A blood plasma aging clock, Part 2

Quite a few people recently looked at Part 1 which curated “Undulating changes in human plasma proteome across lifespan are linked to disease” in December 2019. Let’s start with a 2023 human study coauthored by Part 1’s lead researcher:

“The aim of this study is to identify a set of proteins in human plasma associated with aging by integration of data of four independent, large-scaled datasets. We identified a set of 273 plasma proteins significantly associated with aging (aging proteins, APs) across these cohorts consisting of healthy individuals and individuals with comorbidities and highlight their biological functions.

Although these presented proteins may be different compared to other presented proteomic clocks [like Part 1’s], this can be explained due to a variety of factors. Across studies there may be several technical factors, such as used anti-coagulants, and biological differences, such as different age ranges, ethnicity and corrections for BMI, which may influence the plasma proteome in the cohorts. To overcome these differences, we focused on the overlap between the different studies as they also present several of these confounding factors.

We show that individuals presenting accelerated or decelerated aging based on their plasma proteome, respectively have a more aged or younger systemic environment. These results provide novel insights in understanding the aging process and its underlying mechanisms and highlight potential modulators contributing to healthy aging.”

https://www.frontiersin.org/articles/10.3389/fragi.2023.1112109/full “Markers of aging: Unsupervised integrated analyses of the human plasma proteome”

A 2023 human study cited the above study and found:

“Our cross-sectional study of adults adherent and non-adherent to recommended lifestyle habits established strong group differences for 39 proteins primarily related to innate immunity and lipoprotein metabolism. Many of these protein differences were best explained by group contrasts in adiposity and visceral fat. The relatively small number of upregulated and downregulated proteins associated with good lifestyle habits should facilitate development of a targeted lifestyle proteomic panel that can be used in future studies to determine efficacy of various prevention and treatment strategies.”

https://www.researchsquare.com/article/rs-3097901/v1 “Adherence to Lifestyle Recommendations Linked to Innate Immunity and Lipoprotein Metabolism: A Cross-Sectional Comparison Using Untargeted Proteomics”

A 2023 human study from Google-owned Calico:

“In most cases, direction of effects between cause-specific and all-cause mortality was concordant, but all-cause mortality association was not statistically significant. Neither do we have insight into conditional causal effects of these proteins nor interaction effects between them.”

https://www.researchsquare.com/article/rs-2626017/v1 “Plasma Proteomic Determinants of Common Causes of Mortality”

“Undulating” in Part 1 described plasma proteins changing over time with peaks at ages 34, 60, and 78. Those peaks don’t provide a base for linearly extrapolating all-cause mortality.

A 2023 rodent study did a touch better with one of Part 1’s 46 proteins of a conserved aging signature that changed in the same direction with mice and humans, although it didn’t fully investigate protein expression over time.