Dynamic modeling of antagonism between enteric infection and undernutritionin infancy

项目总结 NIAID指导研究科学家发展奖(K01)的目标是提供候选人， Gwenyth O.Lee教授在培训和时间保障下，建立了一个独立的、跨学科的研究项目。 该计划将把计算建模方法和基于社区的研究联系起来，以解决关键问题 肠道疾病流行病学领域的问题。发育迟缓是一种严重的负面后果 低收入和中等收入国家五岁以下儿童经常发生肠道感染。一位少校 制定有效的干预措施以预防发育迟缓的障碍一直是方法论问题。生长 儿童的步履蹒跚是感染性接触和饮食之间复杂相互作用的累积结果 不足之处。传统的统计方法能够，但不能很好地解决这些问题 相互作用，以确定哪些可修改的因素具有最大的潜在影响。另一方面，系统 方法被明确地设计为捕获多个因果关系之间的复杂交互作用。 因此，该提案的总体目标是开发一种机制模型，该模型能够捕获 感染、饮食和短期生长动态之间的反馈，并预测单个婴儿可能 生长，因为在生命的头两年经历了不同的感染模式。该模型将用于测试 感染与营养不良之间的反馈是否有可能导致“临界点”，从而 只有当肠道感染的负面影响超过婴儿的生物学影响时，这些感染才会影响发育迟缓 追赶增长的能力。该模型还将被用来研究饮食 不足之处修改了肠道感染和生长之间的关系，并将肠道病原体- 具体的增长影响。该模型将基于1)来自三个厄瓜多尔出生队列的数据， 在城乡梯度上采样，肠道暴露的负担不同，但婴儿相对同质 喂养实践和2)来自多个LMIC环境的六个出生队列的数据(Mal-Ed研究)，其中肠道 暴露在人群中，但饮食和社会条件也是不同的。要与建议的 在科学工作方面，候选人将寻求结合她在社区肠道疾病研究方面的经验 具有以下培训领域：计算建模，包括1)系统动力学和2)基于代理 3)儿童生长发育的营养调控。 此培训将以职业发展为补充。 获得领导力和职业成长所必需的工具的活动。专家们强调，有必要 更好地将计算建模与应用流行病学研究相结合，从而创建能够 更好地预测因地制宜的干预策略。该项目建立在独特的资源和 通过密歇根大学为候选人提供指导，以提供先进的方法 培训，支持竞争性资金申请，在更广泛的研究界传播研究成果 实现研究自主性。

PROJECT SUMMARY The goal of this NIAID Mentored Research Scientist Development Award (K01) is to provide the candidate, Gwenyth O. Lee, with the training and protected time to build an independent, interdisciplinary research program. This program would bridge computational modeling methods and community-based research to address key questions in the field of enteric disease epidemiology. Stunted growth is a significant negative outcome of frequent enteric infection among children under five years in low and middle- income countries (LMICs). A major obstacle to the development of impactful interventions to prevent stunting has been methodological. Growth faltering in children is the cumulative result of complex interactions between infectious exposures and dietary inadequacies. Conventional statistical approaches are capable of, but not well-suited to, disentangling these interactions to identify those modifiable factors with the greatest potential impact. On the other hand, systems approaches are explicitly designed to capture complex interactions between multiple causal relationships. Therefore, the overall objective of this proposal is to develop a mechanistic model that captures patterns of feedback between infection, diet, and short-term growth dynamics and predicts how an individual infant might grow, given different patterns of infection experienced in the first two years of life. This model will be used to test whether feedback between infection and undernutrition has the potential to result in a ‘tipping point’, such that enteric infections impact stunting only when the negative effect of these infections outpaces the infant’s biological capacity for catch-up growth. The model will also be used to investigate mechanisms by which dietary inadequacies modify the relationship between enteric infections and growth and to compare enteropathogen- specific growth impacts. The model will be empirically based in 1) data from three Ecuadorian birth cohorts, sampled across rural-urban gradient with variable burdens of enteric exposures but relatively homogenous infant feeding practices and 2) data from six birth cohorts from multiple LMIC contexts (‘MAL-ED’ study), where enteric exposures but also dietary and social conditions are heterogeneous between cohorts. To match the proposed scientific work, the candidate will seek to integrate her experience in community-based enteric disease research with the following training areas: computational modeling, including 1) system dynamics and 2) agent-based modeling, and 3) the nutritional regulation of child growth. This training will be augmented with 4) career-building activities to acquire essential tools for leadership and professional growth. Experts have emphasized the need to better integrate computational modeling into applied epidemiology research, thereby creating models that can better predict contextually-tailored intervention strategies. The project builds upon the unique resources and mentoring available to the candidate through the University of Michigan to provide advanced methodological training, bolster applications for competitive funding, disseminate findings across the wider research community and attain research independence.