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Geographic modeling of very low birth weights around and near Texas federal super

德克萨斯州联邦超级银行周围和附近极低出生体重的地理模型

基本信息

批准号：
7873368
负责人：
JAMES A THOMPSON
金额：
$ 7.33万
依托单位：
TEXAS A&M AGRILIFE RESEARCH
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-01 至 2011-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7873368
关键词：
Affect African American Behavioral Behavioral Genetics Birth Caucasians Caucasoid Race Complex Databases Development Disabled Persons Disease Disease Clusterings Environmental Exposure Epidemiology Funding Future Genetic Genetic Predisposition to Disease Geographic Locations Goals Health Hospitals Individual Infant Investigation Label Location Malignant Childhood Neoplasm Malignant Neoplasms Maps Markov Chains Modeling Neighborhoods Outcome Pattern Personal Behavior Publishing Race Reproduction Research Resistance Risk Risk Factors Science Surface Testing Texas Toxicant exposure Toxicology Training Very Low Birth Weight Infant Weight Work adverse outcome base college design environmental toxicology experience genome wide association study high risk innovation interest prenatal programs public health relevance racial difference reproductive superfund site

项目摘要

DESCRIPTION (provided by applicant): Fifteen percent of babies born with a weight less than 1500 g die before being discharged from the hospital and 2 to 5% die within two years from complications. Those infants who survive two years are frequently disabled and prone to a lifetime of multiple health risks. These babies are labeled as very low birth weight (VLBW) but are also considered preterm. The combined conditions affect African-American babies more than twice as commonly as Caucasian babies. The causes are multi-factorial and include genetic susceptibility, environmental exposures and personal behavior risk factors. All three groups of factors are considered as contributory to the racial disparity. A critical need exists in parsing these confounded components. We propose an approach to model these effects that has been made possible by recent developments in Bayesian risk modeling as implemented by Markov Chain Monte Carlo. The study will evaluate the extent for which VLBW belongs in a broader cluster of adverse birth outcomes (ABO) and will identify high-risk locations and any spatial patterns of race-to-race variability. The results will be crucial for designing and justifying the locations and diseases to evaluate in a forthcoming R01 application. We will be proposing a study that employs a genome wide association study (GWAS) to further parse individual risk factors into genetic and behavioral in the presence of hierarchical or geographic risks. The objective of this application is to use an existing database to evaluate very low birth weight as an adverse birth outcome that is potentially correlated to infant cancers. The central hypothesis is VLBW has similar risk patterns to childhood cancer around federal superfund sites in Texas. This hypothesis will be tested by two specific aims: Specific Aim 1 will model the geographic risks for very low birth weights around the 47 federal superfund sites in Texas. Specific Aim 2 will model the spatial correlation among risks for very low birth weight and childhood cancer around the 47 federal superfund sites in Texas. This proposal is innovative because it will exploit two new developments, fully conditional hierarchical modeling and Multivariate modeling. Demonstration of correlation between very low birth weights and infant cancer would validate a powerful extension to a future genome-wide association study. With the results of this study, a future GWAS could parse personal risk factors into genetic and behavioral when confounding and interaction with geographic risk factors is possible. . PUBLIC HEALTH RELEVANCE: The proposed research is significant because the inclusion of a common condition, very low birth weight, with relatively rare childhood cancers in a common cluster of diseases would greatly strengthen future investigations. A critical need exists in parsing the confounded components of race-based genetics, race-based exposures and racial behavioral differences. We propose an approach to model these effects that has been made possible by recent developments in Bayesian risk modeling as implemented by Markov Chain Monte Carlo. These advances include both Multivariate and hierarchical modeling. The results will be crucial for designing and justifying the locations and diseases to evaluate in a forthcoming application.

描述（由申请人提供）：15%出生时体重低于1500克的婴儿在出院前死亡，2%至5%在两年内死于并发症。那些存活两年的婴儿往往是残疾的，一生中容易面临多种健康风险。这些婴儿被标记为极低出生体重（VLBW），但也被认为是早产。这些综合病症影响非裔美国婴儿的几率是白人婴儿的两倍多。病因是多因素的，包括遗传易感性、环境暴露和个人行为风险因素。所有这三组因素都被认为是造成种族差异的原因。在解析这些混淆的组件时，存在一个关键的需求。我们提出了一种方法来模拟这些影响，这是由马尔可夫链蒙特卡罗实现的贝叶斯风险建模的最新发展所实现的。该研究将评估VLBW属于更广泛的不良出生结局（ABO）集群的程度，并将确定高风险地点和种族间差异的任何空间模式。结果将是设计和证明在即将到来的R01应用中评估的位置和疾病的关键。我们将提出一项研究，采用基因组全关联研究（GWAS）来进一步分析个体风险因素在存在等级或地理风险的情况下的遗传和行为。本应用程序的目的是使用现有的数据库来评估极低的出生体重作为与婴儿癌症潜在相关的不良出生结果。核心假设是，VLBW的风险模式与德克萨斯州联邦超级基金所在地的儿童癌症相似。这一假设将通过两个具体目标来验证：具体目标1将模拟德克萨斯州47个联邦超级基金所在地附近出生体重过低的地理风险。具体目标2将为德克萨斯州47个联邦超级基金所在地的极低出生体重和儿童癌症风险之间的空间相关性建模。这个建议是创新的，因为它将利用两个新的发展，全条件分层建模和多元建模。极低出生体重与婴儿癌症之间的相关性证明将有力地扩展到未来的全基因组关联研究。根据本研究的结果，未来的GWAS可以在可能与地理风险因素混淆和相互作用的情况下，将个人风险因素解析为遗传和行为。