Heart failure proteomics: an epidemiology study

心力衰竭蛋白质组学：流行病学研究

• 批准号: 10929212
• 负责人: Veronique Roger
• 金额: $ 195.01万
• 依托单位: NATIONAL HEART, LUNG, AND BLOOD INSTITUTE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10929212
• 关键词: American Heart Association; Attenuated; Bioinformatics; Biological Markers; Books; Cardiology; Cessation of life; Characteristics; Chronic Kidney Insufficiency; Clinic; Clinical; Clinical Data; Collaborations; Communities; Complex; Confidence Intervals; Congestive Heart Failure; Data; EFRAC; Electronic Health Record; Epidemiology; Equipment and supply inventories; Etiology; European; Foundations; Framingham Heart Study; Heart failure; Immune response; Individual; Inflammation; Infrastructure; International; Ischemia; Kidney Diseases; Machine Learning; Manuscripts; Measurement; Meta-Analysis; Pathway Analysis; Pathway interactions; Patients; Phenotype; Plasma Proteins; Play; Population Heterogeneity; Population Study; Preparation; Proteins; Proteomics; Renal function; Risk; Role; Sampling Studies; Societies; Syndrome; Technology; Therapeutic; Validation; Work; angiogenesis; aptamer; biobank; cohort; comorbidity; design; effective therapy; epidemiology study; imaging program; meetings; mortality; mortality risk; novel; predictive signature; prognostic; prognostic value; proteomic signature; risk stratification

We studied proteomic signatures associated with death in a HF community cohort and quantified 7,335 plasma proteins using an aptamer-based technology. With machine learning, we identified proteomics cluster signatures associated with mortality and examined how these signatures predicted death while adjusting extensively for clinical parameters included in the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) score, and comorbidity burden. We applied 10-fold cross-validation to optimize the generalizability of our clustering results, and explored mechanistic pathways with bioinformatics enrichment and pathway analyses. Among 1388 individuals with HF, machine learning identified 2 clusters based on 405 proteins associated with death. Cluster assignment was univariately associated with the risk of death with a large increased risk in Cluster 2 patients (HR 2.41, 95% confidence intervals CI 2.12 - 2.75; p <0.0001). This association was only slightly attenuated after adjustment for the MAGGIC score and comorbidity burden (HR: 1.82; 95% CI, 1.58 - 2.08, p<0.0001) and did not differ by ejection fraction. Mechanistic pathways were mainly related to matrix remodeling, immune response, inflammation, and angiogenesis. In summary, taken collectively, these results indicate that proteomics provides important information on the phenotypes of the HF syndrome and the proteomic signatures play an important role in risk stratification. Using machine learning, our initial studies led to the identification of proteomic signatures associated with the risk of death independently of clinical factors. Key mechanistic pathways were identified laying the foundation for mechanistic therapeutic approaches. To pursue this work on deep phenotyping of HF using proteomics, we are currently examining how proteomic signature differ by the presentation of the HF syndrome including specific etiologies (ischemic versus nonischemic) and critical comorbidities (kidney disease). Studies planned for year 2023 include collaboration with the Framingham Heart Study and deployment of the requisite infrastructure for the urgently needed expansion to diverse populations through partnership with the imaging program in place at MedStar. This work has been or will be presented at several national and international meetings listed below. Corresponding manuscripts are submitted or in preparation. Annual Meeting of the Society for Epidemiology Research in June 2022 Proteomic Signatures in Heart Failure: a population-based study Kayode O Kuku Hoyoung Park, Suzette J. Bielinski, Nicholas B. Larson Jungnam Joo, Veronique L. Roger 2022-Abstract-Book.pdf (epiresearch.org). European Society of Cardiology meeting in August 2022. Proteomic Signatures of Heart Failure Mortality in the Community Kayode O Kuku, Hoyoung Park, Brittany Dulek, Suzette J. Bielinski, Jungnam Joo, Veronique L. Roger American Heart Association Scientific Sessions in November 2022 o Proteomic Assessment of Novel Kidney Function Biomarkers in Heart Failure: A Community Study Joseph J. Shearer, Hoyoung Park, Jungnam Joo, Kayode O Kuku, Suzette J. Bielinski, Sheila M. Manemann, Veronique L. Roger o Proteomic Signatures Of Ischemic And Non-ischemic Heart Failure In A Community Cohort Kayode O Kuku, Hoyoung Park ,Joseph J. Shearer, Jungnam Joo, Brittany Dulek, Suzette, J. Bielinski MEd, Veronique L. Roger, MD, MPH American Heart Association Epidemiology Meeting, March 2023 o Proteomic Assessment of Progressive Chronic Renal Insufficiency Risk and Mortality in a Heart Failure Community Study Joseph J. Shearer, Christine P. Limonte, Kayode O. Kuku, Jungnam Joo, Nicholas B. Larson, Suzette J. Bielinski, Vronique L. Roger

我们研究了HF社区队列中与死亡相关的蛋白质组特征，并使用基于适体的技术定量了7，335种血浆蛋白。通过机器学习，我们确定了与死亡率相关的蛋白质组学聚类特征，并研究了这些特征如何预测死亡，同时广泛调整了荟萃分析全球慢性心力衰竭组（MAGGIC）评分中包含的临床参数和合并症负担。我们应用10倍交叉验证来优化我们的聚类结果的可推广性，并通过生物信息学富集和途径分析来探索机制途径。 在1388名HF患者中，机器学习基于405种与死亡相关的蛋白质确定了2个聚类。聚类分配与死亡风险单变量相关，聚类2患者的风险大幅增加（HR 2.41，95%置信区间CI 2.12 - 2.75; p <0.0001）。在调整MAGGIC评分和合并症负担后，这种相关性仅略有减弱（HR：1.82; 95%CI，1.58 - 2.08，p<0.0001），射血分数无差异。机制途径主要与基质重塑、免疫反应、炎症和血管生成有关。 总之，总的来说，这些结果表明，蛋白质组学提供了关于HF综合征表型的重要信息，蛋白质组学特征在风险分层中起着重要作用。使用机器学习，我们的初步研究导致了与临床因素无关的死亡风险相关的蛋白质组特征的识别。确定了关键的机制途径，为机制治疗方法奠定了基础。为了利用蛋白质组学对HF进行深入的表型分析，我们目前正在研究HF综合征的蛋白质组特征如何不同，包括特定病因（缺血性与非缺血性）和关键合并症（肾脏疾病）。计划于二零二三年进行的研究包括与心脏病研究合作，并通过与MedStar的成像计划合作，部署必要的基础设施，以满足对不同人群的迫切需求。 这项工作已经或将在下列几个国家和国际会议上提出。相应的手稿已提交或正在编写中。 2022年6月流行病学研究学会年会心力衰竭中的蛋白质组学特征：一项基于人群的研究Kayode O Kuku Hoyoung Park，Suzette J. Bielinski，Nicholas B。作者：Larson Jungnam Joo，Veronique L. Roger 2022-Abstract-Book.pdf（epiresearch.org）。 2022年8月欧洲心脏病学会会议。社区心力衰竭死亡率的蛋白质组学特征Kayode O Kuku，Hoyoung Park，布列塔尼Dulek，Suzette J. Bielinski，Jungnam Joo，Veronique L.罗杰 2022年11月美国心脏协会科学会议 O 心力衰竭中新型肾功能生物标志物的蛋白质组学评估：社区研究Joseph J. Shearer，Hoyoung Park，Jungnam Joo，Kayode O Kuku，Suzette J. Bielinski，Sheila M.马内曼罗杰 O 社区队列缺血性和非缺血性心力衰竭的蛋白质组学特征Kayode O Kuku，Hoyoung Park，Joseph J. Shearer，Jungnam Joo，布列塔尼Dulek，Suzette，J. Bielinski MEd，Veronique L. Roger，MD，MPH 美国心脏协会流行病学会议，2023年3月 O 进行性慢性肾功能不全风险的蛋白质组学评估 心力衰竭社区研究中的死亡率 作者：Joseph J. Shearer，克莉丝汀P. Limonte，Kayode O.作者：Kuku，Jungnam Joo，Nicholas B.放大图片作者：John W.罗杰