A large UK and US cohort study links accelerated biological aging to early-onset solid cancer risk, pointing to new ways scientists may track and prevent cancer in younger adults.
Study: Biological aging and generational shifts in early-onset cancer risk. Image Credit: Lightspring / Shutterstock
A recent study published in the journal Nature Medicine found that people in later birth cohorts showed greater systemic biological aging, and that greater biological aging was associated with a higher risk of early-onset solid cancers.
Analyzing data from more than 150,000 participants in the United Kingdom Biobank (UKB), researchers found a 23% higher standardized PhenoAge-defined age gap among those born between 1965 and 1974 than among those born between 1950 and 1954. These findings suggest that individuals from later birth cohorts may have more advanced biological aging profiles, linked to a higher risk of early-onset cancer, and that biological aging clocks may eventually help inform risk-stratification research and prevention studies if the findings are validated in further studies.
The incidence of early-onset cancer has been increasing worldwide in recent generations. Scientists are therefore exploring the impact of emerging generational risk factors on cancer risk. Biological aging clocks are increasingly being explored to assess whether generational shifts in biological aging help explain early-onset cancer risk. Researchers believe that organ-specific and systemic aging, collectively, may represent the cumulative effects of exposures. Such approaches could help identify high-risk individuals and prioritize resource allocation to individuals who need them the most.
About the study
In the present study, researchers examined how early-onset cancer risk (ages 18-55) differs across generations. They used PhenoAge, a well-known biological age calculator, to assess systemic aging among 154,169 UKB participants (92% White, 55% female). The team identified new-onset cancer cases by linking UKB data with national cancer registries and death records, using the International Classification of Diseases, tenth revision (ICD-10) codes.
Participants completed food frequency questionnaires (FFQ) and self-reported demographic and lifestyle factors such as sex, age, race, educational attainment, smoking habits, pack-years, alcohol consumption, and history of cancer among family members. They also reported menarche onset, oral contraceptive usage, menopause onset, and comorbidities. The study excluded individuals with any cancer, except nonmelanoma skin cancer, diagnosed before or within six months of study baseline, those with a body mass index (BMI) below 18.5 kg/m², and people with missing information on genetic ancestry.
The researchers used the Townsend Deprivation Index and the International Physical Activity Questionnaire (IPAQ) to assess socioeconomic status and physical activity, respectively. They calculated polygenic risk scores (PRS) to assess genetic predisposition. They used generalized additive models (GAMs) to visualize trends in age gaps by birth year. They then estimated hazard ratios (HRs) using Cox proportional hazards regression models.
To validate the findings, the team calculated age gaps using the Klemera-Doubal method (KDM) and nuclear magnetic resonance (NMR) metabolomics. They also used proteomic-based organ-specific biological aging clocks to assess the contribution of organ-specific aging to the risk of developing early-onset solid cancers. They additionally compared the results with those obtained for 10,262 individuals from the United States All of Us Research Program. They also performed a mediation analysis to evaluate whether PhenoAge-derived biological aging mediated the association between lifestyle factors and the development of early-onset solid cancers.
Results
Among the participants, systemic aging increased in recent birth cohorts. UKB participants born between 1965 and 1974 showed a 23% higher standardized PhenoAge-defined age gap than those born between 1950 and 1954. People with greater systemic aging, reflected by a higher PhenoAge-determined age gap, showed an increased likelihood of developing early-onset cancers (HR, 1.08 per standard-deviation increase). Higher age gaps were also linked to an increased likelihood of developing cancers of the lungs, gastrointestinal tract, and uterus. The increase in risk was observed after adjustment for genetic risk factors.
The organ-specific analyses showed associations between immune tissue aging and early-onset lung cancer (HR, 1.89). The analyses also linked adipose tissue aging to an increased likelihood of developing early colorectal cancers (HR, 1.60). These findings remained robust after adjusting for systemic aging. People with greater age gaps, i.e., advanced biological age compared to the chronological age, had a higher risk of developing early-onset solid cancers.
Alternative systemic aging measures showed directionally consistent but generally weaker overall associations, with stronger site-specific signals depending on the clock used. KDM-defined age gap was associated with early-onset lung and gastrointestinal cancers, while metabolomic-based age gap was associated with early-onset lung and uterine cancers. Among participants of the All of Us Research Program, individuals born between 1990 and 1999 also showed a 92% higher standardized PhenoAge-defined age gap than those born between 1965 and 1969.
The findings remained largely unchanged after excluding individuals who could be followed for less than 2 years and after adjusting for leukocyte telomere length. Risk determination using a cut-off of <50 years also yielded results largely similar to those with the <50 years cut-off. The PhenoAge results only modestly mediated the link between lifestyle-based factors and the risk of early-onset solid cancers.
Conclusion
The findings suggest that individuals in more recent birth cohorts and those with advanced biological aging relative to their chronological age may be more likely to develop early-onset solid cancers. However, because this was an observational study, residual confounding cannot be excluded, and the organ-specific analyses should be considered exploratory. If validated in subsequent studies, biological aging clocks could help refine future research on early-onset cancer risk and prevention strategies, rather than being used immediately in clinical decision-making.
Researchers should explore underlying biological mechanisms and include longitudinal aging measurements across more diverse geographic populations to identify physical, social, and sociopolitical risk factors. Such assessments could eventually inform population-level policy-making and individual-level clinical decision-making to improve the effectiveness of early-onset cancer prevention strategies.
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