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. 2022 Nov 11;13:6830. doi: 10.1038/s41467-022-34515-y

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 9 — The schematic illustrates major scenarios by which PAAIs could influence aging phenotypes. First, interventions could have no measurable effect on a set of phenotypes or even accentuate age-dependent phenotypic change. ASPs countered by an intervention could be influenced in ways consistent with a targeting of the mechanisms underlying age-dependent phenotypic change: In this case, PAAI effects should become apparent only after the onset of aging-associated phenotypic change, but not at younger ages (rate effect). PAAI effects at a young age (prior to the age when age-dependent phenotypic change becomes first detectable) indicate that it is not the age-dependent change that is being targeted (baseline effect). Although our studies revealed examples of both rate and baseline effects, many ‘anti-aging’ effects fell into the latter category (age-independent effects that do not provide evidence for a slowed aging rate). Ignoring this distinction would lead to a substantial overestimation of the extent by which PAAIs slow the aging process. Created with BioRender.com.