Differential Risks of Exposure to Acrylamide in Adult Asthma Clusters
Authors: Yuan‐Ting Hsu, Chao‐Chien Wu, Chin‐Chou Wang, Chau‐Chyun Sheu, Yi‐Hsin Yang, Ming‐Yen Cheng, Ruay‐Sheng Lai, Meng‐Hsuan Cheng, Huang‐Chi Chen, Chih‐Jen Yang, Chien‐Jen Wang, Huei‐Ju Liu, Hua‐Ling Chen, Chih‐Hsing Hung, Chon‐Lin Lee, Ming‐Shyan Huang, Shau‐Ku Huang
Journal: Allergy (2025)

The increase in asthma prevalence can be attributed more to environmental factors than genetic factors [1], and a significant degree of phenotypic heterogeneity in asthma is well recognized, but the underlying causes remain unclear. Exposure to acrylamide is primarily from occupational exposure, smoking, or dietary intake [2], posing a risk to the nervous and reproductive systems [3], but its potential adverse effect on asthma and the immune system remains to be defined. To evaluate the exposure to acrylamide and its varying effects on asthma and its phenotypes, we conducted a case–control study consisting of 365 participants with current asthma and 235 healthy controls (Tables S1 and S2). The study was approved by the ethics committees of the National Health Research Institutes in Miaoli, Taiwan, the ethics committees of the participating hospitals, and informed consent from all participants. The case group had significantly higher levels of acrylamide metabolite in urine compared to controls (Table 1; median, 15.99 vs. 12.82 μg/g creatinine, p < 0.001). Logistic regression and multinomial logistic regression analyses revealed that higher urinary acrylamide metabolites levels were significantly associated with increased odds ratios for current asthma (ORs: 1.52, 95% CI: 1.04–2.24; Figure 1 and Table S3). Stratified by asthma phenotypes [4], significant acrylamide exposure risk was observed in Clusters E (young male current smokers with early-onset; OR: 1.84, 95% CI:1.16–2.92), A (older non-atopic females with late-onset; OR: 1.80, 95% CI: 1.28–2.51), C (older males with second-hand smoke exposure; OR: 1.70, 95% CI: 1.23–2.36), F (never smoking atopic males with early-onset; OR: 1.48, 95% CI: 1.02–2.13), and D (atopic females with high BMI and poor lung function; OR: 1.53, 95% CI: 1.05–2.21). To identify factors contributing to differences in acrylamide exposure, we performed a stratified analysis considering both occupational and non-occupational factors (Table S4). Among non-occupational factors, smoking, second-hand smoke exposure, and daily transportation by car and motorcycle were identified as potential contributors to the association with current asthma. Notably, we observed that commerce was associated with the highest levels of acrylamide exposure. Analysis revealed that the primary contribution to this exposure came from Cluster A. Additionally, the level of an oxidative stress marker, HEL [5], was significantly correlated with urinary acrylamide metabolite in both cases and controls (Table 1; ρ = 0.48 and ρ = 0.25, respectively), as well as in Clusters A and B (ρ = 0.69 and ρ = 0.60, respectively). Clusters C and E, which had a history of smoking, highlight the increased risk of acrylamide exposure and the importance of identifying its sources [6]. For Cluster F, acrylamide exposure may have been associated with both workplace and household second-hand smoke exposure. Moreover, Cluster A mainly comprised non-atopic asthma females, whereas Cluster D was dominated by atopic females with a higher BMI. Cluster B mainly consisted of atopic females with a lower BMI, but urinary acrylamide metabolites were not considered crucial factors in environmental exposure. These three clusters highlighted potential variations in the sources of acrylamide exposure. In summary, this study provides novel evidence linking acrylamide exposure to current asthma, using urinary metabolites as a reliable marker of exposure from various sources [7]. We observed significant differences in acrylamide exposure risk between asthma cases and controls, with varying levels across phenotypic clusters, highlighting the diverse environmental risk factors and their impact on asthma phenotypes. However, our study had some limitations, including the cross-sectional study design and the lack of dietary and detailed occupational exposure data, which limited our ability to establish causality and fully characterize acrylamide exposure sources. While identifying exposure sources is critical for public health, the primary aim of this study is to raise awareness of acrylamide exposure among asthma researchers and emphasize the need for future studies that integrate comprehensive exposure data and employ longitudinal designs to better understand the heterogeneity and its effects on asthma control. Yuan-Ting Hsu: methodology, software, validation, formal analysis, data curation, writing – original draft, writing – review and editing, and visualization. Chao-Chien Wu, Chin-Chou Wang, Chau-Chyun Sheu, Yi-Hsin Yang, Ming-Yen Cheng, Ruay-Sheng Lai, Meng-Hsuan Cheng, Huang-Chi Chen, Chih-Jen Yang, Chien-Jen Wang, Huei-Ju Liu, and Hua-Ling Chen: investigation, resources, and data curation. Chih-Hsing Hung, Chon-Lin Lee, and Ming-Shyan Huang: conceptualization, methodology, validation, formal analysis, investigation, resources, writing – review and editing, supervision, project administration, and funding acquisition. Shau-Ku Huang: conceptualization, methodology, validation, formal analysis, investigation, resources, writing – original draft, writing – review and editing, supervision, project administration, and funding acquisition. All of the authors' approval of final document. We thank Ms. Wei-Ling Chou, Ms. Ssu-Ting Liu and Ms. Yu-Chun Lin for their outstanding technical and administrative support. 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. Data S1. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. 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