Age-associated diseases can develop at varying stages of life in different individuals. While lifestyle and genetics clearly play a significant role, our understanding of the molecular mechanisms involved is lacking.
Here, researchers took a new approach to this problem. They monitored the molecular profiles of 106 people over 2 years using a wide range of techniques. Based on this data, many previously identified and unidentified markers were found to correlate with age, which the authors used to categorise individuals into one four distinct ageing patterns or ‘ageotypes’. These ‘ageotypes’ are: Immune, kidney, liver and metabolic.
Metabolic agers, for example, may be at a higher risk for type 2 diabetes as they grow older. Immune agers may generate more inflammation, and therefore be at higher risk for immune-related disease. It could be that liver and kidney ageotypes may be more prone to liver or kidney diseases, respectively. There are likely other pathways, such as cardio agers who may be more prone to heart attacks, for example, but this study was limited to four main aging pathways.Michael Snyder, a professor and the chair of genetics at the Stanford University School of Medicine
These categories are ultimately artificial, and some individuals did not fall strongly into one of these ageotypes, while some fell into multiple categories. Nevertheless, there is potential in designing a clinical test to quickly identify an individual’s ageotype. It would then be possible to determine personalised lifestyle and drug interventions that could slow ageing on an individual basis.
The research team behind the study sorted 43 people into aging categories, or “ageotypes,” based on biological samples collected over the course of two years. The samples included blood, inflammatory substances, microbes, genetic material, proteins and by-products of metabolic processes. By tracking how the samples changed over time, the team identified about 600 so-called markers of aging — values that predict the functional capacity of a tissue and essentially estimate its “biological age.”
So far, the team has identified four distinct ageotypes: Immune, kidney, liver and metabolic. Some people fit squarely in one category, but others may meet the criteria for all four, depending on how their biological systems hold up with age.
“Now, it’s going to be a lot more than just four categories,” said senior author Michael Snyder, a professor and the chair of genetics at the Stanford University School of Medicine in California. For instance, one participant in the study appeared to be a cardiovascular ager, meaning their cardiac muscle accumulates wear-and-tear at a greater rate than other parts of their body. “If we [surveyed] 1,000 people, I’m sure we’ll find other cardio agers and that category will become better defined.” And with more research, even more patterns of aging may emerge, Snyder added.Michael Snyder, a professor and the chair of genetics at the Stanford University School of Medicine
The molecular changes that occur with aging are not well understood1,2,3,4. Here, we performed longitudinal and deep multiomics profiling of 106 healthy individuals from 29 to 75 years of age and examined how different types of ‘omic’ measurements, including transcripts, proteins, metabolites, cytokines, microbes and clinical laboratory values, correlate with age. We identified both known and new markers that associated with age, as well as distinct molecular patterns of aging in insulin-resistant as compared to insulin-sensitive individuals. In a longitudinal setting, we identified personal aging markers whose levels changed over a short time frame of 2–3 years. Further, we defined different types of aging patterns in different individuals, termed ‘ageotypes’, on the basis of the types of molecular pathways that changed over time in a given individual. Ageotypes may provide a molecular assessment of personal aging, reflective of personal lifestyle and medical history, that may ultimately be useful in monitoring and intervening in the aging process.Ahadi, S., Zhou, W., Schüssler-Fiorenza Rose, S.M. et al. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat Med 26, 83–90 (2020). https://doi.org/10.1038/s41591-019-0719-5
Personal aging markers and ageotypes revealed by deep longitudinal profiling: https://www.nature.com/articles/s41591-019-0719-5