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We performed a meta-analysis of blood DNAm samples from 475 dementia-free subjects aged over 65\u00a0years across two independent cohorts, the Framingham Heart Study (FHS) at Exam 9 and the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). We adjusted for sex and immune cell-type proportions and corrected batch effects and genomic inflation. Integrative analyses included pathway enrichment, mQTL analysis, colocalization with Alzheimer\u2019s disease and related dementia (ADRD) GWAS summary statistics, brain-blood DNAm correlations, and comparison to independent AD methylation studies. We identified 3758 CpGs and 556 differentially methylated regions (DMRs) consistently associated with chronological age in both cohorts at a 5% false discovery rate. Our pathway enrichment analyses highlighted metabolic regulation and synaptic signaling, processes previously implicated in Alzheimer\u2019s disease. Colocalization with ADRD GWAS summary statistics identified 32 genomic regions consistent with shared genetic signals for DNAm and ADRD risk. Roughly one-third of aging-associated CpGs overlapped CpGs associated with AD or AD neuropathology in external studies. Finally, we prioritized nine promoter CpGs (including those located in\u00a0\n                    <jats:italic>PDE1B<\/jats:italic>\n                    ,\n                    <jats:italic>ELOVL2<\/jats:italic>\n                    , and\n                    <jats:italic>PODXL2<\/jats:italic>\n                    ) showing strong positive blood-to-brain methylation concordance and external AD associations, nominating them as candidate blood-based biomarkers. Our study demonstrated that\u00a0late-life aging signatures in blood DNAm converge on processes implicated in AD and intersect with dementia genetics. A small set of CpGs with blood-brain concordance and external AD support offers promising candidate blood-based biomarkers for future validation.\n                  <\/jats:p>","DOI":"10.1007\/s11357-026-02195-x","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:52:50Z","timestamp":1776131570000},"page":"3185-3203","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The aging epigenome: integrative analyses reveal intersection with Alzheimer\u2019s disease"],"prefix":"10.1007","volume":"48","author":[{"given":"Wei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Lukacsovich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan I.","family":"Young","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lissette","family":"Gomez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael A.","family":"Schmidt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian W.","family":"Kunkle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eden R.","family":"Martin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8311-4251","authenticated-orcid":false,"given":"Lily","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,14]]},"reference":[{"key":"2195_CR1","doi-asserted-by":"crossref","unstructured":"2022 Alzheimer's disease facts and figures. 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