High-fat diet and obesity are associated with differential angiogenic gene expression in epithelial ovarian cancer.

OBJECTIVE: We sought to evaluate the association between diet and angiogenic biomarkers in KpB mice, and the association between these markers, body mass index (BMI), and overall survival (OS) in high-grade serous cancers (HGSC). METHODS: Tumors previously obtained from KpB mice subjected to high-fat diets (HFD, n = 10) or low-fat diets (LFD, n = 10) were evaluated for angiogenesis based on CD-31 microvessel density (MVD). Data from prior microarray analysis (Agilent 244 K arrays) conducted in 10 mice were utilized to assess associations between diet and angiogenetic biomarkers. Agilent (mouse) and Affymetrix Human Genome U133a probes were linked to 162 angiogenic-related genes. The associations between biomarkers, BMI, and OS were evaluated in an HGSC internal database (IDB) (n = 40). Genes with unadjusted p < 0.05 were evaluated for association with OS in the TCGA-OV database (n = 339). RESULTS: There was no association between CD-31 and diet in mice (p = 0.66). Sixteen angiogenic-related genes passed the p < 0.05 threshold for association with HFD vs. LFD. Transforming growth factor-alpha (TGFA) demonstrated 72% higher expression in HFD vs. LFD mice (p = 0.04). Similar to the mouse study, in our HGSC IDB, higher TGFA expression correlated with higher BMI (p = 0.01) and shorter survival (p = 0.001). In the TCGA-OV dataset, BMI data was not available and there was no association between TGFA and OS (p = 0.48). CONCLUSIONS: HFD and obesity may promote tumor progression via differential modulation of TGFA. We were unable to confirm this finding in the TCGA dataset. Further evaluation of TGFA is needed to determine if this is a target unique to obesity-driven HGSC.

Comparison of vaccination coverage of four childhood vaccines in New Zealand and New York State.

AIM: To ensure that children are vaccinated, different national governments use diverse strategies. We compared childhood vaccination coverage rates between New York State (NYS) and New Zealand (NZ) as the vaccination strategies are different. METHODS: We used vaccination records from the NYS Immunisation Information System and the National Immunisation Register of NZ to measure (i) vaccination coverage by school entry and by age six; (ii) coverage of different socio-demographic groups; and (iii) trend in vaccination coverage between 2011 and 2015. RESULTS: We analysed the records of 583 767 NYS children and 269 800 NZ children 7 years of age. NZ children were 3.3-21.5% more likely than NYS children to receive each of the vaccines. Compared to NYS, NZ children were 39.6% more likely to be up-to-date by the start of school and 28.1% more likely to be up-to-date by age 6 years. Both NYS and NZ had statistically significant increases in the proportion of children who were up to date on each vaccine and all vaccines by the start of school and by 6 years of age (P < 0.001). CONCLUSIONS: We identified under-vaccinated groups and examined the point in the vaccine series where children were most vulnerable to being under-vaccinated. This information is useful in targeting future investigations and interventions aimed at mitigating disparities in vaccine coverage. This comparison of regions with different vaccination programmes and policies is important when considering whether the particular vaccination coverage strategies of one region could be adapted and applied for the benefit of another.