(2024) 22:289
Miao et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03511-y

BMC Medicine

RESEARCH

Open AccessFive years of change in adult
twins: longitudinal changes of genetic
and environmental influence on epigenetic
clocks
Ke Miao1,2, Shunkai Liu1,2, Weihua Cao1,2, Jun Lv1,2, Canqing Yu1,2, Tao Huang1,2, Dianjianyi Sun1,2,
Chunxiao Liao1,2, Yuanjie Pang1,2, Runhua Hu1,2, Zengchang Pang3, Min Yu4, Hua Wang5, Xianping Wu6, Yu Liu7,
Wenjing Gao1,2* and Liming Li1,2*

Abstract
Background Epigenetic clocks were known as promising biomarkers of aging, including original clocks trainedby individual CpG sites and principal component (PC) clocks trained by PCs of CpG sites. The effects of genetic
and environmental factors on epigenetic clocks are still unclear, especially for PC clocks.
Methods We constructed univariate twin models in 477 same-sex twin pairs from the Chinese National Twin
Registry (CNTR) to estimate the heritability of five epigenetic clocks (GrimAge, PhenoAge, DunedinPACE, PCGrimAge, and PCPhenoAge). Besides, we investigated the longitudinal changes of genetic and environmental influenceson epigenetic clocks across 5 years in 134 same-sex twin pairs.
Results Heritability of epigenetic clocks ranged from 0.45 to 0.70, and those for PC clocks were higher than thosefor original clocks. For five epigenetic clocks, the longitudinal stability was moderate to high and was largely due
to genetic effects. The genetic correlations between baseline and follow-up epigenetic clocks were moderate to high.
Special unique environmental factors emerged both at baseline and at follow-up. PC clocks showed higher longitudinal stability and unique environmental correlations than original clocks.
Conclusions For five epigenetic clocks, they have the potential to identify aging interventions. High longitudinalstability is mainly due to genetic factors, and changes of epigenetic clocks over time are primarily due to changes
in unique environmental factors. Given the disparities in genetic and environmental factors as well as longitudinalstability between PC and original clocks, the results of studies with original clocks need to be further verified with PC
clocks.
Keywords Epigenetic clock, Aging, Twin, Heritability*Correspondence:
Wenjing Gao
pkuepigwj@126.com
Liming Li
lmlee@vip.163.com
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Miao et al. BMC Medicine(2024) 22:289

Background
The process of aging is complex, and individuals of thesame chronological age usually are on different aging
trajectories [1]. Owing to the association between DNA
methylation and aging, DNA methylation-based biomarkers of aging have been developed, called epigenetic
clocks. Three compelling epigenetic clocks (GrimAge,
PhenoAge, and DunedinPACE) have attracted widespread attention [2].
Epigenetic clocks were trained on age-associatedmolecular and physiological measures using machinelearning [3–5]. The GrimAge clock was based on 1030
cytosine-phosphate-guanine (CpG) sites associated with
7 plasma proteins and smoking and had an excellent
prediction of lifespan and healthspan [3]. The PhenoAge clock was developed by identifying 513 CpG sites
derived from chronological age and 9 blood measures[5]. The DunedinPACE, based on a set of 173 CpG sites,
was developed by using the longitudinal change rate of 19
biological measures over 20 years as training phenotypes[4]. Because of various training phenotypes, epigenetic
clocks capture different aspects of aging and fall into two
categories: (1) represent the progress of aging (GrimAge
and PhenoAge) and (2) represent the pace of aging (DunedinPACE) [2].
At present, the methylation level of most individuals is
detected by beadchip, and there is great technical noisefrom individual CpG sites [6]. Technical noise threatens
the repeatability of epigenetic clocks [7]. Hence, the proposed principal component (PC) version of epigenetic
clocks, called PC clocks (PCGrimAge and PCPhenoAge),have been trained by PCs of clock-related CpG sites toreduce technical noise and have shown more reliability inlongitudinal studies [8].
Aging process differs among individuals, as it is driven
by genetic and environmental factors [9, 10]. Based on
DNA methylation information, epigenetic clocks are also
influenced by both genetic and environmental factors. On
the one hand, it has been proved in previous studies that
epigenetic clocks are associated with lifestyle, BMI, andsocioeconomic status [11, 12]. On the other hand, there
are twin studies using a cross-sectional design to obtain
the heritability of original clocks (GrimAge, PhenoAge,
and DunedinPACE) [13–15], but the heritability of PC
clocks remains unknown. Only one longitudinal twin
study explored the longitudinal changes of the PhenoAge
clock and found that the genetic factors of the PhenoAge
clock at two measured times overlapped [16]. Longitudinal twin study provides a valuable design for examiningchanges in heritability and assessing the genetic and environmental influence on the longitudinal stability of epigenetic clocks over time [17]. Such a design also allows forassessing the overlap of genes and environmental effects

Page 2 of 10

in the epigenetic clocks over time [18]. Given the complexity of aging, we can use the longitudinal twin study
and epigenetic clocks as aging biomarkers to explore thecontinuity of genetic factors at different stages of aging,as well as the relationship between genetic and environmental factors in the progression of aging.
Currently, to our knowledge, no studies have exploredthe heritability of PC clocks and the difference in heritability between original and PC clocks. Only one relevant
study focused on the longitudinal changes in the genetic
and environmental effects on the PhenoAge clock. Therefore, based on twins from the Chinese National Twin
Registry (CNTR), we first explored the heritability oforiginal and PC clocks in Chinese populations. Then, we
conducted a longitudinal design to estimate the longitudinal changes of genetic and environmental effects on
original and PC clocks, to explore the reasons for the discrepancy between original and PC clocks.

Methods
Study populations

The twins were assessed in two thematic surveys in 2013
and 2017–2018 within the CNTR [19]. For baseline andfollow-up surveys, twins completed questionnaires andprovided fasting blood samples after informed consent.
A total of 477 same-sex twin pairs (954 twins) were
included in the present study who had questionnaires
and DNA methylation information in only one survey.
Among them, 134 same-sex twin pairs (268 twins) had
information in both baseline and follow-up surveys.
Zygosity diagnosis of twins was conducted using a set of
59 SNPs based on DNA methylation information [20].
Ethical approval was obtained from the Biomedical Ethics Committee at Peking University (IRB00001052-13022,
IRB00001052-14021).
DNA methylation profiling

Genomic DNA was extracted from fasting peripheral
blood samples. Of 954 twins who participated in only one
survey, 253 twins used the Illumina Human Methylation
450 K BeadChip array and 701 twins used the Illumina
Infinium Methylation EPIC (850 K) BeadChip array toobtain DNA methylation data. For 268 twins who participated in both two surveys, the 450 K array was employedon 107 twins for the baseline survey, and the 850 K array
was employed on 161 twins for the baseline survey andon 268 twins for the follow-up survey. We used the R
package minfi (1.40.0) to combine the methylation datafrom different arrays and then preprocess [21]. Only
overlapped probes on both 450 K and 850 K arrays wereretained for subsequent analyses, using the “combineArrays” function. We compared each probe with thebackground signal level to calculate detection P values

Miao et al. BMC Medicine(2024) 22:289

by using the “detection” function. Bad-quality probes(detection P values > 0.05 in more than 1% of samples)and samples of poor quality (detection P values > 0.01)
were removed from further analysis. We also excluded
probes that were multi-hit probes, were SNP-related,or were located in chromosome X and Y. We used thequantile normalization method to normalize methylation
data and used β-value (number of methylated probes/[number of both methylated and unmethylated probes])
to represent the DNA methylation level. We used the“ComBat” function of R package sva (3.42.0) to adjust for
known batch effects within the DNA methylation data[22]. Finally, 409,303 CpG sites remained for the calculation of epigenetic clocks.
Epigenetic clocks

We calculated five epigenetic clocks. The first two clocks,
GrimAge and PhenoAge, are composite measures of 1030
[3] and 513 [5] CpG sites, respectively. GrimAge and PhenoAge were assessed using the online calculator (http://​dnama​ge.​genet​ics.​ucla.​edu/) [23]. Beta values were used
as input, and the normalization method implemented in
the calculator was utilized. These two epigenetic clocks
were calculated by aggregating the weighted averagesof β-values at specific CpG sites. For the unreliabilityof individual CpG sites, we also calculated PC-based
GrimAge and PhenoAge (“PCGrimAge” and “PCPhenoAge”), which constructed principal component analysis(PCA) on the DNA methylation data to extract covariance among multicollinear CpG sites [8]. The PCs consistof multiple CpG sites, and technical noise is unlikely tocovary with age-related signals [8]. Thus, the PCs of CpG
sites captured the majority of age-related signals while
minimizing the influence of technical noise. PC clocks
were obtained by using PCs to retrain the original clocks.
PC clocks were calculated using the R code given by the
researchers (https://​github.​com/​Morga​nLevi​neLab/​PC-​
Clocks/) [8]. For these epigenetic clocks, age acceleration(AA) was defined as the residuals obtained from regressing the epigenetic clocks on six blood cell components
and chronological age (GrimAA, PhenoAA, PCGrimAA,
and PCPhenoAA, respectively). We used the Houseman
method to assess the six blood cell components, including ­CD4+ T cells, C­ D8+ T cells, B cells, monocytes,
granulocytes, and natural killer cells [24]. AA indicatedthe degree of aging compared to the chronological age.
The fifth clock was DunedinPACE, which represented
the pace of biological aging [4]. DunedinPACE was calculated by using the public R package “DunedinPACE”(https://​github.​com/​danbe​lsky/​Duned​inPACE). DunedinPACE removes unreliable CpG sites during the training
process, so it does not have a PC version. Four measuresof AA and DunedinPACE were epigenetic age metrics. To

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make the results comparable, we normalized epigenetic
age metrics before inputting in hereafter twin modeling.
Statistical analyses
Univariate twin models

First, we calculated the heritability of five epigenetic age
metrics in 954 twins using univariate structural equation
modelling (SEM). The variance of epigenetic age metrics was decomposed into four latent variables, including additive genetic (A), common environmental (C),or dominant genetic (D) and unique environmental (E)components [18]. A represents the cumulative impact
of individual alleles at various loci affecting epigenetic
age metrics. C represents the common environment
twins share. D represents the interplay among alleles atthe same locus or different loci. E represents the different environment among twins and measurement error.
Monozygotic twins (MZ) share 100% of their genes (alladditive and dominant effects), because they developed
from the same fertilised egg. Dizygotic twins (DZ) shareon average 50% of their segregating genes, that is 25%
for the dominant effects and 50% for the additive effects[25]. The design with twins reared together cannot provide sufficient information to estimate both C and D, so
we can only conduct either an ACE or an ADE model[26]. For the ACE model, the heritability of the epigenetic
age metric is the proportion of the variance of A to thetotal variance; for the ADE model, the heritability is the
proportion of the variance of A and D to the total variance. Then, A, C or D were dropped respectively to fit
sub-models of ACE or ADE. We conducted a comparative analysis between the ACE or ADE model and thefully-saturated model, as well as between the sub-models
and the ACE or ADE model, using the likelihood-ratio
test (LRT). The not statistically significant (P>0.05) resultof LRT indicates that the more parsimonious model fits
the data as well as the more complex model. Besides, weused Akaike’s information criterion (AIC) value to measure model parsimony: the smaller the AIC, the more parsimonious the fit [27]. We chose the most parsimonious
model that fits the data as the optimal model.
Longitudinal bivariate twin models

We used bivariate Cholesky twin models to explore thelongitudinal changes of the genetic and environmental influence on epigenetic age metrics in two surveys[17]. For the baseline survey, the variance of epigenetic
age metrics was decomposed into A ­(a112), C ­(c112) or D­(d112), and E (­ e112), respectively. For the follow-up survey,
the variance of epigenetic age metrics was decomposed
into A ­(a222), C ­(c222) or D ­(d222), and E ­(e222), which were
all independent of genetic and environmental effects atbaseline. The bivariate ACE/ADE model also decomposesMiao et al. BMC Medicine(2024) 22:289

the covariance of epigenetic age metrics across time into
A, C or D, and E components as a­ 212, ­c212 or d
­ 212, and ­e212(Fig. 1). For the ACE model, the heritability at baseline
was given by ­a112/(a112 + ­c112 + ­e112), whereas the heritability at follow-up was given by ­(a212 + ­a222)/(a212 + ­a222+ ­c212 + ­c222 + ­e212 + ­e222). Moreover, we calculated thecross-twin cross-trait (CTCT) correlations for MZ and
DZ twins, which describe the correlation between twin
1’s epigenetic age metrics at baseline and the co-twin’s
epigenetic age metrics at follow-up. If the CTCT for MZ
twins is higher than that for DZ twins, genetic factors
affect the change of epigenetic age metrics across time.
We calculated the phenotypic correlation ­(Rph) of epigenetic age metrics across times and decomposed R­ ph into
common genetic correlation (­Ra), common environmental correlation (­Rc), and unique environmental correlation ­(Re) [18]. Since it is the same variable across time,
we used the phenotype correlation to represent the longitudinal stability of epigenetic age metrics. The proportion of the R­ ph can be derived from genetic ­(Pa), common
environmental ­(Pc), and unique environmental (­Pe) parts[18]. The model selection criteria is as described above.
Twin models were performed using the OpenMx (version 2.20.6) package, version R 4.1.2. Because AA was theresidual from epigenetic clocks regression to chronological age, the models of four AAs were adjusted for only

Page 4 of 10sex, and the models for DunedinPACE were adjusted forsex and chronological age. P < 0.05 indicated statisticalsignificance.

Results
Descriptive statistics

Among the 954 (477 pairs) adult twins included in the
cross-sectional study, 79.7% were MZ twins; 70.2% weremales; and the mean (standard deviation [SD]) age was50.30 (11.86) years (range 19.00 ~ 82.00 years). The means(SD) of original and PC clocks were as follows: GrimAge 50.30 (10.44) years, PCGrimAge 65.46 (9.79) years,
PhenoAge 47.65 (9.79) years, PCPhenoAge 56.10 (10.09)
years, and DunedinPACE 1.14 (0.12) units. The longitudinal analysis included 268 (134 pairs) twins who participated in baseline and follow-up surveys, with means ageof 50.24 (range 26.00 ~ 77.00) years at baseline and 54.87(range 31.00 ~ 82.00) years at follow-up (Table 1).
The correlation between GrimAge and PCGrimAge(r = 0.98, P < 0.001) was higher than that between PhenoAge and PCPhenoAge (r = 0.88, P < 0.001) (Additional
file 1: Fig. S1). The correlations between epigenetic clocks
and chronological age ranged from 0.38 to 0.97. The
highest was for PCGrimAge (r = 0.97), and the lowest wasfor DunedinPACE (r = 0.38). The correlations between
PC clocks and chronological age exceeded that between

Fig. 1 Bivariate Cholesky model. Note. Components of variance: ­a112, the variance of A at baseline; ­c112, the variance of C at baseline; ­d112,
the variance of D at baseline; ­e112, the variance of E at baseline; ­a222, the variance of A unique at follow-up; ­c222, the variance of C uniqueat follow-up; ­d222, the variance of D unique at follow-up; ­e222, the variance of E unique at follow-up. Components of covariance: ­a212, the covarianceof A between baseline and follow-up; ­c212, the covariance of C between baseline and follow-up; ­d212, the covariance of D between baseline
and follow-up; ­e212, the covariance of E between baseline and follow-up. Orange color indicates the effect of A; green color indicates the effect of C
or D; blue color indicates the effect of E

Miao et al. BMC Medicine(2024) 22:289

Page 5 of 10

Table 1 Characteristics of the study population
Cross-sectional study

Longitudinal study
Baseline

Follow-up

N

954

268

268

Chronological age

50.30 ± 11.86

50.24 ± 10.27

54.87 ± 10.21

Age group≤ 25 years

22 (2.3)

0 (0.0)0 (0.0)25 ~ 45 years

282 (29.6)

84 (31.4)

48 (17.9)

45–60 years

458 (48.0)

140 (52.2)

142 (53.0)> 60 years

192 (20.1)

44 (16.4)

78 (29.1)

MZ pairs (%)

380 (79.7)

95 (70.9)

DZ pairs (%)

97 (20.3)

39 (29.1)

Male (%)

670 (70.2)

168 (62.7)

GrimAge50.30 ± 10.44

49.45 ± 9.08

54.00 ± 8.56

PCGrimAge

65.46 ± 9.79

65.00 ± 8.47

68.87 ± 8.31

PhenoAge

47.65 ± 9.79

49.13 ± 8.87

50.54 ± 9.23

PCPhenoAge

56.10 ± 10.09

58.16 ± 8.79

62.25 ± 8.28

a

GrimAA

0.10 ± 3.76

− 0.03 ± 3.81

0.03 ± 3.56

a

PCGrimAA

0.06 ± 2.42

0.00 ± 2.42

0.00 ± 2.37

aPhenoAA− 0.08 ± 4.61

− 0.23 ± 5.05

− 0.30 ± 4.96

a

PCPhenoAA

0.10 ± 3.98

− 0.22 ± 4.13

− 0.26 ± 3.52

DunedinPACE

1.14 ± 0.12

1.13 ± 0.10

1.14 ± 0.11

Data are presented as n (%) or mean ± standard deviation
Abbreviations: MZ monozygotic, DZ dizygotica

Epigenetic age accelerations (AA) are derived from adjusting epigenetic age forblood cell counts and chronological age

original clocks and chronological age (Additional file 1:
Fig. S2).
Heritability of epigenetic age metrics

In univariate analyses, for all the five epigenetic age metrics, ACE models provided better fits to the data thanADE models (Additional file 2: Table S1). Then, we conducted sub-models of the ACE model. We found that thebest-fit models for four epigenetic age metrics (GrimAA,
PCGrimAA, PhenoAA, and PCPhenoAA) were the AE
model, but that for DunedinPACE was the ACE model(Additional file 2: Table S1).
Table 2 shows the variance explained by three components (A, C, and E) and the heritability of five epigenetic
age metrics. The heritability of GrimAA and PCGrimAA
were 0.61 and 0.69, respectively, and no significant difference existed between them according to the overlapped
95% confidence interval (CI). The heritability of PhenoAA (0.45, 95% CI 0.38 ~ 0.53) was significantly lower
than that of PCPhenoAA (0.70, 95% CI 0.65 ~ 0.75). The
heritability of DunedinPACE was 0.59.
Genetic and environmental evolution of epigenetic age
metrics over time

The ­Rph of epigenetic age metrics at baseline and followup ranged from 0.50 to 0.85, indicating the longitudinalstability was moderate to strong. The R­ ph of PC clocks
were higher than their corresponding original clocks.
The CTCT correlations of epigenetic age metrics were
all higher within MZ twin pairs than that within DZ
twin pairs, suggesting the influence of genetic factors(Table 3). Four epigenetic age metrics (GrimAA, PCGrimAA, PhenoAA, and DunedinPACE) showed the optimal model was the AE model, while that for PCPhenoAA
was the ACE model (Additional file 2: Table S2).
Modest decreases were obtained in the heritability of
PCGrimAA (from 0.79 to 0.75) and DunedinPACE (from0.70 to 0.69) over time, but this trend was not obtainedin GrimAA (from 0.69 to 0.72), PhenoAA (from 0.52 to0.57), and PCPhenoAA (from 0.47 to 0.66) (Fig. 2, Additional file 2: Table S3).
Figure 3 shows the variance and covariance of threecomponents (A, C, and E) for five epigenetic age metrics. For GrimAA, PCGrimAA, PhenoAA, and DunedinPACE, the specific variance of A at baseline (­a112) wereTable 2 Parameter estimates (95% CI) from the univariate model of epigenetic age metrics (N = 954)
Epigenetic age metrics

a2(95% CI)c2(95% CI)

e2(95% CI)h2(95% CI)e2/total variance(95% CI)

GrimAA

0.48(0.41, 0.57)–

0.30(0.26, 0.35)

0.61(0.55, 0.67)

0.39(0.33, 0.45)

PCGrimAA

0.52(0.45, 0.61)–

0.23(0.20, 0.27)

0.69(0.64, 0.74)

0.31(0.26, 0.36)

PhenoAA

0.45(0.35, 0.56)–

0.54(0.47, 0.62)

0.45(0.38, 0.53)

0.55(0.47, 0.62)

PCPhenoAA

0.69(0.59, 0.81)–

0.30(0.26, 0.34)

0.70(0.65, 0.75)

0.30(0.25, 0.35)

DunedinPACE

0.52(0.25, 0.61)

0.00(0.00, 0.25)

0.35(0.31, 0.41)

0.59(0.29, 0.65)

0.41(0.35, 0.47)

a2 variance explained by additive genetic component, c2 variance explained by common environmental component, e2 variance explained by unique environmental
component, h2 heritability, that is, the ratio of ­a2 to the total varianceMiao et al. BMC Medicine(2024) 22:289

Page 6 of 10

Table 3 The correlations from bivariate models of epigenetic age metrics (N = 268)
Epigenetic age metrics
GrimAA

PCGrimAA

PhenoAA

PCPhenoAA

DunedinPACE

CTCT r

Rph

Ra

Re

Pa

Pe

MZ

0.50
(0.34, 0.64)

0.70(0.62, 0.76)

0.85(0.74, 0.94)

0.38(0.21, 0.54)

86%

14%

DZ

0.41
(0.11, 0.64)

MZ

0.69
(0.56, 0.78)

0.85(0.81, 0.88)

0.94(0.89, 0.98)

0.61(0.47, 0.72)

85%

15%

DZ

0.17
(− 0.15, 0.46)

MZ

0.37
(0.18, 0.53)

0.50(0.39, 0.59)

0.78(0.56, 1.00)

0.22(0.03, 0.40)

86%

14%

DZ

0.19
(− 0.13, 0.48)

MZ

0.48
(0.31, 0.62)

0.60(0.50, 0.68)

0.81(0.58, 1.00)

0.67(0.55, 0.77)

75%

25%

DZ

0.07
(− 0.25, 0.38)

MZ

0.60
(0.45, 0.71)

0.76(0.70, 0.81)

0.99(0.91, 1.00)

0.36(0.20, 0.52)

91%

9%

DZ

0.44
(0.15, 0.66)

CTCT r, cross-twin cross-trait (CTCT) correlations; Rph, phenotypic correlation; Ra, genetic correlation; Re, unique environmental correlation; Pa, the proportion of ­Rph
due to genetic factors; Pe, the proportion of ­Rph due to unique environmental factors

Fig. 2 Heritability of epigenetic age metrics in bivariate twin models (N = 268)

higher than that specific at follow-up ­(a222). However, for
PCPhenoAA, ­a222 was higher than ­a112. The covariance ofA between baseline and follow-up ­(a212) were lower thanor equal to a­ 222 for four epigenetic age metrics (not DunedinPACE). Except for PhenoAA, the specific varianceof E at baseline ­(e112) were lower than those specific atfollow-up ­(e222). The covariance of E (­e212) were all lower
than ­e222. For PCPhenoAA, the variance of C specific atfollow-up ­(c222) and the covariance of C ­(c212) were close
to zero, indicating that the role of common environmental factors declines over time.
The genetic correlations (­Ra) were all high, especiallyof PCGrimAA ­(Ra = 0.94) and DunedinPACE ­(Ra = 0.99).
The unique environmental correlations ­(Re) were low tomoderate (ranging from 0.22 to 0.67). PC clocks showed
higher ­Ra and R­ e than original clocks. For epigenetic age

Miao et al. BMC Medicine(2024) 22:289

Page 7 of 10

Fig. 3 Bivariate twin models for epigenetic age metrics (N = 268). Note: Orange color indicates the effect of A; green color indicates the effect of C
or D; blue color indicates the effect of E

metrics, ­Rph were largely due to genetic effects, with theproportion of R
­ ph due to genetic factors (­ Pa) ranging from75 to 91% (Table 3).

Discussion
Overall, we conducted univariate twin models of epigenetic age metrics in 954 twins and conducted longitudinalbivariate twin models in 268 twins who participated inbaseline and follow-up surveys. Results from univariate
models showed the moderate to high heritability of epigenetic age metrics (range from 0.45 to 0.70). The resultsfrom longitudinal bivariate models showed genetic contributions to epigenetic age metrics decreased, andunique environmental contributions increased acrosstime. There was a large overlap in genetic effects and asmall overlap in unique environmental effects across
5 years. However, for all epigenetic clocks, new genetic
and new unique environmental influences emerged
across time. Moreover, the phenotypic correlation representing the longitudinal stability of epigenetic clocks
were moderate to strong, mainly due to genetic factors.
PC clocks showed higher correlations with chronologicalage, higher heritability, greater longitudinal stability, andhigher genetic and unique environmental correlations
than original clocks.

To our knowledge, a few studies have investigated theheritability of epigenetic clocks, and virtually no study
has explored the heritability of PC clocks. Some studies
used polygenic models to measure heritability of epigenetic age accelerations and obtained low to moderate
SNP-based heritability estimates: GrimAA at 0.10 ~ 0.30
[3, 28] and PhenoAA at 0.10 ~ 0.33 [5, 28]. However, similar to our results, other twin studies have reported themoderate to high heritability of epigenetic clocks [13–15]. A twin study estimated the heritability of GrimAA at
0.58 (95% CI 0.51 ~ 0.65) involving 1424 twins on average
34.5 years (age ranged 21 ~ 73 years) [13]. A study calculated the heritability in two twin cohorts and found thatthe heritability of GrimAA and PhenoAA in the young
cohort (age ranged 21 ~ 25 years) were 0.62 and 0.64, andthat in the older cohort (age ranged 55 ~ 72 years) were0.58 and 0.60, respectively [14]. Only one study reported
on the heritability of DunedinPACE at 0.68 (95% CI
0.52 ~ 0.82), which involved 730 twins with a mean age of
22.4 years [15]. It also estimated the heritability of GrimAA at 0.73 (95% CI 0.66 ~ 0.80) and PhenoAA at 0.65
(95% CI 0.56 ~ 0.74) [15]. All three studies involved twinsfrom the Finnish Twin Cohort, fitted the AE model for
epigenetic clocks and had a higher estimation of the heritability of epigenetic clocks (GrimAA, PhenoAA, andDunedinPACE) than our results. This may be due to theMiao et al. BMC Medicine(2024) 22:289

older age of our participants (age ranged 19 ~ 82 years,
mean age of 50.3 years). From the longitudinal bivariate models, we proved that the estimate of heritability
for DunedinPACE decreased over time. We utilized thecross-sectional design (954 twins) to obtain the heritability in the Chinese population. Besides, we used a longitudinal design (268 twins) to understand changes of theheritability.
Even so, new genetic factors still emerged at follow-up.
Moreover, the genetic correlations of epigenetic clocks
were high, and it could either be due to a large overlap ingenes across time or due to persistent effects of genetic
factors at baseline [16]. Given the emerging genetic factors at follow-up, we advocated the latter assumption.
The genetic components for epigenetic clocks acrosstime appeared pleiotropic, indicating the mechanisms ofgenes behind aging are complex. Our study confirms thatage affects gene expression and that the influence of early
genetic factors can extend into later periods. A previous
longitudinal twin study investigated the PhenoAge clock
and thought that the high genetic correlation of PhenoAge was attributed to the large overlap of genes, because
no new genetic effects emerged during the follow-up
period [16]. The discrepancy in outcomes may be attributed to the fact that the participants in the previous study
had a mean age of 69.8 years at baseline and 78.9 years
at follow-up, which is older than our study participants.
Age significantly impacts gene expression, and the effectis more pronounced in older adults [29].
Contributions of unique environmental factors
increased over time. New unique environmental factors emerged at follow-up, consistent with the previous
study [16], indicating that epigenetic clocks would still
be responsive to environmental factors even at old age,which further reveals the potential of epigenetic clocks asbiomarkers of aging for the identification and evaluationof longevity interventions. However, we found significant
unique environmental correlation across time, whichwere not found in the previous study [16]. The length of
the follow-up period may be the reason for the discrepancy of unique environmental correlation between thetwo studies. Epigenetic clocks showed moderate to highlongitudinal stability, most of which was due to genetic
factors, and changes of epigenetic clocks over time weremainly due to changes in unique environmental factors,
consistent with previous longitudinal twin studies forclock-related CpG sites and the PhenoAge clock [16, 17].
The difference between original and PC clocks liesin CpG sites. The former was trained from individual
CpG sites, and the latter was trained from PCs of CpG
sites to minimize the technical noise [8]. PCA was used
to extract information from higher dimensions (several
possibly correlated variables, as individual CpG sites) toPage 8 of 10

lower dimensions (a smaller number of uncorrelated variables, as PCs) [30]. PC clocks discarded the low-variance
PCs that primarily represent noise or otherwise do notcontribute to prediction [31]. Notably, PC clocks weremore strongly correlated with chronological age because
the information of chronological age was contained in
the PCs with a large variance, which was retained in PC
clocks [8]. As expected, we found that PC clocks showed
higher longitudinal stability than original clocks. The
higher longitudinal stability of PC clocks was mainly due
to removing technical noise which is expected to belong
to the unique environmental component. We also foundthat the heritability and unique environmental correlation of PCPhenoAA were significantly higher than thatof PhenoAA. The removal of technical noise allowed thepreservation of truly unique environmental factors andincreased the proportion of genetic factors. As demonstrated in previous studies [8, 32], compared to PCGrimAge and GrimAge, the correlation between PCPhenoAge
and PhenoAge was weaker. This may be due to the factthat PCPhenoAge chose a smaller proportion of principal
components (PCPhenoAge: 1000 of 4504 PhenoAge PCs;
PCGrimAge: 1936 of 3934 GrimAge PCs) in training.
As far as we know, only one study has applied bothoriginal and PC clocks to examine the degree of aging[33]. Only a few longitudinal studies used PC clocks toevaluate the influence factors or interventions of aging[32, 34–38]. Because of the different study designs andpopulations, it is difficult to compare results from PC
clocks with those from original clocks. However, due tothe differences between PC and original clocks in genetic
and environmental effects and longitudinal stability, theresults of previous studies using original clocks, especially longitudinal studies, should be verified with PC
clocks. Future longitudinal studies also need to take into
account PC clocks instead of original clocks as biomarkers of aging.
The strengths of our study include the longitudinal
design to investigate genetic and environmental effects
behind the changes of epigenetic clocks over time, andthe comparison between original and PC clocks. Nevertheless, some limitations should be considered. First, werelied on confidence intervals to judge the difference ofgenetic and environmental effects between original and
PC clocks. When confidence intervals overlapped, we
could not clearly judge whether there was a significant
difference. In addition, given the limited sample size, further analysis cannot be taken into consideration, suchas using the sex-limitation model to explore the effectof sex. Finally, the structural equation model can only
approximate the impact of genetic and environmental factors on epigenetic clocks, and the specific genetic

Miao et al. BMC Medicine(2024) 22:289

mechanism and environmental factors still need to be
explored in future studies.

Conclusions
In conclusion, we found the moderate to high heritability of epigenetic clocks and explored the extent towhich the effects of genetic and environmental factors
contribute to epigenetic clocks across 5 years. High longitudinal stability is mainly due to genetic factors, andchanges of epigenetic clocks over time are mainly due tochanges in unique environmental factors. The effects ofgenetic factors at baseline are persistent, and the mechanisms of genes behind epigenetic clocks are complex.
The potential of epigenetic clocks as markers of aging
for identifying interventions affecting aging was demonstrated, even at old age. PC clocks showed higher heritability and longitudinal stability than original clocks dueto the reduction in technical noise belonging to uniqueenvironmental factors. Due to the differences between
PC and original clocks in the genetic and environmental effects and longitudinal stability, the results of studies
with the original clocks need to be further verified with
PC clocks. Further studies should focus on PC clocks to
explore the aging mechanism.
Abbreviations
A	Additive genetic component
AA	Age acceleration
AIC	Akaike’s information criterion
C	Common environmental component
CI	Confidence interval
CNTR	Chinese National Twin Registry
CpG	Cytosine-phosphate-guanine
CTCT​	Cross-twin cross-trait
D	Dominant genetic component
DZ	Dizygotic twinE	Unique environmental component
LRT	Likelihood-ratio test
MZ	Monozygotic twinPa	The proportion of ­Rph due to genetic factors
Pc	The proportion of ­Rph due to common environmental factors
PC	Principal component
PCA	Principal component analysis
Pe	The proportion of ­Rph due to unique environmental factors
Ra	Common genetic correlation
Rc	Common environmental correlation
Re	Unique environmental correlationRph	Phenotypic correlation
SD	Standard deviation
SEM	Structural equation modeling

Page 9 of 10

Additional file 2: Table S1-S3. Table.S1- [Univariate model fit comparisons
for the epigenetic age metrics in the cross-sectional analysis]. Table.S2[Bivariate Model fit comparisons for the epigenetic age metrics in longitudinal analysis]. Table.S3- [Parameter estimates (95%CI) from bivariate twinmodels of epigenetic age metrics (N=268)].
Acknowledgements
We thank all the participants and project staff who participated in the Chinese
National Twin Registry for their contributions.
Authors’ contributions
KM: Software, Writing - Original Draft. SKL, WHC, JL, CY, TH, DJYS, CXL, YJP and
RHH: Writing - Review & Editing; ZCP, MY, HW, XPW and YL: Investigation; WJG:
Conceptualization, Methodology, Writing - Review & Editing; LML: Supervision,
Project administration, Funding acquisition; All authors read and approved thefinal manuscript.
Funding
The authors gratefully acknowledge the support of the National Key Researchand Development Program of China (2023YFC2705900), National Natural
Science Foundation of China (82373659, 82073633), Special Fund for Health
Scientific Research in Public Welfare (201502006, 201002007), and Peking
University Outstanding Discipline Construction Project of Epidemiology andBiostatistics.
Availability of data and materials
The datasets generated and analysed during the current study are not publicly
available but are available from the corresponding author upon reasonable
request.

Declarations
Ethics approval and consent to participate
This study was granted by the Biomedical Ethics Committee at Peking
University, Beijing, China (approval number: IRB00001052-13022, IRB0000105214021). All participants provided written informed consent when investigated.
All methods were carried out in accordance with the relevant guidelines andregulations.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Department of Epidemiology and Biostatistics, School of Public Health,Peking University, Beijing 100191, China. 2 Key Laboratory of Epidemiologyof Major Diseases, (Peking University), Ministry of Education, Beijing 100191,
China. 3 Qingdao Center for Disease Control and Prevention, Qingdao, China.
4
Zhejiang Center for Disease Control and Prevention, Hangzhou, China.
5
Jiangsu Center for Disease Control and Prevention, Nanjing, China. 6 Sichuan
Center for Disease Control and Prevention, Chengdu, China. 7 Heilongjiang
Center for Disease Control and Prevention, Harbin, China.
Received: 11 March 2024 Accepted: 2 July 2024

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03511-y.
Additional file 1: Figs. S1-S2. Fig.S1- [Correlations between the original
clocks and their PC clock proxies in the overall samples]. Fig.S2- [Matrix
heatmap of epigenetic age and chronological age].

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