Chronic pain conditions and internalizing psychopathology, a genetic epidemiology investigation.
慢性疼痛状况和内化精神病理学,遗传流行病学调查。
基本信息
- 批准号:10755801
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-10-01 至 2025-09-30
- 项目状态:未结题
- 来源:
- 关键词:AddressAlgorithmsAmericanAnxiety DisordersAttentionBiometryChromosome MappingChronic Fatigue SyndromeClinical DataClinical ResearchDataDevelopmentDiagnosisDiseaseEconomicsElectronic Health RecordEtiologyEvaluationFibromyalgiaGenesGeneticGenetic DeterminismGenetic ModelsGenetic Predisposition to DiseaseGoalsGrantHealth Care CostsHealth Services AccessibilityHeritabilityImpairmentImprove AccessIndividualInterventionInvestigationIrritable Bowel SyndromeKnowledgeLinkLinkage DisequilibriumLiteratureMapsMediatingMendelian randomizationMental DepressionMental disordersMentorsMethodologyMethodsMigraineModelingMolecular GeneticsMood DisordersPainPain intensityPhenotypePost-Traumatic Stress DisordersPrognosisPsychopathologyPublic HealthQuality of lifeQuantitative GeneticsReadingResearchResearch EthicsResearch PersonnelResourcesRiskRoleSample SizeSamplingSeriesSeveritiesSingle Nucleotide PolymorphismSusceptibility GeneTemporomandibular Joint DisordersTension HeadacheTestingTherapeutic InterventionTrainingTranslational ResearchTreatment outcomeTwin Multiple BirthTwin StudiesUnited States Department of Veterans AffairsVeteransWritingchronic painchronic painful conditioncomorbiditycostdesignepidemiology studygenetic epidemiologygenetic variantgenome wide association studygenome-wideimprovedmilitary veterannovelobservational cohort studyopioid epidemicopioid usepersonalized medicineprecision medicineprescription opioidprogramsresearch and developmentresearch studyrisk sharingsexskillssocialsymposiumtrait
项目摘要
ABSTRACT. Veterans with chronic pain conditions such as migraine headache, fibromyalgia, and irritable
bowel syndrome frequently manifest comorbid internalizing psychiatric conditions such as mood and anxiety
disorders. The comorbidity between chronic pain conditions and internalizing disorders exacerbates the course
of both, contributes to increased opiate use and low quality of life, and leads to worse treatment outcomes.
While twin studies have indicated substantial overlap in genetic and environmental influences between
individual chronic pain conditions and internalizing disorders, progress in identifying specific genetic variants
underlying this relationship has been slow. To date, no studies have evaluated the relationship among chronic
pain conditions and internalizing disorders using molecular genetic methodology, those that allow for the
identification of specific genes, representing a significant knowledge gap in our understanding of the etiology of
these conditions. To address the glaring research gap, the present CDA-2 includes an observational cohort
study using data available from the Million Veteran Program (MVP) to examine the relationship between
chronic pain conditions and internalizing disorders. The MVP represents a unique and powerful resource for
evaluating these relationships through the combination of extensive electronic health record (EHR) and
genome-wide genetic data. Using data from this large and representative sample, the specific aims are to: 1)
derive chronic pain and internalizing disorder phenotypes from EHR; 2) evaluate the genetic overlap between
chronic pain conditions and internalizing disorders using genome-wide association study and linkage
disequilibrium (LD)-score regression; and 3) explore the causal relationships among chronic pain, internalizing
disorders, and opioid use with Mendelian randomization methods. Strengths of the proposal include the use of
large-scale EHR and genetic data to reveal the genetic contributions to the comorbidity among chronic pain
conditions and internalizing disorders, and the evaluation of multiple chronic pain conditions and internalizing
disorders at once. Understanding the shared genetic etiologies between chronic pain conditions and
internalizing disorders as well as potential causal mechanisms will provide targets for advancing therapeutic
intervention and facilitate the progression from genetic epidemiology to personalized medicine.
The proposed CDA-2 will provide the candidate the training and research opportunities necessary to
advance a model of genetic comorbidity among chronic pain conditions and internalizing disorders and support
her long-term goal of becoming an independent researcher within the VA with a focus on genetic epidemiology
of chronic pain and psychiatric disorders, and the development of precision medicine approaches to the
treatment of Veterans with these conditions. Specific training goals include 1) gaining proficiency in the use of
EHR data for clinical and translational research; 2) formal training in molecular genetics; 3) expanding
knowledge of substantive issues underlying pain mechanisms and opioid medication use; and 4) research
ethics, grant writing, and professional development. The training plan consists of an interlocking program of
coursework, intensive mentoring, reading groups, seminar series, and conferences, with special attention to
training in research ethics. Direct mentoring from leading experts in each of the proposed training domains is a
fundamental feature of this proposal: primary mentor, Dr. Niloofar Afari (pain phenotyping), co-mentors Drs.
Richard Hauger (MVP expertise), Murray Stein (EHR data and psychiatric phenotyping), and Caroline
Nievergelt (statistical genetics), and consultants Drs. Matthew Panizzon (quantitative genetics), James Murphy
(EHR), Mark Wallace (pain and opioid use), and Wesley Thompson (biostatistics). The candidate will use these
skills and resources to accomplish the specific research aims of the study. Findings from this CDA-2 have the
potential to substantially increase understanding of the mechanisms that link chronic pain and internalizing
disorders and lead to targeted precision medicine interventions of these debilitating conditions in Veterans.
摘要。患有慢性疼痛疾病的退伍军人,如偏头痛,纤维肌痛和易怒
肠综合征经常表现为共病的内在精神疾病,如情绪和焦虑
紊乱慢性疼痛状况和内化障碍之间的共病加重了病程
两者的结合导致阿片类药物使用增加和生活质量降低,并导致治疗结果恶化。
虽然双胞胎研究表明,在遗传和环境的影响,
个体慢性疼痛状况和内化障碍,识别特定遗传变异的进展
这种关系的基础是缓慢的。到目前为止,还没有研究评估慢性疾病之间的关系。
疼痛条件和内化障碍,使用分子遗传学方法,那些允许
特定基因的鉴定,这代表了我们在理解疾病病因学方面的一个重大知识缺口。
了以下条件为了解决这一明显的研究差距,目前的CDA-2包括一个观察性队列,
研究使用百万退伍军人计划(MVP)提供的数据,以检查
慢性疼痛病症和内化障碍。MVP代表了一种独特而强大的资源,
通过结合广泛的电子健康记录(EHR)和
全基因组遗传数据。利用这一大型代表性样本的数据,具体目标是:1)
从EHR中推导出慢性疼痛和内化障碍表型; 2)评估
使用全基因组关联研究和连锁的慢性疼痛状况和内化障碍
不平衡(LD)评分回归; 3)探讨慢性疼痛,内化,
疾病和阿片类药物使用孟德尔随机化方法。该提案的优点包括使用
大规模EHR和遗传数据揭示慢性疼痛中的遗传贡献
条件和内化障碍,以及评估多种慢性疼痛条件和内化
一次混乱。了解慢性疼痛状况和慢性疼痛之间的共同遗传病因
内化障碍以及潜在的因果机制将为推进治疗提供目标。
干预和促进从遗传流行病学到个性化医疗的进展。
拟议的CDA-2将为候选人提供必要的培训和研究机会,
推进慢性疼痛疾病和内化障碍之间的遗传共病模型,
她的长期目标是成为退伍军人事务部的一名独立研究人员,重点是遗传流行病学
慢性疼痛和精神疾病,以及精确医学方法的发展,
对待有这些条件的退伍军人。具体培训目标包括:1)熟练使用
用于临床和转化研究的EHR数据; 2)分子遗传学的正式培训; 3)扩大
了解疼痛机制和阿片类药物使用的实质性问题;以及4)研究
道德、赠款写作和专业发展。培训计划包括一个环环相扣的程序,
课程作业、强化辅导、阅读小组、系列研讨会和会议,特别关注
研究伦理培训。由每个拟议培训领域的主要专家直接指导,
该提案的基本特征:主要导师,Niloofar Afari博士(疼痛表型),共同导师Dr.
Richard Hauger(MVP专家)、Murray Stein(EHR数据和精神病表型)和Caroline
Nievergelt(统计遗传学),以及顾问Matthew Panizzon博士(数量遗传学),James Murphy
(EHR),Mark Wallace(疼痛和阿片类药物使用)和Wesley Thompson(生物统计学)。候选人将使用这些
技能和资源,以实现研究的具体研究目标。从这个CDA-2的调查结果有
有可能大大增加对慢性疼痛和内化之间联系机制的理解
疾病,并导致有针对性的精确医学干预这些衰弱的条件退伍军人。
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)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Marianna Gasperi其他文献
Marianna Gasperi的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Marianna Gasperi', 18)}}的其他基金
Chronic pain conditions and internalizing psychopathology, a genetic epidemiology investigation.
慢性疼痛状况和内化精神病理学,遗传流行病学调查。
- 批准号:
10316156 - 财政年份:2020
- 资助金额:
-- - 项目类别:
Chronic pain conditions and internalizing psychopathology, a genetic epidemiology investigation.
慢性疼痛状况和内化精神病理学,遗传流行病学调查。
- 批准号:
10595497 - 财政年份:2020
- 资助金额:
-- - 项目类别:
Chronic pain conditions and internalizing psychopathology, a genetic epidemiology investigation.
慢性疼痛状况和内化精神病理学,遗传流行病学调查。
- 批准号:
10008287 - 财政年份:2020
- 资助金额:
-- - 项目类别:
相似海外基金
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
- 批准号:
EP/Y029089/1 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
- 批准号:
2337776 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
- 批准号:
2338816 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
- 批准号:
2338846 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
- 批准号:
2348261 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
- 批准号:
2348346 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
- 批准号:
2348457 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
- 批准号:
2404989 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
- 批准号:
2339669 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Continuing Grant