Measuring changes in transmission of neglected tropical diseases, malaria, and enteric pathogens from quantitative antibody levels.
Measuring changes in transmission of neglected tropical diseases, malaria, and enteric pathogens from quantitative antibody levels.
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DOI：
10.1371/journal.pntd.0005616
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发表时间：
2017-05
影响因子：
3.8
通讯作者：
Lammie PJ
中科院分区：
文献类型：
--
作者：
Arnold BF;van der Laan MJ;Hubbard AE;Steel C;Kubofcik J;Hamlin KL;Moss DM;Nutman TB;Priest JW;Lammie PJ
血清抗体水平是病原体暴露的敏感标志，多重检测的进展为大规模综合传染病监测创造了巨大潜力。分析抗体测量的大多数方法将定量抗体水平降低到血清阳性和血清阴性组，但这对于许多病原体可能是困难的，并且可能提供比定量水平更低的分辨率信息。分析方法主要保持单一的疾病焦点，但综合监测平台将受益于多重检测中包含的跨多种病原体工作的方法。我们开发了一种方法来测量定量抗体水平的传播变化，可应用于全球重要的各种病原体。我们在重复的横断面调查中比较了多种病原体传播差异的人群之间的年龄依赖性免疫球蛋白G曲线，包括：淋巴丝虫病在库克群岛Mauke进行大规模药物给药前后测量的班氏吴策线虫疟疾在尼日利亚的Garki项目中，和肠道原生动物（微小隐孢子虫、肠贾第鞭毛虫、溶组织内阿米巴）、细菌（产肠毒素大肠杆菌、沙门氏菌），以及生活在海地和美国的儿童中的病毒（诺如病毒I组和II组）。使用集成机器学习拟合的抗体依赖性曲线遵循病原体的特征形状，与体液免疫基本机制的预测一致。病原体传播的差异导致拟合的抗体曲线发生变化，这些曲线在病原体、检测和人群中非常一致。平均抗体水平与传播强度的传统测量密切相关，例如恶性疟原虫的昆虫学接种率（斯皮尔曼rho = 0.75）。在高传播和低传播情况下，平均抗体曲线显示了人群平均抗体水平的变化，这些变化被血清阳性率措施掩盖，因为变化发生在血清阳性截止值以上或以下。抗体依赖性曲线和汇总平均值为传播变化提供了一个可靠和敏感的衡量标准，其中幼儿的敏感性最高。该方法推广到可以在高通量、多重血清学测定中测量的病原体，并扩展到需要高时空分辨率的监测活动。我们的研究结果表明，定量抗体水平将是特别有用的，以衡量暴露的病原体，引起短暂的抗体反应或监测人群非常高或非常低的传播，当血清阳性率是信息较少的差异。该方法为针对被忽视的热带病、疟疾和其他具有明确抗原靶点的传染病进行综合血清学监测提供了新的机会。全球消除传染病（如被忽视的热带病和疟疾）的战略依赖于对病原体传播的准确估计，以确定和评估控制方案。循环抗体水平可以是近期病原体暴露的敏感指标，但没有广泛适用的方法直接从定量抗体水平测量传播的变化。我们开发了一种新方法，该方法应用了机器学习和数据科学的最新进展，以灵活地拟合年龄依赖性抗体曲线。在对许多全球重要病原体（蠕虫、疟疾、肠道感染）进行评估时，年龄依赖性抗体曲线的变化提供了非常一致、敏感的传播变化指标。该方法在不同应用中的通用性和性能表明其在传染病综合血清学监测中的广泛潜力。
Serological antibody levels are a sensitive marker of pathogen exposure, and advances in multiplex assays have created enormous potential for large-scale, integrated infectious disease surveillance. Most methods to analyze antibody measurements reduce quantitative antibody levels to seropositive and seronegative groups, but this can be difficult for many pathogens and may provide lower resolution information than quantitative levels. Analysis methods have predominantly maintained a single disease focus, yet integrated surveillance platforms would benefit from methodologies that work across diverse pathogens included in multiplex assays. We developed an approach to measure changes in transmission from quantitative antibody levels that can be applied to diverse pathogens of global importance. We compared age-dependent immunoglobulin G curves in repeated cross-sectional surveys between populations with differences in transmission for multiple pathogens, including: lymphatic filariasis (Wuchereria bancrofti) measured before and after mass drug administration on Mauke, Cook Islands, malaria (Plasmodium falciparum) before and after a combined insecticide and mass drug administration intervention in the Garki project, Nigeria, and enteric protozoans (Cryptosporidium parvum, Giardia intestinalis, Entamoeba histolytica), bacteria (enterotoxigenic Escherichia coli, Salmonella spp.), and viruses (norovirus groups I and II) in children living in Haiti and the USA. Age-dependent antibody curves fit with ensemble machine learning followed a characteristic shape across pathogens that aligned with predictions from basic mechanisms of humoral immunity. Differences in pathogen transmission led to shifts in fitted antibody curves that were remarkably consistent across pathogens, assays, and populations. Mean antibody levels correlated strongly with traditional measures of transmission intensity, such as the entomological inoculation rate for P. falciparum (Spearman’s rho = 0.75). In both high- and low transmission settings, mean antibody curves revealed changes in population mean antibody levels that were masked by seroprevalence measures because changes took place above or below the seropositivity cutoff. Age-dependent antibody curves and summary means provided a robust and sensitive measure of changes in transmission, with greatest sensitivity among young children. The method generalizes to pathogens that can be measured in high-throughput, multiplex serological assays, and scales to surveillance activities that require high spatiotemporal resolution. Our results suggest quantitative antibody levels will be particularly useful to measure differences in exposure for pathogens that elicit a transient antibody response or for monitoring populations with very high- or very low transmission, when seroprevalence is less informative. The approach represents a new opportunity to conduct integrated serological surveillance for neglected tropical diseases, malaria, and other infectious diseases with well-defined antigen targets. Global elimination strategies for infectious diseases like neglected tropical diseases and malaria rely on accurate estimates of pathogen transmission to target and evaluate control programs. Circulating antibody levels can be a sensitive measure of recent pathogen exposure, but no broadly applicable method exists to measure changes in transmission directly from quantitative antibody levels. We developed a novel method that applies recent advances in machine learning and data science to flexibly fit age-dependent antibody curves. Shifts in age-dependent antibody curves provided remarkably consistent, sensitive measures of transmission changes when evaluated across many globally important pathogens (filarial worms, malaria, enteric infections). The method’s generality and performance in diverse applications demonstrate its broad potential for integrated serological surveillance of infectious diseases.