TY - JOUR
T1 - Immunoglobulin genes expressed in lymphoblastoid cell lines discern and predict lithium response in bipolar disorder patients
AU - Mizrahi, Liron
AU - Choudhary, Ashwani
AU - Ofer, Polina
AU - Goldberg, Gabriela
AU - Milanesi, Elena
AU - Kelsoe, John R.
AU - Gurwitz, David
AU - Alda, Martin
AU - Gage, Fred H.
AU - Stern, Shani
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/10
Y1 - 2023/10
N2 - 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 response. We discovered a set of differentially expressed genes (DEGs) from the lymphoblastoid cell lines (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 machine learning algorithms on our datasets that predicted the lithium response of BD subtypes with minimal 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.
AB - 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 response. We discovered a set of differentially expressed genes (DEGs) from the lymphoblastoid cell lines (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 machine learning algorithms on our datasets that predicted the lithium response of BD subtypes with minimal 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.
UR - http://www.scopus.com/inward/record.url?scp=85165596260&partnerID=8YFLogxK
U2 - 10.1038/s41380-023-02183-z
DO - 10.1038/s41380-023-02183-z
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
C2 - 37488168
AN - SCOPUS:85165596260
SN - 1359-4184
VL - 28
SP - 4280
EP - 4293
JO - Molecular Psychiatry
JF - Molecular Psychiatry
IS - 10
ER -