OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.