I think you'll find this open access work on a potential biomarker of aging to be interesting; the researchers use it to assess the results of different lifestyle choices, finding that some of those known to shorten life expectancy produce a higher measure of biological age in their biomarker. This seems a small step closer to validating the usefulness of such biomarkers. A number of research groups are presently developing biomarkers of aging based on characteristic patterns of epigenetic modifications or altered protein levels. We should expect to find common patterns because the cell and tissue damage that causes aging, and the evolved reactions to that damage, are the same in everyone. The challenge lies in identifying these common patterns amidst the complex, varied alterations that occur due to individual circumstances and environment, but solid progress has being made in recent years.
Human ageing is associated with a number of changes in how the body and its organs function. On the molecular level, ageing is associated with numerous processes, such as telomere length reduction, changes in metabolic and gene-transcription profiles and an altered DNA-methylation pattern. In addition to chronological time, lifestyle factors such as smoking or stress can affect both the pattern of DNA-methylation and telomere length and thereby the aging of an individual. Ageing and lifestyle are the strongest known risk factors for many common non-communicable diseases, hence, various predictor models have been developed using measures of facial morphology, physical fitness and physiology, telomere length and methylation pattern to predict ones chronological age.
Comparisons of the actual chronological age with the predicted age, sometimes denoted the biological age, can be used as an indicator of health status, monitor the effect of lifestyle changes and even aid in the decision on treatment strategies. To date, no current models have explored the potential of using the plasma protein profile for age prediction. We have previously characterized abundance levels of 144 circulating plasma proteins and have found over 40% of investigated proteins to be significantly correlated with one or more of the following factors, age, weight, length and hip circumference. We therefore reasoned that the plasma protein profile might also be predictive of these traits. Here we demonstrate for the first time that the profile of circulating plasma proteins can be used to accurately predict chronological age, as well as anthropometrical measures such as height, weight and hip circumference. Moreover, we used the plasma protein-based model to identify lifestyle choices that accelerate or decelerate the predicted age.
Here we demonstrate by analysis of 77 plasma proteins in 976 individuals, that the abundance of circulating proteins accurately predicts chronological age, as well as anthropometrical measurements such as weight, height and hip circumference. The plasma protein profile described herein is highly accurate in predicting chronologic age. The plasma protein profile can also be used to identify lifestyle factors that accelerate and decelerate ageing. We found smoking, high BMI and consumption of sugar-sweetened beverages to increase the predicted chronological age by 2-6 years, while consumption of fatty fish, drinking moderate amounts of coffee and exercising reduced the predicted age by approximately the same amount.