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Objective: To determine whether cognition is associated with mortality among older US adults. Methods: We studied 5,989 National Health and Nutrition Examination Survey participants age 60+ in years 1999–2014 with mortality follow-up through 2015. Cognitive function was measured in one standard deviation decrements using the Digit Symbol Substitution Test (DSST), Animal Fluency (AnFl), and two Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) tests. Results: Each decrement in cognitive function was associated with increased risk of mortality overall (DSST HR: 1.36, 95% CI: 1.25, 1.48), among women only (AnFl: 1.51, 95% CI: 1.02, 2.24), and among those with less than a high school education only (AnFl HR: 1.46, 95% CI: 1.09, 1.97; CERAD-WL HR: 1.34, 95% CI: 1.07, 1.67; and CERAD-DR HR: 1.38, 95% CI: 1.05, 1.82). Discussion: Among US adults, lower cognitive functioning was associated with mortality; associations were stronger among women and those with less education.

SummaryER and PR regulate growth and differentiation in normal ovaries and fallopian tubes and in HGSC transformation and progression. Higher PR expression was associated with improved survival outcomes, while high ER expression was associated with worse survival in patients with HGSC. Here, we show that patients with ER+PR+ tumors have longer overall survival and confirm the role of PR as a prognostic marker of survival and response to chemotherapy. Gene expression analysis demonstrated up-regulation of the ATM signaling pathway in the ER+PR+ subgroup when compared to ER+PR− tumors. Up-regulation of interferon alpha, beta and gamma signaling, and antigen presentation pathways were identified in ER+PR− compared to ER−PR+. In summary, this study elucidated that the genomic and transcriptomic signatures related to ER/PR status in HGSC have clinical prognostic value.

Heatmap of protein functions

The human microbiome has been strongly correlated with disease pathology and outcomes, yet remains relatively underexplored in patients with malignant endometrial disease. In this study, vaginal microbiome samples were prospectively collected at the time of hysterectomy from 61 racially and ethnically diverse patients from three disease conditions: (i) benign gynecologic disease (controls, n = 11), (ii) low-grade endometrial carcinoma (n = 30), and (iii) high-grade endometrial carcinoma (n = 20). Extracted DNA underwent shotgun metagenomics sequencing, and microbial α and β diversities were calculated. Hierarchical clustering was used to describe community state types (CST), which were then compared by microbial diversity and grade. Differential abundance was calculated, and machine learning utilized to assess the predictive value of bacterial abundance to distinguish grade and histology. Both α- and β-diversity were associated with patient tumor grade. Four vaginal CST were identified that associated with grade of disease. Different histologies also demonstrated variation in CST within tumor grades. Using supervised clustering algorithms, critical microbiome markers at the species level were used to build models that predicted benign versus carcinoma, high-grade carcinoma versus benign, and high-grade versus low-grade carcinoma with high accuracy. These results confirm that the vaginal microbiome segregates not just benign disease from endometrial cancer, but is predictive of histology and grade. Further characterization of these findings in large, prospective studies is needed to elucidate their potential clinical applications.Significance:The vaginal microbiome reliably segregates not just benign gynecologic condition from endometrial cancer, but also predicts cancer grade and histology. Patterns of microbial abundance and gene expression should be increasingly considered as a factor in the evolution of precision medicine approaches, especially as they relate to cancer screening, disease pathogenesis, and patient-centered outcomes.

Heatmap of abundance values

Additional statistical methods

Biomarker discovery by histology

Individual Specimen Reads

Analysis of Confounders

Functional pathway analysis

Microbial alpha diversities

Microbial abundance by histotype

The human microbiome has been strongly correlated with disease pathology and outcomes, yet remains relatively underexplored in patients with malignant endometrial disease. In this study, vaginal microbiome samples were prospectively collected at the time of hysterectomy from 61 racially and ethnically diverse patients from three disease conditions: (i) benign gynecologic disease (controls, n = 11), (ii) low-grade endometrial carcinoma (n = 30), and (iii) high-grade endometrial carcinoma (n = 20). Extracted DNA underwent shotgun metagenomics sequencing, and microbial α and β diversities were calculated. Hierarchical clustering was used to describe community state types (CST), which were then compared by microbial diversity and grade. Differential abundance was calculated, and machine learning utilized to assess the predictive value of bacterial abundance to distinguish grade and histology. Both α- and β-diversity were associated with patient tumor grade. Four vaginal CST were identified that associated with grade of disease. Different histologies also demonstrated variation in CST within tumor grades. Using supervised clustering algorithms, critical microbiome markers at the species level were used to build models that predicted benign versus carcinoma, high-grade carcinoma versus benign, and high-grade versus low-grade carcinoma with high accuracy. These results confirm that the vaginal microbiome segregates not just benign disease from endometrial cancer, but is predictive of histology and grade. Further characterization of these findings in large, prospective studies is needed to elucidate their potential clinical applications. Significance: The vaginal microbiome reliably segregates not just benign gynecologic condition from endometrial cancer, but also predicts cancer grade and histology. Patterns of microbial abundance and gene expression should be increasingly considered as a factor in the evolution of precision medicine approaches, especially as they relate to cancer screening, disease pathogenesis, and patient-centered outcomes.