Nowak Stefan, Neidhart Johannes, Szendro Ivan G, Rzezonka Jonas, Marathe Rahul, Krug Joachim
Systems Biology of Ageing Cologne (Sybacol), University of Cologne, 50931 Cologne, Germany.
Institut für Theoretische Physik, Universität zu Köln, 50937 Cologne, Germany.
Biology (Basel). 2018 Jan 6;7(1):6. doi: 10.3390/biology7010006.
A long-standing problem in ageing research is to understand how different factors contributing to longevity should be expected to act in combination under the assumption that they are independent. Standard interaction analysis compares the extension of mean lifespan achieved by a combination of interventions to the prediction under an additive or multiplicative null model, but neither model is fundamentally justified. Moreover, the target of longevity interventions is not mean life span but the entire survival curve. Here we formulate a mathematical approach for predicting the survival curve resulting from a combination of two independent interventions based on the survival curves of the individual treatments, and quantify interaction between interventions as the deviation from this prediction. We test the method on a published data set comprising survival curves for all combinations of four different longevity interventions in . We find that interactions are generally weak even when the standard analysis indicates otherwise.
衰老研究中一个长期存在的问题是,在假设不同的长寿影响因素相互独立的情况下,理解它们共同作用时应如何预期。标准的相互作用分析将联合干预措施所实现的平均寿命延长与加性或乘性零模型下的预测进行比较,但这两种模型都没有根本依据。此外,长寿干预的目标不是平均寿命,而是整个生存曲线。在此,我们基于个体治疗的生存曲线,制定了一种数学方法来预测两种独立干预联合产生的生存曲线,并将干预之间的相互作用量化为与该预测的偏差。我们在一个已发表的数据集上测试了该方法,该数据集包含了四种不同长寿干预措施所有组合的生存曲线。我们发现,即使标准分析显示存在其他情况,相互作用通常也很微弱。