当FamilyTree遇上MeTree,家族史助力识别疾病风险
2016/03/13
家庭树(Family Tree),中文里叫家谱或者族谱,把英语直译过来就是“家庭树”。杜克大学开发了一款针对五种疾病(血栓症、乳腺癌、卵巢癌、结直肠癌、遗传性癌症综合征)设计并为其提供建议的产品,叫做MeTree,旨在分析家族疾病史,识别高风险人群。

Family Tree:用DNA来追踪家族史

家庭树(Family Tree),中文里叫家谱或者族谱,把英语直译过来就是“家庭树”。也就是说,每个家族都有自己的枝枝蔓蔓。通过使用Family Tree DNA ,用户能够很好地理解家庭树的具体形象感知,并通过DNA检测、个人资料、相册、影片以及关于祖先或世代家人的网络文档等来追踪和溯源自己的祖先和家族起源。

在美国,有一家叫做Family Tree DNA 的公司,旨在让用户能够更好地理解家庭树的具体形象感知,并通过DNA检测、个人资料、相册、影片以及关于祖先或世代家人的网络文档等来追踪和溯源自己的祖先和家族起源。2008年,该公司与美国全球最大的家庭社区MyHeritag成为战略合作伙伴关系,将一起致力于帮助互联网用户利用DNA来追踪家族史。

MeTree:分析家族疾病史,识别高风险人群


近期的《Genetics in Medicine》期刊上发布了杜克大学的一项研究:研究者纳入了488例患者,通过一个名叫MeTree的网络程序,对这些患者的家族健康史进行了分析。MeTree是针对五种疾病设计(血栓症、乳腺癌、卵巢癌、结直肠癌、遗传性癌症综合征)并为其提供建议。在174例疾病风险升高的患者中,只有2例患者接受过深入的疾病监控。

该研究的主要作者杜克大学医学副教授Lori Orlando医生说道,他们意外地发现了这种分析方法可以使患者获益。研究者利用的这项深入了解家族疾病史的方法大约需要25分钟完成,能够识别出过去认为有疾病高风险而实际上风险并未升高的患者。Lori Orlando医生表示:有些人进行每年一次的乳腺癌MRI筛查,希望用这种方法来免去侵入性或昂贵的检查方法,而实际上有些人是没有必要进行这种检查的;可是患者却热衷于这些检查,而临床医生对此持保留意见。

杜克大学研究小组目前正将这项研究结果扩展到更加的广泛的领域。Lori Orlando医生表示大家很快就能看到他们这项研究的价值;目前有公司意见开发了类似的工具,但是没有研究探讨这种明确家族健康史的项目的价值。

根据Duke Health的一项新研究显示,通过分析患者的家系谱,可以更好地识别其他的疾病风险因素,促进患者更加及时的筛查。对于心脏病和癌症等与遗传相关疾病,医务工作者往往会通过询问或让患者填写一份简短的清单来明确患者的疾病家族史。

推荐阅读:

MeTree Family History Tool

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  • Development and validation of a primary care-based family health history and decision support program (MeTree).

    INTRODUCTION: Family health history is a strong predictor of disease risk. To reduce the morbidity and mortality of many chronic diseases, risk-stratified evidence-based guidelines strongly encourage the collection and synthesis of family health history to guide selection of primary prevention strategies. However, the collection and synthesis of such information is not well integrated into clinical practice. To address barriers to collection and use of family health histories, the Genomedical Connection developed and validated MeTree, a Web-based, patient-facing family health history collection and clinical decision support tool. MeTree is designed for integration into primary care practices as part of the genomic medicine model for primary care. METHODS: We describe the guiding principles, operational characteristics, algorithm development, and coding used to develop MeTree. Validation was performed through stakeholder cognitive interviewing, a genetic counseling pilot program, and clinical practice pilot programs in 2 community-based primary care clinics. RESULTS: Stakeholder feedback resulted in changes to MeTree's interface and changes to the phrasing of clinical decision support documents. The pilot studies resulted in the identification and correction of coding errors and the reformatting of clinical decision support documents. MeTree's strengths in comparison with other tools are its seamless integration into clinical practice and its provision of action-oriented recommendations guided by providers' needs. LIMITATIONS: The tool was validated in a small cohort. CONCLUSION: MeTree can be integrated into primary care practices to help providers collect and synthesize family health history information from patients with the goal of improving adherence to risk-stratified evidence-based guidelines.

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