Author(s)

Longxing Fan^1*, Wenjie Zhang^2*, Huanhuan Cui³, Yanqing Liu³, Ziquan Liu^3#

¹Ministry of Education Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fuzhou, China.
²Tianjin Center for Disease Control and Prevention, Tianjin, China.
³Institute of Disaster Medicine, Tianjin University, Tianjin, China.

Objective: To investigate the occurrence pattern of abnormal bone density in male long-distance runners from several different regions of China, and provide a basis for elucidating the influences of geo-environmental differences on bone density. Methods: We employed a set of well-designed exclusion-inclusion criteria to recruit study subjects, in which compounding factors were managed and regional environmental traits were fully incorporated. WHO (World Health Organization) criteria for the diagnosis of osteoporosis were then used to examine the subjects to determine occurrence of abnormal bone density. The resulting data were analyzed using methods of spatial statistics, which included several approaches, such as spatial autocorrelation, hot spot analysis, and Geodetector Software analysis, to depict and analyze the spatial distribution of abnormal bone density in male athletes from different regions in China, thereby investigating the influences of geo-environmental factors (e.g., temperature, humidity, and altitude) on bone density. Results: A total of 685 subjects were effectively examined in this study, including 486 with normal bone density, 185 with osteopenia, and 14 with osteoporosis. Spatial distribution analysis revealed that the distribution of subjects with abnormal bone density overall exhibited a pattern indicating that the level of abnormal bone density in the eastern regions was higher than that in the western regions and that the levels of abnormal bone density in the southern and northern regions were higher than that in the middle regions. Spatial autocorrelation analysis revealed a Moran’s I = 0.136, Z-score = 1.114, and P value = 0.265 and indicated that the athletes with abnormal bone density were randomly distributed in each region. Hot spot analysis revealed that Tibet and Qinghai displayed distributions of cold spots. Geodetector Software analysis yielded a Q value for annual average temperature of 1.000 and a corresponding P value of 0.000, and the results revealed that temperature significantly affected bone density and that altitude, relative humidity, sunlight hours, and temperature variations displayed synergistic effects on bone density and could diminish the influences of temperature on bone density. Conclusion: Our data revealed that different regions displayed different distribution patterns of abnormal bone density such that the level in the eastern regions was higher than that in the western ones and that the levels in the southern and northern regions were higher than that in the middle regions; specifically, the provinces of Yunnan, Heilongjiang, Hainan, and Inner Mongolia had high rates of abnormal bone density, whereas Tibet and Qinghai had relatively good conditions of bone density. Our data suggested that suitable temperature changes and appropriate levels of temperature variations can decrease the occurrence rates of osteopenia and osteoporosis.

Spatial Epidemiology, Regions, Athletes, Bone Density

Fan, L. , Zhang, W. , Cui, H. , Liu, Y. and Liu, Z. (2021) Analysis of the Spatial Distribution Characteristics of Abnormal Bone Density in Male Long-Distance Runners from Different Regions in China. Open Journal of Epidemiology, 11, 80-91. doi: 10.4236/ojepi.2021.111008.