Prevalence of Alzheimer’s disease (AD) will reach epidemic proportions in the United States and worldwide in the coming decades, and with substantially higher rates in African Americans (AAs) than in Whites. Older age, family history, low levels of education, and ɛ4 allele of the apolipoprotein E (APOE) gene are recognized risk factors for the neurodegeneration in AD and related disorders. In AAs, the contributions of APOE gene to AD risk continue to engender a considerable debate. In addition to the established role of cardiovascular disease (CVD) risk in vascular dementia, it is now believed that CVD risk and its endophenotype may directly comediate AD phenotype. Given the pleiotropic effects of APOE on CVD and AD risks, the higher rates of CVD risks in AAs than in Whites, it is likely that CVD risks contribute to the disproportionately higher rates of AD in AAs. Though the advantageous effects of aerobic exercise on cognition is increasingly recognized, this evidence is hardly definitive, and data on AAs is lacking. In this paper, we will discuss the roles of CVD risk factors in the development of AD and related dementias, the susceptibility of these risk factors to physiologic adaptation, and fitness-related improvements in cognitive function. Its relevance to AD prevention in AAs is emphasized.

Nonalcoholic steatohepatitis (NASH) is associated with obesity, metabolic syndrome, and dysbiosis of the gut microbiome. Cholecystokinin (CCK) is released by saturated fats and plays an important role in bile acid secretion. CCK receptors are expressed on cholangiocytes, and CCK-B receptor expression increases in the livers of mice with NASH. The farnesoid X receptor (FXR) is involved in bile acid transport and is a target for novel therapeutics for NASH. The aim of this study was to examine the role of proglumide, a CCK receptor inhibitor, in a murine model of NASH and its interaction at FXR. Mice were fed a choline deficient ethionine (CDE) diet to induce NASH. Some CDE-fed mice received proglumide-treated drinking water. Blood was collected and liver tissues were examined histologically. Proglumide’s interaction at FXR was evaluated by computer modeling, a luciferase reporter assay, and tissue FXR expression. Stool microbiome was analyzed by RNA-Sequencing. CDE-fed mice developed NASH and the effect was prevented by proglumide. Computer modeling demonstrated specific binding of proglumide to FXR. Proglumide binding in the reporter assay was consistent with a partial agonist at the FXR with a mean binding affinity of 215 nM. FXR expression was significantly decreased in livers of CDE-fed mice compared to control livers, and proglumide restored FXR expression to normal levels. Proglumide therapy altered the microbiome signature by increasing beneficial and decreasing harmful bacteria. These data highlight the potential novel mechanisms by which proglumide therapy may improve NASH through interaction with the FXR and consequent alteration of the gut microbiome.

Previous studies report that low levels cognitive function and history of smoking are associated with increased mortality risk. Elderly smokers may have increased risk of dementia, but risk in former smokers is unclear. We tested the hypotheses that the harmful effect of impaired cognitive function as related to mortality is greater in persons smoking at baseline than in others. Further, we used serum cotinine levels to assess recall bias of smoking history by cognitive function level. Data were analyzed from a longitudinal mortality follow-up study of 4,916 American men and women aged 60 years and over, examined in 1988–1994 with complete data followed an average 8.5 years. Measurements at baseline included smoking history, a short index of cognitive function (SICF), serum cotinine and socio-demographics. Death during follow-up occurred in 1,919 persons. In proportional hazards regression analysis, a significant interaction of current smoking with cognitive function was not found; but there was a significant age-smoking interaction. After adjusting for confounding by age or multiple variables, current smoking associated with over 2-fold increased mortality (hazards ratio and 95% confidence limits current versus never smoking 2.13, 1.75–2.59) and SICF with 32% reduction in mortality; top versus bottom SICF stratum 0.68, 0.53–0.88). Serum cotinine data revealed substantial recall bias of smoking history in persons with cognitive impairment. However analyses correcting for this bias did not alter the main conclusions: In a nationwide cohort of older Americans, analyses demonstrated a lower risk of death independent of confounders among those with high SICF scores and never smokers, without a significant interaction of the two.

Despite being disproportionately impacted by health disparities, Black, Hispanic, Indigenous, and other underrepresented populations account for a significant minority of graduates in biomedical data science-related disciplines. Given their commitment to educating underrepresented students and trainees, minority serving institutions (MSIs) can play a significant role in enhancing diversity in the biomedical data science workforce. Little has been published about the reach, curricular breadth, and best practices for delivering these data science training programs. The purpose of this paper is to summarize six Research Centers in Minority Institutions (RCMIs) awarded funding from the National Institute of Minority Health Disparities (NIMHD) to develop new data science training programs. A cross-sectional survey was conducted to better understand the demographics of learners served, curricular topics covered, methods of instruction and assessment, challenges, and recommendations by program directors. Programs demonstrated overall success in reach and curricular diversity, serving a broad range of students and faculty, while also covering a broad range of topics. The main challenges highlighted were a lack of resources and infrastructure and teaching learners with varying levels of experience and knowledge. Further investments in MSIs are needed to sustain training efforts and develop pathways for diversifying the biomedical data science workforce.