Associations between Dietary Fiber, the Fecal Microbiota and Estrogen Metabolism in Postmenopausal Women with Breast Cancer.

Breast cancer is a hormonally-driven cancer, and various dietary factors are associated with estrogen metabolism, including dietary fiber. Several studies report associations between dietary fiber and breast cancer; however, research on whether fiber influences circulating estrogens through the gut microbiota is rare. The objective of this cross-sectional study among 29 newly-diagnosed (stage 0-II), post-menopausal breast cancer patients is to examine associations between dietary fiber and the gut microbiota that are linked with ß-glucuronidase activity, and purportedly increase circulating estrogens. Spearman's and partial correlations controlling for body mass index and age were performed using dietary recall data, Illumina MiSeq generated microbiota relative abundance, and HPLC-mass spectrometry-derived estradiol and estrone levels.Major findings are: (1) total dietary fiber is inversely associated with Clostridium hathewayi (r= -0.419; p = 0.024); (2) soluble fiber is inversely associated with Clostridium (r=-0.11; p = 0.02); (3) insoluble fiber is positively associated with Bacteroides uniformis sp. (r = 0.382; p = 0.041); and (4) serum estradiol and estrone levels are not correlated with species/genera or dietary fiber, though there is a trend toward an inverse association between soluble fiber and estradiol levels (r= -0.30; p = 0.12). More studies are needed to understand the complex interaction between dietary fiber, intestinal microbiota, and hormonal levels in older females.

Challenges and Lessons Learned From a Telehealth Community Paramedicine Program for the Prevention of Hypoglycemia: Pre-Post Pilot Feasibility Study.

BACKGROUND: Prevention through Intervention is a community paramedicine program developed by Birmingham Fire and Rescue Services in Alabama. This program aims to reduce dependency on emergency medical services (EMS) for nonemergency-related events through education and to lower the frequency of emergency calls in underserved populations. A telehealth intervention with an emphasis on hypoglycemia was implemented to (1) tailor the intervention to meet the educational needs of participants and (2) facilitate follow-ups. A pre-post pilot feasibility evaluation of the telehealth intervention was conducted. OBJECTIVE: This paper describes the results of the feasibility evaluation, implementation challenges, and the lessons learned about the deployment of a hypoglycemia prevention program in an underserved area and its evaluation. METHODS: This single-arm pretest-posttest intervention included (1) an initial in-person visit (week 1), (2) 3 weekly telecoaching calls (weeks 2-4), (3) 1 biweekly call (week 6), and (4) a final in-person visit (week 8) for collecting posttest data from individuals who called EMS due to hypoglycemic events. In-person visits included educational sessions conducted by EMS personnel. Participants' education included tailored content related to hypoglycemia. Weekly telecoaching calls focused on hypoglycemia symptom monitoring and education reinforcement via a telehealth dashboard. The primary measures focused on feasibility measures, and exploratory measures focused on the fear of hypoglycemia, self-efficacy, and a knowledge of diabetes. RESULTS: A total of 40 participants participated in the intervention. However, the study was marred with high attrition. The various factors behind the low retention rate were discussed. There was a decreasing trend in all three subdomains of the fear of hypoglycemia from pretest to posttest. There was also a significant increase in participants' self-efficacy in hypoglycemia self-management (P=.03). CONCLUSIONS: This study shows preliminary and promising results for a community-based intervention specifically for hypoglycemia. However, the socioeconomic setting in which the intervention was delivered may have resulted in high dropout rates and low attendance during the intervention, which are considerations for future telehealth studies. TRIAL REGISTRATION: ClinicalTrials.gov NCT03665870; https://clinicaltrials.gov/ct2/show/NCT03665870.

Secondary analysis of gut hormone data from children with and without in utero exposure to gestational diabetes: Differences in the associations among ghrelin, GLP-1, and insulin secretion.

BACKGROUND: Intrauterine exposure to gestational diabetes mellitus (GDM) increases risk for type 2 diabetes (T2D). Ghrelin and GLP-1 have opposite functions in nutritional homeostasis and are associated with insulin secretion, but it is not known if individuals exposed to GDM exhibit dysregulation in these associations. OBJECTIVE: Test the hypothesis that children exposed to GDM in utero will exhibit dysregulation among ghrelin, GLP-1, and C-peptide (reflecting insulin secretion). METHODS: Data from N = 43 children aged 5 to 10 years were included in this secondary analysis of ghrelin, GLP-1, and C-peptide response to a liquid meal test. Repeated measures mixed model analyses were used to measure associations among hormones. RESULTS: The association of ghrelin and GLP-1 was moderated by GDM group (P < .01), such that ghrelin was inversely associated with GLP-1 in children without GDM exposure, but not for those exposed to GDM. GLP-1 was positively associated with C-peptide in both groups, but the association was stronger in those exposed to GDM (estimate = 1.06 vs 1.01). CONCLUSIONS: Differences in the associations among ghrelin, GLP-1, and C-peptide displayed here suggest novel lines of research about whether the regulation of gut hormones and insulin secretion contribute to obesity and risk for T2D in children exposed to GDM.

Identifying research themes and trends in the top 20 cancer journals through textual analysis.

BACKGROUND: The rapid growth in cancer research continues expanding the literature. Text mining approaches help make sense of large bodies of scientific literature and integrate the mounting data into the health care delivery system. Our objective is to generate a comprehensive understanding of the themes and trends in cancer research. METHODS: We employed a three-step text mining process of corpus generation and term-list creation and analysis, including latent semantic analysis for dimension reduction and factor analysis for topic identification to analyze 93,423 abstracts from the top 20 cancer/oncology journals for the period between 1999 and 2020. RESULTS: We identified 14 distinct topics in cancer literature. The results revealed the uptrend topics - including cell signaling (T-2), immunotherapy (T-3), clinical trials (T-5), disparities and epidemiology (T-7), cancer practice and policy (T-8), outcome research (T-9), and molecular therapeutics (T-10). - and downtrend topics such as cell death (T-1), early phase clinical trials (T-4), angiogenesis (T-6), cancer screening (T-12), and transplant (T-13). The topics of biomarkers(T-11) and cancer genetics(T-16) remained relatively stable. While the topics of angiogenesis (n = 10,490) and cell death (n = 10,258) included the highest number of abstracts, biomarkers (n = 3203), and cancer genetic (n = 4322) themes included the least number of articles. These findings suggest that despite having the lowest numbers of publications, certain topics such as cancer genetic (T-16) and biomarkers (T-11) have been exhibiting a stable trend and drawing a steady amount of attention from cancer researchers over the past 20 years. CONCLUSION: Findings of this study contribute explanatory insight about themes and trends in cancer research, which can help researchers and stakeholders to identify areas for future studies. POLICY SUMMARY STATEMENT: The findings indicate the increasing efforts to improve cancer practice and cancer care through policy efforts. Therefore, policymakers and other stakeholders may use the findings in prioritization and funding of specific topics.

A critical analysis of COVID-19 research literature: Text mining approach.

OBJECTIVE: Among the stakeholders of COVID-19 research, clinicians particularly experience difficulty keeping up with the deluge of SARS-CoV-2 literature while performing their much needed clinical duties. By revealing major topics, this study proposes a text-mining approach as an alternative to navigating large volumes of COVID-19 literature. MATERIALS AND METHODS: We obtained 85,268 references from the NIH COVID-19 Portfolio as of November 21. After the exclusion based on inadequate abstracts, 65,262 articles remained in the final corpus. We utilized natural language processing to curate and generate the term list. We applied topic modeling analyses and multiple correspondence analyses to reveal the major topics and the associations among topics, journal countries, and publication sources. RESULTS: In our text mining analyses of NIH's COVID-19 Portfolio, we discovered two sets of eleven major research topics by analyzing abstracts and titles of the articles separately. The eleven major areas of COVID-19 research based on abstracts included the following topics: 1) Public Health, 2) Patient Care & Outcomes, 3) Epidemiologic Modeling, 4) Diagnosis and Complications, 5) Mechanism of Disease, 6) Health System Response, 7) Pandemic Control, 8) Protection/Prevention, 9) Mental/Behavioral Health, 10) Detection/Testing, 11) Treatment Options. Further analyses revealed that five (2,3,4,5, and 9) of the eleven abstract-based topics showed a significant correlation (ranked from moderate to weak) with title-based topics. CONCLUSION: By offering up the more dynamic, scalable, and responsive categorization of published literature, our study provides valuable insights to the stakeholders of COVID-19 research, particularly clinicians.