DNA methylation age from peripheral blood predicts progression to Alzheimer’s disease, white matter disease burden, and cortical atrophy

doi:10.21203/rs.3.rs-5273529/v1

Download PDF

Article

DNA methylation age from peripheral blood predicts progression to Alzheimer’s disease, white matter disease burden, and cortical atrophy

https://doi.org/10.21203/rs.3.rs-5273529/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Cross-sectional studies suggest a limited relationship between accelerated epigenetic aging derived from epigenetic clocks, and Alzheimer’s disease (AD) pathophysiology or risk. However, most prior analyses have not utilized longitudinal analyses or whole-brain neuroimaging biomarkers of AD. Herein, we employed longitudinal modeling and structural neuroimaging analyses to test the hypothesis that accelerated epigenetic aging would predict AD progression. Using survival analyses, we found that two second generation epigenetic clocks, DNAmPhenoAge and DNAmGrimAge, predicted progression from cognitively normal aging to mild cognitive impairment or AD and worse longitudinal cognitive outcomes. Epigenetic age was also strongly associated with cortical thinning in AD-relevant regions and white matter disease burden. Thus, in contrast to earlier work suggesting limited applicability of blood-based epigenetic clocks in AD, our novel analytic framework suggests that second-generation epigenetic clocks have broad utility and may represent promising predictors of AD risk and pathophysiology.

Health sciences/Diseases/Neurological disorders/Neurodegenerative diseases/Alzheimers disease

Biological sciences/Genetics/Genetic markers

DNA methylation

epigenetic clock

neuroimaging

Converging evidence from the study of model organisms and human brain tissue suggests that epigenetic aging – a metric of biologic age based on DNA methylation patterns – plays a critical role in Alzheimer’s disease (AD) pathophysiology. Briefly defined, epigenetics and epigenetic aging are measurable changes to DNA (often via DNA methylation) that occur over the lifespan which are regulated by genetic variation, behavior, environment, and human disease. Transgenic mouse models of AD demonstrate unique epigenetic alterations associated with AD pathology^1,2 and studies of human brain tissue show marked DNA methylation differences in AD when compared to normal aging brain^3,4. By contrast, data from clinical studies has been mixed. For example, a recent systematic review largely using cross-sectional studies found no strong evidence that epigenetic age estimates were associated with risk for dementia or mild cognitive impairment (MCI),⁵ while other smaller studies have suggested a limited though promising relationship with risk for AD or related disease biomarkers^4,6.

There are multiple possible explanations for these surprisingly discrepant findings as insights from mouse models and postmortem human tissue are translated into clinical settings. First, most clinical studies performed to date have been cross-sectional rather than longitudinal – limiting evaluation for disease risk-modifying effects which may not appear on same-visit cognitive scores or biomarker measurements. Second, multiple generations of epigenetic clocks have been developed for analysis, with each generation and associated score based on distinct analytic underpinnings and resulting in distinct interpretations – making comparison and replication of results challenging. Finally, many studies have focused on one or two related outcomes such as disease status and cognitive scores, which may not be sensitive to interindividual differences in disease trajectory. Taken together, these findings suggest the need for additional studies using more sensitive longitudinal and biomarker data paired with well-established and validated epigenetic clocks.

Therefore, we investigated the relationship between two well-validated second-generation epigenetic clocks, DNAmPhenoAge and DNAmGrimAge^7–9, and risk for MCI or AD using longitudinal analyses and multimodal neuroimaging. Specifically, we analyzed the rate of progression from cognitively normal (CN) aging to either MCI or AD, cortical thinning and white matter hyperintensities (WMH) on magnetic resonance imaging (MRI), and longitudinal cognitive changes. We hypothesized that combined longitudinal and multimodal imaging analyses would increase our ability to detect the modulation of AD risk by epigenetic age and that epigenetic age would predict AD risk independent of chronologic age.

Participant Characteristics

This study utilized samples from 404 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study with DNA methylation and structural neuroimaging data available. At baseline, 121 participants were considered CN, 236 were diagnosed with MCI, and 47 were diagnosed with AD. ADNI is a longitudinal study of subjects across the US that includes multimodal neuroimaging, blood biomarkers, and clinical markers of AD and has been described previously^10,11. As part of the study, patients undergo a rigorous clinical exam, which includes neurologic examination, neuropsychiatric evaluation, cognitive testing, blood sampling, and brain MRI¹². Participants were diagnosed as CN or with MCI or AD based on a structured protocol that integrated clinical data, cognitive testing, and brain MRI results, which has been described previously¹³. Clinical characteristics are detailed in Table 1. Clinical symptoms severity was measured using the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) score¹⁴. Cognitive changes over time were measured using the Montreal Cognitive Assessment (MoCA)¹⁵. Written and informed consent was obtained from study participants in compliance with local IRB and ADNI protocols as has been previously described¹⁶.

Image Processing

Structural MRI images from all participants were segmented using two pipelines to evaluate cortical structure and white matter disease. Cortical segmentation and structural analysis was performed using FreeSurfer version 5.1 using T1-weighted images as previously described^17,18. Briefly, all images were segmented using FreeSurfer’s automated pipeline and then manually checked for segmentation accuracy, with segmentation errors manually corrected¹⁹. Cortical thickness measurements from 68 cortical regions of interest (ROI; 34 for each hemisphere) in the Desikan-Killiany atlas¹⁸ were retained for analysis. White matter disease was estimated using a pipeline developed at the University of California, Davis, which quantifies WMH volumes via Bayesian modeling based on participants’ 3D T1 and FLAIR images²⁰.

DNA Methylation Assessment

DNA methylation was profiled from blood samples of ADNI participants using the Illumina Infinium Human MethylationEPIC V1 BeadChip Array, which covers ~ 866,000 CpGs (illumina.com). Briefly, data was normalized using the dasen method in the wateRmelon R package²¹, and quality control procedures were performed, including removal of samples with abnormal CpG detection p-values > 0.05, checking the ratio of X/Y chromosome probe intensities for sex concordance, and comparison of targeted SNP genotypes to genotype microarray data as previously described²². Estimates of epigenetic age for the Levine 513 CpG site DNAmPhenoAge clock⁸ and DNA methylation-based mortality risk assessment (DNAmGrimAge)⁷ were calculated using R scripts. Mean imputation was utilized for missing values. The first time point for each peripheral blood sample was used for epigenetic age estimation. For a subset of patients, technical replicates were present, and in these situations, we averaged the mean epigenetic age across replicates for all downstream analyses.

Statistical Analyses

All statistical analyses were completed using R 4.1.2²³. Longitudinal analyses of the effect of epigenetic age on disease progression were performed using Cox proportional hazard modeling with the R package ‘survival’²⁴, controlling for APOE ε4 allele dosage, sex, years of education, and CDR-SB. Conversion to disease was analyzed in CN participants and was defined as a change in clinical diagnosis from CN to MCI or CN to AD. The proportional hazards assumption was tested for each model using the ‘cox.zph’ function and was not statistically significant (p > 0.05 level for all analyses shown). Mixed-effects linear regression analyses were used to assess the relationship between baseline epigenetic age and longitudinal MoCA scores, controlling for baseline and time interactions of baseline MoCA score, sex, years of education, and APOE 𝜀4 dosage. We used the following linear mixed-effect model:

ΔMoCA = 𝛽₀ + 𝛽₁Δ𝑡 + 𝛽₂MoCA_baseline*Δ𝑡 + 𝛽₃Sex*Δ𝑡 + 𝛽₄Education*Δ𝑡 + 𝛽₅𝐴𝑃𝑂𝐸𝜀4*Δ𝑡 + 𝑒

Associations between epigenetic age and cortical thickness were conducted using multiple regression after covarying for APOE ε4 dosage, sex, years of education, CDR-SB, and total intracranial volume. Volume-rendered images of multiple regression analysis results were created using the R package ‘fsbrain’²⁵.

As has been done in prior work²⁶ and in line with our hypotheses, we first performed all analyses using epigenetic age alone to predict disease progression and neuroimaging biomarkers. For outcomes which demonstrated a significant relationship at p < 0.05, we specifically tested whether epigenetic age provided independent information above and beyond chronologic age by covarying for chronologic age in addition to all previously listed covariates.

Where applicable, correction for multiple comparisons was completed using the false discovery rate (FDR) technique²⁷.

Approval and Consent

All ADNI research activities were approved by Institutional Review Boards at each patient recruitment site. Written informed consent was obtained from all patients.

Cohort

Data from 404 participants diagnosed as CN, MCI, or AD with DNA methylation data as well as quantitative neuroimaging data were included in this study (Table 1). The cohort was balanced with respect to sex and education (both p > 0.05) with expected differences in APOE ε4 dosage, CDR-SB score, and MoCA score (all p < 0.001). Interestingly, there was a significant difference (p = 0.02) in chronologic age, with the MCI group slightly younger (mean 73.2 years) than the CN (mean 75.3 years) and AD (mean 74.7) cohorts. As demonstrated in prior analyses of ADNI data, there were no significant differences between epigenetic age across groups for both DNAmPhenoAge (p = 0.69) or DNAmGrimAge (p = 0.13)⁴. A subset of study participants had processed FreeSurfer data available for analysis (n = 335, see Table S1 for additional details) and was overall similar in composition when compared to the parent cohort shown in Table 1.

Table 1

Cohort Demographics
	CN	MCI	AD
	121	236	47	p-value
Age at baseline (years; mean (SD))	75.3 (6.4)	72.8 (7.5)	74.7 (8.4)	0.01
Sex (Male (%))	57 (47.1%)	134 (56.8%)	30 (63.8%)	0.11
Education (years; mean (SD))	16.4 (2.8)	16.3 (2.7)	16.3 (2.5)	0.47
CDR-SB (mean (SD))	0.1 (0.3)	1.5 (1.1)	4.9 (2.2)	< 0.001
MoCA (mean (SD))	26.0 (2.4)	23.5 (3.3)	18.9 (4.6)	< 0.001
APOE ε4 dosage (Count (%))
0	91 (75.2%)	131 (55.5%)	10 (21.3%)
1	28 (23.1%)	82 (33.1%)	32 (63.8%)	<0.001
2	2 (1.7%)	23 (9.7%)	7 (14.9%)

Table 1 Legend

Summary statistics are shown for study participants summarized by diagnostic category with two-tailed p-values from ANOVA (continuous traits) or chi-square (categorical values) shown. CDR-SB, Clinical Dementia Rating scale Sum of Boxes; CN, cognitively normal; MoCA, Montreal Cognitive Assessment; MCI, mild cognitive impairment; AD, Alzheimer’s disease.

DNAmGrimAge and DNAmPhenoAge predict progression to MCI and AD

Analyses in this section utilized longitudinal clinical follow-up data from participants diagnosed as CN (n = 129) with progression defined as a change in clinical diagnosis from CN to MCI or CN to AD. Participants progressed to MCI or AD in 23.3% (n = 30) of the group over the study period. Using Cox proportional hazard modeling, we found that DNAmPhenoAge (Hazard Ratio [HR] 1.06; 95% confidence intervals [CI] 1.02–1.10; p = 3.45x10^− 3) associated with conversion to MCI or AD (Fig. 1). There was also a trend towards significance for DNAmGrimAge (HR 1.03; 95% CI 1.00-1.06; p = 0.06) (Fig. 1). As a sensitivity analysis, we tested whether the effect of epigenetic age was independent of chronologic age and found that DNAmPhenoAge still significantly predicted progression in CN participants (HR 1.07; 95% CI 1.01–1.13; p = 0.01).

DNAmPhenoAge predicts longitudinal cognitive changes during normal aging

Given the findings above highlighting the importance of epigenetic age acceleration as a predictor of progression to MCI or AD, we next asked whether accelerated epigenetic age was also associated with longitudinal cognitive changes.

Analyzing longitudinal MoCA scores in CN, we found that increasing epigenetic age as measured by DNAmPhenoAge associated with decreasing (i.e., worsening) MoCA scores over time (Fig. 2; β ± Standard Error (SE) = -0.30 ± 0.09; p = 4.26x10^− 4). Importantly, this association persisted even after correcting for chronological age (β ± SE = -0.13 ± 0.04; p = 0.002). No associations were identified for DNAmGrimAge.

Epigenetic age predicts neuroimaging markers of AD across the spectrum of normal aging to AD

Given the associations between measures of increased epigenetic age and progression to MCI or AD, we next evaluated their association with neuroimaging biomarkers of AD, including whole-brain cortical thickness and WMH volumes. Due to differences in processing technique and data availability, only a subset of participants had cortical thickness measurements available (n = 335; see Table S1 for additional details). All participants in the survival analyses described above had WMH quantifications available for analysis.

Across the spectrum of normal aging to AD, accelerated epigenetic age was associated with cortical thinning in nearly every brain region for 60 of 68 ROIs for DNAmPhenoAge and 59 of 68 cortical ROIs for DNAmGrimAge (p_FDR<0.05; Tables S2 and S3; Fig. 3). Interestingly, the strongest associations were predominantly located in the temporal and parietal lobes in regions relevant to AD, such as entorhinal cortex and fusiform gyrus (Fig. 3).

To better understand the factors driving these findings, we conducted exploratory analyses examining DNAmPhenoAge and DNAmGrimAge within each diagnostic group and found these associations between accelerated epigenetic age and cortical atrophy were driven largely by the CN and MCI groups. For example, in the CN cohort there were associations for 42 and 47 of 68 cortical ROIs for DNAmPhenoAge and DNAmGrimAge, respectively (p_FDR<0.05; Table S4). Similar findings were observed in the MCI group with associations for 50 and 47 of 68 cortical ROIs for DNAmPhenoAge and DNAmGrimAge, respectively (p_FDR<0.05; Table S4). In contrast, the AD cohort showed no significant associations after correction for multiple testing.

We next repeated the above analyses while adding chronological age to the multiple regression model to better understand whether accelerated epigenetic age provided additional disease-relevant signal beyond that explained by advancing chronological age. In the CN subgroup, the association between epigenetic age and cortical thinning was mildly attenuated though remained significant at a p_FDR<0.05 value for 14 of 68 ROIs for DNAmPhenoAge and 42 of 68 ROIs for DNAmGrimAge. Of note, many of the regions that remained significant after covarying for chronological age are those relevant to AD progression, including precuneus, cuneus, and fusiform gyrus.

Transitioning our analyses to examine WMH, we found that DNAmPhenoAge and DNAmGrimAge both strongly associated with WMH volumes in the combined cohort (p_raw<0.001, Table 2) and when each diagnostic subgroup was considered individually (all p_raw<0.05; Table 2). The associations between epigenetic age and WMH volumes were strongest in patients diagnosed with AD, where DNAmPhenoAge and DNAmGrimAge associated with WMH volume after covarying for chronologic age (p_raw<0.05; Table 2).

Table 2

Epigenetic Age Predicts White Matter Hyperintensity Volumes
	Primary Analysis			Covarying for Chronologic Age
	Diagnosis (N)	Beta ± SE	p-value	Beta ± SE	p-value
DNAmPhenoAge	All Cohort (404)	0.32 ± 0.05	4.54E-09	0.13 ± 0.08	0.09
	CN (121)	0.39 ± 0.14	5.34E-03	0.27 ± 0.18	0.15
	MCI (236)	0.28 ± 0.06	5.13E-06	4.98E-3 ± 0.08	0.96
	AD (47)	0.41 ± 0.12	1.81E-03	0.44 ± 0.20	0.04
DNAmGrimAge	All Cohort (404)	0.49 ± 0.07	4.99E-10	0.18 ± 0.16	0.25
	CN (121)	0.51 ± 0.21	1.65E-02	0.17 ± 0.40	0.67
	MCI (236)	0.49 ± 0.08	1.79E-08	0.08 ± 0.17	0.63
	AD (47)	0.54 ± 0.17	2.94E-03	0.98 ± 0.43	0.03

Table 2 Legend

White matter hyperintensity (WMH) volume associates with two key measures of epigenetic age (DNAmPhenoAge and DNAmGrimAge) across the spectrum of normal aging to neurodegenerative disease. In our primary analysis, epigenetic age associated with WMH volume in the overall cohort as well as within each diagnostic subgrouping available for analysis. In our sensitivity analyses covarying for chronologic age, epigenetic age remained a significant predictor of WMH volume in Alzheimer’s disease. All analyses were conducted using multiple regression covarying for APOE ε4 status, sex, years of education, CDR-SB, and total intracranial volume. AD – Alzheimer’s disease, CN – cognitively normal, MCI – Mild cognitive impairment, SE – Standard Error.

Among CN individuals, we found that accelerated epigenetic age associates with progression to either MCI or AD and with cognitive decline independent of chronological age. Furthermore, accelerated epigenetic age based on DNAmPhenoAge and DNAmGrimAge associated with cortical thinning in AD-relevant regions and WMH burden across the spectrum of normal aging to neurodegenerative disease. Interestingly, the relationship between epigenetic age and cortical thinning in AD-implicated regions appeared most robust in CN participants while the association between epigenetic age and WMH was greatest in participants with AD. Considered together, our findings suggest that advanced epigenetic age modulates risk for AD and cognitive decline even during their most nascent stages.

Our findings suggest that epigenetic age – as measured from peripheral blood – plays an important role in AD risk above and beyond that explained by chronological age, especially in CN individuals. This lends support to the hypothesis that the processes underlying biological aging contribute significantly to the development and progression of AD. Further, our data indicates that epigenetic age is more sensitive than chronological age in proxying this underlying biology. Indeed, even after covarying for chronologic age, elevated DNAmPhenoAge remained significantly associated with clinical progression, worsened MoCA scores, and cortical thinning. Given this, epigenetic age biomarkers may play a key role in identifying patients at risk for AD (and other diseases of aging) prior to symptom onset. For example, while amyloid can be detected up to 15 years prior to clinical symptoms²⁸ it can be challenging to measure due to cost (e.g., PET scan²⁹) or invasiveness (e.g., lumbar puncture³⁰). n future clinical practice, estimates of epigenetic age may provide simple and minimally invasive metrics of AD risk that may also be relevant to other age-related conditions and diseases.

Building on prior work, our results suggest that employing a combination of analytic strategies, including longitudinal cognitive analyses and use of neuroimaging biomarkers of disease, may be useful for the validation of current and future epigenetic clocks. Of the prior studies showing a positive effect, one of the most conclusive also employed a survival analysis framework, though there was no significant association for DNAmPhenoAge⁴. While this study used cognitive and diagnostic data from the ADNI cohort, the authors did not perform survival analyses using ADNI data, nor did the study involve analysis of neuroimaging data. In addition, this study did not formally test whether epigenetic age was associated with longitudinal cognitive changes. Other promising studies examining epigenetics that have shown an association disease progression and neuroimaging biomarkers have focused on MCI rather than CN³¹ or have focused on overall mortality rather than AD³². Our findings suggest that analytic framework, rather than lack of a biologic relationship, may explain the seeming paucity of evidence linking findings from model organisms and human brain tissue to clinical populations.

We observed stronger associations between DNAmPhenoAge and disease progression whereas DNAmGrimAge associated most strongly with neuroimaging biomarkers. While speculative, we hypothesize that these differences are due to differential capture of AD-related risk factors by each epigenetic clock. For example, DNAmGrimAge differs from DNAmPhenoAge, a general lifespan clock, in that it was partially trained on serum proteins that predict mortality and smoking history²⁶. As such, DNAmGrimAge strongly associated with time to death, time to coronary heart disease, time to cancer, and multiple other mortality-associated outcomes²⁶ which may not be captured by DNAmPhenoAge but could be reflected by changes on a brain MRI. Evaluating the relative strengths and weaknesses of each generation of epigenetic clock and their subscores is beyond the scope of this article but may be a fruitful area for future research.

Our study benefits from our use of a thoroughly characterized cohort spanning the spectrum of normal aging to AD as well as our use of multiple strategies to test whether epigenetic age associates with AD risk. Limitations of our study include use of only two second-generation epigenetic clocks and lack of a suitable replication cohort given our multimodal approach. Our reliance on peripheral blood methylation data is both a strength, given its ease of acquisition, and a limitation, given it does not directly measure methylation in brain. However, data from multiple studies has shown that peripheral blood methylation changes are a reasonable proxy for brain with r > 0.85^33,34.

In summary, we found that epigenetic age associates with progression to MCI or AD and cognitive decline in CN individuals as well as cortical thinning in AD-relevant regions and increased WMH across the spectrum of normal aging to neurodegenerative disease. Our analytic framework was critical to the successful identification of these associations and using a similar technique with other epigenetic clocks and neurodegenerative as well as other age-related diseases may be useful in future work. More generally, this study underscores the importance of advanced biologic age – above and beyond chronologic age – as a risk factor for AD and as a potential biomarker for clinical use.

COMPETING INTERESTS:

All authors declare no financial or non-financial competing interests.

Author Contribution

L.B, D.S., J.Y., and M.C. participated in the conception and design of the work. L.B. and A.P. performed statistical analyses under the supervision of J.Y. and M.C. The manuscript was drafted by L.B, D.S., J.Y., and M.C. Interpretation of data and substantial revision of the work was performed by L.B., D.S., A.P, L.S., H.S.G., A.I., B.M., J.Y., and M.C. All authors reviewed the manuscript.

ACKNOWLEDGMENTS:

Author Funding:

J.S.Y. receives funding from NIH-NIA R01AG062588, R01AG057234, P30AG062422, P01AG019724, and U19AG079774; NIH-NINDS U54NS123985; NIH-NIDA 75N95022C00031; the Rainwater Charitable Foundation; the Bluefield Project to Cure Frontotemporal Dementia; the Alzheimer’s Association; the Global Brain Health Institute; the French Foundation; and the Mary Oakley Foundation. J.S.Y. serves on the scientific advisory board for the Epstein Family Alzheimer’s Research Collaboration. L.W.B receives funding from NIH-NIBIB T32EB001631 and RSNA R&E Foundation RR24-217. A.I. is supported by grants from ReDLat (National Institutes of Health and the Fogarty International Center (FIC), National Institute on Aging R01AG057234, R01AG075775, R01AG021051, R01AG083799, Alzheimer's Association SG-20-725707, the Rainwater Charitable Foundation, the Bluefield Project to Cure FTD, and the Global Brain Health Institute, ANID/FONDECYT Regular (1210195, 1210176, and 1220995); and ANID/FONDAP/15150012. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, RSNA R&E Foundation, or other funding institutions.

ADNI Funding:

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Data Availability

Data from ADNI is available after application through a publicly accessible research portal (ADNI: adni.loni.usc.edu/data-samples/access-data/).

Sanchez-Mut, J. V. et al. DNA methylation map of mouse and human brain identifies target genes in Alzheimer’s disease. Brain 136, 3018–3027 (2013).
Griñán-Ferré, C. et al. Understanding Epigenetics in the Neurodegeneration of Alzheimer’s Disease: SAMP8 Mouse Model. Journal of Alzheimer’s Disease 62, 943–963 (2018).
Lang, A.-L. et al. Methylation differences in Alzheimer’s disease neuropathologic change in the aged human brain. Acta Neuropathologica Communications 10, 174 (2022).
Sugden, K. et al. Association of Pace of Aging Measured by Blood-Based DNA Methylation with Age-Related Cognitive Impairment and Dementia. Neurology 99, e1402–e1413 (2022).
Zhou, A. et al. Epigenetic aging as a biomarker of dementia and related outcomes: a systematic review. Epigenomics 14, 1125–1138 (2022).
Milicic, L. et al. Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume. GeroScience 44, 1807–1823 (2022).
Lu, A. T. et al. DNA methylation GrimAge version 2. Aging (Albany NY) 14, 9484–9549 (2022).
Levine, M. E. et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY) 10, 573–591 (2018).
Margiotti, K., Monaco, F., Fabiani, M., Mesoraca, A. & Giorlandino, C. Epigenetic Clocks: In Aging-Related and Complex Diseases. Cytogenet Genome Res.
Petersen, R. C. et al. Alzheimer’s Disease Neuroimaging Initiative (ADNI): Clinical characterization. Neurology 74, 201–209 (2009).
Veitch, D. P. et al. The Alzheimer’s Disease Neuroimaging Initiative in the era of Alzheimer’s disease treatment: A review of ADNI studies from 2021 to 2022. Alzheimers Dement (2023) doi:10.1002/alz.13449.
ADNI General Procedures Manual. 1–120 (2006).
Bonham, L. W. et al. Insulin-Like Growth Factor Binding Protein 2 Is Associated with Biomarkers of Alzheimer’s Disease Pathology and Shows Differential Expression in Transgenic Mice. Front Neurosci 12, 476 (2018).
Morris, J. C. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43, 2412–2414 (1993).
Nasreddine, Z. S. et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 53, 695–699 (2005).
Petersen, R. C. et al. Alzheimer’s Disease Neuroimaging Initiative (ADNI). Neurology 74, 201–209 (2010).
Fischl, B. FreeSurfer. Neuroimage 62, 774–781 (2012).
Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).
Hartig, M. et al. ADNI FreeSurfer Methods and QC. (2014).
DeCarli, C., Maillard, P. & Fletcher, E. Four Tissue Segmentation in ADNI II. (2013).
Pidsley, R. et al. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics 14, 293 (2013).
Davis, J. W., Vasanthakumar, A., Nho, K., Kim, Sungeun & Saykin, A. J. Microarray Whole-Genome DNA Methylation Profiling of ADNI participants. (2017).
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing.
Therneau, Terry M. A Package for Survival Analysis in R. (2024).
Schäfer, T. & Ecker, C. fsbrain: an R package for the visualization of structural neuroimaging data. 2020.09.18.302935 Preprint at https://doi.org/10.1101/2020.09.18.302935 (2020).
Lu, A. T. et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging 11, 303–327 (2019).
Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological) 57, 289–300 (1995).
Jack, C. R. et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement 14, 535–562 (2018).
Witte, M. M. et al. Clinical use of amyloid-positron emission tomography neuroimaging: Practical and bioethical considerations. Alzheimers Dement (Amst) 1, 358–367 (2015).
Peskind, E. R. et al. Safety and Acceptability of the Research Lumbar Puncture. Alzheimer Disease & Associated Disorders 19, 220 (2005).
Liu, H., Lutz, M., Luo, S., & Alzheimer’s Disease Neuroimaging Initiative. Genetic Association Between Epigenetic Aging-Acceleration and the Progression of Mild Cognitive Impairment to Alzheimer’s Disease. The Journals of Gerontology: Series A 77, 1734–1742 (2022).
Hillary, R. F. et al. An epigenetic predictor of death captures multi-modal measures of brain health. Mol Psychiatry 26, 3806–3816 (2021).
Nishitani, S. et al. Cross-tissue correlations of genome-wide DNA methylation in Japanese live human brain and blood, saliva, and buccal epithelial tissues. Transl Psychiatry 13, 72 (2023).
Braun, P. R. et al. Genome-wide DNA methylation comparison between live human brain and peripheral tissues within individuals. Transl Psychiatry 9, 1–10 (2019).

No competing interests reported.

SupplementaryTables.docx

Download PDF

Reviews received at journal
08 Nov, 2024
Reviewers agreed at journal
05 Nov, 2024
Reviewers agreed at journal
30 Oct, 2024
Reviewers invited by journal
30 Oct, 2024
Editor assigned by journal
23 Oct, 2024
Submission checks completed at journal
18 Oct, 2024
First submitted to journal
16 Oct, 2024

You are reading this latest preprint version

DNA methylation age from peripheral blood predicts progression to Alzheimer’s disease, white matter disease burden, and cortical atrophy

Status:

Version 1

Abstract

Figures

INTRODUCTION

METHODS

Participant Characteristics

Image Processing

DNA Methylation Assessment

Statistical Analyses

Approval and Consent

RESULTS

Cohort

DNAmGrimAge and DNAmPhenoAge predict progression to MCI and AD

DNAmPhenoAge predicts longitudinal cognitive changes during normal aging

Epigenetic age predicts neuroimaging markers of AD across the spectrum of normal aging to AD

DISCUSSION

Declarations

COMPETING INTERESTS:

Author Contribution

ACKNOWLEDGMENTS:

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1