Abstract

Epidemiological studies have demonstrated that various kinds of urinary element concentrations were different between healthy, prediabetes, and diabetes patients. Meanwhile, many studies have explored the relationship between element concentration and fasting blood glucose (FBG), but the association between joint exposure to co-existing elements and FBG level has not been well understood. The study explored the associations of joint exposure to co-existing urinary elements with FBG level in a cross-sectional design. 275 retired elderly people were recruited from Beijing, China. The questionnaire survey was conducted, and biological samples were collected. The generalized linear model (GLM) and two-phase Bayesian kernel machine regression (BKMR) model were used to perform in-depth association analysis between urinary elements and FBG. The GLM analysis showed that Zn, Sr, and Cd were significantly correlated with the FBG level, under control potential confounding factors. The BKMR analysis demonstrated 8 elements (Zn, Se, Fe, Cr, Ni, Cd, Mn, and Al) had a higher influence on FBG (posterior inclusion probabilities > 0.1). Further intensive analyses result of the BKMR model indicated that the overall estimated exposure of 8 elements was positively correlated with the FBG level and was statistically significant when all creatinine-adjusted element concentrations were at their 65th percentile. Meanwhile, the BKMR analysis showed that Cd and Zn had a statistically significant association with FBG levels when other co-existing elements were controlled at different levels (25th, 50th, or 75th percentile), respectively. The results of the GLM and BKMR model were inconsistent. The BKMR model could flexibly calculate the joint exposure to co-existing elements, evaluate the possible interaction effects and nonlinear correlations. The meaningful conclusions were found that it was difficult to get by traditional methods. This study will provide methodological reference and experimental evidence for the association between joint exposure to co-existing elements and FBG in elderly people.

Highlights

  • Type 2 diabetes mellitus (T2DM) is an endocrine and metabolic disease characterized by a disturbance of blood glucose, protein, and lipid metabolism disturbance(Henning 2018)

  • Bayesian kernel machine regression (BKMR) calculated Posterior inclusion probabilities (PIPs), which rank each element by importance to the effect in the co-existing elements, showed that part of the 15 element concentration changes had contributed little to the effect on fasting blood glucose (FBG)

  • (1) In general, the overall estimated exposure of 8 elements joint association analysis was positively correlated with the FBG level and was statistically significant when overall element concentrations were at their 65th percentile, as compared to when overall elements were at their median values (Fig. 1). (2) In order to analyze the nonlinear exposureresponse relationship between the single element and FBG level under control influenced by other coexisting elements, the univariate exposure-response relationship between each element and FBG level was estimated by BKMR model

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Summary

Introduction

Type 2 diabetes mellitus (T2DM) is an endocrine and metabolic disease characterized by a disturbance of blood glucose, protein, and lipid metabolism disturbance(Henning 2018). T2DM is a major threat to human health worldwide and may result in an increased incidence and high prevalence. According to the World Health Organization (WHO), there were 422 million diabetes patients, with 80% of them belonging to low and middle-income countries (Jaacks et al 2016; WHO 2021). The International Diabetes Federation (IDF) estimated that the number of patients with diabetes was about 116 million in China. Further investigation revealed that the number of patients with diabetes in China had increased by 62.7% over the past decade, and it is one of the countries with the highest estimated number of patients with diabetes, accounting for nearly 30% of the total world (Liu et al 2019; Williams 2021). With the increases in the size of the aging population, changes in lifestyle, economic and environmental factors, the prevalence of T2DM is increasing consistently

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