Background & aims: Few epidemiological studies have investigated the relationships of urinary essential and non-essential elements with serum albumin, an indicator of nutritional status, especially for the elderly in China.
Methods: A community-based study among elderly participants (n = 275) was conducted in Beijing from November to December 2016. We measured 15 urinary elements concentrations and serum albumin levels. Three statistical methods including the generalized linear model (GLM), quantile g-computation model (qgcomp) and bayesian kernel machine regression (BKMR) were adapted.
Results: In GLM analysis, we observed decreased serum albumin levels associated with elevated urinary concentrations of aluminum, arsenic, barium, cobalt, chromium, copper, iron, manganese, selenium, strontium, and zinc. Compared with the lowest tertile, the highest tertile of cadmium and cesium was also negatively associated with serum albumin. Urinary selenium concentration had the most significant negative contribution (30.05%) in the qgcomp analysis. The negative correlations of element mixtures with serum albumin were also observed in BKMR analysis.
Conclusions: Our findings suggested the negative associations of essential and non-essential elements with serum albumin among the elderly. Large-scare cohort studies among the general population are required to validate our findings and elucidate the relevant underlying mechanisms.
Keywords: bayesian kernel machine regression; elements; nutritional status; quantile g-computation; serum albumin.
Copyright © 2022 Li, Zhou, Mei, Zhao, Liu, Zhao, Xu, Ge and Xu.