Genetic risk for depression linked to other medical conditions

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Genetic risk for depression linked to other medical conditions

Major depressive disorder (MDD) is not only a leading cause of disability on its own, but this psychiatric condition often occurs in conjunction with, and worsens outcomes associated with, physical diseases. Whether this is due to shared genetic risk—and, if so, to what extent and for which conditions—was a question researchers from U-M’s Medical School, College of LSA, and School of Public Health explored.

Using data from more than 46,000 participants in the Michigan Genomics Initiative (MGI), the researchers performed a phenome-wide association study (PheWAS) to test for associations between a genome-wide polygenic risk score for MDD and other medical and psychiatric traits. They observed associations with a range of conditions, such as respiratory, digestive, neurological, and urinary-system conditions, cancers, and other mental disorders. The results are published in the journal Biological Psychiatry.

“Prior research has found that individuals who experience depression are at elevated risk for not only other mental-health conditions, but also physical-health conditions, such as cardiovascular disease, diabetes, chronic lung disease, and cancer,” said senior author Leah Richmond-Rakerd, PhD, an assistant professor of psychology. “There is also evidence that the co-occurrence of depression with some physical diseases is attributable to shared genetic influences. Our goal was to better understand this shared genetic risk by testing associations between a molecular-genetic risk score for major depressive disorder and the broad spectrum of physical-health and mental-health conditions in the electronic health record [EHR]. We did this using integrated genetic and EHR data.”

MGI’s linking of clinical with genetic data was key for this research. “MGI is an incredibly valuable resource,” said author Srijan Sen MD, PhD, director of the Frances and Kenneth Eisenberg and Family Depression Center and co-lead of the PROMPT Precision Health Study. “With a very large sample size, in-depth longitudinal data on diagnoses and treatments, and genomic data, MGI is a wonderful dataset for mental health researchers to understand the relationship between different mental health diagnoses and how mental health conditions are linked to other medical conditions. It’s a resource we hope our Depression Center’s Data and Design Core can help investigators take advantage of for high-impact studies.”

By integrating genetic tools with EHR data, researchers were able to test genetic associations with a broader range of clinical conditions, and by using a genome-wide polygenic risk score for MDD, instead of a single genetic variant, they could learn more about clinical and genetic associations, including which diseases share genetic risk with MDD and how useful genetic measures could be in clinical disease prediction.

Said Richmond-Rakerd, “Findings from this study can inform genetic-discovery efforts for depression. Our results suggest that genome-wide association studies (GWAS) for depression and the predictive power of polygenic risk scores for depression may be enhanced by including in GWAS individuals diagnosed with not only depression, but also other health conditions that share genetic risk with depression.”

The study also considered the temporal significance of the findings. “Depression and other mental-health conditions tend to emerge in adolescence and young adulthood, while noninfectious physical diseases and neurodegenerative conditions peak later in life. Our findings suggest that early screening and prevention efforts among individuals at high genetic risk for depression might benefit later-life—not just early-life—health,” said Richmond-Rakerd.

By plotting time-at-first-diagnosis for depression and comparing that to time-at-first-diagnosis for conditions that had the most genetic overlap with depression, the study found that “several diagnoses tended to disproportionately succeed or precede depression,” said first author Yu Fang, MSE, a research area specialist lead in the Sen Lab. “Nearly three times as many patients received their first chronic-pain diagnosis and over twice as many received their first substance addiction and disorders diagnosis after their first MDD diagnosis than before. By contrast, over twice as many patients received their first asthma diagnosis before their first MDD diagnosis.”

These findings have potential implications for prevention. For instance, “If this link is causal, it suggests that ameliorating depression might also help to ameliorate risk for chronic pain,” added Richmond-Rakerd.

This study was a valuable foray into the genetic overlap between depression and other conditions, but more work needs to be done. “Our results suggest that in the future, integrating information about genetic liability to depression with other clinical risk factors may help to improve risk prediction for both medical and psychiatric conditions. However, polygenic scores for depression will need to predict health outcomes for individuals with a higher level of precision before they can be used in such clinical applications,” said Richmond-Rakerd.

The study also highlighted the importance of not relying solely on genetics, but keeping environmental risk factors in mind. “We detected associations between the depression polygenic score and a range of medical and psychiatric conditions,” said Richmond-Rakerd. “However, of all the diagnoses included in our analyses, the majority were not associated with the score. This reinforces that it is important to consider not just genetic factors, but also environmental factors that help to explain the co-occurrence of depression with other mental-health and physical-health conditions.”

She adds, “Our findings can inform future interdisciplinary research efforts to identify the factors that connect genetic liability to depression with poor physical health across development. These efforts will require integrating molecular-genetic tools into longitudinal cohort studies, and collaborative cross-talk between researchers and clinicians across such fields as developmental science, genomics, psychiatry, and geriatric medicine.”

This study was supported by the National Institute of Mental Health (Grant No. MH101459 [to Sen]) and the National Institute of Child Health and Human Development (Grant No. HD065563 through the Duke Population Research Center [to Richmond-Rakerd]).