Responses of regional environmental factors to the reference value of neutrophil-lymphocyte ratio in healthy adults
- VernacularTitle:区域环境因素对健康成年人中性粒细胞与淋巴细胞比值参考值的响应
- Author:
Jiaxin LI
1
;
Miao GE
1
;
Lei ZHANG
1
;
Zehua PEI
1
;
Wenjie YANG
1
;
Jinwei HE
2
;
Congxia WANG
3
Author Information
- Publication Type:Journal Article
- Keywords: COVID-19; neutrophil-to-lymphocyte ratio (NLR); natural environment; random forest; disjunctive Kriging
- From: Journal of Xi'an Jiaotong University(Medical Sciences) 2022;43(2):302-308
- CountryChina
- Language:Chinese
-
Abstract:
【Objective】 This paper screened the factors that may influence the spatial differentiation of Neutrophil-to-lymphocyte ratio (NLR) reference values in healthy adults in China and explored the trend of NLR reference values in China. 【Methods】 For this research, we collected the NLR of 162 681 healthy adults from 62 cities in China. Spearman regression analysis was used to analyze the correlation between NLR and 25 geography secondary indexes. We extracted 9 indexes with significant correlation, built a random forest (RF) model, and predicted the country’s urban healthy adults’ NLR reference value. By using the disjunctive Kriging method, we obtained the geographical distribution of NLR reference value of healthy adults in China. 【Results】 The reference value of NLR of healthy adults in China was significantly correlated with the 9 secondary indexes, namely, altitude, sunshine duration, annual average temperature, annual average relative humidity, annual temperature range, annual average wind speed, content of organic matter in topsoil, cation exchange capacity in topsoil (clay), and total amount of CaSO
4 in soil. The geographical distribution of NLR values of healthy adults in China showed a trend of being higher in Southeast China and lower in Northwest China, higher in coastal areas and lower in inland areas. 【Conclusion】 This study lays a foundation for further research on the mechanism of different influencing factors on the reference value of NLR index. A random forest model composed of significant influencing factors has been established to provide the basis for formulating reference criteria for the prognostic factors of the novel coronavirus using NLR reference values in different regions.