Wei De-Zhi, Ge Miao, Wang Cong-Xia, Lin Qian-Yi, Li Meng-Jiao, Li Peng
Institute of Health Geography, College of Tourism and Environment, Shaanxi Normal University, Xi'an 710119, China.E-mail:
Nan Fang Yi Ke Da Xue Xue Bao. 2016 Nov 20;36(11):1555-1560.
To explore the relationship between serum creatinine (Scr) reference values in healthy adults and geographic factors and provide evidence for establishing Scr reference values in different regions.
We collected 29 697 Scr reference values from healthy adults measured by 347 medical facilities from 23 provinces, 4 municipalities and 5 autonomous regions. We chose 23 geographical factors and analyzed their correlation with Scr reference values to identify the factors correlated significantly with Scr reference values. According to the Principal component analysis and Ridge regression analysis, two predictive models were constructed and the optimal model was chosen after comparison of the two model's fitting degree of predicted results and measured results. The distribution map of Scr reference values was drawn using the Kriging interpolation method.
Seven geographic factors, including latitude, annual sunshine duration, annual average temperature, annual average relative humidity, annual precipitation, annual temperature range and topsoil (silt) cation exchange capacity were found to correlate significantly with Scr reference values. The overall distribution of Scr reference values featured a pattern that the values were high in the south and low in the north, varying consistently with the latitude change.
The data of the geographic factors in a given region allows the prediction of the Scr values in healthy adults. Analysis of these geographical factors can facilitate the determination of the reference values specific to a region to improve the accuracy for clinical diagnoses.
探讨健康成年人血清肌酐(Scr)参考值与地理因素之间的关系,为制定不同地区的Scr参考值提供依据。
收集来自23个省、4个直辖市和5个自治区的347家医疗机构检测的29697例健康成年人的Scr参考值。选取23个地理因素,分析它们与Scr参考值的相关性,以确定与Scr参考值显著相关的因素。根据主成分分析和岭回归分析构建两个预测模型,并比较两个模型预测结果与测量结果的拟合度,选择最优模型。采用克里金插值法绘制Scr参考值分布图。
发现纬度、年日照时长、年平均气温、年平均相对湿度、年降水量、年气温较差和表土(粉砂)阳离子交换量7个地理因素与Scr参考值显著相关。Scr参考值的总体分布呈现出南高北低的格局,与纬度变化一致。
利用某一地区的地理因素数据可预测健康成年人的Scr值。对这些地理因素进行分析有助于确定特定地区的参考值,提高临床诊断的准确性。