{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T13:15:29Z","timestamp":1767014129166,"version":"3.48.0"},"reference-count":49,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"vor","delay-in-days":69,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alz &amp;amp; Dem Diag Ass &amp;amp; Dis Mo"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>INTRODUCTION<\/jats:title>\n                    <jats:p>The diagnosis of Alzheimer's disease (AD) traditionally relies on cerebrospinal fluid and plasma levels of amyloid beta and phosphorylated tau. Although informative, these biomarkers represent a narrow, hypothesis\u2010driven approach to intercept the disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>\n                      Data\u2010driven analysis was applied on demographic data, apolipoprotein E (\n                      <jats:italic>APOE<\/jats:italic>\n                      ) \u03b54 allele, and 82 biomarkers obtained from blood tests of healthy controls (HC), mild cognitive impairment that remained stable within 36 months following blood collection (sMCI), and patients with AD.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      Statistical analyses revealed differences among groups in many cholesterol\u2010related analytes.\n                      <jats:italic>APOE<\/jats:italic>\n                      \u03b54 and analytes such as amino acids, lipoproteins, and fatty acids emerged as the most influential features in machine learning (ML) classification algorithms. Glycolysis\u2010related metabolites and amino and fatty acids were predictive for distinguishing sMCI and AD from HC.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>These findings support the hypothesis that systemic alterations also occur during the preclinical stages of dementia, which can be detected by ML models on blood biomarkers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Machine learning on blood tests detects preclinical cognitive decline.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Glycolysis metabolites are predictive for distinguishing stable MCI and AD from HC.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Amino acids, lipoproteins, and fatty acids are the most predictive features.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Inflammatory and metabolic biomarkers represent a biosignature of cognitive health.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/dad2.70228","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T04:55:17Z","timestamp":1765342517000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning models of Alzheimer's disease spectrum using blood tests"],"prefix":"10.1002","volume":"17","author":[{"given":"Nicola Walter","family":"Falasca","sequence":"first","affiliation":[{"name":"Department of Neuroscience, Imaging, and Clinical Sciences G. d'Annunzio University of Chieti\u2010Pescara  Chieti Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio","family":"Ferretti","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, Imaging, and Clinical Sciences G. d'Annunzio University of Chieti\u2010Pescara  Chieti Italy"},{"name":"UdA\u2010TechLab Research Center University \"G. d'Annunzio\" of Chieti\u2010Pescara  Chieti Italy"},{"name":"Institute for Advanced Biomedical Technologies G. d'Annunzio University of Chieti\u2010Pescara  Chieti Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alberto","family":"Granzotto","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, Imaging, and Clinical Sciences G. d'Annunzio University of 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