A time-series analysis of blood-based biomarkers within a 25-year longitudinal dolphin cohort.
A time-series analysis of blood-based biomarkers within a 25-year longitudinal dolphin cohort.
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DOI：
10.1371/journal.pcbi.1010890
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发表时间：
2023-03
影响因子：
4.3
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中科院分区：
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了解临床相关生物标志物之间的因果相互作用和相关性非常重要，既可以为潜在的医疗干预提供信息，也可以预测任何个体随着年龄的增长可能的健康轨迹。这些相互作用和相关性可能难以在人类中建立，这是由于常规采样和控制个体差异（例如，饮食、社会经济地位、药物）。由于海豚是长寿的哺乳动物，表现出与人类相似的几个年龄相关的现象，我们分析了一个良好控制的25年纵向队列的144只海豚的数据。这项研究的数据已经在早些时候报道过，包括44种临床相关的生物标志物。这个时间序列数据表现出三种截然不同的影响：（A）生物标志物之间的定向相互作用，（B）生物变异的来源，可以关联或去关联不同的生物标志物，和（C）随机观察噪声，结合测量误差和非常快速的波动海豚的生物标志物。重要的是，生物变异的来源（B型）在数量上很大，通常与观察误差（C型）相当，并且大于定向相互作用（A型）的影响。试图恢复A型相互作用而不考虑B型和C型变化可能导致大量的假阳性和假阴性。使用广义回归拟合的纵向数据与线性模型占所有三个影响，我们表明，海豚表现出许多显着的直接相互作用（A型），以及强相关的变化（B型），几对生物标志物之间。此外，这些相互作用中的许多与高龄相关，这表明可以监测和/或靶向这些相互作用以预测并潜在地影响衰老。身体是一个非常复杂的系统，有许多相互作用的组成部分，其中绝大多数实际上是不可能测量的。此外，我们还不知道随着身体的衰老，我们可以测量的成分中有多少是相互影响的。在这项研究中，我们试图朝着回答这个问题迈出一小步。我们使用来自一组仔细控制的海豚的纵向数据来帮助我们建立一个简单的衰老模型。虽然我们使用的纵向数据确实测量了许多重要的生物标志物，但显然还有更多的生物标志物没有被测量。我们的简单模型通过假设它们的累积效应类似于复杂动力系统研究中经常使用的一种“噪声”来解释这些“缺失”的测量。通过这个简单的模型，我们能够找到这些生物标志物之间几种重要相互作用的证据。我们发现的相互作用也可能在其他长寿哺乳动物的衰老中发挥作用，并且可能值得进一步研究以更好地了解人类衰老。
Causal interactions and correlations between clinically-relevant biomarkers are important to understand, both for informing potential medical interventions as well as predicting the likely health trajectory of any individual as they age. These interactions and correlations can be hard to establish in humans, due to the difficulties of routine sampling and controlling for individual differences (e.g., diet, socio-economic status, medication). Because bottlenose dolphins are long-lived mammals that exhibit several age-related phenomena similar to humans, we analyzed data from a well controlled 25-year longitudinal cohort of 144 dolphins. The data from this study has been reported on earlier, and consists of 44 clinically relevant biomarkers. This time-series data exhibits three starkly different influences: (A) directed interactions between biomarkers, (B) sources of biological variation that can either correlate or decorrelate different biomarkers, and (C) random observation-noise which combines measurement error and very rapid fluctuations in the dolphin’s biomarkers. Importantly, the sources of biological variation (type-B) are large in magnitude, often comparable to the observation errors (type-C) and larger than the effect of the directed interactions (type-A). Attempting to recover the type-A interactions without accounting for the type-B and type-C variation can result in an abundance of false-positives and false-negatives. Using a generalized regression which fits the longitudinal data with a linear model accounting for all three influences, we demonstrate that the dolphins exhibit many significant directed interactions (type-A), as well as strong correlated variation (type-B), between several pairs of biomarkers. Moreover, many of these interactions are associated with advanced age, suggesting that these interactions can be monitored and/or targeted to predict and potentially affect aging. The body is a very complicated system with many interacting components, the vast majority of which are practically impossible to measure. Furthermore, it is still not understood how many of the components that we can measure influence one another as the body ages. In this study we try and take a small step towards answering this question. We use longitudinal data from a carefully controlled cohort of dolphins to help us build a simple model of aging. While the longitudinal data we use does measure many important biomarkers, there are obviously a much larger number of biomarkers that haven’t been measured. Our simple model accounts for these ‘missing’ measurements by assuming that their accumulated effect is similar to a kind of ‘noise’ often used in the study of complicated dynamical systems. With this simple model we are able to find evidence of several significant interactions between these biomarkers. The interactions we find may also play a role in the aging of other long-lived mammals, and may be worth investigating further to better understand human aging.
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