Healthy Lifespan Institute, Department of Infection, Immunity, and Cardiovascular Disease, University of Sheffield, Sheffield, UK; Healthy Lifespan Institute, Department of Oncology & Metabolism, University of Sheffield, UK; The Bateson Centre, University of Sheffield, Firth Court, Western Bank, Sheffield, UK.
Healthy Lifespan Institute, Department of Oncology & Metabolism, University of Sheffield, UK.
Mech Ageing Dev. 2021 Jan;193:111391. doi: 10.1016/j.mad.2020.111391. Epub 2020 Nov 2.
Over 60 % of people over the age of 65 will suffer from multiple diseases concomitantly but the common approach is to treat each disease separately. As age-associated diseases have common underlying mechanisms there is potential to tackle many diseases with the same pharmacological intervention. These are known as geroprotectors and could overcome the problems related to polypharmacy seen with the use of the single disease model. With some geroprotectors now reaching the end stage of preclinical studies and early clinical trials, there is a need to review the evidence and assess how they can be translated practically and effectively into routine practice. Despite promising evidence, there are many gaps and challenges in our understanding that must be addressed to make geroprotective medicine effective in the treatment of age-associated multimorbidity. Here we highlight the key barriers to clinical translation and discuss whether geroprotectors such as metformin, rapamycin and senolytics can tackle all age-associated diseases at the same dose, or whether a more nuanced approach is required. The evidence suggests that geroprotectors' mode of action may differ in different tissues or in response to different inducers of accelerating ageing, suggesting that a blunt 'one drug for many diseases' approach may not work. We make the case for the use of artificial intelligence to better understand multimorbidity, allowing identification of clusters and networks of diseases that are significantly associated beyond chance and the underpinning molecular pathway of ageing causal to each cluster. This will allow us to better understand the development of multimorbidity, select a more homogenous group of patients for intervention, match them with the appropriate geroprotector and identify biomarkers specific to the cluster.
超过 60%的 65 岁以上的人将同时患有多种疾病，但常见的方法是分别治疗每种疾病。由于与年龄相关的疾病有共同的潜在机制，因此有可能用相同的药理学干预来治疗多种疾病。这些药物被称为 geroprotectors，可以克服使用单一疾病模型所带来的与多药治疗相关的问题。随着一些 geroprotectors 现在进入临床前研究和早期临床试验的最后阶段，有必要审查证据，并评估它们如何实际有效地转化为常规实践。尽管有有希望的证据，但我们对 geroprotective medicine 在治疗与年龄相关的多种疾病方面的理解仍存在许多差距和挑战，需要加以解决。在这里，我们强调临床转化的关键障碍，并讨论二甲双胍、雷帕霉素和 senolytics 等 geroprotectors 是否可以在同一剂量下治疗所有与年龄相关的疾病，或者是否需要更细致的方法。有证据表明，geroprotectors 的作用机制在不同组织或对不同的加速衰老诱导物可能不同，这表明一种简单的“一种药物治疗多种疾病”的方法可能行不通。我们主张使用人工智能来更好地理解多种疾病，从而能够识别出显著相关的疾病簇和网络，而不仅仅是偶然的关联，以及与每个簇相关的衰老的潜在分子途径。这将使我们能够更好地了解多种疾病的发展，选择更同质的干预患者群体，将他们与适当的 geroprotector 匹配，并识别出特定于该簇的生物标志物。
