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Genetic Architecture for Comorbid Depression, Coronary Artery Disease Emerges from Study

NEW YORK – A team led by researchers at Vanderbilt University has tracked down genetic risk features in individuals diagnosed with both depression and coronary artery disease (CAD), highlighting the role of inflammatory and cardiomyopathy-related pathways in these comorbid conditions.

The findings suggested that "genes associated with both phenotypes were not necessarily those most strongly associated with depression or CAD independently," senior and corresponding author Lea Davis, a researcher at Vanderbilt University Medical Center (VUMC), and her colleagues wrote in Nature Mental Health on Friday.

Starting with summary statistics from prior genome-wide association studies involving 296,525 CAD and nearly 500,200 depression cases or controls, together with cross-tissue gene expression profiles predicted in silico from expression quantitative trait locus data from version 8 of GTEx, the researchers searched for overlap between 1,455 CAD-related and 928 depression-related genes with the help of a transcriptome-wide association analysis.

"Depression and CAD are highly comorbid conditions and the molecular mechanisms underlying this common comorbidity remain understudied," the authors wrote, noting that comorbid CAD and major depression coincides with lifespans up to two decades shorter.

The team's analyses highlighted 185 genes with overlapping ties to CAD and depression, pointing to an overrepresentation of genes and pathways involved in cardiomyopathy or inflammation.

Consequently, the authors suggested that "comorbid [depression and CAD] may have a partially unique genetic architecture that overlaps a subset of genes associated with depression and CAD," the authors explained.

Similarly, a follow-up electronic health record-based analysis suggested that cardiomyopathy diagnoses are found more frequently in individuals with both depression and CAD than in individuals with either condition alone.

"This study used population-level genetic data and data from the electronic health record to identify genes that increase risk for both depression and cardiovascular disease," VUMC's Davis explained in an email. "When we examined these genes all together, we discovered that many of them had roles in inflammatory processes and some of them also increase the risk for cardiomyopathy."

While almost 1.6 percent of individuals with depression were diagnosed with cardiomyopathy, and cardiomyopathy turned up in just over 11 percent of those with CAD, cardiomyopathy was recorded in nearly 15 percent of individuals in the comorbid depression/CAD group.

Those results were further shored up when the researchers analyzed EHR data from the All of Us project and a Mass General Brigham cohort, where cardiomyopathy diagnoses in patients with both depression and CAD outpaced those in patients with CAD or depression alone.

"This work, taken together with the rest of the emerging literature, suggests that chronic low-level inflammation may be a significant contributor to both depression and cardiovascular disease," Davis said.

Along with prior studies pointing to potential anti-inflammatory effects of some anti-depressant treatments, she explained, the findings further highlight the possibility of targeting inflammation to address depression and heart disease.

"More research is needed to investigate optimal treatment mechanisms," Davis cautioned, "but at a minimum this work suggests that patient heart and brain health should be considered together when developing management plans to treat depression or cardiovascular disease."

More generally, the authors noted that the strategy used in the current study "highlights the power of in silico transcriptome-wide studies to motivate hypotheses that can be tested in large EHR databases."