Deji Zhuoga, Tong Yuantao, Huang Honglian, Zhang Zeyu, Fang Meng, Crabbe M James C, Zhang Xiaoyan, Wang Ying
Research Center for Translational Medicine, Shanghai East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.
Information School, The University of Sheffield, Sheffield, United Kingdom.
Interact J Med Res. 2024 Mar 25;13:e43585. doi: 10.2196/43585.
The novel coronavirus SARS-CoV-2 caused the global COVID-19 pandemic. Emerging reports support lower mortality and reduced case numbers in highland areas; however, comparative studies on the cumulative impact of environmental factors and viral genetic diversity on COVID-19 infection rates have not been performed to date.
The aims of this study were to determine the difference in COVID-19 infection rates between high and low altitudes, and to explore whether the difference in the pandemic trend in the high-altitude region of China compared to that of the lowlands is influenced by environmental factors, population density, and biological mechanisms.
We examined the correlation between population density and COVID-19 cases through linear regression. A zero-shot model was applied to identify possible factors correlated to COVID-19 infection. We further analyzed the correlation of meteorological and air quality factors with infection cases using the Spearman correlation coefficient. Mixed-effects multiple linear regression was applied to evaluate the associations between selected factors and COVID-19 cases adjusting for covariates. Lastly, the relationship between environmental factors and mutation frequency was evaluated using the same correlation techniques mentioned above.
Among the 24,826 confirmed COVID-19 cases reported from 40 cities in China from January 23, 2020, to July 7, 2022, 98.4% (n=24,430) were found in the lowlands. Population density was positively correlated with COVID-19 cases in all regions (ρ=0.641, P=.003). In high-altitude areas, the number of COVID-19 cases was negatively associated with temperature, sunlight hours, and UV index (P=.003, P=.001, and P=.009, respectively) and was positively associated with wind speed (ρ=0.388, P<.001), whereas no correlation was found between meteorological factors and COVID-19 cases in the lowlands. After controlling for covariates, the mixed-effects model also showed positive associations of fine particulate matter (PM2.5) and carbon monoxide (CO) with COVID-19 cases (P=.002 and P<.001, respectively). Sequence variant analysis showed lower genetic diversity among nucleotides for each SARS-CoV-2 genome (P<.001) and three open reading frames (P<.001) in high altitudes compared to 300 sequences analyzed from low altitudes. Moreover, the frequencies of 44 nonsynonymous mutations and 32 synonymous mutations were significantly different between the high- and low-altitude groups (P<.001, mutation frequency>0.1). Key nonsynonymous mutations showed positive correlations with altitude, wind speed, and air pressure and showed negative correlations with temperature, UV index, and sunlight hours.
By comparison with the lowlands, the number of confirmed COVID-19 cases was substantially lower in high-altitude regions of China, and the population density, temperature, sunlight hours, UV index, wind speed, PM2.5, and CO influenced the cumulative pandemic trend in the highlands. The identified influence of environmental factors on SARS-CoV-2 sequence variants adds knowledge of the impact of altitude on COVID-19 infection, offering novel suggestions for preventive intervention.
新型冠状病毒SARS-CoV-2引发了全球新冠肺炎大流行。新出现的报告表明高地地区的死亡率较低且病例数减少；然而，迄今为止尚未对环境因素和病毒基因多样性对新冠肺炎感染率的累积影响进行比较研究。
本研究的目的是确定高海拔和低海拔地区新冠肺炎感染率的差异，并探讨中国高海拔地区与低地地区大流行趋势的差异是否受环境因素、人口密度和生物学机制的影响。
我们通过线性回归研究了人口密度与新冠肺炎病例之间的相关性。应用零样本模型来识别与新冠肺炎感染相关的可能因素。我们进一步使用斯皮尔曼相关系数分析了气象和空气质量因素与感染病例之间的相关性。应用混合效应多元线性回归来评估选定因素与新冠肺炎病例之间的关联，并对协变量进行调整。最后，使用上述相同的相关技术评估环境因素与突变频率之间的关系。
在2020年1月23日至2022年7月7日期间中国40个城市报告的24826例新冠肺炎确诊病例中，98.4%（n = 24430）出现在低地地区。所有地区的人口密度与新冠肺炎病例呈正相关（ρ = 0.641，P = 0.003）。在高海拔地区，新冠肺炎病例数与温度、日照时长和紫外线指数呈负相关（分别为P = 0.003、P = 0.001和P = 0.009），与风速呈正相关（ρ = 0.388，P < 0.001），而在低地地区气象因素与新冠肺炎病例之间未发现相关性。在控制协变量后，混合效应模型也显示细颗粒物（PM2.5）和一氧化碳（CO）与新冠肺炎病例呈正相关（分别为P = 0.002和P < 0.001）。序列变异分析显示，与从低海拔地区分析的300个序列相比，高海拔地区每个SARS-CoV-2基因组的核苷酸以及三个开放阅读框中的基因多样性较低（P < 0.001）。此外，高海拔和低海拔组之间44个非同义突变和32个同义突变的频率存在显著差异（P < 0.001，突变频率>0.1）。关键的非同义突变与海拔、风速和气压呈正相关，与温度、紫外线指数和日照时长呈负相关。
与低地地区相比，中国高海拔地区的新冠肺炎确诊病例数显著更低，且人口密度、温度、日照时长、紫外线指数、风速、PM2.5和CO影响了高海拔地区的累积大流行趋势。所确定的环境因素对SARS-CoV-2序列变异的影响增加了对海拔对新冠肺炎感染影响的认识，为预防性干预提供了新的建议。
