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Results of a multi-omics study that profiled the molecular changes that occur as part of the aging process were recently published in Nature Aging. In sum, they found that there are non-linear patterns in the majority of the -omics related to aging and that two distinct periods of substantial dysregulation occur, one at age 44 and the other at age 60. Rather than a gradual and continuous linear process, their data suggests more discrete periods of abrupt change.
The cohort of participants included in this study was comprised of 108 men and women (52% female) living in California and tracked for a median of 1.7 years, but as long as nearly 7 years for 1 participant. They ranged in age from 25 to 75 and were sampled every 3-6 months, only during periods they self-rated as “healthy.” Samples of blood and stool as well as skin, oral and nasal swabs were taken at each collection period, providing for transcriptomics, proteomics, metabolomics, lipidomics, along with plasma cytokine analysis, clinical lab tests, and stool, skin, nasal, and oral microbiome analyses. An astonishing 135,239 biological features and 246,507,456,400 data points were included in this deep analysis of biological aging. Rather than one snapshot, this ongoing longitudinal analysis allows for a far richer understanding of the complex molecular changes which occur over time.
They found that only a small percentage (6.6%) of molecules and microbes change linearly over time, while the vast majority (81%) change nonlinearly. The -omics that correlated most closely with aging were metabolomics, cytokines, and the oral microbiome, suggesting that these biomarkers could perhaps be used to track aging. They also identified several clusters of molecules that changed in a straightforward pattern over the lifespan; for example, one of these clusters remained fairly stable throughout the lifespan until age 55 to 60, after which it rapidly declined, whereas the other 2 clusters had a sharp increase after age 60. This time frame was consistent in both men and women, reducing the likelihood that this transition point was directly related to menopause.
Within these clusters, several specific molecules also had nonlinear trends related to aging. For example, blood urea nitrogen (BUN) was included in the cluster, which increased between ages 55-60, suggesting that a decline in kidney function is not linear but may happen abruptly. Similarly, plasma glucose levels were in this same cluster, pointing to a greater risk for diabetes, cardiovascular disease, and kidney disease which spikes around age 55 to 60. This is consistent with epidemiological observations; as the authors point out, the prevalence of cardiovascular disease jumps from about 40% between the ages of 40 to 59 to approximately 75% by age 60 to 70, and is as high as 86% among people older than 80. Prediabetes and diabetes together have a similar spike, with a prevalence between 50-80% among adults 65+. Similar inflections around age 40 and 65 have also been observed for neurodegenerative diseases, including Alzheimer’s and Parkinson’s disease. Thus the (non-linear) trends in -omics observed in this study are consistent with chronic disease epidemiology.
Multiple other pathways emerged as significant from this study. Reduced unsaturated fatty acid biosynthesis and increased phenylalanine metabolism both emerged as significant findings, as well as a drop in DNA repair capability between the ages of 50 and 56 (which stabilizes by age 75) and a sharp increase in oxidative stress that occurs after age 60. Increases in phenylalanine levels are thought to be a consequence of decreased hepatic catabolism, have previously been associated with cardiac aging, and are now recognized to be an important contributor to heart failure, as well as a robust predictor of prognosis in people with heart failure. Similarly, polyunsaturated fatty acid metabolism influences multiple physiological processes, including membrane structure, lipid metabolism, inflammation, etc., and has been linked to both cardiovascular as well as neurodegenerative disease risk. Many other anomalies were observed, including dysregulation in skin and muscle functioning at ages 40 and 60 and a decline in caffeine metabolism, which occurs at both age 40 and 60.
Given the limited duration and sample size (and several other confounding factors), it’s difficult to formulate a specific roadmap that utilizes the findings of this study to slow the pace of aging. However, it does at least provide a much deeper insight into the nature of aging and highlights some biological pathways that could be future targets. Substantial evidence indicates that lifestyle interventions can at least slow some of the dysfunction outlined by the -omics data. For example, the well-known Diabetes Prevention Program (DPP) reduced the incidence of diabetes with exercise and dietary changes, with effects that persisted over 10 years. Additionally, while DPP reduced the risk of diabetes in all age groups, it was the most effective among people over 60. Perhaps this type of deep dive into the physiology of aging will lend itself to individualized recommendations, i.e., identifying both the specific dysregulation and its onset could lead to targeted therapies, such as oxidative stress or fatty acid support, insulin sensitivity enhancement, etc., to mitigate the factors which drive the aging process.
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