A blood-based predictor for neocortical Aβ burden in Alzheimer’s disease : results from the AIBL study

Dementia is a global epidemic with Alzheimer’s disease (AD) being the leading cause. Early identification of patients at risk of developing AD is now becoming an international priority. Neocortical Aβ (extracellular β-amyloid) burden (NAB), as assessed by positron emission tomography (PET), represents one such marker for early identification. These scans are expensive and are not widely available, thus, there is a need for cheaper and more widely accessible alternatives. Addressing this need, a blood biomarker-based signature having efficacy for the prediction of NAB and which can be easily adapted for population screening is described. Blood data (176 analytes measured in plasma) and Pittsburgh Compound B (PiB)-PET measurements from 273 participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study were utilised. Univariate analysis was conducted to assess the difference of plasma measures between high and low NAB groups, and cross-validated machine-learning models were generated for predicting NAB. These models were applied to 817 non-imaged AIBL subjects and 82 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) for validation. Five analytes showed significant difference between subjects with high compared to low NAB. A machine-learning model (based on nine markers) achieved sensitivity and specificity of 80 and 82%, respectively, for predicting NAB. Validation using the ADNI cohort yielded similar results (sensitivity 79% and specificity 76%). These results show that a panel of blood-based biomarkers is able to accurately predict NAB, supporting the hypothesis for a relationship between a blood-based signature and Aβ accumulation, therefore, providing a platform for developing a population-based screen.

The interactive effect of demographic and clinical factors on hippocampal volume: A multicohort study on 1958 cognitively normal individuals

Alzheimer's disease is characterized by hippocampal atrophy. Other factors also influence the hippocampal volume, but their interactive effect has not been investigated before in cognitively healthy individuals. The aim of this study is to evaluate the interactive effect of key demographic and clinical factors on hippocampal volume, in contrast to previous studies frequently investigating these factors in a separate manner. Also, to investigate how comparable the control groups from ADNI, AIBL, and AddNeuroMed are with five population-based cohorts. In this study, 1958 participants were included (100 AddNeuroMed, 226 ADNI, 155 AIBL, 59 BRC, 295 GENIC, 279 BioFiNDER, 398 PIVUS, and 446 SNAC-K). ANOVA and random forest were used for testing between-cohort differences in demographic-clinical variables. Multiple regression was used to study the influence of demographic-clinical variables on hippocampal volume. ANCOVA was used to analyze whether between-cohort differences in demographic-clinical variables explained between-cohort differences in hippocampal volume. Age and global brain atrophy were the most important variables in explaining variability in hippocampal volume. These variables were not only important themselves but also in interaction with gender, education, MMSE, and total intracranial volume. AddNeuroMed, ADNI, and AIBL differed from the population-based cohorts in several demographic-clinical variables that had a significant effect on hippocampal volume. Variability in hippocampal volume in individuals with normal cognition is high. Differences that previously tended to be related to disease mechanisms could also be partly explained by demographic and clinical factors independent from the disease. Furthermore, cognitively normal individuals especially from ADNI and AIBL are not representative of the general population. These findings may have important implications for future research and clinical trials, translating imaging biomarkers to the general population, and validating current diagnostic criteria for Alzheimer's disease and predementia stages

The interactive effect of demographic and clinical factors on hippocampal volume: A multicohort study on 1958 cognitively normal individuals

Alzheimer's disease is characterized by hippocampal atrophy. Other factors also influence the hippocampal volume, but their interactive effect has not been investigated before in cognitively healthy individuals. The aim of this study is to evaluate the interactive effect of key demographic and clinical factors on hippocampal volume, in contrast to previous studies frequently investigating these factors in a separate manner. Also, to investigate how comparable the control groups from ADNI, AIBL, and AddNeuroMed are with five population-based cohorts. In this study, 1958 participants were included (100 AddNeuroMed, 226 ADNI, 155 AIBL, 59 BRC, 295 GENIC, 279 BioFiNDER, 398 PIVUS, and 446 SNAC-K). ANOVA and random forest were used for testing between-cohort differences in demographic-clinical variables. Multiple regression was used to study the influence of demographic-clinical variables on hippocampal volume. ANCOVA was used to analyze whether between-cohort differences in demographic-clinical variables explained between-cohort differences in hippocampal volume. Age and global brain atrophy were the most important variables in explaining variability in hippocampal volume. These variables were not only important themselves but also in interaction with gender, education, MMSE, and total intracranial volume. AddNeuroMed, ADNI, and AIBL differed from the population-based cohorts in several demographic-clinical variables that had a significant effect on hippocampal volume. Variability in hippocampal volume in individuals with normal cognition is high. Differences that previously tended to be related to disease mechanisms could also be partly explained by demographic and clinical factors independent from the disease. Furthermore, cognitively normal individuals especially from ADNI and AIBL are not representative of the general population. These findings may have important implications for future research and clinical trials, translating imaging biomarkers to the general population, and validating current diagnostic criteria for Alzheimer's disease and predementia stages