NRASQ61R mutation drives elevated angiopoietin-2 expression in human endothelial cells and a genetic mouse model.

BACKGROUND: Angiopoietin-2 (Ang-2) is increased in the blood of patients with kaposiform lymphangiomatosis (KLA) and kaposiform hemangioendothelioma (KHE). While the genetic causes of KHE are not clear, a somatic activating NRASQ61R mutation has been found in the lesions of KLA patients. PROCEDURE: Our study tested the hypothesis that the NRASQ61R mutation drives elevated Ang-2 expression in endothelial cells. Ang-2 was measured in human endothelial progenitor cells (EPC) expressing NRASQ61R and a genetic mouse model with endothelial targeted NRASQ61R. To determine the signaling pathways driving Ang-2, NRASQ61R EPC were treated with signaling pathway inhibitors. RESULTS: Ang-2 levels were increased in EPC expressing NRASQ61R compared to NRASWT by Western blot analysis of cell lysates and ELISA of the cell culture media. Ang-2 levels were elevated in the blood of NRASQ61R mutant mice. NRASQ61R mutant mice also had reduced platelet counts and splenomegaly with hypervascular lesions, like some KLA patients. mTOR inhibitor rapamycin attenuated Ang-2 expression by NRASQ61R EPC. However, MEK1/2 inhibitor trametinib was more effective blocking increases in Ang-2. CONCLUSIONS: Our studies show that the NRASQ61R mutation in endothelial cells induces Ang-2 expression in vitro and in vivo. In cultured human endothelial cells, NRASQ61R drives elevated Ang-2 through MAP kinase and mTOR-dependent signaling pathways.

A machine learning model to predict therapeutic inertia in type 2 diabetes using electronic health record data.

OBJECTIVE: To estimate the therapeutic inertia prevalence for patients with type 2 diabetes, develop and validate a machine learning model predicting therapeutic inertia, and determine the added predictive value of area-level social determinants of health (SDOH). METHODS: This prognostic study with a retrospective cohort design used OneFlorida data (linked electronic health records (EHRs) from 1240 practices/clinics in Florida). The study cohort included adults (aged ≥ 18) with type 2 diabetes, HbA1C ≥ 7% (53 mmol/mol), ≥one ambulatory visit, and ≥one antihyperglycemic medication prescribed (excluded patients prescribed insulin before HbA1C). The outcome was therapeutic inertia, defined as absence of treatment intensification within six months after HbA1C ≥ 7% (53 mmol/mol). The predictors were patient, provider, and healthcare system factors. Machine learning methods included gradient boosting machines (GBM), random forests (RF), elastic net (EN), and least absolute shrinkage and selection operator (LASSO). The DeLong test compared the discriminative ability (represented by C-statistics) between models. RESULTS: The cohort included 31,087 patients with type 2 diabetes (mean age = 58.89 (SD = 13.27) years, 50.50% male, 58.89% White). The therapeutic inertia prevalence was 39.80% among the 68,445 records. GBM outperformed (C-statistic from testing sample = 0.84, 95% CI = 0.83-0.84) RF (C-statistic = 0.80, 95% CI = 0.79-0.80), EN (C-statistic = 0.80, 95% CI = 0.80-0.81), and LASSO (C-statistic = 0.80, 95% CI = 0.80-0.81), p < 0.05. Area-level SDOH significantly increased the discriminative ability versus models without SDOH (C-statistic for GBM = 0.84, 95% CI = 0.84-0.85 vs. 0.84, 95% CI = 0.83-0.84), p < 0.05. CONCLUSIONS: Using EHRs of patients with type 2 diabetes from a large state, machine learning predicted therapeutic inertia (prevalence = 40%). The model's ability to predict patients at high risk of therapeutic inertia is clinically applicable to diabetes care.