant to Metabolon, Inc. MM reports grants from NIH R01 HL139634, during the conduct of the study. YV reports grants from NIH/NIAID, during the conduct of the study (Grant # K23AI130408). Rest authors declare no conflict of interest.

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The systematic use of metabolomic epidemiology, biobanks, and electronic medical records for precision medicine initiatives in asthma: findings suggest new guidelines to optimize treatment

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