{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T09:09:47Z","timestamp":1779181787903,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T00:00:00Z","timestamp":1775433600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"National Health and Medical Research Council of Australia","award":["2009965; 1197190"],"award-info":[{"award-number":["2009965; 1197190"]}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["AG064803; P30AG072958"],"award-info":[{"award-number":["AG064803; P30AG072958"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Early identification of individuals at elevated risk for Alzheimer's disease (AD) is critical for prevention. Blood-based biomarkers offer scalable alternatives to cerebrospinal fluid and imaging, but the prognostic value of longitudinal plasma lipid trajectories remains unclear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate whether multi-year plasma lipid trajectories improve prediction of AD conversion beyond demographic, clinical, genetic, and neuropsychological measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We studied 1150 cognitively normal or mildly impaired Alzheimer's Disease Neuroimaging Initiative participants; 329 progressed to AD dementia over a mean follow-up of 2.3 years. Plasma lipidomics quantified 749 lipid species by high-resolution LC\u2013MS. Trajectories were summarized using functional principal component analysis and related to time to conversion using covariate-adjusted Cox proportional hazards models. Predictive performance was assessed by concordance index and likelihood-ratio tests.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Cross-sectional lipid profiles modestly improved prediction beyond demographic and clinical covariates, and longitudinal lipid trajectories yielded small additional gains. Ether-linked triglycerides showed the strongest longitudinal associations with conversion, with TG(O-50:1) [NL-18:1] exhibiting the most robust signal. Neuropsychological measures provided substantially stronger discrimination, and lipid trajectories added limited value once cognitive information was included. Nevertheless, longitudinal lipid changes contributed consistent improvements in models without neuropsychological predictors, supporting their role as complementary blood-based markers in settings where standardized cognitive assessments are unavailable or impractical.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Plasma lipid trajectories capture biologically relevant metabolic change associated with AD progression and provide complementary predictive information. Longitudinal lipidomic profiling may support early risk stratification and cohort enrichment when cognitive assessments are unavailable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877261435212","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T14:40:07Z","timestamp":1775486407000},"page":"651-662","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Plasma lipid trajectories improve prediction of future Alzheimer's disease"],"prefix":"10.1177","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3595-8716","authenticated-orcid":false,"given":"Haotian","family":"Zou","sequence":"first","affiliation":[{"name":"Duke University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingting","family":"Wang","sequence":"additional","affiliation":[{"name":"Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia"},{"name":"Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin","family":"Huynh","sequence":"additional","affiliation":[{"name":"Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia"},{"name":"Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia"},{"name":"Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter J.","family":"Meikle","sequence":"additional","affiliation":[{"name":"Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia"},{"name":"Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, Australia"},{"name":"Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rima","family":"Kaddurah-Daouk","sequence":"additional","affiliation":[{"name":"Duke Institute for Brain Sciences"},{"name":"Duke University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Luo","sequence":"additional","affiliation":[{"name":"Duke University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"The Alzheimer's Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"The Alzheimer's Disease Metabolomics Consortium","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,4,6]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1202753"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2011.03.003"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2018.02.018"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(21)00361-6"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.12134"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(20)30071-5"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamaneurol.2016.6117"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awab223"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000008081"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00401-018-1932-x"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2013.01.013"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrn3820"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1046\/j.1471-4159.2002.00997.x"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/tp.2014.127"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1038\/nm.3466"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-2011-110405"},{"key":"e_1_3_3_18_2","first-page":"5698","article-title":"Concordant peripheral lipidome signatures in two large clinical studies of Alzheimer\u2019s disease","volume":"11","author":"Huynh K","year":"2020","unstructured":"Huynh K, Lim WLF, Giles C, et al. 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