DNA methylation age acceleration is associated with incident cognitive impairment in the health and retirement study

Abstract

Background

DNA methylation clocks have emerged as promising biomarkers for cognitive impairment and dementia. Longitudinal studies exploring the association between DNA methylation clocks and cognitive decline have been constrained by limited sample sizes and a lack of diversity.

Objective

Our study aimed to investigate associations between DNA methylation clocks and incident cognitive impairment using a larger and US nationally-representative sample from the Health and Retirement Study.

Methods

We measured DNA methylation age acceleration in 2016 by regressing the DNA methylation clocks, including GrimAge, against chronological age. Cognitive change over time was determined by Langa-Weir cognition status from 2016 to 2018. Multivariable logistic regression evaluated the association between DNA methylation age acceleration and cognitive change, adjusting for cell-type proportions, demographic, and health factors. We also applied inverse probability weighting to address potential selection bias from varying loss-to-follow-up rates.

Results

The analytic sample (N = 2713) was 54% female, 8.4% Black/African American, 86% White, 7.5% Hispanic, and 68 years old at baseline. During the two years of follow-up, 12% experienced cognitive change and had higher baseline GrimAge (mean = 1.2 years) acceleration compared to those maintaining normal cognition (mean = −0.8 years). A one-year increase in GrimAge acceleration was associated with 1.05 times higher adjusted and survey-weighted odds of cognitive change during follow-up (95% CI: 1.01–1.10). This association was consistent after accounting for loss-to-follow-up (OR = 1.07, 95% CI: 1.04–1.11).

Conclusions

Our study offers insights into DNA methylation age acceleration associated with cognitive change over time, suggesting avenues for improved prevention, diagnosis, and treatment.

Keywords

Alzheimer's disease cognitive impairment cohort dementia DNA methylation age epigenetics

Introduction

The aging population structure in the United States (US) is leading to an increasing public health challenge, especially with age-related diseases like dementia. In 2020, it was estimated that around 6.07 million individuals in the US had clinical Alzheimer's disease, and another 12.23 million exhibited mild cognitive impairments.¹ By 2060, these figures are projected to rise to 13.85 million and 21.55 million, respectively.¹ Both Alzheimer's disease and cognitive impairments severely restrict an individual's quality of life and necessitate substantial caregiving. Despite extensive research, there are only a few effective treatments available, and those that do exist seem to work best when administered early in the progression of the disease.² Therefore, pinpointing those at high risk for developing dementia or cognitive impairments is crucial for suggesting preventive measures and initiating timely interventions.

A promising biomarker for cognitive impairment and dementia is epigenetic aging. Rather than altering the DNA sequence itself, epigenetic factors like histone modifications and DNA methylation modify the regulation and expression of DNA. Among these, DNA methylation patterns have been particularly adept at forecasting chronological age and mortality risks.³ By integrating DNA methylation levels from multiple genomic sites, DNA methylation clocks attempt to provide a more accurate depiction of biological aging.⁴ Residualizing DNA methylation clocks against chronological age creates a measure of whether an individual's DNA methylation age is older (accelerated aging) or younger (not accelerated) than would be expected.^5,6 The initial and first-generation of DNA methylation clocks, including Horvath and Hannum, were developed to predict chronological age based solely on DNA methylation patterns.^7,8 The second generation, often termed ‘phenotypic clocks,’ went beyond the prediction of chronological age and were designed to forecast age-related health decline as well as all-cause mortality, by training on additional lifestyle and health factors besides DNA methylation patterns.^7–9 Consequently, these updated clocks might serve as more reliable biomarkers for cognitive deterioration and dementia.

Prior research has explored the associations between DNA methylation clocks and outcomes related to dementia and cognition, yet the findings have been inconclusive.¹⁰ A recent systematic review identified 10 studies examining associations between DNA methylation clocks and dementia, including Alzheimer's disease, cognitive impairment, and frontotemporal dementia.¹⁰ Among these, only four had a longitudinal design, with just one reporting a significant finding.^11–14 Moreover, the sample sizes of these longitudinal studies were smaller, ranging from 52 to 578 participants.¹⁰ In contrast, larger cross-sectional studies (ranging from 29 to 4535 participants) investigating DNA methylation age and cognitive metrics have yielded stronger associations, particularly with second-generation clocks like GrimAge.¹⁰ However, for DNA methylation age acceleration to function as a viable biomarker, differences must precede clinical cognitive decline.

To bridge these knowledge gaps, we examined the relationships between DNA methylation clocks and the onset of cognitive change. This analysis involved 2713 participants from the Health and Retirement Study (HRS), a nationally representative longitudinal cohort study focused on older adults in the US.

Methods

Study population and design

This study is based on data collected from the 2016 and 2018 waves of the HRS. The HRS, funded by the National Institute of Aging and the Social Security Administration, is a longitudinal panel study of older adults (>50 years of age) in the US, with sample replenishment every six years with younger cohorts.¹⁵ HRS collects demographic, economic, and health metrics every two years, with participants alternating between face-to-face interviews and telephone surveys, such that each participant completes a face-to-face interview every 4-years. In 2016, all community-dwelling HRS participants who completed an interview and did not respond via proxy were asked to consent to a venous blood draw. A total of 9934 participants consented. From the banked venous blood samples, the HRS team selected a non-random subsample (N = 4104), designed to be representative of the full HRS sample, for DNA methylation assays. The DNA methylation sample included all the participants of the 2016 cognitive assessments.¹⁶

We considered DNA methylation measures in 2016 as the study baseline and we followed-up cognitive status into the 2018 wave (most recent available). All participants in the HRS provide informed consent. The current analysis of existing publicly available data was approved by the University of Michigan Institutional Review Board (HUM00128220).

DNA methylation age measures

The primary exposure variable for this analysis was DNA methylation age acceleration. DNA methylation was measured using the Infinium Methylation EPIC BeadChip at the University of Minnesota. To minimize batch confounding, samples were randomized across plates by demographic variables (age, cohort, sex, education, race/ethnicity). To assess technical variability, 40 pairs of blinded technical replicate samples were included; technical replicates showed high correlation (>0.97) across all DNA methylation sites. DNA methylation data were preprocessed and quality control was performed by HRS staff,¹⁶ using the minfi package in R.¹⁷ Briefly, DNA methylation probes with a detection P-value below 0.01 were excluded from the final data set (29,431 sites excluded out of 866,091 sites measured). Among 4104 people whose DNA methylation data were collected, 58 samples which failed preprocessing with unsatisfactory probe performance (5% of sites below detection p-value threshold), control samples (blinded duplicates, cell lines), and samples who had a mismatch between DNA-methylation sex and self-reported sex were excluded, leaving 4018 samples with quality-controlled DNA methylation data. Prior to estimation of DNA methylation clocks, the HRS imputed missing DNA methylation probe values using the mean value of the given probe across all samples.¹⁶

Thirteen epigenetic clocks were constructed by the HRS. We focused on five widely recognized epigenetic clocks based on a priori hypotheses. Our primary interest was in one phenotypic clock: GrimAge.⁶ GrimAge showed significant relationships with many aging-related health conditions and performed well in predicting all-cause mortality in various population studies.^6,18 Existing research also reported that GrimAge outperforms other DNA methylation clocks when examining the relationship with cognitive health.⁹ As a secondary analysis, we also included other two phenotypic (second-generation) and two chronological clocks (first-generation): DunedinPoAm (MPOA),¹⁸ and PhenoAge,¹⁹ Horvath²⁰ and Hannum.²¹ Four of the clocks provided DNA methylation age estimates in years. MPOA was provided as a rate, and we transformed it to years by dividing by the participant age to be consistent with the remaining clocks.¹⁸

To account for chronologic age and compute DNA methylation age acceleration, we regressed each clock against the chronological age at the time of measurement, and we extracted the residuals.^5,6 We visualized patterns in participant measures in the clocks, chronologic age, and DNA methylation age acceleration (residuals) using scatter plots and computed Pearson correlation coefficients (Supplemental Figure 1).We examined the pairwise correlation between five epigenetic clocks using a Pearson correlation plot (Supplemental Figure 2). In descriptive analyses, we dichotomized accelerated DNA methylation ages (residuals greater than 0) and non-accelerated DNA methylation ages (residuals 0 or less).

Cognitive measures

Cognition was measured at baseline in 2016 and at follow-up in 2018 using the Langa-Weir three-level cognition status (normal cognition, cognitively impaired non-dementia, and dementia).²² Langa-Weir classification is a recommended method to measure cognitive function from HRS²³ and it has been widely used in various research studies on cognitive health.^24–31 Each wave, the HRS team provides imputed values for missing cognitive tasks, which are then included in an overall 27-point score.³² The Langa-Weir cognition status is calculated differently for self-respondents and proxy respondents. No proxy respondents were included in the baseline wave, though 23 respondents transitioned to proxy status due to cognition concerns in 2018. Of these 23 respondents, nine were categorized as having dementia, and seven each were classified as cognitive impairment, non-dementia and normal cognition. For self-respondents, a 27-point cumulative cognition score based on four cognitive tasks administered to participants is used for classification: immediate and delayed 10-noun free recall task (0–20 points), serial sevens subtraction task (0–5 points), and backward counting task (0–2 points). The 27-point summary score is then categorized into normal cognition (12–27 points), cognitive impairment non-dementia (7–11 points) and dementia (0–6 points).

While no participants from the 2016 baseline venous blood draw sample were proxy respondents, 23 respondents transitioned to proxy status in the 2018 wave. For those proxy respondents, Langa-Weir cognition status was determined using an 11-point scale, constructed using a proxy's assessment of the subject's memory (ranging from excellent to poor), limitations in instrumental activities of daily living, and an interviewer's assessment of the subject's cognitive impairment. Higher scores indicated worse cognition. As with the score for self-respondents, missing data is addressed through imputation. The aggregate 11-point score is then classified into normal cognition (0–2 points), cognitive impairment non-dementia (3–5 points), and dementia (6–11 points).²² The cognitive status measure that has been clinically validated with a 74% sensitivity.²²

We visualized transitions between cognitive states between baseline and follow-up using an alluvial plot. For analyses of cognitive change over time, we excluded participants with dementia at baseline, since they could not undergo any further cognitive decline, as well as those with a stable, non-normal cognitive conditions. To examine cognitive change between baseline and follow-up, we categorized participants who underwent any decline in Langa-Weir cognitive status between 2016 and 2018 – specifically, transitions from normal to impairment, normal to dementia, or impairment to dementia. This category was contrasted with participants who consistently maintained a cognitively normal status (remained cognitively normal from 2016 to 2018). For clinical translation, our primary analyses used these Langa-Weir cognitive status transitions as our outcome measure.

In sensitivity analyses, we also assessed the association using the 27-point continuous cognitive score as the outcome measure. Specifically, we examined the score at the end of follow-up (2018) and separately examined the difference in scores between baseline (2016) and follow-up (2018).

Covariate measures

We sought to identify potential confounders and precision variables. Considering that each cell type possesses its own distinct DNA methylation profile, which can be associated with health outcomes, it's crucial to adjust for cell type distributions.³³ To this end, we used the proportions of granulocytes, lymphocytes, and monocytes in venous blood samples from HRS participants measured using complete blood counts.

We accounted for various demographic variables, including chronological age in 2016 (years), self-reported race (Black/African-American, White, or Other), self-reported Hispanic ethnicity (Hispanic or not Hispanic), sex (male, female), years of education, and marital status in 2016 (single or married/partnered).

We also integrated several health-related factors: cigarette smoking status in 2016 (never or ever smoker), any alcohol consumption in 2016 (any or none), physical activity in 2016 (indicator variable for any self-reported light, moderate or vigorous physical activity more than once per week, with its structure echoing the World Health Organization's guidelines for seniors³⁴), body mass index (BMI) calculated from self-reported height (feet and inches) and weight (pounds), and an indicator variable reflecting whether a participant had been diagnosed by a physician with multiple comorbid conditions (including high blood pressure, diabetes, cancer, lung disease, heart disease, stroke, psychiatric problems, and stroke). Given that many in this aging cohort reported at least one health condition, this indicator was defined as more than one versus one or no comorbid conditions.

The apolipoprotein E (APOE) gene is a recognized genetic risk factor for late onset Alzheimer's disease. Specifically, individuals carrying the APOE ε4 allele face a heightened risk for dementia compared to those with the APOE ε3 and APOE ε2 alleles.³⁵ Between 2006 and 2012, participants in the HRS underwent face-to-face interviews, during which they provided saliva samples for genotyping. These samples were analyzed using the Illumina Human Omni2.5 microarray platform and later imputed with the 1000 Genomes Project reference panel.³⁶ Given the rarity of individuals possessing two copies of APOE ε4, participants were categorized as having any versus no copies of APOE ε4 using the phased genetic data. Since a proportion of participants lacked genetic data, we included APOE ε4 status as a variable in our sensitivity analysis.

Sampling weights

We applied the HRS survey weights specifically designed for use with DNA methylation data.³⁷ We used these sample weights for both descriptive statistics and multivariable models to ensure our findings are nationally representative.

Sample inclusion and exclusion

Only participants with quality-controlled DNA methylation data from the 2016 venous blood sample were eligible for inclusion in this analysis, and analyses were therefore necessarily restricted to individuals who (1) were community-dwelling and (2) did not respond via proxy in 2016. Participants could, however, be proxy-respondents in 2018. We performed a complete case analysis, where we excluded participants lacking cell type data, demographic information, or health factors variables. Those classified with dementia in 2016 were excluded since they were not supposed to experience further cognitive change. Anyone without cognition data from the 2018 HRS wave was also excluded, as we couldn't evaluate their cognitive change over time. Additionally, participants with non-normal cognition that remained stable or improved between 2016 and 2018 were also excluded. We visualized participant exclusion and inclusion using a flow chart.

Statistical analysis

All analyses were conducted in R statistical software (version 4.2.2). We described the distributions of continuous variables using mean and standard deviation and of categorical variables using count and frequency. We compared the distributions of variables in the included and excluded samples. For bivariate analysis among the included sample, we took survey weight into account and compared the distributions of variables by binary cognitive change status using Wilcoxon rank-sum test for complex survey samples to assess mean differences in continuous variables and Pearson's Chi-squared test with Rao & Scott's second-order correction for categorical variables. In sensitivity analyses, we examined subtypes of cognitive change (normal to cognitive impairment non-dementia, normal to dementia, and cognitive impairment non-dementia to dementia). Raincloud plots were used to visualize the distribution and provide summary statistics of the DNA methylation age acceleration by cognitive change over time.

To assess the relationship between DNA methylation age acceleration and cognitive change over time, we used multivariable logistic regression, accounting for potential confounders and precision variables. Our primary multivariable models focused on continuous GrimAge acceleration, and the remaining clocks were used in sensitivity analyses. Our primary analysis encompassed three distinct regression analyses: (1) base model: controlled for cell-type proportions; (2) demographic model: adjusted for demographic variables previously mentioned, in addition to cell-type proportions; and (3) health factors model: adjusted for the health-related factors specified earlier, in addition to all variables included within the demographic model. We also assessed if chronological age modified the association between DNA methylation age acceleration and cognitive change by adding product term between chronological age and DNA methylation age acceleration in the demographic and health factors models. To better represent those participants who had complete data in 2016, we applied survey weight to these models. We reported odds ratios (OR), 95% confidence intervals (CI), and p-values for the variables of interest.

Sensitivity analyses

In sensitivity analyses, we considered a binary GrimAge acceleration predictor. In addition, to determine whether the association between DNA methylation age acceleration and cognition change over time was specific to the GrimAge estimator, we examined four alternate DNA methylation age estimators (continuous and binary) including Levine, MPOA, Horvath, and Hannum. To assess whether GrimAge acceleration had a linear relationship with cognitive function, we modelled cognitive function as a continuous outcome by using 27-point continuous cognitive score at the end of the follow-up and the difference in continuous cognitive score from 2016 to 2018. To address potential confounding by the APOE genotype, we additionally controlled for any APOE ε4 alleles in a sensitivity analysis. All these models were survey sampling weighted.

To address the possible selection bias caused by differing loss-to-follow-up rates between 2016 and 2018, we conducted an inverse probability weighting analysis. First, we compared the distribution of variables among participants who had baseline DNA methylation and cognition measures but did not have cognition measures at follow-up (loss-to follow-up sample) to the analytic sample (that had cognition measures at follow-up). Next, we calculated the inverse probability for both treatment (referring to DNA methylation age acceleration) and censoring (specifically for participants without cognitive data in 2018). For our sensitivity analysis, we multiplied the inverse probabilities of treatment and censoring weights by the given sampling weights. These adjusted weights were then applied to our multivariable models.

To assess if GrimAge acceleration provides increased predictive accuracy beyond standard demographic variables in predicting future cognitive change, we performed an area under the receiver operator curve analysis (AUROC) using the pROC package (version 1.18.0).³⁸ We considered our base model to be a multivariable logistic regression model of cognitive change with demographic and cell type variables as predictors. We added GrimAge acceleration to this model and tested for an association using a likelihood ratio test. We visualized these results using a receiver operator curve, calculated C-statistics for each model, and used a DeLong test to compare performance.

Results

Description of the analytic sample

Of the initial 4104 participants tested with DNA methylation assays, 4018 met the HRS quality control standards. After accounting for those with incomplete covariate data, the sample was reduced to 3713 participants. From this group, 439 were excluded because of stable non-normal cognition, and another 417 lacked cognition data for 2018. Ultimately, 2713 participants were included in the final analysis (Figure 1). During follow up, 333 participants experienced some form of cognitive change (Supplemental Figure 3).

Figure 1.

Consolidated standards of reporting trials diagram for inclusions and exclusions for an analysis of DNA methylation clocks and cognitive change over time among a subset of participants in the health and retirement study.

Among the analytic sample, according to self-report, 54% of participants were female, 8.4% were Black/African American, 86% were White, and 7.5% were Hispanic. Compared to excluded participants, those in the analytic sample were more likely to self-identify as non-Hispanic White, be currently married or partnered, and have more education. They also exhibited fewer chronic conditions, were more likely to exercise at least once per week, and were more often ever-drinkers (Supplemental Table 1). Within our final analytic sample, 54% of the participants identified as female. In addition, 86% identified as White, 8.4% as Black, and 7.5% as Hispanic (Table 1).

Table 1.

Survey weighted bivariate descriptive statistics of participant baseline (2016) characteristics in the health and retirement study by cognitive change status over follow-up (2018).

	Cognitive change				Binary GrimAge acceleration
Baseline characteristics	Overall (N = 2713)^a	Any decline (N = 333)^a	Normal to normal (N = 2380)^a	p^b	Not accelerated (N = 1636)^a	Accelerated (N = 1077)^a	p^b
DNA methylation age acceleration (y)^c
GrimAge	−0.7 (4.7)	1.2 (4.5)	−0.8 (4.6)	<0.001	−3.5 (2.4)	4.2 (3.4)	<0.001
PhenoAge	0 (7)	0 (7)	0 (7)	0.4	−2 (6)	2 (7)	<0.001
MPOA	−1 (6)	1 (7)	−1 (6)	<0.001	−3 (5)	3 (6)	<0.001
Horvath	0.1 (6.0)	−0.4 (6.6)	0.2 (6.0)	0.2	−0.3 (5.4)	0.9 (7.0)	<0.001
Hannum	0.0 (4.9)	0.3 (4.9)	0.0 (4.9)	0.5	−0.9 (4.5)	1.5 (5.3)	<0.001
Baseline cognition				<0.001			0.001
Normal	98% (0.00)	82% (0.02)	100% (0.00)		99% (0.00)	97% (0.01)
CIND	1.7% (0.00)	18% (0.02)	0% (0.00)		1.1% (0.00)	2.7% (0.01)
Chronologic age (y)	68 (8)	73 (10)	67 (8)	<0.001	68 (8)	68 (9)	0.024
Sex (Female)	54% (0.01)	48% (0.03)	54% (0.01)	0.11	64% (0.01)	36% (0.02)	<0.001
Race				<0.001			<0.001
White	86% (0.01)	77% (0.03)	87% (0.01)		88% (0.01)	83% (0.01)
Black/African American	8.4% (0.01)	17% (0.02)	7.6% (0.01)		6.6% (0.01)	11% (0.01)
Other	5.2% (0.00)	6.6% (0.02)	5.0% (0.01)		5.1% (0.01)	5.3% (0.01)
Ethnicity (Hispanic)	7.5% (0.01)	12% (0.02)	7.1% (0.01)	0.008	7.5% (0.01)	7.5% (0.01)	>0.9
Educational attainment (y)	13.77 (2.67)	12.33 (3.19)	13.92 (2.57)	<0.001	14.07 (2.68)	13.27 (2.58)	<0.001
Marital status (single)	35% (0.01)	47% (0.03)	34% (0.01)	<0.001	33% (0.01)	38% (0.02)	0.025
Exercise status (more than once per week)	79% (0.01)	63% (0.03)	80% (0.01)	<0.001	83% (0.01)	72% (0.02)	<0.001
Drinking status (ever)	64% (0.01)	47% (0.03)	66% (0.01)	<0.001	65% (0.01)	62% (0.02)	0.2
BMI (kg/m²)	29.0 (6.2)	28.7 (6.9)	29.0 (6.1)	0.5	28.6 (5.9)	29.6 (6.6)	<0.001
Chronic conditions (more than one)	64% (0.01)	77% (0.03)	63% (0.01)	<0.001	60% (0.02)	72% (0.02)	<0.001
Smoking status (ever smoker)	55% (0.01)	59% (0.03)	54% (0.01)	0.2	41% (0.01)	77% (0.02)	<0.001
APOE status, any copy of ε4	22% (0.01)	30% (0.03)	22% (0.01)	0.009	22% (0.01)	24% (0.02)	0.3
Granulocytes (%)	62 (9)	64 (10)	62 (9)	0.002	61 (8)	64 (9)	<0.001
Lymphocytes (%)	30 (8)	28 (9)	30 (8)	0.005	31 (8)	27 (9)	<0.001
Monocytes (%)	8.54 (2.28)	8.28 (2.23)	8.57 (2.28)	0.082	8.52 (2.28)	8.58 (2.28)	0.6

Mean (SD); frequency% (SE(frequency%)).

Wilcoxon rank-sum test for complex survey samples; chi-squared test with Rao & Scott's second-order correction.

Second-generation of DNA methylation clocks: GrimAge, PhenoAge, MPOA; First-generation of DNA methylation clocks: Horvath, Hannum.

Bivariate descriptive statistics

Participants who experienced cognitive change during follow-up were, on average, older and had fewer years of education, compared to those with normal cognition (Table 1). They were also more likely to be Black, Hispanic, or single. At baseline, they were less likely to exercise at least once a week or report any current alcohol consumption. Participants who experienced cognitive change over time were more likely to having multiple chronic conditions and at least one copy of APOE ε4 (Table 1).

Participants who experienced cognitive change during follow-up had higher baseline GrimAge (mean = 1.2 years) acceleration compared to those who maintained normal cognitive function (mean = −0.8 years, p < 0.001) (Table 1 and Supplemental Figure 4). Participants who experienced cognitive change during follow-up also had higher baseline MPOA (mean = 1.0 years) acceleration compared to those who maintained normal cognitive function (mean = −1.0 years, p < 0.001). This difference was not observed with other epigenetic clocks. Within the cognitive change group, those progressing from normal cognition to cognitive impairment non-dementia had lower GrimAge acceleration (mean = 0.94) than those progressing either from normal cognition to dementia (mean = 1.27) or from cognitive impairment non-dementia to dementia (mean = 1.97, Figure 2).

Figure 2.

Grimage acceleration by cognitive change status (between 2016 and 2018) in the health and retirement study sample with DNA methylation measurements and complete covariate data (N = 2713). CIND: Cognitive impairment non-dementia. For each cognitive change category, three plots are provided: Ridge plot (on the top), boxplot (in the middle), and histogram (on the bottom). Darker colors indicate higher participant counts per category. P value was calculated using the Kruskal-Wallis test.

Multivariable association between GrimAge acceleration and cognitive change over time

Based on a priori hypotheses, we chose GrimAge as the primary predictor in our multivariable models. In survey weighted multivariable logistic regressions, we observed that every additional year of increased GrimAge acceleration at baseline was associated with 1.08 (95% CI: 1.05, 1.11, p = 1.0 × 10⁻⁸) times higher odds of cognitive change, after adjusting for cell-type proportions. Controlling for demographic variables slightly attenuated the odds ratio (OR = 1.05, 95% CI: 1.02, 1.09, p = 0.003), and results were similar when we additionally controlling for health variables (OR = 1.05, 95% CI: 1.01, 1.10, p = 0.02). We did not observe a significant interaction between chronological age and GrimAge acceleration in either demographic model or health factors model (Supplemental Table 2).

Sensitivity analyses for parameterization, other clocks, and APOE adjustment

When we analyzed the data using a binary parameterization of the exposure, i.e., comparing those with accelerated GrimAge (residuals > 0) against those without (residuals ≤ 0), the results remained consistent (Table 2). Full results are available in the Supplemental Materials. We observed an association between binary MPOA acceleration and odds of cognitive change (OR = 1.44, 95% CI: 1.03, 2.02, p = 0.03) in the survey weighted fully adjusted (Supplemental Table 3). We did not observe associations between the remaining three clocks (PhenoAge, Horvath, Hannum) with cognitive change over time. In the survey weighted models using continuous cognitive score as outcomes, both continuous and binary GrimAge acceleration were significantly associated with cognitive score at the end of follow-up after adjusting for health variables. When using the change of cognitive score from 2016 to 2018 as the outcome, the significance only remained for the model with binary GrimAge acceleration after adjusting for health variables (Supplemental Table 4).

Table 2.

Results from multivariable regression model, predicting any cognitive change versus stable normal cognition with accelerated epigenetic aging (GrimAge) as the main predictor.

	Model	N	Survey weighted			Unweighted
GrimAge acceleration measure	Model	N	OR	95% CI	p	OR	95% CI	p
Continuous (1-year increase)	Cell-type adjusted only^a	2713	1.08	1.05, 1.11	1.0 × 10⁻⁸	1.07	1.04, 1.09	2.0 × 10⁻⁷
	Demographic variables^b	2713	1.05	1.02, 1.09	0.003	1.04	1.01, 1.07	0.005
	Health variables^c	2713	1.05	1.01, 1.10	0.02	1.03	1.00, 1.07	0.08
Binary (acceleration > 0 versus ≤0, reference)	Cell-type adjusted only^a	2713	2.13	1.59, 2.84	4.0 × 10⁻⁷	1.82	1.44, 2.31	1.0 × 10⁻⁶
	Demographic variables^b	2713	1.58	1.12, 2.21	0.01	1.42	1.09, 1.84	0.01
	Health variables^c	2713	1.52	1.03, 2.24	0.03	1.31	0.98, 1.75	0.07

OR: odds ratio; CI: confidence interval.

Controlled for: Granulocytes, Lymphocytes.

Controlled for: sex, self-reported race and ethnicity (whether Hispanic), age (2016), years of education, single status (2016), Granulocytes, Lymphocytes.

Controlled for all variables in (2) and: any exercise more than once per week (2016), smoking status (2016), drinking status (ever drinker) (2016), self-reported BMI (body mass index = kg/m2) (2016), more than one chronic condition.

In sensitivity analyses in a subset of participants (N = 1976) where we were able to control for the APOE ε4 allele status, results were consistent with the primary analysis that adjusted for other health variables (OR = 1.05, 95% CI: 1.00, 1.10, p = 0.08, Supplemental Table 5).

Sensitivity analyses accounting for loss-to-follow-up

For 417 participants with baseline DNA methylation and cognitive status measures, we were unable to evaluate the cognitive change over time due to missing cognitive status at follow-up. Participants missing cognitive data at follow-up had higher baseline GrimAge acceleration (mean: 1.5 years) compared to participants with cognitive data at follow-up (mean: −0.4 years). In addition, participants who were lost to follow-up were more likely to have a smoking history and less likely to have exercise, compared to those who remained in the study (Supplemental Table 6).

To address the potential bias introduced by the missing 2018 cognition data, we conducted a sensitivity analysis using inverse probability weighting (Supplemental Table 7). After adjusting for all cell-type, demographic, and health variables, the results mirrored those of the primary multivariable regression (OR = 1.07, 95% CI: 1.04, 1.11, p = 1.0 × 10⁻⁰⁴).

Classification of cognitive status

To assess whether GrimAge acceleration improved classification of cognitive change over time, we used receiver operating curve analyses. The AUC for the base model (cell types and demographics) was 0.755. Additionally using GrimAge acceleration resulted in the same AUC (0.755). There was no classification improvement with the inclusion of GrimAge acceleration (DeLong test p-value = 0.3; see Supplemental Figure 5).

Discussion

DNA methylation clocks have emerged as a potential biomarker for cognitive impairment and dementia. However, most longitudinal studies exploring the association between DNA methylation clocks and incident cognitive impairment have been limited by small sample sizes (previous N's: 52–578). To address this, we investigated the association between epigenetic clocks and the progression of cognitive change over time in a larger sample (N = 2713) from a longitudinal study focusing on older US adults. We found that participants who experienced cognitive change over two years of follow up had elevated baseline GrimAge age acceleration, when contrasted with participants maintaining stable cognitive function over follow-up. Specifically, we observed a 1-year increase in baseline GrimAge acceleration was associated with 1.05 (95% CI: (1.01, 1.10)) times higher odds of cognitive change over follow-up, in fully adjusted models. Moreover, this association remained evident even when accounting for loss-to-follow-up using inverse probability weighting modeling (OR = 1.07, 95% CI: (1.04, 1.11)). In conclusion, our study presents compelling evidence that DNA methylation clocks, particularly GrimAge age acceleration, are biomarkers associated with the progression of cognitive change in older adults in the United States.

Our study highlights the potential of DNA methylation clocks as biomarkers for impaired cognition. Substantial evidence indicates that alterations in blood DNA methylation patterns are indicative of cognitive dysfunction and senescence of the brain.^9,11,39–43 Notably, a cross-sectional analysis within the Whitehall II imaging sub-study (N = 47) revealed that DNA methylation aging acceleration, as quantified by the Hannum clock, correlated with mean diffusivity and the global fractional anisotropy of the brain.⁴⁰ In this study, we noted pronounced differences in cognitive change exclusively with second-generation phenotypic clocks (GrimAge and MPOA), which were trained on health factors to better capture the health conditions and mortality. Consistent with the prior literature, these findings further emphasize the second-generation clock's enhanced abilities to predict age-related health declines compared to their first-generation counterparts (Horvath and Hannum), which were trained to solely predict chronological age, which was adjusted for separately in our models. Our findings that GrimAge had the strongest signal with cognitive function, relative to the other two second-generation clocks tested, are also consistent with the Irish Longitudinal Study on Aging, where 490 participants monitored over a decade exhibited a marked correlation between GrimAge clock metrics and age-associated cognitive decline.⁹ In a similar vein, initial findings from the VITAL-DEP (VITamin D and OmegA-3 TriaL-Depression Endpoint Prevention) study's pilot cohort (N = 45) observed a significant association between GrimAge and rapid declines in overall cognitive function over a two-year span.³⁹ These studies collectively affirm the viability of DNA methylation clocks, especially GrimAge, as potential biomarkers for early detection of cognitive impairment, which is critical for timely therapeutic intervention.

On the other hand, some prior studies have reported no association between DNA methylation clocks and cognitive decline.^12–14,44 For instance, a case-cohort investigation within the ASPirin in Reducing Events in the Elderly (ASPREE) study, encompassing 160 participants, discerned no differential age acceleration—as measured by Horvath, Hannum, GrimAge, and PhenoAge DNA methylation clocks—between dementia cases and control subjects.¹³ Similarly, a cross-sectional analysis utilizing data from 640 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database also reported no correlations between age acceleration (measured in Horvath, PhenoAge, and GrimAge clocks) and cognitive metrics.⁴⁴ Such discrepancies in findings may be attributable to methodological variances, including study design and sample size. Our investigation, leveraging a substantial cohort from the HRS, benefits from a larger sample size, which confers enhanced statistical power.

Our study has several notable strengths that underscore its significance and influence. First, we employed a variety of epigenetic clocks for exposure measurement to examine differences in cognitive change over time (Supplemental Table 3). This is important as varying associations may exist across these different epigenetic clocks, a notion further supported by our results. Second, compared to earlier longitudinal studies, our sample size is not only larger but also encompasses a nationally representative sample of participants from diverse racial and ethnic groups. This diversity enhances the robustness of our results and conclusions. By incorporating survey-weighting, we ensured that our findings are more representative and thus can be more readily generalized to the broader population of older adults in the US. Moreover, we meticulously accounted for numerous potential confounders in our analysis. We also carried out a sensitivity analysis using inverse probability weighting to tackle potential selection bias.

However, there are certain limitations to our study that must be considered when interpreting our results. Notably, the DNA methylation clocks were derived from blood samples rather than brain tissue, which can provide a more direct insight into neural changes and their potential correlation with cognitive change. Nonetheless, acquiring brain samples from living individuals is not feasible, so this inherent limitation could not be overcome in our study. Many of the epigenetic clocks were designed to be robust across tissues.⁴⁵ The generalizability may also be limited by DNA methylation preprocessing process. Additionally, the cognitive status outcome in our study was derived from cognition tests instead of a physician's diagnosis. This could lead to potential misclassification, and our inability to distinguish between dementia subtypes might result in imprecise conclusions. Furthermore, our study experienced attrition with 417 participants lost to follow-up. This phenomenon could be due to a variety of factors, including withdrawal of participants or mortality stemming from causes unrelated to the study. It is important to consider that such attrition might be non-random and associated with baseline cognitive abilities or rates of epigenetic aging. Lastly, the follow-up period for our analytic sample is limited, spanning only two years of cognitive changes. A more extended follow-up would likely yield clearer and more robust findings.

In conclusion, our study aimed to examine the relationships between DNA methylation age acceleration measured using epigenetic clocks and the onset of cognitive change. After accounting for specific confounders and potential selection bias, we identified accelerated GrimAge at baseline was associated with cognitive change over follow-up. By highlighting this association, our study deepens the understanding of the role epigenetic aging clocks might play as reliable predictors for cognitive change over time. These insights can pave the way for enhanced prevention, diagnosis, and treatment strategies. By addressing the study's limitations and leveraging its strengths, the scientific community is better positioned to unravel the intricate ties between epigenetic clocks and cognitive change.

Supplemental Material

sj-docx-1-alz-10.1177_13872877251333707 - Supplemental material for DNA methylation age acceleration is associated with incident cognitive impairment in the health and retirement study

Supplemental material, sj-docx-1-alz-10.1177_13872877251333707 for DNA methylation age acceleration is associated with incident cognitive impairment in the health and retirement study by Freida Blostein, Kelly M Bakulski, Mingzhou Fu, Herong Wang, Matthew Zawistowski and Erin B Ware in Journal of Alzheimer's Disease

Footnotes

Acknowledgments

We thank the participants and staff of the Health and Retirement Study.

ORCID iDs

Freida Blostein

Kelly M Bakulski

Mingzhou Fu

Herong Wang

Matthew Zawistowski

Erin B Ware

Ethical considerations

This study was approved by the University of Michigan Institutional Review Board (HUM00128220) on May 27, 2020.

Consent to participate

All participants in the HRS provide written informed consent.

Author contributions

Freida Blostein (Data curation; Formal analysis; Visualization; Writing – original draft); Kelly M Bakulski (Conceptualization; Funding acquisition; Writing – review & editing); Mingzhou Fu (Writing – original draft); Herong Wang (Formal analysis); Matthew Zawistowski (Writing – review & editing); Erin B Ware (Conceptualization; Funding acquisition).

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Health and Retirement Study is supported by the National Institute on Aging (U01 AG009740). This analysis was supported by the National Institute on Aging (R01 AG072396, P30 AG072931, R01 AG070897, R01 AG067592).

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The Health and Retirement Study data used in this analysis are publicly available (https://hrsdata.isr.umich.edu/). The code used to process the data and produce the analyses are publicly available ().

Supplemental material

Supplemental material for this article is available online.

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Supplementary Material

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