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Predicting Mental Illnesses Using Epigenetics

According to the National Institute of Mental Health, approximately 20% of adults (around 51.5 million people) experience a mental illness each year. I believe that is 51.5 million people too many!

There is a HUGE need for the ability to predict mental illness, as the current diagnostic process has many limitations and challenges.

By analyzing epigenetic markers associated with mental disorders, we can actually predict the likelihood of developing these conditions and tailor personalized treatment plans for improved outcomes.

Predicting mental illness using epigenetics is paramount for early intervention, personalized medicine, and improved outcomes. With DNA methylation marks in peripheral tissues serving as predictive biomarkers, healthcare professionals can identify those at high risk and initiate targeted support.

Early detection enables timely interventions, potentially mitigating the severity and progression of these disorders. By leveraging cutting-edge technologies like artificial intelligence and natural language processing, we can even analyze social media data to predict suicidal thoughts and behaviors, revolutionizing suicide prevention strategies.

In this week’s Everything Epigenetics podcast, Zach and I chat about his work which primarily concentrates on identifying the epigenetic factors that contribute to psychiatric diseases, specifically focusing on mood disorders.

We discuss the microarray technology he utilizes to conduct genome-wide exploratory analyses, aiming to discover disease associations in both human subjects and animal models. We focus on Zach’s investigations which encompass a range of conditions, including major depression, postpartum depression, and suicide.

Another significant area of Zach’s research that we explore is centered around the development of predictive biomarkers for disease risk, using DNA methylation patterns in peripheral tissues.

Furthermore, we talk about his research program that involves the development and application of artificial intelligence-driven natural language processing techniques, and how he applies these techniques to social media data to predict the likelihood of future suicidal thoughts and behaviors.

Additionally, Zach is focused on creating and evaluating innovative digitally delivered suicide interventions that make use of these technologies.

In this Everything Epigenetics episode, you’ll learn about:

– Zach’s story starting with, “I met a girl…”
– Zach’s focus on suicide, PTSD, and post-partum depression epigenetics
– Dionysus digital health
– Why epigenetics is giving researchers hope as a diagnostic tool
– Epigenetics being the common denominator of nature and nurture
– Stress vulnerability and epigenetic variation
– The importance of replication and validation studies
– Molecular regulation and neuroimaging consequences of SKA2
– Modifying the epigenetic code at SKA2
– Cell type heterogeneity in the brain
– Using artificial intelligence and Twitter data to help identify those with the greatest risk of -suicide
– Suicide intervention app
– Where estrogen changes methylation in the brain and how this relates to PPD
– The HP1BP3 gene
– How to find replicated loci in epigenetic methylation studies
– How we can commercialize this type of work
– The stigma against mental illness
– Zach’s future work involving AI responses on Twitter

 
Dr. Kaminsky received his PhD from the University of Toronto in 2008 and trained in one of the first labs studying epigenetics in psychiatry. In 2010, he developed a research program at Johns Hopkins using genome-wide DNA methylation microarrays to study brain and peripheral samples in PPD, suicide, and PTSD, generating some of the first epigenetic biomarkers in psychiatry. As Chair of Suicide Prevention Research at The Royal’s IMHR, Dr. Kaminsky studies human populations in attempts to better understand both the molecular epigenetic underpinnings of psychiatric phenotypes and environmental stressors influencing their development in order to enable the generation of true “bench to bedside” translational findings. Dr. Kaminsky’s research program also develops artificial intelligence driven natural language processing techniques applied to social media data for the purpose of prediction of future risk to suicidal thoughts and behaviors. His group is also developing and evaluating novel digitally delivered suicide interventions leveraging these technologies. As a member of Suicide Prevention Ottawa, Dr. Kaminsky is involved in the implementation of suicide prevention initiatives for suicidal youth in clinical populations.

About this Guest

Zachary Kaminsky
Zachary Kaminsky, PhD, is a pioneer in epigenetic biomarkers in psychiatry, focusing on the molecular epigenetic aspects of psychiatric conditions and developing AI-driven techniques for predicting and intervening in suicide risk.

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