Geomagnetic disturbances may be environmental risk factor for multiple sclerosis: an ecological study of 111 locations in 24 countries
Geomagnetic disturbances may be environmental risk factor for multiple sclerosis: an ecological study of 111 locations in 24 countries
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DOI：
10.1186/1471-2377-12-100
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发表时间：
2012-09-24
期刊：
影响因子：
2.6
通讯作者：
Abdollahi, Fahimeh
中科院分区：
文献类型：
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作者：
Sajedi, Seyed Aidin;Abdollahi, Fahimeh
背景：我们注意到基于地磁扰动（GMD）影响的假设能够解释多发性硬化症（MS）的特殊特征。地磁纬度 60 度 (GM60L) 周围的区域经历的 GMD 量最大。评估我们的假设的最简单方法是测试 MS 患病率 (MSP) 与地磁 60 度纬度角距离 (AMAG60) 的关联，并将其与已知的 MS 与地理纬度 (GL) 的关联进行比较。我们以地理纬度 60 度角距离 (AGRAPH60) 作为对照，进行了同样的操作。方法：从 PubMed 检索在欧洲 (EUR)、北美 (NA) 或澳大利亚 (AUS) 发表的带有 MSP 关键词的英文论文。确定每个位置的地磁坐标，并计算 AMAG60 作为其地磁纬度与 GM60L 之间的数值差的绝对值。通过荟萃回归分析的生态学研究，分别评估了MSP与GL、AMAG60和AGRAPH60的关系。 MSP 数据按流行病例数的平方根加权。通过调整后的 R 平方 (AR(2)) 和估计标准误差 (SEE) 对模型进行比较。结果：研究中输入了 111 个 MSP 数据。在各大洲，AMAG60 与 MSP 的相关性最好，AR(2) 最大（欧元、北美和澳大利亚分别为 0.47、0.42 和 0.84），SEE 最小。合并两个半球数据，AMAG60 用最少的 SEE 解释了 56% 的 MSP 变化（R = 0.75，AR(2) = 0.56，SEE = 57），而 GL 用最高的 SEE 解释了 12% 的变化（R = 0.41，AR(2) = 0.17，SEE = 78.5），AGRAPH60 解释了 12% 的变化。 0.35，AR(2) = 0.12，SEE = 80.5）。结论：我们的结果证实 AMAG60 是 MSP 变异的最佳描述者，并且与 MSP 分布具有最强的关联性。他们澄清，众所周知的MSP纬度梯度实际上可能是与GM60L相关的梯度。此外，GM60L的位置可以解释为什么MSP在北半球和南半球分别具有抛物线和线性梯度。这一初步评估支持 GMD 可能是 MS 的神秘环境风险因素。我们认为这一假设值得考虑进行进一步的验证研究。
Background: We noticed that a hypothesis based on the effect of geomagnetic disturbances (GMD) has the ability to explain special features of multiple sclerosis (MS). Areas around geomagnetic 60 degree latitude (GM60L) experience the greatest amount of GMD. The easiest way to evaluate our hypothesis was to test the association of MS prevalence (MSP) with angular distance to geomagnetic 60 degree latitude (AMAG60) and compare it with the known association of MS with geographical latitude (GL). We did the same with angular distance to geographic 60 degree latitude (AGRAPH60) as a control.Methods: English written papers with MSP keywords, done in Europe (EUR), North America (NA) or Australasia (AUS) were retrieved from the PubMed. Geomagnetic coordinates were determined for each location and AMAG60 was calculated as absolute value of numerical difference between its geomagnetic latitude from GM60L. By an ecological study with using meta-regression analyses, the relationship of MSP with GL, AMAG60 and AGRAPH60 were evaluated separately. MSP data were weighted by square root of number of prevalent cases. Models were compared by their adjusted R square (AR(2)) and standard error of estimate (SEE).Results: 111 MSP data were entered in the study. In each continent, AMAG60 had the best correlation with MSP, the largest AR(2) (0.47, 0.42 and 0.84 for EUR, NA and AUS, respectively) and the least SEE. Merging both hemispheres data, AMAG60 explained 56% of MSP variations with the least SEE (R = 0.75, AR(2) = 0.56, SEE = 57), while GL explained 17% (R = 0.41, AR(2) = 0.17, SEE = 78.5) and AGRAPH60 explained 12% of that variations with the highest SEE (R = 0.35, AR(2) = 0.12, SEE = 80.5).Conclusions: Our results confirmed that AMAG60 is the best describer of MSP variations and has the strongest association with MSP distribution. They clarified that the well- known latitudinal gradient of MSP may be actually a gradient related to GM60L. Moreover, the location of GM60L can elucidate why MSP has parabolic and linear gradient in the north and south hemisphere, respectively. This preliminary evaluation supported that GMD can be the mysterious environmental risk factor for MS. We believe that this hypothesis deserves to be considered for further validation studies.