We aimed to investigate the relationships between clinical, cognitive, genetic and biological features in Subjective Cognitive Decline (SCD) and to assess how these features influence the risk of progression to dementia, with the goal of informing a targeted management protocol.

SCD is a common condition among elderly and middle-aged people. It is getting increasing attention by the research community as it represent the earliest manifestation of  Alzheimer's Disease (AD). Nevertheless, there is currently no consensus on the optimal clinical management of individuals with SCD, leading to considerable heterogeneity across centers, variability in diagnostic and therapeutic approaches.

440 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.

Women 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 important predictive factors.

This 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.