Alzheimer's disease biomarkers in Black and non-Hispanic White cohorts: A contextualized review of the evidence.

Black Americans are disproportionately affected by dementia. To expand our understanding of mechanisms of this disparity, we look to Alzheimer's disease (AD) biomarkers. In this review, we summarize current data, comparing the few studies presenting these findings. Further, we contextualize the data using two influential frameworks: the National Institute on Aging-Alzheimer's Association (NIA-AA) Research Framework and NIA's Health Disparities Research Framework. The NIA-AA Research Framework provides a biological definition of AD that can be measured in vivo. However, current cut-points for determining pathological versus non-pathological status were developed using predominantly White cohorts-a serious limitation. The NIA's Health Disparities Research Framework is used to contextualize findings from studies identifying racial differences in biomarker levels, because studying biomakers in isolation cannot explain or reduce inequities. We offer recommendations to expand study beyond initial reports of racial differences. Specifically, life course experiences associated with racialization and commonly used study enrollment practices may better account for observations than exclusively biological explanations.

Stressful Life Events and Racial Disparities in Cognition Among Middle-Aged and Older Adults.

BACKGROUND: It is well-documented that African Americans have elevated risk for cognitive impairment and dementia in late life, but reasons for the racial disparities remain unknown. Stress processes have been linked to premature age-related morbidity, including Alzheimer's and related dementias (ADRD), and plausibly contribute to social disparities in cognitive aging. OBJECTIVE: We examined the relationship between stressful life events and cognitive decline among African American and White participants enrolled in the Wisconsin Registry for Alzheimer's Prevention (WRAP). METHODS: Linear mixed models including demographic, literacy, and health-related covariates were used to estimate (1) relationships between a life event index score and decline in cognitive test performance in two domains of executive function (Speed & Flexibility, Working Memory) and one domain of episodic memory (Verbal Learning & Memory) among 1,241 WRAP enrollees, stratified by race, and (2) contributions of stressful life events to racial differences in cognition within the full sample. RESULTS: African Americans (N = 50) reported more stressful life events than Whites (N = 1,191). Higher stress scores associated with poorer Speed & Flexibility performance in both groups, though not with declines across time, and partially explained racial differentials in this domain. Among African Americans only, stressor exposure also associated with age-related decline in Verbal Learning & Memory. Stressor-cognition relationships were independent of literacy and health-related variables. CONCLUSIONS: Greater lifetime stress predicted poorer later-life cognition, and, in a small sample of African Americans, faster declines in a key domain of episodic memory. These preliminary findings suggest that future work in large minority aging cohorts should explore stress as an important source of modifiable, socially-rooted risk for impairment and ADRD in African Americans, who are disproportionately exposed to adverse experiences across the life course.

Association between enrollment factors and incident cognitive impairment in Blacks and Whites: Data from the Alzheimer's Disease Center.

INTRODUCTION: We examined the influence of enrollment factors demonstrated to differ by race on incident mild cognitive impairment and dementia using Alzheimer's Disease Center data. METHODS: Differences in rates of incident impairment between non-Latino Whites and Blacks (n = 12,242) were examined with age-at-progression survival models. Models included race, sex, education, source of recruitment, health factors, and family history of dementia. RESULTS: No significant race differences in progression were observed in cognitively unimpaired participants. In those with mild cognitive impairment at baseline, Whites evidenced greater risk for progression than Blacks. Enrollment factors, for example, referral source, were significantly related to progression. DISCUSSION: The finding that Blacks demonstrated lower rate of progression than Whites is contrary to the extant literature. Nested-regression analyses suggested that selection-related factors, differing by race, may account for these findings and influence our ability to accurately estimate risk for progression. It is potentially problematic to make racial comparisons using Alzheimer's Disease Center data sets.

Distinguishing lung tumours from normal lung based on a small set of genes.

Identifying specific molecular markers and developing sensitive detection methods are two of the fundamental requirements for detection and differential diagnosis of cancer. Toward this goal, we first performed cDNA array analysis using 65 non-small cell lung cancer and non-involved normal lung tissues. We then used several complementary statistical and analytical methods to examine gene expression profiles generated by us and others from four independent sets of normal and neoplastic lung tissues. We report here that several sets of roughly 20 genes were sufficient to provide a robust distinction between normal and neoplastic tissues of the lung. Next we assessed the predictive ability of these gene sets by using Flow-Thru Chips (FTC) (MetriGenix, Baltimore, MD) containing 20 genes to screen 48 primary lung tumours and normal lung tissues. Gene expression changes detected by FTC distinguished lung cancers from the normal lung tissues using an RNA amount equivalent to that present in as few as 300 cells. We also used an independent set of 24 genes and showed that their expression profile was equally effective when measured by quantitative polymerase chain reaction (Q-PCR). Our results demonstrate that lung cancers can be identified based on the expression patterns of just 20 genes and that this approach is applicable for cancer diagnosis, prognosis, and monitoring using small amount of tumour or biopsy samples.

Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management.

Recent technical advances in combinatorial chemistry, genomics, and proteomics have made available large databases of biological and chemical information that have the potential to dramatically improve our understanding of cancer biology at the molecular level. Such an understanding of cancer biology could have a substantial impact on how we detect, diagnose, and manage cancer cases in the clinical setting. One of the biggest challenges facing clinical oncologists is how to extract clinically useful knowledge from the overwhelming amount of raw molecular data that are currently available. In this paper, we discuss how the exploratory data analysis techniques of machine learning and high-dimensional visualization can be applied to extract clinically useful knowledge from a heterogeneous assortment of molecular data. After an introductory overview of machine learning and visualization techniques, we describe two proprietary algorithms (PURS and RadViz) that we have found to be useful in the exploratory analysis of large biological data sets. We next illustrate, by way of three examples, the applicability of these techniques to cancer detection, diagnosis, and management using three very different types of molecular data. We first discuss the use of our exploratory analysis techniques on proteomic mass spectroscopy data for the detection of ovarian cancer. Next, we discuss the diagnostic use of these techniques on gene expression data to differentiate between squamous and adenocarcinoma of the lung. Finally, we illustrate the use of such techniques in selecting from a database of chemical compounds those most effective in managing patients with melanoma versus leukemia.