The Aging Epigenome: Integrative Analyses Reveal Functional Overlap with Alzheimer’s Disease

The Aging Epigenome: Integrative Analyses Reveal Functional Overlap with Alzheimer’s Disease | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search The Aging Epigenome: Integrative Analyses Reveal Functional Overlap with Alzheimer’s Disease Wei Zhang , David Lukacsovich , Juan I. Young , Lissette Gomez , Michael A. Schmidt , Brian W. Kunkle , Xi Chen , Eden R. Martin , View ORCID Profile Lily Wang doi: https://doi.org/10.1101/2025.06.08.25329218 Wei Zhang 1 Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine , Miami, FL 33136, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site David Lukacsovich 1 Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine , Miami, FL 33136, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Juan I. Young 2 Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine , Miami, FL 33136, USA 3 John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine , Miami, FL 33136, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lissette Gomez 3 John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine , Miami, FL 33136, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael A. Schmidt 2 Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine , Miami, FL 33136, USA 3 John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine , Miami, FL 33136, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brian W. Kunkle 2 Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine , Miami, FL 33136, USA 3 John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine , Miami, FL 33136, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xi Chen 1 Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine , Miami, FL 33136, USA 4 Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine , Miami, FL 33136, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eden R. Martin 2 Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine , Miami, FL 33136, USA 3 John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine , Miami, FL 33136, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lily Wang 1 Division of Biostatistics, Department of Public Health Sciences, University of Miami, Miller School of Medicine , Miami, FL 33136, USA 2 Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine , Miami, FL 33136, USA 3 John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine , Miami, FL 33136, USA 4 Sylvester Comprehensive Cancer Center, University of Miami, Miller School of Medicine , Miami, FL 33136, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lily Wang For correspondence: lily.wangg{at}gmail.com Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Aging is the strongest risk factor for Alzheimer’s disease (AD), yet the role of age-associated DNA methylation (DNAm) changes in blood and their relevance to AD remains poorly understood. In this study, we performed a meta-analysis of blood DNAm samples from 475 dementia-free subjects aged over 65 years across two independent cohorts, the Framingham Heart Study (FHS) at Exam 9 and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). After adjusting for age, sex, and immune cell type proportions, and correcting for batch effects and genomic inflation, we identified 3758 CpGs and 556 differentially methylated regions (DMRs) consistently associated with aging in both cohorts at a 5% false discovery rate. Our pathway enrichment analyses highlighted immune response, metabolic regulation, and synaptic plasticity, all of which are key biological processes implicated in AD. Moreover, our colocalization analysis revealed 32 genomic regions where shared genetic variants influenced both DNAm and dementia risk. Adjusting for age and other covariate variables, we found roughly one-third of aging-associated CpGs are also associated with AD or AD neuropathology in independent studies external to the ADNI and FHS datasets. Finally, we prioritized 9 aging-associated CpGs, located in promoter regions of PDE1B, ELOVL2, PODXL2 , and other genomic regions, that showed strong positive blood-to-brain methylation concordance, as well as association with AD or AD neuropathology in independent studies, after adjusting for age and other covariates. Our findings provided insights into the functional overlap between the aging processes and AD, and nominated promising blood-based biomarkers for future AD research. INTRODUCTION Alzheimer’s disease (AD) is the leading cause of dementia worldwide, it already affects more than 6 million people in the United States alone; its prevalence is projected to grow to 13.8 million by 2060 as the population ages. 1 Chronological age remains the single strongest non-modifiable risk factor, with the majority of cases occurring after age 65. Understanding the molecular changes that accompany “normal” aging, and how they intersect with pathologic processes that culminate in AD, is therefore central to both risk stratification and early-intervention strategies. Epigenetic modifications, particularly DNA methylation (DNAm), accumulate throughout life and influence transcriptional programs that underlie immune, metabolic, and neurodegenerative pathways. 2 Multi-tissue “epigenetic clocks” such as Horvath’s 353-CpG model demonstrate that a subset of age-related DNAm changes faithfully track biological aging across diverse tissues. 3 In addition, large-scale blood epigenome-wide association studies (EWAS) have confirmed thousands of CpG sites and differentially methylated regions (DMRs) with reproducible age-dependent shifts. 4 - 7 Growing literature also links DNAm signatures both to incident dementia 8 and to early alterations in AD neuropathology 9 . However, because most AD EWAS treat age as a confounder to be adjusted for, the specific contribution of age-related DNAm changes in AD remains unclear. Several recent studies have explored the molecular intersections of aging and AD. In the temporal cortex, age-related and AD-associated transcriptional shifts show extensive concordance, with most genes changing in the same direction. 10 Meng et al. (2016) further demonstrated that epigenomic aging signatures across multiple brain regions converge with AD-responsive genes in immune and developmental pathways. 11 In Li et al. (2016), genome-wide blood DNAm profiles from aging individuals were systematically compared to brain DNAm profiles of AD patients, and the authors provided the first evidence that peripheral epigenetic aging signatures partially overlap with AD-associated changes in the brain. 12 However, these previous studies have used DNAm samples measured by the older 27k/450k Illumina arrays, and did not directly compare blood DNAm differences in aging with blood DNAm differences in AD. To address these gaps, we performed a comprehensive analysis to examine aging-associated blood DNAm and its implications for dementia, by leveraging DNAm data measured in blood from 475 dementia-free adults older than 65 years. These data were generated from two large clinical cohorts, the Framingham Heart Study (Exam 9) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). All the DNAm samples were measured using the Illumina HumanMethylation EPIC beadchip, which included more than 850,000 CpGs 13 . We performed a meta-analysis to identify age-associated CpGs and DMRs, mapped their genomic distributions, and evaluated their transcriptional and functional relevance by integrative eQTm (DNAm-to-gene expression associations), brain-to-blood correlations in DNA methylation levels, and pathway analyses. In addition, we explored shared genetic architecture of the aging processes and dementia, by integrating our results with large-scale mQTL resources and genome-wide association summary statistics from recent dementia GWAS, and performing colocalization analysis. Finally, we compared our findings with independent AD methylation studies external to FHS and ADNI datasets. RESULTS Study datasets Our meta-analysis included 475 participants from two cohorts: 282 individuals from the FHS Offspring cohort at Exam 9 (FHS9) and 193 individuals from the ADNI. In ADNI, we analyzed each participant’s earliest visit with available DNAm data. In FHS9, to avoid inflation due to family structure, we selected only one individual per family, prioritizing the sample with the highest bisulfite conversion rate. The average follow-up durations for the subjects were 5.0 ± 2.3 years in FHS9 and 5.9 ± 3.0 years in ADNI. We excluded individuals who developed dementia during the follow-up period. All participants were over 65 years of age. In FHS9, the average age was 74.3 ± 6.6 years, with 56.7% females. In ADNI, the average age was 77.0 ± 6.5 years, with 50.8% females ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1 Characteristics of subjects included in the meta-analysis of the Framingham Heart Study Exam 9 (FHS9) and Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohorts. Genomic distribution of aging-associated blood DNAm differences at individual CpGs and differentially methylated regions (DMRs) After adjusting for age, sex, and immune cell type proportions, and correcting for batch effects and genomic inflation (see Methods), we identified 3758 CpGs with a nominal P- value < 1×10⁻⁵ and a false discovery rate (FDR) < 0.05 using the inverse-variance fixed-effects meta-analysis ( Figure 1 , Supplementary Table 1, Table 2 ). All these CpGs had a consistent direction of change in the FHS9 and ADNI datasets. Among them, about half (55.7%, 2092 CpGs) were hypermethylated with increasing chronological age, and more than half (64.4%, 2,422 CpGs) were located within CpG islands or shores. Notably, among the 2092 hypermethylated CpGs, the majority (64.1%, 1340 CpGs) were located in gene promoter regions ( 2 kb from the TSS). Download figure Open in new tab Figure 1 Manhattan plot of significant DNA methylation differences associated with chronological age in meta-analysis of FHS9 (FHS at exam 9) and ADNI datasets. The X-axis indicates chromosome number. The Y-axis shows –log 10 ( P- value) of meta-analysis, with red line indicating significance threshold of 5% False Discovery Rate (FDR) and P- value < 10 -5 . View this table: View inline View popup Download powerpoint Table 2 Top 20 most significant CpGs associated with chronological age in meta-analysis of ADNI and FHS9 datasets. For each CpG, annotations include the location of the CpG (Chr, Position) and nearby genes based on GREAT software. The inverse-variance weighted meta-analysis regression model results include estimated effect size (estimate) where CpGs that are hyper-methylated with increased age have positive values, standard error (stdErr), P- value (pValue), and false discovery rate (fdr) accounting for multiple comparison corrections. The last column (direction) indicates the direction of effects in ADNI and FHS9 cohorts. All P- values are two-sided. In GREAT annotation, the numbers in parentheses indicate distance from the TSS. Highlighted in red text are promoter regions associated with the significant CpGs. In region-based analysis, after multiple comparisons correction, the comb-p software 14 identified 629 significant DMRs associated with chronological age at a 5% Sidak-adjusted P- value. Among them, 556 DMRs were also identified by the coMethDMR software 15 at a 5% FDR (Supplementary Table 2, Table 3 ). These 556 DMRs contained an average of 4.3 CpGs per region. The majority of these DMRs (88.3%, 491 out of 556) were hypermethylated with increasing age, and most (90.3 %, 502 DMRs) were located within CpG islands or shores. Consistent with significant age-associated CpGs, among the 491 hypermethylated DMRs, the majority (75.2%, 369 DMRs) were found in gene promoter regions. On the other hand, among the 65 hypomethylated DMRs, most (56.9%, 37 DMRs) were located in distal regions. View this table: View inline View popup Download powerpoint Table 3 Top 20 most significant differentially methylated regions (DMRs) associated with chronological age identified by both comb-p and coMethDMR software in meta-analysis of ADNI and FHS9 datasets. For each DMR, annotations include location of the DMR (DMR) and nearby genes based on GREAT. Comb-p results include the number of probes (nProbes), nominal P-value (pValue), multiple comparison corrected P-value based on Sidak method (Sidak P), and direction of each CpG within the DMR (direction_CpGs). All P-values are two-sided. In GREAT annotation, the numbers in parentheses indicate distance from the TSS. Highlighted in red text are gene promoter regions associated with the DMRs. Correlation of significant DNAm with expression of nearby genes To better understand the functional role of aging-associated DNAm, we overlapped the significant CpGs and DMRs with previously identified DNAm-to-RNA associations (eQTm), which were computed using paired blood DNA methylation and gene expression data collected from the same subjects in FHS. 16 We identified 73 CpGs significantly correlated in cis (within 500 kb of the CpG) with target gene expression (Supplementary Table 3). More than half (62.1%, 64 out of 103) of these DNAm-to-RNA associations were negative. Among significant individual CpGs, the five most significant DNAm-to-RNA associations involved target genes MX1, PDPR, MX2, SCP2 , and ZNF418 . Similarly, among CpGs within DMRs, the five most significant DNAm-to-RNA associations involved target genes SCP2, ZNF577 , and ZNF418 . MX1 and MX2 encode interferon-induced GTPases involved in immune response and antiviral defense pathways; dysregulation of these genes has been implicated in chronic inflammation associated with aging and AD 17 . PDPR is involved in energy metabolism through regulation of pyruvate dehydrogenase activity, and is linked to mitochondrial dysfunction observed in aging and AD pathology 18 . SCP2 participates in lipid metabolism and cholesterol transport; these processes are critical in maintaining neuronal membrane integrity and potentially influencing amyloid-beta aggregation 19 . ZNF577 and ZNF418 are zinc finger transcription factors involved in gene regulation; altered expression of zinc finger proteins could disrupt transcriptional networks in aging brains and may contribute to AD pathology through epigenetic modulation 20 . These results highlight the functional relevance of aging-associated CpGs in regulating gene expression changes implicated in age-related inflammation, metabolism, and neurodegeneration. Pathway enrichment of aging-associated DNA methylation differences highlights biological hallmarks of aging and Alzheimer’s disease pathways To better understand biological pathways enriched with significant aging-associated DNAm differences, we next performed pathway analysis using the methylGSA software 21 . At a 5% false discovery rate (FDR), we identified 26 KEGG pathways and 27 Reactome pathways significantly enriched with aging-associated DNAm (Supplementary Table 4, Table 4 ). Consistent with the 12 established biological hallmarks of aging 22 , these significant pathways highlight a number of critical molecular and cellular processes implicated in age-related functional decline. Several enriched pathways, such as Type II diabetes mellitus , Integration of energy metabolism , and Peptide hormone metabolism , are associated with the hallmark of deregulated nutrient sensing, which encompasses insulin/IGF-1 signaling and metabolic dysregulation in aging 22 . Pathways associated with the altered intercellular communication hallmark include Gap junction , Tight junction , Cell-adhesion molecules , ECM–receptor interaction , and Protein-protein interactions at synapses , underscore that impaired cell-cell communication and structural remodeling accompany aging 23 . Moreover, pathways such as the Wnt signaling pathway and Hedgehog signaling pathway are associated with the stem-cell exhaustion hallmark and altered developmental cues, highlighting aging-related deterioration in regenerative capacity and tissue homeostasis 24 . Finally, mitochondrial dysfunction hallmark is represented by enrichment of the Calcium signaling pathway and Purine metabolism , which affect mitochondrial energy production and oxidative stress 25 . View this table: View inline View popup Download powerpoint Table 4 The methylGSA software identified 26 KEGG pathways and 27 Reactome pathways significantly enriched with aging-associated CpGs at 5% FDR. Importantly, several pathways enriched with aging-associated DNAm are directly relevant to AD. For example, the most significant KEGG pathway is the Calcium signaling pathway , which plays a central role in AD pathology by influencing amyloid-beta accumulation, tau phosphorylation, synaptic dysfunction, and neuronal death 26 . Pathways related to neuronal communication and synaptic function, including Neurotransmitter receptors and postsynaptic signal transmission , Long-term potentiation , and Protein-protein interactions at synapses , are also closely linked to cognitive impairment observed in AD 27 . Additionally, the Wnt signaling pathway has been implicated in AD through its influence on synaptic plasticity, neuronal survival, and amyloid-beta production 28 . ECM–receptor interaction , Cell adhesion molecules , and Focal adhesion pathways have roles in neuronal connectivity and integrity, processes often compromised during AD pathogenesis 29 . Moreover, enrichment of the Type II diabetes mellitus and Integration of energy metabolism pathways supports the hypothesis of AD as a metabolic disease 30 , consistent with the increased recognition that metabolic dysfunction contributes to AD pathology. Notably, the KEGG pathway Alzheimer’s disease is significantly enriched with aging-associated CpGs ( P- value = 2.07×10 -4 , FDR = 0.0038). Collectively, these pathways enriched with aging-associated DNAm highlight multiple biological processes linked to AD pathology, suggesting that age-related epigenetic changes may contribute significantly to the disease. Co-localization analysis nominated genetic variants associated with both dementia and aging-associated DNA methylation To further understand the functional roles of the aging-associated CpGs and DMRs in AD, we performed integrative analyses of aging-associated DNAm with genetic data. First, to identify methylation quantitative trait loci (mQTLs) for the significant aging-associated DNAm differences, we searched the GoDMC database 31 . Among the 3758 aging-associated CpGs (Supplementary Table 1) and the 2409 CpGs located in significant DMRs (Supplementary Table 2), we found that 1696 CpGs (45.1%) had 311,054 mQTLs (Supplementary Table 5). This proportion is consistent with findings from a recent large mQTL meta-analysis in blood, which estimated that approximately 45% of CpGs on the Illumina array are influenced by genetic variants 31 . As the aging processes and dementia may share common genetic factors, we next evaluated whether these mQTLs overlapped with genetic risk loci implicated in dementia, by comparing them with the genetic variants identified in a recent ADRD (Alzheimer’s and related dementia) meta-analysis. 32 While no mQTLs overlapped with genome-wide significant loci ( P- value< 5 ×10 -8 ) for ADRD, we found 1168 mQTLs overlapped with genetic variants reaching a suggestive significance threshold at P < 1×10 -5 (Supplementary Table 6). Given the observed overlap between mQTLs and ADRD genetic risk loci, we sought to determine whether the association signals at these loci (variant to CpG methylation levels and variant to ADRD status) were driven by a single shared causal variant or by distinct variants in proximity. To this end, we performed a co-localization analysis using the method described in Giambartolomei et al. (2014) 33 . The results provided strong evidence 34 (PP3+PP4 > 0.90, PP4 > 0.8 and PP4/PP3 > 5) supporting a shared causal variant in 32 genomic regions influencing both traits. Among the associated CpGs, several were located in promoter regions of genes including ABI3, RELN, ZNF233, DLG4, HIVEP3, DNTT, HSD3B7, RNF39, PPM1E, and HSD3B7 (Supplementary Table 7). Aging-associated DNAm differences are significantly associated with Alzheimer’s disease in independent studies To further investigate the relevance of aging-associated DNAm differences to AD, we next examined their presence in previous AD-related DNAm studies using our recently developed MIAMI-AD database ( https://miami-ad.org/ ) 35 . We considered AD phenotypes including clinical diagnoses of mild cognitive impairment (MCI), AD, or dementia; AD-related neuropathology in brain tissue; and cerebrospinal fluid (CSF) AD biomarkers. To ensure independence from our aging study, we excluded datasets that used samples from the ADNI or FHS studies. Applying a stringent Bonferroni correction for 5362 CpGs (including 3758 significant individual CpGs and 1604 CpGs located within DMRs, at a significance threshold of P- value < 9.32 ×10 -6 ), we identified 33 CpGs that were also significant in independent studies of AD. These studies analyzed brain tissue from the prefrontal cortex (PFC), temporal cortex (TC), parahippocampal gyrus (PHG), or middle temporal gyrus (MTG) (Supplementary Table 8). Notably, all these external studies adjusted for age and other covariates, so the observed DNAm-to-AD associations are independent of age effects. These 33 CpGs were located in the promoter regions of ACADS, CHST9, DIO3, EDARADD, GP5, GPR56, HTR4, LIMD1 , PENK, SLC24A3 genes, and other genomic regions. At the more relaxed nominal significance threshold ( P- value < 0.05), we found that 1846 (34.4%) of the 5362 aging-associated CpGs were associated with AD phenotypes in prior studies. Among them, 1159 (62.8%) were identified in brain-based DNAm studies, 503 (27.2%) in blood-based studies, and 184 (10.0%) were observed in both blood and brain DNAm studies of AD. The higher number of overlapping CpGs in brain studies may reflect both the greater number of brain-based AD studies compared to blood-based studies included in the MIAMI-AD database, and the larger biological variability in blood-based DNAm data 36 , which can reduce statistical power relative to brain-based studies. Limiting to blood-based studies, we next compared the direction of methylation changes in aging and AD among the 687 CpGs (503 + 184 CpGs), we found less than half (278 CpGs, 40.5%) showed concordant changes: 81 CpGs were hypomethylated and 197 CpGs were hypermethylated in both aging and AD. The remaining 409 CpGs (59.5%) were discordant: 45.4% of the CpGs (312 CpGs) were hypermethylated in aging but hypomethylated in AD, and the remaining 14.1% CpGs (97 CpGs) showed hypomethylation in aging but hypermethylation in AD. At a 5% FDR, enrichment analysis showed that CpGs with concordant DNAm changes in aging and AD were significantly over-represented in the phasic smooth muscle contraction pathway, reflecting vascular dysfunction common in AD, as well as in the developmental pathways tripartite regional subdivision and anterior/posterior axis specification in embryo . On the other hand, CpGs showing discordant DNAm changes between aging and AD were significantly enriched in neuroactive ligand signaling and neuron migration pathways, key regulators of neuronal communication and synaptic plasticity that are dysregulated in AD (Supplementary Table 9). Brain-to-blood DNAm correlation analysis identified aging-associated blood DNAm with concordant cross-tissue changes To identify aging-associated DNA methylation (DNAm) with the potential to serve as biomarkers, we evaluated brain-to-blood DNA methylation correlations of the aging-associated DNAm. To this end, we utilized the London cohort dataset, which included 69 pairs of matched brain and blood samples 37 , and computed the Spearman correlations between brain and blood DNA methylation levels, after removing the effects of estimated cell type proportions, batch effects, age, and sex in brain and blood samples separately (Methods). Among the 3758 significant individual CpGs associated with aging and 1604 CpGs located in aging DMRs, DNAm at 23 CpGs showed significant brain-to-blood correlations ( FDR < 0.05) (Supplementary Table 10). All 23 CpGs showed a significant positive association, ranging from 0.423 to 0.626. These CpGs were located in the promoter of CNTNAP2, PODXL2, MARCH11, OCIAD2, SCGN, ZNF233, C3orf18, ELOVL2, ZNF442, PDE1B genes and other genomic regions. The consistent cross-tissue methylation patterns at these CpGs may indicate important shared regulatory roles during aging. By intersecting these 23 CpGs with the 1846 CpGs significantly associated with both aging and AD described above, we identified 9 CpGs, located in the promoter regions of PDE1B , ELOVL2 , PODXL2 genes and other genomic regions, that showed both strong concordance in brain-to-blood DNAm levels, as well as association with AD diagnosis or AD neuropathology in independent studies (Supplementary Table 11). One notable example is cg26019680 in the promoter of PODXL2 , which showed high correlation of DNAm levels between blood and four brain regions (prefrontal cortex, entorhinal cortex, superior temporal gyrus, and cerebellum) (Supplementary Figure 1). This CpG is part of a hypermethylated DMR associated with aging and also displays hypermethylation in male AD cases 38 . Such CpGs represent promising candidates for future biomarker development. DISCUSSION We performed a comprehensive meta-analysis of two large, independent, blood-based DNAm datasets generated by the FHS and ADNI studies, which were measured using the same Infinium MethylationEPIC BeadChip. To characterize DNAm changes associated with chronological aging, we analyzed samples collected from participants older than 65 years at the time of blood collection who remained dementia-free throughout follow-up in both cohorts. To minimize false positives, we applied two complementary methods to identify DMRs, selecting only those genomic regions significantly associated with chronological age in both analyses after multiple-testing correction, and have consistent directionality across all CpGs within each region. To explore molecular intersections between normal aging and dementia, we integrated our findings with multi-omics data from recent large-scale studies, including expression quantitative trait methylation, methylation quantitative trait loci, ADRD GWAS summary statistics, brain-to-blood DNAm correlations, and AD-associated DNAm studies manually curated in the MIAMI-AD database. At a 5% FDR, we identified 3758 CpGs and 556 DMRs consistently associated with chronological age in both the FHS and ADNI datasets. Our findings are consistent with previous studies of the aging epigenome. 39 Notably, most of the hypermethylated CpGs were located within promoter regions, which have previously been reported to undergo epigenetic silencing with age 3 , 40 , 41 . In contrast, hypomethylated CpGs predominantly mapped to distal genomic regions, consistent with prior observations of global hypomethylation in intergenic areas during aging 42 . These patterns were further supported by our region-based analysis, in which 88.3% of DMRs showed age-associated hypermethylation, with the vast majority (91.0%) located in gene promoter regions. However, it is important to note that the results of this study are limited to the probe content of the Illumina EPIC array. Future studies using sequencing-based technology may offer a more comprehensive view of the aging methylome. Importantly, a review of recent literature revealed that many of the aging-associated CpGs and DMRs we identified in this study have previously been implicated in AD. Among the top 20 CpGs ( Table 2 ), the three most significant CpGs were located in the ELOVL2 gene, whose hypermethylation has consistently been demonstrated to be a robust epigenetic biomarker of chronological aging across multiple tissues, including brain 43 , blood 44 , and saliva 45 . Additionally, the EpiAge clock, built solely on these three CpGs (cg16867657, cg21572722, and cg24724428) shows significant acceleration in individuals with mild cognitive impairment (MCI) compared to controls. 45 A recent GWAS also identified a genetic variant in ELOVL2 significantly associated with an increased risk of AD, potentially through alterations in lipid metabolism. 46 TRIM59 is another gene that shows age-associated hypermethylation. A recent study reported hypermethylation of TRIM59 in blood samples from AD patients compared to healthy controls. 47 This hypermethylation was correlated with abnormalities in DNA repair and cell cycle regulation, two critical processes involved in AD pathology. CACNA1G encodes a calcium channel, and its expression decreases with age in both human and mouse brains, a decline further exacerbated in AD. This downregulation may disrupt calcium homeostasis, promote amyloid-beta production, and contribute to cognitive decline. 48 Finally, FHL2 gene is also hypermethylated with age, it regulates inflammatory responses and adipose tissue metabolism, both of which are increasingly recognized as important contributors to AD pathophysiology. 49 Similarly, among the top 20 most significant DMRs ( Table 3 ), PCDHAC1 is a member of the protocadherin family involved in synapse formation and stabilization, processes whose disruption has also been implicated in AD pathology. 50 The GALNT17 gene encodes an enzyme in the GALNT family, which initiates glycosylation (the addition of sugar molecules to proteins), a process recently linked to microglial-driven neuroinflammation and exacerbation of AD pathology in the brain. 51 Also, the ALDH1A2 gene encodes a key enzyme in retinoic acid synthesis, a pathway known to support neuroplasticity and memory. Reduced ALDH1A2 expression has been observed in multiple AD mouse models, even at early disease stages. This decline may play an initiating role in AD pathogenesis, and pharmacologic restoration of RA signaling has shown therapeutic promise 52 . Another noteworthy gene is NEFM (Neurofilament Medium Polypeptide). In AD mouse models, elevated NEFM expressions were associated with neuronal dysfunction, including axonal damage and disrupted cytoskeletal integrity. Notably, reducing NEFM gene expression through IGF1R inhibition provided neuroprotection, leading to improved neuronal function and reduced neuroinflammation. 53 Finally, GPR158 encodes a G protein-coupled receptor predominantly localized to neurons in the cortical and hippocampal regions, areas critical for synaptic architecture and plasticity, which are disrupted in AD. In a recent functional genomics study 54 , GPR158 was identified as one of five hub genes significantly downregulated in the temporal cortex of AD patients. Gene ontology analysis revealed that these hub genes, including GPR158 , are enriched in pathways related to synaptic function and memory processes, suggesting that their reduced expression may contribute to synaptic failure in AD. Notably, GPR158 expression was inversely correlated with β-secretase (BACE1) activity in AD brain samples, indicating that lower GPR158 levels are associated with increased BACE1 activity, which could in turn enhance Aβ production from Amyloid Precursor Protein (APP). In addition to corroborating previous findings, our analyses also identified several novel differentially methylated genes that have potential implications in AD. For instance, in the comparison of aging-associated CpGs with those reported in prior AD studies, cg02336827, located in the promoter region of the LIMD1 gene and hypomethylated with age, emerged as one of the most significant CpGs, showing a P- value of 4.63×10 -7 and consistent hypomethylation in AD (Supplementary Table 8). 55 LIMD1 encodes a scaffold protein involved in key cellular functions, including transcriptional repression, microRNA-mediated gene silencing, and cytoskeletal organization. Although LIMD1 itself has not been directly implicated in AD, other LIM domain-containing proteins have been linked to neurodegenerative processes. Notably, LIM kinase 1 ( LIMK1 ), which regulates actin cytoskeleton dynamics, has been associated with synaptic dysfunction in AD. 56 These findings suggest that LIMD1 may represent a novel candidate for future AD studies. To reveal additional biological insights linking age-associated blood methylation to dementia risk, we performed several additional integrative analyses. Our pathway enrichment analysis revealed that aging-associated CpGs were significantly overrepresented in KEGG and Reactome pathways related to calcium signaling , Wnt/Hedgehog signaling , extracellular matrix (ECM) and cell adhesion , and metabolic regulation , systems repeatedly implicated in AD pathophysiology ( Table 4 ). Colocalization analyses identified 32 loci where the same causal variant likely influences both DNAm and ADRD risk. Notably, shared signals which harbored both mQTLs and AD GWAS hits were observed at ABI3 , RELN , and DLG4, genes involved in microglial activation, synaptic organization, and neuronal plasticity. 57 - 59 Furthermore, we found that approximately one-third of aging-associated CpGs were also associated with AD or AD neuropathology in independent AD DNAm studies after adjusting for age and other covariates, with about 40% displaying concordant direction of effect (Supplementary Table 8). Divergent patterns (e.g., hypermethylation with aging but hypomethylation in AD) may reflect a dysregulation of epigenetic programs in normal aging, whereby protective or adaptive methylation changes that typically accumulate with age are either not established or are actively reversed in AD. This supports the model proposed by Berger and colleagues that AD is not merely an acceleration of aging but also involves a disruption of homeostatic epigenetic regulation that contributes to neurodegeneration 60 . Taken together, our results suggest that a number of age-related epigenetic changes in peripheral blood function not only as biomarkers of aging, but also reflect relevant changes in AD or AD neuropathology, and maybe influenced by genetic risk variants for AD. We further demonstrated that 23 aging-associated CpGs showed significant positive correlations between blood and brain frontal cortex (Supplementary Table 10). Among the genes associated with these CpGs, the CNTNAP2 gene encodes a cell adhesion molecule involved in various critical neuronal functions, including axonal organization, synaptic regulation, and neuronal migration. Recent studies have identified genetic variants in the CNTNAP2 gene significantly associated with AD 61 , and altered CNTNAP2 expression levels have been observed in the brains of AD patients 62 . Also, the SCGN gene encodes the calcium-binding protein secretagogin. Importantly, SCGN has been postulated to have neuroprotective effects against neurodegeneration, as neurons expressing SCGN were largely resistant to cell death in human hippocampus. 63 Moreover, 9 of the 23 CpGs, including loci in ELOVL2, PODXL2, and PDE1B , were also significantly associated with AD or AD neuropathology in independent datasets, after adjusting for age and other covariates (Supplementary Table 11). ELOVL2 , in particular, is a well-established component of several epigenetic clocks 3 , 41 , 64 and has been linked to both neuronal lipid homeostasis and cognitive decline 65 . Prospective evaluation of these CpGs in longitudinal cohorts will clarify their utility for early dementia risk stratification. The strengths of this study include the careful selection of samples, rigorous harmonization and quality control procedures, and robust analytical approaches applied to two well-characterized cohorts. In addition to meta-analyses of individual CpGs and genomic regions, we also performed comprehensive integrative analyses that incorporated information from eQTMs, mQTLs, ADRD GWAS, brain-to-blood DNAm correlations, pathway enrichment, and the manually curated MIAMI-AD database. Several limitations of this study are in order. First, the DNAm was measured in whole blood, which may not fully capture cell-type-specific changes. Future work leveraging single-cell technology will offer more insight into the specific cell types affected by the aging-associated DNAm differences discovered in this study. Second, given that brain and blood derive from distinct cell lineages, blood-based DNAm markers may not accurately reflect brain-specific methylation changes. To address this, we prioritized DNAm changes with concordant brain-to-blood associations using a large publicly available DNAm dataset with matched brain and blood DNA methylation levels. Third, both the ADNI and FHS cohorts are predominantly of European ancestry, limiting generalizability to more diverse populations. Fourth, we analyzed FHS samples collected at Exam 9, and the earliest ADNI visit with available DNAm data; thus, the cross-sectional nature of our analysis precludes causal inference. Large independent longitudinal cohorts with incident dementia outcomes are needed to validate and prioritize the DNAm differences identified here as potential early markers of disease onset. In summary, our results reveal that hypermethylation within gene promoters and hypomethylation in distal regions form a dominant aging signature in peripheral blood. These aging-associated DNAm differences converges on immune, metabolic, and synaptic pathways implicated in AD. We further prioritized a number of CpGs where DNAm is influenced by mQTLs colocalizing with dementia risk loci, or show strong blood-to-brain concordance and are associated with AD in independent studies adjusting for age and other covariates, highlighting their promise as blood-based AD biomarkers in future research. METHODS Study datasets Our meta-analysis included 475 blood DNA methylation (DNAm) samples generated by two independent studies: the ADNI and the FHS Offspring cohort study. In ADNI, we analyzed each participant’s earliest visit with available DNAm data, and for the FHS Offspring cohort, we used DNAm data from Exam 9. Pre-processing of DNA methylation data Supplementary Table 12 shows the number of CpGs and samples at each quality control (QC) step. The pre-processing procedures were previously described elsewhere 8 . Briefly, for each dataset, the QC of probes involved selecting probes with a detection P- value < 0.01 in 90% or more of the samples, probes that start with “cg”, and removing probes that are located on X and Y chromosomes, are cross-reactive 66 , or located close to single nucleotide polymorphism (SNPs). The QC of samples included removing samples with bisulfite conversion rate lower than 85%, as well as samples for which the DNAm predicted sex status differed from the recorded sex status. In addition, we performed principal component analysis (PCA) using the 50,000 most variable CpGs to identify outliers. Samples outside the range of ±3 standard deviations from the mean of PC1 and PC2 were excluded. The quality-controlled data was next normalized using the dasen method, as implemented in the wateRmelon R package 67 . To correct batch effects from methylation plates, we used the BEclear R package 68 . Immune cell type proportions (B lymphocytes, natural killer cells, CD4+ T cells, CD8+ T cells, monocytes, neutrophils, and eosinophils) were estimated using the EpiDISH R package 69 . Granulocyte proportions were computed as the sum of neutrophil and eosinophil proportions 36 , 38 , 70 , since both neutrophils and eosinophils are classified as granular leukocytes. For both studies, we included samples from self-reported non-Hispanic white individuals over 65 years of age. To avoid inflation due to family structure in FHS9, we chose only one individual per family, prioritizing the sample with the highest bisulfite conversion rate. In ADNI and FHS9, the participants were followed for up to 11.11 and 7.72 years after their blood draw, respectively. We excluded samples from individuals who developed dementia at the time of blood draw or during the follow-up period. Association of DNA methylation at individual CpGs with dementia For each dataset, the association between CpG methylation levels and chronological age at blood draw was assessed using linear statistical models. Given that methylation M- values (logit transformation of methylation beta values) have better statistical properties (i.e., homoscedasticity) for linear regression models 71 , we used the M- values as the outcome variable in our statistical models. For both the ADNI and FHS9 datasets, we adjusted for potential confounding factors including sex and estimated major immune cell-type proportions in the samples. The linear model we used is methylation M value ∼ age + sex + immune cell-type proportions (B, NK, CD4T, Mono, Gran). Inflation assessment and correction Genomic inflation factors (lambda values) were estimated using both the conventional approach 72 and the bacon method 73 , which was proposed specifically for EWAS. Using the conventional approach, the estimated λ values were 1.097 for ADNI and 1.229 for FHS9. The inflation factors estimated by the bacon approach (λ.bacon) were 1.033 and 1.136 for the ADNI and FHS9 datasets, respectively. The estimated bias from the bacon method were -0.048 for ADNI and -0.044 for FHS9. After genomic correction using the bacon method 73 , as implemented in the bacon R package, the estimated bias were -1.35×10 -4 and 4.70×10 -4 , and the estimated inflation factors were λ = 1.049 and 1.063, and λ.bacon = 1.002 and 1.006 for the ADNI and FHS9 and datasets, respectively. The bacon method was then used to compute bacon-corrected effect sizes, standard errors, and P- values for each dataset. Meta-analysis To meta-analyze individual CpG results across both the FHS9 and ADNI datasets, we used the inverse-variance weighted fixed-effects model 74 , as implemented in the meta R package. To correct for multiple comparisons, we computed the false discovery rate (FDR). We considered CpGs with an FDR less than 5% in meta-analysis of the FHS9 and ADNI datasets, with a consistent direction of change in estimated effect sizes, and a nominal P- value less than 1×10 -5 as statistically significant. Differentially methylated regions analysis To identify differentially methylated regions (DMRs) significantly associated with chronological age, we used two analytical approaches, the comb-p 14 approach and the coMethDMR 15 approach, and selected significant DMRs identified by both methods. Briefly, comb-p takes single CpG P- values and locations of CpG sites to scan the genome for regions enriched with a series of adjacent low P- values. In our analysis, we used meta-analysis P -values of the two blood sample cohorts as input for comb-p. We used the default parameter setting for our comb-p analysis, with parameters --seed 1e-3 and --dist 200, which required a P- value of 10 -3 to start a region and extend the region if another P- value was within 200 base pairs. As comb-p uses the Sidak method 75 to adjust for multiple comparisons, we selected DMRs with Sidak P- values less than 0.05. In addition, we further required the selected DMRs to have at least 3 CpGs and a consistent direction of change across all CpGs mapped within the region. In the coMethDMR approach, the “contiguous genomic regions” are genomic regions on the Illumina array covered with clusters of contiguous CpGs where the maximum separation between any two consecutive probes is 200 base pairs. First, coMethDMR selects co-methylated sub-regions within the contiguous genomic regions. Next, we summarized methylation M values within these co-methylated sub-regions using medians and tested them against chronological age at blood draw, adjusting for sex, and estimated immune cell-type proportions using linear regression models. The bacon method 73 was next applied to cohort-specific coMethDMR test statistics to obtain inflation-corrected effect sizes, standard errors, and P- values, which were then combined by inverse-variance weighted meta-analysis models using R package meta. We considered co-methylated DMRs with at least 3 CpGs, with a consistent direction of change in estimated effect sizes in both datasets, and a meta-analysis FDR < 0.05 to be significant in the coMethDMR analysis. Finally, we selected significant DMRs identified by both comb-p and coMethDMR approaches for subsequent analyses. Functional annotation of significant methylation associations Significant methylation at individual CpGs and DMRs was annotated using both the Illumina (UCSC) gene annotation and Genomic Regions Enrichment of Annotations Tool (GREAT) software 76 , which associates genomic regions with target genes. Pathway analysis To identify biological pathways enriched with significant DNA methylation differences, we used the methylRRA function in the methylGSA R package 21 , which used P -values from the meta-analysis of FHS9 and ADNI datasets as input. Briefly, methylGSA first computes a gene-wise ρ value by aggregating P -values from multiple CpGs mapped to each gene. Next, the different number of CpGs on each gene is adjusted by Bonferroni correction. Finally, a Gene Set Enrichment Analysis 77 (in pre-rank analysis mode) is performed to identify pathways enriched with significant CpGs. We analyzed pathways in the KEGG and REACTOME databases. Pathways with FDR less than 0.05 were considered to be statistically significant. Integrative analyses with gene expression, genetic variants, and brain-to-blood correlations To evaluate the effect of DNA methylation on the expression of nearby genes, we overlapped our aging-associated CpGs, including both significant individual CpGs and those located within DMRs, with eQTm analysis results in Supplementary Tables 2 and 3 of Yao et al. (2021) 16 . For correlation and overlap of aging-associated CpGs with genetic susceptibility loci, we searched the GoDMC database. 17 To select significant blood mQTLs of aging-associated CpGs in GoDMC, we used the same criteria as the original study, 31 that is, considering a cis P- value smaller than 10 -8 and a trans P- value smaller than 10 -14 as significant. The genome-wide summary statistics for genetic variants associated with dementia described in Bellenguez et al. (2022) 32 were obtained from the European Bioinformatics Institute GWAS Catalog under accession no. GCST90027158. Colocalization analysis was performed using the coloc R package. To assess the correlation of aging-associated CpGs and DMRs methylation levels in blood and brain samples, we used the London dataset, which consisted of 69 samples with matched PFC and blood samples 37 . We assessed the association of brain and blood methylation levels at aging-associated CpGs by performing an adjusted correlation analysis using methylation residuals ( r resid ), in which we first removed the effects of estimated neuron proportions in brain samples (or estimated immune cell-type proportions in blood samples), array, age at death for brain samples (or age at blood draw for blood samples), and sex from DNA methylation M -values. Validation using independent datasets To compare our results with previous independent AD studies, we searched aging-associated CpGs (both significant individual CpGs and those located in DMRs) using the CpG Query tool in the MIAMI-AD database 78 . For input on phenotype, we selected “AD Biomarker”, “AD Neuropathology”, “Dementia Clinical Diagnosis”, and “Mild Cognitive Impairment”. Only studies external to the ADNI and FHS datasets were included. Data availability The ADNI and Framingham Heart Study datasets can be accessed from http://adni.loni.usc.edu and the dbGap database (accession: phs000974.v5.p4). Code availability The scripts for the analyses performed in this study are at https://github.com/TransBioInfoLab/AD-Aging-blood-sample-analysis . Data Availability The ADNI and Framingham Heart Study datasets can be accessed from http://adni.loni.usc.edu and the dbGap database (accession: phs000974.v5.p4). http://adni.loni.usc.edu https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000974.v6.p5 Author Contributions L.W., J.Y., E.R.M., and W.Z. designed the computational analyses. W.Z., D.L., L.G., M.A.S., and L.W. analyzed the data. L.W., J.Y., E.R.M, X.C., and B.W.K. contributed to the interpretation of the results. L.W., W.Z. wrote the paper, and all authors participated in the review and revision of the manuscript. L.W. conceived the original idea and supervised the project. Competing Interest The authors declare that they have no conflict of interest. Acknowledgment This research was supported by US National Institutes of Health grants R61NS135587 (L.W.), RF1NS128145 (L.W.), and R01AG062634 (E.R.M, B.W.K., L.W.). Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf Data collection and sharing for the ADNI dataset was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). 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. Footnotes Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( http://adni.loni.usc.edu ). As such, the ADNI investigators contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators is available at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf . REFERENCES 1. ↵ 2022 Alzheimer’s disease facts and figures . Alzheimers Dement 18 , 700 – 789 ( 2022 ). OpenUrl CrossRef PubMed 2. ↵ De Plano , L.M. , Saitta , A. , Oddo , S. & Caccamo , A . Epigenetic Changes in Alzheimer’s Disease: DNA Methylation and Histone Modification . Cells 13 ( 2024 ). 3. ↵ Horvath , S . DNA methylation age of human tissues and cell types . Genome Biol 14 , R115 ( 2013 ). 4. ↵ Seale , K. , Teschendorff , A. , Reiner , A.P. , Voisin , S. & Eynon , N . A comprehensive map of the aging blood methylome in humans . Genome Biol 25 , 240 ( 2024 ). 5. Reynolds , L.M. et al. Age-related variations in the methylome associated with gene expression in human monocytes and T cells . Nat Commun 5 , 5366 ( 2014 ). OpenUrl CrossRef PubMed 6. Bacalini , M.G. et al. A meta-analysis on age-associated changes in blood DNA methylation: results from an original analysis pipeline for Infinium 450k data . Aging (Albany NY ) 7 , 97 – 109 ( 2015 ). OpenUrl PubMed 7. ↵ Marttila , S. et al. Ageing-associated changes in the human DNA methylome: genomic locations and effects on gene expression . BMC Genomics 16 , 179 ( 2015 ). 8. ↵ Zhang , W. et al. Blood DNA methylation signature for incident dementia: Evidence from longitudinal cohorts . Alzheimers Dement 21 , e14496 ( 2025 ). OpenUrl PubMed 9. ↵ De Jager , P.L. et al. Alzheimer’s disease: early alterations in brain DNA methylation at ANK1 , BI N1 , RHBDF2 and other loci. Nat Neurosci 17 , 1156-63 ( 2014 ). 10. ↵ Avramopoulos , D. , Szymanski , M. , Wang , R. & Bassett , S . Gene expression reveals overlap between normal aging and Alzheimer’s disease genes . Neurobiol Aging 32 , 2319 e27 – 34 ( 2011 ). OpenUrl 11. ↵ Meng , G. , Zhong , X. & Mei , H . A Systematic Investigation into Aging Related Genes in Brain and Their Relationship with Alzheimer’s Disease . PLoS One 11 , e0150624 ( 2016 ). OpenUrl CrossRef PubMed 12. ↵ Li , H. et al. Common DNA methylation alterations of Alzheimer’s disease and aging in peripheral whole blood . Oncotarget 7 , 19089 – 98 ( 2016 ). OpenUrl PubMed 13. ↵ Pidsley , R. et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling . Genome Biol 17 , 208 ( 2016 ). 14. ↵ Pedersen , B.S. , Schwartz , D.A. , Yang , I.V. & Kechris , K.J . Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values . Bioinformatics 28 , 2986 – 8 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 15. ↵ Gomez , L. et al. coMethDMR: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies with continuous phenotypes . Nucleic Acids Res 47 , e98 ( 2019 ). OpenUrl CrossRef PubMed 16. ↵ Yao , C. et al. Epigenome-wide association study of whole blood gene expression in Framingham Heart Study participants provides molecular insight into the potential role of CHRNA5 in cigarette smoking-related lung diseases . Clin Epigenetics 13 , 60 ( 2021 ). 17. ↵ Cao , W . In sickness and in health-Type I interferon and the brain . Front Aging Neurosci 16 , 1403142 ( 2024 ). 18. ↵ Stacpoole , P.W . The pyruvate dehydrogenase complex as a therapeutic target for age-related diseases . Aging Cell 11 , 371 – 7 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 19. ↵ Galano , M. , Venugopal , S. & Papadopoulos , V . Role of STAR and SCP2/SCPx in the Transport of Cholesterol and Other Lipids . Int J Mol Sci 23 ( 2022 ). 20. ↵ Bu , S. , Lv , Y. , Liu , Y. , Qiao , S. & Wang , H . Zinc Finger Proteins in Neuro-Related Diseases Progression . Front Neurosci 15 , 760567 ( 2021 ). 21. ↵ Ren , X. & Kuan , P.F . methylGSA: a Bioconductor package and Shiny app for DNA methylation data length bias adjustment in gene set testing . Bioinformatics 35 , 1958 – 1959 ( 2019 ). OpenUrl CrossRef PubMed 22. ↵ Lopez-Otin , C. , Blasco , M.A. , Partridge , L. , Serrano , M. & Kroemer , G . Hallmarks of aging: An expanding universe . Cell 186 , 243 – 278 ( 2023 ). OpenUrl CrossRef PubMed 23. ↵ Franceschi , C. , Garagnani , P. , Parini , P. , Giuliani , C. & Santoro , A . Inflammaging: a new immune-metabolic viewpoint for age-related diseases . Nat Rev Endocrinol 14 , 576 – 590 ( 2018 ). OpenUrl CrossRef PubMed 24. ↵ Nusse , R. & Clevers , H. Wnt/beta-Catenin Signaling, Disease, and Emerging Therapeutic Modalities. Cell 169 , 985 – 999 ( 2017 ). OpenUrl PubMed 25. ↵ Sun , N. , Youle , R.J. & Finkel , T . The Mitochondrial Basis of Aging . Mol Cell 61 , 654 – 666 ( 2016 ). OpenUrl CrossRef PubMed 26. ↵ Bezprozvanny , I. & Mattson , M.P . Neuronal calcium mishandling and the pathogenesis of Alzheimer’s disease . Trends Neurosci 31 , 454 – 63 ( 2008 ). OpenUrl CrossRef PubMed Web of Science 27. ↵ Selkoe , D.J . Alzheimer’s disease is a synaptic failure . Science 298 , 789 – 91 ( 2002 ). OpenUrl Abstract / FREE Full Text 28. ↵ Inestrosa , N.C. & Varela-Nallar , L . Wnt signaling in the nervous system and in Alzheimer’s disease . J Mol Cell Biol 6 , 64 – 74 ( 2014 ). OpenUrl CrossRef PubMed 29. ↵ Dityatev , A. , Schachner , M. & Sonderegger , P . The dual role of the extracellular matrix in synaptic plasticity and homeostasis . Nat Rev Neurosci 11 , 735 – 46 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 30. ↵ de la Monte , S.M. & Tong , M . Brain metabolic dysfunction at the core of Alzheimer’s disease . Biochem Pharmacol 88 , 548 – 59 ( 2014 ). OpenUrl CrossRef PubMed 31. ↵ Min , J.L. et al. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation . Nat Genet 53 , 1311 – 1321 ( 2021 ). OpenUrl CrossRef PubMed 32. ↵ Bellenguez , C. et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias . Nat Genet 54 , 412 – 436 ( 2022 ). OpenUrl CrossRef PubMed 33. ↵ Giambartolomei , C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics . PLoS Genet 10 , e1004383 ( 2014 ). OpenUrl CrossRef PubMed 34. ↵ Guo , H. et al. Integration of disease association and eQTL data using a Bayesian colocalisation approach highlights six candidate causal genes in immune-mediated diseases . Hum Mol Genet 24 , 3305 – 13 ( 2015 ). OpenUrl CrossRef PubMed 35. ↵ Lukacsovich , D. et al. MIAMI-AD (Methylation in Aging and Methylation in AD): an integrative knowledgebase that facilitates explorations of DNA methylation across sex, aging, and Alzheimer’s disease . Database (Oxford ) 2024( 2024 ). 36. ↵ T, C.S., et al. Cross-tissue analysis of blood and brain epigenome-wide association studies in Alzheimer’s disease . Nat Commun 13 , 4852 ( 2022 ). OpenUrl CrossRef PubMed 37. ↵ Hannon , E. , Lunnon , K. , Schalkwyk , L. & Mill , J . Interindividual methylomic variation across blood, cortex, and cerebellum: implications for epigenetic studies of neurological and neuropsychiatric phenotypes . Epigenetics 10 , 1024 – 32 ( 2015 ). OpenUrl CrossRef PubMed 38. ↵ T, C.S., et al. Distinct sex-specific DNA methylation differences in Alzheimer’s disease . Alzheimers Res Ther 14 , 133 ( 2022 ). 39. ↵ Jones , M.J. , Goodman , S.J. & Kobor , M.S . DNA methylation and healthy human aging . Aging Cell 14 , 924 – 32 ( 2015 ). OpenUrl CrossRef PubMed 40. ↵ Rakyan , V.K. et al. Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains . Genome Res 20 , 434 – 9 ( 2010 ). OpenUrl Abstract / FREE Full Text 41. ↵ Hannum , G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates . Mol Cell 49 , 359 – 367 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 42. ↵ Heyn , H. et al. Distinct DNA methylomes of newborns and centenarians . Proc Natl Acad Sci U S A 109 , 10522 – 7 ( 2012 ). OpenUrl Abstract / FREE Full Text 43. ↵ Bacalini , M.G. et al. Systemic Age-Associated DNA Hypermethylation of ELOVL2 Gene: In Vivo and In Vitro Evidences of a Cell Replication Process . J Gerontol A Biol Sci Med Sci 72 , 1015 – 1023 ( 2017 ). OpenUrl PubMed 44. ↵ Gim , J.A . Integrative Approaches of DNA Methylation Patterns According to Age, Sex and Longitudinal Changes . Curr Genomics 23 , 385 – 399 ( 2023 ). OpenUrl PubMed 45. ↵ Cheishvili , D. et al. EpiAge: a next-generation sequencing-based ELOVL2 epigenetic clock for biological age assessment in saliva and blood across health and disease . Aging (Albany NY ) 17 , 131 – 160 ( 2025 ). OpenUrl PubMed 46. ↵ Hammouda , S. et al. Genetic variants in FADS1 and ELOVL2 increase level of arachidonic acid and the risk of Alzheimer’s disease in the Tunisian population . Prostaglandins Leukot Essent Fatty Acids 160 , 102159 ( 2020 ). 47. ↵ Wezyk , M. et al. Hypermethylation of TRIM59 and KLF14 Influences Cell Death Signaling in Familial Alzheimer’s Disease . Oxid Med Cell Longev 2018, 6918797 ( 2018 ). 48. ↵ Rice , R.A. , Berchtold , N.C. , Cotman , C.W. & Green , K.N . Age-related downregulation of the CaV3.1 T-type calcium channel as a mediator of amyloid beta production . Neurobiol Aging 35 , 1002 – 11 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 49. ↵ Habibe , J.J. , Clemente-Olivo , M.P. & de Vries , C.J . How (Epi)Genetic Regulation of the LIM-Domain Protein FHL2 Impacts Multifactorial Disease . Cells 10 ( 2021 ). 50. ↵ Mancini , M. , Bassani , S. & Passafaro , M . Right Place at the Right Time: How Changes in Protocadherins Affect Synaptic Connections Contributing to the Etiology of Neurodevelopmental Disorders . Cells 9 ( 2020 ). 51. ↵ Haukedal , H. & Freude , K.K . Implications of Glycosylation in Alzheimer’s Disease . Front Neurosci 14 , 625348 ( 2020 ). 52. ↵ Khatib , T. , Chisholm , D.R. , Whiting , A. , Platt , B. & McCaffery , P . Decay in Retinoic Acid Signaling in Varied Models of Alzheimer’s Disease and In-Vitro Test of Novel Retinoic Acid Receptor Ligands (RAR-Ms) to Regulate Protective Genes . J Alzheimers Dis 73 , 935 – 954 ( 2020 ). OpenUrl PubMed 53. ↵ George , C. et al. The Alzheimer’s disease transcriptome mimics the neuroprotective signature of IGF-1 receptor-deficient neurons . Brain 140 , 2012 – 2027 ( 2017 ). OpenUrl CrossRef PubMed 54. ↵ Zhu , M. , Jia , L. , Li , F. & Jia , J . Identification of KIAA0513 and Other Hub Genes Associated With Alzheimer Disease Using Weighted Gene Coexpression Network Analysis . Front Genet 11 , 981 ( 2020 ). OpenUrl CrossRef PubMed 55. ↵ Pellegrini , C. et al. A Meta-Analysis of Brain DNA Methylation Across Sex, Age, and Alzheimer’s Disease Points for Accelerated Epigenetic Aging in Neurodegeneration . Front Aging Neurosci 13 , 639428 ( 2021 ). 56. ↵ Sollazzo , R. et al. Structural and functional alterations of neurons derived from sporadic Alzheimer’s disease hiPSCs are associated with downregulation of the LIMK1-cofilin axis . Alzheimers Res Ther 16 , 267 ( 2024 ). 57. ↵ Sims , R. et al. Rare coding variants in PLCG2 , AB I3 , and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease. Nat Genet 49 , 1373-1384 ( 2017 ). 58. Yi , L.X. , Zeng , L. , Wang , Q. , Tan , E.K. & Zhou , Z.D . Reelin links Apolipoprotein E4, Tau, and Amyloid-beta in Alzheimer’s disease . Ageing Res Rev 98 , 102339 ( 2024 ). 59. ↵ Bustos , F.J. et al. Epigenetic editing of the Dlg4/PSD95 gene improves cognition in aged and Alzheimer’s disease mice . Brain 140 , 3252 – 3268 ( 2017 ). OpenUrl CrossRef PubMed 60. ↵ Nativio , R. et al. Dysregulation of the epigenetic landscape of normal aging in Alzheimer’s disease . Nat Neurosci 21 , 497 – 505 ( 2018 ). OpenUrl CrossRef PubMed 61. ↵ Hirano , A. et al. A genome-wide association study of late-onset Alzheimer’s disease in a Japanese population . Psychiatr Genet 25 , 139 – 46 ( 2015 ). OpenUrl PubMed 62. ↵ van Abel , D. et al. Direct downregulation of CNTNAP2 by STOX1A is associated with Alzheimer’s disease . J Alzheimers Dis 31 , 793 – 800 ( 2012 ). OpenUrl PubMed 63. ↵ Attems , J. et al. Calcium-binding protein secretagogin-expressing neurones in the human hippocampus are largely resistant to neurodegeneration in Alzheimer’s disease . Neuropathol Appl Neurobiol 34 , 23 – 32 ( 2008 ). OpenUrl PubMed Web of Science 64. ↵ Garagnani , P. et al. Methylation of ELOVL2 gene as a new epigenetic marker of age . Aging Cell 11 , 1132 – 4 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 65. ↵ Li , X. et al. Lipid metabolism dysfunction induced by age-dependent DNA methylation accelerates aging . Signal Transduct Target Ther 7 , 162 ( 2022 ). 66. ↵ Chen , Y.A. et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray . Epigenetics 8 , 203 – 9 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 67. ↵ Pidsley , R. et al. A data-driven approach to preprocessing Illumina 450K methylation array data . BMC Genomics 14 , 293 ( 2013 ). 68. ↵ Akulenko , R. , Merl , M. & Helms , V . BEclear: Batch Effect Detection and Adjustment in DNA Methylation Data . PLoS One 11 , e0159921 ( 2016 ). OpenUrl CrossRef PubMed 69. ↵ Teschendorff , A.E. , Breeze , C.E. , Zheng , S.C. & Beck , S . A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies . BMC Bioinformatics 18 , 105 ( 2017 ). 70. ↵ Nabais , M.F. et al. Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders . Genome Biol 22 , 90 ( 2021 ). 71. ↵ Du , P. et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis . BMC Bioinformatics 11 , 587 ( 2010 ). 72. ↵ Delvin , B. & Roeder , K . Genomic Control for Association Studies . Biometrics 55 , 997 – 1004 ( 1999 ). OpenUrl CrossRef PubMed Web of Science 73. ↵ van Iterson , M. , van Zwet , E.W. , Consortium , B. & Heijmans , B.T . Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution . Genome Biol 18 , 19 ( 2017 ). 74. ↵ Rice , K. , Higgins , J.P.T. & Lumley , T . A re-evaluation of fixed effect(s) meta-analysis . J. R. Statist. Soc. A 181 , 205 – 227 ( 2018 ). OpenUrl 75. ↵ Sidak , Z . Rectangular confidence region for the means of multivariate normal distributions . J. Am. Stat. Assoc . 62 , 626 – 633 ( 1967 ). OpenUrl CrossRef Web of Science 76. ↵ McLean , C.Y. et al. GREAT improves functional interpretation of cis-regulatory regions . Nat Biotechnol 28 , 495 – 501 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 77. ↵ Subramanian , A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles . Proc Natl Acad Sci U S A 102 , 15545 – 50 ( 2005 ). OpenUrl Abstract / FREE Full Text 78. ↵ Lukacsovich , D. et al. MIAMI-AD (Methylation in Aging and Methylation in AD): an integrative knowledgebase that facilitates explorations of DNA methylation across sex, aging, and Alzheimer’s disease . medRxiv ( 2023 ). View the discussion thread. Back to top Previous Next Posted June 09, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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