RESUMO
Objective: Type 2 diabetes (T2DM) poses a significant public health challenge, with pronounced disparities in control and outcomes. Social determinants of health (SDoH) significantly contribute to these disparities, affecting healthcare access, neighborhood environments, and social context. We discuss the design, development, and use of an innovative web-based application integrating real-world data (electronic health record and geospatial files), to enhance comprehension of the impact of SDoH on T2 DM health disparities. Methods: We identified a patient cohort with diabetes from the institutional Diabetes Registry (N = 67,699) within the Duke University Health System. Patient-level information (demographics, comorbidities, service utilization, laboratory results, and medications) was extracted to Tableau. Neighborhood-level socioeconomic status was assessed via the Area Deprivation Index (ADI), and geospatial files incorporated additional data related to points of interest (i.e., parks/green space). Interactive Tableau dashboards were developed to understand risk and contextual factors affecting diabetes management at the individual, group, neighborhood, and population levels. Results: The Tableau-powered digital health tool offers dynamic visualizations, identifying T2DM-related disparities. The dashboard allows for the exploration of contextual factors affecting diabetes management (e.g., food insecurity, built environment) and possesses capabilities to generate targeted patient lists for personalized diabetes care planning. Conclusion: As part of a broader health equity initiative, this application meets the needs of a diverse range of users. The interactive dashboard, incorporating clinical, sociodemographic, and environmental factors, enhances understanding at various levels and facilitates targeted interventions to address disparities in diabetes care and outcomes. Ultimately, this transformative approach aims to manage SDoH and improve patient care.
RESUMO
Whether treatment with folic acid (FA) affects human breast cancer positively or negatively remains unclear. We subjected human Michigan Cancer Foundation-7 cells, a human breast cancer cell line, to suboptimal FA at low levels (10 nM; LF) and high levels (50 µM; HF) and investigated the molecular mechanisms underlying their effects through metabolic flux and systematic proteomics analyses. The data indicated that LF induced and HF aggravated 2-fold higher mitochondrial toxicity in terms of suppressed oxidative respiration, increased fermented glycolysis, and enhanced anchorage-independent oncospheroid formation. Quantitative proteomics and Gene Ontology enrichment analysis were used to profile LF- and HF-altered proteins involved in metabolism, apoptosis, and malignancy pathways. Through STRING analysis, we identified a connection network between LF- and HF-altered proteins with mammalian target of rapamycin (mTOR). Rapamycin-induced blockage of mTOR complex 1 (mTORC1) signaling, which regulates metabolism, differentially inhibited LF- and HF-modulated protein signatures of mitochondrial NADH dehydrogenase ubiquinone flavoprotein 2, mitochondrial glutathione peroxidase 4, kynureninase, and alpha-crystallin B chain as well as programmed cell death 5 in transcript levels; it subsequently diminished apoptosis and oncospheroid formation in LF/HF-exposed cells. Taken together, our data indicate that suboptimal FA treatment rewired oncogenic metabolism and mTORC1-mediated proteomics signatures to promote breast cancer development.