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近年来，逐渐兴起的功能医学为临床医生提供了新的思维模式。既往传统的临床循证医学研究多以随机安慰剂对照实验（RCT）为基础，随着基础医学的发展，基因组学、蛋白质组学、代谢组学等多维度基础研究数据的不断呈现，以及医学模式向着“生物—心理—社会医学模式”不断转化过程中，越来越多的医学科学专家对RCT研究提出了更多的思考。 今天，本平台为临床医生提供美国功能医学之父Jeffrey S. Bland博士的发表在《Integrative Medicine》（Vol. 18, No.3*June 2019）的最新文章<什么是21世纪的循证功能医学>，由美国IFM功能医学研究院认证医师王东生主任翻译，本文分析了传统的循证医学模式，提出了“功能性评估与新的生物特征学和生物信息学工具相结合”的未来医学发展模式，或许，一种新的临床研究模式正在悄然兴起…… 非常专业的一篇文章，希望本文可以给医学专业人员带来思考和启示。 Abstract The 21st century has already demonstrated itself to be an era of change for medicine and science. There is a new openness- to ideas, to a shift in perspectives, to a redefinition of evidence and the many ways it can be gathered. New interest in real-world data, patient-experience information has also become an increasingly important contributor to the evaluation of treatment effectiveness. It is a fertile time on many fronts, including an expanded reach for a systems biology formalism and the Functional Medicine movement. 21世纪已经证明是医学和科学变革的时代。许多的思想和认识都在转变，并能以全新的开放性的姿态来审视，包括对于医学证据的重新定义以及收集这些证据的方式。目前对于真实世界和患者体验方面数据的关注日益增加，已然成为疗效评估的重要方法。对许多前沿学科来说，这是一个产出颇丰的时期，包括了系统生物学的广泛运用和功能医学的兴起。 Jeffrey S. Bland, PhD, FACN, FACB, is the president and founder of the Personalized Lifestyle Medicine Institute in Seattle, Washington. He has been an internationally recognized leader in nutrition medicine for more than 25 years. Dr Bland is the cofounder of the Institute for Functional Medicine (IFM) and is chairman emeritus of IFM' Board of Directors. He is the author of the 2014 book The Disease Delusion: Conquering the Causes of Chronic Illness for a Healthier, Longer, and Happier Life. Jeffrey S. Bland博士（PhD，FACN，FACB），是华盛顿州西雅图市个性化生活方式医学研究所的总裁和创始人。25年以来，他一直是国际上公认的营养医学的领导者。Bland博士是美国功能医学研究院（IFM）的联合创始人之一和IFM董事会的退休名誉主席。他是《疾病狂想：征服慢性疾病的根本原因 - 为了更健康、长寿和幸福的生活》一书的作者，该书于2014年出版。 The randomized placebo controlled trial (RCT) has achieved iconic status in the field of medical research. For many decades it has represented a scientific gold standard in which clinicians invest both their trust and confidence. The RCT model is, indeed, a useful tool for a variety of reasons, but- like most icons- it is neither perfect nor infallible. 随机安慰剂对照试验（RCT）在医学研究领域有着不可替代的地位。几十年来，它是科学的金标准，临床医生们对于RCT的结论有着充分的信任和信心。从不同的角度来说，RCT模型确实是有用的工具，但是像大多数膜拜偶像一样——它既不完美也不是万无一失的。 In 2015, an article was published in Nature that invited readers to consider some powerful truths about RCTs. Written by Nicholas Schork, PhD, who is affiliated with the J. Craig Venter Institute, the University of California at San Diego, and the Translational Genomics Institute, this paper-' Personalized Medicine: Time for One- Person Trials' raises important questions about whether we are presently using the right type of evidence to validate the effectiveness of some specific therapies. 2015年，《自然》杂志发表了一篇文章，邀请读者们思考一些关于RCT的相关问题。文章由Nicholas Schork博士撰写，Schork博士来自位于圣地亚哥的加州大学J. Craig Venter研究所和转化基因组学研究所。这篇标题为“个性化医学：是进行个体临床试验的时机了”的文章，提出了如何使用正确的证据类型来验证某些特定疗法有效性的重要问题。 Dr Schork highlights 10 of the most commonly prescribed pharmaceutical drugs and describes RCT-derived outcomes data that indicates very limited therapeutic success (from 1-in-4 to 1-in-25 patients).1 Why is the translation of RCT data to patient outcome experience so poor? This is a question I examined at length in my 2014 book The Disease Delusion: Conquering the Causes of Chronic illness for a Healthier, Longer, and Happier Life.2 To a large extent it is due to the fact that the common diseases these medications are designed to treat - depression, cardiovascular disease, inflammation, inflammatory bowel disease, Crohn's disease, rheumatoid arthritis, esophageal reflux disorder, psoriasis, asthma, and schizophrenia- all have multiple triggering factors derived from the unique way that a person's genes interact with lifestyle, diet, and environment. While symptoms related to these diseases are common in terms of presentation, the triggering events that initiate the onset of disease in each individual are highly variable. Because drug development RCTs are inherently based on a ” one size fits all”approach, trials that are large in size are often necessary to demonstrate a reproducible P< .05 level of significance of evidence of effectiveness in an RCT. Schork博士列举出了10种最常使用的处方药品，及其来源于RCT所产生的数据结果，结果表明这些最常用处方药物的治疗成功率实际非常有限（ 仅有1/4—1/25的患者获得治疗成功）1。为什么将RCT数据转化为病人的结果体验会如此之差？这是我在2014年出版书籍中详细研究过的一个问题，即《疾病狂想：征服慢性疾病的根本原因 - 为了更健康、长寿和幸福的生活》一书2。在很大程度上，这是由于存在如下的事实，即这些药物设计用来治疗的常见疾病，包括抑郁症、心血管疾病、炎症、炎症性肠病、克罗恩病、类风湿性关节炎、食管反流性疾病、银屑病、哮喘和精神分裂症等等，都存在多种疾病的触发因素，而这些因素每个又有着独特的病理路径，其来源于每个人的基因与生活方式、饮食和环境的相互作用。虽然这些疾病的症状表现形式是常见的，但在每个个体当中，触发疾病起始的初始事件在不同的人之间是高度不同的。因为药物开发操作的RCT本质上是基于“ 一个尺寸适合所有”的评价方法，临床试验往往要求具备足够大的样本量，以证明可重复性P <0.05水平的显著性，来表明其有效性的证据。 In 2018, Dr. Schork published a follow-up paper in Social Science o Medicine in which he outlines the limitations of RCTs in the genomic age. Since it is now recognized that there is significant biological heterogeneity within any specific disease diagnostic group, he argues for the need to apply new approaches that integrate developments in biometrics, bioinformatics, and N-of-1 trial designs into criteria that we use to measure evidence of effectiveness.3 This approach could be described as moving from population-based data to that of individualized responses. A good example of this dynamic of generating evidence related to impact on overall health can be found in a published study describing a multi-micronutrient intervention in Brazilian children and teens that examined both physiological and psychological functional responses.4 在2018年，Schork博士发表了一篇后续论文刊登在《医学的社会化科学》期刊上，文中他概述了RCT在基因组年龄中的局限性。文中提出——既然现在认识到在任何特定的疾病诊断组中，都存在显著的生物异质性，需要应用新的方法，以整合生物识别、生物信息学、和N-of-1的试验设计方法的新进展，以制定评估有效性的标准3。这种方法可以描述为从基于人群学的数据到个体化反应的转变。在一篇发表的研究中，可以找到这种动态产生影响整体健康的数据的很好的例子，即在巴西儿童和青少年中进行的多种微量营养素的干预研究，观察了基于生理和和心理功能反应的结果4。 In the new trial models being explored, there are numerous ways that participant data related to physical, metabolic, cognitive, and behavioral functions could be statistically evaluated using nearest- neighbor analyses to determine which individuals share common sensitivities or responses to a given intervention or challenge. Two functional measures that can be used to stratify and study potential interventions are grip strength and male reproductive function. Grip strength has been determined to be a highly significant variable in assessing the risk to all-cause and cardiovascular mortality in both males and females over the age of 35.5 Using grip strength as a marker of function, patients could be segmented into specific risk groups for evaluation of the underlying contribution to their risk profile, which could then lead to personalized interventions. In males, reproductive problems have been associated with a significant decline in both sperm count and quality that has occurred over the past 50 years.6,7 As a result, sperm count and sperm viability indices could be functional assessment measures that would be useful in individualizing interventions such as medical nutritional therapies, lifestyle modification, detoxification, or hormonal therapies. 在新的探索性的试验模型中，参与者的数据，涉及体格检查、代谢、认知和行为功能，可以有多种方法使用最近邻分析来进行统计评估，以确定哪些个体有共同的敏感性，或对特定干预或挑战有回应。有两种功能性的方法可用于对潜在的干预措施进行分层，包括握力和男性生殖功能检测。已经确定握力检测在35岁以上的男性和女性的全因性死亡和心血管疾病性死亡的风险评估中是一个呈高度显著性的变量5。使用握力作为功能性检测的标记，患者可以划分进入特定的风险组，以评估导致风险的各种潜在因素，从而能够提出个性化干预方案。在过去50年中，男性的生殖问题与精子数量和质量的显著下降有关6,7。因此，精子计数和精子活力指数可以作为功能性评估的指标，对个体化干预措施有价值（如医学营养疗法、生活方式改变、解毒或荷尔蒙疗法）。 Evildence for Health Decision Making -Beyond RCTs Functional assessment in combination with new biometrics and bioinformatics tools represents a powerful step forward in the development of innovative approaches to collecting and documenting evidence in support of patient-specific interventions. In 2017. Thomas Frieden, MD MPH, who is the former director of the US Centers for Disease Control and Prevention, published an article in the New England Journal of Medicine describing a number of methods outside of the traditional RCT model for obtaining evidence related to the effectiveness of individualized therapies.8 Such methods may be relevant not only to the study of disease, but also the evaluation of wellness.9 This new way of thinking about study design moves us beyond a limited focus on population risk to a higher-level, enhanced understanding of individual functional uniqueness, setting the stage for a future in which precision personalized lifestyle medicine can be successfully developed and applied to patient care.10 功能性评估与新的生物特征学和生物信息学工具相结合，代表着创新方法的发展向前迈出了一大步，以收集和记录支持患者应用特定干预的证据。2017年，美国疾病控制和预防中心前主任，医学博士Thomas Frieden，在《新英格兰医学杂志》上发表了一篇文章，描述了传统RCT模型之外的许多方法，以获得与个体化治疗有效性相关的证据8。这些方法可能不止对疾病的研究有关，也对健康的评估有关9。这种研究设计的新思维方式使我们超越了对人群风险的有限关注，进入了更高的层次，增强了对个体功能独特性的理解，为未来精确的个性化生活方式药物的成功开发和应用奠定了基础10。 What types of studies might one day equal- or even replace the RCT model? Options currently include prospective cohort studies, retrospective cohort studies, pragmatic and large observational trials, nested case reports, and N-of-1 studies. Two researchers- Stacey Chang BS, and Thomas H. Lee, MD MSc- have introduced another concept into the mix: interpersonal medicine. In a 2018 article titled 'Beyond Evidence Based Medicine,' they describe the important context of a patient's social experiences and preferences, influence of caregivers and other support people on the outcome, and the quality of the communication surrounding biological, social, and humanistic concerns. Furthermore, they emphasize that practitioner experience should be coupled with clinical and biological evidence in the therapeutic decision-making process.11 哪种类型的研究有可能在某一天能等同于或甚至取代RCT模型呢？目前的选择包括前瞻性队列研究、回顾性队列研究、实效性和大型观察性试验、嵌套式病例报告和N-of-1研究。两位研究人员—Stacey Chang和Thomas H. Lee博士 - 在这一组合中引入了另一个概念：人际医学。在2018年题为“超越循证医学”的文章中，他们描述了患者的社会经验和偏好，护理者和其他支持人员对结果的影响，以及围绕生物、社会和人文问题的交流质量等方面的重要关联。此外，他们强调，在治疗决策过程中，从业者经验应与临床和生物学的证据相结合11。 Historically, RCT have been built around population statistics derived from parametric Gaussian distribution analyses, and this is now recognized to be a serious limitation. In most types of biological research, the data from humans is nonparametric and may be multimodal. It also has a long tail due to significant genomic variation. Often, biological functions or biometrics evaluated in a clinical study are the result of many contributing effects, and therefore these represent isolated components that are part of a much larger biological network. In order to understand the presence of a specific biomarker in an individual, it is necessary to also understand the status of the biological network that regulates that biomarker-a concept that is often referred to as systems biology. The field of network medicine has emerged to address complex data analysis derived from systems biology.12 A foundational tenet that underlies network medicine is the recognition that conditions identified as 'comorbidities' or 'disease adjacencies' may actually be functional perturbations of the same underlying biological network expressed in different cells, tissues, or organs.13 The network medicine model has already been successfully applied. One well- known example is the revelation that type 2 diabetes is not one disease, but rather at least three different subtypes that are distinguished by unique functional changes in metabolism.14 In the near future, treatment of type 2 diabetes will likely become very personalized, with less focus on the disease itself and more emphasis on evidence related to the unique functional metabolic disturbance in the patient. In a similar fashion, noted neurologist and author Dale Bredesen, MD, has reported that Alzheimer's disease exists in at least three different subtypes involving functional disturbances in brain metabolism, each of which requires personalization of therapy.15 Both of these conditions -type 2 diabetes and Alzheimer's disease- affect millions of people every year, and yet they serve as excellent examples of the need to gather evidence focused on the individual versus the group. 历史上，RCT是建立在由Gaussian参数分布分析得出的人口统计数据的基础上的，现在这被认为是一个严重的限制。在大多数类型的生物学研究中，来自人类的数据是非参数的，可能是多模态的。由于显著的基因组变异，分布曲线会带有一条长的尾巴。通常，在临床研究中评估的生物功能或生物特征是许多促成因素综合作用的结果，因此它们只代表了作为更大生物网络中一小部分的孤立的组成成分。为了了解个体中特定生物标志物的存在，还需要了解调节该生物标志物的生物网络的状态——这一概念通常被称为系统生物学12。网络医学领域的出现是为了解决从系统生物学得出的复杂数据分析问题。网络医学的一个基本原则是认识到被确定为“共病”或“疾病邻近”的疾病实际上可能是同一潜在生物网络在不同细胞、组织或器官中表达的功能性扰动13。网络医学模型已经成功应用,一个众所周知的例子是揭示2型糖尿病实际不是一种疾病，而是至少三种不同的亚型，它们能以独特的代谢功能变化来区分14。在不久的将来，2型糖尿病的治疗可能会变得非常个性化，对疾病本身的关注较少，而对与患者独特的功能代谢紊乱有关证据的重视会更多。著名的神经病学家和作者Dale Bredesen博士也以类似的方式报告说，阿尔茨海默病至少存在三种不同的亚型，这些亚型涉及不同的脑代谢的功能紊乱，每一种都需要个性化治疗15。这两种疾病：2型糖尿病和阿尔茨海默病，每年都会影响到数以百万计的人，但它们可以作为收集个体和人群间证据的最佳例子。 The growth of personalized medicine as a field has intersected with significant advancements in personal technology, especially the growing interest in and use of wearable medical devices. Michael Snyder, PhD, who is director of the Center for Genomics and Personalized Medicine at Stanford University, has emerged to be a leading expert on this topic. In various publications, Dr Snyder and his team describe front-edge biometric devices that can continuously measure a range of personalized data points in real time: sleep quality and duration, heart rate, blood oxygen levels, blood sugar, blood pressure, and body composition.16As the technology marketplace continues to evolve and grow, so too will the ability to assess individual functional capabilities both at rest and under stress, which will further inform and guide the personalization of therapies.17 个性化医疗领域的发展与个人技术的显著进步相互交叉，特别是体现在对可穿戴医疗设备的兴趣和使用上。Michael Snyder博士是斯坦福大学基因组学和个性化医学中心的主任，他已经成为这方面的重要专家。在各种出版物中，Snyder博士和他的团队描述了前沿生物识别设备，这些设备可以实时连续测量一系列时间点的个性化数据：睡眠质量和持续时间、心率、血氧水平、血糖、血压和身体组成成分等16。随着技术市场的不断发展和增长，在静息和压力状态下评估个体功能的能力也同样在发展和增长，这都将有助于进一步阐释和指导个性化的治疗17。 References 1. Schork NI. 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Combined Cellular and Biochemical Profiling to identify Predictive Drug Response Biomarkers for Kinase Inhibitors Approved for clinical use between 2013 and 2017. Mol cancer Ther. 2019 Feb 18(2):470 481. 22. Hirakawa A. Asano J Sato H. Teramukai s. Master protocol trials in oncology: Review and new trial designs. Contemp Clin Triads Commun. 2018 Aug 24;12:1-8 23.Grimaldi KA, van Ommen B, Ordovas JM, et al. Proposed guidelines to evaluate scientific validity and evidence for genotype-based dietary advice. Genes Nutr. 2017 Dec 15;12:35 24.Cantley LC Cancer, metabolism. fructose, artificial sweeteners, and going cold turkey on sugar. BMC Biol. 2013 Jan 31:12:8. 25.Hopkins BD, Pauli C, Du x, et al Suppression of insulin feedback enhances the efficacy of PI3K inhibitors. Nature. 2018 Aug;560(7719):499- 503. 26.Goncabves MD. Lu C, Tutnauer J, et al. High-fructose corn syrup enhances intestinal tumor growth in mice. Science. 2019 Mar 22:363(6433):1345-1349. 27. Mukherjee. 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