Association Between Di‐(2‐Ethylhexyl) Phthalate and Childhood Asthma Through Plasma Metabolome Alterations
Bibliographic record
Abstract
Exposure to environmental factors has been linked to increased asthma risk. However, few studies have investigated the complex relationships among exposome, metabolites, and asthma. Existing research suggests that environmental exposures shape omics profiles associated with respiratory outcomes. A recent study of preschool children found that metabolomic clusters associated with asthma risk varied by neighborhood resources, suggesting that environmental triggers may induce both metabolic and disease severity [1]. The Human Early Life Exposome project linked prenatal and childhood exposures to serum metabolomic shifts potentially predisposing children to asthma [2]. Despite this progress, the interplay among exposome, metabolites, and disease pathogenesis remains insufficiently explored, particularly in childhood asthma. This study aimed to elucidate the integrated relationships among urinary exposome, plasma metabolites, and childhood asthma. We analyzed 139 children aged 6–7 years from the general population-based ECHO-COCOA (Exposome and Child Health with Omics–Cohort for Childhood Origin of Asthma and allergic diseases) birth cohort study, 26 children with asthma and 113 children without asthma and atopic dermatitis (Table S1). Informed consent was obtained from all individual participants included in the study. Mass spectrometry–based methods were used to quantify 74 urinary exposome components (Table S2) and plasma metabolites (Table S3). Global metabolomic profiling identified asthma-related metabolites, followed by targeted quantification of selected features, mainly glycerophospholipids, amino acids, and derivatives. These target metabolome data were analyzed in relation to urinary exposures and asthma-related outcomes. Asthma-associated urinary exposures were initially identified (Figure 1). Then, metabolites related to these asthma-associated exposures were explored. Asthma-associated exposures included arsenic, phenylmercuric acetate, and mono-n-butyl phthalate, which were linked to increased acetylornithine. Phosphatidylcholine (PC) (12:0/12:0), phosphatidylethanolamine (PE) (16:0/16:0), taurine, and spermidine were significantly associated with diethylhexyl phthalate (DEHP) metabolites (mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP), mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP)) (Figure 2A). Notably, PE (16:0/16:0), taurine, and spermidine also correlated with asthma-related clinical markers. Taurine showed a negative correlation with PC20 and a positive association with eosinophil count. PE (16:0/16:0) and spermidine positively correlated with eosinophils, while PE (16:0/16:0) also associated with total IgE levels. Mendelian randomization (MR) analysis revealed possible causal relationships between taurine and PC20; PE (16:0/16:0) and IgE; and taurine, spermidine, PE (16:0/16:0) and eosinophils (Table S4). These metabolites are also associated with IL-1β, a central pro-inflammatory cytokine in asthma. These findings suggest that taurine, spermidine, and PE (16:0/16:0) could be promising candidate biomarkers of asthma in the context of DEHP exposure. Taurine, spermidine, and PEs are recognized regulators of autophagy and antioxidants, mitigating oxidative stress and inflammation in asthma [3-5]. Elevated levels of these metabolites might reflect a compensatory response or indicate regulatory mechanisms of autophagy. Additionally, arachidonic acid metabolism—implicated in asthma inflammation—has been linked to taurine efflux, which may explain the increased taurine levels in asthmatics [6]. Elevated spermidine and PEs have also been observed in adult asthma. However, mechanisms underlying DEHP's effects on these metabolites remain unclear. Nonetheless, these metabolites may serve as potential biomarkers for DEHP-associated childhood asthma (Figure 2B,C). Although DEHP has a short half-life (~1 day), its widespread presence in consumer products results in chronic, low-level pseudo-persistence in the body. Although the low prevalence of childhood asthma in the general population of Korea may limit statistical power, our results are strengthened by MR analysis and receiver operating characteristic (ROC) analysis. These methods helped us minimize the influence of confounding factors and distinguish metabolite biomarkers between asthmatic and non-asthmatic children. Further research, especially longitudinal and extended cohort and mechanistic validations, is needed to validate our result. Multiple urinary sampling or 24-h urine collection could improve the assessment of long-term exposures. Overall, our findings highlight taurine, spermidine, and PE (16:0/16:0) as potential biomarkers of phthalate-related childhood asthma. Methodology: M. J. Kim, S. J. Kim, H. E. Song, and H. Lee, Resources: S. H. Lee, M. J. Kang, S.-I. Yang, H.-B. Kim, S. Y. Lee, J.-H. Kim, H. Im, H. J. Seong, Y. J. Park, J. Yeom, E. J. Choi, D. I. Suh, K. W. Kim, K. Ahn, Y. H. Shin, and S.-J. Hong, Data analysis and interpretation of data: J.-H. Oh, S. Hong and H. J. Yoo, Writing, review and editing: M. J. Kim, S. H. Lee, S.-J. Hong, and H. J. Yoo. The authors declare no conflicts of interest. The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Table S1: Clinical characteristics of study subjects. p-values were calculated using Chi-squared test or Mann–Whitney test. Table S2: 74 environmental substances were measured in the urine of the study subjects. Table S3: List of target metabolites measured in the plasma of the study subjects. Table S4: One sample bi-directional Mendelian randomization. p-value and bonf.p-value represents the raw.p-value and Bonferroni.p-value, respectively. N.snps: the number of SNPs used in constructing genetic risk scores. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".