MacCormick Ian James Callum, Zhang Bo, Hill Daniel, Cordeiro Maria Francesca, Small Dylan S
Centre for Inflammation Research University of Edinburgh The Queen's Medical Research Institute Edinburgh BioQuarter Edinburgh UK.
Wharton Department of Statistics and Data Science University of Pennsylvania Philadelphia Pennsylvania USA.
Alzheimers Dement (Amst). 2022 Jun 21;14(1):e12327. doi: 10.1002/dad2.12327. eCollection 2022.
Propose a theoretical framework for retinal biomarkers of Alzheimer's disease (AD).
The retina and brain share important biological features that are relevant to AD. Developing retinal biomarkers of AD is a strategic priority but as yet none have been validated for clinical use. Part of the reason may be that fundamental inferential assumptions have been overlooked. Failing to recognize these assumptions will disadvantage biomarker discovery and validation, but incorporating them into analyses could facilitate translation.
The biological assumption that a disease causes analogous effects in the brain and retina can be expressed within a Bayesian network. This allows inferences about abstract theory and individual events, and provides an opportunity to falsify the foundational hypothesis of retina-brain analogy. Graphical representation of the relationships between variables simplifies comparison between studies and facilitates judgements about whether key assumptions are valid given the current state of knowledge.
The framework provides a visual approach to retinal biomarkers and may help to rationalize analysis of future studies. It suggests possible reasons for inconsistent results in existing literature on AD biomarkers.
The framework can be modified to describe alternative theories of retinal biomarker biology, such as retrograde degeneration resulting from brain disease, and can incorporate confounding factors such as co-existent glaucoma or macular degeneration. Parallels with analogue confirmation theory and surrogate marker validation suggest strengths and weaknesses of the framework that can be anticipated when developing analysis plans.
Retinal biomarkers hold great promise for Alzheimer's disease (AD), but none are currently used clinically.Assumptions about the cause of retinal and brain changes are often overlooked, and this may disadvantage biomarker discovery and validation.We present a new approach to retinal biomarkers that describes cause and effect graphically in a Bayesian network.We show how this allows a more complete assessment of how well a biomarker might reflect the brain, and how data from right and left eyes can be used to rule out poor biomarker candidates.
提出一个关于阿尔茨海默病（AD）视网膜生物标志物的理论框架。
视网膜和大脑具有与AD相关的重要生物学特征。开发AD的视网膜生物标志物是一项战略重点，但目前尚无一种生物标志物经过验证可用于临床。部分原因可能是基本的推理假设被忽视了。未能认识到这些假设将不利于生物标志物的发现和验证，但将它们纳入分析中可能有助于转化应用。
疾病在大脑和视网膜中产生类似效应这一生物学假设可以在贝叶斯网络中得到表达。这允许对抽象理论和个体事件进行推理，并提供了一个机会来证伪视网膜 - 大脑类比的基础假设。变量之间关系的图形表示简化了研究之间的比较，并有助于根据当前的知识状态判断关键假设是否有效。
该框架为视网膜生物标志物提供了一种可视化方法，可能有助于使未来研究的分析更加合理。它提示了现有AD生物标志物文献中结果不一致的可能原因。
该框架可以修改以描述视网膜生物标志物生物学的其他理论，例如由脑部疾病导致的逆行性变性，并且可以纳入诸如并存青光眼或黄斑变性等混杂因素。与类似确证理论和替代标志物验证的平行关系表明了在制定分析计划时可以预期的该框架的优缺点。
视网膜生物标志物对阿尔茨海默病（AD）具有巨大潜力，但目前尚无一种用于临床。关于视网膜和大脑变化原因的假设常常被忽视，这可能不利于生物标志物的发现和验证。我们提出了一种新的视网膜生物标志物方法，该方法在贝叶斯网络中以图形方式描述因果关系。我们展示了这如何能够更全面地评估生物标志物反映大脑的程度，以及如何利用左右眼的数据排除不良的生物标志物候选物。
