Mild Behavioral Impairment and Cerebrovascular Profiles Are Associated with Early Cognitive Impairment in a Community-Based Southeast Asian Cohort.

Background: Mild behavioral impairment (MBI) is one of the earliest observable changes when a person experiences cognitive decline and could be an early manifestation of underlying Alzheimer's disease neuropathology. Limited attention has been given to investigating the clinical applicability of behavioral biomarkers for detection of prodromal dementia. Objective: This study compared the prevalence of self-reported MBI and vascular risk factors in Southeast Asian adults to identify early indicators of cognitive impairment and dementia. Methods: This cohort study utilized baseline data from the Biomarkers and Cognition Study, Singapore (BIOCIS). 607 participants were recruited and classified into three groups: cognitively normal (CN), subjective cognitive decline (SCD), and mild cognitive impairment (MCI). Group comparisons of cognitive-behavioral, neuroimaging, and blood biomarkers data were applied using univariate analyses. Multivariate logistic regression analyses were conducted to investigate the association between cerebrovascular disease, vascular profiles, and cognitive impairment. Results: SCD had significantly higher depression scores and poorer quality of life (QOL) compared to CN. MCI had significantly higher depression scores; total MBI symptoms, MBI-interest, MBI-mood, and MBI-beliefs; poorer sleep quality; and poorer QOL compared to CN. Higher Staals scores, glucose levels, and systolic blood pressure were significantly associated with MCI classification. Fasting glucose levels were significantly correlated with depression, anxiety, MBI-social, and poorer sleep quality. Conclusions: The results reflect current research that behavioral changes are among the first symptoms noticeable to the person themselves as they begin to experience cognitive decline. Self-reported questionnaires may aid in early diagnoses of prodromal dementia. Behavioral changes and diabetes could be potential targets for preventative healthcare for dementia.

The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes.

BACKGROUND: The use of structural and perfusion brain imaging in combination with behavioural information in the prediction of cognitive syndromes using a data-driven approach remains to be explored. Here, we thus examined the contribution of brain structural and perfusion imaging and behavioural features to the existing classification of cognitive syndromes using a data-driven approach. METHODS: Study participants belonged to the community-based Biomarker and Cognition Cohort Study in Singapore who underwent neuropsychological assessments, structural-functional MRI and blood biomarkers. Participants had a diagnosis of cognitively normal (CN), subjective cognitive impairment (SCI), mild cognitive impairment (MCI) and dementia. Cross-sectional structural and cerebral perfusion imaging, behavioural scale data including mild behaviour impairment checklist, Pittsburgh Sleep Quality Index and Depression, Anxiety and Stress scale data were obtained. RESULTS: Three hundred seventy-three participants (mean age 60.7 years; 56% female sex) with complete data were included. Principal component analyses demonstrated that no single modality was informative for the classification of cognitive syndromes. However, multivariate glmnet analyses revealed a specific combination of frontal perfusion and temporo-frontal grey matter volume were key protective factors while the severity of mild behaviour impairment interest sub-domain and poor sleep quality were key at-risk factors contributing to the classification of CN, SCI, MCI and dementia (p < 0.0001). Moreover, the glmnet model showed best classification accuracy in differentiating between CN and MCI cognitive syndromes (AUC = 0.704; sensitivity = 0.698; specificity = 0.637). CONCLUSIONS: Brain structure, perfusion and behavioural features are important in the classification of cognitive syndromes and should be incorporated by clinicians and researchers. These findings illustrate the value of using multimodal data when examining syndrome severity and provide new insights into how cerebral perfusion and behavioural impairment influence classification of cognitive syndromes.