Liu Yasmine J, McIntyre Rebecca L, Janssens Georges E
Laboratory Genetic Metabolic Diseases, Amsterdam Gastroenterology, Endocrinology, and Metabolism, Amsterdam Cardiovascular Sciences, Amsterdam UMC location University of Amsterdam, Amsterdam, Netherlands.
Front Aging. 2022 Apr 25;3:903049. doi: 10.3389/fragi.2022.903049. eCollection 2022.
Public attention and interest for longevity interventions are growing. These can include dietary interventions such as intermittent fasting, physical interventions such as various exercise regimens, or through supplementation of nutraceuticals or administration of pharmaceutics. However, it is unlikely that most interventions identified in model organisms will translate to humans, or that every intervention will benefit each person equally. In the worst case, even detrimental health effects may occur. Therefore, identifying longevity interventions using human data and tracking the aging process in people is of paramount importance as we look towards longevity interventions for the public. In this work, we illustrate how to identify candidate longevity interventions using population data in humans, an approach we have recently employed. We consider metformin as a case-study for potential confounders that influence effectiveness of a longevity intervention, such as lifestyle, sex, genetics, age of administration and the microbiome. Indeed, metformin, like most other longevity interventions, may end up only benefitting a subgroup of individuals. Fortunately, technologies have emerged for tracking the rate of 'biological' aging in individuals, which greatly aids in assessing effectiveness. Recently, we have demonstrated that even wearable devices, accessible to everyone, can be used for this purpose. We therefore propose how to use such approaches to test interventions in the general population. In summary, we advocate that 1) not all interventions will be beneficial for each individual and therefore 2) it is imperative that individuals track their own aging rates to assess healthy aging interventions.
公众对长寿干预措施的关注和兴趣与日俱增。这些措施包括饮食干预,如间歇性禁食;身体干预,如各种锻炼方案;或者通过补充营养保健品或服用药物。然而,在模式生物中确定的大多数干预措施不太可能适用于人类,而且并非每种干预措施都会对每个人产生同等的益处。在最坏的情况下,甚至可能会产生有害的健康影响。因此,在我们寻求面向公众的长寿干预措施时,利用人类数据识别长寿干预措施并追踪人类的衰老过程至关重要。在这项工作中,我们阐述了如何利用人类群体数据来识别候选长寿干预措施,这是我们最近采用的一种方法。我们将二甲双胍作为一个案例研究,探讨影响长寿干预措施有效性的潜在混杂因素,如生活方式、性别、遗传因素、用药年龄和微生物群。实际上,与大多数其他长寿干预措施一样,二甲双胍最终可能只会使一部分个体受益。幸运的是,已经出现了一些技术来追踪个体的“生物”衰老速度,这极大地有助于评估干预措施的有效性。最近,我们已经证明,即使是人人都能使用的可穿戴设备也可用于此目的。因此,我们提出了如何利用这些方法在普通人群中测试干预措施。总之,我们主张:1)并非所有干预措施都会对每个个体有益,因此2)个体必须追踪自己的衰老速度,以评估健康衰老干预措施。