Ecotoxicology and environmental safety · 2025
Relationship between acrylamide and glycidamide hemoglobin adduct levels and chronic kidney disease: A NHANES analysis.
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Xu Wen-Tao, Zheng Wen-Cai, Chen Chao-Ran, Lin Fei, Chen Shao-Hao, Li Xiao-Dong, Sun Xiong-Lin, Zheng Qing-Shui, Wei Yong, Xue Xue-Yi, Ke Zhi-Bin, Xu Ning
Аннотация
BACKGROUND: Acrylamide (AA) is a dietary contaminant with experimental nephrotoxicity, yet its relationship with chronic kidney disease (CKD) remains unclear.
METHODS: Utilizing NHANES data (2003-2006, 2013-2016), we analyzed 10,431 participants to examine associations between hemoglobin-bound AA (HbAA), glycidamide (HbGA), and CKD prevalence. Associations were evaluated through weighted multivariate logistic regression and dose-response analysis with restricted cubic splines. Additionally, toxicological profiling, target identification, and pathway enrichment analysis were performed.
RESULTS: Higher HbAA, HbGA, and combined biomarkers were non-linearly associated, with lower CKD prevalence at higher quartiles and evidence of threshold effects on RCS. A significant negative association was observed for HbAA (highest vs. lowest quartile OR = 0.78, 95 % CI: 0.59-1.01, P-trend = 0.033) and HbGA (OR = 0.74, 95 % CI: 0.54-1.01, P-trend = 0.041). Dose-response analysis indicated a non-linear relationship, suggesting threshold effects potentially explained by hormesis, younger demographics, or confounding dietary and lifestyle factors. Computational modeling identified potential key molecular targets (TP53, AKT1, BCL2, JUN, MAPK3, TNF) that may mediate AA's biological effects.
CONCLUSION: The inverse relationship between AA exposure biomarkers and CKD identified in this large population-based study challenges current toxicological paradigms. This may be due to hormesis, demographic confounding, or methodological limitations. Longitudinal studies are needed to validate these paradoxical results.
METHODS: Utilizing NHANES data (2003-2006, 2013-2016), we analyzed 10,431 participants to examine associations between hemoglobin-bound AA (HbAA), glycidamide (HbGA), and CKD prevalence. Associations were evaluated through weighted multivariate logistic regression and dose-response analysis with restricted cubic splines. Additionally, toxicological profiling, target identification, and pathway enrichment analysis were performed.
RESULTS: Higher HbAA, HbGA, and combined biomarkers were non-linearly associated, with lower CKD prevalence at higher quartiles and evidence of threshold effects on RCS. A significant negative association was observed for HbAA (highest vs. lowest quartile OR = 0.78, 95 % CI: 0.59-1.01, P-trend = 0.033) and HbGA (OR = 0.74, 95 % CI: 0.54-1.01, P-trend = 0.041). Dose-response analysis indicated a non-linear relationship, suggesting threshold effects potentially explained by hormesis, younger demographics, or confounding dietary and lifestyle factors. Computational modeling identified potential key molecular targets (TP53, AKT1, BCL2, JUN, MAPK3, TNF) that may mediate AA's biological effects.
CONCLUSION: The inverse relationship between AA exposure biomarkers and CKD identified in this large population-based study challenges current toxicological paradigms. This may be due to hormesis, demographic confounding, or methodological limitations. Longitudinal studies are needed to validate these paradoxical results.