Objective: We hypothesized that nonalcoholic fatty liver disease (NAFLD) is independently associated with cognitive impairment in a representative sample of the general US population regardless of the presence of cardiovascular disease (CVD) or its risk factors. Methods: This was a cross-sectional study of 4,472 adults aged 20–59 years who participated in the Third National Health and Nutritional Examination Survey. The participants underwent assessment of liver enzyme activity and hepatic steatosis by ultrasound, and underwent cognitive evaluation using the following computer-administered tests: the Simple Reaction Time Test (SRTT), the Symbol-Digit Substitution Test (SDST), and the Serial Digit Learning Test (SDLT). We defined NAFLD as moderate/severe steatosis as determined by ultrasound in the absence of hepatitis B or C or excessive alcohol consumption. We used multiple linear regression models to examine the association between NAFLD and cognitive function while controlling for potential confounders. Results: Participants with NAFLD showed lower overall performance on the SDLT (β = 0.726, 95% confidence interval [CI] 0.105–1.347), while associations with SRTT and SDST did not reach significance. Increased activity of the liver enzymes alanine aminotransferase (β = 0.018, 95% CI 0.006–0.030) and aspartate aminotransferase (β = 0.021, 95% CI 0.005–0.037) correlated with lower performance on the SDLT, while increased alanine aminotransferase was also correlated with lower performance in the SDST (β = 0.002, 95% CI 0.0001–0.004). Conclusions: NAFLD was independently associated with lower cognitive performance independent of CVD and its risk factors. Given the scarcity of risk factors associated with age-related cognitive decline, these findings may have significant implications.

Increased O -GlcNAcylation can confer neuroprotective effects against necroptosis in the context of Alzheimer’s disease.

Brain age estimation from anatomical features has been attracting more attention in recent years. This interest in brain age estimation is motivated by the importance of biological age prediction in health informatics, with an application to early prediction of neurocognitive disorders. It is well-known that normal brain aging follows a specific pattern, which enables researchers and practitioners to predict the age of a human's brain from its degeneration. In this paper, we model brain age predicted by cortical thickness data gathered from large cohort brain images. We collected 2,911 cognitively normal subjects (age 45–91 years) at a single medical center and acquired their brain magnetic resonance (MR) images. All images were acquired using the same scanner with the same protocol. We propose to first apply Sparse Group Lasso (SGL) for feature selection by utilizing the brain's anatomical grouping. Once the features are selected, a non-parametric non-linear regression using the Gaussian Process Regression (GPR) algorithm is applied to fit the final age prediction model. Experimental results demonstrate that the proposed method achieves the mean absolute error of 4.05 years, which is comparable with or superior to several recent methods. Our method can also be a critical tool for clinicians to differentiate patients with neurodegenerative brain disease by extracting a cortical thinning pattern associated with normal aging.

Alzheimer disease (AD) and subcortical vascular dementia are considered to be the most common types of dementia. Most studies on mild cognitive impairment (MCI) have focused on the amnestic MCI (aMCI) form, which is a prodromal stage of AD. In contrast, few studies have involved patients in the prodromal stages of subcortical vascular dementia.^1-3 Results from prior studies^4,5 from our group suggest that the prodromal stage of subcortical vascular dementia, referred to as subcortical vascular MCI (svMCI), is distinctive from aMCI in terms of neuropsychological and neurobehavioral findings as well as the topography of glucose metabolism.

Background: A large number of Alzheimer’s disease (AD) studies have focused on medial temporal and cortical atrophy, while changes in the basal ganglia or thalamus have received less attention. Objective: The aim of this study was to investigate the