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1.
Int J Endocrinol ; 2023: 8080578, 2023.
Artículo en Inglés | MEDLINE | ID: mdl-36704419

RESUMEN

Background: Currently, both metabolic syndrome and hyperuricaemia have attracted extensive attention in public health. The correlation between uric acid and metabolic syndrome is controversial. Research on the relationship between uric acid and metabolic syndrome in community-dwelling elderly people is relatively lacking. The purpose of this study is to explore the relationship between uric acid and metabolic syndrome in the community-dwelling elderly people. Design: Cross-sectional study. Methods: We collected the physical examination data of 1,267 elderly people in Gutian community in Wuhan and used SPSS IBM 25.0 for data analysis. Correlation and logistic regression analyses were performed, and ROC curves were drawn. Results: The uric acid level of the nonmetabolic syndrome group was lower than that of the metabolic syndrome group (337.31 vs. 381.91 µmol/L; P < 0.05). Uric acid was positively correlated with systolic blood pressure (r = 0.177, P < 0.001), diastolic blood pressure (r = 0.135, P < 0.001), body mass index (r = 0.234, P < 0.001), waist circumference (r = 0.283, P < 0.001), and triglycerides (r = 0.217, P < 0.05). High-density lipoprotein cholesterol (r = -0.268, P < 0.001) showed the opposite trend. Logistic regression analysis results suggested that uric acid is a risk factor for metabolic syndrome. The result is described as exp (B) and 95% CI (1.003 [1.001, 1.005]). Based on the receiver operating characteristic curve, we found that the area under the curve of uric acid to diagnose metabolic syndrome was 0.64 (sensitivity: 79.3%, specificity: 45.1%). Conclusion: We observed an association between uric acid levels and metabolic syndrome in the elderly Chinese population. The best threshold value for uric acid in predicting metabolic syndrome diagnosis was 314.5 µmol/l.

2.
Huan Jing Ke Xue ; 38(10): 4236-4244, 2017 Oct 08.
Artículo en Chino | MEDLINE | ID: mdl-29965207

RESUMEN

The distribution patterns of human activities affecting groundwater vulnerability vary with time. Studying the temporal and spatial changes in groundwater vulnerability, exploring the distribution characteristics of each period, and predicting the trends of development are important to formulate an effective development plan and reduce the risk of groundwater pollution at the same time. Based on the hydrogeological data as well as humanities and social data for 2004, 2010, and 2016 for the Chaoyang District of Beijing, a comprehensive evaluation model considering the human factors such as the land use types was established using the DRASTIC model. The spatiotemporal pattern of groundwater vulnerability was quantitatively characterized by calculating the Global Moran's Ⅰ and Getis-Ord Gi* index, and the distribution characteristics and variations in groundwater vulnerability were analyzed by the centroid of the G index and the standard deviation ellipse of the study area. The results indicate that in 2004, 2010, and 2016, the areas of high vulnerability have gradually reduced. The groundwater vulnerability in the study area shows a strong spatial aggregation; high concentration areas are mainly distributed in the northeast and southwest regions. The vulnerability of the northeast region has been decreasing each year, while the vulnerability of the northwest region has not changed much. The main reasons are the land use changes and the reductions in fertilizer use.

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