To investigate the concentrations of rare earth elements in cereals and assess human health risk through cereal consumption, a total of 327 cereal samples were collected from rare earth mining area and control area in Shandong, China. The contents of 14 rare earth elements were determined by Inductively Coupled Plasma—Mass Spectrometry (ICP—MS). The medians of total rare earth elements in cereals from mining and control areas were 74.22 μg/kg and 47.83 μg/kg, respectively, and the difference was statistically significant (P < 0.05). The wheat had the highest rare earth elements concentrations (109.39 μg/kg and 77.96 μg/kg for mining and control areas, respectively) and maize had the lowest rare earth elements concentrations (42.88 μg/kg and 30.25 μg/kg for mining and control areas, respectively). The rare earth elements distribution patterns for both areas were characterized by enrichment of light rare earth elements. The health risk assessment demonstrated that the estimated daily intakes of rare earth elements through cereal consumption were considerably lower than the acceptable daily intake (70 μg/kg bw). The damage to adults can be neglected, but more attention should be paid to the effects of continuous exposure to rare earth elements on children.

This study aimed to assess the association of dietary fiber intake with serum uric acid (SUA) levels and risk of hyperuricemia (HUA) among Chinese adults using the latest nationally representative data. A total of 66,427 Chinese adults aged 18 years and over from the China Adult Chronic Disease and Nutrition Surveillance in 2015 were included in this study. Dietary intakes were measured with a 3-day 24 h dietary recall and the household condiment weighing method. Mixed-effect linear and logistic regression models were used to evaluate the associations of dietary fiber intake with SUA levels and risk of HUA, respectively. Compared to the lowest intake group, the coefficient and 95% confidence in the highest intake group of total fiber were −0.06 (−0.08, −0.04) (p-trend < 0.001), −0.18 (−0.2, −0.16) (p-trend < 0.001) for cereal fiber, 0.03 (0.01, 0.04) (p-trend = 0.051) for legume fiber, 0 (−0.01, 0.02) (p-trend = 0.869) for vegetable fiber and 0.01 (−0.001, 0.04) (p-trend = 0.296) for fruit fiber. The odds ratio (OR) and 95% confidence interval (CI) of HUA for the highest vs. lowest intake group of total fiber were 0.88 (0.84, 0.91) (p-trend = 0.001), 0.67 (0.63, 0.71) (p-trend < 0.001) for cereal fiber, 1.05 (1, 1.09) (p-trend = 0.248) for legume fiber, 1.01 (0.97, 1.05) (p-trend = 0.982) for vegetable fiber and 1.06 (1, 1.12) (p-trend = 0.264) for fruit fiber. Our findings suggest that consumption of total fiber and cereal fiber were significantly inversely associated with SUA levels and HUA risk among the Chinese adult population. Developing and implementing effective public education programs are urgently needed to increase the intake of dietary fiber, especially cereal fiber among Chinese adults.

This study is aimed at exploring the relationship between serum ferritin and blood lipids and the influence of diabetes and different hs-CRP levels. A total of 8163 subjects were analyzed. Participators were classified according to serum ferritin, diabetes, and two hs-CRP levels. Blood lipids were determined using standardized methods and conditions. Except for HDL-C, there was a significant increase in blood lipids in the progressive ferritin group with normal hs-CRP levels (P<0.05). But HDL-C was just the opposite (P<0.0001). In nondiabetic patients, TG, TC, and LDL-C were significantly elevated in the progressive ferritin group (P<0.05). And, HDL-C was just the opposite (P<0.05). The generalized linear model and the parsimonious model showed that serum TG was positively correlated with ferritin, and LDL-C was negatively correlated with ferritin (P<0.05). But the correlation between LDL-C and ferritin was broken (P>0.05). After a sufficient adjustment, there was a positive correlation between serum TG and ferritin and a negative correlation between LDL-C and ferritin. Nonetheless, a negative correlation between LDL-C and ferritin is influenced by diabetes frailly. And, there was no change of relationship between lipids and ferritin in different hs-CRP levels. We found a real relationship between ferritin and lipids after sufficient adjustment for confounders.

Background: Stroke is the leading cause of death in China, and its burdens are rapidly increasing. The prevalence and control of stroke risk factors among stroke patients in China are unknown. Objective: We investigated the stroke risk factors of stroke patients in China. Design: We examined stroke risk factors in 6580 stroke patients aged 18 years or older in the China National Chronic Diseases and Nutrition Surveillance of Adults (2015–2017). With regard to the basic characteristics of the study participants, categorical variables were described as frequency (percent). The chi-square test was used to analyze the difference between men and women. The multivariate logistic regression model was used in the multivariate analysis. Results: Among the 6580 stroke patients, hypertension was the most common stroke risk factor identified in most cases (78.51%), followed by overweight or obesity (61.58%), dyslipidemia (54.38%), smoking (24.04%), diabetes (21.75%), family history of stroke (17.43%), lack of exercise (16.35%), and atrial fibrillation (4.47%). Drinking stroke patients had a lower rate of hypertension, diabetes, and dyslipidemia. Patients with hyperuricemia had a higher rate of hypertension and dyslipidemia than no-hyperuricemia patients. The hypertension awareness, treatment, and control rates among hypertension stroke patients were 73.62%, 70.19%, and 17.79%, respectively. The diabetes awareness, treatment, and control rates among diabetes patients were 69.74%, 65.83%, and 34.59%, respectively. The dyslipidemia awareness, treatment, and control rates among dyslipidemia patients were 42.37%, 29.4%, and 20.07%, respectively. Among treated hypertension patients, the rates of taking medicine as medically advised, controlled diet, increased exercise, and blood pressure monitoring were 91.31%, 58.88%, 45.78%, and 73.99%, respectively. Among treated diabetes patients, the rates of oral antidiabetic medications, insulin injection, diet control, and blood glucose monitoring were 78.24%, 34.71%, 85.77%, and 78.24%, respectively. Among treated dyslipidemic patients, the rate of taking medicine as medical advice, controlled diet, increased exercise, and regular blood lipid monitoring was 80.61%, 77.57%, 56.46%, and 40.3%, respectively. Conclusions: The most common risk factors for community stroke patients in China are hypertension, dyslipidemia, and overweight or obesity. The stroke community patients’ suboptimal awareness and treatment of hypertension, and suboptimal awareness, treatment, and control of diabetes, and dyslipidemia are significant problems in China.

This study aimed to explore the association between dietary BCAAs, blood lipid levels and risk of dyslipidemia. In this case–control study, a total of 9541 subjects with normal blood lipids were included as a control group, and 9792 patients with dyslipidemia were included as a case group. Dietary BCAA intake data were measured using 3-day 24 h meal recalls and household condiment weighing. All samples were from China Nutrition and Health Surveillance (2015). Generalized linear model, logistic regression, and restricted cubic spline (RCS) were used to evaluate the relationship between dietary BCAAs, blood lipids and dyslipidemia. After adjusting for confounding factors, dietary BCAAs were positively correlated with TC and LDL-C (p < 0.05). Higher dietary BCAAs were associated with higher OR for Hypercholesteremia (Q4 vs. Q1, OR = 1.29, 95% CI: 1.05–1.58, p-trend = 0.034). The ORs of Hyper-LDL-cholesterolemia showed inverted U-shaped with increasing dietary BCAAs (Q3 vs. Q1, OR = 1.20, 95% CI: 1.03–1.39; Q2 vs. Q1, OR = 1.05, 95% CI: 1.01–1.31). The relationship between dietary BCAAs and the risk of Hypercholesteremia and Hyper-LDL-cholesterolemia were both nonlinear (p nonlinearity = 0.0059, 0.0198). Our study reveals that dietary BCAAs are associated with specific types of lipids and risk of dyslipidemia, some of which may be non-linear.