Epigenetic clocks have become a mainstay of longevity research over the past few years, with new clocks regularly being established for different species and tissues as well as promising research that uses and builds on these clocks. An international research team has recently created a different kind of clock, an inflammatory aging clock that predicts biological age based on inflammatory and immune markers .
Measuring the immunome
The researchers built their clock using data from the Stanford 1000 Immunomes project, a longitudinal study of aging and vaccination that collected blood samples between 2007 and 2016 from 1,001 participants aged 8 to 96. The samples were subjected to deep immune phenotyping using rigorously standardized procedures. Analyses included gene expression, serum cytokine levels, cell subset composition, and cellular responses to various stimuli.
The researchers wanted to use this data to construct a metric for age-related chronic inflammation that could be used to assess a person’s inflammatory burden. To that end, they used an unbiased deep-learning neural network built to compactly capture the nonlinear structure of of immune networks. The model was trained with ten age-related clinical features as outcomes, ranging from cancer and cardiovascular disease to psychiatric evaluations.
A metric for inflammation
This resulted in a metric that the team dubbed the ‘inflammatory clock of aging’, or iAge, which reflects the total inflammatory burden. Analysis showed that iAge correlated well with multimorbidity, or total age-related diseases, as well as with frailty and cardiovascular aging. In other words, like other aging clocks, iAge predicts biological age – in this case, based on inflammatory burden – rather than chronological age.
The team validated iAge in a second cohort, which included hundreds of people over 60 and 19 centenarians. Remarkably, iAge grouped the centenarians together with younger individuals, while older people were separated into a separate group.
Learning from the clock
Rather than resting on their laurels, the researchers immediately used iAge to improve our understanding of age-related inflammation. The most significant contributor to the iAge score was CXCL9, a chemokine produced by cells in inflammatory lesions. Levels of the CXCL9 protein increased significantly with age, reinforcing the notion that it might be an important player.
CXCL9 is strongly expressed in endothelial cells. To understand how it contributes to age-related disorders, the researchers silenced CXCL9 in endothelial cell cultures. They found that this reduced inflammation as well as cellular senescence and malfunction. However, CXCL9 is also important for immune surveillance, so knocking down this gene isn’t a viable, straightforward longevity therapy. Nevertheless, it offers an entry point to improving our understanding of age-related chronic inflammation and, particularly, the importance of the vascular system in this process.
While many diseases of aging have been linked to the immunological system, immune metrics capable of identifying the most at-risk individuals are lacking. From the blood immunome of 1,001 individuals aged 8–96 years, we developed a deep-learning method based on patterns of systemic age-related inflammation. The resulting inflammatory clock of aging (iAge) tracked with multimorbidity, immunosenescence, frailty and cardiovascular aging, and is also associated with exceptional longevity in centenarians. The strongest contributor to iAge was the chemokine CXCL9, which was involved in cardiac aging, adverse cardiac remodeling and poor vascular function. Furthermore, aging endothelial cells in human and mice show loss of function, cellular senescence and hallmark phenotypes of arterial stiffness, all of which are reversed by silencing CXCL9. In conclusion, we identify a key role of CXCL9 in age-related chronic inflammation and derive a metric for multimorbidity that can be utilized for the early detection of age-related clinical phenotypes.
Building on the concept of aging clocks by applying them to other aspects of biological aging seems like a rewarding approach. In addition to the obvious benefit of providing tools to measure other dimensions of how we age and assess how therapeutics affect them, research like this also provides an opportunity to identify and investigate important players in the various processes that make up aging and age-related decline.
 Sayed, N. et al. An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging. Nature Aging (2021), doi: 10.1038/s43587-021-00082-y