{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T09:57:56Z","timestamp":1790675876348,"version":"4.1.0"},"reference-count":65,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Telomere dysfunction contributes to cellular aging and genome instability, but telomere-associated transcription in Alzheimer's disease (AD) is poorly characterized. Telomere genes are commonly tested gene by gene, leaving it uncertain whether they organize into reproducible, disease-linked co-expression programs across cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To identify AD linked telomere gene modules and evaluate their diagnostic and biological relevance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We curated 157 telomere maintenance genes and analyzed two GPL570-platform GEO Series datasets: GSE5281 (n\u2009=\u2009161; AD\u2009=\u200987, control\u2009=\u200974) and GSE48350 (n\u2009=\u2009253; AD\u2009=\u200980, control\u2009=\u2009173). In GSE5281 hippocampus, RMA + age-adjusted limma identified 38 AD-associated telomere genes (FDR&lt;0.05). Ward clustering (silhouette) defined two modules, summarized as eigengenes (module PC1). Eigengenes were evaluated across seven classifiers with nested stratified 5\u2009\u00d7\u20095 cross-validation; Youden's J thresholds from out-of-fold predictions were fixed and applied to GSE48350 using frozen z-scoring (GSE5281 \u03bc\/\u03c3) and fixed cutoffs. Robustness used 500-bootstrap stability; hub genes from the dominant module were interpreted with BRETIGEA and Reactome\/GO enrichment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      PCA and t-SNE showed AD-associated structure in telomere-gene expression. With only two module features, models achieved ROC-AUC 0.722\u20130.780 internally and 0.689\u20130.695 externally. Bootstrap resampling converged on one dominant axis: Cluster 2 was consistently the strongest feature and was reduced in AD (Cohen's d\u2009=\u2009\u22121.07; Welch p\u2009=\u20098.13\u2009\u00d7\u200910\n                      <jats:sup>\u2212<\/jats:sup>\n                      <jats:sup>1<\/jats:sup>\n                      <jats:sup>0<\/jats:sup>\n                      ). Hub genes (TSPYL5, TUBB3, PLCL2, NHP2) were downregulated, tracked neuronal signatures positively, varied inversely with microglial\/astrocytic signatures, and mapped to telomere\/chromosome maintenance, DNA repair, and cell-cycle regulation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AD features a reproducible, resampling-stable telomere genome-maintenance module that provides an interpretable systems-level disease axis for mechanistic follow-up and integrative biomarker development.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877261476085","type":"journal-article","created":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T17:46:43Z","timestamp":1786211203000},"page":"1192-1210","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrative machine learning reveals telomere-associated gene modules reflecting neuronal dysregulation in Alzheimer's disease"],"prefix":"10.1177","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4722-6570","authenticated-orcid":false,"given":"Syeda Ummul Khair","family":"Fatima","sequence":"first","affiliation":[{"name":"Shahjalal University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdul","family":"Jabber","sequence":"additional","affiliation":[{"name":"Shahjalal University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mosammat Halima","family":"Khanam","sequence":"additional","affiliation":[{"name":"Shahjalal University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jubayer","family":"Khan","sequence":"additional","affiliation":[{"name":"Shahjalal University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Mostafij","family":"Talukder","sequence":"additional","affiliation":[{"name":"Shahjalal University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5644-2671","authenticated-orcid":false,"given":"Mohammad Abul","family":"Hasnat","sequence":"additional","affiliation":[{"name":"Shahjalal University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40120-022-00338-8"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41582-022-00749-z"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1172\/JCI158453"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-89980-4"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/cells8010073"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mad.2018.03.013"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awr133"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/s43587-021-00146-z"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbih.2021.100227"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12943-019-1035-x"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2024.04.011"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1093\/plphys\/kiae078"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-50885-8"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-30892-6"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1186\/s43556-025-00305-3"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1009363"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-37836-0"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12863-018-0617-8"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkad1025"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg405"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/4.2.249"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btm254"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv007"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24277-4"},{"key":"e_1_3_3_27_2","unstructured":"Kolde R. pheatmap: Pretty Heatmaps https:\/\/CRAN.R-project.org\/package=pheatmap 2025."},{"key":"e_1_3_3_28_2","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa F","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, et al. 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