{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:03:28Z","timestamp":1760058208085,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Genomic Medicine"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CIMB"],"abstract":"<jats:p>The rising prevalence of Alzheimer\u2019s disease (AD), particularly among older adults, has driven increased research into its underlying mechanisms and risk factors. Aging, genetic susceptibility, and cardiovascular health are recognized contributors to AD, but how the age of onset affects disease progression remains underexplored. This study investigates the role of early- versus late-onset Alzheimer\u2019s disease (EOAD and LOAD, respectively) in shaping the trajectory of cognitive decline. Leveraging data from the Religious Orders Study and Memory and Aging Project (ROSMAP), two cohorts were established: individuals with early-onset AD and those with late-onset AD. Comprehensive analyses, including differential gene expression profiling, pathway enrichment, and gene co-expression network construction, were conducted to identify distinct molecular signatures associated with each cohort. Network modularity learning algorithms were used to discern the inner structure of co-expression networks and their related functional features. Computed network descriptors provided deeper insights into the influence of age at onset on the biological progression of AD.<\/jats:p>","DOI":"10.3390\/cimb47030200","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T04:34:43Z","timestamp":1742272483000},"page":"200","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Gene Co-Expression Analysis Reveals Functional Differences Between Early- and Late-Onset Alzheimer\u2019s Disease"],"prefix":"10.3390","volume":"47","author":[{"given":"Abel Isa\u00edas","family":"Guti\u00e9rrez Cruz","sequence":"first","affiliation":[{"name":"Computational Genomics Division, National Institute of Genomic Medicine, Mexico City 14610, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guillermo","family":"de Anda-J\u00e1uregui","sequence":"additional","affiliation":[{"name":"Computational Genomics Division, National Institute of Genomic Medicine, Mexico City 14610, Mexico"},{"name":"Center for Complexity Sciences, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Mexico City 04510, Mexico"},{"name":"Investigadores por Mexico, Consejo Nacional de Ciencia y Tecnolog\u00eda (CONAHCYT), Mexico City 03940, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1872-1397","authenticated-orcid":false,"given":"Enrique","family":"Hern\u00e1ndez-Lemus","sequence":"additional","affiliation":[{"name":"Computational Genomics Division, National Institute of Genomic Medicine, Mexico City 14610, Mexico"},{"name":"Center for Complexity Sciences, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Mexico City 04510, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"97","DOI":"10.14283\/jpad.2023.61","article-title":"Burden of illness in people with Alzheimer\u2019s disease: A systematic review of epidemiology, comorbidities and mortality","volume":"11","author":"Eichinger","year":"2024","journal-title":"J. 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