Pinkevych Mykola, Chelimo Kiprotich, Vulule John, Kazura James W, Moormann Ann M, Davenport Miles P
Centre for Vascular Research, University of New South Wales Australia, Kensington NSW 2052, Sydney, Australia.
BMC Med. 2015 Jan 30;13:19. doi: 10.1186/s12916-014-0252-9.
The identification of protective immune responses to P. falciparum infection is an important goal for the development of a vaccine for malaria. This requires the identification of susceptible and resistant individuals, so that their immune responses may be studied. Time-to-infection studies are one method for identifying putative susceptible individuals (infected early) versus resistant individuals (infected late). However, the timing of infection is dependent on random factors, such as whether the subject was bitten by an infected mosquito, as well as individual factors, such as their level of immunity. It is important to understand how much of the observed variation in infection is simply due to chance.
We analyse previously published data from a treatment-time-to-infection study of 201 individuals aged 0.5 to 78 years living in Western Kenya. We use a mathematical modelling approach to investigate the role of immunity versus random factors in determining time-to-infection in this cohort. We extend this analysis using a modelling approach to understand what factors might increase or decrease the utility of these studies for identifying susceptible and resistant individuals.
We find that, under most circumstances, the observed distribution of time-to-infection is consistent with this simply being a random process. We find that age, method for detection of infection (PCR versus microscopy), and underlying force of infection are all factors in determining whether time-to-infection is a useful correlate of immunity.
Many epidemiological studies of P. falciparum infection assume that the observed variation in infection outcomes, such as time-to-infection or presence or absence of infection, is determined by host resistance or susceptibility. However, under most circumstances, this distribution appears largely due to the random timing of infection, particularly in children. More direct measurements, such as parasite growth rate, may be more useful than time-to-infection in segregating patients based on their level of immunity.
确定针对恶性疟原虫感染的保护性免疫反应是开发疟疾疫苗的重要目标。这需要识别易感个体和抗性个体,以便研究他们的免疫反应。感染时间研究是识别假定的易感个体(早期感染)与抗性个体(晚期感染)的一种方法。然而,感染时间取决于随机因素,例如受试者是否被感染的蚊子叮咬,以及个体因素,例如他们的免疫水平。了解观察到的感染差异中有多少仅仅是由于偶然因素造成的很重要。
我们分析了先前发表的一项针对肯尼亚西部201名年龄在0.5至78岁之间的个体的治疗至感染时间研究的数据。我们使用数学建模方法来研究免疫与随机因素在确定该队列感染时间方面的作用。我们使用建模方法扩展此分析,以了解哪些因素可能会增加或降低这些研究在识别易感个体和抗性个体方面的效用。
我们发现,在大多数情况下,观察到的感染时间分布与这仅仅是一个随机过程一致。我们发现年龄、感染检测方法(PCR与显微镜检查)以及潜在的感染力都是决定感染时间是否是免疫有用关联指标的因素。
许多关于恶性疟原虫感染的流行病学研究假设,观察到的感染结果差异,如感染时间或感染的有无,是由宿主抗性或易感性决定的。然而,在大多数情况下,这种分布似乎主要是由于感染的随机时间,特别是在儿童中。在根据患者的免疫水平区分患者时,更直接的测量方法,如寄生虫生长率,可能比感染时间更有用。