Relating education, brain structure, and cognition: the role of cardiovascular disease risk factors

Loss to follow-up associated factors in patients with chronic pulmonary aspergillosis and its impact on the disease prognosis

Objective

Pulmonary aspergillosis is a rare but challenging pulmonary disease. The conditions of patients with chronic pulmonary aspergillosis (CPA) can be even more complicated. The mortality rate of CPA remains high, and the prognostic factors are not well established due to a high proportion of loss to follow-up. In this study, we aim to explore factors associated with loss to follow-up in CPA patients and their impact on the disease prognosis after withdrawing anti-fungal treatments.

Methods

Patients with confirmed CPA, who were admitted to the Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University from March 2017 to November 2019, were enrolled in this prospective study. The enrolled patients were followed up for 6 months after discharge. For loss to follow-up patients, the reasons for loss to follow-up and their prognosis after withdrawing anti-fungal treatments during loss to follow-up were recorded by telephone communication. Multivariate logistic regression analysis was performed to determine factors associated with loss to follow-up.

Results

The 199 out of 298 screened patients were included in the study. Except for 67 cases with regular follow-up, the rest 132 cases were lost to follow-up. Factors, including age > 60 years (OR = 2.036, P = 0.03), monthly income ≤ $583 (OR = 5.568, P = 0.0001), education ≤ 6 years (OR = 7.474, P = 0.0001), and non-local residence (OR = 5.893, P = 0.0001) were associated with the loss to follow-up according to multivariate logistic regression analysis. The most common reasons for loss to follow-up were economic factors and clinic visit distance. The overall case fatality rate (CFR) within 180 days in patients with regular follow-up and patients who stopped anti-fungal treatment during the loss to follow-up was 0% and 19.65%, respectively.

Conclusion

The proportion of loss to follow-up in CPA patients remained high. Age (>60 years), poor financial status, low education, and non-local residence were the key factors associated with the loss to follow-up in this study. Our study reveals the need to optimize the follow-up procedures and improve the patients understanding about the benefits and limitations of follow-up to reduce the CFR.

Novel Functional, Health, and Genetic Determinants of Cognitive Terminal Decline: Kuakini Honolulu Heart Program/Honolulu-Asia Aging Study

To investigate interindividual differences in cognitive terminal decline and identify determinants including functional, health, and genetic risk and protective factors, data from the Honolulu Heart Program/Honolulu-Asia Aging Study, a prospective cohort study of Japanese American men, were analyzed. The sample was recruited in 1965-1968 (ages 45-68 years). Longitudinal performance of cognitive abilities and mortality status were assessed from Exam 4 (1991-1993) through June 2014. Latent class analysis revealed 2 groups: maintainers retained relatively high levels of cognitive functioning until death and decliners demonstrated significant cognitive waning several years prior to death. Maintainers were more likely to have greater education, diagnosed coronary heart disease, and presence of the apolipoprotein E (APOE) ε2 allele and FOXO3 G allele (SNP rs2802292). Decliners were more likely to be older and have prior stroke, Parkinson's disease, dementia, and greater depressive symptoms at Exam 4, and the APOE ε4 allele. Findings support terminal decline using distance to death as the basis for modeling change. Significant differences were observed between maintainers and decliners 15 years prior to death, a finding much earlier compared to the majority of previous investigations.

Association of Neighborhood Context, Cognitive Decline, and Cortical Change in an Unimpaired Cohort

OBJECTIVE

To test the hypothesis that neighborhood-level disadvantage is associated with longitudinal measures of neurodegeneration and cognitive decline in an unimpaired cohort.

METHODS

Longitudinal MRI and cognitive testing data were collected from 601 cognitively unimpaired participants in the Wisconsin Registry for Alzheimer's Prevention Study and the Wisconsin Alzheimer's Disease Research Center clinical cohort. Area Deprivation Index was geospatially determined based on participant residence geocode and ranked relative to state of residence. Linear regression models were fitted to test associations between neighborhood-level disadvantage and longitudinal change in cortical thickness and cognitive test performance. Mediation tests were used to assess whether neurodegeneration and cognitive decline were associated with neighborhood-level disadvantage along the same theoretical causal path.

RESULTS

In our middle- to older-aged study population (mean baseline age 59 years), living in the 20% most disadvantaged neighborhoods (n = 19) relative to state of residence was associated with cortical thinning in Alzheimer signature regions (p = 0.002) and decline in the Preclinical Alzheimer's Disease Cognitive Composite (p = 0.04), particularly the Trail-Making Test, part B (p < 0.001), but not Rey Auditory Verbal Learning Test (p = 0.77) or Story Memory Delayed Recall (p = 0.49) subtests. Associations were attenuated but remained significant after controlling for racial and demographic differences between neighborhood-level disadvantage groups. Cortical thinning partially mediated the association between neighborhood-level disadvantage and cognitive decline.

CONCLUSIONS

In this longitudinal study of cognitively unimpaired adults, living in the most highly disadvantaged neighborhoods was associated with accelerated degeneration in Alzheimer signature regions and cognitive decline. This study provides further evidence for neighborhood-level disadvantage as a risk factor for preclinical neurodegeneration and cognitive decline in certain populations. Limitations of the present study, including a small number of participants from highly disadvantaged neighborhoods and a circumscribed geographic setting, should be explored in larger and more diverse study cohorts.

A randomized clinical trial examining the impact of LGG probiotic supplementation on psychological status in middle-aged and older adults

Cognitive decline is common in older adults and more than 5 million Americans suffer from Alzheimer's disease (AD). A number of physiological processes including systemic inflammation, excess adiposity, and impaired glucoregulation arise from a combination of genetic and behavioral factors and increase risk for developing AD or other forms of dementia. Recent research suggests that the gut microbiome may moderate these pathological processes and possibly influence cognitive outcomes. This paper reviews the methodology for a double-blind, randomized clinical trial examining the influence of Lactobacillus GG (LGG) probiotic supplementation on mood and cognitive functioning in middle-aged and older adults. Our two primary hypotheses include: 1) Participants randomized to the probiotic group will show greater improvements in psychological status compared to participants in the placebo group; 2) Participants randomized to the probiotic group will show greater improvements in executive functioning and processing speed, as evidenced through performance on neuropsychological testing, than participants in the placebo group. We anticipate these results will inform future efforts on the feasibility of LGG probiotic supplementation as an intervention for psychological status and cognitive functioning and further elucidate the link between the gut microbiome and cognitive health.

Biological Brain Age Prediction Using Cortical Thickness Data: A Large Scale Cohort Study

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.