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.
Authors
Affiliations (9)
Affiliations (9)
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy.
- Vita-Salute San Raffaele University, Milan, Italy.
- IRCCS Policlinico San Donato, San Donato Milanese, Italy.
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy.
- SOD Neurologia, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy.
- Regional Referral Centre for Relational Criticalities - Tuscany Region, Florence, Italy.
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.
- IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy.
- SODC Neurofisiopatologia, Azienda Ospedaliero-universitaria Careggi, Florence, Italy.
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, <i>APOE</i> 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 <i>APOE</i> ε4 prevalence, and differed in neuropsychological performance compared to men. Older patients (age >60) exhibited a higher prevalence of <i>APOE</i> 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 <i>APOE</i> genotype as the most predictive factors.ConclusionsThis study highlights the role of age, gender, <i>APOE</i> 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.<b>Trial registration number:</b> NCT05569083, registration date: 2019-05-30.