The ratio of circulatory levels of sphingolipids to steroids predicts asthma exacerbations

Abstract

The lack of biomarkers to identify individuals at risk of asthma exacerbations remains a significant limitation to improving patient outcomes. To address this need, we analyze data from three asthma cohorts, combining up to 25 years of electronic medical records with sequential metabolomics studies, to develop and replicate a predictive model for asthma exacerbation risk. We identify asthma-associated biochemical pathways via global circulatory metabolomics and then apply targeted mass spectrometry methods to quantify selected steroids, sphingolipids, and microbial-derived metabolites. The sphingolipid-to-steroid ratios robustly associate with 5-year exacerbation risk (discovery p value = 1.63×10⁻²⁶-0.029; replication p value = 1.89×10⁻³⁶-0.033). Based upon these findings, we derive and replicate a simple 5-year predictive model of asthma exacerbations using 21 sphingolipid-to-steroid ratios that outperforms current clinical measures (discovery AUC = 0.90; replication AUC = 0.89). These findings underscore the value of metabolomic profiling to develop a practical, cost-effective clinical assay for asthma exacerbation risk that may improve patient care.

Asthma exacerbations remain hard to predict with routine tests. Here, the authors show that simple blood sphingolipid-to-steroid ratios predict five-year exacerbation risk and can underpin a practical, low-cost assay that outperforms standard clinical measures.

Introduction

Asthma exacerbations are a major health care burden and cause of disease morbidity, leading to progressive loss of lung function, airway remodeling, and enhanced disease severity^1,2. The heterogeneity of asthma complicates identifying individuals at high risk for future exacerbations^3,4, and there are no current clinical tests or biomarkers that effectively identify those at risk for asthma exacerbations^5–7, making this a critical unmet need^8–10.

Metabolomics studies have been successful in developing clinical biomarkers^11–14 and have the distinct advantage of reflecting both acute and long-term environmental influences in the context of underlying genetic predisposition. To date, asthma metabolomics studies have identified disruptions in several metabolic pathways, including sphingolipids, steroids, and microbial-derived metabolites^15–19, that characterize the overall disease state across a spectrum of symptoms. Despite this progress, limitations in current asthma metabolomics studies persist, including small sample size, lack of validation, incomplete capture of metabolites²⁰, and failure to consider the heterogeneity of the disease. Moreover, few studies have considered how metabolomics findings may be translated into clinical care. Metabolomics applications that characterize asthma heterogeneity and/or predict exacerbation risk may prove particularly useful for reducing the health burden and improving treatment efficacy.

For metabolomics studies to effectively transition into clinical applications, it is imperative to move beyond discovery metabolomics and perform targeted analyses that delve deeply into the disrupted metabolic pathways and interrogate the clinical applicability of the initial findings. Targeted assays aid in identifying candidate biomarkers by providing robust data with a high level of confidence in metabolite identity and are more appropriate for adaptation into clinical applications in terms of cost, reproducibility and overall feasibility. Despite the logical progression to move beyond discovery metabolomics, targeted studies for multiple diseases, including asthma, remain limited.^21,22 Among the targeted assays that did build on discovery metabolomics, there are notable successes in the development of clinical assays, for example, the application of ceramide ratios to assess risk for cardiac events²³. Validation of the metabolic pathways implicated to date in asthma, followed by further characterization of their overall impact on the disease—in particular their potential to predict exacerbation risk—is a necessary next step to understand how these findings may have clinical impact. When discovery metabolomics implicates multiple pathways for a disease, identifying the interrelationships between these pathways may bring a more complete understanding of the underlying disease mechanisms for biomarker development²⁴. Evaluating the impact of cross-pathway metabolite ratios for asthma is compelling for several reasons. These measures are straightforward to calculate and have been successfully used to develop predictive disease biomarkers previously²⁵. Metabolite ratios have the advantage of providing biological insights into the interplay between two metabolic processes and are analytically more robust.

The objective of this study was to build on the existing metabolomics literature to enable practical clinical translation in the context of real-life electronic medical records (EMRs). We utilized three well-characterized asthma cohorts, totaling 2513 participants, with up to 25 years of longitudinal data from EMRs and implemented discovery and independent replication across all stages of the study. We first validated the major dysregulated asthma pathways via discovery metabolomics. We then designed targeted assays to quantify the primary metabolites in the implicated pathways. We characterized the relationship between metabolites and multiple clinical traits for asthma and focused on the development and validation of a simple, cost-effective predictive biomarker panel to assess asthma exacerbation risk. In this process, we also used cross-pathway metabolite ratios to reflect the interrelationship of biological processes across metabolic pathways. This process established a framework that leverages global metabolomics followed by targeted assays to create a reliable and clinically relevant biomarker.

Results

This study included three asthma cohorts: the Mass General Brigham Biobank–Karolinska Asthma Study (MGBB-KAS, n = 1040), Mass General Brigham Biobank-Asthma (MGBB-Asthma, n = 610), and Mass General Brigham Biobank-Longitudinal Lung Function (MGBB-LLF, n = 823; formerly Omic Determinants of Longitudinal Lung Function in Asthma [ODOLLFA]), totaling 2513 individuals (Fig. 1). Together, these cohorts represent a broad range of clinical and demographic characteristics common in adult asthma (Table 1.1, 1.2). The asthma cases in MGBB-KAS and MGBB-Asthma had moderate-to-severe disease, as indicated by elevated blood eosinophil and neutrophil counts, reduced pulmonary function, and higher IgE levels. Across both cohorts, patients exhibited varying patterns of inhaled corticosteroid (ICS) and oral corticosteroid (OCS) over the clinical course of disease, including the 5-year period after metabolomic profiling.

Fig. 1. Study workflow.

MGBB-KAS The Mass General Brigham-Karolinska Asthma Study, MGBB-asthma The Mass General Brigham Biobank-Asthma, MGBB-LLF Mass General Brigham Biobank–Longitudinal Lung Function. This figure was created in BioRender. Su, J. (2025) https://BioRender.com/iqdbinq.

Global metabolomic profiling to identify and validate asthma-associated pathways

Discovery metabolomic profiling in the MGBB-KAS cohort identified 154 significant metabolite associations with asthma using a False Discovery Rate (FDR ≤ 0.05) correction for multiple testing. Of these metabolites, 46 replicated on a pathway level at nominal significance (p ≤ 0.05) in the MGBB-Asthma study, implicating eight metabolic pathways (Fig. 2, Supplemental Data 1). Significant asthma-associated metabolites included microbial–derived metabolites, involved in four metabolic pathways that have been previously reported: 1) creatine metabolism^26,27; 2) glycine, serine, and threonine metabolism^28–30; 3) methionine, cysteine, taurine metabolism^31,32; and 4) tryptophan metabolism^33–36. We observed an inverse relationship between asthma status and steroid metabolites while adjusting for ICS treatment, which was supported by extensive prior literature^16,31,37,38. Sphingolipids were strongly associated with asthma in the MGBB-KAS cohort, and their role in asthma has been substantiated in prior studies^18,19,39; however, they were not included in the older global metabolomics platforms used for the MGBB-Asthma cohort, which prevented internal validation of these associations. We also observed increases in amino sugar and mannose metabolites with asthma diagnosis; however, these pathways were less substantiated in prior studies^26,40. When considering these results in conjunction with the asthma metabolomics literature^15,16,18, we identified steroids, sphingolipids, and microbial-derived metabolites for follow-up investigation via targeted assays.

Fig. 2. Forest plot of significant global metabolomics association results in MGBB-KAS (discovery cohort, N = 1080) and MGBB-Asthma (replication cohort, N = 610).

K MGBB-KAS cohort, A MGBB-Asthma cohort, OR odds ratio. Points show adjusted odds ratios (ORs) from logistic regression; the error bars are 95% confidence intervals, and the center is the point estimate (OR). The x-axis is on a log scale. Two-sided Wald tests of β = 0 are reported with test statistic z = β/SE(β), and exact P values are reported in Supplemental Data 1. The significance levels of the regression analyses were FDR ≤ 0.05 for MGBB-KAS discovery and p value ≤ 0.05 for MGBB cohort replication. The Super Pathway is the broader metabolic category (e.g., lipid metabolism), and the Sub Pathway is the more specific functional grouping within that category (e.g., sphingolipid metabolism). The references indicated in the figure are also cited in the main text. This figure summarizes the global profiling associations from the pathways we selected to study further via targeted assays. These pathways were selected based on their significant metabolite associations using MGBB-KAS in the global profiling, further supported by our validation study (MGBB-Asthma) and prior literature.

Associations between targeted metabolites with asthma traits

We quantified 77 sphingolipids, 18 steroids, and 71 microbial-derived metabolites in serum samples from the MGBB-KAS cohort and observed 93 significant metabolite associations with asthma at an FDR ≤ 0.05 across six clinical measures related to asthma, including asthma diagnosis, pulmonary lung function, total serum IgE levels, and asthma exacerbations (Fig. 3, Supplemental Fig. 1.1, Supplemental Data 2). The strongest associations across all targeted assays were observed for asthma exacerbations: 29 sphingolipids and 17 microbial-derived metabolites were positively associated with exacerbations, while 7 steroids were negatively associated with exacerbations. Overall, androgen, glucocorticoid, and progestogen metabolites were inversely associated with asthma diagnosis and exacerbations and positively associated with forced expiratory volume in one second (FEV₁) and forced vital capacity (FVC). In particular, dehydroepiandrosterone sulfate (DHEAS), cortisone, and pregnenolone sulfate were associated with both asthma diagnosis (p value = 1.63 × 10⁻¹⁰, 5.33 × 10⁻⁹, 1.09 × 10⁻⁵, respectively) and decreased asthma exacerbations (p value = 3.11 × 10⁻⁸, 9.14 × 10⁻¹², 7.22 × 10⁻⁵, respectively). While there were only a few significant microbial-derived metabolites associated with asthma (arginine: p value = 7.3 × 10⁻⁵; indol-3-propionate: p value = 3.4 × 10⁻⁶) and lung function (FVC: kynurenine, p value = 9.23 × 10⁻⁴; FEV₁/FVC: betaine, p value = 2.87 × 10⁻⁴), 16 microbial-derived metabolites were associated with significant increases in exacerbations, including phenylacetylglycine, indoxyl sulfate, kynurenate, and quinolinate (p values = 6.66 × 10⁻¹⁰, 1.28 × 10⁻⁹, 8.67 × 10⁻⁹, and 3.69 × 10⁻⁸, respectively). Sphinganine-1-phosphate (sphinganine-1P) was positively associated with asthma (p value = 2.1 × 10⁻⁴). Many sphingolipid subclasses were associated with both increases in FEV₁ and FVC; however, there were no associations with FEV₁/FVC. Positive associations were observed with exacerbations across all sphingolipid classes, with Cer(d18:1/20:1), lactosylceramide(d18:1/14:0) (LacCer), and sphingomyelin(d18:1/14:0) (SM) exhibiting the most pronounced associations (p values = 2.48 × 10⁻⁶, 3.59 × 10⁻⁶, and 2.78 × 10⁻⁶, respectively). To further explore whether these associations were influenced by sex, we conducted sex-stratified analyses and present the results in Supplemental Data 3. We observed that males and females often exhibited distinct sets of significant metabolites for the same asthma phenotypes. In cases where overlapping metabolites were identified across sexes, the directions of association (β coefficients) were consistent, suggesting shared biological mechanisms despite potential sex-specific sensitivity. These findings indicate that while metabolite associations may vary by sex, the core directional relationships remain stable.

Fig. 3. Forest plot of significant targeted metabolites and metabolite ratio associations with asthma exacerbations in the MGBB-KAS cohort.

A Targeted steroid panel; B Targeted microbial-derived metabolite panel; C Targeted sphingolipid panel; D Sphingolipid/microbial-derived metabolite ratios; E Sphingolipid/steroid ratios; F microbial-derived metabolite/steroid ratios. Points show adjusted regression coefficients (β) from linear regression; the error bars are 95% confidence intervals based on model-based standard errors, and the center is the point estimate. Two-sided t tests for β = 0 are reported with test statistic t(df) = value, and exact P values are reported in Supplemental Data 3. The significance levels of the metabolite ratio regression analyses were FDR ≤ 0.05. The black metabolites represent those with p value ≤ 0.05 in the regression model, while the red metabolites indicate those with FDR ≤ 0.05 in the regression model.

Metabolite ratios strongly associate with asthma traits

We observed a marked elevation in the percentage of significant associations between asthma traits and metabolite ratios compared to individual metabolites (Fig. 3, Supplemental Fig. 1.2, Supplemental Data 4). We assessed the associations with asthma traits and 3898 sphingolipid to microbial-derived metabolite ratios (sphingolipid:microbial), 794 microbial-derived to steroid metabolite ratios (microbial:steroid), and 1248 sphingolipid to steroid ratios (sphingolipid:steroid). The overall association patterns identified consistent relationships between ratios of specific metabolite subclasses and asthma traits, either displaying strong overall significance or no significance at all (Fig. 4A, B). Thousands of associations between ratios and asthma traits were found to be FDR significant. The strongest associations among these were between asthma exacerbations and sphingolipid:steroid ratios, where 59.9% of these ratios tested were FDR significant. Ceramide/sphingomyelin to DHEAS ratios had the strongest associations with asthma exacerbations (p value = 1.63 × 10⁻²⁶ – 1.63 × 10⁻²²); however, significance with exacerbations was observed across a broad range of sphingolipid and steroid subclasses (Figs. 3 and  4). The ratios of multiple sphingolipids to DHEAS were also associated with asthma diagnosis (p value = 3.86 × 10⁻¹⁶ – 8.56 × 10⁻¹⁴), while ratios between ceramides/sphingomyelins and estrone/deoxycorticosterone were negatively associated with log(IgE). These findings are biologically plausible given that sphingolipids, especially ceramides, are known to modulate steroidogenesis through the regulation of steroidogenic gene expression and intracellular signaling. Alterations in sphingolipid:steroid ratios may reflect imbalances in hormonal regulation and lipid signaling, both of which are critical in the inflammatory and immune processes underlying asthma.

Fig. 4. Distinct metabolite ratios associated with asthma clinical measures.

A The percentage of significant metabolite ratios by metabolite assay for different asthma clinical measures across the MGBB-KAS and MGBB-LLF. B Overview of the most significant metabolite ratios consistently identified across MGBB-KAS and MGBB-LLF. C Manhattan plots of associations between prevalent asthma exacerbation and the sphingolipid:steroid ratios across MGBB-KAS and MGBB-LLF. FEV₁ forced expiratory volume in 1 second, FVC forced vital capacity, FEV₁/FVC the ratio of FEV₁ to FVC. Note: “Significant” refers to associations passing an FDR threshold of ≤0.05. Points = individual sphingolipid:steroid ratios (logistic regression). y = −log10(P); shape = effect direction (▼ OR < 1;—~0; ▲ OR > 1); color = numerator sub-pathway. Two-sided t tests for β = 0 are reported with test statistic t(df) = value, and exact P values are reported in Supplemental Data 4.

There were 873 FDR-significant associations between microbial:steroid ratios and asthma traits. The strongest microbial:steroid ratios were observed with asthma exacerbations (p values = 1.20 × 10⁻²³ – 1.55 × 10⁻¹⁷), with DHEAS in the denominator, whereas a broader range of metabolite ratios were associated with asthma diagnosis (p values = 2.37 × 10⁻¹³ – 1.79 × 10⁻¹⁰), FEV₁, FVC, and log(IgE) levels. There were 1603 FDR-significant associations between sphingolipid:microbial ratios and asthma traits. Specific metabolite subclasses were associated with asthma diagnosis (e.g., sphingosines, sphinganines, serotonin, indole-3-propionate), FEV₁ (e.g., ceramides, sphingomyelins, kynurenine), FVC (e.g., sphingomyelins, quinolinate), and asthma exacerbations (e.g., ceramides, methylobutyrate, isobutyrate); however, there were no significant associations with FEV₁/FVC or log(IgE).

Replication of metabolite ratio associations and asthma traits in an independent cohort

The significant metabolite ratio associations were independently replicated using the MGBB-LLF cohort (Supplemental Data 5). Similar association patterns were observed across both cohorts between clinical measures and pathway-level metabolite ratios. In both cohorts, the largest proportion of significant metabolite ratios was associated with asthma exacerbations, whereas a relatively small proportion of metabolite ratios was associated with lung function and log(IgE). The most significant metabolite ratios in MGBB-KAS replicated in MGBB-LLF, identifying ratios of specific metabolites and/or metabolite subclasses associated with distinct asthma traits: 1) Exacerbations: ceramides, sphingomyelin, DHEAS, isobutyrate; 2) FEV₁ and FVC: ceramides, sphingomyelins, kynurenine, quinolinate, and testosterone; and 3) log(IgE): ceramides, deoxycorticosterone, indoles, and estrone.

In both MGBB-KAS and MGBB-LLF, the highest proportion of FDR-significant associations was between 5-year incident and prevalent exacerbations and sphingolipid:steroid ratios. Manhattan plots of the exacerbations to sphingolipid:steroid ratios associations demonstrated that replication was consistent across the same subclasses of sphingolipid:steroid ratios in MGBB-LLF, with stronger overall statistical significance. Association p values in both cohorts ranged between 1.0 × 10⁻³⁰–1.0 × 10⁻¹⁰ and were as low 1.39 × 10⁻³⁶ in MGBB-LLF (Fig. 4C). Both cohorts showed the strongest associations for ratios with androgens, followed by glucocorticoids and progestogens. Significant ratios included multiple sphingolipid subclasses and were particularly elevated for those involving DHEAS, pregnenolone sulfate, androstenedione, cortisone, cortisol, and corticosterone in both cohorts. Further interrogation between asthma exacerbations and the sphingolipid:steroid ratios found that overall significance was driven by the combined impact of both the numerator and denominator of the metabolite ratios, rather than being driven by either the denominator or numerator alone (Supplemental Fig. 2). Associations were not driven by outliers in the ratio distributions (Supplemental Fig. 4) and were more correlated with distinct clusters of the sphingolipid:steroid ratios and correlated with specific sphingolipid and/or steroid metabolites (Supplemental Fig. 3). Results were robust to various covariate adjustments.

Sphingolipid:steroid ratios predict asthma exacerbations

To assess whether sphingolipid:steroid ratios can improve upon current clinical metrics in predicting exacerbations, we first evaluated the associations between clinical metrics available in the EMR and incident exacerbations. Despite nominal associations between higher neutrophil counts and exacerbators in the MGBB-KAS cohort (p = 0.028) and lower FVC among exacerbators in the MGBB-LLF cohort (p = 0.049), no clinical measures differed significantly between exacerbators and non-exacerbators across both cohorts (Fig. 5A). This suggests that current clinical characteristics alone are insufficient to distinguish which individuals will experience an asthma exacerbation in the next five years. We then assessed the value of including sphingolipid:steroid ratios to predict incident exacerbations alone and when used in combination with clinical measures.

Fig. 5. Sphingolipid:androgen ratios are predictive of asthma exacerbations.

A The basic demographic and clinical characteristics between individuals with and without 5-year incident asthma exacerbations are presented for both the MGBB-KAS and MGBB-LLF cohorts; B ROC curves for incident asthma exacerbation classifiers across MGBB-KAS and MGBB-LLF; C Cox model results and cumulative incidence plots for the time until the first asthma exacerbation revealed by all 21 selected ratios in the predictive models in MGBB-KAS. FEV1 forced expiratory volume in 1 second, FVC forced vital capacity, FEV1/FVC the ratio of FEV1 to FVC, ROC receiver operating characteristic, AUC area under the curve, ICS inhaled corticosteroids. Curves show cumulative incidence = 1−Kaplan–Meier S(t) for strata Q1 and Q4. The center is the estimated cumulative incidence; shaded ribbons denote 95% pointwise confidence intervals.

Using elastic net regression, the feature selection retained 21 sphingolipid:steroid ratios in the predictive model for incident exacerbations. Individual Cox models of all 21 sphingolipid:steroid ratios were significant and able to differentiate time until the first asthma exacerbation between exacerbation prone (Q1) and non-exacerbation prone (Q4) individuals in MGBB-KAS (Fig. 5C). The difference in mean days to the first exacerbation between the two groups varied across the sphingolipid:steroid ratio, with most differences exceeding 100 days. Ratios with cortisone in the denominator, including Cer(d18:1/20:1):cortisone, HexCer(d18:1/24:1):cortisone, Sphinganine(d18:0):cortisone, and SM(d18:1/20:0):cortisone showed the strongest differentiations, with mean differences ranging from 265 to 366 days in the time until first exacerbation between the two groups (p value range = 2.50 × 10⁻¹² – .02 × 10⁻⁹). When the metabolite ratios were available, we evaluated these associations in MGBB-LLF and identified that the biggest differences in the time until exacerbations were with ratios where DHEAS is in the denominator (p value range = 9.98 × 10⁻¹² – 7.3 × 10⁻⁷), replicating these findings in MGBB-KAS. Ratios with cortisone did not replicate our findings in MGBB-KAS (Supplemental Fig. 5).

The prediction models that included the 21 sphingolipid:steroid ratios had the greatest predictive accuracy when compared with other clinical and baseline variables. The best predictive accuracy for 5-year incident asthma exacerbations, including 21 sphingolipid:steroid ratios serum IgE level, and baseline variables (race, ICS medication), achieving an area under the curve (AUC) of 0.901 (Supplemental Data 6). This predictive accuracy was validated in the MGBB-LLF cohort with a similar AUC of 0.893, despite the unavailability of 11-deoxycortisol in the MGBB-LLF cohort, which reduced the number of ratios to 16. In the predictive model, we included only individuals with complete data for selected input variables. Participants with missing values for candidate predictors (such as FEV₁, IgE, or eosinophils) were excluded whenever these variables were part of model training (Table 2 summarizes missingness). When serum IgE was excluded from the model, the predictive model remained robust, with AUC values of 0.793 in MGBB-KAS and 0.724 in MGBB-LLF, indicating that the sphingolipid:steroid ratios alone have excellent predictive power. Recognizing the potential for selection bias when restricting the prediction modeling to asthma cases with specific asthma measures (e.g., IgE levels, FEV₁, eosinophils, etc.), we conducted additional analyses to evaluate how these patterns of missingness might impact our study conclusions. Specifically, we evaluated how other clinical characteristics of asthma cases may vary when IgE and/or FEV₁ measures were missing. We found no significant differences in other asthma-related measures, such as eosinophil and neutrophil counts/percentage, lung function, between cases with and without these data. These findings suggest that missingness in IgE and/or FEV₁ in the use of asthma cases is unlikely to introduce systematic bias, and therefore is unlikely to affect the development of the prediction model.

Table 2.

Characterization of asthma cases in MGBB-KAS and MGBB-LLF by relevant asthma measures and medication use

Characteristics	MGBB-KAS		MGBB-LLF
	Asthmatic (N = 540)	Missing (%)	Asthmatic (N = 823)	Missing (%)
Eosinophils (K/uL, mean ± SD)	0.201 (0.182)	75 (13.9%)	0.185 (0.164)	82 (10.0%)
Neutrophils (K/uL, mean ± SD)	4.85 (2.07)	75 (13.9%)	5.06 (2.27)	82 (10.0%)
FEV1 (L, mean ± SD)	2.40 (0.907)	405 (75.0%)	2.23 (0.803)	444 (53.9%)
FVC (L, mean ± SD)	3.19 (1.06)	411 (76.1%)	3.02 (0.958)	471 (57.2%)
FEV1/FVC (mean ± SD)	0.741 (0.107)	411 (76.1%)	0.729 (0.116)	471 (57.2%)
Log10(IgE) (K/uL, mean ± SD)	1.84 (0.658)	363 (67.2%)	1.91 (0.72)	468 (56.9%)
ICS prescriptions (past 5 years, mean ± SD)	11.3 (13.2)	12 (2.2%)	10.9 (12.8)	0 (0%)
ICS prescriptions (next 5 years, mean ± SD)	7.86 (10.4)	56 (10.4%)	7.41 (11.2)	0 (0%)
OCS prescriptions (past 5 years, mean ± SD)	6.51 (10.7)	12 (2.2%)	8.56 (14.0)	0 (0%)
OCS prescriptions (next 5 years, mean ± SD)	8.40 (15.2)	56 (10.4%)	8.98	0 (0%)

MGBB-KAS Mass General Brigham Biobank–Karolinska Asthma Study, MGBB-LLF Mass General Brigham Biobank–Longitudinal Lung Function, FEV₁ forced expiratory volume in 1 second, FVC forced vital capacity, FEV₁/FVC the ratio of FEV₁ to FVC, ICS inhaled corticosteroids, OCS oral corticosteroids.

ICS and OCS values represent the mean number of prescriptions per patient over the specified 5-year period, not percentage use.

In comparison, the baseline model of race and ICS medication achieved an AUC of 0.498. To further evaluate the predictive contribution of prior exacerbation history and traditional clinical markers, we constructed additional models incorporating OCS prescriptions, FEV₁, eosinophils, and neutrophils. Prior exacerbation history (baseline model plus OCS prescriptions history) alone achieved an AUC of 0.726 in MGBB-KAS and 0.781 in MGBB-LLF. Including metabolite ratios alongside prior exacerbation history (Race, ICS, OCS, metabolite ratios) further improved the AUC to 0.845 and 0.844, respectively. Similarly, adding FEV₁ to the race and ICS baseline model modestly improved prediction (AUC = 0.610–0.588), and combining FEV₁ with metabolite ratios (Race, ICS, FEV₁, metabolite ratios) achieved an AUC of 0.809 in MGBB-KAS and 0.733 in MGBB-LLF.

In parallel, we constructed models incorporating eosinophils and neutrophils alongside metabolite ratios. These models resulted in lower AUC values (e.g., 0.795) compared to those that included IgE (AUC = 0.901), as shown in Supplementary Data 7. Models including only clinical markers (eosinophils, neutrophils, FEV₁, or IgE without metabolite ratios) performed worse (AUC range: 0.515 to 0.654), all substantially lower than those incorporating metabolite ratios (Fig. 5B). These findings demonstrate that while blood eosinophils, FEV₁, and prior exacerbation history are clinically informative, combining sphingolipid:steroid metabolite ratios with selected clinical variables yields the highest predictive accuracy for asthma exacerbations in our cohort.

Discussion

Using three large asthma cohorts with up to 25 years of EMRs, this study moves beyond global metabolomics profiling by following up primary findings with targeted assays to develop and validate an accurate predictive biomarker for 5-year incident asthma exacerbation. Throughout this process, we validated key metabolic pathways for asthma and characterized the relationship between metabolites and metabolite ratios with a spectrum of asthma traits. Our findings reinforce the important role of steroids, sphingolipids, and microbial-derived metabolites in asthma and identify reproducible relationships between distinct metabolite ratios and specific asthma traits. The strongest relationship was between sphingolipid:steroid ratios and exacerbations, where single ratios could differentiate high and low exacerbation proneness by up to one year. Based on these ratios, we constructed a validated predictive model for asthma exacerbations incorporating only 12 sphingolipids and 4 steroids, which outperformed conventional clinical predictors—including prior exacerbation history, FEV₁, eosinophil count, and IgE level—in both discovery and replication cohorts. The simple prediction models for exacerbations developed here offer cost-efficiency, increased accuracy, and straightforward implementation, suggesting a viable option for clinical assay development that may be useful in improving asthma treatment efficacy.

After validating asthma-association metabolic pathways via global metabolomics, we selected steroids, sphingolipids, and microbial-derived metabolites for detailed investigation using targeted assays, ultimately focusing on the relationship between steroid and sphingolipid metabolism and 5-year asthma exacerbation risk. Sphingolipids and steroids have independently demonstrated important roles in asthma. Low ceramide levels in utero and early life have been linked to abnormal lung development and asthma^41,42. While the specific mechanisms remain unclear, evidence suggests that environmental triggers, including allergens and infections, induce sphingolipid signaling and mast cell activation to instigate inflammation during asthma exacerbations^43,44. Genetic studies have also shown that ORMDL3 polymorphisms, which inhibit serine palmitoyltransferase (SPT) and regulate sphingolipid homeostasis, contribute to asthma⁴⁵. These findings demonstrate that sphingolipids play a complex, multifactorial role in asthma, yet more remains to be explored. The primary mechanisms of steroidogenesis are better understood and form the basis for corticosteroid treatment in asthma. Steroid deficiencies lead to increases in inflammation in the lung and are a risk factor for asthma exacerbations that start in utero and persist over the life course^46–48. While ICS treatment effectively reduces lung inflammation and is efficacious for asthma control, evidence suggests that prolonged ICS treatment may suppress adrenal function, further exacerbating the underlying physiologic state¹⁶. The inherent link between endogenous steroid production and exogenous steroid treatment, therefore, complicates its role in the disease process. Nevertheless, this makes steroid metabolism an ideal candidate for optimizing asthma control.

While we observed associations between asthma exacerbations and both sphingolipids and steroids, the associations and prediction accuracy were vastly improved with sphingolipid:steroid ratios. Importantly, the significant exacerbation associations replicated consistently across subclasses of sphingolipid:steroid ratios, with the most pronounced associations replicating for androgens, followed by glucocorticoid and progestogen species with that respective order across both cohorts.

The use of metabolite ratios enables us to capture relative imbalances between metabolic pathways, offering insights that may not be apparent from individual metabolite levels alone. In asthma, where inflammatory lipid signaling and endocrine function are closely intertwined, sphingolipid-to-steroid ratios may reflect meaningful shifts in these biological processes. The strong associations between sphingolipid:steroid ratios and asthma exacerbations are further supported by the established interrelationship between these metabolite classes. Multiple sphingolipid species act as secondary modulators/regulators of steroidogenesis^49–51, with distinct cellular functions that include regulating steroidogenic gene transcription^52–54 and acting as both intracellular second messengers^55,56 and extracellular paracrine/autocrine regulators^57–59. Ceramides—produced through either de novo synthesis or via the hydrolysis of sphingomyelin - serve as the precursor to all sphingolipids and, therefore, play a particularly important role in sphingolipid metabolism. Ceramides are powerful in steroidogenesis because they can both directly and indirectly modulate steroid hormone production through their metabolism into other bioactive sphingolipids. Ceramides also act directly to suppress androgens and progestogens, further validating why these ratios may be clinically relevant⁶⁰. Our findings broadly substantiate these mechanistic studies with particularly strong associations observed between ratios of ceramide, sphingomyelin, and Hex/Lac ceramide species to androgen, glucocorticoid, and progestogen species.

Even single sphingolipid:steroid ratios demonstrated excellent discriminatory ability with direct clinical relevance, with single ratios effectively differentiating high and low exacerbators by as much as one year, highlighting the potential importance of this approach for clinical applications. While steroid regulation is important in asthma, the suppression of steroids is likely both a part of the disease etiology and a consequence of long-term ICS treatment. Yet, there is more to learn about how this pathway may impact asthma and its treatment. The relationship with the sphingolipids provides a new angle for how we might understand their impact. While individuals who have the same levels of specific steroids overall, when examined in combination with sphingolipids, some individuals are at much higher risk of an exacerbation, while others remain at low risk. This phenomenon sheds new light on steroids overall, where we can accurately discriminate between two people with similar cortisone or DHEAS levels into low and high-risk groups. This work suggests that the interaction between sphingolipids and steroids, rather than their isolated effects, may play a pivotal role in more fully understanding the condition and suggests that increased study into the interaction of these pathways is merited. While metabolomics research has primarily focused on the dysregulation of metabolites within a pathway, the interrelation between dysregulated pathways - captured in metabolite ratios—may be a crucial metric to describe disease-specific perturbations.

Much of the progress with metabolite ratios to date has focused on alterations within a given metabolic pathway, while a relatively limited amount of epidemiological research has explored the relationship between metabolites across pathways. The use of within-pathway ratios, such as ceramide ratios for cardiovascular disease (CVD)⁶¹, kynurenine:tryptophan ratio for cancers⁶² and CVD⁶³, and various lipid ratios for CVD⁶⁴ haas marked some of the substantial advancements toward clinical translation. While between-pathway relationships are less well-studied, the approach we implemented here, by first limiting the metabolite ratios to metabolites from select pathways that are implicated in asthma, offers an initial approach to limiting the total number of ratios. Additional mechanistic work, such as what has been described, the interdependence between sphingolipids, steroids, and specific microbial-derived metabolites provides another approach to identify biologically relevant metabolite ratios^65–67 that then may be studied further via targeted assays. However, the process of translating these findings into a clinical assay requires that the selected metabolites be vetted as viable candidates for clinical use. Sphingolipid:steroid ratios are not only predictive but would be relatively straightforward to implement into a clinical setting. These molecules are readily amenable to clinical assay development because they are abundant, stable, and the analytical methods for their quantification are relatively straightforward and inexpensive. If these ratios can accurately predict exacerbation risk within the next 6 months, that would enable the implementation of preventative measures to protect against an exacerbation. In addition, these ratios may identify those asthmatics who have poorly controlled asthma as demonstrated by increased numbers/frequency of exacerbation, suggesting that they should receive alternative treatment (e.g., biologics).

In addition to the finding between spinngolipid:steroid ratios and exacerbations, the distinct patterns observed between specific metabolite ratios and other asthma traits offer several viable findings for follow-up. Specific ratios, such as ceramide/sphingomyelin to kynurenine/quinolinate, were strongly associated with lung function measures, but not other asthma traits. The phenotypic specificity between key metabolite ratios and specific clinical outcomes may also be important for understanding the biological mechanisms of asthma. Prior metabolomic studies have identified strong comorbidity across disease phenotypes⁶⁸; however, there is a lack of specificity between metabolites and specific clinical outcomes. The specificity observed in the ratio associations suggests that these may serve as potential biomarkers for specific clinical outcomes.

The current study has several limitations. First, the discovery metabolomics analyses conducted in the MGBB-KAS and MGBB-Asthma cohorts were performed using different platforms, resulting in discrepancies in metabolite annotations. Consequently, the validation of global association findings was performed at the sub-pathway level rather than at the level of individual metabolites. While targeted assays were applied in the MGBB-KAS cohort, the validation process in the MGBB-LLF cohort relied on global metabolomics data, which meant that not every metabolite or ratio identified in MGBB-KAS could be directly replicated in MGBB-LLF. This limitation underlines the challenges of cross-platform metabolomics analysis and highlights the importance of using targeted assays for validation, because they offer greater reproducibility and comparability across studies and laboratories⁶⁹. Further follow-up work can focus on additional targeted assays covering other metabolic pathways. Second, while asthma clinical phenotypes used in this study were extracted from the EMR data, it is essential to recognize that real-world data, such as EMRs, might not match the quality of data derived from clinical studies or surveys. This discrepancy arises because EMRs are primarily tailored for clinical care rather than research. To address EMR-related concerns, we enhanced data reliability by calculating median laboratory test values over a five-year period around the data collection date, ensuring a robust dataset for our analysis. Our study utilizes EMR data from adult populations, which captures typical clinical practice. In our cohorts, IgE results were heterogeneously captured, leading to substantial missingness. Despite this, our analyses indicate that such missingness does not introduce meaningful bias into our predictive model. Moreover, the addition of IgE measurements significantly enhances the predictive accuracy of our model, suggesting IgE may reflect key physiological mechanisms underlying exacerbation susceptibility. Pending further validation, our results advocate for the regular assessment of IgE in adults with asthma to improve overall disease management. Additionally, while we used OCS prescriptions as a proxy for asthma exacerbations, we acknowledge that OCS may be prescribed for conditions unrelated to asthma. To assess the validity of this proxy, we conducted a correlation analysis between OCS prescription count and documented asthma exacerbation diagnoses in the EMR, which yielded a moderate-to-strong correlation (r = 0.573). This suggests reasonable specificity in our definition, although we recognize the need for future studies with more granular, temporally linked clinical data to further validate this endpoint. Furthermore, we observed variations in sample characteristics across the three cohorts, with the MGBB-KAS cohort utilizing serum for metabolomics measurements, in contrast to plasma used in the other cohorts. The strong validation suggested that our findings were robust to these cohort and matrix differences. Last, the study considered the impact of ICS treatment on steroid levels by adjusting the analytical model for ICS usage, addressing potential confounding effects and ensuring the validity of our results. Although faced with these constraints, we demonstrated robust replication of our findings. The use of targeted assays provided biochemical insights that were instrumental in refining disease prediction models and enhancing our understanding of the mechanisms underlying asthma and its diverse manifestations. These findings underscore the potential of metabolomics as a pivotal tool in the advancement of precision medicine. Moving forward, the next step will involve further refining these predictive models by incorporating additional asthma-related features, which will be crucial for enhancing the precision and application of metabolomics in precision medicine.

As we move toward precision medicine, there is a need to translate the findings from large-scale omics studies into viable clinical biomarkers. This study focuses on bridging the gap to translation by focusing on asthma exacerbations, which comprise a major portion of the overall disease burden. We used the findings from global metabolomics to develop targeted assays that quantified select steroids, sphingolipids, and microbial-derived metabolites. We then developed a 5-year predictive model for asthma exacerbation risk using 21 sphingolipid-to-steroid ratios with high predictive accuracy that outperforms current clinical measures (discovery AUC = 0.90; replication AUC = 0.89). These findings underscore the value of metabolomics profiling and metabolite ratios to develop a practical, cost-effective clinical assay for asthma exacerbation risk that may improve patient care. Metabolite ratios have the potential to add to the predictive space and further inform us about biological mechanisms that contribute to important clinical outcomes.

Methods

Overall study design

The overall goal of this study was to develop an accurate predictive model of exacerbations using global metabolomics profiling followed by selected targeted assays across three asthma cohorts (n = 2513) (Fig. 1). The study design includes three stages: 1) Stage 1: Discovery metabolomics to identify asthma metabolic pathways related to asthma; 2) Stage II: Targeted assays for follow-up study and identification of top metabolites and metabolite ratios associated with asthma; 3) Stage III: Predictive model development for 5-year asthma exacerbation risk (Fig. 1). Stage I employed two case-control studies, MGBB-KAS (n = 1080) and MGBB-Asthma, n = 610), with discovery circulatory metabolomics profiling to identify and validate top metabolite pathways associated with asthma. Combining these findings with existing metabolomics literature, top pathways were identified for further study via targeted assays. In Stage II, three targeted assays were developed to quantify 166 selected steroid, sphingolipid, and microbial-derived metabolites across the top pathways. Using asthma cases from two studies, MGBB-KAS and the MGBB-LLF (n = 823), the relationship between targeted metabolites and their ratios with asthma traits was evaluated to identify the strongest candidates for prediction modeling. In Stage III, a 5-year prediction model for asthma exacerbation risk was developed and compared with the performance of current clinical measures.

Cohort descriptions

The MGBB (https://biobank.partners.org) resulted from a project led by MGB (formerly known as Partners HealthCare) in which DNA, plasma, and serum samples were connected to clinical data from the EMR obtained from over 125,000 consented patients as of June 2021. Informed consent was obtained in written form from all participants. Patients involved in the biobank have provided consent through either in-person recruitment or electronic informed consent (eIC). Their involvement in the biobank allowed for blood sample data, EMR data, and survey data, including lifestyle, environmental, and family history information to be collected. The MGBB is linked with the MGB Research Patient Data Registry (RPDR), which stores EMR data on over 4.6 million patients in a SQL Server database, allowing researchers to query the RPDR through the MGBB Portal. The RPDR includes demographic data, diagnoses, procedures, medications, inpatient and outpatient encounter information, provider information, laboratory data, imaging and pathology data, and insurance information. EMR data are available from as early as 1999, and recruitment into the MGBB began in 2011. As such, individual participants may have up to 25 years of longitudinal EMR data, though the actual span varies by individual. For enrolled participants, extensive longitudinal EMR data, including relevant phenotypic information pertaining to asthma, can be extracted.

MGB worked with the Institutional Review Board (IRB: 2014P001109) for approval for this collection of biospecimen data and the use of human participants for research through both the MGBB and the RPDR. The information obtained is approved for use in all types of research on human health, including genomics, biomarker analyses, epidemiology, and cell line creation. In this study, three independent asthma studies selected individuals from the MGBB using distinct study designs. Clinical phenotypes related to asthma, including lung function measures (FEV₁, FVC and FEV₁/FVC), blood differentials (eosinophil and neutrophil counts), IgE levels, ICS treatment, and asthma exacerbations defined via OCS treatments, were harmonized across all three cohorts. For some clinical phenotypes, such as lung function measures, blood differentials, and IgE levels, multiple values were often available per participant due to repeated clinical visits. To ensure temporal consistency with the metabolomics data, we selected the value closest in time to the serum sample collection date for each individual for analysis.

Discovery cohort: MGB–KAS

The MGBB-KAS is a matched case-control study of asthma from the MGBB, consisting of 540 asthma cases and 540 non-asthmatic controls. An individual with asthma was defined by all the following criteria: 1) an asthma diagnosis defined by the asthma prediction algorithm⁷⁰ in the RPDR (positive predictive value > 85%); 2) at least one oral steroid prescription or one inhaled steroid prescription (ICS of ICS/LABA ≥ 1). Control individuals were identified by the following: 1) no diagnosis of asthma (negative predictive value > 99%); 2) no ICS or OCS medications; 3) matched on age, sex, and self-reported race. All 1140 participants of MGBB-KAS were non-smokers and had serum samples available. Participants with missing serum collection dates and BMI information were excluded from subsequent analyses, along with their matched participants. As a result, 540 pairs of asthma cases and controls were included in the analyses (total n = 1080; Table 1).

Table 1.

Description of baseline characteristics of the participants in the biobank/electronic medical record (EMR)-based asthma cases and matched controls cohort (MGBB-KAS), one EMR-based asthma and control cohort (MGBB-Asthma), and another EMR-based asthma cases cohort (MGBB-LLF)

Characteristics		MGBB-KAS			MGBB-Asthma			MGBB-LLF
		Asthmatic (N = 540)	Control (N = 540)	Total (N = 1080)	Asthmatic (N = 287)	Control (N = 323)	Total (N = 610)	Asthmatic (N = 823)
Age (mean, SD, year)		55.5 (15.6)	55.4 (15.6)	55.4 (15.6)	33.1 (6.62)	32.4 (3.67)	32.7 (5.27)	55.2 (15.9)
BMI (mean, SD, year)		30.7 (7.61)	28.0 (5.63)	29.3 (6.83)	28.3 (8.05)	23.2 (3.11)	25.6 (6.47)	30.2 (7.99)
Sex
	Female	411 (76.1%)	411 (76.1%)	822 (76.1%)	208 (72.5%)	151 (46.7%)	359 (58.9%)	584 (71.0%)
	Male	129 (23.9%)	129 (23.9%)	258 (23.9%)	79 (27.5%)	172 (53.3%)	251 (41.1%)	239 (29.0%)
Race
	Black	34 (6.3%)	34 (6.3%)	68 (6.3%)	34 (11.8%)	17 (5.3%)	51 (8.4%)	63 (7.7%)
	Other	70 (13.0%)	70 (13.0%)	140 (13.0%)	31 (10.8%)	53 (16.4%)	84 (13.8%)	90 (10.9%)
	White	436 (80.7%)	436 (80.7%)	872 (80.7%)	222 (77.4%)	253 (78.3%)	475 (77.9%)	670 (81.4%)
Ethnicity
	Hispanic	46 (8.5%)	42 (7.8%)	88 (8.1%)	9 (3.1%)	24 (7.4%)	33 (5.4%)	14 (1.7%)
	Non-Hispanic	494 (91.5%)	498 (92.2%)	992 (91.9%)	278 (96.9%)	299 (92.6%)	577 (94.6%)	809 (98.3%)
Smoking status
	Non-smoker	540 (100%)	540 (100%)	1080 (100%)	210 (73.2%)	272 (84.2%)	482 (79.0%)	840 (100%)
	Smoker	0 (0%)	0 (0%)	0 (0%)	77 (26.8%)	51 (15.8%)	128 (21.0%)	0 (0%)

MGBB-KAS Mass General Brigham Biobank–Karolinska Asthma Study, MGBB-asthma The Mass General Brigham Biobank-Asthma, MGBB-LLF Mass General Brigham Biobank–Longitudinal Lung Function, SD standard deviation, BMI body mass index.

Validation cohort: MGBB-asthma

The MGBB-Asthma is an independent asthma case-control study in MGBB¹⁶ with 287 asthma cases and 323 control cases (total n = 610; Table 1), with no overlap from the MGBB-KAS cohort. A validated phenotyping algorithm⁷⁰ in the RPDR was used for asthma diagnosis and identified 287 individuals with asthma (positive predictive value > 85%) and 323 controls (negative predictive value > 99%) to generate the MGBB-Asthma population (total n  =  610 individuals; Table 1). Non-fasting plasma samples for the MGBB-Asthma cohort were collected between October 2010 and March 2017 and were stored immediately (within 4 hours) in a −80 °C freezer. Controls were randomly selected from the pool of individuals without asthma with available plasma samples.

Validation cohort: MGBB-LLF

The MGBB-LLF is also an independent MGBB cohort of 823 severe asthma cases, with no overlap from the MGBB-KAS and MGBB-Asthma cohorts. An individual with severe asthma was defined by all the following: 1) an asthma diagnosis defined by the asthma prediction algorithm⁷⁰ based on the RPDR; 2) evidence of persistent disease activity, defined as at least three lung function assessments and/or ICS treatment in conjunction with adrenocorticotropic hormone (ACTH) testing. All participants of MGBB-LLF had available plasma samples and were non-smokers (total n = 823, Table 1).

Metabolomics profiling

Untargeted assays

Discovery untargeted metabolomics data for MGBB-KAS

Samples were assayed on three liquid chromatography–mass spectrometry (LC-MS) platforms (HILIC-positive, lipidomics-positive, and lipidomics-negative) using an Agilent QToF 6550 interfaced with a Rapid Resolution separation module. For data quality control (QC) and pre-processing, coefficients of variation (CV%) were first computed, and features with CV% ≥ 25% were removed. Missingness was calculated across each feature and each sample. Features missing ≥ 75% were also excluded. Missing values were imputed as half the minimum value across all samples for each feature. The resulting plots of principal component analysis (PCA) were examined, and the interquartile range (IQR) and skewness of each feature were computed. All features were subsequently log-10 transformed and Pareto-scaled, and IQR and skewness were recalculated after transformation. PCA was performed again, and the distribution of PCs according to demographic variables was examined. After QC, 2338 features from the HILIC-positive platform, 2672 features from the lipidomics-positive platform, and 1918 features from the lipidomics-negative platform were retained. Features with less than or equal to 30% missing were included in subsequent analyses (2317 from HILIC-positive, 2650 from lipidomics-positive, and 1885 from lipidomics-negative platforms). Metabolite annotations were assigned to features using the MS-DIAL software, resulting in 1902 metabolite identifications^71,72. Unidentified features and xenobiotics were not included in further analysis. A detailed description of the data acquisition and the metabolite annotation workflow is provided in the supplemental material.

Global untargeted metabolomics data for MGBB-Asthma and MGBB-LLF

Untargeted global plasma metabolomics profiling was generated by Metabolon Inc. (Durham, North Carolina, USA). CV were measured in blinded QC samples randomly distributed among study samples. Batch variation was controlled for in the analysis. Sample preparation and global metabolomics profiling were performed according to methods described previously^73–75. Metabolomic profiling was performed using four liquid chromatography tandem mass spectrometry (LC-MS) methods that measure complementary sets of metabolite classes described previously: 1) Amines and polar metabolites that ionize in the positive ion mode; 2) Central metabolites and polar metabolites that ionize in the negative ion mode; 3) Polar and non-polar lipids; 4) Free fatty acids, bile acids, and metabolites of intermediate polarity. All reagents and columns for this project were purchased in bulk from a single lot, and all instruments were calibrated daily for mass resolution and mass accuracy.

Metabolite peaks were quantified using the AUC. Raw area counts for each metabolite in each sample were normalized to correct for variation resulting from instrument inter-day tuning differences by the median value for each run-day; therefore, the medians were set to 1.0 for each run. Metabolites were identified by automated comparison of the ion features in the experimental samples to a reference library of ~8,000 chemical standard entries that include retention time, molecular weight (m/z), preferred adducts, and in-source fragments as well as associated MS spectra and curated by visual inspection for QC using software developed at Metabolon, Inc.⁷⁶. Identification of known chemical entities was based on comparison to metabolomic library entries of purified standards. Additional mass spectral entries were created for structurally unnamed biochemicals, which were identified by virtue of their recurrent nature. These compounds have the potential to be identified by future acquisition of a matching purified standard or by classical structural analysis. Missing metabolite values were imputed by replacement with half the minimum value for each metabolite in all samples. Metabolites with an IQRs of 0 were excluded from further analysis, with 904 metabolites remaining for the analysis. The remaining metabolites were subsequently log-10 transformed and Pareto-scaled.

Targeted assays in MGBB-KAS

Sphingolipids

The sphingolipid profiles were quantified in all serum samples of MGBB-KAS participants (N = 1080) using LC-MS/MS. Details of the sphingolipid profiling method can be found in previous studies^18,77. A total of 77 sphingolipids were reported, of which 42 were quantified using external calibration curves. The 35 remaining sphingolipids were identified based on the predicted retention time for the selected reaction monitoring (SRM) transition. For these 36 compounds, a pseudo-quantitation was performed based on compounds sharing similar structures and internal standards. Concentrations of sphingolipids were reported in nanomolar (nM). Two types of QC samples were included: QC of extraction and QC of injection. CV corresponding to each type of QC samples was calculated. Skewness of each sphingolipid was also calculated. The main sphingolipid subgroups included in this panel were dihydroceramides (dhCer), ceramides (Cer), ceramide-1-phosphates (Cer1P), sphingosines, sphingosine-1-phosphates (S1P), sphingomyelins (SM), hexosylceramides (HexCer), and lactosylceramides (LacCer), in addition to 3-ketosphinganine (3-KS), sphinganine, sphinganine-1-phosphate (Spa1P), and glucosylsphingosine. All sphingolipids had <7% missing values; thus, none were removed based on missing percentages. Sphingolipid concentrations were standardized (original value minus mean, then divided by standard deviation) to obtain effect estimates comparable among different sphingolipids.

Microbial-derived metabolites

A separate batch of serum samples from the same participants was sent to Precion Inc. for quantification of a panel of microbial-derived metabolites that included 71 metabolites in the following pathways: short-chain fatty acids, tryptophan metabolism, dietary aromatic compounds, phenylalanine and tyrosine metabolism, polyamine metabolism, trimethylamine N-oxide biosynthesis, secondary bile acid metabolism, histidine metabolism, and vitamin biosynthesis. The microbial-derived metabolites panel analysis consists of two extractions of serum sample aliquots (50 and 100 μl). The extracts are analyzed with three different LC-MS/MS methods for the 71 analytes of interest. The concentration of microbial-derived metabolites was reported in μg/mL. This targeted panel was developed by Precion Inc., which described it as a “Microbial Metabolite Panel”. While some metabolites are indeed microbially produced, others are of dietary or host origin. To improve clarity, we use the term “microbial-derived metabolites” throughout the manuscript, and a comprehensive list of all 71 profiled metabolites is provided in Supplemental Data 2. Fifty-one metabolites missing≤30% were included in subsequent analyses. Microbial-derived metabolites' concentrations were standardized (original value minus mean, then divided by standard deviation) to obtain effect estimates comparable among different metabolites.

Steroids

A separate batch of serum samples from the same participants was sent to Precion Inc. for quantification of a steroid hormone panel, including 16 endogenous steroids in the following sub-pathways: androgens, estrogens, progestogens, glucocorticoids, and mineralocorticoids. The Comprehensive Steroid Hormone Panel utilizes 200 µl of serum or plasma, calibrated across analytes and conducted via three LC-MS/MS methods: Method 1 for aldosterone, estrone, and estradiol in ESI negative mode; Method 2 for non-conjugated steroids including prednisone and prednisolone in ESI positive mode; and Method 3 for steroid sulfates in ESI negative mode. Each method employs stable labeled internal standards for quantitation through linear regression analysis, with calibration and QC standards maintaining accuracy within ±15% of the nominal value and precision ≤10%. Steroid concentrations were standardized (original value minus mean, then divided by standard deviation) to obtain effect estimates comparable among different steroids.

Statistical analysis

Cohort summary

We summarized the basic demographic characteristics of participants in MGBB-KAS, MGBB-Asthma, and MGBB-LLF, stratified by asthma status (Table 1). Additionally, Table 2 provides a summary of the clinical measures relevant to asthma cases in MGBB-KAS and MGBB-LLF.

Global metabolomics: association with asthma status in the MGBB-KAS cohort and replication in the MGBB-Asthma cohort

We employed conditional logistic regression models using the clogit function from the survival R package to evaluate the associations between asthma status and individual metabolite features in the MGBB-KAS cohort, which followed a matched case-control framework. Matching was performed on age, race, and sex, and models were additionally adjusted for body mass index (BMI) values recorded closest to the serum collection date. To account for multiple testing, statistical significance was determined using a False Discovery Rate (FDR) correction.

In the MGBB-Asthma cohort, we applied multivariable logistic regression models using the glm function (family = binomial) from the stats package, adjusting for age, sex, race, BMI, and smoking status, to replicate the significant asthma finding in MGBB-KAS. A finding was deemed to replicate if (1) the observed effect size (odds ratio, OR) aligned in direction with the initial association and (2) the association's p value was < 0.05.

Targeted metabolomics: association with asthma and its clinical measures in MGBB-KAS and replication in MGBB-LLF

Using the global asthma metabolomics findings of the MGBB-KAS cohort and the scientific literature, we identified sphingolipid metabolism, steroid metabolism, and microbial-derived metabolites as priority pathways for further study. We expanded our study of these pathways through three targeted assays that quantified 77 sphingolipids, 71 microbial-derived metabolites, and 18 steroids. For these quantified metabolites, we used conditional logistic regression models implemented via the survival R package, accounting for the matched study design (matched on age, sex, and race). All participants were non-smokers, and BMI was included as an additional covariate to adjust for residual confounding. Additionally, we investigated the associations between targeted metabolites and asthma clinical measures among individuals with asthma. This investigation utilized multivariable linear (lm function, stats package) and Quasi-Poisson regression models (glm function with family = quasiposson) to evaluate various asthma clinical measures. Linear regression models focused on lung function measures such as FEV₁, FVC, and the FEV₁/FVC ratio, in addition to log(IgE). Quasi-Poisson models were employed to analyze the consumption of oral corticosteroids (OCS) over the past five years. The regression models were adjusted for age, sex, and race, ethnicity, BMI from the closest record to the serum collection date and the count of ICS use within the last five years. Specifically, for lung function measures (FEV₁, FVC, and FEV₁/FVC ratio), we additionally adjusted for height. Given the smaller number and higher reliability of targeted metabolites, we applied a more stringent FDR ≤ 0.05 threshold to correct for multiple testing and ensure higher specificity of the results.

In MGBB-KAS, the metabolite ratios were calculated using targeted metabolites from different panels, where the numerator and denominator originated from separate panels. The ratios were determined by dividing the raw concentration of one metabolite by another. Ratios were computed only when both the numerator and denominator metabolite values were available. Ratios with missing values of less than 30% were considered for further analyses. To ensure comparability and enhance model performance, all metabolite ratios were log₁₀-transformed prior to analysis. Sensitivity analyses demonstrated that this transformation improved model diagnostics, including residual normality and homoscedasticity, as shown in Supplemental Data 4. In this study, we focused on the ratio generated from three targeted panels (sphingolipids, microbial-derived metabolites, and steroids panels). These ratios consisted of three different varieties: sphingolipid/microbial-derived metabolite ratios (Sph/Microb, number of ratios = 3898), sphingolipid/steroid ratios (Sph/Str, number of ratios = 1248), and microbial-derived metabolites/steroid ratios (Microb/Str, number of ratios = 794). The same multivariable linear/Quasi-Poisson regression models were utilized to assess the association between log-transformed metabolite ratios with asthma and its phenotypes within asthma cases, where the number of participants varied for each metabolite ratio. To correct for multiple testing, we applied a stringent FDR significance threshold (FDR ≤ 0.05).

We extracted 62 sphingolipids, 46 steroids, and 118 microbial-derived metabolites to replicate our findings in the MGBB-LLF cohort. microbial-derived metabolites involved in the following pathways were selected: Phenylalanine Metabolism, Tyrosine Metabolism, Tryptophan Metabolism, Short-Chain Fatty Acid, Polyamine Metabolism, Secondary Bile Acid Metabolism, Carnitine Metabolism, and Histidine Metabolism. We calculated the ratios using raw values of these metabolites, ensuring the numerator and denominator were derived from the same three distinct categories as in MGBB-KAS. To ensure a normal distribution of the ratios, we applied a log10 transformation to all metabolite ratios. This process resulted in the generation of the same three varieties of ratios as identified in MGBB-KAS: Sphingolipid:microbial (number of ratios = 7316), sphingolipid:steroid (number of ratios = 2852), and microbial:steroid (number of ratios = 5428). To replicate the associations between metabolite ratios and asthma clinical measures, we employed regression models similar to those used in MGBB-KAS, adjusting for variables such as age, sex, race, and BMI (based on the closest record to the serum collection date), as well as ICS usage within the past five years. For lung function measures specifically, adjustments were also made for height. Additionally, we applied an FDR threshold of ≤ 0.05 in our statistical analysis.

Identifying biomarkers for asthma exacerbations

In our study, the presence of asthma exacerbations was assessed through the utilization of OCS medication. Participants who had not used OCS medication in the past five years were considered not to have experienced prevalent asthma exacerbations. Conversely, those whose OCS medication usage was equal to or exceeded the average (7 OCS prescriptions) within the last five years were classified as having prevalent asthma exacerbations. This same threshold was used to identify cases of incident asthma exacerbation over the next five years.

The elastic net approach was employed to identify biomarkers indicative of asthma exacerbations in MGBB-KAS. In initial modeling, we included all FDR-significant metabolite ratios from both sphingolipid:steroid and microbial-derived metabolite:steroid classes to evaluate their combined predictive performance. After comparison, we subsequently focused on the sphingolipid:steroid ratios, which showed better performance and feasibility for assay development. Utilizing the glmnet R package, we trained binomial regression models for asthma exacerbations within the last 5 years on FDR-significant sphingolipid:steroid ratios alongside confounders such as age, sex, race, ethnicity, BMI (based on the most recent record before serum collection), and ICS usage. To maximize the AUC, we set the alpha parameter to 0.6. Through 10-fold cross-validation, an optimal lambda was selected based on the alpha parameter. Demographic variables like sex, race, and ethnicity were encoded using one-hot encoding. Ultimately, only those features with a non-zero coefficient were chosen as significant for identifying prevalent asthma exacerbation. This model was then tested to predict asthma exacerbations in the next 5 years in MGBB-KAS. The performance of the model was assessed using the AUC value and receiver operating characteristic (ROC) curve. The model was subsequently validated in the independent MGBB-LLF cohort using mapped selected features from the logistic regression analysis.

To determine the discriminatory power of the single selected ratios for predicting incident asthma exacerbation, survival analysis was conducted using the Kaplan–Meier estimator to compare survival times between incident asthma exacerbation or not based on the single selected ratio through the survfit function from the survival R package. The event of interest was defined as the time gap to the first OCS medication use after the sample collection date. To further investigate the relationship between the single selected ratio and the time to first OCS medication, a Cox proportional hazards model was fitted using the coxph function. The model included covariates such as age at collection, BMI, gender, race, ethnicity, and ICS usage in the past five years. The mean time to the first OCS medication after the sample collection date was also calculated for participants with and without asthma exacerbation, based on the single selected ratio.

All analyses are conducted in R version 4.1.2⁷⁸.

Inclusion & ethics

Ethical approval was obtained from the IRB (2014P001109) of Brigham and Women’s Hospital. Informed consent was obtained from all participants. We ensured the inclusion of participants irrespective of gender, race, or socioeconomic status.

Reporting summary

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

Peer review

Peer review information

Nature Communications thanks Sally Wenzel, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Data availability

Metabolomics data used in this study for the discovery cohort have been deposited in the Metabolomics Workbench (https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Project&ProjectID=PR002674) under Project ID PR002674. Requests for other data and materials will be reviewed by the cohort, and contact PIs Dr. Jessica Lasky-Su at rejas@channing.harvard.edu for the studies to determine if the request is subject to intellectual property or confidentiality obligations. We anticipate responding to requests within approximately 2–4 weeks. 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.

Code availability

All custom R code used for data processing and statistical analyses is openly available at https://github.com/yuluc/metabolite_ratio_project.

Competing interests

J.A.L.S. is a scientific advisor to Precion Inc. and TruDiagnostic Inc. S.T.W. receives royalties from UpToDate and is on the Board of Histolix. Other authors have no relevant competing interests to disclose.

Supplementary information

The online version contains supplementary material available at 10.1038/s41467-025-67436-7.