OBJECTIVE: The present study investigated the clinical characteristics of Alzheimer's disease (AD) dementia with low brain amyloid-beta (Aβ-AD) burden comparing with AD dementia with high amyloid-beta burden (Aβ+AD). We also developed a prediction model for the amyloid positivity on ¹¹C-labelled Pittsburgh Compound B (PiB) positron emission tomography (PET) with distinct clinical variables in AD dementia patients. METHODS: Fifty-nine clinically defined AD dementia individuals, who participated in the Korean Brain Aging Study for Early diagnosis and prediction of AD (KBASE) study, were included. All the subjects received comprehensive clinical evaluations and PiB-PET. Based on cerebral PiB retention, all subjects were divided into Aβ+AD (n=47) and Aβ-AD (n=12) subgroups. To develop a prediction model for amyloid positivity, stepwise multiple logistic regression analysis was conducted. RESULTS: When compared to Aβ+AD, Aβ-AD showed older age, later age-at-onset, and lower education. In regard of risk factors for dementia, Aβ-AD had higher frequency of hypertension and diabetes mellitus as well as lower frequency of apolipoprotein E (APOE) ε4 allele. Although there was no between group difference in Clinical Dementia Rating (CDR) or CDR sum-of-boxes scores, mini-mental state examination and constructional recall scores were higher for Aβ-AD than Aβ+AD. The final amyloid positivity prediction model included APOE4 genotype, hypertension, and diabetes mellitus. CONCLUSION: The findings from this study indicated that clinically diagnosed AD dementia may have high possibility of not being pathological AD if they have older age and higher vascular risks, and did not have APOE4 genotype.
Age of Onset ; Aging ; Alleles ; Alzheimer Disease* ; Amyloid* ; Apolipoprotein E4 ; Apolipoproteins ; Brain ; Dementia* ; Diabetes Mellitus ; Early Diagnosis ; Education ; Genotype ; Humans ; Hypertension ; Logistic Models ; Positron-Emission Tomography ; Risk Factors

Objective: Anosognosia is a common phenomenon in individuals with dementia. Anosognosia Questionnaire for dementia (AQ-D) is a well-known scale for evaluating anosognosia. This study aimed to establish a Korean version of the AQ-D (AQ-D-K) and to evaluate the reliability and validity of the AQ-D-K in patients with Alzheimer’s disease (AD) dementia. Methods: We translated the original English version of AQ-D into Korean (AQ-D-K). Eighty-four subjects with very mild or mild AD dementia and their caregivers participated. Reliability of AQ-D-K was assessed by internal consistency and one-month test-retest reliability. Construct validity and concurrent validity were also evaluated. Results: Internal consistencies of the AQ-D-K patient form and caregiver form were high (Cronbach alpha 0.95 and 0.93, respectively). The test-retest reliability of AQ-D-K measured by intra-class correlation coefficient was 0.84. Three factors were identified: 1) anosognosia of instrumental activity of daily living; 2) anosognosia basic activity of daily living; and 3) anosognosia of depression and disinhibition. AQ-D-K score was significantly correlated with the clinician-rated anosognosia rating scale (ARS), center for epidemiological studies-depression scale (CES-D) and state-trait anxiety inventory (STAI). Conclusion The findings suggest that the AQ-D-K is a reliable and valid scale for evaluating anosognosia for AD dementia patients using Korean language.

Objective: Anosognosia is a common phenomenon in individuals with dementia. Anosognosia Questionnaire for dementia (AQ-D) is a well-known scale for evaluating anosognosia. This study aimed to establish a Korean version of the AQ-D (AQ-D-K) and to evaluate the reliability and validity of the AQ-D-K in patients with Alzheimer’s disease (AD) dementia. Methods: We translated the original English version of AQ-D into Korean (AQ-D-K). Eighty-four subjects with very mild or mild AD dementia and their caregivers participated. Reliability of AQ-D-K was assessed by internal consistency and one-month test-retest reliability. Construct validity and concurrent validity were also evaluated. Results: Internal consistencies of the AQ-D-K patient form and caregiver form were high (Cronbach alpha 0.95 and 0.93, respectively). The test-retest reliability of AQ-D-K measured by intra-class correlation coefficient was 0.84. Three factors were identified: 1) anosognosia of instrumental activity of daily living; 2) anosognosia basic activity of daily living; and 3) anosognosia of depression and disinhibition. AQ-D-K score was significantly correlated with the clinician-rated anosognosia rating scale (ARS), center for epidemiological studies-depression scale (CES-D) and state-trait anxiety inventory (STAI). Conclusion The findings suggest that the AQ-D-K is a reliable and valid scale for evaluating anosognosia for AD dementia patients using Korean language.

Purpose: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Brain atrophy in this disease spectrum begins in the medial temporal lobe structure, which can be recognized by magnetic resonance imaging. To overcome the unsatisfactory inter-observer reliability of visual evaluation, quantitative brain volumetry has been developed and widely investigated for the diagnosis of MCI and AD. The aim of this study was to assess the prediction accuracy of quantitative brain volumetry using a fully automated segmentation software package, NeuroQuant®, for the diagnosis of MCI. Materials and Methods: A total of 418 subjects from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer’s Disease cohort were included in our study. Each participant was allocated to either a cognitively normal old group (n = 285) or an MCI group (n = 133). Brain volumetric data were obtained from T1-weighted images using the NeuroQuant software package. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to investigate relevant brain regions and their prediction accuracies. Results: Multivariate logistic regression analysis revealed that normative percentiles of the hippocampus (P < 0.001), amygdala (P = 0.003), frontal lobe (P = 0.049), medial parietal lobe (P = 0.023), and third ventricle (P = 0.012) were independent predictive factors for MCI. In ROC analysis, normative percentiles of the hippocampus and amygdala showed fair accuracies in the diagnosis of MCI (area under the curve: 0.739 and 0.727, respectively). Conclusion Normative percentiles of the hippocampus and amygdala provided by the fully automated segmentation software could be used for screening MCI with a reasonable post-processing time. This information might help us interpret structural MRI in patients with cognitive impairment.

Purpose: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Brain atrophy in this disease spectrum begins in the medial temporal lobe structure, which can be recognized by magnetic resonance imaging. To overcome the unsatisfactory inter-observer reliability of visual evaluation, quantitative brain volumetry has been developed and widely investigated for the diagnosis of MCI and AD. The aim of this study was to assess the prediction accuracy of quantitative brain volumetry using a fully automated segmentation software package, NeuroQuant®, for the diagnosis of MCI. Materials and Methods: A total of 418 subjects from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer’s Disease cohort were included in our study. Each participant was allocated to either a cognitively normal old group (n = 285) or an MCI group (n = 133). Brain volumetric data were obtained from T1-weighted images using the NeuroQuant software package. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to investigate relevant brain regions and their prediction accuracies. Results: Multivariate logistic regression analysis revealed that normative percentiles of the hippocampus (P < 0.001), amygdala (P = 0.003), frontal lobe (P = 0.049), medial parietal lobe (P = 0.023), and third ventricle (P = 0.012) were independent predictive factors for MCI. In ROC analysis, normative percentiles of the hippocampus and amygdala showed fair accuracies in the diagnosis of MCI (area under the curve: 0.739 and 0.727, respectively). Conclusion Normative percentiles of the hippocampus and amygdala provided by the fully automated segmentation software could be used for screening MCI with a reasonable post-processing time. This information might help us interpret structural MRI in patients with cognitive impairment.