Managing bipolar disorder - understanding the genetic signatures that lead to variability in lithium treatment
Zuckerman Faculty Scholar Shani Stern new preprint.
Bipolar disorder (BD) is a neuropsychiatric mood disorder manifested by recurrent episodes of mania and depression. More than half of BD patients are non-responsive to lithium, the first-line treatment drug, complicating BD clinical management. Given its unknown etiology, it is pertinent to understand the genetic signatures that lead to variability in lithium treatment. We discovered a set of differentially expressed genes from the LCLs of 10 controls and 19 BD patients belonging mainly to the immunoglobulin gene family that can be used as potential biomarkers to diagnose and treat BD. Importantly, we trained a machine learning algorithm on our datasets that predicted the lithium response of BD subtypes with no errors, even when used on a different cohort of 24 BD patients acquired by a different laboratory. This proves the scalability of our methodology for predicting lithium response in BD and for a prompt and suitable decision on therapeutic interventions.