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In analyses applying machine learning methods to all 187 metabolite concentrations assayed, we find a modest signal in the BLSA with distinct metabolites associated with the preclinical and symptomatic stages of AD, whereas the same methods gave poor classification accuracies in the AGES\u2010RS samples.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>We believe that ours is the largest blood biomarker study of preclinical AD to date. These findings underscore the importance of large\u2010scale independent validation of index findings from biomarker studies with relatively small sample sizes.<\/jats:p><\/jats:sec>","DOI":"10.1016\/j.jalz.2015.12.008","type":"journal-article","created":{"date-parts":[[2016,1,21]],"date-time":"2016-01-21T20:01:29Z","timestamp":1453406489000},"page":"815-822","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":152,"title":["Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals"],"prefix":"10.1002","volume":"12","author":[{"given":"Ramon","family":"Casanova","sequence":"first","affiliation":[{"name":"Department of Biostatistical Sciences Wake Forest School of Medicine  Winston\u2010Salem NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sudhir","family":"Varma","sequence":"additional","affiliation":[{"name":"HiThru Analytics LLC  Laurel MD 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