Epidemiologic studies have suggested that elevated concentrations of zinc are associated with a decreased risk of lung cancer, but the underlying mechanisms remain to be investigated. The metabolites are highly sensitive to environmental stress, which will help to reveal the linkages between zinc exposure and lung cancer risk. We designed a nested case-control study including 101 incident lung cancer cases and 1:2 age- and sex-frequency-matched 202 healthy controls from the Dongfeng-Tongji cohort. Their plasma level of zinc was determined by using inductively coupled plasma-mass spectrometry (ICP-MS) and plasma profiles of metabolites were detected by using an untargeted metabolomics approach. The generalized linear models (GLM) were applied to assess the associations of plasma zinc with metabolites, and the mediation effects of zinc-related metabolites on zinc-lung cancer association were further testified. The concentrations of 55 metabolites had linear dose-response relationships with plasma zinc at a false discovery rate (FDR) < 0.05, among which L-proline, phosphatidylcholine (PC, 34:2), phosphatidylethanolamine (PE, O-36:5), L-altrose, and sphingomyelin (SM, 40:3) showed different levels between lung cancer cases and healthy controls (fold change = 0.92, 0.95, 1.07, 0.90, and 1.08, respectively, and all P < 0.05). The plasma concentration of SM(40:3) was negatively associated with incident risk of lung cancer [OR(95%CI) = 0.71(0.55, 0.91), P = 0.007] and could mediate 41.7% of the association between zinc and lung cancer risk (P = 0.004). Moreover, compared to the traditional factors, addition of SM(40:3) exerted improved prediction performance for incident risk of lung cancer [AUC(95%CIs) = 0.714(0.654, 0.775) vs. 0.663(0.600, 0.727), P = 0.030]. Our findings revealed metabolic profiles with zinc exposure and provide new insight into the alternations of metabolites underpinning the links between zinc exposure and lung cancer development.
Authors: Yansen Bai, Qiang Cao, Xin Guan, Hua Meng, Yue Feng, Chenming Wang, Ming Fu, Shiru Hong, Yuhan Zhou, Fangfang Yuan, Xiaomin Zhang, Meian He, Huan Guo
; Full Source: The Science of the total environment 2022 May 10;155796. doi: 10.1016/j.scitotenv.2022.155796.