吉林大学学报(医学版) ›› 2017, Vol. 43 ›› Issue (03): 600-606.doi: 10.13481/j.1671-587x.20170326

• 临床研究 • 上一篇    下一篇

地理环境与健康人血尿素氮参考值的关系

韦德智1,2, 葛淼1,2, 温国涛1,2, 王聪霞3   

  1. 1. 陕西师范大学旅游与环境学院健康地理研究所, 陕西 西安 710119;
    2. 地理学国家级实验教学示范中心(陕西师范大学), 陕西 西安 710119;
    3. 西安交通大学医学院第二附属医院心内科, 陕西 西安 710004
  • 收稿日期:2016-10-10 出版日期:2017-05-28 发布日期:2017-06-01
  • 通讯作者: 葛淼,教授,研究员,博士研究生导师(Tel:029-85310525,E-mail:gemiao@snnu.edu.cn) E-mail:gemiao@snnu.edu.cn
  • 作者简介:韦德智(1993-),男,广西壮族自治区河池市人,在读环境科学硕士,主要从事环境地理与健康的关系方面的研究。
  • 基金资助:
    国家自然科学基金资助课题(40971060);中央高校基本科研业务费专项资金资助课题(2016CSY012)

Relationship between geographic environment and blood urea nitrogen reference values of healthy people

WEI Dezhi1,2, GE Miao1,2, WEN Guotao1,2, WANG Congxia3   

  1. 1. Institute of Health Geography, College of Tourism and Environment, Shaanxi Normal University, Xi'an 710119, China;
    2. National Demonstration Center for Experimental Geography Education(Shaanxi Normal University), Xi'an 710119, China;
    3. Department of Cardiovascular Medicine, Second Affiliated Hospital, Medical School of Xi'an Jiaotong University, Xi'an 710004, China
  • Received:2016-10-10 Online:2017-05-28 Published:2017-06-01

摘要: 目的:探讨地理环境与健康人血尿素氮BUN参考值的关系,阐明我国健康人BUN参考值的分布规律,为制订不同地区BUN参考值的标准提供科学依据。方法:搜集23个省、4个直辖市、5个自治区403所医疗机构测定的33 521名健康人BUN参考值,选取23项地理数据与健康人BUN参考值进行分析。利用空间自相关分析确定数据空间自相关性,利用相关分析确定与BUN参考值显著相关的地理因素,采用多元线性回归、主成分和岭回归构建预测模型,利用配对样本t检验选取最优预测模型,利用地统计分析构建中国健康人BUN参考值 空间分布图。结果:健康人BUN参考值与纬度(X2)、海拔(X3)、年平均气温(X5)、年平均相对湿度(X6)和年降水量(X7)5项地理因素指标显著相关,最优预测模型回归方程为?=5.112+0.000 127 1X2+0.000 094 61X3-0.000 140 5X5-0.000 136 8X6-0.000 139 1X7±0.531 0;可得出BUN预测参考值分布图。结论:我国健康人BUN参考值的总体分布趋势为东低西高,BUN参考值与海拔高度呈负相关关系。若已知某一地区的地理数据便可以进行健康人BUN参考值的预测。

关键词: 血尿素氮, 地理因素, 主成分分析, 岭回归分析, 回归分析

Abstract: Objective: To discuss the relationship between the geographic environment and blood urea nitrogen(BUN) reference values of the healthy people, and to explore the distributional rule of BUN reference values of the healthy people, and to provide the scientific foundation for establishing the BUN reference value standards of different areas. Methods: A total of 23 geographic factors and 33521 BUN reference values of healthy adults measured by 403 medical facilities from 23 provinces, 4 municipalities and 5 autonomous regions were collected. The spatial autocorrelation analysis was used to determine the spatial autocorrelation of the sample data; the correlation analysis was used to detect the factors which correlated significantly with the BUN reference values;the multiple linear regression, principle component analysis and ridge regression analysis were respectively used to construct the predicted models; the paired-sample t test was used to choose the optimal model; the distribution map of BUN reference values was built by geostatistic analysis. Results: There were 5 geographic factors, latitude(X2), altitude(X3), annual mean temperature(X5), annual mean relative humidity(X6) and annual precipitation(X7), correlated significantly with the BUN reference values. The regression equation of optimal model was ?=5.112+0.000 127 1X2+0.000 094 61X3-0.000 140 5X5-0.000 136 8X6-0.000 139 1X7±0.531 0; the distribution map of predicted values of the BUN reference values was obtained. Conclusion: The overall trend of BUN reference values is low in the east and high in the west. The BUN reference value is negatively associated with the altitude. If the geographic data of a certain region could be obtained, the BUN reference value of this region will be predicted.

Key words: geographical factors, principal component analysis, blood urea nitrogen, ridge regression analysis

中图分类号: 

  • K992.7