Direct and indirect effects of obesity genes on multiple sclerosis
肥胖基因对多发性硬化症的直接和间接影响
基本信息
- 批准号:8984235
- 负责人:
- 金额:$ 3.69万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2015
- 资助国家:美国
- 起止时间:2015-08-01 至 2018-07-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAdolescentAdultAlzheimer&aposs DiseaseAutoimmune Diseases of the Nervous SystemAutoimmune ProcessBiologicalBirthBody mass indexCaliforniaCaringCharacteristicsChildhoodClinicalDataData SetDementiaDemyelinating DiseasesDiseaseEducationElderlyEnvironmentEnvironmental Risk FactorEtiologyFatty acid glycerol estersFutureGenesGeneticGenetic studyGenomeGenotypeHealthImmuneImpaired cognitionIndividualInflammatoryInterventionLife Cycle StagesLife StyleLiteratureMalignant NeoplasmsMeasurementMeasuresMediatingMediationMedicalMethodologyMultiple SclerosisNeurologicObesityObservational StudyOnset of illnessOutcomeParticipantPathogenesisPathway AnalysisPathway interactionsPatient Self-ReportPositioning AttributePredispositionProteinsQuality of lifeRecording of previous eventsResearchRiskRisk FactorsRoleSeveritiesSeverity of illnessSocioeconomic StatusTestingTherapeuticTimeVariantWeightbasebrain volumecase controldisabilitydisorder riskgenetic variantinsightlink proteinmiddle agenervous system disordernovelprogramspublic health relevancerisk variantsocioeconomicstrait
项目摘要
DESCRIPTION (provided by applicant): The objective of this project is to investigate how variation within obesity genes may influence the onset and severity of multiple sclerosis (MS). MS is an immune-mediated, demyelinating disorder believed to be caused by both genetic and environmental factors. Recently, individuals with childhood or adolescent obesity have demonstrated greater than a twofold increased risk of MS compared to those at a normal weight in numerous studies, though the biological mechanism through which this occurs is unknown. It is plausible that there are common genetic and biologic pathways that contribute to obesity and result in susceptibility of MS, as both MS and obesity are characterized as inflammatory diseases. Forty genes dispersed throughout the genome have been found to be associated with obesity-related traits; however, how they may relate to MS onset or disease severity has not been previously investigated. The overall hypothesis of this project is that genetic variants associated with obesity will demonstrate both direct and indirect effects on MS onset or severity. This project will utilize high-quality genotype data, detailed body mass index histories, and other
clinical characteristics from ~1,500 cases and ~12,000 controls to address three related hypotheses. First, genetic variants associated with obesity will demonstrate both direct and indirect effects via body mass index on MS onset or severity. A regression-based mediation analysis, adjusting for known confounders and genetic ancestry, will be utilized to test for the presence of these effects. Second, pathway analysis of obesity and established MS genes will identify biological pathways that may influence risk of MS onset or severity. Pathway analyses of established obesity and MS associated genes will be conducted using the Disease Association Protein-Protein Link Evaluator (DAPPLE). Third, observed effects of obesity variants on MS onset or severity will be replicated in an independent dataset of MS cases and controls. Analyses will be pursued in a second dataset of MS cases and controls to confirm significant findings. The importance in MS susceptibility and severity of both genetic and environmental factors, including obesity, will be demonstrated through this research and provide new insight into the etiology of this debilitating condition and others where a role for obesity is
suspected.
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Milena Anne Gianfrancesco其他文献
Milena Anne Gianfrancesco的其他文献
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{{ truncateString('Milena Anne Gianfrancesco', 18)}}的其他基金
Computational methods using electronic health records and registry data to detect and predict clinical outcomes in rheumatic disease
使用电子健康记录和登记数据检测和预测风湿病临床结果的计算方法
- 批准号:
9912723 - 财政年份:2019
- 资助金额:
$ 3.69万 - 项目类别:
Computational methods using electronic health records and registry data to detect and predict clinical outcomes in rheumatic disease
使用电子健康记录和登记数据检测和预测风湿病临床结果的计算方法
- 批准号:
10349472 - 财政年份:2019
- 资助金额:
$ 3.69万 - 项目类别:
Computational methods using electronic health records and registry data to detect and predict clinical outcomes in rheumatic disease
使用电子健康记录和登记数据检测和预测风湿病临床结果的计算方法
- 批准号:
10400540 - 财政年份:2019
- 资助金额:
$ 3.69万 - 项目类别:
Examining the causal effect of sociodemographic and genetic factors on patient safety outcomes in individuals prescribed high-risk immunosuppressive medications
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- 批准号:
9327592 - 财政年份:2017
- 资助金额:
$ 3.69万 - 项目类别:
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