Joseph Ippolito, MD, PhD

Assistant Professor of Radiology

Sex differences in nutrient uptake and metabolism Read More

Email: ippolitoj@wustl.edu
Lab Phone: (314) 362-2928
Website: Ippolito lab
Lab Location: East Building 2313
Keywords: cancer, metabolism, imaging, sex differences, glioblastoma

Sex differences in nutrient uptake and metabolism

In cancers throughout the body, males not only have a higher incidence, but higher mortality than women. Although the reason for this is not yet clear, sex differences in nutrient uptake and metabolism are seen early in development and are carried into adulthood. My group is identifying specific metabolic pathways that underlie this phenomenon, operating under the hypothesis that sex differences in nutrient uptake and metabolism underlie sex differences in survival. The projects in the lab include:

  1. Development and implementation of novel animal models for sex differences in GBM. We are collaborating with Dr. Joshua Rubin’s lab to develop new models with transgenic mice and CRISPR of astrocyte cell lines to identify novel drivers of sex differences in GBM.
  2. Targeting sex differences in glutamine consumption as a driver for sex differences in GBM phenotype: We are using stable isotopes, mass spectrometry, NMR, and metabolic PET tracers to understand how and why male GBM cells consume more glutamine that may allow us to explain increased proliferation in male GBM
  3. Sex-specific effects of diet on brain and GBM tumor metabolism. We are investigating the effects of the ketogenic diet on sex-specific growth of GBM in mice, using the above tools to identify sex-specific effects on tumor, brain, and systemic metabolis.
  4. Clinical assessment of sex differences in GBM and host metabolism: On the clinical end, we are developing ways to assess prognostic sex differences in both tumor and patient metabolism with imaging using PET, MRI, and CT. We have successfully developed new obesity metrics with abdominal CT to predict sex-specific outcomes in many cancers including GBM and are developing novel software algorithms to predict the metabolic state of the individual.