Intermittent explosive disorder (IED) is a psychiatric condition defined by impulsive aggression and poorly regulated emotional control, often leading to significant personal and societal consequences. Yet, despite its disruptive nature, IED remains understudied and underdiagnosed in psychiatry. A new study led by Yanli Zhang-James, MD, PhD, leverages advancements in big data to shed light on the web of comorbidities associated with IED, emphasizing the transformative potential of modern databases and advanced statistical modeling in mental health research. Zhang-James is an Associate Professor of Psychiatry and Behavioral Sciences at Upstate Medical University.

Yanli Zhang-James, MD, PhD, used data from thousands of patients to better understand the complex relationships between Intermittent explosive disorder and other psychiatric diagnoses.

Patterns of Comorbidity: A Groundbreaking Analysis

The study, published in JAMA Psychiatry in early 2025, posed a question; are there patterns of comorbidity between IED and other disorders? Analyzing electronic health records from the TriNetX Research Network, the researchers identified over 33,000 patients with a lifetime IED diagnosis. 95.7% of these individuals had at least one additional psychiatric diagnosis, highlighting the pervasive overlap between IED and other mental health conditions.

However, the findings went beyond psychiatric comorbidities. The study uncovered significant associations between IED and a range of neurological and somatic disorders, suggesting that IED’s impact extends far beyond its defining symptoms of impulsive aggression. These results underscore the need for integrated care approaches that address both the psychological and physical health challenges faced by individuals with IED.

Big Data Revolutionizing Mental Health Research

Zhang-James’ study exemplifies the power of big data in advancing modern medicine. By utilizing TriNetX, a database containing de-identified electronic medical records from millions of patients, the team could identify patterns that would have been nearly impossible to detect using traditional research methods. “Until now, we've never been able to look at a disorder such as IED and find such a huge and specific real-world patient cohort,” she says. “The insights we can gain from analyzing these disease profiles is extraordinary.”

Big data analytics has dramatically expanded researchers' ability to explore mental health conditions like IED. With more accessible and larger datasets than ever before, scientists can investigate the prevalence, progression, and treatment patterns of disorders on an unprecedented scale. “Gaining a better understanding of IED’s comorbidities and treatment trajectories may lead to better diagnosis and care in the future,” Dr. Zhang-James notes.

Implications for Diagnosis and Treatment

The findings of this study raise critical questions about the way IED is diagnosed and treated in real-world clinical settings. Currently, many physicians view impulsive aggression as a symptom rather than a standalone disorder. Zhang-James and her colleagues argue that recognizing IED as a distinct diagnosis has significant advantages. “If it’s diagnosed, we can track its prevalence, study the other conditions people with this disorder may have, and understand how it’s treated in the real world,” she explains. “Are patients being treated for other conditions first, leaving their impulsive aggression under-addressed? How can we better tailor treatment plans to more effectively manage both IED and its comorbidities?”

Dr. Zhang-James’ interest in IED initially stemmed from her research on ADHD, another condition associated with impulsive behavior. Over the past decade, her focus has shifted from traditional lab bench science to computational research, driven by advancements in artificial intelligence (AI) and machine learning. “I’m fascinated by the potential of AI to predict disease progression and identify the most effective treatments for individual patients,” she says. “Hopefully, we can use this enormous amount of data to build decision-making tools that improve personalized care.”

The Path Forward

While the study highlights the extensive comorbidities associated with IED, it also acknowledges important limitations— reliance on medical records and the low diagnostic rates of IED caution against overgeneralizing the findings. Prospective studies and more inclusive diagnostic practices are needed to validate and expand upon these results.

Still, the implications are clear: IED’s complex relationship with other disorders demands a more nuanced and integrated approach to diagnosis and treatment. By embracing big data and leveraging tools like AI, researchers and clinicians can uncover hidden patterns, optimize treatment strategies, and ultimately improve outcomes for individuals with this challenging condition.

“This line of research is so exciting because it allows us to see the bigger picture,” says Zhang-James. “With the right tools and data, we can transform our understanding of IED and many other disorders, paving the way for a more comprehensive and personalized approach to mental health care.”

You can read the whole paper published in JAMA Psychiatry here- https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2829332