Researchers have developed a groundbreaking proteomic aging clock, employing machine learning to analyze proteins in the blood and estimate individuals' biological age. The study identifies 25 lifestyle and environmental factors associated with mortality and proteomic aging, revealing that these factors wield significant influence over health outcomes. Remarkably, 23 of these factors are modifiable, highlighting the potential for public health interventions to mitigate their effects. Conducted by a team of scientists, the study underscores the profound impact of lifestyle choices on aging and mortality, suggesting that genetic predisposition plays a much smaller role than previously believed.
The research indicates that smoking, socioeconomic status, physical activity, and living conditions are the most influential factors affecting mortality and biological aging. Smoking alone correlates with 21 diseases, while socioeconomic factors and tiredness frequency are linked to 19 diseases. These findings emphasize the need for targeted public health strategies to address these critical factors. The study demonstrates that environmental and lifestyle elements are nearly ten times more significant in explaining mortality risk than genetic predisposition, with genetics accounting for less than 2% of the risk. Despite this, environmental factors only explain about 17% of the variation in the risk of death.
The proteomic aging clock serves as a powerful predictor of mortality and disease multimorbidity, offering a single measure associated with future risk of 18 major chronic diseases. It functions like a stopwatch, measuring internal aging rather than simply counting years. This innovative approach provides new insights into the interplay between lifestyle, environment, and aging.
"This study provides compelling evidence supporting the long-held understanding that lifestyle and environmental factors play a crucial role in determining health outcomes, often outweighing genetic predispositions. The finding that environmental exposures account for 17% of mortality risk compared to less than 2% from genetics is particularly striking and highlights the significant potential for public health interventions," noted Wael Harb, MD.
The researchers utilized the proteomic aging clock to identify exposures critical for aging. They selected exposures consistently associated with both mortality and the proteomic age clock. This method allowed them to pinpoint lifestyle and environmental factors with substantial impacts on health.
"In a previous publication, we built a proteomic aging clock by taking proteins in the blood and using machine learning to estimate (a) participant’s biological age. We showed that this proteomic aging clock is a powerful predictor of mortality and disease multimorbidity — it is a single measure associated with future risk of 18 major chronic diseases," explained Austin Argentieri, PhD.
The study suggests that aging is not entirely predetermined by genetics but is significantly shaped by environmental influences. This presents an opportunity for individuals to take control of their health through lifestyle changes and policy interventions aimed at reducing exposure to harmful environments.
"This research should give us all hope that aging is not fully predetermined in our genes, but it is something that is shaped in our environments. This means that we have the power to take our own health in our hands preventatively through not just changes to lifestyle but also through policy and intervention efforts aimed at reducing our exposures to harmful environments," Argentieri said.
The findings advocate for comprehensive public health strategies focusing on modifiable factors such as smoking cessation programs, increased physical activity, and improved socioeconomic conditions. Addressing these areas could potentially slow biological aging and reduce premature mortality.
"The next logical step would be to dive deeper into the specific mechanisms by which key environmental factors accelerate aging and contribute to disease. Longitudinal studies focusing on intervention strategies — such as smoking cessation programs, physical activity promotion, and improvements in socioeconomic conditions — would help validate whether modifying these exposures can indeed slow biological aging and reduce premature mortality," Harb suggested.
Additionally, integrating multi-omics data, including genomics, proteomics, and metabolomics, could provide a more holistic view of how environmental and genetic factors interact. This approach might lead to more personalized preventive care strategies.
"Additionally, integrating multi-omics data — genomics, proteomics, and metabolomics — could offer a more comprehensive view of how environmental and genetic factors interplay, leading to more personalized approaches to preventive care," Cheng-Han Chen, MD, added.
Replicating these findings across diverse populations would ensure broad applicability of the conclusions drawn from this study. Such efforts would enhance understanding of how various factors influence aging and disease development globally.
"It would also be valuable to replicate these findings in diverse populations to ensure the conclusions are broadly applicable," Chen emphasized.
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