BackgroundSubjective cognitive decline (SCD) represents the first early symptomatic stage of Alzheimer's disease (AD).ObjectiveWe aimed to investigate the relationships between features in SCD and to assess the importance of these features in the future development of dementia to inform a targeted management protocol.Methods440 SCD patients underwent neurological and neuropsychological assessments, MRI scans, APOE genotyping, and AD biomarker evaluations. 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.ResultsWomen comprising 68.9% of the cohort, were generally younger at onset, had lower APOE ε4 prevalence, and differed in neuropsychological performance compared to men. Older patients (age >60) exhibited a higher prevalence of APOE ε4 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 APOE genotype as the most predictive factors.ConclusionsThis study highlights the role of age, gender, APOE 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.Trial registration number: NCT05569083, registration date: 2019-05-30.

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 clinic / Salvatore Mazzeo, Michael Lassi , Sonia Padiglioni, Alberto Arturo Vergani , Valentina Moschini , Maenia Scarpino , Giulia Giacomucci , Rachele Burali, Carmen Morinelli , Carlo Fabbiani , Giulia Galdo, Lorenzo Gaetano Amato, Silvia Bagnoli , Filippo Emiliani, Assunta Ingannato, Benedetta Nacmias , Sandro Sorbi , Antonello Grippo , Alberto Mazzoni , Valentina Bessi. - In: JOURNAL OF ALZHEIMER'S DISEASE. - ISSN 1875-8908. - ELETTRONICO. - (2026), pp. 0-0.

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 clinic

Salvatore Mazzeo;Sonia Padiglioni;Maenia Scarpino;Giulia Giacomucci;Carlo Fabbiani;Giulia Galdo;Silvia Bagnoli;Filippo Emiliani;Assunta Ingannato;Benedetta Nacmias;Sandro Sorbi;Antonello Grippo
;
Valentina Bessi
2026

Abstract

BackgroundSubjective cognitive decline (SCD) represents the first early symptomatic stage of Alzheimer's disease (AD).ObjectiveWe aimed to investigate the relationships between features in SCD and to assess the importance of these features in the future development of dementia to inform a targeted management protocol.Methods440 SCD patients underwent neurological and neuropsychological assessments, MRI scans, APOE genotyping, and AD biomarker evaluations. 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.ResultsWomen comprising 68.9% of the cohort, were generally younger at onset, had lower APOE ε4 prevalence, and differed in neuropsychological performance compared to men. Older patients (age >60) exhibited a higher prevalence of APOE ε4 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 APOE genotype as the most predictive factors.ConclusionsThis study highlights the role of age, gender, APOE 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.Trial registration number: NCT05569083, registration date: 2019-05-30.
2026
0
0
Salvatore Mazzeo, Michael Lassi , Sonia Padiglioni, Alberto Arturo Vergani , Valentina Moschini , Maenia Scarpino , Giulia Giacomucci , Rachele Bural...espandi
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1452434
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