What this is
- This research analyzes the relationship between epigenetic aging and frailty across midlife to old age.
- It uses data from the Swedish Adoption/Twin Study of Aging, involving 1,309 repeated measures from 524 individuals.
- The study examines how changes in epigenetic clocks relate to frailty over time, focusing particularly on the clock.
Essence
- Changes in the clock predict subsequent increases in frailty, while traditional epigenetic clocks do not show dynamic associations with frailty over time.
Key takeaways
- The () increases nonlinearly with age, particularly accelerating after age 75. This contrasts with epigenetic clocks, which generally increase linearly.
- shows a unidirectional relationship with the , where higher predicts greater frailty, indicating its potential as an early marker of physiological decline.
- Other epigenetic clocks (PCHorvathAge, PCHannumAge, PCPhenoAge, and PCGrimAge) do not exhibit dynamic associations with the , suggesting they may not effectively capture changes in frailty.
Caveats
- The study's sample size is relatively small, limiting the ability to explore sex-specific effects or other confounders.
- Observational results do not establish causation, and sample attrition may have influenced the findings.
- The generalizability of results is uncertain as the sample consists solely of older Swedish twins.
Definitions
- Frailty Index (FI): A measure of frailty calculated from self-reported health deficits, ranging from 0 to 100%, where higher scores indicate greater frailty.
- DunedinPACE: A pace of aging clock that reflects the rate of biological aging based on changes in 19 biomarkers of organ-system integrity.
AI simplified
Method
Study Population
We used data from the Swedish Adoption/Twin Study of Aging (SATSA) (22), which was drawn from the population-based Swedish Twin Registry (23). SATSA is a longitudinal study of same-sex twins consisting of up to 10 in-person testing (IPT) waves performed at approximately 3-year intervals from 1984 to 2014. Each IPT included a health examination and blood sample collection. For this analysis, we included only data from the third, fifth, sixth, eighth, ninth, and tenth IPT waves when DNA methylation data were available. A total of 524 individuals who participated in at least one IPT and had information on both frailty and DNA methylation were included.
This study was approved by the Regional Ethics Review Board in Stockholm (Dnr 2016/1888-31/1). Informed consent was obtained from all participants.
Epigenetic Clocks
Whole blood DNA methylation levels were measured using Illumina’s Infinium Human Methylation 450K or MethylationEPIC BeadChip, and the raw data were preprocessed using a rigorous quality control pipeline as detailed elsewhere (24). DNA methylation-based epigenetic clocks, including HorvathAge, HannumAge, PhenoAge, and GrimAge have previously been derived in SATSA using methylation data in 353, 71, 513, and 1030 CpGs, respectively (4). However, recent studies suggested that these clocks can be rather unreliable due to technical noise from the individual CpGs, especially in longitudinal settings (25). Hence, we used the proposed principal component (PC) versions of epigenetic clocks in this analysis, including PCHorvathAge, PCHannumAge, PCPhenoAge, and PCGrimAge, which were trained from the PCs of CpGs that capture the majority of the age-related signals and have shown to be more reliable for studying longitudinal trajectories (25). We also included DunedinPACE as a measure of the pace of aging, which was trained on longitudinal data capturing within-individual decline in 19 biomarkers of organ-system integrity (representing cardiovascular, metabolic, renal, hepatic, immune, dental, and pulmonary systems) in individuals of the same chronological age (9). As its construction already excluded the unreliable CpGs, a PC training is not needed for the DunedinPACE (9). The 4 PC-clocks were measured in years, whereas DunedinPACE was measured in biological year per chronological year (a value of 1 can be interpreted as a rate of 1 year of biological aging per year of chronological aging (9)).
Frailty Index
Frailty was measured using a 42-item FI, which was developed and validated in SATSA using deficit items from self-reported diseases, signs, symptoms, and activities of daily living (Supplementary Table 1) (26). In accordance with the deficit accumulation model (12), the FI at each wave was calculated as the sum of the deficits divided by the total number of items considered, yielding a score of 0–100%, where a higher score represents a higher degree of frailty.
Statistical Analysis
Pearson’s correlations between the epigenetic clocks and the FI at baseline were calculated. We used DCSMs to study changes in the epigenetic clocks and FI and with age independently in univariate models and jointly in bivariate models (27). For modeling purposes, we split the data into 2-year age intervals from 50 to <52 years through 88 to <90 years (data from 90 years onwards were sparse and therefore excluded from the analysis). In all models, chronological age (in 2-year bins) was adjusted for as the underlying timescale, and sex was adjusted for by regressing it on the intercepts and slopes.
A series of univariate DCSMs was first fitted to characterize the longitudinal trajectory of each measure (path diagram of the model is shown in the upper and lower parts of Figure 1). Similar to latent growth curve models, the univariate DCSMs consist of an intercept and a slope for assessing the mean trajectory and individual differences around change in each measure over age, thus capturing both within-person and between-person differences in change. Two components of change were incorporated in the model: a static linear change component, which is defined as α (normally set to 1) multiplied by the slope factor (FIS and ClockS), and a proportional change component (βFI and βClock) that depends on the previous score. For example, the equation for change in the FI at age t, without considering epigenetic clocks, can be written as ΔFIt = α × FIS + βFI × FIt−1. The model also estimates the means, variances, and covariances of the intercept and slope, as well as the residual variance. We calculated the variances and covariances for the intercept and linear slope at both individual- and twin pair-levels to account for twin relatedness. To test whether there is evidence of a nonlinear change for the FI and epigenetic clocks over age, we compared the goodness of fit of the full univariate models specified above with models removing the proportional change parameter (ie, leaving only the static linear parameter α × FIS or α × ClockS).
Based on the best-fitting univariate models, we then fitted bivariate DCSMs to assess dynamic associations between the epigenetic clocks and FI. On top of the univariate models, the bivariate DCSMs additionally include cross-trait covariances of the intercepts and slopes to measure static associations, as well as 2 coupling parameters from FI to epigenetic clock and vice versa (γFI→Clock and γClock→FI) to test for dynamic lead-lag relationships (Figure 1). The equation for change in the FI at age t in relation to previous levels of epigenetic clock can then be written as ΔFIt = α × FIS + βFI × FIt−1 + γClock→FI × Clockt−1. Same as the proportional change component, the coupling parameters depend on the score at the previous occasion and as such represent a dynamic relationship on top of the static correlations between intercepts and slopes. By comparing models with and without the coupling parameters, we can therefore test whether there is evidence of a temporal, dynamic association between the 2 processes above and beyond the static associations. Evidence of such coupling effects is in line with, but not proof of a causal relationship. We first compared a full-coupling (bidirectional) model to a no-coupling model removing both coupling parameters. The full-coupling model was further compared to 2 models including each of the coupling parameters to test for unidirectional associations.
All analyses were performed in R v.4.2.3, and the DCSMs were fitted using full-information maximum likelihood estimation in OpenMx (version 2.20.6). Comparisons of the goodness of fit of models were done by likelihood ratio tests, where a p value < .05 comparing a constrained model with a full model indicates a significant loss in model fit.
Path diagram of the bivariate dual change score model in assessing the relationship between age changes in the frailty index (FI) and epigenetic clocks. “Clock” denotes each of the 5 DNA methylation-derived epigenetic clocks: PCHorvathAge, PCHannumAge, PCPhenoAge, PCGrimAge, and DunedinPACE. The FI and epigenetic clocks are modeled in 2-year age intervals from 50 to <52 years (FI, Clock) through 88 to <90 years (FI, Clock). FIand Clockrepresent intercepts (at age 50) and FIand Clockrepresent their linear slopes. 𝜇and 𝜇represent the estimated mean levels of the intercepts and slopes, and σand σrepresent individual variations around the mean intercepts and slopes. αand αrepresent constant change related to the slope factors FIand Clock, which are fixed to 1 in the model. βand βrepresent the proportional (nonlinear) change effects in FI and epigenetic clocks (βwas not included in the models for PCHorvathAge, PCHannumAge, and DunedinPACE due to nonsignificant proportional effect estimates), which are relative to the level at the previous occasion. γand γrepresent the cross-trait coupling effects, where FI at the previous occasion can influence change in the clocks, and vice versa. Sex is added as a covariate in the model, where βand βrepresent the regression coefficients of sex on the intercepts and slopes. All unlabeled single-headed arrows are set to 1. The unlabeled 2-headed arrows in the left represent the covariances between the intercepts and slopes. All the systematic variance and covariance are also estimated on the twin-pair level to account for twin relatedness. Residual variance (σ, indicating variation not accounted for by the model) is assumed to be constant within each trait at each age. Residual covariance between the FI and epigenetic clocks is also estimated but not shown in the figure. 50 50 88 88 I I S S FI Clock FI Clock FI Clock S S FI Clock Clock FI→Clock Clock→FI Sex.FI Sex.Clock res 2 2 2
Results
Sample Characteristics
The study sample included 1 309 repeated measures in 524 individuals, the mean age of which was 68.2 years (SD 9.2) at baseline and 58.6% were women (Table 1). Participants were followed up to 6 waves spanning a maximum of 20 years. At baseline, all the epigenetic clocks were modestly correlated with the FI (rs ranged from 0.21 to 0.35); these correlations were largely attenuated after adjusted for chronological age, with the strongest correlation observed for DunedinPACE and FI (r = 0.16; Supplementary Figure 1).
| Characteristic | or MeanN |
|---|---|
| No. of individuals | 524 |
| No. of observations | 1 309 |
| No. of available measurements per person | |
| One | 157 |
| Two | 123 |
| Three | 113 |
| Four | 89 |
| Five | 41 |
| Six | 1 |
| Age, y, mean ()SD | 68.2 (9.2) |
| Women,(%)n | 307 (58.6) |
| Zygosity | |
| Monozygotic | 181 (34.5) |
| Dizygotic | 342 (65.3) |
| Unknown | 1 (0.2) |
| FI (%), mean ()SD | 9.77 (8.20) |
| PCHorvathAge, y, mean ()SD | 59.98 (8.79) |
| PCHannumAge, y, mean ()SD | 63.32 (8.53) |
| PCPhenoAge, y, mean ()SD | 61.60 (8.60) |
| PCGrimAge, y, mean ()SD | 76.74 (7.07) |
| DunedinPACE, mean ()SD | 1.05 (0.15) |
Univariate Trajectories of Epigenetic Clocks and Frailty Index
We first fitted univariate DCSMs to examine the trajectories of the FI and epigenetic clocks. As shown in Supplementary Table 2, removing the proportional change parameter resulted in a significantly reduced fit for the univariate models of the FI, PCPhenoAge, and PCGrimAge (all p < .01), but not for the models of PCHorvathAge, PCHannumAge, and DunedinPACE (p > .05). This suggested evidence of nonlinearity in the longitudinal changes in the FI, PCPhenoAge, and PCGrimAge, although a nonlinear change was most evident for the FI, which showed an accelerated growth after age 75 years (Figure 2). For instance, the FI, PCPhenoAge, and PCGrimAge on average increased 1.3%, 6.6 years, and 6.8 years between ages 50 and 60, and increased 10.0%, 8.6 years, and 8.0 years between ages 80 and 90, respectively. Meanwhile, PCHorvathAge, PCHannumAge, and DunedinPACE had a steady 10-year increase of 5.6 years, 6.3 years, and 0.03 across all ages, respectively (Figure 2). These patterns can similarly be seen from the parameter estimates of the best-fitting models (Supplementary Table 3); the mean FI level at age 50 was 6.01%, with an overall negative linear slope of −0.69 but a positive proportional effect (βFI = 0.15) that drives up the FI across age. In contrast, there was a positive mean slope for all the epigenetic clocks, and a positive proportional effect for PCPhenoAge and PCGrimAge, so that the clocks generally increased linearly with age (Figure 2).
Trajectories of the frailty index and the 5 epigenetic clocks in SATSA (= 524). (a) Frailty index; (b) PCHorvathAge; (c) PCHannumAge; (d) PCPhenoAge; (e) PCGrimAge; and (f) DunedinPACE. The thick black lines represent the estimated trajectories from the best-fitting univariate dual change score models of each trait. SATSA, Swedish Adoption/Twin Study of Aging. n
Longitudinal Associations Between Epigenetic Clocks and Frailty Index
Bivariate DCSMs were then fitted to examine dynamic associations between epigenetic clocks and FI over age. For the bivariate models between the 4 PC-clocks and the FI, removing both coupling parameters did not lead to a significant loss in model fit, indicating no evidence of a dynamic association after accounting for the static correlations between intercepts and slopes (ie, neither the PC-clocks nor the FI predict subsequent changes of the other; Supplementary Table 4). Despite lack of a coupling effect, we observed positive correlations between intercepts of the PC-clocks and the FI (eg, covariance between intercepts of PCGrimAge and FI = 3.46), thus suggesting a between-person, static association between PC-clocks and the FI such that their values were positively correlated at age 50 (Table 2). This is also consistent with the positive Pearson’s correlations observed between the clocks and the FI at baseline (Supplementary Figure 1). Moreover, individuals with higher PCHannum and PCPhenoAge at age 50 tended to have a significantly slower growth in the FI, as shown by the negative covariances between slope of the clocks and intercept of the FI, which were −1.24 and −1.32, respectively (Table 2).
On the other hand, there was a significant coupling effect from DunedinPACE to FI (γClock→FI = 1.19), but not from FI to DunedinPACE, indicating a unidirectional association such that DunedinPACE was a positive leading indicator of subsequent changes in the FI (Supplementary Table 4; Table 2). In addition, compared to the univariate model of the FI, the negative linear slope was stronger (FIS = −12.31) and the positive proportional effect attenuated (βFI = 0.06) in the bivariate model (Table 2). Together with the coupling parameter, this indicates that changes in DunedinPACE have substantial effects on subsequent estimates of the FI, where a low DunedinPACE predicts a stable or even decreasing FI, whereas a high DunedinPACE predicts a substantial increase in the FI. To visualize the dynamic relationship between DunedinPACE and the FI over time, we presented a vector field plot in Figure 3, which is usually used as a graphical display of coupled dynamical systems (28). Each arrow in the plot represents the expected direction and relative magnitude of changes in DunedinPACE and FI for each combination of their initial values. The plot also displays the actual data points and a 95% ellipse; the focus should be on the arrows contained within the ellipse (28). Along the horizontal lines (eg, when FI equals 15%), as DunedinPACE increases, the arrows point further upwards, indicating a greater increase in the FI (ie, a positive coupling from DunedinPACE to changes in FI). On the contrary, as FI increases along the vertical lines, there is no obvious change in the length of arrows in the horizontal direction, indicating no significant coupling from FI to changes in DunedinPACE.
Vector field plot visualizing the dynamic relationship between DunedinPACE and frailty index. The vector field is constructed based on the bivariate dual change score model for frailty index and DunedinPACE. The values for DunedinPACE were in the original scale, where a value of 1 is interpreted as a rate of 1 year of biological aging per year of chronological aging. Each arrow in the plot represents the direction and relative magnitude of the expected changes in both the frailty index (y-axis) and DunedinPACE (x-axis) for every given pair of values. The dashed line represents the 95% ellipse, and the dots represent the actual data points (= 524). n
| PCHorvathAge | PCHannumAge | PCPhenoAge | PCGrimAge | DunedinPACE | |
|---|---|---|---|---|---|
| Estimate ()SE | Estimate ()SE | Estimate ()SE | Estimate ()SE | Estimate ()SE | |
| Best-fitting model | No coupling | No coupling | No coupling | No coupling | DunedinPACE to FI |
| FI parameters | |||||
| Mean intercept at age 50 | 6.03 (0.51)* | 6.08 (0.51)* | 6.04 (0.51)* | 6.07 (0.50)* | 6.55 (0.58)* |
| Mean slope | −0.73 (0.23)* | −0.77 (0.23)* | −0.73 (0.23)* | −0.77 (0.23)* | −12.31 (4.35)* |
| Proportional change effect (β)FI | 0.15 (0.02)* | 0.16 (0.02)* | 0.15 (0.02)* | 0.16 (0.02)* | 0.06 (0.03) |
| Effect of sex on intercept (women vs men) | 2.39 (0.79)* | 2.40 (0.79)* | 2.46 (0.78)* | 2.50 (0.77)* | 0.60 (1.10) |
| Effect of sex on slope (women vs men) | −0.36 (0.15)* | −0.37 (0.15)* | −0.37 (0.15)* | −0.39 (0.15)* | 0.29 (0.28) |
| Variance of intercept, individual level | 19.95 (5.05)* | 20.40 (4.93)* | 20.58 (5.09)* | 19.82 (4.78)* | 9.94 (5.10) |
| Variance of slope, individual level | 0.52 (0.19)* | 0.56 (0.20)* | 0.54 (0.19)* | 0.54 (0.19)* | 1.10 (0.56) |
| Covariance of intercept and slope, individual level | −3.15 (0.96)* | −3.31 (0.96)* | −3.26 (0.95)* | −3.22 (0.92)* | −2.24 (1.06)* |
| Variance of intercept, twin pair level | 6.71 (4.48) | 6.63 (4.30) | 6.04 (4.65) | 6.54 (4.33) | 2.66 (4.16) |
| Variance of slope, twin pair level | 0.12 (0.15) | 0.12 (0.15) | 0.10 (0.15) | 0.12 (0.15) | 0.71 (0.60) |
| Covariance of intercept and slope, twin pair level | −0.91 (0.81) | −0.92 (0.80) | −0.79 (0.84) | −0.90 (0.81) | 0.29 (0.92) |
| Residual variance | 15.52 (0.94)* | 15.48 (0.93)* | 15.48 (0.93)* | 15.65 (0.94)* | 14.52 (0.95)* |
| Epigenetic age parameters | |||||
| Mean intercept at age 50 | 50.49 (0.61)* | 52.77 (0.60)* | 49.79 (0.54)* | 65.00 (0.29)* | 10.04 (0.13)* |
| Mean slope | 1.12 (0.04)* | 1.25 (0.04)* | 0.37 (0.38) | 0.58 (0.24)* | 0.06 (0.01)* |
| Proportional change effect (β)Clock | — | — | 0.02 (0.01)* | 0.01 (0.00)* | — |
| Effect of sex on intercept (women vs men) | −2.43 (1.23) | −2.07 (1.21) | −1.03 (0.87) | −2.90 (0.47)* | 0.01 (0.22) |
| Effect of sex on slope (women vs men) | 0.05 (0.07) | 0.00 (0.08) | −0.04 (0.07) | 0.03 (0.03) | −0.03 (0.02)* |
| Variance of intercept, individual level | 5.89 (3.65) | 4.53 (4.01) | 9.67 (3.60)* | 4.45 (1.04)* | 1.01 (0.30)* |
| Variance of slope, individual level | 0.06 (0.04) | 0.02 (0.04) | 0.06 (0.04) | 0.01 (0.01) | 0.00 (0.00)* |
| Covariance of intercept and slope, individual level | −0.26 (0.37) | 0.07 (0.42) | −0.35 (0.36) | −0.20 (0.09)* | −0.05 (0.02)* |
| Variance of intercept, twin pair level | 54.16 (8.44)* | 43.53 (8.15)* | 18.13 (4.51)* | 5.29 (1.27)* | 0.56 (0.11)* |
| Variance of slope, twin pair level | 0.00 (0.03) | 0.03 (0.04) | 0.02 (0.03) | 0.01 (0.01) | — |
| Covariance of intercept and slope, twin pair level | −0.61 (0.45) | −0.70 (0.49) | −0.58 (0.36) | −0.15 (0.09) | — |
| Residual variance | 9.85 (0.58)* | 12.24 (0.70)* | 6.21 (0.37)* | 1.60 (0.10)* | 0.99 (0.05)* |
| Bivariate parameters | |||||
| Coupling effect, clock to FI (γ)Clock→FI | — | — | — | — | 1.19 (0.43)* |
| Coupling effect, FI to clock (γ)FI→Clock | — | — | — | — | — |
| Covariance intercept FI—intercept clock, individual level | 6.27 (3.19)* | 6.89 (3.42)* | 8.00 (3.54)* | 3.46 (1.63)* | 1.94 (1.15) |
| Covariance slope FI—intercept clock, individual level | −1.06 (0.57) | −1.24 (0.63)* | −1.32 (0.63)* | −0.60 (0.31) | −0.96 (0.26)* |
| Covariance intercept FI—slope clock, individual level | −0.47 (0.33) | −0.47 (0.35) | −0.55 (0.35) | −0.22 (0.14) | −0.13 (0.12) |
| Covariance slope FI—slope clock, individual level | 0.08 (0.06) | 0.09 (0.06) | 0.10 (0.06) | 0.04 (0.03) | 0.04 (0.02)* |
| Covariance intercept FI—intercept clock, twin pair level | −2.41 (3.42) | −4.37 (4.54) | −0.50 (4.30) | −0.61 (1.53) | −0.17 (0.54) |
| Covariance slope FI—intercept clock, twin pair level | 0.65 (0.59) | 1.04 (0.79) | 0.26 (0.75) | 0.22 (0.27) | −0.64 (0.29)* |
| Covariance intercept FI—slope clock, twin pair level | 0.12 (0.26) | 0.26 (0.32) | 0.03 (0.34) | 0.02 (0.12) | — |
| Covariance slope FI—slope clock, twin pair level | −0.03 (0.04) | −0.06 (0.06) | −0.02 (0.06) | −0.01 (0.02) | — |
| Residual covariance between FI and clock | −0.05 (0.53) | −0.01 (0.58) | 0.30 (0.41) | 0.20 (0.21) | 0.03 (0.15) |
Discussion
By applying DCSMs in a longitudinal twin study of aging, this study assessed the longitudinal trajectories of frailty and 5 epigenetic clocks and examined the direction of their interrelationships across the older adulthood. In particular, we demonstrated a unidirectional, dynamic association between the pace of aging clock and the FI, where a higher level in DunedinPACE temporally preceded increase in the FI. However, the first- and second-generation epigenetic clocks (ie, PCHorvathAge, PCHannumAge, PCPhenoAge, and PCGrimAge) did not appear to be dynamically coupled with the FI over age, although we did observe static associations as indicated by their correlated levels at age 50.
From the univariate DCSMs, we observed an accelerated increase for the FI, PCPhenoAge, and PCGrimAge across age, but not for the PCHorvathAge, PCHannumAge, and DunedinPACE. The PCHorvathAge (5) and PCHannumAge (6) are first-generation clocks designed for chronological age prediction, thus inherently implying a linear progression with age. On the other hand, the second-generation clocks (PCPhenoAge and PCGrimAge) (7,8) and the FI (11) are measures for predicting mortality risk, and the DunedinPACE is a measure of the rate of aging where a linear growth would be indicative of an accelerated change (9). Therefore, these findings potentially suggest that the rate of aging becomes faster as individuals approach the later stages of life.
Few studies have assessed the longitudinal associations between epigenetic clocks and frailty, and most of them were limited to the use of first- and second-generation clocks that were trained on cross-sectional measures of age and aging-related traits. Using data from the Canadian Longitudinal Study on Aging, Verschoor et al. showed that HannumAge and GrimAge were associated with a small increase in the FI over 3 years of follow-up (19). Contrarily, Seligman et al. found only correlations between the FI and HannumAge, PhenoAge, and GrimAge at baseline of the MOBILIZE Boston cohort, but none of these clocks were associated with changes in the FI over 18 months (18). Other studies similarly reported absence of a longitudinal association between epigenetic clocks and markers of physical frailty (17,29). In the present study, using bivariate DCSMs, we only observed positive correlations between levels of the PC-clocks and FI at age 50 years and negative correlations between levels of PCHannumAge and PCPhenoAge at age 50 and the FI slope, but no other intercept-slope correlations or dynamic coupling effect over age. Taken together, the current evidence does not support for a temporal, causal connection between the first- and second-generation clocks and frailty. Thus, any dynamics may occur at earlier ages than examined here, an unknown or unmodeled factor may contribute to the associations between the PC-clocks and FI, or the PC-clocks may be less sensitive indices of change.
Meanwhile, we found a significant coupling effect from DunedinPACE, a pace of aging clock, to the FI. Different from the earlier versions of epigenetic clocks, DunedinPACE is a novel measure of biological aging reflecting the ongoing rate of deterioration in system integrity (9). As such, DunedinPACE may be more sensitive to capture age-related changes at the molecular level and more indicative of an acceleration in the underlying aging processes compared to other clocks. Many of the biomarkers used in construction of the DunedinPACE such as glycated hemoglobin, cholesterols, and C-reactive protein have also been associated with frailty (30). The observed unidirectional, dynamic relationship may therefore imply a temporal order in the aging process, where a higher pace of aging measured by molecular or epigenetic markers could lead to a subsequent increase in deficit accumulation at organ or system levels. Alternatively, other factors such as genes affecting biological aging may first lead to changes on the molecular and cellular levels, before their effects manifest on tissue and whole organismal level. These results also provide support for the geroscience hypothesis, suggesting that slowing down the aging processes at the molecular or cellular level may delay or prevent many age-related diseases and frailty (2,20). Additionally, these results strengthen the potential use of DunedinPACE as an early marker to monitor the overall physiological decline during aging, although it would be important for future studies to replicate our results and assess if DunedinPACE may be associated with physical frailty and other age-related diseases.
Strengths of this study include the long follow-up and multiple testing occasions per person, allowing us to apply the powerful DCSMs to study dynamic interactions between frailty and epigenetic clocks over age. Instead of the traditional epigenetic clocks calculated on individual CpGs, we included the newer versions of PC-based clocks, which are more reliable in studying longitudinal changes (25). Nevertheless, some limitations should also be considered. Given the relatively small sample size and limited statistical power, we were unable to perform further analysis, such as studying sex-specific effects or investigating the impacts of other potential confounders. Although the longitudinal nature of the data allowed us to examine potential causal connections between epigenetic clocks and the FI, our observational results do not provide proof of causal relationships. As in other longitudinal studies, sample attrition over time could have resulted in a more selected sample of healthy individuals, although the full-information maximum likelihood estimation is beneficial in handling missing data due to attrition. Finally, as our sample included only older Swedish twin individuals, more studies are needed to test whether our findings are generalizable to other populations and ethnic groups.
In conclusion, we did not find support for a dynamic relationship between the first- and second-generation epigenetic clocks and frailty beyond their correlated levels at age 50. However, for DunedinPACE that is trained on changes in biomarkers and reflects the pace of aging, it is unidirectionally linked to frailty such that within-person changes in DunedinPACE temporally precede changes in the FI. These findings provide new insights into the nature of the relationships between epigenetic aging and frailty and potentially indicate a temporal, hierarchical nature of aging such that molecular changes occur prior to physiological decline at the organismal level.
Supplementary Material
Acknowledgments
The authors acknowledge the Swedish Twin Registry for access to data. This study was accomplished within the context of the Swedish National Graduate School on Ageing and Health (SWEAH).
Contributor Information
Jonathan K L Mak, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Ida K Karlsson, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Bowen Tang, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Yunzhang Wang, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
Nancy L Pedersen, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Sara Hägg, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Juulia Jylhävä, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Faculty of Social Sciences (Health Sciences) and Gerontology Research Center (GEREC), University of Tampere, Tampere, Finland.
Chandra A Reynolds, Department of Psychology, University of California, Riverside, California, USA; Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA.
Lewis A Lipsitz, (Medical Sciences Section).
Funding
The Swedish Adoption/Twin Study of Aging (SATSA) cohort was supported by NIH grants R01 AG04563, AG10175, and AG028555; the MacArthur Foundation Research Network on Successful Aging; the Swedish Council for Working Life and Social Research (FAS/FORTE) (97:0147:1B, 2009-0795); and the Swedish Research Council (825-2007-7460, 825-2009-6141, and 521-2013-8689). This study was supported by the Swedish Research Council (2018-02077, 2019-01272, 2020-06101, 2022-01608), the Loo & Hans Osterman Foundation, the Karolinska Institutet Foundation, the Strategic Research Program in Epidemiology at Karolinska Institutet, the King Gustaf V and Queen Victoria’s Foundation of Freemasons, the Yrjö Jahnsson Foundation, and Sigrid Jusélius Foundation. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council under the grant number 2017-00641.
Conflict of Interest
None.
Data Availability
Methylation data are available in EMBL-EBI under accession number S-BSST1206 (https://www.ebi.ac.uk/biostudies/studies/S-BSST1206↗), whereas phenotypic data are available in the National Archive of Computerized Data on Aging under accession number ICPSR 3843 (https://www.icpsr.umich.edu/web/NACDA/studies/3843↗). Codes used for data analysis are provided at the Open Science Framework platform (https://osf.io/6cyde/?view_only=5ac1866f01a24294901e85f623288a2e↗).
Author Contributions
J.K.L.M. and C.A.R. designed the study and drafted the manuscript. J.K.L.M., I.K.K., and C.A.R. contributed to the methodology. J.K.L.M. performed statistical analyses. B.T. and Y.W. contributed to the preparation of the study variables. S.H., J.J., and C.A.R. were involved in supervision. N.L.P. is the founder and principal investigator of the Swedish Adoption/Twin Study of Aging (SATSA) study. All authors contributed to the interpretation of the results and read and approved the final manuscript.
References
Associated Data
Supplementary Materials
Data Availability Statement
Methylation data are available in EMBL-EBI under accession number S-BSST1206 (https://www.ebi.ac.uk/biostudies/studies/S-BSST1206↗), whereas phenotypic data are available in the National Archive of Computerized Data on Aging under accession number ICPSR 3843 (https://www.icpsr.umich.edu/web/NACDA/studies/3843↗). Codes used for data analysis are provided at the Open Science Framework platform (https://osf.io/6cyde/?view_only=5ac1866f01a24294901e85f623288a2e↗).