While estradiol and progesterone rise and fall over the course of the menstrual cycle, the hormones plummet to their lowest levels just before and during menses. Suicide attempts occur most frequently either right before, during or right after menstrual bleeding in women.
“In most women, stable estradiol and progesterone are associated with feelings of well-being and calm,” said Tory Eisenlohr-Moul, assistant professor of psychiatry in the UIC College of Medicine, and principal investigator on the grant. “In our previous research in females with chronic suicidality, stabilizing both estradiol and progesterone protected women against increased depression and thoughts of suicide around menses and may have mitigated the negative effects of withdrawal from these hormones. In this study, we want to investigate the mechanisms by which hormone stabilization may protect against suicide.”
Eisenlohr-Moul and her colleagues will recruit 90 females who had suicidal symptoms in the previous 30 days but who are not on birth control. They will compare symptoms of suicidality among the participants across two experimental conditions: one menstrual cycle where placebos are given during the perimenstrual period and one cycle where estradiol and progesterone are stabilized during the perimenstrual period. In between each condition, participants will be allowed to freely cycle for one menstrual cycle with no hormone patches or placebos.
Participants will record levels of depression, hopelessness, and suicidality daily on their smartphones. Blood will be drawn each week during experimental conditions to evaluate levels of estradiol and progesterone as well as inflammatory markers and changes in gene expression that have been linked to depression and suicide.
Participants will also use an app developed at the University of Illinois at Chicago called BiAffect. The app unobtrusively monitors keyboard dynamics metadata, such as typing speed and rhythm, mistakes in texts, and the use of backspace and auto-correct. The metadata, but not the content of the text, is analyzed using an artificial intelligence-based machine learning approach to identify digital biomarkers of manic and depressive episodes in people with bipolar disorder.
Participants also will provide blood samples to determine levels of hormone metabolites such as allopregnanolone, and the expression of genes related to the brain’s ability to create allopregnanolone. Allopregnanolone is known as the “feel-good hormone” — it is a metabolite of the hormone progesterone, one of the two major female hormones (the other being estrogen). Allopregnanolone binds to receptors for the neurotransmitter gamma-aminobutyric acid (GABA) in the brain. These receptors also are the targets of anti-anxiety drugs such as benzodiazepines. The first-ever drug to treat postpartum depression, approved this year, is a synthetic version of allopregnanolone. Graziano Pinna, associate professor of psychiatry in the UIC College of Medicine and a co-investigator on the project, is a world-renowned expert on allopregnanolone and its role in mental health disorders.
“If we can identify biomarkers through our blood testing and through analyzing data from BiAffect, we may be able to more accurately predict who is at the highest risk and at what times, and take action to help that particular person, such as ensuring they get extra support or treatment during certain times in their cycle,” Eisenlohr-Moul said.
Dr. Alex Leow of the University of Illinois at Chicago is a co-investigator on the grant.