Hub for Metabolic Psychiatry Special Lecture
Jan
21

Hub for Metabolic Psychiatry Special Lecture

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We are delighted to welcome guest speaker, Prof Mark Frye, to Edinburgh for our first Hub for Metabolic Psychiatry Special Lecture of 2026!

Mark A. Frye, M.D., is a Consultant in the Department of Psychiatry & Psychology at Mayo Clinic. He is the past Chair of the Department of Psychiatry & Psychology (2010-2020) and is recognised with the distinction of the Stephen and Shelley Jackson Family Professorship in Individualised Medicine.

Prof Frye and his team established the Mayo Clinic Individualised Medicine Biobank for Bipolar Disorder to identify the underpinning mechanisms of bipolar disorder through genomic studies. An active clinical investigator, he has received research support from NIMH, NIAAA, Mayo Foundation, Brain & Behavior Research Foundation, and industry partners, and has published more than 500 peer-reviewed papers.

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INCR Metabolic Interactions Workshop
Mar
27

INCR Metabolic Interactions Workshop

Colleagues based at the Institute of Neuroscience and Cardiovascular Research (INCR) at the University of Edinburgh are invited to come along to this half day workshop where we will discuss collaborations and funding opportunities across the Institute at the interface of neuroscience, psychiatry and cardiovascular health.

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Hub for Metabolic Psychiatry Special Lecture
Mar
5

Hub for Metabolic Psychiatry Special Lecture

We are delighted to welcome guest speaker, Dr Benjamin Perry, to Edinburgh for the first of our quarterly Hub for Metabolic Psychiatry Lectures!

In this talk, Ben will describe his work developing PsyMetRiC, the first cardiometabolic risk prediction algorithm tailored and accurate for young people with SMI. Validated in eight countries across four continents, PsyMetRiC is now being revised and updated for accuracy, equity and usefulness. This presentation will highlight some of the challenges of implementing prediction models into clinical practice, and shine a light on the growing recognition of inequities baked into our health record datasets.

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