Mount Sinai researchers have discovered that women are over ten times more likely to develop postpartum psychosis if their sister has experienced the condition. This research, published on May 19 in the American Journal of Psychiatry and discussed at the American Psychiatric Association’s annual meeting, points to genetic and shared environmental factors as contributors.
Postpartum psychosis is a rare but serious mental illness affecting mothers shortly after childbirth, with potential risks including suicide and infanticide if untreated. Symptoms include severe mood swings, hallucinations, disorganized thinking, insomnia, paranoia, and self-harm thoughts. Despite its severity, postpartum psychosis remains understudied and difficult for physicians to diagnose.
Dr. Veerle Bergink, Director of the Women’s Mental Health Center at Mount Sinai and co-senior author of the study, emphasized the importance of awareness: “Every woman of childbearing age and their physicians need to know about the existence of, severity, symptoms, and familial risk for postpartum psychosis so it can be promptly diagnosed and, hopefully, prevented.”
The research team analyzed records from over 1.6 million women in Swedish nationwide registries. They identified 2,514 cases of postpartum psychosis occurring within three months post-childbirth. The study revealed that having a sister with bipolar disorder doubles a woman’s risk for postpartum psychosis. Those with sisters who have both bipolar disorder and postpartum psychosis face a 14-fold increased risk.
While the relative recurrent risk for developing postpartum psychosis is high among siblings, Dr. Bergink noted that “the absolute risk for women with an affected sister is still low at 1.6 percent.” She added that this distinction supports understanding postpartum psychosis as separate from bipolar disorder despite some overlap.
This study lays groundwork for further research aimed at early recognition and prevention of postpartum psychosis. Dr. Behrang Mahjani’s lab at Mount Sinai is working on identifying genes responsible for the condition using complex molecular data: “Knowing what specific genes are involved will help us dive into the mechanisms and triggers… And that could lead us to novel treatments.”
