You May Be Historically Older Than Your “Real Age” Reuters/Toru Hanai
Years ago, when I began discussing the concept of aging, the “biological clock” emerged as a key topic. This term, synonymous with the aging clock and “true age” measurement, highlights the difference between chronological age—the number of years since birth—and biological age, which indicates the actual aging process within our bodies.
Generally, biological aging follows a predictable pattern: a gradual decline in physical and mental functions throughout adulthood. Our intuitive judgments of age often incorporate visible signs like wrinkles, gray hair, and variations in posture, gait, mental sharpness, and voice.
The goal of determining biological age is to encapsulate this aging process into a single measurable figure. This provides insight into an individual’s health trajectory, emphasizing that some people age significantly faster than others.
Most individuals find their biological age within a few years of their chronological age. However, discrepancies can be stark: one 56-year-old may exhibit a biological age akin to someone in their 30s, while another may resemble a person in their 70s. Notably, biological age can increase or decrease at a different rate than chronological age.
Understanding biological age serves as a valuable tool, offering individuals clear, understandable insights into their health. This information can motivate lifestyle modifications and help assess the effectiveness of interventions like diet and exercise. The demand for biological age assessments is evident, as numerous companies now offer testing services, albeit often at a premium.
For scientists investigating anti-aging strategies, biological age measurements serve as immediate indicators of intervention success, eliminating the need for long-term studies involving human or animal subjects. Furthermore, tracking biological age enables us to comprehend the inner workings of our bodies as they age.
Despite its advantages, the concept of biological age requires refinement. The initial biological clocks were based on epigenetic markers—molecular indicators that alter gene expression. Innovators like Steve Horvath from UCLA discovered that these markers change predictably throughout life, allowing for the estimation of biological age through complex algorithms.
Yet, epigenetics isn’t the sole estimation approach. Various other biological markers—such as blood proteins, telomeres, urine metabolites, facial imagery, and even X-rays—can also inform biological age assessments. However, the inconsistency between these different measurement methods raises concerns about their reliability.
For instance, according to a recent analysis of the CALERIE trial, which examined caloric restriction as an anti-aging intervention, five different aging clocks were applied to a cohort of 220 adults. Only two showed a significant decline in biological age among calorie-restricted participants, leaving questions about which clock to trust—a dilemma faced by both individuals and researchers utilizing aging assessments.
Another challenge is the misleading perception of accuracy. Most companies report a single biological age figure without indicating a margin of error, leading to potential misinterpretations. A recent study published in npj Aging pointed out that many existing biological clocks do not perform as anticipated, which could lead to unnecessary anxiety regarding health outcomes.
But does this imply that biological clocks are without value? Not entirely. Research indicates that many limitations associated with these methods could be addressed. According to Dmitri Kulikov and fellow researchers from the Skolkovo Institute of Science and Technology, overcoming these challenges is feasible, although determining whether it is worth pursuing these improvements remains an open question.
Meanwhile, innovative solutions are on the horizon. Emerging methodologies that utilize artificial intelligence, particularly large-scale health models (LHM), hold promise. These AI-driven models, akin to those powering systems like ChatGPT, analyze vast datasets to assess individual risks related to mortality and the development of age-related conditions. A recent study in Natural Medicine suggests these modern methods may outperform traditional biological clocks.
As LHM continues to evolve, it may address many current limitations of biological age assessments. Thus, if you are contemplating determining your biological age, proceed with caution. If you’ve already done so, take the outcomes with a degree of skepticism. In future reflections on aging, I promise to approach this subject with a more critical perspective, blending newfound knowledge with experience.
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Source: www.newscientist.com
