Association analyses between urinary concentrations of multiple trace elements and gastric precancerous lesions and gastric cancer in Anhui province, eastern China

Abstract

Background: Limited epidemiological evidence suggests that exposure to trace elements adversely impacts the development of gastric precancerous lesions (GPL) and gastric cancer (GC). This study aimed to estimate the association of individual urinary exposure to multiple elements with GPL and GC.

Methods: A case-control investigation was conducted in Anhui Province from March 2021 to December 2022. A total of 528 subjects (randomly sampled from 1,020 patients with GPL, 200 patients with GC, and 762 normal controls) were included in our study. Urinary levels of iron (Fe), copper (Cu), zinc (Zn), nickel (Ni), strontium (Sr), and Cesium (Cs) were measured using inductively coupled plasma mass spectrometry (ICP-MS). Four different statistical approaches were employed to explore the risk of GPL and GC with mixed exposure, including multivariate logistic regression, weighted quantile regression (WQS), quantile g-computation (Qgcomp), and the Bayesian kernel machine regression (BKMR) model.

Results: The WQS model indicated that urinary exposure to a mixture of elements is positively correlated with both GPL and GC, with ORs for the mixture exposure of 1.34 (95% CI: 1.34-1.61) for GPL and 1.38 (95% CI: 1.27-1.50) for GC. The Qgcomp and BKMR models also demonstrated a statistically significant positive correlation between the mixture and both GPL and GC.

Conclusion: Considering the limitations of case-control studies, future prospective studies are warranted to elucidate the combined effects and mechanisms of trace elements exposure on human health.

Keywords: BKMR; GC; WQS; case-control study; combined effect.

Figure 1

Spearman correlations between concentrations of urinary trace elements in the study participant. (A) Presented the correlation coefficients between metals in the GPL group and controls; (B) presented the correlation coefficients between metals in the GC group and controls.

Figure 2

WQS model regression index weights for GPL and GC. The relative contribution of each trace element to the total effect of the mixture in urine with positive and negative associations with GPL (A, B) and GC (C, D). The model was adjusted for sex, BMI, age, education, income level, occupation, family history of GC, lifestyle (such as smoking and drinking), and H. pylori infection. Q, quartile; Zn, Zinc; Sr, strontium; Ni, Nickel; Fe, Iron; Cu, Copper; Cs: cesium.

Figure 3

Association between Qgcomp index and GPL (A) and GC (B). The model was adjusted for sex, BMI, age, education, income level, occupation, family history of GC, lifestyle (such as smoking and drinking), and H. pylori infection.

Figure 4

Univariate exposure–response functions and 95% confidence interval (gray part) for the association between urinary levels of single trace element exposure and odds of GPL (A) and GC (B) when concentrations of other elements were held at their median concentrations.

Figure 5

Estimated joint effects of trace elements mixtures on GPL (A) and GC (B) by BKMR. Adjusted variables included sex, BMI, age, education, income level, occupation, family history of GC, lifestyle (such as smoking and drinking), and H. pylori infection.