Innovative Methodologic Advances for Mixtures Research in Epidemiology
流行病学混合物研究的创新方法进展
基本信息
- 批准号:10087927
- 负责人:
- 金额:$ 44.82万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-02-01 至 2024-01-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAdverse effectsAffectBiologicalBiological MonitoringCharacteristicsChemical ExposureChemicalsCommunitiesComplexComputer softwareCoupledDataData SetDetectionDevelopmentDoseEndocrineEpidemiologistEpidemiologyEvaluationExposure toFoodGoalsHealthHeritabilityHumanIndividualInvestigationMeasurementMeasuresMediatingMethodologyMethodsModelingModificationNational Health and Nutrition Examination SurveyNutrientNutritionalOdds RatioOutcomePathway AnalysisPathway interactionsPerformancePopulationResearchResearch PersonnelResearch Project GrantsRiskRoleStatistical MethodsTestingToxic effectToxicant exposureToxicologyUncertaintyVariantWorkbasecardiometabolismchemical groupepidemiologic dataepidemiology studyexposure pathwayexposure routeflexibilityfood consumptiongenome wide association studygenome-widehealth assessmenthigh dimensionalityimprovedinnovationinterestnovelpersistent organic pollutantspollutantresponsesimulationtoxicanttrait
项目摘要
Abstract
Human biomonitoring for chemical exposures has generated large amounts of data. Analysis of those data
presents a challenging problem to epidemiologists and biostatisticians. One prominent characteristic of these
environmental data is that exposures are always mixtures of chemicals and the chemicals in a mixture are
often moderately or highly correlated. The adverse effect of an individual chemical on any health outcome is
usually small due to the low exposure level. However, effects of exposure to chemicals in mixtures can
accumulate and act synergistically on health outcomes. The overarching goal of this project is to develop better
statistical methods for understanding the detrimental health impacts of exposure to mixtures of chemicals. To
accomplish this goal, we propose improvements over the existing genome-wide complex trait analysis
approach so that the accumulative effects and the total interaction effects of exposure to chemical mixtures
can be estimated with minimal bias. We further propose to estimate the individual chemical effects as the
average causal effect through the propensity score adjustment. The estimates will serve as the basis for
toxicity assessment of chemicals. Lastly, we propose a flexible network analysis approach to understand the
potential causal pathways from exposure to mixtures to health outcomes. The methods will be applied to a
number of datasets on which the research team has been working to answer important scientific questions with
regards to the associations of persistent organic pollutant exposures with endocrine and cardio-metabolic
outcomes, and biological pathways and nutrients relevant to these associations. The datasets also serve as
testing formats for developing and using the software package implementing the proposed methods. The
software package will be made freely available to environmental research community. The results of this
project are expected to substantially improve our ability to understand complex relationships among the many
chemical exposures found in human populations and detrimental health outcomes. Our development of
innovative methods will potentially facilitate the investigation of biological pathways mediating these
relationships and enhance our understanding of nutritional and other factors that may in part ameliorate
adverse effects of toxicants.
摘要
人类对化学品暴露的生物监测产生了大量数据。分析这些数据
对流行病学家和生物统计学家提出了一个具有挑战性的问题。其中一个突出的特点是,
环境数据是,暴露总是化学品的混合物,混合物中的化学品是
通常是中度或高度相关的。个别化学品对任何健康结果的不利影响是
通常由于曝光水平低而较小。然而,接触混合物中的化学品的影响,
积累并协同作用于健康成果。这个项目的首要目标是更好地开发
了解接触化学品混合物的有害健康影响的统计方法。到
为了实现这一目标,我们提出了对现有全基因组复杂性状分析的改进
这种方法使接触化学混合物的累积效应和总的相互作用效应
可以以最小的偏差进行估计。我们进一步建议将单个化学效应估计为
通过倾向评分调整的平均因果效应。这些估计数将作为
化学品毒性评估。最后,我们提出了一种灵活的网络分析方法,以了解
从接触混合物到健康结果的潜在因果途径。这些方法将应用于
研究团队一直致力于回答重要科学问题的数据集数量,
关于持久性有机污染物接触与内分泌和心脏代谢之间的联系
结果,以及与这些关联相关的生物途径和营养物质。这些数据集还可用作
用于开发和使用实现所提出的方法的软件包的测试格式。的
软件包将免费提供给环境研究界。的结果
项目预计将大大提高我们的能力,以了解复杂的关系,在许多
在人群中发现的化学品暴露和有害的健康后果。我们发展
创新的方法将有可能促进对介导这些的生物学途径的研究。
关系,提高我们对营养和其他因素的理解,
有毒物质的不良影响。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Statistical Methods for Assessing the Explained Variation of a Health Outcome by a Mixture of Exposures.
- DOI:10.3390/ijerph19052693
- 发表时间:2022-02-25
- 期刊:
- 影响因子:0
- 作者:Chen HY;Li H;Argos M;Persky VW;Turyk ME
- 通讯作者:Turyk ME
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{{ truncateString('HUA YUN CHEN', 18)}}的其他基金
Novel Statistical Methods for Data with Missing Values
缺失值数据的新统计方法
- 批准号:
7072231 - 财政年份:2005
- 资助金额:
$ 44.82万 - 项目类别:
Novel Statistical Methods for Data with Missing Values
缺失值数据的新统计方法
- 批准号:
7237205 - 财政年份:2005
- 资助金额:
$ 44.82万 - 项目类别:
Novel Statistical Methods for Data with Missing Values
缺失值数据的新统计方法
- 批准号:
6986543 - 财政年份:2005
- 资助金额:
$ 44.82万 - 项目类别:
A Multivariate Probit Model for Health Services Research
卫生服务研究的多元概率模型
- 批准号:
6820885 - 财政年份:2004
- 资助金额:
$ 44.82万 - 项目类别:
A Multivariate Probit Model for Health Services Research
卫生服务研究的多元概率模型
- 批准号:
6925409 - 财政年份:2004
- 资助金额:
$ 44.82万 - 项目类别:
A Multivariate Probit Model for Health Services Research
卫生服务研究的多元概率模型
- 批准号:
7062106 - 财政年份:2004
- 资助金额:
$ 44.82万 - 项目类别:
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