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

Abstract

Article Figures and data Abstract Editor's evaluation eLife digest Introduction Methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics 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.). Editor's evaluation This is an important article that is methodologically compelling that provides evidence that an unhealthy lifestyle during adolescence accelerates epigenetic age in adulthood and that these associations are largely explained by the effect of shared genetic influences. The main strengths of this article are the relatively large sample size, longitudinal assessment of lifestyle factors, and sophisticated statistical analyses. The article will be of interest to a broad audience, including individuals working on methylation, epidemiology, and/or aging. https://doi.org/10.7554/eLife.80729.sa0 Decision letter Reviews on Sciety eLife's review process eLife digest 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. Introduction Epidemiological studies of life course have indicated that exposures during early life have long-term effects on later health (Kuh et al., 2003). Unhealthy environments and lifestyle habits during rapid cell division can affect the structure or functions of organs, tissues, or body systems, and these changes can subsequently affect health and disease in later life (Biro and Deardorff, 2013; Power et al., 2013). For example, lower birth weight and fast growth during childhood predispose individuals to coronary heart disease and increased blood pressure in adulthood (Osmond and Barker, 2000). In addition to infancy and childhood, adolescence is also a critical period of growth. Adolescence is characterized by pubertal maturation and growth spurts. Early pubertal development is linked to worse health conditions, such as obesity and cardiometabolic risk factors in adulthood (Prentice and Viner, 2013). However, childhood obesity can lead to early onset of puberty, especially among girls (Li et al., 2017; Richardson et al., 2020), and, therefore, can confound the observed associations between early pubertal development and worse later health. Moreover, early pubertal development is linked to substance use and other risky behaviors in adolescence (Hartman et al., 2017; Savage et al., 2018), but the associations are partly explained by familial factors (Savage et al., 2018). Many unhealthy lifestyle choices, such as smoking initiation, alcohol use, and a physically inactive lifestyle, are already made in adolescence and increase the risk of developing several noncommunicable diseases over the following decades (Lopez et al., 2006). Once initiated, unhealthy habits are likely to persist into adulthood (Latvala et al., 2014; Maggs and Schulenberg, 2005; Rovio et al., 2018; Salin et al., 2019). A recent systematic review showed that healthy habits tend to cluster during childhood and adolescence, and typically, about half of the adolescents fall into subgroups characterized by healthy lifestyle habits (Whitaker et al., 2021). However, small minorities of adolescents are classified as heavy substance users or as having multiple other risk behaviors (Whitaker et al., 2021). The long-term consequences of the accumulation of unhealthy adolescent behaviors on health in later life have been rarely studied. An unhealthy lifestyle in adolescence can affect biological mechanisms of aging at the molecular level and, subsequently, morbidity. Epigenetic alterations, including age-related changes in DNA methylation (DNAm), constitute a primary hallmark of biological aging (López-Otín et al., 2013). Epigenetic clocks are algorithms that aim to quantify biological aging using DNAm levels within specific CpG sites. The first-generation clocks, Horvath’s and Hannum’s clocks, were trained to predict chronological age (Hannum et al., 2013; Horvath, 2013), whereas the second-generation clocks, such as DNAm PhenoAge and GrimAge, are better predictors of health span and lifespan (Levine et al., 2018; Lu et al., 2019). For epigenetic clocks, the difference between an individual’s epigenetic age estimate and chronological age provides a measure of age acceleration (AA). The DunedinPoAm estimator differs from its predecessors in that it has been developed to predict the pace of aging (Belsky et al., 2020). The pace of aging describes longitudinal changes over 12 years in several biomarkers of organ-system integrity among same-aged individuals. Recently, the DunedinPACE estimator, which constitutes an advance on the original DunedinPoAm, was published (Belsky et al., 2022). DunedinPACE was trained to predict pace of aging measured over 20-year follow-up, and only the reliable probes were used in the prediction. From the life-course perspective, epigenetic aging measures are useful tools to assess biological aging at all ages and detect changes induced by lifetime exposures. Previous studies have linked several lifestyle-related factors, such as higher body mass index (BMI), smoking, alcohol use, and lower leisure-time physical activity (LTPA), with accelerated biological aging measured using epigenetic clocks (Oblak et al., 2021; Quach et al., 2017). However, most of these studies were based on cross-sectional data on older adults. The first studies on the associations of adolescent lifestyle-related exposures with biological aging assessed with epigenetic aging measures indicated that advanced pubertal development, higher BMI, and smoking are associated with accelerated biological aging in adolescence (Etzel et al., 2022; Raffington et al., 2021; Simpkin et al., 2017). The few previous studies conducted on this topic have focused on single lifestyle factors, and a comprehensive understanding of the role of adolescent lifestyle in later biological aging remains unclear. Our first aim is to define the types of lifestyle behavior patterns that can be identified in adolescence using data-driven latent class analysis (LCA). The second aim is to investigate whether the identified behavioral subgroups differ in biological aging in young adulthood and whether the associations are independent of baseline pubertal development. The third aim is to assess the genetic and environmental influences shared between biological aging and adolescent lifestyle behavior patterns. Methods The participants were Finnish twins and members of the longitudinal FinnTwin12 study (born during 1983–1987) (Kaprio, 2013; Rose et al., 2019). A total of 5600 twins and their families initially enrolled in the study. At the baseline, the twins filled out the questionnaires regarding their lifestyle-related habits at 11–12 years of age, and follow-up assessments were conducted at ages 14 and 17.5 years. The response rates were high for each assessment (85–90%). In young adulthood, at an average age of 22 years, blood samples for DNA analyses were collected during in-person clinical studies after written informed consent was signed. The data on health-related behaviors were collected with questionnaires and interviews. A total of 1295 twins of the FinnTwin12 cohort were examined and measured either in-person or through telephonic interviews. DNAm was determined and biological aging was assessed for 847 twins, out of which 824 twins had also information on lifestyle-related habits in adolescence. Data collection was conducted in accordance with the Declaration of Helsinki. The Indiana University IRB and the ethics committees of the University of Helsinki and Helsinki University Central Hospital approved the study protocol (113/E3/2001and 346/E0/05). DNAm and assessment of biological age Genomic DNA was extracted from peripheral blood samples using commercial kits. High molecular weight DNA samples (1 μg) were bisulfite converted using EZ-96 DNA methylation-Gold Kit (Zymo Research, Irvine, CA) according to the manufacturer’s protocol. The twins and co-twins were randomly distributed across plates, with both twins from a pair on the same plate. DNAm profiles were obtained using Illumina’s Infinium HumanMethylation450 BeadChip or the Infinium MethylationEPIC BeadChip (Illumina, San Diego, CA). The Illumina BeadChips measure single-CpG resolution DNAm levels across the human genome. With these assays, it is possible to interrogate over 450,000 (450k) or 850,000 (EPIC) methylation sites quantitatively across the genome at single-nucleotide resolution. Of the samples included in this study, 744 were assayed using 450k and 80 samples using EPIC arrays. Methylation data from different platforms was combined and preprocessed together using R package minfi (Aryee et al., 2014). We calculated detection p-values comparing total signal for each probe to the background signal level to evaluate the quality of the samples (Maksimovic et al., 2016). Samples of poor quality (mean detection p>0.01) were excluded from further analysis. Data were normalized by using the single-sample Noob normalization method, which is suitable for datasets originating from different platforms (Fortin et al., 2017). We also used Beta-Mixture Quantile (BMIQ) normalization (Teschendorff et al., 2013). Beta values representing CpG methylation levels were calculated as the ratio of methylated intensities (M) to the overall intensities (beta value = M/(M + U + 100), where U is unmethylated probe intensity). These preprocessed beta values were used as input in the calculations of the estimates of epigenetic aging. We utilized six epigenetic clocks. The first four clocks, namely, Horvath’s and Hannum’s epigenetic clocks (Hannum et al., 2013; Horvath, 2013) and DNAm PhenoAge and DNAm GrimAge estimators (Levine et al., 2018; Lu et al., 2019), produced DNAm-based epigenetic age estimates in years by using a publicly available online calculator (https://dnamage.genetics.ucla.edu/new) (normalization method implemented in the calculator was utilized, as well). For these measures, AA was defined as the residual obtained from regressing the estimated epigenetic age on chronological age (AAHorvath, AAHannum, AAPheno, and AAGrim, respectively). The fifth and sixth clocks, namely, DunedinPoAm and DunedinPACE estimators, provided an estimate for the pace of biological aging in years per calendar year (Belsky et al., 2020; Belsky et al., 2022). DunedinPoAm and DunedinPACE were calculated using publicly available R packages (https://github.com/danbelsky/DunedinPoAm38; Belsky et al., 2020 and https://github.com/danbelsky/DunedinPACE; Belsky et al., 2022, respectively). The epigenetic aging measures were screened for outliers (>5 standard deviations away from mean). One outlier was detected according to DunedinPACE and was recoded as a missing value. The components of DNAm GrimAge (adjusted for age) were also obtained, including DNAm-based smoking pack-years and the surrogates for plasma proteins (DNAm-based plasma proteins): DNAm adrenomedullin (ADM), DNAm beta-2-microglobulin (B2M), DNAm cystatin C, DNAm growth differentiation factor 15 (GDF15), DNAm leptin, DNAm plasminogen activator inhibitor 1 (PAI-1), and DNAm tissue inhibitor metalloproteinases 1 (TIMP-1). Lifestyle-related factors in adolescence BMI at ages 12, 14, and 17 years BMI (kg/m2) was calculated based on self-reported height and weight. LTPA at ages 12, 14, and 17 years The frequency of LTPA at the age of 12 years was assessed with the question ‘How often do you engage in sports (i.e., team sports and training)?’ The answers were classified as 0 = less than once a week, 1 = once a week, and 2 = every day. At ages 14 and 17 years, the question differed slightly: ‘How often do you engage in physical activity or sports during your leisure time (excluding physical education)?’ The answers were classified as 0 = less than once a week, 1 = once a week, 2 = 2–5 times a week, and 3 = every day. Smoking status at ages 14 and 17 years Smoking status was determined using the self-reported frequency of smoking and classified as 0 = never smoker, 1 = former smoker, 2 = occasional smoker, and 3 = daily smoker. Alcohol use (binge drinking) at ages 14 and 17 years The frequency of drinking to intoxication had the following classes: ‘How often do you get really drunk?’ 0 = never, 1 = less than once a month, 2 = approximately once or twice a month, and 3 = once a week or more. Pubertal development at age 12 years Baseline pubertal development was assessed using a modified five-item Pubertal Development Scale (PDS) questionnaire (Petersen et al., 1988; Wehkalampi et al., 2008). Both sexes answered three questions each concerning growth in height, body hair, and skin changes. Moreover, boys were asked questions about the development of facial hair and voice change, while girls were asked about breast development and menarche. Each question had response categories 1 = growth/change has not begun, 2 = growth/change has barely started, and 3 = growth/change is definitely underway, except for menarche, which was dichotomous, 1 = has not occurred or 3 = has occurred (see also Wehkalampi et al., 2008). PDS was calculated as the mean score of the five items, and higher values indicated more advanced pubertal development at age 12 years. Lifestyle-related factors in young adulthood at age 21–25 years BMI (kg/m2) was calculated based on the measured height and weight. LTPA was assessed using the Baecke questionnaire (Baecke et al., 1982). A sport index was based on the mean scores of four questions on sports activity described by Baecke et al., 1982 and Mustelin et al., 2012 for the FinnTwin12 study. The sport index is a reliable and valid instrument to measure high-intensity physical activity (Richardson et al., 1995). Smoking was self-reported and classified as never, former, or current smoker. Alcohol use (100% alcohol grams/day) was derived from the Semi-Structured Assessment for the Genetics of Alcoholism (Bucholz et al., 1994) and based on quantity and frequency of use and the content of alcoholic beverages, assessed by trained interviewers. Statistical analysis Patterns of lifestyle behaviors in adolescence To identify the patterns of lifestyle behaviors in adolescence, an LCA was conducted, which is a data-driven approach to identify homogenous subgroups in a heterogeneous population. The classification was based on BMI and LTPA at ages 12, 14, and 17 years and smoking status and alcohol use at ages 14 and 17 years (10 indicator variables). All variables were treated as ordinal variables, except for continuous BMI. The classification was based on the thresholds of the ordinal variables and the means and variances of BMI. An LCA model with 1–8 classes was fitted. The following fit indices were used to evaluate the goodness of fit: Akaike’s information criterion, Bayesian information criterion (BIC), and sample size-adjusted BIC. The lower values of the information criteria indicated a better fit for the model. Moreover, we used the Vuong–Lo–Mendell–Rubin likelihood ratio (VLMR) test and the Lo–Mendell–Rubin (LMR) test to determine the optimal number of classes. The estimated model was compared with the model with one class less, and the low p-value suggested that the model with one class less should be rejected. At each step, the classification quality was assessed using the average posterior probabilities for most likely latent class membership (AvePP). AvePP values close to 1 indicate a clear classification. In addition to the model fit, the final model for further analyses was chosen based on the parsimony and interpretability of the classes. Differences in biological aging The mean differences in biological aging between the lifestyle behavior patterns were studied using the Bolck–Croon–Hagenaars approach (Asparouhov and Muthén, 2021). The class-specific weights for each participant were computed and saved during the latent class model estimation. After that, a secondary model conditional on the latent lifestyle behavior patterns was specified using weights as training data: Epigenetic aging measures were treated as distal outcome one at a time, and the mean differences across classes were studied while adjusting for sex, age and baseline pubertal development. Similarly, the mean differences in the components of DNAm GrimAge and lifestyle-related factors in young adulthood were studied. The models of epigenetic aging measures were additionally adjusted for BMI in adulthood. To evaluate the effect sizes, standardized mean differences (SMDs) were calculated. Genetic and environmental influences Genetic and environmental influences on biological aging in common with lifestyle behavior patterns were studied using quantitative genetic modeling. For simplicity, we adjusted the epigenetic aging variables for sex, age, and baseline pubertal development prior to the analysis. We first carried out univariate modeling to study genetic and environmental influences on epigenetic aging measures (Neale and Cardon, 1992). The variance in the epigenetic aging measures was decomposed into the latent variables representing additive genetic (A), dominant genetic (D), or shared environmental (C) and non-shared environmental (E) components (ACE model or ADE model). The sequences of the models were fitted (ACE, ADE, AE, CE, and E). Because dominance in the absence of additive effects is rare, the model including D and E components (DE-model) was omitted. We used Satorra–Bentler scaled chi-squared (χ2) test, comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root-mean-square residual (SRMR) to evaluate the goodness of fit of the models. The model fits the data well if the χ2 test is not statistically significant (p>0.05), CFI and TLI values are close to 0.95, the RMSEA value is below 0.06, and the SRMR value is below 0.08 (Hu and Bentler, 1999). Moreover, BIC was used to compare non-nested models. A lower BIC value indicates a better model fit. The most parsimonious model with the sufficient fit to the data was considered optimal. On the one hand, as described above, total variance in biological aging was decomposed in the components explained by genetic, shared, and unshared environmental factors (aTot2+ cTot2+eTot2)(=VarTot) (Figure 1A). On the other hand, we can use the secondary model to study the differences in biological aging between the adolescent lifestyle behavior patterns, as described above, and decompose the variance in biological aging into the variance explained by the adolescent lifestyle behavior patterns Var(Model) and the variance of the residual term Var(Res). We also conducted univariate modeling for the residual term of biological aging, which corresponds to the variation in biological aging not explained by the adolescent lifestyle behavior patterns (aRes2+ cRes2+eRes2) (= VarRes) (Figure 1B). The residual terms were obtained by specifying a latent variable corresponding to the residuals of the secondary model described above (without including covariates), and the factor scores were saved. Finally, the proportion of variation in biological aging explained by the genetic factors shared with adolescent lifestyle patterns was evaluated as follows: (aTot2-aRes2)/ VarTot. The proportion of variation in epigenetic aging explained by the environmental factors was evaluated similarly. These proportions reflect the extent to which the same genetic/environmental factors contribute to the association between the adolescent lifestyle patterns and biological aging (i.e., size of the genetic and environmental correlations between the phenotypes). Figure 1 Download asset Open asset Decomposition of (A) total variation in biological aging and (B) the variation of the residual term. Missing data were assumed to be missing at random (MAR). The model parameters were estimated using the full information maximum likelihood (FIML) method with robust standard errors. Under the MAR assumption, the FIML method produced unbiased parameter estimates. The standard errors of the latent class models and secondary models were corrected for nested sampling (TYPE = COMPLEX). Descriptive statistics were calculated using IBM SPSS Statistics for Windows, version 20.0 (IBM Corp, Armonk, NY), and further modeling was conducted using Mplus, version 8.2 (Muthén and Muthén, 1998). Results The descriptive statistics of the study variables are presented in Table 1. A total of 5114 twins answered questionnaires on lifestyle-related behaviors during their adolescent years at least once. For 824 twins, epigenetic aging estimates were obtained. The mean age (SD) of the twins having information on biological aging was 22.4 (0.7) years. The means of the epigenetic age estimates were estimated as follows: Horvath’s clock 28.9 (3.6), Hannum’s clock 18.2 (3.3), DNAm PhenoAge 13.0 (5.3), and DNAm GrimAge 25.2 (3.3) years. The intraclass correlation coefficients (ICCs) of epigenetic aging measures were consistently higher in MZ twin pairs than in DZ twin pairs (Table 2). This suggests an underlying genetic component in biological aging. The correlations between the different epigenetic aging measures ranged from –0.12 to 0.73. The lowest correlation was observed between AAHorvath and DunedinPoAm and between AAHorvath and DunedinPACE. All other correlations were positive. The highest correlations (>0.5) were observed between AAHannum and AAPheno, AAGrim and DunedinPoAm, and DunedinPoAm and DunedinPACE. Table 1 Descriptive statistics of the adolescent lifestyle-related variables in all twins and in the subsample of twins with information on biological aging. All twins (n = 5114)Subsample (n = 824)nMean (SD) or %nMean (SD) or %Zygosity4852824 MZ165034.033540.7 Same-sex DZ160333.026231.8 Opposite-sex DZ159933.022727.5Sex5114824 Female258450.547057.0 Male253049.535443.0At age 12 Pubertal development (1–3)51111.6 (0.5)8231.6 (0.5) Body mass index491317.6 (2.6)79317.7 (2.6) Leisure-time physical activity5038813 Less than once a week187737.329535.3 Once a week249949.641651.2 Every day66213.110212.5At age 14 Body mass index447319.3 (2.7)78719.5 (2.6) Leisure-time physical activity4590799 Less than once a week68815.011013.8 Once a week79617.314918.6 2–5 times a week218247.537046.3 Every day92420.117021.3 Smoking status4570800 Never395486.568785.9 Former2966.5577.1 Occasional1222.7243.0 Daily smoker1984.3324.0 Alcohol use (binge drinking)4565796 Never350176.760275.6 Less than once a month75616.613517 Once or twice a month2756.0506.3 Once a week or more330.791.1At age 17 Body mass index415821.4 (3.0)76021.4 (2.7) Leisure-time physical activity4208766 Less than once a week74817.813217.2 Once a week68616.313017.0 2–5 times a week197747.036347.4 Every day79718.914118.4 Smoking status4190762 Never241957.745459.7 Former49311.88310.9 Occasional2135.1486.3 Daily smoker106525.417623.1 Alcohol use (binge drinking)4217766 Never88120.915219.8 Less than once a month180742.934044.4 Once or twice a month124029.422229.0 Once a week or more2896.9526.8 MZ, monozygotic twins; DZ, dizygotic twins; SD, standard deviation. Table 2 The intraclass correlation coefficients (ICCs) of epigenetic aging measures by zygosity and correlation coefficients between the measures (n = 824). ICCs (95% CI)Correlation coefficients (95% CI) off-diagonal and means (standard deviations) on the diagonalMZ twin pairsDZ twin pairsAAHorvathAAHannumAAPhenoAAGrimDunedinPoAmDunedinPACEAAHorvath0.71 (0.63, 0.79)0.40 (0.24, 0.55)0.00 (3.51)AAHannum0.66 (0.56, 0.76)0.32 (0.16, 0.48)0.40 (0.33, 0.48)0.00 (3.27)AAPheno0.69 (0.60, 0.78)0.16 (0.00, 0.33)0.36 (0.29, 0.44)0.61 (0.56, 0.66)0.00 (5.25)AAGrim0.72 (0.63, 0.80)0.35 (0.15, 0.55)0.08 (0.01, 0.16)0.32 (0.24, 0.40)0.39 (0.33, 0.46)0.00 (3.24)DunedinPoAm0.62 (0.52, 0.71)0.42 (0.24, 0.60)–0.05 (-0.12, 0.03)0.20 (0.13, 0.27)0.41 (0.35, 0.47)0.57 (0.52, 0.63)1.00 (0.07)DunedinPACE0.71 (0.64, 0.78)0.46 (0.31, 0.61)–0.04 (–0.11, 0.04)0.30 (0.22, 0.38)0.49 (0.43, 0.55)0.55 (0.49, 0.61)0.62 (0.57, 0.67)0.88 (0.10) CIs were corrected for nested sampling. CI, confidence interval; AA, age acceleration; MZ, monozygotic; DZ, dizygotic. Patterns of lifestyle behaviors Increasing the number of classes continued to improve AIC, BIC, and ABIC (Table 3). However, the VLMR and LMR tests indicated that even a solution with four classes would be sufficient. In the fifth step, a class of participants with high BMI was extracted. Previous studies have shown the role of being overweight or obese in biological aging (Lundgren et al., 2022). After including the sixth class, the information criteria still showed considerable improvement, but the AvePPs for several cla