Current Status and Future Opportunities in Modeling Clinical Characteristics of Multiple Sclerosis.

Development of effective treatments requires understanding of disease mechanisms. For diseases of the central nervous system (CNS), such as multiple sclerosis (MS), human pathology studies and animal models tend to identify candidate disease mechanisms. However, these studies cannot easily link the identified processes to clinical outcomes, such as MS severity, required for causality assessment of candidate mechanisms. Technological advances now allow the generation of thousands of biomarkers in living human subjects, derived from genes, transcripts, medical images, and proteins or metabolites in biological fluids. These biomarkers can be assembled into computational models of clinical value, provided such models are generalizable. Reproducibility of models increases with the technical rigor of the study design, such as blinding, control implementation, the use of large cohorts that encompass the entire spectrum of disease phenotypes and, most importantly, model validation in independent cohort(s). To facilitate the growth of this important research area, we performed a meta-analysis of publications (n = 302) that model MS clinical outcomes extracting effect sizes, while also scoring the technical quality of the study design using predefined criteria. Finally, we generated a Shiny-App-based website that allows dynamic exploration of the data by selective filtering. On average, the published studies fulfilled only one of the seven criteria of study design rigor. Only 15.2% of the studies used any validation strategy, and only 8% used the gold standard of independent cohort validation. Many studies also used small cohorts, e.g., for magnetic resonance imaging (MRI) and blood biomarker predictors, the median sample size was <100 subjects. We observed inverse relationships between reported effect sizes and the number of study design criteria fulfilled, expanding analogous reports from non-MS fields, that studies that fail to limit bias overestimate effect sizes. In conclusion, the presented meta-analysis represents a useful tool for researchers, reviewers, and funders to improve the design of future modeling studies in MS and to easily compare new studies with the published literature. We expect that this will accelerate research in this important area, leading to the development of robust models with proven clinical value.

开发有效的治疗方法需要了解疾病机制。对于中枢神经系统（CNS）疾病，如多发性硬化症（MS），人体病理学研究和动物模型往往能确定候选疾病机制。然而，这些研究无法轻易地将所确定的过程与临床结果（如MS严重程度）联系起来，而这种联系对于候选机制的因果关系评估是必需的。技术的进步现在使得能够在活体人类受试者中产生数千种生物标志物，这些生物标志物来自基因、转录本、医学图像以及生物体液中的蛋白质或代谢物。如果这些模型具有普遍性，就可以将这些生物标志物组合成具有临床价值的计算模型。模型的可重复性随着研究设计的技术严谨性而提高，例如设盲、实施对照、使用涵盖疾病表型全谱的大型队列，以及最重要的是在独立队列中进行模型验证。为了促进这一重要研究领域的发展，我们对302篇对MS临床结果进行建模的出版物进行了荟萃分析，提取了效应量，同时还使用预先定义的标准对研究设计的技术质量进行了评分。最后，我们创建了一个基于Shiny - App的网站，允许通过选择性筛选对数据进行动态探索。平均而言，已发表的研究仅满足研究设计严谨性的七个标准中的一个。只有15.2%的研究使用了任何验证策略，只有8%使用了独立队列验证的金标准。许多研究还使用了小型队列，例如，对于磁共振成像（MRI）和血液生物标志物预测因子，样本量中位数小于100名受试者。我们观察到报告的效应量与满足的研究设计标准数量之间呈反比关系，这扩展了来自非MS领域的类似报告，即未能限制偏倚的研究高估了效应量。总之，所呈现的荟萃分析为研究人员、评审人员和资助者提供了一个有用的工具，可用于改进MS未来建模研究的设计，并便于将新研究与已发表的文献进行比较。我们期望这将加速这一重要领域的研究，从而开发出具有经证实的临床价值的可靠模型。