How to Eliminate Uncertainty in Clinical Medicine - Clues from Creation of Mathematical Models Followed by Scientific Data Mining.
How to Eliminate Uncertainty in Clinical Medicine - Clues from Creation of Mathematical Models Followed by Scientific Data Mining.
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DOI：
10.1016/j.ebiom.2018.07.001
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发表时间：
2018-08
期刊：
影响因子：
11.1
通讯作者：
Asano Y
中科院分区：
文献类型：
--
作者：
Asano Y
医学和科学是通过强有力的信息积累、适当的分析和适当的评估过程而发展起来的。预测个体疾病进展风险、判断治疗效果和准确预测预后的能力是精密医学的核心原则[1]。鉴于我们现在可以全面搜索临床指标和生物标记物并收集这些信息，是时候在实践中使用这些能力了。但如何做到呢？临床研究人员调查疾病表型，积极收集临床信息，以便提供早期诊断，预测预后，获得更好的临床结果。生物医学研究人员仔细观察DNA、RNA、蛋白质或代谢物水平的变化，并确定新的生物标志物[2]。为了将基础科学转化为临床干预，我们进行了大规模的临床试验来验证假说，并实践了床到床的过程[3]。这一过程已成为近期临床流行病学研究的流行策略。当目标分子机制明确，并且有一个中等规模的受影响人群时，找到个体存活时间和感兴趣因素之间的关系是至关重要的。传染病、免疫性疾病和癌症就是最好的例子。随着疾病特征的显现，生物标志物的证据价值进一步增强。这类疾病的特征是基于生物标志物的平均值，而每个病例的特征可以通过疾病群体的平均值来解释，即“平均药物”。然而，在多因素疾病(如心血管疾病、糖尿病和肾脏疾病)中，使用“平均药物”方法寻找生物标记物未必有效。多因素疾病具有多个独立的、多样化的、与多种遗传和环境因素交织在一起的调节因子或预后参数。疾病进展可能是不可预测的，因为传统方法未能考虑多个标记物的相互作用[4]。换句话说，用一个平均参数来表示患有某种疾病的受影响人群是不可取的，因为其他参数的重要信息和多样性可能会丢失。过去的许多研究集中于特定的疾病人群，使用特定的生物标记物将他们分为两组或更多组，并询问指标的平均值是否在组之间显示出流行病学上的显著差异。然而，当候选标记数量的指数增加需要大的多重测试校正系数时，这是不希望的，这可能会掩盖重要性。例如，高血压不一定会导致心肌梗死，而心肌梗死可能是由高血压以外的其他症状引起的。因此，对于这些疾病，我们一定要追求“个体化药物”，而不是“一般药物”。具体地说，我们必须避免丢失个体多参数的临床信息，寻找在医疗中利用患者数据的新方法，实现精准医疗。此外，在处理多个生物标志物时，必须注意消除选择过程中的随意性。为了消除不确定性，我们需要一种方法，使用数学计算(不通过研究人员的手)从大量数据中发现特定规则，并将这些规则应用于临床实践。这些努力将代表着计算资源时代的有效解决方案[5]…
Medicine and science have evolved through processes of robust information accumulation, appropriate analyses, and proper evaluation. The ability to predict individual risk of disease progression, judge therapeutic effects, and predict accurate prognosis is a core principle of precision medicine [1]. Given that we can now comprehensively search clinical indicators and biomarkers and collect such information, it is time to use these abilities in practice. But how? Clinical researchers investigate disease phenotypes and actively collected clinical information in order to provide early diagnosis, predict prognosis, and achieve better clinical outcomes. Biomedical researchers carefully observe changes in levels of DNA, RNA, protein, or metabolites and identify novel biomarkers [2]. To translate basic science to clinical intervention, we have conducted large-scale clinical trials to validate hypotheses and practice the bench-to-bedside process [3]. This process has been a popular strategy for recent clinical epidemiological research. When the target molecular mechanisms are clear and a moderately sized affected population is available, it is critically useful to find relationships between individual survival times and factors of interest. Infectious diseases, immune diseases, and cancer are among the best examples of such conditions. As the characteristics of a disease are manifested, the evidentiary value of biomarkers is further strengthened. Such diseases are characterized based on average values of the biomarkers, and the characteristics of each individual case can be explained by average values of the disease population, ie,“average medicine”. However, in multifactorial diseases (eg, cardiovascular diseases, diabetes, and kidney diseases), searching for biomarkers using the “average medicine” approach may not necessarily be effective. Multifactorial diseases have multiple regulators or prognostic parameters that are independent, diverse, and intertwined with a wide variety of genetic and environmental factors. Disease progression can be unpredictable because conventional approaches have failed to consider multiple marker interactions [4]. In other words, it is undesirable for an affected population with a certain disease to be represented by one averaged parameter, because the important information and the diversity of other parameters can be lost. Many past studies focused on specific disease populations, divided them into two or more groups using specific biomarkers, and asked whether the average values of indicators exhibited epidemiologically significant differences among groups. However, this is undesirable when an exponential increase in the number of candidate markers requires a large multiple-testing correction factor, which can conceal significance. For example, hypertension does not necessarily result in myocardial infarction, and myocardial infarction can be caused by syndromes other than hypertension. Therefore, for these diseases, we must pursue “individual medicine” rather than “average medicine”. Specifically, we must avoid losing clinical information about multiple parameters of individuals, find new methods for making use of patient data in medical care, and achieve precision medicine. In addition, when dealing with multiple biomarkers, careful attention must be paid to eliminating arbitrariness in the selection process. To eliminate uncertainty, we need a method for discovering specific rules from large amounts of data using mathematical calculations (without going through researchers' hands) and applying these rules to clinical practice. Such efforts would represent effective solutions in the era of computational resources [5 …
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影响因子：
11.1
作者：
Fukuda H;Shindo K;Sakamoto M;Ide T;Kinugawa S;Fukushima A;Tsutsui H;Ito S;Ishii A;Washio T;Kitakaze M
通讯作者：
Kitakaze M
影响因子：
2.7
作者：
Relator RT;Terada A;Sese J
通讯作者：
Sese J
DOI：
10.1089/omi.2010.0023
发表时间：
2011-03
期刊：
Omics : a journal of integrative biology
影响因子：
--
作者：
Abu-Asab MS;Chaouchi M;Alesci S;Galli S;Laassri M;Cheema AK;Atouf F;VanMeter J;Amri H
通讯作者：
Amri H
影响因子：
168.9
作者：
Kitakaze, Masafumi;Asakura, Masanori;Kitamura, Soichiro
通讯作者：
Kitamura, Soichiro
影响因子：
3.4
作者：
Kim, J;Washio, T;Kitakaze, M
通讯作者：
Kitakaze, M