Mapping amyotrophic lateral sclerosis lake risk factors across northern New England.
Mapping amyotrophic lateral sclerosis lake risk factors across northern New England.
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DOI：
10.1186/1476-072x-13-1
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发表时间：
2014-01-02
影响因子：
4.9
通讯作者：
Caller T
中科院分区：
文献类型：
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作者：
Torbick N;Hession S;Stommel E;Caller T
肌萎缩侧索硬化症(ALS)是一种进行性的、致命的神经退行性疾病，一生的风险为1/700。尽管最近关于ALS的遗传学有了许多发现，但散发性ALS的病因仍然很大程度上仍不清楚，基因-环境相互作用被怀疑是一个驱动因素。水质和蓝藻产生的毒素β-甲氨基丙氨酸可能是环境诱因。我们的目标是开发一种生态流行病学建模方法，以表征ALS发病率高于预期的地区与来自卫星遥感的湖泊水质风险因素之间的空间关系，作为暴露的替代标志。我们的生态流行病学建模方法从实施空间聚类分析开始，该分析由当地的空间自相关指标提供信息，以确定整个新英格兰北部人口普查区域水平的归一化过剩ALS计数的位置。接下来，使用陆地卫星TM波段比回归技术生成所有超过6公顷的湖泊(n = 4,453)的水质数据，并通过现场湖泊采样进行校准。得到的湖泊水质风险图包括叶绿素a(Chl-a)、塞奇深度(SD)和总氮(TN)。最后，采用空间感知的Logistic回归建模方法，描述了提取的湖泊水质指标与ALS热点之间的关系。在整个地区发现了几个不同的ALS热点。成功地提取了遥感湖泊水质指标；基于样本外验证，这些指标的调整R2值在0.62-0.88之间。从这些指标得出的MAP产品代表了该地区湖泊水质的第一个全面衡量标准。对全区局部热点，即ALS计数高于预期的人口普查区域ALS病例成员的Logistic回归模型显示：半径30公里内平均SD的增加对应于属于ALS热点的几率降低59%；半径30公里内的平均TN和半径10公里内的平均Chl-a浓度对应于属于ALS热点的几率分别增加167%和4%。卫星遥感信息的优势可以帮助克服传统的领域限制和时空数据空白，为公共卫生界提供宝贵的暴露数据。在评估生态过程、风险因素和人类健康结果之间的关系时，需要认真考虑地理尺度。总的来说，我们发现，较差的湖泊水质与该地区属于肌萎缩侧索硬化症集群的几率增加显著相关。这些发现支持这样一种假设，即散发性ALS(SALS)可能部分由环境水质指标和促进有害藻类大量繁殖的湖泊条件引发。
Amyotrophic lateral sclerosis (ALS) is a progressive, fatal neurodegenerative disease with a lifetime risk of developing as 1 in 700. Despite many recent discoveries about the genetics of ALS, the etiology of sporadic ALS remains largely unknown with gene-environment interaction suspected as a driver. Water quality and the toxin beta methyl-amino-alanine produced by cyanobacteria are suspected environmental triggers. Our objective was to develop an eco-epidemiological modeling approach to characterize the spatial relationships between areas of higher than expected ALS incidence and lake water quality risk factors derived from satellite remote sensing as a surrogate marker of exposure. Our eco-epidemiological modeling approach began with implementing a spatial clustering analysis that was informed by local indicators of spatial autocorrelation to identify locations of normalized excess ALS counts at the census tract level across northern New England. Next, water quality data for all lakes over 6 hectares (n = 4,453) were generated using Landsat TM band ratio regression techniques calibrated with in situ lake sampling. Derived lake water quality risk maps included chlorophyll-a (Chl-a), Secchi depth (SD), and total nitrogen (TN). Finally, a spatially-aware logistic regression modeling approach was executed characterizing relationships between the derived lake water quality metrics and ALS hot spots. Several distinct ALS hot spots were identified across the region. Remotely sensed lake water quality indicators were successfully derived; adjusted R2 values ranged between 0.62-0.88 for these indicators based on out-of-sample validation. Map products derived from these indicators represent the first wall-to-wall metrics of lake water quality across the region. Logistic regression modeling of ALS case membership in localized hot spots across the region, i.e., census tracts with higher than expected ALS counts, showed the following: increasing average SD within a radius of 30 km corresponds with a decrease in the odds of belonging to an ALS hot spot by 59%; increasing average TN within a radius of 30 km and average Chl-a concentration within a radius of 10 km correspond with increased odds of belonging to an ALS hot spot by 167% and 4%, respectively. The strengths of satellite remote sensing information can help overcome traditional field limitations and spatiotemporal data gaps to provide the public health community valuable exposure data. Geographic scale needs to be diligently considered when evaluating relationships among ecological processes, risk factors, and human health outcomes. Broadly, we found that poorer lake water quality was significantly associated with increased odds of belonging to an ALS cluster in the region. These findings support the hypothesis that sporadic ALS (sALS) can, in part, be triggered by environmental water-quality indicators and lake conditions that promote harmful algal blooms.