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. Author manuscript; available in PMC: 2025 Mar 1.
Published in final edited form as: Allergy. 2024 Jan 23;79(3):643–655. doi: 10.1111/all.16000

Plasma Protein Signatures of Adult Asthma

Gordon J Smilnak 1, Yura Lee 2, Abhijnan Chattopadhyay 1, Annah B Wyss 1, Julie D White 1,3, Sinjini Sikdar 1,4, Jianping Jin 5, Andrew J Grant 6, Alison A Motsinger-Reif 7, Jian-Liang Li 8, Mikyeong Lee 1, Bing Yu 2, Stephanie J London 1
PMCID: PMC10994188  NIHMSID: NIHMS1973341  PMID: 38263798

Abstract

Background:

Adult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma.

Methods:

Protein abundance in plasma was measured in individuals from the Agricultural Lung Health Study (ALHS) (761 asthma, 1,095 non-case) and the Atherosclerosis Risk in Communities study (470 asthma, 10,669 non-case) using the SOMAScan 5K array. Associations with asthma were estimated using covariate adjusted logistic regression and meta-analyzed using inverse-variance weighting. Additionally, in ALHS, we examined phenotypes based on both asthma and seroatopy (asthma with atopy (n=207), asthma without atopy (n=554), atopy without asthma (n=147), compared to neither (n=948)).

Results:

Meta-analysis of 4,860 proteins identified 115 significantly (FDR<0.05) associated with asthma. Multiple signaling pathways related to airway inflammation and pulmonary injury were enriched (FDR<0.05) among these proteins. A proteomic score generated using machine learning provided predictive value for asthma (AUC=0.77, 95% CI=0.75–0.79 in training set; AUC=0.72, 95% CI=0.69–0.75 in validation set). Twenty proteins are targeted by approved or investigational drugs for asthma or other conditions, suggesting potential drug repurposing. The combined asthma-atopy phenotype showed significant associations with 20 proteins, including five not identified in the overall asthma analysis.

Conclusion:

This first large-scale proteomics study identified over 100 plasma proteins associated with current asthma in adults. In addition to validating previous associations, we identified many novel proteins that could inform development of diagnostic biomarkers and therapeutic targets in asthma management.

Keywords: allergy, area under curve, biomarkers, precision medicine, proteomics

Introduction

Asthma is a chronic respiratory disease affecting over 300 million individuals worldwide.1 Along with environmental factors that influence asthma occurrence and severity, hundreds of genetic variants have been identified through large consortium GWAS.2 Because asthma is clinically heterogenous and multifactorial, disease mechanisms remain poorly described.3,4 A more complete understanding of underlying pathophysiology is likely necessary to improve asthma management.

To improve diagnosis and management of asthma and gain insight into mechanisms, a few studies have examined circulating proteomic biomarkers.5–7 These studies have examined a limited number of candidate proteins and included up to a few hundred individuals.5,7 Technologic advances such as the Slow Off-rate Modified Aptamer (SOMA) Scan™ assay can reliably measure thousands of well-annotated proteins simultaneously at high throughput.8

In the present study, we employed SOMAScan technology to examine associations between about 5,000 human plasma proteins and current asthma among nearly 13,000 adults by combining data from two large studies using meta-analysis. In one of the two studies with seroatopy data, we examined associations with asthma in the presence or absence of atopy, which has been less examined in population-based studies and could help further understand the etiology of adult asthma.9,10 To our knowledge, this is the largest proteomic study of adult asthma to date.

Methods

Details can be found in the Online Supplement.

Study populations and asthma definition

We included individuals from two population-based studies: the Agricultural Lung Health Study (ALHS) and the Atherosclerosis Risk in Communities (ARIC) study. The ALHS, a case-control study of current asthma nested within the Agricultural Health Study (AHS), enrolled 3,301 adults between 2009 and 2013 (data version R3REL201209.00). Current asthma (n=1,223) was based on self-reported diagnosed asthma and/or current symptoms or medication use.11 Noncases (n=2,078) were randomly selected from AHS participants who were not identified as cases. The ARIC study is a prospective cohort of 15,792 Black and white participants.12,13 Asthma was determined by self-report of doctor diagnosis and affirmation that asthma was still present. Each study was approved by its institutional review board. All study participants provided written informed consent.

Proteomic measurement

Relative abundance of plasma proteins was determined using the SOMAScan V4 (5K) assay (SomaLogic, Inc., Boulder, CO). Aptamers, short sequences of DNA, are synthetically designed to bind a specific human protein or protein complex with high specificity. Binding is reported in relative fluorescence units (RFU), a semi-quantitative measure of protein abundance. We analyzed 4,979 aptamers, targeting a total of 4,860 unique human proteins. Because most proteins (98%) were tagged by a unique aptamer we refer to aptamers as proteins throughout this paper but indicate proteins targeted by multiple aptamers in the tables. Analyses included 1,856 ALHS participants (all European ancestry) and 11,139 ARIC participants (8,770 European ancestry, 2,369 African ancestry).

Associations between proteins and asthma

We examined differences in log odds of having current asthma per unit change in log2-transformed abundance in each study using logistic regression. The ALHS analysis was adjusted for age (years), sex, smoking status (current or former, each relative to never-smokers), pack-years, body mass index (kg/m2), study center, time in transit, and season of blood draw. The ARIC analysis was adjusted for all the above covariates without pack-years, time in transit or season. European ancestry (EA) and African ancestry (AA) ARIC participants were analyzed separately. We meta-analyzed association results from ALHS, ARIC EA and ARIC AA with inverse variance weighted fixed effects models14 implemented in the Metafor R package (version 3.8–1).15 Statistical significance was defined by a Benjamini-Hochberg false discovery rate (FDR) <0.05.16 All analyses were performed in R v4.1.1.

Pathway enrichment analysis

Enrichment for canonical pathways among our significant (FDR<0.05) proteins was determined via Ingenuity Pathway Analysis Version 01-20-04 (QIAGEN Inc.).17 For pathway analyses, FDR<0.05 was used as the significance threshold for enrichment.

Functional protein network analysis

The STRING database (https://string-db.org, version 12.0) was used to construct a functional protein-protein interaction network from proteins identified as FDR-significantly related to asthma.

Proteomic score calculation

We constructed a proteomic score for asthma using the ALHS and tested its performance in the ARIC. Our prediction model included proteins that were either FDR significant in the one-by-one analysis of proteins or selected via a machine learning method, penalized logistic regression with the least absolute shrinkage and selection operator (LASSO)18 in analysis of all proteins together. We then fit a logistic regression model on the combined protein list. We calculated a score for each ALHS participant by summing the product of each protein’s abundance and its corresponding logistic regression coefficient. Receiver operating characteristics (ROC) curves were generated first using only the covariates and next using the proteomic score with the covariates. We then validated the proteomic score generated using ALHS data in the ARIC study.

Mendelian randomization (MR)

We used two-sample MR analysis to assess possible causal relationships between FDR-significant proteins and asthma.19 Previously identified cis-protein quantitative trait loci (pQTLs) for each protein were used as the exposure dataset and the outcome dataset was derived from a large genome-wide association meta-analysis of asthma.20,21 Because the genomic reference datasets are ancestry-specific and the EA sample is much larger, we performed MR analysis on proteins significantly associated with asthma in a meta-analysis of ALHS and EA ARIC participants. Additionally, we performed Bayesian colocalization analysis of proteins identified as nominally (p<0.05) significant through MR to determine the probability that plasma protein level and asthma status share a causal variant, rather than possess distinct causal variants, at a given genetic locus.22

Search for druggable targets

Proteins significantly associated with asthma in the overall meta-analysis were searched by UniProt ID in ChEMBL (ebi.ac.uk/chembl) release 29 (updated July 2021), a curated database of genomic and biochemical data, for approved or investigational drugs that target them.23

Combined asthma and atopy phenotypes

Seroatopy was determined in ALHS participants by a serum immunoglobulin E (IgE) level ≥0.70 IU/mL24 to at least one of ten common antigens: Bermuda grass, Timothy grass, ragweed, mountain cedar, Alternaria, dust mite, cat dander, milk, egg, and wheat. To explore joint and separate associations of asthma and atopy with the plasma proteome, we examined three mutually exclusive phenotypes using logistic regression: asthma with atopy, asthma without atopy, and atopy without asthma, each relative to neither asthma nor atopy.

Results

The ALHS sample comprised 1,856 individuals of EA: 761 with asthma and 1,095 without (Table 1). The ARIC sample comprised 11,139 individuals: 470 asthma cases and 10,669 non-cases. There were 2,369 participants of African ancestry and the remainder were EA. Our workflow can be found in Figure 1.

Table 1.

Characteristics of study participants.

ALHS ARIC
Variable Asthma Case (n=761) Non-Case (n=1095) Asthma Case (n=470) Non-Case (n=10,669)
Age, yr. 62 (11) 63 (11) 60 (5.9) 60 (5.7)
Sex, % Female 55% 45% 62% 54%
Ancestry, %
 African 0% 0% 21% 21%
 European 100% 100% 79% 79%
Body Mass Index, kg/m2 31.3 (6.3) 29.4 (5.7) 29.7 (6.4) 28.5 (5.5)
Smoking Status, %
 Never 67% 65% 41% 40%
 Former 30% 30% 45% 41%
 Current 3.30% 5.30% 14% 18%
Pack-Years (among ever smokers)1 13 (17) 21 (22) - -
Study center2, %
 NC 29% 31% - -
 IA 71% 69% - -
 Forsyth County, NC - - 28% 26%
 Jackson, MS - - 18% 18%
 Minneapolis, MN - - 28% 28%
 Washington County, MD - - 26% 28%
Season, % Fall 28% 19% - -
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Comparison of covariates between asthma cases and controls for both the Agricultural Lung Health Study (ALHS) and Atherosclerosis Risk in Communities (ARIC) Study. Unless otherwise noted, all variable distributions are reported as mean (SD).

1

Pack-years data was unavailable for ARIC Study participants at this time point.

2

Study center was state of residence (NC or IA) for ALHS and study site (Forsyth County, NC; Jackson, MS; Minneapolis, MN; or Washington County, MD) for ARIC.

Figure 1.

Figure 1.

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Overview of our proteome and asthma study.

Each study examined associations between plasma protein levels and asthma. Participating studies were ALHS (Agricultural Lung Health Study) and ARIC (Atherosclerosis Risk in Communities). Our meta-analysis included three datasets from two ancestries: European ancestry (EA) and African ancestry (AA). Functional follow-up analyses included pathway enrichment analysis, Mendelian randomization, and a druggable targets search. We validated 19 of our significant findings in a proteome study (n=85). In our proteomic score analysis, we included proteins either FDR-significant or selected via LASSO feature selection. We derived the proteomic score in ALHS and validated in ARIC.

Plasma proteins associated with asthma

Meta-analysis of ALHS and ARIC participants together (n=12,995, 1,231 asthma, 11,764 noncases) identified 115 proteins significantly (FDR<0.05) related to asthma (Table E1, Figure 2, top 25 proteins in Table 2). A cutoff using Bonferroni correction would be 0.05/4979=p<1.004 ×10−5 and 37 met this criterion. Of the 115 proteins, 51 had positive and 64 had negative associations with asthma. Proteins with positive effect sizes included cysteine-rich secretory protein LCCL domain-containing 2 (CRLD2), pappalysin-1 (PAPP-A) and, unsurprisingly, immunoglobulin E (IgE); those with negative effect sizes included roundabout homolog 2 (ROBO2), contactin-1 (CNTN1), and neurogenic locus notch homolog protein 1 (Notch 1). Associations were robust across datasets as demonstrated in forest plots (Figure E1). Complete meta-analysis results are in the Online Supplement.

Figure 2.

Figure 2.

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Associations between plasma proteins and current asthma in adults.

Meta-analyzed beta estimates for each protein are plotted against the negative log10-transformed p-value of the estimate; each point represents one protein. Proteins either significant after Bonferroni correction (blue) or validated in a proteome study (n=85) (red) are labeled with the corresponding target protein name. Horizontal dashed line represents the significance thresholds after multiple-testing correction (FDR<0.05).

Table 2.

Top 25 proteins significantly associated with asthma based on P values from the overall.

SeqId OR1 95% CI P Target Protein Name UniProt
4135–84 1.20 1.16–1.24 3.39E-25 IgE P01854
4148–49 1.86 1.63–2.11 1.11E-20 PAPP-A Q13219
2974–61 0.32 0.24–0.43 6.65E-14 contactin-1 Q12860
5315–22 1.68 1.45–1.95 7.82E-12 Troponin T P45379
9015–1 1.57 1.37–1.79 8.05E-11 PRG3 Q9Y2Y8
3044–3 1.77 1.49–2.1 8.09E-11 PARC P55774
8841–65 0.58 0.49–0.69 4.90E-10 CILP2 Q8IUL8
5688–65 0.54 0.44–0.66 9.31E-10 CBLN4 Q9NTU7
8479–4 1.58 1.36–1.84 2.79E-09 MMP-10 P09238
8885–6 0.52 0.42–0.65 2.80E-09 CA2D3 Q8IZS8
5116–62 0.32 0.22–0.46 3.47E-09 ROBO2 Q9HCK4
3041–55 0.48 0.37–0.62 8.97E-09 MRC2 Q9UBG0
8233–2 0.56 0.46–0.69 1.50E-08 ITIH5 Q86UX2
5691–2 1.30 1.19–1.42 1.83E-08 CRLD2 Q9H0B8
5628–21 0.49 0.38–0.63 2.04E-08 SEM3G Q9NS98
6470–19 0.39 0.28–0.55 5.73E-08 fibulin 1 P23142
8900–28 0.42 0.3–0.59 2.79E-07 NEO1 Q92859
18376–19 1.23 1.13–1.34 6.58E-07 Myosin light chain 1 P08590
8289–8 0.54 0.42–0.69 8.37E-07 GPNMB:ECD Q14956
2744–57 0.48 0.36–0.64 8.84E-07 IgG P01857
9793–145 0.53 0.42–0.69 9.41E-07 IGDC4 Q8TDY8
2999–6 0.40 0.28–0.58 1.08E-06 LSAMP Q13449
7258–5 1.54 1.3–1.84 1.10E-06 EMBP P13727
8908–14 0.53 0.41–0.68 1.35E-06 KCE1L:CD Q9UJ90
9595–11 0.41 0.29–0.59 1.55E-06 B4GT2 O60909
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1

Logistic regression model adjusted for covariates. Covariates were age, sex, smoking status (never/former/current), pack-years, body mass index, study center (NC/IA), time in transit, and season of blood draw in ALHS. In ARIC, all the above covariates were included with study center (MD/NC/MS/MN) and without packyears or time in transit or season. ORs indicate the multiplicative change in the odds of having asthma in relation to an increase in protein abundance.

Meta-analysis restricted to the 10,626 EA participants (ALHS and ARIC EA) revealed 120 FDR-significant proteins, including 95 that were significant with consistent direction of association in meta-analysis of all participants (EA and AA) (Table E2). Effect estimates in the EA specific meta-analysis were similar in ARIC AA participants (Table E2). The smaller number of statistically significant findings in the AA analysis reflects the much smaller sample size (100 AA vs 1,131 EA asthma cases).

Enriched pathways

We identified enrichment of multiple pro-inflammatory cellular pathways among the 115 FDR-significant proteins in our meta-analysis. Enriched (FDR<0.05) pathways included adhesion and diapedesis of granulocytes, pulmonary healing, airway inflammation in asthma, and Notch signaling (Figure 3A, Table E3). Significant pathways tended to be interrelated (Figure 3B).

Figure 3.

Figure 3.

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Functionally enriched (FDR<0.05) pathways among proteins related (FDR<0.05) to adult asthma.

A. Bar plot of -log10(FDR) for enriched Ingenuity Pathway Analysis (IPA) canonical pathways, sorted by p-value. Dashed line represents the significance threshold (FDR<0.05). B. Interrelationships among enriched (FDR<0.05) pathways.

Functional protein network

A functional interaction network of proteins FDR-significantly related to asthma status generated by the STRING database contained significantly more associations than expected by chance (23 interactions vs. 13 expected, enrichment p-value = 0.005; Figure 4). Cellular processes enriched within the network included chemotaxis and amyloidosis (Table E4).

Figure 4.

Figure 4.

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Network of functional associations between proteins significant at FDR<0.05.

This protein network had significantly more interactions than expected by chance when compared to a statistical background of all 4,860 measured proteins (P= 0.005). Colored nodes represent individual proteins and gray lines real or predicted interactions; line thickness denotes strength of evidence for association. Network made using STRING database version 12.0.

Proteomic score for asthma

In ALHS, to generate a proteomic score for asthma classification, we first identified 38 proteins after FDR<0.05 correction in standard one-by-one analysis. We applied LASSO on the entire set of 4,860 proteins which selected 29 proteins, including 12 not overlapping with those 38 selected based on FDR<0.05) (Table E5). To achieve better power, we included the 50 (38+12) proteins that were either FDR-significant or selected through LASSO in our prediction model. We calculated a proteomic score, which is the weighted sum of protein abundance for the 50 selected proteins for each participant (median=0.384 log2-transformed RFU, IQR=0.350 log2-transformed RFU) multiplied by the coefficients for each protein from the prediction analysis. A receiver operating characteristics (ROC) curve constructed from the proteomic score plus relevant covariates classified ALHS participants by asthma status significantly better than covariates alone (AUC=0.77, 95% CI=0.75–0.79 vs. AUC=0.64, 95% CI=0.61–0.66; p<0.001 for difference) (Figure 5A). When the proteomic score was tested in the ARIC EA cohort, as expected the AUC was slightly lower but still performed significantly better than covariates alone (AUC=0.72, 95% CI=0.69–0.75 vs. AUC=0.57, 95% CI=0.54–0.61; p<0.001 for difference) (Figure 5B).

Figure 5.

Figure 5.

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Performance of proteomic score in derivation (ALHS) and validation (ARIC) cohorts.

A. Receiver operating characteristics (ROC) curves comparing performance of the proteomic score with covariates (blue line) to the covariates alone (grey line) at classifying ALHS participants by asthma status. Area under the curve (AUC) of the proteomic score curve was significantly greater than the covariates-only curve. Covariates used: age (yrs.), sex, body mass index (kg/m2), smoking status (current vs. former, relative to never), pack-years of smoking, study center (IA or NC), season (fall vs. not), time in transit (hrs.). B. Displays the same comparison when the proteomic score is applied to classify ARIC EA participants. The proteomic score curve still performed significantly better than the covariates-only curve, though AUCs were lower than in the derivation cohort. Covariates used: age (yrs.), sex, body mass index (kg/m2), smoking status (current vs. former, relative to never), study center (NC, MS, MN, or MD).

Mendelian randomization (MR)

Given the cross-sectional nature of our analysis, we anticipated that significant associations would largely represent biomarkers rather than causes of asthma. However, to consider possible causal associations with asthma, we performed two-sample MR analysis.19 Because of the ancestry-specific nature of genomic reference data, the 120 proteins we included in MR analysis were from our meta-analysis of only EA individuals (ALHS and ARIC EA). Of the 120 proteins, 90 were amenable to two-sample MR by virtue of having previously identified pQTLs and adequate proxy SNPs. The two sample MR on these 90 did not identify significant evidence of causal association with asthma at FDR<0.05. However, seven proteins gave nominal evidence (p<0.05) of causal association with asthma (Table E6). Although colocalization analysis of these seven proteins did not generate evidence for the presence of any shared causal genetic variants with risk for asthma (i.e. all PP H4<0.8), we found a high posterior probability (PP) for the presence of two distinct causal variants at the C-reactive protein (CRP) locus (PP H3=0.98) (Table E7).

Druggable targets of implicated proteins

In the ChEMBL database, 20 of the 115 FDR-significant proteins identified by our overall meta-analysis annotated to at least one approved or investigational drug target in Phase 3 or higher (Table E8). These include omalizumab and ligelizumab, IgE inhibitors already approved for treatment of asthma, and dupilumab, an IL-4 receptor alpha-antagonist targeting IL-13 and IL-4, also approved for treatment of moderate-to-severe asthma and other allergy outcomes. We also identified >15 proteins that are targeted by approved or investigations drugs for other conditions (Table E8), representing possible candidates for drug repurposing.

Plasma proteins associated with combined asthma-atopy phenotypes in ALHS

We were able to examine associations of the plasma proteome with asthma with and without seroatopy in ALHS; seroatopy was not assessed in ARIC. We categorized ALHS participants into four groups: asthma with atopy (n=207), asthma without atopy (n=554), atopy without asthma (n=147), and neither asthma nor atopy (n=948) (Table E9). Relative to individuals with neither asthma nor atopy, the number of differentially abundant proteins (FDR<0.05) was 10 for asthma with atopy, 11 for asthma without atopy, and one for atopy without asthma (Table E10). Two proteins, pappalysin-1 (PAPP-A) and contactin-1 (CNTN1), were significantly related to asthma with or without atopy. As expected, IgE was associated both with asthma with atopy and atopy alone. Two proteins, alkaline phosphatase, placental-like (PPBN) and septin-6 (SEPT6), were significantly associated with the asthma with atopy phenotype but not with asthma in the overall meta-analysis of asthma ignoring atopic status.

Comparison of results to published studies

No large studies using recent SOMAScan proteomics assays have investigated associations between proteins and asthma compared to noncases. However, in a study comparing asthma (n=51) to COPD (n=34) using the older SOMAScan 1.3K assay,25 45 of the 115 proteins associated with asthma in our study (FDR<0.05) were available and 19 were related to asthma (vs COPD) (p<0.001=0.05/45) (Table E11). Conversely, we examined whether proteins identified in that study were associated with asthma in our data. Of 365 proteins associated with asthma vs COPD,25 351 were available in our data and nine were significantly related to asthma in our meta-analysis (p<1.4×10−4=0.05/351) (Table E12). We also interrogated proteins related to asthma subtypes in prior studies using the Olink Inflammation panel of 92 proteins (n~2000).26,27 Of 41 proteins related to obese asthma phenotypes,26 36 were available in our data and four were related to asthma (p<0.0014=0.05/36) (Table E13). Of 10 proteins related to eosinophilic and neutrophilic asthma,27 eight were available in our data and four were related to asthma (p<0.0063=0.05/8) (Table E13).

Discussion

Through meta-analysis of two large studies and the use of a proteomics platform interrogating 4,860 proteins, we associated differential abundance of over one hundred peripheral blood proteins with adult asthma. We confirmed some findings of previous asthma studies, but most were not previously reported in proteomic studies of asthma and thus represent novel results. Notably, some proteins related to asthma in our study are targets of investigational or approved drugs for asthma, but others are targeted by drugs approved for other conditions, representing potential candidates for drug repurposing. Our proteomic score predicted asthma status better than covariates alone.

Examining plasma allowed for a whole-body approach to disease characterization in a readily accessible tissue and illustration of physiologic differences between individuals with and without asthma. Additionally, this investigation concentrated on adults and examined associations of combined asthma-atopy phenotypes with the proteome, two areas which have been underdiscussed in the literature.10,28

Among our significant asthma findings, some have been associated with relevant respiratory outcomes. For example, immunoglobulin E (IgE) is a key immune and inflammation related molecule29 and cysteine-rich secretory protein LCCL domain-containing 2 (CRLD2) has been implicated with glucocorticoid response.30 Pappalysin-1 (PAPP-A), a known pregnancy-related protein, was reported to be more abundant in asthma cases and positively associated with asthma severity (N=55)31 and has been proposed as a biomarker of airway remodeling.32 We note that in our ALHS data women’s mean ages did not differ by asthma (62) versus not (63, p=0.16). This protein has been related to lung cancer development and growth.33–35 Identification of these proteins in plasma together with known acute phase reactants c-reactive protein (CRP) and serum amyloid A-1 (SAA1) is consistent with asthma promoting up-regulation of circulating pro-inflammatory mediators in addition to airway-specific changes.36,37

Pathway analysis highlighted mechanisms highly relevant to asthma, including the acute phase response and pulmonary injury-related signaling. Additionally, we found pathways related to epithelial-mesenchymal transition which plays an important role in airway remodeling in asthma as well as pathways related to axonal guidance signaling reflecting the role of innervation of the airways in asthma pathogenesis.38,39

A functional interaction network (Figure 4) constructed from proteins related to asthma contained significantly more associations than expected by chance, supporting the biological plausibility of our findings. Cellular processes found to be enriched within the network included chemotaxis and amyloidosis, suggesting a role for the systemic inflammatory response in asthma pathogenesis. Several proteins implicated in acute phase signaling and pulmonary injury (including CRP, SAA1, and MMP-9) are central nodes within the network, which corresponds closely with the results of our pathway analysis.

The proteomic signals we identified were useful in predicting asthma status and performed better than covariates alone. As expected, the proteomic score trained in ALHS had a lower AUC when tested in the ARIC study but still performed better than covariates alone. Several factors could contribute to different performance of the score in the two populations. Questionnaire items used to ascertain asthma were more detailed in ALHS. Additionally, ALHS was a nested case-control study that oversampled on asthma which provided more cases (761 vs. 470 in ARIC) and thus had greater power to successfully classify participants by asthma status using their proteome. Regardless, our ROC curve analysis serves as proof of concept that the plasma proteome may be used to portend an asthma phenotype.

In our proteomic score calculation, we used ALHS as the derivation cohort and ARIC EA as the validation cohort because ALHS, a nested case control study of asthma, had more asthma cases (761 in ALHS versus 370 in ARIC EA) and asthma status was defined in more detail using multiple questions. However, we also considered analyses switching the derivation and validation cohort. Using instead ARIC EA as the derivation cohort, 68 proteins were significant at FDR<0.05 and 194 proteins selected via LASSO for a total of 227 nonoverlapping proteins included in the final proteomic score model, resulting in AUCs of 0.92 (95 % CI: 0.89–0.94) in the derivation cohort (ARIC EA) and 0.79 (95% CI: 0.77– 0.81) in the validation cohort (ALHS). These higher AUCs might seem promising, but caution is advised in interpretation. In LASSO feature selection using ARIC EA, with the lower proportion of cases (4%, compared to 41% in ALHS) and lower total number of cases, the model needed 227 proteins, a lot more than the 50 proteins selected in ALHS, to classify asthma cases vs noncases. This much larger number of selected proteins likely reflect overfitting from using relatively imbalanced data in the ARIC cohort.

Our proteomic score approach is an application of a standard genetic risk score method to proteomic data.40 Studies using genome-wide association analyses to generate genetic risk scores have reported AUCs of models ranging between 0.68 and 0.84 for complex diseases.41–44 Our AUCs were in line with other proteomic studies reported for asthma using specific candidate proteins45,46 or for complex diseases using an earlier SOMAScan array or different proteomic assays.47–49 Compared to AUCs of 0.51–0.70 from previous large genetic studies for asthma,50,51 AUCs from our proteomic score prediction models are encouraging. The benefit of adding the proteome score to a covariates-only prediction model is reflected by the improved performance (gain in AUC) in our data: from 0.64 to 0.77 for ALHS and from 0.57 to 0.72 for ARIC EA. The proteome score we generated can be tested in future studies. Going forward, integrative omics studies might improve asthma prediction.

A recent paper used LASSO followed by backward elimination to identify robust sets of features because their LASSO results were highly variable.52 When we applied backward elimination after feature selection in our proteomic data, 32 proteins (out of 50) remained based on Akaike information criteria (AIC) in the derivation cohort (ALHS) (Table E5). Using this reduced selection of 32 proteins, the AUCs were almost identical to using all 50: 0.76 (95% CI 0.74–0.78) in the derivation cohort (ALHS) and 0.70 (95 % CI: 0.68– 0.72) in the validation cohort (ARIC EA) compared with 0.77 in ALHS and 0.72 in ARIC EA using all 50 proteins. As expected, with reduced number of proteins we observed reduced AUCs. We note that unlike the paper using backward elimination,52 results from LASSO in our proteomics study were not highly variable. Using our proteomic score approach, we provide a set of proteins and their weights so that other researchers can use them in their proteomics research. In contrast, data dimension reduction methods, such as Fisher discriminant analysis or latent class analysis, do not provide effect sizes for each feature selected and therefore results would not be transportable to other datasets. Perhaps for this reason, in contrast to the proteomic score approach that we applied, these methods have not been widely used in these settings.

Given the cross-sectional nature of our analysis, we expected that significant associations would reflect biomarkers of asthma rather than causes of the disease. Nonetheless, we performed Mendelian randomization analysis to examine whether proteins identified by our meta-analysis might play causal roles in asthma pathogenesis. While none met significance after multiple-testing correction, seven proteins gave nominally significant causal evidence. When considering these cross-sectional results and the polygenic and multifactorial nature of asthma, it is more likely that these plasma proteins represent biomarkers of the disease process rather than causal actors.

Several proteins associated with asthma in our analysis are direct targets of approved or investigational drugs, increasing clinical relevance of our findings. Three are already approved for asthma treatment which serves as a proof of principle. Two are the IgE inhibitors omalizumab and ligelizumab. Dupilumab is approved for moderate-to-severe asthma and regarded as a promising new treatment.53 Additionally we identified proteins that are targeted by approved or investigational agents for conditions other than asthma. The protein complement C5 (C5) and its cleavage fragment C5a anaphylatoxin (C5a) annotated to drugs, namely, ravulizumab and eculizumab, approved for paroxysmal nocturnal hemoglobinuria (PNH), that were recently investigated for treatment of respiratory conditions among COVID-19 patients.54,55 Given their role in regulating immune responses, complement C5 (C5) and C5a anaphylatoxin (C5a) have also been proposed as potential therapeutic targets for asthma.56,57 In addition, andecaliximab and sotatercept, which are in Phase 3 clinical development for gastric cancer and pulmonary arterial hypertension respectively, may have some biological relevance for asthma given their respective roles in targeting matrix metalloproteinase 9 (MMP-9), a potential marker of airway inflammation58–60 and reversing smooth muscle hyperplasia in pulmonary vasculature.61,62 These could be potential candidates for drug repurposing for treatment of asthma.63

In ALHS, seroatopy data were available to evaluate whether proteomic signals for asthma might differ by atopy. This analysis uncovered several plasma proteins significantly associated with combined asthma-atopy phenotypes but not with asthma overall, including septin-6 (SEPT6). Septins play a crucial role in inflammation and have potential in therapeutic treatments.64

A recent study of asthma genetics implicated 174 genes.65 Of these, one (IL4R) was associated with asthma in our data. The gene encodes interleukin-4 receptor subunit alpha that can bind interleukin 4 and interleukin 13 to regulate IgE production. Notably, a genetic variant in the gene roundabout homolog 2 (ROBO2) was related to inhaled corticosteroid response in a genome-wide association study of childhood asthma.37 Our proteomic findings complement findings from genetic studies.

We note that participants in the two studies were enrolled at very different time periods (ALHS from 2009–13; ARIC visit 3 from 1993–95) when different medications were in common use and medication records were collected differently. Inhaled corticosteroid use was similar between the two studies; inhaled bronchodilator information was not available in ARIC (Table E14). Regarding commonly used medications for other conditions, frequency of cholesterol lowering medications and antihypertensive medications did not show consistent differences by asthma status between ARIC and ALHS (Table E15). As the mean age of our study participants was around 60, we observed individuals with comorbidities including coronary heart disease, diabetes, and hypertension, but the distribution did not differ by asthma status (Table E16) and thus it is not likely that such comorbidities explain our asthma findings.

This study has several limitations. For both the ALHS and ARIC, participants were visited at their home or traveled to a dedicated study center and thus were unlikely to be experiencing an acute asthma exacerbation or recovering from a recent hospitalization when blood samples were drawn, limiting the range of asthma severity. Accordingly, few participants (n=29) were taking oral steroids at the time of their visit which limited power to examine effects on the plasma proteome. We did not validate our FDR-significant proteomic signals with a secondary quantitation method such as an antibody-based assay. However, several studies have demonstrated the validity and stability of SOMAScan plasma analyses.66–68 Finally, while composition of the circulating plasma proteome can vary greatly with renal function, we were unable to adjust for glomerular filtration rate (GFR) in both studies. However, our findings were not significantly altered by additional adjustment for cystatin-C, a plasma protein increasingly being used as an unbiased estimator of GFR (107 FDR-significant proteins, including 96/115 (75%) significant in original meta-analysis).69

A major strength of this study lies in its size: meta-analysis of proteomic results from two distinct cohorts raised our total sample size to almost 13,000 individuals, including 1,231 current asthma cases. Another strength is that the proteomics platform, SOMAScan assay (V4.0), allows for relative quantitation of a far greater number of plasma proteins than in previous studies of adult asthma.3,70 Our association analyses considered individuals of both European and African ancestry. ALHS participants also underwent allergen serology testing, enabling evaluation of co-occurrence of asthma and atopy, which has been understudied in the genomic literature. In clinical settings, 100 IU/ml is a common cut point for allergy, especially when using total IgE but also for specific IgE when used in combination with other patient history. However, large-scale epidemiologic studies comparing individuals with positive antibody levels vs not71 have commonly used a less restrictive cut point of 0.35 IU/mL.72,73 We used a stricter cutoff of 0.7 IU/mL for greater clinical relevance.24 Furthermore, although assessment of asthma was based on questionnaires, the use of population-based cohorts increases relevance of our findings to the daily experience of individuals living with asthma.

In summary, we associate over 100 plasma proteins with current adult asthma through meta-analysis of proteomic results from two large, population-based studies. We provide validation for some published studies, but most of our findings are novel. By applying the SOMAScan assay to the plasma proteome, we examined whole-body physiologic differences between individuals with and without asthma and in ALHS evaluated asthma by atopic status. Pathway analysis of our proteomic signatures suggests activation of both lung-related and generalized inflammatory signaling in asthma. We also demonstrate that the plasma proteome can be used in aggregate to provide prognostic information on asthma status. Finally, we suggest that some of these proteins may have value as novel druggable targets or for drug repurposing in future management of asthma.

Supplementary Material

Fig. E1 legends
Online methods
Fig. E1
Tables E
Table S

ACKNOWLEDGEMENTS

We are grateful to all the participants of the Agricultural Lung Health Study and Atherosclerosis Risk in Communities study for their invaluable contribution to this work. SomaLogic Inc. conducted the SOMAScan assays in exchange for use of ARIC data. We would also like to thank Marie Richards-Barber (Westat, Inc.) for additional support.

Financial Support:

This work was supported by the Intramural Research Program of the National Institutes of Health, National Institute of Environmental Health Sciences (NIEHS) (Z01-ES049030 and Z01-ES102385 and for ABW, contract no. HHSN273201600003I) and National Cancer Institute (Z01-CP010119), and in part by American Recovery and Reinvestment Act (ARRA) funds through NIEHS contract number N01-ES55546. The Atherosclerosis Risk in Communities Study has been funded in whole or in part by federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services (contract nos. 75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, 75N92022D00005). This work was supported in part by NIH/NHLBI grant R01 HL134320. BY is in part supported by the JLH Foundation. JJ is in part supported by grant no. HHSN273201600003I.

Footnotes

Conflict of interests: All authors have no conflict of interest within the scope of the submitted work.

Data availability:

Complete summary statistics and association results are available in the supplementary material. For ALHS, requests for the underlying data should be submitted to the executive committee of the parent cohort Agricultural Health Study by emailing Dr. Mikyeong Lee. Data requests will be evaluated based on pre-existing data sharing policies (https://aghealth.nih.gov/collaboration/process.html). For ARIC, proteome and phenotypic data will be available via application through the ARIC Data Coordinating Center (https://sites.cscc.unc.edu/aric/distribution-agreements).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Fig. E1 legends
NIHMS1973341-supplement-Fig__E1_legends.docx (12.4KB, docx)
Online methods
NIHMS1973341-supplement-Online_methods.docx (105KB, docx)
Fig. E1
NIHMS1973341-supplement-Fig__E1.pdf (179.3KB, pdf)
Tables E
NIHMS1973341-supplement-Tables_E.docx (223.7KB, docx)
Table S
NIHMS1973341-supplement-Table_S.txt (677.2KB, txt)

Data Availability Statement

Complete summary statistics and association results are available in the supplementary material. For ALHS, requests for the underlying data should be submitted to the executive committee of the parent cohort Agricultural Health Study by emailing Dr. Mikyeong Lee. Data requests will be evaluated based on pre-existing data sharing policies (https://aghealth.nih.gov/collaboration/process.html). For ARIC, proteome and phenotypic data will be available via application through the ARIC Data Coordinating Center (https://sites.cscc.unc.edu/aric/distribution-agreements).

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