S1 Data set -

This study combined geographic factors to predict Chinese healthy male RBP reference values from a geographic perspective, with the aim of exploring the spatial distribution and regional differences in Chinese healthy male Retinol-Binding Protein(RBP) reference values, and then providing a theoretical basis for medical diagnosis of healthy male RBP reference values in different regions of China. Using the actual measured RBP values of 24,502 healthy men in 256 cities in China combined with 16 geographical factors as the base data, the spatial autocorrelation, correlation analysis and support vector machine were used to predict the RBP reference values of healthy men in 2322 cities in China, and to generate a spatial distribution map of the RBP reference values of healthy men in China. It was found that the spatial distribution of healthy male RBP reference values in China showed a trend of gradual increase from the first to the third terrain steps. Combined with the distribution map, it is suggested that the RBP reference values of healthy men in China should be divided into the low value zone of the first-level terrain step (25mg/L~40mg/L), the middle value zone of the second-level terrain step (40mg/L~45mg/L) and the high value zone of the third-level terrain step (45mg/L~52mg/L).</div

Correlation analysis between geographical factors.

Correlation analysis between geographical factors.</p

A Taylor plot of the RBP prediction model.

This study combined geographic factors to predict Chinese healthy male RBP reference values from a geographic perspective, with the aim of exploring the spatial distribution and regional differences in Chinese healthy male Retinol-Binding Protein(RBP) reference values, and then providing a theoretical basis for medical diagnosis of healthy male RBP reference values in different regions of China. Using the actual measured RBP values of 24,502 healthy men in 256 cities in China combined with 16 geographical factors as the base data, the spatial autocorrelation, correlation analysis and support vector machine were used to predict the RBP reference values of healthy men in 2322 cities in China, and to generate a spatial distribution map of the RBP reference values of healthy men in China. It was found that the spatial distribution of healthy male RBP reference values in China showed a trend of gradual increase from the first to the third terrain steps. Combined with the distribution map, it is suggested that the RBP reference values of healthy men in China should be divided into the low value zone of the first-level terrain step (25mg/L~40mg/L), the middle value zone of the second-level terrain step (40mg/L~45mg/L) and the high value zone of the third-level terrain step (45mg/L~52mg/L).</div

Ridge plot of the RBP.

This study combined geographic factors to predict Chinese healthy male RBP reference values from a geographic perspective, with the aim of exploring the spatial distribution and regional differences in Chinese healthy male Retinol-Binding Protein(RBP) reference values, and then providing a theoretical basis for medical diagnosis of healthy male RBP reference values in different regions of China. Using the actual measured RBP values of 24,502 healthy men in 256 cities in China combined with 16 geographical factors as the base data, the spatial autocorrelation, correlation analysis and support vector machine were used to predict the RBP reference values of healthy men in 2322 cities in China, and to generate a spatial distribution map of the RBP reference values of healthy men in China. It was found that the spatial distribution of healthy male RBP reference values in China showed a trend of gradual increase from the first to the third terrain steps. Combined with the distribution map, it is suggested that the RBP reference values of healthy men in China should be divided into the low value zone of the first-level terrain step (25mg/L~40mg/L), the middle value zone of the second-level terrain step (40mg/L~45mg/L) and the high value zone of the third-level terrain step (45mg/L~52mg/L).</div

Flow chart of the study.

This study combined geographic factors to predict Chinese healthy male RBP reference values from a geographic perspective, with the aim of exploring the spatial distribution and regional differences in Chinese healthy male Retinol-Binding Protein(RBP) reference values, and then providing a theoretical basis for medical diagnosis of healthy male RBP reference values in different regions of China. Using the actual measured RBP values of 24,502 healthy men in 256 cities in China combined with 16 geographical factors as the base data, the spatial autocorrelation, correlation analysis and support vector machine were used to predict the RBP reference values of healthy men in 2322 cities in China, and to generate a spatial distribution map of the RBP reference values of healthy men in China. It was found that the spatial distribution of healthy male RBP reference values in China showed a trend of gradual increase from the first to the third terrain steps. Combined with the distribution map, it is suggested that the RBP reference values of healthy men in China should be divided into the low value zone of the first-level terrain step (25mg/L~40mg/L), the middle value zone of the second-level terrain step (40mg/L~45mg/L) and the high value zone of the third-level terrain step (45mg/L~52mg/L).</div

Moran plot of RBP predicted values.

This study combined geographic factors to predict Chinese healthy male RBP reference values from a geographic perspective, with the aim of exploring the spatial distribution and regional differences in Chinese healthy male Retinol-Binding Protein(RBP) reference values, and then providing a theoretical basis for medical diagnosis of healthy male RBP reference values in different regions of China. Using the actual measured RBP values of 24,502 healthy men in 256 cities in China combined with 16 geographical factors as the base data, the spatial autocorrelation, correlation analysis and support vector machine were used to predict the RBP reference values of healthy men in 2322 cities in China, and to generate a spatial distribution map of the RBP reference values of healthy men in China. It was found that the spatial distribution of healthy male RBP reference values in China showed a trend of gradual increase from the first to the third terrain steps. Combined with the distribution map, it is suggested that the RBP reference values of healthy men in China should be divided into the low value zone of the first-level terrain step (25mg/L~40mg/L), the middle value zone of the second-level terrain step (40mg/L~45mg/L) and the high value zone of the third-level terrain step (45mg/L~52mg/L).</div

SVM variable importance of RBP.

This study combined geographic factors to predict Chinese healthy male RBP reference values from a geographic perspective, with the aim of exploring the spatial distribution and regional differences in Chinese healthy male Retinol-Binding Protein(RBP) reference values, and then providing a theoretical basis for medical diagnosis of healthy male RBP reference values in different regions of China. Using the actual measured RBP values of 24,502 healthy men in 256 cities in China combined with 16 geographical factors as the base data, the spatial autocorrelation, correlation analysis and support vector machine were used to predict the RBP reference values of healthy men in 2322 cities in China, and to generate a spatial distribution map of the RBP reference values of healthy men in China. It was found that the spatial distribution of healthy male RBP reference values in China showed a trend of gradual increase from the first to the third terrain steps. Combined with the distribution map, it is suggested that the RBP reference values of healthy men in China should be divided into the low value zone of the first-level terrain step (25mg/L~40mg/L), the middle value zone of the second-level terrain step (40mg/L~45mg/L) and the high value zone of the third-level terrain step (45mg/L~52mg/L).</div

Trend surface analysis of the predicted RBP values.

Trend surface analysis of the predicted RBP values.</p

Spatial distribution plot of the predicted RBP values.

(This map is based on the standard map with review number GS (2020) 4619, which is publicly available from the website of the Standard Map Service of the Ministry of Natural Resources of China (http://bzdt.ch.mnr.gov.cn/download.html?searchText=GS(2020)4619), and there are no modifications to the base map).</p

Correlation test for geographical factors.

This study combined geographic factors to predict Chinese healthy male RBP reference values from a geographic perspective, with the aim of exploring the spatial distribution and regional differences in Chinese healthy male Retinol-Binding Protein(RBP) reference values, and then providing a theoretical basis for medical diagnosis of healthy male RBP reference values in different regions of China. Using the actual measured RBP values of 24,502 healthy men in 256 cities in China combined with 16 geographical factors as the base data, the spatial autocorrelation, correlation analysis and support vector machine were used to predict the RBP reference values of healthy men in 2322 cities in China, and to generate a spatial distribution map of the RBP reference values of healthy men in China. It was found that the spatial distribution of healthy male RBP reference values in China showed a trend of gradual increase from the first to the third terrain steps. Combined with the distribution map, it is suggested that the RBP reference values of healthy men in China should be divided into the low value zone of the first-level terrain step (25mg/L~40mg/L), the middle value zone of the second-level terrain step (40mg/L~45mg/L) and the high value zone of the third-level terrain step (45mg/L~52mg/L).</div