Impact of biobanks on research outcomes in rare diseases: a systematic review.
Impact of biobanks on research outcomes in rare diseases: a systematic review.
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DOI：
10.1186/s13023-018-0942-z
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发表时间：
2018-11-12
影响因子：
3.7
通讯作者：
Wang W
中科院分区：
文献类型：
--
作者：
Garcia M;Downs J;Russell A;Wang W
减轻罕见疾病的负担需要研究新的诊断和治疗策略。我们进行了一项系统性综述，以确定和比较独立登记、生物库登记和罕见病生物库对罕见病研究结果的影响。使用系统性综述和荟萃分析的首选报告项目（PRISMA声明）进行系统性综述和荟萃分析。英文出版物来源于PubMed、Medline、Scopus和Web of Science。考虑了报告临床、流行病学、基础或转化研究结果的原始研究论文，这些研究结果来源于独立登记、生物库登记和罕见病生物库中的数据。使用JBI-QARI的关键评价工具对入选的文章进行评估。每篇文章都被完整阅读，并使用JBI-QARI的在线数据提取软件提取结果。包括28种罕见病资源在内的30项研究纳入综述。其中，14项登记与生物库基础设施无关，9项登记与生物库基础设施相关，6项是罕见病生物库资源。独立的登记册有能力揭示疾病的自然史，并有助于循证实践。当与生物库基础设施相结合时，注册表还可以识别和验证生物标志物，发现新基因，在组学水平上阐明发病机制，并开发新的治疗策略。本次审查中的罕见病生物库具有类似的生物学研究能力，但此外，具有更大的样本数量和更高质量的实验室技术用于质量保证过程。我们检查了三个特定人群的研究结果：独立登记处，生物库登记处和独立罕见疾病生物库，并证明这些资源之间存在关键差异。这些差异是资源的设计，目的和目标的函数，每个资源在为罕见疾病研究的知识体系做出贡献方面具有独特而重要的作用。虽然独立的登记系统有能力揭示疾病的自然史，制定最佳实践，取代临床试验，改善患者结局，但它们进行基础研究的能力有限。基础研究在罕见病研究中的作用至关重要;科学家必须首先了解疾病的途径，然后才能开发适当的干预措施。另一方面，罕见疾病生物库（特别是大型生物库）拥有进行基础研究所需的关键基础设施，进行新的组学发现，识别和验证生物标志物，发现新基因，并开发新的治疗策略。然而，这些独立的罕见病生物库并没有像罕见病登记处那样收集全面的数据或对临床观察的影响。罕见病研究不仅对罕见病很重要，对常见病也很重要。例如，对家族性高胆固醇血症这种罕见疾病中低密度脂蛋白（LDL）受体的研究导致了他汀类药物的发现，这是一种现在常规用于预防心脏病的药物疗法。罕见疾病在世界范围内仍处于研究不足状态。该综述提出了一个重要的观察结果，即具有生物库的登记处具有独立登记处（收集全面临床和流行病学数据的能力）和独立罕见疾病生物库（促进组学研究的能力）的功能。我们发现，生物库注册为罕见病研究提供了一种独特、实用、具有成本效益和影响力的解决方案。独立登记与罕见病生物库的联系将为基础研究有效转化为临床实践提供所需的适当资源。此外，协作、参与、混合招聘、主动营销、广泛同意和“虚拟生物库”在线目录等促进因素，如果得到利用，将有助于这些资源的成功。这些重要的观察结果可以指导未来的罕见病研究工作，最终改善患者的预后，减轻临床医生，医院，社会，最重要的是患者及其家属与罕见病相关的重大负担。本文的在线版本（10.1186/s13023-018-0942-z）包含补充材料，可供授权用户使用。
Alleviating the burden of rare diseases requires research into new diagnostic and therapeutic strategies. We undertook a systematic review to identify and compare the impact of stand-alone registries, registries with biobanks, and rare disease biobanks on research outcomes in rare diseases. A systematic review and meta-aggregation was conducted using the preferred reporting items for systematic reviews and meta-analyses (the PRISMA statement). English language publications were sourced from PubMed, Medline, Scopus, and Web of Science. Original research papers that reported clinical, epidemiological, basic or translational research findings derived from data contained in stand-alone registries, registries with biobanks, and rare disease biobanks were considered. Articles selected for inclusion were assessed using the critical appraisal instruments by JBI-QARI. Each article was read in its entirety and findings were extracted using the online data extraction software from JBI-QARI. Thirty studies including 28 rare disease resources were included in the review. Of those, 14 registries were not associated to biobank infrastructure, 9 registries were associated with biobank infrastructure, and 6 were rare disease biobank resources. Stand-alone registries had the capacity to uncover the natural history of disease and contributed to evidence-based practice. When annexed to biobank infrastructure, registries could also identify and validate biomarkers, uncover novel genes, elucidate pathogenesis at the Omics level, and develop new therapeutic strategies. Rare disease biobanks in this review had similar capacity for biological investigations, but in addition, had far greater sample numbers and higher quality laboratory techniques for quality assurance processes. We examined the research outcomes of three specific populations: stand-alone registries, registries with biobanks, and stand-alone rare disease biobanks and demonstrated that there are key differences among these resources. These differences are a function of the resources’ design, aims, and objectives, with each resource having a distinctive and important role in contributing to the body of knowledge for rare disease research. Whilst stand-alone registries had the capacity to uncover the natural history of disease, develop best practice, replace clinical trials, and improve patient outcomes, they were limited in their capacity to conduct basic research. The role of basic research in rare disease research is vital; scientists must first understand the pathways of disease before they can develop appropriate interventions. Rare disease biobanks, on the other hand (particularly larger biobanks), had the key infrastructure required to conduct basic research, making novel Omics discoveries, identify and validate biomarkers, uncover novel genes, and develop new therapeutic strategies. However, these stand-alone rare disease biobanks did not collect comprehensive data or impact on clinical observations like a rare disease registry. Rare disease research is important not only for rare diseases, but also for also common diseases. For example, research of low-density lipoprotein (LDL)-receptors in the rare disease known as familial hypercholesterolemia led to the discovery of statins, a drug therapy that is now used routinely to prevent heart disease. Rare diseases are still under-researched worldwide. This review made the important observation that registries with biobanks had the function of both stand-alone registries (the capacity to collect comprehensive clinical and epidemiological data) and stand-alone rare disease biobanks (the ability to contribute to Omics research). We found registries with biobanks offer a unique, practical, cost-effective, and impactful solution for rare disease research. Linkage of stand-alone registries to rare disease biobanks will provide the appropriate resources required for the effective translation of basic research into clinical practice. Furthermore, facilitators such as collaboration, engagement, blended recruitment, pro-active marketing, broad consent, and “virtual biobank” online catalogues will, if utilised, add to the success of these resources. These important observations can serve to direct future rare diseases research efforts, ultimately improve patient outcomes and alleviate the significant burden associated with rare disease for clinicians, hospitals, society, and most importantly, the patients and their families. The online version of this article (10.1186/s13023-018-0942-z) contains supplementary material, which is available to authorized users.
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影响因子：
3.7
作者：
Filocamo M;Baldo C;Goldwurm S;Renieri A;Angelini C;Moggio M;Mora M;Merla G;Politano L;Garavaglia B;Casareto L;Bricarelli FD;Telethon Network of Genetic Biobanks Staff
通讯作者：
Telethon Network of Genetic Biobanks Staff
影响因子：
3.7
作者：
Kakkis ED;O'Donovan M;Cox G;Hayes M;Goodsaid F;Tandon PK;Furlong P;Boynton S;Bozic M;Orfali M;Thornton M
通讯作者：
Thornton M
影响因子：
9.9
作者：
Fehr, Stephanie;Wong, Kingsley;Leonard, Helen
通讯作者：
Leonard, Helen
DOI：
10.1016/j.pmr.2012.08.003
发表时间：
2012-11-01
影响因子：
1.7
作者：
Hoffman, Eric P.;Reeves, Erica;Bushby, Kate
通讯作者：
Bushby, Kate
影响因子：
1.6
作者：
Li, Yanjie;Polak, Urszula;Napierala, Marek
通讯作者：
Napierala, Marek