Association of Urinary Zinc Concentrations with Dyslipidemia and Its Subtypes: Baseline Data from the Chinese Multi-Ethnic Cohort (CMEC) Study

Associations of urinary zinc exposure with blood lipid profiles and dyslipidemia: Mediating effect of serum uric acid

The relationship between zinc (Zn) exposure and abnormal blood lipids including dyslipidemia is contentious. Serum uric acid (SUA) has been reported to be correlated to both Zn exposure and dyslipidemia. The underlying mechanisms of Zn exposure associated with blood lipids and the mediating effects of SUA remain unclear. Therefore, this study analyzed the data from Chinese 2110 adults (mean age: 59.0 years old) in rural areas across China to explore the associations of Zn exposure with blood lipid profiles and dyslipidemia, and to further estimate the mediating effects of SUA in these relationships. The study data showed that urinary Zn was associated with increased levels of blood lipid components triglyceride (TG) and low-density lipoprotein cholesterol (LDL-C). Moreover, an increased risk of dyslipidemia was observed in the study participants who had higher urinary Zn levels. Compared with the first quartile, the fourth quartile of urinary Zn concentration corresponded to the increase of TG (β = 0.20, 95 % CI: 0.12, 0.28), LDL-C (β = 0.06, 95 % CI: 0.01, 0.10) and dyslipidemia risk (OR = 2.16, 95 % CI: 1.50, 3.10), respectively. Elevated urinary Zn was also associated with higher levels of SUA and hyperuricemia risk. The SUA levels were positively related to total cholesterol (TC), TG, LDL-C levels and dyslipidemia risk. Mediation analyses revealed that SUA mediated 31.75 %, 46.16 % and 19.25 % of the associations of urinary Zn with TG, LDL-C levels and dyslipidemia risk, respectively. The subgroup and sensitivity analyses confirmed the positive associations between urinary Zn and blood lipid profiles and the mediating effect of SUA. The national population-based study further enhanced our understanding of the associations between Zn exposure and blood lipid profiles and mediating effect of SUA among generally healthy, middle-aged, and elderly individuals.

Study on the correlation and interaction between metals and dyslipidemia: a case-control study in Chinese community-dwelling elderly

Previous studies on the association between metals and dyslipidemia are not completely consistent. There are few studies investigating the relationship between mixed metal exposure and dyslipidemia as well as the effects of metals on dyslipidemia in community-dwelling elderly. To evaluate the correlations and interaction effect between the urinary concentrations of metals and the risk of dyslipidemia in community-dwelling elderly. We designed a case-control study to assess the correlation between urine metals and dyslipidemia in elderly people in the Yinchuan. The urinary levels of 13 metals, including calcium, vanadium, iron, cobalt, zinc, copper, arsenic, selenium, molybdenum, cadmium, tellurium, and thallium, were measured by inductively coupled plasma-mass spectrometry (ICP-MS), and the blood biochemical analyzer was used to measure the blood lipid levels of 3384 senior individuals from four different areas of Yinchuan city. Logistic regression and restricted cubic splines (RCS) were used to explore the correlation and dose-response relationship between urinary metals and the risk of dyslipidemia. Least absolute shrinkage and selection operator (LASSO) regression was used to select metals, and then weighted quantile sum (WQS) regression was used to explore the weight of each metal in mixed metals. Bayesian kernel machine regression (BKMR) was used to explore the interactions between metals on dyslipidemia risk. (1) After selection by LASSO regression, in the multi-metal model, compared with the lowest quartile, the adjusted ORs (95%CI) of the highest quartiles were 0.47 (0.37-0.60) for Fe, 1.43 (1.13-1.83) for Zn, 1.46 (1.11-1.92) for As, 0.59 (0.44-0.80) for Se, 1.53 (1.18-2.00) for Mo, and 1.36 (1.07-1.73) for Te. (2) In the WQS regression model, Fe and Mo accounted for the largest weight in the negative and positive effects of dyslipidemia, respectively. (3) In the BKMR model, there may be a positive interaction between Te and Se on dyslipidemia. Among the mixed metals, Fe, As, Se, Mo, and Te were associated with the prevalence of dyslipidemia, with Fe and Mo contributing the most. There may be certain interactions between Te and Se.