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INTRODUCTION
People age chronologically at the same rate but show
substantial individual differences in their rates of
biological aging, or the gradual, multi-system decline in
physiology that occurs with aging. Recent advances are
underway to quantify biological aging. DNA
methylation (DNAm)-based measures, including first-
and second-generation epigenetic clocks and pace of
aging measures, are promising aging biomarkers that
predict morbidity and mortality independent of
chronological age [1–3].
Links between DNAm measures and risk for cognitive
decline have been less well characterized, despite the
substantial and growing burden of cognitive decline and
dementia [4]. The majority of existing evidence on
DNAm measures and neuropsychologically assessed
cognitive function is cross-sectional [5] and cannot
address whether changes in biological aging are
associated with changes in cognition. Four studies to
date [6–9] have examined but did not find changes in
cognitive function relating to changes in first- or
second-generation epigenetic clocks.
First-generation clocks, including Horvath [10] and
Hannum [11], were trained to predict chronological
age. Therefore, Horvath and Hannum clocks exhibit
high correlations with chronological age; however, they
predict morbidity and mortality more weakly than
www.aging-us.com AGING 2022, Vol. 14, No. 23
Research Paper
DNA methylation-based measures of biological aging and cognitive
decline over 16-years: preliminary longitudinal findings in midlife
Rebecca G. Reed1, Judith E. Carroll2, Anna L. Marsland1, Stephen B. Manuck1
1Department of Psychology, Dietrich School of Arts and Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
2Cousins Center for Psychoneuroimmunology, Department of Psychiatry and Biobehavioral Science, Jane and
Terry Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of
California, Los Angeles, CA 90095, USA
Correspondence to: Rebecca G. Reed; email: rebecca.reed@pitt.edu
Keywords: epigenetic age, aging biomarker, pace of aging, geroscience, cognitive aging
Received: July 8, 2022 Accepted: October 29, 2022 Published: October 11, 2022
Copyright: © 2022 Reed et al. This is an open access article distributed under the terms of the Creative Commons Attribution
License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original
author and source are credited.
ABSTRACT
DNA methylation-based (DNAm) measures of biological aging associate with increased risk of morbidity and
mortality, but their links with cognitive decline are less established. This study examined changes over a 16-
year interval in epigenetic clocks (the traditional and principal components [PC]-based Horvath, Hannum,
PhenoAge, GrimAge) and pace of aging measures (Dunedin PoAm, Dunedin PACE) in 48 midlife adults enrolled
in the longitudinal arm of the Adult Health and Behavior project (56% Female, baseline AgeM = 44.7 years),
selected for discrepant cognitive trajectories. Cognitive Decliners (N = 24) were selected based on declines in a
composite score derived from neuropsychological tests and matched with participants who did not show any
decline, Maintainers (N = 24). Multilevel models with repeated DNAm measures within person tested the main
effects of time, group, and group by time interactions. DNAm measures significantly increased over time
generally consistent with elapsed time between study visits. There were also group differences: overall,
Cognitive Decliners had an older PC-GrimAge and faster pace of aging (Dunedin PoAm, Dunedin PACE) than
Cognitive Maintainers. There were no significant group by time interactions, suggesting accelerated epigenetic
aging in Decliners remained constant over time. Older PC-GrimAge and faster pace of aging may be particularly
sensitive to cognitive decline in midlife.
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second-generation clocks [12, 13]. Second-generation
clocks, including PhenoAge [14] and GrimAge [12],
were optimized for lifespan prediction. Specifically,
PhenoAge and GrimAge were developed to capture
DNAm patterns that not only change with
chronological age, but also account for differences in
risk for morbidity and mortality. Finally, the latest
DNAm measures include epigenetic “pace of aging”
metrics [2, 3] and principal components (PC)-based
clocks [15]. Pace of aging measures differ from first-
and second-generation clocks in that they were trained
to predict longitudinal changes in multi-system
biomarkers [2, 3]. Specifically, Dunedin PoAm was
trained in individuals of the same chronological age to
predict changes in 18 biomarkers across 12 years (age
26 to 38), and Dunedin PACE, an updated version, was
trained to predict changes in 19 biomarkers across 20
years (age 26 to 45) [2, 3]. Last, PC-based clocks were
developed to enhance the reliability of traditional
epigenetic clocks (Horvath, Hannum, PhenoAge, and
GrimAge), which use individual CpG sites that are
noisy and unreliable [16]. Instead, PC-based clocks use
principal components (shared systematic variation
across many CpG sites) rather than individual CpGs to
estimate PC-clock ages [15] (see Supplementary
Materials for additional DNAm clock descriptions).
These latest DNAm measures (Dunedin PoAm,
Dunedin PACE, and PC-clocks) may be particularly
robust predictors of cognitive decline, but these
associations have yet to be thoroughly examined,
including longitudinally.
This preliminary study examined overall levels and
changes in traditional and PC-based first- and second-
generation epigenetic clocks and pace of aging
measures in participants selected from a larger
prospective cohort to represent extremes of maintained
and declining cognitive function (termed Maintainers
and Decliners, respectively) between a baseline visit
when participants were in midlife and a second visit
approximately 16 years later. We hypothesized that
overall, cognitive Decliners would be biologically older
compared to cognitive Maintainers. We also explored
whether cognitive Decliners would show faster
biological aging (i.e., steeper increases in DNAm over
time) compared to cognitive Maintainers; and whether
particular cognitive domains associated more strongly
than others with measures of biological aging. We
expected that PC-based clocks of enhanced reliability
would outperform traditional clocks and that second-
generation clocks and pace of aging measures trained to
predict morbidity, mortality, and multi-system decline
would outperform first-generation clocks optimized for
age prediction. Notably, we tested several DNAm
measures because a comparative analysis approach is
recommended to simultaneously evaluate the utility of
many DNAm measures and determine which ones are
associated with aging outcomes of interest [17].
RESULTS
Neuropsychological tests were administered and
biological age was estimated at both time 1 (T1) and
time 2 (T2) for 24 people who declined in cognitive
function (Decliners) and 24 who maintained cognitive
function (Maintainers) from T1 to T2 (mean years
between assessments = 15.9, range: 15.4 to 16.9),
selected using an extreme groups approach (see
Methods). Table 1 summarizes study participant
characteristics. Decliners and Maintainers did not
significantly differ on chronological age, sex, education,
race, body mass index, smoking status, or T1 cognition
(a composite score derived from neuropsychological
tests for spatial reasoning, working memory, processing
speed, executive function, and attention; see Methods).
Decliners’ cognitive composite decreased from T1 to
T2 (T1M = 67.61; T2M = 53.89, p < 0.001) whereas
Maintainers’ cognitive composite did not change over
time (T1M = 66.48; T2M = 67.56, p = .189). The
observed cognitive decline was more than a standard
deviation decline, a clinically noticeable change in
cognitive performance associated with risk for future
cognitive impairments. Normative values on several
neuropsychological tests were further examined to
contextualize changes in the cognitive composite. As
the sample performed above average at T1, the
Decliners’ change can be interpreted as moving from
above average to average, whereas the Maintainers
remained slightly above average at both time points (see
Supplementary Results). All individuals in the Decliner
and Maintainer groups denied being diagnosed with
dementia. Adjudications were not performed, so clinical
determinations regarding mild cognitive impairment
(MCI) cannot be made.
Table 2 summarizes descriptive statistics for the DNAm
measures: Horvath, Hannum, PhenoAge, GrimAge,
Dunedin PoAm, Dunedin PACE, PC-Horvath, PC-
Hannum, PC-PhenoAge, and PC-GrimAge (see
Methods). All DNAm measures exhibited rank-order
stability between baseline and follow-up (r’s ranged
from 0.71 to 0.93); GrimAge and PC-GrimAge had the
highest test-retest correlations (both r = .93) and
Dunedin PACE (r = .73) and Dunedin PoAm (r = .71)
were lower. In addition, there were strong and similar
inter-correlations among DNAm measures within each
time point (Figure 1). The exceptions were Dunedin
PoAm and Dunedin PACE, which only correlated with
each other (r = .66–.77) and with GrimAge (r = 0.45–
0.61) and PC-GrimAge (r = .40–.58) at T1 and T2.
DNAm measures independent of chronological age
(denoted Age Acceleration, AA) are displayed in
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Table 1. Characteristics of cognitive decliners (n = 24) and maintainers (n = 24).
Total
Decliners
Maintainers
p-valuea
Age (yrs), mean (SD)
T1
44.79 (6.34)
44.57 (6.43)
45.01 (6.38)
0.815
T2
60.67 (6.27)
60.42 (6.34)
60.91 (6.32)
0.791
Education (yrs), mean (SD)
T1
15.15 (2.45)
14.46 (2.38)
15.83 (2.37)
0.051
T2
15.46 (2.71)
14.79 (2.54)
16.12 (2.77)
0.089
Sex, N (%)
Male
21 (43.8)
11 (45.8)
10 (41.7)
0.239
Female
27 (56.2)
13 (54.2)
14 (58.3)
Race, N (%)
White
44 (91.7)
22 (91.7)
22 (91.7)
0.656
Black or African American
4 (8.3)
2 (8.3)
2 (8.3)
Time between T1 and T2, mean (SD)
15.87 (0.33)
15.85 (0.27)
15.90 (0.39)
0.592
BMI (kg/m2), mean (SD)
T1
24.70 (3.60)
25.13 (3.37)
24.27 (3.85)
0.411
T2
26.80 (5.18)
27.79 (5.35)
25.85 (4.93)
0.203
Current smoker, N (%)
T1, No
36 (75.0)
19 (79.2)
17 (70.8)
0.107
T1, Yes
12 (25.0)
5 (20.8)
7 (29.2)
T2, No
39 (81.2)
19 (79.2)
20 (83.3)
0.261
T2, Yes
9 (18.8)
5 (20.8)
4 (16.7)
Cognitive Composite, mean (SD)
T1
67.05 (8.60)
67.61 (9.17)
66.48 (8.15)
0.653
T2
60.73 (11.19)
53.89 (10.05)
67.56 (7.58)
<0.001
Total sample size is 48. Means and standard deviations (SD) are displayed for continuous measures; sample size (N) and
percentages (%) are shown otherwise. T1: time 1; T2: time 2. ap-value comparing groups. Dependent t-tests were used for
continuous variables; chi-square tests were used for categorical variables. p-values are bold if <0.05.
Table 2. Descriptive statistics among the DNAm measures.
T1, M (SD)
T2, M (SD)
Change per year, M (SD)
Test-retest (r)
Chronological Age
44.79 (6.34)
60.67 (6.27)
1.00 (0.00)
1.00
Horvath
46.32 (6.52)
59.07 (6.44)
0.80 (0.19)
0.89
Hannum
37.27 (6.75)
50.38 (6.69)
0.83 (0.18)
0.91
PhenoAge
34.46 (8.15)
49.76 (9.03)
0.96 (0.31)
0.85
GrimAge
48.43 (6.68)
60.86 (7.07)
0.78 (0.16)
0.93
Dunedin PoAm
1.01 (0.08)
1.04 (0.08)
0.002 (0.004)
0.71
Dunedin PACE
0.91 (0.12)
0.97 (0.13)
0.003 (0.006)
0.73
PC-Horvath
46.77 (6.25)
58.49 (6.26)
0.74 (0.15)
0.93
PC-Hannum
52.97 (6.38)
65.40 (6.30)
0.78 (0.18
0.90
PC-PhenoAge
44.07 (8.23)
59.19 (8.05)
0.95 (0.29)
0.85
PC-GrimAge
58.09 (5.97)
70.98 (6.30)
0.81 (0.15)
0.93
Means (M) and standard deviations (SD) are shown for Time 1 (T1) and Time 2 (T2) DNAm measures. Change per year
represents the average rate of change in each DNAm measure per year. Test-retest correlations are displayed as Pearson r.
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Figure 2. As compared to raw DNAm measures, the
inter-correlations among DNAmAA measures were
smaller within each time point, with the exception of
Dunedin PoAm-AA and Dunedin PACE-AA, which
were more strongly correlated with GrimAgeAA (r =
.69–.77) and PC-GrimAgeAA (r = .68–.76), as well as
with PhenoAgeAA (r = .46–.59) and PC-PhenoAgeAA (r
= .37–.57) at T1 and T2.
Time and group main and interacting effects on
DNAm
The traditional and PC-based epigenetic clocks and
pace of aging measures significantly increased over
time, generally consistent with or underestimating the
time elapsed between study visits (Table 3 and
Supplementary Table 1). With respect to group
Figure 1. Pearson correlations among DNAm measures at Time 1 (left) and Time 2 (right). Correlations greater than r = .29 are
statistically significant at p < .05.
Figure 2. Pearson correlations among DNAm measures independent of chronological age (denoted Age Acceleration, AA) at
Time 1 (left) and Time 2 (right). Correlations greater than r = .29 are statistically significant at p < .05.
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Table 3. Main effects of group and time on PC-Clocks and pace of aging measures.
Predictors
PC-Horvath
PC-Hannum
PC-PhenoAge
PC-GrimAge
Dunedin PoAm
Dunedin PACE
γ (CI)
p
γ (CI)
p
γ (CI)
p
γ (CI)
p
γ (CI)
p
γ (CI)
p
Intercept
48.78
(47.22–50.35)
<0.001
54.46
(52.90–56.02)
<0.001
44.14
(41.88–46.40)
<0.001
58.14
(56.41–59.87)
<0.001
0.98
(0.95–1.02)
<0.001
0.88
(0.82–0.94)
<0.001
Female
−2.90
(−4.58–−1.22)
0.002
−2.26
(−3.91–−0.61)
0.010
−1.21
(−3.59–1.17)
0.323
−1.91
(−3.77–−0.05)
0.050
0.01
(−0.03–0.05)
0.714
−0.00
(−0.07–0.06)
0.914
Baseline
Age
0.83
(0.70–0.96)
<0.001
0.85
(0.72–0.98)
<0.001
1.05
(0.86–1.24)
<0.001
0.78
(0.64–0.93)
<0.001
0.00
(−0.00–0.00)
0.528
0.00
(−0.00–0.01)
0.672
Group-
Decliners
−0.77
(−2.43–0.90)
0.372
−0.43
(−2.07–1.21)
0.608
1.22
(−1.14–3.58)
0.315
2.05
(0.20–3.90)
0.035
0.04
(0.00–0.08)
0.045
0.08
(0.01–0.14)
0.027
Time
11.72
(11.04–12.39)
<0.001
12.42
(11.60–13.24)
<0.001
15.12
(13.82–16.41)
<0.001
12.89
(12.23–13.55)
<0.001
0.03
(0.02–0.05)
0.001
0.05
(0.03–0.08)
<0.001
95% Confidence Intervals (CI) are reported.
differences, Decliners overall had an older PC-GrimAge
(γ = 2.05, SE = .94, t(44) = 2.18, p = .035) and a faster
pace of aging on both Dunedin PoAm (γ = .042, SE =
.021, t(44) = 2.06, p = .045) and Dunedin PACE (γ =
.075, SE = .033, t(44) = 2.28, p = .027) than Maintainers
(Table 3, Figure 3). (Decliners did not significantly differ
from Maintainers on PC-PhenoAge (γ = 1.22, SE = 1.20,
t(44) = 1.02, p = .31)). In other words, Decliners were on
average 2.05 years older than Maintainers using PC-
GrimAge; in terms of pace of aging, Decliners
biologically aged at rates .042 (Dunedin PoAm) and .075
(Dunedin PACE) faster than Maintainers. For example, if
Maintainers age at a rate of 1.0 biological year per
chronological year, Decliners age at 1.042 (Dunedin
PoAm) and 1.075 (Dunedin PACE) biological years per
chronological year. In analyses that adjusted for multiple
comparisons using the Benjamini-Hochberg correction
[18] (see Data Analyses), these group differences
remained statistically significant at a false discovery rate
(FDR) of .10 but not .05. In addition, in sensitivity
analyses that further controlled for percentages of CD8 T
cells, CD4 T cells, NK cells, plasma blasts, monocytes,
and granulocytes, these group differences remained
statistically significant (Supplementary Table 2).
Furthermore, results were similar from logistic regression
models that regressed Cognitive Decliner group
membership (1) [vs. Cognitive Maintainer (0)] on
average biological age, controlling for sex and baseline
chronological age: a 1-year increase in PC-GrimAge was
associated with a .22 increased log-odds of being in the
Cognitive Decliner group (p = .049); in addition, a 1-year
rate increase in Dunedin PoAm and Dunedin PACE were
associated with 9.91 (p = .061) and 6.03 (p = .034)
increased log odds of being in the Cognitive Decliner
group. The Dunedin PoAm finding is no longer
statistically significant likely due to power loss moving
from a multilevel modeling framework to logistic
regression. In the main analyses, there were no group by
time interactions (ps > .24).
Exploring specific cognitive components on DNAm
To further explore whether the several components of
cognitive functioning associated differentially with
PC-GrimAge and pace of aging measures, we
conducted secondary analyses using the same
adjusted multilevel model predicting T1 and T2
DNAm, but instead of the categorical Group
predictor, we tested the continuous scaled version of
each cognitive component at T2 to determine which
cognition component(s) were significantly associated
with DNAm-based measures of biological aging. We
focused on T2 cognitive components because this
was the time point that differentiated the two groups
(see Supplementary Table 3).
Results are depicted in Table 4. In terms of executive
function, worse performance on T2 Trail A-B was
significantly associated with older PC-GrimAge (p =
.013) and faster pace of aging for Dunedin PoAm (p =
.016) and for Dunedin PACE (p = .019). In addition,
worse performance on Stroop Color-Word was
significantly associated with older PC-GrimAge (p =
.017). In terms of processing speed, slower Trail A and
worse performance on Stroop Word were associated with
faster pace of aging for Dunedin PACE (Trail A: p = .046,
Stroop Word: p =.035). Finally, in terms of spatial
reasoning, worse matrix reasoning was associated with
faster pace of aging for Dunedin PACE (p = .041). The
following components of cognition at T2 were not
significantly associated with DNAm: working memory
(Digit Span forward and backward), attention (Digit
Vigilance), and one measure of processing speed (Stroop
Word). In analyses that adjusted for multiple
comparisons, only the associations with the executive
function tests remained statistically significant at an FDR
of .05 or .1 (Trail A-B: all padj = .019; Stroop Color-Word:
padj = .051). Results further adjusted for cell percentages
did not differ and are in Supplementary Table 4.
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Table 4. Main effects of scaled Time 2 cognitive components on PC-GrimAge and pace of aging measures.
PC-GrimAge
Dunedin PoAm
Dunedin PACE
γ (CI)
p
γ (CI)
p
γ (CI)
p
Matrix Reasoning
−0.049
(−0.120–0.021)
0.179
−0.001
(−0.002–0.001)
0.467
−0.003
(−0.005–−0.000)
0.041
DS-Forward
−0.026
(−0.074–0.022)
0.286
−0.000
(−0.001–0.001)
0.601
−0.001
(−0.003–0.001)
0.231
DS-Backward
−0.035
(−0.086–0.015)
0.180
−0.000
(−0.001–0.001)
0.521
−0.001
(−0.003–0.001)
0.220
Trail A
−0.163
(−0.365–0.039)
0.120
−0.004
(−0.008–0.001)
0.092
−0.007
(−0.014–−0.000)
0.046
Trail A-B
−0.097
(−0.171–−0.024)
0.013
−0.002
(−0.004–−0.000)
0.016
−0.003
(−0.006–−0.001)
0.019
Stroop Word
−0.054
(−0.113–0.005)
0.079
−0.001
(−0.002–0.000)
0.122
−0.002
(−0.004–−0.000)
0.035
Stroop Color
−0.057
(−0.115–0.002)
0.065
−0.001
(−0.002–0.000)
0.093
−0.001
(−0.003–0.001)
0.236
Stroop Color-Word
−0.068
(−0.121–−0.014)
0.017
−0.001
(−0.002–0.000)
0.089
−0.002
(−0.004–0.000)
0.111
Digit Vigilance-pg1
−0.052
(−0.123–0.019)
0.162
−0.002
(−0.003–0.000)
0.117
−0.001
(−0.004–0.002)
0.414
Digit Vigilance-pg2
−0.063
(−0.138–0.012)
0.109
−0.002
(−0.004–0.000)
0.078
−0.002
(−0.005–0.001)
0.167
95% Confidence Intervals (CI) are reported. Models included female, baseline age, and time (estimates not shown). Higher scaled cognitive scores indicate
better performance. Abbreviation: DS: digit span.
Figure 3. Boxplots of significant group effects on PC-GrimAge, dunedin PoAm, and dunedin PACE. Two values are shown per
person, but analyses accounted for repeated measures within person.
DISCUSSION
This is the first report to explore changes over time in
several of the latest DNAm biological aging measures –
including traditional and PC-based epigenetic clocks
and pace of aging measures – in an age-, race-, sex-,
education-, cognition-, and body mass index- matched
case control comparison and where cases were selected
for having cognitive performance declines on objective
neuropsychological tests. There were no group
differences in DNAm slopes over time, which may be
due to low statistical power, but is in line with the few
previous studies that have examined only first- and
second-generation epigenetic clocks [6–9]. However,
cognitive decline was related to an overall older PC-
GrimAge and a faster pace of aging (Dunedin PoAm
and Dunedin PACE) compared to those without
cognitive decline over this 16-year time frame. These
group differences remained statistically significant
when corrected for multiple comparisons at a false
discovery rate of .10.
There was no evidence of associations between the first-
generation epigenetic clocks and cognitive decline.
Rather, our findings point to the second-generation
clock PC-GrimAge as being more sensitive to cognitive
change, which aligns with others who report
associations between GrimAge, but not Horvath or
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Hannum, and worse cognitive performance cross-
sectionally [19], worse future cognitive performance
[8], and cognitive decline from adolescence to age 45
[3] and from age 70 to 79 [20]. Notably, we did not
observe associations with (PC)-PhenoAge and cognitive
decline, which may be due to limited power, but is also
consistent with other reports [3, 8]. Although PhenoAge
and GrimAge are both second-generation clocks, they
differ in how they were trained: PhenoAge was created
by identifying CpGs that predict a composite measure
of mortality-related blood biomarkers (see
Supplementary Materials for biomarker list) and
chronological age [14]. Conversely, GrimAge was
created by generating DNAm surrogates of morbidity-
and mortality-related plasma proteins (see
Supplementary Materials) and smoking pack-years;
then time-to-death was regressed onto these DNAm
surrogates, chronological age, and sex to identify the
CpGs [12]. The blood-based biomarkers across both
epigenetic clocks reflect the functioning of similar
physiological systems (e.g., immune, kidney,
metabolic), but GrimAge also explicitly includes the
effects of smoking, which is an established risk factor
for cognitive decline and dementia [21]. In addition, of
the first- and second-generation clocks, GrimAge and
PC-GrimAge tend to have the highest reliability due to
its two-step DNAm calculation [3, 15]; thus, this
measurement property may also explain why GrimAge
tends to outperform other clocks, including PhenoAge.
However, these reasons remain speculative and future
studies with DNAm data should continue to evaluate
and report associations across multiple DNAm
measures (including the newest pace of aging measures,
below) to facilitate comparison across studies, reconcile
inconsistencies, and facilitate their inclusion in future
meta-analyses and systematic reviews.
In addition to PC-GrimAge, faster pace of aging was
associated with cognitive decline. This report is the first
to replicate Belsky and colleagues’ [2, 3] findings of
Dunedin PoAm and Dunedin PACE associating with
cognitive decline. Our findings suggest that pace of
aging measures, which were developed from Dunedin
Study participants aged 26–45, can inform cognitive
outcomes in middle-aged and older adults. Pace of
aging measures may be particularly sensitive to pre-
clinical cognitive changes because they are indexed by a
longitudinal panel of biomarkers across multiple
physiological systems, which may more closely reflect
the mechanisms of cognitive decline, relative to first-
generation epigenetic clocks that are optimized for age
prediction. Interestingly, the epigenetic clocks that pace
of aging was most strongly correlated with at T1 and T2
were GrimAge and PC-GrimAge (Figures 1, 2),
suggesting that these DNAm measures may be detecting
some shared biological aging signals. A limitation to the
current DNAm measures is a lack of mechanistic
understanding of their underlying biology. Current work
is underway to deconstruct these DNAm composite
measures into distinct “modules” that may reflect
functionally related biological changes [22]. Each
epigenetic clock is comprised of differing proportions
of CpGs from a given module; however, in line with our
findings, GrimAge and DunedinPoAm share a similar
composition of modules and have higher quantities of
modules that are stronger predictors of morbidity and
mortality, as compared to PhenoAge, Horvath, and
Hannum [22]. Continued efforts to examine the
underlying mechanisms of DNAm measures will aid
our understanding of why certain clocks outperform
others in predicting health outcomes, including
cognitive health.
All DNAm measures significantly increased over time;
however, these estimates of biological aging did not
increase between T1 and T2 more steeply in Decliners,
compared to Maintainers, as evidenced by the absence
of a significant group by time interaction. In other
words, DNAm estimates of biological aging were
associated with the 16-year change in cognitive
functioning, but did not progress more rapidly in
Decliners than among Maintainers, which may suggest
that Decliners’ accelerated profile of epigenetic aging
was established prior to the initial assessment.
However, we note that we had limited power to detect
small and moderate effects (particularly interaction
effects); therefore, we cannot confidently infer
whether the non-significant group by time interactions
are due to truly null effects and/or due to the smaller
sample size.
In exploring whether particular cognitive domains
may covary with PC-GrimAge and pace of aging
measures more strongly than others, executive
function showed the most consistent associations, as
well as withstanding correction for multiple
comparisons. One previous report links older
epigenetic age estimated from other clocks, including
Horvath’s intrinsic and Hannum derived extrinsic
epigenetic age acceleration and PhenoAge, but not
GrimAge, to poorer executive function in African
Americans with HIV and a control group [23]; others
report null associations between GrimAge and
executive function composites [24, 25], and between
Dunedin PACE and one test of executive function,
Trails B [26]. Therefore, converging evidence for
associations between DNAm and specific cognitive
domains remains inconclusive. Future studies will
benefit from investigating separate cognitive domains
(in addition to general composites, which is more
commonly done), to shed light on which components
of cognition may be more or less affected.
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The current study focused on neuropsychologically-
assessed cognitive decline, which can indicate future
risk for dementia [27]. Indeed, in other studies, DNAm
measures predicted MCI and clinical diagnosis of
Alzheimer’s Disease (e.g., [26, 28]). No participants in
our sample reported having a dementia diagnosis, but
adjudications were not performed, so MCI status could
not be assessed. However, descriptively, the group with
cognitive performance decrements over time
experienced greater than a standard deviation change in
their average composite score, an indication they may
be at future cognitive risk, with their T2 assessments
falling slightly below normative values on several
neuropsychological tests (see Supplemental Results). It
remains unclear whether these individuals will manifest
future cognitive impairments, but this magnitude of
decline is considered clinically meaningful [29].
Strengths of this study include the longitudinal design
with a relatively long follow-up of 16 years; the
comprehensive assessment of cognition across several
domains known to decline with age; and the
recommended analysis of multiple DNAm measures
[17] that allowed for comparisons across traditional and
PC-based epigenetic clocks and pace of aging measures.
However, this preliminary study had limited power to
detect small and moderate effects (particularly
interaction effects), although we maximized our ability
to detect effects by selecting cognitive groups from the
tails or extremes of the distribution of cognitive change.
In addition, the cognition composite approach used to
identify Cognitive Decliners vs. Maintainers assumed
that the neuropsychological tests have the same
meaning and factor structure across the 16-year time
frame in both groups; our smaller, multi-group sample
does not meet sample size recommendations for testing
measurement invariance [30, 31]. However, using a
latent variable approach and testing measurement
invariance is an important future direction for cognitive
change research, and may yield stronger effects than a
composite approach (e.g., [32]). Other limitations
include only two time points for longitudinal analysis;
limited generalizability in terms of education and race;
and DNAm measured in blood but not the brain,
although blood-brain global DNAm profiles are highly
correlated (r = .86) [33].
In conclusion, these preliminary results suggest PC-
GrimAge and DNAm based pace of aging measures
(Dunedin PoAm and PACE) associate with 16-year,
neuropsychologically-validated cognitive decline in
midlife. The results warrant a larger-scale study to
better examine longitudinal associations between
changes in DNAm measures and changes across
multiple cognitive domains. Ultimately, establishing
DNAm measures as biomarkers of cognitive function in
midlife may offer pre-clinical markers of a molecular
aging mechanism that can help identify individuals at
increased risk for cognitive impairment and dementia in
later life.
METHODS
Participants
Participants were selected from a longitudinal arm of
the Adult Health and Behavior (AHAB)-1 study, which
comprises a registry of behavioral and biological
measurements for the study of midlife individual
differences [34]. AHAB-1 participants were first
recruited at 30–54 years of age via mass-mail
solicitation from southwestern Pennsylvania and were
relatively healthy. Study exclusions at the time of initial
recruitment (time 1) were a reported history of
atherosclerotic cardiovascular disease, chronic kidney
or liver disease, cancer treatment in the preceding year,
and major neurological disorders, schizophrenia, or
other psychotic illness. Other exclusions included
pregnancy and reported use of insulin, glucocorticoid,
antiarrhythmic, psychotropic, or prescription weight-
loss medications. Baseline (T1) assessments occurred
between 2001 and 2005 and follow-up (T2) assessments
began in 2017 and are ongoing, with additional subjects
being added at the time of writing.
Selection of participant groups
Using an extreme groups approach, a subset of AHAB-
1 participants was selected for the current study: 24
Cognitive Decliners (i.e., those who showed the most
decline in cognition from T1 to T2 based on changes in
a cognitive composite score, described below) and 24
matched Cognitive Maintainers (i.e., those who
maintained cognitive composite levels from T1 to T2,
matched to Decliners on demographics and health). The
selection was carried out in the following steps: First,
from the 300 available AHAB-1 participants with both
T1 and T2 data who were enrolled for follow-up (T2)
evaluation between June, 2017 and March, 2020, we
excluded those who reported medical conditions having
potential cognitive sequelae, as might be associated
with Alzheimer’s disease, stroke, transient ischemic
attack, multiple sclerosis, Parkinson’s disease, epilepsy,
brain cancer, or brain cyst, and people who endorsed
having a head injury, concussion, or spinal cord injury.
We also excluded people with diagnosed diabetes or
HbA1c greater than or equal to 7%; individuals who
reported exposure in the previous 12 months to any of
the neurocognitive tests administered here; were
missing more than 3 of 10 cognitive measurements used
in the present analyses; or for whom we lack a stored
T1 blood sample sufficient for DNA extraction and
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DNAm profiling. These exclusions resulted in 167
remaining participants. From the 167, we selected the
24 most extreme cognitive decliners, identified using
the cognitive composite (described below). Next, we
identified the 50 most extreme cognitive maintainers,
and from those 50, matched on sex, race, T1 age, T1
education, T1 cognitive composite, and T1 body mass
index to obtain the matched 24 cognitive maintainers.
One-to-one multivariate matching based on
Mahalanobis distance was performed using the Match
function in R (Matching package) [35]. Matching was
performed without replacement and by randomly
breaking ties. Groups (Decliners, Maintainers) were
identified blind and prior to assessment of DNAm
measures.
Procedure
Sociodemographic, cognitive, psychosocial, and
instrumented biological measurements were collected
over multiple study visits at both T1 and T2. At T1, the
neuropsychological tests used in the present analyses
were administered at visit 1 and blood was drawn at
visit 2. On average, there were 30.85 days between
visits 1 and 2 for the sample analyzed (median = 25.5,
range: 2 to 98). At T2, the neuropsychological tests
used in the present analyses were administered at visits
2 and 3 and blood was drawn at visit 2. On average,
there were 26.1 days between visits 2 and 3 for the
sample analyzed (median = 16.5, range: 8 to 102).
AHAB was approved by the University of Pittsburgh
Institutional Review Board, and all participants
provided written informed consent.
Measures
Demographic and health characteristics
Self-reported sex, race, years of education, and
smoking status were assessed. Measures of height and
weight were obtained to determine body mass index
(in kg/m2).
Cognition
T1 and T2 neuropsychological tests used in the present
analyses capture several domains of cognitive function:
spatial reasoning, working memory, visuomotor
processing speed, executive function, and attention. A
cognition composite was used (described below).
Spatial reasoning
The Matrix Reasoning subtest from the Wechsler
Abbreviated Scale of Intelligence [36, 37] was used to
assess spatial perception and reasoning. This test
involves viewing an incomplete matrix and selecting the
response option that completes the matrix. Higher
scores correspond to better spatial reasoning.
Working memory
Working memory was assessed with the Digit Span
subtest from the Wechsler Adult Intelligence Scale – III
(WAIS-III) [37]. The participant is read sequences of
numbers and is asked to recall the numbers in the same
order (forward) or in reverse order (backward). Higher
scores indicate better working memory.
Visuomotor processing speed
Participants completed the first parts of the Trail
Making Test [38] and the Stroop Color-Word Test [39]
to assess processing speed. Part A (in seconds) of the
Trail Making Test requires participants to draw a line
connecting circles numbered from 1 to 25 as quickly as
possible. Higher scores correspond to poorer processing
speed. The first two parts of the Stroop Color-Word
Test require participants to (A) read aloud a list of color
names (i.e., red, green, blue) printed in black ink and
(B) name the colors of the inks (i.e., “XXXX” written in
blue ink) as quickly as possible. Scores are the number
of correct responses within a 45-second period, with
higher scores indicating better performance.
Executive function
Participants were administered two tests of executive
functioning: task switching on Part B of the Trail
Making Test [38] and the interference score of the
Stroop Color-Word Test [39]. The Trail Making Test
Part B requires subjects to draw a line connecting
numbered and lettered circles as quickly as possible,
alternating between numbers and letters in ascending
numerical and alphabetical order (e.g., 1-A-2-B-3-C…,
etc.). To derive a measure of executive function
relatively independent of psychomotor speed, time to
completion of Part B is subtracted from Part A, such that
higher scores indicate better performance. Assessing
ability to resist cognitive interference, the Stroop Color-
Word Test requires subjects to read aloud as quickly as
possible from 3 pages of color word lists: pages 1 and 2
provide tests of processing speed, previously described.
On Page 3 individuals are asked to report the color of the
ink used to print the name of incongruent colors (e.g.,
“blue” for blue ink used to spell the color name “red”),
thus requiring participants to inhibit a prepotent response
(color word naming). Scores are the number of correct
responses within a 45-second period, with higher scores
indicating better performance.
Attention
Digit Vigilance pages 1 and 2 [40] was administered to
assess vigilant visual tracking and capacity for sustained
attention. This test requires participants to rapidly scan
a page of numbers arrayed in rows and to cross out only
digits designated as targets as quickly as possible. Time
(in seconds) was recorded. Higher scores correspond to
lower performance.
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Cognition composite
A cognition composite was calculated using raw (not
standardized or normed) test scores. First, the Trail
Making Test Part A and Digit Vigilance Times were
multiplied by (-1) so that higher scores correspond to
better performance; then the proportion of maximum
scaling approach [41] was applied to the individual
subtests. This approach transforms each score to a
metric from 0 (minimum observed) to 1 (maximum
observed) by first transforming the score range from 0
to the highest observed value and then dividing by the
highest observed value. The resulting value between 0
and 1 was multiplied by 100. This approach does not
change the multivariate distribution and covariate
matrix of the transformed variables and is the
recommended approach for longitudinal data [42].
The scaled individual tests (Matrix Reasoning, Digit
Span forward and backward, Trail Making Test A and
A-B, Stroop word, Stroop color, and Stroop color-
word, and Digit Vigilance pages 1 and 2) were
averaged together to create a cognition composite
using all available data. At T1, no cognition data were
missing. At T2, 1 participant was missing the Stroop
test and 19 were missing Digit Vigilance pages 1 and
2 and 1 was missing just page 2. Higher composite
scores indicate better cognition. Notably, this
composite approach assumes that the individual
neuropsychological tests have the same meaning and
factor structure over time. The composite’s multilevel
reliability was calculated using coefficient omega
(omegaSEM function in the multilevelTools package)
and was adequate at both the between- (ω = .80, 95%
CI [.62, .98]) and within-person levels (ω = .85, 95%
CI [.79, .91]).
Tissue acquisition and processing
Fasting blood was collected by a trained phlebotomist
between 8:00am and 10:00am. Whole blood samples
were frozen in −80°C until time of DNA extraction and
analysis. DNA was extracted using the DNeasy Blood
and Tissue Kit (Qiagen) at the UCLA Cousins Center
for Psychoneuroimmunology. Purified DNA was
concentrated using GeneJET PCR Purification Kit
(Thermo Fisher) and suspended in the elution buffer to
a minimum of 12.5 ng/ul before plating in a 96-well
plate. DNA was quantified using the Quant-iT dsDNA
Assay Kit, high sensitivity (Invitrogen).
Consideration for variability across assay chips was
addressed by organizing samples from the same
individual to be placed together on the same chip but
randomly assigned by ID. In addition, samples from
Decliners and Maintainers were assured to be evenly
distributed within each chip, and position within chip
was randomized.
DNA methylation data pre-processing
Bisulfite conversion using the Zymo EZ DNA Methylation
Kit (ZymoResearch, Orange, CA, USA) and subsequent
hybridization of the Human Methylation 850 K EPIC chip
(Illumina, San Diego, CA, USA) and scanning (iScan,
Illumina) were performed by the UCLA Neuroscience
Genomics Core facilities according to the manufacturer’s
protocols. DNA methylation image data were processed in
R statistical software (version 4.1.1) using the minfi
Bioconductor package (version 1.38.0) [43]. We checked
for samples with >1% of sites with detection p-values
>0.01 (n = 0) and for samples with DNA methylation
predicted sex discordant with recorded sex (n = 0). The
minfi preprocessNoob function was used to normalize dye
bias and apply background correction before obtaining
methylation beta-values.
Epigenetic clocks and pace of aging measures
The following traditional first- and second-generation
epigenetic clocks were estimated using available online
software (http://dnamage.genetics.ucla.edu/new, with the
“Normalize Data” and “Advanced Analysis” options
selected for blood samples): Horvath (353 CpGs) [10],
Hannum (71 CpGs) [11], PhenoAge (513 CpGs) [14], and
GrimAge (1030 CpGs) [12]. Given the low reliability of
existing epigenetic clocks [15], we used available R code
that uses principal component (PC) analyses to improve
reliability of epigenetic clocks and calculated the following
“PC” clocks: PC-Horvath, PC-Hannum, PC-PhenoAge, and
PC-GrimAge. Finally, we also calculated Dunedin pace of
aging measures using available R code: DunedinPoAm (46
CpGs) [2] and DunedinPACE (173 CpGs) [3].
Covariates
Analyses were adjusted for participant age and sex.
Additionally, because DNAm profiles may differ
between cell subtypes [44] and cell composition
changes with age, the percentages of six cell subtypes
(CD8 total, CD4 total, NK cells, plasma blasts,
monocytes, and granulocytes) were estimated from
Horvath’s website using the Houseman method [45]
(and see [46] for validation) and further controlled for
in sensitivity analyses. Some may consider controlling
for cell subtypes to be unnecessary adjustment or
overadjustment because cell subtypes may contribute to
the observed differences in DNAm or be on a mediation
pathway linking DNAm to aging outcomes; however,
we present results both ways for interested readers.
Data analysis
All analyses were conducted using the traditional and
PC-based epigenetic clocks and pace of aging measures.
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Further mention of DNAm refers to all measures unless
specified.
The DNAm measures were modeled individually in two
multilevel models with repeated measures nested within
person. Model 1 included the main effect of group
(Maintainers, Decliners) and time (T1 and T2) on DNAm.
Model 2 included the interaction between group and time
to explore group differences in change in DNAm over
time. All models controlled for baseline chronological age
(grand mean centered at 44.79 years) and sex (0 = male, 1
= female, as a factor variable). Notably, because these
statistical models control for level 2 (time-invariant)
chronological age and include level 1 (time-varying) time
as a predictor, our findings can be considered in terms of
“age acceleration”, which in cross-sectional studies is
achieved by controlling for chronological age or outputting
residuals from DNAm age regressed on chronological age.
Sensitivity analyses further controlled for the percentages
of six cell subtypes (CD8 T cells, CD4 T cells, NK cells,
plasma blasts, monocytes, and granulocytes), treated as
time-varying covariates.
Statistical analyses were conducted in R version 4.1.1
using the nlme package (version 3.1.152). The variance-
covariance structure was modeled as a random intercept
in all models. Gamma weights (γ), analogous to
unstandardized beta weights (i.e., a 1-unit change in the
predictor [Decliner vs. Maintainer, or T1 vs. T2] is
associated with γ-year change in the outcome), are
reported with their 95% confidence intervals (CIs) in
tables. We adjusted for multiple comparisons using the
Benjamini-Hochberg (BH) correction (using the
p.adjust function in R) [18]. To examine different levels
of stringency, false discovery rates (FDRs) of .05 and
.10 were calculated and chosen to ensure no true
discoveries were missed while balancing the number of
false positives. FDRs can be interpreted as the expected
proportion of false positives among all statistically
significant tests.
Power considerations
We selected 24 participants per group to balance funding
constraints with generating preliminary data. Although
we maximized our ability to detect effects by selecting
cognitive groups from extremes of the distribution of
change in cognitive performance, the smaller sample size
affects our power nonetheless. There is no conventional
method for computing power in a multilevel model;
however, for a parallel two-group independent t-test with
24 participants per group and alpha set to .05, power of
0.80 can detect approximately Cohen’s d = 0.82 (see
power curve plotted in Supplementary Figure 1).
Therefore, the current study was powered to detect large
effects for comparing DNAm measures between groups;
we had low statistical power to explore group by time
interactions on DNAm measures.
Abbreviations
AHAB: Adult Health and Behavior; CI: confidence
interval; DNAm: DNA methylation; PACE: pace of
aging calculated from the epigenome; PC: principal
component; PoAm: pace of aging from methylation; T:
time; WAIS: Wechsler Adult Intelligence Scale.
AUTHOR CONTRIBUTIONS
R.G.R.: Conceptualization, funding acquisition, data
curation, investigation, methodology, formal analysis,
visualization, writing the original draft. J.E.C., A.L.M,
and S.B.M.: Conceptualization, methodology,
resources, reviewing and editing the manuscript.
ACKNOWLEDGMENTS
R.G.R acknowledges training received from the
University of Michigan Genomics for Social Scientists
Workshop (NIA R25 AG053227). R.G.R. also thanks
Kelly Rentscher for assistance with DNAm processing
R code and Albert Higgins-Chen and Kyra Thursh for
assistance with PC-Clock R code.
CONFLICTS OF INTEREST
The authors declare no conflicts of interest related to
this study.
ETHICAL STATEMENT AND CONSENT
AHAB was approved by the University of Pittsburgh
Institutional Review Board, and all participants
provided written informed consent.
FUNDING
This work was supported by the National Institute on
Aging (HL040962, AG056043, AG056635) and a
University of Pittsburgh Momentum Funds Grant.
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www.aging-us.com 9437 AGING
SUPPLEMENTARY MATERIALS
Introduction
Overview of DNAm clocks
The DNAm clock measures were developed using
supervised machine learning techniques to derive
algorithms that capture DNAm patterns that predict a
dependent variable of interest, or a surrogate of
“biological age”. The dependent variables differ across
the different types of clocks.
First-generation clocks
The first-generation clocks were trained to predict
chronological age.
Hannum et al. [1] developed an epigenetic clock (71
CpGs) using whole blood samples from 656 individuals
(426 Caucasian and 120 Hispanic) aged 19 to 101. The
Hannum clock used in the current study does not
include cell distribution data. However, for
completeness, there is a version of the Hannum clock
known as extrinsic epigenetic age acceleration (EEAA)
that is a weighted average of Hannum’s estimate with
naïve and exhausted CD8 T cells and plasma blasts and
adjusted for chronological age [2].
Horvath [3] developed a multi-tissue epigenetic clock
(353 CpGs) from 8,000 samples (82 different datasets)
representing people across the lifespan. The Horvath
clock used in the current study does not include cell
distribution data; there is a version of the Horvath clock
defined as the residual resulting from regressing
Horvath’s DNAm age on chronological age and 7 blood
cell types (naïve and exhausted CD8 T cells, plasma
blasts, CD4 T cells, NK cells, monocytes, and
granulocytes) and is known as intrinsic epigenetic age
acceleration (IEAA) [4].
Second-generation clocks
The second-generation clocks were optimized for
lifespan prediction. Levine et al. [5] proposed the
“PhenoAge” clock, which was developed in two steps.
First, using data from the National Health and Nutrition
Examination Survey (9,926 people ages 20 and over),
they developed a measure of “phenotypic age” by
selecting from 42 blood-based clinical markers those that
predicted mortality. Based on this analysis, 9 blood-based
clinical markers (see table below) and chronological age
were selected and combined into a phenotypic age
estimate and validated in a new sample to predict all-
cause mortality. In the second step, data from 465
participants aged 21–100 years in the Invecchiare in
Chianti (InCHIANTI) study were used to regress
phenotypic age on CpG sites. From this, the PhenoAge
clock (513 CpGs) was developed, which strongly relates
to all-cause mortality and aging-related morbidity [5].
Phenotypic age
Role
Albumin
Liver
Alkaline phosphatase
Liver
Creatinine
Kidney
Glucose, serum
Metabolic
C-reactive protein
Inflammation
Lymphocyte percent
Immune
Mean (red) cell volume
Immune
Red cell distribution width
Immune
White Blood cell count
Immune
Lu et al. [6] developed the “GrimAge” epigenetic clock
in two steps. First, DNAm-based surrogates for self-
reported smoking pack-years and a selection of plasma
proteins associated with morbidity and mortality were
constructed from 2,356 individuals from the
Framingham Heart Study offspring cohort (average age:
66 years). Second, time-to-death due to all-cause
mortality was regressed on age, sex, DNAm-based
pack-years, and 7 DNAm-based surrogate plasma
markers (see table below). The resulting mortality risk
estimate was transformed into an age estimate, called
GrimAge (1030 CpGs).
DNAm based surrogates for plasma proteins
Role
Adrenomedullin
Multiple functions
Beta-2-microglublin
Immune
www.aging-us.com 9438 AGING
Cystatin C
Kidney
GDF-15
Stress response
Leptin
Metabolic
Plasminogen activator inhibitor-1 (PAI-1)
Fibrinolytic
Tissue inhibitor matrix metalloproteinase 1 (TIMP-1)
Matrix regulation
Pace of aging measures
Most recently, “pace of aging” measures were
developed, which have been referred to as the third-
generation of DNAm clocks. Pace of aging measures
differ from first- and second-generation clocks in that
they are trained to predict longitudinal biomarker data.
Belsky and colleagues developed the Dunedin PoAm
(Pace of Aging from methylation; [7]) and Dunedin
PACE (Pace of Aging Calculated from the Epigenome;
[8]) measures. Both measures were developed using the
Dunedin Study (52% male, 93% white), a longitudinal
investigation of individuals born between April 1972
and March 1973 in Dunedin, New Zealand.
The pace of aging measures were developed in two
steps, with slight differences highlighted. First, mixed-
effects growth curve models were used to estimate
longitudinal changes over time in many blood-
chemistry and organ-system-function biomarkers across
physiological systems (18 biomarkers for Dunedin
PoAm; 19 biomarkers for Dunedin PACE – see table
below). Biomarkers for Dunedin PoAm were measured
across 12 years, at ages 26, 32, and 38. Biomarkers for
Dunedin PACE were measured across 20 years, at ages
26, 32, 38, and 45. In other words, these measures were
trained in a cohort of same-aged individuals. The slopes
were composited across the 18 or 19 biomarkers to
calculate a participant’s “pace of aging” across 12 years
(Dunedin PoAm) or 20 years (Dunedin PACE). Second,
elastic-net regression analyses were used to select CpGs
that predict the longitudinal pace of aging measures,
resulting in Dunedin PoAm (46 CpGs) and Dunedin
PACE (173 CpGs). Additional details for developing
Dunedin PACE, including the selection of reliable CpG
probes, are discussed in Belsky et al. [8].
(Bio)marker
Role
Dunedin PoAm
Dunedin PACE
Glycated hemoglobin (HbA1C)
Metabolic
X
X
Cardiorespiratory fitness (VO2Max)
Cardiovascular
X
X
Waist-hip ratio
Anthropometric
X
X
Body mass index
Anthropometric
X
X
FEV1/FVC ratio
Pulmonary
X
X
FEV1
Pulmonary
X
X
Mean arterial pressure
Cardiovascular
X
X
Leukocyte telomere length
Immune
X
(not included)
Creatinine clearance (eGFR)
Kidney
X
X
Blood urea nitrogen
Kidney
X
X
Triglycerides
Metabolic
X
X
Total cholesterol
Metabolic
X
X
HDL cholesterol
Metabolic
X
X
Lipoprotein (a)
Metabolic
X
X
Apolipoprotein B100/A1 ratio
Metabolic
X
X
Gum health (combined attachment loss)
Periodontal
X
X
Caries-affected tooth surfaces
Periodontal
(not included)
X
White blood cell count
Immune
X
X
High-sensitivity C-reactive protein
Inflammation
X
X
Leptin
Metabolic
(not included)
X
www.aging-us.com 9439 AGING
Principal components (PC)-based clocks
Traditional epigenetic clocks use individual CpG sites as
inputs to the epigenetic age algorithms, but individual
CpGs are unreliable and noisy [9]. Therefore, Higgins-
Chen et al. proposed [10] that principal components
analysis (PCA) can be used to enhance the reliability of
traditional epigenetic clocks by extracting shared
systematic variation across CpG sites (principal
components, PCs) and feeding those PCs into the elastic
net regressions to predict chronological age or other health
phenotype. Higgins-Chen et al. provides R code that has
users project their own DNAm data onto the original PCA
space, which then allows PC-based clock outcomes to be
estimated from new data. PC-based clocks show
agreement between technical replicates (the same sample
measured twice) within 0 to 1.5 years and more stable
trajectories in longitudinal studies [10]. PC-based clocks
have been used in other published studies (e.g., [11]).
Supplementary Results
Normed neuropsychological test scores
The average normed scores for several individual
neuropsychological tests at Time 1 and Time 2 are
displayed below for each cognitive group (Decliners,
Maintainers). The normed scores are represented as T-
scores (M[SD] = 50 [10]), with corresponding z-scores
and percentile information.
At T1, both cognitive groups had average or slightly
above average normed test scores; when averaged
across individual tests, Decliners were at the 60th
percentile and Maintainers the 62nd percentile. At T2,
Decliners were at the 49th percentile whereas
Maintainers were at the 73nd percentile.
Decliners
Time 1 (T1)
Time 2 (T2)
T-score
z-score
Percentile
T-score
z-score
Percentile
Matrix Reasoning
57.58
0.76
77.6
59.21
0.92
82.2
Digit Span total
57.4
0.74
77.0
51.4
0.14
55.6
Stroop Word
48.79
−0.12
45.2
43.96
−0.60
27.3
Stroop Color
48.25
−0.18
43.1
44.46
−0.55
29
Stroop Color-Word
51.25
0.13
55
49.96
0
49.8
Average
52.65
0.266
59.58
49.79
−0.018
48.78
Maintainers
Time 1 (T1)
Time 2 (T2)
T-score
z-score
Percentile
T-score
z-score
Percentile
Matrix Reasoning
58.21
0.82
79.4
63.42
1.3
91.0
Digit Span total
54.3
0.43
66.6
57.2
0.72
76.5
Stroop Word
53.08
0.31
62.1
52.65
0.27
60.5
Stroop Color
51.67
0.17
56.6
52.35
0.24
59.3
Stroop Color-Word
48.88
−0.11
45.5
57.13
0.71
76.2
Average
53.23
0.32
62.04
56.55
0.65
72.7
Supplementary References
1. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G,
Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y,
Deconde R, Chen M, Rajapakse I, et al. Genome-wide
methylation profiles reveal quantitative views of
human aging rates. Mol Cell. 2013; 49:359–67.
https://doi.org/10.1016/j.molcel.2012.10.016
PMID:23177740
2. Chen BH, Marioni RE, Colicino E, Peters MJ, Ward-
Caviness CK, Tsai PC, Roetker NS, Just AC, Demerath
EW, Guan W, Bressler J, Fornage M, Studenski S, et al.
DNA methylation-based measures of biological age:
meta-analysis predicting time to death. Aging (Albany
NY). 2016; 8:1844–65.
https://doi.org/10.18632/aging.101020
PMID:27690265
3. Horvath S. DNA methylation age of human tissues
and cell types. Genome Biol. 2013; 14:R115.
https://doi.org/10.1186/gb-2013-14-10-r115
PMID:24138928
4. Horvath S, Gurven M, Levine ME, Trumble BC,
Kaplan H, Allayee H, Ritz BR, Chen B, Lu AT,
Rickabaugh TM, Jamieson BD, Sun D, Li S, et al. An
epigenetic clock analysis of race/ethnicity, sex, and
coronary heart disease. Genome Biol. 2016;
17:171.
https://doi.org/10.1186/s13059-016-1030-0
PMID:27511193
www.aging-us.com 9440 AGING
5. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL,
Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y,
Whitsel EA, Wilson JG, Reiner AP, et al. An epigenetic
biomarker of aging for lifespan and healthspan. Aging
(Albany NY). 2018; 10:573–91.
https://doi.org/10.18632/aging.101414
PMID:29676998
6. Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K,
Hou L, Baccarelli AA, Li Y, Stewart JD, Whitsel EA,
Assimes TL, Ferrucci L, Horvath S. DNA methylation
GrimAge strongly predicts lifespan and healthspan.
Aging (Albany NY). 2019; 11:303–27.
https://doi.org/10.18632/aging.101684
PMID:30669119
7. Belsky DW, Caspi A, Arseneault L, Baccarelli A,
Corcoran DL, Gao X, Hannon E, Harrington HL,
Rasmussen LJ, Houts R, Huffman K, Kraus WE, Kwon
D, et al. Quantification of the pace of biological aging
in humans through a blood test, the DunedinPoAm
DNA methylation algorithm. Elife. 2020; 9:e54870.
https://doi.org/10.7554/eLife.54870
PMID:32367804
8. Belsky DW, Caspi A, Corcoran DL, Sugden K, Poulton
R, Arseneault L, Baccarelli A, Chamarti K, Gao X,
Hannon E, Harrington HL, Houts R, Kothari M, et al.
DunedinPACE, a DNA methylation biomarker of the
pace of aging. Elife. 2022; 11:e73420.
https://doi.org/10.7554/eLife.73420
PMID:35029144
9. Sugden K, Hannon EJ, Arseneault L, Belsky DW,
Corcoran DL, Fisher HL, Houts RM, Kandaswamy R,
Moffitt TE, Poulton R, Prinz JA, Rasmussen LJH,
Williams BS, et al. Patterns of reliability: Assessing the
reproducibility and integrity of DNA methylation
measurement. Patterns. Elsevier; 2020; 1. Available
from:
https://www.cell.com/patterns/abstract/S2666-
3899(20)30014-3.
10. Higgins-Chen AT, Thrush KL, Wang Y, Minteer CJ, Kuo
PL, Wang M, Niimi P, Sturm G, Lin J, Moore AZ,
Bandinelli S, Vinkers CH, Vermetten E, et al. A
computational solution for bolstering reliability of
epigenetic clocks: Implications for clinical trials and
longitudinal tracking. Nat Aging. 2022; 2:644–61.
https://doi.org/10.1038/s43587-022-00248-2
PMID:36277076
11. Pang APS, Higgins-Chen AT, Comite F, Raica I,
Arboleda C, Went H, Mendez T, Schotsaert M,
Dwaraka V, Smith R, Levine ME, Ndhlovu LC, Corley
MJ. Longitudinal Study of DNA Methylation and
Epigenetic Clocks Prior to and Following Test-
Confirmed COVID-19 and mRNA Vaccination. Front
Genet. 2022; 13:819749.
https://doi.org/10.3389/fgene.2022.819749
PMID:35719387
www.aging-us.com 9441 AGING
Supplementary Figure
Supplementary Figure 1. Power curve for two-sample independent t-test with 24 participants per group and α = .05. Dashed
horizontal line indicates power of 0.80.
www.aging-us.com 9442 AGING
Supplementary Tables
Supplementary Table 1. Main effects of group and time for traditional epigenetic clocks.
Horvath
Hannum
PhenoAge
GrimAge
γ (CI)
p
γ (CI)
p
γ (CI)
p
γ (CI)
p
Intercept
47.40
(45.49– 49.31)
<0.001
39.65
(37.98–41.32)
<0.001
34.80
(32.47–37.13)
<0.001
48.35
(46.06–50.65)
<0.001
Female
−0.89
(−2.93–1.16)
0.398
−2.83
(−4.61–−1.05)
0.003
−0.58
(−3.02–1.87)
0.646
−1.56
(−4.05–0.93)
0.226
Baseline Age
0.81
(0.65–0.98)
<0.001
0.88
(0.74–1.03)
<0.001
1.12
(0.93–1.31)
<0.001
0.81
(0.61–1.01)
<0.001
Group-Decliners
−1.17
(−3.19–0.86)
0.265
−1.58
(−3.35–0.19)
0.086
−0.03
(−2.45–2.40)
0.984
1.91
(−0.56–4.38)
0.136
Time
12.75
(11.86–13.64)
<0.001
13.11
(12.29–13.93)
<0.001
15.30
(13.91–16.70)
<0.001
12.42
(11.70–13.15)
<0.001
Random Effects
σ2
4.71
3.99
11.54
3.15
τ00
9.76 ahabid
7.19 ahabid
11.58 ahabid
16.38 habid
N
48 ahabid
48 ahabid
48 ahabid
48 ahabid
Observations
96
96
96
96
95% Confidence Intervals (CI) are reported.
Supplementary Table 2. Main effects of group and time on PC-clocks and pace of aging measures, controlling for cell
percentages.
PC-Horvath
PC-Hannum
PC-PhenoAge
PC-GrimAge
Dunedin PoAm
Dunedin PACE
γ (CI)
p
γ (CI)
p
γ (CI)
p
γ (CI)
p
γ (CI)
p
γ (CI)
p
Intercept
49.26
(47.71–50.81)
<0.001
55.18
(53.63–56.73)
<0.001
45.66
(43.39–47.93)
<0.001
58.91
(57.14–60.67)
<0.001
1.00
(0.96–1.04)
<0.001
0.90
(0.84–0.96)
<0.001
Female
−3.06
(−4.70–−1.41)
0.001
−2.51
(−4.16–−0.87)
0.006
−1.83
(−4.28–0.61)
0.163
−2.09
(−4.01–−0.17)
0.045
0.01
(-0.03–0.05)
0.742
−0.01
(−0.07–0.06)
0.882
Baseline Age
0.84
(0.71–0.97)
<0.001
0.86
(0.73–0.99)
<0.001
1.06
(0.86–1.25)
<0.001
0.79
(0.64–0.94)
<0.001
0.00
(-0.00–0.00)
0.399
0.00
(−0.00–0.01)
0.677
Group-
Decliners
−0.59
(−2.23–1.04)
0.495
−0.22
(−1.85–1.41)
0.799
1.66
(−0.77–4.09)
0.201
2.15
(0.24–4.05)
0.038
0.04
(0.00–0.09)
0.048
0.08
(0.01–0.14)
0.030
Time
11.25
(10.58–11.91)
<0.001
11.74
(11.07–12.41)
<0.001
13.73
(12.97–14.48)
<0.001
12.18
(11.74–12.61)
<0.001
0.02
(0.01–0.04)
0.012
0.03
(0.00–0.06)
0.030
CD8 T cell
−11.78
(−94.81–71.25)
0.789
−14.70
(−98.24–68.84)
0.740
67.84
(−27.49–163.18)
0.184
−10.84
(−66.86–45.17)
0.715
−1.83
(−3.70–0.03)
0.069
−0.59
(−3.90–2.72)
0.738
CD4 T cell
6.63
(−36.09–49.34)
0.770
19.75
(−23.22–62.73)
0.388
48.93
(−0.11–97.98)
0.065
21.35
(−7.47–50.16)
0.167
−0.67
(−1.63–0.29)
0.192
0.28
(−1.43–1.98)
0.760
NK cell
30.17
(−17.47–77.80)
0.236
49.66
(1.73–97.58)
0.056
78.80
(24.13–133.47)
0.009
23.22
(−8.89–55.34)
0.177
−0.20
(−1.27–0.87)
0.720
0.16
(−1.74–2.06)
0.873
Plasma
Blasts
0.28
(−5.08–5.64)
0.921
1.47
(−3.92–6.86)
0.606
0.36
(−5.81–6.54)
0.912
0.55
(−3.09–4.18)
0.776
−0.10
(−0.22–0.03)
0.141
0.03
(−0.19–0.24)
0.819
Monocytes
6.43
(−47.64–60.50)
0.822
28.13
(−26.27–82.53)
0.332
70.73
(8.56–132.89)
0.037
27.31
(−9.25–63.87)
0.163
−0.74
(−1.96–0.47)
0.252
−0.30
(−2.45–1.86)
0.795
Granulocytes
19.49
(−17.07–56.05)
0.317
35.31
(−1.48–72.09)
0.075
80.82
(38.88–122.76)
0.001
33.02
(8.40–57.65)
0.015
−0.09
(−0.91–0.73)
0.836
0.53
(−0.93–1.99)
0.494
Random Effects
σ2
1.97
1.99
2.48
0.84
0.00
0.00
τ00
6.75 ahabid
6.69 ahabid
15.96 ahabid
10.22 ahabid
0.00 ahabid
0.01 ahabid
N
48 ahabid
48 ahabid
48 ahabid
48 ahabid
48 ahabid
48 ahabid
Obs
96
96
96
96
96
96
95% Confidence Intervals (CI) are reported.
www.aging-us.com 9443 AGING
Supplementary Table 3. Scaled cognitive components for decliners (n = 24) and maintainers (n = 24).
Scaled cognitive component
n missa
Overall
Decliners
Maintainers
p-valueb
Matrix Reasoning
T1
0
74.31 (12.21)
74.23 (10.04)
74.38 (14.28)
0.966
T2
0
70.45 (13.93)
65.59 (15.81)
75.31 (9.87)
0.014
Digit Span – Forward
T1
0
52.84 (24.93)
56.44 (21.94)
49.24 (27.59)
0.322
T2
0
50.19 (21.22)
43.56 (19.14)
56.82 (21.49)
0.029
Digit Span – Backwards
T1
0
53.82 (24.31)
56.25 (24.97)
51.39 (23.91)
0.494
T2
0
44.44 (19.85)
40.63 (15.98)
48.26 (22.79)
0.185
Trail Making Test (A)
T1
0
95.86 (2.70)
95.19 (2.90)
96.52 (2.37)
0.087
T2
0
94.35 (4.90)
92.36 (5.97)
96.34 (2.28)
0.004
Trail Making Test (A-B)
T1
0
87.18 (9.52)
86.80 (10.80)
87.57 (8.26)
0.784
T2
0
84.28 (12.45)
80.82 (15.13)
87.73 (7.94)
0.053
Stroop Word
T1
0
53.19 (15.60)
49.67 (16.37)
56.70 (14.27)
0.12
T2
1
43.83 (16.42)
35.69 (15.47)
52.33 (12.88)
<0.001
Stroop Color
T1
0
57.19 (14.90)
54.61 (14.59)
59.77 (15.07)
0.234
T2
1
50.08 (17.17)
42.46 (16.25)
58.02 (14.52)
0.001
Stroop Color-Word
T1
0
38.24 (15.86)
40.63 (17.53)
35.85 (13.96)
0.302
T2
1
36.14 (17.40)
29.47 (14.70)
43.09 (17.55)
0.006
Digit Vigilance – page 1
T1
0
79.20 (12.61)
81.17 (11.04)
77.23 (13.96)
0.284
T2
19
71.42 (19.84)
50.24 (24.60)
79.49 (9.59)
<0.001
Digit Vigilance – page 2
T1
0
78.65 (16.08)
81.15 (11.44)
76.16 (19.61)
0.287
T2
20
72.18 (17.50)
53.70 (18.13)
78.34 (12.46)
<0.001
Means and standard deviations (SD, in parentheses) are displayed for all scaled cognitive measures (scaled using the
proportion of maximum scaling method, range: 0-100, see Methods). Higher scores indicate better performance. T1 = time
1; T2 = time 2. aAt T1, no cognition data were missing. At T2, 1 participant was missing the Stroop test and 19 were missing
Digit Vigilance pages 1 and 2 and 1 was missing just page 2. bp-value comparing groups using dependent t-tests. p-values are
bold if <0.05.
www.aging-us.com 9444 AGING
Supplementary Table 4. Main effects of scaled T2 cognitive components on PC-GrimAge and pace of aging measures,
controlling for cell percentages.
PC-GrimAge
Dunedin PoAm
Dunedin PACE
γ (CI)
p
γ (CI)
T2 Predictors
γ (CI)
p
Matrix Reasoning
−0.066
(−0.138–0.005)
0.086
−0.001
(−0.003–0.001)
0.255
−0.003
(−0.005–−0.001)
0.021
DS-Forward
−0.024
(−0.074–0.025)
0.356
−0.000
(−0.001–0.001)
0.794
−0.001
(−0.003–0.001)
0.278
DS-Backward
−0.034
(−0.086–0.019)
0.229
−0.000
(−0.001–0.001)
0.602
−0.001
(−0.003–0.001)
0.277
Trail A
−0.177
(−0.385–0.030)
0.113
−0.004
(−0.008–0.001)
0.140
−0.008
(−0.015–−0.001)
0.045
Trail A-B
−0.110
(−0.184–−0.035)
0.008
−0.002
(−0.004–−0.001)
0.012
−0.004
(−0.006–−0.001)
0.012
Stroop Word
−0.059
(−0.119–0.002)
0.074
−0.001
(−0.002–0.000)
0.116
−0.002
(−0.005–−0.000)
0.038
Stroop Color
−0.060
(−0.120–0.000)
0.066
−0.001
(−0.002–0.000)
0.087
−0.001
(−0.004–0.001)
0.225
Stroop Color-Word
−0.079
(−0.133–−0.024)
0.009
−0.001
(−0.002–0.000)
0.097
−0.002
(−0.004–0.000)
0.087
Digit Vigilance-pg1
−0.056
(−0.131–0.018)
0.173
−0.001
(−0.003–0.000)
0.165
−0.001
(−0.004–0.002)
0.466
Digit Vigilance-pg2
−0.068
(−0.146–0.010)
0.122
−0.002
(−0.004–0.000)
0.123
−0.002
(−0.005–0.001)
0.227
95% Confidence Intervals (CI) are reported. Models included female, baseline age, time, and cell percentages (estimates not
shown). Higher scaled cognitive scores indicate better performance. Abbreviation: DS: digit span.
