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. 2022 Nov 8:11:e80729.
doi: 10.7554/eLife.80729.

The role of adolescent lifestyle habits in biological aging: A prospective twin study

Anna Kankaanpää  1 , Asko Tolvanen  2 , Aino Heikkinen  3 , Jaakko Kaprio  3 , Miina Ollikainen  3 , Elina Sillanpää  1   3
Affiliations

The role of adolescent lifestyle habits in biological aging: A prospective twin study

Anna Kankaanpää et al. Elife. .

Abstract

Background: Adolescence is a stage of fast growth and development. Exposures during puberty can have long-term effects on health in later life. This study aims to investigate the role of adolescent lifestyle in biological aging.

Methods: The study participants originated from the longitudinal FinnTwin12 study (n = 5114). Adolescent lifestyle-related factors, including body mass index (BMI), leisure-time physical activity, smoking, and alcohol use, were based on self-reports and measured at ages 12, 14, and 17 years. For a subsample, blood-based DNA methylation (DNAm) was used to assess biological aging with six epigenetic aging measures in young adulthood (21-25 years, n = 824). A latent class analysis was conducted to identify patterns of lifestyle behaviors in adolescence, and differences between the subgroups in later biological aging were studied. Genetic and environmental influences on biological aging shared with lifestyle behavior patterns were estimated using quantitative genetic modeling.

Results: We identified five subgroups of participants with different adolescent lifestyle behavior patterns. When DNAm GrimAge, DunedinPoAm, and DunedinPACE estimators were used, the class with the unhealthiest lifestyle and the class of participants with high BMI were biologically older than the classes with healthier lifestyle habits. The differences in lifestyle-related factors were maintained into young adulthood. Most of the variation in biological aging shared with adolescent lifestyle was explained by common genetic factors.

Conclusions: These findings suggest that an unhealthy lifestyle during pubertal years is associated with accelerated biological aging in young adulthood. Genetic pleiotropy may largely explain the observed associations.

Funding: This work was supported by the Academy of Finland (213506, 265240, 263278, 312073 to J.K., 297908 to M.O. and 341750, 346509 to E.S.), EC FP5 GenomEUtwin (J.K.), National Institutes of Health/National Heart, Lung, and Blood Institute (grant HL104125), EC MC ITN Project EPITRAIN (J.K. and M.O.), the University of Helsinki Research Funds (M.O.), Sigrid Juselius Foundation (J.K. and M.O.), Yrjö Jahnsson Foundation (6868), Juho Vainio Foundation (E.S.) and Päivikki and Sakari Sohlberg foundation (E.S.).

Keywords: DNA methylation; adolescence; biological age; dna methylation; epidemiology; epigenetic aging; global health; lifespan; lifestyle; pace of aging.

Plain language summary

For most animals, events that occur early in life can have a lasting impact on individuals’ health. In humans, adolescence is a particularly vulnerable time when rapid growth and development collide with growing independence and experimentation. An unhealthy lifestyle during this period of rapid cell growth can contribute to later health problems like heart disease, lung disease, and premature death. This is due partly to accelerated biological aging, where the body deteriorates faster than what would be expected for an individual’s chronological age. One way to track the effects of lifestyle on biological aging is by measuring epigenetic changes. Epigenetic changes consist on adding or removing chemical ‘tags’ on genes. These tags can switch the genes on or off without changing their sequences. Scientists can measure certain epigenetic changes by measuring the levels of methylated DNA – DNA with a chemical ‘tag’ known as a methyl group – in blood samples. Several algorithms – known as ‘epigenetic clocks’ – are available that estimate how fast an individual is aging biologically based on DNA methylation. Kankaanpää et al. show that unhealthy lifestyles during adolescence may lead to accelerated aging in early adulthood. For their analysis, Kankaanpää et al. used data on the levels of DNA methylation in blood samples from 824 twins between 21 and 25 years old. The twins were participants in the FinnTwin12 study and had completed a survey about their lifestyles at ages 12, 14, and 17. Kankaanpää et al. classified individuals into five groups depending on their lifestyles. The first three groups, which included most of the twins, contained individuals that led relatively healthy lives. The fourth group contained individuals with a higher body mass index based on their height and weight. Finally, the last group included individuals with unhealthy lifestyles who binge drank, smoked and did not exercise. After estimating the biological ages for all of the participants, Kankaanpää et al. found that both the individuals with higher body mass indices and those in the group with unhealthy lifestyles aged faster than those who reported healthier lifestyles. However, the results varied depending on which epigenetic clock Kankaanpää et al. used to measure biological aging: clocks that had been developed earlier showed fewer differences in aging between groups; while newer clocks consistently found that individuals in the higher body mass index and unhealthy groups were older. Kankaanpää et al. also showed that shared genetic factors explained both unhealthy lifestyles and accelerated biological aging. The experiments performed by Kankaanpää et al. provide new insights into the vital role of an individual’s genetics in unhealthy lifestyles and cellular aging. These insights might help scientists identify at risk individuals early in life and try to prevent accelerated aging.

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Conflict of interest statement

AK, AT, AH, JK, MO, ES No competing interests declared

Figures

Figure 1.
Figure 1.. Decomposition of (A) total variation in biological aging and (B) the variation of the residual term.
Figure 2.
Figure 2.. Classes with different lifestyle patterns (n = 5114).
Mean and probability profiles (95% confidence intervals) of the indicator variables utilized in the classification: (A) body mass index, (B) regular leisure-time physical activity (LTPA) (several times a week), (C) daily smoking, and (D) regular alcohol use (once a month or more). For categorical variables, the probabilities of belonging to the highest categories are presented.
Figure 2—figure supplement 1.
Figure 2—figure supplement 1.. Lifestyle-related factors in adulthood (21–25 years) according to the adolescent lifestyle behavior classes in the subsample of participants with information on biological aging (n = 824).
(A) Body mass index (BMI), (B) leisure-time physical activity (LTPA), (C) prevalence of daily smokers, and (D) alcohol use. Means and 95% confidence intervals are presented. C1, the class with the healthiest lifestyle; C2, the class with low-normal BMI; C3, the class with healthy lifestyle and high-normal BMI; C4, the class with high BMI; C5, the class with the unhealthiest lifestyle pattern. The model was controlled for sex (female), age, and baseline pubertal development.
Figure 3.
Figure 3.. Mean differences between the adolescent lifestyle behavior patterns in biological aging measured with (A) DNAm PhenoAge, (B) DNAm GrimAge, (C) DunedinPoAm, and (D) DunedinPACE estimators (n = 824).
The analysis was adjusted for sex (female), standardized age, and baseline pubertal development. Means and 95% confidence intervals are presented. C1, the class with the healthiest lifestyle pattern; C2, the class with low-normal body mass index (BMI); C3, the class with a healthy lifestyle and high-normal BMI; C4, the class with high BMI; C5, the class with the unhealthiest lifestyle pattern; AA, age acceleration.
Figure 3—figure supplement 1.
Figure 3—figure supplement 1.. DNA methylation (DNAm)-based plasma proteins and smoking pack-years according to the adolescent lifestyle behavior patterns (n = 824).
(A) DNAm adrenomedullin (ADM), (B) DNAm beta-2 microglobulin (B2M), (C) DNAm growth differentiation factor (GDF15), (D) DNAm cystatin C, (E) DNAm leptin, (F) DNAm plasminogen activation inhibitor 1 (PAI-1), DNAm tissue inhibitor metalloproteinase 1 (TIMP-1), and (H) DNAm smoking pack-years (packyrs). Means and 95% confidence intervals are presented. C1, the class with the healthiest lifestyle pattern; C2, the class with low-normal body mass index (BMI); C3, the class with healthy lifestyle and high-normal BMI; C4, the class with high BMI; C5, the class with the unhealthiest lifestyle pattern. The model was controlled for sex (female), age, and baseline pubertal development.
Figure 4.
Figure 4.. Proportions of the total variation in biological aging explained by genetic and (unshared) environmental factors shared with adolescent lifestyle patterns among young adult twin pairs (MZ n = 154, DZ n = 211).
The results are based on the model including additive genetic and non-shared environmental component (AE model). AA, age acceleration.
Author response image 1.
Author response image 1.. Correlations between epigenetic age acceleration (AA) measures assessed with different clocks.
Author response image 2.
Author response image 2.. Mean differences between the adolescent lifestyle behavior patterns in biological aging measured with original and PC-based (A) DNAm PhenoAge and (B) DNAm GrimAge.
The analysis was adjusted for sex (female), standardized age and baseline pubertal development. Means and 95% confidence intervals are presented. C1 = the class with the healthiest lifestyle pattern, C2 = the class with low–normal BMI, C3 = the class with a healthy lifestyle and high–normal BMI, C4 = the class with high BMI, C5 = the class with the unhealthiest lifestyle pattern. AA, age acceleration.
See this image and copyright information in PMC

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