Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests.

OBJECTIVE: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids). MATERIALS AND METHODS: We harmonized survey data from the Personalized Environment and Genes Study (PEGS) on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. RESULTS: Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant or suggestive predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. DISCUSSION: Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal but can support hypothesis generation. CONCLUSION: This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests.

Objective: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (e.g., endometriosis, ovarian cyst, and uterine fibroids). Materials and Methods: We harmonized survey data from the Personalized Environment and Genes Study on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. Results: Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. Discussion: Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal, but can support hypothesis generation. Conclusion: This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

Metformin is associated with reduced COVID-19 severity in patients with prediabetes.

AIMS: Studies suggest that metformin is associated with reduced COVID-19 severity in individuals with diabetes compared to other antihyperglycemics. We assessed if metformin is associated with reduced incidence of severe COVID-19 for patients with prediabetes or polycystic ovary syndrome (PCOS), common diseases that increase the risk of severe COVID-19. METHODS: This observational, retrospective study utilized EHR data from 52 hospitals for COVID-19 patients with PCOS or prediabetes treated with metformin or levothyroxine/ondansetron (controls). After balancing via inverse probability score weighting, associations with COVID-19 severity were assessed by logistic regression. RESULTS: In the prediabetes cohort, when compared to levothyroxine, metformin was associated with a significantly lower incidence of COVID-19 with "mild-ED" or worse (OR [95% CI]: 0.636, [0.455-0.888]) and "moderate" or worse severity (0.493 [0.339-0.718]). Compared to ondansetron, metformin was associated with lower incidence of "mild-ED" or worse severity (0.039 [0.026-0.057]), "moderate" or worse (0.045 [0.03-0.069]), "severe" or worse (0.183 [0.077-0.431]), and "mortality/hospice" (0.223 [0.071-0.694]). For PCOS, metformin showed no significant differences in severity compared to levothyroxine, but was associated with a significantly lower incidence of "mild-ED" or worse (0.101 [0.061-0.166]), and "moderate" or worse (0.094 [0.049-0.18]) COVID-19 outcome compared to ondansetron. CONCLUSIONS: Metformin use is associated with less severe COVID-19 in patients with prediabetes or PCOS.

AXL/MERTK inhibitor ONO-7475 potently synergizes with venetoclax and overcomes venetoclax resistance to kill FLT3-ITD acute myeloid leukemia.

FMS-like Tyrosine Kinase 3 (FLT3) mutation is associated with poor survival in acute myeloid leukemia (AML). The specific Anexelekto/MER Tyrosine Kinase (AXL) inhibitor, ONO-7475, kills FLT3-mutant AML cells with targets including Extracellular- signal Regulated Kinase (ERK) and Myeloid Cell Leukemia 1 (MCL1). ERK and MCL1 are known resistance factors for Venetoclax (ABT-199), a popular drug for AML therapy, prompting the investigation of the efficacy of ONO-7475 in combination with ABT-199 in vitro and in vivo. ONO-7475 synergizes with ABT-199 to potently kill FLT3-mutant acute myeloid leukemia cell lines and primary cells. ONO-7475 is effective against ABT-199-resistant cells including cells that overexpress MCL1. Proteomic analyses revealed that ABT-199-resistant cells expressed elevated levels of pro-growth and anti-apoptotic proteins compared to parental cells, and that ONO-7475 reduced the expression of these proteins in both the parental and ABT-199-resistant cells. ONO-7475 treatment significantly extended survival as a single in vivo agent using acute myeloid leukemia cell lines and PDX models. Compared to ONO-7474 monotherapy, the combination of ONO-7475/ABT-199 was even more potent in reducing leukemic burden and prolonging the survival of mice in both model systems. These results suggest that the ONO-7475/ABT-199 combination may be effective for AML therapy.

The Eµ-hnRNP K Murine Model of Lymphoma: Novel Insights into the Role of hnRNP K in B-Cell Malignancies.

B-cell lymphomas are one of the most biologically and molecularly heterogeneous group of malignancies. The inherent complexity of this cancer subtype necessitates the development of appropriate animal model systems to characterize the disease with the ultimate objective of identifying effective therapies. In this article, we discuss a new driver of B-cell lymphomas - hnRNP K (heterogenous nuclear ribonucleoprotein K)-an RNA-binding protein. We introduce the Eµ-Hnrnpk mouse model, a murine model characterized by hnRNP K overexpression in B cells, which develops B-cell lymphomas with high penetrance. Molecular analysis of the disease developed in this model reveals an upregulation of the c-Myc oncogene via post-transcriptional and translational mechanisms underscoring the impact of non-genomic MYC activation in B-cell lymphomas. Finally, the transplantability of the disease developed in Eµ-Hnrnpk mice makes it a valuable pre-clinical platform for the assessment of novel therapeutics.