Background: The relationship between the apolipoprotein E (ApoE) genotype and the risk for developing Alzheimer’s disease (AD) or age at onset of AD is relatively well established in Caucasians, but less established in other ethnicities. We examined the association between the ApoE genotype and age at onset of AD in a quadriethnic group of community-dwelling AD patients. Methods: AD patients were evaluated at 2 university-based outpatient memory disorder clinics. The ethnic distribution was as follows: Caucasians (n = 1,083), Hispanics (n = 55), African Americans (n = 84) and Koreans (n = 87). All were diagnosed with probable AD according to NINCDS-ADRDA diagnostic criteria. Results: After adjusting for ethnicity, the Ε4 allele was significantly associated with earlier age at onset (p < 0.0001) for the combined group. Within ethnic groups, the effect of Apo Ε4 on age at onset was significant in Caucasians (p < 0.0001) and African Americans (p < 0.05), but nonsignificant in Koreans (p = 0.43) and in the smaller Hispanic (p = 0.07) group. Conclusions: The association between Apo Ε4 and younger age at onset was significant in Caucasians and African Americans, where the Ε4 allele was also most frequent. This study suggests that the impact of ApoE polymorphism on age at onset of AD may be different among distinct ethnic groups.

The aim of this study is to investigate the relationship between Hachinski ischemic score (HIS) and vascular factors as well as between HIS and the cognitive function in elderly community. Demographic characteristics, such as sex, age, education, history of drinking and smoking, family history of dementia and stroke, diabetes mellitus, hypertension, hyperlipidemia, cardiovascular disease, stroke, and dementia, were surveyed. Neurological examination was administered to every subject and HIS was checked by a neurologist. From a total of 392 participants aged 65 and over in a rural community, 348 completed the survey and were finally enrolled. Among the vascular factors, history of hypertension (P=0.008), history of stroke (P<0.001), family history of dementia (P=0.01), and history of cardiac diseases (P=0.012) showed a significant relationship with HIS. In the cognitive function tests, both Korean version of the Mini-Mental State Examination and the Clinical Dementia Rating (Global and Sum of Boxes) had a significant relationship with HIS. Our study suggested HIS may have an association with some vascular factors and cognitive scales in community dwelling elderly. In this study, the HIS seemed to contribute to the evaluation of the quantity of vascular factors and to the prediction of status of cognitive function.

A recent report found that left-handed adolescents were more than three times more likely to have an Apolipoprotein (APOE) ϵ2 allele. This study was unable to replicate this association in young adults (N=166). A meta-analysis of nine other datasets (N=360 to 7559, Power > 0.999) including that of National Alzheimer's Coordinating Center also failed to find an over-representation of ϵ2 among left-handers indicating that this earlier outcome was most likely a statistical artefact.

The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson’s disease (PD) based on [¹⁸F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [¹⁸F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran’s Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [¹⁸F]FP-CIT PET, and its performance was comparable to that of NM physicians.