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Patients were followed for a median of 10 years. Relationships among features were first assessed univariately, focusing on differences across stratified subgroups. To capture multivariate associations, we applied network analysis using a Markov Random Field. Finally, baseline features were related to dementia progression using an XGboost machine learning model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Women comprising 68.9% of the cohort, were generally younger at onset, had lower\n                      <jats:italic toggle=\"yes\">APOE<\/jats:italic>\n                      \u03b54 prevalence, and differed in neuropsychological performance compared to men. Older patients (age &gt;60) exhibited a higher prevalence of\n                      <jats:italic toggle=\"yes\">APOE<\/jats:italic>\n                      \u03b54 and cerebral small vessel disease. Patients with depressive symptoms demonstrated lower cognitive performance across multiple domains. Network analysis indicated complex interconnections among gender, cognitive reserve, SCD severity, and depressive symptoms. The XGboost model achieved 74% accuracy in predicting progression to dementia, identifying age at onset, mini-mental state examination scores, and\n                      <jats:italic toggle=\"yes\">APOE<\/jats:italic>\n                      genotype as the most predictive factors.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      This study highlights the role of age, gender,\n                      <jats:italic toggle=\"yes\">APOE<\/jats:italic>\n                      genotype, and depressive symptoms in the presentation and progression of cognitive decline. By identifying key predictive features, we propose a personalized management protocol aimed at optimizing care for individuals with SCD.\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>Trial registration number:<\/jats:bold>\n                      NCT05569083, registration date: 2019-05-30\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877261416117","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T11:34:50Z","timestamp":1769600090000},"page":"728-745","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Towards the development of a management protocol for subjective cognitive decline: Insights from a cross-sectional and longitudinal analysis of multimodal data from a memory 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